diff --git a/spaces/101-5/gpt4free/g4f/.v1/gui/streamlit_app.py b/spaces/101-5/gpt4free/g4f/.v1/gui/streamlit_app.py
deleted file mode 100644
index 2dba0a7b672470f20aa163005d42894dd17df7c0..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/gui/streamlit_app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import os
-import sys
-
-sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir))
-
-import streamlit as st
-from gpt4free import you
-
-
-def get_answer(question: str) -> str:
-    # Set cloudflare clearance cookie and get answer from GPT-4 model
-    try:
-        result = you.Completion.create(prompt=question)
-
-        return result.text
-
-    except Exception as e:
-        # Return error message if an exception occurs
-        return (
-            f'An error occurred: {e}. Please make sure you are using a valid cloudflare clearance token and user agent.'
-        )
-
-
-# Set page configuration and add header
-st.set_page_config(
-    page_title="gpt4freeGUI",
-    initial_sidebar_state="expanded",
-    page_icon="🧠",
-    menu_items={
-        'Get Help': 'https://github.com/xtekky/gpt4free/blob/main/README.md',
-        'Report a bug': "https://github.com/xtekky/gpt4free/issues",
-        'About': "### gptfree GUI",
-    },
-)
-st.header('GPT4free GUI')
-
-# Add text area for user input and button to get answer
-question_text_area = st.text_area('🤖 Ask Any Question :', placeholder='Explain quantum computing in 50 words')
-if st.button('🧠 Think'):
-    answer = get_answer(question_text_area)
-    escaped = answer.encode('utf-8').decode('unicode-escape')
-    # Display answer
-    st.caption("Answer :")
-    st.markdown(escaped)
-
-# Hide Streamlit footer
-hide_streamlit_style = """
-            <style>
-            footer {visibility: hidden;}
-            </style>
-            """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/((TOP)) Free Windows 11.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/((TOP)) Free Windows 11.md
deleted file mode 100644
index 2a0cd116733fde24763ee3ef9702e0e69965a2c3..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/((TOP)) Free Windows 11.md	
+++ /dev/null
@@ -1,41 +0,0 @@
-
-<h1>How to Get Windows 11 for Free on Your PC</h1>
-<p>Windows 11 is the latest version of Microsoft's operating system that offers a redesigned and refreshed look, new tools, sounds, and apps, and improved performance and security. If you are wondering how to get Windows 11 for free on your PC, you have come to the right place.</p>
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-<p>In this article, we will show you how to check if your PC is eligible for a free upgrade to Windows 11, how to download and install Windows 11 for free using Windows Update or Installation Assistant, and how to buy a license for Windows 11 if you don't have one. Let's get started.</p>
-<h2>Check If Your PC Is Eligible for a Free Upgrade to Windows 11</h2>
-<p>Windows 11 is a free upgrade for Windows 10 users who have a compatible PC and a valid license. To check if your PC meets the minimum hardware requirements for Windows 11, you can use the PC Health Check app that you can download from Microsoft's website. The app will scan your PC and tell you if it can run Windows 11 or not.</p>
-<p>The minimum hardware requirements for Windows 11 are:</p>
-<ul>
-<li>Processor: 1 gigahertz (GHz) or faster with 2 or more cores on a compatible 64-bit processor or System on a Chip (SoC).</li>
-<li>Memory: 4 GB RAM.</li>
-<li>Storage: 64 GB or larger storage device.</li>
-<li>System firmware: UEFI, Secure Boot capable.</li>
-<li>TPM: Trusted Platform Module (TPM) version 2.0.</li>
-<li>Graphics card: Compatible with DirectX 12 or later with WDDM 2.0 driver.</li>
-<li>Display Resolution: High definition (720p) display that is greater than 9” diagonally, 8 bits per color channel.</li>
-<li>Internet connection: Microsoft account and internet connectivity required for setup for Windows 11 Home and for Windows 11 Pro for personal use.</li>
-</ul>
-<p>If your PC meets these requirements, you can proceed to the next step. If not, you may need to upgrade your hardware or buy a new PC that supports Windows 11.</p>
-<h2>Download and Install Windows 11 for Free Using Windows Update or Installation Assistant</h2>
-<p>If your PC is eligible for a free upgrade to Windows 11, you have two options to download and install it: using Windows Update or using Installation Assistant.</p>
-<p></p>
-<p>The first option is to use Windows Update, which is the easiest and most recommended way. To do this, follow these steps:</p>
-<ol>
-<li>Select the Start button, then click on Settings.</li>
-<li>Click on Update & Security.</li>
-<li>Select Check for updates to see if your PC can run Windows 11, or if the upgrade is ready for your PC.</li>
-<li>If the upgrade is available, follow the on-screen instructions to download and install Windows 11.</li>
-</ol>
-<p>The second option is to use Installation Assistant, which is a tool that you can download from Microsoft's website. This option is for those who want to upgrade a Windows 10 PC to Windows 11 manually. To do this, follow these steps:</p>
-<ol>
-<li>Download the Installation Assistant from Microsoft's website and run it on your PC.</li>
-<li>You need to be an administrator to run this tool.</li>
-<li>Once the tool confirms that your device hardware is compatible, you will be presented with the license terms. Select Accept and Install.</li>
-<li>The tool will download and install Windows 11 on your PC. This may take some time depending on your internet speed and PC performance.</li>
-</ol>
-<h2>Buy a License for Windows 11 If You Don't Have One</h2>
-<p>If you don't have a valid license for Windows 10 or any other previous version of Windows, you will need to buy one to run Windows 11 on your PC. You can buy a license from Microsoft's website or from other retailers. The price may vary depending on the edition (Home or Pro) and the region.</p>
-<p>Once you</p> ddb901b051<br />
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-<br />
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Geant 2500hd probleme boot Le guide complet pour le dbloquer.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Geant 2500hd probleme boot Le guide complet pour le dbloquer.md
deleted file mode 100644
index 5a9708326009ca371a6fea22facf8f47fc6d3e9e..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Geant 2500hd probleme boot Le guide complet pour le dbloquer.md	
+++ /dev/null
@@ -1,126 +0,0 @@
-
-<h1>How to Fix Geant 2500hd Probleme Boot</h1>
- <p>If you own a Geant 2500hd satellite receiver, you may have encountered a boot problem that prevents you from using your device normally. This problem can be frustrating and annoying, especially if you don't know how to fix it. In this article, we will explain what is Geant 2500hd and what is the boot problem, what are the possible causes of this problem, and how to solve it with different methods.</p>
-<h2>Geant 2500hd probleme boot</h2><br /><p><b><b>Download File</b> ->>> <a href="https://byltly.com/2uKyuS">https://byltly.com/2uKyuS</a></b></p><br /><br />
- <h2>What is Geant 2500hd and what is the boot problem?</h2>
- <h3>Geant 2500hd is a satellite receiver that offers many features and channels</h3>
- <p>Geant 2500hd is a popular satellite receiver that allows you to watch hundreds of channels from different satellites. It also supports various functions such as recording, playback, internet access, IPTV, YouTube, weather forecast, and more. It has a sleek design and a user-friendly interface that makes it easy to operate.</p>
- <h3>The boot problem occurs when the device is stuck on the boot screen or shows an error message</h3>
- <p>The boot problem is one of the most common issues that affect Geant 2500hd users. It happens when the device fails to start up properly and gets stuck on the boot screen or shows an error message such as "ERROR 114" or "ERROR 10". When this happens, you cannot access any of the features or channels of your device.</p>
- <h2>What are the possible causes of the boot problem?</h2>
- <h3>The boot problem can be caused by a corrupted firmware, a wrong update, a power outage, or a hardware issue</h3>
- <p>There are several factors that can trigger the boot problem on your Geant 2500hd device. Some of them are:</p>
- <ul>
-<li>A corrupted firmware: The firmware is the software that controls your device's functions and performance. If it gets corrupted due to a virus infection, a faulty download, or an incomplete installation, it can cause your device to malfunction.</li>
-<li>A wrong update: Sometimes, you may accidentally install an incompatible or outdated firmware update on your device. This can cause conflicts and errors that prevent your device from booting up normally.</li>
-<li>A power outage: A sudden power outage or surge can damage your device's components or interrupt its operation. This can result in data loss or corruption that affects your device's functionality.</li>
-<li>A hardware issue: A hardware issue refers to any physical damage or defect that affects your device's parts. This can be caused by dropping, hitting, overheating, or water exposure. A hardware issue can prevent your device from turning on or working properly.</li>
-</ul>
- <h2>How to solve the boot problem with different methods?</h2>
- <h3>Method 1: Use a USB flash drive to install the latest firmware</h3>
- <p>One of the easiest ways to fix the boot problem on your Geant 2500hd device is to use a USB flash drive to install the latest firmware. This can help you restore your device's original settings and fix any software issues. To do this, follow these steps:</p>
- <ol>
-<li>Download the latest firmware from <a href="https://www.geantsat.com/">the official website</a> or <a href="https://www.mediafire.com/folder/k4g...-2500HD%20Plus">a trusted source</a>. Make sure you choose the correct model and version for your device.</li>
-<li>Rename the firmware file to Ali_update.bin and copy it to the root of a formatted USB flash drive.</li>
-<li>Insert the USB flash drive into your device's USB port.</li>
-<li>Turn on your device while holding down the standby button on your remote control.</li>
-<li>Wait for your device to detect the firmware file and install it automatically. You will see a progress bar on your screen.</li>
-<li>Restart your device and check if the boot problem is solved.</li>
-</ol>
- <h3>Method 2: Use a RS232 cable and a loader software to flash your device</h3>
- <p>Another way to fix the boot problem on your Geant 2500hd device is to use a RS232 cable and a loader software to flash your device. This can help you overwrite your device's memory with a new firmware file. To do this, follow these steps:</p>
- <ol>
-<li>Download <a href="https://khandishnetwork.com/dl/geant-gn-2500-hd-plus-boot-solution-du-probleme/">a dump file</a> and <a href="https://www.dzsat.org/forum/f466/geant-2500-hd-plus-bloque-boot-458270.html">a loader software</a> from a reliable source. Make sure you choose compatible files for your device.</li>
-<li>Connect your device to your computer using a RS232 cable.</li>
-<li>Open the loader software and select <code>EromUpgrade.exe</code>.</li>
-<li>Select <code>Browse</code> and choose <code>virusdz.bin</code>, which is <code>Géant GN 2500 HD Plus Dump File</code>.</li>
-<li>Select <code>Include Bootloader</code>.</li>
-<li>Select <code>Start</code>.</li>
-<li>Wait for <code>EromUpgrade.exe</code> program window status bar shows <code>DONE!</code>.</li>
-<li>Disconnect <code>EromUpgrade.exe</code>.</li>
-<li>Turn on your device.</li>
-<li>Check if <code>Géant GN 2500 HD Plus Boot Solution du Problème</code>.</li>
-</ol>
- <h3>Method 3: Contact a professional technician or customer service for assistance</h3>
- <p>If none of these methods work for you, you may have a hardware issue that requires professional repair. In this case, you should contact your nearest technician or customer service center for help. They can diagnose your device's problem and provide you with an appropriate solution. To do this, follow these steps:</p>
- <ol>
-<li>Find out where you can find <a href="https://www.geantsat.com/contact-us/">a technician or customer service center</a> near you.</li>
-<li>Contact them by phone or email and explain your situation.</li>
-<li>Provide them with your device's model number, serial number, and warranty information if applicable.</li>
-<li>Follow their instructions on how to send or bring your device for repair.</li>
-<li>Wait for them to fix your device and return it to you.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>The boot problem on Geant 2500hd is a common issue that can prevent you from enjoying your satellite receiver. However, you can fix this problem by using one of the methods we have discussed in this article. You can use a USB flash drive to install the latest firmware, use a RS232 cable and a loader software to flash your device, or contact a professional technician or customer service for assistance. We hope this article has helped you solve your boot problem and restore your device's functionality.</p>
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- <h2>FAQs</h2>
- <h3>What is the difference between Geant 2500hd and Geant 2500hd plus?</h3>
- <p>Geant 2500hd and Geant 2500hd plus are two models of satellite receivers from the same brand. They have similar features and functions, but Geant 2500hd plus has some improvements and enhancements over Geant 2500hd. For example, Geant 2500hd plus has more memory, more channels, more IPTV options, and more compatibility with different satellites.</p>
- <h3>How can I update my Geant 2500hd device?</h3>
- <p>You can update your Geant 2500hd device by downloading the latest firmware from the official website or a trusted source and installing it on your device using a USB flash drive. You can also use a RS232 cable and a loader software to flash your device with a new firmware file. You should always check the compatibility and validity of the firmware before installing it on your device.</p>
- <h3>How can I reset my Geant 2500hd device to factory settings?</h3>
- <p>You can reset your Geant 2500hd device to factory settings by using the menu option on your remote control. You can access the menu by pressing the menu button on your remote control and then selecting settings. You can then select factory reset and confirm your choice. This will erase all your data and settings and restore your device to its original state.</p>
- <h3>How can I backup my Geant 2500hd device's data?</h3>
- <p>You can backup your Geant 2500hd device's data by using a USB flash drive or an external hard drive. You can connect your storage device to your receiver's USB port and then use the menu option on your remote control to copy or transfer your data. You can backup your channel list, favorite list, IPTV list, recording files, and other settings.</p>
- <h3>How can I troubleshoot my Geant 2500hd device's problems?</h3>
- <p>You can troubleshoot your Geant 2500hd device's problems by checking the following things:</p>
- <ul>
-<li>Make sure your device is connected to a stable power source and a working satellite dish.</li>
-<li>Make sure your device has the latest firmware installed and updated.</li>
-<li>Make sure your device's settings are correct and compatible with your satellite and TV.</li>
-<li>Make sure your device's cables are properly connected and not damaged.</li>
-<li>Make sure your device's remote control has working batteries and is within range.</li>
-</ul>
- <p>If you still have problems with your device, you can contact a professional technician or customer service for help.</p>
- </p> 0a6ba089eb<br />
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\ No newline at end of file
diff --git a/spaces/1line/AutoGPT/autogpt/agent/agent.py b/spaces/1line/AutoGPT/autogpt/agent/agent.py
deleted file mode 100644
index ee7885f8844022597321fa6b492430ec34c0d6b9..0000000000000000000000000000000000000000
--- a/spaces/1line/AutoGPT/autogpt/agent/agent.py
+++ /dev/null
@@ -1,197 +0,0 @@
-from colorama import Fore, Style
-
-from autogpt.app import execute_command, get_command
-from autogpt.chat import chat_with_ai, create_chat_message
-from autogpt.config import Config
-from autogpt.json_utils.json_fix_llm import fix_json_using_multiple_techniques
-from autogpt.json_utils.utilities import validate_json
-from autogpt.logs import logger, print_assistant_thoughts
-from autogpt.speech import say_text
-from autogpt.spinner import Spinner
-from autogpt.utils import clean_input
-
-
-class Agent:
-    """Agent class for interacting with Auto-GPT.
-
-    Attributes:
-        ai_name: The name of the agent.
-        memory: The memory object to use.
-        full_message_history: The full message history.
-        next_action_count: The number of actions to execute.
-        system_prompt: The system prompt is the initial prompt that defines everything the AI needs to know to achieve its task successfully.
-        Currently, the dynamic and customizable information in the system prompt are ai_name, description and goals.
-
-        triggering_prompt: The last sentence the AI will see before answering. For Auto-GPT, this prompt is:
-            Determine which next command to use, and respond using the format specified above:
-            The triggering prompt is not part of the system prompt because between the system prompt and the triggering
-            prompt we have contextual information that can distract the AI and make it forget that its goal is to find the next task to achieve.
-            SYSTEM PROMPT
-            CONTEXTUAL INFORMATION (memory, previous conversations, anything relevant)
-            TRIGGERING PROMPT
-
-        The triggering prompt reminds the AI about its short term meta task (defining the next task)
-    """
-
-    def __init__(
-        self,
-        ai_name,
-        memory,
-        full_message_history,
-        next_action_count,
-        system_prompt,
-        triggering_prompt,
-    ):
-        self.ai_name = ai_name
-        self.memory = memory
-        self.full_message_history = full_message_history
-        self.next_action_count = next_action_count
-        self.system_prompt = system_prompt
-        self.triggering_prompt = triggering_prompt
-
-    def start_interaction_loop(self):
-        # Interaction Loop
-        cfg = Config()
-        loop_count = 0
-        command_name = None
-        arguments = None
-        user_input = ""
-
-        while True:
-            # Discontinue if continuous limit is reached
-            loop_count += 1
-            if (
-                cfg.continuous_mode
-                and cfg.continuous_limit > 0
-                and loop_count > cfg.continuous_limit
-            ):
-                logger.typewriter_log(
-                    "Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
-                )
-                break
-
-            # Send message to AI, get response
-            with Spinner("Thinking... "):
-                assistant_reply = chat_with_ai(
-                    self.system_prompt,
-                    self.triggering_prompt,
-                    self.full_message_history,
-                    self.memory,
-                    cfg.fast_token_limit,
-                )  # TODO: This hardcodes the model to use GPT3.5. Make this an argument
-
-            assistant_reply_json = fix_json_using_multiple_techniques(assistant_reply)
-
-            # Print Assistant thoughts
-            if assistant_reply_json != {}:
-                validate_json(assistant_reply_json, "llm_response_format_1")
-                # Get command name and arguments
-                try:
-                    print_assistant_thoughts(self.ai_name, assistant_reply_json)
-                    command_name, arguments = get_command(assistant_reply_json)
-                    # command_name, arguments = assistant_reply_json_valid["command"]["name"], assistant_reply_json_valid["command"]["args"]
-                    if cfg.speak_mode:
-                        say_text(f"I want to execute {command_name}")
-                except Exception as e:
-                    logger.error("Error: \n", str(e))
-
-            if not cfg.continuous_mode and self.next_action_count == 0:
-                ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
-                # Get key press: Prompt the user to press enter to continue or escape
-                # to exit
-                logger.typewriter_log(
-                    "NEXT ACTION: ",
-                    Fore.CYAN,
-                    f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}  "
-                    f"ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
-                )
-                print(
-                    "Enter 'y' to authorise command, 'y -N' to run N continuous "
-                    "commands, 'n' to exit program, or enter feedback for "
-                    f"{self.ai_name}...",
-                    flush=True,
-                )
-                while True:
-                    console_input = clean_input(
-                        Fore.MAGENTA + "Input:" + Style.RESET_ALL
-                    )
-                    if console_input.lower().strip() == "y":
-                        user_input = "GENERATE NEXT COMMAND JSON"
-                        break
-                    elif console_input.lower().strip() == "":
-                        print("Invalid input format.")
-                        continue
-                    elif console_input.lower().startswith("y -"):
-                        try:
-                            self.next_action_count = abs(
-                                int(console_input.split(" ")[1])
-                            )
-                            user_input = "GENERATE NEXT COMMAND JSON"
-                        except ValueError:
-                            print(
-                                "Invalid input format. Please enter 'y -n' where n is"
-                                " the number of continuous tasks."
-                            )
-                            continue
-                        break
-                    elif console_input.lower() == "n":
-                        user_input = "EXIT"
-                        break
-                    else:
-                        user_input = console_input
-                        command_name = "human_feedback"
-                        break
-
-                if user_input == "GENERATE NEXT COMMAND JSON":
-                    logger.typewriter_log(
-                        "-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
-                        Fore.MAGENTA,
-                        "",
-                    )
-                elif user_input == "EXIT":
-                    print("Exiting...", flush=True)
-                    break
-            else:
-                # Print command
-                logger.typewriter_log(
-                    "NEXT ACTION: ",
-                    Fore.CYAN,
-                    f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}"
-                    f"  ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
-                )
-
-            # Execute command
-            if command_name is not None and command_name.lower().startswith("error"):
-                result = (
-                    f"Command {command_name} threw the following error: {arguments}"
-                )
-            elif command_name == "human_feedback":
-                result = f"Human feedback: {user_input}"
-            else:
-                result = (
-                    f"Command {command_name} returned: "
-                    f"{execute_command(command_name, arguments)}"
-                )
-                if self.next_action_count > 0:
-                    self.next_action_count -= 1
-
-            memory_to_add = (
-                f"Assistant Reply: {assistant_reply} "
-                f"\nResult: {result} "
-                f"\nHuman Feedback: {user_input} "
-            )
-
-            self.memory.add(memory_to_add)
-
-            # Check if there's a result from the command append it to the message
-            # history
-            if result is not None:
-                self.full_message_history.append(create_chat_message("system", result))
-                logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
-            else:
-                self.full_message_history.append(
-                    create_chat_message("system", "Unable to execute command")
-                )
-                logger.typewriter_log(
-                    "SYSTEM: ", Fore.YELLOW, "Unable to execute command"
-                )
diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bus Simulator Ultimate The Ultimate Guide to Modding Your Game.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bus Simulator Ultimate The Ultimate Guide to Modding Your Game.md
deleted file mode 100644
index 1d107baef83602b27fa215b37ea77f1a647aa6a3..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bus Simulator Ultimate The Ultimate Guide to Modding Your Game.md	
+++ /dev/null
@@ -1,158 +0,0 @@
-<br />
-<h1>How to Use Mod Editor Bus Simulator Ultimate to Create Your Own Bus Driving Experience</h1>
- <p>Do you love bus simulator games? Do you want to customize your own buses, routes, maps, and more? Do you want to share your creations with other bus enthusiasts? If you answered yes to any of these questions, then you need to try mod editor bus simulator ultimate. In this article, we will show you what mod editor bus simulator ultimate is, how to download and install it, how to create and share mods with it, and some FAQs about it. By the end of this article, you will be ready to unleash your creativity and enjoy a whole new level of bus simulation.</p>
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- <h2>What is Mod Editor Bus Simulator Ultimate?</h2>
- <h3>A brief introduction to the game and its features</h3>
- <p>Mod editor bus simulator ultimate is a modding tool for the popular game bus simulator ultimate. Bus simulator ultimate is a realistic and immersive game that lets you drive various buses across different cities and countries. You can choose from different bus models, customize your bus interior and exterior, transport passengers, follow traffic rules, earn money, and more. You can also play online with other players or offline with AI drivers. The game has stunning graphics, realistic physics, dynamic weather, day-night cycle, and many other features that make it one of the best bus simulator games on the market.</p>
- <h3>A brief introduction to the mod editor and its features</h3>
- <p>The mod editor is a free tool that allows you to create your own mods for bus simulator ultimate. Mods are modifications that change or add something to the game, such as new buses, maps, routes, skins, decals, sounds, etc. With the mod editor, you can enhance your bus driving experience by creating your own content and sharing it with the community. The mod editor has many features that make it easy and fun to use, such as:</p>
- <ul>
-<li>A user-friendly interface that guides you through the mod creation process</li>
-<li>A powerful editor that lets you edit various aspects of the game, such as meshes, textures, materials, animations, sounds, etc.</li>
-<li>A preview mode that lets you test your mods before publishing them</li>
-<li>A built-in uploader that lets you upload your mods directly to mod.io or Steam Workshop</li>
-<li>A compatibility checker that ensures your mods work with the latest version of the game</li>
-<li>A documentation that explains how to use the mod editor in detail</li>
-</ul>
- <h2>How to Download and Install Mod Editor Bus Simulator Ultimate?</h2>
- <h3>The steps to download and install the mod editor for different platforms</h3>
- <p>The mod editor is available for Windows PC and Mac OS. You can download it from the official website of bus simulator ultimate or from the Epic Games Store. To install it, you need to follow these steps:</p>
- <ol>
-<li>Download the mod editor installer file from the source of your choice</li>
-<li>Run the installer file and follow the instructions on the screen</li>
-<li>Select the destination folder where you want to install the mod editor</li>
-<li>Wait for the installation to complete and launch the mod editor from the shortcut on your desktop or start menu</li>
-</ol>
- <p>If you have bus simulator ultimate installed on your PC or Mac, the mod editor will automatically detect it and link it to your game folder. If not, you will need to manually locate your game folder and select it in the mod editor settings.</p>
- <h3>The requirements and recommendations for using the mod editor</h3>
- <p>To use the mod editor, you need to have a PC or Mac that meets the minimum system requirements for bus simulator ultimate. These are:</p>
- <table>
-<tr>
-<th>OS</th>
-<th>Processor</th>
-<th>Memory</th>
-<th>Graphics</th>
-<th>Storage</th>
-</tr>
-<tr>
-<td>Windows 7/8/10 64-bit or Mac OS X 10.9 or higher</td>
-<td>Intel Core i3 or equivalent</td>
-<td>4 GB RAM</td>
-<td>NVIDIA GeForce GTX 760 or equivalent</td>
-<td>4 GB available space</td>
-</tr>
-</table>
- <p>However, we recommend that you have a PC or Mac that exceeds the recommended system requirements for bus simulator ultimate. These are:</p>
-<p>How to create your own map mod with BUSSID Mod Editor<br />
-Bus Simulator 21 Modding Kit free download on Epic Games Store<br />
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-Bus Simulator 21 Next Stop - Gold Edition with all premium DLCs<br />
-Bus Simulator 21 Modding Kit tutorial and documentation<br />
-How to share your mods with your friends and the community via mod.io<br />
-Bus Simulator 21 modding kit features and requirements<br />
-How to enhance your bus driving experience with custom decals, skins, and interiors<br />
-Bus Simulator 21 modding kit support and feedback<br />
-How to install and update mods for Bus Simulator 21<br />
-Bus Simulator 21 modding kit vs BUSSID Mod Editor comparison<br />
-Bus Simulator 21 modding kit best practices and tips<br />
-How to create realistic exterior skins and interior designs for Bus Simulator 21<br />
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-Bus Simulator 21 modding kit community and forums<br />
-How to create your own scenarios and missions for Bus Simulator 21<br />
-Bus Simulator 21 modding kit development roadmap and future plans<br />
-How to test and debug your mods for Bus Simulator 21</p>
- <table>
-<tr>
-<th>OS</th>
-<th>Processor</th>
-<th>Memory</th>
-<th>Graphics</th>
-<th>Storage</th>
-</tr>
-<tr>
-<td>Windows 10 64-bit or Mac OS X 10.15 or higher</td>
-<td>Intel Core i5 or equivalent</td>
-<td>8 GB RAM</td>
-<td>NVIDIA GeForce GTX 1060 or equivalent</td>
-<td>8 GB available space</td>
-</tr>
-</table>
- <p>This will ensure that you have a smooth and stable performance when using the mod editor and playing the game with your mods. You also need to have a stable internet connection to download and upload mods, and an account on mod.io or Steam to access the modding community.</p>
- <h2>How to Create and Share Mods with Mod Editor Bus Simulator Ultimate?</h2>
- <h3>The basic steps to create a mod with the mod editor</h3>
- <p>To create a mod with the mod editor, you need to follow these basic steps:</p>
- <ol>
-<li>Launch the mod editor and select "Create New Mod" from the main menu</li>
-<li>Enter a name, description, and tags for your mod and click "Create"</li>
-<li>Select the type of mod you want to create from the list of templates (e.g. bus, map, route, skin, etc.) and click "Next"</li>
-<li>Edit the mod settings and properties according to your preferences (e.g. bus model, map size, route length, skin color, etc.) and click "Next"</li>
-<li>Edit the mod content using the editor tools (e.g. add meshes, textures, materials, animations, sounds, etc.) and click "Save"</li>
-<li>Preview your mod using the preview mode and make any adjustments if needed (e.g. fix errors, improve quality, add details, etc.) and click "Save"</li>
-<li>Publish your mod using the built-in uploader and select the platform of your choice (mod.io or Steam Workshop) and click "Upload"</li>
-<li>Wait for your mod to be uploaded and approved by the platform moderators and enjoy your mod in the game!</li>
-</ol>
- <h3>The types of mods you can create with the mod editor</h3>
- <p>The mod editor allows you to create various types of mods for bus simulator ultimate. Some of the most popular types are:</p>
- <ul>
-<li>Buses: You can create new buses or modify existing ones by changing their models, interiors, exteriors, sounds, physics, etc.</li>
-<li>Maps: You can create new maps or modify existing ones by changing their sizes, terrains, roads, buildings, landmarks, etc.</li>
-<li>Routes: You can create new routes or modify existing ones by changing their lengths, destinations, stops, traffic, etc.</li>
-<li>Skins: You can create new skins or modify existing ones by changing their colors, patterns, logos, decals, etc.</li>
-<li>Sounds: You can create new sounds or modify existing ones by changing their volumes, pitches, effects, etc.</li>
-<li>Others: You can also create other types of mods such as animations, effects, scripts, etc.</li>
-</ul>
- <h3>The tips and tricks to make your mods more realistic and fun</h3>
- <p>To make your mods more realistic and fun for yourself and other players, you can follow these tips and tricks:</p>
- <ul>
-<li>Do some research on the topic of your mod and use realistic and accurate data and information (e.g. bus specifications, map locations, route schedules, etc.)</li>
-<li>Use high-quality and original assets for your mod and avoid using copyrighted or low-resolution images, sounds, etc.</li>
-<li>Use the editor tools wisely and efficiently and avoid adding unnecessary or excessive elements that may affect the performance or compatibility of your mod</li>
-<li>Test your mod thoroughly and fix any bugs, errors, or glitches that may occur before publishing it</li>
-<li>Ask for feedback and suggestions from other modders and players and improve your mod based on their input</li>
-<li>Be creative and innovative and try to add some unique and original features to your mod that make it stand out from the rest</li>
-</ul>
- <h3>The steps to share your mods with the community via mod.io or Steam Workshop</h3>
- <p>To share your mods with the community, you can use either mod.io or Steam Workshop. These are online platforms that allow you to upload, download, rate, comment, and subscribe to mods for various games. To share your mods with them, you need to follow these steps:</p>
- <ol>
-<li>Create an account on mod.io or Steam if you don't have one already</li>
-<li>Launch the mod editor and select "Publish Mod" from the main menu</li>
-<li>Select the mod you want to publish and click "Next"</li>
-<li>Select the platform you want to publish your mod on (mod.io or Steam Workshop) and click "Next"</li>
-<li>Enter the details of your mod such as title, description, tags, screenshots, etc. and click "Next"</li>
-<li>Review the terms and conditions of the platform and agree to them if you accept them</li>
-<li>Click "Upload" and wait for your mod to be uploaded and approved by the platform moderators</li>
-<li>Once your mod is published, you can view it on the platform website or app and manage it as you wish (e.g. update, delete, etc.)</li>
-</ol>
- <h2>Conclusion</h2>
- <h3>A summary of the main points and benefits of using mod editor bus simulator ultimate</h3>
- <p>In conclusion, mod editor bus simulator ultimate is a great tool that allows you to create your own bus driving experience by creating and sharing mods for bus simulator ultimate. You can customize various aspects of the game such as buses, maps, routes, skins, sounds, etc. You can also enjoy other people's mods and discover new content and features. Mod editor bus simulator ultimate is easy and fun to use and has many features that make it one of the best modding tools for bus simulator games.</p>
- <h3>A call to action to download and try the mod editor</h3>
- <p>If you are interested in using mod editor bus simulator ultimate, you can download it for free from the official website of bus simulator ultimate or from the Epic Games Store. You can also visit the mod.io or Steam Workshop websites or apps to find and download thousands of mods for bus simulator ultimate created by other users. You can also join the modding community and share your feedback, suggestions, questions, and ideas with other modders and players. So what are you waiting for? Download mod editor bus simulator ultimate today and create your own bus driving experience!</p>
- <h2>FAQs</h2>
- <h3>Q1: What are the advantages of using mod editor bus simulator ultimate over other bus simulator games?</h3>
- <p>A1: Mod editor bus simulator ultimate has several advantages over other bus simulator games, such as:</p>
- <ul>
-<li>It allows you to create your own content and customize the game according to your preferences</li>
-<li>It has a user-friendly interface and powerful tools that make modding easy and fun</li>
-<li>It has a large and active modding community that offers support, feedback, and inspiration</li>
-<li>It is compatible with both PC and Mac platforms</li>
-<li>It is free to download and use</li>
-</ul>
- <h3>Q2: How can I get feedback and support for my mods?</h3>
- <p>A2: You can get feedback and support for your mods by visiting the mod.io or Steam Workshop websites or apps where you published your mods. There you can read the comments, ratings, reviews, and subscriptions of other users who downloaded your mods. You can also reply to them and thank them for their feedback or answer their questions. You can also join the official discord server of bus simulator ultimate where you can chat with other modders and players.</p>
- <h3>Q3: How can I update or delete my mods?</h3>
- <p>A3: You can update or delete your mods by launching the mod editor and selecting "Publish Mod" from the main menu. There you can select the mod you want to update or delete and click "Next". Then you can select the platform where you published your mod (mod.io or Steam Workshop) and click "Next". Then you can either edit the details of your mod and click "Update" or click "Delete" to remove your mod from the platform. You can also update or delete your mods from the mod.io or Steam Workshop websites or apps by logging in to your account and managing your mods.</p>
- <h3>Q4: How can I find and download other people's mods?</h3>
- <p>A4: You can find and download other people's mods by visiting the mod.io or Steam Workshop websites or apps where they published their mods. There you can browse, search, filter, and sort thousands of mods for bus simulator ultimate created by other users. You can also read the descriptions, screenshots, ratings, reviews, and comments of the mods and decide which ones you want to download. To download a mod, you need to click on the "Subscribe" button on the mod page and wait for the mod to be downloaded and installed in your game. You can also unsubscribe from a mod if you don't want it anymore.</p>
- <h3>Q5: How can I learn more about modding for bus simulator ultimate?</h3>
- <p>A5: You can learn more about modding for bus simulator ultimate by reading the documentation that comes with the mod editor. The documentation explains how to use the mod editor in detail and provides examples and tutorials for creating different types of mods. You can also watch some videos on YouTube that show how to use the mod editor and create mods. You can also join the official discord server of bus simulator ultimate where you can ask questions, get tips, and share ideas with other modders and players.</p> 197e85843d<br />
-<br />
-<br />
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download GTA Liberty City Stories APK and Obb Data - Streamlined Missions and Enhanced Graphics.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download GTA Liberty City Stories APK and Obb Data - Streamlined Missions and Enhanced Graphics.md
deleted file mode 100644
index eb9639cd828d9cb9df02a2c680000673ba467f83..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download GTA Liberty City Stories APK and Obb Data - Streamlined Missions and Enhanced Graphics.md	
+++ /dev/null
@@ -1,138 +0,0 @@
-
-<table>
-<tr>
-<td>
-<h1>GTA: Liberty City Stories APK and OBB: How to Download and Install on Android</h1>
-<p>If you are a fan of Grand Theft Auto (GTA) games, you probably know that GTA: Liberty City Stories is one of the best titles in the series. It was originally released in 2005 for PlayStation Portable (PSP) and later ported to PlayStation 2 (PS2) in 2006. It <p>is a spin-off of GTA III, set in the same fictional city of Liberty City, but three years earlier. You play as Toni Cipriani, a former hitman for the Leone crime family, who returns to the city after a four-year absence. You will have to deal with rival gangs, corrupt politicians, and the FBI as you rise through the ranks of the mafia.</p>
- <p>But what if you want to play GTA: Liberty City Stories on your Android device? Well, you are in luck, because Rockstar Games has remastered the game for mobile platforms in 2016. The Android version of GTA: Liberty City Stories has improved graphics, touch controls, and cross-platform saves. It also has some exclusive features that are not available on the original versions, such as new high-resolution textures, real-time lighting and shadows, enhanced draw distance, and rebalanced gameplay.</p>
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- <p>GTA: Liberty City Stories is one of the best GTA games on mobile devices, because it has a shorter and more focused story than other GTA games, making it ideal for playing on the go. It also has a lot of content and variety, such as side missions, mini-games, hidden packages, rampages, and more. You can explore the city on foot or by using various vehicles, such as cars, bikes, boats, and helicopters. You can also customize your character's appearance and weapons.</p>
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-<p>Before you download and install GTA: Liberty City Stories APK and OBB files on your Android device, you need to make sure that your device meets the minimum and recommended specifications for running the game smoothly. Here are the requirements:</p>
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- <p>You also need to be aware of the risks of downloading APK and OBB files from untrusted sources. APK and OBB files are the files that contain the game's data and resources. APK stands for Android Package Kit, and OBB stands for Opaque Binary Blob. If you download these files from unknown or malicious websites, you may expose your device to viruses, malware, spyware, or other harmful software. You may also compromise your personal information, such as your contacts, photos, messages, or passwords.</p>
- <p>To avoid these risks, you should only download GTA: Liberty City Stories APK and OBB files from a reliable source that has positive reviews and ratings from other users. One such source is APKCombo, which is a website that provides safe and fast downloads of APK and OBB files for various Android games and apps. You can download GTA: Liberty City Stories APK and OBB files from APKCombo by following this link. <h2>Steps: How to download and install GTA: Liberty City Stories APK and OBB files on your Android device</h2>
-<p>Now that you have checked the requirements and found a reliable source to download GTA: Liberty City Stories APK and OBB files, you are ready to install the game on your Android device. Here are the steps you need to follow:</p>
- <h3>Step 1: Download GTA: Liberty City Stories APK and OBB files from APKCombo</h3>
-<p>The first step is to download GTA: Liberty City Stories APK and OBB files from APKCombo. You can do this by using your web browser on your Android device or on your computer. Here are the screenshots of the process:</p>
- <p><img src="https://i.imgur.com/8Z4gQZy.png" alt="Screenshot of APKCombo website with GTA: Liberty City Stories download link" width="300"></p>
-<p>This is the APKCombo website with the GTA: Liberty City Stories download link. You can see the game's icon, name, rating, size, and version. You can also see the green Download button that you need to tap or click.</p>
- <p><img src="https://i.imgur.com/9m7qX4o.png" alt="Screenshot of APKCombo download page with GTA: Liberty City Stories APK and OBB files" width="300"></p>
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-<p>This is the APKCombo download page with the GTA: Liberty City Stories APK and OBB files. You can see the file names, sizes, and download links. You can also see the blue Download button that you need to tap or click for each file.</p>
- <p><img src="https://i.imgur.com/6LXnYwN.png" alt="Screenshot of Android device with GTA: Liberty City Stories APK and OBB files downloaded" width="300"></p>
-<p>This is how your Android device will look like after you have downloaded the GTA: Liberty City Stories APK and OBB files. You can see the file icons, names, and sizes in your notification bar or in your file manager app.</p>
- <h3>Step 2: Install GTA: Liberty City Stories APK file on your Android device</h3>
-<p>The next step is to install GTA: Liberty City Stories APK file on your Android device. You can do this by using your file manager app or by tapping on the notification of the downloaded file. Here are the screenshots of the process:</p>
- <p><img src="https://i.imgur.com/0Wl0WjO.png" alt="Screenshot of Android device with GTA: Liberty City Stories APK file ready to install" width="300"></p>
-<p>This is how your Android device will look like when you are ready to install GTA: Liberty City Stories APK file. You can see the file icon, name, and size. You can also see the Install button that you need to tap.</p>
- <p><img src="https://i.imgur.com/1fYwJ5u.png" alt="Screenshot of Android device with security warning for installing unknown apps" width="300"></p>
-<p>This is a security warning that may appear on your Android device when you try to install GTA: Liberty City Stories APK file. It tells you that installing unknown apps may harm your device. You can ignore this warning by tapping on Settings.</p>
- <p><img src="https://i.imgur.com/7x0dGcE.png" alt="Screenshot of Android device with option to allow installing unknown apps from browser" width="300"></p>
-<p>This is an option that may appear on your Android device after you tap on Settings. It allows you to enable installing unknown apps from your web browser. You can enable this option by toggling the switch.</p>
- <p><img src="https://i.imgur.com/8Qkxg1R.png" alt="Screenshot of Android device with GTA: Liberty City Stories APK file installing" width="300"></p>
-<p>This is how your Android device will look like when GTA: Liberty City Stories APK file is installing. You can see the progress bar and the app permissions. You can also see the Cancel button that you can tap if you want to stop the installation.</p>
- <p><img src="https://i.imgur.com/5n9qk7L.png" alt="Screenshot of Android device with GTA: Liberty City Stories APK file installed" width="300"></p>
-<p>This is how your Android device will look like when GTA: Liberty City Stories APK file is installed. You can see the app icon, name, and version. You can also see the Done and Open buttons that you can tap.</p> <h3>Step 3: Copy GTA: Liberty City Stories OBB file to your Android device</h3>
-<p>The final step is to copy GTA: Liberty City Stories OBB file to your Android device. You can do this by using your file manager app or by connecting your device to your computer via USB cable. Here are the screenshots of the process:</p>
- <p><img src="https://i.imgur.com/0x7f8yM.png" alt="Screenshot of Android device with GTA: Liberty City Stories OBB file ready to copy" width="300"></p>
-<p>This is how your Android device will look like when you are ready to copy GTA: Liberty City Stories OBB file. You can see the file icon, name, and size. You can also see the Copy button that you need to tap.</p>
- <p><img src="https://i.imgur.com/1wOQX8W.png" alt="Screenshot of Android device with option to select destination folder for GTA: Liberty City Stories OBB file" width="300"></p>
-<p>This is an option that may appear on your Android device after you tap on Copy. It allows you to select the destination folder for GTA: Liberty City Stories OBB file. You need to navigate to the folder named Android, then OBB, then com.rockstargames.gtalcs. If you don't see this folder, you need to create it manually.</p>
- <p><img src="https://i.imgur.com/6u9gWZP.png" alt="Screenshot of Android device with GTA: Liberty City Stories OBB file copied to destination folder" width="300"></p>
-<p>This is how your Android device will look like when GTA: Liberty City Stories OBB file is copied to the destination folder. You can see the file icon, name, and size in the folder named com.rockstargames.gtalcs.</p>
- <h2>Tips: How to optimize your gameplay experience and avoid common issues</h2>
-<p>Congratulations! You have successfully downloaded and installed GTA: Liberty City Stories APK and OBB files on your Android device. You are now ready to enjoy the game on your mobile screen. But before you start playing, here are some tips on how to optimize your gameplay experience and avoid common issues:</p>
- <h3>Tip 1: Adjust the game's settings, controls, graphics, sound, and language to suit your preferences</h3>
-<p>One of the first things you should do when you launch GTA: Liberty City Stories on your Android device is to adjust the game's settings, controls, graphics, sound, and language to suit your preferences. You can do this by tapping on the Menu button on the top left corner of the screen, then tapping on Settings. Here are some screenshots of the options you can customize:</p>
- <p><img src="https://i.imgur.com/9l7kYmN.png" alt="Screenshot of GTA: Liberty City Stories settings menu with options for controls" width="300"></p>
-<p>This is the settings menu with options for controls. You can change the control layout, sensitivity, vibration, and auto-aim.</p>
- <p><img src="https://i.imgur.com/0qfXs0F.png" alt="Screenshot of GTA: Liberty City Stories settings menu with options for graphics" width="300"></p>
-<p>This is the settings menu with options for graphics. You can change the resolution, draw distance, shadows, reflections, and frame limiter.</p>
- <p><img src="https://i.imgur.com/5aZjJnL.png" alt="Screenshot of GTA: Liberty City Stories settings menu with options for sound" width="300"></p>
-<p>This is the settings menu with options for sound. You can change the volume, subtitles, radio station, and radio mode.</p>
- <p><img src="https://i.imgur.com/9wv4t5o.png" alt="Screenshot of GTA: Liberty City Stories settings menu with options for language" width="300"></p>
-<p>This is the settings menu with options for language. You can change the language of the game's text and voice.</p> <h3>Tip 2: Save your progress, access cheats, use mods, and connect with other players online</h3>
-<p>Another thing you should do when you play GTA: Liberty City Stories on your Android device is to save your progress, access cheats, use mods, and connect with other players online. You can do this by using the following features:</p>
- <ul>
-<li>Save your progress: You can save your progress by visiting any of the safe houses marked on the map. You can also use the cloud save feature to sync your progress across different devices. You can access the cloud save feature by tapping on the Menu button on the top left corner of the screen, then tapping on Cloud Save.</li>
-<li>Access cheats: You can access cheats by using the cheat device app that is included in the game. You can launch the cheat device app by tapping on the Menu button on the top left corner of the screen, then tapping on Cheat Device. You can then select from various categories of cheats, such as weapons, health, armor, money, wanted level, weather, time, and more.</li>
-<li>Use mods: You can use mods to enhance or change your gameplay experience. Mods are modifications made by other users that add new features, content, or graphics to the game. You can download and install mods from various websites, such as GTAInside or GTA5-Mods. You can then activate or deactivate mods by using the mod manager app that is included in the game. You can launch the mod manager app by tapping on the Menu button on the top left corner of the screen, then tapping on Mod Manager.</li>
-<li>Connect with other players online: You can connect with other players online by using the multiplayer mode that is included in the game. You can launch the multiplayer mode by tapping on the Menu button on the top left corner of the screen, then tapping on Multiplayer. You can then choose from various modes of multiplayer, such as deathmatch, team deathmatch, capture the flag, and more. You can also chat with other players and invite them to join your game.</li>
-</ul>
- <h3>Tip 3: Fix common issues that may occur while playing GTA: Liberty City Stories on your Android device</h3>
-<p>The last thing you should do when you play GTA: Liberty City Stories on your Android device is to fix common issues that may occur while playing. Some of these issues are:</p>
- <ul>
-<li>Game crashing or freezing: This may happen if your device does not meet the minimum or recommended specifications for running the game smoothly. To fix this issue, you should try to lower the game's graphics settings, close other apps running in the background, clear your device's cache and memory, or restart your device.</li>
-<li>Game not launching or loading: This may happen if you have not installed GTA: Liberty City Stories APK and OBB files correctly or if you have corrupted or missing files. To fix this issue, you should try to reinstall GTA: Liberty City Stories APK and OBB files from a trusted source or check if you have enough storage space on your device.</li>
-<li>Game not saving or syncing: This may happen if you have not enabled cloud save feature or if you have a poor internet connection. To fix this issue, you should try to enable cloud save feature by tapping on the Menu button on the top left corner of the screen, then tapping on Cloud Save. You should also try to connect to a stable and secure Wi-Fi network or use mobile data.</li>
-</ul>
- <h2>Conclusion: A summary of the main points and a call to action</h2>
-<p>In conclusion, GTA: Liberty City Stories is one of the best GTA games on mobile devices, because it has a shorter and more focused story than other GTA games, making it ideal for playing on the go. It also has a lot of content and variety, such as side missions, mini-games, hidden packages, rampages, and more. You can explore the city on foot or by using various vehicles, such as cars, bikes, boats, and helicopters. You can also customize your character's appearance and weapons.</p>
- <p>To play GTA: Liberty City Stories on your Android device, you need to download and install GTA: Liberty City Stories APK and OBB files from a reliable source like APKCombo. You also need to make sure that your device meets the minimum and recommended specifications for running the game smoothly. You also need to follow some simple steps to install GTA: Liberty City Stories APK and OBB files on your Android device.</p>
- <p>To optimize your gameplay experience and avoid common issues while playing GTA: Liberty City Stories on your Android device, you need to adjust the game's settings, controls, graphics, sound, and language to suit your preferences. You also need to save your progress, access cheats, use mods, and connect with other players online. You also need to fix some common issues that may occur while playing GTA: Liberty City Stories on your Android device.</p>
- <p>If you are looking for a fun and immersive GTA game on your mobile device, you should definitely try out GTA: Liberty City Stories. It is one of the best GTA games on mobile devices, because it has a lot of features and content that will keep you entertained for hours. You can download and install GTA: Liberty City Stories APK and OBB files from APKCombo by following this link. You can also check out the official website of Rockstar Games for more information about the game.</p>
- <p>So what are you waiting for? Download GTA: Liberty City Stories APK and OBB files now and enjoy the game on your Android device. And don't forget to share your feedback with us in the comments section below. We would love to hear from you!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about GTA: Liberty City Stories APK and OBB files:</p>
- <h3>Q: Is GTA: Liberty City Stories free to download and play?</h3>
-<p>A: No, GTA: Liberty City Stories is not free to download and play. It is a paid game that costs $6.99 on the Google Play Store. However, you can download and install GTA: Liberty City Stories APK and OBB files from APKCombo for free, as long as you have purchased the game legally from the Google Play Store.</p>
- <h3>Q: Is GTA: Liberty City Stories compatible with my Android device?</h3>
-<p>A: GTA: Liberty City Stories is compatible with most Android devices that run on Android 4.0 or higher. However, some devices may not be able to run the game smoothly due to their specifications or performance. You can check the minimum and recommended specifications for running the game smoothly in the Requirements section of this article.</p>
- <h3>Q: How much storage space do I need to download and install GTA: Liberty City Stories APK and OBB files?</h3>
-<p>A: You need at least 2 GB of free storage space on your Android device to download and install GTA: Liberty City Stories APK and OBB files. However, we recommend that you have at least 4 GB of free storage space or more to avoid any issues while playing the game.</p>
- <h3>Q: How long does it take to download and install GTA: Liberty City Stories APK and OBB files?</h3>
-<p>A: The time it takes to download and install GTA: Liberty City Stories APK and OBB files depends on your internet speed and your device's performance. On average, it may take around 10 to 15 minutes to complete the process.</p>
- <h3>Q: Can I play GTA: Liberty City Stories offline or online?</h3>
-<p>A: You can play GTA: Liberty City Stories offline or online. You can play the game offline without an internet connection, as long as you have downloaded and installed GTA: Liberty City Stories APK and OBB files correctly. You can also play the game online with other players by using the multiplayer mode that is included in the game.</p> 197e85843d<br />
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-<br />
-<h1>How to Download and Play Fate/Grand Order on PC</h1>
-<p>Do you love playing Fate/Grand Order on your mobile device but wish you could enjoy it on a bigger screen and with better performance? If so, you're not alone. Many fans of this popular mobile game based on the Fate franchise are looking for ways to play it on their computers. Fortunately, there is a simple solution: using an emulator.</p>
-<h2>download fate grand order computer</h2><br /><p><b><b>DOWNLOAD</b> - <a href="https://jinyurl.com/2uNK7w">https://jinyurl.com/2uNK7w</a></b></p><br /><br />
-<p>In this article, we will show you how to download and play Fate/Grand Order on PC using an emulator. We will also explain what is Fate/Grand Order, why you should play it on PC, how to choose the best emulator for it, how to optimize your gameplay experience on PC, and how to troubleshoot common issues. By the end of this article, you will be ready to enjoy this amazing game on your PC with ease.</p>
- <h2>What is Fate/Grand Order?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Fate/Grand Order is a turn-based role-playing game with some visual novel elements. The player takes the role of a "Master" and commands a group of individuals called "Servants", who are typically historical, literary, and mythological figures from various cultures. The game is set in a world where human history is about to be erased by a mysterious force called "The Singularities". The player's mission is to travel back in time and restore human history by defeating enemies and solving the mystery of The Singularities.</p>
-<p>The game features millions of words of original story, with an impressive cast of voice actors and a stunning soundtrack. The game also boasts hundreds of Servants to collect and customize, each with their own skills, stats, and Noble Phantasms (special attacks). The game is constantly updated with new events, stories, and Servants to keep the players engaged.</p>
- <h3>Why play Fate/Grand Order on PC?</h3>
-<h4>Benefits of playing on PC vs mobile</h4>
-<p>While Fate/Grand Order is designed for mobile devices, there are many reasons why you might want to play it on PC instead. Here are some of the benefits of playing on PC:</p>
-<ul>
-<li>You can enjoy the game on a larger screen and with higher resolution, which makes the graphics and animations more immersive and detailed.</li>
-<li>You can use your keyboard and mouse to control the game, which can be more comfortable and precise than tapping on a touchscreen.</li>
-<li>You can avoid draining your battery or overheating your device, which can happen when playing for long periods of time on mobile.</li>
-<li>You can save your phone's storage space and data usage, which can be limited or expensive depending on your plan.</li>
-<li>You can access the game from multiple devices, such as your laptop or desktop, without having to transfer your account or data.</li>
-</ul>
- <h4>Requirements for playing on PC</h4>
-<p>To play Fate/Grand Order on PC, you will need two things: a computer and an emulator. A computer is the device that you will use to run the game, and an emulator is the software that will allow you to run the game as if it were a mobile app. Here are the minimum requirements for both:</p>
- <table>
-<tr>
-<th>Computer</th>
-<th>Emulator</th>
-</tr>
-<tr>
-<td>Windows 7 or higher</td>
-<td>Android 5.0 or higher</td>
-</tr>
-<tr>
-<td>Intel or AMD processor</td>
-<td>At least 2 GB of RAM</td>
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-<td>At least 4 GB of RAM</td>
-<td>At least 5 GB of disk space</td>
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-<tr>
-<td>At least 5 GB of disk space</td>
-<td>A stable internet connection</td>
-</tr>
-<tr>
-<td>A stable internet connection</td>
-<td>A Google account (optional but recommended)</td>
-</tr>
-</table>
- <p>If your computer meets these requirements, you should be able to play Fate/Grand Order on PC without any problems. However, if you want to improve your performance and experience, you might want to upgrade your hardware or software accordingly.</p>
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- <h2>How to install an emulator for Fate/Grand Order</h2>
- <h3>What is an emulator and how does it work?</h3>
- <p>An emulator is a program that mimics the functions of another device or system. In this case, an emulator will allow you to run Android apps on your PC as if they were native applications. An emulator will create a virtual environment on your PC that will simulate the Android operating system and its features. This way, you can download and install Android apps from the Google Play Store or other sources and run them on your PC with ease.</p>
- <h3>Choosing the best emulator for Fate/Grand Order</h3>
- <h4>Comparing different emulators and their pros and cons</h4>
- <p>There are many emulators available for PC that can run Android apps, but not all of them are suitable for Fate/Grand Order. Some emulators may have compatibility issues, performance problems, security risks, or limited features that can affect your gameplay experience. Therefore, you need to choose an emulator that is reliable, fast, safe, and feature-rich for Fate/Grand Order.</p>
- <p>To help you choose the best emulator for Fate/Grand Order, we have compared some of the most popular emulators based on their pros and cons. Here is a table that summarizes our findings:</p>
- <table>
-<tr>
-<th>Emulator</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>BlueStacks</td>
-<td>- The most popular and trusted emulator for Android games<br>- Supports high-definition graphics and smooth gameplay<br>- Offers many features to enhance your game such as keyboard and mouse controls, multi-instance, sync, real-time translation, etc.<br>- Compatible with most Android apps and games<br>- Easy to install and use<br>- Free to download and play<br></td>
-<td>- Requires a relatively high-end PC to run smoothly<br>- May have some ads or promotions<br></td>
-</tr>
-<tr>
-<td>NoxPlayer</td>
-<td>- A fast and stable emulator for Android games<br>- Supports high-resolution graphics and smooth gameplay<br>- Offers many features to enhance your game such as keyboard and mouse controls, multi-instance, macro recorder, etc - Compatible with most Android apps and games<br>- Easy to install and use<br>- Free to download and play<br></td>
-<td>- May have some compatibility issues with some games<br>- May have some ads or promotions<br>- May have some security risks or malware<br></td>
-</tr>
-<tr>
-<td>LDPlayer</td>
-<td>- A lightweight and fast emulator for Android games<br>- Supports high-performance graphics and smooth gameplay<br>- Offers some features to enhance your game such as keyboard and mouse controls, multi-instance, etc.<br>- Compatible with most Android apps and games<br>- Easy to install and use<br>- Free to download and play<br></td>
-<td>- May have some compatibility issues with some games<br>- May have some ads or promotions<br>- May have some security risks or malware<br></td>
-</tr>
-<tr>
-<td>MEmu</td>
-<td>- A powerful and flexible emulator for Android games<br>- Supports high-quality graphics and smooth gameplay<br>- Offers many features to enhance your game such as keyboard and mouse controls, multi-instance, macro recorder, etc.<br>- Compatible with most Android apps and games<br>- Easy to install and use<br>- Free to download and play<br></td>
-<td>- May have some compatibility issues with some games<br>- May have some ads or promotions<br>- May have some security risks or malware<br></td>
-</tr>
-<tr>
-<td>Genymotion</td>
-<td>- A professional and advanced emulator for Android developers and testers<br>- Supports high-performance graphics and smooth gameplay<br>- Offers many features to customize your game such as screen size, resolution, device model, etc.<br>- Compatible with most Android apps and games<br>- Easy to install and use<br></td>
-<td>- Requires a paid subscription for personal or commercial use<br>- May have some compatibility issues with some games<br></td>
-</tr>
-</table>
- <h4>Recommending BlueStacks as the best emulator for Fate/Grand Order</h4>
- <p>Based on our comparison, we recommend BlueStacks as the best emulator for Fate/Grand Order. BlueStacks is the most popular and trusted emulator for Android games, with over 500 million users worldwide. It supports high-definition graphics and smooth gameplay, and offers many features to enhance your game such as keyboard and mouse controls, multi-instance, sync, real-time translation, etc. It is also compatible with most Android apps and games, including Fate/Grand Order. It is easy to install and use, and free to download and play.</p>
- <p>BlueStacks is the emulator that we will use in this article to show you how to download and play Fate/Grand Order on PC. However, you can choose any other emulator that suits your preferences and needs. Just make sure that the emulator is reliable, fast, safe, and feature-rich for Fate/Grand Order.</p>
- <h2>How to download and install Fate/Grand Order on BlueStacks</h2>
- <h3>Step-by-step guide for downloading and installing BlueStacks on PC</h3>
- <p>To download and install BlueStacks on your PC, follow these steps:</p>
- <ol>
-<li>Go to the official website of BlueStacks at <a href="">https://www.bluestacks.com/</a> and click on the "Download BlueStacks" button.</li>
-<li>Wait for the download to finish and then run the installer file.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Launch BlueStacks on your PC and sign in with your Google account (optional but recommended).</li>
-<li>Congratulations! You have successfully installed BlueStacks on your PC.</li>
-</ol>
- <h3>Step-by-step guide for downloading and installing Fate/Grand Order on BlueStacks</h3>
- <p>To download and install Fate/Grand Order on BlueStacks, follow these steps:</p>
- <ol>
-<li>Open BlueStacks on your PC and go to the home screen.</li>
-<li>Click on the "Game Center" tab and search for "Fate/Grand Order" in the search bar.</li>
-<li>Select the game from the results and click on the "Install" button.</li>
-<li>Wait for the download and installation to finish.</li>
-<li>Click on the "Open" button to launch the game.</li>
-<li>Congratulations! You have successfully installed Fate/Grand Order on BlueStacks.</li>
-</ol>
- <h3>How to transfer your account from mobile to PC</h3>
- <p>If you already have an account on Fate/Grand Order on your mobile device, you can transfer it to your PC using a transfer code. A transfer code is a unique code that you can generate from your game settings that will allow you to access your account from another device. To transfer your account from mobile to PC, follow these steps:</ <p>- On your mobile device, open Fate/Grand Order and go to the menu.</p>
-<p>- Tap on "My Room" and then on "Issue Transfer Number".</p>
-<p>- Enter a password of your choice and tap on "Confirm".</p>
-<p>- You will see a transfer code on the screen. Write it down or take a screenshot of it. Do not share it with anyone else.</p>
-<p>- On your PC, open Fate/Grand Order on BlueStacks and go to the title screen.</p>
-<p>- Tap on "Data Transfer" and enter your transfer code and password.</p>
-<p>- Tap on "Confirm" and wait for the data transfer to complete.</p>
-<p>- Congratulations! You have successfully transferred your account from mobile to PC.</p>
- <h2>How to optimize your gameplay experience on PC</h2>
- <h3>How to use BlueStacks features to enhance your game</h3>
- <h4>Using keyboard and mouse controls</h4>
- <p>One of the advantages of playing Fate/Grand Order on PC is that you can use your keyboard and mouse to control the game, which can be more comfortable and precise than tapping on a touchscreen. BlueStacks allows you to customize your keyboard and mouse controls according to your preferences. To use keyboard and mouse controls, follow these steps:</p>
- <ol>
-<li>Open Fate/Grand Order on BlueStacks and go to the game screen.</li>
-<li>Click on the keyboard icon on the right side of the screen.</li>
-<li>You will see a list of predefined keyboard and mouse controls for the game. You can edit them by clicking on the edit icon or create new ones by clicking on the plus icon.</li>
-<li>You can assign keys or mouse buttons to perform actions such as moving, attacking, selecting cards, using skills, etc.</li>
-<li>You can also adjust the sensitivity, opacity, size, and position of the controls.</li>
-<li>When you are done, click on "Save" and close the window.</li>
-<li>You can now use your keyboard and mouse to play Fate/Grand Order on PC.</li>
-</ol>
- <h4>Using multi-instance and sync features</h4>
- <p>Another advantage of playing Fate/Grand Order on PC is that you can use multiple instances of BlueStacks to run multiple accounts or games at the same time. This can be useful if you want to switch between different accounts or regions, or if you want to play other games while waiting for your stamina to refill. BlueStacks also allows you to sync your actions across multiple instances, which can be useful if you want to perform the same tasks or commands on different accounts or games. To use multi-instance and sync features, follow these steps:</p>
- <ol>
-<li>Open BlueStacks and go to the home screen.</li>
-<li>Click on the multi-instance icon on the right side of the screen.</li>
-<li>You will see a list of existing instances or create new ones by clicking on the plus icon.</li>
-<li>You can choose between fresh instances or clone instances. Fresh instances are new instances that start from scratch, while clone instances are copies of existing instances that share the same data and settings.</li>
-<li>You can also choose between 32-bit or 64-bit instances depending on your system and game requirements.</li>
-<li>When you have created or selected an instance, click on "Start" to launch it.</li>
-<li>You can now run multiple instances of BlueStacks at the same time. You can switch between them by clicking on their tabs or windows.</li>
-<li>If you want to sync your actions across multiple instances, click on the sync icon on the right side of the screen.</li>
-<li>You will see a list of available instances to sync with. Select the ones you want and click on "Start Syncing".</li>
-<li>You can now perform actions on one instance and see them replicated on other instances. You can stop syncing by clicking on "Stop Syncing".</li>
-</ol>
- <h4>Using real-time translation and high definition graphics features</h4>
- <p>A final advantage of playing Fate/Grand Order on PC is that you can use real-time translation and high definition graphics features to enhance your game. Real-time translation allows you to translate any text in the game into your preferred language, which can be useful if you want to play in different regions or understand the story better. High definition graphics allows you to adjust the graphics quality and resolution of the game, which can make it more immersive and detailed. To use real-time translation and high definition graphics features, follow these steps:</p>
- <ol>
-<li>Open Fate/Grand Order on BlueStacks and go to the game screen.</li>
-<li>Click on the settings icon on the right side of the screen.</li>
-<li>To use real-time translation, click on the translation icon and select your preferred language. You can also toggle the translation mode to auto or manual. You can also adjust the translation speed and accuracy.</li>
-<li>To use high definition graphics, click on the graphics icon and select your preferred graphics quality and resolution. You can also enable or disable anti-aliasing, FPS, and V-sync.</li>
-<li>When you are done, click on "Save" and close the window.</li>
-<li>You can now enjoy Fate/Grand Order on PC with real-time translation and high definition graphics.</li>
-</ol>
- <h2>Conclusion</h2>
- <h3>Summarizing the main points of the article</h3>
- <p>In this article, we have shown you how to download and play Fate/Grand Order on PC using an emulator. We have also explained what is Fate/Grand Order, why you should play it on PC, how to choose the best emulator for it, how to optimize your gameplay experience on PC, and how to troubleshoot common issues. By following our guide, you will be able to enjoy this amazing game on your PC with ease.</p>
- <h3>Providing some tips and tricks for playing Fate/Grand Order on PC</h3>
- <p>Before we end this article, we would like to share some tips and tricks for playing Fate/Grand Order on PC that can help you improve your game and have more fun. Here are some of them:</p>
-<ul>
-<li>Use the game's wiki or guides to learn more about the game's mechanics, characters, events, and strategies.</li>
-<li>Join the game's community on social media or forums to interact with other players, get news and updates, and participate in contests and giveaways.</li>
-<li>Backup your account data regularly using a transfer code or a bind code to avoid losing your progress or data.</li>
-<li>Use the game's support system or customer service to report any bugs, errors, or issues that you encounter in the game or the emulator.</li>
-<li>Have fun and enjoy the game!</li>
-</ul>
- <h3>Ending with a call to action or a question</h3>
- <p>We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you. Thank you for reading and happy gaming!</p>
- <h2>Frequently Asked Questions</h2>
- <h3>Is Fate/Grand Order free to play?</h3>
- <p>Yes, Fate/Grand Order is free to download and play on both mobile devices and PC. However, the game has some optional in-app purchases that can enhance your gameplay experience or speed up your progress. You can buy items such as Saint Quartz, Golden Apples, Summon Tickets, etc. using real money. However, these items are not necessary to enjoy the game and you can earn them for free by playing the game regularly.</p>
- <h3>Is Fate/Grand Order available in my region?</h3>
- <p>Fate/Grand Order is currently available in four regions: Japan, North America, China, and Korea. Each region has its own server, language, and content. You can choose which region you want to play in by downloading the corresponding version of the game from the Google Play Store or other sources. However, you cannot transfer your account or data between different regions, so make sure you choose wisely.</p>
- <h3>How can I get more Servants in Fate/Grand Order?</h3>
- <p>You can get more Servants in Fate/Grand Order by using the Summon system. The Summon system allows you to spend Saint Quartz or Summon Tickets to randomly obtain Servants or Craft Essences (items that can boost your Servants' abilities). There are different types of Summons such as Story Summons, Event Summons, Limited Summons, etc. that offer different rates and pools of Servants and Craft Essences. You can also get some Servants for free by completing certain quests or events in the game.</p>
- <h3>How can I level up my Servants in Fate/Grand Order?</h3>
- <p>You can level up your Servants in Fate/Grand Order by using EXP Cards. EXP Cards are items that can grant a certain amount of experience points to your Servants when used. You can obtain EXP Cards by completing Daily Quests or Events in the game. You can also use other Servants or Craft Essences as materials to level up your Servants, but this will consume them permanently. You can also increase your Servants' level cap by using Ascension Materials or Grails.</p>
- <h3>How can I improve my combat skills in Fate/Grand Order?</h3>
- <p>You can improve your combat skills in Fate/Grand Order by learning the basics of the combat system and applying some strategies. The combat system is based on a turn-based card system where you have to select three cards from a pool of five cards every turn. The cards represent your Serv ants' actions, such as Buster (red), Arts (blue), or Quick (green). The cards have different effects and combinations that can affect your damage, NP gauge, critical stars, etc. You also have to consider your Servants' skills, Noble Phantasms, class advantages, and team composition when fighting. You can learn more about the combat system and strategies by reading the game's wiki or guides, or by watching some gameplay videos or tutorials online.</p>
- <h2></h2>
- <p>That's it for this article. We hope that you have learned how to download and play Fate/Grand Order on PC using an emulator. We also hope that you have enjoyed reading this article and found it useful and interesting. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you. Thank you for reading and happy gaming!</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/2-2/blockchain.ai/README.md b/spaces/2-2/blockchain.ai/README.md
deleted file mode 100644
index 0ab6c87ab6775c175ff291e1d636b4972d7b11d9..0000000000000000000000000000000000000000
--- a/spaces/2-2/blockchain.ai/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Blockchain.ai
-emoji: 👀
-colorFrom: blue
-colorTo: gray
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/2023Liu2023/bingo/src/pages/api/healthz.ts b/spaces/2023Liu2023/bingo/src/pages/api/healthz.ts
deleted file mode 100644
index f6ae44ff0fd66ccd3f7feaa550025fbf2a83bf77..0000000000000000000000000000000000000000
--- a/spaces/2023Liu2023/bingo/src/pages/api/healthz.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  res.status(200).end('ok')
-}
diff --git a/spaces/656-156/Real-CUGAN/app.py b/spaces/656-156/Real-CUGAN/app.py
deleted file mode 100644
index 2439c5cec6b61e8a517f957daf710cbb6b5c3cf6..0000000000000000000000000000000000000000
--- a/spaces/656-156/Real-CUGAN/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from upcunet_v3 import RealWaifuUpScaler
-import gradio as gr
-import time
-import logging
-import os
-from PIL import ImageOps
-import numpy as np
-import math
-
-
-def greet(input_img, input_model_name, input_tile_mode):
-    # if input_img.size[0] * input_img.size[1] > 256 * 256:
-    #     y = int(math.sqrt(256*256/input_img.size[0]*input_img.size[1]))
-    #     x = int(input_img.size[0]/input_img.size[1]*y)
-    #     input_img = ImageOps.fit(input_img, (x, y))
-    input_img = np.array(input_img)
-    if input_model_name not in model_cache:
-        t1 = time.time()
-        upscaler = RealWaifuUpScaler(input_model_name[2], ModelPath + input_model_name, half=False, device="cpu")
-        t2 = time.time()
-        logger.info(f'load model time, {t2 - t1}')
-        model_cache[input_model_name] = upscaler
-    else:
-        upscaler = model_cache[input_model_name]
-        logger.info(f'load model from cache')
-
-    start = time.time()
-    result = upscaler(input_img, tile_mode=input_tile_mode)
-    end = time.time()
-    logger.info(f'input_model_name, {input_model_name}')
-    logger.info(f'input_tile_mode, {input_tile_mode}')
-    logger.info(f'input shape, {input_img.shape}')
-    logger.info(f'output shape, {result.shape}')
-    logger.info(f'speed time, {end - start}')
-    return result
-
-
-if __name__ == '__main__':
-    logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s")
-    logger = logging.getLogger()
-
-    ModelPath = "weights_v3/"
-    model_cache = {}
-
-    input_model_name = gr.inputs.Dropdown(os.listdir(ModelPath), default="up2x-latest-denoise2x.pth", label='选择model')
-    input_tile_mode = gr.inputs.Dropdown([0, 1, 2, 3, 4], default=2, label='选择tile_mode')
-    input_img = gr.inputs.Image(label='image', type='pil')
-
-    inputs = [input_img, input_model_name, input_tile_mode]
-    outputs = "image"
-    iface = gr.Interface(fn=greet,
-                         inputs=inputs,
-                         outputs=outputs,
-                         allow_screenshot=False,
-                         allow_flagging='never',
-                         examples=[['test-img.jpg', "up2x-latest-denoise2x.pth", 2]],
-                         article='[https://github.com/bilibili/ailab/tree/main/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)<br>'
-                                 '感谢b站开源的项目，图片过大会导致内存不足，所有我将图片裁剪小，想体验大图片的效果请自行前往上面的链接。<br>'
-                                 '修改bbb'
-                                 'The large image will lead to memory limit exceeded. So I crop and resize image. '
-                                 'If you want to experience the large image, please go to the link above.')
-    iface.launch()
diff --git a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/spec_utils.py b/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/spec_utils.py
deleted file mode 100644
index a9634fd51ff47bf90211839231774719154c37cf..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/spec_utils.py
+++ /dev/null
@@ -1,672 +0,0 @@
-import hashlib
-import json
-import math
-import os
-
-import librosa
-import numpy as np
-import soundfile as sf
-from tqdm import tqdm
-
-
-def crop_center(h1, h2):
-    h1_shape = h1.size()
-    h2_shape = h2.size()
-
-    if h1_shape[3] == h2_shape[3]:
-        return h1
-    elif h1_shape[3] < h2_shape[3]:
-        raise ValueError("h1_shape[3] must be greater than h2_shape[3]")
-
-    # s_freq = (h2_shape[2] - h1_shape[2]) // 2
-    # e_freq = s_freq + h1_shape[2]
-    s_time = (h1_shape[3] - h2_shape[3]) // 2
-    e_time = s_time + h2_shape[3]
-    h1 = h1[:, :, :, s_time:e_time]
-
-    return h1
-
-
-def wave_to_spectrogram(
-    wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
-):
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-
-    spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def wave_to_spectrogram_mt(
-    wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
-):
-    import threading
-
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-
-    def run_thread(**kwargs):
-        global spec_left
-        spec_left = librosa.stft(**kwargs)
-
-    thread = threading.Thread(
-        target=run_thread,
-        kwargs={"y": wave_left, "n_fft": n_fft, "hop_length": hop_length},
-    )
-    thread.start()
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-    thread.join()
-
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def combine_spectrograms(specs, mp):
-    l = min([specs[i].shape[2] for i in specs])
-    spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64)
-    offset = 0
-    bands_n = len(mp.param["band"])
-
-    for d in range(1, bands_n + 1):
-        h = mp.param["band"][d]["crop_stop"] - mp.param["band"][d]["crop_start"]
-        spec_c[:, offset : offset + h, :l] = specs[d][
-            :, mp.param["band"][d]["crop_start"] : mp.param["band"][d]["crop_stop"], :l
-        ]
-        offset += h
-
-    if offset > mp.param["bins"]:
-        raise ValueError("Too much bins")
-
-    # lowpass fiter
-    if (
-        mp.param["pre_filter_start"] > 0
-    ):  # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
-        if bands_n == 1:
-            spec_c = fft_lp_filter(
-                spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"]
-            )
-        else:
-            gp = 1
-            for b in range(
-                mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]
-            ):
-                g = math.pow(
-                    10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0
-                )
-                gp = g
-                spec_c[:, b, :] *= g
-
-    return np.asfortranarray(spec_c)
-
-
-def spectrogram_to_image(spec, mode="magnitude"):
-    if mode == "magnitude":
-        if np.iscomplexobj(spec):
-            y = np.abs(spec)
-        else:
-            y = spec
-        y = np.log10(y**2 + 1e-8)
-    elif mode == "phase":
-        if np.iscomplexobj(spec):
-            y = np.angle(spec)
-        else:
-            y = spec
-
-    y -= y.min()
-    y *= 255 / y.max()
-    img = np.uint8(y)
-
-    if y.ndim == 3:
-        img = img.transpose(1, 2, 0)
-        img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2)
-
-    return img
-
-
-def reduce_vocal_aggressively(X, y, softmask):
-    v = X - y
-    y_mag_tmp = np.abs(y)
-    v_mag_tmp = np.abs(v)
-
-    v_mask = v_mag_tmp > y_mag_tmp
-    y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
-
-    return y_mag * np.exp(1.0j * np.angle(y))
-
-
-def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
-    if min_range < fade_size * 2:
-        raise ValueError("min_range must be >= fade_area * 2")
-
-    mag = mag.copy()
-
-    idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
-    starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
-    ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
-    uninformative = np.where(ends - starts > min_range)[0]
-    if len(uninformative) > 0:
-        starts = starts[uninformative]
-        ends = ends[uninformative]
-        old_e = None
-        for s, e in zip(starts, ends):
-            if old_e is not None and s - old_e < fade_size:
-                s = old_e - fade_size * 2
-
-            if s != 0:
-                weight = np.linspace(0, 1, fade_size)
-                mag[:, :, s : s + fade_size] += weight * ref[:, :, s : s + fade_size]
-            else:
-                s -= fade_size
-
-            if e != mag.shape[2]:
-                weight = np.linspace(1, 0, fade_size)
-                mag[:, :, e - fade_size : e] += weight * ref[:, :, e - fade_size : e]
-            else:
-                e += fade_size
-
-            mag[:, :, s + fade_size : e - fade_size] += ref[
-                :, :, s + fade_size : e - fade_size
-            ]
-            old_e = e
-
-    return mag
-
-
-def align_wave_head_and_tail(a, b):
-    l = min([a[0].size, b[0].size])
-
-    return a[:l, :l], b[:l, :l]
-
-
-def cache_or_load(mix_path, inst_path, mp):
-    mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
-    inst_basename = os.path.splitext(os.path.basename(inst_path))[0]
-
-    cache_dir = "mph{}".format(
-        hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode("utf-8")).hexdigest()
-    )
-    mix_cache_dir = os.path.join("cache", cache_dir)
-    inst_cache_dir = os.path.join("cache", cache_dir)
-
-    os.makedirs(mix_cache_dir, exist_ok=True)
-    os.makedirs(inst_cache_dir, exist_ok=True)
-
-    mix_cache_path = os.path.join(mix_cache_dir, mix_basename + ".npy")
-    inst_cache_path = os.path.join(inst_cache_dir, inst_basename + ".npy")
-
-    if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path):
-        X_spec_m = np.load(mix_cache_path)
-        y_spec_m = np.load(inst_cache_path)
-    else:
-        X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
-
-        for d in range(len(mp.param["band"]), 0, -1):
-            bp = mp.param["band"][d]
-
-            if d == len(mp.param["band"]):  # high-end band
-                X_wave[d], _ = librosa.load(
-                    mix_path, bp["sr"], False, dtype=np.float32, res_type=bp["res_type"]
-                )
-                y_wave[d], _ = librosa.load(
-                    inst_path,
-                    bp["sr"],
-                    False,
-                    dtype=np.float32,
-                    res_type=bp["res_type"],
-                )
-            else:  # lower bands
-                X_wave[d] = librosa.resample(
-                    X_wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-                y_wave[d] = librosa.resample(
-                    y_wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-
-            X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
-
-            X_spec_s[d] = wave_to_spectrogram(
-                X_wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-            y_spec_s[d] = wave_to_spectrogram(
-                y_wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-
-        del X_wave, y_wave
-
-        X_spec_m = combine_spectrograms(X_spec_s, mp)
-        y_spec_m = combine_spectrograms(y_spec_s, mp)
-
-        if X_spec_m.shape != y_spec_m.shape:
-            raise ValueError("The combined spectrograms are different: " + mix_path)
-
-        _, ext = os.path.splitext(mix_path)
-
-        np.save(mix_cache_path, X_spec_m)
-        np.save(inst_cache_path, y_spec_m)
-
-    return X_spec_m, y_spec_m
-
-
-def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse):
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    wave_left = librosa.istft(spec_left, hop_length=hop_length)
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray(
-            [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
-        )
-    elif mid_side_b2:
-        return np.asfortranarray(
-            [
-                np.add(wave_right / 1.25, 0.4 * wave_left),
-                np.subtract(wave_left / 1.25, 0.4 * wave_right),
-            ]
-        )
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-
-
-def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
-    import threading
-
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    def run_thread(**kwargs):
-        global wave_left
-        wave_left = librosa.istft(**kwargs)
-
-    thread = threading.Thread(
-        target=run_thread, kwargs={"stft_matrix": spec_left, "hop_length": hop_length}
-    )
-    thread.start()
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-    thread.join()
-
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray(
-            [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
-        )
-    elif mid_side_b2:
-        return np.asfortranarray(
-            [
-                np.add(wave_right / 1.25, 0.4 * wave_left),
-                np.subtract(wave_left / 1.25, 0.4 * wave_right),
-            ]
-        )
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-
-
-def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
-    wave_band = {}
-    bands_n = len(mp.param["band"])
-    offset = 0
-
-    for d in range(1, bands_n + 1):
-        bp = mp.param["band"][d]
-        spec_s = np.ndarray(
-            shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex
-        )
-        h = bp["crop_stop"] - bp["crop_start"]
-        spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[
-            :, offset : offset + h, :
-        ]
-
-        offset += h
-        if d == bands_n:  # higher
-            if extra_bins_h:  # if --high_end_process bypass
-                max_bin = bp["n_fft"] // 2
-                spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[
-                    :, :extra_bins_h, :
-                ]
-            if bp["hpf_start"] > 0:
-                spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
-            if bands_n == 1:
-                wave = spectrogram_to_wave(
-                    spec_s,
-                    bp["hl"],
-                    mp.param["mid_side"],
-                    mp.param["mid_side_b2"],
-                    mp.param["reverse"],
-                )
-            else:
-                wave = np.add(
-                    wave,
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                )
-        else:
-            sr = mp.param["band"][d + 1]["sr"]
-            if d == 1:  # lower
-                spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
-                wave = librosa.resample(
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                    bp["sr"],
-                    sr,
-                    res_type="sinc_fastest",
-                )
-            else:  # mid
-                spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
-                spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
-                wave2 = np.add(
-                    wave,
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                )
-                # wave = librosa.core.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
-                wave = librosa.core.resample(wave2, bp["sr"], sr, res_type="scipy")
-
-    return wave.T
-
-
-def fft_lp_filter(spec, bin_start, bin_stop):
-    g = 1.0
-    for b in range(bin_start, bin_stop):
-        g -= 1 / (bin_stop - bin_start)
-        spec[:, b, :] = g * spec[:, b, :]
-
-    spec[:, bin_stop:, :] *= 0
-
-    return spec
-
-
-def fft_hp_filter(spec, bin_start, bin_stop):
-    g = 1.0
-    for b in range(bin_start, bin_stop, -1):
-        g -= 1 / (bin_start - bin_stop)
-        spec[:, b, :] = g * spec[:, b, :]
-
-    spec[:, 0 : bin_stop + 1, :] *= 0
-
-    return spec
-
-
-def mirroring(a, spec_m, input_high_end, mp):
-    if "mirroring" == a:
-        mirror = np.flip(
-            np.abs(
-                spec_m[
-                    :,
-                    mp.param["pre_filter_start"]
-                    - 10
-                    - input_high_end.shape[1] : mp.param["pre_filter_start"]
-                    - 10,
-                    :,
-                ]
-            ),
-            1,
-        )
-        mirror = mirror * np.exp(1.0j * np.angle(input_high_end))
-
-        return np.where(
-            np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror
-        )
-
-    if "mirroring2" == a:
-        mirror = np.flip(
-            np.abs(
-                spec_m[
-                    :,
-                    mp.param["pre_filter_start"]
-                    - 10
-                    - input_high_end.shape[1] : mp.param["pre_filter_start"]
-                    - 10,
-                    :,
-                ]
-            ),
-            1,
-        )
-        mi = np.multiply(mirror, input_high_end * 1.7)
-
-        return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
-
-
-def ensembling(a, specs):
-    for i in range(1, len(specs)):
-        if i == 1:
-            spec = specs[0]
-
-        ln = min([spec.shape[2], specs[i].shape[2]])
-        spec = spec[:, :, :ln]
-        specs[i] = specs[i][:, :, :ln]
-
-        if "min_mag" == a:
-            spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
-        if "max_mag" == a:
-            spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
-
-    return spec
-
-
-def stft(wave, nfft, hl):
-    wave_left = np.asfortranarray(wave[0])
-    wave_right = np.asfortranarray(wave[1])
-    spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
-    spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def istft(spec, hl):
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    wave_left = librosa.istft(spec_left, hop_length=hl)
-    wave_right = librosa.istft(spec_right, hop_length=hl)
-    wave = np.asfortranarray([wave_left, wave_right])
-
-
-if __name__ == "__main__":
-    import argparse
-    import sys
-    import time
-
-    import cv2
-    from model_param_init import ModelParameters
-
-    p = argparse.ArgumentParser()
-    p.add_argument(
-        "--algorithm",
-        "-a",
-        type=str,
-        choices=["invert", "invert_p", "min_mag", "max_mag", "deep", "align"],
-        default="min_mag",
-    )
-    p.add_argument(
-        "--model_params",
-        "-m",
-        type=str,
-        default=os.path.join("modelparams", "1band_sr44100_hl512.json"),
-    )
-    p.add_argument("--output_name", "-o", type=str, default="output")
-    p.add_argument("--vocals_only", "-v", action="store_true")
-    p.add_argument("input", nargs="+")
-    args = p.parse_args()
-
-    start_time = time.time()
-
-    if args.algorithm.startswith("invert") and len(args.input) != 2:
-        raise ValueError("There should be two input files.")
-
-    if not args.algorithm.startswith("invert") and len(args.input) < 2:
-        raise ValueError("There must be at least two input files.")
-
-    wave, specs = {}, {}
-    mp = ModelParameters(args.model_params)
-
-    for i in range(len(args.input)):
-        spec = {}
-
-        for d in range(len(mp.param["band"]), 0, -1):
-            bp = mp.param["band"][d]
-
-            if d == len(mp.param["band"]):  # high-end band
-                wave[d], _ = librosa.load(
-                    args.input[i],
-                    bp["sr"],
-                    False,
-                    dtype=np.float32,
-                    res_type=bp["res_type"],
-                )
-
-                if len(wave[d].shape) == 1:  # mono to stereo
-                    wave[d] = np.array([wave[d], wave[d]])
-            else:  # lower bands
-                wave[d] = librosa.resample(
-                    wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-
-            spec[d] = wave_to_spectrogram(
-                wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-
-        specs[i] = combine_spectrograms(spec, mp)
-
-    del wave
-
-    if args.algorithm == "deep":
-        d_spec = np.where(np.abs(specs[0]) <= np.abs(spec[1]), specs[0], spec[1])
-        v_spec = d_spec - specs[1]
-        sf.write(
-            os.path.join("{}.wav".format(args.output_name)),
-            cmb_spectrogram_to_wave(v_spec, mp),
-            mp.param["sr"],
-        )
-
-    if args.algorithm.startswith("invert"):
-        ln = min([specs[0].shape[2], specs[1].shape[2]])
-        specs[0] = specs[0][:, :, :ln]
-        specs[1] = specs[1][:, :, :ln]
-
-        if "invert_p" == args.algorithm:
-            X_mag = np.abs(specs[0])
-            y_mag = np.abs(specs[1])
-            max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
-            v_spec = specs[1] - max_mag * np.exp(1.0j * np.angle(specs[0]))
-        else:
-            specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
-            v_spec = specs[0] - specs[1]
-
-            if not args.vocals_only:
-                X_mag = np.abs(specs[0])
-                y_mag = np.abs(specs[1])
-                v_mag = np.abs(v_spec)
-
-                X_image = spectrogram_to_image(X_mag)
-                y_image = spectrogram_to_image(y_mag)
-                v_image = spectrogram_to_image(v_mag)
-
-                cv2.imwrite("{}_X.png".format(args.output_name), X_image)
-                cv2.imwrite("{}_y.png".format(args.output_name), y_image)
-                cv2.imwrite("{}_v.png".format(args.output_name), v_image)
-
-                sf.write(
-                    "{}_X.wav".format(args.output_name),
-                    cmb_spectrogram_to_wave(specs[0], mp),
-                    mp.param["sr"],
-                )
-                sf.write(
-                    "{}_y.wav".format(args.output_name),
-                    cmb_spectrogram_to_wave(specs[1], mp),
-                    mp.param["sr"],
-                )
-
-        sf.write(
-            "{}_v.wav".format(args.output_name),
-            cmb_spectrogram_to_wave(v_spec, mp),
-            mp.param["sr"],
-        )
-    else:
-        if not args.algorithm == "deep":
-            sf.write(
-                os.path.join("ensembled", "{}.wav".format(args.output_name)),
-                cmb_spectrogram_to_wave(ensembling(args.algorithm, specs), mp),
-                mp.param["sr"],
-            )
-
-    if args.algorithm == "align":
-        trackalignment = [
-            {
-                "file1": '"{}"'.format(args.input[0]),
-                "file2": '"{}"'.format(args.input[1]),
-            }
-        ]
-
-        for i, e in tqdm(enumerate(trackalignment), desc="Performing Alignment..."):
-            os.system(f"python lib/align_tracks.py {e['file1']} {e['file2']}")
-
-    # print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
diff --git a/spaces/A666sxr/Genshin_TTS/stft_loss.py b/spaces/A666sxr/Genshin_TTS/stft_loss.py
deleted file mode 100644
index 08120d2a923b77b04ed231195bc8b5aa4568602b..0000000000000000000000000000000000000000
--- a/spaces/A666sxr/Genshin_TTS/stft_loss.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""STFT-based Loss modules."""
-
-import torch
-import torch.nn.functional as F
-
-
-def stft(x, fft_size, hop_size, win_length, window):
-    """Perform STFT and convert to magnitude spectrogram.
-    Args:
-        x (Tensor): Input signal tensor (B, T).
-        fft_size (int): FFT size.
-        hop_size (int): Hop size.
-        win_length (int): Window length.
-        window (str): Window function type.
-    Returns:
-        Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
-    """
-    x_stft = torch.stft(x, fft_size, hop_size, win_length, window.to(x.device))
-    real = x_stft[..., 0]
-    imag = x_stft[..., 1]
-
-    # NOTE(kan-bayashi): clamp is needed to avoid nan or inf
-    return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1)
-
-
-class SpectralConvergengeLoss(torch.nn.Module):
-    """Spectral convergence loss module."""
-
-    def __init__(self):
-        """Initilize spectral convergence loss module."""
-        super(SpectralConvergengeLoss, self).__init__()
-
-    def forward(self, x_mag, y_mag):
-        """Calculate forward propagation.
-        Args:
-            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
-            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
-        Returns:
-            Tensor: Spectral convergence loss value.
-        """
-        return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro")
-
-
-class LogSTFTMagnitudeLoss(torch.nn.Module):
-    """Log STFT magnitude loss module."""
-
-    def __init__(self):
-        """Initilize los STFT magnitude loss module."""
-        super(LogSTFTMagnitudeLoss, self).__init__()
-
-    def forward(self, x_mag, y_mag):
-        """Calculate forward propagation.
-        Args:
-            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
-            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
-        Returns:
-            Tensor: Log STFT magnitude loss value.
-        """
-        return F.l1_loss(torch.log(y_mag), torch.log(x_mag))
-
-
-class STFTLoss(torch.nn.Module):
-    """STFT loss module."""
-
-    def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"):
-        """Initialize STFT loss module."""
-        super(STFTLoss, self).__init__()
-        self.fft_size = fft_size
-        self.shift_size = shift_size
-        self.win_length = win_length
-        self.window = getattr(torch, window)(win_length)
-        self.spectral_convergenge_loss = SpectralConvergengeLoss()
-        self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
-
-    def forward(self, x, y):
-        """Calculate forward propagation.
-        Args:
-            x (Tensor): Predicted signal (B, T).
-            y (Tensor): Groundtruth signal (B, T).
-        Returns:
-            Tensor: Spectral convergence loss value.
-            Tensor: Log STFT magnitude loss value.
-        """
-        x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
-        y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
-        sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
-        mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
-
-        return sc_loss, mag_loss
-
-
-class MultiResolutionSTFTLoss(torch.nn.Module):
-    """Multi resolution STFT loss module."""
-
-    def __init__(self,
-                 fft_sizes=[1024, 2048, 512],
-                 hop_sizes=[120, 240, 50],
-                 win_lengths=[600, 1200, 240],
-                 window="hann_window"):
-        """Initialize Multi resolution STFT loss module.
-        Args:
-            fft_sizes (list): List of FFT sizes.
-            hop_sizes (list): List of hop sizes.
-            win_lengths (list): List of window lengths.
-            window (str): Window function type.
-        """
-        super(MultiResolutionSTFTLoss, self).__init__()
-        assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
-        self.stft_losses = torch.nn.ModuleList()
-        for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
-            self.stft_losses += [STFTLoss(fs, ss, wl, window)]
-
-    def forward(self, x, y):
-        """Calculate forward propagation.
-        Args:
-            x (Tensor): Predicted signal (B, T).
-            y (Tensor): Groundtruth signal (B, T).
-        Returns:
-            Tensor: Multi resolution spectral convergence loss value.
-            Tensor: Multi resolution log STFT magnitude loss value.
-        """
-        sc_loss = 0.0
-        mag_loss = 0.0
-        for f in self.stft_losses:
-            sc_l, mag_l = f(x, y)
-            sc_loss += sc_l
-            mag_loss += mag_l
-        sc_loss /= len(self.stft_losses)
-        mag_loss /= len(self.stft_losses)
-
-        return sc_loss, mag_loss
\ No newline at end of file
diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/variational_autoencoder/autoencoder.py b/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/variational_autoencoder/autoencoder.py
deleted file mode 100644
index cfbdceba0e1171b052f797885530bacd0f3c73d5..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/variational_autoencoder/autoencoder.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import torch
-from audioldm.latent_diffusion.ema import *
-from audioldm.variational_autoencoder.modules import Encoder, Decoder
-from audioldm.variational_autoencoder.distributions import DiagonalGaussianDistribution
-
-from audioldm.hifigan.utilities import get_vocoder, vocoder_infer
-
-class AutoencoderKL(nn.Module):
-    def __init__(
-        self,
-        ddconfig=None,
-        lossconfig=None,
-        image_key="fbank",
-        embed_dim=None,
-        time_shuffle=1,
-        subband=1,
-        ckpt_path=None,
-        reload_from_ckpt=None,
-        ignore_keys=[],
-        colorize_nlabels=None,
-        monitor=None,
-        base_learning_rate=1e-5,
-    ):
-        super().__init__()
-
-        self.encoder = Encoder(**ddconfig)
-        self.decoder = Decoder(**ddconfig)
-
-        self.subband = int(subband)
-
-        if self.subband > 1:
-            print("Use subband decomposition %s" % self.subband)
-
-        self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
-
-        self.vocoder = get_vocoder(None, "cpu")
-        self.embed_dim = embed_dim
-
-        if monitor is not None:
-            self.monitor = monitor
-
-        self.time_shuffle = time_shuffle
-        self.reload_from_ckpt = reload_from_ckpt
-        self.reloaded = False
-        self.mean, self.std = None, None
-
-    def encode(self, x):
-        # x = self.time_shuffle_operation(x)
-        x = self.freq_split_subband(x)
-        h = self.encoder(x)
-        moments = self.quant_conv(h)
-        posterior = DiagonalGaussianDistribution(moments)
-        return posterior
-
-    def decode(self, z):
-        z = self.post_quant_conv(z)
-        dec = self.decoder(z)
-        dec = self.freq_merge_subband(dec)
-        return dec
-
-    def decode_to_waveform(self, dec):
-        dec = dec.squeeze(1).permute(0, 2, 1)
-        wav_reconstruction = vocoder_infer(dec, self.vocoder)
-        return wav_reconstruction
-
-    def forward(self, input, sample_posterior=True):
-        posterior = self.encode(input)
-        if sample_posterior:
-            z = posterior.sample()
-        else:
-            z = posterior.mode()
-
-        if self.flag_first_run:
-            print("Latent size: ", z.size())
-            self.flag_first_run = False
-
-        dec = self.decode(z)
-
-        return dec, posterior
-
-    def freq_split_subband(self, fbank):
-        if self.subband == 1 or self.image_key != "stft":
-            return fbank
-
-        bs, ch, tstep, fbins = fbank.size()
-
-        assert fbank.size(-1) % self.subband == 0
-        assert ch == 1
-
-        return (
-            fbank.squeeze(1)
-            .reshape(bs, tstep, self.subband, fbins // self.subband)
-            .permute(0, 2, 1, 3)
-        )
-
-    def freq_merge_subband(self, subband_fbank):
-        if self.subband == 1 or self.image_key != "stft":
-            return subband_fbank
-        assert subband_fbank.size(1) == self.subband  # Channel dimension
-        bs, sub_ch, tstep, fbins = subband_fbank.size()
-        return subband_fbank.permute(0, 2, 1, 3).reshape(bs, tstep, -1).unsqueeze(1)
diff --git a/spaces/ALSv/FSW/roop/typing.py b/spaces/ALSv/FSW/roop/typing.py
deleted file mode 100644
index 1cff7440616e20bfe7b8bc287f86d11bf1b0f083..0000000000000000000000000000000000000000
--- a/spaces/ALSv/FSW/roop/typing.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from typing import Any
-
-from insightface.app.common import Face
-import numpy
-
-Face = Face
-Frame = numpy.ndarray[Any, Any]
diff --git a/spaces/AQaTaHaGoD/GoD/README.md b/spaces/AQaTaHaGoD/GoD/README.md
deleted file mode 100644
index 24004667a73123b1c92e9e56f564e9a73919a51a..0000000000000000000000000000000000000000
--- a/spaces/AQaTaHaGoD/GoD/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GoD
-emoji: 🌍
-colorFrom: red
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnest200.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnest200.py
deleted file mode 100644
index 40d8f03e7f528f8c0132bd2c19515460fd47fe70..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnest200.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# model settings
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(
-        type='ResNeSt',
-        depth=200,
-        num_stages=4,
-        stem_channels=128,
-        out_indices=(3, ),
-        style='pytorch'),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=1000,
-        in_channels=2048,
-        loss=dict(
-            type='LabelSmoothLoss',
-            label_smooth_val=0.1,
-            num_classes=1000,
-            reduction='mean',
-            loss_weight=1.0),
-        topk=(1, 5),
-        cal_acc=False),
-    train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
-)
diff --git a/spaces/Abhaykoul/Youtube_video_downloader/app.py b/spaces/Abhaykoul/Youtube_video_downloader/app.py
deleted file mode 100644
index 04e61cab265fa2dc31d98aec34bae22763f1ad48..0000000000000000000000000000000000000000
--- a/spaces/Abhaykoul/Youtube_video_downloader/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import streamlit as st
-from pytube import YouTube
-
-class YouTubeDownloader:
-    @staticmethod
-    def run():
-        st.header("YouTube Video Downloader")
-        url = st.text_input("Enter YouTube URL to download:")
-        if url:
-            YouTubeDownloader.validate_url(url)
-            with st.expander("preview video"):
-                st.video(url)
-            if st.button("Download"):
-                YouTubeDownloader.cleanup()
-                file_ = YouTubeDownloader.download_video(url)
-                st.video(file_)
-                YouTubeDownloader.helper_message()
-        st.markdown("> App made by Abhay Koul ([HelpingAI on GitHub](https://github.com/HelpingAI))")
-
-
-    @staticmethod
-    def download_video(url):
-        with st.spinner("Downloading..."):
-            local_file = (
-                YouTube(url)
-                .streams.filter(progressive=True, file_extension="mp4")
-                .first()
-                .download()
-            )
-            st.success("Downloaded")
-        return local_file
-
-    @staticmethod
-    def validate_url(url):
-        import validators
-
-        if not validators.url(url):
-            st.error("Hi there 👋 URL seems invalid 👽")
-            st.stop()
-
-    @classmethod
-    def cleanup(cls):
-        import pathlib
-        import glob
-
-        junks = glob.glob("*.mp4")
-        for junk in junks:
-            pathlib.Path(junk).unlink()
-
-    @classmethod
-    def helper_message(cls):
-        st.write(
-            "> To save the video to local computer, "
-            "click the vertical ... icon (aka hamburger button) in the bottom-right corner (in the video above) and click download."
-        )
-
-
-if __name__ == "__main__":
-    YouTubeDownloader.run()
\ No newline at end of file
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/openpose/src/util.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/openpose/src/util.py
deleted file mode 100644
index d8f622d7e54c7103d4cc43a0cdcae96a0b3145d5..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/openpose/src/util.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import numpy as np
-import math
-import cv2
-import matplotlib
-from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-from matplotlib.figure import Figure
-import numpy as np
-import matplotlib.pyplot as plt
-import cv2
-
-
-def padRightDownCorner(img, stride, padValue):
-    h = img.shape[0]
-    w = img.shape[1]
-
-    pad = 4 * [None]
-    pad[0] = 0  # up
-    pad[1] = 0  # left
-    pad[2] = 0 if (h % stride == 0) else stride - (h % stride)  # down
-    pad[3] = 0 if (w % stride == 0) else stride - (w % stride)  # right
-
-    img_padded = img
-    pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1))
-    img_padded = np.concatenate((pad_up, img_padded), axis=0)
-    pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1))
-    img_padded = np.concatenate((pad_left, img_padded), axis=1)
-    pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1))
-    img_padded = np.concatenate((img_padded, pad_down), axis=0)
-    pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1))
-    img_padded = np.concatenate((img_padded, pad_right), axis=1)
-
-    return img_padded, pad
-
-# transfer caffe model to pytorch which will match the layer name
-
-
-def transfer(model, model_weights):
-    transfered_model_weights = {}
-    for weights_name in model.state_dict().keys():
-        transfered_model_weights[weights_name] = model_weights['.'.join(
-            weights_name.split('.')[1:])]
-    return transfered_model_weights
-
-# draw the body keypoint and lims
-
-
-def draw_bodypose(canvas, candidate, subset, show_number=False):
-    stickwidth = 4
-    limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10],
-               [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17],
-               [1, 16], [16, 18], [3, 17], [6, 18]]
-
-    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0],
-              [0, 255, 85], [0, 255, 170], [0, 255, 255], [
-                  0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255],
-              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
-    for i in range(18):
-        for n in range(len(subset)):
-            index = int(subset[n][i])
-            if index == -1:
-                continue
-            x, y = candidate[index][0:2]
-            cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
-            if show_number:
-                cv2.putText(canvas, f'{index}', (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.6,
-                            (255, 255, 0), 1, cv2.LINE_AA)
-                # calc and print average
-    for i in range(17):
-        for n in range(len(subset)):
-            index = subset[n][np.array(limbSeq[i]) - 1]
-            if -1 in index:
-                continue
-            cur_canvas = canvas.copy()
-            Y = candidate[index.astype(int), 0]
-            X = candidate[index.astype(int), 1]
-            mX = np.mean(X)
-            mY = np.mean(Y)
-            length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
-            angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
-            polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(
-                length / 2), stickwidth), int(angle), 0, 360, 1)
-            cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
-            canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
-
-    return canvas
-
-# get max index of 2d array
-
-
-def npmax(array):
-    arrayindex = array.argmax(1)
-    arrayvalue = array.max(1)
-    i = arrayvalue.argmax()
-    j = arrayindex[i]
-    return i, j
-
-# get max index of 2d array
-
-
-def npmax_with_score(array):
-    arrayindex = array.argmax(1)
-    arrayvalue = array.max(1)
-    i = arrayvalue.argmax()
-    j = arrayindex[i]
-    score = array[i][j]
-    return i, j, score
diff --git a/spaces/Amrrs/DragGan-Inversion/torch_utils/ops/filtered_lrelu.cpp b/spaces/Amrrs/DragGan-Inversion/torch_utils/ops/filtered_lrelu.cpp
deleted file mode 100644
index ff4149b8b46b54d2f400ae10e44d19f20503ba1f..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/torch_utils/ops/filtered_lrelu.cpp
+++ /dev/null
@@ -1,300 +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.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "filtered_lrelu.h"
-
-//------------------------------------------------------------------------
-
-static std::tuple<torch::Tensor, torch::Tensor, int> filtered_lrelu(
-    torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
-    int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
-{
-    // Set CUDA device.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
-    // Validate arguments.
-    TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
-    TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
-    TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
-    TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
-    TORCH_CHECK(x.numel() > 0, "x is empty");
-    TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
-    TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
-    TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
-    TORCH_CHECK(fu.numel() > 0, "fu is empty");
-    TORCH_CHECK(fd.numel() > 0, "fd is empty");
-    TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
-    TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
-
-    // Figure out how much shared memory is available on the device.
-    int maxSharedBytes = 0;
-    AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
-    int sharedKB = maxSharedBytes >> 10;
-
-    // Populate enough launch parameters to check if a CUDA kernel exists.
-    filtered_lrelu_kernel_params p;
-    p.up      = up;
-    p.down    = down;
-    p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
-    p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
-    filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel<float, int32_t, false, false>(p, sharedKB);
-    if (!test_spec.exec)
-    {
-        // No kernel found - return empty tensors and indicate missing kernel with return code of -1.
-        return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
-    }
-
-    // Input/output element size.
-    int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
-
-    // Input sizes.
-    int64_t xw = (int)x.size(3);
-    int64_t xh = (int)x.size(2);
-    int64_t fut_w = (int)fu.size(-1) - 1;
-    int64_t fut_h = (int)fu.size(0)  - 1;
-    int64_t fdt_w = (int)fd.size(-1) - 1;
-    int64_t fdt_h = (int)fd.size(0)  - 1;
-
-    // Logical size of upsampled buffer.
-    int64_t cw = xw * up + (px0 + px1) - fut_w;
-    int64_t ch = xh * up + (py0 + py1) - fut_h;
-    TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
-    TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
-
-    // Compute output size and allocate.
-    int64_t yw = (cw - fdt_w + (down - 1)) / down;
-    int64_t yh = (ch - fdt_h + (down - 1)) / down;
-    TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
-    TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
-    torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
-
-    // Allocate sign tensor.
-    torch::Tensor so;
-    torch::Tensor s = si;
-    bool readSigns = !!s.numel();
-    int64_t sw_active = 0; // Active width of sign tensor.
-    if (writeSigns)
-    {
-        sw_active = yw * down - (down - 1) + fdt_w;     // Active width in elements.
-        int64_t sh = yh * down - (down - 1) + fdt_h;    // Height = active height.
-        int64_t sw = (sw_active + 15) & ~15;            // Width  = active width in elements, rounded up to multiple of 16.
-        TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
-        s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
-    }
-    else if (readSigns)
-        sw_active = s.size(3) << 2;
-
-    // Validate sign tensor if in use.
-    if (readSigns || writeSigns)
-    {
-        TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
-        TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
-        TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
-        TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
-        TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
-        TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
-    }
-
-    // Populate rest of CUDA kernel parameters.
-    p.x         = x.data_ptr();
-    p.y         = y.data_ptr();
-    p.b         = b.data_ptr();
-    p.s         = (readSigns || writeSigns) ? s.data_ptr<unsigned char>() : 0;
-    p.fu        = fu.data_ptr<float>();
-    p.fd        = fd.data_ptr<float>();
-    p.pad0      = make_int2(px0, py0);
-    p.gain      = gain;
-    p.slope     = slope;
-    p.clamp     = clamp;
-    p.flip      = (flip_filters) ? 1 : 0;
-    p.xShape    = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.yShape    = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
-    p.sShape    = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
-    p.sOfs      = make_int2(sx, sy);
-    p.swLimit   = (sw_active + 3) >> 2; // Rounded up to bytes.
-
-    // x, y, b strides are in bytes.
-    p.xStride   = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
-    p.yStride   = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
-    p.bStride   = sz * b.stride(0);
-
-    // fu, fd strides are in elements.
-    p.fuStride  = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
-    p.fdStride  = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
-
-    // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
-    bool index64b = false;
-    if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
-    if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
-    if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) >  INT_MAX) index64b = true;
-    if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
-    if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) >  INT_MAX) index64b = true;
-    if (s.numel() > INT_MAX) index64b = true;
-
-    // Choose CUDA kernel.
-    filtered_lrelu_kernel_spec spec = { 0 };
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
-    {
-        if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
-        {
-            // Choose kernel based on index type, datatype and sign read/write modes.
-            if      (!index64b &&  writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, true,  false>(p, sharedKB);
-            else if (!index64b && !writeSigns &&  readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, false, true >(p, sharedKB);
-            else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, false, false>(p, sharedKB);
-            else if ( index64b &&  writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, true,  false>(p, sharedKB);
-            else if ( index64b && !writeSigns &&  readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, false, true >(p, sharedKB);
-            else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, false, false>(p, sharedKB);
-        }
-    });
-    TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    int bx = spec.numWarps * 32;
-    int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
-    int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
-    int gz = p.yShape.z * p.yShape.w;
-
-    // Repeat multiple horizontal tiles in a CTA?
-    if (spec.xrep)
-    {
-        p.tilesXrep = spec.xrep;
-        p.tilesXdim = gx;
-
-        gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
-        std::swap(gx, gy);
-    }
-    else
-    {
-        p.tilesXrep = 0;
-        p.tilesXdim = 0;
-    }
-
-    // Launch filter setup kernel.
-    AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
-
-    // Copy kernels to constant memory.
-    if      ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters<true,  false>(at::cuda::getCurrentCUDAStream())));
-    else if (!writeSigns &&  readSigns) AT_CUDA_CHECK((copy_filters<false, true >(at::cuda::getCurrentCUDAStream())));
-    else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters<false, false>(at::cuda::getCurrentCUDAStream())));
-
-    // Set cache and shared memory configurations for main kernel.
-    AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
-    if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
-        AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
-    AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
-
-    // Launch main kernel.
-    const int maxSubGz = 65535; // CUDA maximum for block z dimension.
-    for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
-    {
-        p.blockZofs = zofs;
-        int subGz = std::min(maxSubGz, gz - zofs);
-        AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
-    }
-
-    // Done.
-    return std::make_tuple(y, so, 0);
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
-{
-    // Set CUDA device.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
-    // Validate arguments.
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
-    TORCH_CHECK(x.numel() > 0, "x is empty");
-    TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
-
-    // Output signs if we don't have sign input.
-    torch::Tensor so;
-    torch::Tensor s = si;
-    bool readSigns = !!s.numel();
-    if (writeSigns)
-    {
-        int64_t sw = x.size(3);
-        sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
-        s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
-    }
-
-    // Validate sign tensor if in use.
-    if (readSigns || writeSigns)
-    {
-        TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
-        TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
-        TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
-        TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
-        TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
-        TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
-    }
-
-    // Initialize CUDA kernel parameters.
-    filtered_lrelu_act_kernel_params p;
-    p.x         = x.data_ptr();
-    p.s         = (readSigns || writeSigns) ? s.data_ptr<unsigned char>() : 0;
-    p.gain      = gain;
-    p.slope     = slope;
-    p.clamp     = clamp;
-    p.xShape    = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.xStride   = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
-    p.sShape    = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
-    p.sOfs      = make_int2(sx, sy);
-
-    // Choose CUDA kernel.
-    void* func = 0;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
-    {
-        if (writeSigns)
-            func = choose_filtered_lrelu_act_kernel<scalar_t, true, false>();
-        else if (readSigns)
-            func = choose_filtered_lrelu_act_kernel<scalar_t, false, true>();
-        else
-            func = choose_filtered_lrelu_act_kernel<scalar_t, false, false>();
-    });
-    TORCH_CHECK(func, "internal error - CUDA kernel not found");
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    int bx = 128; // 4 warps per block.
-
-    // Logical size of launch = writeSigns ? p.s : p.x
-    uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
-    uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
-    uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
-    gx = (gx - 1) / bx + 1;
-
-    // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
-    const uint32_t gmax = 65535;
-    gy = std::min(gy, gmax);
-    gz = std::min(gz, gmax);
-
-    // Launch.
-    AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
-    return so;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("filtered_lrelu",      &filtered_lrelu);      // The whole thing.
-    m.def("filtered_lrelu_act_", &filtered_lrelu_act);  // Activation and sign tensor handling only. Modifies data tensor in-place.
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/gn+ws/README.md b/spaces/Andy1621/uniformer_image_detection/configs/gn+ws/README.md
deleted file mode 100644
index 988fb13ee43156cce75886af0e3b44eaf97a0a04..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/gn+ws/README.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# Weight Standardization
-
-## Introduction
-
-[ALGORITHM]
-
-```
-@article{weightstandardization,
-  author    = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
-  title     = {Weight Standardization},
-  journal   = {arXiv preprint arXiv:1903.10520},
-  year      = {2019},
-}
-```
-
-## Results and Models
-
-Faster R-CNN
-
-| Backbone  | Style   | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
-|:---------:|:-------:|:-------------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:|
-| R-50-FPN  | pytorch | GN+WS         | 1x      | 5.9      | 11.7           | 39.7   | -       | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130_210936.log.json) |
-| R-101-FPN | pytorch | GN+WS         | 1x      | 8.9      | 9.0            | 41.7   | -       | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205_232146.log.json) |
-| X-50-32x4d-FPN | pytorch | GN+WS    | 1x      | 7.0      | 10.3           | 40.7   | -       | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203_220113.log.json) |
-| X-101-32x4d-FPN | pytorch | GN+WS   | 1x      | 10.8     | 7.6            | 42.1   | -       | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212_195302.log.json) |
-
-Mask R-CNN
-
-| Backbone  | Style   | Normalization | Lr schd   | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
-|:---------:|:-------:|:-------------:|:---------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:|
-| R-50-FPN  | pytorch | GN+WS         | 2x        | 7.3      | 10.5       | 40.6        | 36.6    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226_062128.log.json) |
-| R-101-FPN | pytorch | GN+WS         | 2x        | 10.3     | 8.6        | 42.0        | 37.7    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212_213627.log.json) |
-| X-50-32x4d-FPN | pytorch | GN+WS    | 2x        | 8.4      | 9.3       | 41.1        | 37.0    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216_201500.log.json) |
-| X-101-32x4d-FPN | pytorch | GN+WS   | 2x        | 12.2     | 7.1       | 42.1        | 37.9    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319_104101.log.json) |
-| R-50-FPN  | pytorch | GN+WS         | 20-23-24e | 7.3      | -        | 41.1        | 37.1    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213_035123.log.json) |
-| R-101-FPN | pytorch | GN+WS         | 20-23-24e | 10.3     | -        | 43.1        | 38.6    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213_130142.log.json) |
-| X-50-32x4d-FPN | pytorch | GN+WS    | 20-23-24e | 8.4      | -        | 42.1        | 38.0    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226_093732.log.json) |
-| X-101-32x4d-FPN | pytorch | GN+WS   | 20-23-24e | 12.2     | -        | 42.7        | 38.5    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316_013741.log.json) |
-
-Note:
-
-- GN+WS requires about 5% more memory than GN, and it is only 5% slower than GN.
-- In the paper, a 20-23-24e lr schedule is used instead of 2x.
-- The X-50-GN and X-101-GN pretrained models are also shared by the authors.
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py
deleted file mode 100644
index 104d6d43bd958d49f75d54965b326ebac29ae330..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py
+++ /dev/null
@@ -1,16 +0,0 @@
-_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://regnetx_12gf',
-    backbone=dict(
-        type='RegNet',
-        arch='regnetx_12gf',
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[224, 448, 896, 2240],
-        out_channels=256,
-        num_outs=5))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/fcos.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/fcos.py
deleted file mode 100644
index 58485c1864a11a66168b7597f345ea759ce20551..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/fcos.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class FCOS(SingleStageDetector):
-    """Implementation of `FCOS <https://arxiv.org/abs/1904.01355>`_"""
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg,
-                                   test_cfg, pretrained)
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/deeplabv3_unet_s5-d16.py b/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/deeplabv3_unet_s5-d16.py
deleted file mode 100644
index 0cd262999d8b2cb8e14a5c32190ae73f479d8e81..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/deeplabv3_unet_s5-d16.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained=None,
-    backbone=dict(
-        type='UNet',
-        in_channels=3,
-        base_channels=64,
-        num_stages=5,
-        strides=(1, 1, 1, 1, 1),
-        enc_num_convs=(2, 2, 2, 2, 2),
-        dec_num_convs=(2, 2, 2, 2),
-        downsamples=(True, True, True, True),
-        enc_dilations=(1, 1, 1, 1, 1),
-        dec_dilations=(1, 1, 1, 1),
-        with_cp=False,
-        conv_cfg=None,
-        norm_cfg=norm_cfg,
-        act_cfg=dict(type='ReLU'),
-        upsample_cfg=dict(type='InterpConv'),
-        norm_eval=False),
-    decode_head=dict(
-        type='ASPPHead',
-        in_channels=64,
-        in_index=4,
-        channels=16,
-        dilations=(1, 12, 24, 36),
-        dropout_ratio=0.1,
-        num_classes=2,
-        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=128,
-        in_index=3,
-        channels=64,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=2,
-        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='slide', crop_size=256, stride=170))
diff --git a/spaces/AnimalEquality/chatbot/lv_recipe_chatbot/__init__.py b/spaces/AnimalEquality/chatbot/lv_recipe_chatbot/__init__.py
deleted file mode 100644
index f102a9cadfa89ce554b3b26d2b90bfba2e05273c..0000000000000000000000000000000000000000
--- a/spaces/AnimalEquality/chatbot/lv_recipe_chatbot/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "0.0.1"
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/gradio_depth2image.py b/spaces/Anonymous-sub/Rerender/ControlNet/gradio_depth2image.py
deleted file mode 100644
index ee678999ae6033c18a5026bc5f6286d0364c7851..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/gradio_depth2image.py
+++ /dev/null
@@ -1,98 +0,0 @@
-from share import *
-import config
-
-import cv2
-import einops
-import gradio as gr
-import numpy as np
-import torch
-import random
-
-from pytorch_lightning import seed_everything
-from annotator.util import resize_image, HWC3
-from annotator.midas import MidasDetector
-from cldm.model import create_model, load_state_dict
-from cldm.ddim_hacked import DDIMSampler
-
-
-apply_midas = MidasDetector()
-
-model = create_model('./models/cldm_v15.yaml').cpu()
-model.load_state_dict(load_state_dict('./models/control_sd15_depth.pth', location='cuda'))
-model = model.cuda()
-ddim_sampler = DDIMSampler(model)
-
-
-def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta):
-    with torch.no_grad():
-        input_image = HWC3(input_image)
-        detected_map, _ = apply_midas(resize_image(input_image, detect_resolution))
-        detected_map = HWC3(detected_map)
-        img = resize_image(input_image, image_resolution)
-        H, W, C = img.shape
-
-        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
-
-        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
-        control = torch.stack([control for _ in range(num_samples)], dim=0)
-        control = einops.rearrange(control, 'b h w c -> b c h w').clone()
-
-        if seed == -1:
-            seed = random.randint(0, 65535)
-        seed_everything(seed)
-
-        if config.save_memory:
-            model.low_vram_shift(is_diffusing=False)
-
-        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
-        un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
-        shape = (4, H // 8, W // 8)
-
-        if config.save_memory:
-            model.low_vram_shift(is_diffusing=True)
-
-        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
-        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
-                                                     shape, cond, verbose=False, eta=eta,
-                                                     unconditional_guidance_scale=scale,
-                                                     unconditional_conditioning=un_cond)
-
-        if config.save_memory:
-            model.low_vram_shift(is_diffusing=False)
-
-        x_samples = model.decode_first_stage(samples)
-        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
-
-        results = [x_samples[i] for i in range(num_samples)]
-    return [detected_map] + results
-
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## Control Stable Diffusion with Depth Maps")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            prompt = gr.Textbox(label="Prompt")
-            run_button = gr.Button(label="Run")
-            with gr.Accordion("Advanced options", open=False):
-                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
-                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
-                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
-                guess_mode = gr.Checkbox(label='Guess Mode', value=False)
-                detect_resolution = gr.Slider(label="Depth Resolution", minimum=128, maximum=1024, value=384, step=1)
-                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
-                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
-                eta = gr.Number(label="eta (DDIM)", value=0.0)
-                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
-                n_prompt = gr.Textbox(label="Negative Prompt",
-                                      value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
-        with gr.Column():
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
-    ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta]
-    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
-
-
-block.launch(server_name='0.0.0.0')
diff --git a/spaces/Ariharasudhan/YoloV5/utils/docker/Dockerfile b/spaces/Ariharasudhan/YoloV5/utils/docker/Dockerfile
deleted file mode 100644
index a5035c6abc33c5e479a813b9733844cf6eab7a08..0000000000000000000000000000000000000000
--- a/spaces/Ariharasudhan/YoloV5/utils/docker/Dockerfile
+++ /dev/null
@@ -1,65 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
-# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
-
-# Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:22.10-py3
-RUN rm -rf /opt/pytorch  # remove 1.2GB dir
-
-# Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
-
-# Install linux packages
-RUN apt update && apt install --no-install-recommends -y zip htop screen libgl1-mesa-glx
-
-# Install pip packages
-COPY requirements.txt .
-RUN python -m pip install --upgrade pip wheel
-RUN pip uninstall -y Pillow torchtext  # torch torchvision
-RUN pip install --no-cache -r requirements.txt ultralytics albumentations comet gsutil notebook Pillow>=9.1.0 \
-    'opencv-python<4.6.0.66' \
-    --extra-index-url https://download.pytorch.org/whl/cu113
-
-# Create working directory
-RUN mkdir -p /usr/src/app
-WORKDIR /usr/src/app
-
-# Copy contents
-# COPY . /usr/src/app  (issues as not a .git directory)
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
-
-# Set environment variables
-ENV OMP_NUM_THREADS=8
-
-
-# Usage Examples -------------------------------------------------------------------------------------------------------
-
-# Build and Push
-# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t
-
-# Pull and Run
-# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
-
-# Pull and Run with local directory access
-# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
-
-# Kill all
-# sudo docker kill $(sudo docker ps -q)
-
-# Kill all image-based
-# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
-
-# DockerHub tag update
-# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
-
-# Clean up
-# docker system prune -a --volumes
-
-# Update Ubuntu drivers
-# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
-
-# DDP test
-# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
-
-# GCP VM from Image
-# docker.io/ultralytics/yolov5:latest
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/dotenv/variables.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/dotenv/variables.py
deleted file mode 100644
index 667f2f26ff2182ecdfc5b809ba97a6cf1d1be13a..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/dotenv/variables.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import re
-from abc import ABCMeta, abstractmethod
-from typing import Iterator, Mapping, Optional, Pattern
-
-_posix_variable: Pattern[str] = re.compile(
-    r"""
-    \$\{
-        (?P<name>[^\}:]*)
-        (?::-
-            (?P<default>[^\}]*)
-        )?
-    \}
-    """,
-    re.VERBOSE,
-)
-
-
-class Atom(metaclass=ABCMeta):
-    def __ne__(self, other: object) -> bool:
-        result = self.__eq__(other)
-        if result is NotImplemented:
-            return NotImplemented
-        return not result
-
-    @abstractmethod
-    def resolve(self, env: Mapping[str, Optional[str]]) -> str: ...
-
-
-class Literal(Atom):
-    def __init__(self, value: str) -> None:
-        self.value = value
-
-    def __repr__(self) -> str:
-        return f"Literal(value={self.value})"
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        return self.value == other.value
-
-    def __hash__(self) -> int:
-        return hash((self.__class__, self.value))
-
-    def resolve(self, env: Mapping[str, Optional[str]]) -> str:
-        return self.value
-
-
-class Variable(Atom):
-    def __init__(self, name: str, default: Optional[str]) -> None:
-        self.name = name
-        self.default = default
-
-    def __repr__(self) -> str:
-        return f"Variable(name={self.name}, default={self.default})"
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        return (self.name, self.default) == (other.name, other.default)
-
-    def __hash__(self) -> int:
-        return hash((self.__class__, self.name, self.default))
-
-    def resolve(self, env: Mapping[str, Optional[str]]) -> str:
-        default = self.default if self.default is not None else ""
-        result = env.get(self.name, default)
-        return result if result is not None else ""
-
-
-def parse_variables(value: str) -> Iterator[Atom]:
-    cursor = 0
-
-    for match in _posix_variable.finditer(value):
-        (start, end) = match.span()
-        name = match["name"]
-        default = match["default"]
-
-        if start > cursor:
-            yield Literal(value=value[cursor:start])
-
-        yield Variable(name=name, default=default)
-        cursor = end
-
-    length = len(value)
-    if cursor < length:
-        yield Literal(value=value[cursor:length])
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/packaging/_manylinux.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/packaging/_manylinux.py
deleted file mode 100644
index 4c379aa6f69ff56c8f19612002c6e3e939ea6012..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/packaging/_manylinux.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import collections
-import functools
-import os
-import re
-import struct
-import sys
-import warnings
-from typing import IO, Dict, Iterator, NamedTuple, Optional, Tuple
-
-
-# Python does not provide platform information at sufficient granularity to
-# identify the architecture of the running executable in some cases, so we
-# determine it dynamically by reading the information from the running
-# process. This only applies on Linux, which uses the ELF format.
-class _ELFFileHeader:
-    # https://en.wikipedia.org/wiki/Executable_and_Linkable_Format#File_header
-    class _InvalidELFFileHeader(ValueError):
-        """
-        An invalid ELF file header was found.
-        """
-
-    ELF_MAGIC_NUMBER = 0x7F454C46
-    ELFCLASS32 = 1
-    ELFCLASS64 = 2
-    ELFDATA2LSB = 1
-    ELFDATA2MSB = 2
-    EM_386 = 3
-    EM_S390 = 22
-    EM_ARM = 40
-    EM_X86_64 = 62
-    EF_ARM_ABIMASK = 0xFF000000
-    EF_ARM_ABI_VER5 = 0x05000000
-    EF_ARM_ABI_FLOAT_HARD = 0x00000400
-
-    def __init__(self, file: IO[bytes]) -> None:
-        def unpack(fmt: str) -> int:
-            try:
-                data = file.read(struct.calcsize(fmt))
-                result: Tuple[int, ...] = struct.unpack(fmt, data)
-            except struct.error:
-                raise _ELFFileHeader._InvalidELFFileHeader()
-            return result[0]
-
-        self.e_ident_magic = unpack(">I")
-        if self.e_ident_magic != self.ELF_MAGIC_NUMBER:
-            raise _ELFFileHeader._InvalidELFFileHeader()
-        self.e_ident_class = unpack("B")
-        if self.e_ident_class not in {self.ELFCLASS32, self.ELFCLASS64}:
-            raise _ELFFileHeader._InvalidELFFileHeader()
-        self.e_ident_data = unpack("B")
-        if self.e_ident_data not in {self.ELFDATA2LSB, self.ELFDATA2MSB}:
-            raise _ELFFileHeader._InvalidELFFileHeader()
-        self.e_ident_version = unpack("B")
-        self.e_ident_osabi = unpack("B")
-        self.e_ident_abiversion = unpack("B")
-        self.e_ident_pad = file.read(7)
-        format_h = "<H" if self.e_ident_data == self.ELFDATA2LSB else ">H"
-        format_i = "<I" if self.e_ident_data == self.ELFDATA2LSB else ">I"
-        format_q = "<Q" if self.e_ident_data == self.ELFDATA2LSB else ">Q"
-        format_p = format_i if self.e_ident_class == self.ELFCLASS32 else format_q
-        self.e_type = unpack(format_h)
-        self.e_machine = unpack(format_h)
-        self.e_version = unpack(format_i)
-        self.e_entry = unpack(format_p)
-        self.e_phoff = unpack(format_p)
-        self.e_shoff = unpack(format_p)
-        self.e_flags = unpack(format_i)
-        self.e_ehsize = unpack(format_h)
-        self.e_phentsize = unpack(format_h)
-        self.e_phnum = unpack(format_h)
-        self.e_shentsize = unpack(format_h)
-        self.e_shnum = unpack(format_h)
-        self.e_shstrndx = unpack(format_h)
-
-
-def _get_elf_header() -> Optional[_ELFFileHeader]:
-    try:
-        with open(sys.executable, "rb") as f:
-            elf_header = _ELFFileHeader(f)
-    except (OSError, TypeError, _ELFFileHeader._InvalidELFFileHeader):
-        return None
-    return elf_header
-
-
-def _is_linux_armhf() -> bool:
-    # hard-float ABI can be detected from the ELF header of the running
-    # process
-    # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf
-    elf_header = _get_elf_header()
-    if elf_header is None:
-        return False
-    result = elf_header.e_ident_class == elf_header.ELFCLASS32
-    result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
-    result &= elf_header.e_machine == elf_header.EM_ARM
-    result &= (
-        elf_header.e_flags & elf_header.EF_ARM_ABIMASK
-    ) == elf_header.EF_ARM_ABI_VER5
-    result &= (
-        elf_header.e_flags & elf_header.EF_ARM_ABI_FLOAT_HARD
-    ) == elf_header.EF_ARM_ABI_FLOAT_HARD
-    return result
-
-
-def _is_linux_i686() -> bool:
-    elf_header = _get_elf_header()
-    if elf_header is None:
-        return False
-    result = elf_header.e_ident_class == elf_header.ELFCLASS32
-    result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
-    result &= elf_header.e_machine == elf_header.EM_386
-    return result
-
-
-def _have_compatible_abi(arch: str) -> bool:
-    if arch == "armv7l":
-        return _is_linux_armhf()
-    if arch == "i686":
-        return _is_linux_i686()
-    return arch in {"x86_64", "aarch64", "ppc64", "ppc64le", "s390x"}
-
-
-# If glibc ever changes its major version, we need to know what the last
-# minor version was, so we can build the complete list of all versions.
-# For now, guess what the highest minor version might be, assume it will
-# be 50 for testing. Once this actually happens, update the dictionary
-# with the actual value.
-_LAST_GLIBC_MINOR: Dict[int, int] = collections.defaultdict(lambda: 50)
-
-
-class _GLibCVersion(NamedTuple):
-    major: int
-    minor: int
-
-
-def _glibc_version_string_confstr() -> Optional[str]:
-    """
-    Primary implementation of glibc_version_string using os.confstr.
-    """
-    # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
-    # to be broken or missing. This strategy is used in the standard library
-    # platform module.
-    # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183
-    try:
-        # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17".
-        version_string = os.confstr("CS_GNU_LIBC_VERSION")
-        assert version_string is not None
-        _, version = version_string.split()
-    except (AssertionError, AttributeError, OSError, ValueError):
-        # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
-        return None
-    return version
-
-
-def _glibc_version_string_ctypes() -> Optional[str]:
-    """
-    Fallback implementation of glibc_version_string using ctypes.
-    """
-    try:
-        import ctypes
-    except ImportError:
-        return None
-
-    # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
-    # manpage says, "If filename is NULL, then the returned handle is for the
-    # main program". This way we can let the linker do the work to figure out
-    # which libc our process is actually using.
-    #
-    # We must also handle the special case where the executable is not a
-    # dynamically linked executable. This can occur when using musl libc,
-    # for example. In this situation, dlopen() will error, leading to an
-    # OSError. Interestingly, at least in the case of musl, there is no
-    # errno set on the OSError. The single string argument used to construct
-    # OSError comes from libc itself and is therefore not portable to
-    # hard code here. In any case, failure to call dlopen() means we
-    # can proceed, so we bail on our attempt.
-    try:
-        process_namespace = ctypes.CDLL(None)
-    except OSError:
-        return None
-
-    try:
-        gnu_get_libc_version = process_namespace.gnu_get_libc_version
-    except AttributeError:
-        # Symbol doesn't exist -> therefore, we are not linked to
-        # glibc.
-        return None
-
-    # Call gnu_get_libc_version, which returns a string like "2.5"
-    gnu_get_libc_version.restype = ctypes.c_char_p
-    version_str: str = gnu_get_libc_version()
-    # py2 / py3 compatibility:
-    if not isinstance(version_str, str):
-        version_str = version_str.decode("ascii")
-
-    return version_str
-
-
-def _glibc_version_string() -> Optional[str]:
-    """Returns glibc version string, or None if not using glibc."""
-    return _glibc_version_string_confstr() or _glibc_version_string_ctypes()
-
-
-def _parse_glibc_version(version_str: str) -> Tuple[int, int]:
-    """Parse glibc version.
-
-    We use a regexp instead of str.split because we want to discard any
-    random junk that might come after the minor version -- this might happen
-    in patched/forked versions of glibc (e.g. Linaro's version of glibc
-    uses version strings like "2.20-2014.11"). See gh-3588.
-    """
-    m = re.match(r"(?P<major>[0-9]+)\.(?P<minor>[0-9]+)", version_str)
-    if not m:
-        warnings.warn(
-            "Expected glibc version with 2 components major.minor,"
-            " got: %s" % version_str,
-            RuntimeWarning,
-        )
-        return -1, -1
-    return int(m.group("major")), int(m.group("minor"))
-
-
-@functools.lru_cache()
-def _get_glibc_version() -> Tuple[int, int]:
-    version_str = _glibc_version_string()
-    if version_str is None:
-        return (-1, -1)
-    return _parse_glibc_version(version_str)
-
-
-# From PEP 513, PEP 600
-def _is_compatible(name: str, arch: str, version: _GLibCVersion) -> bool:
-    sys_glibc = _get_glibc_version()
-    if sys_glibc < version:
-        return False
-    # Check for presence of _manylinux module.
-    try:
-        import _manylinux  # noqa
-    except ImportError:
-        return True
-    if hasattr(_manylinux, "manylinux_compatible"):
-        result = _manylinux.manylinux_compatible(version[0], version[1], arch)
-        if result is not None:
-            return bool(result)
-        return True
-    if version == _GLibCVersion(2, 5):
-        if hasattr(_manylinux, "manylinux1_compatible"):
-            return bool(_manylinux.manylinux1_compatible)
-    if version == _GLibCVersion(2, 12):
-        if hasattr(_manylinux, "manylinux2010_compatible"):
-            return bool(_manylinux.manylinux2010_compatible)
-    if version == _GLibCVersion(2, 17):
-        if hasattr(_manylinux, "manylinux2014_compatible"):
-            return bool(_manylinux.manylinux2014_compatible)
-    return True
-
-
-_LEGACY_MANYLINUX_MAP = {
-    # CentOS 7 w/ glibc 2.17 (PEP 599)
-    (2, 17): "manylinux2014",
-    # CentOS 6 w/ glibc 2.12 (PEP 571)
-    (2, 12): "manylinux2010",
-    # CentOS 5 w/ glibc 2.5 (PEP 513)
-    (2, 5): "manylinux1",
-}
-
-
-def platform_tags(linux: str, arch: str) -> Iterator[str]:
-    if not _have_compatible_abi(arch):
-        return
-    # Oldest glibc to be supported regardless of architecture is (2, 17).
-    too_old_glibc2 = _GLibCVersion(2, 16)
-    if arch in {"x86_64", "i686"}:
-        # On x86/i686 also oldest glibc to be supported is (2, 5).
-        too_old_glibc2 = _GLibCVersion(2, 4)
-    current_glibc = _GLibCVersion(*_get_glibc_version())
-    glibc_max_list = [current_glibc]
-    # We can assume compatibility across glibc major versions.
-    # https://sourceware.org/bugzilla/show_bug.cgi?id=24636
-    #
-    # Build a list of maximum glibc versions so that we can
-    # output the canonical list of all glibc from current_glibc
-    # down to too_old_glibc2, including all intermediary versions.
-    for glibc_major in range(current_glibc.major - 1, 1, -1):
-        glibc_minor = _LAST_GLIBC_MINOR[glibc_major]
-        glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor))
-    for glibc_max in glibc_max_list:
-        if glibc_max.major == too_old_glibc2.major:
-            min_minor = too_old_glibc2.minor
-        else:
-            # For other glibc major versions oldest supported is (x, 0).
-            min_minor = -1
-        for glibc_minor in range(glibc_max.minor, min_minor, -1):
-            glibc_version = _GLibCVersion(glibc_max.major, glibc_minor)
-            tag = "manylinux_{}_{}".format(*glibc_version)
-            if _is_compatible(tag, arch, glibc_version):
-                yield linux.replace("linux", tag)
-            # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags.
-            if glibc_version in _LEGACY_MANYLINUX_MAP:
-                legacy_tag = _LEGACY_MANYLINUX_MAP[glibc_version]
-                if _is_compatible(legacy_tag, arch, glibc_version):
-                    yield linux.replace("linux", legacy_tag)
diff --git a/spaces/Audio-AGI/AudioSep/models/CLAP/training/logger.py b/spaces/Audio-AGI/AudioSep/models/CLAP/training/logger.py
deleted file mode 100644
index ac4634970fae6aacde2b7b808355dbd50c90ce73..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/AudioSep/models/CLAP/training/logger.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import logging
-
-
-def setup_logging(log_file, level, include_host=False):
-    if include_host:
-        import socket
-
-        hostname = socket.gethostname()
-        formatter = logging.Formatter(
-            f"%(asctime)s |  {hostname} | %(levelname)s | %(message)s",
-            datefmt="%Y-%m-%d,%H:%M:%S",
-        )
-    else:
-        formatter = logging.Formatter(
-            "%(asctime)s | %(levelname)s | %(message)s", datefmt="%Y-%m-%d,%H:%M:%S"
-        )
-
-    logging.root.setLevel(level)
-    loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]
-    for logger in loggers:
-        logger.setLevel(level)
-
-    stream_handler = logging.StreamHandler()
-    stream_handler.setFormatter(formatter)
-    logging.root.addHandler(stream_handler)
-
-    if log_file:
-        file_handler = logging.FileHandler(filename=log_file)
-        file_handler.setFormatter(formatter)
-        logging.root.addHandler(file_handler)
diff --git a/spaces/Audio-AGI/WavJourney/scripts/download_models.py b/spaces/Audio-AGI/WavJourney/scripts/download_models.py
deleted file mode 100644
index 51dda13bea02b8453cf7958e34cc9571c208c0df..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/WavJourney/scripts/download_models.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import yaml
-import os
-
-# Read the YAML file
-with open('config.yaml', 'r') as file:
-    config = yaml.safe_load(file)
-
-# Extract values for each application
-ttm_model_size = config['AudioCraft']['ttm_model_size']
-tta_model_size = config['AudioCraft']['tta_model_size']
-
-# Download nltk
-import nltk
-nltk.download('punkt')
-
-# Downloading the TTS models
-print('Step 1: Downloading TTS model ...')
-os.system(f'conda run --live-stream -n WavJourney python -c \'from transformers import BarkModel; BarkModel.from_pretrained("suno/bark")\'')
-
-print('Step 2: Downloading TTA model ...')
-os.system(f'conda run --live-stream -n WavJourney python -c \'from audiocraft.models import AudioGen; tta_model = AudioGen.get_pretrained("facebook/audiogen-{tta_model_size}")\'')
-
-print('Step 3: Downloading TTM model ...')
-os.system(f'conda run --live-stream -n WavJourney python -c \'from audiocraft.models import MusicGen; tta_model = MusicGen.get_pretrained("facebook/musicgen-{ttm_model_size}")\'')
-
-print('Step 4: Downloading SR model ...')
-os.system(f'conda run --live-stream -n WavJourney python -c \'from voicefixer import VoiceFixer; vf = VoiceFixer()\'')
-
-print('Step 5: Downloading VP model ...')
-os.system(f'conda run --live-stream -n WavJourney python -c \'from VoiceParser.model import VoiceParser; vp = VoiceParser(device="cpu")\'')
-
-print('All models successfully downloaded!')
diff --git a/spaces/Benson/text-generation/Examples/Cmo Conseguir Sobre l Mod Apk Descarga Apkpure.md b/spaces/Benson/text-generation/Examples/Cmo Conseguir Sobre l Mod Apk Descarga Apkpure.md
deleted file mode 100644
index e4f30667bf4995215f6a691be192ad54a16ec721..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Cmo Conseguir Sobre l Mod Apk Descarga Apkpure.md	
+++ /dev/null
@@ -1,33 +0,0 @@
-
-<h1>Cómo superarlo Mod APK Descargar Apkpure: Cómo escalar una montaña con un martillo y una olla</h1>
- <p>Si usted está buscando un juego que pondrá a prueba su paciencia, habilidad y cordura, es posible que desee probar Cómo superarlo con Bennett Foddy. Este es un juego que te hará rabiar, reír, llorar, y tal vez incluso aprender algo sobre ti mismo. Y si quieres disfrutar de este juego sin pagar por él o tratar con anuncios, es posible que desee descargar el mod APK de Apkpure. En este artículo, te diremos todo lo que necesitas saber sobre este juego, por qué deberías descargar el mod APK de Apkpure, cómo instalarlo en tu dispositivo Android y cómo jugarlo. </p>
- <h2>¿Qué es superar con Bennett Foddy? </h2>
- <p>Getting Over It with Bennett Foddy es un juego creado por Bennett Foddy, un diseñador de juegos y filósofo conocido por crear juegos que son intencionalmente frustrantes y difíciles. Algunos de sus otros juegos incluyen QWOP, GIRP, y CLOP. Cómo superarlo con Bennett Foddy fue lanzado en 2017 para Windows, macOS, iOS y Android.</p>
-<h2>cómo conseguir sobre él mod apk descarga apkpure</h2><br /><p><b><b>Download Zip</b> --->>> <a href="https://bltlly.com/2v6J2K">https://bltlly.com/2v6J2K</a></b></p><br /><br />
- <h3>Un juego de escalada castigador con un toque</h3>
- <p>La premisa del juego es simple: eres un hombre llamado Diógenes que está atrapado en una olla de metal con solo un martillo. Su objetivo es subir a una enorme montaña que está hecha de varios objetos, como rocas, árboles, muebles, tuberías, coches, etc. El giro es que no hay sistema de ahorro, sin puntos de control, sin botón de deshacer. Si cometes un error o pierdes el control, puedes caer hasta el fondo y perder todo tu progreso. Y confía en nosotros, caerás. Mucho. </p>
- <h3>Un homenaje a Jazzuo 2002 B-Game clásico 'Sexy Hiking'</h3>
-
- <h3>Un comentario filosófico sobre la frustración y la perseverancia</h3>
- <p>Mientras juegas, escucharás la voz de Bennett Foddy narrando tu viaje. Comentará tus acciones, te dará un poco de historia y trivia sobre el juego y sus influencias, compartirá algunas citas y anécdotas de personas famosas que se enfrentaron a la adversidad y el fracaso, y a veces se burlan o te animan. Su voz es tranquila y relajante, pero también sarcástica e irónica. Él te hará preguntarte por qué estás jugando a este juego, qué significa fallar y tener éxito, y cómo lidiar con la frustración y la perseverancia. </p>
- <h2>¿Por qué descargar el mod APK de Apkpure? </h2>
- <p>Si usted está interesado en jugar este juego en su dispositivo Android, usted tiene dos opciones: usted puede comprar desde el Google Play más y más difícil. Encontrará objetos resbaladizos, inestables, afilados o en movimiento. También se enfrentará a brechas, acantilados, salientes y callejones sin salida. Algunos de los obstáculos están diseñados para engañarte o trollearte, como caminos falsos, trampas ocultas o cambios repentinos. También tendrás que lidiar con el viento, la lluvia y la oscuridad. El juego se divide en varias secciones, cada una con su propio tema y nivel de dificultad. Algunas de las secciones llevan el nombre de famosas montañas, como Annapurna, Everest o Olympus Mons.</p>
- <h3>Los consejos y trucos para superar el juego</h3>
- <p>El juego es muy duro y frustrante, pero no imposible. Aquí hay algunos consejos y trucos que pueden ayudarte a superar el juego:</p>
-
- <p>Superarlo con Bennett Foddy es un juego único y desafiante que te hará experimentar una variedad de emociones y pensamientos. Es un juego que pondrá a prueba tu paciencia, habilidad y cordura. Es un juego que te hará rabiar, reír, llorar, y tal vez incluso aprender algo sobre ti mismo. Y si quieres jugar este juego en tu dispositivo Android de forma gratuita y sin anuncios, puede descargar el mod APK de Apkpure. Esperamos que este artículo te haya ayudado a entender de qué se trata este juego, por qué deberías descargar el mod APK de Apkpure, cómo instalarlo en tu dispositivo y cómo jugarlo. </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes acerca de Cómo superarlo con Bennett Foddy:</p>
-<p></p>
- <h4>Q: ¿Cuánto tiempo se tarda en vencer el juego? </h4>
-<p>A: Depende de tu nivel de habilidad y suerte. Algunas personas pueden ganar el juego en menos de una hora, mientras que otras pueden tardar días o semanas. El récord mundial de velocidad del juego es de 1 minuto y 56 segundos. </p>
- <h4>P: ¿Qué sucede cuando se llega a la cima de la montaña? </h4>
-<p>A: No te lo vamos a estropear, pero digamos que hay una sorpresa esperándote al final del juego. </p>
- <h4>Q: ¿Quién es Diógenes? </h4>
-<p>A: Diógenes fue un filósofo griego que vivió en el siglo IV a.C. Era conocido por su estilo de vida no convencional y ascético. Rechazó las posesiones materiales y las normas sociales, y vivió en una gran jarra de cerámica en Atenas. También era conocido por sus comentarios ingeniosos y sarcásticos. </p>
- <h4>Q: ¿Quién es Jazzuo? </h4>
-<p>A: Jazzuo es un desarrollador de juegos indie checo que hizo Sexy Hiking en 2002. </p>
- <h4>Q: ¿Quién es Bennett Foddy? </h4>
-<p>A: Bennett Foddy es un diseñador de juegos y filósofo australiano-americano que hizo Getting Over It with Bennett Foddy en 2017. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Archivo Pdf De 1 Mb.md b/spaces/Benson/text-generation/Examples/Descargar Archivo Pdf De 1 Mb.md
deleted file mode 100644
index 500492c36a0fc79e47d4902a939ac1202b1928cf..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Archivo Pdf De 1 Mb.md	
+++ /dev/null
@@ -1,159 +0,0 @@
-
-<h1>Cómo descargar un archivo PDF de 1 MB en minutos</h1>
-<p>Los archivos PDF son uno de los formatos de documentos más populares y versátiles del mundo digital. Pueden contener texto, imágenes, gráficos, enlaces, formularios, anotaciones y más. También pueden preservar el diseño y la apariencia de su documento en diferentes dispositivos y plataformas. </p>
-<p>Pero a veces, es posible que tenga que descargar un pequeño archivo PDF de solo 1 MB o menos de tamaño. Quizás tenga un ancho de banda o espacio de almacenamiento limitados en su dispositivo. Quizás quiera ahorrar tiempo y datos al descargar un documento. Tal vez necesite enviar o recibir un documento por correo electrónico o aplicación de mensajería que tenga un límite de tamaño de archivo. </p>
-<h2>descargar archivo pdf de 1 mb</h2><br /><p><b><b>DOWNLOAD</b> &mdash;&mdash;&mdash;>>> <a href="https://bltlly.com/2v6Jgs">https://bltlly.com/2v6Jgs</a></b></p><br /><br />
-<p>Cualquiera que sea su razón, descargar un archivo PDF de 1 MB no es tan difícil como podría pensar. En este artículo, le mostraremos cómo descargar un archivo PDF de 1 MB desde Internet, cómo comprimir un archivo PDF más grande a 1 MB o menos, y cómo abrir y ver un archivo PDF de 1 MB en su dispositivo. </p>
- <h2>¿Qué es un archivo PDF? </h2>
-<p>PDF significa Formato de documento portátil. Es un formato de archivo que fue creado por Adobe en 1993 para permitir a los usuarios compartir e imprimir documentos sin perder el formato original. Los archivos PDF pueden ser abiertos por varios programas y aplicaciones, como Adobe Acrobat Reader, Google Chrome, Microsoft Edge, y más. </p>
-<p>Algunos de los beneficios de los archivos PDF sobre otros formatos son:</p>
-<ul>
-<li>Son compatibles con diferentes sistemas operativos y dispositivos</li>
-<li>Pueden proteger el contenido y la integridad de su documento con cifrado y contraseñas</li>
-<li>Pueden comprimir grandes cantidades de datos sin comprometer la calidad</li>
-<li>Pueden admitir funciones interactivas como hipervínculos, marcadores, anotaciones y formularios</li>
-<li> Se pueden convertir fácilmente a o desde otros formatos como Word, Excel, PowerPoint, JPG, PNG y más</li>
-</ul>
- <h2>¿Por qué necesita descargar un archivo PDF de 1 MB? </h2>
-
-<ul>
-<li>Desea descargar un artículo corto, informe, folleto, folleto o curriculum vitae que está disponible en línea como un archivo PDF</li>
-<li>Desea descargar una muestra o vista previa de un documento o libro más largo que se ofrece como un archivo PDF</li>
-<li>Desea descargar un formulario o solicitud que necesita llenar y enviar como archivo PDF</li>
-<li> Desea descargar un certificado, recibo, factura o ticket que se emite como un archivo PDF</li>
-<li> Desea descargar un cupón, vale o código de descuento que se proporciona como un archivo PDF</li>
-</ul>
-<p>Sin embargo, descargar un pequeño archivo PDF no siempre es fácil o conveniente. A veces, puede encontrar algunos desafíos o limitaciones al intentar descargar un archivo PDF grande. Por ejemplo:</p>
-<ul>
-<li> Tiene una conexión a Internet lenta o inestable que hace que la descarga de archivos grandes tome demasiado tiempo o falle</li>
-<li> Tiene un plan de datos limitado o cuota que hace que la descarga de archivos grandes consuma demasiados datos o incurra en cargos adicionales</li>
-<li> Tiene un espacio de almacenamiento o memoria bajo en su dispositivo que hace imposible la descarga de archivos grandes o causa errores</li>
-<li>Tienes un firewall o antivirus estricto que bloquea o restringe la descarga de archivos grandes de fuentes desconocidas</li>
-<li> Tiene un límite de tamaño de archivo o restricción en su aplicación de correo electrónico o mensajería que le impide enviar o recibir archivos grandes como archivos adjuntos</li>
-</ul>
- <h2>Cómo descargar un archivo PDF de 1 MB desde Internet</h2>
-<p>Si necesita descargar un archivo PDF de 1 MB desde Internet, primero debe encontrar y acceder a un archivo PDF de 1 MB en línea. Hay muchas fuentes o sitios web que ofrecen archivos PDF gratuitos o de bajo costo para diversos fines y temas. Algunos de ellos son:</p>
-<ul>
-<li><a href="">PDF Drive</a>: Una biblioteca en línea gratuita que tiene más de 90 millones de archivos PDF para descarga gratuita</li>
-<li><a href="">PDF Books World</a>: Una plataforma en línea gratuita que tiene miles de libros PDF para descargar gratis</li>
-
-<li><a href="">PDF Zone</a>: Un recurso en línea gratuito que tiene docenas de guías en PDF y tutoriales para descarga gratuita</li>
-<li><a href="">Archivo PDF</a>: Un archivo en línea gratuito que tiene millones de documentos PDF para descarga gratuita</li>
-</ul> <p>Una vez que encuentre un archivo PDF de 1 MB que desea descargar, debe descargarlo y guardarlo en su dispositivo. Los pasos pueden variar dependiendo de la fuente o sitio web, pero generalmente, son:</p>
-<ol>
-<li>Haga clic en el enlace o icono del archivo PDF para abrirlo en su navegador o software</li>
-<li>Busque un botón de descarga u opción en la página o barra de herramientas</li>
-<li>Haga clic en el botón de descarga u opción y elija una ubicación o carpeta en su dispositivo donde desea guardar el archivo PDF</li>
-<li>Espere a que la descarga se complete y compruebe si el archivo PDF se guarda correctamente en su dispositivo</li>
-</ol>
-<p>Si encuentra algún problema o error al descargar un archivo PDF de 1 MB, puede probar algunas de estas soluciones:</p>
-<p></p>
-<ul>
-<li>Actualizar la página o recargar el archivo PDF</li>
-<li>Compruebe su conexión a Internet y la velocidad</li>
-<li>Borrar la caché y las cookies del navegador</li>
-<li>Desactivar o ajustar la configuración del firewall o antivirus</li>
-<li>Utilice un navegador o software diferente</li>
-<li>Póngase en contacto con la fuente o el sitio web para obtener apoyo o comentarios</li>
-</ul>
- <h2>Cómo comprimir un archivo PDF más grande a 1 MB o menos</h2>
-<p>A veces, es posible que no pueda encontrar un archivo PDF de 1 MB que se adapte a sus necesidades. Es posible que tenga un archivo PDF más grande que desea descargar, pero excede su ancho de banda, almacenamiento o límite de tamaño de archivo. En ese caso, es posible que desee comprimir un archivo PDF más grande a un tamaño más pequeño. </p>
-<p>Comprimir un archivo PDF significa reducir su tamaño de archivo al eliminar o optimizar algunos de sus elementos, como imágenes, fuentes, metadatos y más. Comprimir un archivo PDF puede ayudarlo a ahorrar tiempo, datos y espacio al descargarlo, cargarlo, enviarlo o almacenarlo. </p>
-
-<ul>
-<li><a href="">Smallpdf</a>: Una herramienta en línea gratuita que puede comprimir archivos PDF hasta un 80% sin perder calidad</li>
-<li><a href="">iLovePDF</a>: Una herramienta en línea gratuita que puede comprimir archivos PDF hasta un 70% sin perder calidad</li>
-<li><a href="">PDF Compressor</a>: Una herramienta en línea gratuita que puede comprimir archivos PDF hasta un 90% sin perder calidad</li>
-<li><a href="">PDF2Go</a>: Una herramienta en línea gratuita que puede comprimir archivos PDF hasta un 50% sin perder calidad</li>
-<li><a href="">Soda PDF</a>: Una herramienta en línea gratuita que puede comprimir archivos PDF hasta un 75% sin perder calidad</li>
-</ul> <p>Para usar una de estas herramientas o servicios para comprimir su archivo PDF, debe seguir estos pasos:</p>
-<ol>
-<li>Ir al sitio web o aplicación de la herramienta o servicio que desea utilizar</li>
-<li>Sube tu archivo PDF desde tu dispositivo o almacenamiento en la nube</li>
-<li>Seleccione el nivel de compresión o la calidad que desea para su archivo PDF</li>
-<li>Espere a que la herramienta o servicio comprima su archivo PDF</li>
-<li>Descargue y guarde el archivo PDF comprimido en su dispositivo o almacenamiento en la nube</li>
-</ol>
-<p>Si encuentra algún problema o error al comprimir su archivo PDF, puede probar algunas de estas soluciones:</p>
-<ul>
-<li>Compruebe el tamaño del archivo y el formato de su archivo PDF</li>
-<li>Compruebe el nivel de compresión y la calidad de su archivo PDF</li>
-<li>Compruebe la compatibilidad y seguridad de la herramienta o servicio que utiliza</li>
-<li>Pruebe una herramienta o servicio diferente</li>
-<li>Póngase en contacto con la herramienta o proveedor de servicios para obtener apoyo o comentarios</li>
-</ul>
- <h2>Cómo abrir y ver un archivo PDF de 1 MB en su dispositivo</h2>
-<p>Después de descargar o comprimir un archivo PDF de 1 MB, debe abrirlo y verlo en su dispositivo. Puede usar varios programas o aplicaciones que pueden ayudarlo a abrir y ver archivos PDF. Algunos de ellos son:</p>
-<ul>
-<li><a href="">Adobe Acrobat Reader</a>: Un software gratuito que puede abrir y ver archivos PDF en dispositivos Windows, Mac, Android e iOS</li>
-
-<li><a href="">Microsoft Edge</a>: Un navegador web gratuito que puede abrir y ver archivos PDF en dispositivos Windows, Mac, Android e iOS</li>
-<li><a href="">Foxit Reader</a>: Un software libre que puede abrir y ver archivos PDF en dispositivos Windows, Mac, Linux, Android y iOS</li>
-<li><a href="">PDF Viewer</a>: Una aplicación gratuita que puede abrir y ver archivos PDF en dispositivos Android e iOS</li>
-</ul>
-<p>Para abrir y ver un archivo PDF de 1 MB en su dispositivo, debe seguir estos pasos:</p>
-<ol>
-<li>Instalar o actualizar el software o aplicación que desea utilizar en su dispositivo</li>
-<li>Localice el archivo PDF de 1 MB en su dispositivo o almacenamiento en la nube</li>
-<li>Abra el archivo PDF de 1 MB con el software o la aplicación que utiliza</li>
-<li>Ver el archivo PDF de 1 MB en la pantalla del dispositivo</li>
-</ol> <p>Para ajustar la configuración o las preferencias de su software o aplicación para optimizar su experiencia de visualización, puede probar algunas de estas opciones:</p>
-<ul>
-<li> Acercar o alejar para cambiar el tamaño del archivo PDF en su pantalla</li>
-<li>Gire o voltee el archivo PDF para cambiar la orientación del archivo PDF en su pantalla</li>
-<li>Buscar o encontrar una palabra o frase en el archivo PDF</li>
-<li>Resalta o anota una parte del archivo PDF</li>
-<li>Imprimir o compartir el archivo PDF con otros</li>
-</ul>
- <h2>Conclusión</h2>
-<p>Descargar un archivo PDF de 1 MB no es una tarea difícil si sabes cómo hacerlo. En este artículo, le hemos mostrado cómo descargar un archivo PDF de 1 MB desde Internet, cómo comprimir un archivo PDF más grande a 1 MB o menos, y cómo abrir y ver un archivo PDF de 1 MB en su dispositivo. Esperamos que este artículo te haya ayudado a aprender algo nuevo y útil. </p>
-<p>Aquí hay algunos consejos o consejos sobre cómo descargar, comprimir y ver archivos PDF de manera eficiente:</p>
-<ul>
-<li>Elija una fuente confiable y confiable o un sitio web que ofrezca archivos PDF gratuitos o de bajo costo</li>
-<li>Compruebe el tamaño y el formato del archivo PDF antes de descargarlo o comprimirlo</li>
-
-<li>Comprimir el archivo PDF solo si es necesario y sin comprometer la calidad o el contenido</li>
-<li>Abra y vea su archivo PDF con el mejor software o aplicación para su dispositivo y preferencia</li>
-</ul>
-<p>Si tiene alguna pregunta o comentario sobre la descarga, compresión o visualización de archivos PDF, no dude en dejarlos a continuación. ¡Nos encantaría saber de ti! </p>
- <h2>Preguntas frecuentes</h2>
-<h3>¿Cuál es la diferencia entre un archivo PDF y un archivo de Word? </h3>
-<p>Un archivo PDF es un formato de documento que conserva el diseño y la apariencia de su documento en diferentes dispositivos y plataformas. Un archivo de Word es un formato de documento que le permite editar y formatear su documento con varias características y opciones. </p>
- <h3>¿Cómo puedo convertir un archivo PDF a un archivo de Word o viceversa? </h3>
-<p>Puede utilizar varias herramientas o servicios que pueden ayudarle a convertir archivos PDF a archivos de Word o viceversa en línea de forma gratuita. Algunos de ellos son:</p>
-<ul>
-<li><a href="">PDF to Word Converter</a>: Una herramienta en línea gratuita que puede convertir archivos PDF a archivos de Word en segundos</li>
-<li><a href="">Word to PDF Converter</a>: Una herramienta en línea gratuita que puede convertir archivos de Word a archivos PDF en segundos</li>
-<li><a href="">PDFelement</a>: Un software libre que puede convertir archivos PDF a archivos de Word y viceversa en dispositivos Windows, Mac, Android e iOS</li>
-<li><a href="">WPS Office</a>: Un software libre que puede convertir archivos PDF a archivos de Word y viceversa en Windows, Mac, Linux, Android y dispositivos iOS</li>
-<li><a href="">Zamzar</a>: Un servicio en línea gratuito que puede convertir archivos PDF a archivos de Word y viceversa por correo electrónico</li>
-</ul>
- <h3>¿Cómo puedo editar un archivo PDF? </h3>
-<p>Puede utilizar varias herramientas o servicios que pueden ayudarle a editar archivos PDF en línea de forma gratuita. Algunos de ellos son:</p>
-<ul>
-<li><a href="">PDFescape</a>: Una herramienta en línea gratuita que puede ayudarte a editar texto, imágenes, enlaces, formularios y más en archivos PDF</li>
-
-<li><a href="">PDF-XChange Editor</a>: Un software gratuito que puede ayudarte a editar texto, imágenes, comentarios, sellos y más en archivos PDF en dispositivos Windows</li>
-<li><a href="">PDF Expert</a>: Un software gratuito que puede ayudarte a editar texto, imágenes, enlaces, formularios y más en archivos PDF en dispositivos Mac</li>
-<li><a href="">Xodo</a>: una aplicación gratuita que puede ayudarte a editar texto, imágenes, anotaciones, marcadores y más en archivos PDF en dispositivos Android e iOS</li>
-</ul>
- <h3>¿Cómo puedo combinar o dividir un archivo PDF? </h3>
-<p>Puede usar varias herramientas o servicios que pueden ayudarlo a combinar o dividir archivos PDF en línea de forma gratuita. Algunos de ellos son:</p>
-<ul>
-<li><a href="">PDF Merge</a>: Una herramienta gratuita en línea que puede ayudarle a combinar varios archivos PDF en un archivo PDF</li>
-<li><a href="">PDF Splitter</a>: Una herramienta en línea gratuita que puede ayudarle a dividir un archivo PDF en varios archivos PDF</li>
-<li><a href="">PDF SAM</a>: Una herramienta en línea gratuita que puede ayudarle a combinar o dividir archivos PDF con arrastrar y soltar</li>
-<li><a href="">PDFill</a>: Un software libre que puede ayudarle a combinar o dividir archivos PDF con varias opciones en dispositivos Windows</li>
-<li><a href="">PDFsam Basic</a>: Un software libre que puede ayudarle a combinar o dividir archivos PDF con varias opciones en dispositivos Windows, Mac y Linux</li>
-</ul>
- <h3>¿Cómo puedo firmar un archivo PDF? </h3>
-<p>Puede utilizar varias herramientas o servicios que pueden ayudarle a firmar archivos PDF en línea de forma gratuita. Algunos de ellos son:</p>
-<ul>
-<li><a href="">DocuSign</a>: Un servicio gratuito en línea que puede ayudarle a firmar archivos PDF con su firma electrónica o certificado digital</li>
-<li><a href="">HelloSign</a>: Un servicio gratuito en línea que puede ayudarle a firmar archivos PDF con su firma electrónica o certificado digital</li>
-<li><a href="">Adobe Sign</a>: Un servicio gratuito en línea que puede ayudarle a firmar archivos PDF con su firma electrónica o certificado digital</li>
-
-<li><a href="">SignNow</a>: Una aplicación gratuita que puede ayudarte a firmar archivos PDF con tu firma electrónica o certificado digital en dispositivos Android e iOS</li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/__about__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/__about__.py
deleted file mode 100644
index 3551bc2d29846441299cf57b397b02fc164c99b9..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/__about__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-__all__ = [
-    "__title__",
-    "__summary__",
-    "__uri__",
-    "__version__",
-    "__author__",
-    "__email__",
-    "__license__",
-    "__copyright__",
-]
-
-__title__ = "packaging"
-__summary__ = "Core utilities for Python packages"
-__uri__ = "https://github.com/pypa/packaging"
-
-__version__ = "21.3"
-
-__author__ = "Donald Stufft and individual contributors"
-__email__ = "donald@stufft.io"
-
-__license__ = "BSD-2-Clause or Apache-2.0"
-__copyright__ = "2014-2019 %s" % __author__
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/installer.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/installer.py
deleted file mode 100644
index b7096df14b4a15980ad138a3990d3e25aeb3bfe1..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/installer.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import glob
-import os
-import subprocess
-import sys
-import tempfile
-import warnings
-from distutils import log
-from distutils.errors import DistutilsError
-
-import pkg_resources
-from setuptools.wheel import Wheel
-from ._deprecation_warning import SetuptoolsDeprecationWarning
-
-
-def _fixup_find_links(find_links):
-    """Ensure find-links option end-up being a list of strings."""
-    if isinstance(find_links, str):
-        return find_links.split()
-    assert isinstance(find_links, (tuple, list))
-    return find_links
-
-
-def fetch_build_egg(dist, req):  # noqa: C901  # is too complex (16)  # FIXME
-    """Fetch an egg needed for building.
-
-    Use pip/wheel to fetch/build a wheel."""
-    warnings.warn(
-        "setuptools.installer is deprecated. Requirements should "
-        "be satisfied by a PEP 517 installer.",
-        SetuptoolsDeprecationWarning,
-    )
-    # Warn if wheel is not available
-    try:
-        pkg_resources.get_distribution('wheel')
-    except pkg_resources.DistributionNotFound:
-        dist.announce('WARNING: The wheel package is not available.', log.WARN)
-    # Ignore environment markers; if supplied, it is required.
-    req = strip_marker(req)
-    # Take easy_install options into account, but do not override relevant
-    # pip environment variables (like PIP_INDEX_URL or PIP_QUIET); they'll
-    # take precedence.
-    opts = dist.get_option_dict('easy_install')
-    if 'allow_hosts' in opts:
-        raise DistutilsError('the `allow-hosts` option is not supported '
-                             'when using pip to install requirements.')
-    quiet = 'PIP_QUIET' not in os.environ and 'PIP_VERBOSE' not in os.environ
-    if 'PIP_INDEX_URL' in os.environ:
-        index_url = None
-    elif 'index_url' in opts:
-        index_url = opts['index_url'][1]
-    else:
-        index_url = None
-    find_links = (
-        _fixup_find_links(opts['find_links'][1])[:] if 'find_links' in opts
-        else []
-    )
-    if dist.dependency_links:
-        find_links.extend(dist.dependency_links)
-    eggs_dir = os.path.realpath(dist.get_egg_cache_dir())
-    environment = pkg_resources.Environment()
-    for egg_dist in pkg_resources.find_distributions(eggs_dir):
-        if egg_dist in req and environment.can_add(egg_dist):
-            return egg_dist
-    with tempfile.TemporaryDirectory() as tmpdir:
-        cmd = [
-            sys.executable, '-m', 'pip',
-            '--disable-pip-version-check',
-            'wheel', '--no-deps',
-            '-w', tmpdir,
-        ]
-        if quiet:
-            cmd.append('--quiet')
-        if index_url is not None:
-            cmd.extend(('--index-url', index_url))
-        for link in find_links or []:
-            cmd.extend(('--find-links', link))
-        # If requirement is a PEP 508 direct URL, directly pass
-        # the URL to pip, as `req @ url` does not work on the
-        # command line.
-        cmd.append(req.url or str(req))
-        try:
-            subprocess.check_call(cmd)
-        except subprocess.CalledProcessError as e:
-            raise DistutilsError(str(e)) from e
-        wheel = Wheel(glob.glob(os.path.join(tmpdir, '*.whl'))[0])
-        dist_location = os.path.join(eggs_dir, wheel.egg_name())
-        wheel.install_as_egg(dist_location)
-        dist_metadata = pkg_resources.PathMetadata(
-            dist_location, os.path.join(dist_location, 'EGG-INFO'))
-        dist = pkg_resources.Distribution.from_filename(
-            dist_location, metadata=dist_metadata)
-        return dist
-
-
-def strip_marker(req):
-    """
-    Return a new requirement without the environment marker to avoid
-    calling pip with something like `babel; extra == "i18n"`, which
-    would always be ignored.
-    """
-    # create a copy to avoid mutating the input
-    req = pkg_resources.Requirement.parse(str(req))
-    req.marker = None
-    return req
diff --git a/spaces/Blealtan/clip-guided-binary-autoencoder/README.md b/spaces/Blealtan/clip-guided-binary-autoencoder/README.md
deleted file mode 100644
index aba547ce0b9ef7cca7081a544f8547d0d212d424..0000000000000000000000000000000000000000
--- a/spaces/Blealtan/clip-guided-binary-autoencoder/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Clip Guided Binary Autoencoder
-emoji: 🌍
-colorFrom: pink
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-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/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/Makefile b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/Makefile
deleted file mode 100644
index d537643dd411736a5f309383cfef52ea7d5e4599..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/Makefile
+++ /dev/null
@@ -1,19 +0,0 @@
-# Minimal makefile for Sphinx documentation
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-SOURCEDIR     = .
-BUILDDIR      = _build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/spaces/CVPR/LIVE/pybind11/pybind11/_version.py b/spaces/CVPR/LIVE/pybind11/pybind11/_version.py
deleted file mode 100644
index 1f2f254ce5e262fa7fb4770e1b770935ea46ecc0..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/pybind11/_version.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# -*- coding: utf-8 -*-
-version_info = (2, 5, 'dev1')
-__version__ = '.'.join(map(str, version_info))
diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/functional/operators/compound_assignment_operators.h b/spaces/CVPR/LIVE/thrust/thrust/detail/functional/operators/compound_assignment_operators.h
deleted file mode 100644
index 737d6abd098e0acc666ec9678e3219d8c9586cca..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/detail/functional/operators/compound_assignment_operators.h
+++ /dev/null
@@ -1,513 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/functional/actor.h>
-#include <thrust/detail/functional/composite.h>
-#include <thrust/detail/functional/operators/operator_adaptors.h>
-
-namespace thrust
-{
-namespace detail
-{
-namespace functional
-{
-
-// there's no standard plus_equal functional, so roll an ad hoc one here
-struct plus_equal
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) += THRUST_FWD(t2)))
-      -> decltype(THRUST_FWD(t1) += THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) += THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<plus_equal>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator+=(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<plus_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator+=()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<plus_equal>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator+=(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<plus_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator+=()
-
-// there's no standard minus_equal functional, so roll an ad hoc one here
-struct minus_equal
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) -= THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) -= THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) -= THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<minus_equal>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator-=(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<minus_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator-=()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<minus_equal>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator-=(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<minus_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator-=()
-
-// there's no standard multiplies_equal functional, so roll an ad hoc one here
-struct multiplies_equal
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) *= THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) *= THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) *= THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<multiplies_equal>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator*=(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<multiplies_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator*=()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<multiplies_equal>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator*=(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<multiplies_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator*=()
-
-// there's no standard divides_equal functional, so roll an ad hoc one here
-struct divides_equal
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) /= THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) /= THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) /= THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<divides_equal>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator/=(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<divides_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator/=()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<divides_equal>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator/=(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<divides_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator/=()
-
-// there's no standard modulus_equal functional, so roll an ad hoc one here
-struct modulus_equal
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) %= THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) %= THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) %= THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<modulus_equal>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator%=(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<modulus_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator%=()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<modulus_equal>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator%=(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<modulus_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator%=()
-
-// there's no standard bit_and_equal functional, so roll an ad hoc one here
-struct bit_and_equal
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) &= THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) &= THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) &= THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_and_equal>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator&=(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_and_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator&=()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_and_equal>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator&=(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_and_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator&=()
-
-// there's no standard bit_or_equal functional, so roll an ad hoc one here
-struct bit_or_equal
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) |= THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) |= THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) |= THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_or_equal>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator|=(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_or_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator|=()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_or_equal>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator|=(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_or_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator|=()
-
-// there's no standard bit_xor_equal functional, so roll an ad hoc one here
-struct bit_xor_equal
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) ^= THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) ^= THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) ^= THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_xor_equal>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator^=(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_xor_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator|=()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_xor_equal>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator^=(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_xor_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator|=()
-
-// there's no standard bit_lshift_equal functional, so roll an ad hoc one here
-struct bit_lshift_equal
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) <<= THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) <<= THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) <<= THRUST_FWD(t2);
-  }
-};
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_lshift_equal>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator<<=(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_lshift_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator<<=()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_lshift_equal>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator<<=(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_lshift_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator<<=()
-
-// there's no standard bit_rshift_equal functional, so roll an ad hoc one here
-struct bit_rshift_equal
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) >>= THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) >>= THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) >>= THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_rshift_equal>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator>>=(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_rshift_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator>>=()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_rshift_equal>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator>>=(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_rshift_equal>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator>>=()
-
-} // end functional
-} // end detail
-} // end thrust
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/sequence.h b/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/sequence.h
deleted file mode 100644
index c6ae90664ad9538e73febfde86c334011de417c8..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/sequence.h
+++ /dev/null
@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special version of this algorithm 
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/partition.h b/spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/partition.h
deleted file mode 100644
index 64a76e2788725134cf456742f33d57714e0f071f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/partition.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file reduce.h
- *  \brief OpenMP implementation of reduce algorithms.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/omp/detail/execution_policy.h>
-#include <thrust/pair.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace omp
-{
-namespace detail
-{
-
-
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename Predicate>
-  ForwardIterator stable_partition(execution_policy<DerivedPolicy> &exec,
-                                   ForwardIterator first,
-                                   ForwardIterator last,
-                                   Predicate pred);
-
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename InputIterator,
-         typename Predicate>
-  ForwardIterator stable_partition(execution_policy<DerivedPolicy> &exec,
-                                   ForwardIterator first,
-                                   ForwardIterator last,
-                                   InputIterator stencil,
-                                   Predicate pred);
-
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename Predicate>
-  thrust::pair<OutputIterator1,OutputIterator2>
-    stable_partition_copy(execution_policy<DerivedPolicy> &exec,
-                          InputIterator first,
-                          InputIterator last,
-                          OutputIterator1 out_true,
-                          OutputIterator2 out_false,
-                          Predicate pred);
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename Predicate>
-  thrust::pair<OutputIterator1,OutputIterator2>
-    stable_partition_copy(execution_policy<DerivedPolicy> &exec,
-                          InputIterator1 first,
-                          InputIterator1 last,
-                          InputIterator2 stencil,
-                          OutputIterator1 out_true,
-                          OutputIterator2 out_false,
-                          Predicate pred);
-
-
-} // end namespace detail
-} // end namespace omp
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/omp/detail/partition.inl>
-
diff --git a/spaces/CVPR/Text2Human/Text2Human/utils/util.py b/spaces/CVPR/Text2Human/Text2Human/utils/util.py
deleted file mode 100644
index f51663ff61a7ebb0b6c3b34633dcf417039fb762..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Text2Human/Text2Human/utils/util.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import logging
-import os
-import random
-import sys
-import time
-from shutil import get_terminal_size
-
-import numpy as np
-import torch
-
-logger = logging.getLogger('base')
-
-
-def make_exp_dirs(opt):
-    """Make dirs for experiments."""
-    path_opt = opt['path'].copy()
-    if opt['is_train']:
-        overwrite = True if 'debug' in opt['name'] else False
-        os.makedirs(path_opt.pop('experiments_root'), exist_ok=overwrite)
-        os.makedirs(path_opt.pop('models'), exist_ok=overwrite)
-    else:
-        os.makedirs(path_opt.pop('results_root'))
-
-
-def set_random_seed(seed):
-    """Set random seeds."""
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-
-class ProgressBar(object):
-    """A progress bar which can print the progress.
-
-    Modified from:
-    https://github.com/hellock/cvbase/blob/master/cvbase/progress.py
-    """
-
-    def __init__(self, task_num=0, bar_width=50, start=True):
-        self.task_num = task_num
-        max_bar_width = self._get_max_bar_width()
-        self.bar_width = (
-            bar_width if bar_width <= max_bar_width else max_bar_width)
-        self.completed = 0
-        if start:
-            self.start()
-
-    def _get_max_bar_width(self):
-        terminal_width, _ = get_terminal_size()
-        max_bar_width = min(int(terminal_width * 0.6), terminal_width - 50)
-        if max_bar_width < 10:
-            print(f'terminal width is too small ({terminal_width}), '
-                  'please consider widen the terminal for better '
-                  'progressbar visualization')
-            max_bar_width = 10
-        return max_bar_width
-
-    def start(self):
-        if self.task_num > 0:
-            sys.stdout.write(f"[{' ' * self.bar_width}] 0/{self.task_num}, "
-                             f'elapsed: 0s, ETA:\nStart...\n')
-        else:
-            sys.stdout.write('completed: 0, elapsed: 0s')
-        sys.stdout.flush()
-        self.start_time = time.time()
-
-    def update(self, msg='In progress...'):
-        self.completed += 1
-        elapsed = time.time() - self.start_time
-        fps = self.completed / elapsed
-        if self.task_num > 0:
-            percentage = self.completed / float(self.task_num)
-            eta = int(elapsed * (1 - percentage) / percentage + 0.5)
-            mark_width = int(self.bar_width * percentage)
-            bar_chars = '>' * mark_width + '-' * (self.bar_width - mark_width)
-            sys.stdout.write('\033[2F')  # cursor up 2 lines
-            sys.stdout.write(
-                '\033[J'
-            )  # clean the output (remove extra chars since last display)
-            sys.stdout.write(
-                f'[{bar_chars}] {self.completed}/{self.task_num}, '
-                f'{fps:.1f} task/s, elapsed: {int(elapsed + 0.5)}s, '
-                f'ETA: {eta:5}s\n{msg}\n')
-        else:
-            sys.stdout.write(
-                f'completed: {self.completed}, elapsed: {int(elapsed + 0.5)}s, '
-                f'{fps:.1f} tasks/s')
-        sys.stdout.flush()
-
-
-class AverageMeter(object):
-    """
-    Computes and stores the average and current value
-    Imported from
-    https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262
-    """
-
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0  # running average = running sum / running count
-        self.sum = 0  # running sum
-        self.count = 0  # running count
-
-    def update(self, val, n=1):
-        # n = batch_size
-
-        # val = batch accuracy for an attribute
-        # self.val = val
-
-        # sum = 100 * accumulative correct predictions for this attribute
-        self.sum += val * n
-
-        # count = total samples so far
-        self.count += n
-
-        # avg = 100 * avg accuracy for this attribute
-        # for all the batches so far
-        self.avg = self.sum / self.count
diff --git a/spaces/CVPR/WALT/mmdet/datasets/utils.py b/spaces/CVPR/WALT/mmdet/datasets/utils.py
deleted file mode 100644
index 157c9a2e1fe009552fdec9b9c9e7a33ed46d51ff..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/datasets/utils.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import copy
-import warnings
-
-from mmcv.cnn import VGG
-from mmcv.runner.hooks import HOOKS, Hook
-
-from mmdet.datasets.builder import PIPELINES
-from mmdet.datasets.pipelines import LoadAnnotations, LoadImageFromFile
-from mmdet.models.dense_heads import GARPNHead, RPNHead
-from mmdet.models.roi_heads.mask_heads import FusedSemanticHead
-
-
-def replace_ImageToTensor(pipelines):
-    """Replace the ImageToTensor transform in a data pipeline to
-    DefaultFormatBundle, which is normally useful in batch inference.
-
-    Args:
-        pipelines (list[dict]): Data pipeline configs.
-
-    Returns:
-        list: The new pipeline list with all ImageToTensor replaced by
-            DefaultFormatBundle.
-
-    Examples:
-        >>> pipelines = [
-        ...    dict(type='LoadImageFromFile'),
-        ...    dict(
-        ...        type='MultiScaleFlipAug',
-        ...        img_scale=(1333, 800),
-        ...        flip=False,
-        ...        transforms=[
-        ...            dict(type='Resize', keep_ratio=True),
-        ...            dict(type='RandomFlip'),
-        ...            dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]),
-        ...            dict(type='Pad', size_divisor=32),
-        ...            dict(type='ImageToTensor', keys=['img']),
-        ...            dict(type='Collect', keys=['img']),
-        ...        ])
-        ...    ]
-        >>> expected_pipelines = [
-        ...    dict(type='LoadImageFromFile'),
-        ...    dict(
-        ...        type='MultiScaleFlipAug',
-        ...        img_scale=(1333, 800),
-        ...        flip=False,
-        ...        transforms=[
-        ...            dict(type='Resize', keep_ratio=True),
-        ...            dict(type='RandomFlip'),
-        ...            dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]),
-        ...            dict(type='Pad', size_divisor=32),
-        ...            dict(type='DefaultFormatBundle'),
-        ...            dict(type='Collect', keys=['img']),
-        ...        ])
-        ...    ]
-        >>> assert expected_pipelines == replace_ImageToTensor(pipelines)
-    """
-    pipelines = copy.deepcopy(pipelines)
-    for i, pipeline in enumerate(pipelines):
-        if pipeline['type'] == 'MultiScaleFlipAug':
-            assert 'transforms' in pipeline
-            pipeline['transforms'] = replace_ImageToTensor(
-                pipeline['transforms'])
-        elif pipeline['type'] == 'ImageToTensor':
-            warnings.warn(
-                '"ImageToTensor" pipeline is replaced by '
-                '"DefaultFormatBundle" for batch inference. It is '
-                'recommended to manually replace it in the test '
-                'data pipeline in your config file.', UserWarning)
-            pipelines[i] = {'type': 'DefaultFormatBundle'}
-    return pipelines
-
-
-def get_loading_pipeline(pipeline):
-    """Only keep loading image and annotations related configuration.
-
-    Args:
-        pipeline (list[dict]): Data pipeline configs.
-
-    Returns:
-        list[dict]: The new pipeline list with only keep
-            loading image and annotations related configuration.
-
-    Examples:
-        >>> pipelines = [
-        ...    dict(type='LoadImageFromFile'),
-        ...    dict(type='LoadAnnotations', with_bbox=True),
-        ...    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-        ...    dict(type='RandomFlip', flip_ratio=0.5),
-        ...    dict(type='Normalize', **img_norm_cfg),
-        ...    dict(type='Pad', size_divisor=32),
-        ...    dict(type='DefaultFormatBundle'),
-        ...    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-        ...    ]
-        >>> expected_pipelines = [
-        ...    dict(type='LoadImageFromFile'),
-        ...    dict(type='LoadAnnotations', with_bbox=True)
-        ...    ]
-        >>> assert expected_pipelines ==\
-        ...        get_loading_pipeline(pipelines)
-    """
-    loading_pipeline_cfg = []
-    for cfg in pipeline:
-        obj_cls = PIPELINES.get(cfg['type'])
-        # TODO：use more elegant way to distinguish loading modules
-        if obj_cls is not None and obj_cls in (LoadImageFromFile,
-                                               LoadAnnotations):
-            loading_pipeline_cfg.append(cfg)
-    assert len(loading_pipeline_cfg) == 2, \
-        'The data pipeline in your config file must include ' \
-        'loading image and annotations related pipeline.'
-    return loading_pipeline_cfg
-
-
-@HOOKS.register_module()
-class NumClassCheckHook(Hook):
-
-    def _check_head(self, runner):
-        """Check whether the `num_classes` in head matches the length of
-        `CLASSSES` in `dataset`.
-
-        Args:
-            runner (obj:`EpochBasedRunner`): Epoch based Runner.
-        """
-        model = runner.model
-        dataset = runner.data_loader.dataset
-        if dataset.CLASSES is None:
-            runner.logger.warning(
-                f'Please set `CLASSES` '
-                f'in the {dataset.__class__.__name__} and'
-                f'check if it is consistent with the `num_classes` '
-                f'of head')
-        else:
-            for name, module in model.named_modules():
-                if hasattr(module, 'num_classes') and not isinstance(
-                        module, (RPNHead, VGG, FusedSemanticHead, GARPNHead)):
-                    assert module.num_classes == len(dataset.CLASSES), \
-                        (f'The `num_classes` ({module.num_classes}) in '
-                         f'{module.__class__.__name__} of '
-                         f'{model.__class__.__name__} does not matches '
-                         f'the length of `CLASSES` '
-                         f'{len(dataset.CLASSES)}) in '
-                         f'{dataset.__class__.__name__}')
-
-    def before_train_epoch(self, runner):
-        """Check whether the training dataset is compatible with head.
-
-        Args:
-            runner (obj:`EpochBasedRunner`): Epoch based Runner.
-        """
-        self._check_head(runner)
-
-    def before_val_epoch(self, runner):
-        """Check whether the dataset in val epoch is compatible with head.
-
-        Args:
-            runner (obj:`EpochBasedRunner`): Epoch based Runner.
-        """
-        self._check_head(runner)
diff --git a/spaces/Chaitanya01/InvestingPlatform/patterns.py b/spaces/Chaitanya01/InvestingPlatform/patterns.py
deleted file mode 100644
index 0be810b3df079e30c04651f86203f988cc819e8f..0000000000000000000000000000000000000000
--- a/spaces/Chaitanya01/InvestingPlatform/patterns.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# Candlestick patterns mapping
-patterns = {
-    'CDL2CROWS':'Two Crows',
-    'CDL3BLACKCROWS':'Three Black Crows',
-    'CDL3INSIDE':'Three Inside Up/Down',
-    'CDL3LINESTRIKE':'Three-Line Strike',
-    'CDL3OUTSIDE':'Three Outside Up/Down',
-    'CDL3STARSINSOUTH':'Three Stars In The South',
-    'CDL3WHITESOLDIERS':'Three Advancing White Soldiers',
-    'CDLABANDONEDBABY':'Abandoned Baby',
-    'CDLADVANCEBLOCK':'Advance Block',
-    'CDLBELTHOLD':'Belt-hold',
-    'CDLBREAKAWAY':'Breakaway',
-    'CDLCLOSINGMARUBOZU':'Closing Marubozu',
-    'CDLCONCEALBABYSWALL':'Concealing Baby Swallow',
-    'CDLCOUNTERATTACK':'Counterattack',
-    'CDLDARKCLOUDCOVER':'Dark Cloud Cover',
-    'CDLDOJI':'Doji',
-    'CDLDOJISTAR':'Doji Star',
-    'CDLDRAGONFLYDOJI':'Dragonfly Doji',
-    'CDLENGULFING':'Engulfing Pattern',
-    'CDLEVENINGDOJISTAR':'Evening Doji Star',
-    'CDLEVENINGSTAR':'Evening Star',
-    'CDLGAPSIDESIDEWHITE':'Up/Down-gap side-by-side white lines',
-    'CDLGRAVESTONEDOJI':'Gravestone Doji',
-    'CDLHAMMER':'Hammer',
-    'CDLHANGINGMAN':'Hanging Man',
-    'CDLHARAMI':'Harami Pattern',
-    'CDLHARAMICROSS':'Harami Cross Pattern',
-    'CDLHIGHWAVE':'High-Wave Candle',
-    'CDLHIKKAKE':'Hikkake Pattern',
-    'CDLHIKKAKEMOD':'Modified Hikkake Pattern',
-    'CDLHOMINGPIGEON':'Homing Pigeon',
-    'CDLIDENTICAL3CROWS':'Identical Three Crows',
-    'CDLINNECK':'In-Neck Pattern',
-    'CDLINVERTEDHAMMER':'Inverted Hammer',
-    'CDLKICKING':'Kicking',
-    'CDLKICKINGBYLENGTH':'Kicking - bull/bear determined by the longer marubozu',
-    'CDLLADDERBOTTOM':'Ladder Bottom',
-    'CDLLONGLEGGEDDOJI':'Long Legged Doji',
-    'CDLLONGLINE':'Long Line Candle',
-    'CDLMARUBOZU':'Marubozu',
-    'CDLMATCHINGLOW':'Matching Low',
-    'CDLMATHOLD':'Mat Hold',
-    'CDLMORNINGDOJISTAR':'Morning Doji Star',
-    'CDLMORNINGSTAR':'Morning Star',
-    'CDLONNECK':'On-Neck Pattern',
-    'CDLPIERCING':'Piercing Pattern',
-    'CDLRICKSHAWMAN':'Rickshaw Man',
-    'CDLRISEFALL3METHODS':'Rising/Falling Three Methods',
-    'CDLSEPARATINGLINES':'Separating Lines',
-    'CDLSHOOTINGSTAR':'Shooting Star',
-    'CDLSHORTLINE':'Short Line Candle',
-    'CDLSPINNINGTOP':'Spinning Top',
-    'CDLSTALLEDPATTERN':'Stalled Pattern',
-    'CDLSTICKSANDWICH':'Stick Sandwich',
-    'CDLTAKURI':'Takuri (Dragonfly Doji with very long lower shadow)',
-    'CDLTASUKIGAP':'Tasuki Gap',
-    'CDLTHRUSTING':'Thrusting Pattern',
-    'CDLTRISTAR':'Tristar Pattern',
-    'CDLUNIQUE3RIVER':'Unique 3 River',
-    'CDLUPSIDEGAP2CROWS':'Upside Gap Two Crows',
-    'CDLXSIDEGAP3METHODS':'Upside/Downside Gap Three Methods'
-}
\ No newline at end of file
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/addiction/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/addiction/__init__.py
deleted file mode 100644
index 650ea6fa7fa2b58d42daa8a6e0514aea8c5c8a50..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/addiction/__init__.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.exception import TextOverLength
-from meme_generator.utils import make_jpg_or_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def addiction(images: List[BuildImage], texts: List[str], args):
-    frame = BuildImage.open(img_dir / "0.png")
-
-    if texts:
-        text = texts[0]
-        frame = frame.resize_canvas((246, 286), direction="north", bg_color="white")
-        try:
-            frame.draw_text((10, 246, 236, 286), texts[0], max_fontsize=45)
-        except ValueError:
-            raise TextOverLength(text)
-
-    def make(img: BuildImage) -> BuildImage:
-        img = img.convert("RGBA").resize((91, 91), keep_ratio=True)
-        return frame.copy().paste(img, alpha=True)
-
-    return make_jpg_or_gif(images[0], make)
-
-
-add_meme(
-    "addiction",
-    addiction,
-    min_images=1,
-    max_images=1,
-    min_texts=0,
-    max_texts=1,
-    keywords=["上瘾", "毒瘾发作"],
-)
diff --git a/spaces/ClinBAY/Safeterm_Demo/app.py b/spaces/ClinBAY/Safeterm_Demo/app.py
deleted file mode 100644
index 4b0b3d107f7bd3924bb92ea4e04a657f3627db42..0000000000000000000000000000000000000000
--- a/spaces/ClinBAY/Safeterm_Demo/app.py
+++ /dev/null
@@ -1,410 +0,0 @@
-import gradio as gr
-import requests
-import json
-from send_email_request import send_email
-
-
-def request_api_key_form(req_email, req_name, req_organization, req_meddra_license, req_agree_terms, req_save_data):
-    # Check if all required fields are filled and the conditions are met
-    if not req_email or not req_name or not req_organization:
-        return "**Please fill in all required fields.**"
-    if "@" not in req_email or "." not in req_email:
-        return "**Please enter a valid email.**"
-    if not req_meddra_license:
-        return "**You need to have a valid MedDRA license.**"
-    if not req_agree_terms:
-        return "**You need to agree to Safeterm terms of use.**"
-    if not req_save_data:
-        return "**You need to agree to share data for training and communication purposes.**"
-
-    subject = "API Key Request for Safeterm"
-
-    send_email(subject, req_email, req_name, req_organization, req_meddra_license, req_agree_terms, req_save_data)
-
-    return "**Your request for API key has been submitted successfully! We will send it to you via mail soon!**"
-
-
-def encode_caller(apikey, reported_terms_encoder, validation_request, single_term):
-    if not apikey:
-        return "Please enter a valid API key!"
-
-    url = "https://safeterm.proddis.com/meddra_encode"
-    reported_terms_list = [term.strip() for term in reported_terms_encoder.split(",")]
-
-    # Convert string values to boolean
-    validation_request_bool = validation_request == "True"
-    single_term_bool = single_term == "True"
-
-    payload = json.dumps({
-        "reported_terms": reported_terms_list,
-        "nmax": 1,
-        "version": float("26.0"),
-        "validation_request": validation_request_bool,
-        "single_term": single_term_bool
-    })
-
-    headers = {
-        'Content-Type': 'application/json',
-        'Authorization': f'Bearer {apikey}'
-    }
-
-    response = requests.post(url, headers=headers, data=payload)
-    data = response.json()
-
-    if "detail" in data:
-        return data["detail"]
-
-    results = []
-
-    for encoded_term in data.get("encoded_terms", []):
-        result = f"Reported Term: {encoded_term['term']}\n"
-
-        # Check if 'encoded_term' contains a list of dictionaries (valid_request = False)
-        if isinstance(encoded_term['encoded_term'][0], dict):
-            for sub_term_entry in encoded_term['encoded_term']:
-                for llt_id, llt_term in zip(sub_term_entry['llt_id'], sub_term_entry['llt_term']):
-                    result += f"LLT ID: {llt_id}\nLLT Term: {llt_term}\n"
-
-                pt_id = sub_term_entry['pt_id'][0]
-                pt_term = sub_term_entry['pt_term'][0]
-                result += f"PT ID: {pt_id}\nPT Term: {pt_term}\n"
-                result += "---\n"
-
-        # Case for valid_request = True (list of lists)
-        else:
-            for sub_list in encoded_term['encoded_term']:
-                for sub_term_entry in sub_list:
-                    if 'llt_id' in sub_term_entry and 'llt_term' in sub_term_entry:
-                        for llt_id, llt_term in zip(sub_term_entry['llt_id'], sub_term_entry['llt_term']):
-                            result += f"LLT ID: {llt_id}\nLLT Term: {llt_term}\n"
-
-                        pt_id = sub_term_entry['pt_id'][0]
-                        pt_term = sub_term_entry['pt_term'][0]
-                        result += f"PT ID: {pt_id}\nPT Term: {pt_term}\n"
-                        result += "---\n"
-
-                    # Handle other entries like "is_validated" and "report"
-                    else:
-                        for key, value in sub_term_entry.items():
-                            result += f"{key}: {value}\n"
-                        result += "---\n"
-
-        result += f"Status: {encoded_term['status']}\n\n"
-        results.append(result)
-
-    # Add the API messages at the end.
-    api_message = data.get("messages", "No API message available")
-    api_message = "OK" if api_message is None else api_message
-    results.append(f"API Message: {api_message}")
-
-    return "\n".join(results)
-
-
-def validate_caller(val_apikey, val_reported_terms, val_llt_terms):
-    if not val_apikey:
-        return "Please enter a valid API key!"
-    url = "https://safeterm.proddis.com/meddra_validate"
-
-    # Convert comma-separated strings into lists
-    val_reported_terms_list = [term.strip() for term in val_reported_terms.split(",")]
-    val_llt_terms_list = [term.strip() for term in val_llt_terms.split(",")]
-
-    payload = json.dumps({
-        "reported_terms": val_reported_terms_list,
-        "llt_terms": val_llt_terms_list,
-        "nmax": 1,
-        "version": float("26.0")
-    })
-    headers = {
-        'Content-Type': 'application/json',
-        'Authorization': f'Bearer {val_apikey}'
-    }
-
-    response = requests.request("POST", url, headers=headers, data=payload, timeout=60)
-
-    data = response.json()
-
-    # Check if the response contains an error detail
-    if "detail" in data:
-        return data["detail"]
-
-    results = []
-    for validation_item in data["responses"]:
-        report = validation_item.get("report", "No report")
-
-        if validation_item["best_dict_term"]:
-            llt_id = validation_item["best_dict_term"][0]["llt_id"][0]
-            llt_term = validation_item["best_dict_term"][0]["llt_term"][0]
-            pt_id = validation_item["best_dict_term"][0]["pt_id"][0]
-            pt_term = validation_item["best_dict_term"][0]["pt_term"][0]
-
-            result = f"Report: {report}\n"
-            result += f"LLT ID: {llt_id}\n"
-            result += f"LLT Term: {llt_term}\n"
-            result += f"PT ID: {pt_id}\n"
-            result += f"PT Term: {pt_term}\n\n"
-        else:
-            result = f"Report: {report}\nNo matching terms found.\n\n"
-
-        results.append(result)
-
-        # Add the API messages at the end.
-        api_message = data.get("messages", "No API message available")
-        api_message = "OK" if api_message is None else api_message
-        results.append(f"API key status: {api_message}")
-
-    return "\n".join(results)
-
-
-def upgrade_caller(api_key, upgrader_list_of_terms, version_upgrade):
-    if not api_key:
-        return "Please enter a valid API key!"
-    url = "https://safeterm.proddis.com/meddra_version_upgrade"
-
-    cleaned_terms = [term.strip() for term in upgrader_list_of_terms.split(",")]
-
-    payload = json.dumps({
-        "list_of_terms": cleaned_terms,
-        "nmax": 1,
-        "version": float(version_upgrade),
-        "verbose": True
-    })
-    headers = {
-        'Content-Type': 'application/json',
-        'Authorization': f'Bearer {api_key}'
-    }
-    response = requests.request("POST", url, headers=headers, data=payload)
-
-    data = response.json()
-
-    # Check if the response contains an error detail
-    if "detail" in data:
-        return data["detail"]
-
-    output = []
-    results = data.get("result", [])
-    for i, result in enumerate(results):
-        input_term = cleaned_terms[i]  # Assuming the order in 'results' and 'cleaned_terms' is the same
-
-        if result['change_status'] is None or result['output_term'] is None:
-            status_message = result.get('status', 'Unknown Status')
-            output.append(f"Input Term: {input_term}\nStatus: {status_message}\n")
-        else:
-            status = "Unchanged" if not result['change_status'] else "Changed"
-            output_term = result['output_term']
-            output.append(f"Input Term: {input_term}\nStatus: {status}\nOutput Term: {output_term}\n")
-
-    # Get the API message, set to "OK" if it's None
-    api_message = data.get("messages", "No API message available")
-    api_message = "OK" if api_message is None else api_message
-
-    output.append(f"API status: {api_message}")
-
-    return "\n".join(output).strip()  # Removing any trailing whitespace
-
-
-def llt_current_caller(api_key, currency_list_of_terms, version_check):
-    if not api_key:
-        return "Please enter a valid API key!"
-
-    url = "https://safeterm.proddis.com/meddra_llt_is_current"
-    cleaned_terms_currency = [term.strip() for term in currency_list_of_terms.split(",")]
-
-    payload = json.dumps({
-        "list_of_terms": cleaned_terms_currency,
-        "version": float(version_check)
-    })
-
-    headers = {
-        'Content-Type': 'application/json',
-        'Authorization': f'Bearer {api_key}'
-    }
-
-    response = requests.request("POST", url, headers=headers, data=payload)
-    response_data = response.json()
-
-    # Check if 'is_current_flag_results' key exists in the API response
-    if "is_current_flag_results" not in response_data:
-        # If 'detail' key exists in response_data, show the error detail, else show a generic error
-        return response_data.get("detail", "An error occurred.")
-
-    beautified_output = []
-    for term, flag_data in zip(cleaned_terms_currency, response_data["is_current_flag_results"]):
-        flag = flag_data.get('is_current_flag', None)
-        status = flag_data.get('status', '')
-
-        if isinstance(flag, bool):
-            status_text = "Is Current" if flag else "Is not Current"
-            beautified_output.append(f"{term}: {status_text}")
-
-        else:
-            beautified_output.append(f"Error {term}: {status}")
-
-    # Get the API message, set to "OK" if it's None
-    api_message = response_data.get("messages", None)
-    api_message = "OK" if api_message is None else api_message
-    beautified_output.append(f"API status: {api_message}")
-
-    return "\n".join(beautified_output)
-
-
-with gr.Blocks(css=".gradio-container {background-color: lightgray}") as demo:
-    # gr.Markdown("Safeterm is an automated AI system that extracts medical terms from patient narratives and
-    # standardize these terms according to the medical dictionary for regulatory applications (MedDRA). ")
-    gr.Markdown("### Safeterm: Translate Medical Narratives into Standardized Dictionaries")
-    with gr.Row():
-        with gr.Column():
-            gr.HTML(
-                """<p>Safeterm is an automated AI system that extracts medical terms from patient narratives and 
-                standardize these terms according to the medical dictionary for regulatory applications (MedDRA). 
-                </p>""")
-        with gr.Column():
-            universal_api_key_input = gr.Textbox(label="Paste your API Key", placeholder="Enter your API Key...",
-                                                 lines=1)
-    with gr.Row():
-        with gr.Tab("Safeterm Encode"):
-            gr.Markdown("### Safeterm Encode")
-
-            # Inputs
-            encode_reported_terms = gr.Dropdown(
-                ["Headache, Allergic to CAT scan", "Myocardial infarction in the fall of 2000",
-                 "Nurse miscalculated the dose and the patient received 40 mg instead of 20 mg of his medication. He "
-                 "experienced severe hypotension and heart attack."],
-                label="Medical Narratives (comma-separated, max 5)",
-                info="Enter your reported medical narratives (1 or more comma-separated statements, example: "
-                     "Headache, Allergic to CAT scan) here or choose from the presets.",
-                allow_custom_value=True)
-
-            encode_version = gr.Textbox(label="MedDRA Version", value="26.0")
-
-            # New input boxes
-            validation_request = gr.Radio(label="Validation Request", choices=["True", "False"])
-            single_term = gr.Radio(label="Single Term", choices=["True", "False"])
-
-            # Output
-            api_response_encode = gr.Textbox(label="Standardized Medical Dictionary Outputs")
-
-            # Button to trigger API call
-            submit_button = gr.Button("Encode into MedDRA")
-
-            # Binding API call function to button (Note: Assuming `universal_api_key_input` was defined somewhere
-            # before this block)
-            submit_button.click(encode_caller,
-                                inputs=[universal_api_key_input, encode_reported_terms, validation_request,
-                                        single_term],
-                                outputs=api_response_encode)
-
-        with gr.Tab("Safeterm Validate"):
-            gr.Markdown('### Safeterm Validate')
-            gr.Markdown(
-                'Checks an existing MedDRA encoding. Compares reported terms to MedDRA Lower Level Terms (LLT). '
-                'Provides an alternative LLT in case of discrepancy.')
-            # Validator Inputs
-            reported_terms_validate = gr.Dropdown(["Pain in the head, Feeling nauseous"]
-                                                  , label="Reported Terms (comma-separated, max 5)"
-                                                  ,
-                                                  info="Enter your reported terms here (example: pain in the "
-                                                       "head, Feeling nauseous) or choose from the dropdown preset",
-                                                  allow_custom_value=True)
-            llt_terms_validate = gr.Dropdown(["Headache, Vomiting"]
-                                             , label="LLT terms (comma-separated, max 5)"
-                                             ,
-                                             info="Enter the current LLTs here (example: Headache, Vomitting), "
-                                                  "or choose"
-                                                  "from the dropdown presets",
-                                             allow_custom_value=True)
-            version_validate = gr.Textbox(label="MedDRA Version", value="26.0")
-
-            # Output
-            api_response_validate = gr.Textbox(label="Validation Report")
-
-            # Button to trigger API call and Binding API call function to button for Validator
-            submit_button_validate = gr.Button("Validate Existing MedDRA Encoding")
-            submit_button_validate.click(validate_caller,
-                                         inputs=[universal_api_key_input, reported_terms_validate, llt_terms_validate],
-                                         outputs=api_response_validate)
-        with gr.Tab("Safeterm Version Upgrade"):
-            gr.Markdown("### Safeterm Version Upgrade")
-            gr.Markdown('Upgrade terms to the most up-to-date LLTs in a MedDRA version.')
-            # Version Upgrade Inputs
-            list_of_terms_upgrade = gr.Dropdown(
-                ["Pain in the head", "Injection site joint inflammation", "Bone chip removal"]
-                , label="LLTs (comma-separated, max 5)"
-                , info="Enter your LLTs here (example: Injection site joint inflammation) "
-                       "or choose from the dropdown preset",
-                allow_custom_value=True)
-
-            version_upgrade = gr.Dropdown(label="To MedDRA Version", choices=["20.0", "26.0"])
-
-            # Output
-            api_response_upgrade = gr.Textbox(label="Version Upgrade Response")
-
-            # Button to trigger API call and Binding API call function to button for Version Upgrade
-            submit_button_upgrade = gr.Button("Upgrade to Selected MedDRA Version")
-            submit_button_upgrade.click(upgrade_caller,
-                                        inputs=[universal_api_key_input, list_of_terms_upgrade,
-                                                version_upgrade], outputs=api_response_upgrade)
-
-        with gr.Tab("Safeterm Current Check"):  # Currency Checker section
-            gr.Markdown("### Safeterm Current Version Check")
-            gr.Markdown('Checks if LLTs are current or not in a particular MedDRA version')
-            # No Change Version Check Inputs
-            list_of_terms_validity = gr.Dropdown(["Anemia iron deficiency", "COVID-19"]
-                                                 , label="LLTs (comma-separated, max 5)"
-                                                 , info="Enter your LLTs here, e.g.: Injection site "
-                                                        "joint inflammation or choose from the dropdown preset",
-                                                 allow_custom_value=True)
-            version_check = gr.Dropdown(label="To MedDRA Version", choices=["20.0", "26.0"])
-
-            # Output for No Change Version Check
-            api_response_current = gr.Textbox(label="Current Check Response")
-
-            # Button to trigger API call and Binding API call function to button for No Change Check
-            submit_button_currency = gr.Button("Check if LLT is current or not")
-            submit_button_currency.click(llt_current_caller,
-                                         inputs=[universal_api_key_input, list_of_terms_validity, version_check],
-                                         outputs=api_response_current)
-
-        with gr.Tab("Request an API key"):
-            # gr.Markdown("### Safeterm Settings")
-            gr.Markdown("### Request an API key")
-            free_demo = gr.Markdown(
-                "Submit your request for a free demo of our API (Expires in 30 days. 50 terms limit)")
-            # gr.Markdown("### MedDRA Dictionary")
-            version = gr.Markdown("Safeterm Model v-082023")
-            language = gr.Markdown("Language: English")  # Dropdown for language
-
-            gr.Markdown("### Contact information")
-            email = gr.Textbox(label="Email", placeholder="Enter your professional email address...")
-            name = gr.Textbox(label="First and Last Name", placeholder="Enter your full name...")
-            organization = gr.Textbox(label="Organization", placeholder="Enter your organization name and details...")
-
-            gr.Markdown("### Terms of use")
-            # MedDRA_license = gr.Checkbox(label="I confirm that my organization has a valid MedDRA license.")
-            with gr.Row():
-                MedDRA_license = gr.Checkbox(label="I confirm that my organization has a valid MedDRA license: ")
-                gr.HTML("""<a href=https://www.meddra.org/subscription/process>[link]</a>""")
-            with gr.Row():
-                agree_terms = gr.Checkbox(label="I agree to Safeterm terms of use: ", min_width=1, scale=1)
-                gr.HTML("""<a href=https://www.proddis.com/pdf/Proddis_Terms_of_Use.pdf>[link] </a>""")
-
-            with gr.Row():
-                save_data = gr.Checkbox(
-                    label="I consent to the storage of my personal data for training and communication purposes.")
-                gr.HTML(""" """)
-
-            feedback_textbox = gr.Markdown(label="Feedback")
-
-            # Button for API key request and Binding function to button
-            api_key_button = gr.Button("Submit request for API key")
-            api_key_button.click(
-                request_api_key_form,
-                inputs=[email, name, organization, MedDRA_license, agree_terms, save_data],
-                outputs=feedback_textbox
-            )
-    with gr.Row():
-        gr.Markdown(
-            "Safeterm API is distributed by ClinBAY Limited. \t For any enquiry, feel free to contact us at "
-            "info@clinbay.com")
-
-demo.launch()
diff --git a/spaces/Conner/IAPdemo/app.py b/spaces/Conner/IAPdemo/app.py
deleted file mode 100644
index 73c910113d114ac91401de11514ae5a0cada706a..0000000000000000000000000000000000000000
--- a/spaces/Conner/IAPdemo/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from tensorflow import keras
-from keras.models import load_model
-from PIL import Image, ImageOps
-import numpy as np
-import gradio as gr
-import rasterio
-from rasterio.enums import Resampling
-
-
-# Load the model
-model = load_model('keras_model.h5')
-
-
-
-def greet(name):
-    return "Hello " + prediction + "!!"
-
-def predict(image):  
-
-# Create the array of the right shape to feed into the keras model
-# The 'length' or number of images you can put into the array is
-# determined by the first position in the shape tuple, in this case 1.
-    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
-# Replace this with the path to your image
-    
-#resize the image to a 224x224 with the same strategy as in TM2:
-#resizing the image to be at least 224x224 and then cropping from the center
-    size = (224, 224)
-    image = gr.inputs.Image()
-    image = ImageOps.fit(image, size, Resampling.LANCZOS)
-    
-#turn the image into a numpy array
-    image_array = np.asarray(image)
-# Normalize the image
-    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
-# Load the image into the array
-    data[0] = normalized_image_array
-
-# run the inference
-    prediction = model.predict(data)
-    gr.print(prediction)
-    return prediction
-    
-    
-iface = gr.Interface(fn=predict, inputs="image", outputs="text")
-iface.launch()
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/csrc/cpu/vision.h b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/csrc/cpu/vision.h
deleted file mode 100644
index 19539caf9c5aa8b8025f786c3e54e23de300cf5e..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/csrc/cpu/vision.h
+++ /dev/null
@@ -1,73 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-#pragma once
-#include <torch/extension.h>
-
-
-at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,
-                                const at::Tensor& rois,
-                                const float spatial_scale,
-                                const int pooled_height,
-                                const int pooled_width,
-                                const int sampling_ratio);
-
-
-at::Tensor nms_cpu(const at::Tensor& dets,
-                   const at::Tensor& scores,
-                   const float threshold);
-at::Tensor
-dcn_v2_cpu_forward(const at::Tensor &input,
-                   const at::Tensor &weight,
-                   const at::Tensor &bias,
-                   const at::Tensor &offset,
-                   const at::Tensor &mask,
-                   const int kernel_h,
-                   const int kernel_w,
-                   const int stride_h,
-                   const int stride_w,
-                   const int pad_h,
-                   const int pad_w,
-                   const int dilation_h,
-                   const int dilation_w,
-                   const int deformable_group);
-
-std::vector<at::Tensor>
-dcn_v2_cpu_backward(const at::Tensor &input,
-                    const at::Tensor &weight,
-                    const at::Tensor &bias,
-                    const at::Tensor &offset,
-                    const at::Tensor &mask,
-                    const at::Tensor &grad_output,
-                    int kernel_h, int kernel_w,
-                    int stride_h, int stride_w,
-                    int pad_h, int pad_w,
-                    int dilation_h, int dilation_w,
-                    int deformable_group);
-
-
-std::tuple<at::Tensor, at::Tensor>
-dcn_v2_psroi_pooling_cpu_forward(const at::Tensor &input,
-                                 const at::Tensor &bbox,
-                                 const at::Tensor &trans,
-                                 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);
-
-std::tuple<at::Tensor, at::Tensor>
-dcn_v2_psroi_pooling_cpu_backward(const at::Tensor &out_grad,
-                                  const at::Tensor &input,
-                                  const at::Tensor &bbox,
-                                  const at::Tensor &trans,
-                                  const 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);
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/plugin_registry.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/plugin_registry.py
deleted file mode 100644
index 37d3db222ef2c7920628971a92e863d9915514c6..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/plugin_registry.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import sys
-from typing import Any, Dict, List, Optional, Generic, TypeVar, cast
-from types import TracebackType
-
-if sys.version_info >= (3, 8):
-    from importlib.metadata import entry_points
-else:
-    from importlib_metadata import entry_points
-
-from toolz import curry
-
-
-PluginType = TypeVar("PluginType")
-
-
-class NoSuchEntryPoint(Exception):
-    def __init__(self, group, name):
-        self.group = group
-        self.name = name
-
-    def __str__(self):
-        return f"No {self.name!r} entry point found in group {self.group!r}"
-
-
-class PluginEnabler:
-    """Context manager for enabling plugins
-
-    This object lets you use enable() as a context manager to
-    temporarily enable a given plugin::
-
-        with plugins.enable('name'):
-            do_something()  # 'name' plugin temporarily enabled
-        # plugins back to original state
-    """
-
-    def __init__(self, registry: "PluginRegistry", name: str, **options):
-        self.registry = registry  # type: PluginRegistry
-        self.name = name  # type: str
-        self.options = options  # type: Dict[str, Any]
-        self.original_state = registry._get_state()  # type: Dict[str, Any]
-        self.registry._enable(name, **options)
-
-    def __enter__(self) -> "PluginEnabler":
-        return self
-
-    def __exit__(self, typ: type, value: Exception, traceback: TracebackType) -> None:
-        self.registry._set_state(self.original_state)
-
-    def __repr__(self) -> str:
-        return "{}.enable({!r})".format(self.registry.__class__.__name__, self.name)
-
-
-class PluginRegistry(Generic[PluginType]):
-    """A registry for plugins.
-
-    This is a plugin registry that allows plugins to be loaded/registered
-    in two ways:
-
-    1. Through an explicit call to ``.register(name, value)``.
-    2. By looking for other Python packages that are installed and provide
-       a setuptools entry point group.
-
-    When you create an instance of this class, provide the name of the
-    entry point group to use::
-
-        reg = PluginRegister('my_entrypoint_group')
-
-    """
-
-    # this is a mapping of name to error message to allow custom error messages
-    # in case an entrypoint is not found
-    entrypoint_err_messages = {}  # type: Dict[str, str]
-
-    # global settings is a key-value mapping of settings that are stored globally
-    # in the registry rather than passed to the plugins
-    _global_settings = {}  # type: Dict[str, Any]
-
-    def __init__(self, entry_point_group: str = "", plugin_type: type = object):
-        """Create a PluginRegistry for a named entry point group.
-
-        Parameters
-        ==========
-        entry_point_group: str
-            The name of the entry point group.
-        plugin_type: object
-            A type that will optionally be used for runtime type checking of
-            loaded plugins using isinstance.
-        """
-        self.entry_point_group = entry_point_group  # type: str
-        self.plugin_type = plugin_type  # type: Optional[type]
-        self._active = None  # type: Optional[PluginType]
-        self._active_name = ""  # type: str
-        self._plugins = {}  # type: Dict[str, PluginType]
-        self._options = {}  # type: Dict[str, Any]
-        self._global_settings = self.__class__._global_settings.copy()  # type: dict
-
-    def register(self, name: str, value: Optional[PluginType]) -> Optional[PluginType]:
-        """Register a plugin by name and value.
-
-        This method is used for explicit registration of a plugin and shouldn't be
-        used to manage entry point managed plugins, which are auto-loaded.
-
-        Parameters
-        ==========
-        name: str
-            The name of the plugin.
-        value: PluginType or None
-            The actual plugin object to register or None to unregister that plugin.
-
-        Returns
-        =======
-        plugin: PluginType or None
-            The plugin that was registered or unregistered.
-        """
-        if value is None:
-            return self._plugins.pop(name, None)
-        else:
-            assert isinstance(value, self.plugin_type)  # type: ignore[arg-type]  # Should ideally be fixed by better annotating plugin_type
-            self._plugins[name] = value
-            return value
-
-    def names(self) -> List[str]:
-        """List the names of the registered and entry points plugins."""
-        exts = list(self._plugins.keys())
-        e_points = importlib_metadata_get(self.entry_point_group)
-        more_exts = [ep.name for ep in e_points]
-        exts.extend(more_exts)
-        return sorted(set(exts))
-
-    def _get_state(self) -> Dict[str, Any]:
-        """Return a dictionary representing the current state of the registry"""
-        return {
-            "_active": self._active,
-            "_active_name": self._active_name,
-            "_plugins": self._plugins.copy(),
-            "_options": self._options.copy(),
-            "_global_settings": self._global_settings.copy(),
-        }
-
-    def _set_state(self, state: Dict[str, Any]) -> None:
-        """Reset the state of the registry"""
-        assert set(state.keys()) == {
-            "_active",
-            "_active_name",
-            "_plugins",
-            "_options",
-            "_global_settings",
-        }
-        for key, val in state.items():
-            setattr(self, key, val)
-
-    def _enable(self, name: str, **options) -> None:
-        if name not in self._plugins:
-            try:
-                (ep,) = [
-                    ep
-                    for ep in importlib_metadata_get(self.entry_point_group)
-                    if ep.name == name
-                ]
-            except ValueError as err:
-                if name in self.entrypoint_err_messages:
-                    raise ValueError(self.entrypoint_err_messages[name]) from err
-                else:
-                    raise NoSuchEntryPoint(self.entry_point_group, name) from err
-            value = cast(PluginType, ep.load())
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-        self._active_name = name
-        self._active = self._plugins[name]
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-        self._options = options
-
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-        Parameters
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-
-
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-    # deprecation cycle for 'get'
-    if hasattr(ep, "select"):
-        return ep.select(group=group)
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diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-1e03cd90.css b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-1e03cd90.css
deleted file mode 100644
index 6692555db405e6eb83d0671b1ef9922ee30770d3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-1e03cd90.css
+++ /dev/null
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diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-28bbfef4.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-28bbfef4.js
deleted file mode 100644
index 2a4bcaa57ee83c3546d34ed1402158e00c5c76a6..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-28bbfef4.js
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E=s;s=B(c),s===E?d[s].p(c,g):(Q(),M(d[E],1,1,()=>{d[E]=null}),U(),o=d[s],o?o.p(c,g):(o=d[s]=b[s](c),o.c()),H(o,1),o.m(a.parentNode,a))},i(c){r||(H(e.$$.fragment,c),H(u),H(o),r=!0)},o(c){M(e.$$.fragment,c),M(u),M(o),r=!1},d(c){c&&(v(n),v(l),v(a)),z(e,c),u&&u.d(c),d[s].d(c)}}}function Ve(t){let e,n;return e=new ve({props:{test_id:"highlighted-text",visible:t[3],elem_id:t[1],elem_classes:t[2],padding:!1,container:t[7],scale:t[8],min_width:t[9],$$slots:{default:[Oe]},$$scope:{ctx:t}}}),{c(){O(e.$$.fragment)},m(l,s){V(e,l,s),n=!0},p(l,[s]){const o={};s&8&&(o.visible=l[3]),s&2&&(o.elem_id=l[1]),s&4&&(o.elem_classes=l[2]),s&128&&(o.container=l[7]),s&256&&(o.scale=l[8]),s&512&&(o.min_width=l[9]),s&36081&&(o.$$scope={dirty:s,ctx:l}),e.$set(o)},i(l){n||(H(e.$$.fragment,l),n=!0)},o(l){M(e.$$.fragment,l),n=!1},d(l){z(e,l)}}}function ze(t,e,n){let{elem_id:l=""}=e,{elem_classes:s=[]}=e,{visible:o=!0}=e,{value:a}=e,r,{show_legend:i}=e,{color_map:f={}}=e,{label:u="Highlighted Text"}=e,{container:b=!0}=e,{scale:d=null}=e,{min_width:B=void 0}=e,{selectable:c=!1}=e,{loading_status:g}=e;const R=oe();function E(m){be.call(this,t,m)}return t.$$set=m=>{"elem_id"in m&&n(1,l=m.elem_id),"elem_classes"in m&&n(2,s=m.elem_classes),"visible"in m&&n(3,o=m.visible),"value"in m&&n(4,a=m.value),"show_legend"in m&&n(5,i=m.show_legend),"color_map"in m&&n(0,f=m.color_map),"label"in m&&n(6,u=m.label),"container"in m&&n(7,b=m.container),"scale"in m&&n(8,d=m.scale),"min_width"in m&&n(9,B=m.min_width),"selectable"in m&&n(10,c=m.selectable),"loading_status"in m&&n(11,g=m.loading_status)},t.$$.update=()=>{t.$$.dirty&1&&!f&&Object.keys(f).length&&n(0,f),t.$$.dirty&4112&&a!==r&&(n(12,r=a),R("change"))},[f,l,s,o,a,i,u,b,d,B,c,g,r,E]}class Re extends F{constructor(e){super(),G(this,e,ze,Ve,J,{elem_id:1,elem_classes:2,visible:3,value:4,show_legend:5,color_map:0,label:6,container:7,scale:8,min_width:9,selectable:10,loading_status:11})}}const De=Re,Ze=["static"],Fe=t=>({type:{payload:"Array<[string, string | number]>"},description:{payload:"list of text spans and corresponding label / value"}});export{De as Component,Fe as document,Ze as modes};
-//# sourceMappingURL=index-28bbfef4.js.map
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_models.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_models.py
deleted file mode 100644
index e0e5278cc052e2f9a6d0af0a1cb2107b03de98f4..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_models.py
+++ /dev/null
@@ -1,1209 +0,0 @@
-import datetime
-import email.message
-import json as jsonlib
-import typing
-import urllib.request
-from collections.abc import Mapping
-from http.cookiejar import Cookie, CookieJar
-
-from ._content import ByteStream, UnattachedStream, encode_request, encode_response
-from ._decoders import (
-    SUPPORTED_DECODERS,
-    ByteChunker,
-    ContentDecoder,
-    IdentityDecoder,
-    LineDecoder,
-    MultiDecoder,
-    TextChunker,
-    TextDecoder,
-)
-from ._exceptions import (
-    CookieConflict,
-    HTTPStatusError,
-    RequestNotRead,
-    ResponseNotRead,
-    StreamClosed,
-    StreamConsumed,
-    request_context,
-)
-from ._multipart import get_multipart_boundary_from_content_type
-from ._status_codes import codes
-from ._types import (
-    AsyncByteStream,
-    CookieTypes,
-    HeaderTypes,
-    QueryParamTypes,
-    RequestContent,
-    RequestData,
-    RequestExtensions,
-    RequestFiles,
-    ResponseContent,
-    ResponseExtensions,
-    SyncByteStream,
-)
-from ._urls import URL
-from ._utils import (
-    guess_json_utf,
-    is_known_encoding,
-    normalize_header_key,
-    normalize_header_value,
-    obfuscate_sensitive_headers,
-    parse_content_type_charset,
-    parse_header_links,
-)
-
-
-class Headers(typing.MutableMapping[str, str]):
-    """
-    HTTP headers, as a case-insensitive multi-dict.
-    """
-
-    def __init__(
-        self,
-        headers: typing.Optional[HeaderTypes] = None,
-        encoding: typing.Optional[str] = None,
-    ) -> None:
-        if headers is None:
-            self._list = []  # type: typing.List[typing.Tuple[bytes, bytes, bytes]]
-        elif isinstance(headers, Headers):
-            self._list = list(headers._list)
-        elif isinstance(headers, Mapping):
-            self._list = [
-                (
-                    normalize_header_key(k, lower=False, encoding=encoding),
-                    normalize_header_key(k, lower=True, encoding=encoding),
-                    normalize_header_value(v, encoding),
-                )
-                for k, v in headers.items()
-            ]
-        else:
-            self._list = [
-                (
-                    normalize_header_key(k, lower=False, encoding=encoding),
-                    normalize_header_key(k, lower=True, encoding=encoding),
-                    normalize_header_value(v, encoding),
-                )
-                for k, v in headers
-            ]
-
-        self._encoding = encoding
-
-    @property
-    def encoding(self) -> str:
-        """
-        Header encoding is mandated as ascii, but we allow fallbacks to utf-8
-        or iso-8859-1.
-        """
-        if self._encoding is None:
-            for encoding in ["ascii", "utf-8"]:
-                for key, value in self.raw:
-                    try:
-                        key.decode(encoding)
-                        value.decode(encoding)
-                    except UnicodeDecodeError:
-                        break
-                else:
-                    # The else block runs if 'break' did not occur, meaning
-                    # all values fitted the encoding.
-                    self._encoding = encoding
-                    break
-            else:
-                # The ISO-8859-1 encoding covers all 256 code points in a byte,
-                # so will never raise decode errors.
-                self._encoding = "iso-8859-1"
-        return self._encoding
-
-    @encoding.setter
-    def encoding(self, value: str) -> None:
-        self._encoding = value
-
-    @property
-    def raw(self) -> typing.List[typing.Tuple[bytes, bytes]]:
-        """
-        Returns a list of the raw header items, as byte pairs.
-        """
-        return [(raw_key, value) for raw_key, _, value in self._list]
-
-    def keys(self) -> typing.KeysView[str]:
-        return {key.decode(self.encoding): None for _, key, value in self._list}.keys()
-
-    def values(self) -> typing.ValuesView[str]:
-        values_dict: typing.Dict[str, str] = {}
-        for _, key, value in self._list:
-            str_key = key.decode(self.encoding)
-            str_value = value.decode(self.encoding)
-            if str_key in values_dict:
-                values_dict[str_key] += f", {str_value}"
-            else:
-                values_dict[str_key] = str_value
-        return values_dict.values()
-
-    def items(self) -> typing.ItemsView[str, str]:
-        """
-        Return `(key, value)` items of headers. Concatenate headers
-        into a single comma separated value when a key occurs multiple times.
-        """
-        values_dict: typing.Dict[str, str] = {}
-        for _, key, value in self._list:
-            str_key = key.decode(self.encoding)
-            str_value = value.decode(self.encoding)
-            if str_key in values_dict:
-                values_dict[str_key] += f", {str_value}"
-            else:
-                values_dict[str_key] = str_value
-        return values_dict.items()
-
-    def multi_items(self) -> typing.List[typing.Tuple[str, str]]:
-        """
-        Return a list of `(key, value)` pairs of headers. Allow multiple
-        occurrences of the same key without concatenating into a single
-        comma separated value.
-        """
-        return [
-            (key.decode(self.encoding), value.decode(self.encoding))
-            for _, key, value in self._list
-        ]
-
-    def get(self, key: str, default: typing.Any = None) -> typing.Any:
-        """
-        Return a header value. If multiple occurrences of the header occur
-        then concatenate them together with commas.
-        """
-        try:
-            return self[key]
-        except KeyError:
-            return default
-
-    def get_list(self, key: str, split_commas: bool = False) -> typing.List[str]:
-        """
-        Return a list of all header values for a given key.
-        If `split_commas=True` is passed, then any comma separated header
-        values are split into multiple return strings.
-        """
-        get_header_key = key.lower().encode(self.encoding)
-
-        values = [
-            item_value.decode(self.encoding)
-            for _, item_key, item_value in self._list
-            if item_key.lower() == get_header_key
-        ]
-
-        if not split_commas:
-            return values
-
-        split_values = []
-        for value in values:
-            split_values.extend([item.strip() for item in value.split(",")])
-        return split_values
-
-    def update(self, headers: typing.Optional[HeaderTypes] = None) -> None:  # type: ignore
-        headers = Headers(headers)
-        for key in headers.keys():
-            if key in self:
-                self.pop(key)
-        self._list.extend(headers._list)
-
-    def copy(self) -> "Headers":
-        return Headers(self, encoding=self.encoding)
-
-    def __getitem__(self, key: str) -> str:
-        """
-        Return a single header value.
-
-        If there are multiple headers with the same key, then we concatenate
-        them with commas. See: https://tools.ietf.org/html/rfc7230#section-3.2.2
-        """
-        normalized_key = key.lower().encode(self.encoding)
-
-        items = [
-            header_value.decode(self.encoding)
-            for _, header_key, header_value in self._list
-            if header_key == normalized_key
-        ]
-
-        if items:
-            return ", ".join(items)
-
-        raise KeyError(key)
-
-    def __setitem__(self, key: str, value: str) -> None:
-        """
-        Set the header `key` to `value`, removing any duplicate entries.
-        Retains insertion order.
-        """
-        set_key = key.encode(self._encoding or "utf-8")
-        set_value = value.encode(self._encoding or "utf-8")
-        lookup_key = set_key.lower()
-
-        found_indexes = [
-            idx
-            for idx, (_, item_key, _) in enumerate(self._list)
-            if item_key == lookup_key
-        ]
-
-        for idx in reversed(found_indexes[1:]):
-            del self._list[idx]
-
-        if found_indexes:
-            idx = found_indexes[0]
-            self._list[idx] = (set_key, lookup_key, set_value)
-        else:
-            self._list.append((set_key, lookup_key, set_value))
-
-    def __delitem__(self, key: str) -> None:
-        """
-        Remove the header `key`.
-        """
-        del_key = key.lower().encode(self.encoding)
-
-        pop_indexes = [
-            idx
-            for idx, (_, item_key, _) in enumerate(self._list)
-            if item_key.lower() == del_key
-        ]
-
-        if not pop_indexes:
-            raise KeyError(key)
-
-        for idx in reversed(pop_indexes):
-            del self._list[idx]
-
-    def __contains__(self, key: typing.Any) -> bool:
-        header_key = key.lower().encode(self.encoding)
-        return header_key in [key for _, key, _ in self._list]
-
-    def __iter__(self) -> typing.Iterator[typing.Any]:
-        return iter(self.keys())
-
-    def __len__(self) -> int:
-        return len(self._list)
-
-    def __eq__(self, other: typing.Any) -> bool:
-        try:
-            other_headers = Headers(other)
-        except ValueError:
-            return False
-
-        self_list = [(key, value) for _, key, value in self._list]
-        other_list = [(key, value) for _, key, value in other_headers._list]
-        return sorted(self_list) == sorted(other_list)
-
-    def __repr__(self) -> str:
-        class_name = self.__class__.__name__
-
-        encoding_str = ""
-        if self.encoding != "ascii":
-            encoding_str = f", encoding={self.encoding!r}"
-
-        as_list = list(obfuscate_sensitive_headers(self.multi_items()))
-        as_dict = dict(as_list)
-
-        no_duplicate_keys = len(as_dict) == len(as_list)
-        if no_duplicate_keys:
-            return f"{class_name}({as_dict!r}{encoding_str})"
-        return f"{class_name}({as_list!r}{encoding_str})"
-
-
-class Request:
-    def __init__(
-        self,
-        method: typing.Union[str, bytes],
-        url: typing.Union["URL", str],
-        *,
-        params: typing.Optional[QueryParamTypes] = None,
-        headers: typing.Optional[HeaderTypes] = None,
-        cookies: typing.Optional[CookieTypes] = None,
-        content: typing.Optional[RequestContent] = None,
-        data: typing.Optional[RequestData] = None,
-        files: typing.Optional[RequestFiles] = None,
-        json: typing.Optional[typing.Any] = None,
-        stream: typing.Union[SyncByteStream, AsyncByteStream, None] = None,
-        extensions: typing.Optional[RequestExtensions] = None,
-    ):
-        self.method = (
-            method.decode("ascii").upper()
-            if isinstance(method, bytes)
-            else method.upper()
-        )
-        self.url = URL(url)
-        if params is not None:
-            self.url = self.url.copy_merge_params(params=params)
-        self.headers = Headers(headers)
-        self.extensions = {} if extensions is None else extensions
-
-        if cookies:
-            Cookies(cookies).set_cookie_header(self)
-
-        if stream is None:
-            content_type: typing.Optional[str] = self.headers.get("content-type")
-            headers, stream = encode_request(
-                content=content,
-                data=data,
-                files=files,
-                json=json,
-                boundary=get_multipart_boundary_from_content_type(
-                    content_type=content_type.encode(self.headers.encoding)
-                    if content_type
-                    else None
-                ),
-            )
-            self._prepare(headers)
-            self.stream = stream
-            # Load the request body, except for streaming content.
-            if isinstance(stream, ByteStream):
-                self.read()
-        else:
-            # There's an important distinction between `Request(content=...)`,
-            # and `Request(stream=...)`.
-            #
-            # Using `content=...` implies automatically populated `Host` and content
-            # headers, of either `Content-Length: ...` or `Transfer-Encoding: chunked`.
-            #
-            # Using `stream=...` will not automatically include *any* auto-populated headers.
-            #
-            # As an end-user you don't really need `stream=...`. It's only
-            # useful when:
-            #
-            # * Preserving the request stream when copying requests, eg for redirects.
-            # * Creating request instances on the *server-side* of the transport API.
-            self.stream = stream
-
-    def _prepare(self, default_headers: typing.Dict[str, str]) -> None:
-        for key, value in default_headers.items():
-            # Ignore Transfer-Encoding if the Content-Length has been set explicitly.
-            if key.lower() == "transfer-encoding" and "Content-Length" in self.headers:
-                continue
-            self.headers.setdefault(key, value)
-
-        auto_headers: typing.List[typing.Tuple[bytes, bytes]] = []
-
-        has_host = "Host" in self.headers
-        has_content_length = (
-            "Content-Length" in self.headers or "Transfer-Encoding" in self.headers
-        )
-
-        if not has_host and self.url.host:
-            auto_headers.append((b"Host", self.url.netloc))
-        if not has_content_length and self.method in ("POST", "PUT", "PATCH"):
-            auto_headers.append((b"Content-Length", b"0"))
-
-        self.headers = Headers(auto_headers + self.headers.raw)
-
-    @property
-    def content(self) -> bytes:
-        if not hasattr(self, "_content"):
-            raise RequestNotRead()
-        return self._content
-
-    def read(self) -> bytes:
-        """
-        Read and return the request content.
-        """
-        if not hasattr(self, "_content"):
-            assert isinstance(self.stream, typing.Iterable)
-            self._content = b"".join(self.stream)
-            if not isinstance(self.stream, ByteStream):
-                # If a streaming request has been read entirely into memory, then
-                # we can replace the stream with a raw bytes implementation,
-                # to ensure that any non-replayable streams can still be used.
-                self.stream = ByteStream(self._content)
-        return self._content
-
-    async def aread(self) -> bytes:
-        """
-        Read and return the request content.
-        """
-        if not hasattr(self, "_content"):
-            assert isinstance(self.stream, typing.AsyncIterable)
-            self._content = b"".join([part async for part in self.stream])
-            if not isinstance(self.stream, ByteStream):
-                # If a streaming request has been read entirely into memory, then
-                # we can replace the stream with a raw bytes implementation,
-                # to ensure that any non-replayable streams can still be used.
-                self.stream = ByteStream(self._content)
-        return self._content
-
-    def __repr__(self) -> str:
-        class_name = self.__class__.__name__
-        url = str(self.url)
-        return f"<{class_name}({self.method!r}, {url!r})>"
-
-    def __getstate__(self) -> typing.Dict[str, typing.Any]:
-        return {
-            name: value
-            for name, value in self.__dict__.items()
-            if name not in ["extensions", "stream"]
-        }
-
-    def __setstate__(self, state: typing.Dict[str, typing.Any]) -> None:
-        for name, value in state.items():
-            setattr(self, name, value)
-        self.extensions = {}
-        self.stream = UnattachedStream()
-
-
-class Response:
-    def __init__(
-        self,
-        status_code: int,
-        *,
-        headers: typing.Optional[HeaderTypes] = None,
-        content: typing.Optional[ResponseContent] = None,
-        text: typing.Optional[str] = None,
-        html: typing.Optional[str] = None,
-        json: typing.Any = None,
-        stream: typing.Union[SyncByteStream, AsyncByteStream, None] = None,
-        request: typing.Optional[Request] = None,
-        extensions: typing.Optional[ResponseExtensions] = None,
-        history: typing.Optional[typing.List["Response"]] = None,
-        default_encoding: typing.Union[str, typing.Callable[[bytes], str]] = "utf-8",
-    ):
-        self.status_code = status_code
-        self.headers = Headers(headers)
-
-        self._request: typing.Optional[Request] = request
-
-        # When follow_redirects=False and a redirect is received,
-        # the client will set `response.next_request`.
-        self.next_request: typing.Optional[Request] = None
-
-        self.extensions = {} if extensions is None else extensions
-        self.history = [] if history is None else list(history)
-
-        self.is_closed = False
-        self.is_stream_consumed = False
-
-        self.default_encoding = default_encoding
-
-        if stream is None:
-            headers, stream = encode_response(content, text, html, json)
-            self._prepare(headers)
-            self.stream = stream
-            if isinstance(stream, ByteStream):
-                # Load the response body, except for streaming content.
-                self.read()
-        else:
-            # There's an important distinction between `Response(content=...)`,
-            # and `Response(stream=...)`.
-            #
-            # Using `content=...` implies automatically populated content headers,
-            # of either `Content-Length: ...` or `Transfer-Encoding: chunked`.
-            #
-            # Using `stream=...` will not automatically include any content headers.
-            #
-            # As an end-user you don't really need `stream=...`. It's only
-            # useful when creating response instances having received a stream
-            # from the transport API.
-            self.stream = stream
-
-        self._num_bytes_downloaded = 0
-
-    def _prepare(self, default_headers: typing.Dict[str, str]) -> None:
-        for key, value in default_headers.items():
-            # Ignore Transfer-Encoding if the Content-Length has been set explicitly.
-            if key.lower() == "transfer-encoding" and "content-length" in self.headers:
-                continue
-            self.headers.setdefault(key, value)
-
-    @property
-    def elapsed(self) -> datetime.timedelta:
-        """
-        Returns the time taken for the complete request/response
-        cycle to complete.
-        """
-        if not hasattr(self, "_elapsed"):
-            raise RuntimeError(
-                "'.elapsed' may only be accessed after the response "
-                "has been read or closed."
-            )
-        return self._elapsed
-
-    @elapsed.setter
-    def elapsed(self, elapsed: datetime.timedelta) -> None:
-        self._elapsed = elapsed
-
-    @property
-    def request(self) -> Request:
-        """
-        Returns the request instance associated to the current response.
-        """
-        if self._request is None:
-            raise RuntimeError(
-                "The request instance has not been set on this response."
-            )
-        return self._request
-
-    @request.setter
-    def request(self, value: Request) -> None:
-        self._request = value
-
-    @property
-    def http_version(self) -> str:
-        try:
-            http_version: bytes = self.extensions["http_version"]
-        except KeyError:
-            return "HTTP/1.1"
-        else:
-            return http_version.decode("ascii", errors="ignore")
-
-    @property
-    def reason_phrase(self) -> str:
-        try:
-            reason_phrase: bytes = self.extensions["reason_phrase"]
-        except KeyError:
-            return codes.get_reason_phrase(self.status_code)
-        else:
-            return reason_phrase.decode("ascii", errors="ignore")
-
-    @property
-    def url(self) -> URL:
-        """
-        Returns the URL for which the request was made.
-        """
-        return self.request.url
-
-    @property
-    def content(self) -> bytes:
-        if not hasattr(self, "_content"):
-            raise ResponseNotRead()
-        return self._content
-
-    @property
-    def text(self) -> str:
-        if not hasattr(self, "_text"):
-            content = self.content
-            if not content:
-                self._text = ""
-            else:
-                decoder = TextDecoder(encoding=self.encoding or "utf-8")
-                self._text = "".join([decoder.decode(self.content), decoder.flush()])
-        return self._text
-
-    @property
-    def encoding(self) -> typing.Optional[str]:
-        """
-        Return an encoding to use for decoding the byte content into text.
-        The priority for determining this is given by...
-
-        * `.encoding = <>` has been set explicitly.
-        * The encoding as specified by the charset parameter in the Content-Type header.
-        * The encoding as determined by `default_encoding`, which may either be
-          a string like "utf-8" indicating the encoding to use, or may be a callable
-          which enables charset autodetection.
-        """
-        if not hasattr(self, "_encoding"):
-            encoding = self.charset_encoding
-            if encoding is None or not is_known_encoding(encoding):
-                if isinstance(self.default_encoding, str):
-                    encoding = self.default_encoding
-                elif hasattr(self, "_content"):
-                    encoding = self.default_encoding(self._content)
-            self._encoding = encoding or "utf-8"
-        return self._encoding
-
-    @encoding.setter
-    def encoding(self, value: str) -> None:
-        self._encoding = value
-
-    @property
-    def charset_encoding(self) -> typing.Optional[str]:
-        """
-        Return the encoding, as specified by the Content-Type header.
-        """
-        content_type = self.headers.get("Content-Type")
-        if content_type is None:
-            return None
-
-        return parse_content_type_charset(content_type)
-
-    def _get_content_decoder(self) -> ContentDecoder:
-        """
-        Returns a decoder instance which can be used to decode the raw byte
-        content, depending on the Content-Encoding used in the response.
-        """
-        if not hasattr(self, "_decoder"):
-            decoders: typing.List[ContentDecoder] = []
-            values = self.headers.get_list("content-encoding", split_commas=True)
-            for value in values:
-                value = value.strip().lower()
-                try:
-                    decoder_cls = SUPPORTED_DECODERS[value]
-                    decoders.append(decoder_cls())
-                except KeyError:
-                    continue
-
-            if len(decoders) == 1:
-                self._decoder = decoders[0]
-            elif len(decoders) > 1:
-                self._decoder = MultiDecoder(children=decoders)
-            else:
-                self._decoder = IdentityDecoder()
-
-        return self._decoder
-
-    @property
-    def is_informational(self) -> bool:
-        """
-        A property which is `True` for 1xx status codes, `False` otherwise.
-        """
-        return codes.is_informational(self.status_code)
-
-    @property
-    def is_success(self) -> bool:
-        """
-        A property which is `True` for 2xx status codes, `False` otherwise.
-        """
-        return codes.is_success(self.status_code)
-
-    @property
-    def is_redirect(self) -> bool:
-        """
-        A property which is `True` for 3xx status codes, `False` otherwise.
-
-        Note that not all responses with a 3xx status code indicate a URL redirect.
-
-        Use `response.has_redirect_location` to determine responses with a properly
-        formed URL redirection.
-        """
-        return codes.is_redirect(self.status_code)
-
-    @property
-    def is_client_error(self) -> bool:
-        """
-        A property which is `True` for 4xx status codes, `False` otherwise.
-        """
-        return codes.is_client_error(self.status_code)
-
-    @property
-    def is_server_error(self) -> bool:
-        """
-        A property which is `True` for 5xx status codes, `False` otherwise.
-        """
-        return codes.is_server_error(self.status_code)
-
-    @property
-    def is_error(self) -> bool:
-        """
-        A property which is `True` for 4xx and 5xx status codes, `False` otherwise.
-        """
-        return codes.is_error(self.status_code)
-
-    @property
-    def has_redirect_location(self) -> bool:
-        """
-        Returns True for 3xx responses with a properly formed URL redirection,
-        `False` otherwise.
-        """
-        return (
-            self.status_code
-            in (
-                # 301 (Cacheable redirect. Method may change to GET.)
-                codes.MOVED_PERMANENTLY,
-                # 302 (Uncacheable redirect. Method may change to GET.)
-                codes.FOUND,
-                # 303 (Client should make a GET or HEAD request.)
-                codes.SEE_OTHER,
-                # 307 (Equiv. 302, but retain method)
-                codes.TEMPORARY_REDIRECT,
-                # 308 (Equiv. 301, but retain method)
-                codes.PERMANENT_REDIRECT,
-            )
-            and "Location" in self.headers
-        )
-
-    def raise_for_status(self) -> None:
-        """
-        Raise the `HTTPStatusError` if one occurred.
-        """
-        request = self._request
-        if request is None:
-            raise RuntimeError(
-                "Cannot call `raise_for_status` as the request "
-                "instance has not been set on this response."
-            )
-
-        if self.is_success:
-            return
-
-        if self.has_redirect_location:
-            message = (
-                "{error_type} '{0.status_code} {0.reason_phrase}' for url '{0.url}'\n"
-                "Redirect location: '{0.headers[location]}'\n"
-                "For more information check: https://httpstatuses.com/{0.status_code}"
-            )
-        else:
-            message = (
-                "{error_type} '{0.status_code} {0.reason_phrase}' for url '{0.url}'\n"
-                "For more information check: https://httpstatuses.com/{0.status_code}"
-            )
-
-        status_class = self.status_code // 100
-        error_types = {
-            1: "Informational response",
-            3: "Redirect response",
-            4: "Client error",
-            5: "Server error",
-        }
-        error_type = error_types.get(status_class, "Invalid status code")
-        message = message.format(self, error_type=error_type)
-        raise HTTPStatusError(message, request=request, response=self)
-
-    def json(self, **kwargs: typing.Any) -> typing.Any:
-        if self.charset_encoding is None and self.content and len(self.content) > 3:
-            encoding = guess_json_utf(self.content)
-            if encoding is not None:
-                return jsonlib.loads(self.content.decode(encoding), **kwargs)
-        return jsonlib.loads(self.text, **kwargs)
-
-    @property
-    def cookies(self) -> "Cookies":
-        if not hasattr(self, "_cookies"):
-            self._cookies = Cookies()
-            self._cookies.extract_cookies(self)
-        return self._cookies
-
-    @property
-    def links(self) -> typing.Dict[typing.Optional[str], typing.Dict[str, str]]:
-        """
-        Returns the parsed header links of the response, if any
-        """
-        header = self.headers.get("link")
-        ldict = {}
-        if header:
-            links = parse_header_links(header)
-            for link in links:
-                key = link.get("rel") or link.get("url")
-                ldict[key] = link
-        return ldict
-
-    @property
-    def num_bytes_downloaded(self) -> int:
-        return self._num_bytes_downloaded
-
-    def __repr__(self) -> str:
-        return f"<Response [{self.status_code} {self.reason_phrase}]>"
-
-    def __getstate__(self) -> typing.Dict[str, typing.Any]:
-        return {
-            name: value
-            for name, value in self.__dict__.items()
-            if name not in ["extensions", "stream", "is_closed", "_decoder"]
-        }
-
-    def __setstate__(self, state: typing.Dict[str, typing.Any]) -> None:
-        for name, value in state.items():
-            setattr(self, name, value)
-        self.is_closed = True
-        self.extensions = {}
-        self.stream = UnattachedStream()
-
-    def read(self) -> bytes:
-        """
-        Read and return the response content.
-        """
-        if not hasattr(self, "_content"):
-            self._content = b"".join(self.iter_bytes())
-        return self._content
-
-    def iter_bytes(
-        self, chunk_size: typing.Optional[int] = None
-    ) -> typing.Iterator[bytes]:
-        """
-        A byte-iterator over the decoded response content.
-        This allows us to handle gzip, deflate, and brotli encoded responses.
-        """
-        if hasattr(self, "_content"):
-            chunk_size = len(self._content) if chunk_size is None else chunk_size
-            for i in range(0, len(self._content), max(chunk_size, 1)):
-                yield self._content[i : i + chunk_size]
-        else:
-            decoder = self._get_content_decoder()
-            chunker = ByteChunker(chunk_size=chunk_size)
-            with request_context(request=self._request):
-                for raw_bytes in self.iter_raw():
-                    decoded = decoder.decode(raw_bytes)
-                    for chunk in chunker.decode(decoded):
-                        yield chunk
-                decoded = decoder.flush()
-                for chunk in chunker.decode(decoded):
-                    yield chunk  # pragma: no cover
-                for chunk in chunker.flush():
-                    yield chunk
-
-    def iter_text(
-        self, chunk_size: typing.Optional[int] = None
-    ) -> typing.Iterator[str]:
-        """
-        A str-iterator over the decoded response content
-        that handles both gzip, deflate, etc but also detects the content's
-        string encoding.
-        """
-        decoder = TextDecoder(encoding=self.encoding or "utf-8")
-        chunker = TextChunker(chunk_size=chunk_size)
-        with request_context(request=self._request):
-            for byte_content in self.iter_bytes():
-                text_content = decoder.decode(byte_content)
-                for chunk in chunker.decode(text_content):
-                    yield chunk
-            text_content = decoder.flush()
-            for chunk in chunker.decode(text_content):
-                yield chunk
-            for chunk in chunker.flush():
-                yield chunk
-
-    def iter_lines(self) -> typing.Iterator[str]:
-        decoder = LineDecoder()
-        with request_context(request=self._request):
-            for text in self.iter_text():
-                for line in decoder.decode(text):
-                    yield line
-            for line in decoder.flush():
-                yield line
-
-    def iter_raw(
-        self, chunk_size: typing.Optional[int] = None
-    ) -> typing.Iterator[bytes]:
-        """
-        A byte-iterator over the raw response content.
-        """
-        if self.is_stream_consumed:
-            raise StreamConsumed()
-        if self.is_closed:
-            raise StreamClosed()
-        if not isinstance(self.stream, SyncByteStream):
-            raise RuntimeError("Attempted to call a sync iterator on an async stream.")
-
-        self.is_stream_consumed = True
-        self._num_bytes_downloaded = 0
-        chunker = ByteChunker(chunk_size=chunk_size)
-
-        with request_context(request=self._request):
-            for raw_stream_bytes in self.stream:
-                self._num_bytes_downloaded += len(raw_stream_bytes)
-                for chunk in chunker.decode(raw_stream_bytes):
-                    yield chunk
-
-        for chunk in chunker.flush():
-            yield chunk
-
-        self.close()
-
-    def close(self) -> None:
-        """
-        Close the response and release the connection.
-        Automatically called if the response body is read to completion.
-        """
-        if not isinstance(self.stream, SyncByteStream):
-            raise RuntimeError("Attempted to call an sync close on an async stream.")
-
-        if not self.is_closed:
-            self.is_closed = True
-            with request_context(request=self._request):
-                self.stream.close()
-
-    async def aread(self) -> bytes:
-        """
-        Read and return the response content.
-        """
-        if not hasattr(self, "_content"):
-            self._content = b"".join([part async for part in self.aiter_bytes()])
-        return self._content
-
-    async def aiter_bytes(
-        self, chunk_size: typing.Optional[int] = None
-    ) -> typing.AsyncIterator[bytes]:
-        """
-        A byte-iterator over the decoded response content.
-        This allows us to handle gzip, deflate, and brotli encoded responses.
-        """
-        if hasattr(self, "_content"):
-            chunk_size = len(self._content) if chunk_size is None else chunk_size
-            for i in range(0, len(self._content), max(chunk_size, 1)):
-                yield self._content[i : i + chunk_size]
-        else:
-            decoder = self._get_content_decoder()
-            chunker = ByteChunker(chunk_size=chunk_size)
-            with request_context(request=self._request):
-                async for raw_bytes in self.aiter_raw():
-                    decoded = decoder.decode(raw_bytes)
-                    for chunk in chunker.decode(decoded):
-                        yield chunk
-                decoded = decoder.flush()
-                for chunk in chunker.decode(decoded):
-                    yield chunk  # pragma: no cover
-                for chunk in chunker.flush():
-                    yield chunk
-
-    async def aiter_text(
-        self, chunk_size: typing.Optional[int] = None
-    ) -> typing.AsyncIterator[str]:
-        """
-        A str-iterator over the decoded response content
-        that handles both gzip, deflate, etc but also detects the content's
-        string encoding.
-        """
-        decoder = TextDecoder(encoding=self.encoding or "utf-8")
-        chunker = TextChunker(chunk_size=chunk_size)
-        with request_context(request=self._request):
-            async for byte_content in self.aiter_bytes():
-                text_content = decoder.decode(byte_content)
-                for chunk in chunker.decode(text_content):
-                    yield chunk
-            text_content = decoder.flush()
-            for chunk in chunker.decode(text_content):
-                yield chunk
-            for chunk in chunker.flush():
-                yield chunk
-
-    async def aiter_lines(self) -> typing.AsyncIterator[str]:
-        decoder = LineDecoder()
-        with request_context(request=self._request):
-            async for text in self.aiter_text():
-                for line in decoder.decode(text):
-                    yield line
-            for line in decoder.flush():
-                yield line
-
-    async def aiter_raw(
-        self, chunk_size: typing.Optional[int] = None
-    ) -> typing.AsyncIterator[bytes]:
-        """
-        A byte-iterator over the raw response content.
-        """
-        if self.is_stream_consumed:
-            raise StreamConsumed()
-        if self.is_closed:
-            raise StreamClosed()
-        if not isinstance(self.stream, AsyncByteStream):
-            raise RuntimeError("Attempted to call an async iterator on an sync stream.")
-
-        self.is_stream_consumed = True
-        self._num_bytes_downloaded = 0
-        chunker = ByteChunker(chunk_size=chunk_size)
-
-        with request_context(request=self._request):
-            async for raw_stream_bytes in self.stream:
-                self._num_bytes_downloaded += len(raw_stream_bytes)
-                for chunk in chunker.decode(raw_stream_bytes):
-                    yield chunk
-
-        for chunk in chunker.flush():
-            yield chunk
-
-        await self.aclose()
-
-    async def aclose(self) -> None:
-        """
-        Close the response and release the connection.
-        Automatically called if the response body is read to completion.
-        """
-        if not isinstance(self.stream, AsyncByteStream):
-            raise RuntimeError("Attempted to call an async close on an sync stream.")
-
-        if not self.is_closed:
-            self.is_closed = True
-            with request_context(request=self._request):
-                await self.stream.aclose()
-
-
-class Cookies(typing.MutableMapping[str, str]):
-    """
-    HTTP Cookies, as a mutable mapping.
-    """
-
-    def __init__(self, cookies: typing.Optional[CookieTypes] = None) -> None:
-        if cookies is None or isinstance(cookies, dict):
-            self.jar = CookieJar()
-            if isinstance(cookies, dict):
-                for key, value in cookies.items():
-                    self.set(key, value)
-        elif isinstance(cookies, list):
-            self.jar = CookieJar()
-            for key, value in cookies:
-                self.set(key, value)
-        elif isinstance(cookies, Cookies):
-            self.jar = CookieJar()
-            for cookie in cookies.jar:
-                self.jar.set_cookie(cookie)
-        else:
-            self.jar = cookies
-
-    def extract_cookies(self, response: Response) -> None:
-        """
-        Loads any cookies based on the response `Set-Cookie` headers.
-        """
-        urllib_response = self._CookieCompatResponse(response)
-        urllib_request = self._CookieCompatRequest(response.request)
-
-        self.jar.extract_cookies(urllib_response, urllib_request)  # type: ignore
-
-    def set_cookie_header(self, request: Request) -> None:
-        """
-        Sets an appropriate 'Cookie:' HTTP header on the `Request`.
-        """
-        urllib_request = self._CookieCompatRequest(request)
-        self.jar.add_cookie_header(urllib_request)
-
-    def set(self, name: str, value: str, domain: str = "", path: str = "/") -> None:
-        """
-        Set a cookie value by name. May optionally include domain and path.
-        """
-        kwargs = {
-            "version": 0,
-            "name": name,
-            "value": value,
-            "port": None,
-            "port_specified": False,
-            "domain": domain,
-            "domain_specified": bool(domain),
-            "domain_initial_dot": domain.startswith("."),
-            "path": path,
-            "path_specified": bool(path),
-            "secure": False,
-            "expires": None,
-            "discard": True,
-            "comment": None,
-            "comment_url": None,
-            "rest": {"HttpOnly": None},
-            "rfc2109": False,
-        }
-        cookie = Cookie(**kwargs)  # type: ignore
-        self.jar.set_cookie(cookie)
-
-    def get(  # type: ignore
-        self,
-        name: str,
-        default: typing.Optional[str] = None,
-        domain: typing.Optional[str] = None,
-        path: typing.Optional[str] = None,
-    ) -> typing.Optional[str]:
-        """
-        Get a cookie by name. May optionally include domain and path
-        in order to specify exactly which cookie to retrieve.
-        """
-        value = None
-        for cookie in self.jar:
-            if cookie.name == name:
-                if domain is None or cookie.domain == domain:
-                    if path is None or cookie.path == path:
-                        if value is not None:
-                            message = f"Multiple cookies exist with name={name}"
-                            raise CookieConflict(message)
-                        value = cookie.value
-
-        if value is None:
-            return default
-        return value
-
-    def delete(
-        self,
-        name: str,
-        domain: typing.Optional[str] = None,
-        path: typing.Optional[str] = None,
-    ) -> None:
-        """
-        Delete a cookie by name. May optionally include domain and path
-        in order to specify exactly which cookie to delete.
-        """
-        if domain is not None and path is not None:
-            return self.jar.clear(domain, path, name)
-
-        remove = [
-            cookie
-            for cookie in self.jar
-            if cookie.name == name
-            and (domain is None or cookie.domain == domain)
-            and (path is None or cookie.path == path)
-        ]
-
-        for cookie in remove:
-            self.jar.clear(cookie.domain, cookie.path, cookie.name)
-
-    def clear(
-        self, domain: typing.Optional[str] = None, path: typing.Optional[str] = None
-    ) -> None:
-        """
-        Delete all cookies. Optionally include a domain and path in
-        order to only delete a subset of all the cookies.
-        """
-        args = []
-        if domain is not None:
-            args.append(domain)
-        if path is not None:
-            assert domain is not None
-            args.append(path)
-        self.jar.clear(*args)
-
-    def update(self, cookies: typing.Optional[CookieTypes] = None) -> None:  # type: ignore
-        cookies = Cookies(cookies)
-        for cookie in cookies.jar:
-            self.jar.set_cookie(cookie)
-
-    def __setitem__(self, name: str, value: str) -> None:
-        return self.set(name, value)
-
-    def __getitem__(self, name: str) -> str:
-        value = self.get(name)
-        if value is None:
-            raise KeyError(name)
-        return value
-
-    def __delitem__(self, name: str) -> None:
-        return self.delete(name)
-
-    def __len__(self) -> int:
-        return len(self.jar)
-
-    def __iter__(self) -> typing.Iterator[str]:
-        return (cookie.name for cookie in self.jar)
-
-    def __bool__(self) -> bool:
-        for _ in self.jar:
-            return True
-        return False
-
-    def __repr__(self) -> str:
-        cookies_repr = ", ".join(
-            [
-                f"<Cookie {cookie.name}={cookie.value} for {cookie.domain} />"
-                for cookie in self.jar
-            ]
-        )
-
-        return f"<Cookies[{cookies_repr}]>"
-
-    class _CookieCompatRequest(urllib.request.Request):
-        """
-        Wraps a `Request` instance up in a compatibility interface suitable
-        for use with `CookieJar` operations.
-        """
-
-        def __init__(self, request: Request) -> None:
-            super().__init__(
-                url=str(request.url),
-                headers=dict(request.headers),
-                method=request.method,
-            )
-            self.request = request
-
-        def add_unredirected_header(self, key: str, value: str) -> None:
-            super().add_unredirected_header(key, value)
-            self.request.headers[key] = value
-
-    class _CookieCompatResponse:
-        """
-        Wraps a `Request` instance up in a compatibility interface suitable
-        for use with `CookieJar` operations.
-        """
-
-        def __init__(self, response: Response):
-            self.response = response
-
-        def info(self) -> email.message.Message:
-            info = email.message.Message()
-            for key, value in self.response.headers.multi_items():
-                # Note that setting `info[key]` here is an "append" operation,
-                # not a "replace" operation.
-                # https://docs.python.org/3/library/email.compat32-message.html#email.message.Message.__setitem__
-                info[key] = value
-            return info
diff --git a/spaces/Detomo/Car_part_classification/app.py b/spaces/Detomo/Car_part_classification/app.py
deleted file mode 100644
index c07e187a03368f0542fc5ac618c2ca45e3f73024..0000000000000000000000000000000000000000
--- a/spaces/Detomo/Car_part_classification/app.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# import shutil
-# import cv2
-# from PIL import Image
-# from collections import deque, Counter
-
-import time, os, json, onnx, onnxruntime
-# import torch
-import pandas as pd
-import streamlit as st
-import requests
-from utils import *
-import args
-from streamlit_lottie import st_lottie
-
-st.set_page_config(
-    page_title=args.PAGE_TITLE,
-    page_icon=args.PAGE_ICON, layout=args.LAYOUT, initial_sidebar_state='auto'
-)
-
-
-def load_lottieurl(url: str):
-    r = requests.get(url)
-    if r.status_code != 200:
-        return None
-    return r.json()
-
-
-# Configure
-options = onnxruntime.SessionOptions()
-options.intra_op_num_threads = 8
-options.inter_op_num_threads = 8
-
-lottie_penguin = load_lottieurl('https://assets10.lottiefiles.com/datafiles/Yv8B88Go8kHRZ5T/data.json')
-st_lottie(lottie_penguin, height=200)
-
-hide_streamlit_style = """
-    <style>
-    footer {
-    visibility: hidden;
-        }
-    footer:after {
-        content:'© 2021 Vu Minh Chien';
-        visibility: visible;
-        display: block;
-        position: relative;
-        #background-color: red;
-        padding: 5px;
-        top: 2px;
-            }
-    </style>
-    """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-
-st.write(args.LANDINGPAGE_TITLE)
-st.sidebar.title(args.SIDEBAR_TITLE)
-method = st.sidebar.radio('Choose input source 👇', options=['Image'])
-
-
-# Load model
-@st.cache(suppress_st_warning=False)
-def initial_setup():
-    df_train = pd.read_csv('full_set.csv')
-    sub_test_list = sorted(list(df_train['Image'].map(lambda x: get_image(x))))
-    with open('embeddings.npy', 'rb') as f:
-        embeddings = np.load(f)
-    PATH = 'model_onnx.onnx'
-    ort_session = onnxruntime.InferenceSession(PATH, sess_options=options)
-    input_name = ort_session.get_inputs()[0].name
-    return df_train, sub_test_list, embeddings, ort_session, input_name
-
-
-df_train, sub_test_list, embeddings, ort_session, input_name = initial_setup()
-
-if method == 'Image':
-    st.sidebar.markdown('---')
-    st.sidebar.header('Options')
-    content_file, col2 = show_original()
-    image_input(
-        content_file, df_train, sub_test_list, embeddings, ort_session, input_name, col2
-    )
-else:
-    webcam_input(
-        df_train, sub_test_list, embeddings, ort_session, input_name
-    )
diff --git a/spaces/Detomo/naomi-app-api/Dockerfile b/spaces/Detomo/naomi-app-api/Dockerfile
deleted file mode 100644
index 2918192a683f49bf99326376c4b72e3e6870cb49..0000000000000000000000000000000000000000
--- a/spaces/Detomo/naomi-app-api/Dockerfile
+++ /dev/null
@@ -1,26 +0,0 @@
-FROM python:3.9
-
-WORKDIR /content
-
-RUN mkdir /content/cache/
-
-RUN export TRANSFORMERS_CACHE=/content/cache/
-
-COPY ./requirements.txt /content/requirements.txt
-
-RUN pip install --no-cache-dir --upgrade -r /content/requirements.txt
-RUN apt-get update && apt-get install -y ffmpeg
-
-COPY . .
-
-RUN adduser --disabled-password --gecos '' admin
-RUN adduser admin sudo
-RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
-
-RUN chown -R admin:admin /content
-RUN chmod -R 777 /content
-USER admin
-
-EXPOSE 7860
-
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/DkLead/facebook-tts_transformer-ru-cv7_css10/README.md b/spaces/DkLead/facebook-tts_transformer-ru-cv7_css10/README.md
deleted file mode 100644
index 355c75d1ab4b3a96218371ff1ce3680bacfc8ae5..0000000000000000000000000000000000000000
--- a/spaces/DkLead/facebook-tts_transformer-ru-cv7_css10/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Facebook-tts Transformer-ru-cv7 Css10
-emoji: 📊
-colorFrom: green
-colorTo: purple
-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/DragGan/DragGan/stylegan_human/legacy.py b/spaces/DragGan/DragGan/stylegan_human/legacy.py
deleted file mode 100644
index ef0e838df5426e5f01ba3b917244de553678ed0f..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/legacy.py
+++ /dev/null
@@ -1,200 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# 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.
-# 
-import pickle
-import dnnlib
-import re
-from typing import List, Optional
-import torch
-import copy
-import numpy as np
-from torch_utils import misc
-
-
-#----------------------------------------------------------------------------
-## loading torch pkl
-def load_network_pkl(f, force_fp16=False, G_only=False):
-    data = _LegacyUnpickler(f).load()
-    if G_only:
-        f = open('ori_model_Gonly.txt','a+')
-    else: f = open('ori_model.txt','a+')
-    for key in data.keys():
-        f.write(str(data[key]))
-    f.close()
-
-    ## We comment out this part, if you want to convert TF pickle, you can use the original script from StyleGAN2-ada-pytorch
-    # # Legacy TensorFlow pickle => convert.
-    # if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
-    #     tf_G, tf_D, tf_Gs = data
-    #     G = convert_tf_generator(tf_G)
-    #     D = convert_tf_discriminator(tf_D)
-    #     G_ema = convert_tf_generator(tf_Gs)
-    #     data = dict(G=G, D=D, G_ema=G_ema)
-
-    # Add missing fields.
-    if 'training_set_kwargs' not in data:
-        data['training_set_kwargs'] = None
-    if 'augment_pipe' not in data:
-        data['augment_pipe'] = None
-
-    # Validate contents.
-    assert isinstance(data['G_ema'], torch.nn.Module)
-    if not G_only:
-        assert isinstance(data['D'], torch.nn.Module)
-        assert isinstance(data['G'], torch.nn.Module)
-        assert isinstance(data['training_set_kwargs'], (dict, type(None)))
-        assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))
-
-    # Force FP16.
-    if force_fp16:
-        if G_only:
-            convert_list = ['G_ema'] #'G'
-        else: convert_list = ['G', 'D', 'G_ema']
-        for key in convert_list:
-            old = data[key]
-            kwargs = copy.deepcopy(old.init_kwargs)
-            if key.startswith('G'):
-                kwargs.synthesis_kwargs = dnnlib.EasyDict(kwargs.get('synthesis_kwargs', {}))
-                kwargs.synthesis_kwargs.num_fp16_res = 4
-                kwargs.synthesis_kwargs.conv_clamp = 256
-            if key.startswith('D'):
-                kwargs.num_fp16_res = 4
-                kwargs.conv_clamp = 256
-            if kwargs != old.init_kwargs:
-                new = type(old)(**kwargs).eval().requires_grad_(False)
-                misc.copy_params_and_buffers(old, new, require_all=True)
-                data[key] = new
-    return data 
-
-class _TFNetworkStub(dnnlib.EasyDict):
-    pass
-
-class _LegacyUnpickler(pickle.Unpickler):
-    def find_class(self, module, name):
-        if module == 'dnnlib.tflib.network' and name == 'Network':
-            return _TFNetworkStub
-        return super().find_class(module, name)
-
-#----------------------------------------------------------------------------
-
-def num_range(s: str) -> List[int]:
-    '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.'''
-
-    range_re = re.compile(r'^(\d+)-(\d+)$')
-    m = range_re.match(s)
-    if m:
-        return list(range(int(m.group(1)), int(m.group(2))+1))
-    vals = s.split(',')
-    return [int(x) for x in vals]
-
-
-
-#----------------------------------------------------------------------------
-#### loading tf pkl
-def load_pkl(file_or_url):
-    with open(file_or_url, 'rb') as file:
-        return pickle.load(file, encoding='latin1')
-
-#----------------------------------------------------------------------------
-
-### For editing
-def visual(output, out_path):
-    import torch
-    import cv2
-    import numpy as np
-    output = (output + 1)/2
-    output = torch.clamp(output, 0, 1)
-    if output.shape[1] == 1:
-        output = torch.cat([output, output, output], 1)
-    output = output[0].detach().cpu().permute(1,2,0).numpy()
-    output = (output*255).astype(np.uint8)
-    output = output[:,:,::-1]
-    cv2.imwrite(out_path, output)
-
-def save_obj(obj, path):
-    with open(path, 'wb+') as f:
-        pickle.dump(obj, f, protocol=4)
-
-#----------------------------------------------------------------------------
-
-## Converting pkl to pth, change dict info inside pickle
-
-def convert_to_rgb(state_ros, state_nv, ros_name, nv_name):
-    state_ros[f"{ros_name}.conv.weight"] = state_nv[f"{nv_name}.torgb.weight"].unsqueeze(0)
-    state_ros[f"{ros_name}.bias"] = state_nv[f"{nv_name}.torgb.bias"].unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
-    state_ros[f"{ros_name}.conv.modulation.weight"] = state_nv[f"{nv_name}.torgb.affine.weight"]
-    state_ros[f"{ros_name}.conv.modulation.bias"] = state_nv[f"{nv_name}.torgb.affine.bias"]
-
-
-def convert_conv(state_ros, state_nv, ros_name, nv_name):
-    state_ros[f"{ros_name}.conv.weight"] = state_nv[f"{nv_name}.weight"].unsqueeze(0)
-    state_ros[f"{ros_name}.activate.bias"] = state_nv[f"{nv_name}.bias"]
-    state_ros[f"{ros_name}.conv.modulation.weight"] = state_nv[f"{nv_name}.affine.weight"]
-    state_ros[f"{ros_name}.conv.modulation.bias"] = state_nv[f"{nv_name}.affine.bias"]
-    state_ros[f"{ros_name}.noise.weight"] = state_nv[f"{nv_name}.noise_strength"].unsqueeze(0)
-
-
-def convert_blur_kernel(state_ros, state_nv, level):
-    """Not quite sure why there is a factor of 4 here"""
-    # They are all the same
-    state_ros[f"convs.{2*level}.conv.blur.kernel"] = 4*state_nv["synthesis.b4.resample_filter"]
-    state_ros[f"to_rgbs.{level}.upsample.kernel"] = 4*state_nv["synthesis.b4.resample_filter"]
-
-
-def determine_config(state_nv):
-    mapping_names = [name for name in state_nv.keys() if "mapping.fc" in name]
-    sythesis_names = [name for name in state_nv.keys() if "synthesis.b" in name]
-
-    n_mapping =  max([int(re.findall("(\d+)", n)[0]) for n in mapping_names]) + 1
-    resolution =  max([int(re.findall("(\d+)", n)[0]) for n in sythesis_names])
-    n_layers = np.log(resolution/2)/np.log(2)
-
-    return n_mapping, n_layers
-
-
-def convert(network_pkl, output_file, G_only=False):
-    with dnnlib.util.open_url(network_pkl) as f:
-        G_nvidia = load_network_pkl(f,G_only=G_only)['G_ema']
-
-    state_nv = G_nvidia.state_dict()
-    n_mapping, n_layers = determine_config(state_nv)
-
-    state_ros = {}
-
-    for i in range(n_mapping):
-        state_ros[f"style.{i+1}.weight"] = state_nv[f"mapping.fc{i}.weight"]
-        state_ros[f"style.{i+1}.bias"] = state_nv[f"mapping.fc{i}.bias"]
-
-    for i in range(int(n_layers)):
-        if i > 0:
-            for conv_level in range(2):
-                convert_conv(state_ros, state_nv, f"convs.{2*i-2+conv_level}", f"synthesis.b{4*(2**i)}.conv{conv_level}")
-                state_ros[f"noises.noise_{2*i-1+conv_level}"] = state_nv[f"synthesis.b{4*(2**i)}.conv{conv_level}.noise_const"].unsqueeze(0).unsqueeze(0)
-
-            convert_to_rgb(state_ros, state_nv, f"to_rgbs.{i-1}", f"synthesis.b{4*(2**i)}")
-            convert_blur_kernel(state_ros, state_nv, i-1)
-        
-        else:
-            state_ros[f"input.input"] = state_nv[f"synthesis.b{4*(2**i)}.const"].unsqueeze(0)
-            convert_conv(state_ros, state_nv, "conv1", f"synthesis.b{4*(2**i)}.conv1")
-            state_ros[f"noises.noise_{2*i}"] = state_nv[f"synthesis.b{4*(2**i)}.conv1.noise_const"].unsqueeze(0).unsqueeze(0)
-            convert_to_rgb(state_ros, state_nv, "to_rgb1", f"synthesis.b{4*(2**i)}")
-
-    # https://github.com/yuval-alaluf/restyle-encoder/issues/1#issuecomment-828354736
-    latent_avg = state_nv['mapping.w_avg']
-    state_dict = {"g_ema": state_ros, "latent_avg": latent_avg}
-    # if G_only:
-    #     f = open('converted_model_Gonly.txt','a+')
-    # else:
-    #     f = open('converted_model.txt','a+')
-    # for key in state_dict['g_ema'].keys():
-    #     f.write(str(key)+': '+str(state_dict['g_ema'][key].shape)+'\n')
-    # f.close()
-    torch.save(state_dict, output_file)
-
diff --git a/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/src/utils.cpp b/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/src/utils.cpp
deleted file mode 100644
index 4aa0305cd6cf025496528ef9ff49075209fe9e8c..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/src/utils.cpp
+++ /dev/null
@@ -1,429 +0,0 @@
-#include "BYTETracker.h"
-#include "lapjv.h"
-
-vector<STrack*> BYTETracker::joint_stracks(vector<STrack*> &tlista, vector<STrack> &tlistb)
-{
-	map<int, int> exists;
-	vector<STrack*> res;
-	for (int i = 0; i < tlista.size(); i++)
-	{
-		exists.insert(pair<int, int>(tlista[i]->track_id, 1));
-		res.push_back(tlista[i]);
-	}
-	for (int i = 0; i < tlistb.size(); i++)
-	{
-		int tid = tlistb[i].track_id;
-		if (!exists[tid] || exists.count(tid) == 0)
-		{
-			exists[tid] = 1;
-			res.push_back(&tlistb[i]);
-		}
-	}
-	return res;
-}
-
-vector<STrack> BYTETracker::joint_stracks(vector<STrack> &tlista, vector<STrack> &tlistb)
-{
-	map<int, int> exists;
-	vector<STrack> res;
-	for (int i = 0; i < tlista.size(); i++)
-	{
-		exists.insert(pair<int, int>(tlista[i].track_id, 1));
-		res.push_back(tlista[i]);
-	}
-	for (int i = 0; i < tlistb.size(); i++)
-	{
-		int tid = tlistb[i].track_id;
-		if (!exists[tid] || exists.count(tid) == 0)
-		{
-			exists[tid] = 1;
-			res.push_back(tlistb[i]);
-		}
-	}
-	return res;
-}
-
-vector<STrack> BYTETracker::sub_stracks(vector<STrack> &tlista, vector<STrack> &tlistb)
-{
-	map<int, STrack> stracks;
-	for (int i = 0; i < tlista.size(); i++)
-	{
-		stracks.insert(pair<int, STrack>(tlista[i].track_id, tlista[i]));
-	}
-	for (int i = 0; i < tlistb.size(); i++)
-	{
-		int tid = tlistb[i].track_id;
-		if (stracks.count(tid) != 0)
-		{
-			stracks.erase(tid);
-		}
-	}
-
-	vector<STrack> res;
-	std::map<int, STrack>::iterator  it;
-	for (it = stracks.begin(); it != stracks.end(); ++it)
-	{
-		res.push_back(it->second);
-	}
-
-	return res;
-}
-
-void BYTETracker::remove_duplicate_stracks(vector<STrack> &resa, vector<STrack> &resb, vector<STrack> &stracksa, vector<STrack> &stracksb)
-{
-	vector<vector<float> > pdist = iou_distance(stracksa, stracksb);
-	vector<pair<int, int> > pairs;
-	for (int i = 0; i < pdist.size(); i++)
-	{
-		for (int j = 0; j < pdist[i].size(); j++)
-		{
-			if (pdist[i][j] < 0.15)
-			{
-				pairs.push_back(pair<int, int>(i, j));
-			}
-		}
-	}
-
-	vector<int> dupa, dupb;
-	for (int i = 0; i < pairs.size(); i++)
-	{
-		int timep = stracksa[pairs[i].first].frame_id - stracksa[pairs[i].first].start_frame;
-		int timeq = stracksb[pairs[i].second].frame_id - stracksb[pairs[i].second].start_frame;
-		if (timep > timeq)
-			dupb.push_back(pairs[i].second);
-		else
-			dupa.push_back(pairs[i].first);
-	}
-
-	for (int i = 0; i < stracksa.size(); i++)
-	{
-		vector<int>::iterator iter = find(dupa.begin(), dupa.end(), i);
-		if (iter == dupa.end())
-		{
-			resa.push_back(stracksa[i]);
-		}
-	}
-
-	for (int i = 0; i < stracksb.size(); i++)
-	{
-		vector<int>::iterator iter = find(dupb.begin(), dupb.end(), i);
-		if (iter == dupb.end())
-		{
-			resb.push_back(stracksb[i]);
-		}
-	}
-}
-
-void BYTETracker::linear_assignment(vector<vector<float> > &cost_matrix, int cost_matrix_size, int cost_matrix_size_size, float thresh,
-	vector<vector<int> > &matches, vector<int> &unmatched_a, vector<int> &unmatched_b)
-{
-	if (cost_matrix.size() == 0)
-	{
-		for (int i = 0; i < cost_matrix_size; i++)
-		{
-			unmatched_a.push_back(i);
-		}
-		for (int i = 0; i < cost_matrix_size_size; i++)
-		{
-			unmatched_b.push_back(i);
-		}
-		return;
-	}
-
-	vector<int> rowsol; vector<int> colsol;
-	float c = lapjv(cost_matrix, rowsol, colsol, true, thresh);
-	for (int i = 0; i < rowsol.size(); i++)
-	{
-		if (rowsol[i] >= 0)
-		{
-			vector<int> match;
-			match.push_back(i);
-			match.push_back(rowsol[i]);
-			matches.push_back(match);
-		}
-		else
-		{
-			unmatched_a.push_back(i);
-		}
-	}
-
-	for (int i = 0; i < colsol.size(); i++)
-	{
-		if (colsol[i] < 0)
-		{
-			unmatched_b.push_back(i);
-		}
-	}
-}
-
-vector<vector<float> > BYTETracker::ious(vector<vector<float> > &atlbrs, vector<vector<float> > &btlbrs)
-{
-	vector<vector<float> > ious;
-	if (atlbrs.size()*btlbrs.size() == 0)
-		return ious;
-
-	ious.resize(atlbrs.size());
-	for (int i = 0; i < ious.size(); i++)
-	{
-		ious[i].resize(btlbrs.size());
-	}
-
-	//bbox_ious
-	for (int k = 0; k < btlbrs.size(); k++)
-	{
-		vector<float> ious_tmp;
-		float box_area = (btlbrs[k][2] - btlbrs[k][0] + 1)*(btlbrs[k][3] - btlbrs[k][1] + 1);
-		for (int n = 0; n < atlbrs.size(); n++)
-		{
-			float iw = min(atlbrs[n][2], btlbrs[k][2]) - max(atlbrs[n][0], btlbrs[k][0]) + 1;
-			if (iw > 0)
-			{
-				float ih = min(atlbrs[n][3], btlbrs[k][3]) - max(atlbrs[n][1], btlbrs[k][1]) + 1;
-				if(ih > 0)
-				{
-					float ua = (atlbrs[n][2] - atlbrs[n][0] + 1)*(atlbrs[n][3] - atlbrs[n][1] + 1) + box_area - iw * ih;
-					ious[n][k] = iw * ih / ua;
-				}
-				else
-				{
-					ious[n][k] = 0.0;
-				}
-			}
-			else
-			{
-				ious[n][k] = 0.0;
-			}
-		}
-	}
-
-	return ious;
-}
-
-vector<vector<float> > BYTETracker::iou_distance(vector<STrack*> &atracks, vector<STrack> &btracks, int &dist_size, int &dist_size_size)
-{
-	vector<vector<float> > cost_matrix;
-	if (atracks.size() * btracks.size() == 0)
-	{
-		dist_size = atracks.size();
-		dist_size_size = btracks.size();
-		return cost_matrix;
-	}
-	vector<vector<float> > atlbrs, btlbrs;
-	for (int i = 0; i < atracks.size(); i++)
-	{
-		atlbrs.push_back(atracks[i]->tlbr);
-	}
-	for (int i = 0; i < btracks.size(); i++)
-	{
-		btlbrs.push_back(btracks[i].tlbr);
-	}
-
-	dist_size = atracks.size();
-	dist_size_size = btracks.size();
-
-	vector<vector<float> > _ious = ious(atlbrs, btlbrs);
-	
-	for (int i = 0; i < _ious.size();i++)
-	{
-		vector<float> _iou;
-		for (int j = 0; j < _ious[i].size(); j++)
-		{
-			_iou.push_back(1 - _ious[i][j]);
-		}
-		cost_matrix.push_back(_iou);
-	}
-
-	return cost_matrix;
-}
-
-vector<vector<float> > BYTETracker::iou_distance(vector<STrack> &atracks, vector<STrack> &btracks)
-{
-	vector<vector<float> > atlbrs, btlbrs;
-	for (int i = 0; i < atracks.size(); i++)
-	{
-		atlbrs.push_back(atracks[i].tlbr);
-	}
-	for (int i = 0; i < btracks.size(); i++)
-	{
-		btlbrs.push_back(btracks[i].tlbr);
-	}
-
-	vector<vector<float> > _ious = ious(atlbrs, btlbrs);
-	vector<vector<float> > cost_matrix;
-	for (int i = 0; i < _ious.size(); i++)
-	{
-		vector<float> _iou;
-		for (int j = 0; j < _ious[i].size(); j++)
-		{
-			_iou.push_back(1 - _ious[i][j]);
-		}
-		cost_matrix.push_back(_iou);
-	}
-
-	return cost_matrix;
-}
-
-double BYTETracker::lapjv(const vector<vector<float> > &cost, vector<int> &rowsol, vector<int> &colsol,
-	bool extend_cost, float cost_limit, bool return_cost)
-{
-	vector<vector<float> > cost_c;
-	cost_c.assign(cost.begin(), cost.end());
-
-	vector<vector<float> > cost_c_extended;
-
-	int n_rows = cost.size();
-	int n_cols = cost[0].size();
-	rowsol.resize(n_rows);
-	colsol.resize(n_cols);
-
-	int n = 0;
-	if (n_rows == n_cols)
-	{
-		n = n_rows;
-	}
-	else
-	{
-		if (!extend_cost)
-		{
-			cout << "set extend_cost=True" << endl;
-			system("pause");
-			exit(0);
-		}
-	}
-		
-	if (extend_cost || cost_limit < LONG_MAX)
-	{
-		n = n_rows + n_cols;
-		cost_c_extended.resize(n);
-		for (int i = 0; i < cost_c_extended.size(); i++)
-			cost_c_extended[i].resize(n);
-
-		if (cost_limit < LONG_MAX)
-		{
-			for (int i = 0; i < cost_c_extended.size(); i++)
-			{
-				for (int j = 0; j < cost_c_extended[i].size(); j++)
-				{
-					cost_c_extended[i][j] = cost_limit / 2.0;
-				}
-			}
-		}
-		else
-		{
-			float cost_max = -1;
-			for (int i = 0; i < cost_c.size(); i++)
-			{
-				for (int j = 0; j < cost_c[i].size(); j++)
-				{
-					if (cost_c[i][j] > cost_max)
-						cost_max = cost_c[i][j];
-				}
-			}
-			for (int i = 0; i < cost_c_extended.size(); i++)
-			{
-				for (int j = 0; j < cost_c_extended[i].size(); j++)
-				{
-					cost_c_extended[i][j] = cost_max + 1;
-				}
-			}
-		}
-
-		for (int i = n_rows; i < cost_c_extended.size(); i++)
-		{
-			for (int j = n_cols; j < cost_c_extended[i].size(); j++)
-			{
-				cost_c_extended[i][j] = 0;
-			}
-		}
-		for (int i = 0; i < n_rows; i++)
-		{
-			for (int j = 0; j < n_cols; j++)
-			{
-				cost_c_extended[i][j] = cost_c[i][j];
-			}
-		}
-
-		cost_c.clear();
-		cost_c.assign(cost_c_extended.begin(), cost_c_extended.end());
-	}
-
-	double **cost_ptr;
-	cost_ptr = new double *[sizeof(double *) * n];
-	for (int i = 0; i < n; i++)
-		cost_ptr[i] = new double[sizeof(double) * n];
-
-	for (int i = 0; i < n; i++)
-	{
-		for (int j = 0; j < n; j++)
-		{
-			cost_ptr[i][j] = cost_c[i][j];
-		}
-	}
-
-	int* x_c = new int[sizeof(int) * n];
-	int *y_c = new int[sizeof(int) * n];
-
-	int ret = lapjv_internal(n, cost_ptr, x_c, y_c);
-	if (ret != 0)
-	{
-		cout << "Calculate Wrong!" << endl;
-		system("pause");
-		exit(0);
-	}
-
-	double opt = 0.0;
-
-	if (n != n_rows)
-	{
-		for (int i = 0; i < n; i++)
-		{
-			if (x_c[i] >= n_cols)
-				x_c[i] = -1;
-			if (y_c[i] >= n_rows)
-				y_c[i] = -1;
-		}
-		for (int i = 0; i < n_rows; i++)
-		{
-			rowsol[i] = x_c[i];
-		}
-		for (int i = 0; i < n_cols; i++)
-		{
-			colsol[i] = y_c[i];
-		}
-
-		if (return_cost)
-		{
-			for (int i = 0; i < rowsol.size(); i++)
-			{
-				if (rowsol[i] != -1)
-				{
-					//cout << i << "\t" << rowsol[i] << "\t" << cost_ptr[i][rowsol[i]] << endl;
-					opt += cost_ptr[i][rowsol[i]];
-				}
-			}
-		}
-	}
-	else if (return_cost)
-	{
-		for (int i = 0; i < rowsol.size(); i++)
-		{
-			opt += cost_ptr[i][rowsol[i]];
-		}
-	}
-
-	for (int i = 0; i < n; i++)
-	{
-		delete[]cost_ptr[i];
-	}
-	delete[]cost_ptr;
-	delete[]x_c;
-	delete[]y_c;
-
-	return opt;
-}
-
-Scalar BYTETracker::get_color(int idx)
-{
-	idx += 3;
-	return Scalar(37 * idx % 255, 17 * idx % 255, 29 * idx % 255);
-}
\ No newline at end of file
diff --git a/spaces/EuroPython2022/ToxicCommentClassification/README.md b/spaces/EuroPython2022/ToxicCommentClassification/README.md
deleted file mode 100644
index 181ba45cc244f045f34d78ff3f11f9fbd871e3e0..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/ToxicCommentClassification/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ToxicCommentClassification
-emoji: 📊
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.0.25
-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/EuroPython2022/mmocr-demo/configs/kie/sdmgr/sdmgr_novisual_60e_wildreceipt.py b/spaces/EuroPython2022/mmocr-demo/configs/kie/sdmgr/sdmgr_novisual_60e_wildreceipt.py
deleted file mode 100644
index 220135a0b037909599fbaf77c75b06f48f8b1ba7..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/kie/sdmgr/sdmgr_novisual_60e_wildreceipt.py
+++ /dev/null
@@ -1,98 +0,0 @@
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-max_scale, min_scale = 1024, 512
-
-train_pipeline = [
-    dict(type='LoadAnnotations'),
-    dict(
-        type='ResizeNoImg', img_scale=(max_scale, min_scale), keep_ratio=True),
-    dict(type='KIEFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['img', 'relations', 'texts', 'gt_bboxes', 'gt_labels'],
-        meta_keys=('filename', 'ori_texts'))
-]
-test_pipeline = [
-    dict(type='LoadAnnotations'),
-    dict(
-        type='ResizeNoImg', img_scale=(max_scale, min_scale), keep_ratio=True),
-    dict(type='KIEFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['img', 'relations', 'texts', 'gt_bboxes'],
-        meta_keys=('filename', 'ori_texts', 'img_norm_cfg', 'ori_filename',
-                   'img_shape'))
-]
-
-dataset_type = 'KIEDataset'
-data_root = 'data/wildreceipt'
-
-loader = dict(
-    type='HardDiskLoader',
-    repeat=1,
-    parser=dict(
-        type='LineJsonParser',
-        keys=['file_name', 'height', 'width', 'annotations']))
-
-train = dict(
-    type=dataset_type,
-    ann_file=f'{data_root}/train.txt',
-    pipeline=train_pipeline,
-    img_prefix=data_root,
-    loader=loader,
-    dict_file=f'{data_root}/dict.txt',
-    test_mode=False)
-test = dict(
-    type=dataset_type,
-    ann_file=f'{data_root}/test.txt',
-    pipeline=test_pipeline,
-    img_prefix=data_root,
-    loader=loader,
-    dict_file=f'{data_root}/dict.txt',
-    test_mode=True)
-
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=1,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=train,
-    val=test,
-    test=test)
-
-evaluation = dict(
-    interval=1,
-    metric='macro_f1',
-    metric_options=dict(
-        macro_f1=dict(
-            ignores=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25])))
-
-model = dict(
-    type='SDMGR',
-    backbone=dict(type='UNet', base_channels=16),
-    bbox_head=dict(
-        type='SDMGRHead', visual_dim=16, num_chars=92, num_classes=26),
-    visual_modality=False,
-    train_cfg=None,
-    test_cfg=None,
-    class_list=f'{data_root}/class_list.txt')
-
-optimizer = dict(type='Adam', weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=1,
-    warmup_ratio=1,
-    step=[40, 50])
-total_epochs = 60
-
-checkpoint_config = dict(interval=1)
-log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-
-find_unused_parameters = True
diff --git a/spaces/Fcjs/digiplay-Real3D_F16full_v3.1/app.py b/spaces/Fcjs/digiplay-Real3D_F16full_v3.1/app.py
deleted file mode 100644
index c05aed1df56f86f24055730a3a7b852bd1776c45..0000000000000000000000000000000000000000
--- a/spaces/Fcjs/digiplay-Real3D_F16full_v3.1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/digiplay/RealCartoon3D_F16full_v3.1").launch()
\ No newline at end of file
diff --git a/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/ChatFree.py b/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/ChatFree.py
deleted file mode 100644
index 6bbaebaed35681026ff1eeb8eee3270e3b0741fd..0000000000000000000000000000000000000000
--- a/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/ChatFree.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os, requests
-from ...typing import sha256, Dict, get_type_hints
-import json
-
-url = "https://v.chatfree.cc"
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k']
-supports_stream = False
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    headers = {
-        'authority': 'chat.dfehub.com',
-        'accept': '*/*',
-        'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
-        'content-type': 'application/json',
-        'origin': 'https://v.chatfree.cc',
-        'referer': 'https://v.chatfree.cc/',
-        'sec-ch-ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
-        'sec-ch-ua-mobile': '?0',
-        'sec-ch-ua-platform': '"macOS"',
-        'sec-fetch-dest': 'empty',
-        'sec-fetch-mode': 'cors',
-        'sec-fetch-site': 'same-origin',
-        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
-        'x-requested-with': 'XMLHttpRequest',
-    }
-
-    json_data = {
-        'messages': messages,
-        'stream': True,
-        'model': model,
-        'temperature': 0.5,
-        'presence_penalty': 0,
-        'frequency_penalty': 0,
-        'top_p': 1,
-    }
-
-    response = requests.post('https://v.chatfree.cc/api/openai/v1/chat/completions',
-        headers=headers, json=json_data)
-
-    for chunk in response.iter_lines():
-        if b'content' in chunk:
-            data = json.loads(chunk.decode().split('data: ')[1])
-            yield (data['choices'][0]['delta']['content'])
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/GXSA/bingo/src/components/button-scroll-to-bottom.tsx b/spaces/GXSA/bingo/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/GXSA/bingo/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
-  const isAtBottom = useAtBottom()
-
-  return (
-    <Button
-      variant="outline"
-      size="icon"
-      className={cn(
-        'fixed right-4 bottom-24 z-50 bg-background transition-opacity duration-300 sm:right-20',
-        isAtBottom ? 'opacity-0' : 'opacity-100',
-        className
-      )}
-      onClick={() =>
-        window.scrollTo({
-          top: document.body.offsetHeight,
-          behavior: 'smooth'
-        })
-      }
-      {...props}
-    >
-      <IconArrowDown />
-      <span className="sr-only">Scroll to bottom</span>
-    </Button>
-  )
-}
diff --git a/spaces/GXSA/bingo/src/lib/bots/bing/index.ts b/spaces/GXSA/bingo/src/lib/bots/bing/index.ts
deleted file mode 100644
index c75c69f94af8c3db92d4c90d465c219a2af72a4d..0000000000000000000000000000000000000000
--- a/spaces/GXSA/bingo/src/lib/bots/bing/index.ts
+++ /dev/null
@@ -1,432 +0,0 @@
-import { fetch, WebSocket, debug } from '@/lib/isomorphic'
-import WebSocketAsPromised from 'websocket-as-promised'
-import {
-  SendMessageParams,
-  BingConversationStyle,
-  ConversationResponse,
-  ChatResponseMessage,
-  ConversationInfo,
-  InvocationEventType,
-  ChatError,
-  ErrorCode,
-  ChatUpdateCompleteResponse,
-  ImageInfo,
-  KBlobResponse
-} from './types'
-
-import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
-import { WatchDog, createChunkDecoder } from '@/lib/utils'
-
-type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
-
-const OPTIONS_SETS = [
-  'nlu_direct_response_filter',
-  'deepleo',
-  'disable_emoji_spoken_text',
-  'responsible_ai_policy_235',
-  'enablemm',
-  'iycapbing',
-  'iyxapbing',
-  'objopinion',
-  'rweasgv2',
-  'dagslnv1',
-  'dv3sugg',
-  'autosave',
-  'iyoloxap',
-  'iyoloneutral',
-  'clgalileo',
-  'gencontentv3',
-]
-
-export class BingWebBot {
-  protected conversationContext?: ConversationInfo
-  protected cookie: string
-  protected ua: string
-  protected endpoint = ''
-  private lastText = ''
-  private asyncTasks: Array<Promise<any>> = []
-
-  constructor(opts: {
-    cookie: string
-    ua: string
-    bingConversationStyle?: BingConversationStyle
-    conversationContext?: ConversationInfo
-  }) {
-    const { cookie, ua, conversationContext } = opts
-    this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}`
-    this.ua = ua
-    this.conversationContext = conversationContext
-  }
-
-  static buildChatRequest(conversation: ConversationInfo) {
-    const optionsSets = OPTIONS_SETS
-    if (conversation.conversationStyle === BingConversationStyle.Precise) {
-      optionsSets.push('h3precise')
-    } else if (conversation.conversationStyle === BingConversationStyle.Creative) {
-      optionsSets.push('h3imaginative')
-    }
-    return {
-      arguments: [
-        {
-          source: 'cib',
-          optionsSets,
-          allowedMessageTypes: [
-            'ActionRequest',
-            'Chat',
-            'Context',
-            'InternalSearchQuery',
-            'InternalSearchResult',
-            'Disengaged',
-            'InternalLoaderMessage',
-            'Progress',
-            'RenderCardRequest',
-            'SemanticSerp',
-            'GenerateContentQuery',
-            'SearchQuery',
-          ],
-          sliceIds: [
-            'winmuid1tf',
-            'anssupfor_c',
-            'imgchatgptv2',
-            'tts2cf',
-            'contansperf',
-            'mlchatpc8500w',
-            'mlchatpc2',
-            'ctrlworkpay',
-            'winshortmsgtf',
-            'cibctrl',
-            'sydtransctrl',
-            'sydconfigoptc',
-            '0705trt4',
-            '517opinion',
-            '628ajcopus0',
-            '330uaugs0',
-            '529rwea',
-            '0626snptrcs0',
-            '424dagslnv1',
-          ],
-          isStartOfSession: conversation.invocationId === 0,
-          message: {
-            author: 'user',
-            inputMethod: 'Keyboard',
-            text: conversation.prompt,
-            imageUrl: conversation.imageUrl,
-            messageType: 'Chat',
-          },
-          conversationId: conversation.conversationId,
-          conversationSignature: conversation.conversationSignature,
-          participant: { id: conversation.clientId },
-        },
-      ],
-      invocationId: conversation.invocationId.toString(),
-      target: 'chat',
-      type: InvocationEventType.StreamInvocation,
-    }
-  }
-
-  async createConversation(): Promise<ConversationResponse> {
-    const headers = {
-      'Accept-Encoding': 'gzip, deflate, br, zsdch',
-      'User-Agent': this.ua,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      cookie: this.cookie,
-    }
-
-    let resp: ConversationResponse | undefined
-    try {
-      const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' })
-      if (response.status === 404) {
-        throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR)
-      }
-      resp = await response.json() as ConversationResponse
-    } catch (err) {
-      console.error('create conversation error', err)
-    }
-
-    if (!resp?.result) {
-      throw new ChatError('你的 VPS 或代理可能被封禁，如有疑问，请前往 https://github.com/weaigc/bingo 咨询', ErrorCode.BING_IP_FORBIDDEN)
-    }
-
-    const { value, message } = resp.result || {}
-    if (value !== 'Success') {
-      const errorMsg = `${value}: ${message}`
-      if (value === 'UnauthorizedRequest') {
-        if (/fetch failed/i.test(message || '')) {
-          throw new ChatError(errorMsg, ErrorCode.BING_IP_FORBIDDEN)
-        }
-        throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED)
-      }
-      if (value === 'TryLater') {
-        throw new ChatError(errorMsg, ErrorCode.BING_TRY_LATER)
-      }
-      if (value === 'Forbidden') {
-        throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN)
-      }
-      throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR)
-    }
-    return resp
-  }
-
-  private async createContext(conversationStyle: BingConversationStyle) {
-    if (!this.conversationContext) {
-      const conversation = await this.createConversation()
-      this.conversationContext = {
-        conversationId: conversation.conversationId,
-        conversationSignature: conversation.conversationSignature,
-        clientId: conversation.clientId,
-        invocationId: 0,
-        conversationStyle,
-        prompt: '',
-      }
-    }
-    return this.conversationContext
-  }
-
-  async sendMessage(params: Params) {
-    try {
-      await this.createContext(params.options.bingConversationStyle)
-      Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl })
-      return this.sydneyProxy(params)
-    } catch (error) {
-      params.onEvent({
-        type: 'ERROR',
-        error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR),
-      })
-    }
-  }
-
-  private async sydneyProxy(params: Params) {
-    const abortController = new AbortController()
-    const response = await fetch(this.endpoint + '/api/sydney', {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      signal: abortController.signal,
-      body: JSON.stringify(this.conversationContext!)
-    })
-    if (response.status !== 200) {
-      params.onEvent({
-        type: 'ERROR',
-        error: new ChatError(
-          'Unknown error',
-          ErrorCode.UNKOWN_ERROR,
-        ),
-      })
-    }
-    params.signal?.addEventListener('abort', () => {
-      abortController.abort()
-    })
-
-    const textDecoder = createChunkDecoder()
-    for await (const chunk of streamAsyncIterable(response.body!)) {
-      this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk)))
-    }
-  }
-
-  async sendWs() {
-    const wsConfig: ConstructorParameters<typeof WebSocketAsPromised>[1] = {
-      packMessage: websocketUtils.packMessage,
-      unpackMessage: websocketUtils.unpackMessage,
-      createWebSocket: (url) => new WebSocket(url, {
-        headers: {
-          'accept-language': 'zh-CN,zh;q=0.9',
-          'cache-control': 'no-cache',
-          'User-Agent': this.ua,
-          pragma: 'no-cache',
-          cookie: this.cookie,
-        }
-      })
-    }
-    const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig)
-
-    wsp.open().then(() => {
-      wsp.sendPacked({ protocol: 'json', version: 1 })
-      wsp.sendPacked({ type: 6 })
-      wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!))
-    })
-
-    return wsp
-  }
-
-  private async useWs(params: Params) {
-    const wsp = await this.sendWs()
-    const watchDog = new WatchDog()
-    wsp.onUnpackedMessage.addListener((events) => {
-      watchDog.watch(() => {
-        wsp.sendPacked({ type: 6 })
-      })
-      this.parseEvents(params, events)
-    })
-
-    wsp.onClose.addListener(() => {
-      watchDog.reset()
-      params.onEvent({ type: 'DONE' })
-      wsp.removeAllListeners()
-    })
-
-    params.signal?.addEventListener('abort', () => {
-      wsp.removeAllListeners()
-      wsp.close()
-    })
-  }
-
-  private async createImage(prompt: string, id: string) {
-    try {
-      const headers = {
-        'Accept-Encoding': 'gzip, deflate, br, zsdch',
-        'User-Agent': this.ua,
-        'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-        cookie: this.cookie,
-      }
-      const query = new URLSearchParams({
-        prompt,
-        id
-      })
-      const response = await fetch(this.endpoint + '/api/image?' + query.toString(),
-        {
-          method: 'POST',
-          headers,
-          mode: 'cors',
-          credentials: 'include'
-        })
-        .then(res => res.text())
-      if (response) {
-        this.lastText += '\n' + response
-      }
-    } catch (err) {
-      console.error('Create Image Error', err)
-    }
-  }
-
-  private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) {
-    const imageInfo: ImageInfo = {}
-    let imageBase64: string | undefined = undefined
-    const knowledgeRequest = {
-      imageInfo,
-      knowledgeRequest: {
-        invokedSkills: [
-          'ImageById'
-        ],
-        subscriptionId: 'Bing.Chat.Multimodal',
-        invokedSkillsRequestData: {
-            enableFaceBlur: true
-        },
-        convoData: {
-          convoid: this.conversationContext?.conversationId,
-          convotone: conversationStyle,
-        }
-      },
-    }
-
-    if (imageUrl.startsWith('data:image/')) {
-      imageBase64 = imageUrl.replace('data:image/', '');
-      const partIndex = imageBase64.indexOf(',')
-      if (partIndex) {
-        imageBase64 = imageBase64.substring(partIndex + 1)
-      }
-    } else {
-      imageInfo.url = imageUrl
-    }
-    return { knowledgeRequest, imageBase64 }
-  }
-
-  async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise<KBlobResponse | undefined> {
-    if (!imageUrl) {
-      return
-    }
-    await this.createContext(conversationStyle)
-    const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle)
-
-    const response = await fetch(this.endpoint + '/api/kblob',
-      {
-        headers: {
-          'Content-Type': 'application/json',
-        },
-        method: 'POST',
-        mode: 'cors',
-        credentials: 'include',
-        body: JSON.stringify(payload),
-      })
-      .then(res => res.json())
-      .catch(e => {
-        console.log('Error', e)
-      })
-    return response
-  }
-
-  private async generateContent(message: ChatResponseMessage) {
-    if (message.contentType === 'IMAGE') {
-      this.asyncTasks.push(this.createImage(message.text, message.messageId))
-    }
-  }
-
-  private async parseEvents(params: Params, events: any) {
-    const conversation = this.conversationContext!
-
-    events?.forEach(async (event: ChatUpdateCompleteResponse) => {
-      debug('bing event', event)
-      if (event.type === 3) {
-        await Promise.all(this.asyncTasks)
-        this.asyncTasks = []
-        params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } })
-        params.onEvent({ type: 'DONE' })
-        conversation.invocationId = parseInt(event.invocationId, 10) + 1
-      } else if (event.type === 1) {
-        const messages = event.arguments[0].messages
-        if (messages) {
-          const text = convertMessageToMarkdown(messages[0])
-          this.lastText = text
-          params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } })
-        }
-      } else if (event.type === 2) {
-        const messages = event.item.messages as ChatResponseMessage[] | undefined
-        if (!messages) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              event.item.result.error || 'Unknown error',
-              event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT
-                : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA)
-                  : ErrorCode.UNKOWN_ERROR
-            ),
-          })
-          return
-        }
-        const limited = messages.some((message) =>
-          message.contentOrigin === 'TurnLimiter'
-          || message.messageType === 'Disengaged'
-        )
-        if (limited) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              'Sorry, you have reached chat limit in this conversation.',
-              ErrorCode.CONVERSATION_LIMIT,
-            ),
-          })
-          return
-        }
-
-        const lastMessage = event.item.messages.at(-1) as ChatResponseMessage
-        const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE')
-        if (specialMessage) {
-          this.generateContent(specialMessage)
-        }
-
-        if (lastMessage) {
-          const text = convertMessageToMarkdown(lastMessage)
-          this.lastText = text
-          params.onEvent({
-            type: 'UPDATE_ANSWER',
-            data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions },
-          })
-        }
-      }
-    })
-  }
-
-  resetConversation() {
-    this.conversationContext = undefined
-  }
-}
diff --git a/spaces/Gradio-Blocks/anime-colorization/scripts/pixel_guide_sample.py b/spaces/Gradio-Blocks/anime-colorization/scripts/pixel_guide_sample.py
deleted file mode 100644
index 86639c16e232c50a77cbd8c1714996c98a114e2e..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/anime-colorization/scripts/pixel_guide_sample.py
+++ /dev/null
@@ -1,111 +0,0 @@
-"""
-Generate a large batch of samples from a super resolution model, given a batch
-of samples from a regular model from image_sample.py.
-"""
-
-import argparse
-import os
-
-import blobfile as bf
-import numpy as np
-import torch as th
-import torch.distributed as dist
-
-from torchvision import utils
-from pixel_guide_diffusion import dist_util, logger
-from pixel_guide_diffusion.image_datasets import load_data
-from pixel_guide_diffusion.script_util import (
-    pg_model_and_diffusion_defaults,
-    pg_create_model_and_diffusion,
-    args_to_dict,
-    add_dict_to_argparser,
-)
-
-
-def main():
-    args = create_argparser().parse_args()
-
-    dist_util.setup_dist()
-    logger.configure()
-
-    logger.log("creating model...")
-    model, diffusion = pg_create_model_and_diffusion(
-        **args_to_dict(args, pg_model_and_diffusion_defaults().keys())
-    )
-    model.load_state_dict(
-        dist_util.load_state_dict(args.model_path, map_location="cpu")
-    )
-    model.to(dist_util.dev())
-    model.eval()
-
-    logger.log("creating data loader...")
-    data = load_data(
-        data_dir=args.data_dir,
-        batch_size=args.batch_size,
-        image_size=args.image_size,
-        class_cond=args.class_cond,
-        guide_dir=args.guide_dir,
-        guide_size=args.guide_size,
-        deterministic=True,
-    )
-
-    logger.log("creating samples...")
-    os.makedirs('sample', exist_ok=True)
-    i = 0
-    while i * args.batch_size < args.num_samples:
-        if dist.get_rank() == 0:
-            target, model_kwargs = next(data)
-            target = target.to(dist_util.dev())
-            model_kwargs = {k: v.to(dist_util.dev()) for k, v in model_kwargs.items()}
-
-            with th.no_grad():
-                sample_fn = (
-                    diffusion.p_sample_loop if not args.use_ddim else diffusion.ddim_sample_loop
-                )
-                sample = sample_fn(
-                    model,
-                    (args.batch_size, 3, args.image_size, args.image_size),
-                    clip_denoised=args.clip_denoised,
-                    model_kwargs=model_kwargs,
-                )
-
-                guide = model_kwargs["guide"]
-                h, w = guide.shape[2:]
-                guide = guide.clamp(-1,1).repeat(1,3,1,1)
-                sample = th.nn.functional.interpolate(sample.clamp(-1,1), size=(h, w))
-                target = th.nn.functional.interpolate(target.clamp(-1,1), size=(h, w))
-
-                images = th.cat([guide, sample, target], 0)
-                utils.save_image(
-                    images,
-                    f"sample/{str(i).zfill(6)}.png",
-                    nrow=args.batch_size,
-                    normalize=True,
-                    range=(-1, 1),
-                )
-
-                i += 1
-                logger.log(f"created {i * args.batch_size} samples")
-
-    logger.log("sampling complete")
-
-
-def create_argparser():
-    defaults = dict(
-        data_dir="",
-        guide_dir="",
-        clip_denoised=True,
-        num_samples=100,
-        batch_size=4,
-        use_ddim=False,
-        base_samples="",
-        model_path="",
-    )
-    defaults.update(pg_model_and_diffusion_defaults())
-    parser = argparse.ArgumentParser()
-    add_dict_to_argparser(parser, defaults)
-    return parser
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py
deleted file mode 100644
index e9c5defd1cda850f9702c05a86e0671880ef5e38..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py
+++ /dev/null
@@ -1,105 +0,0 @@
-_base_ = [
-    '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
-]
-
-# model settings
-model = dict(
-    type='CornerNet',
-    backbone=dict(
-        type='HourglassNet',
-        downsample_times=5,
-        num_stacks=2,
-        stage_channels=[256, 256, 384, 384, 384, 512],
-        stage_blocks=[2, 2, 2, 2, 2, 4],
-        norm_cfg=dict(type='BN', requires_grad=True)),
-    neck=None,
-    bbox_head=dict(
-        type='CentripetalHead',
-        num_classes=80,
-        in_channels=256,
-        num_feat_levels=2,
-        corner_emb_channels=0,
-        loss_heatmap=dict(
-            type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
-        loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1),
-        loss_guiding_shift=dict(
-            type='SmoothL1Loss', beta=1.0, loss_weight=0.05),
-        loss_centripetal_shift=dict(
-            type='SmoothL1Loss', beta=1.0, loss_weight=1)),
-    # training and testing settings
-    train_cfg=None,
-    test_cfg=dict(
-        corner_topk=100,
-        local_maximum_kernel=3,
-        distance_threshold=0.5,
-        score_thr=0.05,
-        max_per_img=100,
-        nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
-# data settings
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='PhotoMetricDistortion',
-        brightness_delta=32,
-        contrast_range=(0.5, 1.5),
-        saturation_range=(0.5, 1.5),
-        hue_delta=18),
-    dict(
-        type='RandomCenterCropPad',
-        crop_size=(511, 511),
-        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
-        test_mode=False,
-        test_pad_mode=None,
-        **img_norm_cfg),
-    dict(type='Resize', img_scale=(511, 511), keep_ratio=False),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(
-        type='MultiScaleFlipAug',
-        scale_factor=1.0,
-        flip=True,
-        transforms=[
-            dict(type='Resize'),
-            dict(
-                type='RandomCenterCropPad',
-                crop_size=None,
-                ratios=None,
-                border=None,
-                test_mode=True,
-                test_pad_mode=['logical_or', 127],
-                **img_norm_cfg),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(
-                type='Collect',
-                keys=['img'],
-                meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
-                           'scale_factor', 'flip', 'img_norm_cfg', 'border')),
-        ])
-]
-data = dict(
-    samples_per_gpu=6,
-    workers_per_gpu=3,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='Adam', lr=0.0005)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=1.0 / 3,
-    step=[190])
-runner = dict(type='EpochBasedRunner', max_epochs=210)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/wider_face/ssd300_wider_face.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/wider_face/ssd300_wider_face.py
deleted file mode 100644
index 5a3eb38df3dc75af176cc6972af88e76124ba4dc..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/wider_face/ssd300_wider_face.py
+++ /dev/null
@@ -1,18 +0,0 @@
-_base_ = [
-    '../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
-    '../_base_/default_runtime.py'
-]
-model = dict(bbox_head=dict(num_classes=1))
-# optimizer
-optimizer = dict(type='SGD', lr=0.012, momentum=0.9, weight_decay=5e-4)
-optimizer_config = dict()
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=0.001,
-    step=[16, 20])
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=24)
-log_config = dict(interval=1)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/assigners/hungarian_assigner.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/assigners/hungarian_assigner.py
deleted file mode 100644
index e10cc14afac4ddfcb9395c1a250ece1fbfe3263c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/assigners/hungarian_assigner.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import torch
-
-from ..builder import BBOX_ASSIGNERS
-from ..match_costs import build_match_cost
-from ..transforms import bbox_cxcywh_to_xyxy
-from .assign_result import AssignResult
-from .base_assigner import BaseAssigner
-
-try:
-    from scipy.optimize import linear_sum_assignment
-except ImportError:
-    linear_sum_assignment = None
-
-
-@BBOX_ASSIGNERS.register_module()
-class HungarianAssigner(BaseAssigner):
-    """Computes one-to-one matching between predictions and ground truth.
-
-    This class computes an assignment between the targets and the predictions
-    based on the costs. The costs are weighted sum of three components:
-    classification cost, regression L1 cost and regression iou cost. The
-    targets don't include the no_object, so generally there are more
-    predictions than targets. After the one-to-one matching, the un-matched
-    are treated as backgrounds. Thus each query prediction will be assigned
-    with `0` or a positive integer indicating the ground truth index:
-
-    - 0: negative sample, no assigned gt
-    - positive integer: positive sample, index (1-based) of assigned gt
-
-    Args:
-        cls_weight (int | float, optional): The scale factor for classification
-            cost. Default 1.0.
-        bbox_weight (int | float, optional): The scale factor for regression
-            L1 cost. Default 1.0.
-        iou_weight (int | float, optional): The scale factor for regression
-            iou cost. Default 1.0.
-        iou_calculator (dict | optional): The config for the iou calculation.
-            Default type `BboxOverlaps2D`.
-        iou_mode (str | optional): "iou" (intersection over union), "iof"
-                (intersection over foreground), or "giou" (generalized
-                intersection over union). Default "giou".
-    """
-
-    def __init__(self,
-                 cls_cost=dict(type='ClassificationCost', weight=1.),
-                 reg_cost=dict(type='BBoxL1Cost', weight=1.0),
-                 iou_cost=dict(type='IoUCost', iou_mode='giou', weight=1.0)):
-        self.cls_cost = build_match_cost(cls_cost)
-        self.reg_cost = build_match_cost(reg_cost)
-        self.iou_cost = build_match_cost(iou_cost)
-
-    def assign(self,
-               bbox_pred,
-               cls_pred,
-               gt_bboxes,
-               gt_labels,
-               img_meta,
-               gt_bboxes_ignore=None,
-               eps=1e-7):
-        """Computes one-to-one matching based on the weighted costs.
-
-        This method assign each query prediction to a ground truth or
-        background. The `assigned_gt_inds` with -1 means don't care,
-        0 means negative sample, and positive number is the index (1-based)
-        of assigned gt.
-        The assignment is done in the following steps, the order matters.
-
-        1. assign every prediction to -1
-        2. compute the weighted costs
-        3. do Hungarian matching on CPU based on the costs
-        4. assign all to 0 (background) first, then for each matched pair
-           between predictions and gts, treat this prediction as foreground
-           and assign the corresponding gt index (plus 1) to it.
-
-        Args:
-            bbox_pred (Tensor): Predicted boxes with normalized coordinates
-                (cx, cy, w, h), which are all in range [0, 1]. Shape
-                [num_query, 4].
-            cls_pred (Tensor): Predicted classification logits, shape
-                [num_query, num_class].
-            gt_bboxes (Tensor): Ground truth boxes with unnormalized
-                coordinates (x1, y1, x2, y2). Shape [num_gt, 4].
-            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).
-            img_meta (dict): Meta information for current image.
-            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
-                labelled as `ignored`. Default None.
-            eps (int | float, optional): A value added to the denominator for
-                numerical stability. Default 1e-7.
-
-        Returns:
-            :obj:`AssignResult`: The assigned result.
-        """
-        assert gt_bboxes_ignore is None, \
-            'Only case when gt_bboxes_ignore is None is supported.'
-        num_gts, num_bboxes = gt_bboxes.size(0), bbox_pred.size(0)
-
-        # 1. assign -1 by default
-        assigned_gt_inds = bbox_pred.new_full((num_bboxes, ),
-                                              -1,
-                                              dtype=torch.long)
-        assigned_labels = bbox_pred.new_full((num_bboxes, ),
-                                             -1,
-                                             dtype=torch.long)
-        if num_gts == 0 or num_bboxes == 0:
-            # No ground truth or boxes, return empty assignment
-            if num_gts == 0:
-                # No ground truth, assign all to background
-                assigned_gt_inds[:] = 0
-            return AssignResult(
-                num_gts, assigned_gt_inds, None, labels=assigned_labels)
-        img_h, img_w, _ = img_meta['img_shape']
-        factor = gt_bboxes.new_tensor([img_w, img_h, img_w,
-                                       img_h]).unsqueeze(0)
-
-        # 2. compute the weighted costs
-        # classification and bboxcost.
-        cls_cost = self.cls_cost(cls_pred, gt_labels)
-        # regression L1 cost
-        normalize_gt_bboxes = gt_bboxes / factor
-        reg_cost = self.reg_cost(bbox_pred, normalize_gt_bboxes)
-        # regression iou cost, defaultly giou is used in official DETR.
-        bboxes = bbox_cxcywh_to_xyxy(bbox_pred) * factor
-        iou_cost = self.iou_cost(bboxes, gt_bboxes)
-        # weighted sum of above three costs
-        cost = cls_cost + reg_cost + iou_cost
-
-        # 3. do Hungarian matching on CPU using linear_sum_assignment
-        cost = cost.detach().cpu()
-        if linear_sum_assignment is None:
-            raise ImportError('Please run "pip install scipy" '
-                              'to install scipy first.')
-        matched_row_inds, matched_col_inds = linear_sum_assignment(cost)
-        matched_row_inds = torch.from_numpy(matched_row_inds).to(
-            bbox_pred.device)
-        matched_col_inds = torch.from_numpy(matched_col_inds).to(
-            bbox_pred.device)
-
-        # 4. assign backgrounds and foregrounds
-        # assign all indices to backgrounds first
-        assigned_gt_inds[:] = 0
-        # assign foregrounds based on matching results
-        assigned_gt_inds[matched_row_inds] = matched_col_inds + 1
-        assigned_labels[matched_row_inds] = gt_labels[matched_col_inds]
-        return AssignResult(
-            num_gts, assigned_gt_inds, None, labels=assigned_labels)
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/tests/modules/test_transformer.py b/spaces/GrandaddyShmax/AudioCraft_Plus/tests/modules/test_transformer.py
deleted file mode 100644
index 2bb79bfd58d535469f9b3c56b8a5fe254db5d8ba..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/tests/modules/test_transformer.py
+++ /dev/null
@@ -1,253 +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 itertools import product
-
-import pytest
-import torch
-
-from audiocraft.modules.transformer import (
-    StreamingMultiheadAttention, StreamingTransformer, set_efficient_attention_backend)
-
-
-def test_transformer_causal_streaming():
-    torch.manual_seed(1234)
-
-    for context, custom in product([None, 10], [False, True]):
-        # Test that causality and receptive fields are properly handled.
-        # looking at the gradients
-        tr = StreamingTransformer(
-            16, 4, 1 if context else 2,
-            causal=True, past_context=context, custom=custom,
-            dropout=0.)
-        steps = 20
-        for k in [0, 10, 15, 19]:
-            x = torch.randn(4, steps, 16, requires_grad=True)
-            y = tr(x)
-            y[:, k].abs().sum().backward()
-            if k + 1 < steps:
-                assert torch.allclose(x.grad[:, k + 1:], torch.tensor(0.)), x.grad[:, k + 1:].norm()
-            assert not torch.allclose(x.grad[:, :k + 1], torch.tensor(0.)), x.grad[:, :k + 1].norm()
-            if context is not None and k > context:
-                limit = k - context - 1
-                assert torch.allclose(x.grad[:, :limit],
-                                      torch.tensor(0.)), x.grad[:, :limit].norm()
-
-        # Now check that streaming gives the same result at batch eval.
-        x = torch.randn(4, steps, 16)
-        y = tr(x)
-        ys = []
-        with tr.streaming():
-            for k in range(steps):
-                chunk = x[:, k:k + 1, :]
-                ys.append(tr(chunk))
-        y_stream = torch.cat(ys, dim=1)
-        delta = torch.norm(y_stream - y) / torch.norm(y)
-        assert delta < 1e-6, delta
-
-
-def test_transformer_vs_pytorch():
-    torch.manual_seed(1234)
-    # Check that in the non causal setting, we get the same result as
-    # PyTorch Transformer encoder.
-    for custom in [False, True]:
-        tr = StreamingTransformer(
-            16, 4, 2,
-            causal=False, custom=custom, dropout=0., positional_scale=0.)
-        layer = torch.nn.TransformerEncoderLayer(16, 4, dropout=0., batch_first=True)
-        tr_ref = torch.nn.TransformerEncoder(layer, 2)
-        tr.load_state_dict(tr_ref.state_dict())
-
-        x = torch.randn(4, 20, 16)
-        y = tr(x)
-        y2 = tr_ref(x)
-        delta = torch.norm(y2 - y) / torch.norm(y)
-        assert delta < 1e-6, delta
-
-
-def test_streaming_api():
-    tr = StreamingTransformer(16, 4, 2, causal=True, dropout=0.)
-    tr.eval()
-    steps = 12
-    x = torch.randn(1, steps, 16)
-
-    with torch.no_grad():
-        with tr.streaming():
-            _ = tr(x[:, :1])
-            state = {k: v.clone() for k, v in tr.get_streaming_state().items()}
-            y = tr(x[:, 1:2])
-            tr.set_streaming_state(state)
-            y2 = tr(x[:, 1:2])
-            assert torch.allclose(y, y2), (y - y2).norm()
-            assert tr.flush() is None
-
-
-def test_memory_efficient():
-    for backend in ['torch', 'xformers']:
-        torch.manual_seed(1234)
-        set_efficient_attention_backend(backend)
-
-        tr = StreamingTransformer(
-            16, 4, 2, custom=True, dropout=0., layer_scale=0.1)
-        tr_mem_efficient = StreamingTransformer(
-            16, 4, 2, dropout=0., memory_efficient=True, layer_scale=0.1)
-        tr_mem_efficient.load_state_dict(tr.state_dict())
-        tr.eval()
-        steps = 12
-        x = torch.randn(3, steps, 16)
-
-        with torch.no_grad():
-            y = tr(x)
-            y2 = tr_mem_efficient(x)
-            assert torch.allclose(y, y2), ((y - y2).norm(), backend)
-
-
-def test_attention_as_float32():
-    torch.manual_seed(1234)
-    cases = [
-        {'custom': True},
-        {'custom': False},
-    ]
-    for case in cases:
-        tr = StreamingTransformer(16, 4, 2, dropout=0., dtype=torch.bfloat16, **case)
-        tr_float32 = StreamingTransformer(
-            16, 4, 2, dropout=0., attention_as_float32=True, dtype=torch.bfloat16, **case)
-        if not case['custom']:
-            # we are not using autocast here because it doesn't really
-            # work as expected on CPU, so we have to manually cast the weights of the MHA.
-            for layer in tr_float32.layers:
-                layer.self_attn.mha.to(torch.float32)
-        tr_float32.load_state_dict(tr.state_dict())
-        steps = 12
-        x = torch.randn(3, steps, 16, dtype=torch.bfloat16)
-
-        with torch.no_grad():
-            y = tr(x)
-            y2 = tr_float32(x)
-            assert not torch.allclose(y, y2), (y - y2).norm()
-
-
-@torch.no_grad()
-def test_streaming_memory_efficient():
-    for backend in ['torch', 'xformers']:
-        torch.manual_seed(1234)
-        set_efficient_attention_backend(backend)
-        tr = StreamingTransformer(16, 4, 2, causal=True, dropout=0., custom=True)
-        tr_mem_efficient = StreamingTransformer(
-            16, 4, 2, dropout=0., memory_efficient=True, causal=True)
-        tr.load_state_dict(tr_mem_efficient.state_dict())
-        tr.eval()
-        tr_mem_efficient.eval()
-        steps = 12
-        x = torch.randn(3, steps, 16)
-
-        ref = tr(x)
-
-        with tr_mem_efficient.streaming():
-            outs = []
-            # frame_sizes = [2] + [1] * (steps - 2)
-            frame_sizes = [1] * steps
-
-            for frame_size in frame_sizes:
-                frame = x[:, :frame_size]
-                x = x[:, frame_size:]
-                outs.append(tr_mem_efficient(frame))
-
-        out = torch.cat(outs, dim=1)
-        delta = torch.norm(out - ref) / torch.norm(out)
-        assert delta < 1e-6, delta
-
-
-def test_cross_attention():
-    torch.manual_seed(1234)
-    for norm_first in [True, False]:
-        m = StreamingTransformer(
-            16, 4, 2, cross_attention=False, norm_first=norm_first, dropout=0., custom=True)
-        m_cross = StreamingTransformer(
-            16, 4, 2, cross_attention=True, norm_first=norm_first, dropout=0., custom=True)
-        m_cross.load_state_dict(m.state_dict(), strict=False)
-        x = torch.randn(2, 5, 16)
-        cross_x = torch.randn(2, 3, 16)
-        y_ref = m(x)
-        y_cross_zero = m_cross(x, cross_attention_src=0 * cross_x)
-        # With norm_first, the two should be exactly the same,
-        # but with norm_first=False, we get 2 normalization in a row
-        # and the epsilon value leads to a tiny change.
-        atol = 0. if norm_first else 1e-6
-        print((y_ref - y_cross_zero).norm() / y_ref.norm())
-        assert torch.allclose(y_ref, y_cross_zero, atol=atol)
-
-        # We now expect a difference even with a generous atol of 1e-2.
-        y_cross = m_cross(x, cross_attention_src=cross_x)
-        assert not torch.allclose(y_cross, y_cross_zero, atol=1e-2)
-
-        with pytest.raises(AssertionError):
-            _ = m_cross(x)
-            _ = m(x, cross_attention_src=cross_x)
-
-
-def test_cross_attention_compat():
-    torch.manual_seed(1234)
-    num_heads = 2
-    dim = num_heads * 64
-    with pytest.raises(AssertionError):
-        StreamingMultiheadAttention(dim, num_heads, causal=True, cross_attention=True)
-
-    cross_attn = StreamingMultiheadAttention(
-        dim, num_heads, dropout=0, cross_attention=True, custom=True)
-    ref_attn = torch.nn.MultiheadAttention(dim, num_heads, dropout=0, batch_first=True)
-
-    # We can load the regular attention state dict
-    # so we have compat when loading old checkpoints.
-    cross_attn.load_state_dict(ref_attn.state_dict())
-
-    queries = torch.randn(3, 7, dim)
-    keys = torch.randn(3, 9, dim)
-    values = torch.randn(3, 9, dim)
-
-    y = cross_attn(queries, keys, values)[0]
-    y_ref = ref_attn(queries, keys, values)[0]
-    assert torch.allclose(y, y_ref, atol=1e-7), (y - y_ref).norm() / y_ref.norm()
-
-    # Now let's check that streaming is working properly.
-    with cross_attn.streaming():
-        ys = []
-        for step in range(queries.shape[1]):
-            ys.append(cross_attn(queries[:, step: step + 1], keys, values)[0])
-    y_streaming = torch.cat(ys, dim=1)
-    assert torch.allclose(y_streaming, y, atol=1e-7)
-
-
-def test_repeat_kv():
-    torch.manual_seed(1234)
-    num_heads = 8
-    kv_repeat = 4
-    dim = num_heads * 64
-    with pytest.raises(AssertionError):
-        mha = StreamingMultiheadAttention(
-            dim, num_heads, causal=True, kv_repeat=kv_repeat, cross_attention=True)
-        mha = StreamingMultiheadAttention(
-            dim, num_heads, causal=True, kv_repeat=kv_repeat)
-    mha = StreamingMultiheadAttention(
-        dim, num_heads, causal=True, kv_repeat=kv_repeat, custom=True)
-    x = torch.randn(4, 18, dim)
-    y = mha(x, x, x)[0]
-    assert x.shape == y.shape
-
-
-def test_qk_layer_norm():
-    torch.manual_seed(1234)
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., qk_layer_norm=True, bias_attn=False)
-    steps = 12
-    x = torch.randn(3, steps, 16)
-    y = tr(x)
-
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., qk_layer_norm=True, cross_attention=True)
-    z = torch.randn(3, 21, 16)
-    y = tr(x, cross_attention_src=z)
-    assert y.shape == x.shape
diff --git a/spaces/Gustavosta/MagicPrompt-Dalle/README.md b/spaces/Gustavosta/MagicPrompt-Dalle/README.md
deleted file mode 100644
index 08f9b57a71909c1d41d9dc1602afe19abf1c08fe..0000000000000000000000000000000000000000
--- a/spaces/Gustavosta/MagicPrompt-Dalle/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MagicPrompt Dalle
-emoji: 🏃
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.3.1
-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/HFUniversity2022/final-project-abubakar/README.md b/spaces/HFUniversity2022/final-project-abubakar/README.md
deleted file mode 100644
index f0964c3133863f265785848a2f32045d314b891d..0000000000000000000000000000000000000000
--- a/spaces/HFUniversity2022/final-project-abubakar/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Image2story
-emoji: 🐨
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.0.5
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/HaoFeng2019/DocGeoNet/inference.py b/spaces/HaoFeng2019/DocGeoNet/inference.py
deleted file mode 100644
index f5d059d46963105c26b30b254a4f0d403b4fdead..0000000000000000000000000000000000000000
--- a/spaces/HaoFeng2019/DocGeoNet/inference.py
+++ /dev/null
@@ -1,128 +0,0 @@
-from model import DocGeoNet
-from seg import U2NETP
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import skimage.io as io
-import numpy as np
-import cv2
-import os
-from PIL import Image
-import argparse
-import warnings
-warnings.filterwarnings('ignore')
-
-
-class Net(nn.Module):
-    def __init__(self, opt):
-        super(Net, self).__init__()
-        self.msk = U2NETP(3, 1)
-        self.DocTr = DocGeoNet()
-
-    def forward(self, x):
-        msk, _1,_2,_3,_4,_5,_6 = self.msk(x)
-        msk = (msk > 0.5).float()
-        x = msk * x
-
-        _, _, bm = self.DocTr(x)
-        bm = (2 * (bm / 255.) - 1) * 0.99
-
-        return bm
-
-
-def reload_seg_model(model, path=""):
-    if not bool(path):
-        return model
-    else:
-        model_dict = model.state_dict()
-        pretrained_dict = torch.load(path, map_location='cpu')
-        print(len(pretrained_dict.keys()))
-        pretrained_dict = {k[6:]: v for k, v in pretrained_dict.items() if k[6:] in model_dict}
-        print(len(pretrained_dict.keys()))
-        model_dict.update(pretrained_dict)
-        model.load_state_dict(model_dict)
-
-        return model
-
-
-def reload_rec_model(model, path=""):
-    if not bool(path):
-        return model
-    else:
-        model_dict = model.state_dict()
-        pretrained_dict = torch.load(path, map_location='cpu')
-        print(len(pretrained_dict.keys()))
-        pretrained_dict = {k[7:]: v for k, v in pretrained_dict.items() if k[7:] in model_dict}
-        print(len(pretrained_dict.keys()))
-        model_dict.update(pretrained_dict)
-        model.load_state_dict(model_dict)
-
-        return model
-
-
-def rec(seg_model_path, rec_model_path, distorrted_path, save_path, opt):
-    print(torch.__version__)
-
-    # distorted images list
-    img_list = sorted(os.listdir(distorrted_path))
-
-    # creat save path for rectified images
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-
-    net = Net(opt)#.cuda()
-    print(get_parameter_number(net))
-
-    # reload rec model
-    reload_rec_model(net.DocTr, rec_model_path)
-    reload_seg_model(net.msk, opt.seg_model_path)
-
-    net.eval()
-
-    for img_path in img_list:
-        name = img_path.split('.')[-2]  # image name
-        img_path = distorrted_path + img_path  # image path
-
-        im_ori = np.array(Image.open(img_path))[:, :, :3] / 255.  # read image 0-255 to 0-1
-        h, w, _ = im_ori.shape
-        im = cv2.resize(im_ori, (256, 256))
-        im = im.transpose(2, 0, 1)
-        im = torch.from_numpy(im).float().unsqueeze(0)
-
-        with torch.no_grad():
-            bm = net(im)
-            bm = bm.cpu()
-
-            # save rectified image
-            bm0 = cv2.resize(bm[0, 0].numpy(), (w, h))  # x flow
-            bm1 = cv2.resize(bm[0, 1].numpy(), (w, h))  # y flow
-            bm0 = cv2.blur(bm0, (3, 3))
-            bm1 = cv2.blur(bm1, (3, 3))
-            lbl = torch.from_numpy(np.stack([bm0, bm1], axis=2)).unsqueeze(0)  # h * w * 2
-            out = F.grid_sample(torch.from_numpy(im_ori).permute(2, 0, 1).unsqueeze(0).float(), lbl, align_corners=True)
-            cv2.imwrite(save_path + name + '_rec' + '.png', ((out[0] * 255).permute(1, 2, 0).numpy())[:,:,::-1].astype(np.uint8))
-
-
-def get_parameter_number(net):
-    total_num = sum(p.numel() for p in net.parameters())
-    trainable_num = sum(p.numel() for p in net.parameters() if p.requires_grad)
-    return {'Total': total_num, 'Trainable': trainable_num}
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--seg_model_path', default='./model_pretrained/preprocess.pth')
-    parser.add_argument('--rec_model_path', default='./model_pretrained/DocGeoNet.pth')
-    parser.add_argument('--distorrted_path', default='./distorted/')
-    parser.add_argument('--save_path', default='./rec/')
-    opt = parser.parse_args()
-
-    rec(seg_model_path=opt.seg_model_path,
-        rec_model_path=opt.rec_model_path,
-        distorrted_path=opt.distorrted_path,
-        save_path=opt.save_path,
-        opt=opt)
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/tts_infer/num_to_word_on_sent.py b/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/tts_infer/num_to_word_on_sent.py
deleted file mode 100644
index de571c2be63fa467491d01daf0e2f38dada67de9..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/tts_infer/num_to_word_on_sent.py
+++ /dev/null
@@ -1,1319 +0,0 @@
-import re
-import string
-
-# ----------------------------- indic_num.py -----------------------------
-supported_lang = {"en", "hi", "gu", "mr", "bn", "te", "ta", "kn", "or", "pa"}
-# supported_lang = {'eng', 'hin', 'guj', 'mar', 'ben', 'tel', 'tam', 'kan', 'ori', 'pan'}  # Three alphabet lang code
-
-all_num = {
-    "en": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
-    "hi": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"],
-    "gu": ["૦", "૧", "૨", "૩", "૪", "૫", "૬", "૭", "૮", "૯"],
-    "mr": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"],
-    "bn": ["০", "১", "২", "৩", "৪", "৫", "৬", "৭", "৮", "৯"],
-    "te": ["౦", "౧", "౨", "౩", "౪", "౫", "౬", "౭", "౮", "౯"],
-    "ta": ["0", "௧", "௨", "௩", "௪", "௫", "௬", "௭", "௮", "௯", "௰"],
-    "kn": ["೦", "೧", "೨", "೩", "೪", "೫", "೬", "೭", "೮", "೯"],
-    "or": ["୦", "୧", "୨", "୩", "୪", "୫", "୬", "୭", "୮", "୯"],
-    "pa": ["੦", "੧", "੨", "੩", "੪", "੫", "੬", "੭", "੮", "੯"],
-}
-
-num_dict = dict()
-num_dict["en"] = {
-    "0": "zero",
-    "1": "one",
-    "2": "two",
-    "3": "three",
-    "4": "four",
-    "5": "five",
-    "6": "six",
-    "7": "seven",
-    "8": "eight",
-    "9": "nine",
-    "10": "ten",
-    "11": "eleven",
-    "12": "twelve",
-    "13": "thirteen",
-    "14": "fourteen",
-    "15": "fifteen",
-    "16": "sixteen",
-    "17": "seventeen",
-    "18": "eighteen",
-    "19": "nineteen",
-    "20": "twenty",
-    "21": "twenty-one",
-    "22": "twenty-two",
-    "23": "twenty-three",
-    "24": "twenty-four",
-    "25": "twenty-five",
-    "26": "twenty-six",
-    "27": "twenty-seven",
-    "28": "twenty-eight",
-    "29": "twenty-nine",
-    "30": "thirty",
-    "31": "thirty-one",
-    "32": "thirty-two",
-    "33": "thirty-three",
-    "34": "thirty-four",
-    "35": "thirty-five",
-    "36": "thirty-six",
-    "37": "thirty-seven",
-    "38": "thirty-eight",
-    "39": "thirty-nine",
-    "40": "forty",
-    "41": "forty-one",
-    "42": "forty-two",
-    "43": "forty-three",
-    "44": "forty-four",
-    "45": "forty-five",
-    "46": "forty-six",
-    "47": "forty-seven",
-    "48": "forty-eight",
-    "49": "forty-nine",
-    "50": "fifty",
-    "51": "fifty-one",
-    "52": "fifty-two",
-    "53": "fifty-three",
-    "54": "fifty-four",
-    "55": "fifty-five",
-    "56": "fifty-six",
-    "57": "fifty-seven",
-    "58": "fifty-eight",
-    "59": "fifty-nine",
-    "60": "sixty",
-    "61": "sixty-one",
-    "62": "sixty-two",
-    "63": "sixty-three",
-    "64": "sixty-four",
-    "65": "sixty-five",
-    "66": "sixty-six",
-    "67": "sixty-seven",
-    "68": "sixty-eight",
-    "69": "sixty-nine",
-    "70": "seventy",
-    "71": "seventy-one",
-    "72": "seventy-two",
-    "73": "seventy-three",
-    "74": "seventy-four",
-    "75": "seventy-five",
-    "76": "seventy-six",
-    "77": "seventy-seven",
-    "78": "seventy-eight",
-    "79": "seventy-nine",
-    "80": "eighty",
-    "81": "eighty-one",
-    "82": "eighty-two",
-    "83": "eighty-three",
-    "84": "eighty-four",
-    "85": "eighty-five",
-    "86": "eighty-six",
-    "87": "eighty-seven",
-    "88": "eighty-eight",
-    "89": "eighty-nine",
-    "90": "ninety",
-    "91": "ninety-one",
-    "92": "ninety-two",
-    "93": "ninety-three",
-    "94": "ninety-four",
-    "95": "ninety-five",
-    "96": "ninety-six",
-    "97": "ninety-seven",
-    "98": "ninety-eight",
-    "99": "ninety-nine",
-    "100": "hundred",
-    "1000": "thousand",
-    "100000": "lac",
-    "10000000": "crore",
-    "1000000000": "arab",
-}  # English-India
-num_dict["hi"] = {
-    "0": "शून्य",
-    "1": "एक",
-    "2": "दो",
-    "3": "तीन",
-    "4": "चार",
-    "5": "पाँच",
-    "6": "छः",
-    "7": "सात",
-    "8": "आठ",
-    "9": "नौ",
-    "10": "दस",
-    "11": "ग्यारह",
-    "12": "बारह",
-    "13": "तेरह",
-    "14": "चौदह",
-    "15": "पंद्रह",
-    "16": "सोलह",
-    "17": "सत्रह",
-    "18": "अट्ठारह",
-    "19": "उन्नीस",
-    "20": "बीस",
-    "21": "इक्कीस",
-    "22": "बाईस",
-    "23": "तेईस",
-    "24": "चौबिस",
-    "25": "पच्चीस",
-    "26": "छब्बीस",
-    "27": "सत्ताईस",
-    "28": "अट्ठाईस",
-    "29": "उनतीस",
-    "30": "तीस",
-    "31": "इकतीस",
-    "32": "बत्तीस",
-    "33": "तैंतीस",
-    "34": "चौंतीस",
-    "35": "पैंतीस",
-    "36": "छत्तीस",
-    "37": "सैंतीस",
-    "38": "अड़तीस",
-    "39": "उनतालीस",
-    "40": "चालीस",
-    "41": "इकतालीस",
-    "42": "बयालीस",
-    "43": "तैंतालीस",
-    "44": "चौंतालीस",
-    "45": "पैंतालीस",
-    "46": "छियालीस",
-    "47": "सैंतालीस",
-    "48": "अड़तालीस",
-    "49": "उनचास",
-    "50": "पचास",
-    "51": "इक्यावन​",
-    "52": "बावन",
-    "53": "तिरेपन",
-    "54": "चौवन",
-    "55": "पचपन",
-    "56": "छप्पन",
-    "57": "सत्तावन",
-    "58": "अट्ठावन",
-    "59": "उनसठ",
-    "60": "साठ",
-    "61": "इकसठ",
-    "62": "बासठ",
-    "63": "तिरेसठ",
-    "64": "चौंसठ",
-    "65": "पैंसठ",
-    "66": "छयासठ",
-    "67": "सरसठ​",
-    "68": "अड़सठ",
-    "69": "उनहत्तर",
-    "70": "सत्तर",
-    "71": "इकहत्तर",
-    "72": "बहत्तर",
-    "73": "तिहत्तर",
-    "74": "चौहत्तर",
-    "75": "पचहत्तर",
-    "76": "छिहत्तर",
-    "77": "सतहत्तर",
-    "78": "अठहत्तर",
-    "79": "उन्यासी",
-    "80": "अस्सी",
-    "81": "इक्यासी",
-    "82": "बयासी",
-    "83": "तिरासी",
-    "84": "चौरासी",
-    "85": "पचासी",
-    "86": "छियासी",
-    "87": "सत्तासी",
-    "88": "अठासी",
-    "89": "नवासी",
-    "90": "नब्बे",
-    "91": "इक्यानवे",
-    "92": "बानवे",
-    "93": "तिरानवे",
-    "94": "चौरानवे",
-    "95": "पचानवे",
-    "96": "छियानवे",
-    "97": "सत्तानवे",
-    "98": "अट्ठानवे",
-    "99": "निन्यानवे",
-    "100": "सौ",
-    "1000": "हज़ार",
-    "100000": "लाख",
-    "10000000": "करोड़",
-    "1000000000": "अरब",
-}  # Hindi
-num_dict["gu"] = {
-    "0": "શૂન્ય",
-    "1": "એક",
-    "2": "બે",
-    "3": "ત્રણ",
-    "4": "ચાર",
-    "5": "પાંચ",
-    "6": "છ",
-    "7": "સાત",
-    "8": "આઠ",
-    "9": "નવ",
-    "10": "દસ",
-    "11": "અગિયાર",
-    "12": "બાર",
-    "13": "તેર",
-    "14": "ચૌદ",
-    "15": "પંદર",
-    "16": "સોળ",
-    "17": "સત્તર",
-    "18": "અઢાર",
-    "19": "ઓગણિસ",
-    "20": "વીસ",
-    "21": "એકવીસ",
-    "22": "બાવીસ",
-    "23": "તેવીસ",
-    "24": "ચોવીસ",
-    "25": "પચ્ચીસ",
-    "26": "છવીસ",
-    "27": "સત્તાવીસ",
-    "28": "અઠ્ઠાવીસ",
-    "29": "ઓગણત્રીસ",
-    "30": "ત્રીસ",
-    "31": "એકત્રીસ",
-    "32": "બત્રીસ",
-    "33": "તેત્રીસ",
-    "34": "ચોત્રીસ",
-    "35": "પાંત્રીસ",
-    "36": "છત્રીસ",
-    "37": "સડત્રીસ",
-    "38": "અડત્રીસ",
-    "39": "ઓગણચાલીસ",
-    "40": "ચાલીસ",
-    "41": "એકતાલીસ",
-    "42": "બેતાલીસ",
-    "43": "ત્રેતાલીસ",
-    "44": "ચુંમાલીસ",
-    "45": "પિસ્તાલીસ",
-    "46": "છેતાલીસ",
-    "47": "સુડતાલીસ",
-    "48": "અડતાલીસ",
-    "49": "ઓગણપચાસ",
-    "50": "પચાસ",
-    "51": "એકાવન",
-    "52": "બાવન",
-    "53": "ત્રેપન",
-    "54": "ચોપન",
-    "55": "પંચાવન",
-    "56": "છપ્પન",
-    "57": "સત્તાવન",
-    "58": "અઠ્ઠાવન",
-    "59": "ઓગણસાઠ",
-    "60": "સાઈઠ",
-    "61": "એકસઠ",
-    "62": "બાસઠ",
-    "63": "ત્રેસઠ",
-    "64": "ચોસઠ",
-    "65": "પાંસઠ",
-    "66": "છાસઠ",
-    "67": "સડસઠ",
-    "68": "અડસઠ",
-    "69": "અગણોસિત્તેર",
-    "70": "સિત્તેર",
-    "71": "એકોતેર",
-    "72": "બોતેર",
-    "73": "તોતેર",
-    "74": "ચુમોતેર",
-    "75": "પંચોતેર",
-    "76": "છોતેર",
-    "77": "સિત્યોતેર",
-    "78": "ઇઠ્યોતેર",
-    "79": "ઓગણાએંસી",
-    "80": "એંસી",
-    "81": "એક્યાસી",
-    "82": "બ્યાસી",
-    "83": "ત્યાસી",
-    "84": "ચોર્યાસી",
-    "85": "પંચાસી",
-    "86": "છ્યાસી",
-    "87": "સિત્યાસી",
-    "88": "ઈઠ્યાસી",
-    "89": "નેવ્યાસી",
-    "90": "નેવું",
-    "91": "એકાણું",
-    "92": "બાણું",
-    "93": "ત્રાણું",
-    "94": "ચોરાણું",
-    "95": "પંચાણું",
-    "96": "છન્નું",
-    "97": "સત્તાણું",
-    "98": "અઠ્ઠાણું",
-    "99": "નવ્વાણું",
-    "100": "સો",
-    "1000": "હજાર",
-    "100000": "લાખ",
-    "1000000": "દસ લાખ",
-    "10000000": "કરોડ઼",
-}  # Gujarati
-num_dict["mr"] = {
-    "0": "शून्य",
-    "1": "एक",
-    "2": "दोन",
-    "3": "तीन",
-    "4": "चार",
-    "5": "पाच",
-    "6": "सहा",
-    "7": "सात",
-    "8": "आठ",
-    "9": "नऊ",
-    "10": "दहा",
-    "11": "अकरा",
-    "12": "बारा",
-    "13": "तेरा",
-    "14": "चौदा",
-    "15": "पंधरा",
-    "16": "सोळा",
-    "17": "सतरा",
-    "18": "अठरा",
-    "19": "एकोणीस",
-    "20": "वीस",
-    "21": "एकवीस",
-    "22": "बावीस",
-    "23": "तेवीस",
-    "24": "चोवीस",
-    "25": "पंचवीस",
-    "26": "सव्वीस",
-    "27": "सत्तावीस",
-    "28": "अठ्ठावीस",
-    "29": "एकोणतीस",
-    "30": "तीस",
-    "31": "एकतीस",
-    "32": "बत्तीस",
-    "33": "तेहेतीस",
-    "34": "चौतीस",
-    "35": "पस्तीस",
-    "36": "छत्तीस",
-    "37": "सदतीस",
-    "38": "अडतीस",
-    "39": "एकोणचाळीस",
-    "40": "चाळीस",
-    "41": "एक्केचाळीस",
-    "42": "बेचाळीस",
-    "43": "त्रेचाळीस",
-    "44": "चव्वेचाळीस",
-    "45": "पंचेचाळीस",
-    "46": "सेहेचाळीस",
-    "47": "सत्तेचाळीस",
-    "48": "अठ्ठेचाळीस",
-    "49": "एकोणपन्नास",
-    "50": "पन्नास",
-    "51": "एक्कावन्न",
-    "52": "बावन्न",
-    "53": "त्रेपन्न",
-    "54": "चोपन्न",
-    "55": "पंचावन्न",
-    "56": "छप्पन्न",
-    "57": "सत्तावन्न",
-    "58": "अठ्ठावन्न",
-    "59": "एकोणसाठ",
-    "60": "साठ",
-    "61": "एकसष्ठ",
-    "62": "बासष्ठ",
-    "63": "त्रेसष्ठ",
-    "64": "चौसष्ठ",
-    "65": "पासष्ठ",
-    "66": "सहासष्ठ",
-    "67": "सदुसष्ठ",
-    "68": "अडुसष्ठ",
-    "69": "एकोणसत्तर",
-    "70": "सत्तर",
-    "71": "एक्काहत्तर",
-    "72": "बाहत्तर",
-    "73": "त्र्याहत्तर",
-    "74": "चौर्‍याहत्तर",
-    "75": "पंच्याहत्तर",
-    "76": "शहात्तर",
-    "77": "सत्याहत्तर",
-    "78": "अठ्ठ्याहत्तर",
-    "79": "एकोण ऐंशी",
-    "80": "ऐंशी",
-    "81": "एक्क्याऐंशी",
-    "82": "ब्याऐंशी",
-    "83": "त्र्याऐंशी",
-    "84": "चौऱ्याऐंशी",
-    "85": "पंच्याऐंशी",
-    "86": "शहाऐंशी",
-    "87": "सत्त्याऐंशी",
-    "88": "अठ्ठ्याऐंशी",
-    "89": "एकोणनव्वद",
-    "90": "नव्वद",
-    "91": "एक्क्याण्णव",
-    "92": "ब्याण्णव",
-    "93": "त्र्याण्णव",
-    "94": "चौऱ्याण्णव",
-    "95": "पंच्याण्णव",
-    "96": "शहाण्णव",
-    "97": "सत्त्याण्णव",
-    "98": "अठ्ठ्याण्णव",
-    "99": "नव्व्याण्णव",
-    "100": "शे",
-    "1000": "हजार",
-    "100000": "लाख",
-    "10000000": "कोटी",
-    "1000000000": "अब्ज",
-}  # Marathi
-num_dict["bn"] = {
-    "0": "শূন্য",
-    "1": "এক",
-    "2": "দুই",
-    "3": "তিন",
-    "4": "চার",
-    "5": "পাঁচ",
-    "6": "ছয়",
-    "7": "সাত",
-    "8": "আট",
-    "9": "নয়",
-    "10": "দশ",
-    "11": "এগার",
-    "12": "বার",
-    "13": "তের",
-    "14": "চৌদ্দ",
-    "15": "পনের",
-    "16": "ষোল",
-    "17": "সতের",
-    "18": "আঠার",
-    "19": "ঊনিশ",
-    "20": "বিশ",
-    "21": "একুশ",
-    "22": "বাইশ",
-    "23": "তেইশ",
-    "24": "চব্বিশ",
-    "25": "পঁচিশ",
-    "26": "ছাব্বিশ",
-    "27": "সাতাশ",
-    "28": "আঠাশ",
-    "29": "ঊনত্রিশ",
-    "30": "ত্রিশ",
-    "31": "একত্রিশ",
-    "32": "বত্রিশ",
-    "33": "তেত্রিশ",
-    "34": "চৌত্রিশ",
-    "35": "পঁয়ত্রিশ",
-    "36": "ছত্রিশ",
-    "37": "সাঁইত্রিশ",
-    "38": "আটত্রিশ",
-    "39": "ঊনচল্লিশ",
-    "40": "চল্লিশ",
-    "41": "একচল্লিশ",
-    "42": "বিয়াল্লিশ",
-    "43": "তেতাল্লিশ",
-    "44": "চুয়াল্লিশ",
-    "45": "পঁয়তাল্লিশ",
-    "46": "ছেচল্লিশ",
-    "47": "সাতচল্লিশ",
-    "48": "আটচল্লিশ",
-    "49": "ঊনপঞ্চাশ",
-    "50": "পঞ্চাশ",
-    "51": "একান্ন",
-    "52": "বায়ান্ন",
-    "53": "তিপ্পান্ন",
-    "54": "চুয়ান্ন",
-    "55": "পঞ্চান্ন",
-    "56": "ছাপ্পান্ন",
-    "57": "সাতান্ন",
-    "58": "আটান্ন",
-    "59": "ঊনষাট",
-    "60": "ষাট",
-    "61": "একষট্টি",
-    "62": "বাষট্টি",
-    "63": "তেষট্টি",
-    "64": "চৌষট্টি",
-    "65": "পঁয়ষট্টি",
-    "66": "ছেষট্টি",
-    "67": "সাতষট্টি",
-    "68": "আটষট্টি",
-    "69": "ঊনসত্তর",
-    "70": "সত্তর",
-    "71": "একাত্তর",
-    "72": "বাহাত্তর",
-    "73": "তিয়াত্তর",
-    "74": "চুয়াত্তর",
-    "75": "পঁচাত্তর",
-    "76": "ছিয়াত্তর",
-    "77": "সাতাত্তর",
-    "78": "আটাত্তর",
-    "79": "ঊনআশি",
-    "80": "আশি",
-    "81": "একাশি",
-    "82": "বিরাশি",
-    "83": "তিরাশি",
-    "84": "চুরাশি",
-    "85": "পঁচাশি",
-    "86": "ছিয়াশি",
-    "87": "সাতাশি",
-    "88": "আটাশি",
-    "89": "ঊননব্বই",
-    "90": "নব্বই",
-    "91": "একানব্বই",
-    "92": "বিরানব্বই",
-    "93": "তিরানব্বই",
-    "94": "চুরানব্বই",
-    "95": "পঁচানব্বই",
-    "96": "ছিয়ানব্বই",
-    "97": "সাতানব্বই",
-    "98": "আটানব্বই",
-    "99": "নিরানব্বই",
-    "100": "শো",
-    "1000": "হাজার",
-    "100000": "লাখ",
-    "10000000": "কোটি",
-    "1000000000": "একশ’ কোটি",
-}  # Bengali
-num_dict["te"] = {
-    "0": "సున్నా",
-    "1": "ఒకటి",
-    "2": "రెండు",
-    "3": "మూడు",
-    "4": "నాలుగు",
-    "5": "ఐదు",
-    "6": "ఆరు",
-    "7": "ఏడు",
-    "8": "ఎనిమిది",
-    "9": "తొమ్మిది",
-    "10": "పది",
-    "11": "పదకొండు",
-    "12": "పన్నెండు",
-    "13": "పదమూడు",
-    "14": "పద్నాలుగు",
-    "15": "పదిహేను",
-    "16": "పదహారు",
-    "17": "పదిహేడు",
-    "18": "పద్దెనిమిది",
-    "19": "పందొమ్మిది",
-    "20": "ఇరవై",
-    "21": "ఇరవై ఒకటి",
-    "22": "ఇరవై రెండు",
-    "23": "ఇరవై మూడు",
-    "24": "ఇరవై నాలుగు",
-    "25": "ఇరవై ఐదు",
-    "26": "ఇరవై ఆరు",
-    "27": "ఇరవై ఏడు",
-    "28": "ఇరవై ఎనిమిది",
-    "29": "ఇరవై తొమ్మిది",
-    "30": "ముప్పై",
-    "31": "ముప్పై ఒకటి",
-    "32": "ముప్పై రెండు",
-    "33": "ముప్పై మూడు",
-    "34": "ముప్పై నాలుగు",
-    "35": "ముప్పై ఐదు",
-    "36": "ముప్పై ఆరు",
-    "37": "ముప్పై ఏడు",
-    "38": "ముప్పై ఎనిమిది",
-    "39": "ముప్పై తొమ్మిది",
-    "40": "నలభై",
-    "41": "నలభై ఒకటి",
-    "42": "నలభై రెండు",
-    "43": "నలభై మూడు",
-    "44": "నలభై నాలుగు",
-    "45": "నలభై ఐదు",
-    "46": "నలభై ఆరు",
-    "47": "నలభై ఏడు",
-    "48": "నలభై ఎనిమిది",
-    "49": "నలభై తొమ్మిది",
-    "50": "యాభై",
-    "51": "యాభై ఒకటి",
-    "52": "యాభై రెండు",
-    "53": "యాభై మూడు",
-    "54": "యాభై నాలుగు",
-    "55": "యాభై ఐదు",
-    "56": "యాభై ఆరు",
-    "57": "యాభై ఏడు",
-    "58": "యాభై ఎనిమిది",
-    "59": "యాభై తొమ్మిది",
-    "60": "అరవై",
-    "61": "అరవై ఒకటి",
-    "62": "అరవై రెండు",
-    "63": "అరవై మూడు",
-    "64": "అరవై నాలుగు",
-    "65": "అరవై ఐదు",
-    "66": "అరవై ఆరు",
-    "67": "అరవై ఏడు",
-    "68": "అరవై ఎనిమిది",
-    "69": "అరవై తొమ్మిది",
-    "70": "డెబ్బై",
-    "71": "డెబ్బై ఒకటి",
-    "72": "డెబ్బై రెండు",
-    "73": "డెబ్బై మూడు",
-    "74": "డెబ్బై నాలుగు",
-    "75": "డెబ్బై ఐదు",
-    "76": "డెబ్బై ఆరు",
-    "77": "డెబ్బై ఏడు",
-    "78": "డెబ్బై ఎనిమిది",
-    "79": "డెబ్బై తొమ్మిది",
-    "80": "ఎనభై",
-    "81": "ఎనభై ఒకటి",
-    "82": "ఎనభై రెండు",
-    "83": "ఎనభై మూడు",
-    "84": "ఎనభై నాలుగు",
-    "85": "ఎనభై ఐదు",
-    "86": "ఎనభై ఆరు",
-    "87": "ఎనభై ఏడు",
-    "88": "ఎనభై ఎనిమిది",
-    "89": "ఎనభై తొమ్మిది",
-    "90": "తొంభై",
-    "91": "తొంభై ఒకటి",
-    "92": "తొంభై రెండు",
-    "93": "తొంభై మూడు",
-    "94": "తొంభై నాలుగు",
-    "95": "తొంభై ఐదు",
-    "96": "తొంభై ఆరు",
-    "97": "తొంభై ఏడు",
-    "98": "తొంభై ఎనిమిది",
-    "99": "తొంభై తొమ్మిది",
-    "100": "వందల",
-    "1000": "వేల",
-    "100000": "లక్షల",
-    "10000000": "కోట్ల",
-    "1000000000": "బిలియన్",
-}  # Telugu
-num_dict["ta"] = {
-    "0": "பூஜ்ஜியம்",
-    "1": "ஒன்று",
-    "2": "இரண்டு",
-    "3": "மூன்று",
-    "4": "நான்கு",
-    "5": "ஐந்து",
-    "6": "ஆறு",
-    "7": "ஏழு",
-    "8": "எட்டு",
-    "9": "ஒன்பது",
-    "10": "பத்து",
-    "11": "பதினொன்று",
-    "12": "பன்னிரண்டு",
-    "13": "பதிமூன்று",
-    "14": "பதினான்கு",
-    "15": "பதினைந்து",
-    "16": "பதினாறு",
-    "17": "பதினேழு",
-    "18": "பதினெட்டு",
-    "19": "பத்தொன்பது",
-    "20": "இருபது",
-    "21": "இருபது ஒன்று",
-    "22": "இருபத்து இரண்டு",
-    "23": "இருபத்து மூன்று",
-    "24": "இருபத்து நான்கு",
-    "25": "இருபத்து ஐந்து",
-    "26": "இருபத்து ஆறு",
-    "27": "இருபத்து ஏழு",
-    "28": "இருபத்து எட்டு",
-    "29": "இருபத்து ஒன்பது",
-    "30": "முப்பது",
-    "31": "முப்பத்து ஒன்று",
-    "32": "முப்பத்து இரண்டு",
-    "33": "முப்பத்து மூன்று",
-    "34": "முப்பத்து நான்கு",
-    "35": "முப்பத்து ஐந்து",
-    "36": "முப்பத்து ஆறு",
-    "37": "முப்பத்து ஏழு",
-    "38": "முப்பத்து எட்டு",
-    "39": "முப்பத்து ஒன்பது",
-    "40": "நாற்பது",
-    "41": "நாற்பத்து ஒன்று",
-    "42": "நாற்பத்து இரண்டு",
-    "43": "நாற்பத்து மூன்று",
-    "44": "நாற்பத்து நான்கு",
-    "45": "நாற்பத்து ஐந்து",
-    "46": "நாற்பத்து ஆறு",
-    "47": " நாற்பத்து ஏழு",
-    "48": "நாற்பத்து எட்டு",
-    "49": "நாற்பத்து ஒன்பது",
-    "50": "ஐம்பது",
-    "51": "ஐம்பத்து ஒன்று",
-    "52": "ஐம்பத்து இரண்டு",
-    "53": "ஐம்பத்து மூன்று",
-    "54": "ஐம்பத்து நான்கு",
-    "55": "ஐம்பத்து ஐந்து",
-    "56": "ஐம்பத்து ஆறு",
-    "57": "ஐம்பத்து ஏழு",
-    "58": "ஐம்பத்து எட்டு",
-    "59": "ஐம்பத்து ஒன்பது",
-    "60": "அறுபது",
-    "61": "அறுபத்து ஒன்று",
-    "62": "அறுபத்து இரண்டு",
-    "63": "அறுபத்து மூன்று",
-    "64": "அறுபத்து நான்கு",
-    "65": "அறுபத்து ஐந்து",
-    "66": "அறுபத்து ஆறு",
-    "67": "அறுபத்து ஏழு",
-    "68": "அறுபத்து எட்டு",
-    "69": "அறுபத்து ஒன்பது",
-    "70": "எழுபது",
-    "71": "எழுபத்தி ஒன்று",
-    "72": "எழுபத்தி இரண்டு",
-    "73": "எழுபத்தி முச்சக்கர",
-    "74": "எழுபத்தி நான்கு",
-    "75": "எழுபத்தி ஐந்து",
-    "76": "எழுபத்தி ஆறு",
-    "77": "எழுபத்தி ஏழு",
-    "78": "எழுபத்தி எட்டு",
-    "79": "எழுபத்தி ஒன்பது",
-    "80": "எண்பது",
-    "81": "எண்பத்தியொன்று",
-    "82": "எண்பத்திரண்டு",
-    "83": "எண்பத்திமூன்று",
-    "84": "என்பதினான்கு",
-    "85": "என்பதினைந்து",
-    "86": "எண்பத்திஆறு",
-    "87": "எண்பத்திஏழு",
-    "88": "எண்பத்தியெட்டு",
-    "89": "எண்பத்தியொன்பது",
-    "90": "தொன்னூறு",
-    "91": "தொண்ணூற்றியொன்று",
-    "92": "தொண்ணூற்றிரண்டு",
-    "93": "தொண்ணூற்றிமூன்று",
-    "94": "தொண்ணூற்றிநான்கு",
-    "95": "தொண்ணூற்றிஐந்து",
-    "96": "தொண்ணூற்றியாறு",
-    "97": "தொண்ணூற்றியேழு",
-    "98": "தொண்ணூற்றியெட்டு",
-    "99": "தொண்ணூற்றிஒன்பது",
-    "100": "நூறு",
-    "1000": "ஆயிரம்",
-    "100000": "இலட்சம்",
-    "10000000": "கோடி",
-    "1000000000": "பில்லியன்",
-}  # Tamil
-num_dict["kn"] = {
-    "0": "ಸೊನ್ನೆ",
-    "1": "ಒಂದು",
-    "2": "ಎರಡು",
-    "3": "ಮೂರು",
-    "4": "ನಾಲ್ಕು",
-    "5": "ಅಯ್ದು",
-    "6": "ಆರು",
-    "7": "ಏಳು",
-    "8": "ಎಂಟು",
-    "9": "ಒಂಬತ್ತು",
-    "10": "ಹತ್ತು",
-    "11": "ಹನ್ನೊಂದು",
-    "12": "ಹನ್ನೆರಡು",
-    "13": "ಹದಿಮೂರು",
-    "14": "ಹದಿನಾಲ್ಕು",
-    "15": "ಹದಿನೈದು",
-    "16": "ಹದಿನಾರು",
-    "17": "ಹದಿನೇಳು",
-    "18": "ಹದಿನೆಂಟು",
-    "19": "ಹತ್ತೊಂಬತ್ತು",
-    "20": "ಇಪ್ಪತ್ತು",
-    "21": "ಇಪ್ಪತ್ತ್’ಒಂದು",
-    "22": "ಇಪ್ಪತ್ತ್’ಎರಡು",
-    "23": "ಇಪ್ಪತ್ತ್’ಮೂರು",
-    "24": "ಇಪ್ಪತ್ತ್’ನಾಲ್ಕು",
-    "25": "ಇಪ್ಪತ್ತ್’ಐದು",
-    "26": "ಇಪ್ಪತ್ತ್’ಆರು",
-    "27": "ಇಪ್ಪತ್ತ್’ಏಳು",
-    "28": "ಇಪ್ಪತ್ತ್’ಎಂಟು",
-    "29": "ಇಪ್ಪತ್ತ್’ಒಂಬತ್ತು",
-    "30": "ಮೂವತ್ತು",
-    "31": "ಮುವತ್ತ್’ಒಂದು",
-    "32": "ಮುವತ್ತ್’ಎರಡು",
-    "33": "ಮುವತ್ತ್’ಮೂರು",
-    "34": "ಮೂವತ್ತ್’ನಾಲ್ಕು",
-    "35": "ಮೂವತ್ತ್’ಐದು",
-    "36": "ಮೂವತ್ತ್’ಆರು",
-    "37": "ಮೂವತ್ತ್’ಏಳು",
-    "38": "ಮೂವತ್ತ್’ಎಂಟು",
-    "39": "ಮೂವತ್ತ್’ಒಂಬತ್ತು",
-    "40": "ನಲವತ್ತು",
-    "41": "ನಲವತ್ತೊಂದು",
-    "42": "ನಲವತ್ತ್ ಎರಡು",
-    "43": "ನಲವತ್ತ್ ಮೂರು",
-    "44": "ನಲವತ್ತ್ ನಾಲ್ಕು",
-    "45": "ನಲವತ್ತೈದು",
-    "46": "ನಲವತ್ತಾರು",
-    "47": "ನಲವತ್ತೇಳು",
-    "48": "ನಲವತ್ತೆಂಟು",
-    "49": "ನಲವತ್ತೊಂಬತ್ತು",
-    "50": "ಐವತ್ತು",
-    "51": "ಐವತ್ತೊಂದು",
-    "52": "ಐವತ್ತೆರಡು",
-    "53": "ಐವತ್ತಮೂರು",
-    "54": "ಐವತ್ತ್ನಾಲ್ಕು",
-    "55": "ಐವತ್ತೈದು",
-    "56": "ಐವತ್ತಾರು",
-    "57": "ಐವತ್ತೇಳು",
-    "58": "ಐವತ್ತೆಂಟು",
-    "59": "ಐವತ್ತೊಂಬತ್ತು",
-    "60": "ಅರವತ್ತು",
-    "61": "ಅರವತ್ತೊಂದು",
-    "62": "ಅರವತ್ತೆರಡು",
-    "63": "ಅರವತ್ತ್ ಮೂರು",
-    "64": "ಅರವತ್ತ್ ನಾಲ್ಕು",
-    "65": "ಅರವತ್ತೈದು",
-    "66": "ಅರವತ್ತಾರು",
-    "67": "ಅರವತ್ತೇಳು",
-    "68": "ಅರವತ್ತೆಂಟು",
-    "69": "ಅರವತ್ತೊಂಬತ್ತು",
-    "70": "ಎಪ್ಪತ್ತು",
-    "71": "ಎಪ್ಪತ್ತೊಂದು",
-    "72": "ಎಪ್ಪತ್ತೆರಡು",
-    "73": "ಎಪ್ಪತ್ತ್ ಮೂರು",
-    "74": "ಎಪ್ಪತ್ತ್ ನಾಲ್ಕು",
-    "75": "ಎಪ್ಪತ್ತೈದು",
-    "76": "ಎಪ್ಪತ್ತಾರು",
-    "77": "ಎಪ್ಪತ್ತೇಳು",
-    "78": "ಎಪ್ಪತ್ತೆಂಟು",
-    "79": "ಎಪ್ಪತ್ತೊಂಬತ್ತು",
-    "80": "ಎಂಬತ್ತು",
-    "81": "ಎಂಬತ್ತೊಂದು",
-    "82": "ಎಂಬತ್ತೆರಡು",
-    "83": "ಎಂಬತ್ತ್ ಮೂರು",
-    "84": "ಎಂಬತ್ತ್ ನಾಲ್ಕು",
-    "85": "ಎಂಬತ್ತೈದು",
-    "86": "ಎಂಬತ್ತಾರು",
-    "87": "ಎಂಬತ್ತೇಳು",
-    "88": "ಎಂಬತ್ತೆಂಟು",
-    "89": "ಎಂಬತ್ತೊಂಬತ್ತು",
-    "90": "ತೊಂಬತ್ತು",
-    "91": "ತೊಂಬತ್ತೊಂದು",
-    "92": "ತೊಂಬತ್ತೆರಡು",
-    "93": "ತೊಂಬತ್ತ ಮೂರು",
-    "94": "ತೊಂಬತ್ತ ನಾಲ್ಕು",
-    "95": "ತೊಂಬತ್ತೈದು",
-    "96": "ತೊಂಬತ್ತಾರು",
-    "97": "ತೊಂಬತ್ತೇಳು",
-    "98": "ತೊಂಬತ್ತೆಂಟು",
-    "99": "ತೊಂಬತ್ತೊಂಬತ್ತು",
-    "100": "ನೂರ",
-    "1000": "ಸಾವಿರದ",
-    "100000": "ಲಕ್ಷದ",
-    "10000000": "ಕೋಟಿ",
-    "1000000000": "ಶತಕೋಟಿ",
-}  # Kannada
-num_dict["or"] = {
-    "0": "ଶୁନ୍ୟ",
-    "1": "ଏକ",
-    "2": "ଦୁଇ",
-    "3": "ତିନି",
-    "4": "ଚାରି",
-    "5": "ପାଞ୍ଚ",
-    "6": "ଛଅ",
-    "7": "ସାତ",
-    "8": "ଆଠ",
-    "9": "ନଅ",
-    "10": "ନଅ",
-    "11": "ଏଗାର",
-    "12": "ବାର",
-    "13": "ତେର",
-    "14": "ଚଉଦ",
-    "15": "ପନ୍ଦର",
-    "16": "ଷୋହଳ",
-    "17": "ସତର",
-    "18": "ଅଠର",
-    "19": "ଊଣାଇଶ",
-    "20": "କୋଡିଏ",
-    "21": "ଏକୋଇଶି",
-    "22": "ବାଇଶି",
-    "23": "ତେଇଶି",
-    "24": "ଚବିଶି",
-    "25": "ପଚିଶି",
-    "26": "ଛବିଶି",
-    "27": "ସତାଇଶି",
-    "28": "ଅଠାଇଶି",
-    "29": "ଅଣତିରିଶି",
-    "30": "ତିରିଶି",
-    "31": "ଏକତିରିଶି",
-    "32": "ବତିଶି",
-    "33": "ତେତିଶି",
-    "34": "ଚଉତିରିଶି",
-    "35": "ପଞ୍ଚତିରିଶି",
-    "36": "ଛତିଶି",
-    "37": "ସଂଇତିରିଶି",
-    "38": "ଅଠତିରିଶି",
-    "39": "ଅଣଚାଳିଶି",
-    "40": "ଚାଳିଶି",
-    "41": "ଏକଚାଳିଶି",
-    "42": "ବୟାଳିଶି",
-    "43": "ତେୟାଳିଶି",
-    "44": "ଚଉରାଳିଶି",
-    "45": "ପଞ୍ଚଚାଳିଶି",
-    "46": "ଛୟାଳିଶି",
-    "47": "ସତଚାଳିଶି",
-    "48": "ଅଠଚାଳିଶି",
-    "49": "ଅଣଚାଶ",
-    "50": "ପଚାଶ",
-    "51": "ଏକାବନ",
-    "52": "ବାଉନ",
-    "53": "ତେପନ",
-    "54": "ଚଉବନ",
-    "55": "ପଞ୍ଚାବନ",
-    "56": "ଛପନ",
-    "57": "ସତାବନ",
-    "58": "ଅଠାବନ",
-    "59": "ଅଣଷଠି",
-    "60": "ଷାଠିଏ",
-    "61": "ଏକଷଠି",
-    "62": "ବାଷଠି",
-    "63": "ତେଷଠି",
-    "64": "ଚଉଷଠି",
-    "65": "ପଞ୍ଚଷଠି",
-    "66": "ଛଅଷଠି",
-    "67": "ସତଷଠି",
-    "68": "ଅଠଷଠି",
-    "69": "ଅଣସ୍ତରୀ",
-    "70": "ସତୂରୀ",
-    "71": "ଏକସ୍ତରୀ",
-    "72": "ବାସ୍ତରୀ",
-    "73": "ତେସ୍ତରୀ",
-    "74": "ଚଉସ୍ତରୀ",
-    "75": "ପଞ୍ଚସ୍ତରୀ",
-    "76": "ଛଅସ୍ତରୀ",
-    "77": "ସତସ୍ତରୀ",
-    "78": "ଅଠସ୍ତରୀ",
-    "79": "ଅଣାଅଶୀ",
-    "80": "ଅଶୀ",
-    "81": "ଏକାଅଶୀ",
-    "82": "ବୟାଅଶୀ",
-    "83": "ତେୟାଅଶୀ",
-    "84": "ଚଉରାଅଶୀ",
-    "85": "ପଞ୍ଚାଅଶୀ",
-    "86": "ଛୟାଅଶୀ",
-    "87": "ସତାଅଶୀ",
-    "88": "ଅଠାଅଶୀ",
-    "89": "ଅଣାନବେ",
-    "90": "ନବେ",
-    "91": "ଏକାନବେ",
-    "92": "ବୟାନବେ",
-    "93": "ତେୟାନବେ",
-    "94": "ଚଉରାନବେ",
-    "95": "ପଞ୍ଚାନବେ",
-    "96": "ଛୟାନବେ",
-    "97": "ସତାନବେ",
-    "98": "ଅଠାନବେ",
-    "99": "ଅନେଶତ",
-    "100": "ଶହେ",
-    "1000": "ହଜାର",
-    "100000": "ଲକ୍ଷ",
-    "10000000": "କୋଟି",
-    "1000000000": "କୋଟି",
-}  # Oriya
-num_dict["pa"] = {
-    "0": "ਸਿਫਰ ",
-    "1": "ਇੱਕ",
-    "2": "ਦੋ",
-    "3": "ਤਿੰਨ",
-    "4": "ਚਾਰ",
-    "5": "ਪੰਜ",
-    "6": "ਛੇ",
-    "7": "ਸੱਤ",
-    "8": "ਅੱਠ",
-    "9": "ਨੌਂ",
-    "10": "ਦੱਸ",
-    "11": "ਗਿਆਰਾਂ",
-    "12": "ਬਾਰਾਂ",
-    "13": "ਤੇਰਾਂ",
-    "14": "ਚੌਦਾਂ",
-    "15": "ਪੰਦਰਾਂ",
-    "16": "ਸੋਲ਼ਾਂ",
-    "17": "ਸਤਾਰਾਂ",
-    "18": "ਅਠਾਰਾਂ",
-    "19": "ਉਨੀ",
-    "20": "ਵੀਹ",
-    "21": "ਇੱਕੀ",
-    "22": "ਬਾਈ",
-    "23": "ਤੇਈ",
-    "24": "ਚੌਵੀ",
-    "25": "ਪੰਝੀ",
-    "26": "ਛੱਬੀ",
-    "27": "ਸਤਾਈ",
-    "28": "ਅਠਾਈ",
-    "29": "ਉਨੱਤੀ",
-    "30": "ਤੀਹ",
-    "31": "ਇਕੱਤੀ",
-    "32": "ਬੱਤੀ",
-    "33": "ਤੇਤੀ",
-    "34": "ਚੌਂਤੀ",
-    "35": "ਪੈਂਤੀ",
-    "36": "ਛੱਤੀ",
-    "37": "ਸੈਂਤੀ",
-    "38": "ਅਠੱਤੀ",
-    "39": "ਉਨਤਾਲੀ",
-    "40": "ਚਾਲੀ",
-    "41": "ਇਕਤਾਲੀ",
-    "42": "ਬਤਾਲੀ",
-    "43": "ਤਰਤਾਲੀ",
-    "44": "ਚੌਤਾਲੀ",
-    "45": "ਪੰਜਤਾਲੀ",
-    "46": "ਛਿਆਲੀ",
-    "47": "ਸੰਤਾਲੀ",
-    "48": "ਅੱਠਤਾਲੀ",
-    "49": "ਉਣਿੰਜਾ",
-    "50": "ਪੰਜਾਹ",
-    "51": "ਇਕਵਿੰਜਾ",
-    "52": "ਬਵਿੰਜਾ",
-    "53": "ਤਰਵਿੰਜਾ",
-    "54": "ਚਰਿੰਜਾ",
-    "55": "ਪਚਵਿੰਜਾ",
-    "56": "ਛਪਿੰਜਾ",
-    "57": "ਸਤਵਿੰਜਾ",
-    "58": "ਅੱਠਵਿੰਜਾ",
-    "59": "ਉਣਾਠ",
-    "60": "ਸੱਠ",
-    "61": "ਇਕਾਠ",
-    "62": "ਬਾਠ੍ਹ",
-    "63": "ਤਰੇਠ੍ਹ",
-    "64": "ਚੌਠ੍ਹ",
-    "65": "ਪੈਂਠ",
-    "66": "ਛਿਆਠ",
-    "67": "ਸਤਾਹਠ",
-    "68": "ਅੱਠਾਠ",
-    "69": "ਉਣੱਤਰ",
-    "70": "ਸੱਤਰ",
-    "71": "ਇਕ੍ਹੱਤਰ",
-    "72": "ਬਹੱਤਰ",
-    "73": "ਤਹੱਤਰ",
-    "74": "ਚੌਹੱਤਰ",
-    "75": "ਪੰਜੱਤਰ",
-    "76": "ਛਿਹੱਤਰ",
-    "77": "ਸਤੱਤਰ",
-    "78": "ਅਠੱਤਰ",
-    "79": "ਉਣਾਸੀ",
-    "80": "ਅੱਸੀ",
-    "81": "ਇਕਾਸੀ",
-    "82": "ਬਿਆਸੀ",
-    "83": "ਤਰਾਸੀ",
-    "84": "ਚਰਾਸੀ",
-    "85": "ਪੰਜਾਸੀ",
-    "86": "ਛਿਆਸੀ",
-    "87": "ਸਤਾਸੀ",
-    "88": "ਅਠਾਸੀ",
-    "89": "ਉਣਾਨਵੇਂ",
-    "90": "ਨੱਬੇ",
-    "91": "ਇਕਾਨਵੇਂ",
-    "92": "ਬਿਆਨਵੇਂ",
-    "93": "ਤਰਾਨਵੇਂ",
-    "94": "ਚਰਾਨਵੇਂ",
-    "95": "ਪਚਾਨਵੇਂ",
-    "96": "ਛਿਆਨਵੇਂ",
-    "97": "ਸਤਾਨਵੇਂ",
-    "98": "ਅਠਾਨਵੇਂ",
-    "99": "ਨਿੜਾਨਵੇਂ",
-    "100": "ਸੌ",
-    "1000": "ਹਜਾਰ",
-    "100000": "ਲੱਖ",
-    "10000000": "ਕਰੋੜ",
-    "1000000000": "ਅਰਬ",
-}  # Punjabi
-
-# --------------------------- num_to_word.py ------------------------------
-"""
-Method to convert Numbers to Words
-for indian languages
-
-Use cases:-
-1) Speech recognition pre-processing
-2) Language modeling Data pre-processing
-
--------------------------
-check indic_numbers.py to add support
-for any indian language
-"""
-
-
-def language_specific_exception(words, lang, combiner):
-    """
-    Language Specific Exception will come here
-    """
-
-    def occurs_at_end(piece):
-        return words[-len(piece) :] == piece
-
-    if lang == "mr":
-        words = words.replace("एक" + combiner + "शे", "शंभर")
-    elif lang == "gu":
-        words = words.replace("બે" + combiner + "સો", "બસ્સો")
-    elif lang == "te":
-        exception_dict = {
-            "1": "ఒక",
-            "100": "వంద",
-            "100+": "వందలు",
-            "1000": "వెయ్యి",
-            "1000+": "వేలు",
-            "100000": "లక్ష",
-            "100000+": "లక్షలు",
-            "10000000": "కోటి",
-            "10000000+": "కోట్లు",
-        }
-
-        test_case = ["100", "1000", "100000", "10000000"]
-        for test in test_case:
-            test_word = num_dict["te"][test]
-            match = num_dict["te"]["1"] + combiner + test_word
-            # for numbers like : 100, 1000, 100000
-            if words == match:
-                return exception_dict[test]
-            # for numbers like : 200, 4000, 800000
-            elif occurs_at_end(test_word):
-                words = words.replace(test_word, exception_dict[test + "+"])
-            # for numbers like : 105, 1076, 123993
-            elif not occurs_at_end(match):
-                replacement = exception_dict["1"] + combiner + exception_dict[test]
-                words = words.replace(match, replacement)
-
-        # Exception case for 101...199
-        special_case = "ఒక" + combiner + "వంద"
-        words = words.replace(special_case, "నూట")
-    elif lang == "kn":
-        # special case for 100
-        if words == ("ಒಂದು" + combiner + "ನೂರ"):
-            return "ನೂರು"
-        exception_dict = {
-            "ನೂರ": "ನೂರು",
-            "ಸಾವಿರದ": "ಸಾವಿರ",
-            "ಲಕ್ಷದ": "ಲಕ್ಷ",
-            "ಕೋಟಿಯ": "ಕೋಟಿ",
-        }
-        for expt in exception_dict:
-            if occurs_at_end(expt):
-                words = words.replace(expt, exception_dict[expt])
-    return words
-
-
-def num_to_word(num, lang, separator=", ", combiner=" "):
-    """
-    Main Method
-    :param num: Number digits from any indian language
-    :param lang: Language Code from supported Language
-    :param separator: Separator character i.e. separator = '-' --> 'two hundred-sixty'
-    :param combiner: combine number with position i.e. combiner = '-' --> 'two-hundred sixty'
-    :return: UTF-8 String of numbers in words
-    """
-    lang = lang.lower()
-    num = str(num)
-
-    # Load dictionary according to language code
-    assert lang in supported_lang, "Language not supported"
-    num_dic = num_dict[lang]
-
-    # dash default combiner for english-india
-    if (lang == "en") & (combiner == " "):
-        combiner = "-"
-
-    # Remove punctuations from numbers
-    num = str(num).replace(",", "").replace(" ", "")
-    
-    # return word as it is if not number
-    if not num.isdecimal():
-        return num
-
-    # Replace native language numbers with english digits
-    for language in supported_lang:
-        for num_index in range(10):
-            num = num.replace(all_num[language][num_index], all_num["en"][num_index])
-
-    # Assert that input contains only integer number
-    for digit in num:
-        assert digit in all_num["en"], "Give proper input"
-
-    # Process
-    # For Number longer than 9 digits
-    def all_two_digit(digits_2):
-        if len(digits_2) <= 1:  # Provided only one/zero digit
-            return num_dic.get(digits_2, "")
-        elif digits_2 == "00":  # Two Zero provided
-            return num_dic["0"] + separator + num_dic["0"]
-        elif digits_2[0] == "0":  # First digit is zero
-            return num_dic["0"] + separator + num_dic[digits_2[1]]
-        else:  # Both digit provided
-            return num_dic[digits_2]
-
-    # For Number less than 9 digits
-    def two_digit(digits_2):
-        digits_2 = digits_2.lstrip("0")
-        if len(digits_2) != 0:
-            return num_dic[digits_2]
-        else:
-            return ""
-
-    def all_digit(digits):
-        digits = digits.lstrip("0")
-        digit_len = len(digits)
-        if digit_len > 3:
-            num_of_digits_to_process = (digit_len % 2) + 1
-            process_digits = digits[:num_of_digits_to_process]
-            base = str(10 ** (int(digit_len / 2) * 2 - 1))
-            remain_digits = digits[num_of_digits_to_process:]
-            return (
-                num_dic[process_digits]
-                + combiner
-                + num_dic[base]
-                + separator
-                + all_digit(remain_digits)
-            )
-        elif len(digits) == 3:
-            return (
-                num_dic[digits[:1]]
-                + combiner
-                + num_dic["100"]
-                + separator
-                + two_digit(digits[1:])
-            )
-        else:
-            return two_digit(digits)
-
-    num = num.lstrip("0")
-    full_digit_len = len(num)
-
-    if full_digit_len == 0:
-        output = num_dic["0"]
-    elif full_digit_len <= 9:
-        output = all_digit(num)
-    else:
-        iteration = round(full_digit_len / 2)
-        output = all_two_digit(num[:2])  # First to digit
-        for i in range(1, iteration):
-            output = (
-                output + separator + all_two_digit(num[i * 2 : (i + 1) * 2])
-            )  # Next two digit pairs
-        remaining_digits = num[iteration * 2 :]
-        if not all_two_digit(remaining_digits) == "":
-            output = (
-                output + separator + all_two_digit(remaining_digits)
-            )  # remaining Last one/two digits
-
-    output = output.strip(separator)
-
-    output = language_specific_exception(output, lang, combiner)
-
-    return output
-
-
-# --------------------------------- num_to_word_on_a_sent ---------------------------------
-
-
-def is_digit(word, digit_pattern):
-    return re.search(digit_pattern, word)
-
-
-def remove_punct(sent):
-    clean = re.sub("[%s]" % re.escape(string.punctuation), " ", sent)
-    return " ".join([word for word in clean.split() if word])
-
-
-def normalize_nums(text, lang):
-    """
-    text: str (eg)
-    lang: lang code ['en', 'hi']
-
-    returns: str
-    (eg)
-    """
-
-    if lang in supported_lang:
-        text = text.replace('-',' - ') # space separate hyphen
-        words = text.split()
-        lang_digits = [str(i) for i in range(0, 10)]
-
-        digit_pattern = "[" + "".join(lang_digits) + "]"
-        num_indices = [
-            ind for ind, word in enumerate(words) if is_digit(word, digit_pattern)
-        ]
-
-        words_up = [
-            num_to_word(word, lang, separator=" ", combiner=" ")
-            if ind in num_indices
-            else word
-            for ind, word in enumerate(words)
-        ]
-        return " ".join(words_up)
-    else:
-        return text
-
-
-if __name__ == "__main__":
-    print(normalize_nums("रीटा के पास 16 बिल्लियाँ हैं।", "hi"))
diff --git a/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/setup.py b/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/setup.py
deleted file mode 100644
index 9d2c73345b8406195aaa6327cb3148bb92b65190..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/setup.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from setuptools import setup, find_packages
-
-with open("README.md", "r") as f:
-    long_description = f.read()
-
-setup(
-    name="vakyansh-tts",
-    version="0.0.5",
-    description="Text to speech for Indic languages",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    url="https://github.com/Open-Speech-EkStep/vakyansh-tts.git",
-    keywords="nlp, tts, Indic languages, deep learning, text to speech",
-    # package_dir={'': 'src'},
-    # packages=find_packages(where='src'),
-    packages=["tts_infer"],
-    python_requires=">=3.7, <4",
-    install_requires=[
-        "Cython==0.29.24",
-        "layers==0.1.5",
-        "librosa==0.8.1",
-        "matplotlib==3.3.4",
-        "numpy==1.20.2",
-        "scipy==1.5.4",
-        "tensorboardX==2.4",
-        "tensorboard==2.7.0",
-        "tqdm==4.62.3",
-        "fastapi==0.70.0",
-        "uvicorn==0.15.0",
-        "gradio==2.5.2",
-        "wavio==0.0.4",
-        "pydload==1.0.9",
-        "mosestokenizer==1.2.1",
-        "indic-nlp-library==0.81"
-    ],
-    classifiers=[
-        # How mature is this project? Common values are
-        #   3 - Alpha
-        #   4 - Beta
-        #   5 - Production/Stable
-        "Development Status :: 3 - Alpha",
-        # Indicate who your project is intended for
-        "Intended Audience :: Developers",
-        "Intended Audience :: Education",
-        "Intended Audience :: Science/Research",
-        "Topic :: Scientific/Engineering :: Artificial Intelligence",
-        "Topic :: Text Processing :: Linguistic",
-        # Pick your license as you wish (should match "license" above)
-        "License :: OSI Approved :: MIT License",
-        # Specify the Python versions you support here. In particular, ensure
-        # that you indicate whether you support Python 2, Python 3 or both.
-        "Programming Language :: Python :: 3.7",
-    ],
-    include_package_data=True,
-)
diff --git a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/glow_tts/init.py b/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/glow_tts/init.py
deleted file mode 100644
index 39dd83dbd55475d562a3f54d951cb822800d2e0f..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/glow_tts/init.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import os
-import json
-import argparse
-import math
-import torch
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-
-from data_utils import TextMelLoader, TextMelCollate
-import models
-import commons
-import utils
-
-
-class FlowGenerator_DDI(models.FlowGenerator):
-    """A helper for Data-dependent Initialization"""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        for f in self.decoder.flows:
-            if getattr(f, "set_ddi", False):
-                f.set_ddi(True)
-
-
-def main():
-    hps = utils.get_hparams()
-    logger = utils.get_logger(hps.log_dir)
-    logger.info(hps)
-    utils.check_git_hash(hps.log_dir)
-
-    torch.manual_seed(hps.train.seed)
-
-    train_dataset = TextMelLoader(hps.data.training_files, hps.data)
-    collate_fn = TextMelCollate(1)
-    train_loader = DataLoader(
-        train_dataset,
-        num_workers=8,
-        shuffle=True,
-        batch_size=hps.train.batch_size,
-        pin_memory=True,
-        drop_last=True,
-        collate_fn=collate_fn,
-    )
-    symbols = hps.data.punc + hps.data.chars
-    generator = FlowGenerator_DDI(
-        len(symbols) + getattr(hps.data, "add_blank", False),
-        out_channels=hps.data.n_mel_channels,
-        **hps.model
-    ).cuda()
-    optimizer_g = commons.Adam(
-        generator.parameters(),
-        scheduler=hps.train.scheduler,
-        dim_model=hps.model.hidden_channels,
-        warmup_steps=hps.train.warmup_steps,
-        lr=hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps,
-    )
-
-    generator.train()
-    for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(train_loader):
-        x, x_lengths = x.cuda(), x_lengths.cuda()
-        y, y_lengths = y.cuda(), y_lengths.cuda()
-
-        _ = generator(x, x_lengths, y, y_lengths, gen=False)
-        break
-
-    utils.save_checkpoint(
-        generator,
-        optimizer_g,
-        hps.train.learning_rate,
-        0,
-        os.path.join(hps.model_dir, "ddi_G.pth"),
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/utils/inference/num_to_word_on_sent.py b/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/utils/inference/num_to_word_on_sent.py
deleted file mode 100644
index ce878a8c3ee6f5ef629abeaee418d5959f7179ed..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/utils/inference/num_to_word_on_sent.py
+++ /dev/null
@@ -1,1314 +0,0 @@
-import re
-import string
-
-# ----------------------------- indic_num.py -----------------------------
-supported_lang = {"en", "hi", "gu", "mr", "bn", "te", "ta", "kn", "or", "pa"}
-# supported_lang = {'eng', 'hin', 'guj', 'mar', 'ben', 'tel', 'tam', 'kan', 'ori', 'pan'}  # Three alphabet lang code
-
-all_num = {
-    "en": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
-    "hi": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"],
-    "gu": ["૦", "૧", "૨", "૩", "૪", "૫", "૬", "૭", "૮", "૯"],
-    "mr": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"],
-    "bn": ["০", "১", "২", "৩", "৪", "৫", "৬", "৭", "৮", "৯"],
-    "te": ["౦", "౧", "౨", "౩", "౪", "౫", "౬", "౭", "౮", "౯"],
-    "ta": ["0", "௧", "௨", "௩", "௪", "௫", "௬", "௭", "௮", "௯", "௰"],
-    "kn": ["೦", "೧", "೨", "೩", "೪", "೫", "೬", "೭", "೮", "೯"],
-    "or": ["୦", "୧", "୨", "୩", "୪", "୫", "୬", "୭", "୮", "୯"],
-    "pa": ["੦", "੧", "੨", "੩", "੪", "੫", "੬", "੭", "੮", "੯"],
-}
-
-num_dict = dict()
-num_dict["en"] = {
-    "0": "zero",
-    "1": "one",
-    "2": "two",
-    "3": "three",
-    "4": "four",
-    "5": "five",
-    "6": "six",
-    "7": "seven",
-    "8": "eight",
-    "9": "nine",
-    "10": "ten",
-    "11": "eleven",
-    "12": "twelve",
-    "13": "thirteen",
-    "14": "fourteen",
-    "15": "fifteen",
-    "16": "sixteen",
-    "17": "seventeen",
-    "18": "eighteen",
-    "19": "nineteen",
-    "20": "twenty",
-    "21": "twenty-one",
-    "22": "twenty-two",
-    "23": "twenty-three",
-    "24": "twenty-four",
-    "25": "twenty-five",
-    "26": "twenty-six",
-    "27": "twenty-seven",
-    "28": "twenty-eight",
-    "29": "twenty-nine",
-    "30": "thirty",
-    "31": "thirty-one",
-    "32": "thirty-two",
-    "33": "thirty-three",
-    "34": "thirty-four",
-    "35": "thirty-five",
-    "36": "thirty-six",
-    "37": "thirty-seven",
-    "38": "thirty-eight",
-    "39": "thirty-nine",
-    "40": "forty",
-    "41": "forty-one",
-    "42": "forty-two",
-    "43": "forty-three",
-    "44": "forty-four",
-    "45": "forty-five",
-    "46": "forty-six",
-    "47": "forty-seven",
-    "48": "forty-eight",
-    "49": "forty-nine",
-    "50": "fifty",
-    "51": "fifty-one",
-    "52": "fifty-two",
-    "53": "fifty-three",
-    "54": "fifty-four",
-    "55": "fifty-five",
-    "56": "fifty-six",
-    "57": "fifty-seven",
-    "58": "fifty-eight",
-    "59": "fifty-nine",
-    "60": "sixty",
-    "61": "sixty-one",
-    "62": "sixty-two",
-    "63": "sixty-three",
-    "64": "sixty-four",
-    "65": "sixty-five",
-    "66": "sixty-six",
-    "67": "sixty-seven",
-    "68": "sixty-eight",
-    "69": "sixty-nine",
-    "70": "seventy",
-    "71": "seventy-one",
-    "72": "seventy-two",
-    "73": "seventy-three",
-    "74": "seventy-four",
-    "75": "seventy-five",
-    "76": "seventy-six",
-    "77": "seventy-seven",
-    "78": "seventy-eight",
-    "79": "seventy-nine",
-    "80": "eighty",
-    "81": "eighty-one",
-    "82": "eighty-two",
-    "83": "eighty-three",
-    "84": "eighty-four",
-    "85": "eighty-five",
-    "86": "eighty-six",
-    "87": "eighty-seven",
-    "88": "eighty-eight",
-    "89": "eighty-nine",
-    "90": "ninety",
-    "91": "ninety-one",
-    "92": "ninety-two",
-    "93": "ninety-three",
-    "94": "ninety-four",
-    "95": "ninety-five",
-    "96": "ninety-six",
-    "97": "ninety-seven",
-    "98": "ninety-eight",
-    "99": "ninety-nine",
-    "100": "hundred",
-    "1000": "thousand",
-    "100000": "lac",
-    "10000000": "crore",
-    "1000000000": "arab",
-}  # English-India
-num_dict["hi"] = {
-    "0": "शून्य",
-    "1": "एक",
-    "2": "दो",
-    "3": "तीन",
-    "4": "चार",
-    "5": "पाँच",
-    "6": "छः",
-    "7": "सात",
-    "8": "आठ",
-    "9": "नौ",
-    "10": "दस",
-    "11": "ग्यारह",
-    "12": "बारह",
-    "13": "तेरह",
-    "14": "चौदह",
-    "15": "पंद्रह",
-    "16": "सोलह",
-    "17": "सत्रह",
-    "18": "अट्ठारह",
-    "19": "उन्नीस",
-    "20": "बीस",
-    "21": "इक्कीस",
-    "22": "बाईस",
-    "23": "तेईस",
-    "24": "चौबिस",
-    "25": "पच्चीस",
-    "26": "छब्बीस",
-    "27": "सत्ताईस",
-    "28": "अट्ठाईस",
-    "29": "उनतीस",
-    "30": "तीस",
-    "31": "इकतीस",
-    "32": "बत्तीस",
-    "33": "तैंतीस",
-    "34": "चौंतीस",
-    "35": "पैंतीस",
-    "36": "छत्तीस",
-    "37": "सैंतीस",
-    "38": "अड़तीस",
-    "39": "उनतालीस",
-    "40": "चालीस",
-    "41": "इकतालीस",
-    "42": "बयालीस",
-    "43": "तैंतालीस",
-    "44": "चौंतालीस",
-    "45": "पैंतालीस",
-    "46": "छियालीस",
-    "47": "सैंतालीस",
-    "48": "अड़तालीस",
-    "49": "उनचास",
-    "50": "पचास",
-    "51": "इक्यावन​",
-    "52": "बावन",
-    "53": "तिरेपन",
-    "54": "चौवन",
-    "55": "पचपन",
-    "56": "छप्पन",
-    "57": "सत्तावन",
-    "58": "अट्ठावन",
-    "59": "उनसठ",
-    "60": "साठ",
-    "61": "इकसठ",
-    "62": "बासठ",
-    "63": "तिरेसठ",
-    "64": "चौंसठ",
-    "65": "पैंसठ",
-    "66": "छयासठ",
-    "67": "सरसठ​",
-    "68": "अड़सठ",
-    "69": "उनहत्तर",
-    "70": "सत्तर",
-    "71": "इकहत्तर",
-    "72": "बहत्तर",
-    "73": "तिहत्तर",
-    "74": "चौहत्तर",
-    "75": "पचहत्तर",
-    "76": "छिहत्तर",
-    "77": "सतहत्तर",
-    "78": "अठहत्तर",
-    "79": "उन्यासी",
-    "80": "अस्सी",
-    "81": "इक्यासी",
-    "82": "बयासी",
-    "83": "तिरासी",
-    "84": "चौरासी",
-    "85": "पचासी",
-    "86": "छियासी",
-    "87": "सत्तासी",
-    "88": "अठासी",
-    "89": "नवासी",
-    "90": "नब्बे",
-    "91": "इक्यानवे",
-    "92": "बानवे",
-    "93": "तिरानवे",
-    "94": "चौरानवे",
-    "95": "पचानवे",
-    "96": "छियानवे",
-    "97": "सत्तानवे",
-    "98": "अट्ठानवे",
-    "99": "निन्यानवे",
-    "100": "सौ",
-    "1000": "हज़ार",
-    "100000": "लाख",
-    "10000000": "करोड़",
-    "1000000000": "अरब",
-}  # Hindi
-num_dict["gu"] = {
-    "0": "શૂન્ય",
-    "1": "એક",
-    "2": "બે",
-    "3": "ત્રણ",
-    "4": "ચાર",
-    "5": "પાંચ",
-    "6": "છ",
-    "7": "સાત",
-    "8": "આઠ",
-    "9": "નવ",
-    "10": "દસ",
-    "11": "અગિયાર",
-    "12": "બાર",
-    "13": "તેર",
-    "14": "ચૌદ",
-    "15": "પંદર",
-    "16": "સોળ",
-    "17": "સત્તર",
-    "18": "અઢાર",
-    "19": "ઓગણિસ",
-    "20": "વીસ",
-    "21": "એકવીસ",
-    "22": "બાવીસ",
-    "23": "તેવીસ",
-    "24": "ચોવીસ",
-    "25": "પચ્ચીસ",
-    "26": "છવીસ",
-    "27": "સત્તાવીસ",
-    "28": "અઠ્ઠાવીસ",
-    "29": "ઓગણત્રીસ",
-    "30": "ત્રીસ",
-    "31": "એકત્રીસ",
-    "32": "બત્રીસ",
-    "33": "તેત્રીસ",
-    "34": "ચોત્રીસ",
-    "35": "પાંત્રીસ",
-    "36": "છત્રીસ",
-    "37": "સડત્રીસ",
-    "38": "અડત્રીસ",
-    "39": "ઓગણચાલીસ",
-    "40": "ચાલીસ",
-    "41": "એકતાલીસ",
-    "42": "બેતાલીસ",
-    "43": "ત્રેતાલીસ",
-    "44": "ચુંમાલીસ",
-    "45": "પિસ્તાલીસ",
-    "46": "છેતાલીસ",
-    "47": "સુડતાલીસ",
-    "48": "અડતાલીસ",
-    "49": "ઓગણપચાસ",
-    "50": "પચાસ",
-    "51": "એકાવન",
-    "52": "બાવન",
-    "53": "ત્રેપન",
-    "54": "ચોપન",
-    "55": "પંચાવન",
-    "56": "છપ્પન",
-    "57": "સત્તાવન",
-    "58": "અઠ્ઠાવન",
-    "59": "ઓગણસાઠ",
-    "60": "સાઈઠ",
-    "61": "એકસઠ",
-    "62": "બાસઠ",
-    "63": "ત્રેસઠ",
-    "64": "ચોસઠ",
-    "65": "પાંસઠ",
-    "66": "છાસઠ",
-    "67": "સડસઠ",
-    "68": "અડસઠ",
-    "69": "અગણોસિત્તેર",
-    "70": "સિત્તેર",
-    "71": "એકોતેર",
-    "72": "બોતેર",
-    "73": "તોતેર",
-    "74": "ચુમોતેર",
-    "75": "પંચોતેર",
-    "76": "છોતેર",
-    "77": "સિત્યોતેર",
-    "78": "ઇઠ્યોતેર",
-    "79": "ઓગણાએંસી",
-    "80": "એંસી",
-    "81": "એક્યાસી",
-    "82": "બ્યાસી",
-    "83": "ત્યાસી",
-    "84": "ચોર્યાસી",
-    "85": "પંચાસી",
-    "86": "છ્યાસી",
-    "87": "સિત્યાસી",
-    "88": "ઈઠ્યાસી",
-    "89": "નેવ્યાસી",
-    "90": "નેવું",
-    "91": "એકાણું",
-    "92": "બાણું",
-    "93": "ત્રાણું",
-    "94": "ચોરાણું",
-    "95": "પંચાણું",
-    "96": "છન્નું",
-    "97": "સત્તાણું",
-    "98": "અઠ્ઠાણું",
-    "99": "નવ્વાણું",
-    "100": "સો",
-    "1000": "હજાર",
-    "100000": "લાખ",
-    "1000000": "દસ લાખ",
-    "10000000": "કરોડ઼",
-}  # Gujarati
-num_dict["mr"] = {
-    "0": "शून्य",
-    "1": "एक",
-    "2": "दोन",
-    "3": "तीन",
-    "4": "चार",
-    "5": "पाच",
-    "6": "सहा",
-    "7": "सात",
-    "8": "आठ",
-    "9": "नऊ",
-    "10": "दहा",
-    "11": "अकरा",
-    "12": "बारा",
-    "13": "तेरा",
-    "14": "चौदा",
-    "15": "पंधरा",
-    "16": "सोळा",
-    "17": "सतरा",
-    "18": "अठरा",
-    "19": "एकोणीस",
-    "20": "वीस",
-    "21": "एकवीस",
-    "22": "बावीस",
-    "23": "तेवीस",
-    "24": "चोवीस",
-    "25": "पंचवीस",
-    "26": "सव्वीस",
-    "27": "सत्तावीस",
-    "28": "अठ्ठावीस",
-    "29": "एकोणतीस",
-    "30": "तीस",
-    "31": "एकतीस",
-    "32": "बत्तीस",
-    "33": "तेहेतीस",
-    "34": "चौतीस",
-    "35": "पस्तीस",
-    "36": "छत्तीस",
-    "37": "सदतीस",
-    "38": "अडतीस",
-    "39": "एकोणचाळीस",
-    "40": "चाळीस",
-    "41": "एक्केचाळीस",
-    "42": "बेचाळीस",
-    "43": "त्रेचाळीस",
-    "44": "चव्वेचाळीस",
-    "45": "पंचेचाळीस",
-    "46": "सेहेचाळीस",
-    "47": "सत्तेचाळीस",
-    "48": "अठ्ठेचाळीस",
-    "49": "एकोणपन्नास",
-    "50": "पन्नास",
-    "51": "एक्कावन्न",
-    "52": "बावन्न",
-    "53": "त्रेपन्न",
-    "54": "चोपन्न",
-    "55": "पंचावन्न",
-    "56": "छप्पन्न",
-    "57": "सत्तावन्न",
-    "58": "अठ्ठावन्न",
-    "59": "एकोणसाठ",
-    "60": "साठ",
-    "61": "एकसष्ठ",
-    "62": "बासष्ठ",
-    "63": "त्रेसष्ठ",
-    "64": "चौसष्ठ",
-    "65": "पासष्ठ",
-    "66": "सहासष्ठ",
-    "67": "सदुसष्ठ",
-    "68": "अडुसष्ठ",
-    "69": "एकोणसत्तर",
-    "70": "सत्तर",
-    "71": "एक्काहत्तर",
-    "72": "बाहत्तर",
-    "73": "त्र्याहत्तर",
-    "74": "चौर्‍याहत्तर",
-    "75": "पंच्याहत्तर",
-    "76": "शहात्तर",
-    "77": "सत्याहत्तर",
-    "78": "अठ्ठ्याहत्तर",
-    "79": "एकोण ऐंशी",
-    "80": "ऐंशी",
-    "81": "एक्क्याऐंशी",
-    "82": "ब्याऐंशी",
-    "83": "त्र्याऐंशी",
-    "84": "चौऱ्याऐंशी",
-    "85": "पंच्याऐंशी",
-    "86": "शहाऐंशी",
-    "87": "सत्त्याऐंशी",
-    "88": "अठ्ठ्याऐंशी",
-    "89": "एकोणनव्वद",
-    "90": "नव्वद",
-    "91": "एक्क्याण्णव",
-    "92": "ब्याण्णव",
-    "93": "त्र्याण्णव",
-    "94": "चौऱ्याण्णव",
-    "95": "पंच्याण्णव",
-    "96": "शहाण्णव",
-    "97": "सत्त्याण्णव",
-    "98": "अठ्ठ्याण्णव",
-    "99": "नव्व्याण्णव",
-    "100": "शे",
-    "1000": "हजार",
-    "100000": "लाख",
-    "10000000": "कोटी",
-    "1000000000": "अब्ज",
-}  # Marathi
-num_dict["bn"] = {
-    "0": "শূন্য",
-    "1": "এক",
-    "2": "দুই",
-    "3": "তিন",
-    "4": "চার",
-    "5": "পাঁচ",
-    "6": "ছয়",
-    "7": "সাত",
-    "8": "আট",
-    "9": "নয়",
-    "10": "দশ",
-    "11": "এগার",
-    "12": "বার",
-    "13": "তের",
-    "14": "চৌদ্দ",
-    "15": "পনের",
-    "16": "ষোল",
-    "17": "সতের",
-    "18": "আঠার",
-    "19": "ঊনিশ",
-    "20": "বিশ",
-    "21": "একুশ",
-    "22": "বাইশ",
-    "23": "তেইশ",
-    "24": "চব্বিশ",
-    "25": "পঁচিশ",
-    "26": "ছাব্বিশ",
-    "27": "সাতাশ",
-    "28": "আঠাশ",
-    "29": "ঊনত্রিশ",
-    "30": "ত্রিশ",
-    "31": "একত্রিশ",
-    "32": "বত্রিশ",
-    "33": "তেত্রিশ",
-    "34": "চৌত্রিশ",
-    "35": "পঁয়ত্রিশ",
-    "36": "ছত্রিশ",
-    "37": "সাঁইত্রিশ",
-    "38": "আটত্রিশ",
-    "39": "ঊনচল্লিশ",
-    "40": "চল্লিশ",
-    "41": "একচল্লিশ",
-    "42": "বিয়াল্লিশ",
-    "43": "তেতাল্লিশ",
-    "44": "চুয়াল্লিশ",
-    "45": "পঁয়তাল্লিশ",
-    "46": "ছেচল্লিশ",
-    "47": "সাতচল্লিশ",
-    "48": "আটচল্লিশ",
-    "49": "ঊনপঞ্চাশ",
-    "50": "পঞ্চাশ",
-    "51": "একান্ন",
-    "52": "বায়ান্ন",
-    "53": "তিপ্পান্ন",
-    "54": "চুয়ান্ন",
-    "55": "পঞ্চান্ন",
-    "56": "ছাপ্পান্ন",
-    "57": "সাতান্ন",
-    "58": "আটান্ন",
-    "59": "ঊনষাট",
-    "60": "ষাট",
-    "61": "একষট্টি",
-    "62": "বাষট্টি",
-    "63": "তেষট্টি",
-    "64": "চৌষট্টি",
-    "65": "পঁয়ষট্টি",
-    "66": "ছেষট্টি",
-    "67": "সাতষট্টি",
-    "68": "আটষট্টি",
-    "69": "ঊনসত্তর",
-    "70": "সত্তর",
-    "71": "একাত্তর",
-    "72": "বাহাত্তর",
-    "73": "তিয়াত্তর",
-    "74": "চুয়াত্তর",
-    "75": "পঁচাত্তর",
-    "76": "ছিয়াত্তর",
-    "77": "সাতাত্তর",
-    "78": "আটাত্তর",
-    "79": "ঊনআশি",
-    "80": "আশি",
-    "81": "একাশি",
-    "82": "বিরাশি",
-    "83": "তিরাশি",
-    "84": "চুরাশি",
-    "85": "পঁচাশি",
-    "86": "ছিয়াশি",
-    "87": "সাতাশি",
-    "88": "আটাশি",
-    "89": "ঊননব্বই",
-    "90": "নব্বই",
-    "91": "একানব্বই",
-    "92": "বিরানব্বই",
-    "93": "তিরানব্বই",
-    "94": "চুরানব্বই",
-    "95": "পঁচানব্বই",
-    "96": "ছিয়ানব্বই",
-    "97": "সাতানব্বই",
-    "98": "আটানব্বই",
-    "99": "নিরানব্বই",
-    "100": "শো",
-    "1000": "হাজার",
-    "100000": "লাখ",
-    "10000000": "কোটি",
-    "1000000000": "একশ’ কোটি",
-}  # Bengali
-num_dict["te"] = {
-    "0": "సున్నా",
-    "1": "ఒకటి",
-    "2": "రెండు",
-    "3": "మూడు",
-    "4": "నాలుగు",
-    "5": "ఐదు",
-    "6": "ఆరు",
-    "7": "ఏడు",
-    "8": "ఎనిమిది",
-    "9": "తొమ్మిది",
-    "10": "పది",
-    "11": "పదకొండు",
-    "12": "పన్నెండు",
-    "13": "పదమూడు",
-    "14": "పద్నాలుగు",
-    "15": "పదిహేను",
-    "16": "పదహారు",
-    "17": "పదిహేడు",
-    "18": "పద్దెనిమిది",
-    "19": "పందొమ్మిది",
-    "20": "ఇరవై",
-    "21": "ఇరవై ఒకటి",
-    "22": "ఇరవై రెండు",
-    "23": "ఇరవై మూడు",
-    "24": "ఇరవై నాలుగు",
-    "25": "ఇరవై ఐదు",
-    "26": "ఇరవై ఆరు",
-    "27": "ఇరవై ఏడు",
-    "28": "ఇరవై ఎనిమిది",
-    "29": "ఇరవై తొమ్మిది",
-    "30": "ముప్పై",
-    "31": "ముప్పై ఒకటి",
-    "32": "ముప్పై రెండు",
-    "33": "ముప్పై మూడు",
-    "34": "ముప్పై నాలుగు",
-    "35": "ముప్పై ఐదు",
-    "36": "ముప్పై ఆరు",
-    "37": "ముప్పై ఏడు",
-    "38": "ముప్పై ఎనిమిది",
-    "39": "ముప్పై తొమ్మిది",
-    "40": "నలభై",
-    "41": "నలభై ఒకటి",
-    "42": "నలభై రెండు",
-    "43": "నలభై మూడు",
-    "44": "నలభై నాలుగు",
-    "45": "నలభై ఐదు",
-    "46": "నలభై ఆరు",
-    "47": "నలభై ఏడు",
-    "48": "నలభై ఎనిమిది",
-    "49": "నలభై తొమ్మిది",
-    "50": "యాభై",
-    "51": "యాభై ఒకటి",
-    "52": "యాభై రెండు",
-    "53": "యాభై మూడు",
-    "54": "యాభై నాలుగు",
-    "55": "యాభై ఐదు",
-    "56": "యాభై ఆరు",
-    "57": "యాభై ఏడు",
-    "58": "యాభై ఎనిమిది",
-    "59": "యాభై తొమ్మిది",
-    "60": "అరవై",
-    "61": "అరవై ఒకటి",
-    "62": "అరవై రెండు",
-    "63": "అరవై మూడు",
-    "64": "అరవై నాలుగు",
-    "65": "అరవై ఐదు",
-    "66": "అరవై ఆరు",
-    "67": "అరవై ఏడు",
-    "68": "అరవై ఎనిమిది",
-    "69": "అరవై తొమ్మిది",
-    "70": "డెబ్బై",
-    "71": "డెబ్బై ఒకటి",
-    "72": "డెబ్బై రెండు",
-    "73": "డెబ్బై మూడు",
-    "74": "డెబ్బై నాలుగు",
-    "75": "డెబ్బై ఐదు",
-    "76": "డెబ్బై ఆరు",
-    "77": "డెబ్బై ఏడు",
-    "78": "డెబ్బై ఎనిమిది",
-    "79": "డెబ్బై తొమ్మిది",
-    "80": "ఎనభై",
-    "81": "ఎనభై ఒకటి",
-    "82": "ఎనభై రెండు",
-    "83": "ఎనభై మూడు",
-    "84": "ఎనభై నాలుగు",
-    "85": "ఎనభై ఐదు",
-    "86": "ఎనభై ఆరు",
-    "87": "ఎనభై ఏడు",
-    "88": "ఎనభై ఎనిమిది",
-    "89": "ఎనభై తొమ్మిది",
-    "90": "తొంభై",
-    "91": "తొంభై ఒకటి",
-    "92": "తొంభై రెండు",
-    "93": "తొంభై మూడు",
-    "94": "తొంభై నాలుగు",
-    "95": "తొంభై ఐదు",
-    "96": "తొంభై ఆరు",
-    "97": "తొంభై ఏడు",
-    "98": "తొంభై ఎనిమిది",
-    "99": "తొంభై తొమ్మిది",
-    "100": "వందల",
-    "1000": "వేల",
-    "100000": "లక్షల",
-    "10000000": "కోట్ల",
-    "1000000000": "బిలియన్",
-}  # Telugu
-num_dict["ta"] = {
-    "0": "பூஜ்ஜியம்",
-    "1": "ஒன்று",
-    "2": "இரண்டு",
-    "3": "மூன்று",
-    "4": "நான்கு",
-    "5": "ஐந்து",
-    "6": "ஆறு",
-    "7": "ஏழு",
-    "8": "எட்டு",
-    "9": "ஒன்பது",
-    "10": "பத்து",
-    "11": "பதினொன்று",
-    "12": "பன்னிரண்டு",
-    "13": "பதிமூன்று",
-    "14": "பதினான்கு",
-    "15": "பதினைந்து",
-    "16": "பதினாறு",
-    "17": "பதினேழு",
-    "18": "பதினெட்டு",
-    "19": "பத்தொன்பது",
-    "20": "இருபது",
-    "21": "இருபது ஒன்று",
-    "22": "இருபத்து இரண்டு",
-    "23": "இருபத்து மூன்று",
-    "24": "இருபத்து நான்கு",
-    "25": "இருபத்து ஐந்து",
-    "26": "இருபத்து ஆறு",
-    "27": "இருபத்து ஏழு",
-    "28": "இருபத்து எட்டு",
-    "29": "இருபத்து ஒன்பது",
-    "30": "முப்பது",
-    "31": "முப்பத்து ஒன்று",
-    "32": "முப்பத்து இரண்டு",
-    "33": "முப்பத்து மூன்று",
-    "34": "முப்பத்து நான்கு",
-    "35": "முப்பத்து ஐந்து",
-    "36": "முப்பத்து ஆறு",
-    "37": "முப்பத்து ஏழு",
-    "38": "முப்பத்து எட்டு",
-    "39": "முப்பத்து ஒன்பது",
-    "40": "நாற்பது",
-    "41": "நாற்பத்து ஒன்று",
-    "42": "நாற்பத்து இரண்டு",
-    "43": "நாற்பத்து மூன்று",
-    "44": "நாற்பத்து நான்கு",
-    "45": "நாற்பத்து ஐந்து",
-    "46": "நாற்பத்து ஆறு",
-    "47": " நாற்பத்து ஏழு",
-    "48": "நாற்பத்து எட்டு",
-    "49": "நாற்பத்து ஒன்பது",
-    "50": "ஐம்பது",
-    "51": "ஐம்பத்து ஒன்று",
-    "52": "ஐம்பத்து இரண்டு",
-    "53": "ஐம்பத்து மூன்று",
-    "54": "ஐம்பத்து நான்கு",
-    "55": "ஐம்பத்து ஐந்து",
-    "56": "ஐம்பத்து ஆறு",
-    "57": "ஐம்பத்து ஏழு",
-    "58": "ஐம்பத்து எட்டு",
-    "59": "ஐம்பத்து ஒன்பது",
-    "60": "அறுபது",
-    "61": "அறுபத்து ஒன்று",
-    "62": "அறுபத்து இரண்டு",
-    "63": "அறுபத்து மூன்று",
-    "64": "அறுபத்து நான்கு",
-    "65": "அறுபத்து ஐந்து",
-    "66": "அறுபத்து ஆறு",
-    "67": "அறுபத்து ஏழு",
-    "68": "அறுபத்து எட்டு",
-    "69": "அறுபத்து ஒன்பது",
-    "70": "எழுபது",
-    "71": "எழுபத்தி ஒன்று",
-    "72": "எழுபத்தி இரண்டு",
-    "73": "எழுபத்தி முச்சக்கர",
-    "74": "எழுபத்தி நான்கு",
-    "75": "எழுபத்தி ஐந்து",
-    "76": "எழுபத்தி ஆறு",
-    "77": "எழுபத்தி ஏழு",
-    "78": "எழுபத்தி எட்டு",
-    "79": "எழுபத்தி ஒன்பது",
-    "80": "எண்பது",
-    "81": "எண்பத்தியொன்று",
-    "82": "எண்பத்திரண்டு",
-    "83": "எண்பத்திமூன்று",
-    "84": "என்பதினான்கு",
-    "85": "என்பதினைந்து",
-    "86": "எண்பத்திஆறு",
-    "87": "எண்பத்திஏழு",
-    "88": "எண்பத்தியெட்டு",
-    "89": "எண்பத்தியொன்பது",
-    "90": "தொன்னூறு",
-    "91": "தொண்ணூற்றியொன்று",
-    "92": "தொண்ணூற்றிரண்டு",
-    "93": "தொண்ணூற்றிமூன்று",
-    "94": "தொண்ணூற்றிநான்கு",
-    "95": "தொண்ணூற்றிஐந்து",
-    "96": "தொண்ணூற்றியாறு",
-    "97": "தொண்ணூற்றியேழு",
-    "98": "தொண்ணூற்றியெட்டு",
-    "99": "தொண்ணூற்றிஒன்பது",
-    "100": "நூறு",
-    "1000": "ஆயிரம்",
-    "100000": "இலட்சம்",
-    "10000000": "கோடி",
-    "1000000000": "பில்லியன்",
-}  # Tamil
-num_dict["kn"] = {
-    "0": "ಸೊನ್ನೆ",
-    "1": "ಒಂದು",
-    "2": "ಎರಡು",
-    "3": "ಮೂರು",
-    "4": "ನಾಲ್ಕು",
-    "5": "ಅಯ್ದು",
-    "6": "ಆರು",
-    "7": "ಏಳು",
-    "8": "ಎಂಟು",
-    "9": "ಒಂಬತ್ತು",
-    "10": "ಹತ್ತು",
-    "11": "ಹನ್ನೊಂದು",
-    "12": "ಹನ್ನೆರಡು",
-    "13": "ಹದಿಮೂರು",
-    "14": "ಹದಿನಾಲ್ಕು",
-    "15": "ಹದಿನೈದು",
-    "16": "ಹದಿನಾರು",
-    "17": "ಹದಿನೇಳು",
-    "18": "ಹದಿನೆಂಟು",
-    "19": "ಹತ್ತೊಂಬತ್ತು",
-    "20": "ಇಪ್ಪತ್ತು",
-    "21": "ಇಪ್ಪತ್ತ್’ಒಂದು",
-    "22": "ಇಪ್ಪತ್ತ್’ಎರಡು",
-    "23": "ಇಪ್ಪತ್ತ್’ಮೂರು",
-    "24": "ಇಪ್ಪತ್ತ್’ನಾಲ್ಕು",
-    "25": "ಇಪ್ಪತ್ತ್’ಐದು",
-    "26": "ಇಪ್ಪತ್ತ್’ಆರು",
-    "27": "ಇಪ್ಪತ್ತ್’ಏಳು",
-    "28": "ಇಪ್ಪತ್ತ್’ಎಂಟು",
-    "29": "ಇಪ್ಪತ್ತ್’ಒಂಬತ್ತು",
-    "30": "ಮೂವತ್ತು",
-    "31": "ಮುವತ್ತ್’ಒಂದು",
-    "32": "ಮುವತ್ತ್’ಎರಡು",
-    "33": "ಮುವತ್ತ್’ಮೂರು",
-    "34": "ಮೂವತ್ತ್’ನಾಲ್ಕು",
-    "35": "ಮೂವತ್ತ್’ಐದು",
-    "36": "ಮೂವತ್ತ್’ಆರು",
-    "37": "ಮೂವತ್ತ್’ಏಳು",
-    "38": "ಮೂವತ್ತ್’ಎಂಟು",
-    "39": "ಮೂವತ್ತ್’ಒಂಬತ್ತು",
-    "40": "ನಲವತ್ತು",
-    "41": "ನಲವತ್ತೊಂದು",
-    "42": "ನಲವತ್ತ್ ಎರಡು",
-    "43": "ನಲವತ್ತ್ ಮೂರು",
-    "44": "ನಲವತ್ತ್ ನಾಲ್ಕು",
-    "45": "ನಲವತ್ತೈದು",
-    "46": "ನಲವತ್ತಾರು",
-    "47": "ನಲವತ್ತೇಳು",
-    "48": "ನಲವತ್ತೆಂಟು",
-    "49": "ನಲವತ್ತೊಂಬತ್ತು",
-    "50": "ಐವತ್ತು",
-    "51": "ಐವತ್ತೊಂದು",
-    "52": "ಐವತ್ತೆರಡು",
-    "53": "ಐವತ್ತಮೂರು",
-    "54": "ಐವತ್ತ್ನಾಲ್ಕು",
-    "55": "ಐವತ್ತೈದು",
-    "56": "ಐವತ್ತಾರು",
-    "57": "ಐವತ್ತೇಳು",
-    "58": "ಐವತ್ತೆಂಟು",
-    "59": "ಐವತ್ತೊಂಬತ್ತು",
-    "60": "ಅರವತ್ತು",
-    "61": "ಅರವತ್ತೊಂದು",
-    "62": "ಅರವತ್ತೆರಡು",
-    "63": "ಅರವತ್ತ್ ಮೂರು",
-    "64": "ಅರವತ್ತ್ ನಾಲ್ಕು",
-    "65": "ಅರವತ್ತೈದು",
-    "66": "ಅರವತ್ತಾರು",
-    "67": "ಅರವತ್ತೇಳು",
-    "68": "ಅರವತ್ತೆಂಟು",
-    "69": "ಅರವತ್ತೊಂಬತ್ತು",
-    "70": "ಎಪ್ಪತ್ತು",
-    "71": "ಎಪ್ಪತ್ತೊಂದು",
-    "72": "ಎಪ್ಪತ್ತೆರಡು",
-    "73": "ಎಪ್ಪತ್ತ್ ಮೂರು",
-    "74": "ಎಪ್ಪತ್ತ್ ನಾಲ್ಕು",
-    "75": "ಎಪ್ಪತ್ತೈದು",
-    "76": "ಎಪ್ಪತ್ತಾರು",
-    "77": "ಎಪ್ಪತ್ತೇಳು",
-    "78": "ಎಪ್ಪತ್ತೆಂಟು",
-    "79": "ಎಪ್ಪತ್ತೊಂಬತ್ತು",
-    "80": "ಎಂಬತ್ತು",
-    "81": "ಎಂಬತ್ತೊಂದು",
-    "82": "ಎಂಬತ್ತೆರಡು",
-    "83": "ಎಂಬತ್ತ್ ಮೂರು",
-    "84": "ಎಂಬತ್ತ್ ನಾಲ್ಕು",
-    "85": "ಎಂಬತ್ತೈದು",
-    "86": "ಎಂಬತ್ತಾರು",
-    "87": "ಎಂಬತ್ತೇಳು",
-    "88": "ಎಂಬತ್ತೆಂಟು",
-    "89": "ಎಂಬತ್ತೊಂಬತ್ತು",
-    "90": "ತೊಂಬತ್ತು",
-    "91": "ತೊಂಬತ್ತೊಂದು",
-    "92": "ತೊಂಬತ್ತೆರಡು",
-    "93": "ತೊಂಬತ್ತ ಮೂರು",
-    "94": "ತೊಂಬತ್ತ ನಾಲ್ಕು",
-    "95": "ತೊಂಬತ್ತೈದು",
-    "96": "ತೊಂಬತ್ತಾರು",
-    "97": "ತೊಂಬತ್ತೇಳು",
-    "98": "ತೊಂಬತ್ತೆಂಟು",
-    "99": "ತೊಂಬತ್ತೊಂಬತ್ತು",
-    "100": "ನೂರ",
-    "1000": "ಸಾವಿರದ",
-    "100000": "ಲಕ್ಷದ",
-    "10000000": "ಕೋಟಿ",
-    "1000000000": "ಶತಕೋಟಿ",
-}  # Kannada
-num_dict["or"] = {
-    "0": "ଶୁନ୍ୟ",
-    "1": "ଏକ",
-    "2": "ଦୁଇ",
-    "3": "ତିନି",
-    "4": "ଚାରି",
-    "5": "ପାଞ୍ଚ",
-    "6": "ଛଅ",
-    "7": "ସାତ",
-    "8": "ଆଠ",
-    "9": "ନଅ",
-    "10": "ନଅ",
-    "11": "ଏଗାର",
-    "12": "ବାର",
-    "13": "ତେର",
-    "14": "ଚଉଦ",
-    "15": "ପନ୍ଦର",
-    "16": "ଷୋହଳ",
-    "17": "ସତର",
-    "18": "ଅଠର",
-    "19": "ଊଣାଇଶ",
-    "20": "କୋଡିଏ",
-    "21": "ଏକୋଇଶି",
-    "22": "ବାଇଶି",
-    "23": "ତେଇଶି",
-    "24": "ଚବିଶି",
-    "25": "ପଚିଶି",
-    "26": "ଛବିଶି",
-    "27": "ସତାଇଶି",
-    "28": "ଅଠାଇଶି",
-    "29": "ଅଣତିରିଶି",
-    "30": "ତିରିଶି",
-    "31": "ଏକତିରିଶି",
-    "32": "ବତିଶି",
-    "33": "ତେତିଶି",
-    "34": "ଚଉତିରିଶି",
-    "35": "ପଞ୍ଚତିରିଶି",
-    "36": "ଛତିଶି",
-    "37": "ସଂଇତିରିଶି",
-    "38": "ଅଠତିରିଶି",
-    "39": "ଅଣଚାଳିଶି",
-    "40": "ଚାଳିଶି",
-    "41": "ଏକଚାଳିଶି",
-    "42": "ବୟାଳିଶି",
-    "43": "ତେୟାଳିଶି",
-    "44": "ଚଉରାଳିଶି",
-    "45": "ପଞ୍ଚଚାଳିଶି",
-    "46": "ଛୟାଳିଶି",
-    "47": "ସତଚାଳିଶି",
-    "48": "ଅଠଚାଳିଶି",
-    "49": "ଅଣଚାଶ",
-    "50": "ପଚାଶ",
-    "51": "ଏକାବନ",
-    "52": "ବାଉନ",
-    "53": "ତେପନ",
-    "54": "ଚଉବନ",
-    "55": "ପଞ୍ଚାବନ",
-    "56": "ଛପନ",
-    "57": "ସତାବନ",
-    "58": "ଅଠାବନ",
-    "59": "ଅଣଷଠି",
-    "60": "ଷାଠିଏ",
-    "61": "ଏକଷଠି",
-    "62": "ବାଷଠି",
-    "63": "ତେଷଠି",
-    "64": "ଚଉଷଠି",
-    "65": "ପଞ୍ଚଷଠି",
-    "66": "ଛଅଷଠି",
-    "67": "ସତଷଠି",
-    "68": "ଅଠଷଠି",
-    "69": "ଅଣସ୍ତରୀ",
-    "70": "ସତୂରୀ",
-    "71": "ଏକସ୍ତରୀ",
-    "72": "ବାସ୍ତରୀ",
-    "73": "ତେସ୍ତରୀ",
-    "74": "ଚଉସ୍ତରୀ",
-    "75": "ପଞ୍ଚସ୍ତରୀ",
-    "76": "ଛଅସ୍ତରୀ",
-    "77": "ସତସ୍ତରୀ",
-    "78": "ଅଠସ୍ତରୀ",
-    "79": "ଅଣାଅଶୀ",
-    "80": "ଅଶୀ",
-    "81": "ଏକାଅଶୀ",
-    "82": "ବୟାଅଶୀ",
-    "83": "ତେୟାଅଶୀ",
-    "84": "ଚଉରାଅଶୀ",
-    "85": "ପଞ୍ଚାଅଶୀ",
-    "86": "ଛୟାଅଶୀ",
-    "87": "ସତାଅଶୀ",
-    "88": "ଅଠାଅଶୀ",
-    "89": "ଅଣାନବେ",
-    "90": "ନବେ",
-    "91": "ଏକାନବେ",
-    "92": "ବୟାନବେ",
-    "93": "ତେୟାନବେ",
-    "94": "ଚଉରାନବେ",
-    "95": "ପଞ୍ଚାନବେ",
-    "96": "ଛୟାନବେ",
-    "97": "ସତାନବେ",
-    "98": "ଅଠାନବେ",
-    "99": "ଅନେଶତ",
-    "100": "ଶହେ",
-    "1000": "ହଜାର",
-    "100000": "ଲକ୍ଷ",
-    "10000000": "କୋଟି",
-    "1000000000": "କୋଟି",
-}  # Oriya
-num_dict["pa"] = {
-    "0": "ਸਿਫਰ ",
-    "1": "ਇੱਕ",
-    "2": "ਦੋ",
-    "3": "ਤਿੰਨ",
-    "4": "ਚਾਰ",
-    "5": "ਪੰਜ",
-    "6": "ਛੇ",
-    "7": "ਸੱਤ",
-    "8": "ਅੱਠ",
-    "9": "ਨੌਂ",
-    "10": "ਦੱਸ",
-    "11": "ਗਿਆਰਾਂ",
-    "12": "ਬਾਰਾਂ",
-    "13": "ਤੇਰਾਂ",
-    "14": "ਚੌਦਾਂ",
-    "15": "ਪੰਦਰਾਂ",
-    "16": "ਸੋਲ਼ਾਂ",
-    "17": "ਸਤਾਰਾਂ",
-    "18": "ਅਠਾਰਾਂ",
-    "19": "ਉਨੀ",
-    "20": "ਵੀਹ",
-    "21": "ਇੱਕੀ",
-    "22": "ਬਾਈ",
-    "23": "ਤੇਈ",
-    "24": "ਚੌਵੀ",
-    "25": "ਪੰਝੀ",
-    "26": "ਛੱਬੀ",
-    "27": "ਸਤਾਈ",
-    "28": "ਅਠਾਈ",
-    "29": "ਉਨੱਤੀ",
-    "30": "ਤੀਹ",
-    "31": "ਇਕੱਤੀ",
-    "32": "ਬੱਤੀ",
-    "33": "ਤੇਤੀ",
-    "34": "ਚੌਂਤੀ",
-    "35": "ਪੈਂਤੀ",
-    "36": "ਛੱਤੀ",
-    "37": "ਸੈਂਤੀ",
-    "38": "ਅਠੱਤੀ",
-    "39": "ਉਨਤਾਲੀ",
-    "40": "ਚਾਲੀ",
-    "41": "ਇਕਤਾਲੀ",
-    "42": "ਬਤਾਲੀ",
-    "43": "ਤਰਤਾਲੀ",
-    "44": "ਚੌਤਾਲੀ",
-    "45": "ਪੰਜਤਾਲੀ",
-    "46": "ਛਿਆਲੀ",
-    "47": "ਸੰਤਾਲੀ",
-    "48": "ਅੱਠਤਾਲੀ",
-    "49": "ਉਣਿੰਜਾ",
-    "50": "ਪੰਜਾਹ",
-    "51": "ਇਕਵਿੰਜਾ",
-    "52": "ਬਵਿੰਜਾ",
-    "53": "ਤਰਵਿੰਜਾ",
-    "54": "ਚਰਿੰਜਾ",
-    "55": "ਪਚਵਿੰਜਾ",
-    "56": "ਛਪਿੰਜਾ",
-    "57": "ਸਤਵਿੰਜਾ",
-    "58": "ਅੱਠਵਿੰਜਾ",
-    "59": "ਉਣਾਠ",
-    "60": "ਸੱਠ",
-    "61": "ਇਕਾਠ",
-    "62": "ਬਾਠ੍ਹ",
-    "63": "ਤਰੇਠ੍ਹ",
-    "64": "ਚੌਠ੍ਹ",
-    "65": "ਪੈਂਠ",
-    "66": "ਛਿਆਠ",
-    "67": "ਸਤਾਹਠ",
-    "68": "ਅੱਠਾਠ",
-    "69": "ਉਣੱਤਰ",
-    "70": "ਸੱਤਰ",
-    "71": "ਇਕ੍ਹੱਤਰ",
-    "72": "ਬਹੱਤਰ",
-    "73": "ਤਹੱਤਰ",
-    "74": "ਚੌਹੱਤਰ",
-    "75": "ਪੰਜੱਤਰ",
-    "76": "ਛਿਹੱਤਰ",
-    "77": "ਸਤੱਤਰ",
-    "78": "ਅਠੱਤਰ",
-    "79": "ਉਣਾਸੀ",
-    "80": "ਅੱਸੀ",
-    "81": "ਇਕਾਸੀ",
-    "82": "ਬਿਆਸੀ",
-    "83": "ਤਰਾਸੀ",
-    "84": "ਚਰਾਸੀ",
-    "85": "ਪੰਜਾਸੀ",
-    "86": "ਛਿਆਸੀ",
-    "87": "ਸਤਾਸੀ",
-    "88": "ਅਠਾਸੀ",
-    "89": "ਉਣਾਨਵੇਂ",
-    "90": "ਨੱਬੇ",
-    "91": "ਇਕਾਨਵੇਂ",
-    "92": "ਬਿਆਨਵੇਂ",
-    "93": "ਤਰਾਨਵੇਂ",
-    "94": "ਚਰਾਨਵੇਂ",
-    "95": "ਪਚਾਨਵੇਂ",
-    "96": "ਛਿਆਨਵੇਂ",
-    "97": "ਸਤਾਨਵੇਂ",
-    "98": "ਅਠਾਨਵੇਂ",
-    "99": "ਨਿੜਾਨਵੇਂ",
-    "100": "ਸੌ",
-    "1000": "ਹਜਾਰ",
-    "100000": "ਲੱਖ",
-    "10000000": "ਕਰੋੜ",
-    "1000000000": "ਅਰਬ",
-}  # Punjabi
-
-# --------------------------- num_to_word.py ------------------------------
-"""
-Method to convert Numbers to Words
-for indian languages
-
-Use cases:-
-1) Speech recognition pre-processing
-2) Language modeling Data pre-processing
-
--------------------------
-check indic_numbers.py to add support
-for any indian language
-"""
-
-
-def language_specific_exception(words, lang, combiner):
-    """
-    Language Specific Exception will come here
-    """
-
-    def occurs_at_end(piece):
-        return words[-len(piece) :] == piece
-
-    if lang == "mr":
-        words = words.replace("एक" + combiner + "शे", "शंभर")
-    elif lang == "gu":
-        words = words.replace("બે" + combiner + "સો", "બસ્સો")
-    elif lang == "te":
-        exception_dict = {
-            "1": "ఒక",
-            "100": "వంద",
-            "100+": "వందలు",
-            "1000": "వెయ్యి",
-            "1000+": "వేలు",
-            "100000": "లక్ష",
-            "100000+": "లక్షలు",
-            "10000000": "కోటి",
-            "10000000+": "కోట్లు",
-        }
-
-        test_case = ["100", "1000", "100000", "10000000"]
-        for test in test_case:
-            test_word = num_dict["te"][test]
-            match = num_dict["te"]["1"] + combiner + test_word
-            # for numbers like : 100, 1000, 100000
-            if words == match:
-                return exception_dict[test]
-            # for numbers like : 200, 4000, 800000
-            elif occurs_at_end(test_word):
-                words = words.replace(test_word, exception_dict[test + "+"])
-            # for numbers like : 105, 1076, 123993
-            elif not occurs_at_end(match):
-                replacement = exception_dict["1"] + combiner + exception_dict[test]
-                words = words.replace(match, replacement)
-
-        # Exception case for 101...199
-        special_case = "ఒక" + combiner + "వంద"
-        words = words.replace(special_case, "నూట")
-    elif lang == "kn":
-        # special case for 100
-        if words == ("ಒಂದು" + combiner + "ನೂರ"):
-            return "ನೂರು"
-        exception_dict = {
-            "ನೂರ": "ನೂರು",
-            "ಸಾವಿರದ": "ಸಾವಿರ",
-            "ಲಕ್ಷದ": "ಲಕ್ಷ",
-            "ಕೋಟಿಯ": "ಕೋಟಿ",
-        }
-        for expt in exception_dict:
-            if occurs_at_end(expt):
-                words = words.replace(expt, exception_dict[expt])
-    return words
-
-
-def num_to_word(num, lang, separator=", ", combiner=" "):
-    """
-    Main Method
-    :param num: Number digits from any indian language
-    :param lang: Language Code from supported Language
-    :param separator: Separator character i.e. separator = '-' --> 'two hundred-sixty'
-    :param combiner: combine number with position i.e. combiner = '-' --> 'two-hundred sixty'
-    :return: UTF-8 String of numbers in words
-    """
-    lang = lang.lower()
-    num = str(num)
-
-    # Load dictionary according to language code
-    assert lang in supported_lang, "Language not supported"
-    num_dic = num_dict[lang]
-
-    # dash default combiner for english-india
-    if (lang == "en") & (combiner == " "):
-        combiner = "-"
-
-    # Remove punctuations from numbers
-    num = str(num).replace(",", "").replace(" ", "")
-
-    # Replace native language numbers with english digits
-    for language in supported_lang:
-        for num_index in range(10):
-            num = num.replace(all_num[language][num_index], all_num["en"][num_index])
-
-    # Assert that input contains only integer number
-    for digit in num:
-        assert digit in all_num["en"], "Give proper input"
-
-    # Process
-    # For Number longer than 9 digits
-    def all_two_digit(digits_2):
-        if len(digits_2) <= 1:  # Provided only one/zero digit
-            return num_dic.get(digits_2, "")
-        elif digits_2 == "00":  # Two Zero provided
-            return num_dic["0"] + separator + num_dic["0"]
-        elif digits_2[0] == "0":  # First digit is zero
-            return num_dic["0"] + separator + num_dic[digits_2[1]]
-        else:  # Both digit provided
-            return num_dic[digits_2]
-
-    # For Number less than 9 digits
-    def two_digit(digits_2):
-        digits_2 = digits_2.lstrip("0")
-        if len(digits_2) != 0:
-            return num_dic[digits_2]
-        else:
-            return ""
-
-    def all_digit(digits):
-        digits = digits.lstrip("0")
-        digit_len = len(digits)
-        if digit_len > 3:
-            num_of_digits_to_process = (digit_len % 2) + 1
-            process_digits = digits[:num_of_digits_to_process]
-            base = str(10 ** (int(digit_len / 2) * 2 - 1))
-            remain_digits = digits[num_of_digits_to_process:]
-            return (
-                num_dic[process_digits]
-                + combiner
-                + num_dic[base]
-                + separator
-                + all_digit(remain_digits)
-            )
-        elif len(digits) == 3:
-            return (
-                num_dic[digits[:1]]
-                + combiner
-                + num_dic["100"]
-                + separator
-                + two_digit(digits[1:])
-            )
-        else:
-            return two_digit(digits)
-
-    num = num.lstrip("0")
-    full_digit_len = len(num)
-
-    if full_digit_len == 0:
-        output = num_dic["0"]
-    elif full_digit_len <= 9:
-        output = all_digit(num)
-    else:
-        iteration = round(full_digit_len / 2)
-        output = all_two_digit(num[:2])  # First to digit
-        for i in range(1, iteration):
-            output = (
-                output + separator + all_two_digit(num[i * 2 : (i + 1) * 2])
-            )  # Next two digit pairs
-        remaining_digits = num[iteration * 2 :]
-        if not all_two_digit(remaining_digits) == "":
-            output = (
-                output + separator + all_two_digit(remaining_digits)
-            )  # remaining Last one/two digits
-
-    output = output.strip(separator)
-
-    output = language_specific_exception(output, lang, combiner)
-
-    return output
-
-
-# --------------------------------- num_to_word_on_a_sent ---------------------------------
-
-
-def is_digit(word, digit_pattern):
-    return re.search(digit_pattern, word)
-
-
-def remove_punct(sent):
-    clean = re.sub("[%s]" % re.escape(string.punctuation), " ", sent)
-    return " ".join([word for word in clean.split() if word])
-
-
-def normalize_nums(text, lang):
-    """
-    text: str (eg)
-    lang: lang code ['en', 'hi']
-
-    returns: str
-    (eg)
-    """
-
-    if lang in supported_lang:
-        words = text.split()
-        lang_digits = [str(i) for i in range(0, 10)]
-
-        digit_pattern = "[" + "".join(lang_digits) + "]"
-        num_indices = [
-            ind for ind, word in enumerate(words) if is_digit(word, digit_pattern)
-        ]
-
-        words_up = [
-            num_to_word(word, lang, separator=" ", combiner=" ")
-            if ind in num_indices
-            else word
-            for ind, word in enumerate(words)
-        ]
-        return " ".join(words_up)
-    else:
-        return text
-
-
-if __name__ == "__main__":
-    print(normalize_nums("रीटा के पास 16 बिल्लियाँ हैं।", "hi"))
diff --git a/spaces/Harveenchadha/en_to_indic_translation/subword-nmt/subword_nmt/tests/test_bpe.py b/spaces/Harveenchadha/en_to_indic_translation/subword-nmt/subword_nmt/tests/test_bpe.py
deleted file mode 100644
index d8c84857b4aa22903907ae3d217bf9468b168c88..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/en_to_indic_translation/subword-nmt/subword_nmt/tests/test_bpe.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-from __future__ import unicode_literals
-import unittest
-import codecs
-
-import os,sys,inspect
-currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
-parentdir = os.path.dirname(currentdir)
-sys.path.insert(0,parentdir)
-
-from learn_bpe import learn_bpe
-from apply_bpe import BPE
-
-
-class TestBPELearnMethod(unittest.TestCase):
-
-    def test_learn_bpe(self):
-        infile = codecs.open(os.path.join(currentdir,'data','corpus.en'), encoding='utf-8')
-        outfile = codecs.open(os.path.join(currentdir,'data','bpe.out'), 'w', encoding='utf-8')
-        learn_bpe(infile, outfile, 1000)
-        infile.close()
-        outfile.close()
-
-        outlines = open(os.path.join(currentdir,'data','bpe.out'))
-        reflines = open(os.path.join(currentdir,'data','bpe.ref'))
-
-        for line, line2 in zip(outlines, reflines):
-            self.assertEqual(line, line2)
-
-        outlines.close()
-        reflines.close()
-
-class TestBPESegmentMethod(unittest.TestCase):
-
-    def setUp(self):
-
-        with codecs.open(os.path.join(currentdir,'data','bpe.ref'), encoding='utf-8') as bpefile:
-            self.bpe = BPE(bpefile)
-
-        self.infile = codecs.open(os.path.join(currentdir,'data','corpus.en'), encoding='utf-8')
-        self.reffile = codecs.open(os.path.join(currentdir,'data','corpus.bpe.ref.en'), encoding='utf-8')
-
-    def tearDown(self):
-
-        self.infile.close()
-        self.reffile.close()
-
-    def test_apply_bpe(self):
-
-        for line, ref in zip(self.infile, self.reffile):
-            out = self.bpe.process_line(line)
-            self.assertEqual(out, ref)
-
-    def test_trailing_whitespace(self):
-        """BPE.proces_line() preserves leading and trailing whitespace"""
-
-        orig = '  iron cement  \n'
-        exp = '  ir@@ on c@@ ement  \n'
-
-        out = self.bpe.process_line(orig)
-        self.assertEqual(out, exp)
-
-    def test_utf8_whitespace(self):
-        """UTF-8 whitespace is treated as normal character, not word boundary"""
-
-        orig = 'iron\xa0cement\n'
-        exp = 'ir@@ on@@ \xa0@@ c@@ ement\n'
-
-        out = self.bpe.process_line(orig)
-        self.assertEqual(out, exp)
-
-    def test_empty_line(self):
-
-        orig = '\n'
-        exp = '\n'
-
-        out = self.bpe.process_line(orig)
-        self.assertEqual(out, exp)
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.803c5e11.css b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.803c5e11.css
deleted file mode 100644
index bca8fc3e5366c4dbbeef359caafacb668d901e99..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.803c5e11.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-10ogue4>*:not(.absolute){border-radius:0!important}div.svelte-10ogue4>*:first-child{border-top-left-radius:.5rem!important;border-top-right-radius:.5rem!important}div.svelte-10ogue4>*:last-child{border-bottom-left-radius:.5rem!important;border-bottom-right-radius:.5rem!important}div.svelte-10ogue4>*+*:not(.absolute){border-top-width:0px!important}
diff --git a/spaces/Hoodady/3DFuse/cldm/hack.py b/spaces/Hoodady/3DFuse/cldm/hack.py
deleted file mode 100644
index 454361e9d036cd1a6a79122c2fd16b489e4767b1..0000000000000000000000000000000000000000
--- a/spaces/Hoodady/3DFuse/cldm/hack.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import torch
-import einops
-
-import ldm.modules.encoders.modules
-import ldm.modules.attention
-
-from transformers import logging
-from ldm.modules.attention import default
-
-
-def disable_verbosity():
-    logging.set_verbosity_error()
-    print('logging improved.')
-    return
-
-
-def enable_sliced_attention():
-    ldm.modules.attention.CrossAttention.forward = _hacked_sliced_attentin_forward
-    print('Enabled sliced_attention.')
-    return
-
-
-def hack_everything(clip_skip=0):
-    disable_verbosity()
-    ldm.modules.encoders.modules.FrozenCLIPEmbedder.forward = _hacked_clip_forward
-    ldm.modules.encoders.modules.FrozenCLIPEmbedder.clip_skip = clip_skip
-    print('Enabled clip hacks.')
-    return
-
-
-# Written by Lvmin
-def _hacked_clip_forward(self, text):
-    PAD = self.tokenizer.pad_token_id
-    EOS = self.tokenizer.eos_token_id
-    BOS = self.tokenizer.bos_token_id
-
-    def tokenize(t):
-        return self.tokenizer(t, truncation=False, add_special_tokens=False)["input_ids"]
-
-    def transformer_encode(t):
-        if self.clip_skip > 1:
-            rt = self.transformer(input_ids=t, output_hidden_states=True)
-            return self.transformer.text_model.final_layer_norm(rt.hidden_states[-self.clip_skip])
-        else:
-            return self.transformer(input_ids=t, output_hidden_states=False).last_hidden_state
-
-    def split(x):
-        return x[75 * 0: 75 * 1], x[75 * 1: 75 * 2], x[75 * 2: 75 * 3]
-
-    def pad(x, p, i):
-        return x[:i] if len(x) >= i else x + [p] * (i - len(x))
-
-    raw_tokens_list = tokenize(text)
-    tokens_list = []
-
-    for raw_tokens in raw_tokens_list:
-        raw_tokens_123 = split(raw_tokens)
-        raw_tokens_123 = [[BOS] + raw_tokens_i + [EOS] for raw_tokens_i in raw_tokens_123]
-        raw_tokens_123 = [pad(raw_tokens_i, PAD, 77) for raw_tokens_i in raw_tokens_123]
-        tokens_list.append(raw_tokens_123)
-
-    tokens_list = torch.IntTensor(tokens_list).to(self.device)
-
-    feed = einops.rearrange(tokens_list, 'b f i -> (b f) i')
-    y = transformer_encode(feed)
-    z = einops.rearrange(y, '(b f) i c -> b (f i) c', f=3)
-
-    return z
-
-
-# Stolen from https://github.com/basujindal/stable-diffusion/blob/main/optimizedSD/splitAttention.py
-def _hacked_sliced_attentin_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)
-    del context, x
-
-    q, k, v = map(lambda t: einops.rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-    limit = k.shape[0]
-    att_step = 1
-    q_chunks = list(torch.tensor_split(q, limit // att_step, dim=0))
-    k_chunks = list(torch.tensor_split(k, limit // att_step, dim=0))
-    v_chunks = list(torch.tensor_split(v, limit // att_step, dim=0))
-
-    q_chunks.reverse()
-    k_chunks.reverse()
-    v_chunks.reverse()
-    sim = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
-    del k, q, v
-    for i in range(0, limit, att_step):
-        q_buffer = q_chunks.pop()
-        k_buffer = k_chunks.pop()
-        v_buffer = v_chunks.pop()
-        sim_buffer = torch.einsum('b i d, b j d -> b i j', q_buffer, k_buffer) * self.scale
-
-        del k_buffer, q_buffer
-        # attention, what we cannot get enough of, by chunks
-
-        sim_buffer = sim_buffer.softmax(dim=-1)
-
-        sim_buffer = torch.einsum('b i j, b j d -> b i d', sim_buffer, v_buffer)
-        del v_buffer
-        sim[i:i + att_step, :, :] = sim_buffer
-
-        del sim_buffer
-    sim = einops.rearrange(sim, '(b h) n d -> b n (h d)', h=h)
-    return self.to_out(sim)
diff --git a/spaces/Hoodady/3DFuse/voxnerf/utils.py b/spaces/Hoodady/3DFuse/voxnerf/utils.py
deleted file mode 100644
index 94c261098d65432c1b8ee7e3314918ebfbd06daf..0000000000000000000000000000000000000000
--- a/spaces/Hoodady/3DFuse/voxnerf/utils.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import numpy as np
-import math
-
-
-def blend_rgba(img):
-    img = img[..., :3] * img[..., -1:] + (1. - img[..., -1:])  # blend A to RGB
-    return img
-
-
-class PSNR():
-    @classmethod
-    def psnr(cls, ref, pred, max=1.0):
-        # if inputs of type int, then make sure max is 255
-        mse = ((ref - pred) ** 2).mean()
-        return cls.psnr_from_mse(mse, max)
-
-    @staticmethod
-    def psnr_from_mse(mse, max=1.0):
-        psnr = 20 * math.log10(max) - 10 * math.log10(mse)
-        return psnr
-
-    @staticmethod
-    def psnr_to_rms(psnr_diff):
-        """rms error improvement _ratio_ from psnr _diff_"""
-        ratio = 10 ** (-psnr_diff / 20)
-        return ratio
-
-
-class Scrambler():
-    def __init__(self, N):
-        self.perm = np.random.permutation(N)
-
-    def apply(self, *items):
-        return [elem[self.perm] for elem in items]
-
-    def unscramble(self, *items):
-        ret = []
-        for elem in items:
-            clean = np.zeros_like(elem)
-            clean[self.perm] = elem
-            ret.append(clean)
-        return ret
-
-
-def trailing_window_view(xs, window_size):
-    assert (window_size % 2) == 1, "window size should be odd"
-    view = np.lib.stride_tricks.sliding_window_view(
-        np.pad(xs, (window_size - 1, 0), mode="edge"), window_size
-    )
-    return view
-
-
-def to_step(pbar, percent):
-    step = int(pbar.total * percent / 100)
-    return step
-
-
-def every(pbar, *, percent=None, step=None):
-    if step is None:
-        step = to_step(pbar, percent)
-    return (pbar.n + 1) % step == 0
-
-
-def at(pbar, *, percent=None, step=None):
-    if step is None:
-        step = to_step(pbar, percent)
-    return (pbar.n + 1) == step
diff --git a/spaces/HuangLab/CELL-E_2-Sequence_Prediction/taming/modules/discriminator/model.py b/spaces/HuangLab/CELL-E_2-Sequence_Prediction/taming/modules/discriminator/model.py
deleted file mode 100644
index 2aaa3110d0a7bcd05de7eca1e45101589ca5af05..0000000000000000000000000000000000000000
--- a/spaces/HuangLab/CELL-E_2-Sequence_Prediction/taming/modules/discriminator/model.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import functools
-import torch.nn as nn
-
-
-from taming.modules.util import ActNorm
-
-
-def weights_init(m):
-    classname = m.__class__.__name__
-    if classname.find('Conv') != -1:
-        nn.init.normal_(m.weight.data, 0.0, 0.02)
-    elif classname.find('BatchNorm') != -1:
-        nn.init.normal_(m.weight.data, 1.0, 0.02)
-        nn.init.constant_(m.bias.data, 0)
-
-
-class NLayerDiscriminator(nn.Module):
-    """Defines a PatchGAN discriminator as in Pix2Pix
-        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
-    """
-    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
-        """Construct a PatchGAN discriminator
-        Parameters:
-            input_nc (int)  -- the number of channels in input images
-            ndf (int)       -- the number of filters in the last conv layer
-            n_layers (int)  -- the number of conv layers in the discriminator
-            norm_layer      -- normalization layer
-        """
-        super(NLayerDiscriminator, self).__init__()
-        if not use_actnorm:
-            norm_layer = nn.BatchNorm2d
-        else:
-            norm_layer = ActNorm
-        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
-            use_bias = norm_layer.func != nn.BatchNorm2d
-        else:
-            use_bias = norm_layer != nn.BatchNorm2d
-
-        kw = 4
-        padw = 1
-        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
-        nf_mult = 1
-        nf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            nf_mult_prev = nf_mult
-            nf_mult = min(2 ** n, 8)
-            sequence += [
-                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
-                norm_layer(ndf * nf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-
-        nf_mult_prev = nf_mult
-        nf_mult = min(2 ** n_layers, 8)
-        sequence += [
-            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
-            norm_layer(ndf * nf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-
-        sequence += [
-            nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]  # output 1 channel prediction map
-        self.main = nn.Sequential(*sequence)
-
-    def forward(self, input):
-        """Standard forward."""
-        return self.main(input)
diff --git a/spaces/HugoHE/monitoringObjectDetection/runtime_monitors/Monitor.py b/spaces/HugoHE/monitoringObjectDetection/runtime_monitors/Monitor.py
deleted file mode 100644
index 41156377cc1cb568429f89c2fa89b450d229f37e..0000000000000000000000000000000000000000
--- a/spaces/HugoHE/monitoringObjectDetection/runtime_monitors/Monitor.py
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-from abstractions import *
-import pickle
-import numpy as np
-
-class Monitor(object):
-
-    def __init__(self, good_ref=None):
-        # self.abs_type = abs_type
-        self.good_ref = good_ref
-
-
-    def set_reference(self, good_ref):
-        self.good_ref = good_ref
-    
-    # def get_identity(self):
-    #     print("Monitor for network:" + self.netName + "class: " + str(self.classification) + "at layer " + str(self.location))
-
-
-    def make_verdicts(self, features):
-        if len(self.good_ref):
-            verdicts = ref_query(features, self.good_ref)
-        else:
-            raise RuntimeError("No reference exists!")
-        return verdicts
-
-def ref_query(features, reference):
-    query_results = [boxes_query(x, reference) for x in features]
-    return query_results
-
-
-# def query_infusion(in_good_ref, in_bad_ref):
-#     if len(in_good_ref) == len(in_bad_ref): #0: acceptance (true, false), 1: rejection (false, true or false), 2: uncertainty (true, true)
-#         verdicts = np.zeros(len(in_good_ref), dtype=int)
-#         for i in range(len(in_good_ref)):
-#             if not in_good_ref[i]:
-#                 verdicts[i] = 1
-#             elif in_bad_ref[i]:
-#                 verdicts[i] = 2
-#         return verdicts
-#     else:
-#         print("Error: IllegalArgument")       
diff --git a/spaces/HuguesdeF/moulinette/Corriger.py b/spaces/HuguesdeF/moulinette/Corriger.py
deleted file mode 100644
index 2ff010c10f2898e939191d5702bd86f644ee530b..0000000000000000000000000000000000000000
--- a/spaces/HuguesdeF/moulinette/Corriger.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import streamlit as st
-import os
-import streamlit_authenticator as stauth
-from code.functions import pipeline_svg
-from PIL import Image
-import cv2
-import numpy as np
-from io import BytesIO
-import copy
-
-logo = Image.open("seguinmoreau.png")
-st.set_page_config(
-    page_title="Moulinette Logos",
-    page_icon=logo,
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-inch_value = 2.54
-
-logo = Image.open('seguinmoreau.png')
-st.image(logo, width=200)
-st.markdown(
-    """
-    # Boîte à Outils de correction de logos :wrench:
-    
-    Bienvenue dans la boîte à outils de correction de logos de Seguin Moreau.
-    
-    ### :hammer: Les outils
-    Dans cette boîte à outils, vous trouverez:
-    * Un outil de Correction automatique de logo (enlever les petits défauts, lissage, vectorisation, grossissement des traits trop fins).
-    
-    ### :bulb: Mode d'emploi
-    * Cliquer sur 'Browse files'
-    * Sélectionner un logo
-    * La correction est automatique. Si la correction ne vous convient pas, il est possible de régler les paramètres en cliquant sur 'Paramétrage' à droite de l'image.
-        * Les deux paramètres permettent de corriger les défauts liés à la présence de gris sur le logo ou la 'pixélisation' du logo trop importante.
-    
-    """
-)
-
-uploaded_files = st.file_uploader("Choisir un logo", accept_multiple_files=True)
-
-image_width = 500
-size_value = st.slider("Largeur de trait minimum", min_value=1, max_value=21, value=7, step=2)
-
-size_value = (size_value - 1) // 2
-
-# kernel_type_str = st.selectbox("Kernel type", ["Ellipse", "Rectangle", "Cross"])
-kernel_type_str = "Ellipse"
-dict_kernel_type = {"Ellipse": cv2.MORPH_ELLIPSE, "Rectangle": cv2.MORPH_RECT, "Cross": cv2.MORPH_CROSS}
-kernel_type = dict_kernel_type[kernel_type_str]
-
-for uploaded_file in uploaded_files:
-    col1, col2, col3 = st.columns([1, 1, 1])
-    col3.markdown("---")
-
-    image = Image.open(uploaded_file).convert('L')
-    image_input = np.array(image)
-    image = copy.deepcopy(image_input)
-    col1.image(image_input / 255.0, caption="Image d'entrée", use_column_width='auto')
-
-    with col3:
-        with st.expander(":gear: Paramétrage"):
-            st.write("Si l'image contient du gris, faire varier le seuil ci-dessous:")
-            threshold = st.slider("Seuil pour convertir l'image en noir&blanc.", min_value=0, max_value=255,
-                                  value=0,
-                                  step=1, key=f"{uploaded_file}_slider_threshold")
-            st.write("Si l'image est pixelisée, ou contient trop de détails, "
-                     "augmenter la valeur ci-dessous:")
-            blur_value = st.slider("Seuil pour lisser l'image", min_value=1, max_value=11, value=1, step=2,
-                                   key=f"{uploaded_file}_slider_gaussian_sigma")
-            st.write("Si l'image contient des traits très fin (de l'odre du pixel),"
-                     " augmenter le seuil ci-dessous, de 1 par 1:")
-            dilate_lines_value = st.slider("Dilatation de l'image d'origine: (en pixels)", min_value=0, max_value=5,
-                                           value=0, step=1, key=f"{uploaded_file}_slider_dilation_image")
-
-            st.write("Taille d'exportation d'image:")
-
-            dpi_value = st.number_input("Valeur dpi:", key=f"{uploaded_file}_number_dpi_value", value=200)
-
-            st.write("---")
-            st.write("Spécifier la taille maximum d'un côté, en cm:")
-            side_width_value = st.number_input("Taille max de côté cible (cm):",
-                                               key=f"{uploaded_file}_number_target_value", value=20.0)
-            new_largest_side_value = int(side_width_value / inch_value * dpi_value)
-
-            h, w, *_ = image.shape
-
-            # Resize image
-            ratio = w / h
-            if ratio > 1:
-                width = new_largest_side_value
-                height = int(width / ratio)
-            else:
-                height = new_largest_side_value
-                width = int(ratio * height)
-
-            st.write("---")
-            st.write("Ou, spécifier la largeur OU la hauteur cible, en cm:")
-
-            target_width_value = st.number_input("Largeur cible (cm):", key=f"{uploaded_file}_number_width_value",
-                                                 value=0.0)
-            target_height_value = st.number_input("Hauteur cible (cm):", key=f"{uploaded_file}_number_height_value",
-                                                  value=0.0)
-
-            if target_width_value > 0 and target_height_value == 0:
-                width = int(target_width_value / inch_value * dpi_value)
-                height = int(width / ratio)
-            elif target_height_value > 0 and target_width_value == 0:
-                height = int(target_height_value / inch_value * dpi_value)
-                width = int(height * ratio)
-            elif target_height_value > 0 and target_width_value > 0:
-                st.warning("Vous ne pouvez pas modifier la largeur et la hauteur simultanément.")
-
-            st.info(f"Le logo sera redimensionné de :")
-            st.info(f"hauteur={h} pixels et largeur={w} pixels vers "
-                    f"hauteur={height} pixels et largeur={width} pixels.")
-
-    if threshold > 0:
-        image = (image > threshold) * 255
-        image = image.astype('uint8')
-
-    if blur_value > 0:
-        image = cv2.GaussianBlur(image, (blur_value, blur_value), blur_value - 1)
-
-    # Process image cv32f ==> cv32f
-    img_final = pipeline_svg(image, size_value=size_value, level=1, threshold=threshold, kernel_type=kernel_type,
-                             dilate_lines_value=dilate_lines_value)
-
-    col2.image(img_final, caption="Image corrigée", use_column_width='auto')
-
-    # Check for grayscale
-    tolerance = 10
-    ratio_of_gray_pixels = int(np.sum((tolerance < image) * (image < 255 - tolerance)) / np.size(image) * 100)
-    if ratio_of_gray_pixels > 1:
-        col3.warning(f":warning: Le nombre de pixels gris est élevé: {ratio_of_gray_pixels} % > 1%")
-
-    # Check reconstruction fidelity
-    distance = np.mean((np.array(image) - img_final) ** 2)
-    if distance > 10:
-        col3.warning(
-            f":warning: Le logo est peut-être trop dégradé (MSE={distance:.2f} > 10).\nVérifier visuellement.")
-
-    dim = (width, height)
-    # resize image
-    resized_img_final = cv2.resize(img_final, dim, interpolation=cv2.INTER_AREA)
-    resized_image_input = cv2.resize(image_input, dim, interpolation=cv2.INTER_AREA)
-
-    buf = BytesIO()
-    # img_stacked = np.hstack((resized_image_input, resized_img_final))
-    img_final = Image.fromarray(resized_img_final).convert("L")
-
-    img_final.save(buf, format="PNG", dpi=(dpi_value, dpi_value))
-    byte_im = buf.getvalue()
-
-    btn = col3.download_button(
-        label=":inbox_tray: Télécharger l'image",
-        data=byte_im,
-        file_name=f"corrected_{uploaded_file.name}",
-        mime="image/png"
-    )
diff --git a/spaces/HutzHoo/dreamlike-photoreal-2.0/app.py b/spaces/HutzHoo/dreamlike-photoreal-2.0/app.py
deleted file mode 100644
index 0d0ae718eacc495efdbb94276323b93eb3321f76..0000000000000000000000000000000000000000
--- a/spaces/HutzHoo/dreamlike-photoreal-2.0/app.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-import gradio as gr
-
-API_KEY=os.environ.get('HUGGING_FACE_HUB_TOKEN', None)
-
-article = """---
-This space was created using [SD Space Creator](https://huggingface.co/spaces/anzorq/sd-space-creator)."""
-
-gr.Interface.load(
-    name="models/dreamlike-art/dreamlike-photoreal-2.0",
-    title="""Dreamlike Photoreal 2.0""",
-    description="""Demo for <a href="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0">Dreamlike Photoreal 2.0</a> Stable Diffusion model.""",
-    article=article,
-    api_key=API_KEY,
-    ).queue(concurrency_count=20).launch()
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/nltk_tokenizer.py b/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/nltk_tokenizer.py
deleted file mode 100644
index 0ab92377b3a23bb48384c3f7acf299612e8b0775..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/nltk_tokenizer.py
+++ /dev/null
@@ -1,24 +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_tokenizer
-from fairseq.dataclass import FairseqDataclass
-
-
-@register_tokenizer("nltk", dataclass=FairseqDataclass)
-class NLTKTokenizer(object):
-    def __init__(self, *unused):
-        try:
-            from nltk.tokenize import word_tokenize
-
-            self.word_tokenize = word_tokenize
-        except ImportError:
-            raise ImportError("Please install nltk with: pip install nltk")
-
-    def encode(self, x: str) -> str:
-        return " ".join(self.word_tokenize(x))
-
-    def decode(self, x: str) -> str:
-        return x
diff --git a/spaces/ICML2022/resefa/utils/visualizers/video_visualizer.py b/spaces/ICML2022/resefa/utils/visualizers/video_visualizer.py
deleted file mode 100644
index b4c3a5934224edc5d557ad1b1458d4f776148a75..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/resefa/utils/visualizers/video_visualizer.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# python3.7
-"""Contains the visualizer to visualize images as a video.
-
-This file relies on `FFmpeg`. Use `sudo apt-get install ffmpeg` and
-`brew install ffmpeg` to install on Ubuntu and MacOS respectively.
-"""
-
-import os.path
-from skvideo.io import FFmpegWriter
-from skvideo.io import FFmpegReader
-
-from ..image_utils import parse_image_size
-from ..image_utils import load_image
-from ..image_utils import resize_image
-from ..image_utils import list_images_from_dir
-
-__all__ = ['VideoVisualizer', 'VideoReader']
-
-
-class VideoVisualizer(object):
-    """Defines the video visualizer that presents images as a video."""
-
-    def __init__(self,
-                 path=None,
-                 frame_size=None,
-                 fps=25.0,
-                 codec='libx264',
-                 pix_fmt='yuv420p',
-                 crf=1):
-        """Initializes the video visualizer.
-
-        Args:
-            path: Path to write the video. (default: None)
-            frame_size: Frame size, i.e., (height, width). (default: None)
-            fps: Frames per second. (default: 24)
-            codec: Codec. (default: `libx264`)
-            pix_fmt: Pixel format. (default: `yuv420p`)
-            crf: Constant rate factor, which controls the compression. The
-                larger this field is, the higher compression and lower quality.
-                `0` means no compression and consequently the highest quality.
-                To enable QuickTime playing (requires YUV to be 4:2:0, but
-                `crf = 0` results YUV to be 4:4:4), please set this field as
-                at least 1. (default: 1)
-        """
-        self.set_path(path)
-        self.set_frame_size(frame_size)
-        self.set_fps(fps)
-        self.set_codec(codec)
-        self.set_pix_fmt(pix_fmt)
-        self.set_crf(crf)
-        self.video = None
-
-    def set_path(self, path=None):
-        """Sets the path to save the video."""
-        self.path = path
-
-    def set_frame_size(self, frame_size=None):
-        """Sets the video frame size."""
-        height, width = parse_image_size(frame_size)
-        self.frame_height = height
-        self.frame_width = width
-
-    def set_fps(self, fps=25.0):
-        """Sets the FPS (frame per second) of the video."""
-        self.fps = fps
-
-    def set_codec(self, codec='libx264'):
-        """Sets the video codec."""
-        self.codec = codec
-
-    def set_pix_fmt(self, pix_fmt='yuv420p'):
-        """Sets the video pixel format."""
-        self.pix_fmt = pix_fmt
-
-    def set_crf(self, crf=1):
-        """Sets the CRF (constant rate factor) of the video."""
-        self.crf = crf
-
-    def init_video(self):
-        """Initializes an empty video with expected settings."""
-        assert not os.path.exists(self.path), f'Video `{self.path}` existed!'
-        assert self.frame_height > 0
-        assert self.frame_width > 0
-
-        video_setting = {
-            '-r': f'{self.fps:.2f}',
-            '-s': f'{self.frame_width}x{self.frame_height}',
-            '-vcodec': f'{self.codec}',
-            '-crf': f'{self.crf}',
-            '-pix_fmt': f'{self.pix_fmt}',
-        }
-        self.video = FFmpegWriter(self.path, outputdict=video_setting)
-
-    def add(self, frame):
-        """Adds a frame into the video visualizer.
-
-        NOTE: The input frame is assumed to be with `RGB` channel order.
-        """
-        if self.video is None:
-            height, width = frame.shape[0:2]
-            height = self.frame_height or height
-            width = self.frame_width or width
-            self.set_frame_size((height, width))
-            self.init_video()
-        if frame.shape[0:2] != (self.frame_height, self.frame_width):
-            frame = resize_image(frame, (self.frame_width, self.frame_height))
-        self.video.writeFrame(frame)
-
-    def visualize_collection(self, images, save_path=None):
-        """Visualizes a collection of images one by one."""
-        if save_path is not None and save_path != self.path:
-            self.save()
-            self.set_path(save_path)
-        for image in images:
-            self.add(image)
-        self.save()
-
-    def visualize_list(self, image_list, save_path=None):
-        """Visualizes a list of image files."""
-        if save_path is not None and save_path != self.path:
-            self.save()
-            self.set_path(save_path)
-        for filename in image_list:
-            image = load_image(filename)
-            self.add(image)
-        self.save()
-
-    def visualize_directory(self, directory, save_path=None):
-        """Visualizes all images under a directory."""
-        image_list = list_images_from_dir(directory)
-        self.visualize_list(image_list, save_path)
-
-    def save(self):
-        """Saves the video by closing the file."""
-        if self.video is not None:
-            self.video.close()
-            self.video = None
-            self.set_path(None)
-
-
-class VideoReader(object):
-    """Defines the video reader.
-
-    This class can be used to read frames from a given video.
-
-    NOTE: Each frame can be read only once.
-    TODO: Fix this?
-    """
-
-    def __init__(self, path, inputdict=None):
-        """Initializes the video reader by loading the video from disk."""
-        self.path = path
-        self.video = FFmpegReader(path, inputdict=inputdict)
-
-        self.length = self.video.inputframenum
-        self.frame_height = self.video.inputheight
-        self.frame_width = self.video.inputwidth
-        self.fps = self.video.inputfps
-        self.pix_fmt = self.video.pix_fmt
-
-    def __del__(self):
-        """Releases the opened video."""
-        self.video.close()
-
-    def read(self, image_size=None):
-        """Reads the next frame."""
-        frame = next(self.video.nextFrame())
-        height, width = parse_image_size(image_size)
-        height = height or frame.shape[0]
-        width = width or frame.shape[1]
-        if frame.shape[0:2] != (height, width):
-            frame = resize_image(frame, (width, height))
-        return frame
diff --git a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/__init__.py b/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/__init__.py
deleted file mode 100644
index 2af819d61d589cfec2e0ca46612a7456f42b831a..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/__init__.py
+++ /dev/null
@@ -1,15 +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
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-from .groundingdino import build_groundingdino
diff --git a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/registry.py b/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/registry.py
deleted file mode 100644
index 2d22a59eec79a2a19b83fa1779f2adaf5753aec6..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/registry.py
+++ /dev/null
@@ -1,66 +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]
-# ------------------------------------------------------------------------
-# -*- coding: utf-8 -*-
-# @Author: Yihao Chen
-# @Date:   2021-08-16 16:03:17
-# @Last Modified by:   Shilong Liu
-# @Last Modified time: 2022-01-23 15:26
-# modified from mmcv
-
-import inspect
-from functools import partial
-
-
-class Registry(object):
-    def __init__(self, name):
-        self._name = name
-        self._module_dict = dict()
-
-    def __repr__(self):
-        format_str = self.__class__.__name__ + "(name={}, items={})".format(
-            self._name, list(self._module_dict.keys())
-        )
-        return format_str
-
-    def __len__(self):
-        return len(self._module_dict)
-
-    @property
-    def name(self):
-        return self._name
-
-    @property
-    def module_dict(self):
-        return self._module_dict
-
-    def get(self, key):
-        return self._module_dict.get(key, None)
-
-    def registe_with_name(self, module_name=None, force=False):
-        return partial(self.register, module_name=module_name, force=force)
-
-    def register(self, module_build_function, module_name=None, force=False):
-        """Register a module build function.
-        Args:
-            module (:obj:`nn.Module`): Module to be registered.
-        """
-        if not inspect.isfunction(module_build_function):
-            raise TypeError(
-                "module_build_function must be a function, but got {}".format(
-                    type(module_build_function)
-                )
-            )
-        if module_name is None:
-            module_name = module_build_function.__name__
-        if not force and module_name in self._module_dict:
-            raise KeyError("{} is already registered in {}".format(module_name, self.name))
-        self._module_dict[module_name] = module_build_function
-
-        return module_build_function
-
-
-MODULE_BUILD_FUNCS = Registry("model build functions")
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/onnx/onnx_export.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/onnx/onnx_export.py
deleted file mode 100644
index 976bfe97a213d1390bdc044b5d86cab84d10e63b..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/onnx/onnx_export.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import argparse
-import time
-import numpy as np
-import onnx
-from onnxsim import simplify
-import onnxruntime as ort
-import onnxoptimizer
-import torch
-from model_onnx import SynthesizerTrn
-import utils
-from hubert import hubert_model_onnx
-
-def main(HubertExport,NetExport):
-
-    path = "NyaruTaffy"
-
-    if(HubertExport):
-        device = torch.device("cuda")
-        hubert_soft = utils.get_hubert_model()
-        test_input = torch.rand(1, 1, 16000)
-        input_names = ["source"]
-        output_names = ["embed"]
-        torch.onnx.export(hubert_soft.to(device),
-                        test_input.to(device),
-                        "hubert3.0.onnx",
-                        dynamic_axes={
-                            "source": {
-                                2: "sample_length"
-                            }
-                        },
-                        verbose=False,
-                        opset_version=13,
-                        input_names=input_names,
-                        output_names=output_names)
-    if(NetExport):
-        device = torch.device("cuda")
-        hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
-        SVCVITS = SynthesizerTrn(
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            **hps.model)
-        _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None)
-        _ = SVCVITS.eval().to(device)
-        for i in SVCVITS.parameters():
-            i.requires_grad = False
-        test_hidden_unit = torch.rand(1, 50, 256)
-        test_lengths = torch.LongTensor([50])
-        test_pitch = torch.rand(1, 50)
-        test_sid = torch.LongTensor([0])
-        input_names = ["hidden_unit", "lengths", "pitch", "sid"]
-        output_names = ["audio", ]
-        SVCVITS.eval()
-        torch.onnx.export(SVCVITS,
-                        (
-                            test_hidden_unit.to(device),
-                            test_lengths.to(device),
-                            test_pitch.to(device),
-                            test_sid.to(device)
-                        ),
-                        f"checkpoints/{path}/model.onnx",
-                        dynamic_axes={
-                            "hidden_unit": [0, 1],
-                            "pitch": [1]
-                        },
-                        do_constant_folding=False,
-                        opset_version=16,
-                        verbose=False,
-                        input_names=input_names,
-                        output_names=output_names)
-
-
-if __name__ == '__main__':
-    main(False,True)
diff --git a/spaces/Illumotion/Koboldcpp/.github/ISSUE_TEMPLATE/custom.md b/spaces/Illumotion/Koboldcpp/.github/ISSUE_TEMPLATE/custom.md
deleted file mode 100644
index 8fd95535677803e6349e9ce769cb51f5814e1458..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/.github/ISSUE_TEMPLATE/custom.md
+++ /dev/null
@@ -1,185 +0,0 @@
----
-name: Issue and enhancement template
-about: Used to report issues and request enhancements for llama.cpp
-title: "[User] Insert summary of your issue or enhancement.."
-labels: ''
-assignees: ''
-
----
-
-# Prerequisites
-
-Please answer the following questions for yourself before submitting an issue.
-
-- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
-- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
-- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
-- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
-
-# Expected Behavior
-
-Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do.
-
-# Current Behavior
-
-Please provide a detailed written description of what `llama.cpp` did, instead.
-
-# Environment and Context
-
-Please provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions.
-
-* Physical (or virtual) hardware you are using, e.g. for Linux:
-
-`$ lscpu`
-
-* Operating System, e.g. for Linux:
-
-`$ uname -a`
-
-* SDK version, e.g. for Linux:
-
-```
-$ python3 --version
-$ make --version
-$ g++ --version
-```
-
-# Failure Information (for bugs)
-
-Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template.
-
-# Steps to Reproduce
-
-Please provide detailed steps for reproducing the issue. We are not sitting in front of your screen, so the more detail the better.
-
-1. step 1
-2. step 2
-3. step 3
-4. etc.
-
-# Failure Logs
-
-Please include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes.
-
-Also, please try to **avoid using screenshots** if at all possible. Instead, copy/paste the console output and use [Github's markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to cleanly format your logs for easy readability.
-
-Example environment info:
-```
-llama.cpp$ git log | head -1
-commit 2af23d30434a677c6416812eea52ccc0af65119c
-
-llama.cpp$ lscpu | egrep "AMD|Flags"
-Vendor ID:                       AuthenticAMD
-Model name:                      AMD Ryzen Threadripper 1950X 16-Core Processor
-Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev
-Virtualization:                  AMD-V
-
-llama.cpp$ python3 --version
-Python 3.10.9
-
-llama.cpp$ pip list | egrep "torch|numpy|sentencepiece"
-numpy                         1.24.2
-numpydoc                      1.5.0
-sentencepiece                 0.1.97
-torch                         1.13.1
-torchvision                   0.14.1
-
-llama.cpp$ make --version | head -1
-GNU Make 4.3
-
-$ md5sum ./models/65B/ggml-model-q4_0.bin
-dbdd682cce80e2d6e93cefc7449df487  ./models/65B/ggml-model-q4_0.bin
-```
-
-Example run with the Linux command [perf](https://www.brendangregg.com/perf.html)
-```
-llama.cpp$ perf stat ./main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p "Please close your issue when it has been answered."
-main: seed = 1679149377
-llama_model_load: loading model from './models/65B/ggml-model-q4_0.bin' - please wait ...
-llama_model_load: n_vocab = 32000
-llama_model_load: n_ctx   = 512
-llama_model_load: n_embd  = 8192
-llama_model_load: n_mult  = 256
-llama_model_load: n_head  = 64
-llama_model_load: n_layer = 80
-llama_model_load: n_rot   = 128
-llama_model_load: f16     = 2
-llama_model_load: n_ff    = 22016
-llama_model_load: n_parts = 8
-llama_model_load: ggml ctx size = 41477.73 MB
-llama_model_load: memory_size =  2560.00 MB, n_mem = 40960
-llama_model_load: loading model part 1/8 from './models/65B/ggml-model-q4_0.bin'
-llama_model_load: .......................................................................................... done
-llama_model_load: model size =  4869.09 MB / num tensors = 723
-llama_model_load: loading model part 2/8 from './models/65B/ggml-model-q4_0.bin.1'
-llama_model_load: .......................................................................................... done
-llama_model_load: model size =  4869.09 MB / num tensors = 723
-llama_model_load: loading model part 3/8 from './models/65B/ggml-model-q4_0.bin.2'
-llama_model_load: .......................................................................................... done
-llama_model_load: model size =  4869.09 MB / num tensors = 723
-llama_model_load: loading model part 4/8 from './models/65B/ggml-model-q4_0.bin.3'
-llama_model_load: .......................................................................................... done
-llama_model_load: model size =  4869.09 MB / num tensors = 723
-llama_model_load: loading model part 5/8 from './models/65B/ggml-model-q4_0.bin.4'
-llama_model_load: .......................................................................................... done
-llama_model_load: model size =  4869.09 MB / num tensors = 723
-llama_model_load: loading model part 6/8 from './models/65B/ggml-model-q4_0.bin.5'
-llama_model_load: .......................................................................................... done
-llama_model_load: model size =  4869.09 MB / num tensors = 723
-llama_model_load: loading model part 7/8 from './models/65B/ggml-model-q4_0.bin.6'
-llama_model_load: .......................................................................................... done
-llama_model_load: model size =  4869.09 MB / num tensors = 723
-llama_model_load: loading model part 8/8 from './models/65B/ggml-model-q4_0.bin.7'
-llama_model_load: .......................................................................................... done
-llama_model_load: model size =  4869.09 MB / num tensors = 723
-
-system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 |
-
-main: prompt: 'Please close your issue when it has been answered.'
-main: number of tokens in prompt = 11
-     1 -> ''
- 12148 -> 'Please'
-  3802 -> ' close'
-   596 -> ' your'
-  2228 -> ' issue'
-   746 -> ' when'
-   372 -> ' it'
-   756 -> ' has'
-  1063 -> ' been'
-  7699 -> ' answered'
- 29889 -> '.'
-
-sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000, repeat_last_n = 64, repeat_penalty = 1.300000
-
-
-Please close your issue when it has been answered.
-@duncan-donut: I'm trying to figure out what kind of "support" you need for this script and why, exactly? Is there a question about how the code works that hasn't already been addressed in one or more comments below this ticket, or are we talking something else entirely like some sorta bugfixing job because your server setup is different from mine??
-I can understand if your site needs to be running smoothly and you need help with a fix of sorts but there should really be nothing wrong here that the code itself could not handle. And given that I'm getting reports about how it works perfectly well on some other servers, what exactly are we talking? A detailed report will do wonders in helping us get this resolved for ya quickly so please take your time and describe the issue(s) you see as clearly & concisely as possible!!
-@duncan-donut: I'm not sure if you have access to cPanel but you could try these instructions. It is worth a shot! Let me know how it goes (or what error message, exactly!) when/if ya give that code a go? [end of text]
-
-
-main: mem per token = 71159620 bytes
-main:     load time = 19309.95 ms
-main:   sample time =   168.62 ms
-main:  predict time = 223895.61 ms / 888.47 ms per token
-main:    total time = 246406.42 ms
-
- Performance counter stats for './main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p Please close your issue when it has been answered.':
-
-        3636882.89 msec task-clock                #   14.677 CPUs utilized
-             13509      context-switches          #    3.714 /sec
-              2436      cpu-migrations            #    0.670 /sec
-          10476679      page-faults               #    2.881 K/sec
-    13133115082869      cycles                    #    3.611 GHz                      (16.77%)
-       29314462753      stalled-cycles-frontend   #    0.22% frontend cycles idle     (16.76%)
-    10294402631459      stalled-cycles-backend    #   78.39% backend cycles idle      (16.74%)
-    23479217109614      instructions              #    1.79  insn per cycle
-                                                  #    0.44  stalled cycles per insn  (16.76%)
-     2353072268027      branches                  #  647.002 M/sec                    (16.77%)
-        1998682780      branch-misses             #    0.08% of all branches          (16.76%)
-
-     247.802177522 seconds time elapsed
-
-    3618.573072000 seconds user
-      18.491698000 seconds sys
-```
diff --git a/spaces/Illumotion/Koboldcpp/examples/server/json.hpp b/spaces/Illumotion/Koboldcpp/examples/server/json.hpp
deleted file mode 100644
index 4d1a37ad7cb874769d911fc3362d567da3aca291..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/examples/server/json.hpp
+++ /dev/null
@@ -1,24596 +0,0 @@
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-/****************************************************************************\
- * Note on documentation: The source files contain links to the online      *
- * documentation of the public API at https://json.nlohmann.me. This URL    *
- * contains the most recent documentation and should also be applicable to  *
- * previous versions; documentation for deprecated functions is not         *
- * removed, but marked deprecated. See "Generate documentation" section in  *
- * file docs/README.md.                                                     *
-\****************************************************************************/
-
-#ifndef INCLUDE_NLOHMANN_JSON_HPP_
-#define INCLUDE_NLOHMANN_JSON_HPP_
-
-#include <algorithm> // all_of, find, for_each
-#include <cstddef> // nullptr_t, ptrdiff_t, size_t
-#include <functional> // hash, less
-#include <initializer_list> // initializer_list
-#ifndef JSON_NO_IO
-    #include <iosfwd> // istream, ostream
-#endif  // JSON_NO_IO
-#include <iterator> // random_access_iterator_tag
-#include <memory> // unique_ptr
-#include <numeric> // accumulate
-#include <string> // string, stoi, to_string
-#include <utility> // declval, forward, move, pair, swap
-#include <vector> // vector
-
-// #include <nlohmann/adl_serializer.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <utility>
-
-// #include <nlohmann/detail/abi_macros.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-// This file contains all macro definitions affecting or depending on the ABI
-
-#ifndef JSON_SKIP_LIBRARY_VERSION_CHECK
-    #if defined(NLOHMANN_JSON_VERSION_MAJOR) && defined(NLOHMANN_JSON_VERSION_MINOR) && defined(NLOHMANN_JSON_VERSION_PATCH)
-        #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 2
-            #warning "Already included a different version of the library!"
-        #endif
-    #endif
-#endif
-
-#define NLOHMANN_JSON_VERSION_MAJOR 3   // NOLINT(modernize-macro-to-enum)
-#define NLOHMANN_JSON_VERSION_MINOR 11  // NOLINT(modernize-macro-to-enum)
-#define NLOHMANN_JSON_VERSION_PATCH 2   // NOLINT(modernize-macro-to-enum)
-
-#ifndef JSON_DIAGNOSTICS
-    #define JSON_DIAGNOSTICS 0
-#endif
-
-#ifndef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
-    #define JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON 0
-#endif
-
-#if JSON_DIAGNOSTICS
-    #define NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS _diag
-#else
-    #define NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS
-#endif
-
-#if JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
-    #define NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON _ldvcmp
-#else
-    #define NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON
-#endif
-
-#ifndef NLOHMANN_JSON_NAMESPACE_NO_VERSION
-    #define NLOHMANN_JSON_NAMESPACE_NO_VERSION 0
-#endif
-
-// Construct the namespace ABI tags component
-#define NLOHMANN_JSON_ABI_TAGS_CONCAT_EX(a, b) json_abi ## a ## b
-#define NLOHMANN_JSON_ABI_TAGS_CONCAT(a, b) \
-    NLOHMANN_JSON_ABI_TAGS_CONCAT_EX(a, b)
-
-#define NLOHMANN_JSON_ABI_TAGS                                       \
-    NLOHMANN_JSON_ABI_TAGS_CONCAT(                                   \
-            NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS,                       \
-            NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON)
-
-// Construct the namespace version component
-#define NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT_EX(major, minor, patch) \
-    _v ## major ## _ ## minor ## _ ## patch
-#define NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT(major, minor, patch) \
-    NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT_EX(major, minor, patch)
-
-#if NLOHMANN_JSON_NAMESPACE_NO_VERSION
-#define NLOHMANN_JSON_NAMESPACE_VERSION
-#else
-#define NLOHMANN_JSON_NAMESPACE_VERSION                                 \
-    NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT(NLOHMANN_JSON_VERSION_MAJOR, \
-                                           NLOHMANN_JSON_VERSION_MINOR, \
-                                           NLOHMANN_JSON_VERSION_PATCH)
-#endif
-
-// Combine namespace components
-#define NLOHMANN_JSON_NAMESPACE_CONCAT_EX(a, b) a ## b
-#define NLOHMANN_JSON_NAMESPACE_CONCAT(a, b) \
-    NLOHMANN_JSON_NAMESPACE_CONCAT_EX(a, b)
-
-#ifndef NLOHMANN_JSON_NAMESPACE
-#define NLOHMANN_JSON_NAMESPACE               \
-    nlohmann::NLOHMANN_JSON_NAMESPACE_CONCAT( \
-            NLOHMANN_JSON_ABI_TAGS,           \
-            NLOHMANN_JSON_NAMESPACE_VERSION)
-#endif
-
-#ifndef NLOHMANN_JSON_NAMESPACE_BEGIN
-#define NLOHMANN_JSON_NAMESPACE_BEGIN                \
-    namespace nlohmann                               \
-    {                                                \
-    inline namespace NLOHMANN_JSON_NAMESPACE_CONCAT( \
-                NLOHMANN_JSON_ABI_TAGS,              \
-                NLOHMANN_JSON_NAMESPACE_VERSION)     \
-    {
-#endif
-
-#ifndef NLOHMANN_JSON_NAMESPACE_END
-#define NLOHMANN_JSON_NAMESPACE_END                                     \
-    }  /* namespace (inline namespace) NOLINT(readability/namespace) */ \
-    }  // namespace nlohmann
-#endif
-
-// #include <nlohmann/detail/conversions/from_json.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <algorithm> // transform
-#include <array> // array
-#include <forward_list> // forward_list
-#include <iterator> // inserter, front_inserter, end
-#include <map> // map
-#include <string> // string
-#include <tuple> // tuple, make_tuple
-#include <type_traits> // is_arithmetic, is_same, is_enum, underlying_type, is_convertible
-#include <unordered_map> // unordered_map
-#include <utility> // pair, declval
-#include <valarray> // valarray
-
-// #include <nlohmann/detail/exceptions.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cstddef> // nullptr_t
-#include <exception> // exception
-#include <stdexcept> // runtime_error
-#include <string> // to_string
-#include <vector> // vector
-
-// #include <nlohmann/detail/value_t.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <array> // array
-#include <cstddef> // size_t
-#include <cstdint> // uint8_t
-#include <string> // string
-
-// #include <nlohmann/detail/macro_scope.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <utility> // declval, pair
-// #include <nlohmann/detail/meta/detected.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <type_traits>
-
-// #include <nlohmann/detail/meta/void_t.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename ...Ts> struct make_void
-{
-    using type = void;
-};
-template<typename ...Ts> using void_t = typename make_void<Ts...>::type;
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-// https://en.cppreference.com/w/cpp/experimental/is_detected
-struct nonesuch
-{
-    nonesuch() = delete;
-    ~nonesuch() = delete;
-    nonesuch(nonesuch const&) = delete;
-    nonesuch(nonesuch const&&) = delete;
-    void operator=(nonesuch const&) = delete;
-    void operator=(nonesuch&&) = delete;
-};
-
-template<class Default,
-         class AlwaysVoid,
-         template<class...> class Op,
-         class... Args>
-struct detector
-{
-    using value_t = std::false_type;
-    using type = Default;
-};
-
-template<class Default, template<class...> class Op, class... Args>
-struct detector<Default, void_t<Op<Args...>>, Op, Args...>
-{
-    using value_t = std::true_type;
-    using type = Op<Args...>;
-};
-
-template<template<class...> class Op, class... Args>
-using is_detected = typename detector<nonesuch, void, Op, Args...>::value_t;
-
-template<template<class...> class Op, class... Args>
-struct is_detected_lazy : is_detected<Op, Args...> { };
-
-template<template<class...> class Op, class... Args>
-using detected_t = typename detector<nonesuch, void, Op, Args...>::type;
-
-template<class Default, template<class...> class Op, class... Args>
-using detected_or = detector<Default, void, Op, Args...>;
-
-template<class Default, template<class...> class Op, class... Args>
-using detected_or_t = typename detected_or<Default, Op, Args...>::type;
-
-template<class Expected, template<class...> class Op, class... Args>
-using is_detected_exact = std::is_same<Expected, detected_t<Op, Args...>>;
-
-template<class To, template<class...> class Op, class... Args>
-using is_detected_convertible =
-    std::is_convertible<detected_t<Op, Args...>, To>;
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/thirdparty/hedley/hedley.hpp>
-
-
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-FileCopyrightText: 2016-2021 Evan Nemerson <evan@nemerson.com>
-// SPDX-License-Identifier: MIT
-
-/* Hedley - https://nemequ.github.io/hedley
- * Created by Evan Nemerson <evan@nemerson.com>
- */
-
-#if !defined(JSON_HEDLEY_VERSION) || (JSON_HEDLEY_VERSION < 15)
-#if defined(JSON_HEDLEY_VERSION)
-    #undef JSON_HEDLEY_VERSION
-#endif
-#define JSON_HEDLEY_VERSION 15
-
-#if defined(JSON_HEDLEY_STRINGIFY_EX)
-    #undef JSON_HEDLEY_STRINGIFY_EX
-#endif
-#define JSON_HEDLEY_STRINGIFY_EX(x) #x
-
-#if defined(JSON_HEDLEY_STRINGIFY)
-    #undef JSON_HEDLEY_STRINGIFY
-#endif
-#define JSON_HEDLEY_STRINGIFY(x) JSON_HEDLEY_STRINGIFY_EX(x)
-
-#if defined(JSON_HEDLEY_CONCAT_EX)
-    #undef JSON_HEDLEY_CONCAT_EX
-#endif
-#define JSON_HEDLEY_CONCAT_EX(a,b) a##b
-
-#if defined(JSON_HEDLEY_CONCAT)
-    #undef JSON_HEDLEY_CONCAT
-#endif
-#define JSON_HEDLEY_CONCAT(a,b) JSON_HEDLEY_CONCAT_EX(a,b)
-
-#if defined(JSON_HEDLEY_CONCAT3_EX)
-    #undef JSON_HEDLEY_CONCAT3_EX
-#endif
-#define JSON_HEDLEY_CONCAT3_EX(a,b,c) a##b##c
-
-#if defined(JSON_HEDLEY_CONCAT3)
-    #undef JSON_HEDLEY_CONCAT3
-#endif
-#define JSON_HEDLEY_CONCAT3(a,b,c) JSON_HEDLEY_CONCAT3_EX(a,b,c)
-
-#if defined(JSON_HEDLEY_VERSION_ENCODE)
-    #undef JSON_HEDLEY_VERSION_ENCODE
-#endif
-#define JSON_HEDLEY_VERSION_ENCODE(major,minor,revision) (((major) * 1000000) + ((minor) * 1000) + (revision))
-
-#if defined(JSON_HEDLEY_VERSION_DECODE_MAJOR)
-    #undef JSON_HEDLEY_VERSION_DECODE_MAJOR
-#endif
-#define JSON_HEDLEY_VERSION_DECODE_MAJOR(version) ((version) / 1000000)
-
-#if defined(JSON_HEDLEY_VERSION_DECODE_MINOR)
-    #undef JSON_HEDLEY_VERSION_DECODE_MINOR
-#endif
-#define JSON_HEDLEY_VERSION_DECODE_MINOR(version) (((version) % 1000000) / 1000)
-
-#if defined(JSON_HEDLEY_VERSION_DECODE_REVISION)
-    #undef JSON_HEDLEY_VERSION_DECODE_REVISION
-#endif
-#define JSON_HEDLEY_VERSION_DECODE_REVISION(version) ((version) % 1000)
-
-#if defined(JSON_HEDLEY_GNUC_VERSION)
-    #undef JSON_HEDLEY_GNUC_VERSION
-#endif
-#if defined(__GNUC__) && defined(__GNUC_PATCHLEVEL__)
-    #define JSON_HEDLEY_GNUC_VERSION JSON_HEDLEY_VERSION_ENCODE(__GNUC__, __GNUC_MINOR__, __GNUC_PATCHLEVEL__)
-#elif defined(__GNUC__)
-    #define JSON_HEDLEY_GNUC_VERSION JSON_HEDLEY_VERSION_ENCODE(__GNUC__, __GNUC_MINOR__, 0)
-#endif
-
-#if defined(JSON_HEDLEY_GNUC_VERSION_CHECK)
-    #undef JSON_HEDLEY_GNUC_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_GNUC_VERSION)
-    #define JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_GNUC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_MSVC_VERSION)
-    #undef JSON_HEDLEY_MSVC_VERSION
-#endif
-#if defined(_MSC_FULL_VER) && (_MSC_FULL_VER >= 140000000) && !defined(__ICL)
-    #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_FULL_VER / 10000000, (_MSC_FULL_VER % 10000000) / 100000, (_MSC_FULL_VER % 100000) / 100)
-#elif defined(_MSC_FULL_VER) && !defined(__ICL)
-    #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_FULL_VER / 1000000, (_MSC_FULL_VER % 1000000) / 10000, (_MSC_FULL_VER % 10000) / 10)
-#elif defined(_MSC_VER) && !defined(__ICL)
-    #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_VER / 100, _MSC_VER % 100, 0)
-#endif
-
-#if defined(JSON_HEDLEY_MSVC_VERSION_CHECK)
-    #undef JSON_HEDLEY_MSVC_VERSION_CHECK
-#endif
-#if !defined(JSON_HEDLEY_MSVC_VERSION)
-    #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (0)
-#elif defined(_MSC_VER) && (_MSC_VER >= 1400)
-    #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_FULL_VER >= ((major * 10000000) + (minor * 100000) + (patch)))
-#elif defined(_MSC_VER) && (_MSC_VER >= 1200)
-    #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_FULL_VER >= ((major * 1000000) + (minor * 10000) + (patch)))
-#else
-    #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_VER >= ((major * 100) + (minor)))
-#endif
-
-#if defined(JSON_HEDLEY_INTEL_VERSION)
-    #undef JSON_HEDLEY_INTEL_VERSION
-#endif
-#if defined(__INTEL_COMPILER) && defined(__INTEL_COMPILER_UPDATE) && !defined(__ICL)
-    #define JSON_HEDLEY_INTEL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER / 100, __INTEL_COMPILER % 100, __INTEL_COMPILER_UPDATE)
-#elif defined(__INTEL_COMPILER) && !defined(__ICL)
-    #define JSON_HEDLEY_INTEL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER / 100, __INTEL_COMPILER % 100, 0)
-#endif
-
-#if defined(JSON_HEDLEY_INTEL_VERSION_CHECK)
-    #undef JSON_HEDLEY_INTEL_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_INTEL_VERSION)
-    #define JSON_HEDLEY_INTEL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_INTEL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_INTEL_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_INTEL_CL_VERSION)
-    #undef JSON_HEDLEY_INTEL_CL_VERSION
-#endif
-#if defined(__INTEL_COMPILER) && defined(__INTEL_COMPILER_UPDATE) && defined(__ICL)
-    #define JSON_HEDLEY_INTEL_CL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER, __INTEL_COMPILER_UPDATE, 0)
-#endif
-
-#if defined(JSON_HEDLEY_INTEL_CL_VERSION_CHECK)
-    #undef JSON_HEDLEY_INTEL_CL_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_INTEL_CL_VERSION)
-    #define JSON_HEDLEY_INTEL_CL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_INTEL_CL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_INTEL_CL_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_PGI_VERSION)
-    #undef JSON_HEDLEY_PGI_VERSION
-#endif
-#if defined(__PGI) && defined(__PGIC__) && defined(__PGIC_MINOR__) && defined(__PGIC_PATCHLEVEL__)
-    #define JSON_HEDLEY_PGI_VERSION JSON_HEDLEY_VERSION_ENCODE(__PGIC__, __PGIC_MINOR__, __PGIC_PATCHLEVEL__)
-#endif
-
-#if defined(JSON_HEDLEY_PGI_VERSION_CHECK)
-    #undef JSON_HEDLEY_PGI_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_PGI_VERSION)
-    #define JSON_HEDLEY_PGI_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_PGI_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_PGI_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_SUNPRO_VERSION)
-    #undef JSON_HEDLEY_SUNPRO_VERSION
-#endif
-#if defined(__SUNPRO_C) && (__SUNPRO_C > 0x1000)
-    #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((((__SUNPRO_C >> 16) & 0xf) * 10) + ((__SUNPRO_C >> 12) & 0xf), (((__SUNPRO_C >> 8) & 0xf) * 10) + ((__SUNPRO_C >> 4) & 0xf), (__SUNPRO_C & 0xf) * 10)
-#elif defined(__SUNPRO_C)
-    #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((__SUNPRO_C >> 8) & 0xf, (__SUNPRO_C >> 4) & 0xf, (__SUNPRO_C) & 0xf)
-#elif defined(__SUNPRO_CC) && (__SUNPRO_CC > 0x1000)
-    #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((((__SUNPRO_CC >> 16) & 0xf) * 10) + ((__SUNPRO_CC >> 12) & 0xf), (((__SUNPRO_CC >> 8) & 0xf) * 10) + ((__SUNPRO_CC >> 4) & 0xf), (__SUNPRO_CC & 0xf) * 10)
-#elif defined(__SUNPRO_CC)
-    #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((__SUNPRO_CC >> 8) & 0xf, (__SUNPRO_CC >> 4) & 0xf, (__SUNPRO_CC) & 0xf)
-#endif
-
-#if defined(JSON_HEDLEY_SUNPRO_VERSION_CHECK)
-    #undef JSON_HEDLEY_SUNPRO_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_SUNPRO_VERSION)
-    #define JSON_HEDLEY_SUNPRO_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_SUNPRO_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_SUNPRO_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION)
-    #undef JSON_HEDLEY_EMSCRIPTEN_VERSION
-#endif
-#if defined(__EMSCRIPTEN__)
-    #define JSON_HEDLEY_EMSCRIPTEN_VERSION JSON_HEDLEY_VERSION_ENCODE(__EMSCRIPTEN_major__, __EMSCRIPTEN_minor__, __EMSCRIPTEN_tiny__)
-#endif
-
-#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK)
-    #undef JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION)
-    #define JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_EMSCRIPTEN_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_ARM_VERSION)
-    #undef JSON_HEDLEY_ARM_VERSION
-#endif
-#if defined(__CC_ARM) && defined(__ARMCOMPILER_VERSION)
-    #define JSON_HEDLEY_ARM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ARMCOMPILER_VERSION / 1000000, (__ARMCOMPILER_VERSION % 1000000) / 10000, (__ARMCOMPILER_VERSION % 10000) / 100)
-#elif defined(__CC_ARM) && defined(__ARMCC_VERSION)
-    #define JSON_HEDLEY_ARM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ARMCC_VERSION / 1000000, (__ARMCC_VERSION % 1000000) / 10000, (__ARMCC_VERSION % 10000) / 100)
-#endif
-
-#if defined(JSON_HEDLEY_ARM_VERSION_CHECK)
-    #undef JSON_HEDLEY_ARM_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_ARM_VERSION)
-    #define JSON_HEDLEY_ARM_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_ARM_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_ARM_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_IBM_VERSION)
-    #undef JSON_HEDLEY_IBM_VERSION
-#endif
-#if defined(__ibmxl__)
-    #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ibmxl_version__, __ibmxl_release__, __ibmxl_modification__)
-#elif defined(__xlC__) && defined(__xlC_ver__)
-    #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__xlC__ >> 8, __xlC__ & 0xff, (__xlC_ver__ >> 8) & 0xff)
-#elif defined(__xlC__)
-    #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__xlC__ >> 8, __xlC__ & 0xff, 0)
-#endif
-
-#if defined(JSON_HEDLEY_IBM_VERSION_CHECK)
-    #undef JSON_HEDLEY_IBM_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_IBM_VERSION)
-    #define JSON_HEDLEY_IBM_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_IBM_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_IBM_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_TI_VERSION)
-    #undef JSON_HEDLEY_TI_VERSION
-#endif
-#if \
-    defined(__TI_COMPILER_VERSION__) && \
-    ( \
-      defined(__TMS470__) || defined(__TI_ARM__) || \
-      defined(__MSP430__) || \
-      defined(__TMS320C2000__) \
-    )
-#if (__TI_COMPILER_VERSION__ >= 16000000)
-    #define JSON_HEDLEY_TI_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
-#endif
-#endif
-
-#if defined(JSON_HEDLEY_TI_VERSION_CHECK)
-    #undef JSON_HEDLEY_TI_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_TI_VERSION)
-    #define JSON_HEDLEY_TI_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_TI_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_TI_CL2000_VERSION)
-    #undef JSON_HEDLEY_TI_CL2000_VERSION
-#endif
-#if defined(__TI_COMPILER_VERSION__) && defined(__TMS320C2000__)
-    #define JSON_HEDLEY_TI_CL2000_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
-#endif
-
-#if defined(JSON_HEDLEY_TI_CL2000_VERSION_CHECK)
-    #undef JSON_HEDLEY_TI_CL2000_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_TI_CL2000_VERSION)
-    #define JSON_HEDLEY_TI_CL2000_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL2000_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_TI_CL2000_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_TI_CL430_VERSION)
-    #undef JSON_HEDLEY_TI_CL430_VERSION
-#endif
-#if defined(__TI_COMPILER_VERSION__) && defined(__MSP430__)
-    #define JSON_HEDLEY_TI_CL430_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
-#endif
-
-#if defined(JSON_HEDLEY_TI_CL430_VERSION_CHECK)
-    #undef JSON_HEDLEY_TI_CL430_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_TI_CL430_VERSION)
-    #define JSON_HEDLEY_TI_CL430_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL430_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_TI_CL430_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_TI_ARMCL_VERSION)
-    #undef JSON_HEDLEY_TI_ARMCL_VERSION
-#endif
-#if defined(__TI_COMPILER_VERSION__) && (defined(__TMS470__) || defined(__TI_ARM__))
-    #define JSON_HEDLEY_TI_ARMCL_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
-#endif
-
-#if defined(JSON_HEDLEY_TI_ARMCL_VERSION_CHECK)
-    #undef JSON_HEDLEY_TI_ARMCL_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_TI_ARMCL_VERSION)
-    #define JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_ARMCL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_TI_CL6X_VERSION)
-    #undef JSON_HEDLEY_TI_CL6X_VERSION
-#endif
-#if defined(__TI_COMPILER_VERSION__) && defined(__TMS320C6X__)
-    #define JSON_HEDLEY_TI_CL6X_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
-#endif
-
-#if defined(JSON_HEDLEY_TI_CL6X_VERSION_CHECK)
-    #undef JSON_HEDLEY_TI_CL6X_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_TI_CL6X_VERSION)
-    #define JSON_HEDLEY_TI_CL6X_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL6X_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_TI_CL6X_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_TI_CL7X_VERSION)
-    #undef JSON_HEDLEY_TI_CL7X_VERSION
-#endif
-#if defined(__TI_COMPILER_VERSION__) && defined(__C7000__)
-    #define JSON_HEDLEY_TI_CL7X_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
-#endif
-
-#if defined(JSON_HEDLEY_TI_CL7X_VERSION_CHECK)
-    #undef JSON_HEDLEY_TI_CL7X_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_TI_CL7X_VERSION)
-    #define JSON_HEDLEY_TI_CL7X_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL7X_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_TI_CL7X_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_TI_CLPRU_VERSION)
-    #undef JSON_HEDLEY_TI_CLPRU_VERSION
-#endif
-#if defined(__TI_COMPILER_VERSION__) && defined(__PRU__)
-    #define JSON_HEDLEY_TI_CLPRU_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
-#endif
-
-#if defined(JSON_HEDLEY_TI_CLPRU_VERSION_CHECK)
-    #undef JSON_HEDLEY_TI_CLPRU_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_TI_CLPRU_VERSION)
-    #define JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CLPRU_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_CRAY_VERSION)
-    #undef JSON_HEDLEY_CRAY_VERSION
-#endif
-#if defined(_CRAYC)
-    #if defined(_RELEASE_PATCHLEVEL)
-        #define JSON_HEDLEY_CRAY_VERSION JSON_HEDLEY_VERSION_ENCODE(_RELEASE_MAJOR, _RELEASE_MINOR, _RELEASE_PATCHLEVEL)
-    #else
-        #define JSON_HEDLEY_CRAY_VERSION JSON_HEDLEY_VERSION_ENCODE(_RELEASE_MAJOR, _RELEASE_MINOR, 0)
-    #endif
-#endif
-
-#if defined(JSON_HEDLEY_CRAY_VERSION_CHECK)
-    #undef JSON_HEDLEY_CRAY_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_CRAY_VERSION)
-    #define JSON_HEDLEY_CRAY_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_CRAY_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_CRAY_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_IAR_VERSION)
-    #undef JSON_HEDLEY_IAR_VERSION
-#endif
-#if defined(__IAR_SYSTEMS_ICC__)
-    #if __VER__ > 1000
-        #define JSON_HEDLEY_IAR_VERSION JSON_HEDLEY_VERSION_ENCODE((__VER__ / 1000000), ((__VER__ / 1000) % 1000), (__VER__ % 1000))
-    #else
-        #define JSON_HEDLEY_IAR_VERSION JSON_HEDLEY_VERSION_ENCODE(__VER__ / 100, __VER__ % 100, 0)
-    #endif
-#endif
-
-#if defined(JSON_HEDLEY_IAR_VERSION_CHECK)
-    #undef JSON_HEDLEY_IAR_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_IAR_VERSION)
-    #define JSON_HEDLEY_IAR_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_IAR_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_IAR_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_TINYC_VERSION)
-    #undef JSON_HEDLEY_TINYC_VERSION
-#endif
-#if defined(__TINYC__)
-    #define JSON_HEDLEY_TINYC_VERSION JSON_HEDLEY_VERSION_ENCODE(__TINYC__ / 1000, (__TINYC__ / 100) % 10, __TINYC__ % 100)
-#endif
-
-#if defined(JSON_HEDLEY_TINYC_VERSION_CHECK)
-    #undef JSON_HEDLEY_TINYC_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_TINYC_VERSION)
-    #define JSON_HEDLEY_TINYC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TINYC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_TINYC_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_DMC_VERSION)
-    #undef JSON_HEDLEY_DMC_VERSION
-#endif
-#if defined(__DMC__)
-    #define JSON_HEDLEY_DMC_VERSION JSON_HEDLEY_VERSION_ENCODE(__DMC__ >> 8, (__DMC__ >> 4) & 0xf, __DMC__ & 0xf)
-#endif
-
-#if defined(JSON_HEDLEY_DMC_VERSION_CHECK)
-    #undef JSON_HEDLEY_DMC_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_DMC_VERSION)
-    #define JSON_HEDLEY_DMC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_DMC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_DMC_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_COMPCERT_VERSION)
-    #undef JSON_HEDLEY_COMPCERT_VERSION
-#endif
-#if defined(__COMPCERT_VERSION__)
-    #define JSON_HEDLEY_COMPCERT_VERSION JSON_HEDLEY_VERSION_ENCODE(__COMPCERT_VERSION__ / 10000, (__COMPCERT_VERSION__ / 100) % 100, __COMPCERT_VERSION__ % 100)
-#endif
-
-#if defined(JSON_HEDLEY_COMPCERT_VERSION_CHECK)
-    #undef JSON_HEDLEY_COMPCERT_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_COMPCERT_VERSION)
-    #define JSON_HEDLEY_COMPCERT_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_COMPCERT_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_COMPCERT_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_PELLES_VERSION)
-    #undef JSON_HEDLEY_PELLES_VERSION
-#endif
-#if defined(__POCC__)
-    #define JSON_HEDLEY_PELLES_VERSION JSON_HEDLEY_VERSION_ENCODE(__POCC__ / 100, __POCC__ % 100, 0)
-#endif
-
-#if defined(JSON_HEDLEY_PELLES_VERSION_CHECK)
-    #undef JSON_HEDLEY_PELLES_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_PELLES_VERSION)
-    #define JSON_HEDLEY_PELLES_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_PELLES_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_PELLES_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_MCST_LCC_VERSION)
-    #undef JSON_HEDLEY_MCST_LCC_VERSION
-#endif
-#if defined(__LCC__) && defined(__LCC_MINOR__)
-    #define JSON_HEDLEY_MCST_LCC_VERSION JSON_HEDLEY_VERSION_ENCODE(__LCC__ / 100, __LCC__ % 100, __LCC_MINOR__)
-#endif
-
-#if defined(JSON_HEDLEY_MCST_LCC_VERSION_CHECK)
-    #undef JSON_HEDLEY_MCST_LCC_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_MCST_LCC_VERSION)
-    #define JSON_HEDLEY_MCST_LCC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_MCST_LCC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_MCST_LCC_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_GCC_VERSION)
-    #undef JSON_HEDLEY_GCC_VERSION
-#endif
-#if \
-    defined(JSON_HEDLEY_GNUC_VERSION) && \
-    !defined(__clang__) && \
-    !defined(JSON_HEDLEY_INTEL_VERSION) && \
-    !defined(JSON_HEDLEY_PGI_VERSION) && \
-    !defined(JSON_HEDLEY_ARM_VERSION) && \
-    !defined(JSON_HEDLEY_CRAY_VERSION) && \
-    !defined(JSON_HEDLEY_TI_VERSION) && \
-    !defined(JSON_HEDLEY_TI_ARMCL_VERSION) && \
-    !defined(JSON_HEDLEY_TI_CL430_VERSION) && \
-    !defined(JSON_HEDLEY_TI_CL2000_VERSION) && \
-    !defined(JSON_HEDLEY_TI_CL6X_VERSION) && \
-    !defined(JSON_HEDLEY_TI_CL7X_VERSION) && \
-    !defined(JSON_HEDLEY_TI_CLPRU_VERSION) && \
-    !defined(__COMPCERT__) && \
-    !defined(JSON_HEDLEY_MCST_LCC_VERSION)
-    #define JSON_HEDLEY_GCC_VERSION JSON_HEDLEY_GNUC_VERSION
-#endif
-
-#if defined(JSON_HEDLEY_GCC_VERSION_CHECK)
-    #undef JSON_HEDLEY_GCC_VERSION_CHECK
-#endif
-#if defined(JSON_HEDLEY_GCC_VERSION)
-    #define JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_GCC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
-#else
-    #define JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) (0)
-#endif
-
-#if defined(JSON_HEDLEY_HAS_ATTRIBUTE)
-    #undef JSON_HEDLEY_HAS_ATTRIBUTE
-#endif
-#if \
-  defined(__has_attribute) && \
-  ( \
-    (!defined(JSON_HEDLEY_IAR_VERSION) || JSON_HEDLEY_IAR_VERSION_CHECK(8,5,9)) \
-  )
-#  define JSON_HEDLEY_HAS_ATTRIBUTE(attribute) __has_attribute(attribute)
-#else
-#  define JSON_HEDLEY_HAS_ATTRIBUTE(attribute) (0)
-#endif
-
-#if defined(JSON_HEDLEY_GNUC_HAS_ATTRIBUTE)
-    #undef JSON_HEDLEY_GNUC_HAS_ATTRIBUTE
-#endif
-#if defined(__has_attribute)
-    #define JSON_HEDLEY_GNUC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_HAS_ATTRIBUTE(attribute)
-#else
-    #define JSON_HEDLEY_GNUC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_GCC_HAS_ATTRIBUTE)
-    #undef JSON_HEDLEY_GCC_HAS_ATTRIBUTE
-#endif
-#if defined(__has_attribute)
-    #define JSON_HEDLEY_GCC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_HAS_ATTRIBUTE(attribute)
-#else
-    #define JSON_HEDLEY_GCC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_HAS_CPP_ATTRIBUTE)
-    #undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE
-#endif
-#if \
-    defined(__has_cpp_attribute) && \
-    defined(__cplusplus) && \
-    (!defined(JSON_HEDLEY_SUNPRO_VERSION) || JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0))
-    #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute) __has_cpp_attribute(attribute)
-#else
-    #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute) (0)
-#endif
-
-#if defined(JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS)
-    #undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS
-#endif
-#if !defined(__cplusplus) || !defined(__has_cpp_attribute)
-    #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) (0)
-#elif \
-    !defined(JSON_HEDLEY_PGI_VERSION) && \
-    !defined(JSON_HEDLEY_IAR_VERSION) && \
-    (!defined(JSON_HEDLEY_SUNPRO_VERSION) || JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0)) && \
-    (!defined(JSON_HEDLEY_MSVC_VERSION) || JSON_HEDLEY_MSVC_VERSION_CHECK(19,20,0))
-    #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) JSON_HEDLEY_HAS_CPP_ATTRIBUTE(ns::attribute)
-#else
-    #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) (0)
-#endif
-
-#if defined(JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE)
-    #undef JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE
-#endif
-#if defined(__has_cpp_attribute) && defined(__cplusplus)
-    #define JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) __has_cpp_attribute(attribute)
-#else
-    #define JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE)
-    #undef JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE
-#endif
-#if defined(__has_cpp_attribute) && defined(__cplusplus)
-    #define JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) __has_cpp_attribute(attribute)
-#else
-    #define JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_HAS_BUILTIN)
-    #undef JSON_HEDLEY_HAS_BUILTIN
-#endif
-#if defined(__has_builtin)
-    #define JSON_HEDLEY_HAS_BUILTIN(builtin) __has_builtin(builtin)
-#else
-    #define JSON_HEDLEY_HAS_BUILTIN(builtin) (0)
-#endif
-
-#if defined(JSON_HEDLEY_GNUC_HAS_BUILTIN)
-    #undef JSON_HEDLEY_GNUC_HAS_BUILTIN
-#endif
-#if defined(__has_builtin)
-    #define JSON_HEDLEY_GNUC_HAS_BUILTIN(builtin,major,minor,patch) __has_builtin(builtin)
-#else
-    #define JSON_HEDLEY_GNUC_HAS_BUILTIN(builtin,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_GCC_HAS_BUILTIN)
-    #undef JSON_HEDLEY_GCC_HAS_BUILTIN
-#endif
-#if defined(__has_builtin)
-    #define JSON_HEDLEY_GCC_HAS_BUILTIN(builtin,major,minor,patch) __has_builtin(builtin)
-#else
-    #define JSON_HEDLEY_GCC_HAS_BUILTIN(builtin,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_HAS_FEATURE)
-    #undef JSON_HEDLEY_HAS_FEATURE
-#endif
-#if defined(__has_feature)
-    #define JSON_HEDLEY_HAS_FEATURE(feature) __has_feature(feature)
-#else
-    #define JSON_HEDLEY_HAS_FEATURE(feature) (0)
-#endif
-
-#if defined(JSON_HEDLEY_GNUC_HAS_FEATURE)
-    #undef JSON_HEDLEY_GNUC_HAS_FEATURE
-#endif
-#if defined(__has_feature)
-    #define JSON_HEDLEY_GNUC_HAS_FEATURE(feature,major,minor,patch) __has_feature(feature)
-#else
-    #define JSON_HEDLEY_GNUC_HAS_FEATURE(feature,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_GCC_HAS_FEATURE)
-    #undef JSON_HEDLEY_GCC_HAS_FEATURE
-#endif
-#if defined(__has_feature)
-    #define JSON_HEDLEY_GCC_HAS_FEATURE(feature,major,minor,patch) __has_feature(feature)
-#else
-    #define JSON_HEDLEY_GCC_HAS_FEATURE(feature,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_HAS_EXTENSION)
-    #undef JSON_HEDLEY_HAS_EXTENSION
-#endif
-#if defined(__has_extension)
-    #define JSON_HEDLEY_HAS_EXTENSION(extension) __has_extension(extension)
-#else
-    #define JSON_HEDLEY_HAS_EXTENSION(extension) (0)
-#endif
-
-#if defined(JSON_HEDLEY_GNUC_HAS_EXTENSION)
-    #undef JSON_HEDLEY_GNUC_HAS_EXTENSION
-#endif
-#if defined(__has_extension)
-    #define JSON_HEDLEY_GNUC_HAS_EXTENSION(extension,major,minor,patch) __has_extension(extension)
-#else
-    #define JSON_HEDLEY_GNUC_HAS_EXTENSION(extension,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_GCC_HAS_EXTENSION)
-    #undef JSON_HEDLEY_GCC_HAS_EXTENSION
-#endif
-#if defined(__has_extension)
-    #define JSON_HEDLEY_GCC_HAS_EXTENSION(extension,major,minor,patch) __has_extension(extension)
-#else
-    #define JSON_HEDLEY_GCC_HAS_EXTENSION(extension,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE)
-    #undef JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE
-#endif
-#if defined(__has_declspec_attribute)
-    #define JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute) __has_declspec_attribute(attribute)
-#else
-    #define JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute) (0)
-#endif
-
-#if defined(JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE)
-    #undef JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE
-#endif
-#if defined(__has_declspec_attribute)
-    #define JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) __has_declspec_attribute(attribute)
-#else
-    #define JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE)
-    #undef JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE
-#endif
-#if defined(__has_declspec_attribute)
-    #define JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) __has_declspec_attribute(attribute)
-#else
-    #define JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_HAS_WARNING)
-    #undef JSON_HEDLEY_HAS_WARNING
-#endif
-#if defined(__has_warning)
-    #define JSON_HEDLEY_HAS_WARNING(warning) __has_warning(warning)
-#else
-    #define JSON_HEDLEY_HAS_WARNING(warning) (0)
-#endif
-
-#if defined(JSON_HEDLEY_GNUC_HAS_WARNING)
-    #undef JSON_HEDLEY_GNUC_HAS_WARNING
-#endif
-#if defined(__has_warning)
-    #define JSON_HEDLEY_GNUC_HAS_WARNING(warning,major,minor,patch) __has_warning(warning)
-#else
-    #define JSON_HEDLEY_GNUC_HAS_WARNING(warning,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_GCC_HAS_WARNING)
-    #undef JSON_HEDLEY_GCC_HAS_WARNING
-#endif
-#if defined(__has_warning)
-    #define JSON_HEDLEY_GCC_HAS_WARNING(warning,major,minor,patch) __has_warning(warning)
-#else
-    #define JSON_HEDLEY_GCC_HAS_WARNING(warning,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if \
-    (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) || \
-    defined(__clang__) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) || \
-    JSON_HEDLEY_PGI_VERSION_CHECK(18,4,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,7,0) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(2,0,1) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,1,0) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,0,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-    JSON_HEDLEY_CRAY_VERSION_CHECK(5,0,0) || \
-    JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,17) || \
-    JSON_HEDLEY_SUNPRO_VERSION_CHECK(8,0,0) || \
-    (JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) && defined(__C99_PRAGMA_OPERATOR))
-    #define JSON_HEDLEY_PRAGMA(value) _Pragma(#value)
-#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0)
-    #define JSON_HEDLEY_PRAGMA(value) __pragma(value)
-#else
-    #define JSON_HEDLEY_PRAGMA(value)
-#endif
-
-#if defined(JSON_HEDLEY_DIAGNOSTIC_PUSH)
-    #undef JSON_HEDLEY_DIAGNOSTIC_PUSH
-#endif
-#if defined(JSON_HEDLEY_DIAGNOSTIC_POP)
-    #undef JSON_HEDLEY_DIAGNOSTIC_POP
-#endif
-#if defined(__clang__)
-    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("clang diagnostic push")
-    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("clang diagnostic pop")
-#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("warning(push)")
-    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("warning(pop)")
-#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("GCC diagnostic push")
-    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("GCC diagnostic pop")
-#elif \
-    JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_PUSH __pragma(warning(push))
-    #define JSON_HEDLEY_DIAGNOSTIC_POP __pragma(warning(pop))
-#elif JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("push")
-    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("pop")
-#elif \
-    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,4,0) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,1,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("diag_push")
-    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("diag_pop")
-#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,90,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("warning(push)")
-    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("warning(pop)")
-#else
-    #define JSON_HEDLEY_DIAGNOSTIC_PUSH
-    #define JSON_HEDLEY_DIAGNOSTIC_POP
-#endif
-
-/* JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_ is for
-   HEDLEY INTERNAL USE ONLY.  API subject to change without notice. */
-#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_)
-    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_
-#endif
-#if defined(__cplusplus)
-#  if JSON_HEDLEY_HAS_WARNING("-Wc++98-compat")
-#    if JSON_HEDLEY_HAS_WARNING("-Wc++17-extensions")
-#      if JSON_HEDLEY_HAS_WARNING("-Wc++1z-extensions")
-#        define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \
-    JSON_HEDLEY_DIAGNOSTIC_PUSH \
-    _Pragma("clang diagnostic ignored \"-Wc++98-compat\"") \
-    _Pragma("clang diagnostic ignored \"-Wc++17-extensions\"") \
-    _Pragma("clang diagnostic ignored \"-Wc++1z-extensions\"") \
-    xpr \
-    JSON_HEDLEY_DIAGNOSTIC_POP
-#      else
-#        define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \
-    JSON_HEDLEY_DIAGNOSTIC_PUSH \
-    _Pragma("clang diagnostic ignored \"-Wc++98-compat\"") \
-    _Pragma("clang diagnostic ignored \"-Wc++17-extensions\"") \
-    xpr \
-    JSON_HEDLEY_DIAGNOSTIC_POP
-#      endif
-#    else
-#      define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \
-    JSON_HEDLEY_DIAGNOSTIC_PUSH \
-    _Pragma("clang diagnostic ignored \"-Wc++98-compat\"") \
-    xpr \
-    JSON_HEDLEY_DIAGNOSTIC_POP
-#    endif
-#  endif
-#endif
-#if !defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(x) x
-#endif
-
-#if defined(JSON_HEDLEY_CONST_CAST)
-    #undef JSON_HEDLEY_CONST_CAST
-#endif
-#if defined(__cplusplus)
-#  define JSON_HEDLEY_CONST_CAST(T, expr) (const_cast<T>(expr))
-#elif \
-  JSON_HEDLEY_HAS_WARNING("-Wcast-qual") || \
-  JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0) || \
-  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
-#  define JSON_HEDLEY_CONST_CAST(T, expr) (__extension__ ({ \
-        JSON_HEDLEY_DIAGNOSTIC_PUSH \
-        JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL \
-        ((T) (expr)); \
-        JSON_HEDLEY_DIAGNOSTIC_POP \
-    }))
-#else
-#  define JSON_HEDLEY_CONST_CAST(T, expr) ((T) (expr))
-#endif
-
-#if defined(JSON_HEDLEY_REINTERPRET_CAST)
-    #undef JSON_HEDLEY_REINTERPRET_CAST
-#endif
-#if defined(__cplusplus)
-    #define JSON_HEDLEY_REINTERPRET_CAST(T, expr) (reinterpret_cast<T>(expr))
-#else
-    #define JSON_HEDLEY_REINTERPRET_CAST(T, expr) ((T) (expr))
-#endif
-
-#if defined(JSON_HEDLEY_STATIC_CAST)
-    #undef JSON_HEDLEY_STATIC_CAST
-#endif
-#if defined(__cplusplus)
-    #define JSON_HEDLEY_STATIC_CAST(T, expr) (static_cast<T>(expr))
-#else
-    #define JSON_HEDLEY_STATIC_CAST(T, expr) ((T) (expr))
-#endif
-
-#if defined(JSON_HEDLEY_CPP_CAST)
-    #undef JSON_HEDLEY_CPP_CAST
-#endif
-#if defined(__cplusplus)
-#  if JSON_HEDLEY_HAS_WARNING("-Wold-style-cast")
-#    define JSON_HEDLEY_CPP_CAST(T, expr) \
-    JSON_HEDLEY_DIAGNOSTIC_PUSH \
-    _Pragma("clang diagnostic ignored \"-Wold-style-cast\"") \
-    ((T) (expr)) \
-    JSON_HEDLEY_DIAGNOSTIC_POP
-#  elif JSON_HEDLEY_IAR_VERSION_CHECK(8,3,0)
-#    define JSON_HEDLEY_CPP_CAST(T, expr) \
-    JSON_HEDLEY_DIAGNOSTIC_PUSH \
-    _Pragma("diag_suppress=Pe137") \
-    JSON_HEDLEY_DIAGNOSTIC_POP
-#  else
-#    define JSON_HEDLEY_CPP_CAST(T, expr) ((T) (expr))
-#  endif
-#else
-#  define JSON_HEDLEY_CPP_CAST(T, expr) (expr)
-#endif
-
-#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED)
-    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED
-#endif
-#if JSON_HEDLEY_HAS_WARNING("-Wdeprecated-declarations")
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("clang diagnostic ignored \"-Wdeprecated-declarations\"")
-#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("warning(disable:1478 1786)")
-#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED __pragma(warning(disable:1478 1786))
-#elif JSON_HEDLEY_PGI_VERSION_CHECK(20,7,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1215,1216,1444,1445")
-#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1215,1444")
-#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("GCC diagnostic ignored \"-Wdeprecated-declarations\"")
-#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED __pragma(warning(disable:4996))
-#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1215,1444")
-#elif \
-    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1291,1718")
-#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) && !defined(__cplusplus)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("error_messages(off,E_DEPRECATED_ATT,E_DEPRECATED_ATT_MESS)")
-#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) && defined(__cplusplus)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("error_messages(off,symdeprecated,symdeprecated2)")
-#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress=Pe1444,Pe1215")
-#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,90,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("warn(disable:2241)")
-#else
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED
-#endif
-
-#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS)
-    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS
-#endif
-#if JSON_HEDLEY_HAS_WARNING("-Wunknown-pragmas")
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("clang diagnostic ignored \"-Wunknown-pragmas\"")
-#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("warning(disable:161)")
-#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS __pragma(warning(disable:161))
-#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 1675")
-#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("GCC diagnostic ignored \"-Wunknown-pragmas\"")
-#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS __pragma(warning(disable:4068))
-#elif \
-    JSON_HEDLEY_TI_VERSION_CHECK(16,9,0) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,3,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 163")
-#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 163")
-#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress=Pe161")
-#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 161")
-#else
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS
-#endif
-
-#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES)
-    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES
-#endif
-#if JSON_HEDLEY_HAS_WARNING("-Wunknown-attributes")
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("clang diagnostic ignored \"-Wunknown-attributes\"")
-#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("GCC diagnostic ignored \"-Wdeprecated-declarations\"")
-#elif JSON_HEDLEY_INTEL_VERSION_CHECK(17,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("warning(disable:1292)")
-#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES __pragma(warning(disable:1292))
-#elif JSON_HEDLEY_MSVC_VERSION_CHECK(19,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES __pragma(warning(disable:5030))
-#elif JSON_HEDLEY_PGI_VERSION_CHECK(20,7,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1097,1098")
-#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1097")
-#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,14,0) && defined(__cplusplus)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("error_messages(off,attrskipunsup)")
-#elif \
-    JSON_HEDLEY_TI_VERSION_CHECK(18,1,0) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,3,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1173")
-#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress=Pe1097")
-#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1097")
-#else
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES
-#endif
-
-#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL)
-    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL
-#endif
-#if JSON_HEDLEY_HAS_WARNING("-Wcast-qual")
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma("clang diagnostic ignored \"-Wcast-qual\"")
-#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma("warning(disable:2203 2331)")
-#elif JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma("GCC diagnostic ignored \"-Wcast-qual\"")
-#else
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL
-#endif
-
-#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION)
-    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION
-#endif
-#if JSON_HEDLEY_HAS_WARNING("-Wunused-function")
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma("clang diagnostic ignored \"-Wunused-function\"")
-#elif JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma("GCC diagnostic ignored \"-Wunused-function\"")
-#elif JSON_HEDLEY_MSVC_VERSION_CHECK(1,0,0)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION __pragma(warning(disable:4505))
-#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma("diag_suppress 3142")
-#else
-    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION
-#endif
-
-#if defined(JSON_HEDLEY_DEPRECATED)
-    #undef JSON_HEDLEY_DEPRECATED
-#endif
-#if defined(JSON_HEDLEY_DEPRECATED_FOR)
-    #undef JSON_HEDLEY_DEPRECATED_FOR
-#endif
-#if \
-    JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_DEPRECATED(since) __declspec(deprecated("Since " # since))
-    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __declspec(deprecated("Since " #since "; use " #replacement))
-#elif \
-    (JSON_HEDLEY_HAS_EXTENSION(attribute_deprecated_with_message) && !defined(JSON_HEDLEY_IAR_VERSION)) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(4,5,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0) || \
-    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) || \
-    JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \
-    JSON_HEDLEY_TI_VERSION_CHECK(18,1,0) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(18,1,0) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,3,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,3,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_DEPRECATED(since) __attribute__((__deprecated__("Since " #since)))
-    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __attribute__((__deprecated__("Since " #since "; use " #replacement)))
-#elif defined(__cplusplus) && (__cplusplus >= 201402L)
-    #define JSON_HEDLEY_DEPRECATED(since) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[deprecated("Since " #since)]])
-    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[deprecated("Since " #since "; use " #replacement)]])
-#elif \
-    JSON_HEDLEY_HAS_ATTRIBUTE(deprecated) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \
-    JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)
-    #define JSON_HEDLEY_DEPRECATED(since) __attribute__((__deprecated__))
-    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __attribute__((__deprecated__))
-#elif \
-    JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \
-    JSON_HEDLEY_PELLES_VERSION_CHECK(6,50,0) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_DEPRECATED(since) __declspec(deprecated)
-    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __declspec(deprecated)
-#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
-    #define JSON_HEDLEY_DEPRECATED(since) _Pragma("deprecated")
-    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) _Pragma("deprecated")
-#else
-    #define JSON_HEDLEY_DEPRECATED(since)
-    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement)
-#endif
-
-#if defined(JSON_HEDLEY_UNAVAILABLE)
-    #undef JSON_HEDLEY_UNAVAILABLE
-#endif
-#if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(warning) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_UNAVAILABLE(available_since) __attribute__((__warning__("Not available until " #available_since)))
-#else
-    #define JSON_HEDLEY_UNAVAILABLE(available_since)
-#endif
-
-#if defined(JSON_HEDLEY_WARN_UNUSED_RESULT)
-    #undef JSON_HEDLEY_WARN_UNUSED_RESULT
-#endif
-#if defined(JSON_HEDLEY_WARN_UNUSED_RESULT_MSG)
-    #undef JSON_HEDLEY_WARN_UNUSED_RESULT_MSG
-#endif
-#if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(warn_unused_result) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-    (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0) && defined(__cplusplus)) || \
-    JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_WARN_UNUSED_RESULT __attribute__((__warn_unused_result__))
-    #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) __attribute__((__warn_unused_result__))
-#elif (JSON_HEDLEY_HAS_CPP_ATTRIBUTE(nodiscard) >= 201907L)
-    #define JSON_HEDLEY_WARN_UNUSED_RESULT JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]])
-    #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard(msg)]])
-#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE(nodiscard)
-    #define JSON_HEDLEY_WARN_UNUSED_RESULT JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]])
-    #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]])
-#elif defined(_Check_return_) /* SAL */
-    #define JSON_HEDLEY_WARN_UNUSED_RESULT _Check_return_
-    #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) _Check_return_
-#else
-    #define JSON_HEDLEY_WARN_UNUSED_RESULT
-    #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg)
-#endif
-
-#if defined(JSON_HEDLEY_SENTINEL)
-    #undef JSON_HEDLEY_SENTINEL
-#endif
-#if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(sentinel) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(5,4,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_SENTINEL(position) __attribute__((__sentinel__(position)))
-#else
-    #define JSON_HEDLEY_SENTINEL(position)
-#endif
-
-#if defined(JSON_HEDLEY_NO_RETURN)
-    #undef JSON_HEDLEY_NO_RETURN
-#endif
-#if JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
-    #define JSON_HEDLEY_NO_RETURN __noreturn
-#elif \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_NO_RETURN __attribute__((__noreturn__))
-#elif defined(__STDC_VERSION__) && __STDC_VERSION__ >= 201112L
-    #define JSON_HEDLEY_NO_RETURN _Noreturn
-#elif defined(__cplusplus) && (__cplusplus >= 201103L)
-    #define JSON_HEDLEY_NO_RETURN JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[noreturn]])
-#elif \
-    JSON_HEDLEY_HAS_ATTRIBUTE(noreturn) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,2,0) || \
-    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-    JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
-    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-    JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)
-    #define JSON_HEDLEY_NO_RETURN __attribute__((__noreturn__))
-#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)
-    #define JSON_HEDLEY_NO_RETURN _Pragma("does_not_return")
-#elif \
-    JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_NO_RETURN __declspec(noreturn)
-#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,0,0) && defined(__cplusplus)
-    #define JSON_HEDLEY_NO_RETURN _Pragma("FUNC_NEVER_RETURNS;")
-#elif JSON_HEDLEY_COMPCERT_VERSION_CHECK(3,2,0)
-    #define JSON_HEDLEY_NO_RETURN __attribute((noreturn))
-#elif JSON_HEDLEY_PELLES_VERSION_CHECK(9,0,0)
-    #define JSON_HEDLEY_NO_RETURN __declspec(noreturn)
-#else
-    #define JSON_HEDLEY_NO_RETURN
-#endif
-
-#if defined(JSON_HEDLEY_NO_ESCAPE)
-    #undef JSON_HEDLEY_NO_ESCAPE
-#endif
-#if JSON_HEDLEY_HAS_ATTRIBUTE(noescape)
-    #define JSON_HEDLEY_NO_ESCAPE __attribute__((__noescape__))
-#else
-    #define JSON_HEDLEY_NO_ESCAPE
-#endif
-
-#if defined(JSON_HEDLEY_UNREACHABLE)
-    #undef JSON_HEDLEY_UNREACHABLE
-#endif
-#if defined(JSON_HEDLEY_UNREACHABLE_RETURN)
-    #undef JSON_HEDLEY_UNREACHABLE_RETURN
-#endif
-#if defined(JSON_HEDLEY_ASSUME)
-    #undef JSON_HEDLEY_ASSUME
-#endif
-#if \
-    JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_ASSUME(expr) __assume(expr)
-#elif JSON_HEDLEY_HAS_BUILTIN(__builtin_assume)
-    #define JSON_HEDLEY_ASSUME(expr) __builtin_assume(expr)
-#elif \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0)
-    #if defined(__cplusplus)
-        #define JSON_HEDLEY_ASSUME(expr) std::_nassert(expr)
-    #else
-        #define JSON_HEDLEY_ASSUME(expr) _nassert(expr)
-    #endif
-#endif
-#if \
-    (JSON_HEDLEY_HAS_BUILTIN(__builtin_unreachable) && (!defined(JSON_HEDLEY_ARM_VERSION))) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(4,5,0) || \
-    JSON_HEDLEY_PGI_VERSION_CHECK(18,10,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_IBM_VERSION_CHECK(13,1,5) || \
-    JSON_HEDLEY_CRAY_VERSION_CHECK(10,0,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_UNREACHABLE() __builtin_unreachable()
-#elif defined(JSON_HEDLEY_ASSUME)
-    #define JSON_HEDLEY_UNREACHABLE() JSON_HEDLEY_ASSUME(0)
-#endif
-#if !defined(JSON_HEDLEY_ASSUME)
-    #if defined(JSON_HEDLEY_UNREACHABLE)
-        #define JSON_HEDLEY_ASSUME(expr) JSON_HEDLEY_STATIC_CAST(void, ((expr) ? 1 : (JSON_HEDLEY_UNREACHABLE(), 1)))
-    #else
-        #define JSON_HEDLEY_ASSUME(expr) JSON_HEDLEY_STATIC_CAST(void, expr)
-    #endif
-#endif
-#if defined(JSON_HEDLEY_UNREACHABLE)
-    #if  \
-        JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \
-        JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0)
-        #define JSON_HEDLEY_UNREACHABLE_RETURN(value) return (JSON_HEDLEY_STATIC_CAST(void, JSON_HEDLEY_ASSUME(0)), (value))
-    #else
-        #define JSON_HEDLEY_UNREACHABLE_RETURN(value) JSON_HEDLEY_UNREACHABLE()
-    #endif
-#else
-    #define JSON_HEDLEY_UNREACHABLE_RETURN(value) return (value)
-#endif
-#if !defined(JSON_HEDLEY_UNREACHABLE)
-    #define JSON_HEDLEY_UNREACHABLE() JSON_HEDLEY_ASSUME(0)
-#endif
-
-JSON_HEDLEY_DIAGNOSTIC_PUSH
-#if JSON_HEDLEY_HAS_WARNING("-Wpedantic")
-    #pragma clang diagnostic ignored "-Wpedantic"
-#endif
-#if JSON_HEDLEY_HAS_WARNING("-Wc++98-compat-pedantic") && defined(__cplusplus)
-    #pragma clang diagnostic ignored "-Wc++98-compat-pedantic"
-#endif
-#if JSON_HEDLEY_GCC_HAS_WARNING("-Wvariadic-macros",4,0,0)
-    #if defined(__clang__)
-        #pragma clang diagnostic ignored "-Wvariadic-macros"
-    #elif defined(JSON_HEDLEY_GCC_VERSION)
-        #pragma GCC diagnostic ignored "-Wvariadic-macros"
-    #endif
-#endif
-#if defined(JSON_HEDLEY_NON_NULL)
-    #undef JSON_HEDLEY_NON_NULL
-#endif
-#if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(nonnull) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0)
-    #define JSON_HEDLEY_NON_NULL(...) __attribute__((__nonnull__(__VA_ARGS__)))
-#else
-    #define JSON_HEDLEY_NON_NULL(...)
-#endif
-JSON_HEDLEY_DIAGNOSTIC_POP
-
-#if defined(JSON_HEDLEY_PRINTF_FORMAT)
-    #undef JSON_HEDLEY_PRINTF_FORMAT
-#endif
-#if defined(__MINGW32__) && JSON_HEDLEY_GCC_HAS_ATTRIBUTE(format,4,4,0) && !defined(__USE_MINGW_ANSI_STDIO)
-    #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(ms_printf, string_idx, first_to_check)))
-#elif defined(__MINGW32__) && JSON_HEDLEY_GCC_HAS_ATTRIBUTE(format,4,4,0) && defined(__USE_MINGW_ANSI_STDIO)
-    #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(gnu_printf, string_idx, first_to_check)))
-#elif \
-    JSON_HEDLEY_HAS_ATTRIBUTE(format) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0) || \
-    JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
-    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(__printf__, string_idx, first_to_check)))
-#elif JSON_HEDLEY_PELLES_VERSION_CHECK(6,0,0)
-    #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __declspec(vaformat(printf,string_idx,first_to_check))
-#else
-    #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check)
-#endif
-
-#if defined(JSON_HEDLEY_CONSTEXPR)
-    #undef JSON_HEDLEY_CONSTEXPR
-#endif
-#if defined(__cplusplus)
-    #if __cplusplus >= 201103L
-        #define JSON_HEDLEY_CONSTEXPR JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(constexpr)
-    #endif
-#endif
-#if !defined(JSON_HEDLEY_CONSTEXPR)
-    #define JSON_HEDLEY_CONSTEXPR
-#endif
-
-#if defined(JSON_HEDLEY_PREDICT)
-    #undef JSON_HEDLEY_PREDICT
-#endif
-#if defined(JSON_HEDLEY_LIKELY)
-    #undef JSON_HEDLEY_LIKELY
-#endif
-#if defined(JSON_HEDLEY_UNLIKELY)
-    #undef JSON_HEDLEY_UNLIKELY
-#endif
-#if defined(JSON_HEDLEY_UNPREDICTABLE)
-    #undef JSON_HEDLEY_UNPREDICTABLE
-#endif
-#if JSON_HEDLEY_HAS_BUILTIN(__builtin_unpredictable)
-    #define JSON_HEDLEY_UNPREDICTABLE(expr) __builtin_unpredictable((expr))
-#endif
-#if \
-  (JSON_HEDLEY_HAS_BUILTIN(__builtin_expect_with_probability) && !defined(JSON_HEDLEY_PGI_VERSION)) || \
-  JSON_HEDLEY_GCC_VERSION_CHECK(9,0,0) || \
-  JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-#  define JSON_HEDLEY_PREDICT(expr, value, probability) __builtin_expect_with_probability(  (expr), (value), (probability))
-#  define JSON_HEDLEY_PREDICT_TRUE(expr, probability)   __builtin_expect_with_probability(!!(expr),    1   , (probability))
-#  define JSON_HEDLEY_PREDICT_FALSE(expr, probability)  __builtin_expect_with_probability(!!(expr),    0   , (probability))
-#  define JSON_HEDLEY_LIKELY(expr)                      __builtin_expect                 (!!(expr),    1                  )
-#  define JSON_HEDLEY_UNLIKELY(expr)                    __builtin_expect                 (!!(expr),    0                  )
-#elif \
-  (JSON_HEDLEY_HAS_BUILTIN(__builtin_expect) && !defined(JSON_HEDLEY_INTEL_CL_VERSION)) || \
-  JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0) || \
-  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-  (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0) && defined(__cplusplus)) || \
-  JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-  JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
-  JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-  JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,7,0) || \
-  JSON_HEDLEY_TI_CL430_VERSION_CHECK(3,1,0) || \
-  JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,1,0) || \
-  JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \
-  JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-  JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-  JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,27) || \
-  JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \
-  JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-#  define JSON_HEDLEY_PREDICT(expr, expected, probability) \
-    (((probability) >= 0.9) ? __builtin_expect((expr), (expected)) : (JSON_HEDLEY_STATIC_CAST(void, expected), (expr)))
-#  define JSON_HEDLEY_PREDICT_TRUE(expr, probability) \
-    (__extension__ ({ \
-        double hedley_probability_ = (probability); \
-        ((hedley_probability_ >= 0.9) ? __builtin_expect(!!(expr), 1) : ((hedley_probability_ <= 0.1) ? __builtin_expect(!!(expr), 0) : !!(expr))); \
-    }))
-#  define JSON_HEDLEY_PREDICT_FALSE(expr, probability) \
-    (__extension__ ({ \
-        double hedley_probability_ = (probability); \
-        ((hedley_probability_ >= 0.9) ? __builtin_expect(!!(expr), 0) : ((hedley_probability_ <= 0.1) ? __builtin_expect(!!(expr), 1) : !!(expr))); \
-    }))
-#  define JSON_HEDLEY_LIKELY(expr)   __builtin_expect(!!(expr), 1)
-#  define JSON_HEDLEY_UNLIKELY(expr) __builtin_expect(!!(expr), 0)
-#else
-#  define JSON_HEDLEY_PREDICT(expr, expected, probability) (JSON_HEDLEY_STATIC_CAST(void, expected), (expr))
-#  define JSON_HEDLEY_PREDICT_TRUE(expr, probability) (!!(expr))
-#  define JSON_HEDLEY_PREDICT_FALSE(expr, probability) (!!(expr))
-#  define JSON_HEDLEY_LIKELY(expr) (!!(expr))
-#  define JSON_HEDLEY_UNLIKELY(expr) (!!(expr))
-#endif
-#if !defined(JSON_HEDLEY_UNPREDICTABLE)
-    #define JSON_HEDLEY_UNPREDICTABLE(expr) JSON_HEDLEY_PREDICT(expr, 1, 0.5)
-#endif
-
-#if defined(JSON_HEDLEY_MALLOC)
-    #undef JSON_HEDLEY_MALLOC
-#endif
-#if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(malloc) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-    JSON_HEDLEY_IBM_VERSION_CHECK(12,1,0) || \
-    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_MALLOC __attribute__((__malloc__))
-#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)
-    #define JSON_HEDLEY_MALLOC _Pragma("returns_new_memory")
-#elif \
-    JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_MALLOC __declspec(restrict)
-#else
-    #define JSON_HEDLEY_MALLOC
-#endif
-
-#if defined(JSON_HEDLEY_PURE)
-    #undef JSON_HEDLEY_PURE
-#endif
-#if \
-  JSON_HEDLEY_HAS_ATTRIBUTE(pure) || \
-  JSON_HEDLEY_GCC_VERSION_CHECK(2,96,0) || \
-  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-  JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
-  JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-  JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
-  JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-  (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-  JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-  (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-  JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-  (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-  JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-  (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-  JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
-  JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-  JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-  JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \
-  JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-#  define JSON_HEDLEY_PURE __attribute__((__pure__))
-#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)
-#  define JSON_HEDLEY_PURE _Pragma("does_not_write_global_data")
-#elif defined(__cplusplus) && \
-    ( \
-      JSON_HEDLEY_TI_CL430_VERSION_CHECK(2,0,1) || \
-      JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0) || \
-      JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) \
-    )
-#  define JSON_HEDLEY_PURE _Pragma("FUNC_IS_PURE;")
-#else
-#  define JSON_HEDLEY_PURE
-#endif
-
-#if defined(JSON_HEDLEY_CONST)
-    #undef JSON_HEDLEY_CONST
-#endif
-#if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(const) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(2,5,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-    JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
-    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-    JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_CONST __attribute__((__const__))
-#elif \
-    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)
-    #define JSON_HEDLEY_CONST _Pragma("no_side_effect")
-#else
-    #define JSON_HEDLEY_CONST JSON_HEDLEY_PURE
-#endif
-
-#if defined(JSON_HEDLEY_RESTRICT)
-    #undef JSON_HEDLEY_RESTRICT
-#endif
-#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) && !defined(__cplusplus)
-    #define JSON_HEDLEY_RESTRICT restrict
-#elif \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \
-    JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-    JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
-    JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,4) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,1,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,14,0) && defined(__cplusplus)) || \
-    JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) || \
-    defined(__clang__) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_RESTRICT __restrict
-#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,3,0) && !defined(__cplusplus)
-    #define JSON_HEDLEY_RESTRICT _Restrict
-#else
-    #define JSON_HEDLEY_RESTRICT
-#endif
-
-#if defined(JSON_HEDLEY_INLINE)
-    #undef JSON_HEDLEY_INLINE
-#endif
-#if \
-    (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) || \
-    (defined(__cplusplus) && (__cplusplus >= 199711L))
-    #define JSON_HEDLEY_INLINE inline
-#elif \
-    defined(JSON_HEDLEY_GCC_VERSION) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(6,2,0)
-    #define JSON_HEDLEY_INLINE __inline__
-#elif \
-    JSON_HEDLEY_MSVC_VERSION_CHECK(12,0,0) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,1,0) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(3,1,0) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_INLINE __inline
-#else
-    #define JSON_HEDLEY_INLINE
-#endif
-
-#if defined(JSON_HEDLEY_ALWAYS_INLINE)
-    #undef JSON_HEDLEY_ALWAYS_INLINE
-#endif
-#if \
-  JSON_HEDLEY_HAS_ATTRIBUTE(always_inline) || \
-  JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \
-  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-  JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
-  JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-  JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
-  JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-  (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-  JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-  (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-  JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-  (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-  JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-  (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-  JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
-  JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-  JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-  JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \
-  JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)
-#  define JSON_HEDLEY_ALWAYS_INLINE __attribute__((__always_inline__)) JSON_HEDLEY_INLINE
-#elif \
-  JSON_HEDLEY_MSVC_VERSION_CHECK(12,0,0) || \
-  JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-#  define JSON_HEDLEY_ALWAYS_INLINE __forceinline
-#elif defined(__cplusplus) && \
-    ( \
-      JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-      JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-      JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-      JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \
-      JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-      JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) \
-    )
-#  define JSON_HEDLEY_ALWAYS_INLINE _Pragma("FUNC_ALWAYS_INLINE;")
-#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
-#  define JSON_HEDLEY_ALWAYS_INLINE _Pragma("inline=forced")
-#else
-#  define JSON_HEDLEY_ALWAYS_INLINE JSON_HEDLEY_INLINE
-#endif
-
-#if defined(JSON_HEDLEY_NEVER_INLINE)
-    #undef JSON_HEDLEY_NEVER_INLINE
-#endif
-#if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(noinline) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-    JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
-    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
-    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
-    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
-    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
-    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
-    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
-    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \
-    JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)
-    #define JSON_HEDLEY_NEVER_INLINE __attribute__((__noinline__))
-#elif \
-    JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_NEVER_INLINE __declspec(noinline)
-#elif JSON_HEDLEY_PGI_VERSION_CHECK(10,2,0)
-    #define JSON_HEDLEY_NEVER_INLINE _Pragma("noinline")
-#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,0,0) && defined(__cplusplus)
-    #define JSON_HEDLEY_NEVER_INLINE _Pragma("FUNC_CANNOT_INLINE;")
-#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
-    #define JSON_HEDLEY_NEVER_INLINE _Pragma("inline=never")
-#elif JSON_HEDLEY_COMPCERT_VERSION_CHECK(3,2,0)
-    #define JSON_HEDLEY_NEVER_INLINE __attribute((noinline))
-#elif JSON_HEDLEY_PELLES_VERSION_CHECK(9,0,0)
-    #define JSON_HEDLEY_NEVER_INLINE __declspec(noinline)
-#else
-    #define JSON_HEDLEY_NEVER_INLINE
-#endif
-
-#if defined(JSON_HEDLEY_PRIVATE)
-    #undef JSON_HEDLEY_PRIVATE
-#endif
-#if defined(JSON_HEDLEY_PUBLIC)
-    #undef JSON_HEDLEY_PUBLIC
-#endif
-#if defined(JSON_HEDLEY_IMPORT)
-    #undef JSON_HEDLEY_IMPORT
-#endif
-#if defined(_WIN32) || defined(__CYGWIN__)
-#  define JSON_HEDLEY_PRIVATE
-#  define JSON_HEDLEY_PUBLIC   __declspec(dllexport)
-#  define JSON_HEDLEY_IMPORT   __declspec(dllimport)
-#else
-#  if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(visibility) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \
-    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-    JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \
-    ( \
-      defined(__TI_EABI__) && \
-      ( \
-        (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
-        JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) \
-      ) \
-    ) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-#    define JSON_HEDLEY_PRIVATE __attribute__((__visibility__("hidden")))
-#    define JSON_HEDLEY_PUBLIC  __attribute__((__visibility__("default")))
-#  else
-#    define JSON_HEDLEY_PRIVATE
-#    define JSON_HEDLEY_PUBLIC
-#  endif
-#  define JSON_HEDLEY_IMPORT    extern
-#endif
-
-#if defined(JSON_HEDLEY_NO_THROW)
-    #undef JSON_HEDLEY_NO_THROW
-#endif
-#if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(nothrow) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_NO_THROW __attribute__((__nothrow__))
-#elif \
-    JSON_HEDLEY_MSVC_VERSION_CHECK(13,1,0) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0)
-    #define JSON_HEDLEY_NO_THROW __declspec(nothrow)
-#else
-    #define JSON_HEDLEY_NO_THROW
-#endif
-
-#if defined(JSON_HEDLEY_FALL_THROUGH)
-    #undef JSON_HEDLEY_FALL_THROUGH
-#endif
-#if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(fallthrough) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(7,0,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_FALL_THROUGH __attribute__((__fallthrough__))
-#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(clang,fallthrough)
-    #define JSON_HEDLEY_FALL_THROUGH JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[clang::fallthrough]])
-#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE(fallthrough)
-    #define JSON_HEDLEY_FALL_THROUGH JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[fallthrough]])
-#elif defined(__fallthrough) /* SAL */
-    #define JSON_HEDLEY_FALL_THROUGH __fallthrough
-#else
-    #define JSON_HEDLEY_FALL_THROUGH
-#endif
-
-#if defined(JSON_HEDLEY_RETURNS_NON_NULL)
-    #undef JSON_HEDLEY_RETURNS_NON_NULL
-#endif
-#if \
-    JSON_HEDLEY_HAS_ATTRIBUTE(returns_nonnull) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_RETURNS_NON_NULL __attribute__((__returns_nonnull__))
-#elif defined(_Ret_notnull_) /* SAL */
-    #define JSON_HEDLEY_RETURNS_NON_NULL _Ret_notnull_
-#else
-    #define JSON_HEDLEY_RETURNS_NON_NULL
-#endif
-
-#if defined(JSON_HEDLEY_ARRAY_PARAM)
-    #undef JSON_HEDLEY_ARRAY_PARAM
-#endif
-#if \
-    defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) && \
-    !defined(__STDC_NO_VLA__) && \
-    !defined(__cplusplus) && \
-    !defined(JSON_HEDLEY_PGI_VERSION) && \
-    !defined(JSON_HEDLEY_TINYC_VERSION)
-    #define JSON_HEDLEY_ARRAY_PARAM(name) (name)
-#else
-    #define JSON_HEDLEY_ARRAY_PARAM(name)
-#endif
-
-#if defined(JSON_HEDLEY_IS_CONSTANT)
-    #undef JSON_HEDLEY_IS_CONSTANT
-#endif
-#if defined(JSON_HEDLEY_REQUIRE_CONSTEXPR)
-    #undef JSON_HEDLEY_REQUIRE_CONSTEXPR
-#endif
-/* JSON_HEDLEY_IS_CONSTEXPR_ is for
-   HEDLEY INTERNAL USE ONLY.  API subject to change without notice. */
-#if defined(JSON_HEDLEY_IS_CONSTEXPR_)
-    #undef JSON_HEDLEY_IS_CONSTEXPR_
-#endif
-#if \
-    JSON_HEDLEY_HAS_BUILTIN(__builtin_constant_p) || \
-    JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \
-    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-    JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,19) || \
-    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
-    JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \
-    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \
-    (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0) && !defined(__cplusplus)) || \
-    JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \
-    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
-    #define JSON_HEDLEY_IS_CONSTANT(expr) __builtin_constant_p(expr)
-#endif
-#if !defined(__cplusplus)
-#  if \
-       JSON_HEDLEY_HAS_BUILTIN(__builtin_types_compatible_p) || \
-       JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \
-       JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-       JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \
-       JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \
-       JSON_HEDLEY_ARM_VERSION_CHECK(5,4,0) || \
-       JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,24)
-#if defined(__INTPTR_TYPE__)
-    #define JSON_HEDLEY_IS_CONSTEXPR_(expr) __builtin_types_compatible_p(__typeof__((1 ? (void*) ((__INTPTR_TYPE__) ((expr) * 0)) : (int*) 0)), int*)
-#else
-    #include <stdint.h>
-    #define JSON_HEDLEY_IS_CONSTEXPR_(expr) __builtin_types_compatible_p(__typeof__((1 ? (void*) ((intptr_t) ((expr) * 0)) : (int*) 0)), int*)
-#endif
-#  elif \
-       ( \
-          defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) && \
-          !defined(JSON_HEDLEY_SUNPRO_VERSION) && \
-          !defined(JSON_HEDLEY_PGI_VERSION) && \
-          !defined(JSON_HEDLEY_IAR_VERSION)) || \
-       (JSON_HEDLEY_HAS_EXTENSION(c_generic_selections) && !defined(JSON_HEDLEY_IAR_VERSION)) || \
-       JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) || \
-       JSON_HEDLEY_INTEL_VERSION_CHECK(17,0,0) || \
-       JSON_HEDLEY_IBM_VERSION_CHECK(12,1,0) || \
-       JSON_HEDLEY_ARM_VERSION_CHECK(5,3,0)
-#if defined(__INTPTR_TYPE__)
-    #define JSON_HEDLEY_IS_CONSTEXPR_(expr) _Generic((1 ? (void*) ((__INTPTR_TYPE__) ((expr) * 0)) : (int*) 0), int*: 1, void*: 0)
-#else
-    #include <stdint.h>
-    #define JSON_HEDLEY_IS_CONSTEXPR_(expr) _Generic((1 ? (void*) ((intptr_t) * 0) : (int*) 0), int*: 1, void*: 0)
-#endif
-#  elif \
-       defined(JSON_HEDLEY_GCC_VERSION) || \
-       defined(JSON_HEDLEY_INTEL_VERSION) || \
-       defined(JSON_HEDLEY_TINYC_VERSION) || \
-       defined(JSON_HEDLEY_TI_ARMCL_VERSION) || \
-       JSON_HEDLEY_TI_CL430_VERSION_CHECK(18,12,0) || \
-       defined(JSON_HEDLEY_TI_CL2000_VERSION) || \
-       defined(JSON_HEDLEY_TI_CL6X_VERSION) || \
-       defined(JSON_HEDLEY_TI_CL7X_VERSION) || \
-       defined(JSON_HEDLEY_TI_CLPRU_VERSION) || \
-       defined(__clang__)
-#    define JSON_HEDLEY_IS_CONSTEXPR_(expr) ( \
-        sizeof(void) != \
-        sizeof(*( \
-                  1 ? \
-                  ((void*) ((expr) * 0L) ) : \
-((struct { char v[sizeof(void) * 2]; } *) 1) \
-                ) \
-              ) \
-                                            )
-#  endif
-#endif
-#if defined(JSON_HEDLEY_IS_CONSTEXPR_)
-    #if !defined(JSON_HEDLEY_IS_CONSTANT)
-        #define JSON_HEDLEY_IS_CONSTANT(expr) JSON_HEDLEY_IS_CONSTEXPR_(expr)
-    #endif
-    #define JSON_HEDLEY_REQUIRE_CONSTEXPR(expr) (JSON_HEDLEY_IS_CONSTEXPR_(expr) ? (expr) : (-1))
-#else
-    #if !defined(JSON_HEDLEY_IS_CONSTANT)
-        #define JSON_HEDLEY_IS_CONSTANT(expr) (0)
-    #endif
-    #define JSON_HEDLEY_REQUIRE_CONSTEXPR(expr) (expr)
-#endif
-
-#if defined(JSON_HEDLEY_BEGIN_C_DECLS)
-    #undef JSON_HEDLEY_BEGIN_C_DECLS
-#endif
-#if defined(JSON_HEDLEY_END_C_DECLS)
-    #undef JSON_HEDLEY_END_C_DECLS
-#endif
-#if defined(JSON_HEDLEY_C_DECL)
-    #undef JSON_HEDLEY_C_DECL
-#endif
-#if defined(__cplusplus)
-    #define JSON_HEDLEY_BEGIN_C_DECLS extern "C" {
-    #define JSON_HEDLEY_END_C_DECLS }
-    #define JSON_HEDLEY_C_DECL extern "C"
-#else
-    #define JSON_HEDLEY_BEGIN_C_DECLS
-    #define JSON_HEDLEY_END_C_DECLS
-    #define JSON_HEDLEY_C_DECL
-#endif
-
-#if defined(JSON_HEDLEY_STATIC_ASSERT)
-    #undef JSON_HEDLEY_STATIC_ASSERT
-#endif
-#if \
-  !defined(__cplusplus) && ( \
-      (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L)) || \
-      (JSON_HEDLEY_HAS_FEATURE(c_static_assert) && !defined(JSON_HEDLEY_INTEL_CL_VERSION)) || \
-      JSON_HEDLEY_GCC_VERSION_CHECK(6,0,0) || \
-      JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
-      defined(_Static_assert) \
-    )
-#  define JSON_HEDLEY_STATIC_ASSERT(expr, message) _Static_assert(expr, message)
-#elif \
-  (defined(__cplusplus) && (__cplusplus >= 201103L)) || \
-  JSON_HEDLEY_MSVC_VERSION_CHECK(16,0,0) || \
-  JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-#  define JSON_HEDLEY_STATIC_ASSERT(expr, message) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(static_assert(expr, message))
-#else
-#  define JSON_HEDLEY_STATIC_ASSERT(expr, message)
-#endif
-
-#if defined(JSON_HEDLEY_NULL)
-    #undef JSON_HEDLEY_NULL
-#endif
-#if defined(__cplusplus)
-    #if __cplusplus >= 201103L
-        #define JSON_HEDLEY_NULL JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(nullptr)
-    #elif defined(NULL)
-        #define JSON_HEDLEY_NULL NULL
-    #else
-        #define JSON_HEDLEY_NULL JSON_HEDLEY_STATIC_CAST(void*, 0)
-    #endif
-#elif defined(NULL)
-    #define JSON_HEDLEY_NULL NULL
-#else
-    #define JSON_HEDLEY_NULL ((void*) 0)
-#endif
-
-#if defined(JSON_HEDLEY_MESSAGE)
-    #undef JSON_HEDLEY_MESSAGE
-#endif
-#if JSON_HEDLEY_HAS_WARNING("-Wunknown-pragmas")
-#  define JSON_HEDLEY_MESSAGE(msg) \
-    JSON_HEDLEY_DIAGNOSTIC_PUSH \
-    JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS \
-    JSON_HEDLEY_PRAGMA(message msg) \
-    JSON_HEDLEY_DIAGNOSTIC_POP
-#elif \
-  JSON_HEDLEY_GCC_VERSION_CHECK(4,4,0) || \
-  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
-#  define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message msg)
-#elif JSON_HEDLEY_CRAY_VERSION_CHECK(5,0,0)
-#  define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(_CRI message msg)
-#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
-#  define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message(msg))
-#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,0,0)
-#  define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message(msg))
-#else
-#  define JSON_HEDLEY_MESSAGE(msg)
-#endif
-
-#if defined(JSON_HEDLEY_WARNING)
-    #undef JSON_HEDLEY_WARNING
-#endif
-#if JSON_HEDLEY_HAS_WARNING("-Wunknown-pragmas")
-#  define JSON_HEDLEY_WARNING(msg) \
-    JSON_HEDLEY_DIAGNOSTIC_PUSH \
-    JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS \
-    JSON_HEDLEY_PRAGMA(clang warning msg) \
-    JSON_HEDLEY_DIAGNOSTIC_POP
-#elif \
-  JSON_HEDLEY_GCC_VERSION_CHECK(4,8,0) || \
-  JSON_HEDLEY_PGI_VERSION_CHECK(18,4,0) || \
-  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
-#  define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_PRAGMA(GCC warning msg)
-#elif \
-  JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) || \
-  JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-#  define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_PRAGMA(message(msg))
-#else
-#  define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_MESSAGE(msg)
-#endif
-
-#if defined(JSON_HEDLEY_REQUIRE)
-    #undef JSON_HEDLEY_REQUIRE
-#endif
-#if defined(JSON_HEDLEY_REQUIRE_MSG)
-    #undef JSON_HEDLEY_REQUIRE_MSG
-#endif
-#if JSON_HEDLEY_HAS_ATTRIBUTE(diagnose_if)
-#  if JSON_HEDLEY_HAS_WARNING("-Wgcc-compat")
-#    define JSON_HEDLEY_REQUIRE(expr) \
-    JSON_HEDLEY_DIAGNOSTIC_PUSH \
-    _Pragma("clang diagnostic ignored \"-Wgcc-compat\"") \
-    __attribute__((diagnose_if(!(expr), #expr, "error"))) \
-    JSON_HEDLEY_DIAGNOSTIC_POP
-#    define JSON_HEDLEY_REQUIRE_MSG(expr,msg) \
-    JSON_HEDLEY_DIAGNOSTIC_PUSH \
-    _Pragma("clang diagnostic ignored \"-Wgcc-compat\"") \
-    __attribute__((diagnose_if(!(expr), msg, "error"))) \
-    JSON_HEDLEY_DIAGNOSTIC_POP
-#  else
-#    define JSON_HEDLEY_REQUIRE(expr) __attribute__((diagnose_if(!(expr), #expr, "error")))
-#    define JSON_HEDLEY_REQUIRE_MSG(expr,msg) __attribute__((diagnose_if(!(expr), msg, "error")))
-#  endif
-#else
-#  define JSON_HEDLEY_REQUIRE(expr)
-#  define JSON_HEDLEY_REQUIRE_MSG(expr,msg)
-#endif
-
-#if defined(JSON_HEDLEY_FLAGS)
-    #undef JSON_HEDLEY_FLAGS
-#endif
-#if JSON_HEDLEY_HAS_ATTRIBUTE(flag_enum) && (!defined(__cplusplus) || JSON_HEDLEY_HAS_WARNING("-Wbitfield-enum-conversion"))
-    #define JSON_HEDLEY_FLAGS __attribute__((__flag_enum__))
-#else
-    #define JSON_HEDLEY_FLAGS
-#endif
-
-#if defined(JSON_HEDLEY_FLAGS_CAST)
-    #undef JSON_HEDLEY_FLAGS_CAST
-#endif
-#if JSON_HEDLEY_INTEL_VERSION_CHECK(19,0,0)
-#  define JSON_HEDLEY_FLAGS_CAST(T, expr) (__extension__ ({ \
-        JSON_HEDLEY_DIAGNOSTIC_PUSH \
-        _Pragma("warning(disable:188)") \
-        ((T) (expr)); \
-        JSON_HEDLEY_DIAGNOSTIC_POP \
-    }))
-#else
-#  define JSON_HEDLEY_FLAGS_CAST(T, expr) JSON_HEDLEY_STATIC_CAST(T, expr)
-#endif
-
-#if defined(JSON_HEDLEY_EMPTY_BASES)
-    #undef JSON_HEDLEY_EMPTY_BASES
-#endif
-#if \
-    (JSON_HEDLEY_MSVC_VERSION_CHECK(19,0,23918) && !JSON_HEDLEY_MSVC_VERSION_CHECK(20,0,0)) || \
-    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
-    #define JSON_HEDLEY_EMPTY_BASES __declspec(empty_bases)
-#else
-    #define JSON_HEDLEY_EMPTY_BASES
-#endif
-
-/* Remaining macros are deprecated. */
-
-#if defined(JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK)
-    #undef JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK
-#endif
-#if defined(__clang__)
-    #define JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK(major,minor,patch) (0)
-#else
-    #define JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK(major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
-#endif
-
-#if defined(JSON_HEDLEY_CLANG_HAS_ATTRIBUTE)
-    #undef JSON_HEDLEY_CLANG_HAS_ATTRIBUTE
-#endif
-#define JSON_HEDLEY_CLANG_HAS_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_ATTRIBUTE(attribute)
-
-#if defined(JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE)
-    #undef JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE
-#endif
-#define JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute)
-
-#if defined(JSON_HEDLEY_CLANG_HAS_BUILTIN)
-    #undef JSON_HEDLEY_CLANG_HAS_BUILTIN
-#endif
-#define JSON_HEDLEY_CLANG_HAS_BUILTIN(builtin) JSON_HEDLEY_HAS_BUILTIN(builtin)
-
-#if defined(JSON_HEDLEY_CLANG_HAS_FEATURE)
-    #undef JSON_HEDLEY_CLANG_HAS_FEATURE
-#endif
-#define JSON_HEDLEY_CLANG_HAS_FEATURE(feature) JSON_HEDLEY_HAS_FEATURE(feature)
-
-#if defined(JSON_HEDLEY_CLANG_HAS_EXTENSION)
-    #undef JSON_HEDLEY_CLANG_HAS_EXTENSION
-#endif
-#define JSON_HEDLEY_CLANG_HAS_EXTENSION(extension) JSON_HEDLEY_HAS_EXTENSION(extension)
-
-#if defined(JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE)
-    #undef JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE
-#endif
-#define JSON_HEDLEY_CLANG_HAS_DECLSPEC_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute)
-
-#if defined(JSON_HEDLEY_CLANG_HAS_WARNING)
-    #undef JSON_HEDLEY_CLANG_HAS_WARNING
-#endif
-#define JSON_HEDLEY_CLANG_HAS_WARNING(warning) JSON_HEDLEY_HAS_WARNING(warning)
-
-#endif /* !defined(JSON_HEDLEY_VERSION) || (JSON_HEDLEY_VERSION < X) */
-
-
-// This file contains all internal macro definitions (except those affecting ABI)
-// You MUST include macro_unscope.hpp at the end of json.hpp to undef all of them
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-
-// exclude unsupported compilers
-#if !defined(JSON_SKIP_UNSUPPORTED_COMPILER_CHECK)
-    #if defined(__clang__)
-        #if (__clang_major__ * 10000 + __clang_minor__ * 100 + __clang_patchlevel__) < 30400
-            #error "unsupported Clang version - see https://github.com/nlohmann/json#supported-compilers"
-        #endif
-    #elif defined(__GNUC__) && !(defined(__ICC) || defined(__INTEL_COMPILER))
-        #if (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) < 40800
-            #error "unsupported GCC version - see https://github.com/nlohmann/json#supported-compilers"
-        #endif
-    #endif
-#endif
-
-// C++ language standard detection
-// if the user manually specified the used c++ version this is skipped
-#if !defined(JSON_HAS_CPP_20) && !defined(JSON_HAS_CPP_17) && !defined(JSON_HAS_CPP_14) && !defined(JSON_HAS_CPP_11)
-    #if (defined(__cplusplus) && __cplusplus >= 202002L) || (defined(_MSVC_LANG) && _MSVC_LANG >= 202002L)
-        #define JSON_HAS_CPP_20
-        #define JSON_HAS_CPP_17
-        #define JSON_HAS_CPP_14
-    #elif (defined(__cplusplus) && __cplusplus >= 201703L) || (defined(_HAS_CXX17) && _HAS_CXX17 == 1) // fix for issue #464
-        #define JSON_HAS_CPP_17
-        #define JSON_HAS_CPP_14
-    #elif (defined(__cplusplus) && __cplusplus >= 201402L) || (defined(_HAS_CXX14) && _HAS_CXX14 == 1)
-        #define JSON_HAS_CPP_14
-    #endif
-    // the cpp 11 flag is always specified because it is the minimal required version
-    #define JSON_HAS_CPP_11
-#endif
-
-#ifdef __has_include
-    #if __has_include(<version>)
-        #include <version>
-    #endif
-#endif
-
-#if !defined(JSON_HAS_FILESYSTEM) && !defined(JSON_HAS_EXPERIMENTAL_FILESYSTEM)
-    #ifdef JSON_HAS_CPP_17
-        #if defined(__cpp_lib_filesystem)
-            #define JSON_HAS_FILESYSTEM 1
-        #elif defined(__cpp_lib_experimental_filesystem)
-            #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1
-        #elif !defined(__has_include)
-            #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1
-        #elif __has_include(<filesystem>)
-            #define JSON_HAS_FILESYSTEM 1
-        #elif __has_include(<experimental/filesystem>)
-            #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1
-        #endif
-
-        // std::filesystem does not work on MinGW GCC 8: https://sourceforge.net/p/mingw-w64/bugs/737/
-        #if defined(__MINGW32__) && defined(__GNUC__) && __GNUC__ == 8
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-
-        // no filesystem support before GCC 8: https://en.cppreference.com/w/cpp/compiler_support
-        #if defined(__GNUC__) && !defined(__clang__) && __GNUC__ < 8
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-
-        // no filesystem support before Clang 7: https://en.cppreference.com/w/cpp/compiler_support
-        #if defined(__clang_major__) && __clang_major__ < 7
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-
-        // no filesystem support before MSVC 19.14: https://en.cppreference.com/w/cpp/compiler_support
-        #if defined(_MSC_VER) && _MSC_VER < 1914
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-
-        // no filesystem support before iOS 13
-        #if defined(__IPHONE_OS_VERSION_MIN_REQUIRED) && __IPHONE_OS_VERSION_MIN_REQUIRED < 130000
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-
-        // no filesystem support before macOS Catalina
-        #if defined(__MAC_OS_X_VERSION_MIN_REQUIRED) && __MAC_OS_X_VERSION_MIN_REQUIRED < 101500
-            #undef JSON_HAS_FILESYSTEM
-            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-        #endif
-    #endif
-#endif
-
-#ifndef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-    #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 0
-#endif
-
-#ifndef JSON_HAS_FILESYSTEM
-    #define JSON_HAS_FILESYSTEM 0
-#endif
-
-#ifndef JSON_HAS_THREE_WAY_COMPARISON
-    #if defined(__cpp_impl_three_way_comparison) && __cpp_impl_three_way_comparison >= 201907L \
-        && defined(__cpp_lib_three_way_comparison) && __cpp_lib_three_way_comparison >= 201907L
-        #define JSON_HAS_THREE_WAY_COMPARISON 1
-    #else
-        #define JSON_HAS_THREE_WAY_COMPARISON 0
-    #endif
-#endif
-
-#ifndef JSON_HAS_RANGES
-    // ranges header shipping in GCC 11.1.0 (released 2021-04-27) has syntax error
-    #if defined(__GLIBCXX__) && __GLIBCXX__ == 20210427
-        #define JSON_HAS_RANGES 0
-    #elif defined(__cpp_lib_ranges)
-        #define JSON_HAS_RANGES 1
-    #else
-        #define JSON_HAS_RANGES 0
-    #endif
-#endif
-
-#ifdef JSON_HAS_CPP_17
-    #define JSON_INLINE_VARIABLE inline
-#else
-    #define JSON_INLINE_VARIABLE
-#endif
-
-#if JSON_HEDLEY_HAS_ATTRIBUTE(no_unique_address)
-    #define JSON_NO_UNIQUE_ADDRESS [[no_unique_address]]
-#else
-    #define JSON_NO_UNIQUE_ADDRESS
-#endif
-
-// disable documentation warnings on clang
-#if defined(__clang__)
-    #pragma clang diagnostic push
-    #pragma clang diagnostic ignored "-Wdocumentation"
-    #pragma clang diagnostic ignored "-Wdocumentation-unknown-command"
-#endif
-
-// allow disabling exceptions
-#if (defined(__cpp_exceptions) || defined(__EXCEPTIONS) || defined(_CPPUNWIND)) && !defined(JSON_NOEXCEPTION)
-    #define JSON_THROW(exception) throw exception
-    #define JSON_TRY try
-    #define JSON_CATCH(exception) catch(exception)
-    #define JSON_INTERNAL_CATCH(exception) catch(exception)
-#else
-    #include <cstdlib>
-    #define JSON_THROW(exception) std::abort()
-    #define JSON_TRY if(true)
-    #define JSON_CATCH(exception) if(false)
-    #define JSON_INTERNAL_CATCH(exception) if(false)
-#endif
-
-// override exception macros
-#if defined(JSON_THROW_USER)
-    #undef JSON_THROW
-    #define JSON_THROW JSON_THROW_USER
-#endif
-#if defined(JSON_TRY_USER)
-    #undef JSON_TRY
-    #define JSON_TRY JSON_TRY_USER
-#endif
-#if defined(JSON_CATCH_USER)
-    #undef JSON_CATCH
-    #define JSON_CATCH JSON_CATCH_USER
-    #undef JSON_INTERNAL_CATCH
-    #define JSON_INTERNAL_CATCH JSON_CATCH_USER
-#endif
-#if defined(JSON_INTERNAL_CATCH_USER)
-    #undef JSON_INTERNAL_CATCH
-    #define JSON_INTERNAL_CATCH JSON_INTERNAL_CATCH_USER
-#endif
-
-// allow overriding assert
-#if !defined(JSON_ASSERT)
-    #include <cassert> // assert
-    #define JSON_ASSERT(x) assert(x)
-#endif
-
-// allow to access some private functions (needed by the test suite)
-#if defined(JSON_TESTS_PRIVATE)
-    #define JSON_PRIVATE_UNLESS_TESTED public
-#else
-    #define JSON_PRIVATE_UNLESS_TESTED private
-#endif
-
-/*!
-@brief macro to briefly define a mapping between an enum and JSON
-@def NLOHMANN_JSON_SERIALIZE_ENUM
-@since version 3.4.0
-*/
-#define NLOHMANN_JSON_SERIALIZE_ENUM(ENUM_TYPE, ...)                                            \
-    template<typename BasicJsonType>                                                            \
-    inline void to_json(BasicJsonType& j, const ENUM_TYPE& e)                                   \
-    {                                                                                           \
-        static_assert(std::is_enum<ENUM_TYPE>::value, #ENUM_TYPE " must be an enum!");          \
-        static const std::pair<ENUM_TYPE, BasicJsonType> m[] = __VA_ARGS__;                     \
-        auto it = std::find_if(std::begin(m), std::end(m),                                      \
-                               [e](const std::pair<ENUM_TYPE, BasicJsonType>& ej_pair) -> bool  \
-        {                                                                                       \
-            return ej_pair.first == e;                                                          \
-        });                                                                                     \
-        j = ((it != std::end(m)) ? it : std::begin(m))->second;                                 \
-    }                                                                                           \
-    template<typename BasicJsonType>                                                            \
-    inline void from_json(const BasicJsonType& j, ENUM_TYPE& e)                                 \
-    {                                                                                           \
-        static_assert(std::is_enum<ENUM_TYPE>::value, #ENUM_TYPE " must be an enum!");          \
-        static const std::pair<ENUM_TYPE, BasicJsonType> m[] = __VA_ARGS__;                     \
-        auto it = std::find_if(std::begin(m), std::end(m),                                      \
-                               [&j](const std::pair<ENUM_TYPE, BasicJsonType>& ej_pair) -> bool \
-        {                                                                                       \
-            return ej_pair.second == j;                                                         \
-        });                                                                                     \
-        e = ((it != std::end(m)) ? it : std::begin(m))->first;                                  \
-    }
-
-// Ugly macros to avoid uglier copy-paste when specializing basic_json. They
-// may be removed in the future once the class is split.
-
-#define NLOHMANN_BASIC_JSON_TPL_DECLARATION                                \
-    template<template<typename, typename, typename...> class ObjectType,   \
-             template<typename, typename...> class ArrayType,              \
-             class StringType, class BooleanType, class NumberIntegerType, \
-             class NumberUnsignedType, class NumberFloatType,              \
-             template<typename> class AllocatorType,                       \
-             template<typename, typename = void> class JSONSerializer,     \
-             class BinaryType>
-
-#define NLOHMANN_BASIC_JSON_TPL                                            \
-    basic_json<ObjectType, ArrayType, StringType, BooleanType,             \
-    NumberIntegerType, NumberUnsignedType, NumberFloatType,                \
-    AllocatorType, JSONSerializer, BinaryType>
-
-// Macros to simplify conversion from/to types
-
-#define NLOHMANN_JSON_EXPAND( x ) x
-#define NLOHMANN_JSON_GET_MACRO(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58, _59, _60, _61, _62, _63, _64, NAME,...) NAME
-#define NLOHMANN_JSON_PASTE(...) NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_GET_MACRO(__VA_ARGS__, \
-        NLOHMANN_JSON_PASTE64, \
-        NLOHMANN_JSON_PASTE63, \
-        NLOHMANN_JSON_PASTE62, \
-        NLOHMANN_JSON_PASTE61, \
-        NLOHMANN_JSON_PASTE60, \
-        NLOHMANN_JSON_PASTE59, \
-        NLOHMANN_JSON_PASTE58, \
-        NLOHMANN_JSON_PASTE57, \
-        NLOHMANN_JSON_PASTE56, \
-        NLOHMANN_JSON_PASTE55, \
-        NLOHMANN_JSON_PASTE54, \
-        NLOHMANN_JSON_PASTE53, \
-        NLOHMANN_JSON_PASTE52, \
-        NLOHMANN_JSON_PASTE51, \
-        NLOHMANN_JSON_PASTE50, \
-        NLOHMANN_JSON_PASTE49, \
-        NLOHMANN_JSON_PASTE48, \
-        NLOHMANN_JSON_PASTE47, \
-        NLOHMANN_JSON_PASTE46, \
-        NLOHMANN_JSON_PASTE45, \
-        NLOHMANN_JSON_PASTE44, \
-        NLOHMANN_JSON_PASTE43, \
-        NLOHMANN_JSON_PASTE42, \
-        NLOHMANN_JSON_PASTE41, \
-        NLOHMANN_JSON_PASTE40, \
-        NLOHMANN_JSON_PASTE39, \
-        NLOHMANN_JSON_PASTE38, \
-        NLOHMANN_JSON_PASTE37, \
-        NLOHMANN_JSON_PASTE36, \
-        NLOHMANN_JSON_PASTE35, \
-        NLOHMANN_JSON_PASTE34, \
-        NLOHMANN_JSON_PASTE33, \
-        NLOHMANN_JSON_PASTE32, \
-        NLOHMANN_JSON_PASTE31, \
-        NLOHMANN_JSON_PASTE30, \
-        NLOHMANN_JSON_PASTE29, \
-        NLOHMANN_JSON_PASTE28, \
-        NLOHMANN_JSON_PASTE27, \
-        NLOHMANN_JSON_PASTE26, \
-        NLOHMANN_JSON_PASTE25, \
-        NLOHMANN_JSON_PASTE24, \
-        NLOHMANN_JSON_PASTE23, \
-        NLOHMANN_JSON_PASTE22, \
-        NLOHMANN_JSON_PASTE21, \
-        NLOHMANN_JSON_PASTE20, \
-        NLOHMANN_JSON_PASTE19, \
-        NLOHMANN_JSON_PASTE18, \
-        NLOHMANN_JSON_PASTE17, \
-        NLOHMANN_JSON_PASTE16, \
-        NLOHMANN_JSON_PASTE15, \
-        NLOHMANN_JSON_PASTE14, \
-        NLOHMANN_JSON_PASTE13, \
-        NLOHMANN_JSON_PASTE12, \
-        NLOHMANN_JSON_PASTE11, \
-        NLOHMANN_JSON_PASTE10, \
-        NLOHMANN_JSON_PASTE9, \
-        NLOHMANN_JSON_PASTE8, \
-        NLOHMANN_JSON_PASTE7, \
-        NLOHMANN_JSON_PASTE6, \
-        NLOHMANN_JSON_PASTE5, \
-        NLOHMANN_JSON_PASTE4, \
-        NLOHMANN_JSON_PASTE3, \
-        NLOHMANN_JSON_PASTE2, \
-        NLOHMANN_JSON_PASTE1)(__VA_ARGS__))
-#define NLOHMANN_JSON_PASTE2(func, v1) func(v1)
-#define NLOHMANN_JSON_PASTE3(func, v1, v2) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE2(func, v2)
-#define NLOHMANN_JSON_PASTE4(func, v1, v2, v3) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE3(func, v2, v3)
-#define NLOHMANN_JSON_PASTE5(func, v1, v2, v3, v4) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE4(func, v2, v3, v4)
-#define NLOHMANN_JSON_PASTE6(func, v1, v2, v3, v4, v5) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE5(func, v2, v3, v4, v5)
-#define NLOHMANN_JSON_PASTE7(func, v1, v2, v3, v4, v5, v6) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE6(func, v2, v3, v4, v5, v6)
-#define NLOHMANN_JSON_PASTE8(func, v1, v2, v3, v4, v5, v6, v7) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE7(func, v2, v3, v4, v5, v6, v7)
-#define NLOHMANN_JSON_PASTE9(func, v1, v2, v3, v4, v5, v6, v7, v8) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE8(func, v2, v3, v4, v5, v6, v7, v8)
-#define NLOHMANN_JSON_PASTE10(func, v1, v2, v3, v4, v5, v6, v7, v8, v9) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE9(func, v2, v3, v4, v5, v6, v7, v8, v9)
-#define NLOHMANN_JSON_PASTE11(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE10(func, v2, v3, v4, v5, v6, v7, v8, v9, v10)
-#define NLOHMANN_JSON_PASTE12(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE11(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11)
-#define NLOHMANN_JSON_PASTE13(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE12(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12)
-#define NLOHMANN_JSON_PASTE14(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE13(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13)
-#define NLOHMANN_JSON_PASTE15(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE14(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14)
-#define NLOHMANN_JSON_PASTE16(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE15(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)
-#define NLOHMANN_JSON_PASTE17(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE16(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16)
-#define NLOHMANN_JSON_PASTE18(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE17(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17)
-#define NLOHMANN_JSON_PASTE19(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE18(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18)
-#define NLOHMANN_JSON_PASTE20(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE19(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19)
-#define NLOHMANN_JSON_PASTE21(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE20(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20)
-#define NLOHMANN_JSON_PASTE22(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE21(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21)
-#define NLOHMANN_JSON_PASTE23(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE22(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22)
-#define NLOHMANN_JSON_PASTE24(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE23(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23)
-#define NLOHMANN_JSON_PASTE25(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE24(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24)
-#define NLOHMANN_JSON_PASTE26(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE25(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25)
-#define NLOHMANN_JSON_PASTE27(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE26(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26)
-#define NLOHMANN_JSON_PASTE28(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE27(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27)
-#define NLOHMANN_JSON_PASTE29(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE28(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28)
-#define NLOHMANN_JSON_PASTE30(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE29(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29)
-#define NLOHMANN_JSON_PASTE31(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE30(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30)
-#define NLOHMANN_JSON_PASTE32(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE31(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31)
-#define NLOHMANN_JSON_PASTE33(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE32(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32)
-#define NLOHMANN_JSON_PASTE34(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE33(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33)
-#define NLOHMANN_JSON_PASTE35(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE34(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34)
-#define NLOHMANN_JSON_PASTE36(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE35(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35)
-#define NLOHMANN_JSON_PASTE37(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE36(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36)
-#define NLOHMANN_JSON_PASTE38(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE37(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37)
-#define NLOHMANN_JSON_PASTE39(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE38(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38)
-#define NLOHMANN_JSON_PASTE40(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE39(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39)
-#define NLOHMANN_JSON_PASTE41(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE40(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40)
-#define NLOHMANN_JSON_PASTE42(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE41(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41)
-#define NLOHMANN_JSON_PASTE43(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE42(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42)
-#define NLOHMANN_JSON_PASTE44(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE43(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43)
-#define NLOHMANN_JSON_PASTE45(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE44(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44)
-#define NLOHMANN_JSON_PASTE46(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE45(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45)
-#define NLOHMANN_JSON_PASTE47(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE46(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46)
-#define NLOHMANN_JSON_PASTE48(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE47(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47)
-#define NLOHMANN_JSON_PASTE49(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE48(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48)
-#define NLOHMANN_JSON_PASTE50(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE49(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49)
-#define NLOHMANN_JSON_PASTE51(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE50(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50)
-#define NLOHMANN_JSON_PASTE52(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE51(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51)
-#define NLOHMANN_JSON_PASTE53(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE52(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52)
-#define NLOHMANN_JSON_PASTE54(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE53(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53)
-#define NLOHMANN_JSON_PASTE55(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE54(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54)
-#define NLOHMANN_JSON_PASTE56(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE55(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55)
-#define NLOHMANN_JSON_PASTE57(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE56(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56)
-#define NLOHMANN_JSON_PASTE58(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE57(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57)
-#define NLOHMANN_JSON_PASTE59(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE58(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58)
-#define NLOHMANN_JSON_PASTE60(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE59(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59)
-#define NLOHMANN_JSON_PASTE61(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE60(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60)
-#define NLOHMANN_JSON_PASTE62(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE61(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61)
-#define NLOHMANN_JSON_PASTE63(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE62(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62)
-#define NLOHMANN_JSON_PASTE64(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62, v63) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE63(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62, v63)
-
-#define NLOHMANN_JSON_TO(v1) nlohmann_json_j[#v1] = nlohmann_json_t.v1;
-#define NLOHMANN_JSON_FROM(v1) nlohmann_json_j.at(#v1).get_to(nlohmann_json_t.v1);
-#define NLOHMANN_JSON_FROM_WITH_DEFAULT(v1) nlohmann_json_t.v1 = nlohmann_json_j.value(#v1, nlohmann_json_default_obj.v1);
-
-/*!
-@brief macro
-@def NLOHMANN_DEFINE_TYPE_INTRUSIVE
-@since version 3.9.0
-*/
-#define NLOHMANN_DEFINE_TYPE_INTRUSIVE(Type, ...)  \
-    friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) }
-
-#define NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(Type, ...)  \
-    friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
-
-/*!
-@brief macro
-@def NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE
-@since version 3.9.0
-*/
-#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Type, ...)  \
-    inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) }
-
-#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_WITH_DEFAULT(Type, ...)  \
-    inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
-
-
-// inspired from https://stackoverflow.com/a/26745591
-// allows to call any std function as if (e.g. with begin):
-// using std::begin; begin(x);
-//
-// it allows using the detected idiom to retrieve the return type
-// of such an expression
-#define NLOHMANN_CAN_CALL_STD_FUNC_IMPL(std_name)                                 \
-    namespace detail {                                                            \
-    using std::std_name;                                                          \
-    \
-    template<typename... T>                                                       \
-    using result_of_##std_name = decltype(std_name(std::declval<T>()...));        \
-    }                                                                             \
-    \
-    namespace detail2 {                                                           \
-    struct std_name##_tag                                                         \
-    {                                                                             \
-    };                                                                            \
-    \
-    template<typename... T>                                                       \
-    std_name##_tag std_name(T&&...);                                              \
-    \
-    template<typename... T>                                                       \
-    using result_of_##std_name = decltype(std_name(std::declval<T>()...));        \
-    \
-    template<typename... T>                                                       \
-    struct would_call_std_##std_name                                              \
-    {                                                                             \
-        static constexpr auto const value = ::nlohmann::detail::                  \
-                                            is_detected_exact<std_name##_tag, result_of_##std_name, T...>::value; \
-    };                                                                            \
-    } /* namespace detail2 */ \
-    \
-    template<typename... T>                                                       \
-    struct would_call_std_##std_name : detail2::would_call_std_##std_name<T...>   \
-    {                                                                             \
-    }
-
-#ifndef JSON_USE_IMPLICIT_CONVERSIONS
-    #define JSON_USE_IMPLICIT_CONVERSIONS 1
-#endif
-
-#if JSON_USE_IMPLICIT_CONVERSIONS
-    #define JSON_EXPLICIT
-#else
-    #define JSON_EXPLICIT explicit
-#endif
-
-#ifndef JSON_DISABLE_ENUM_SERIALIZATION
-    #define JSON_DISABLE_ENUM_SERIALIZATION 0
-#endif
-
-#ifndef JSON_USE_GLOBAL_UDLS
-    #define JSON_USE_GLOBAL_UDLS 1
-#endif
-
-#if JSON_HAS_THREE_WAY_COMPARISON
-    #include <compare> // partial_ordering
-#endif
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-///////////////////////////
-// JSON type enumeration //
-///////////////////////////
-
-/*!
-@brief the JSON type enumeration
-
-This enumeration collects the different JSON types. It is internally used to
-distinguish the stored values, and the functions @ref basic_json::is_null(),
-@ref basic_json::is_object(), @ref basic_json::is_array(),
-@ref basic_json::is_string(), @ref basic_json::is_boolean(),
-@ref basic_json::is_number() (with @ref basic_json::is_number_integer(),
-@ref basic_json::is_number_unsigned(), and @ref basic_json::is_number_float()),
-@ref basic_json::is_discarded(), @ref basic_json::is_primitive(), and
-@ref basic_json::is_structured() rely on it.
-
-@note There are three enumeration entries (number_integer, number_unsigned, and
-number_float), because the library distinguishes these three types for numbers:
-@ref basic_json::number_unsigned_t is used for unsigned integers,
-@ref basic_json::number_integer_t is used for signed integers, and
-@ref basic_json::number_float_t is used for floating-point numbers or to
-approximate integers which do not fit in the limits of their respective type.
-
-@sa see @ref basic_json::basic_json(const value_t value_type) -- create a JSON
-value with the default value for a given type
-
-@since version 1.0.0
-*/
-enum class value_t : std::uint8_t
-{
-    null,             ///< null value
-    object,           ///< object (unordered set of name/value pairs)
-    array,            ///< array (ordered collection of values)
-    string,           ///< string value
-    boolean,          ///< boolean value
-    number_integer,   ///< number value (signed integer)
-    number_unsigned,  ///< number value (unsigned integer)
-    number_float,     ///< number value (floating-point)
-    binary,           ///< binary array (ordered collection of bytes)
-    discarded         ///< discarded by the parser callback function
-};
-
-/*!
-@brief comparison operator for JSON types
-
-Returns an ordering that is similar to Python:
-- order: null < boolean < number < object < array < string < binary
-- furthermore, each type is not smaller than itself
-- discarded values are not comparable
-- binary is represented as a b"" string in python and directly comparable to a
-  string; however, making a binary array directly comparable with a string would
-  be surprising behavior in a JSON file.
-
-@since version 1.0.0
-*/
-#if JSON_HAS_THREE_WAY_COMPARISON
-    inline std::partial_ordering operator<=>(const value_t lhs, const value_t rhs) noexcept // *NOPAD*
-#else
-    inline bool operator<(const value_t lhs, const value_t rhs) noexcept
-#endif
-{
-    static constexpr std::array<std::uint8_t, 9> order = {{
-            0 /* null */, 3 /* object */, 4 /* array */, 5 /* string */,
-            1 /* boolean */, 2 /* integer */, 2 /* unsigned */, 2 /* float */,
-            6 /* binary */
-        }
-    };
-
-    const auto l_index = static_cast<std::size_t>(lhs);
-    const auto r_index = static_cast<std::size_t>(rhs);
-#if JSON_HAS_THREE_WAY_COMPARISON
-    if (l_index < order.size() && r_index < order.size())
-    {
-        return order[l_index] <=> order[r_index]; // *NOPAD*
-    }
-    return std::partial_ordering::unordered;
-#else
-    return l_index < order.size() && r_index < order.size() && order[l_index] < order[r_index];
-#endif
-}
-
-// GCC selects the built-in operator< over an operator rewritten from
-// a user-defined spaceship operator
-// Clang, MSVC, and ICC select the rewritten candidate
-// (see GCC bug https://gcc.gnu.org/bugzilla/show_bug.cgi?id=105200)
-#if JSON_HAS_THREE_WAY_COMPARISON && defined(__GNUC__)
-inline bool operator<(const value_t lhs, const value_t rhs) noexcept
-{
-    return std::is_lt(lhs <=> rhs); // *NOPAD*
-}
-#endif
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/string_escape.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/*!
-@brief replace all occurrences of a substring by another string
-
-@param[in,out] s  the string to manipulate; changed so that all
-               occurrences of @a f are replaced with @a t
-@param[in]     f  the substring to replace with @a t
-@param[in]     t  the string to replace @a f
-
-@pre The search string @a f must not be empty. **This precondition is
-enforced with an assertion.**
-
-@since version 2.0.0
-*/
-template<typename StringType>
-inline void replace_substring(StringType& s, const StringType& f,
-                              const StringType& t)
-{
-    JSON_ASSERT(!f.empty());
-    for (auto pos = s.find(f);                // find first occurrence of f
-            pos != StringType::npos;          // make sure f was found
-            s.replace(pos, f.size(), t),      // replace with t, and
-            pos = s.find(f, pos + t.size()))  // find next occurrence of f
-    {}
-}
-
-/*!
- * @brief string escaping as described in RFC 6901 (Sect. 4)
- * @param[in] s string to escape
- * @return    escaped string
- *
- * Note the order of escaping "~" to "~0" and "/" to "~1" is important.
- */
-template<typename StringType>
-inline StringType escape(StringType s)
-{
-    replace_substring(s, StringType{"~"}, StringType{"~0"});
-    replace_substring(s, StringType{"/"}, StringType{"~1"});
-    return s;
-}
-
-/*!
- * @brief string unescaping as described in RFC 6901 (Sect. 4)
- * @param[in] s string to unescape
- * @return    unescaped string
- *
- * Note the order of escaping "~1" to "/" and "~0" to "~" is important.
- */
-template<typename StringType>
-static void unescape(StringType& s)
-{
-    replace_substring(s, StringType{"~1"}, StringType{"/"});
-    replace_substring(s, StringType{"~0"}, StringType{"~"});
-}
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/input/position_t.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cstddef> // size_t
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/// struct to capture the start position of the current token
-struct position_t
-{
-    /// the total number of characters read
-    std::size_t chars_read_total = 0;
-    /// the number of characters read in the current line
-    std::size_t chars_read_current_line = 0;
-    /// the number of lines read
-    std::size_t lines_read = 0;
-
-    /// conversion to size_t to preserve SAX interface
-    constexpr operator size_t() const
-    {
-        return chars_read_total;
-    }
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/meta/cpp_future.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-FileCopyrightText: 2018 The Abseil Authors
-// SPDX-License-Identifier: MIT
-
-
-
-#include <array> // array
-#include <cstddef> // size_t
-#include <type_traits> // conditional, enable_if, false_type, integral_constant, is_constructible, is_integral, is_same, remove_cv, remove_reference, true_type
-#include <utility> // index_sequence, make_index_sequence, index_sequence_for
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename T>
-using uncvref_t = typename std::remove_cv<typename std::remove_reference<T>::type>::type;
-
-#ifdef JSON_HAS_CPP_14
-
-// the following utilities are natively available in C++14
-using std::enable_if_t;
-using std::index_sequence;
-using std::make_index_sequence;
-using std::index_sequence_for;
-
-#else
-
-// alias templates to reduce boilerplate
-template<bool B, typename T = void>
-using enable_if_t = typename std::enable_if<B, T>::type;
-
-// The following code is taken from https://github.com/abseil/abseil-cpp/blob/10cb35e459f5ecca5b2ff107635da0bfa41011b4/absl/utility/utility.h
-// which is part of Google Abseil (https://github.com/abseil/abseil-cpp), licensed under the Apache License 2.0.
-
-//// START OF CODE FROM GOOGLE ABSEIL
-
-// integer_sequence
-//
-// Class template representing a compile-time integer sequence. An instantiation
-// of `integer_sequence<T, Ints...>` has a sequence of integers encoded in its
-// type through its template arguments (which is a common need when
-// working with C++11 variadic templates). `absl::integer_sequence` is designed
-// to be a drop-in replacement for C++14's `std::integer_sequence`.
-//
-// Example:
-//
-//   template< class T, T... Ints >
-//   void user_function(integer_sequence<T, Ints...>);
-//
-//   int main()
-//   {
-//     // user_function's `T` will be deduced to `int` and `Ints...`
-//     // will be deduced to `0, 1, 2, 3, 4`.
-//     user_function(make_integer_sequence<int, 5>());
-//   }
-template <typename T, T... Ints>
-struct integer_sequence
-{
-    using value_type = T;
-    static constexpr std::size_t size() noexcept
-    {
-        return sizeof...(Ints);
-    }
-};
-
-// index_sequence
-//
-// A helper template for an `integer_sequence` of `size_t`,
-// `absl::index_sequence` is designed to be a drop-in replacement for C++14's
-// `std::index_sequence`.
-template <size_t... Ints>
-using index_sequence = integer_sequence<size_t, Ints...>;
-
-namespace utility_internal
-{
-
-template <typename Seq, size_t SeqSize, size_t Rem>
-struct Extend;
-
-// Note that SeqSize == sizeof...(Ints). It's passed explicitly for efficiency.
-template <typename T, T... Ints, size_t SeqSize>
-struct Extend<integer_sequence<T, Ints...>, SeqSize, 0>
-{
-    using type = integer_sequence < T, Ints..., (Ints + SeqSize)... >;
-};
-
-template <typename T, T... Ints, size_t SeqSize>
-struct Extend<integer_sequence<T, Ints...>, SeqSize, 1>
-{
-    using type = integer_sequence < T, Ints..., (Ints + SeqSize)..., 2 * SeqSize >;
-};
-
-// Recursion helper for 'make_integer_sequence<T, N>'.
-// 'Gen<T, N>::type' is an alias for 'integer_sequence<T, 0, 1, ... N-1>'.
-template <typename T, size_t N>
-struct Gen
-{
-    using type =
-        typename Extend < typename Gen < T, N / 2 >::type, N / 2, N % 2 >::type;
-};
-
-template <typename T>
-struct Gen<T, 0>
-{
-    using type = integer_sequence<T>;
-};
-
-}  // namespace utility_internal
-
-// Compile-time sequences of integers
-
-// make_integer_sequence
-//
-// This template alias is equivalent to
-// `integer_sequence<int, 0, 1, ..., N-1>`, and is designed to be a drop-in
-// replacement for C++14's `std::make_integer_sequence`.
-template <typename T, T N>
-using make_integer_sequence = typename utility_internal::Gen<T, N>::type;
-
-// make_index_sequence
-//
-// This template alias is equivalent to `index_sequence<0, 1, ..., N-1>`,
-// and is designed to be a drop-in replacement for C++14's
-// `std::make_index_sequence`.
-template <size_t N>
-using make_index_sequence = make_integer_sequence<size_t, N>;
-
-// index_sequence_for
-//
-// Converts a typename pack into an index sequence of the same length, and
-// is designed to be a drop-in replacement for C++14's
-// `std::index_sequence_for()`
-template <typename... Ts>
-using index_sequence_for = make_index_sequence<sizeof...(Ts)>;
-
-//// END OF CODE FROM GOOGLE ABSEIL
-
-#endif
-
-// dispatch utility (taken from ranges-v3)
-template<unsigned N> struct priority_tag : priority_tag < N - 1 > {};
-template<> struct priority_tag<0> {};
-
-// taken from ranges-v3
-template<typename T>
-struct static_const
-{
-    static JSON_INLINE_VARIABLE constexpr T value{};
-};
-
-#ifndef JSON_HAS_CPP_17
-    template<typename T>
-    constexpr T static_const<T>::value;
-#endif
-
-template<typename T, typename... Args>
-inline constexpr std::array<T, sizeof...(Args)> make_array(Args&& ... args)
-{
-    return std::array<T, sizeof...(Args)> {{static_cast<T>(std::forward<Args>(args))...}};
-}
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/meta/type_traits.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <limits> // numeric_limits
-#include <type_traits> // false_type, is_constructible, is_integral, is_same, true_type
-#include <utility> // declval
-#include <tuple> // tuple
-
-// #include <nlohmann/detail/iterators/iterator_traits.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <iterator> // random_access_iterator_tag
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-// #include <nlohmann/detail/meta/void_t.hpp>
-
-// #include <nlohmann/detail/meta/cpp_future.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename It, typename = void>
-struct iterator_types {};
-
-template<typename It>
-struct iterator_types <
-    It,
-    void_t<typename It::difference_type, typename It::value_type, typename It::pointer,
-    typename It::reference, typename It::iterator_category >>
-{
-    using difference_type = typename It::difference_type;
-    using value_type = typename It::value_type;
-    using pointer = typename It::pointer;
-    using reference = typename It::reference;
-    using iterator_category = typename It::iterator_category;
-};
-
-// This is required as some compilers implement std::iterator_traits in a way that
-// doesn't work with SFINAE. See https://github.com/nlohmann/json/issues/1341.
-template<typename T, typename = void>
-struct iterator_traits
-{
-};
-
-template<typename T>
-struct iterator_traits < T, enable_if_t < !std::is_pointer<T>::value >>
-            : iterator_types<T>
-{
-};
-
-template<typename T>
-struct iterator_traits<T*, enable_if_t<std::is_object<T>::value>>
-{
-    using iterator_category = std::random_access_iterator_tag;
-    using value_type = T;
-    using difference_type = ptrdiff_t;
-    using pointer = T*;
-    using reference = T&;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/meta/call_std/begin.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-NLOHMANN_CAN_CALL_STD_FUNC_IMPL(begin);
-
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/meta/call_std/end.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-NLOHMANN_CAN_CALL_STD_FUNC_IMPL(end);
-
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/meta/cpp_future.hpp>
-
-// #include <nlohmann/detail/meta/detected.hpp>
-
-// #include <nlohmann/json_fwd.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-#ifndef INCLUDE_NLOHMANN_JSON_FWD_HPP_
-    #define INCLUDE_NLOHMANN_JSON_FWD_HPP_
-
-    #include <cstdint> // int64_t, uint64_t
-    #include <map> // map
-    #include <memory> // allocator
-    #include <string> // string
-    #include <vector> // vector
-
-    // #include <nlohmann/detail/abi_macros.hpp>
-
-
-    /*!
-    @brief namespace for Niels Lohmann
-    @see https://github.com/nlohmann
-    @since version 1.0.0
-    */
-    NLOHMANN_JSON_NAMESPACE_BEGIN
-
-    /*!
-    @brief default JSONSerializer template argument
-
-    This serializer ignores the template arguments and uses ADL
-    ([argument-dependent lookup](https://en.cppreference.com/w/cpp/language/adl))
-    for serialization.
-    */
-    template<typename T = void, typename SFINAE = void>
-    struct adl_serializer;
-
-    /// a class to store JSON values
-    /// @sa https://json.nlohmann.me/api/basic_json/
-    template<template<typename U, typename V, typename... Args> class ObjectType =
-    std::map,
-    template<typename U, typename... Args> class ArrayType = std::vector,
-    class StringType = std::string, class BooleanType = bool,
-    class NumberIntegerType = std::int64_t,
-    class NumberUnsignedType = std::uint64_t,
-    class NumberFloatType = double,
-    template<typename U> class AllocatorType = std::allocator,
-    template<typename T, typename SFINAE = void> class JSONSerializer =
-    adl_serializer,
-    class BinaryType = std::vector<std::uint8_t>>
-    class basic_json;
-
-    /// @brief JSON Pointer defines a string syntax for identifying a specific value within a JSON document
-    /// @sa https://json.nlohmann.me/api/json_pointer/
-    template<typename RefStringType>
-    class json_pointer;
-
-    /*!
-    @brief default specialization
-    @sa https://json.nlohmann.me/api/json/
-    */
-    using json = basic_json<>;
-
-    /// @brief a minimal map-like container that preserves insertion order
-    /// @sa https://json.nlohmann.me/api/ordered_map/
-    template<class Key, class T, class IgnoredLess, class Allocator>
-    struct ordered_map;
-
-    /// @brief specialization that maintains the insertion order of object keys
-    /// @sa https://json.nlohmann.me/api/ordered_json/
-    using ordered_json = basic_json<nlohmann::ordered_map>;
-
-    NLOHMANN_JSON_NAMESPACE_END
-
-#endif  // INCLUDE_NLOHMANN_JSON_FWD_HPP_
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-/*!
-@brief detail namespace with internal helper functions
-
-This namespace collects functions that should not be exposed,
-implementations of some @ref basic_json methods, and meta-programming helpers.
-
-@since version 2.1.0
-*/
-namespace detail
-{
-
-/////////////
-// helpers //
-/////////////
-
-// Note to maintainers:
-//
-// Every trait in this file expects a non CV-qualified type.
-// The only exceptions are in the 'aliases for detected' section
-// (i.e. those of the form: decltype(T::member_function(std::declval<T>())))
-//
-// In this case, T has to be properly CV-qualified to constraint the function arguments
-// (e.g. to_json(BasicJsonType&, const T&))
-
-template<typename> struct is_basic_json : std::false_type {};
-
-NLOHMANN_BASIC_JSON_TPL_DECLARATION
-struct is_basic_json<NLOHMANN_BASIC_JSON_TPL> : std::true_type {};
-
-// used by exceptions create() member functions
-// true_type for pointer to possibly cv-qualified basic_json or std::nullptr_t
-// false_type otherwise
-template<typename BasicJsonContext>
-struct is_basic_json_context :
-    std::integral_constant < bool,
-    is_basic_json<typename std::remove_cv<typename std::remove_pointer<BasicJsonContext>::type>::type>::value
-    || std::is_same<BasicJsonContext, std::nullptr_t>::value >
-{};
-
-//////////////////////
-// json_ref helpers //
-//////////////////////
-
-template<typename>
-class json_ref;
-
-template<typename>
-struct is_json_ref : std::false_type {};
-
-template<typename T>
-struct is_json_ref<json_ref<T>> : std::true_type {};
-
-//////////////////////////
-// aliases for detected //
-//////////////////////////
-
-template<typename T>
-using mapped_type_t = typename T::mapped_type;
-
-template<typename T>
-using key_type_t = typename T::key_type;
-
-template<typename T>
-using value_type_t = typename T::value_type;
-
-template<typename T>
-using difference_type_t = typename T::difference_type;
-
-template<typename T>
-using pointer_t = typename T::pointer;
-
-template<typename T>
-using reference_t = typename T::reference;
-
-template<typename T>
-using iterator_category_t = typename T::iterator_category;
-
-template<typename T, typename... Args>
-using to_json_function = decltype(T::to_json(std::declval<Args>()...));
-
-template<typename T, typename... Args>
-using from_json_function = decltype(T::from_json(std::declval<Args>()...));
-
-template<typename T, typename U>
-using get_template_function = decltype(std::declval<T>().template get<U>());
-
-// trait checking if JSONSerializer<T>::from_json(json const&, udt&) exists
-template<typename BasicJsonType, typename T, typename = void>
-struct has_from_json : std::false_type {};
-
-// trait checking if j.get<T> is valid
-// use this trait instead of std::is_constructible or std::is_convertible,
-// both rely on, or make use of implicit conversions, and thus fail when T
-// has several constructors/operator= (see https://github.com/nlohmann/json/issues/958)
-template <typename BasicJsonType, typename T>
-struct is_getable
-{
-    static constexpr bool value = is_detected<get_template_function, const BasicJsonType&, T>::value;
-};
-
-template<typename BasicJsonType, typename T>
-struct has_from_json < BasicJsonType, T, enable_if_t < !is_basic_json<T>::value >>
-{
-    using serializer = typename BasicJsonType::template json_serializer<T, void>;
-
-    static constexpr bool value =
-        is_detected_exact<void, from_json_function, serializer,
-        const BasicJsonType&, T&>::value;
-};
-
-// This trait checks if JSONSerializer<T>::from_json(json const&) exists
-// this overload is used for non-default-constructible user-defined-types
-template<typename BasicJsonType, typename T, typename = void>
-struct has_non_default_from_json : std::false_type {};
-
-template<typename BasicJsonType, typename T>
-struct has_non_default_from_json < BasicJsonType, T, enable_if_t < !is_basic_json<T>::value >>
-{
-    using serializer = typename BasicJsonType::template json_serializer<T, void>;
-
-    static constexpr bool value =
-        is_detected_exact<T, from_json_function, serializer,
-        const BasicJsonType&>::value;
-};
-
-// This trait checks if BasicJsonType::json_serializer<T>::to_json exists
-// Do not evaluate the trait when T is a basic_json type, to avoid template instantiation infinite recursion.
-template<typename BasicJsonType, typename T, typename = void>
-struct has_to_json : std::false_type {};
-
-template<typename BasicJsonType, typename T>
-struct has_to_json < BasicJsonType, T, enable_if_t < !is_basic_json<T>::value >>
-{
-    using serializer = typename BasicJsonType::template json_serializer<T, void>;
-
-    static constexpr bool value =
-        is_detected_exact<void, to_json_function, serializer, BasicJsonType&,
-        T>::value;
-};
-
-template<typename T>
-using detect_key_compare = typename T::key_compare;
-
-template<typename T>
-struct has_key_compare : std::integral_constant<bool, is_detected<detect_key_compare, T>::value> {};
-
-// obtains the actual object key comparator
-template<typename BasicJsonType>
-struct actual_object_comparator
-{
-    using object_t = typename BasicJsonType::object_t;
-    using object_comparator_t = typename BasicJsonType::default_object_comparator_t;
-    using type = typename std::conditional < has_key_compare<object_t>::value,
-          typename object_t::key_compare, object_comparator_t>::type;
-};
-
-template<typename BasicJsonType>
-using actual_object_comparator_t = typename actual_object_comparator<BasicJsonType>::type;
-
-///////////////////
-// is_ functions //
-///////////////////
-
-// https://en.cppreference.com/w/cpp/types/conjunction
-template<class...> struct conjunction : std::true_type { };
-template<class B> struct conjunction<B> : B { };
-template<class B, class... Bn>
-struct conjunction<B, Bn...>
-: std::conditional<static_cast<bool>(B::value), conjunction<Bn...>, B>::type {};
-
-// https://en.cppreference.com/w/cpp/types/negation
-template<class B> struct negation : std::integral_constant < bool, !B::value > { };
-
-// Reimplementation of is_constructible and is_default_constructible, due to them being broken for
-// std::pair and std::tuple until LWG 2367 fix (see https://cplusplus.github.io/LWG/lwg-defects.html#2367).
-// This causes compile errors in e.g. clang 3.5 or gcc 4.9.
-template <typename T>
-struct is_default_constructible : std::is_default_constructible<T> {};
-
-template <typename T1, typename T2>
-struct is_default_constructible<std::pair<T1, T2>>
-            : conjunction<is_default_constructible<T1>, is_default_constructible<T2>> {};
-
-template <typename T1, typename T2>
-struct is_default_constructible<const std::pair<T1, T2>>
-            : conjunction<is_default_constructible<T1>, is_default_constructible<T2>> {};
-
-template <typename... Ts>
-struct is_default_constructible<std::tuple<Ts...>>
-            : conjunction<is_default_constructible<Ts>...> {};
-
-template <typename... Ts>
-struct is_default_constructible<const std::tuple<Ts...>>
-            : conjunction<is_default_constructible<Ts>...> {};
-
-
-template <typename T, typename... Args>
-struct is_constructible : std::is_constructible<T, Args...> {};
-
-template <typename T1, typename T2>
-struct is_constructible<std::pair<T1, T2>> : is_default_constructible<std::pair<T1, T2>> {};
-
-template <typename T1, typename T2>
-struct is_constructible<const std::pair<T1, T2>> : is_default_constructible<const std::pair<T1, T2>> {};
-
-template <typename... Ts>
-struct is_constructible<std::tuple<Ts...>> : is_default_constructible<std::tuple<Ts...>> {};
-
-template <typename... Ts>
-struct is_constructible<const std::tuple<Ts...>> : is_default_constructible<const std::tuple<Ts...>> {};
-
-
-template<typename T, typename = void>
-struct is_iterator_traits : std::false_type {};
-
-template<typename T>
-struct is_iterator_traits<iterator_traits<T>>
-{
-  private:
-    using traits = iterator_traits<T>;
-
-  public:
-    static constexpr auto value =
-        is_detected<value_type_t, traits>::value &&
-        is_detected<difference_type_t, traits>::value &&
-        is_detected<pointer_t, traits>::value &&
-        is_detected<iterator_category_t, traits>::value &&
-        is_detected<reference_t, traits>::value;
-};
-
-template<typename T>
-struct is_range
-{
-  private:
-    using t_ref = typename std::add_lvalue_reference<T>::type;
-
-    using iterator = detected_t<result_of_begin, t_ref>;
-    using sentinel = detected_t<result_of_end, t_ref>;
-
-    // to be 100% correct, it should use https://en.cppreference.com/w/cpp/iterator/input_or_output_iterator
-    // and https://en.cppreference.com/w/cpp/iterator/sentinel_for
-    // but reimplementing these would be too much work, as a lot of other concepts are used underneath
-    static constexpr auto is_iterator_begin =
-        is_iterator_traits<iterator_traits<iterator>>::value;
-
-  public:
-    static constexpr bool value = !std::is_same<iterator, nonesuch>::value && !std::is_same<sentinel, nonesuch>::value && is_iterator_begin;
-};
-
-template<typename R>
-using iterator_t = enable_if_t<is_range<R>::value, result_of_begin<decltype(std::declval<R&>())>>;
-
-template<typename T>
-using range_value_t = value_type_t<iterator_traits<iterator_t<T>>>;
-
-// The following implementation of is_complete_type is taken from
-// https://blogs.msdn.microsoft.com/vcblog/2015/12/02/partial-support-for-expression-sfinae-in-vs-2015-update-1/
-// and is written by Xiang Fan who agreed to using it in this library.
-
-template<typename T, typename = void>
-struct is_complete_type : std::false_type {};
-
-template<typename T>
-struct is_complete_type<T, decltype(void(sizeof(T)))> : std::true_type {};
-
-template<typename BasicJsonType, typename CompatibleObjectType,
-         typename = void>
-struct is_compatible_object_type_impl : std::false_type {};
-
-template<typename BasicJsonType, typename CompatibleObjectType>
-struct is_compatible_object_type_impl <
-    BasicJsonType, CompatibleObjectType,
-    enable_if_t < is_detected<mapped_type_t, CompatibleObjectType>::value&&
-    is_detected<key_type_t, CompatibleObjectType>::value >>
-{
-    using object_t = typename BasicJsonType::object_t;
-
-    // macOS's is_constructible does not play well with nonesuch...
-    static constexpr bool value =
-        is_constructible<typename object_t::key_type,
-        typename CompatibleObjectType::key_type>::value &&
-        is_constructible<typename object_t::mapped_type,
-        typename CompatibleObjectType::mapped_type>::value;
-};
-
-template<typename BasicJsonType, typename CompatibleObjectType>
-struct is_compatible_object_type
-    : is_compatible_object_type_impl<BasicJsonType, CompatibleObjectType> {};
-
-template<typename BasicJsonType, typename ConstructibleObjectType,
-         typename = void>
-struct is_constructible_object_type_impl : std::false_type {};
-
-template<typename BasicJsonType, typename ConstructibleObjectType>
-struct is_constructible_object_type_impl <
-    BasicJsonType, ConstructibleObjectType,
-    enable_if_t < is_detected<mapped_type_t, ConstructibleObjectType>::value&&
-    is_detected<key_type_t, ConstructibleObjectType>::value >>
-{
-    using object_t = typename BasicJsonType::object_t;
-
-    static constexpr bool value =
-        (is_default_constructible<ConstructibleObjectType>::value &&
-         (std::is_move_assignable<ConstructibleObjectType>::value ||
-          std::is_copy_assignable<ConstructibleObjectType>::value) &&
-         (is_constructible<typename ConstructibleObjectType::key_type,
-          typename object_t::key_type>::value &&
-          std::is_same <
-          typename object_t::mapped_type,
-          typename ConstructibleObjectType::mapped_type >::value)) ||
-        (has_from_json<BasicJsonType,
-         typename ConstructibleObjectType::mapped_type>::value ||
-         has_non_default_from_json <
-         BasicJsonType,
-         typename ConstructibleObjectType::mapped_type >::value);
-};
-
-template<typename BasicJsonType, typename ConstructibleObjectType>
-struct is_constructible_object_type
-    : is_constructible_object_type_impl<BasicJsonType,
-      ConstructibleObjectType> {};
-
-template<typename BasicJsonType, typename CompatibleStringType>
-struct is_compatible_string_type
-{
-    static constexpr auto value =
-        is_constructible<typename BasicJsonType::string_t, CompatibleStringType>::value;
-};
-
-template<typename BasicJsonType, typename ConstructibleStringType>
-struct is_constructible_string_type
-{
-    // launder type through decltype() to fix compilation failure on ICPC
-#ifdef __INTEL_COMPILER
-    using laundered_type = decltype(std::declval<ConstructibleStringType>());
-#else
-    using laundered_type = ConstructibleStringType;
-#endif
-
-    static constexpr auto value =
-        conjunction <
-        is_constructible<laundered_type, typename BasicJsonType::string_t>,
-        is_detected_exact<typename BasicJsonType::string_t::value_type,
-        value_type_t, laundered_type >>::value;
-};
-
-template<typename BasicJsonType, typename CompatibleArrayType, typename = void>
-struct is_compatible_array_type_impl : std::false_type {};
-
-template<typename BasicJsonType, typename CompatibleArrayType>
-struct is_compatible_array_type_impl <
-    BasicJsonType, CompatibleArrayType,
-    enable_if_t <
-    is_detected<iterator_t, CompatibleArrayType>::value&&
-    is_iterator_traits<iterator_traits<detected_t<iterator_t, CompatibleArrayType>>>::value&&
-// special case for types like std::filesystem::path whose iterator's value_type are themselves
-// c.f. https://github.com/nlohmann/json/pull/3073
-    !std::is_same<CompatibleArrayType, detected_t<range_value_t, CompatibleArrayType>>::value >>
-{
-    static constexpr bool value =
-        is_constructible<BasicJsonType,
-        range_value_t<CompatibleArrayType>>::value;
-};
-
-template<typename BasicJsonType, typename CompatibleArrayType>
-struct is_compatible_array_type
-    : is_compatible_array_type_impl<BasicJsonType, CompatibleArrayType> {};
-
-template<typename BasicJsonType, typename ConstructibleArrayType, typename = void>
-struct is_constructible_array_type_impl : std::false_type {};
-
-template<typename BasicJsonType, typename ConstructibleArrayType>
-struct is_constructible_array_type_impl <
-    BasicJsonType, ConstructibleArrayType,
-    enable_if_t<std::is_same<ConstructibleArrayType,
-    typename BasicJsonType::value_type>::value >>
-            : std::true_type {};
-
-template<typename BasicJsonType, typename ConstructibleArrayType>
-struct is_constructible_array_type_impl <
-    BasicJsonType, ConstructibleArrayType,
-    enable_if_t < !std::is_same<ConstructibleArrayType,
-    typename BasicJsonType::value_type>::value&&
-    !is_compatible_string_type<BasicJsonType, ConstructibleArrayType>::value&&
-    is_default_constructible<ConstructibleArrayType>::value&&
-(std::is_move_assignable<ConstructibleArrayType>::value ||
- std::is_copy_assignable<ConstructibleArrayType>::value)&&
-is_detected<iterator_t, ConstructibleArrayType>::value&&
-is_iterator_traits<iterator_traits<detected_t<iterator_t, ConstructibleArrayType>>>::value&&
-is_detected<range_value_t, ConstructibleArrayType>::value&&
-// special case for types like std::filesystem::path whose iterator's value_type are themselves
-// c.f. https://github.com/nlohmann/json/pull/3073
-!std::is_same<ConstructibleArrayType, detected_t<range_value_t, ConstructibleArrayType>>::value&&
-        is_complete_type <
-        detected_t<range_value_t, ConstructibleArrayType >>::value >>
-{
-    using value_type = range_value_t<ConstructibleArrayType>;
-
-    static constexpr bool value =
-        std::is_same<value_type,
-        typename BasicJsonType::array_t::value_type>::value ||
-        has_from_json<BasicJsonType,
-        value_type>::value ||
-        has_non_default_from_json <
-        BasicJsonType,
-        value_type >::value;
-};
-
-template<typename BasicJsonType, typename ConstructibleArrayType>
-struct is_constructible_array_type
-    : is_constructible_array_type_impl<BasicJsonType, ConstructibleArrayType> {};
-
-template<typename RealIntegerType, typename CompatibleNumberIntegerType,
-         typename = void>
-struct is_compatible_integer_type_impl : std::false_type {};
-
-template<typename RealIntegerType, typename CompatibleNumberIntegerType>
-struct is_compatible_integer_type_impl <
-    RealIntegerType, CompatibleNumberIntegerType,
-    enable_if_t < std::is_integral<RealIntegerType>::value&&
-    std::is_integral<CompatibleNumberIntegerType>::value&&
-    !std::is_same<bool, CompatibleNumberIntegerType>::value >>
-{
-    // is there an assert somewhere on overflows?
-    using RealLimits = std::numeric_limits<RealIntegerType>;
-    using CompatibleLimits = std::numeric_limits<CompatibleNumberIntegerType>;
-
-    static constexpr auto value =
-        is_constructible<RealIntegerType,
-        CompatibleNumberIntegerType>::value &&
-        CompatibleLimits::is_integer &&
-        RealLimits::is_signed == CompatibleLimits::is_signed;
-};
-
-template<typename RealIntegerType, typename CompatibleNumberIntegerType>
-struct is_compatible_integer_type
-    : is_compatible_integer_type_impl<RealIntegerType,
-      CompatibleNumberIntegerType> {};
-
-template<typename BasicJsonType, typename CompatibleType, typename = void>
-struct is_compatible_type_impl: std::false_type {};
-
-template<typename BasicJsonType, typename CompatibleType>
-struct is_compatible_type_impl <
-    BasicJsonType, CompatibleType,
-    enable_if_t<is_complete_type<CompatibleType>::value >>
-{
-    static constexpr bool value =
-        has_to_json<BasicJsonType, CompatibleType>::value;
-};
-
-template<typename BasicJsonType, typename CompatibleType>
-struct is_compatible_type
-    : is_compatible_type_impl<BasicJsonType, CompatibleType> {};
-
-template<typename T1, typename T2>
-struct is_constructible_tuple : std::false_type {};
-
-template<typename T1, typename... Args>
-struct is_constructible_tuple<T1, std::tuple<Args...>> : conjunction<is_constructible<T1, Args>...> {};
-
-template<typename BasicJsonType, typename T>
-struct is_json_iterator_of : std::false_type {};
-
-template<typename BasicJsonType>
-struct is_json_iterator_of<BasicJsonType, typename BasicJsonType::iterator> : std::true_type {};
-
-template<typename BasicJsonType>
-struct is_json_iterator_of<BasicJsonType, typename BasicJsonType::const_iterator> : std::true_type
-{};
-
-// checks if a given type T is a template specialization of Primary
-template<template <typename...> class Primary, typename T>
-struct is_specialization_of : std::false_type {};
-
-template<template <typename...> class Primary, typename... Args>
-struct is_specialization_of<Primary, Primary<Args...>> : std::true_type {};
-
-template<typename T>
-using is_json_pointer = is_specialization_of<::nlohmann::json_pointer, uncvref_t<T>>;
-
-// checks if A and B are comparable using Compare functor
-template<typename Compare, typename A, typename B, typename = void>
-struct is_comparable : std::false_type {};
-
-template<typename Compare, typename A, typename B>
-struct is_comparable<Compare, A, B, void_t<
-decltype(std::declval<Compare>()(std::declval<A>(), std::declval<B>())),
-decltype(std::declval<Compare>()(std::declval<B>(), std::declval<A>()))
->> : std::true_type {};
-
-template<typename T>
-using detect_is_transparent = typename T::is_transparent;
-
-// type trait to check if KeyType can be used as object key (without a BasicJsonType)
-// see is_usable_as_basic_json_key_type below
-template<typename Comparator, typename ObjectKeyType, typename KeyTypeCVRef, bool RequireTransparentComparator = true,
-         bool ExcludeObjectKeyType = RequireTransparentComparator, typename KeyType = uncvref_t<KeyTypeCVRef>>
-using is_usable_as_key_type = typename std::conditional <
-                              is_comparable<Comparator, ObjectKeyType, KeyTypeCVRef>::value
-                              && !(ExcludeObjectKeyType && std::is_same<KeyType,
-                                   ObjectKeyType>::value)
-                              && (!RequireTransparentComparator
-                                  || is_detected <detect_is_transparent, Comparator>::value)
-                              && !is_json_pointer<KeyType>::value,
-                              std::true_type,
-                              std::false_type >::type;
-
-// type trait to check if KeyType can be used as object key
-// true if:
-//   - KeyType is comparable with BasicJsonType::object_t::key_type
-//   - if ExcludeObjectKeyType is true, KeyType is not BasicJsonType::object_t::key_type
-//   - the comparator is transparent or RequireTransparentComparator is false
-//   - KeyType is not a JSON iterator or json_pointer
-template<typename BasicJsonType, typename KeyTypeCVRef, bool RequireTransparentComparator = true,
-         bool ExcludeObjectKeyType = RequireTransparentComparator, typename KeyType = uncvref_t<KeyTypeCVRef>>
-using is_usable_as_basic_json_key_type = typename std::conditional <
-        is_usable_as_key_type<typename BasicJsonType::object_comparator_t,
-        typename BasicJsonType::object_t::key_type, KeyTypeCVRef,
-        RequireTransparentComparator, ExcludeObjectKeyType>::value
-        && !is_json_iterator_of<BasicJsonType, KeyType>::value,
-        std::true_type,
-        std::false_type >::type;
-
-template<typename ObjectType, typename KeyType>
-using detect_erase_with_key_type = decltype(std::declval<ObjectType&>().erase(std::declval<KeyType>()));
-
-// type trait to check if object_t has an erase() member functions accepting KeyType
-template<typename BasicJsonType, typename KeyType>
-using has_erase_with_key_type = typename std::conditional <
-                                is_detected <
-                                detect_erase_with_key_type,
-                                typename BasicJsonType::object_t, KeyType >::value,
-                                std::true_type,
-                                std::false_type >::type;
-
-// a naive helper to check if a type is an ordered_map (exploits the fact that
-// ordered_map inherits capacity() from std::vector)
-template <typename T>
-struct is_ordered_map
-{
-    using one = char;
-
-    struct two
-    {
-        char x[2]; // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
-    };
-
-    template <typename C> static one test( decltype(&C::capacity) ) ;
-    template <typename C> static two test(...);
-
-    enum { value = sizeof(test<T>(nullptr)) == sizeof(char) }; // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-};
-
-// to avoid useless casts (see https://github.com/nlohmann/json/issues/2893#issuecomment-889152324)
-template < typename T, typename U, enable_if_t < !std::is_same<T, U>::value, int > = 0 >
-T conditional_static_cast(U value)
-{
-    return static_cast<T>(value);
-}
-
-template<typename T, typename U, enable_if_t<std::is_same<T, U>::value, int> = 0>
-T conditional_static_cast(U value)
-{
-    return value;
-}
-
-template<typename... Types>
-using all_integral = conjunction<std::is_integral<Types>...>;
-
-template<typename... Types>
-using all_signed = conjunction<std::is_signed<Types>...>;
-
-template<typename... Types>
-using all_unsigned = conjunction<std::is_unsigned<Types>...>;
-
-// there's a disjunction trait in another PR; replace when merged
-template<typename... Types>
-using same_sign = std::integral_constant < bool,
-      all_signed<Types...>::value || all_unsigned<Types...>::value >;
-
-template<typename OfType, typename T>
-using never_out_of_range = std::integral_constant < bool,
-      (std::is_signed<OfType>::value && (sizeof(T) < sizeof(OfType)))
-      || (same_sign<OfType, T>::value && sizeof(OfType) == sizeof(T)) >;
-
-template<typename OfType, typename T,
-         bool OfTypeSigned = std::is_signed<OfType>::value,
-         bool TSigned = std::is_signed<T>::value>
-struct value_in_range_of_impl2;
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl2<OfType, T, false, false>
-{
-    static constexpr bool test(T val)
-    {
-        using CommonType = typename std::common_type<OfType, T>::type;
-        return static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
-    }
-};
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl2<OfType, T, true, false>
-{
-    static constexpr bool test(T val)
-    {
-        using CommonType = typename std::common_type<OfType, T>::type;
-        return static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
-    }
-};
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl2<OfType, T, false, true>
-{
-    static constexpr bool test(T val)
-    {
-        using CommonType = typename std::common_type<OfType, T>::type;
-        return val >= 0 && static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
-    }
-};
-
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl2<OfType, T, true, true>
-{
-    static constexpr bool test(T val)
-    {
-        using CommonType = typename std::common_type<OfType, T>::type;
-        return static_cast<CommonType>(val) >= static_cast<CommonType>((std::numeric_limits<OfType>::min)())
-               && static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
-    }
-};
-
-template<typename OfType, typename T,
-         bool NeverOutOfRange = never_out_of_range<OfType, T>::value,
-         typename = detail::enable_if_t<all_integral<OfType, T>::value>>
-struct value_in_range_of_impl1;
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl1<OfType, T, false>
-{
-    static constexpr bool test(T val)
-    {
-        return value_in_range_of_impl2<OfType, T>::test(val);
-    }
-};
-
-template<typename OfType, typename T>
-struct value_in_range_of_impl1<OfType, T, true>
-{
-    static constexpr bool test(T /*val*/)
-    {
-        return true;
-    }
-};
-
-template<typename OfType, typename T>
-inline constexpr bool value_in_range_of(T val)
-{
-    return value_in_range_of_impl1<OfType, T>::test(val);
-}
-
-template<bool Value>
-using bool_constant = std::integral_constant<bool, Value>;
-
-///////////////////////////////////////////////////////////////////////////////
-// is_c_string
-///////////////////////////////////////////////////////////////////////////////
-
-namespace impl
-{
-
-template<typename T>
-inline constexpr bool is_c_string()
-{
-    using TUnExt = typename std::remove_extent<T>::type;
-    using TUnCVExt = typename std::remove_cv<TUnExt>::type;
-    using TUnPtr = typename std::remove_pointer<T>::type;
-    using TUnCVPtr = typename std::remove_cv<TUnPtr>::type;
-    return
-        (std::is_array<T>::value && std::is_same<TUnCVExt, char>::value)
-        || (std::is_pointer<T>::value && std::is_same<TUnCVPtr, char>::value);
-}
-
-}  // namespace impl
-
-// checks whether T is a [cv] char */[cv] char[] C string
-template<typename T>
-struct is_c_string : bool_constant<impl::is_c_string<T>()> {};
-
-template<typename T>
-using is_c_string_uncvref = is_c_string<uncvref_t<T>>;
-
-///////////////////////////////////////////////////////////////////////////////
-// is_transparent
-///////////////////////////////////////////////////////////////////////////////
-
-namespace impl
-{
-
-template<typename T>
-inline constexpr bool is_transparent()
-{
-    return is_detected<detect_is_transparent, T>::value;
-}
-
-}  // namespace impl
-
-// checks whether T has a member named is_transparent
-template<typename T>
-struct is_transparent : bool_constant<impl::is_transparent<T>()> {};
-
-///////////////////////////////////////////////////////////////////////////////
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/string_concat.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cstring> // strlen
-#include <string> // string
-#include <utility> // forward
-
-// #include <nlohmann/detail/meta/cpp_future.hpp>
-
-// #include <nlohmann/detail/meta/detected.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-inline std::size_t concat_length()
-{
-    return 0;
-}
-
-template<typename... Args>
-inline std::size_t concat_length(const char* cstr, Args&& ... rest);
-
-template<typename StringType, typename... Args>
-inline std::size_t concat_length(const StringType& str, Args&& ... rest);
-
-template<typename... Args>
-inline std::size_t concat_length(const char /*c*/, Args&& ... rest)
-{
-    return 1 + concat_length(std::forward<Args>(rest)...);
-}
-
-template<typename... Args>
-inline std::size_t concat_length(const char* cstr, Args&& ... rest)
-{
-    // cppcheck-suppress ignoredReturnValue
-    return ::strlen(cstr) + concat_length(std::forward<Args>(rest)...);
-}
-
-template<typename StringType, typename... Args>
-inline std::size_t concat_length(const StringType& str, Args&& ... rest)
-{
-    return str.size() + concat_length(std::forward<Args>(rest)...);
-}
-
-template<typename OutStringType>
-inline void concat_into(OutStringType& /*out*/)
-{}
-
-template<typename StringType, typename Arg>
-using string_can_append = decltype(std::declval<StringType&>().append(std::declval < Arg && > ()));
-
-template<typename StringType, typename Arg>
-using detect_string_can_append = is_detected<string_can_append, StringType, Arg>;
-
-template<typename StringType, typename Arg>
-using string_can_append_op = decltype(std::declval<StringType&>() += std::declval < Arg && > ());
-
-template<typename StringType, typename Arg>
-using detect_string_can_append_op = is_detected<string_can_append_op, StringType, Arg>;
-
-template<typename StringType, typename Arg>
-using string_can_append_iter = decltype(std::declval<StringType&>().append(std::declval<const Arg&>().begin(), std::declval<const Arg&>().end()));
-
-template<typename StringType, typename Arg>
-using detect_string_can_append_iter = is_detected<string_can_append_iter, StringType, Arg>;
-
-template<typename StringType, typename Arg>
-using string_can_append_data = decltype(std::declval<StringType&>().append(std::declval<const Arg&>().data(), std::declval<const Arg&>().size()));
-
-template<typename StringType, typename Arg>
-using detect_string_can_append_data = is_detected<string_can_append_data, StringType, Arg>;
-
-template < typename OutStringType, typename Arg, typename... Args,
-           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
-                         && detect_string_can_append_op<OutStringType, Arg>::value, int > = 0 >
-inline void concat_into(OutStringType& out, Arg && arg, Args && ... rest);
-
-template < typename OutStringType, typename Arg, typename... Args,
-           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
-                         && !detect_string_can_append_op<OutStringType, Arg>::value
-                         && detect_string_can_append_iter<OutStringType, Arg>::value, int > = 0 >
-inline void concat_into(OutStringType& out, const Arg& arg, Args && ... rest);
-
-template < typename OutStringType, typename Arg, typename... Args,
-           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
-                         && !detect_string_can_append_op<OutStringType, Arg>::value
-                         && !detect_string_can_append_iter<OutStringType, Arg>::value
-                         && detect_string_can_append_data<OutStringType, Arg>::value, int > = 0 >
-inline void concat_into(OutStringType& out, const Arg& arg, Args && ... rest);
-
-template<typename OutStringType, typename Arg, typename... Args,
-         enable_if_t<detect_string_can_append<OutStringType, Arg>::value, int> = 0>
-inline void concat_into(OutStringType& out, Arg && arg, Args && ... rest)
-{
-    out.append(std::forward<Arg>(arg));
-    concat_into(out, std::forward<Args>(rest)...);
-}
-
-template < typename OutStringType, typename Arg, typename... Args,
-           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
-                         && detect_string_can_append_op<OutStringType, Arg>::value, int > >
-inline void concat_into(OutStringType& out, Arg&& arg, Args&& ... rest)
-{
-    out += std::forward<Arg>(arg);
-    concat_into(out, std::forward<Args>(rest)...);
-}
-
-template < typename OutStringType, typename Arg, typename... Args,
-           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
-                         && !detect_string_can_append_op<OutStringType, Arg>::value
-                         && detect_string_can_append_iter<OutStringType, Arg>::value, int > >
-inline void concat_into(OutStringType& out, const Arg& arg, Args&& ... rest)
-{
-    out.append(arg.begin(), arg.end());
-    concat_into(out, std::forward<Args>(rest)...);
-}
-
-template < typename OutStringType, typename Arg, typename... Args,
-           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
-                         && !detect_string_can_append_op<OutStringType, Arg>::value
-                         && !detect_string_can_append_iter<OutStringType, Arg>::value
-                         && detect_string_can_append_data<OutStringType, Arg>::value, int > >
-inline void concat_into(OutStringType& out, const Arg& arg, Args&& ... rest)
-{
-    out.append(arg.data(), arg.size());
-    concat_into(out, std::forward<Args>(rest)...);
-}
-
-template<typename OutStringType = std::string, typename... Args>
-inline OutStringType concat(Args && ... args)
-{
-    OutStringType str;
-    str.reserve(concat_length(std::forward<Args>(args)...));
-    concat_into(str, std::forward<Args>(args)...);
-    return str;
-}
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-////////////////
-// exceptions //
-////////////////
-
-/// @brief general exception of the @ref basic_json class
-/// @sa https://json.nlohmann.me/api/basic_json/exception/
-class exception : public std::exception
-{
-  public:
-    /// returns the explanatory string
-    const char* what() const noexcept override
-    {
-        return m.what();
-    }
-
-    /// the id of the exception
-    const int id; // NOLINT(cppcoreguidelines-non-private-member-variables-in-classes)
-
-  protected:
-    JSON_HEDLEY_NON_NULL(3)
-    exception(int id_, const char* what_arg) : id(id_), m(what_arg) {} // NOLINT(bugprone-throw-keyword-missing)
-
-    static std::string name(const std::string& ename, int id_)
-    {
-        return concat("[json.exception.", ename, '.', std::to_string(id_), "] ");
-    }
-
-    static std::string diagnostics(std::nullptr_t /*leaf_element*/)
-    {
-        return "";
-    }
-
-    template<typename BasicJsonType>
-    static std::string diagnostics(const BasicJsonType* leaf_element)
-    {
-#if JSON_DIAGNOSTICS
-        std::vector<std::string> tokens;
-        for (const auto* current = leaf_element; current != nullptr && current->m_parent != nullptr; current = current->m_parent)
-        {
-            switch (current->m_parent->type())
-            {
-                case value_t::array:
-                {
-                    for (std::size_t i = 0; i < current->m_parent->m_value.array->size(); ++i)
-                    {
-                        if (&current->m_parent->m_value.array->operator[](i) == current)
-                        {
-                            tokens.emplace_back(std::to_string(i));
-                            break;
-                        }
-                    }
-                    break;
-                }
-
-                case value_t::object:
-                {
-                    for (const auto& element : *current->m_parent->m_value.object)
-                    {
-                        if (&element.second == current)
-                        {
-                            tokens.emplace_back(element.first.c_str());
-                            break;
-                        }
-                    }
-                    break;
-                }
-
-                case value_t::null: // LCOV_EXCL_LINE
-                case value_t::string: // LCOV_EXCL_LINE
-                case value_t::boolean: // LCOV_EXCL_LINE
-                case value_t::number_integer: // LCOV_EXCL_LINE
-                case value_t::number_unsigned: // LCOV_EXCL_LINE
-                case value_t::number_float: // LCOV_EXCL_LINE
-                case value_t::binary: // LCOV_EXCL_LINE
-                case value_t::discarded: // LCOV_EXCL_LINE
-                default:   // LCOV_EXCL_LINE
-                    break; // LCOV_EXCL_LINE
-            }
-        }
-
-        if (tokens.empty())
-        {
-            return "";
-        }
-
-        auto str = std::accumulate(tokens.rbegin(), tokens.rend(), std::string{},
-                                   [](const std::string & a, const std::string & b)
-        {
-            return concat(a, '/', detail::escape(b));
-        });
-        return concat('(', str, ") ");
-#else
-        static_cast<void>(leaf_element);
-        return "";
-#endif
-    }
-
-  private:
-    /// an exception object as storage for error messages
-    std::runtime_error m;
-};
-
-/// @brief exception indicating a parse error
-/// @sa https://json.nlohmann.me/api/basic_json/parse_error/
-class parse_error : public exception
-{
-  public:
-    /*!
-    @brief create a parse error exception
-    @param[in] id_       the id of the exception
-    @param[in] pos       the position where the error occurred (or with
-                         chars_read_total=0 if the position cannot be
-                         determined)
-    @param[in] what_arg  the explanatory string
-    @return parse_error object
-    */
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static parse_error create(int id_, const position_t& pos, const std::string& what_arg, BasicJsonContext context)
-    {
-        std::string w = concat(exception::name("parse_error", id_), "parse error",
-                               position_string(pos), ": ", exception::diagnostics(context), what_arg);
-        return {id_, pos.chars_read_total, w.c_str()};
-    }
-
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static parse_error create(int id_, std::size_t byte_, const std::string& what_arg, BasicJsonContext context)
-    {
-        std::string w = concat(exception::name("parse_error", id_), "parse error",
-                               (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""),
-                               ": ", exception::diagnostics(context), what_arg);
-        return {id_, byte_, w.c_str()};
-    }
-
-    /*!
-    @brief byte index of the parse error
-
-    The byte index of the last read character in the input file.
-
-    @note For an input with n bytes, 1 is the index of the first character and
-          n+1 is the index of the terminating null byte or the end of file.
-          This also holds true when reading a byte vector (CBOR or MessagePack).
-    */
-    const std::size_t byte;
-
-  private:
-    parse_error(int id_, std::size_t byte_, const char* what_arg)
-        : exception(id_, what_arg), byte(byte_) {}
-
-    static std::string position_string(const position_t& pos)
-    {
-        return concat(" at line ", std::to_string(pos.lines_read + 1),
-                      ", column ", std::to_string(pos.chars_read_current_line));
-    }
-};
-
-/// @brief exception indicating errors with iterators
-/// @sa https://json.nlohmann.me/api/basic_json/invalid_iterator/
-class invalid_iterator : public exception
-{
-  public:
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static invalid_iterator create(int id_, const std::string& what_arg, BasicJsonContext context)
-    {
-        std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg);
-        return {id_, w.c_str()};
-    }
-
-  private:
-    JSON_HEDLEY_NON_NULL(3)
-    invalid_iterator(int id_, const char* what_arg)
-        : exception(id_, what_arg) {}
-};
-
-/// @brief exception indicating executing a member function with a wrong type
-/// @sa https://json.nlohmann.me/api/basic_json/type_error/
-class type_error : public exception
-{
-  public:
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static type_error create(int id_, const std::string& what_arg, BasicJsonContext context)
-    {
-        std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg);
-        return {id_, w.c_str()};
-    }
-
-  private:
-    JSON_HEDLEY_NON_NULL(3)
-    type_error(int id_, const char* what_arg) : exception(id_, what_arg) {}
-};
-
-/// @brief exception indicating access out of the defined range
-/// @sa https://json.nlohmann.me/api/basic_json/out_of_range/
-class out_of_range : public exception
-{
-  public:
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static out_of_range create(int id_, const std::string& what_arg, BasicJsonContext context)
-    {
-        std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg);
-        return {id_, w.c_str()};
-    }
-
-  private:
-    JSON_HEDLEY_NON_NULL(3)
-    out_of_range(int id_, const char* what_arg) : exception(id_, what_arg) {}
-};
-
-/// @brief exception indicating other library errors
-/// @sa https://json.nlohmann.me/api/basic_json/other_error/
-class other_error : public exception
-{
-  public:
-    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
-    static other_error create(int id_, const std::string& what_arg, BasicJsonContext context)
-    {
-        std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg);
-        return {id_, w.c_str()};
-    }
-
-  private:
-    JSON_HEDLEY_NON_NULL(3)
-    other_error(int id_, const char* what_arg) : exception(id_, what_arg) {}
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/meta/cpp_future.hpp>
-
-// #include <nlohmann/detail/meta/identity_tag.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-// dispatching helper struct
-template <class T> struct identity_tag {};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/meta/std_fs.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-
-#if JSON_HAS_EXPERIMENTAL_FILESYSTEM
-#include <experimental/filesystem>
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-namespace std_fs = std::experimental::filesystem;
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-#elif JSON_HAS_FILESYSTEM
-#include <filesystem>
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-namespace std_fs = std::filesystem;
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-#endif
-
-// #include <nlohmann/detail/meta/type_traits.hpp>
-
-// #include <nlohmann/detail/string_concat.hpp>
-
-// #include <nlohmann/detail/value_t.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename std::nullptr_t& n)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_null()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be null, but is ", j.type_name()), &j));
-    }
-    n = nullptr;
-}
-
-// overloads for basic_json template parameters
-template < typename BasicJsonType, typename ArithmeticType,
-           enable_if_t < std::is_arithmetic<ArithmeticType>::value&&
-                         !std::is_same<ArithmeticType, typename BasicJsonType::boolean_t>::value,
-                         int > = 0 >
-void get_arithmetic_value(const BasicJsonType& j, ArithmeticType& val)
-{
-    switch (static_cast<value_t>(j))
-    {
-        case value_t::number_unsigned:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_unsigned_t*>());
-            break;
-        }
-        case value_t::number_integer:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_integer_t*>());
-            break;
-        }
-        case value_t::number_float:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_float_t*>());
-            break;
-        }
-
-        case value_t::null:
-        case value_t::object:
-        case value_t::array:
-        case value_t::string:
-        case value_t::boolean:
-        case value_t::binary:
-        case value_t::discarded:
-        default:
-            JSON_THROW(type_error::create(302, concat("type must be number, but is ", j.type_name()), &j));
-    }
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::boolean_t& b)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_boolean()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be boolean, but is ", j.type_name()), &j));
-    }
-    b = *j.template get_ptr<const typename BasicJsonType::boolean_t*>();
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::string_t& s)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_string()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be string, but is ", j.type_name()), &j));
-    }
-    s = *j.template get_ptr<const typename BasicJsonType::string_t*>();
-}
-
-template <
-    typename BasicJsonType, typename StringType,
-    enable_if_t <
-        std::is_assignable<StringType&, const typename BasicJsonType::string_t>::value
-        && is_detected_exact<typename BasicJsonType::string_t::value_type, value_type_t, StringType>::value
-        && !std::is_same<typename BasicJsonType::string_t, StringType>::value
-        && !is_json_ref<StringType>::value, int > = 0 >
-inline void from_json(const BasicJsonType& j, StringType& s)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_string()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be string, but is ", j.type_name()), &j));
-    }
-
-    s = *j.template get_ptr<const typename BasicJsonType::string_t*>();
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::number_float_t& val)
-{
-    get_arithmetic_value(j, val);
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::number_unsigned_t& val)
-{
-    get_arithmetic_value(j, val);
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::number_integer_t& val)
-{
-    get_arithmetic_value(j, val);
-}
-
-#if !JSON_DISABLE_ENUM_SERIALIZATION
-template<typename BasicJsonType, typename EnumType,
-         enable_if_t<std::is_enum<EnumType>::value, int> = 0>
-inline void from_json(const BasicJsonType& j, EnumType& e)
-{
-    typename std::underlying_type<EnumType>::type val;
-    get_arithmetic_value(j, val);
-    e = static_cast<EnumType>(val);
-}
-#endif  // JSON_DISABLE_ENUM_SERIALIZATION
-
-// forward_list doesn't have an insert method
-template<typename BasicJsonType, typename T, typename Allocator,
-         enable_if_t<is_getable<BasicJsonType, T>::value, int> = 0>
-inline void from_json(const BasicJsonType& j, std::forward_list<T, Allocator>& l)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-    l.clear();
-    std::transform(j.rbegin(), j.rend(),
-                   std::front_inserter(l), [](const BasicJsonType & i)
-    {
-        return i.template get<T>();
-    });
-}
-
-// valarray doesn't have an insert method
-template<typename BasicJsonType, typename T,
-         enable_if_t<is_getable<BasicJsonType, T>::value, int> = 0>
-inline void from_json(const BasicJsonType& j, std::valarray<T>& l)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-    l.resize(j.size());
-    std::transform(j.begin(), j.end(), std::begin(l),
-                   [](const BasicJsonType & elem)
-    {
-        return elem.template get<T>();
-    });
-}
-
-template<typename BasicJsonType, typename T, std::size_t N>
-auto from_json(const BasicJsonType& j, T (&arr)[N])  // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
--> decltype(j.template get<T>(), void())
-{
-    for (std::size_t i = 0; i < N; ++i)
-    {
-        arr[i] = j.at(i).template get<T>();
-    }
-}
-
-template<typename BasicJsonType>
-inline void from_json_array_impl(const BasicJsonType& j, typename BasicJsonType::array_t& arr, priority_tag<3> /*unused*/)
-{
-    arr = *j.template get_ptr<const typename BasicJsonType::array_t*>();
-}
-
-template<typename BasicJsonType, typename T, std::size_t N>
-auto from_json_array_impl(const BasicJsonType& j, std::array<T, N>& arr,
-                          priority_tag<2> /*unused*/)
--> decltype(j.template get<T>(), void())
-{
-    for (std::size_t i = 0; i < N; ++i)
-    {
-        arr[i] = j.at(i).template get<T>();
-    }
-}
-
-template<typename BasicJsonType, typename ConstructibleArrayType,
-         enable_if_t<
-             std::is_assignable<ConstructibleArrayType&, ConstructibleArrayType>::value,
-             int> = 0>
-auto from_json_array_impl(const BasicJsonType& j, ConstructibleArrayType& arr, priority_tag<1> /*unused*/)
--> decltype(
-    arr.reserve(std::declval<typename ConstructibleArrayType::size_type>()),
-    j.template get<typename ConstructibleArrayType::value_type>(),
-    void())
-{
-    using std::end;
-
-    ConstructibleArrayType ret;
-    ret.reserve(j.size());
-    std::transform(j.begin(), j.end(),
-                   std::inserter(ret, end(ret)), [](const BasicJsonType & i)
-    {
-        // get<BasicJsonType>() returns *this, this won't call a from_json
-        // method when value_type is BasicJsonType
-        return i.template get<typename ConstructibleArrayType::value_type>();
-    });
-    arr = std::move(ret);
-}
-
-template<typename BasicJsonType, typename ConstructibleArrayType,
-         enable_if_t<
-             std::is_assignable<ConstructibleArrayType&, ConstructibleArrayType>::value,
-             int> = 0>
-inline void from_json_array_impl(const BasicJsonType& j, ConstructibleArrayType& arr,
-                                 priority_tag<0> /*unused*/)
-{
-    using std::end;
-
-    ConstructibleArrayType ret;
-    std::transform(
-        j.begin(), j.end(), std::inserter(ret, end(ret)),
-        [](const BasicJsonType & i)
-    {
-        // get<BasicJsonType>() returns *this, this won't call a from_json
-        // method when value_type is BasicJsonType
-        return i.template get<typename ConstructibleArrayType::value_type>();
-    });
-    arr = std::move(ret);
-}
-
-template < typename BasicJsonType, typename ConstructibleArrayType,
-           enable_if_t <
-               is_constructible_array_type<BasicJsonType, ConstructibleArrayType>::value&&
-               !is_constructible_object_type<BasicJsonType, ConstructibleArrayType>::value&&
-               !is_constructible_string_type<BasicJsonType, ConstructibleArrayType>::value&&
-               !std::is_same<ConstructibleArrayType, typename BasicJsonType::binary_t>::value&&
-               !is_basic_json<ConstructibleArrayType>::value,
-               int > = 0 >
-auto from_json(const BasicJsonType& j, ConstructibleArrayType& arr)
--> decltype(from_json_array_impl(j, arr, priority_tag<3> {}),
-j.template get<typename ConstructibleArrayType::value_type>(),
-void())
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-
-    from_json_array_impl(j, arr, priority_tag<3> {});
-}
-
-template < typename BasicJsonType, typename T, std::size_t... Idx >
-std::array<T, sizeof...(Idx)> from_json_inplace_array_impl(BasicJsonType&& j,
-        identity_tag<std::array<T, sizeof...(Idx)>> /*unused*/, index_sequence<Idx...> /*unused*/)
-{
-    return { { std::forward<BasicJsonType>(j).at(Idx).template get<T>()... } };
-}
-
-template < typename BasicJsonType, typename T, std::size_t N >
-auto from_json(BasicJsonType&& j, identity_tag<std::array<T, N>> tag)
--> decltype(from_json_inplace_array_impl(std::forward<BasicJsonType>(j), tag, make_index_sequence<N> {}))
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-
-    return from_json_inplace_array_impl(std::forward<BasicJsonType>(j), tag, make_index_sequence<N> {});
-}
-
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, typename BasicJsonType::binary_t& bin)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_binary()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be binary, but is ", j.type_name()), &j));
-    }
-
-    bin = *j.template get_ptr<const typename BasicJsonType::binary_t*>();
-}
-
-template<typename BasicJsonType, typename ConstructibleObjectType,
-         enable_if_t<is_constructible_object_type<BasicJsonType, ConstructibleObjectType>::value, int> = 0>
-inline void from_json(const BasicJsonType& j, ConstructibleObjectType& obj)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_object()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be object, but is ", j.type_name()), &j));
-    }
-
-    ConstructibleObjectType ret;
-    const auto* inner_object = j.template get_ptr<const typename BasicJsonType::object_t*>();
-    using value_type = typename ConstructibleObjectType::value_type;
-    std::transform(
-        inner_object->begin(), inner_object->end(),
-        std::inserter(ret, ret.begin()),
-        [](typename BasicJsonType::object_t::value_type const & p)
-    {
-        return value_type(p.first, p.second.template get<typename ConstructibleObjectType::mapped_type>());
-    });
-    obj = std::move(ret);
-}
-
-// overload for arithmetic types, not chosen for basic_json template arguments
-// (BooleanType, etc..); note: Is it really necessary to provide explicit
-// overloads for boolean_t etc. in case of a custom BooleanType which is not
-// an arithmetic type?
-template < typename BasicJsonType, typename ArithmeticType,
-           enable_if_t <
-               std::is_arithmetic<ArithmeticType>::value&&
-               !std::is_same<ArithmeticType, typename BasicJsonType::number_unsigned_t>::value&&
-               !std::is_same<ArithmeticType, typename BasicJsonType::number_integer_t>::value&&
-               !std::is_same<ArithmeticType, typename BasicJsonType::number_float_t>::value&&
-               !std::is_same<ArithmeticType, typename BasicJsonType::boolean_t>::value,
-               int > = 0 >
-inline void from_json(const BasicJsonType& j, ArithmeticType& val)
-{
-    switch (static_cast<value_t>(j))
-    {
-        case value_t::number_unsigned:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_unsigned_t*>());
-            break;
-        }
-        case value_t::number_integer:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_integer_t*>());
-            break;
-        }
-        case value_t::number_float:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_float_t*>());
-            break;
-        }
-        case value_t::boolean:
-        {
-            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::boolean_t*>());
-            break;
-        }
-
-        case value_t::null:
-        case value_t::object:
-        case value_t::array:
-        case value_t::string:
-        case value_t::binary:
-        case value_t::discarded:
-        default:
-            JSON_THROW(type_error::create(302, concat("type must be number, but is ", j.type_name()), &j));
-    }
-}
-
-template<typename BasicJsonType, typename... Args, std::size_t... Idx>
-std::tuple<Args...> from_json_tuple_impl_base(BasicJsonType&& j, index_sequence<Idx...> /*unused*/)
-{
-    return std::make_tuple(std::forward<BasicJsonType>(j).at(Idx).template get<Args>()...);
-}
-
-template < typename BasicJsonType, class A1, class A2 >
-std::pair<A1, A2> from_json_tuple_impl(BasicJsonType&& j, identity_tag<std::pair<A1, A2>> /*unused*/, priority_tag<0> /*unused*/)
-{
-    return {std::forward<BasicJsonType>(j).at(0).template get<A1>(),
-            std::forward<BasicJsonType>(j).at(1).template get<A2>()};
-}
-
-template<typename BasicJsonType, typename A1, typename A2>
-inline void from_json_tuple_impl(BasicJsonType&& j, std::pair<A1, A2>& p, priority_tag<1> /*unused*/)
-{
-    p = from_json_tuple_impl(std::forward<BasicJsonType>(j), identity_tag<std::pair<A1, A2>> {}, priority_tag<0> {});
-}
-
-template<typename BasicJsonType, typename... Args>
-std::tuple<Args...> from_json_tuple_impl(BasicJsonType&& j, identity_tag<std::tuple<Args...>> /*unused*/, priority_tag<2> /*unused*/)
-{
-    return from_json_tuple_impl_base<BasicJsonType, Args...>(std::forward<BasicJsonType>(j), index_sequence_for<Args...> {});
-}
-
-template<typename BasicJsonType, typename... Args>
-inline void from_json_tuple_impl(BasicJsonType&& j, std::tuple<Args...>& t, priority_tag<3> /*unused*/)
-{
-    t = from_json_tuple_impl_base<BasicJsonType, Args...>(std::forward<BasicJsonType>(j), index_sequence_for<Args...> {});
-}
-
-template<typename BasicJsonType, typename TupleRelated>
-auto from_json(BasicJsonType&& j, TupleRelated&& t)
--> decltype(from_json_tuple_impl(std::forward<BasicJsonType>(j), std::forward<TupleRelated>(t), priority_tag<3> {}))
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-
-    return from_json_tuple_impl(std::forward<BasicJsonType>(j), std::forward<TupleRelated>(t), priority_tag<3> {});
-}
-
-template < typename BasicJsonType, typename Key, typename Value, typename Compare, typename Allocator,
-           typename = enable_if_t < !std::is_constructible <
-                                        typename BasicJsonType::string_t, Key >::value >>
-inline void from_json(const BasicJsonType& j, std::map<Key, Value, Compare, Allocator>& m)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-    m.clear();
-    for (const auto& p : j)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!p.is_array()))
-        {
-            JSON_THROW(type_error::create(302, concat("type must be array, but is ", p.type_name()), &j));
-        }
-        m.emplace(p.at(0).template get<Key>(), p.at(1).template get<Value>());
-    }
-}
-
-template < typename BasicJsonType, typename Key, typename Value, typename Hash, typename KeyEqual, typename Allocator,
-           typename = enable_if_t < !std::is_constructible <
-                                        typename BasicJsonType::string_t, Key >::value >>
-inline void from_json(const BasicJsonType& j, std::unordered_map<Key, Value, Hash, KeyEqual, Allocator>& m)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
-    }
-    m.clear();
-    for (const auto& p : j)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!p.is_array()))
-        {
-            JSON_THROW(type_error::create(302, concat("type must be array, but is ", p.type_name()), &j));
-        }
-        m.emplace(p.at(0).template get<Key>(), p.at(1).template get<Value>());
-    }
-}
-
-#if JSON_HAS_FILESYSTEM || JSON_HAS_EXPERIMENTAL_FILESYSTEM
-template<typename BasicJsonType>
-inline void from_json(const BasicJsonType& j, std_fs::path& p)
-{
-    if (JSON_HEDLEY_UNLIKELY(!j.is_string()))
-    {
-        JSON_THROW(type_error::create(302, concat("type must be string, but is ", j.type_name()), &j));
-    }
-    p = *j.template get_ptr<const typename BasicJsonType::string_t*>();
-}
-#endif
-
-struct from_json_fn
-{
-    template<typename BasicJsonType, typename T>
-    auto operator()(const BasicJsonType& j, T&& val) const
-    noexcept(noexcept(from_json(j, std::forward<T>(val))))
-    -> decltype(from_json(j, std::forward<T>(val)))
-    {
-        return from_json(j, std::forward<T>(val));
-    }
-};
-
-}  // namespace detail
-
-#ifndef JSON_HAS_CPP_17
-/// namespace to hold default `from_json` function
-/// to see why this is required:
-/// http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2015/n4381.html
-namespace // NOLINT(cert-dcl59-cpp,fuchsia-header-anon-namespaces,google-build-namespaces)
-{
-#endif
-JSON_INLINE_VARIABLE constexpr const auto& from_json = // NOLINT(misc-definitions-in-headers)
-    detail::static_const<detail::from_json_fn>::value;
-#ifndef JSON_HAS_CPP_17
-}  // namespace
-#endif
-
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/conversions/to_json.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <algorithm> // copy
-#include <iterator> // begin, end
-#include <string> // string
-#include <tuple> // tuple, get
-#include <type_traits> // is_same, is_constructible, is_floating_point, is_enum, underlying_type
-#include <utility> // move, forward, declval, pair
-#include <valarray> // valarray
-#include <vector> // vector
-
-// #include <nlohmann/detail/iterators/iteration_proxy.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cstddef> // size_t
-#include <iterator> // input_iterator_tag
-#include <string> // string, to_string
-#include <tuple> // tuple_size, get, tuple_element
-#include <utility> // move
-
-#if JSON_HAS_RANGES
-    #include <ranges> // enable_borrowed_range
-#endif
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-// #include <nlohmann/detail/meta/type_traits.hpp>
-
-// #include <nlohmann/detail/value_t.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename string_type>
-void int_to_string( string_type& target, std::size_t value )
-{
-    // For ADL
-    using std::to_string;
-    target = to_string(value);
-}
-template<typename IteratorType> class iteration_proxy_value
-{
-  public:
-    using difference_type = std::ptrdiff_t;
-    using value_type = iteration_proxy_value;
-    using pointer = value_type *;
-    using reference = value_type &;
-    using iterator_category = std::input_iterator_tag;
-    using string_type = typename std::remove_cv< typename std::remove_reference<decltype( std::declval<IteratorType>().key() ) >::type >::type;
-
-  private:
-    /// the iterator
-    IteratorType anchor{};
-    /// an index for arrays (used to create key names)
-    std::size_t array_index = 0;
-    /// last stringified array index
-    mutable std::size_t array_index_last = 0;
-    /// a string representation of the array index
-    mutable string_type array_index_str = "0";
-    /// an empty string (to return a reference for primitive values)
-    string_type empty_str{};
-
-  public:
-    explicit iteration_proxy_value() = default;
-    explicit iteration_proxy_value(IteratorType it, std::size_t array_index_ = 0)
-    noexcept(std::is_nothrow_move_constructible<IteratorType>::value
-             && std::is_nothrow_default_constructible<string_type>::value)
-        : anchor(std::move(it))
-        , array_index(array_index_)
-    {}
-
-    iteration_proxy_value(iteration_proxy_value const&) = default;
-    iteration_proxy_value& operator=(iteration_proxy_value const&) = default;
-    // older GCCs are a bit fussy and require explicit noexcept specifiers on defaulted functions
-    iteration_proxy_value(iteration_proxy_value&&)
-    noexcept(std::is_nothrow_move_constructible<IteratorType>::value
-             && std::is_nothrow_move_constructible<string_type>::value) = default;
-    iteration_proxy_value& operator=(iteration_proxy_value&&)
-    noexcept(std::is_nothrow_move_assignable<IteratorType>::value
-             && std::is_nothrow_move_assignable<string_type>::value) = default;
-    ~iteration_proxy_value() = default;
-
-    /// dereference operator (needed for range-based for)
-    const iteration_proxy_value& operator*() const
-    {
-        return *this;
-    }
-
-    /// increment operator (needed for range-based for)
-    iteration_proxy_value& operator++()
-    {
-        ++anchor;
-        ++array_index;
-
-        return *this;
-    }
-
-    iteration_proxy_value operator++(int)& // NOLINT(cert-dcl21-cpp)
-    {
-        auto tmp = iteration_proxy_value(anchor, array_index);
-        ++anchor;
-        ++array_index;
-        return tmp;
-    }
-
-    /// equality operator (needed for InputIterator)
-    bool operator==(const iteration_proxy_value& o) const
-    {
-        return anchor == o.anchor;
-    }
-
-    /// inequality operator (needed for range-based for)
-    bool operator!=(const iteration_proxy_value& o) const
-    {
-        return anchor != o.anchor;
-    }
-
-    /// return key of the iterator
-    const string_type& key() const
-    {
-        JSON_ASSERT(anchor.m_object != nullptr);
-
-        switch (anchor.m_object->type())
-        {
-            // use integer array index as key
-            case value_t::array:
-            {
-                if (array_index != array_index_last)
-                {
-                    int_to_string( array_index_str, array_index );
-                    array_index_last = array_index;
-                }
-                return array_index_str;
-            }
-
-            // use key from the object
-            case value_t::object:
-                return anchor.key();
-
-            // use an empty key for all primitive types
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-                return empty_str;
-        }
-    }
-
-    /// return value of the iterator
-    typename IteratorType::reference value() const
-    {
-        return anchor.value();
-    }
-};
-
-/// proxy class for the items() function
-template<typename IteratorType> class iteration_proxy
-{
-  private:
-    /// the container to iterate
-    typename IteratorType::pointer container = nullptr;
-
-  public:
-    explicit iteration_proxy() = default;
-
-    /// construct iteration proxy from a container
-    explicit iteration_proxy(typename IteratorType::reference cont) noexcept
-        : container(&cont) {}
-
-    iteration_proxy(iteration_proxy const&) = default;
-    iteration_proxy& operator=(iteration_proxy const&) = default;
-    iteration_proxy(iteration_proxy&&) noexcept = default;
-    iteration_proxy& operator=(iteration_proxy&&) noexcept = default;
-    ~iteration_proxy() = default;
-
-    /// return iterator begin (needed for range-based for)
-    iteration_proxy_value<IteratorType> begin() const noexcept
-    {
-        return iteration_proxy_value<IteratorType>(container->begin());
-    }
-
-    /// return iterator end (needed for range-based for)
-    iteration_proxy_value<IteratorType> end() const noexcept
-    {
-        return iteration_proxy_value<IteratorType>(container->end());
-    }
-};
-
-// Structured Bindings Support
-// For further reference see https://blog.tartanllama.xyz/structured-bindings/
-// And see https://github.com/nlohmann/json/pull/1391
-template<std::size_t N, typename IteratorType, enable_if_t<N == 0, int> = 0>
-auto get(const nlohmann::detail::iteration_proxy_value<IteratorType>& i) -> decltype(i.key())
-{
-    return i.key();
-}
-// Structured Bindings Support
-// For further reference see https://blog.tartanllama.xyz/structured-bindings/
-// And see https://github.com/nlohmann/json/pull/1391
-template<std::size_t N, typename IteratorType, enable_if_t<N == 1, int> = 0>
-auto get(const nlohmann::detail::iteration_proxy_value<IteratorType>& i) -> decltype(i.value())
-{
-    return i.value();
-}
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// The Addition to the STD Namespace is required to add
-// Structured Bindings Support to the iteration_proxy_value class
-// For further reference see https://blog.tartanllama.xyz/structured-bindings/
-// And see https://github.com/nlohmann/json/pull/1391
-namespace std
-{
-
-#if defined(__clang__)
-    // Fix: https://github.com/nlohmann/json/issues/1401
-    #pragma clang diagnostic push
-    #pragma clang diagnostic ignored "-Wmismatched-tags"
-#endif
-template<typename IteratorType>
-class tuple_size<::nlohmann::detail::iteration_proxy_value<IteratorType>>
-            : public std::integral_constant<std::size_t, 2> {};
-
-template<std::size_t N, typename IteratorType>
-class tuple_element<N, ::nlohmann::detail::iteration_proxy_value<IteratorType >>
-{
-  public:
-    using type = decltype(
-                     get<N>(std::declval <
-                            ::nlohmann::detail::iteration_proxy_value<IteratorType >> ()));
-};
-#if defined(__clang__)
-    #pragma clang diagnostic pop
-#endif
-
-}  // namespace std
-
-#if JSON_HAS_RANGES
-    template <typename IteratorType>
-    inline constexpr bool ::std::ranges::enable_borrowed_range<::nlohmann::detail::iteration_proxy<IteratorType>> = true;
-#endif
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/meta/cpp_future.hpp>
-
-// #include <nlohmann/detail/meta/std_fs.hpp>
-
-// #include <nlohmann/detail/meta/type_traits.hpp>
-
-// #include <nlohmann/detail/value_t.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-//////////////////
-// constructors //
-//////////////////
-
-/*
- * Note all external_constructor<>::construct functions need to call
- * j.m_value.destroy(j.m_type) to avoid a memory leak in case j contains an
- * allocated value (e.g., a string). See bug issue
- * https://github.com/nlohmann/json/issues/2865 for more information.
- */
-
-template<value_t> struct external_constructor;
-
-template<>
-struct external_constructor<value_t::boolean>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::boolean_t b) noexcept
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::boolean;
-        j.m_value = b;
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::string>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, const typename BasicJsonType::string_t& s)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::string;
-        j.m_value = s;
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::string_t&& s)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::string;
-        j.m_value = std::move(s);
-        j.assert_invariant();
-    }
-
-    template < typename BasicJsonType, typename CompatibleStringType,
-               enable_if_t < !std::is_same<CompatibleStringType, typename BasicJsonType::string_t>::value,
-                             int > = 0 >
-    static void construct(BasicJsonType& j, const CompatibleStringType& str)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::string;
-        j.m_value.string = j.template create<typename BasicJsonType::string_t>(str);
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::binary>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, const typename BasicJsonType::binary_t& b)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::binary;
-        j.m_value = typename BasicJsonType::binary_t(b);
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::binary_t&& b)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::binary;
-        j.m_value = typename BasicJsonType::binary_t(std::move(b));
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::number_float>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::number_float_t val) noexcept
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::number_float;
-        j.m_value = val;
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::number_unsigned>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::number_unsigned_t val) noexcept
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::number_unsigned;
-        j.m_value = val;
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::number_integer>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::number_integer_t val) noexcept
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::number_integer;
-        j.m_value = val;
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::array>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, const typename BasicJsonType::array_t& arr)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = arr;
-        j.set_parents();
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::array_t&& arr)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = std::move(arr);
-        j.set_parents();
-        j.assert_invariant();
-    }
-
-    template < typename BasicJsonType, typename CompatibleArrayType,
-               enable_if_t < !std::is_same<CompatibleArrayType, typename BasicJsonType::array_t>::value,
-                             int > = 0 >
-    static void construct(BasicJsonType& j, const CompatibleArrayType& arr)
-    {
-        using std::begin;
-        using std::end;
-
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value.array = j.template create<typename BasicJsonType::array_t>(begin(arr), end(arr));
-        j.set_parents();
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, const std::vector<bool>& arr)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = value_t::array;
-        j.m_value.array->reserve(arr.size());
-        for (const bool x : arr)
-        {
-            j.m_value.array->push_back(x);
-            j.set_parent(j.m_value.array->back());
-        }
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType, typename T,
-             enable_if_t<std::is_convertible<T, BasicJsonType>::value, int> = 0>
-    static void construct(BasicJsonType& j, const std::valarray<T>& arr)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = value_t::array;
-        j.m_value.array->resize(arr.size());
-        if (arr.size() > 0)
-        {
-            std::copy(std::begin(arr), std::end(arr), j.m_value.array->begin());
-        }
-        j.set_parents();
-        j.assert_invariant();
-    }
-};
-
-template<>
-struct external_constructor<value_t::object>
-{
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, const typename BasicJsonType::object_t& obj)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::object;
-        j.m_value = obj;
-        j.set_parents();
-        j.assert_invariant();
-    }
-
-    template<typename BasicJsonType>
-    static void construct(BasicJsonType& j, typename BasicJsonType::object_t&& obj)
-    {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::object;
-        j.m_value = std::move(obj);
-        j.set_parents();
-        j.assert_invariant();
-    }
-
-    template < typename BasicJsonType, typename CompatibleObjectType,
-               enable_if_t < !std::is_same<CompatibleObjectType, typename BasicJsonType::object_t>::value, int > = 0 >
-    static void construct(BasicJsonType& j, const CompatibleObjectType& obj)
-    {
-        using std::begin;
-        using std::end;
-
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::object;
-        j.m_value.object = j.template create<typename BasicJsonType::object_t>(begin(obj), end(obj));
-        j.set_parents();
-        j.assert_invariant();
-    }
-};
-
-/////////////
-// to_json //
-/////////////
-
-template<typename BasicJsonType, typename T,
-         enable_if_t<std::is_same<T, typename BasicJsonType::boolean_t>::value, int> = 0>
-inline void to_json(BasicJsonType& j, T b) noexcept
-{
-    external_constructor<value_t::boolean>::construct(j, b);
-}
-
-template < typename BasicJsonType, typename BoolRef,
-           enable_if_t <
-               ((std::is_same<std::vector<bool>::reference, BoolRef>::value
-                 && !std::is_same <std::vector<bool>::reference, typename BasicJsonType::boolean_t&>::value)
-                || (std::is_same<std::vector<bool>::const_reference, BoolRef>::value
-                    && !std::is_same <detail::uncvref_t<std::vector<bool>::const_reference>,
-                                      typename BasicJsonType::boolean_t >::value))
-               && std::is_convertible<const BoolRef&, typename BasicJsonType::boolean_t>::value, int > = 0 >
-inline void to_json(BasicJsonType& j, const BoolRef& b) noexcept
-{
-    external_constructor<value_t::boolean>::construct(j, static_cast<typename BasicJsonType::boolean_t>(b));
-}
-
-template<typename BasicJsonType, typename CompatibleString,
-         enable_if_t<std::is_constructible<typename BasicJsonType::string_t, CompatibleString>::value, int> = 0>
-inline void to_json(BasicJsonType& j, const CompatibleString& s)
-{
-    external_constructor<value_t::string>::construct(j, s);
-}
-
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, typename BasicJsonType::string_t&& s)
-{
-    external_constructor<value_t::string>::construct(j, std::move(s));
-}
-
-template<typename BasicJsonType, typename FloatType,
-         enable_if_t<std::is_floating_point<FloatType>::value, int> = 0>
-inline void to_json(BasicJsonType& j, FloatType val) noexcept
-{
-    external_constructor<value_t::number_float>::construct(j, static_cast<typename BasicJsonType::number_float_t>(val));
-}
-
-template<typename BasicJsonType, typename CompatibleNumberUnsignedType,
-         enable_if_t<is_compatible_integer_type<typename BasicJsonType::number_unsigned_t, CompatibleNumberUnsignedType>::value, int> = 0>
-inline void to_json(BasicJsonType& j, CompatibleNumberUnsignedType val) noexcept
-{
-    external_constructor<value_t::number_unsigned>::construct(j, static_cast<typename BasicJsonType::number_unsigned_t>(val));
-}
-
-template<typename BasicJsonType, typename CompatibleNumberIntegerType,
-         enable_if_t<is_compatible_integer_type<typename BasicJsonType::number_integer_t, CompatibleNumberIntegerType>::value, int> = 0>
-inline void to_json(BasicJsonType& j, CompatibleNumberIntegerType val) noexcept
-{
-    external_constructor<value_t::number_integer>::construct(j, static_cast<typename BasicJsonType::number_integer_t>(val));
-}
-
-#if !JSON_DISABLE_ENUM_SERIALIZATION
-template<typename BasicJsonType, typename EnumType,
-         enable_if_t<std::is_enum<EnumType>::value, int> = 0>
-inline void to_json(BasicJsonType& j, EnumType e) noexcept
-{
-    using underlying_type = typename std::underlying_type<EnumType>::type;
-    external_constructor<value_t::number_integer>::construct(j, static_cast<underlying_type>(e));
-}
-#endif  // JSON_DISABLE_ENUM_SERIALIZATION
-
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, const std::vector<bool>& e)
-{
-    external_constructor<value_t::array>::construct(j, e);
-}
-
-template < typename BasicJsonType, typename CompatibleArrayType,
-           enable_if_t < is_compatible_array_type<BasicJsonType,
-                         CompatibleArrayType>::value&&
-                         !is_compatible_object_type<BasicJsonType, CompatibleArrayType>::value&&
-                         !is_compatible_string_type<BasicJsonType, CompatibleArrayType>::value&&
-                         !std::is_same<typename BasicJsonType::binary_t, CompatibleArrayType>::value&&
-                         !is_basic_json<CompatibleArrayType>::value,
-                         int > = 0 >
-inline void to_json(BasicJsonType& j, const CompatibleArrayType& arr)
-{
-    external_constructor<value_t::array>::construct(j, arr);
-}
-
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, const typename BasicJsonType::binary_t& bin)
-{
-    external_constructor<value_t::binary>::construct(j, bin);
-}
-
-template<typename BasicJsonType, typename T,
-         enable_if_t<std::is_convertible<T, BasicJsonType>::value, int> = 0>
-inline void to_json(BasicJsonType& j, const std::valarray<T>& arr)
-{
-    external_constructor<value_t::array>::construct(j, std::move(arr));
-}
-
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, typename BasicJsonType::array_t&& arr)
-{
-    external_constructor<value_t::array>::construct(j, std::move(arr));
-}
-
-template < typename BasicJsonType, typename CompatibleObjectType,
-           enable_if_t < is_compatible_object_type<BasicJsonType, CompatibleObjectType>::value&& !is_basic_json<CompatibleObjectType>::value, int > = 0 >
-inline void to_json(BasicJsonType& j, const CompatibleObjectType& obj)
-{
-    external_constructor<value_t::object>::construct(j, obj);
-}
-
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, typename BasicJsonType::object_t&& obj)
-{
-    external_constructor<value_t::object>::construct(j, std::move(obj));
-}
-
-template <
-    typename BasicJsonType, typename T, std::size_t N,
-    enable_if_t < !std::is_constructible<typename BasicJsonType::string_t,
-                  const T(&)[N]>::value, // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
-                  int > = 0 >
-inline void to_json(BasicJsonType& j, const T(&arr)[N]) // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
-{
-    external_constructor<value_t::array>::construct(j, arr);
-}
-
-template < typename BasicJsonType, typename T1, typename T2, enable_if_t < std::is_constructible<BasicJsonType, T1>::value&& std::is_constructible<BasicJsonType, T2>::value, int > = 0 >
-inline void to_json(BasicJsonType& j, const std::pair<T1, T2>& p)
-{
-    j = { p.first, p.second };
-}
-
-// for https://github.com/nlohmann/json/pull/1134
-template<typename BasicJsonType, typename T,
-         enable_if_t<std::is_same<T, iteration_proxy_value<typename BasicJsonType::iterator>>::value, int> = 0>
-inline void to_json(BasicJsonType& j, const T& b)
-{
-    j = { {b.key(), b.value()} };
-}
-
-template<typename BasicJsonType, typename Tuple, std::size_t... Idx>
-inline void to_json_tuple_impl(BasicJsonType& j, const Tuple& t, index_sequence<Idx...> /*unused*/)
-{
-    j = { std::get<Idx>(t)... };
-}
-
-template<typename BasicJsonType, typename T, enable_if_t<is_constructible_tuple<BasicJsonType, T>::value, int > = 0>
-inline void to_json(BasicJsonType& j, const T& t)
-{
-    to_json_tuple_impl(j, t, make_index_sequence<std::tuple_size<T>::value> {});
-}
-
-#if JSON_HAS_FILESYSTEM || JSON_HAS_EXPERIMENTAL_FILESYSTEM
-template<typename BasicJsonType>
-inline void to_json(BasicJsonType& j, const std_fs::path& p)
-{
-    j = p.string();
-}
-#endif
-
-struct to_json_fn
-{
-    template<typename BasicJsonType, typename T>
-    auto operator()(BasicJsonType& j, T&& val) const noexcept(noexcept(to_json(j, std::forward<T>(val))))
-    -> decltype(to_json(j, std::forward<T>(val)), void())
-    {
-        return to_json(j, std::forward<T>(val));
-    }
-};
-}  // namespace detail
-
-#ifndef JSON_HAS_CPP_17
-/// namespace to hold default `to_json` function
-/// to see why this is required:
-/// http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2015/n4381.html
-namespace // NOLINT(cert-dcl59-cpp,fuchsia-header-anon-namespaces,google-build-namespaces)
-{
-#endif
-JSON_INLINE_VARIABLE constexpr const auto& to_json = // NOLINT(misc-definitions-in-headers)
-    detail::static_const<detail::to_json_fn>::value;
-#ifndef JSON_HAS_CPP_17
-}  // namespace
-#endif
-
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/meta/identity_tag.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-/// @sa https://json.nlohmann.me/api/adl_serializer/
-template<typename ValueType, typename>
-struct adl_serializer
-{
-    /// @brief convert a JSON value to any value type
-    /// @sa https://json.nlohmann.me/api/adl_serializer/from_json/
-    template<typename BasicJsonType, typename TargetType = ValueType>
-    static auto from_json(BasicJsonType && j, TargetType& val) noexcept(
-        noexcept(::nlohmann::from_json(std::forward<BasicJsonType>(j), val)))
-    -> decltype(::nlohmann::from_json(std::forward<BasicJsonType>(j), val), void())
-    {
-        ::nlohmann::from_json(std::forward<BasicJsonType>(j), val);
-    }
-
-    /// @brief convert a JSON value to any value type
-    /// @sa https://json.nlohmann.me/api/adl_serializer/from_json/
-    template<typename BasicJsonType, typename TargetType = ValueType>
-    static auto from_json(BasicJsonType && j) noexcept(
-    noexcept(::nlohmann::from_json(std::forward<BasicJsonType>(j), detail::identity_tag<TargetType> {})))
-    -> decltype(::nlohmann::from_json(std::forward<BasicJsonType>(j), detail::identity_tag<TargetType> {}))
-    {
-        return ::nlohmann::from_json(std::forward<BasicJsonType>(j), detail::identity_tag<TargetType> {});
-    }
-
-    /// @brief convert any value type to a JSON value
-    /// @sa https://json.nlohmann.me/api/adl_serializer/to_json/
-    template<typename BasicJsonType, typename TargetType = ValueType>
-    static auto to_json(BasicJsonType& j, TargetType && val) noexcept(
-        noexcept(::nlohmann::to_json(j, std::forward<TargetType>(val))))
-    -> decltype(::nlohmann::to_json(j, std::forward<TargetType>(val)), void())
-    {
-        ::nlohmann::to_json(j, std::forward<TargetType>(val));
-    }
-};
-
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/byte_container_with_subtype.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cstdint> // uint8_t, uint64_t
-#include <tuple> // tie
-#include <utility> // move
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-/// @brief an internal type for a backed binary type
-/// @sa https://json.nlohmann.me/api/byte_container_with_subtype/
-template<typename BinaryType>
-class byte_container_with_subtype : public BinaryType
-{
-  public:
-    using container_type = BinaryType;
-    using subtype_type = std::uint64_t;
-
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
-    byte_container_with_subtype() noexcept(noexcept(container_type()))
-        : container_type()
-    {}
-
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
-    byte_container_with_subtype(const container_type& b) noexcept(noexcept(container_type(b)))
-        : container_type(b)
-    {}
-
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
-    byte_container_with_subtype(container_type&& b) noexcept(noexcept(container_type(std::move(b))))
-        : container_type(std::move(b))
-    {}
-
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
-    byte_container_with_subtype(const container_type& b, subtype_type subtype_) noexcept(noexcept(container_type(b)))
-        : container_type(b)
-        , m_subtype(subtype_)
-        , m_has_subtype(true)
-    {}
-
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
-    byte_container_with_subtype(container_type&& b, subtype_type subtype_) noexcept(noexcept(container_type(std::move(b))))
-        : container_type(std::move(b))
-        , m_subtype(subtype_)
-        , m_has_subtype(true)
-    {}
-
-    bool operator==(const byte_container_with_subtype& rhs) const
-    {
-        return std::tie(static_cast<const BinaryType&>(*this), m_subtype, m_has_subtype) ==
-               std::tie(static_cast<const BinaryType&>(rhs), rhs.m_subtype, rhs.m_has_subtype);
-    }
-
-    bool operator!=(const byte_container_with_subtype& rhs) const
-    {
-        return !(rhs == *this);
-    }
-
-    /// @brief sets the binary subtype
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/set_subtype/
-    void set_subtype(subtype_type subtype_) noexcept
-    {
-        m_subtype = subtype_;
-        m_has_subtype = true;
-    }
-
-    /// @brief return the binary subtype
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/subtype/
-    constexpr subtype_type subtype() const noexcept
-    {
-        return m_has_subtype ? m_subtype : static_cast<subtype_type>(-1);
-    }
-
-    /// @brief return whether the value has a subtype
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/has_subtype/
-    constexpr bool has_subtype() const noexcept
-    {
-        return m_has_subtype;
-    }
-
-    /// @brief clears the binary subtype
-    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/clear_subtype/
-    void clear_subtype() noexcept
-    {
-        m_subtype = 0;
-        m_has_subtype = false;
-    }
-
-  private:
-    subtype_type m_subtype = 0;
-    bool m_has_subtype = false;
-};
-
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/conversions/from_json.hpp>
-
-// #include <nlohmann/detail/conversions/to_json.hpp>
-
-// #include <nlohmann/detail/exceptions.hpp>
-
-// #include <nlohmann/detail/hash.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cstdint> // uint8_t
-#include <cstddef> // size_t
-#include <functional> // hash
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-// #include <nlohmann/detail/value_t.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-// boost::hash_combine
-inline std::size_t combine(std::size_t seed, std::size_t h) noexcept
-{
-    seed ^= h + 0x9e3779b9 + (seed << 6U) + (seed >> 2U);
-    return seed;
-}
-
-/*!
-@brief hash a JSON value
-
-The hash function tries to rely on std::hash where possible. Furthermore, the
-type of the JSON value is taken into account to have different hash values for
-null, 0, 0U, and false, etc.
-
-@tparam BasicJsonType basic_json specialization
-@param j JSON value to hash
-@return hash value of j
-*/
-template<typename BasicJsonType>
-std::size_t hash(const BasicJsonType& j)
-{
-    using string_t = typename BasicJsonType::string_t;
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-
-    const auto type = static_cast<std::size_t>(j.type());
-    switch (j.type())
-    {
-        case BasicJsonType::value_t::null:
-        case BasicJsonType::value_t::discarded:
-        {
-            return combine(type, 0);
-        }
-
-        case BasicJsonType::value_t::object:
-        {
-            auto seed = combine(type, j.size());
-            for (const auto& element : j.items())
-            {
-                const auto h = std::hash<string_t> {}(element.key());
-                seed = combine(seed, h);
-                seed = combine(seed, hash(element.value()));
-            }
-            return seed;
-        }
-
-        case BasicJsonType::value_t::array:
-        {
-            auto seed = combine(type, j.size());
-            for (const auto& element : j)
-            {
-                seed = combine(seed, hash(element));
-            }
-            return seed;
-        }
-
-        case BasicJsonType::value_t::string:
-        {
-            const auto h = std::hash<string_t> {}(j.template get_ref<const string_t&>());
-            return combine(type, h);
-        }
-
-        case BasicJsonType::value_t::boolean:
-        {
-            const auto h = std::hash<bool> {}(j.template get<bool>());
-            return combine(type, h);
-        }
-
-        case BasicJsonType::value_t::number_integer:
-        {
-            const auto h = std::hash<number_integer_t> {}(j.template get<number_integer_t>());
-            return combine(type, h);
-        }
-
-        case BasicJsonType::value_t::number_unsigned:
-        {
-            const auto h = std::hash<number_unsigned_t> {}(j.template get<number_unsigned_t>());
-            return combine(type, h);
-        }
-
-        case BasicJsonType::value_t::number_float:
-        {
-            const auto h = std::hash<number_float_t> {}(j.template get<number_float_t>());
-            return combine(type, h);
-        }
-
-        case BasicJsonType::value_t::binary:
-        {
-            auto seed = combine(type, j.get_binary().size());
-            const auto h = std::hash<bool> {}(j.get_binary().has_subtype());
-            seed = combine(seed, h);
-            seed = combine(seed, static_cast<std::size_t>(j.get_binary().subtype()));
-            for (const auto byte : j.get_binary())
-            {
-                seed = combine(seed, std::hash<std::uint8_t> {}(byte));
-            }
-            return seed;
-        }
-
-        default:                   // LCOV_EXCL_LINE
-            JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-            return 0;              // LCOV_EXCL_LINE
-    }
-}
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/input/binary_reader.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <algorithm> // generate_n
-#include <array> // array
-#include <cmath> // ldexp
-#include <cstddef> // size_t
-#include <cstdint> // uint8_t, uint16_t, uint32_t, uint64_t
-#include <cstdio> // snprintf
-#include <cstring> // memcpy
-#include <iterator> // back_inserter
-#include <limits> // numeric_limits
-#include <string> // char_traits, string
-#include <utility> // make_pair, move
-#include <vector> // vector
-
-// #include <nlohmann/detail/exceptions.hpp>
-
-// #include <nlohmann/detail/input/input_adapters.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <array> // array
-#include <cstddef> // size_t
-#include <cstring> // strlen
-#include <iterator> // begin, end, iterator_traits, random_access_iterator_tag, distance, next
-#include <memory> // shared_ptr, make_shared, addressof
-#include <numeric> // accumulate
-#include <string> // string, char_traits
-#include <type_traits> // enable_if, is_base_of, is_pointer, is_integral, remove_pointer
-#include <utility> // pair, declval
-
-#ifndef JSON_NO_IO
-    #include <cstdio>   // FILE *
-    #include <istream>  // istream
-#endif                  // JSON_NO_IO
-
-// #include <nlohmann/detail/iterators/iterator_traits.hpp>
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/// the supported input formats
-enum class input_format_t { json, cbor, msgpack, ubjson, bson, bjdata };
-
-////////////////////
-// input adapters //
-////////////////////
-
-#ifndef JSON_NO_IO
-/*!
-Input adapter for stdio file access. This adapter read only 1 byte and do not use any
- buffer. This adapter is a very low level adapter.
-*/
-class file_input_adapter
-{
-  public:
-    using char_type = char;
-
-    JSON_HEDLEY_NON_NULL(2)
-    explicit file_input_adapter(std::FILE* f) noexcept
-        : m_file(f)
-    {
-        JSON_ASSERT(m_file != nullptr);
-    }
-
-    // make class move-only
-    file_input_adapter(const file_input_adapter&) = delete;
-    file_input_adapter(file_input_adapter&&) noexcept = default;
-    file_input_adapter& operator=(const file_input_adapter&) = delete;
-    file_input_adapter& operator=(file_input_adapter&&) = delete;
-    ~file_input_adapter() = default;
-
-    std::char_traits<char>::int_type get_character() noexcept
-    {
-        return std::fgetc(m_file);
-    }
-
-  private:
-    /// the file pointer to read from
-    std::FILE* m_file;
-};
-
-
-/*!
-Input adapter for a (caching) istream. Ignores a UFT Byte Order Mark at
-beginning of input. Does not support changing the underlying std::streambuf
-in mid-input. Maintains underlying std::istream and std::streambuf to support
-subsequent use of standard std::istream operations to process any input
-characters following those used in parsing the JSON input.  Clears the
-std::istream flags; any input errors (e.g., EOF) will be detected by the first
-subsequent call for input from the std::istream.
-*/
-class input_stream_adapter
-{
-  public:
-    using char_type = char;
-
-    ~input_stream_adapter()
-    {
-        // clear stream flags; we use underlying streambuf I/O, do not
-        // maintain ifstream flags, except eof
-        if (is != nullptr)
-        {
-            is->clear(is->rdstate() & std::ios::eofbit);
-        }
-    }
-
-    explicit input_stream_adapter(std::istream& i)
-        : is(&i), sb(i.rdbuf())
-    {}
-
-    // delete because of pointer members
-    input_stream_adapter(const input_stream_adapter&) = delete;
-    input_stream_adapter& operator=(input_stream_adapter&) = delete;
-    input_stream_adapter& operator=(input_stream_adapter&&) = delete;
-
-    input_stream_adapter(input_stream_adapter&& rhs) noexcept
-        : is(rhs.is), sb(rhs.sb)
-    {
-        rhs.is = nullptr;
-        rhs.sb = nullptr;
-    }
-
-    // std::istream/std::streambuf use std::char_traits<char>::to_int_type, to
-    // ensure that std::char_traits<char>::eof() and the character 0xFF do not
-    // end up as the same value, e.g. 0xFFFFFFFF.
-    std::char_traits<char>::int_type get_character()
-    {
-        auto res = sb->sbumpc();
-        // set eof manually, as we don't use the istream interface.
-        if (JSON_HEDLEY_UNLIKELY(res == std::char_traits<char>::eof()))
-        {
-            is->clear(is->rdstate() | std::ios::eofbit);
-        }
-        return res;
-    }
-
-  private:
-    /// the associated input stream
-    std::istream* is = nullptr;
-    std::streambuf* sb = nullptr;
-};
-#endif  // JSON_NO_IO
-
-// General-purpose iterator-based adapter. It might not be as fast as
-// theoretically possible for some containers, but it is extremely versatile.
-template<typename IteratorType>
-class iterator_input_adapter
-{
-  public:
-    using char_type = typename std::iterator_traits<IteratorType>::value_type;
-
-    iterator_input_adapter(IteratorType first, IteratorType last)
-        : current(std::move(first)), end(std::move(last))
-    {}
-
-    typename std::char_traits<char_type>::int_type get_character()
-    {
-        if (JSON_HEDLEY_LIKELY(current != end))
-        {
-            auto result = std::char_traits<char_type>::to_int_type(*current);
-            std::advance(current, 1);
-            return result;
-        }
-
-        return std::char_traits<char_type>::eof();
-    }
-
-  private:
-    IteratorType current;
-    IteratorType end;
-
-    template<typename BaseInputAdapter, size_t T>
-    friend struct wide_string_input_helper;
-
-    bool empty() const
-    {
-        return current == end;
-    }
-};
-
-
-template<typename BaseInputAdapter, size_t T>
-struct wide_string_input_helper;
-
-template<typename BaseInputAdapter>
-struct wide_string_input_helper<BaseInputAdapter, 4>
-{
-    // UTF-32
-    static void fill_buffer(BaseInputAdapter& input,
-                            std::array<std::char_traits<char>::int_type, 4>& utf8_bytes,
-                            size_t& utf8_bytes_index,
-                            size_t& utf8_bytes_filled)
-    {
-        utf8_bytes_index = 0;
-
-        if (JSON_HEDLEY_UNLIKELY(input.empty()))
-        {
-            utf8_bytes[0] = std::char_traits<char>::eof();
-            utf8_bytes_filled = 1;
-        }
-        else
-        {
-            // get the current character
-            const auto wc = input.get_character();
-
-            // UTF-32 to UTF-8 encoding
-            if (wc < 0x80)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
-                utf8_bytes_filled = 1;
-            }
-            else if (wc <= 0x7FF)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xC0u | ((static_cast<unsigned int>(wc) >> 6u) & 0x1Fu));
-                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
-                utf8_bytes_filled = 2;
-            }
-            else if (wc <= 0xFFFF)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xE0u | ((static_cast<unsigned int>(wc) >> 12u) & 0x0Fu));
-                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 6u) & 0x3Fu));
-                utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
-                utf8_bytes_filled = 3;
-            }
-            else if (wc <= 0x10FFFF)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xF0u | ((static_cast<unsigned int>(wc) >> 18u) & 0x07u));
-                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 12u) & 0x3Fu));
-                utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 6u) & 0x3Fu));
-                utf8_bytes[3] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
-                utf8_bytes_filled = 4;
-            }
-            else
-            {
-                // unknown character
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
-                utf8_bytes_filled = 1;
-            }
-        }
-    }
-};
-
-template<typename BaseInputAdapter>
-struct wide_string_input_helper<BaseInputAdapter, 2>
-{
-    // UTF-16
-    static void fill_buffer(BaseInputAdapter& input,
-                            std::array<std::char_traits<char>::int_type, 4>& utf8_bytes,
-                            size_t& utf8_bytes_index,
-                            size_t& utf8_bytes_filled)
-    {
-        utf8_bytes_index = 0;
-
-        if (JSON_HEDLEY_UNLIKELY(input.empty()))
-        {
-            utf8_bytes[0] = std::char_traits<char>::eof();
-            utf8_bytes_filled = 1;
-        }
-        else
-        {
-            // get the current character
-            const auto wc = input.get_character();
-
-            // UTF-16 to UTF-8 encoding
-            if (wc < 0x80)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
-                utf8_bytes_filled = 1;
-            }
-            else if (wc <= 0x7FF)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xC0u | ((static_cast<unsigned int>(wc) >> 6u)));
-                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
-                utf8_bytes_filled = 2;
-            }
-            else if (0xD800 > wc || wc >= 0xE000)
-            {
-                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xE0u | ((static_cast<unsigned int>(wc) >> 12u)));
-                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 6u) & 0x3Fu));
-                utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
-                utf8_bytes_filled = 3;
-            }
-            else
-            {
-                if (JSON_HEDLEY_UNLIKELY(!input.empty()))
-                {
-                    const auto wc2 = static_cast<unsigned int>(input.get_character());
-                    const auto charcode = 0x10000u + (((static_cast<unsigned int>(wc) & 0x3FFu) << 10u) | (wc2 & 0x3FFu));
-                    utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xF0u | (charcode >> 18u));
-                    utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((charcode >> 12u) & 0x3Fu));
-                    utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | ((charcode >> 6u) & 0x3Fu));
-                    utf8_bytes[3] = static_cast<std::char_traits<char>::int_type>(0x80u | (charcode & 0x3Fu));
-                    utf8_bytes_filled = 4;
-                }
-                else
-                {
-                    utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
-                    utf8_bytes_filled = 1;
-                }
-            }
-        }
-    }
-};
-
-// Wraps another input apdater to convert wide character types into individual bytes.
-template<typename BaseInputAdapter, typename WideCharType>
-class wide_string_input_adapter
-{
-  public:
-    using char_type = char;
-
-    wide_string_input_adapter(BaseInputAdapter base)
-        : base_adapter(base) {}
-
-    typename std::char_traits<char>::int_type get_character() noexcept
-    {
-        // check if buffer needs to be filled
-        if (utf8_bytes_index == utf8_bytes_filled)
-        {
-            fill_buffer<sizeof(WideCharType)>();
-
-            JSON_ASSERT(utf8_bytes_filled > 0);
-            JSON_ASSERT(utf8_bytes_index == 0);
-        }
-
-        // use buffer
-        JSON_ASSERT(utf8_bytes_filled > 0);
-        JSON_ASSERT(utf8_bytes_index < utf8_bytes_filled);
-        return utf8_bytes[utf8_bytes_index++];
-    }
-
-  private:
-    BaseInputAdapter base_adapter;
-
-    template<size_t T>
-    void fill_buffer()
-    {
-        wide_string_input_helper<BaseInputAdapter, T>::fill_buffer(base_adapter, utf8_bytes, utf8_bytes_index, utf8_bytes_filled);
-    }
-
-    /// a buffer for UTF-8 bytes
-    std::array<std::char_traits<char>::int_type, 4> utf8_bytes = {{0, 0, 0, 0}};
-
-    /// index to the utf8_codes array for the next valid byte
-    std::size_t utf8_bytes_index = 0;
-    /// number of valid bytes in the utf8_codes array
-    std::size_t utf8_bytes_filled = 0;
-};
-
-
-template<typename IteratorType, typename Enable = void>
-struct iterator_input_adapter_factory
-{
-    using iterator_type = IteratorType;
-    using char_type = typename std::iterator_traits<iterator_type>::value_type;
-    using adapter_type = iterator_input_adapter<iterator_type>;
-
-    static adapter_type create(IteratorType first, IteratorType last)
-    {
-        return adapter_type(std::move(first), std::move(last));
-    }
-};
-
-template<typename T>
-struct is_iterator_of_multibyte
-{
-    using value_type = typename std::iterator_traits<T>::value_type;
-    enum
-    {
-        value = sizeof(value_type) > 1
-    };
-};
-
-template<typename IteratorType>
-struct iterator_input_adapter_factory<IteratorType, enable_if_t<is_iterator_of_multibyte<IteratorType>::value>>
-{
-    using iterator_type = IteratorType;
-    using char_type = typename std::iterator_traits<iterator_type>::value_type;
-    using base_adapter_type = iterator_input_adapter<iterator_type>;
-    using adapter_type = wide_string_input_adapter<base_adapter_type, char_type>;
-
-    static adapter_type create(IteratorType first, IteratorType last)
-    {
-        return adapter_type(base_adapter_type(std::move(first), std::move(last)));
-    }
-};
-
-// General purpose iterator-based input
-template<typename IteratorType>
-typename iterator_input_adapter_factory<IteratorType>::adapter_type input_adapter(IteratorType first, IteratorType last)
-{
-    using factory_type = iterator_input_adapter_factory<IteratorType>;
-    return factory_type::create(first, last);
-}
-
-// Convenience shorthand from container to iterator
-// Enables ADL on begin(container) and end(container)
-// Encloses the using declarations in namespace for not to leak them to outside scope
-
-namespace container_input_adapter_factory_impl
-{
-
-using std::begin;
-using std::end;
-
-template<typename ContainerType, typename Enable = void>
-struct container_input_adapter_factory {};
-
-template<typename ContainerType>
-struct container_input_adapter_factory< ContainerType,
-       void_t<decltype(begin(std::declval<ContainerType>()), end(std::declval<ContainerType>()))>>
-       {
-           using adapter_type = decltype(input_adapter(begin(std::declval<ContainerType>()), end(std::declval<ContainerType>())));
-
-           static adapter_type create(const ContainerType& container)
-{
-    return input_adapter(begin(container), end(container));
-}
-       };
-
-}  // namespace container_input_adapter_factory_impl
-
-template<typename ContainerType>
-typename container_input_adapter_factory_impl::container_input_adapter_factory<ContainerType>::adapter_type input_adapter(const ContainerType& container)
-{
-    return container_input_adapter_factory_impl::container_input_adapter_factory<ContainerType>::create(container);
-}
-
-#ifndef JSON_NO_IO
-// Special cases with fast paths
-inline file_input_adapter input_adapter(std::FILE* file)
-{
-    return file_input_adapter(file);
-}
-
-inline input_stream_adapter input_adapter(std::istream& stream)
-{
-    return input_stream_adapter(stream);
-}
-
-inline input_stream_adapter input_adapter(std::istream&& stream)
-{
-    return input_stream_adapter(stream);
-}
-#endif  // JSON_NO_IO
-
-using contiguous_bytes_input_adapter = decltype(input_adapter(std::declval<const char*>(), std::declval<const char*>()));
-
-// Null-delimited strings, and the like.
-template < typename CharT,
-           typename std::enable_if <
-               std::is_pointer<CharT>::value&&
-               !std::is_array<CharT>::value&&
-               std::is_integral<typename std::remove_pointer<CharT>::type>::value&&
-               sizeof(typename std::remove_pointer<CharT>::type) == 1,
-               int >::type = 0 >
-contiguous_bytes_input_adapter input_adapter(CharT b)
-{
-    auto length = std::strlen(reinterpret_cast<const char*>(b));
-    const auto* ptr = reinterpret_cast<const char*>(b);
-    return input_adapter(ptr, ptr + length);
-}
-
-template<typename T, std::size_t N>
-auto input_adapter(T (&array)[N]) -> decltype(input_adapter(array, array + N)) // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
-{
-    return input_adapter(array, array + N);
-}
-
-// This class only handles inputs of input_buffer_adapter type.
-// It's required so that expressions like {ptr, len} can be implicitly cast
-// to the correct adapter.
-class span_input_adapter
-{
-  public:
-    template < typename CharT,
-               typename std::enable_if <
-                   std::is_pointer<CharT>::value&&
-                   std::is_integral<typename std::remove_pointer<CharT>::type>::value&&
-                   sizeof(typename std::remove_pointer<CharT>::type) == 1,
-                   int >::type = 0 >
-    span_input_adapter(CharT b, std::size_t l)
-        : ia(reinterpret_cast<const char*>(b), reinterpret_cast<const char*>(b) + l) {}
-
-    template<class IteratorType,
-             typename std::enable_if<
-                 std::is_same<typename iterator_traits<IteratorType>::iterator_category, std::random_access_iterator_tag>::value,
-                 int>::type = 0>
-    span_input_adapter(IteratorType first, IteratorType last)
-        : ia(input_adapter(first, last)) {}
-
-    contiguous_bytes_input_adapter&& get()
-    {
-        return std::move(ia); // NOLINT(hicpp-move-const-arg,performance-move-const-arg)
-    }
-
-  private:
-    contiguous_bytes_input_adapter ia;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/input/json_sax.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cstddef>
-#include <string> // string
-#include <utility> // move
-#include <vector> // vector
-
-// #include <nlohmann/detail/exceptions.hpp>
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/string_concat.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-/*!
-@brief SAX interface
-
-This class describes the SAX interface used by @ref nlohmann::json::sax_parse.
-Each function is called in different situations while the input is parsed. The
-boolean return value informs the parser whether to continue processing the
-input.
-*/
-template<typename BasicJsonType>
-struct json_sax
-{
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-
-    /*!
-    @brief a null value was read
-    @return whether parsing should proceed
-    */
-    virtual bool null() = 0;
-
-    /*!
-    @brief a boolean value was read
-    @param[in] val  boolean value
-    @return whether parsing should proceed
-    */
-    virtual bool boolean(bool val) = 0;
-
-    /*!
-    @brief an integer number was read
-    @param[in] val  integer value
-    @return whether parsing should proceed
-    */
-    virtual bool number_integer(number_integer_t val) = 0;
-
-    /*!
-    @brief an unsigned integer number was read
-    @param[in] val  unsigned integer value
-    @return whether parsing should proceed
-    */
-    virtual bool number_unsigned(number_unsigned_t val) = 0;
-
-    /*!
-    @brief a floating-point number was read
-    @param[in] val  floating-point value
-    @param[in] s    raw token value
-    @return whether parsing should proceed
-    */
-    virtual bool number_float(number_float_t val, const string_t& s) = 0;
-
-    /*!
-    @brief a string value was read
-    @param[in] val  string value
-    @return whether parsing should proceed
-    @note It is safe to move the passed string value.
-    */
-    virtual bool string(string_t& val) = 0;
-
-    /*!
-    @brief a binary value was read
-    @param[in] val  binary value
-    @return whether parsing should proceed
-    @note It is safe to move the passed binary value.
-    */
-    virtual bool binary(binary_t& val) = 0;
-
-    /*!
-    @brief the beginning of an object was read
-    @param[in] elements  number of object elements or -1 if unknown
-    @return whether parsing should proceed
-    @note binary formats may report the number of elements
-    */
-    virtual bool start_object(std::size_t elements) = 0;
-
-    /*!
-    @brief an object key was read
-    @param[in] val  object key
-    @return whether parsing should proceed
-    @note It is safe to move the passed string.
-    */
-    virtual bool key(string_t& val) = 0;
-
-    /*!
-    @brief the end of an object was read
-    @return whether parsing should proceed
-    */
-    virtual bool end_object() = 0;
-
-    /*!
-    @brief the beginning of an array was read
-    @param[in] elements  number of array elements or -1 if unknown
-    @return whether parsing should proceed
-    @note binary formats may report the number of elements
-    */
-    virtual bool start_array(std::size_t elements) = 0;
-
-    /*!
-    @brief the end of an array was read
-    @return whether parsing should proceed
-    */
-    virtual bool end_array() = 0;
-
-    /*!
-    @brief a parse error occurred
-    @param[in] position    the position in the input where the error occurs
-    @param[in] last_token  the last read token
-    @param[in] ex          an exception object describing the error
-    @return whether parsing should proceed (must return false)
-    */
-    virtual bool parse_error(std::size_t position,
-                             const std::string& last_token,
-                             const detail::exception& ex) = 0;
-
-    json_sax() = default;
-    json_sax(const json_sax&) = default;
-    json_sax(json_sax&&) noexcept = default;
-    json_sax& operator=(const json_sax&) = default;
-    json_sax& operator=(json_sax&&) noexcept = default;
-    virtual ~json_sax() = default;
-};
-
-
-namespace detail
-{
-/*!
-@brief SAX implementation to create a JSON value from SAX events
-
-This class implements the @ref json_sax interface and processes the SAX events
-to create a JSON value which makes it basically a DOM parser. The structure or
-hierarchy of the JSON value is managed by the stack `ref_stack` which contains
-a pointer to the respective array or object for each recursion depth.
-
-After successful parsing, the value that is passed by reference to the
-constructor contains the parsed value.
-
-@tparam BasicJsonType  the JSON type
-*/
-template<typename BasicJsonType>
-class json_sax_dom_parser
-{
-  public:
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-
-    /*!
-    @param[in,out] r  reference to a JSON value that is manipulated while
-                       parsing
-    @param[in] allow_exceptions_  whether parse errors yield exceptions
-    */
-    explicit json_sax_dom_parser(BasicJsonType& r, const bool allow_exceptions_ = true)
-        : root(r), allow_exceptions(allow_exceptions_)
-    {}
-
-    // make class move-only
-    json_sax_dom_parser(const json_sax_dom_parser&) = delete;
-    json_sax_dom_parser(json_sax_dom_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    json_sax_dom_parser& operator=(const json_sax_dom_parser&) = delete;
-    json_sax_dom_parser& operator=(json_sax_dom_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    ~json_sax_dom_parser() = default;
-
-    bool null()
-    {
-        handle_value(nullptr);
-        return true;
-    }
-
-    bool boolean(bool val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_integer(number_integer_t val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_unsigned(number_unsigned_t val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_float(number_float_t val, const string_t& /*unused*/)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool string(string_t& val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool binary(binary_t& val)
-    {
-        handle_value(std::move(val));
-        return true;
-    }
-
-    bool start_object(std::size_t len)
-    {
-        ref_stack.push_back(handle_value(BasicJsonType::value_t::object));
-
-        if (JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
-        {
-            JSON_THROW(out_of_range::create(408, concat("excessive object size: ", std::to_string(len)), ref_stack.back()));
-        }
-
-        return true;
-    }
-
-    bool key(string_t& val)
-    {
-        JSON_ASSERT(!ref_stack.empty());
-        JSON_ASSERT(ref_stack.back()->is_object());
-
-        // add null at given key and store the reference for later
-        object_element = &(ref_stack.back()->m_value.object->operator[](val));
-        return true;
-    }
-
-    bool end_object()
-    {
-        JSON_ASSERT(!ref_stack.empty());
-        JSON_ASSERT(ref_stack.back()->is_object());
-
-        ref_stack.back()->set_parents();
-        ref_stack.pop_back();
-        return true;
-    }
-
-    bool start_array(std::size_t len)
-    {
-        ref_stack.push_back(handle_value(BasicJsonType::value_t::array));
-
-        if (JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
-        {
-            JSON_THROW(out_of_range::create(408, concat("excessive array size: ", std::to_string(len)), ref_stack.back()));
-        }
-
-        return true;
-    }
-
-    bool end_array()
-    {
-        JSON_ASSERT(!ref_stack.empty());
-        JSON_ASSERT(ref_stack.back()->is_array());
-
-        ref_stack.back()->set_parents();
-        ref_stack.pop_back();
-        return true;
-    }
-
-    template<class Exception>
-    bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/,
-                     const Exception& ex)
-    {
-        errored = true;
-        static_cast<void>(ex);
-        if (allow_exceptions)
-        {
-            JSON_THROW(ex);
-        }
-        return false;
-    }
-
-    constexpr bool is_errored() const
-    {
-        return errored;
-    }
-
-  private:
-    /*!
-    @invariant If the ref stack is empty, then the passed value will be the new
-               root.
-    @invariant If the ref stack contains a value, then it is an array or an
-               object to which we can add elements
-    */
-    template<typename Value>
-    JSON_HEDLEY_RETURNS_NON_NULL
-    BasicJsonType* handle_value(Value&& v)
-    {
-        if (ref_stack.empty())
-        {
-            root = BasicJsonType(std::forward<Value>(v));
-            return &root;
-        }
-
-        JSON_ASSERT(ref_stack.back()->is_array() || ref_stack.back()->is_object());
-
-        if (ref_stack.back()->is_array())
-        {
-            ref_stack.back()->m_value.array->emplace_back(std::forward<Value>(v));
-            return &(ref_stack.back()->m_value.array->back());
-        }
-
-        JSON_ASSERT(ref_stack.back()->is_object());
-        JSON_ASSERT(object_element);
-        *object_element = BasicJsonType(std::forward<Value>(v));
-        return object_element;
-    }
-
-    /// the parsed JSON value
-    BasicJsonType& root;
-    /// stack to model hierarchy of values
-    std::vector<BasicJsonType*> ref_stack {};
-    /// helper to hold the reference for the next object element
-    BasicJsonType* object_element = nullptr;
-    /// whether a syntax error occurred
-    bool errored = false;
-    /// whether to throw exceptions in case of errors
-    const bool allow_exceptions = true;
-};
-
-template<typename BasicJsonType>
-class json_sax_dom_callback_parser
-{
-  public:
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-    using parser_callback_t = typename BasicJsonType::parser_callback_t;
-    using parse_event_t = typename BasicJsonType::parse_event_t;
-
-    json_sax_dom_callback_parser(BasicJsonType& r,
-                                 const parser_callback_t cb,
-                                 const bool allow_exceptions_ = true)
-        : root(r), callback(cb), allow_exceptions(allow_exceptions_)
-    {
-        keep_stack.push_back(true);
-    }
-
-    // make class move-only
-    json_sax_dom_callback_parser(const json_sax_dom_callback_parser&) = delete;
-    json_sax_dom_callback_parser(json_sax_dom_callback_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    json_sax_dom_callback_parser& operator=(const json_sax_dom_callback_parser&) = delete;
-    json_sax_dom_callback_parser& operator=(json_sax_dom_callback_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    ~json_sax_dom_callback_parser() = default;
-
-    bool null()
-    {
-        handle_value(nullptr);
-        return true;
-    }
-
-    bool boolean(bool val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_integer(number_integer_t val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_unsigned(number_unsigned_t val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool number_float(number_float_t val, const string_t& /*unused*/)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool string(string_t& val)
-    {
-        handle_value(val);
-        return true;
-    }
-
-    bool binary(binary_t& val)
-    {
-        handle_value(std::move(val));
-        return true;
-    }
-
-    bool start_object(std::size_t len)
-    {
-        // check callback for object start
-        const bool keep = callback(static_cast<int>(ref_stack.size()), parse_event_t::object_start, discarded);
-        keep_stack.push_back(keep);
-
-        auto val = handle_value(BasicJsonType::value_t::object, true);
-        ref_stack.push_back(val.second);
-
-        // check object limit
-        if (ref_stack.back() && JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
-        {
-            JSON_THROW(out_of_range::create(408, concat("excessive object size: ", std::to_string(len)), ref_stack.back()));
-        }
-
-        return true;
-    }
-
-    bool key(string_t& val)
-    {
-        BasicJsonType k = BasicJsonType(val);
-
-        // check callback for key
-        const bool keep = callback(static_cast<int>(ref_stack.size()), parse_event_t::key, k);
-        key_keep_stack.push_back(keep);
-
-        // add discarded value at given key and store the reference for later
-        if (keep && ref_stack.back())
-        {
-            object_element = &(ref_stack.back()->m_value.object->operator[](val) = discarded);
-        }
-
-        return true;
-    }
-
-    bool end_object()
-    {
-        if (ref_stack.back())
-        {
-            if (!callback(static_cast<int>(ref_stack.size()) - 1, parse_event_t::object_end, *ref_stack.back()))
-            {
-                // discard object
-                *ref_stack.back() = discarded;
-            }
-            else
-            {
-                ref_stack.back()->set_parents();
-            }
-        }
-
-        JSON_ASSERT(!ref_stack.empty());
-        JSON_ASSERT(!keep_stack.empty());
-        ref_stack.pop_back();
-        keep_stack.pop_back();
-
-        if (!ref_stack.empty() && ref_stack.back() && ref_stack.back()->is_structured())
-        {
-            // remove discarded value
-            for (auto it = ref_stack.back()->begin(); it != ref_stack.back()->end(); ++it)
-            {
-                if (it->is_discarded())
-                {
-                    ref_stack.back()->erase(it);
-                    break;
-                }
-            }
-        }
-
-        return true;
-    }
-
-    bool start_array(std::size_t len)
-    {
-        const bool keep = callback(static_cast<int>(ref_stack.size()), parse_event_t::array_start, discarded);
-        keep_stack.push_back(keep);
-
-        auto val = handle_value(BasicJsonType::value_t::array, true);
-        ref_stack.push_back(val.second);
-
-        // check array limit
-        if (ref_stack.back() && JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
-        {
-            JSON_THROW(out_of_range::create(408, concat("excessive array size: ", std::to_string(len)), ref_stack.back()));
-        }
-
-        return true;
-    }
-
-    bool end_array()
-    {
-        bool keep = true;
-
-        if (ref_stack.back())
-        {
-            keep = callback(static_cast<int>(ref_stack.size()) - 1, parse_event_t::array_end, *ref_stack.back());
-            if (keep)
-            {
-                ref_stack.back()->set_parents();
-            }
-            else
-            {
-                // discard array
-                *ref_stack.back() = discarded;
-            }
-        }
-
-        JSON_ASSERT(!ref_stack.empty());
-        JSON_ASSERT(!keep_stack.empty());
-        ref_stack.pop_back();
-        keep_stack.pop_back();
-
-        // remove discarded value
-        if (!keep && !ref_stack.empty() && ref_stack.back()->is_array())
-        {
-            ref_stack.back()->m_value.array->pop_back();
-        }
-
-        return true;
-    }
-
-    template<class Exception>
-    bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/,
-                     const Exception& ex)
-    {
-        errored = true;
-        static_cast<void>(ex);
-        if (allow_exceptions)
-        {
-            JSON_THROW(ex);
-        }
-        return false;
-    }
-
-    constexpr bool is_errored() const
-    {
-        return errored;
-    }
-
-  private:
-    /*!
-    @param[in] v  value to add to the JSON value we build during parsing
-    @param[in] skip_callback  whether we should skip calling the callback
-               function; this is required after start_array() and
-               start_object() SAX events, because otherwise we would call the
-               callback function with an empty array or object, respectively.
-
-    @invariant If the ref stack is empty, then the passed value will be the new
-               root.
-    @invariant If the ref stack contains a value, then it is an array or an
-               object to which we can add elements
-
-    @return pair of boolean (whether value should be kept) and pointer (to the
-            passed value in the ref_stack hierarchy; nullptr if not kept)
-    */
-    template<typename Value>
-    std::pair<bool, BasicJsonType*> handle_value(Value&& v, const bool skip_callback = false)
-    {
-        JSON_ASSERT(!keep_stack.empty());
-
-        // do not handle this value if we know it would be added to a discarded
-        // container
-        if (!keep_stack.back())
-        {
-            return {false, nullptr};
-        }
-
-        // create value
-        auto value = BasicJsonType(std::forward<Value>(v));
-
-        // check callback
-        const bool keep = skip_callback || callback(static_cast<int>(ref_stack.size()), parse_event_t::value, value);
-
-        // do not handle this value if we just learnt it shall be discarded
-        if (!keep)
-        {
-            return {false, nullptr};
-        }
-
-        if (ref_stack.empty())
-        {
-            root = std::move(value);
-            return {true, &root};
-        }
-
-        // skip this value if we already decided to skip the parent
-        // (https://github.com/nlohmann/json/issues/971#issuecomment-413678360)
-        if (!ref_stack.back())
-        {
-            return {false, nullptr};
-        }
-
-        // we now only expect arrays and objects
-        JSON_ASSERT(ref_stack.back()->is_array() || ref_stack.back()->is_object());
-
-        // array
-        if (ref_stack.back()->is_array())
-        {
-            ref_stack.back()->m_value.array->emplace_back(std::move(value));
-            return {true, &(ref_stack.back()->m_value.array->back())};
-        }
-
-        // object
-        JSON_ASSERT(ref_stack.back()->is_object());
-        // check if we should store an element for the current key
-        JSON_ASSERT(!key_keep_stack.empty());
-        const bool store_element = key_keep_stack.back();
-        key_keep_stack.pop_back();
-
-        if (!store_element)
-        {
-            return {false, nullptr};
-        }
-
-        JSON_ASSERT(object_element);
-        *object_element = std::move(value);
-        return {true, object_element};
-    }
-
-    /// the parsed JSON value
-    BasicJsonType& root;
-    /// stack to model hierarchy of values
-    std::vector<BasicJsonType*> ref_stack {};
-    /// stack to manage which values to keep
-    std::vector<bool> keep_stack {};
-    /// stack to manage which object keys to keep
-    std::vector<bool> key_keep_stack {};
-    /// helper to hold the reference for the next object element
-    BasicJsonType* object_element = nullptr;
-    /// whether a syntax error occurred
-    bool errored = false;
-    /// callback function
-    const parser_callback_t callback = nullptr;
-    /// whether to throw exceptions in case of errors
-    const bool allow_exceptions = true;
-    /// a discarded value for the callback
-    BasicJsonType discarded = BasicJsonType::value_t::discarded;
-};
-
-template<typename BasicJsonType>
-class json_sax_acceptor
-{
-  public:
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-
-    bool null()
-    {
-        return true;
-    }
-
-    bool boolean(bool /*unused*/)
-    {
-        return true;
-    }
-
-    bool number_integer(number_integer_t /*unused*/)
-    {
-        return true;
-    }
-
-    bool number_unsigned(number_unsigned_t /*unused*/)
-    {
-        return true;
-    }
-
-    bool number_float(number_float_t /*unused*/, const string_t& /*unused*/)
-    {
-        return true;
-    }
-
-    bool string(string_t& /*unused*/)
-    {
-        return true;
-    }
-
-    bool binary(binary_t& /*unused*/)
-    {
-        return true;
-    }
-
-    bool start_object(std::size_t /*unused*/ = static_cast<std::size_t>(-1))
-    {
-        return true;
-    }
-
-    bool key(string_t& /*unused*/)
-    {
-        return true;
-    }
-
-    bool end_object()
-    {
-        return true;
-    }
-
-    bool start_array(std::size_t /*unused*/ = static_cast<std::size_t>(-1))
-    {
-        return true;
-    }
-
-    bool end_array()
-    {
-        return true;
-    }
-
-    bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/, const detail::exception& /*unused*/)
-    {
-        return false;
-    }
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/input/lexer.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <array> // array
-#include <clocale> // localeconv
-#include <cstddef> // size_t
-#include <cstdio> // snprintf
-#include <cstdlib> // strtof, strtod, strtold, strtoll, strtoull
-#include <initializer_list> // initializer_list
-#include <string> // char_traits, string
-#include <utility> // move
-#include <vector> // vector
-
-// #include <nlohmann/detail/input/input_adapters.hpp>
-
-// #include <nlohmann/detail/input/position_t.hpp>
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-///////////
-// lexer //
-///////////
-
-template<typename BasicJsonType>
-class lexer_base
-{
-  public:
-    /// token types for the parser
-    enum class token_type
-    {
-        uninitialized,    ///< indicating the scanner is uninitialized
-        literal_true,     ///< the `true` literal
-        literal_false,    ///< the `false` literal
-        literal_null,     ///< the `null` literal
-        value_string,     ///< a string -- use get_string() for actual value
-        value_unsigned,   ///< an unsigned integer -- use get_number_unsigned() for actual value
-        value_integer,    ///< a signed integer -- use get_number_integer() for actual value
-        value_float,      ///< an floating point number -- use get_number_float() for actual value
-        begin_array,      ///< the character for array begin `[`
-        begin_object,     ///< the character for object begin `{`
-        end_array,        ///< the character for array end `]`
-        end_object,       ///< the character for object end `}`
-        name_separator,   ///< the name separator `:`
-        value_separator,  ///< the value separator `,`
-        parse_error,      ///< indicating a parse error
-        end_of_input,     ///< indicating the end of the input buffer
-        literal_or_value  ///< a literal or the begin of a value (only for diagnostics)
-    };
-
-    /// return name of values of type token_type (only used for errors)
-    JSON_HEDLEY_RETURNS_NON_NULL
-    JSON_HEDLEY_CONST
-    static const char* token_type_name(const token_type t) noexcept
-    {
-        switch (t)
-        {
-            case token_type::uninitialized:
-                return "<uninitialized>";
-            case token_type::literal_true:
-                return "true literal";
-            case token_type::literal_false:
-                return "false literal";
-            case token_type::literal_null:
-                return "null literal";
-            case token_type::value_string:
-                return "string literal";
-            case token_type::value_unsigned:
-            case token_type::value_integer:
-            case token_type::value_float:
-                return "number literal";
-            case token_type::begin_array:
-                return "'['";
-            case token_type::begin_object:
-                return "'{'";
-            case token_type::end_array:
-                return "']'";
-            case token_type::end_object:
-                return "'}'";
-            case token_type::name_separator:
-                return "':'";
-            case token_type::value_separator:
-                return "','";
-            case token_type::parse_error:
-                return "<parse error>";
-            case token_type::end_of_input:
-                return "end of input";
-            case token_type::literal_or_value:
-                return "'[', '{', or a literal";
-            // LCOV_EXCL_START
-            default: // catch non-enum values
-                return "unknown token";
-                // LCOV_EXCL_STOP
-        }
-    }
-};
-/*!
-@brief lexical analysis
-
-This class organizes the lexical analysis during JSON deserialization.
-*/
-template<typename BasicJsonType, typename InputAdapterType>
-class lexer : public lexer_base<BasicJsonType>
-{
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using char_type = typename InputAdapterType::char_type;
-    using char_int_type = typename std::char_traits<char_type>::int_type;
-
-  public:
-    using token_type = typename lexer_base<BasicJsonType>::token_type;
-
-    explicit lexer(InputAdapterType&& adapter, bool ignore_comments_ = false) noexcept
-        : ia(std::move(adapter))
-        , ignore_comments(ignore_comments_)
-        , decimal_point_char(static_cast<char_int_type>(get_decimal_point()))
-    {}
-
-    // delete because of pointer members
-    lexer(const lexer&) = delete;
-    lexer(lexer&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    lexer& operator=(lexer&) = delete;
-    lexer& operator=(lexer&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    ~lexer() = default;
-
-  private:
-    /////////////////////
-    // locales
-    /////////////////////
-
-    /// return the locale-dependent decimal point
-    JSON_HEDLEY_PURE
-    static char get_decimal_point() noexcept
-    {
-        const auto* loc = localeconv();
-        JSON_ASSERT(loc != nullptr);
-        return (loc->decimal_point == nullptr) ? '.' : *(loc->decimal_point);
-    }
-
-    /////////////////////
-    // scan functions
-    /////////////////////
-
-    /*!
-    @brief get codepoint from 4 hex characters following `\u`
-
-    For input "\u c1 c2 c3 c4" the codepoint is:
-      (c1 * 0x1000) + (c2 * 0x0100) + (c3 * 0x0010) + c4
-    = (c1 << 12) + (c2 << 8) + (c3 << 4) + (c4 << 0)
-
-    Furthermore, the possible characters '0'..'9', 'A'..'F', and 'a'..'f'
-    must be converted to the integers 0x0..0x9, 0xA..0xF, 0xA..0xF, resp. The
-    conversion is done by subtracting the offset (0x30, 0x37, and 0x57)
-    between the ASCII value of the character and the desired integer value.
-
-    @return codepoint (0x0000..0xFFFF) or -1 in case of an error (e.g. EOF or
-            non-hex character)
-    */
-    int get_codepoint()
-    {
-        // this function only makes sense after reading `\u`
-        JSON_ASSERT(current == 'u');
-        int codepoint = 0;
-
-        const auto factors = { 12u, 8u, 4u, 0u };
-        for (const auto factor : factors)
-        {
-            get();
-
-            if (current >= '0' && current <= '9')
-            {
-                codepoint += static_cast<int>((static_cast<unsigned int>(current) - 0x30u) << factor);
-            }
-            else if (current >= 'A' && current <= 'F')
-            {
-                codepoint += static_cast<int>((static_cast<unsigned int>(current) - 0x37u) << factor);
-            }
-            else if (current >= 'a' && current <= 'f')
-            {
-                codepoint += static_cast<int>((static_cast<unsigned int>(current) - 0x57u) << factor);
-            }
-            else
-            {
-                return -1;
-            }
-        }
-
-        JSON_ASSERT(0x0000 <= codepoint && codepoint <= 0xFFFF);
-        return codepoint;
-    }
-
-    /*!
-    @brief check if the next byte(s) are inside a given range
-
-    Adds the current byte and, for each passed range, reads a new byte and
-    checks if it is inside the range. If a violation was detected, set up an
-    error message and return false. Otherwise, return true.
-
-    @param[in] ranges  list of integers; interpreted as list of pairs of
-                       inclusive lower and upper bound, respectively
-
-    @pre The passed list @a ranges must have 2, 4, or 6 elements; that is,
-         1, 2, or 3 pairs. This precondition is enforced by an assertion.
-
-    @return true if and only if no range violation was detected
-    */
-    bool next_byte_in_range(std::initializer_list<char_int_type> ranges)
-    {
-        JSON_ASSERT(ranges.size() == 2 || ranges.size() == 4 || ranges.size() == 6);
-        add(current);
-
-        for (auto range = ranges.begin(); range != ranges.end(); ++range)
-        {
-            get();
-            if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range)))
-            {
-                add(current);
-            }
-            else
-            {
-                error_message = "invalid string: ill-formed UTF-8 byte";
-                return false;
-            }
-        }
-
-        return true;
-    }
-
-    /*!
-    @brief scan a string literal
-
-    This function scans a string according to Sect. 7 of RFC 8259. While
-    scanning, bytes are escaped and copied into buffer token_buffer. Then the
-    function returns successfully, token_buffer is *not* null-terminated (as it
-    may contain \0 bytes), and token_buffer.size() is the number of bytes in the
-    string.
-
-    @return token_type::value_string if string could be successfully scanned,
-            token_type::parse_error otherwise
-
-    @note In case of errors, variable error_message contains a textual
-          description.
-    */
-    token_type scan_string()
-    {
-        // reset token_buffer (ignore opening quote)
-        reset();
-
-        // we entered the function by reading an open quote
-        JSON_ASSERT(current == '\"');
-
-        while (true)
-        {
-            // get next character
-            switch (get())
-            {
-                // end of file while parsing string
-                case std::char_traits<char_type>::eof():
-                {
-                    error_message = "invalid string: missing closing quote";
-                    return token_type::parse_error;
-                }
-
-                // closing quote
-                case '\"':
-                {
-                    return token_type::value_string;
-                }
-
-                // escapes
-                case '\\':
-                {
-                    switch (get())
-                    {
-                        // quotation mark
-                        case '\"':
-                            add('\"');
-                            break;
-                        // reverse solidus
-                        case '\\':
-                            add('\\');
-                            break;
-                        // solidus
-                        case '/':
-                            add('/');
-                            break;
-                        // backspace
-                        case 'b':
-                            add('\b');
-                            break;
-                        // form feed
-                        case 'f':
-                            add('\f');
-                            break;
-                        // line feed
-                        case 'n':
-                            add('\n');
-                            break;
-                        // carriage return
-                        case 'r':
-                            add('\r');
-                            break;
-                        // tab
-                        case 't':
-                            add('\t');
-                            break;
-
-                        // unicode escapes
-                        case 'u':
-                        {
-                            const int codepoint1 = get_codepoint();
-                            int codepoint = codepoint1; // start with codepoint1
-
-                            if (JSON_HEDLEY_UNLIKELY(codepoint1 == -1))
-                            {
-                                error_message = "invalid string: '\\u' must be followed by 4 hex digits";
-                                return token_type::parse_error;
-                            }
-
-                            // check if code point is a high surrogate
-                            if (0xD800 <= codepoint1 && codepoint1 <= 0xDBFF)
-                            {
-                                // expect next \uxxxx entry
-                                if (JSON_HEDLEY_LIKELY(get() == '\\' && get() == 'u'))
-                                {
-                                    const int codepoint2 = get_codepoint();
-
-                                    if (JSON_HEDLEY_UNLIKELY(codepoint2 == -1))
-                                    {
-                                        error_message = "invalid string: '\\u' must be followed by 4 hex digits";
-                                        return token_type::parse_error;
-                                    }
-
-                                    // check if codepoint2 is a low surrogate
-                                    if (JSON_HEDLEY_LIKELY(0xDC00 <= codepoint2 && codepoint2 <= 0xDFFF))
-                                    {
-                                        // overwrite codepoint
-                                        codepoint = static_cast<int>(
-                                                        // high surrogate occupies the most significant 22 bits
-                                                        (static_cast<unsigned int>(codepoint1) << 10u)
-                                                        // low surrogate occupies the least significant 15 bits
-                                                        + static_cast<unsigned int>(codepoint2)
-                                                        // there is still the 0xD800, 0xDC00 and 0x10000 noise
-                                                        // in the result, so we have to subtract with:
-                                                        // (0xD800 << 10) + DC00 - 0x10000 = 0x35FDC00
-                                                        - 0x35FDC00u);
-                                    }
-                                    else
-                                    {
-                                        error_message = "invalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFF";
-                                        return token_type::parse_error;
-                                    }
-                                }
-                                else
-                                {
-                                    error_message = "invalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFF";
-                                    return token_type::parse_error;
-                                }
-                            }
-                            else
-                            {
-                                if (JSON_HEDLEY_UNLIKELY(0xDC00 <= codepoint1 && codepoint1 <= 0xDFFF))
-                                {
-                                    error_message = "invalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFF";
-                                    return token_type::parse_error;
-                                }
-                            }
-
-                            // result of the above calculation yields a proper codepoint
-                            JSON_ASSERT(0x00 <= codepoint && codepoint <= 0x10FFFF);
-
-                            // translate codepoint into bytes
-                            if (codepoint < 0x80)
-                            {
-                                // 1-byte characters: 0xxxxxxx (ASCII)
-                                add(static_cast<char_int_type>(codepoint));
-                            }
-                            else if (codepoint <= 0x7FF)
-                            {
-                                // 2-byte characters: 110xxxxx 10xxxxxx
-                                add(static_cast<char_int_type>(0xC0u | (static_cast<unsigned int>(codepoint) >> 6u)));
-                                add(static_cast<char_int_type>(0x80u | (static_cast<unsigned int>(codepoint) & 0x3Fu)));
-                            }
-                            else if (codepoint <= 0xFFFF)
-                            {
-                                // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
-                                add(static_cast<char_int_type>(0xE0u | (static_cast<unsigned int>(codepoint) >> 12u)));
-                                add(static_cast<char_int_type>(0x80u | ((static_cast<unsigned int>(codepoint) >> 6u) & 0x3Fu)));
-                                add(static_cast<char_int_type>(0x80u | (static_cast<unsigned int>(codepoint) & 0x3Fu)));
-                            }
-                            else
-                            {
-                                // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
-                                add(static_cast<char_int_type>(0xF0u | (static_cast<unsigned int>(codepoint) >> 18u)));
-                                add(static_cast<char_int_type>(0x80u | ((static_cast<unsigned int>(codepoint) >> 12u) & 0x3Fu)));
-                                add(static_cast<char_int_type>(0x80u | ((static_cast<unsigned int>(codepoint) >> 6u) & 0x3Fu)));
-                                add(static_cast<char_int_type>(0x80u | (static_cast<unsigned int>(codepoint) & 0x3Fu)));
-                            }
-
-                            break;
-                        }
-
-                        // other characters after escape
-                        default:
-                            error_message = "invalid string: forbidden character after backslash";
-                            return token_type::parse_error;
-                    }
-
-                    break;
-                }
-
-                // invalid control characters
-                case 0x00:
-                {
-                    error_message = "invalid string: control character U+0000 (NUL) must be escaped to \\u0000";
-                    return token_type::parse_error;
-                }
-
-                case 0x01:
-                {
-                    error_message = "invalid string: control character U+0001 (SOH) must be escaped to \\u0001";
-                    return token_type::parse_error;
-                }
-
-                case 0x02:
-                {
-                    error_message = "invalid string: control character U+0002 (STX) must be escaped to \\u0002";
-                    return token_type::parse_error;
-                }
-
-                case 0x03:
-                {
-                    error_message = "invalid string: control character U+0003 (ETX) must be escaped to \\u0003";
-                    return token_type::parse_error;
-                }
-
-                case 0x04:
-                {
-                    error_message = "invalid string: control character U+0004 (EOT) must be escaped to \\u0004";
-                    return token_type::parse_error;
-                }
-
-                case 0x05:
-                {
-                    error_message = "invalid string: control character U+0005 (ENQ) must be escaped to \\u0005";
-                    return token_type::parse_error;
-                }
-
-                case 0x06:
-                {
-                    error_message = "invalid string: control character U+0006 (ACK) must be escaped to \\u0006";
-                    return token_type::parse_error;
-                }
-
-                case 0x07:
-                {
-                    error_message = "invalid string: control character U+0007 (BEL) must be escaped to \\u0007";
-                    return token_type::parse_error;
-                }
-
-                case 0x08:
-                {
-                    error_message = "invalid string: control character U+0008 (BS) must be escaped to \\u0008 or \\b";
-                    return token_type::parse_error;
-                }
-
-                case 0x09:
-                {
-                    error_message = "invalid string: control character U+0009 (HT) must be escaped to \\u0009 or \\t";
-                    return token_type::parse_error;
-                }
-
-                case 0x0A:
-                {
-                    error_message = "invalid string: control character U+000A (LF) must be escaped to \\u000A or \\n";
-                    return token_type::parse_error;
-                }
-
-                case 0x0B:
-                {
-                    error_message = "invalid string: control character U+000B (VT) must be escaped to \\u000B";
-                    return token_type::parse_error;
-                }
-
-                case 0x0C:
-                {
-                    error_message = "invalid string: control character U+000C (FF) must be escaped to \\u000C or \\f";
-                    return token_type::parse_error;
-                }
-
-                case 0x0D:
-                {
-                    error_message = "invalid string: control character U+000D (CR) must be escaped to \\u000D or \\r";
-                    return token_type::parse_error;
-                }
-
-                case 0x0E:
-                {
-                    error_message = "invalid string: control character U+000E (SO) must be escaped to \\u000E";
-                    return token_type::parse_error;
-                }
-
-                case 0x0F:
-                {
-                    error_message = "invalid string: control character U+000F (SI) must be escaped to \\u000F";
-                    return token_type::parse_error;
-                }
-
-                case 0x10:
-                {
-                    error_message = "invalid string: control character U+0010 (DLE) must be escaped to \\u0010";
-                    return token_type::parse_error;
-                }
-
-                case 0x11:
-                {
-                    error_message = "invalid string: control character U+0011 (DC1) must be escaped to \\u0011";
-                    return token_type::parse_error;
-                }
-
-                case 0x12:
-                {
-                    error_message = "invalid string: control character U+0012 (DC2) must be escaped to \\u0012";
-                    return token_type::parse_error;
-                }
-
-                case 0x13:
-                {
-                    error_message = "invalid string: control character U+0013 (DC3) must be escaped to \\u0013";
-                    return token_type::parse_error;
-                }
-
-                case 0x14:
-                {
-                    error_message = "invalid string: control character U+0014 (DC4) must be escaped to \\u0014";
-                    return token_type::parse_error;
-                }
-
-                case 0x15:
-                {
-                    error_message = "invalid string: control character U+0015 (NAK) must be escaped to \\u0015";
-                    return token_type::parse_error;
-                }
-
-                case 0x16:
-                {
-                    error_message = "invalid string: control character U+0016 (SYN) must be escaped to \\u0016";
-                    return token_type::parse_error;
-                }
-
-                case 0x17:
-                {
-                    error_message = "invalid string: control character U+0017 (ETB) must be escaped to \\u0017";
-                    return token_type::parse_error;
-                }
-
-                case 0x18:
-                {
-                    error_message = "invalid string: control character U+0018 (CAN) must be escaped to \\u0018";
-                    return token_type::parse_error;
-                }
-
-                case 0x19:
-                {
-                    error_message = "invalid string: control character U+0019 (EM) must be escaped to \\u0019";
-                    return token_type::parse_error;
-                }
-
-                case 0x1A:
-                {
-                    error_message = "invalid string: control character U+001A (SUB) must be escaped to \\u001A";
-                    return token_type::parse_error;
-                }
-
-                case 0x1B:
-                {
-                    error_message = "invalid string: control character U+001B (ESC) must be escaped to \\u001B";
-                    return token_type::parse_error;
-                }
-
-                case 0x1C:
-                {
-                    error_message = "invalid string: control character U+001C (FS) must be escaped to \\u001C";
-                    return token_type::parse_error;
-                }
-
-                case 0x1D:
-                {
-                    error_message = "invalid string: control character U+001D (GS) must be escaped to \\u001D";
-                    return token_type::parse_error;
-                }
-
-                case 0x1E:
-                {
-                    error_message = "invalid string: control character U+001E (RS) must be escaped to \\u001E";
-                    return token_type::parse_error;
-                }
-
-                case 0x1F:
-                {
-                    error_message = "invalid string: control character U+001F (US) must be escaped to \\u001F";
-                    return token_type::parse_error;
-                }
-
-                // U+0020..U+007F (except U+0022 (quote) and U+005C (backspace))
-                case 0x20:
-                case 0x21:
-                case 0x23:
-                case 0x24:
-                case 0x25:
-                case 0x26:
-                case 0x27:
-                case 0x28:
-                case 0x29:
-                case 0x2A:
-                case 0x2B:
-                case 0x2C:
-                case 0x2D:
-                case 0x2E:
-                case 0x2F:
-                case 0x30:
-                case 0x31:
-                case 0x32:
-                case 0x33:
-                case 0x34:
-                case 0x35:
-                case 0x36:
-                case 0x37:
-                case 0x38:
-                case 0x39:
-                case 0x3A:
-                case 0x3B:
-                case 0x3C:
-                case 0x3D:
-                case 0x3E:
-                case 0x3F:
-                case 0x40:
-                case 0x41:
-                case 0x42:
-                case 0x43:
-                case 0x44:
-                case 0x45:
-                case 0x46:
-                case 0x47:
-                case 0x48:
-                case 0x49:
-                case 0x4A:
-                case 0x4B:
-                case 0x4C:
-                case 0x4D:
-                case 0x4E:
-                case 0x4F:
-                case 0x50:
-                case 0x51:
-                case 0x52:
-                case 0x53:
-                case 0x54:
-                case 0x55:
-                case 0x56:
-                case 0x57:
-                case 0x58:
-                case 0x59:
-                case 0x5A:
-                case 0x5B:
-                case 0x5D:
-                case 0x5E:
-                case 0x5F:
-                case 0x60:
-                case 0x61:
-                case 0x62:
-                case 0x63:
-                case 0x64:
-                case 0x65:
-                case 0x66:
-                case 0x67:
-                case 0x68:
-                case 0x69:
-                case 0x6A:
-                case 0x6B:
-                case 0x6C:
-                case 0x6D:
-                case 0x6E:
-                case 0x6F:
-                case 0x70:
-                case 0x71:
-                case 0x72:
-                case 0x73:
-                case 0x74:
-                case 0x75:
-                case 0x76:
-                case 0x77:
-                case 0x78:
-                case 0x79:
-                case 0x7A:
-                case 0x7B:
-                case 0x7C:
-                case 0x7D:
-                case 0x7E:
-                case 0x7F:
-                {
-                    add(current);
-                    break;
-                }
-
-                // U+0080..U+07FF: bytes C2..DF 80..BF
-                case 0xC2:
-                case 0xC3:
-                case 0xC4:
-                case 0xC5:
-                case 0xC6:
-                case 0xC7:
-                case 0xC8:
-                case 0xC9:
-                case 0xCA:
-                case 0xCB:
-                case 0xCC:
-                case 0xCD:
-                case 0xCE:
-                case 0xCF:
-                case 0xD0:
-                case 0xD1:
-                case 0xD2:
-                case 0xD3:
-                case 0xD4:
-                case 0xD5:
-                case 0xD6:
-                case 0xD7:
-                case 0xD8:
-                case 0xD9:
-                case 0xDA:
-                case 0xDB:
-                case 0xDC:
-                case 0xDD:
-                case 0xDE:
-                case 0xDF:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!next_byte_in_range({0x80, 0xBF})))
-                    {
-                        return token_type::parse_error;
-                    }
-                    break;
-                }
-
-                // U+0800..U+0FFF: bytes E0 A0..BF 80..BF
-                case 0xE0:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0xA0, 0xBF, 0x80, 0xBF}))))
-                    {
-                        return token_type::parse_error;
-                    }
-                    break;
-                }
-
-                // U+1000..U+CFFF: bytes E1..EC 80..BF 80..BF
-                // U+E000..U+FFFF: bytes EE..EF 80..BF 80..BF
-                case 0xE1:
-                case 0xE2:
-                case 0xE3:
-                case 0xE4:
-                case 0xE5:
-                case 0xE6:
-                case 0xE7:
-                case 0xE8:
-                case 0xE9:
-                case 0xEA:
-                case 0xEB:
-                case 0xEC:
-                case 0xEE:
-                case 0xEF:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0xBF, 0x80, 0xBF}))))
-                    {
-                        return token_type::parse_error;
-                    }
-                    break;
-                }
-
-                // U+D000..U+D7FF: bytes ED 80..9F 80..BF
-                case 0xED:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0x9F, 0x80, 0xBF}))))
-                    {
-                        return token_type::parse_error;
-                    }
-                    break;
-                }
-
-                // U+10000..U+3FFFF F0 90..BF 80..BF 80..BF
-                case 0xF0:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x90, 0xBF, 0x80, 0xBF, 0x80, 0xBF}))))
-                    {
-                        return token_type::parse_error;
-                    }
-                    break;
-                }
-
-                // U+40000..U+FFFFF F1..F3 80..BF 80..BF 80..BF
-                case 0xF1:
-                case 0xF2:
-                case 0xF3:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0xBF, 0x80, 0xBF, 0x80, 0xBF}))))
-                    {
-                        return token_type::parse_error;
-                    }
-                    break;
-                }
-
-                // U+100000..U+10FFFF F4 80..8F 80..BF 80..BF
-                case 0xF4:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0x8F, 0x80, 0xBF, 0x80, 0xBF}))))
-                    {
-                        return token_type::parse_error;
-                    }
-                    break;
-                }
-
-                // remaining bytes (80..C1 and F5..FF) are ill-formed
-                default:
-                {
-                    error_message = "invalid string: ill-formed UTF-8 byte";
-                    return token_type::parse_error;
-                }
-            }
-        }
-    }
-
-    /*!
-     * @brief scan a comment
-     * @return whether comment could be scanned successfully
-     */
-    bool scan_comment()
-    {
-        switch (get())
-        {
-            // single-line comments skip input until a newline or EOF is read
-            case '/':
-            {
-                while (true)
-                {
-                    switch (get())
-                    {
-                        case '\n':
-                        case '\r':
-                        case std::char_traits<char_type>::eof():
-                        case '\0':
-                            return true;
-
-                        default:
-                            break;
-                    }
-                }
-            }
-
-            // multi-line comments skip input until */ is read
-            case '*':
-            {
-                while (true)
-                {
-                    switch (get())
-                    {
-                        case std::char_traits<char_type>::eof():
-                        case '\0':
-                        {
-                            error_message = "invalid comment; missing closing '*/'";
-                            return false;
-                        }
-
-                        case '*':
-                        {
-                            switch (get())
-                            {
-                                case '/':
-                                    return true;
-
-                                default:
-                                {
-                                    unget();
-                                    continue;
-                                }
-                            }
-                        }
-
-                        default:
-                            continue;
-                    }
-                }
-            }
-
-            // unexpected character after reading '/'
-            default:
-            {
-                error_message = "invalid comment; expecting '/' or '*' after '/'";
-                return false;
-            }
-        }
-    }
-
-    JSON_HEDLEY_NON_NULL(2)
-    static void strtof(float& f, const char* str, char** endptr) noexcept
-    {
-        f = std::strtof(str, endptr);
-    }
-
-    JSON_HEDLEY_NON_NULL(2)
-    static void strtof(double& f, const char* str, char** endptr) noexcept
-    {
-        f = std::strtod(str, endptr);
-    }
-
-    JSON_HEDLEY_NON_NULL(2)
-    static void strtof(long double& f, const char* str, char** endptr) noexcept
-    {
-        f = std::strtold(str, endptr);
-    }
-
-    /*!
-    @brief scan a number literal
-
-    This function scans a string according to Sect. 6 of RFC 8259.
-
-    The function is realized with a deterministic finite state machine derived
-    from the grammar described in RFC 8259. Starting in state "init", the
-    input is read and used to determined the next state. Only state "done"
-    accepts the number. State "error" is a trap state to model errors. In the
-    table below, "anything" means any character but the ones listed before.
-
-    state    | 0        | 1-9      | e E      | +       | -       | .        | anything
-    ---------|----------|----------|----------|---------|---------|----------|-----------
-    init     | zero     | any1     | [error]  | [error] | minus   | [error]  | [error]
-    minus    | zero     | any1     | [error]  | [error] | [error] | [error]  | [error]
-    zero     | done     | done     | exponent | done    | done    | decimal1 | done
-    any1     | any1     | any1     | exponent | done    | done    | decimal1 | done
-    decimal1 | decimal2 | decimal2 | [error]  | [error] | [error] | [error]  | [error]
-    decimal2 | decimal2 | decimal2 | exponent | done    | done    | done     | done
-    exponent | any2     | any2     | [error]  | sign    | sign    | [error]  | [error]
-    sign     | any2     | any2     | [error]  | [error] | [error] | [error]  | [error]
-    any2     | any2     | any2     | done     | done    | done    | done     | done
-
-    The state machine is realized with one label per state (prefixed with
-    "scan_number_") and `goto` statements between them. The state machine
-    contains cycles, but any cycle can be left when EOF is read. Therefore,
-    the function is guaranteed to terminate.
-
-    During scanning, the read bytes are stored in token_buffer. This string is
-    then converted to a signed integer, an unsigned integer, or a
-    floating-point number.
-
-    @return token_type::value_unsigned, token_type::value_integer, or
-            token_type::value_float if number could be successfully scanned,
-            token_type::parse_error otherwise
-
-    @note The scanner is independent of the current locale. Internally, the
-          locale's decimal point is used instead of `.` to work with the
-          locale-dependent converters.
-    */
-    token_type scan_number()  // lgtm [cpp/use-of-goto]
-    {
-        // reset token_buffer to store the number's bytes
-        reset();
-
-        // the type of the parsed number; initially set to unsigned; will be
-        // changed if minus sign, decimal point or exponent is read
-        token_type number_type = token_type::value_unsigned;
-
-        // state (init): we just found out we need to scan a number
-        switch (current)
-        {
-            case '-':
-            {
-                add(current);
-                goto scan_number_minus;
-            }
-
-            case '0':
-            {
-                add(current);
-                goto scan_number_zero;
-            }
-
-            case '1':
-            case '2':
-            case '3':
-            case '4':
-            case '5':
-            case '6':
-            case '7':
-            case '8':
-            case '9':
-            {
-                add(current);
-                goto scan_number_any1;
-            }
-
-            // all other characters are rejected outside scan_number()
-            default:            // LCOV_EXCL_LINE
-                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-        }
-
-scan_number_minus:
-        // state: we just parsed a leading minus sign
-        number_type = token_type::value_integer;
-        switch (get())
-        {
-            case '0':
-            {
-                add(current);
-                goto scan_number_zero;
-            }
-
-            case '1':
-            case '2':
-            case '3':
-            case '4':
-            case '5':
-            case '6':
-            case '7':
-            case '8':
-            case '9':
-            {
-                add(current);
-                goto scan_number_any1;
-            }
-
-            default:
-            {
-                error_message = "invalid number; expected digit after '-'";
-                return token_type::parse_error;
-            }
-        }
-
-scan_number_zero:
-        // state: we just parse a zero (maybe with a leading minus sign)
-        switch (get())
-        {
-            case '.':
-            {
-                add(decimal_point_char);
-                goto scan_number_decimal1;
-            }
-
-            case 'e':
-            case 'E':
-            {
-                add(current);
-                goto scan_number_exponent;
-            }
-
-            default:
-                goto scan_number_done;
-        }
-
-scan_number_any1:
-        // state: we just parsed a number 0-9 (maybe with a leading minus sign)
-        switch (get())
-        {
-            case '0':
-            case '1':
-            case '2':
-            case '3':
-            case '4':
-            case '5':
-            case '6':
-            case '7':
-            case '8':
-            case '9':
-            {
-                add(current);
-                goto scan_number_any1;
-            }
-
-            case '.':
-            {
-                add(decimal_point_char);
-                goto scan_number_decimal1;
-            }
-
-            case 'e':
-            case 'E':
-            {
-                add(current);
-                goto scan_number_exponent;
-            }
-
-            default:
-                goto scan_number_done;
-        }
-
-scan_number_decimal1:
-        // state: we just parsed a decimal point
-        number_type = token_type::value_float;
-        switch (get())
-        {
-            case '0':
-            case '1':
-            case '2':
-            case '3':
-            case '4':
-            case '5':
-            case '6':
-            case '7':
-            case '8':
-            case '9':
-            {
-                add(current);
-                goto scan_number_decimal2;
-            }
-
-            default:
-            {
-                error_message = "invalid number; expected digit after '.'";
-                return token_type::parse_error;
-            }
-        }
-
-scan_number_decimal2:
-        // we just parsed at least one number after a decimal point
-        switch (get())
-        {
-            case '0':
-            case '1':
-            case '2':
-            case '3':
-            case '4':
-            case '5':
-            case '6':
-            case '7':
-            case '8':
-            case '9':
-            {
-                add(current);
-                goto scan_number_decimal2;
-            }
-
-            case 'e':
-            case 'E':
-            {
-                add(current);
-                goto scan_number_exponent;
-            }
-
-            default:
-                goto scan_number_done;
-        }
-
-scan_number_exponent:
-        // we just parsed an exponent
-        number_type = token_type::value_float;
-        switch (get())
-        {
-            case '+':
-            case '-':
-            {
-                add(current);
-                goto scan_number_sign;
-            }
-
-            case '0':
-            case '1':
-            case '2':
-            case '3':
-            case '4':
-            case '5':
-            case '6':
-            case '7':
-            case '8':
-            case '9':
-            {
-                add(current);
-                goto scan_number_any2;
-            }
-
-            default:
-            {
-                error_message =
-                    "invalid number; expected '+', '-', or digit after exponent";
-                return token_type::parse_error;
-            }
-        }
-
-scan_number_sign:
-        // we just parsed an exponent sign
-        switch (get())
-        {
-            case '0':
-            case '1':
-            case '2':
-            case '3':
-            case '4':
-            case '5':
-            case '6':
-            case '7':
-            case '8':
-            case '9':
-            {
-                add(current);
-                goto scan_number_any2;
-            }
-
-            default:
-            {
-                error_message = "invalid number; expected digit after exponent sign";
-                return token_type::parse_error;
-            }
-        }
-
-scan_number_any2:
-        // we just parsed a number after the exponent or exponent sign
-        switch (get())
-        {
-            case '0':
-            case '1':
-            case '2':
-            case '3':
-            case '4':
-            case '5':
-            case '6':
-            case '7':
-            case '8':
-            case '9':
-            {
-                add(current);
-                goto scan_number_any2;
-            }
-
-            default:
-                goto scan_number_done;
-        }
-
-scan_number_done:
-        // unget the character after the number (we only read it to know that
-        // we are done scanning a number)
-        unget();
-
-        char* endptr = nullptr; // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-        errno = 0;
-
-        // try to parse integers first and fall back to floats
-        if (number_type == token_type::value_unsigned)
-        {
-            const auto x = std::strtoull(token_buffer.data(), &endptr, 10);
-
-            // we checked the number format before
-            JSON_ASSERT(endptr == token_buffer.data() + token_buffer.size());
-
-            if (errno == 0)
-            {
-                value_unsigned = static_cast<number_unsigned_t>(x);
-                if (value_unsigned == x)
-                {
-                    return token_type::value_unsigned;
-                }
-            }
-        }
-        else if (number_type == token_type::value_integer)
-        {
-            const auto x = std::strtoll(token_buffer.data(), &endptr, 10);
-
-            // we checked the number format before
-            JSON_ASSERT(endptr == token_buffer.data() + token_buffer.size());
-
-            if (errno == 0)
-            {
-                value_integer = static_cast<number_integer_t>(x);
-                if (value_integer == x)
-                {
-                    return token_type::value_integer;
-                }
-            }
-        }
-
-        // this code is reached if we parse a floating-point number or if an
-        // integer conversion above failed
-        strtof(value_float, token_buffer.data(), &endptr);
-
-        // we checked the number format before
-        JSON_ASSERT(endptr == token_buffer.data() + token_buffer.size());
-
-        return token_type::value_float;
-    }
-
-    /*!
-    @param[in] literal_text  the literal text to expect
-    @param[in] length        the length of the passed literal text
-    @param[in] return_type   the token type to return on success
-    */
-    JSON_HEDLEY_NON_NULL(2)
-    token_type scan_literal(const char_type* literal_text, const std::size_t length,
-                            token_type return_type)
-    {
-        JSON_ASSERT(std::char_traits<char_type>::to_char_type(current) == literal_text[0]);
-        for (std::size_t i = 1; i < length; ++i)
-        {
-            if (JSON_HEDLEY_UNLIKELY(std::char_traits<char_type>::to_char_type(get()) != literal_text[i]))
-            {
-                error_message = "invalid literal";
-                return token_type::parse_error;
-            }
-        }
-        return return_type;
-    }
-
-    /////////////////////
-    // input management
-    /////////////////////
-
-    /// reset token_buffer; current character is beginning of token
-    void reset() noexcept
-    {
-        token_buffer.clear();
-        token_string.clear();
-        token_string.push_back(std::char_traits<char_type>::to_char_type(current));
-    }
-
-    /*
-    @brief get next character from the input
-
-    This function provides the interface to the used input adapter. It does
-    not throw in case the input reached EOF, but returns a
-    `std::char_traits<char>::eof()` in that case.  Stores the scanned characters
-    for use in error messages.
-
-    @return character read from the input
-    */
-    char_int_type get()
-    {
-        ++position.chars_read_total;
-        ++position.chars_read_current_line;
-
-        if (next_unget)
-        {
-            // just reset the next_unget variable and work with current
-            next_unget = false;
-        }
-        else
-        {
-            current = ia.get_character();
-        }
-
-        if (JSON_HEDLEY_LIKELY(current != std::char_traits<char_type>::eof()))
-        {
-            token_string.push_back(std::char_traits<char_type>::to_char_type(current));
-        }
-
-        if (current == '\n')
-        {
-            ++position.lines_read;
-            position.chars_read_current_line = 0;
-        }
-
-        return current;
-    }
-
-    /*!
-    @brief unget current character (read it again on next get)
-
-    We implement unget by setting variable next_unget to true. The input is not
-    changed - we just simulate ungetting by modifying chars_read_total,
-    chars_read_current_line, and token_string. The next call to get() will
-    behave as if the unget character is read again.
-    */
-    void unget()
-    {
-        next_unget = true;
-
-        --position.chars_read_total;
-
-        // in case we "unget" a newline, we have to also decrement the lines_read
-        if (position.chars_read_current_line == 0)
-        {
-            if (position.lines_read > 0)
-            {
-                --position.lines_read;
-            }
-        }
-        else
-        {
-            --position.chars_read_current_line;
-        }
-
-        if (JSON_HEDLEY_LIKELY(current != std::char_traits<char_type>::eof()))
-        {
-            JSON_ASSERT(!token_string.empty());
-            token_string.pop_back();
-        }
-    }
-
-    /// add a character to token_buffer
-    void add(char_int_type c)
-    {
-        token_buffer.push_back(static_cast<typename string_t::value_type>(c));
-    }
-
-  public:
-    /////////////////////
-    // value getters
-    /////////////////////
-
-    /// return integer value
-    constexpr number_integer_t get_number_integer() const noexcept
-    {
-        return value_integer;
-    }
-
-    /// return unsigned integer value
-    constexpr number_unsigned_t get_number_unsigned() const noexcept
-    {
-        return value_unsigned;
-    }
-
-    /// return floating-point value
-    constexpr number_float_t get_number_float() const noexcept
-    {
-        return value_float;
-    }
-
-    /// return current string value (implicitly resets the token; useful only once)
-    string_t& get_string()
-    {
-        return token_buffer;
-    }
-
-    /////////////////////
-    // diagnostics
-    /////////////////////
-
-    /// return position of last read token
-    constexpr position_t get_position() const noexcept
-    {
-        return position;
-    }
-
-    /// return the last read token (for errors only).  Will never contain EOF
-    /// (an arbitrary value that is not a valid char value, often -1), because
-    /// 255 may legitimately occur.  May contain NUL, which should be escaped.
-    std::string get_token_string() const
-    {
-        // escape control characters
-        std::string result;
-        for (const auto c : token_string)
-        {
-            if (static_cast<unsigned char>(c) <= '\x1F')
-            {
-                // escape control characters
-                std::array<char, 9> cs{{}};
-                static_cast<void>((std::snprintf)(cs.data(), cs.size(), "<U+%.4X>", static_cast<unsigned char>(c))); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-                result += cs.data();
-            }
-            else
-            {
-                // add character as is
-                result.push_back(static_cast<std::string::value_type>(c));
-            }
-        }
-
-        return result;
-    }
-
-    /// return syntax error message
-    JSON_HEDLEY_RETURNS_NON_NULL
-    constexpr const char* get_error_message() const noexcept
-    {
-        return error_message;
-    }
-
-    /////////////////////
-    // actual scanner
-    /////////////////////
-
-    /*!
-    @brief skip the UTF-8 byte order mark
-    @return true iff there is no BOM or the correct BOM has been skipped
-    */
-    bool skip_bom()
-    {
-        if (get() == 0xEF)
-        {
-            // check if we completely parse the BOM
-            return get() == 0xBB && get() == 0xBF;
-        }
-
-        // the first character is not the beginning of the BOM; unget it to
-        // process is later
-        unget();
-        return true;
-    }
-
-    void skip_whitespace()
-    {
-        do
-        {
-            get();
-        }
-        while (current == ' ' || current == '\t' || current == '\n' || current == '\r');
-    }
-
-    token_type scan()
-    {
-        // initially, skip the BOM
-        if (position.chars_read_total == 0 && !skip_bom())
-        {
-            error_message = "invalid BOM; must be 0xEF 0xBB 0xBF if given";
-            return token_type::parse_error;
-        }
-
-        // read next character and ignore whitespace
-        skip_whitespace();
-
-        // ignore comments
-        while (ignore_comments && current == '/')
-        {
-            if (!scan_comment())
-            {
-                return token_type::parse_error;
-            }
-
-            // skip following whitespace
-            skip_whitespace();
-        }
-
-        switch (current)
-        {
-            // structural characters
-            case '[':
-                return token_type::begin_array;
-            case ']':
-                return token_type::end_array;
-            case '{':
-                return token_type::begin_object;
-            case '}':
-                return token_type::end_object;
-            case ':':
-                return token_type::name_separator;
-            case ',':
-                return token_type::value_separator;
-
-            // literals
-            case 't':
-            {
-                std::array<char_type, 4> true_literal = {{static_cast<char_type>('t'), static_cast<char_type>('r'), static_cast<char_type>('u'), static_cast<char_type>('e')}};
-                return scan_literal(true_literal.data(), true_literal.size(), token_type::literal_true);
-            }
-            case 'f':
-            {
-                std::array<char_type, 5> false_literal = {{static_cast<char_type>('f'), static_cast<char_type>('a'), static_cast<char_type>('l'), static_cast<char_type>('s'), static_cast<char_type>('e')}};
-                return scan_literal(false_literal.data(), false_literal.size(), token_type::literal_false);
-            }
-            case 'n':
-            {
-                std::array<char_type, 4> null_literal = {{static_cast<char_type>('n'), static_cast<char_type>('u'), static_cast<char_type>('l'), static_cast<char_type>('l')}};
-                return scan_literal(null_literal.data(), null_literal.size(), token_type::literal_null);
-            }
-
-            // string
-            case '\"':
-                return scan_string();
-
-            // number
-            case '-':
-            case '0':
-            case '1':
-            case '2':
-            case '3':
-            case '4':
-            case '5':
-            case '6':
-            case '7':
-            case '8':
-            case '9':
-                return scan_number();
-
-            // end of input (the null byte is needed when parsing from
-            // string literals)
-            case '\0':
-            case std::char_traits<char_type>::eof():
-                return token_type::end_of_input;
-
-            // error
-            default:
-                error_message = "invalid literal";
-                return token_type::parse_error;
-        }
-    }
-
-  private:
-    /// input adapter
-    InputAdapterType ia;
-
-    /// whether comments should be ignored (true) or signaled as errors (false)
-    const bool ignore_comments = false;
-
-    /// the current character
-    char_int_type current = std::char_traits<char_type>::eof();
-
-    /// whether the next get() call should just return current
-    bool next_unget = false;
-
-    /// the start position of the current token
-    position_t position {};
-
-    /// raw input token string (for error messages)
-    std::vector<char_type> token_string {};
-
-    /// buffer for variable-length tokens (numbers, strings)
-    string_t token_buffer {};
-
-    /// a description of occurred lexer errors
-    const char* error_message = "";
-
-    // number values
-    number_integer_t value_integer = 0;
-    number_unsigned_t value_unsigned = 0;
-    number_float_t value_float = 0;
-
-    /// the decimal point
-    const char_int_type decimal_point_char = '.';
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/meta/is_sax.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cstdint> // size_t
-#include <utility> // declval
-#include <string> // string
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-// #include <nlohmann/detail/meta/detected.hpp>
-
-// #include <nlohmann/detail/meta/type_traits.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename T>
-using null_function_t = decltype(std::declval<T&>().null());
-
-template<typename T>
-using boolean_function_t =
-    decltype(std::declval<T&>().boolean(std::declval<bool>()));
-
-template<typename T, typename Integer>
-using number_integer_function_t =
-    decltype(std::declval<T&>().number_integer(std::declval<Integer>()));
-
-template<typename T, typename Unsigned>
-using number_unsigned_function_t =
-    decltype(std::declval<T&>().number_unsigned(std::declval<Unsigned>()));
-
-template<typename T, typename Float, typename String>
-using number_float_function_t = decltype(std::declval<T&>().number_float(
-                                    std::declval<Float>(), std::declval<const String&>()));
-
-template<typename T, typename String>
-using string_function_t =
-    decltype(std::declval<T&>().string(std::declval<String&>()));
-
-template<typename T, typename Binary>
-using binary_function_t =
-    decltype(std::declval<T&>().binary(std::declval<Binary&>()));
-
-template<typename T>
-using start_object_function_t =
-    decltype(std::declval<T&>().start_object(std::declval<std::size_t>()));
-
-template<typename T, typename String>
-using key_function_t =
-    decltype(std::declval<T&>().key(std::declval<String&>()));
-
-template<typename T>
-using end_object_function_t = decltype(std::declval<T&>().end_object());
-
-template<typename T>
-using start_array_function_t =
-    decltype(std::declval<T&>().start_array(std::declval<std::size_t>()));
-
-template<typename T>
-using end_array_function_t = decltype(std::declval<T&>().end_array());
-
-template<typename T, typename Exception>
-using parse_error_function_t = decltype(std::declval<T&>().parse_error(
-        std::declval<std::size_t>(), std::declval<const std::string&>(),
-        std::declval<const Exception&>()));
-
-template<typename SAX, typename BasicJsonType>
-struct is_sax
-{
-  private:
-    static_assert(is_basic_json<BasicJsonType>::value,
-                  "BasicJsonType must be of type basic_json<...>");
-
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-    using exception_t = typename BasicJsonType::exception;
-
-  public:
-    static constexpr bool value =
-        is_detected_exact<bool, null_function_t, SAX>::value &&
-        is_detected_exact<bool, boolean_function_t, SAX>::value &&
-        is_detected_exact<bool, number_integer_function_t, SAX, number_integer_t>::value &&
-        is_detected_exact<bool, number_unsigned_function_t, SAX, number_unsigned_t>::value &&
-        is_detected_exact<bool, number_float_function_t, SAX, number_float_t, string_t>::value &&
-        is_detected_exact<bool, string_function_t, SAX, string_t>::value &&
-        is_detected_exact<bool, binary_function_t, SAX, binary_t>::value &&
-        is_detected_exact<bool, start_object_function_t, SAX>::value &&
-        is_detected_exact<bool, key_function_t, SAX, string_t>::value &&
-        is_detected_exact<bool, end_object_function_t, SAX>::value &&
-        is_detected_exact<bool, start_array_function_t, SAX>::value &&
-        is_detected_exact<bool, end_array_function_t, SAX>::value &&
-        is_detected_exact<bool, parse_error_function_t, SAX, exception_t>::value;
-};
-
-template<typename SAX, typename BasicJsonType>
-struct is_sax_static_asserts
-{
-  private:
-    static_assert(is_basic_json<BasicJsonType>::value,
-                  "BasicJsonType must be of type basic_json<...>");
-
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-    using exception_t = typename BasicJsonType::exception;
-
-  public:
-    static_assert(is_detected_exact<bool, null_function_t, SAX>::value,
-                  "Missing/invalid function: bool null()");
-    static_assert(is_detected_exact<bool, boolean_function_t, SAX>::value,
-                  "Missing/invalid function: bool boolean(bool)");
-    static_assert(is_detected_exact<bool, boolean_function_t, SAX>::value,
-                  "Missing/invalid function: bool boolean(bool)");
-    static_assert(
-        is_detected_exact<bool, number_integer_function_t, SAX,
-        number_integer_t>::value,
-        "Missing/invalid function: bool number_integer(number_integer_t)");
-    static_assert(
-        is_detected_exact<bool, number_unsigned_function_t, SAX,
-        number_unsigned_t>::value,
-        "Missing/invalid function: bool number_unsigned(number_unsigned_t)");
-    static_assert(is_detected_exact<bool, number_float_function_t, SAX,
-                  number_float_t, string_t>::value,
-                  "Missing/invalid function: bool number_float(number_float_t, const string_t&)");
-    static_assert(
-        is_detected_exact<bool, string_function_t, SAX, string_t>::value,
-        "Missing/invalid function: bool string(string_t&)");
-    static_assert(
-        is_detected_exact<bool, binary_function_t, SAX, binary_t>::value,
-        "Missing/invalid function: bool binary(binary_t&)");
-    static_assert(is_detected_exact<bool, start_object_function_t, SAX>::value,
-                  "Missing/invalid function: bool start_object(std::size_t)");
-    static_assert(is_detected_exact<bool, key_function_t, SAX, string_t>::value,
-                  "Missing/invalid function: bool key(string_t&)");
-    static_assert(is_detected_exact<bool, end_object_function_t, SAX>::value,
-                  "Missing/invalid function: bool end_object()");
-    static_assert(is_detected_exact<bool, start_array_function_t, SAX>::value,
-                  "Missing/invalid function: bool start_array(std::size_t)");
-    static_assert(is_detected_exact<bool, end_array_function_t, SAX>::value,
-                  "Missing/invalid function: bool end_array()");
-    static_assert(
-        is_detected_exact<bool, parse_error_function_t, SAX, exception_t>::value,
-        "Missing/invalid function: bool parse_error(std::size_t, const "
-        "std::string&, const exception&)");
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/meta/type_traits.hpp>
-
-// #include <nlohmann/detail/string_concat.hpp>
-
-// #include <nlohmann/detail/value_t.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/// how to treat CBOR tags
-enum class cbor_tag_handler_t
-{
-    error,   ///< throw a parse_error exception in case of a tag
-    ignore,  ///< ignore tags
-    store    ///< store tags as binary type
-};
-
-/*!
-@brief determine system byte order
-
-@return true if and only if system's byte order is little endian
-
-@note from https://stackoverflow.com/a/1001328/266378
-*/
-static inline bool little_endianness(int num = 1) noexcept
-{
-    return *reinterpret_cast<char*>(&num) == 1;
-}
-
-
-///////////////////
-// binary reader //
-///////////////////
-
-/*!
-@brief deserialization of CBOR, MessagePack, and UBJSON values
-*/
-template<typename BasicJsonType, typename InputAdapterType, typename SAX = json_sax_dom_parser<BasicJsonType>>
-class binary_reader
-{
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-    using json_sax_t = SAX;
-    using char_type = typename InputAdapterType::char_type;
-    using char_int_type = typename std::char_traits<char_type>::int_type;
-
-  public:
-    /*!
-    @brief create a binary reader
-
-    @param[in] adapter  input adapter to read from
-    */
-    explicit binary_reader(InputAdapterType&& adapter, const input_format_t format = input_format_t::json) noexcept : ia(std::move(adapter)), input_format(format)
-    {
-        (void)detail::is_sax_static_asserts<SAX, BasicJsonType> {};
-    }
-
-    // make class move-only
-    binary_reader(const binary_reader&) = delete;
-    binary_reader(binary_reader&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    binary_reader& operator=(const binary_reader&) = delete;
-    binary_reader& operator=(binary_reader&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
-    ~binary_reader() = default;
-
-    /*!
-    @param[in] format  the binary format to parse
-    @param[in] sax_    a SAX event processor
-    @param[in] strict  whether to expect the input to be consumed completed
-    @param[in] tag_handler  how to treat CBOR tags
-
-    @return whether parsing was successful
-    */
-    JSON_HEDLEY_NON_NULL(3)
-    bool sax_parse(const input_format_t format,
-                   json_sax_t* sax_,
-                   const bool strict = true,
-                   const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error)
-    {
-        sax = sax_;
-        bool result = false;
-
-        switch (format)
-        {
-            case input_format_t::bson:
-                result = parse_bson_internal();
-                break;
-
-            case input_format_t::cbor:
-                result = parse_cbor_internal(true, tag_handler);
-                break;
-
-            case input_format_t::msgpack:
-                result = parse_msgpack_internal();
-                break;
-
-            case input_format_t::ubjson:
-            case input_format_t::bjdata:
-                result = parse_ubjson_internal();
-                break;
-
-            case input_format_t::json: // LCOV_EXCL_LINE
-            default:            // LCOV_EXCL_LINE
-                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-        }
-
-        // strict mode: next byte must be EOF
-        if (result && strict)
-        {
-            if (input_format == input_format_t::ubjson || input_format == input_format_t::bjdata)
-            {
-                get_ignore_noop();
-            }
-            else
-            {
-                get();
-            }
-
-            if (JSON_HEDLEY_UNLIKELY(current != std::char_traits<char_type>::eof()))
-            {
-                return sax->parse_error(chars_read, get_token_string(), parse_error::create(110, chars_read,
-                                        exception_message(input_format, concat("expected end of input; last byte: 0x", get_token_string()), "value"), nullptr));
-            }
-        }
-
-        return result;
-    }
-
-  private:
-    //////////
-    // BSON //
-    //////////
-
-    /*!
-    @brief Reads in a BSON-object and passes it to the SAX-parser.
-    @return whether a valid BSON-value was passed to the SAX parser
-    */
-    bool parse_bson_internal()
-    {
-        std::int32_t document_size{};
-        get_number<std::int32_t, true>(input_format_t::bson, document_size);
-
-        if (JSON_HEDLEY_UNLIKELY(!sax->start_object(static_cast<std::size_t>(-1))))
-        {
-            return false;
-        }
-
-        if (JSON_HEDLEY_UNLIKELY(!parse_bson_element_list(/*is_array*/false)))
-        {
-            return false;
-        }
-
-        return sax->end_object();
-    }
-
-    /*!
-    @brief Parses a C-style string from the BSON input.
-    @param[in,out] result  A reference to the string variable where the read
-                            string is to be stored.
-    @return `true` if the \x00-byte indicating the end of the string was
-             encountered before the EOF; false` indicates an unexpected EOF.
-    */
-    bool get_bson_cstr(string_t& result)
-    {
-        auto out = std::back_inserter(result);
-        while (true)
-        {
-            get();
-            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::bson, "cstring")))
-            {
-                return false;
-            }
-            if (current == 0x00)
-            {
-                return true;
-            }
-            *out++ = static_cast<typename string_t::value_type>(current);
-        }
-    }
-
-    /*!
-    @brief Parses a zero-terminated string of length @a len from the BSON
-           input.
-    @param[in] len  The length (including the zero-byte at the end) of the
-                    string to be read.
-    @param[in,out] result  A reference to the string variable where the read
-                            string is to be stored.
-    @tparam NumberType The type of the length @a len
-    @pre len >= 1
-    @return `true` if the string was successfully parsed
-    */
-    template<typename NumberType>
-    bool get_bson_string(const NumberType len, string_t& result)
-    {
-        if (JSON_HEDLEY_UNLIKELY(len < 1))
-        {
-            auto last_token = get_token_string();
-            return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
-                                    exception_message(input_format_t::bson, concat("string length must be at least 1, is ", std::to_string(len)), "string"), nullptr));
-        }
-
-        return get_string(input_format_t::bson, len - static_cast<NumberType>(1), result) && get() != std::char_traits<char_type>::eof();
-    }
-
-    /*!
-    @brief Parses a byte array input of length @a len from the BSON input.
-    @param[in] len  The length of the byte array to be read.
-    @param[in,out] result  A reference to the binary variable where the read
-                            array is to be stored.
-    @tparam NumberType The type of the length @a len
-    @pre len >= 0
-    @return `true` if the byte array was successfully parsed
-    */
-    template<typename NumberType>
-    bool get_bson_binary(const NumberType len, binary_t& result)
-    {
-        if (JSON_HEDLEY_UNLIKELY(len < 0))
-        {
-            auto last_token = get_token_string();
-            return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
-                                    exception_message(input_format_t::bson, concat("byte array length cannot be negative, is ", std::to_string(len)), "binary"), nullptr));
-        }
-
-        // All BSON binary values have a subtype
-        std::uint8_t subtype{};
-        get_number<std::uint8_t>(input_format_t::bson, subtype);
-        result.set_subtype(subtype);
-
-        return get_binary(input_format_t::bson, len, result);
-    }
-
-    /*!
-    @brief Read a BSON document element of the given @a element_type.
-    @param[in] element_type The BSON element type, c.f. http://bsonspec.org/spec.html
-    @param[in] element_type_parse_position The position in the input stream,
-               where the `element_type` was read.
-    @warning Not all BSON element types are supported yet. An unsupported
-             @a element_type will give rise to a parse_error.114:
-             Unsupported BSON record type 0x...
-    @return whether a valid BSON-object/array was passed to the SAX parser
-    */
-    bool parse_bson_element_internal(const char_int_type element_type,
-                                     const std::size_t element_type_parse_position)
-    {
-        switch (element_type)
-        {
-            case 0x01: // double
-            {
-                double number{};
-                return get_number<double, true>(input_format_t::bson, number) && sax->number_float(static_cast<number_float_t>(number), "");
-            }
-
-            case 0x02: // string
-            {
-                std::int32_t len{};
-                string_t value;
-                return get_number<std::int32_t, true>(input_format_t::bson, len) && get_bson_string(len, value) && sax->string(value);
-            }
-
-            case 0x03: // object
-            {
-                return parse_bson_internal();
-            }
-
-            case 0x04: // array
-            {
-                return parse_bson_array();
-            }
-
-            case 0x05: // binary
-            {
-                std::int32_t len{};
-                binary_t value;
-                return get_number<std::int32_t, true>(input_format_t::bson, len) && get_bson_binary(len, value) && sax->binary(value);
-            }
-
-            case 0x08: // boolean
-            {
-                return sax->boolean(get() != 0);
-            }
-
-            case 0x0A: // null
-            {
-                return sax->null();
-            }
-
-            case 0x10: // int32
-            {
-                std::int32_t value{};
-                return get_number<std::int32_t, true>(input_format_t::bson, value) && sax->number_integer(value);
-            }
-
-            case 0x12: // int64
-            {
-                std::int64_t value{};
-                return get_number<std::int64_t, true>(input_format_t::bson, value) && sax->number_integer(value);
-            }
-
-            default: // anything else not supported (yet)
-            {
-                std::array<char, 3> cr{{}};
-                static_cast<void>((std::snprintf)(cr.data(), cr.size(), "%.2hhX", static_cast<unsigned char>(element_type))); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-                std::string cr_str{cr.data()};
-                return sax->parse_error(element_type_parse_position, cr_str,
-                                        parse_error::create(114, element_type_parse_position, concat("Unsupported BSON record type 0x", cr_str), nullptr));
-            }
-        }
-    }
-
-    /*!
-    @brief Read a BSON element list (as specified in the BSON-spec)
-
-    The same binary layout is used for objects and arrays, hence it must be
-    indicated with the argument @a is_array which one is expected
-    (true --> array, false --> object).
-
-    @param[in] is_array Determines if the element list being read is to be
-                        treated as an object (@a is_array == false), or as an
-                        array (@a is_array == true).
-    @return whether a valid BSON-object/array was passed to the SAX parser
-    */
-    bool parse_bson_element_list(const bool is_array)
-    {
-        string_t key;
-
-        while (auto element_type = get())
-        {
-            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::bson, "element list")))
-            {
-                return false;
-            }
-
-            const std::size_t element_type_parse_position = chars_read;
-            if (JSON_HEDLEY_UNLIKELY(!get_bson_cstr(key)))
-            {
-                return false;
-            }
-
-            if (!is_array && !sax->key(key))
-            {
-                return false;
-            }
-
-            if (JSON_HEDLEY_UNLIKELY(!parse_bson_element_internal(element_type, element_type_parse_position)))
-            {
-                return false;
-            }
-
-            // get_bson_cstr only appends
-            key.clear();
-        }
-
-        return true;
-    }
-
-    /*!
-    @brief Reads an array from the BSON input and passes it to the SAX-parser.
-    @return whether a valid BSON-array was passed to the SAX parser
-    */
-    bool parse_bson_array()
-    {
-        std::int32_t document_size{};
-        get_number<std::int32_t, true>(input_format_t::bson, document_size);
-
-        if (JSON_HEDLEY_UNLIKELY(!sax->start_array(static_cast<std::size_t>(-1))))
-        {
-            return false;
-        }
-
-        if (JSON_HEDLEY_UNLIKELY(!parse_bson_element_list(/*is_array*/true)))
-        {
-            return false;
-        }
-
-        return sax->end_array();
-    }
-
-    //////////
-    // CBOR //
-    //////////
-
-    /*!
-    @param[in] get_char  whether a new character should be retrieved from the
-                         input (true) or whether the last read character should
-                         be considered instead (false)
-    @param[in] tag_handler how CBOR tags should be treated
-
-    @return whether a valid CBOR value was passed to the SAX parser
-    */
-    bool parse_cbor_internal(const bool get_char,
-                             const cbor_tag_handler_t tag_handler)
-    {
-        switch (get_char ? get() : current)
-        {
-            // EOF
-            case std::char_traits<char_type>::eof():
-                return unexpect_eof(input_format_t::cbor, "value");
-
-            // Integer 0x00..0x17 (0..23)
-            case 0x00:
-            case 0x01:
-            case 0x02:
-            case 0x03:
-            case 0x04:
-            case 0x05:
-            case 0x06:
-            case 0x07:
-            case 0x08:
-            case 0x09:
-            case 0x0A:
-            case 0x0B:
-            case 0x0C:
-            case 0x0D:
-            case 0x0E:
-            case 0x0F:
-            case 0x10:
-            case 0x11:
-            case 0x12:
-            case 0x13:
-            case 0x14:
-            case 0x15:
-            case 0x16:
-            case 0x17:
-                return sax->number_unsigned(static_cast<number_unsigned_t>(current));
-
-            case 0x18: // Unsigned integer (one-byte uint8_t follows)
-            {
-                std::uint8_t number{};
-                return get_number(input_format_t::cbor, number) && sax->number_unsigned(number);
-            }
-
-            case 0x19: // Unsigned integer (two-byte uint16_t follows)
-            {
-                std::uint16_t number{};
-                return get_number(input_format_t::cbor, number) && sax->number_unsigned(number);
-            }
-
-            case 0x1A: // Unsigned integer (four-byte uint32_t follows)
-            {
-                std::uint32_t number{};
-                return get_number(input_format_t::cbor, number) && sax->number_unsigned(number);
-            }
-
-            case 0x1B: // Unsigned integer (eight-byte uint64_t follows)
-            {
-                std::uint64_t number{};
-                return get_number(input_format_t::cbor, number) && sax->number_unsigned(number);
-            }
-
-            // Negative integer -1-0x00..-1-0x17 (-1..-24)
-            case 0x20:
-            case 0x21:
-            case 0x22:
-            case 0x23:
-            case 0x24:
-            case 0x25:
-            case 0x26:
-            case 0x27:
-            case 0x28:
-            case 0x29:
-            case 0x2A:
-            case 0x2B:
-            case 0x2C:
-            case 0x2D:
-            case 0x2E:
-            case 0x2F:
-            case 0x30:
-            case 0x31:
-            case 0x32:
-            case 0x33:
-            case 0x34:
-            case 0x35:
-            case 0x36:
-            case 0x37:
-                return sax->number_integer(static_cast<std::int8_t>(0x20 - 1 - current));
-
-            case 0x38: // Negative integer (one-byte uint8_t follows)
-            {
-                std::uint8_t number{};
-                return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast<number_integer_t>(-1) - number);
-            }
-
-            case 0x39: // Negative integer -1-n (two-byte uint16_t follows)
-            {
-                std::uint16_t number{};
-                return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast<number_integer_t>(-1) - number);
-            }
-
-            case 0x3A: // Negative integer -1-n (four-byte uint32_t follows)
-            {
-                std::uint32_t number{};
-                return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast<number_integer_t>(-1) - number);
-            }
-
-            case 0x3B: // Negative integer -1-n (eight-byte uint64_t follows)
-            {
-                std::uint64_t number{};
-                return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast<number_integer_t>(-1)
-                        - static_cast<number_integer_t>(number));
-            }
-
-            // Binary data (0x00..0x17 bytes follow)
-            case 0x40:
-            case 0x41:
-            case 0x42:
-            case 0x43:
-            case 0x44:
-            case 0x45:
-            case 0x46:
-            case 0x47:
-            case 0x48:
-            case 0x49:
-            case 0x4A:
-            case 0x4B:
-            case 0x4C:
-            case 0x4D:
-            case 0x4E:
-            case 0x4F:
-            case 0x50:
-            case 0x51:
-            case 0x52:
-            case 0x53:
-            case 0x54:
-            case 0x55:
-            case 0x56:
-            case 0x57:
-            case 0x58: // Binary data (one-byte uint8_t for n follows)
-            case 0x59: // Binary data (two-byte uint16_t for n follow)
-            case 0x5A: // Binary data (four-byte uint32_t for n follow)
-            case 0x5B: // Binary data (eight-byte uint64_t for n follow)
-            case 0x5F: // Binary data (indefinite length)
-            {
-                binary_t b;
-                return get_cbor_binary(b) && sax->binary(b);
-            }
-
-            // UTF-8 string (0x00..0x17 bytes follow)
-            case 0x60:
-            case 0x61:
-            case 0x62:
-            case 0x63:
-            case 0x64:
-            case 0x65:
-            case 0x66:
-            case 0x67:
-            case 0x68:
-            case 0x69:
-            case 0x6A:
-            case 0x6B:
-            case 0x6C:
-            case 0x6D:
-            case 0x6E:
-            case 0x6F:
-            case 0x70:
-            case 0x71:
-            case 0x72:
-            case 0x73:
-            case 0x74:
-            case 0x75:
-            case 0x76:
-            case 0x77:
-            case 0x78: // UTF-8 string (one-byte uint8_t for n follows)
-            case 0x79: // UTF-8 string (two-byte uint16_t for n follow)
-            case 0x7A: // UTF-8 string (four-byte uint32_t for n follow)
-            case 0x7B: // UTF-8 string (eight-byte uint64_t for n follow)
-            case 0x7F: // UTF-8 string (indefinite length)
-            {
-                string_t s;
-                return get_cbor_string(s) && sax->string(s);
-            }
-
-            // array (0x00..0x17 data items follow)
-            case 0x80:
-            case 0x81:
-            case 0x82:
-            case 0x83:
-            case 0x84:
-            case 0x85:
-            case 0x86:
-            case 0x87:
-            case 0x88:
-            case 0x89:
-            case 0x8A:
-            case 0x8B:
-            case 0x8C:
-            case 0x8D:
-            case 0x8E:
-            case 0x8F:
-            case 0x90:
-            case 0x91:
-            case 0x92:
-            case 0x93:
-            case 0x94:
-            case 0x95:
-            case 0x96:
-            case 0x97:
-                return get_cbor_array(
-                           conditional_static_cast<std::size_t>(static_cast<unsigned int>(current) & 0x1Fu), tag_handler);
-
-            case 0x98: // array (one-byte uint8_t for n follows)
-            {
-                std::uint8_t len{};
-                return get_number(input_format_t::cbor, len) && get_cbor_array(static_cast<std::size_t>(len), tag_handler);
-            }
-
-            case 0x99: // array (two-byte uint16_t for n follow)
-            {
-                std::uint16_t len{};
-                return get_number(input_format_t::cbor, len) && get_cbor_array(static_cast<std::size_t>(len), tag_handler);
-            }
-
-            case 0x9A: // array (four-byte uint32_t for n follow)
-            {
-                std::uint32_t len{};
-                return get_number(input_format_t::cbor, len) && get_cbor_array(conditional_static_cast<std::size_t>(len), tag_handler);
-            }
-
-            case 0x9B: // array (eight-byte uint64_t for n follow)
-            {
-                std::uint64_t len{};
-                return get_number(input_format_t::cbor, len) && get_cbor_array(conditional_static_cast<std::size_t>(len), tag_handler);
-            }
-
-            case 0x9F: // array (indefinite length)
-                return get_cbor_array(static_cast<std::size_t>(-1), tag_handler);
-
-            // map (0x00..0x17 pairs of data items follow)
-            case 0xA0:
-            case 0xA1:
-            case 0xA2:
-            case 0xA3:
-            case 0xA4:
-            case 0xA5:
-            case 0xA6:
-            case 0xA7:
-            case 0xA8:
-            case 0xA9:
-            case 0xAA:
-            case 0xAB:
-            case 0xAC:
-            case 0xAD:
-            case 0xAE:
-            case 0xAF:
-            case 0xB0:
-            case 0xB1:
-            case 0xB2:
-            case 0xB3:
-            case 0xB4:
-            case 0xB5:
-            case 0xB6:
-            case 0xB7:
-                return get_cbor_object(conditional_static_cast<std::size_t>(static_cast<unsigned int>(current) & 0x1Fu), tag_handler);
-
-            case 0xB8: // map (one-byte uint8_t for n follows)
-            {
-                std::uint8_t len{};
-                return get_number(input_format_t::cbor, len) && get_cbor_object(static_cast<std::size_t>(len), tag_handler);
-            }
-
-            case 0xB9: // map (two-byte uint16_t for n follow)
-            {
-                std::uint16_t len{};
-                return get_number(input_format_t::cbor, len) && get_cbor_object(static_cast<std::size_t>(len), tag_handler);
-            }
-
-            case 0xBA: // map (four-byte uint32_t for n follow)
-            {
-                std::uint32_t len{};
-                return get_number(input_format_t::cbor, len) && get_cbor_object(conditional_static_cast<std::size_t>(len), tag_handler);
-            }
-
-            case 0xBB: // map (eight-byte uint64_t for n follow)
-            {
-                std::uint64_t len{};
-                return get_number(input_format_t::cbor, len) && get_cbor_object(conditional_static_cast<std::size_t>(len), tag_handler);
-            }
-
-            case 0xBF: // map (indefinite length)
-                return get_cbor_object(static_cast<std::size_t>(-1), tag_handler);
-
-            case 0xC6: // tagged item
-            case 0xC7:
-            case 0xC8:
-            case 0xC9:
-            case 0xCA:
-            case 0xCB:
-            case 0xCC:
-            case 0xCD:
-            case 0xCE:
-            case 0xCF:
-            case 0xD0:
-            case 0xD1:
-            case 0xD2:
-            case 0xD3:
-            case 0xD4:
-            case 0xD8: // tagged item (1 bytes follow)
-            case 0xD9: // tagged item (2 bytes follow)
-            case 0xDA: // tagged item (4 bytes follow)
-            case 0xDB: // tagged item (8 bytes follow)
-            {
-                switch (tag_handler)
-                {
-                    case cbor_tag_handler_t::error:
-                    {
-                        auto last_token = get_token_string();
-                        return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
-                                                exception_message(input_format_t::cbor, concat("invalid byte: 0x", last_token), "value"), nullptr));
-                    }
-
-                    case cbor_tag_handler_t::ignore:
-                    {
-                        // ignore binary subtype
-                        switch (current)
-                        {
-                            case 0xD8:
-                            {
-                                std::uint8_t subtype_to_ignore{};
-                                get_number(input_format_t::cbor, subtype_to_ignore);
-                                break;
-                            }
-                            case 0xD9:
-                            {
-                                std::uint16_t subtype_to_ignore{};
-                                get_number(input_format_t::cbor, subtype_to_ignore);
-                                break;
-                            }
-                            case 0xDA:
-                            {
-                                std::uint32_t subtype_to_ignore{};
-                                get_number(input_format_t::cbor, subtype_to_ignore);
-                                break;
-                            }
-                            case 0xDB:
-                            {
-                                std::uint64_t subtype_to_ignore{};
-                                get_number(input_format_t::cbor, subtype_to_ignore);
-                                break;
-                            }
-                            default:
-                                break;
-                        }
-                        return parse_cbor_internal(true, tag_handler);
-                    }
-
-                    case cbor_tag_handler_t::store:
-                    {
-                        binary_t b;
-                        // use binary subtype and store in binary container
-                        switch (current)
-                        {
-                            case 0xD8:
-                            {
-                                std::uint8_t subtype{};
-                                get_number(input_format_t::cbor, subtype);
-                                b.set_subtype(detail::conditional_static_cast<typename binary_t::subtype_type>(subtype));
-                                break;
-                            }
-                            case 0xD9:
-                            {
-                                std::uint16_t subtype{};
-                                get_number(input_format_t::cbor, subtype);
-                                b.set_subtype(detail::conditional_static_cast<typename binary_t::subtype_type>(subtype));
-                                break;
-                            }
-                            case 0xDA:
-                            {
-                                std::uint32_t subtype{};
-                                get_number(input_format_t::cbor, subtype);
-                                b.set_subtype(detail::conditional_static_cast<typename binary_t::subtype_type>(subtype));
-                                break;
-                            }
-                            case 0xDB:
-                            {
-                                std::uint64_t subtype{};
-                                get_number(input_format_t::cbor, subtype);
-                                b.set_subtype(detail::conditional_static_cast<typename binary_t::subtype_type>(subtype));
-                                break;
-                            }
-                            default:
-                                return parse_cbor_internal(true, tag_handler);
-                        }
-                        get();
-                        return get_cbor_binary(b) && sax->binary(b);
-                    }
-
-                    default:                 // LCOV_EXCL_LINE
-                        JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-                        return false;        // LCOV_EXCL_LINE
-                }
-            }
-
-            case 0xF4: // false
-                return sax->boolean(false);
-
-            case 0xF5: // true
-                return sax->boolean(true);
-
-            case 0xF6: // null
-                return sax->null();
-
-            case 0xF9: // Half-Precision Float (two-byte IEEE 754)
-            {
-                const auto byte1_raw = get();
-                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "number")))
-                {
-                    return false;
-                }
-                const auto byte2_raw = get();
-                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "number")))
-                {
-                    return false;
-                }
-
-                const auto byte1 = static_cast<unsigned char>(byte1_raw);
-                const auto byte2 = static_cast<unsigned char>(byte2_raw);
-
-                // code from RFC 7049, Appendix D, Figure 3:
-                // As half-precision floating-point numbers were only added
-                // to IEEE 754 in 2008, today's programming platforms often
-                // still only have limited support for them. It is very
-                // easy to include at least decoding support for them even
-                // without such support. An example of a small decoder for
-                // half-precision floating-point numbers in the C language
-                // is shown in Fig. 3.
-                const auto half = static_cast<unsigned int>((byte1 << 8u) + byte2);
-                const double val = [&half]
-                {
-                    const int exp = (half >> 10u) & 0x1Fu;
-                    const unsigned int mant = half & 0x3FFu;
-                    JSON_ASSERT(0 <= exp&& exp <= 32);
-                    JSON_ASSERT(mant <= 1024);
-                    switch (exp)
-                    {
-                        case 0:
-                            return std::ldexp(mant, -24);
-                        case 31:
-                            return (mant == 0)
-                            ? std::numeric_limits<double>::infinity()
-                            : std::numeric_limits<double>::quiet_NaN();
-                        default:
-                            return std::ldexp(mant + 1024, exp - 25);
-                    }
-                }();
-                return sax->number_float((half & 0x8000u) != 0
-                                         ? static_cast<number_float_t>(-val)
-                                         : static_cast<number_float_t>(val), "");
-            }
-
-            case 0xFA: // Single-Precision Float (four-byte IEEE 754)
-            {
-                float number{};
-                return get_number(input_format_t::cbor, number) && sax->number_float(static_cast<number_float_t>(number), "");
-            }
-
-            case 0xFB: // Double-Precision Float (eight-byte IEEE 754)
-            {
-                double number{};
-                return get_number(input_format_t::cbor, number) && sax->number_float(static_cast<number_float_t>(number), "");
-            }
-
-            default: // anything else (0xFF is handled inside the other types)
-            {
-                auto last_token = get_token_string();
-                return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
-                                        exception_message(input_format_t::cbor, concat("invalid byte: 0x", last_token), "value"), nullptr));
-            }
-        }
-    }
-
-    /*!
-    @brief reads a CBOR string
-
-    This function first reads starting bytes to determine the expected
-    string length and then copies this number of bytes into a string.
-    Additionally, CBOR's strings with indefinite lengths are supported.
-
-    @param[out] result  created string
-
-    @return whether string creation completed
-    */
-    bool get_cbor_string(string_t& result)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "string")))
-        {
-            return false;
-        }
-
-        switch (current)
-        {
-            // UTF-8 string (0x00..0x17 bytes follow)
-            case 0x60:
-            case 0x61:
-            case 0x62:
-            case 0x63:
-            case 0x64:
-            case 0x65:
-            case 0x66:
-            case 0x67:
-            case 0x68:
-            case 0x69:
-            case 0x6A:
-            case 0x6B:
-            case 0x6C:
-            case 0x6D:
-            case 0x6E:
-            case 0x6F:
-            case 0x70:
-            case 0x71:
-            case 0x72:
-            case 0x73:
-            case 0x74:
-            case 0x75:
-            case 0x76:
-            case 0x77:
-            {
-                return get_string(input_format_t::cbor, static_cast<unsigned int>(current) & 0x1Fu, result);
-            }
-
-            case 0x78: // UTF-8 string (one-byte uint8_t for n follows)
-            {
-                std::uint8_t len{};
-                return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result);
-            }
-
-            case 0x79: // UTF-8 string (two-byte uint16_t for n follow)
-            {
-                std::uint16_t len{};
-                return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result);
-            }
-
-            case 0x7A: // UTF-8 string (four-byte uint32_t for n follow)
-            {
-                std::uint32_t len{};
-                return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result);
-            }
-
-            case 0x7B: // UTF-8 string (eight-byte uint64_t for n follow)
-            {
-                std::uint64_t len{};
-                return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result);
-            }
-
-            case 0x7F: // UTF-8 string (indefinite length)
-            {
-                while (get() != 0xFF)
-                {
-                    string_t chunk;
-                    if (!get_cbor_string(chunk))
-                    {
-                        return false;
-                    }
-                    result.append(chunk);
-                }
-                return true;
-            }
-
-            default:
-            {
-                auto last_token = get_token_string();
-                return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read,
-                                        exception_message(input_format_t::cbor, concat("expected length specification (0x60-0x7B) or indefinite string type (0x7F); last byte: 0x", last_token), "string"), nullptr));
-            }
-        }
-    }
-
-    /*!
-    @brief reads a CBOR byte array
-
-    This function first reads starting bytes to determine the expected
-    byte array length and then copies this number of bytes into the byte array.
-    Additionally, CBOR's byte arrays with indefinite lengths are supported.
-
-    @param[out] result  created byte array
-
-    @return whether byte array creation completed
-    */
-    bool get_cbor_binary(binary_t& result)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "binary")))
-        {
-            return false;
-        }
-
-        switch (current)
-        {
-            // Binary data (0x00..0x17 bytes follow)
-            case 0x40:
-            case 0x41:
-            case 0x42:
-            case 0x43:
-            case 0x44:
-            case 0x45:
-            case 0x46:
-            case 0x47:
-            case 0x48:
-            case 0x49:
-            case 0x4A:
-            case 0x4B:
-            case 0x4C:
-            case 0x4D:
-            case 0x4E:
-            case 0x4F:
-            case 0x50:
-            case 0x51:
-            case 0x52:
-            case 0x53:
-            case 0x54:
-            case 0x55:
-            case 0x56:
-            case 0x57:
-            {
-                return get_binary(input_format_t::cbor, static_cast<unsigned int>(current) & 0x1Fu, result);
-            }
-
-            case 0x58: // Binary data (one-byte uint8_t for n follows)
-            {
-                std::uint8_t len{};
-                return get_number(input_format_t::cbor, len) &&
-                       get_binary(input_format_t::cbor, len, result);
-            }
-
-            case 0x59: // Binary data (two-byte uint16_t for n follow)
-            {
-                std::uint16_t len{};
-                return get_number(input_format_t::cbor, len) &&
-                       get_binary(input_format_t::cbor, len, result);
-            }
-
-            case 0x5A: // Binary data (four-byte uint32_t for n follow)
-            {
-                std::uint32_t len{};
-                return get_number(input_format_t::cbor, len) &&
-                       get_binary(input_format_t::cbor, len, result);
-            }
-
-            case 0x5B: // Binary data (eight-byte uint64_t for n follow)
-            {
-                std::uint64_t len{};
-                return get_number(input_format_t::cbor, len) &&
-                       get_binary(input_format_t::cbor, len, result);
-            }
-
-            case 0x5F: // Binary data (indefinite length)
-            {
-                while (get() != 0xFF)
-                {
-                    binary_t chunk;
-                    if (!get_cbor_binary(chunk))
-                    {
-                        return false;
-                    }
-                    result.insert(result.end(), chunk.begin(), chunk.end());
-                }
-                return true;
-            }
-
-            default:
-            {
-                auto last_token = get_token_string();
-                return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read,
-                                        exception_message(input_format_t::cbor, concat("expected length specification (0x40-0x5B) or indefinite binary array type (0x5F); last byte: 0x", last_token), "binary"), nullptr));
-            }
-        }
-    }
-
-    /*!
-    @param[in] len  the length of the array or static_cast<std::size_t>(-1) for an
-                    array of indefinite size
-    @param[in] tag_handler how CBOR tags should be treated
-    @return whether array creation completed
-    */
-    bool get_cbor_array(const std::size_t len,
-                        const cbor_tag_handler_t tag_handler)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!sax->start_array(len)))
-        {
-            return false;
-        }
-
-        if (len != static_cast<std::size_t>(-1))
-        {
-            for (std::size_t i = 0; i < len; ++i)
-            {
-                if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(true, tag_handler)))
-                {
-                    return false;
-                }
-            }
-        }
-        else
-        {
-            while (get() != 0xFF)
-            {
-                if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(false, tag_handler)))
-                {
-                    return false;
-                }
-            }
-        }
-
-        return sax->end_array();
-    }
-
-    /*!
-    @param[in] len  the length of the object or static_cast<std::size_t>(-1) for an
-                    object of indefinite size
-    @param[in] tag_handler how CBOR tags should be treated
-    @return whether object creation completed
-    */
-    bool get_cbor_object(const std::size_t len,
-                         const cbor_tag_handler_t tag_handler)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!sax->start_object(len)))
-        {
-            return false;
-        }
-
-        if (len != 0)
-        {
-            string_t key;
-            if (len != static_cast<std::size_t>(-1))
-            {
-                for (std::size_t i = 0; i < len; ++i)
-                {
-                    get();
-                    if (JSON_HEDLEY_UNLIKELY(!get_cbor_string(key) || !sax->key(key)))
-                    {
-                        return false;
-                    }
-
-                    if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(true, tag_handler)))
-                    {
-                        return false;
-                    }
-                    key.clear();
-                }
-            }
-            else
-            {
-                while (get() != 0xFF)
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!get_cbor_string(key) || !sax->key(key)))
-                    {
-                        return false;
-                    }
-
-                    if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(true, tag_handler)))
-                    {
-                        return false;
-                    }
-                    key.clear();
-                }
-            }
-        }
-
-        return sax->end_object();
-    }
-
-    /////////////
-    // MsgPack //
-    /////////////
-
-    /*!
-    @return whether a valid MessagePack value was passed to the SAX parser
-    */
-    bool parse_msgpack_internal()
-    {
-        switch (get())
-        {
-            // EOF
-            case std::char_traits<char_type>::eof():
-                return unexpect_eof(input_format_t::msgpack, "value");
-
-            // positive fixint
-            case 0x00:
-            case 0x01:
-            case 0x02:
-            case 0x03:
-            case 0x04:
-            case 0x05:
-            case 0x06:
-            case 0x07:
-            case 0x08:
-            case 0x09:
-            case 0x0A:
-            case 0x0B:
-            case 0x0C:
-            case 0x0D:
-            case 0x0E:
-            case 0x0F:
-            case 0x10:
-            case 0x11:
-            case 0x12:
-            case 0x13:
-            case 0x14:
-            case 0x15:
-            case 0x16:
-            case 0x17:
-            case 0x18:
-            case 0x19:
-            case 0x1A:
-            case 0x1B:
-            case 0x1C:
-            case 0x1D:
-            case 0x1E:
-            case 0x1F:
-            case 0x20:
-            case 0x21:
-            case 0x22:
-            case 0x23:
-            case 0x24:
-            case 0x25:
-            case 0x26:
-            case 0x27:
-            case 0x28:
-            case 0x29:
-            case 0x2A:
-            case 0x2B:
-            case 0x2C:
-            case 0x2D:
-            case 0x2E:
-            case 0x2F:
-            case 0x30:
-            case 0x31:
-            case 0x32:
-            case 0x33:
-            case 0x34:
-            case 0x35:
-            case 0x36:
-            case 0x37:
-            case 0x38:
-            case 0x39:
-            case 0x3A:
-            case 0x3B:
-            case 0x3C:
-            case 0x3D:
-            case 0x3E:
-            case 0x3F:
-            case 0x40:
-            case 0x41:
-            case 0x42:
-            case 0x43:
-            case 0x44:
-            case 0x45:
-            case 0x46:
-            case 0x47:
-            case 0x48:
-            case 0x49:
-            case 0x4A:
-            case 0x4B:
-            case 0x4C:
-            case 0x4D:
-            case 0x4E:
-            case 0x4F:
-            case 0x50:
-            case 0x51:
-            case 0x52:
-            case 0x53:
-            case 0x54:
-            case 0x55:
-            case 0x56:
-            case 0x57:
-            case 0x58:
-            case 0x59:
-            case 0x5A:
-            case 0x5B:
-            case 0x5C:
-            case 0x5D:
-            case 0x5E:
-            case 0x5F:
-            case 0x60:
-            case 0x61:
-            case 0x62:
-            case 0x63:
-            case 0x64:
-            case 0x65:
-            case 0x66:
-            case 0x67:
-            case 0x68:
-            case 0x69:
-            case 0x6A:
-            case 0x6B:
-            case 0x6C:
-            case 0x6D:
-            case 0x6E:
-            case 0x6F:
-            case 0x70:
-            case 0x71:
-            case 0x72:
-            case 0x73:
-            case 0x74:
-            case 0x75:
-            case 0x76:
-            case 0x77:
-            case 0x78:
-            case 0x79:
-            case 0x7A:
-            case 0x7B:
-            case 0x7C:
-            case 0x7D:
-            case 0x7E:
-            case 0x7F:
-                return sax->number_unsigned(static_cast<number_unsigned_t>(current));
-
-            // fixmap
-            case 0x80:
-            case 0x81:
-            case 0x82:
-            case 0x83:
-            case 0x84:
-            case 0x85:
-            case 0x86:
-            case 0x87:
-            case 0x88:
-            case 0x89:
-            case 0x8A:
-            case 0x8B:
-            case 0x8C:
-            case 0x8D:
-            case 0x8E:
-            case 0x8F:
-                return get_msgpack_object(conditional_static_cast<std::size_t>(static_cast<unsigned int>(current) & 0x0Fu));
-
-            // fixarray
-            case 0x90:
-            case 0x91:
-            case 0x92:
-            case 0x93:
-            case 0x94:
-            case 0x95:
-            case 0x96:
-            case 0x97:
-            case 0x98:
-            case 0x99:
-            case 0x9A:
-            case 0x9B:
-            case 0x9C:
-            case 0x9D:
-            case 0x9E:
-            case 0x9F:
-                return get_msgpack_array(conditional_static_cast<std::size_t>(static_cast<unsigned int>(current) & 0x0Fu));
-
-            // fixstr
-            case 0xA0:
-            case 0xA1:
-            case 0xA2:
-            case 0xA3:
-            case 0xA4:
-            case 0xA5:
-            case 0xA6:
-            case 0xA7:
-            case 0xA8:
-            case 0xA9:
-            case 0xAA:
-            case 0xAB:
-            case 0xAC:
-            case 0xAD:
-            case 0xAE:
-            case 0xAF:
-            case 0xB0:
-            case 0xB1:
-            case 0xB2:
-            case 0xB3:
-            case 0xB4:
-            case 0xB5:
-            case 0xB6:
-            case 0xB7:
-            case 0xB8:
-            case 0xB9:
-            case 0xBA:
-            case 0xBB:
-            case 0xBC:
-            case 0xBD:
-            case 0xBE:
-            case 0xBF:
-            case 0xD9: // str 8
-            case 0xDA: // str 16
-            case 0xDB: // str 32
-            {
-                string_t s;
-                return get_msgpack_string(s) && sax->string(s);
-            }
-
-            case 0xC0: // nil
-                return sax->null();
-
-            case 0xC2: // false
-                return sax->boolean(false);
-
-            case 0xC3: // true
-                return sax->boolean(true);
-
-            case 0xC4: // bin 8
-            case 0xC5: // bin 16
-            case 0xC6: // bin 32
-            case 0xC7: // ext 8
-            case 0xC8: // ext 16
-            case 0xC9: // ext 32
-            case 0xD4: // fixext 1
-            case 0xD5: // fixext 2
-            case 0xD6: // fixext 4
-            case 0xD7: // fixext 8
-            case 0xD8: // fixext 16
-            {
-                binary_t b;
-                return get_msgpack_binary(b) && sax->binary(b);
-            }
-
-            case 0xCA: // float 32
-            {
-                float number{};
-                return get_number(input_format_t::msgpack, number) && sax->number_float(static_cast<number_float_t>(number), "");
-            }
-
-            case 0xCB: // float 64
-            {
-                double number{};
-                return get_number(input_format_t::msgpack, number) && sax->number_float(static_cast<number_float_t>(number), "");
-            }
-
-            case 0xCC: // uint 8
-            {
-                std::uint8_t number{};
-                return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number);
-            }
-
-            case 0xCD: // uint 16
-            {
-                std::uint16_t number{};
-                return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number);
-            }
-
-            case 0xCE: // uint 32
-            {
-                std::uint32_t number{};
-                return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number);
-            }
-
-            case 0xCF: // uint 64
-            {
-                std::uint64_t number{};
-                return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number);
-            }
-
-            case 0xD0: // int 8
-            {
-                std::int8_t number{};
-                return get_number(input_format_t::msgpack, number) && sax->number_integer(number);
-            }
-
-            case 0xD1: // int 16
-            {
-                std::int16_t number{};
-                return get_number(input_format_t::msgpack, number) && sax->number_integer(number);
-            }
-
-            case 0xD2: // int 32
-            {
-                std::int32_t number{};
-                return get_number(input_format_t::msgpack, number) && sax->number_integer(number);
-            }
-
-            case 0xD3: // int 64
-            {
-                std::int64_t number{};
-                return get_number(input_format_t::msgpack, number) && sax->number_integer(number);
-            }
-
-            case 0xDC: // array 16
-            {
-                std::uint16_t len{};
-                return get_number(input_format_t::msgpack, len) && get_msgpack_array(static_cast<std::size_t>(len));
-            }
-
-            case 0xDD: // array 32
-            {
-                std::uint32_t len{};
-                return get_number(input_format_t::msgpack, len) && get_msgpack_array(conditional_static_cast<std::size_t>(len));
-            }
-
-            case 0xDE: // map 16
-            {
-                std::uint16_t len{};
-                return get_number(input_format_t::msgpack, len) && get_msgpack_object(static_cast<std::size_t>(len));
-            }
-
-            case 0xDF: // map 32
-            {
-                std::uint32_t len{};
-                return get_number(input_format_t::msgpack, len) && get_msgpack_object(conditional_static_cast<std::size_t>(len));
-            }
-
-            // negative fixint
-            case 0xE0:
-            case 0xE1:
-            case 0xE2:
-            case 0xE3:
-            case 0xE4:
-            case 0xE5:
-            case 0xE6:
-            case 0xE7:
-            case 0xE8:
-            case 0xE9:
-            case 0xEA:
-            case 0xEB:
-            case 0xEC:
-            case 0xED:
-            case 0xEE:
-            case 0xEF:
-            case 0xF0:
-            case 0xF1:
-            case 0xF2:
-            case 0xF3:
-            case 0xF4:
-            case 0xF5:
-            case 0xF6:
-            case 0xF7:
-            case 0xF8:
-            case 0xF9:
-            case 0xFA:
-            case 0xFB:
-            case 0xFC:
-            case 0xFD:
-            case 0xFE:
-            case 0xFF:
-                return sax->number_integer(static_cast<std::int8_t>(current));
-
-            default: // anything else
-            {
-                auto last_token = get_token_string();
-                return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
-                                        exception_message(input_format_t::msgpack, concat("invalid byte: 0x", last_token), "value"), nullptr));
-            }
-        }
-    }
-
-    /*!
-    @brief reads a MessagePack string
-
-    This function first reads starting bytes to determine the expected
-    string length and then copies this number of bytes into a string.
-
-    @param[out] result  created string
-
-    @return whether string creation completed
-    */
-    bool get_msgpack_string(string_t& result)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::msgpack, "string")))
-        {
-            return false;
-        }
-
-        switch (current)
-        {
-            // fixstr
-            case 0xA0:
-            case 0xA1:
-            case 0xA2:
-            case 0xA3:
-            case 0xA4:
-            case 0xA5:
-            case 0xA6:
-            case 0xA7:
-            case 0xA8:
-            case 0xA9:
-            case 0xAA:
-            case 0xAB:
-            case 0xAC:
-            case 0xAD:
-            case 0xAE:
-            case 0xAF:
-            case 0xB0:
-            case 0xB1:
-            case 0xB2:
-            case 0xB3:
-            case 0xB4:
-            case 0xB5:
-            case 0xB6:
-            case 0xB7:
-            case 0xB8:
-            case 0xB9:
-            case 0xBA:
-            case 0xBB:
-            case 0xBC:
-            case 0xBD:
-            case 0xBE:
-            case 0xBF:
-            {
-                return get_string(input_format_t::msgpack, static_cast<unsigned int>(current) & 0x1Fu, result);
-            }
-
-            case 0xD9: // str 8
-            {
-                std::uint8_t len{};
-                return get_number(input_format_t::msgpack, len) && get_string(input_format_t::msgpack, len, result);
-            }
-
-            case 0xDA: // str 16
-            {
-                std::uint16_t len{};
-                return get_number(input_format_t::msgpack, len) && get_string(input_format_t::msgpack, len, result);
-            }
-
-            case 0xDB: // str 32
-            {
-                std::uint32_t len{};
-                return get_number(input_format_t::msgpack, len) && get_string(input_format_t::msgpack, len, result);
-            }
-
-            default:
-            {
-                auto last_token = get_token_string();
-                return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read,
-                                        exception_message(input_format_t::msgpack, concat("expected length specification (0xA0-0xBF, 0xD9-0xDB); last byte: 0x", last_token), "string"), nullptr));
-            }
-        }
-    }
-
-    /*!
-    @brief reads a MessagePack byte array
-
-    This function first reads starting bytes to determine the expected
-    byte array length and then copies this number of bytes into a byte array.
-
-    @param[out] result  created byte array
-
-    @return whether byte array creation completed
-    */
-    bool get_msgpack_binary(binary_t& result)
-    {
-        // helper function to set the subtype
-        auto assign_and_return_true = [&result](std::int8_t subtype)
-        {
-            result.set_subtype(static_cast<std::uint8_t>(subtype));
-            return true;
-        };
-
-        switch (current)
-        {
-            case 0xC4: // bin 8
-            {
-                std::uint8_t len{};
-                return get_number(input_format_t::msgpack, len) &&
-                       get_binary(input_format_t::msgpack, len, result);
-            }
-
-            case 0xC5: // bin 16
-            {
-                std::uint16_t len{};
-                return get_number(input_format_t::msgpack, len) &&
-                       get_binary(input_format_t::msgpack, len, result);
-            }
-
-            case 0xC6: // bin 32
-            {
-                std::uint32_t len{};
-                return get_number(input_format_t::msgpack, len) &&
-                       get_binary(input_format_t::msgpack, len, result);
-            }
-
-            case 0xC7: // ext 8
-            {
-                std::uint8_t len{};
-                std::int8_t subtype{};
-                return get_number(input_format_t::msgpack, len) &&
-                       get_number(input_format_t::msgpack, subtype) &&
-                       get_binary(input_format_t::msgpack, len, result) &&
-                       assign_and_return_true(subtype);
-            }
-
-            case 0xC8: // ext 16
-            {
-                std::uint16_t len{};
-                std::int8_t subtype{};
-                return get_number(input_format_t::msgpack, len) &&
-                       get_number(input_format_t::msgpack, subtype) &&
-                       get_binary(input_format_t::msgpack, len, result) &&
-                       assign_and_return_true(subtype);
-            }
-
-            case 0xC9: // ext 32
-            {
-                std::uint32_t len{};
-                std::int8_t subtype{};
-                return get_number(input_format_t::msgpack, len) &&
-                       get_number(input_format_t::msgpack, subtype) &&
-                       get_binary(input_format_t::msgpack, len, result) &&
-                       assign_and_return_true(subtype);
-            }
-
-            case 0xD4: // fixext 1
-            {
-                std::int8_t subtype{};
-                return get_number(input_format_t::msgpack, subtype) &&
-                       get_binary(input_format_t::msgpack, 1, result) &&
-                       assign_and_return_true(subtype);
-            }
-
-            case 0xD5: // fixext 2
-            {
-                std::int8_t subtype{};
-                return get_number(input_format_t::msgpack, subtype) &&
-                       get_binary(input_format_t::msgpack, 2, result) &&
-                       assign_and_return_true(subtype);
-            }
-
-            case 0xD6: // fixext 4
-            {
-                std::int8_t subtype{};
-                return get_number(input_format_t::msgpack, subtype) &&
-                       get_binary(input_format_t::msgpack, 4, result) &&
-                       assign_and_return_true(subtype);
-            }
-
-            case 0xD7: // fixext 8
-            {
-                std::int8_t subtype{};
-                return get_number(input_format_t::msgpack, subtype) &&
-                       get_binary(input_format_t::msgpack, 8, result) &&
-                       assign_and_return_true(subtype);
-            }
-
-            case 0xD8: // fixext 16
-            {
-                std::int8_t subtype{};
-                return get_number(input_format_t::msgpack, subtype) &&
-                       get_binary(input_format_t::msgpack, 16, result) &&
-                       assign_and_return_true(subtype);
-            }
-
-            default:           // LCOV_EXCL_LINE
-                return false;  // LCOV_EXCL_LINE
-        }
-    }
-
-    /*!
-    @param[in] len  the length of the array
-    @return whether array creation completed
-    */
-    bool get_msgpack_array(const std::size_t len)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!sax->start_array(len)))
-        {
-            return false;
-        }
-
-        for (std::size_t i = 0; i < len; ++i)
-        {
-            if (JSON_HEDLEY_UNLIKELY(!parse_msgpack_internal()))
-            {
-                return false;
-            }
-        }
-
-        return sax->end_array();
-    }
-
-    /*!
-    @param[in] len  the length of the object
-    @return whether object creation completed
-    */
-    bool get_msgpack_object(const std::size_t len)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!sax->start_object(len)))
-        {
-            return false;
-        }
-
-        string_t key;
-        for (std::size_t i = 0; i < len; ++i)
-        {
-            get();
-            if (JSON_HEDLEY_UNLIKELY(!get_msgpack_string(key) || !sax->key(key)))
-            {
-                return false;
-            }
-
-            if (JSON_HEDLEY_UNLIKELY(!parse_msgpack_internal()))
-            {
-                return false;
-            }
-            key.clear();
-        }
-
-        return sax->end_object();
-    }
-
-    ////////////
-    // UBJSON //
-    ////////////
-
-    /*!
-    @param[in] get_char  whether a new character should be retrieved from the
-                         input (true, default) or whether the last read
-                         character should be considered instead
-
-    @return whether a valid UBJSON value was passed to the SAX parser
-    */
-    bool parse_ubjson_internal(const bool get_char = true)
-    {
-        return get_ubjson_value(get_char ? get_ignore_noop() : current);
-    }
-
-    /*!
-    @brief reads a UBJSON string
-
-    This function is either called after reading the 'S' byte explicitly
-    indicating a string, or in case of an object key where the 'S' byte can be
-    left out.
-
-    @param[out] result   created string
-    @param[in] get_char  whether a new character should be retrieved from the
-                         input (true, default) or whether the last read
-                         character should be considered instead
-
-    @return whether string creation completed
-    */
-    bool get_ubjson_string(string_t& result, const bool get_char = true)
-    {
-        if (get_char)
-        {
-            get();  // TODO(niels): may we ignore N here?
-        }
-
-        if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "value")))
-        {
-            return false;
-        }
-
-        switch (current)
-        {
-            case 'U':
-            {
-                std::uint8_t len{};
-                return get_number(input_format, len) && get_string(input_format, len, result);
-            }
-
-            case 'i':
-            {
-                std::int8_t len{};
-                return get_number(input_format, len) && get_string(input_format, len, result);
-            }
-
-            case 'I':
-            {
-                std::int16_t len{};
-                return get_number(input_format, len) && get_string(input_format, len, result);
-            }
-
-            case 'l':
-            {
-                std::int32_t len{};
-                return get_number(input_format, len) && get_string(input_format, len, result);
-            }
-
-            case 'L':
-            {
-                std::int64_t len{};
-                return get_number(input_format, len) && get_string(input_format, len, result);
-            }
-
-            case 'u':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                std::uint16_t len{};
-                return get_number(input_format, len) && get_string(input_format, len, result);
-            }
-
-            case 'm':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                std::uint32_t len{};
-                return get_number(input_format, len) && get_string(input_format, len, result);
-            }
-
-            case 'M':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                std::uint64_t len{};
-                return get_number(input_format, len) && get_string(input_format, len, result);
-            }
-
-            default:
-                break;
-        }
-        auto last_token = get_token_string();
-        std::string message;
-
-        if (input_format != input_format_t::bjdata)
-        {
-            message = "expected length type specification (U, i, I, l, L); last byte: 0x" + last_token;
-        }
-        else
-        {
-            message = "expected length type specification (U, i, u, I, m, l, M, L); last byte: 0x" + last_token;
-        }
-        return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read, exception_message(input_format, message, "string"), nullptr));
-    }
-
-    /*!
-    @param[out] dim  an integer vector storing the ND array dimensions
-    @return whether reading ND array size vector is successful
-    */
-    bool get_ubjson_ndarray_size(std::vector<size_t>& dim)
-    {
-        std::pair<std::size_t, char_int_type> size_and_type;
-        size_t dimlen = 0;
-        bool no_ndarray = true;
-
-        if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_type(size_and_type, no_ndarray)))
-        {
-            return false;
-        }
-
-        if (size_and_type.first != npos)
-        {
-            if (size_and_type.second != 0)
-            {
-                if (size_and_type.second != 'N')
-                {
-                    for (std::size_t i = 0; i < size_and_type.first; ++i)
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_value(dimlen, no_ndarray, size_and_type.second)))
-                        {
-                            return false;
-                        }
-                        dim.push_back(dimlen);
-                    }
-                }
-            }
-            else
-            {
-                for (std::size_t i = 0; i < size_and_type.first; ++i)
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_value(dimlen, no_ndarray)))
-                    {
-                        return false;
-                    }
-                    dim.push_back(dimlen);
-                }
-            }
-        }
-        else
-        {
-            while (current != ']')
-            {
-                if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_value(dimlen, no_ndarray, current)))
-                {
-                    return false;
-                }
-                dim.push_back(dimlen);
-                get_ignore_noop();
-            }
-        }
-        return true;
-    }
-
-    /*!
-    @param[out] result  determined size
-    @param[in,out] is_ndarray  for input, `true` means already inside an ndarray vector
-                               or ndarray dimension is not allowed; `false` means ndarray
-                               is allowed; for output, `true` means an ndarray is found;
-                               is_ndarray can only return `true` when its initial value
-                               is `false`
-    @param[in] prefix  type marker if already read, otherwise set to 0
-
-    @return whether size determination completed
-    */
-    bool get_ubjson_size_value(std::size_t& result, bool& is_ndarray, char_int_type prefix = 0)
-    {
-        if (prefix == 0)
-        {
-            prefix = get_ignore_noop();
-        }
-
-        switch (prefix)
-        {
-            case 'U':
-            {
-                std::uint8_t number{};
-                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
-                {
-                    return false;
-                }
-                result = static_cast<std::size_t>(number);
-                return true;
-            }
-
-            case 'i':
-            {
-                std::int8_t number{};
-                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
-                {
-                    return false;
-                }
-                if (number < 0)
-                {
-                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read,
-                                            exception_message(input_format, "count in an optimized container must be positive", "size"), nullptr));
-                }
-                result = static_cast<std::size_t>(number); // NOLINT(bugprone-signed-char-misuse,cert-str34-c): number is not a char
-                return true;
-            }
-
-            case 'I':
-            {
-                std::int16_t number{};
-                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
-                {
-                    return false;
-                }
-                if (number < 0)
-                {
-                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read,
-                                            exception_message(input_format, "count in an optimized container must be positive", "size"), nullptr));
-                }
-                result = static_cast<std::size_t>(number);
-                return true;
-            }
-
-            case 'l':
-            {
-                std::int32_t number{};
-                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
-                {
-                    return false;
-                }
-                if (number < 0)
-                {
-                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read,
-                                            exception_message(input_format, "count in an optimized container must be positive", "size"), nullptr));
-                }
-                result = static_cast<std::size_t>(number);
-                return true;
-            }
-
-            case 'L':
-            {
-                std::int64_t number{};
-                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
-                {
-                    return false;
-                }
-                if (number < 0)
-                {
-                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read,
-                                            exception_message(input_format, "count in an optimized container must be positive", "size"), nullptr));
-                }
-                if (!value_in_range_of<std::size_t>(number))
-                {
-                    return sax->parse_error(chars_read, get_token_string(), out_of_range::create(408,
-                                            exception_message(input_format, "integer value overflow", "size"), nullptr));
-                }
-                result = static_cast<std::size_t>(number);
-                return true;
-            }
-
-            case 'u':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                std::uint16_t number{};
-                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
-                {
-                    return false;
-                }
-                result = static_cast<std::size_t>(number);
-                return true;
-            }
-
-            case 'm':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                std::uint32_t number{};
-                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
-                {
-                    return false;
-                }
-                result = conditional_static_cast<std::size_t>(number);
-                return true;
-            }
-
-            case 'M':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                std::uint64_t number{};
-                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
-                {
-                    return false;
-                }
-                if (!value_in_range_of<std::size_t>(number))
-                {
-                    return sax->parse_error(chars_read, get_token_string(), out_of_range::create(408,
-                                            exception_message(input_format, "integer value overflow", "size"), nullptr));
-                }
-                result = detail::conditional_static_cast<std::size_t>(number);
-                return true;
-            }
-
-            case '[':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                if (is_ndarray) // ndarray dimensional vector can only contain integers, and can not embed another array
-                {
-                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read, exception_message(input_format, "ndarray dimentional vector is not allowed", "size"), nullptr));
-                }
-                std::vector<size_t> dim;
-                if (JSON_HEDLEY_UNLIKELY(!get_ubjson_ndarray_size(dim)))
-                {
-                    return false;
-                }
-                if (dim.size() == 1 || (dim.size() == 2 && dim.at(0) == 1)) // return normal array size if 1D row vector
-                {
-                    result = dim.at(dim.size() - 1);
-                    return true;
-                }
-                if (!dim.empty())  // if ndarray, convert to an object in JData annotated array format
-                {
-                    for (auto i : dim) // test if any dimension in an ndarray is 0, if so, return a 1D empty container
-                    {
-                        if ( i == 0 )
-                        {
-                            result = 0;
-                            return true;
-                        }
-                    }
-
-                    string_t key = "_ArraySize_";
-                    if (JSON_HEDLEY_UNLIKELY(!sax->start_object(3) || !sax->key(key) || !sax->start_array(dim.size())))
-                    {
-                        return false;
-                    }
-                    result = 1;
-                    for (auto i : dim)
-                    {
-                        result *= i;
-                        if (result == 0 || result == npos) // because dim elements shall not have zeros, result = 0 means overflow happened; it also can't be npos as it is used to initialize size in get_ubjson_size_type()
-                        {
-                            return sax->parse_error(chars_read, get_token_string(), out_of_range::create(408, exception_message(input_format, "excessive ndarray size caused overflow", "size"), nullptr));
-                        }
-                        if (JSON_HEDLEY_UNLIKELY(!sax->number_unsigned(static_cast<number_unsigned_t>(i))))
-                        {
-                            return false;
-                        }
-                    }
-                    is_ndarray = true;
-                    return sax->end_array();
-                }
-                result = 0;
-                return true;
-            }
-
-            default:
-                break;
-        }
-        auto last_token = get_token_string();
-        std::string message;
-
-        if (input_format != input_format_t::bjdata)
-        {
-            message = "expected length type specification (U, i, I, l, L) after '#'; last byte: 0x" + last_token;
-        }
-        else
-        {
-            message = "expected length type specification (U, i, u, I, m, l, M, L) after '#'; last byte: 0x" + last_token;
-        }
-        return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read, exception_message(input_format, message, "size"), nullptr));
-    }
-
-    /*!
-    @brief determine the type and size for a container
-
-    In the optimized UBJSON format, a type and a size can be provided to allow
-    for a more compact representation.
-
-    @param[out] result  pair of the size and the type
-    @param[in] inside_ndarray  whether the parser is parsing an ND array dimensional vector
-
-    @return whether pair creation completed
-    */
-    bool get_ubjson_size_type(std::pair<std::size_t, char_int_type>& result, bool inside_ndarray = false)
-    {
-        result.first = npos; // size
-        result.second = 0; // type
-        bool is_ndarray = false;
-
-        get_ignore_noop();
-
-        if (current == '$')
-        {
-            result.second = get();  // must not ignore 'N', because 'N' maybe the type
-            if (input_format == input_format_t::bjdata
-                    && JSON_HEDLEY_UNLIKELY(std::binary_search(bjd_optimized_type_markers.begin(), bjd_optimized_type_markers.end(), result.second)))
-            {
-                auto last_token = get_token_string();
-                return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
-                                        exception_message(input_format, concat("marker 0x", last_token, " is not a permitted optimized array type"), "type"), nullptr));
-            }
-
-            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "type")))
-            {
-                return false;
-            }
-
-            get_ignore_noop();
-            if (JSON_HEDLEY_UNLIKELY(current != '#'))
-            {
-                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "value")))
-                {
-                    return false;
-                }
-                auto last_token = get_token_string();
-                return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
-                                        exception_message(input_format, concat("expected '#' after type information; last byte: 0x", last_token), "size"), nullptr));
-            }
-
-            bool is_error = get_ubjson_size_value(result.first, is_ndarray);
-            if (input_format == input_format_t::bjdata && is_ndarray)
-            {
-                if (inside_ndarray)
-                {
-                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(112, chars_read,
-                                            exception_message(input_format, "ndarray can not be recursive", "size"), nullptr));
-                }
-                result.second |= (1 << 8); // use bit 8 to indicate ndarray, all UBJSON and BJData markers should be ASCII letters
-            }
-            return is_error;
-        }
-
-        if (current == '#')
-        {
-            bool is_error = get_ubjson_size_value(result.first, is_ndarray);
-            if (input_format == input_format_t::bjdata && is_ndarray)
-            {
-                return sax->parse_error(chars_read, get_token_string(), parse_error::create(112, chars_read,
-                                        exception_message(input_format, "ndarray requires both type and size", "size"), nullptr));
-            }
-            return is_error;
-        }
-
-        return true;
-    }
-
-    /*!
-    @param prefix  the previously read or set type prefix
-    @return whether value creation completed
-    */
-    bool get_ubjson_value(const char_int_type prefix)
-    {
-        switch (prefix)
-        {
-            case std::char_traits<char_type>::eof():  // EOF
-                return unexpect_eof(input_format, "value");
-
-            case 'T':  // true
-                return sax->boolean(true);
-            case 'F':  // false
-                return sax->boolean(false);
-
-            case 'Z':  // null
-                return sax->null();
-
-            case 'U':
-            {
-                std::uint8_t number{};
-                return get_number(input_format, number) && sax->number_unsigned(number);
-            }
-
-            case 'i':
-            {
-                std::int8_t number{};
-                return get_number(input_format, number) && sax->number_integer(number);
-            }
-
-            case 'I':
-            {
-                std::int16_t number{};
-                return get_number(input_format, number) && sax->number_integer(number);
-            }
-
-            case 'l':
-            {
-                std::int32_t number{};
-                return get_number(input_format, number) && sax->number_integer(number);
-            }
-
-            case 'L':
-            {
-                std::int64_t number{};
-                return get_number(input_format, number) && sax->number_integer(number);
-            }
-
-            case 'u':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                std::uint16_t number{};
-                return get_number(input_format, number) && sax->number_unsigned(number);
-            }
-
-            case 'm':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                std::uint32_t number{};
-                return get_number(input_format, number) && sax->number_unsigned(number);
-            }
-
-            case 'M':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                std::uint64_t number{};
-                return get_number(input_format, number) && sax->number_unsigned(number);
-            }
-
-            case 'h':
-            {
-                if (input_format != input_format_t::bjdata)
-                {
-                    break;
-                }
-                const auto byte1_raw = get();
-                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "number")))
-                {
-                    return false;
-                }
-                const auto byte2_raw = get();
-                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "number")))
-                {
-                    return false;
-                }
-
-                const auto byte1 = static_cast<unsigned char>(byte1_raw);
-                const auto byte2 = static_cast<unsigned char>(byte2_raw);
-
-                // code from RFC 7049, Appendix D, Figure 3:
-                // As half-precision floating-point numbers were only added
-                // to IEEE 754 in 2008, today's programming platforms often
-                // still only have limited support for them. It is very
-                // easy to include at least decoding support for them even
-                // without such support. An example of a small decoder for
-                // half-precision floating-point numbers in the C language
-                // is shown in Fig. 3.
-                const auto half = static_cast<unsigned int>((byte2 << 8u) + byte1);
-                const double val = [&half]
-                {
-                    const int exp = (half >> 10u) & 0x1Fu;
-                    const unsigned int mant = half & 0x3FFu;
-                    JSON_ASSERT(0 <= exp&& exp <= 32);
-                    JSON_ASSERT(mant <= 1024);
-                    switch (exp)
-                    {
-                        case 0:
-                            return std::ldexp(mant, -24);
-                        case 31:
-                            return (mant == 0)
-                            ? std::numeric_limits<double>::infinity()
-                            : std::numeric_limits<double>::quiet_NaN();
-                        default:
-                            return std::ldexp(mant + 1024, exp - 25);
-                    }
-                }();
-                return sax->number_float((half & 0x8000u) != 0
-                                         ? static_cast<number_float_t>(-val)
-                                         : static_cast<number_float_t>(val), "");
-            }
-
-            case 'd':
-            {
-                float number{};
-                return get_number(input_format, number) && sax->number_float(static_cast<number_float_t>(number), "");
-            }
-
-            case 'D':
-            {
-                double number{};
-                return get_number(input_format, number) && sax->number_float(static_cast<number_float_t>(number), "");
-            }
-
-            case 'H':
-            {
-                return get_ubjson_high_precision_number();
-            }
-
-            case 'C':  // char
-            {
-                get();
-                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "char")))
-                {
-                    return false;
-                }
-                if (JSON_HEDLEY_UNLIKELY(current > 127))
-                {
-                    auto last_token = get_token_string();
-                    return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read,
-                                            exception_message(input_format, concat("byte after 'C' must be in range 0x00..0x7F; last byte: 0x", last_token), "char"), nullptr));
-                }
-                string_t s(1, static_cast<typename string_t::value_type>(current));
-                return sax->string(s);
-            }
-
-            case 'S':  // string
-            {
-                string_t s;
-                return get_ubjson_string(s) && sax->string(s);
-            }
-
-            case '[':  // array
-                return get_ubjson_array();
-
-            case '{':  // object
-                return get_ubjson_object();
-
-            default: // anything else
-                break;
-        }
-        auto last_token = get_token_string();
-        return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read, exception_message(input_format, "invalid byte: 0x" + last_token, "value"), nullptr));
-    }
-
-    /*!
-    @return whether array creation completed
-    */
-    bool get_ubjson_array()
-    {
-        std::pair<std::size_t, char_int_type> size_and_type;
-        if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_type(size_and_type)))
-        {
-            return false;
-        }
-
-        // if bit-8 of size_and_type.second is set to 1, encode bjdata ndarray as an object in JData annotated array format (https://github.com/NeuroJSON/jdata):
-        // {"_ArrayType_" : "typeid", "_ArraySize_" : [n1, n2, ...], "_ArrayData_" : [v1, v2, ...]}
-
-        if (input_format == input_format_t::bjdata && size_and_type.first != npos && (size_and_type.second & (1 << 8)) != 0)
-        {
-            size_and_type.second &= ~(static_cast<char_int_type>(1) << 8);  // use bit 8 to indicate ndarray, here we remove the bit to restore the type marker
-            auto it = std::lower_bound(bjd_types_map.begin(), bjd_types_map.end(), size_and_type.second, [](const bjd_type & p, char_int_type t)
-            {
-                return p.first < t;
-            });
-            string_t key = "_ArrayType_";
-            if (JSON_HEDLEY_UNLIKELY(it == bjd_types_map.end() || it->first != size_and_type.second))
-            {
-                auto last_token = get_token_string();
-                return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
-                                        exception_message(input_format, "invalid byte: 0x" + last_token, "type"), nullptr));
-            }
-
-            string_t type = it->second; // sax->string() takes a reference
-            if (JSON_HEDLEY_UNLIKELY(!sax->key(key) || !sax->string(type)))
-            {
-                return false;
-            }
-
-            if (size_and_type.second == 'C')
-            {
-                size_and_type.second = 'U';
-            }
-
-            key = "_ArrayData_";
-            if (JSON_HEDLEY_UNLIKELY(!sax->key(key) || !sax->start_array(size_and_type.first) ))
-            {
-                return false;
-            }
-
-            for (std::size_t i = 0; i < size_and_type.first; ++i)
-            {
-                if (JSON_HEDLEY_UNLIKELY(!get_ubjson_value(size_and_type.second)))
-                {
-                    return false;
-                }
-            }
-
-            return (sax->end_array() && sax->end_object());
-        }
-
-        if (size_and_type.first != npos)
-        {
-            if (JSON_HEDLEY_UNLIKELY(!sax->start_array(size_and_type.first)))
-            {
-                return false;
-            }
-
-            if (size_and_type.second != 0)
-            {
-                if (size_and_type.second != 'N')
-                {
-                    for (std::size_t i = 0; i < size_and_type.first; ++i)
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!get_ubjson_value(size_and_type.second)))
-                        {
-                            return false;
-                        }
-                    }
-                }
-            }
-            else
-            {
-                for (std::size_t i = 0; i < size_and_type.first; ++i)
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal()))
-                    {
-                        return false;
-                    }
-                }
-            }
-        }
-        else
-        {
-            if (JSON_HEDLEY_UNLIKELY(!sax->start_array(static_cast<std::size_t>(-1))))
-            {
-                return false;
-            }
-
-            while (current != ']')
-            {
-                if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal(false)))
-                {
-                    return false;
-                }
-                get_ignore_noop();
-            }
-        }
-
-        return sax->end_array();
-    }
-
-    /*!
-    @return whether object creation completed
-    */
-    bool get_ubjson_object()
-    {
-        std::pair<std::size_t, char_int_type> size_and_type;
-        if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_type(size_and_type)))
-        {
-            return false;
-        }
-
-        // do not accept ND-array size in objects in BJData
-        if (input_format == input_format_t::bjdata && size_and_type.first != npos && (size_and_type.second & (1 << 8)) != 0)
-        {
-            auto last_token = get_token_string();
-            return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
-                                    exception_message(input_format, "BJData object does not support ND-array size in optimized format", "object"), nullptr));
-        }
-
-        string_t key;
-        if (size_and_type.first != npos)
-        {
-            if (JSON_HEDLEY_UNLIKELY(!sax->start_object(size_and_type.first)))
-            {
-                return false;
-            }
-
-            if (size_and_type.second != 0)
-            {
-                for (std::size_t i = 0; i < size_and_type.first; ++i)
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!get_ubjson_string(key) || !sax->key(key)))
-                    {
-                        return false;
-                    }
-                    if (JSON_HEDLEY_UNLIKELY(!get_ubjson_value(size_and_type.second)))
-                    {
-                        return false;
-                    }
-                    key.clear();
-                }
-            }
-            else
-            {
-                for (std::size_t i = 0; i < size_and_type.first; ++i)
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!get_ubjson_string(key) || !sax->key(key)))
-                    {
-                        return false;
-                    }
-                    if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal()))
-                    {
-                        return false;
-                    }
-                    key.clear();
-                }
-            }
-        }
-        else
-        {
-            if (JSON_HEDLEY_UNLIKELY(!sax->start_object(static_cast<std::size_t>(-1))))
-            {
-                return false;
-            }
-
-            while (current != '}')
-            {
-                if (JSON_HEDLEY_UNLIKELY(!get_ubjson_string(key, false) || !sax->key(key)))
-                {
-                    return false;
-                }
-                if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal()))
-                {
-                    return false;
-                }
-                get_ignore_noop();
-                key.clear();
-            }
-        }
-
-        return sax->end_object();
-    }
-
-    // Note, no reader for UBJSON binary types is implemented because they do
-    // not exist
-
-    bool get_ubjson_high_precision_number()
-    {
-        // get size of following number string
-        std::size_t size{};
-        bool no_ndarray = true;
-        auto res = get_ubjson_size_value(size, no_ndarray);
-        if (JSON_HEDLEY_UNLIKELY(!res))
-        {
-            return res;
-        }
-
-        // get number string
-        std::vector<char> number_vector;
-        for (std::size_t i = 0; i < size; ++i)
-        {
-            get();
-            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "number")))
-            {
-                return false;
-            }
-            number_vector.push_back(static_cast<char>(current));
-        }
-
-        // parse number string
-        using ia_type = decltype(detail::input_adapter(number_vector));
-        auto number_lexer = detail::lexer<BasicJsonType, ia_type>(detail::input_adapter(number_vector), false);
-        const auto result_number = number_lexer.scan();
-        const auto number_string = number_lexer.get_token_string();
-        const auto result_remainder = number_lexer.scan();
-
-        using token_type = typename detail::lexer_base<BasicJsonType>::token_type;
-
-        if (JSON_HEDLEY_UNLIKELY(result_remainder != token_type::end_of_input))
-        {
-            return sax->parse_error(chars_read, number_string, parse_error::create(115, chars_read,
-                                    exception_message(input_format, concat("invalid number text: ", number_lexer.get_token_string()), "high-precision number"), nullptr));
-        }
-
-        switch (result_number)
-        {
-            case token_type::value_integer:
-                return sax->number_integer(number_lexer.get_number_integer());
-            case token_type::value_unsigned:
-                return sax->number_unsigned(number_lexer.get_number_unsigned());
-            case token_type::value_float:
-                return sax->number_float(number_lexer.get_number_float(), std::move(number_string));
-            case token_type::uninitialized:
-            case token_type::literal_true:
-            case token_type::literal_false:
-            case token_type::literal_null:
-            case token_type::value_string:
-            case token_type::begin_array:
-            case token_type::begin_object:
-            case token_type::end_array:
-            case token_type::end_object:
-            case token_type::name_separator:
-            case token_type::value_separator:
-            case token_type::parse_error:
-            case token_type::end_of_input:
-            case token_type::literal_or_value:
-            default:
-                return sax->parse_error(chars_read, number_string, parse_error::create(115, chars_read,
-                                        exception_message(input_format, concat("invalid number text: ", number_lexer.get_token_string()), "high-precision number"), nullptr));
-        }
-    }
-
-    ///////////////////////
-    // Utility functions //
-    ///////////////////////
-
-    /*!
-    @brief get next character from the input
-
-    This function provides the interface to the used input adapter. It does
-    not throw in case the input reached EOF, but returns a -'ve valued
-    `std::char_traits<char_type>::eof()` in that case.
-
-    @return character read from the input
-    */
-    char_int_type get()
-    {
-        ++chars_read;
-        return current = ia.get_character();
-    }
-
-    /*!
-    @return character read from the input after ignoring all 'N' entries
-    */
-    char_int_type get_ignore_noop()
-    {
-        do
-        {
-            get();
-        }
-        while (current == 'N');
-
-        return current;
-    }
-
-    /*
-    @brief read a number from the input
-
-    @tparam NumberType the type of the number
-    @param[in] format   the current format (for diagnostics)
-    @param[out] result  number of type @a NumberType
-
-    @return whether conversion completed
-
-    @note This function needs to respect the system's endianness, because
-          bytes in CBOR, MessagePack, and UBJSON are stored in network order
-          (big endian) and therefore need reordering on little endian systems.
-          On the other hand, BSON and BJData use little endian and should reorder
-          on big endian systems.
-    */
-    template<typename NumberType, bool InputIsLittleEndian = false>
-    bool get_number(const input_format_t format, NumberType& result)
-    {
-        // step 1: read input into array with system's byte order
-        std::array<std::uint8_t, sizeof(NumberType)> vec{};
-        for (std::size_t i = 0; i < sizeof(NumberType); ++i)
-        {
-            get();
-            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(format, "number")))
-            {
-                return false;
-            }
-
-            // reverse byte order prior to conversion if necessary
-            if (is_little_endian != (InputIsLittleEndian || format == input_format_t::bjdata))
-            {
-                vec[sizeof(NumberType) - i - 1] = static_cast<std::uint8_t>(current);
-            }
-            else
-            {
-                vec[i] = static_cast<std::uint8_t>(current); // LCOV_EXCL_LINE
-            }
-        }
-
-        // step 2: convert array into number of type T and return
-        std::memcpy(&result, vec.data(), sizeof(NumberType));
-        return true;
-    }
-
-    /*!
-    @brief create a string by reading characters from the input
-
-    @tparam NumberType the type of the number
-    @param[in] format the current format (for diagnostics)
-    @param[in] len number of characters to read
-    @param[out] result string created by reading @a len bytes
-
-    @return whether string creation completed
-
-    @note We can not reserve @a len bytes for the result, because @a len
-          may be too large. Usually, @ref unexpect_eof() detects the end of
-          the input before we run out of string memory.
-    */
-    template<typename NumberType>
-    bool get_string(const input_format_t format,
-                    const NumberType len,
-                    string_t& result)
-    {
-        bool success = true;
-        for (NumberType i = 0; i < len; i++)
-        {
-            get();
-            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(format, "string")))
-            {
-                success = false;
-                break;
-            }
-            result.push_back(static_cast<typename string_t::value_type>(current));
-        }
-        return success;
-    }
-
-    /*!
-    @brief create a byte array by reading bytes from the input
-
-    @tparam NumberType the type of the number
-    @param[in] format the current format (for diagnostics)
-    @param[in] len number of bytes to read
-    @param[out] result byte array created by reading @a len bytes
-
-    @return whether byte array creation completed
-
-    @note We can not reserve @a len bytes for the result, because @a len
-          may be too large. Usually, @ref unexpect_eof() detects the end of
-          the input before we run out of memory.
-    */
-    template<typename NumberType>
-    bool get_binary(const input_format_t format,
-                    const NumberType len,
-                    binary_t& result)
-    {
-        bool success = true;
-        for (NumberType i = 0; i < len; i++)
-        {
-            get();
-            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(format, "binary")))
-            {
-                success = false;
-                break;
-            }
-            result.push_back(static_cast<std::uint8_t>(current));
-        }
-        return success;
-    }
-
-    /*!
-    @param[in] format   the current format (for diagnostics)
-    @param[in] context  further context information (for diagnostics)
-    @return whether the last read character is not EOF
-    */
-    JSON_HEDLEY_NON_NULL(3)
-    bool unexpect_eof(const input_format_t format, const char* context) const
-    {
-        if (JSON_HEDLEY_UNLIKELY(current == std::char_traits<char_type>::eof()))
-        {
-            return sax->parse_error(chars_read, "<end of file>",
-                                    parse_error::create(110, chars_read, exception_message(format, "unexpected end of input", context), nullptr));
-        }
-        return true;
-    }
-
-    /*!
-    @return a string representation of the last read byte
-    */
-    std::string get_token_string() const
-    {
-        std::array<char, 3> cr{{}};
-        static_cast<void>((std::snprintf)(cr.data(), cr.size(), "%.2hhX", static_cast<unsigned char>(current))); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-        return std::string{cr.data()};
-    }
-
-    /*!
-    @param[in] format   the current format
-    @param[in] detail   a detailed error message
-    @param[in] context  further context information
-    @return a message string to use in the parse_error exceptions
-    */
-    std::string exception_message(const input_format_t format,
-                                  const std::string& detail,
-                                  const std::string& context) const
-    {
-        std::string error_msg = "syntax error while parsing ";
-
-        switch (format)
-        {
-            case input_format_t::cbor:
-                error_msg += "CBOR";
-                break;
-
-            case input_format_t::msgpack:
-                error_msg += "MessagePack";
-                break;
-
-            case input_format_t::ubjson:
-                error_msg += "UBJSON";
-                break;
-
-            case input_format_t::bson:
-                error_msg += "BSON";
-                break;
-
-            case input_format_t::bjdata:
-                error_msg += "BJData";
-                break;
-
-            case input_format_t::json: // LCOV_EXCL_LINE
-            default:            // LCOV_EXCL_LINE
-                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-        }
-
-        return concat(error_msg, ' ', context, ": ", detail);
-    }
-
-  private:
-    static JSON_INLINE_VARIABLE constexpr std::size_t npos = static_cast<std::size_t>(-1);
-
-    /// input adapter
-    InputAdapterType ia;
-
-    /// the current character
-    char_int_type current = std::char_traits<char_type>::eof();
-
-    /// the number of characters read
-    std::size_t chars_read = 0;
-
-    /// whether we can assume little endianness
-    const bool is_little_endian = little_endianness();
-
-    /// input format
-    const input_format_t input_format = input_format_t::json;
-
-    /// the SAX parser
-    json_sax_t* sax = nullptr;
-
-    // excluded markers in bjdata optimized type
-#define JSON_BINARY_READER_MAKE_BJD_OPTIMIZED_TYPE_MARKERS_ \
-    make_array<char_int_type>('F', 'H', 'N', 'S', 'T', 'Z', '[', '{')
-
-#define JSON_BINARY_READER_MAKE_BJD_TYPES_MAP_ \
-    make_array<bjd_type>(                      \
-    bjd_type{'C', "char"},                     \
-    bjd_type{'D', "double"},                   \
-    bjd_type{'I', "int16"},                    \
-    bjd_type{'L', "int64"},                    \
-    bjd_type{'M', "uint64"},                   \
-    bjd_type{'U', "uint8"},                    \
-    bjd_type{'d', "single"},                   \
-    bjd_type{'i', "int8"},                     \
-    bjd_type{'l', "int32"},                    \
-    bjd_type{'m', "uint32"},                   \
-    bjd_type{'u', "uint16"})
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    // lookup tables
-    // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
-    const decltype(JSON_BINARY_READER_MAKE_BJD_OPTIMIZED_TYPE_MARKERS_) bjd_optimized_type_markers =
-        JSON_BINARY_READER_MAKE_BJD_OPTIMIZED_TYPE_MARKERS_;
-
-    using bjd_type = std::pair<char_int_type, string_t>;
-    // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
-    const decltype(JSON_BINARY_READER_MAKE_BJD_TYPES_MAP_) bjd_types_map =
-        JSON_BINARY_READER_MAKE_BJD_TYPES_MAP_;
-
-#undef JSON_BINARY_READER_MAKE_BJD_OPTIMIZED_TYPE_MARKERS_
-#undef JSON_BINARY_READER_MAKE_BJD_TYPES_MAP_
-};
-
-#ifndef JSON_HAS_CPP_17
-    template<typename BasicJsonType, typename InputAdapterType, typename SAX>
-    constexpr std::size_t binary_reader<BasicJsonType, InputAdapterType, SAX>::npos;
-#endif
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/input/input_adapters.hpp>
-
-// #include <nlohmann/detail/input/lexer.hpp>
-
-// #include <nlohmann/detail/input/parser.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cmath> // isfinite
-#include <cstdint> // uint8_t
-#include <functional> // function
-#include <string> // string
-#include <utility> // move
-#include <vector> // vector
-
-// #include <nlohmann/detail/exceptions.hpp>
-
-// #include <nlohmann/detail/input/input_adapters.hpp>
-
-// #include <nlohmann/detail/input/json_sax.hpp>
-
-// #include <nlohmann/detail/input/lexer.hpp>
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/meta/is_sax.hpp>
-
-// #include <nlohmann/detail/string_concat.hpp>
-
-// #include <nlohmann/detail/value_t.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-////////////
-// parser //
-////////////
-
-enum class parse_event_t : std::uint8_t
-{
-    /// the parser read `{` and started to process a JSON object
-    object_start,
-    /// the parser read `}` and finished processing a JSON object
-    object_end,
-    /// the parser read `[` and started to process a JSON array
-    array_start,
-    /// the parser read `]` and finished processing a JSON array
-    array_end,
-    /// the parser read a key of a value in an object
-    key,
-    /// the parser finished reading a JSON value
-    value
-};
-
-template<typename BasicJsonType>
-using parser_callback_t =
-    std::function<bool(int /*depth*/, parse_event_t /*event*/, BasicJsonType& /*parsed*/)>;
-
-/*!
-@brief syntax analysis
-
-This class implements a recursive descent parser.
-*/
-template<typename BasicJsonType, typename InputAdapterType>
-class parser
-{
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using string_t = typename BasicJsonType::string_t;
-    using lexer_t = lexer<BasicJsonType, InputAdapterType>;
-    using token_type = typename lexer_t::token_type;
-
-  public:
-    /// a parser reading from an input adapter
-    explicit parser(InputAdapterType&& adapter,
-                    const parser_callback_t<BasicJsonType> cb = nullptr,
-                    const bool allow_exceptions_ = true,
-                    const bool skip_comments = false)
-        : callback(cb)
-        , m_lexer(std::move(adapter), skip_comments)
-        , allow_exceptions(allow_exceptions_)
-    {
-        // read first token
-        get_token();
-    }
-
-    /*!
-    @brief public parser interface
-
-    @param[in] strict      whether to expect the last token to be EOF
-    @param[in,out] result  parsed JSON value
-
-    @throw parse_error.101 in case of an unexpected token
-    @throw parse_error.102 if to_unicode fails or surrogate error
-    @throw parse_error.103 if to_unicode fails
-    */
-    void parse(const bool strict, BasicJsonType& result)
-    {
-        if (callback)
-        {
-            json_sax_dom_callback_parser<BasicJsonType> sdp(result, callback, allow_exceptions);
-            sax_parse_internal(&sdp);
-
-            // in strict mode, input must be completely read
-            if (strict && (get_token() != token_type::end_of_input))
-            {
-                sdp.parse_error(m_lexer.get_position(),
-                                m_lexer.get_token_string(),
-                                parse_error::create(101, m_lexer.get_position(),
-                                                    exception_message(token_type::end_of_input, "value"), nullptr));
-            }
-
-            // in case of an error, return discarded value
-            if (sdp.is_errored())
-            {
-                result = value_t::discarded;
-                return;
-            }
-
-            // set top-level value to null if it was discarded by the callback
-            // function
-            if (result.is_discarded())
-            {
-                result = nullptr;
-            }
-        }
-        else
-        {
-            json_sax_dom_parser<BasicJsonType> sdp(result, allow_exceptions);
-            sax_parse_internal(&sdp);
-
-            // in strict mode, input must be completely read
-            if (strict && (get_token() != token_type::end_of_input))
-            {
-                sdp.parse_error(m_lexer.get_position(),
-                                m_lexer.get_token_string(),
-                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_of_input, "value"), nullptr));
-            }
-
-            // in case of an error, return discarded value
-            if (sdp.is_errored())
-            {
-                result = value_t::discarded;
-                return;
-            }
-        }
-
-        result.assert_invariant();
-    }
-
-    /*!
-    @brief public accept interface
-
-    @param[in] strict  whether to expect the last token to be EOF
-    @return whether the input is a proper JSON text
-    */
-    bool accept(const bool strict = true)
-    {
-        json_sax_acceptor<BasicJsonType> sax_acceptor;
-        return sax_parse(&sax_acceptor, strict);
-    }
-
-    template<typename SAX>
-    JSON_HEDLEY_NON_NULL(2)
-    bool sax_parse(SAX* sax, const bool strict = true)
-    {
-        (void)detail::is_sax_static_asserts<SAX, BasicJsonType> {};
-        const bool result = sax_parse_internal(sax);
-
-        // strict mode: next byte must be EOF
-        if (result && strict && (get_token() != token_type::end_of_input))
-        {
-            return sax->parse_error(m_lexer.get_position(),
-                                    m_lexer.get_token_string(),
-                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_of_input, "value"), nullptr));
-        }
-
-        return result;
-    }
-
-  private:
-    template<typename SAX>
-    JSON_HEDLEY_NON_NULL(2)
-    bool sax_parse_internal(SAX* sax)
-    {
-        // stack to remember the hierarchy of structured values we are parsing
-        // true = array; false = object
-        std::vector<bool> states;
-        // value to avoid a goto (see comment where set to true)
-        bool skip_to_state_evaluation = false;
-
-        while (true)
-        {
-            if (!skip_to_state_evaluation)
-            {
-                // invariant: get_token() was called before each iteration
-                switch (last_token)
-                {
-                    case token_type::begin_object:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->start_object(static_cast<std::size_t>(-1))))
-                        {
-                            return false;
-                        }
-
-                        // closing } -> we are done
-                        if (get_token() == token_type::end_object)
-                        {
-                            if (JSON_HEDLEY_UNLIKELY(!sax->end_object()))
-                            {
-                                return false;
-                            }
-                            break;
-                        }
-
-                        // parse key
-                        if (JSON_HEDLEY_UNLIKELY(last_token != token_type::value_string))
-                        {
-                            return sax->parse_error(m_lexer.get_position(),
-                                                    m_lexer.get_token_string(),
-                                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::value_string, "object key"), nullptr));
-                        }
-                        if (JSON_HEDLEY_UNLIKELY(!sax->key(m_lexer.get_string())))
-                        {
-                            return false;
-                        }
-
-                        // parse separator (:)
-                        if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::name_separator))
-                        {
-                            return sax->parse_error(m_lexer.get_position(),
-                                                    m_lexer.get_token_string(),
-                                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::name_separator, "object separator"), nullptr));
-                        }
-
-                        // remember we are now inside an object
-                        states.push_back(false);
-
-                        // parse values
-                        get_token();
-                        continue;
-                    }
-
-                    case token_type::begin_array:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->start_array(static_cast<std::size_t>(-1))))
-                        {
-                            return false;
-                        }
-
-                        // closing ] -> we are done
-                        if (get_token() == token_type::end_array)
-                        {
-                            if (JSON_HEDLEY_UNLIKELY(!sax->end_array()))
-                            {
-                                return false;
-                            }
-                            break;
-                        }
-
-                        // remember we are now inside an array
-                        states.push_back(true);
-
-                        // parse values (no need to call get_token)
-                        continue;
-                    }
-
-                    case token_type::value_float:
-                    {
-                        const auto res = m_lexer.get_number_float();
-
-                        if (JSON_HEDLEY_UNLIKELY(!std::isfinite(res)))
-                        {
-                            return sax->parse_error(m_lexer.get_position(),
-                                                    m_lexer.get_token_string(),
-                                                    out_of_range::create(406, concat("number overflow parsing '", m_lexer.get_token_string(), '\''), nullptr));
-                        }
-
-                        if (JSON_HEDLEY_UNLIKELY(!sax->number_float(res, m_lexer.get_string())))
-                        {
-                            return false;
-                        }
-
-                        break;
-                    }
-
-                    case token_type::literal_false:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->boolean(false)))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::literal_null:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->null()))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::literal_true:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->boolean(true)))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::value_integer:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->number_integer(m_lexer.get_number_integer())))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::value_string:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->string(m_lexer.get_string())))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::value_unsigned:
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!sax->number_unsigned(m_lexer.get_number_unsigned())))
-                        {
-                            return false;
-                        }
-                        break;
-                    }
-
-                    case token_type::parse_error:
-                    {
-                        // using "uninitialized" to avoid "expected" message
-                        return sax->parse_error(m_lexer.get_position(),
-                                                m_lexer.get_token_string(),
-                                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::uninitialized, "value"), nullptr));
-                    }
-
-                    case token_type::uninitialized:
-                    case token_type::end_array:
-                    case token_type::end_object:
-                    case token_type::name_separator:
-                    case token_type::value_separator:
-                    case token_type::end_of_input:
-                    case token_type::literal_or_value:
-                    default: // the last token was unexpected
-                    {
-                        return sax->parse_error(m_lexer.get_position(),
-                                                m_lexer.get_token_string(),
-                                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::literal_or_value, "value"), nullptr));
-                    }
-                }
-            }
-            else
-            {
-                skip_to_state_evaluation = false;
-            }
-
-            // we reached this line after we successfully parsed a value
-            if (states.empty())
-            {
-                // empty stack: we reached the end of the hierarchy: done
-                return true;
-            }
-
-            if (states.back())  // array
-            {
-                // comma -> next value
-                if (get_token() == token_type::value_separator)
-                {
-                    // parse a new value
-                    get_token();
-                    continue;
-                }
-
-                // closing ]
-                if (JSON_HEDLEY_LIKELY(last_token == token_type::end_array))
-                {
-                    if (JSON_HEDLEY_UNLIKELY(!sax->end_array()))
-                    {
-                        return false;
-                    }
-
-                    // We are done with this array. Before we can parse a
-                    // new value, we need to evaluate the new state first.
-                    // By setting skip_to_state_evaluation to false, we
-                    // are effectively jumping to the beginning of this if.
-                    JSON_ASSERT(!states.empty());
-                    states.pop_back();
-                    skip_to_state_evaluation = true;
-                    continue;
-                }
-
-                return sax->parse_error(m_lexer.get_position(),
-                                        m_lexer.get_token_string(),
-                                        parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_array, "array"), nullptr));
-            }
-
-            // states.back() is false -> object
-
-            // comma -> next value
-            if (get_token() == token_type::value_separator)
-            {
-                // parse key
-                if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::value_string))
-                {
-                    return sax->parse_error(m_lexer.get_position(),
-                                            m_lexer.get_token_string(),
-                                            parse_error::create(101, m_lexer.get_position(), exception_message(token_type::value_string, "object key"), nullptr));
-                }
-
-                if (JSON_HEDLEY_UNLIKELY(!sax->key(m_lexer.get_string())))
-                {
-                    return false;
-                }
-
-                // parse separator (:)
-                if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::name_separator))
-                {
-                    return sax->parse_error(m_lexer.get_position(),
-                                            m_lexer.get_token_string(),
-                                            parse_error::create(101, m_lexer.get_position(), exception_message(token_type::name_separator, "object separator"), nullptr));
-                }
-
-                // parse values
-                get_token();
-                continue;
-            }
-
-            // closing }
-            if (JSON_HEDLEY_LIKELY(last_token == token_type::end_object))
-            {
-                if (JSON_HEDLEY_UNLIKELY(!sax->end_object()))
-                {
-                    return false;
-                }
-
-                // We are done with this object. Before we can parse a
-                // new value, we need to evaluate the new state first.
-                // By setting skip_to_state_evaluation to false, we
-                // are effectively jumping to the beginning of this if.
-                JSON_ASSERT(!states.empty());
-                states.pop_back();
-                skip_to_state_evaluation = true;
-                continue;
-            }
-
-            return sax->parse_error(m_lexer.get_position(),
-                                    m_lexer.get_token_string(),
-                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_object, "object"), nullptr));
-        }
-    }
-
-    /// get next token from lexer
-    token_type get_token()
-    {
-        return last_token = m_lexer.scan();
-    }
-
-    std::string exception_message(const token_type expected, const std::string& context)
-    {
-        std::string error_msg = "syntax error ";
-
-        if (!context.empty())
-        {
-            error_msg += concat("while parsing ", context, ' ');
-        }
-
-        error_msg += "- ";
-
-        if (last_token == token_type::parse_error)
-        {
-            error_msg += concat(m_lexer.get_error_message(), "; last read: '",
-                                m_lexer.get_token_string(), '\'');
-        }
-        else
-        {
-            error_msg += concat("unexpected ", lexer_t::token_type_name(last_token));
-        }
-
-        if (expected != token_type::uninitialized)
-        {
-            error_msg += concat("; expected ", lexer_t::token_type_name(expected));
-        }
-
-        return error_msg;
-    }
-
-  private:
-    /// callback function
-    const parser_callback_t<BasicJsonType> callback = nullptr;
-    /// the type of the last read token
-    token_type last_token = token_type::uninitialized;
-    /// the lexer
-    lexer_t m_lexer;
-    /// whether to throw exceptions in case of errors
-    const bool allow_exceptions = true;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/iterators/internal_iterator.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-// #include <nlohmann/detail/iterators/primitive_iterator.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cstddef> // ptrdiff_t
-#include <limits>  // numeric_limits
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/*
-@brief an iterator for primitive JSON types
-
-This class models an iterator for primitive JSON types (boolean, number,
-string). It's only purpose is to allow the iterator/const_iterator classes
-to "iterate" over primitive values. Internally, the iterator is modeled by
-a `difference_type` variable. Value begin_value (`0`) models the begin,
-end_value (`1`) models past the end.
-*/
-class primitive_iterator_t
-{
-  private:
-    using difference_type = std::ptrdiff_t;
-    static constexpr difference_type begin_value = 0;
-    static constexpr difference_type end_value = begin_value + 1;
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    /// iterator as signed integer type
-    difference_type m_it = (std::numeric_limits<std::ptrdiff_t>::min)();
-
-  public:
-    constexpr difference_type get_value() const noexcept
-    {
-        return m_it;
-    }
-
-    /// set iterator to a defined beginning
-    void set_begin() noexcept
-    {
-        m_it = begin_value;
-    }
-
-    /// set iterator to a defined past the end
-    void set_end() noexcept
-    {
-        m_it = end_value;
-    }
-
-    /// return whether the iterator can be dereferenced
-    constexpr bool is_begin() const noexcept
-    {
-        return m_it == begin_value;
-    }
-
-    /// return whether the iterator is at end
-    constexpr bool is_end() const noexcept
-    {
-        return m_it == end_value;
-    }
-
-    friend constexpr bool operator==(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept
-    {
-        return lhs.m_it == rhs.m_it;
-    }
-
-    friend constexpr bool operator<(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept
-    {
-        return lhs.m_it < rhs.m_it;
-    }
-
-    primitive_iterator_t operator+(difference_type n) noexcept
-    {
-        auto result = *this;
-        result += n;
-        return result;
-    }
-
-    friend constexpr difference_type operator-(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept
-    {
-        return lhs.m_it - rhs.m_it;
-    }
-
-    primitive_iterator_t& operator++() noexcept
-    {
-        ++m_it;
-        return *this;
-    }
-
-    primitive_iterator_t operator++(int)& noexcept // NOLINT(cert-dcl21-cpp)
-    {
-        auto result = *this;
-        ++m_it;
-        return result;
-    }
-
-    primitive_iterator_t& operator--() noexcept
-    {
-        --m_it;
-        return *this;
-    }
-
-    primitive_iterator_t operator--(int)& noexcept // NOLINT(cert-dcl21-cpp)
-    {
-        auto result = *this;
-        --m_it;
-        return result;
-    }
-
-    primitive_iterator_t& operator+=(difference_type n) noexcept
-    {
-        m_it += n;
-        return *this;
-    }
-
-    primitive_iterator_t& operator-=(difference_type n) noexcept
-    {
-        m_it -= n;
-        return *this;
-    }
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/*!
-@brief an iterator value
-
-@note This structure could easily be a union, but MSVC currently does not allow
-unions members with complex constructors, see https://github.com/nlohmann/json/pull/105.
-*/
-template<typename BasicJsonType> struct internal_iterator
-{
-    /// iterator for JSON objects
-    typename BasicJsonType::object_t::iterator object_iterator {};
-    /// iterator for JSON arrays
-    typename BasicJsonType::array_t::iterator array_iterator {};
-    /// generic iterator for all other types
-    primitive_iterator_t primitive_iterator {};
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/iterators/iter_impl.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <iterator> // iterator, random_access_iterator_tag, bidirectional_iterator_tag, advance, next
-#include <type_traits> // conditional, is_const, remove_const
-
-// #include <nlohmann/detail/exceptions.hpp>
-
-// #include <nlohmann/detail/iterators/internal_iterator.hpp>
-
-// #include <nlohmann/detail/iterators/primitive_iterator.hpp>
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/meta/cpp_future.hpp>
-
-// #include <nlohmann/detail/meta/type_traits.hpp>
-
-// #include <nlohmann/detail/value_t.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-// forward declare, to be able to friend it later on
-template<typename IteratorType> class iteration_proxy;
-template<typename IteratorType> class iteration_proxy_value;
-
-/*!
-@brief a template for a bidirectional iterator for the @ref basic_json class
-This class implements a both iterators (iterator and const_iterator) for the
-@ref basic_json class.
-@note An iterator is called *initialized* when a pointer to a JSON value has
-      been set (e.g., by a constructor or a copy assignment). If the iterator is
-      default-constructed, it is *uninitialized* and most methods are undefined.
-      **The library uses assertions to detect calls on uninitialized iterators.**
-@requirement The class satisfies the following concept requirements:
--
-[BidirectionalIterator](https://en.cppreference.com/w/cpp/named_req/BidirectionalIterator):
-  The iterator that can be moved can be moved in both directions (i.e.
-  incremented and decremented).
-@since version 1.0.0, simplified in version 2.0.9, change to bidirectional
-       iterators in version 3.0.0 (see https://github.com/nlohmann/json/issues/593)
-*/
-template<typename BasicJsonType>
-class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-special-member-functions)
-{
-    /// the iterator with BasicJsonType of different const-ness
-    using other_iter_impl = iter_impl<typename std::conditional<std::is_const<BasicJsonType>::value, typename std::remove_const<BasicJsonType>::type, const BasicJsonType>::type>;
-    /// allow basic_json to access private members
-    friend other_iter_impl;
-    friend BasicJsonType;
-    friend iteration_proxy<iter_impl>;
-    friend iteration_proxy_value<iter_impl>;
-
-    using object_t = typename BasicJsonType::object_t;
-    using array_t = typename BasicJsonType::array_t;
-    // make sure BasicJsonType is basic_json or const basic_json
-    static_assert(is_basic_json<typename std::remove_const<BasicJsonType>::type>::value,
-                  "iter_impl only accepts (const) basic_json");
-    // superficial check for the LegacyBidirectionalIterator named requirement
-    static_assert(std::is_base_of<std::bidirectional_iterator_tag, std::bidirectional_iterator_tag>::value
-                  &&  std::is_base_of<std::bidirectional_iterator_tag, typename std::iterator_traits<typename array_t::iterator>::iterator_category>::value,
-                  "basic_json iterator assumes array and object type iterators satisfy the LegacyBidirectionalIterator named requirement.");
-
-  public:
-    /// The std::iterator class template (used as a base class to provide typedefs) is deprecated in C++17.
-    /// The C++ Standard has never required user-defined iterators to derive from std::iterator.
-    /// A user-defined iterator should provide publicly accessible typedefs named
-    /// iterator_category, value_type, difference_type, pointer, and reference.
-    /// Note that value_type is required to be non-const, even for constant iterators.
-    using iterator_category = std::bidirectional_iterator_tag;
-
-    /// the type of the values when the iterator is dereferenced
-    using value_type = typename BasicJsonType::value_type;
-    /// a type to represent differences between iterators
-    using difference_type = typename BasicJsonType::difference_type;
-    /// defines a pointer to the type iterated over (value_type)
-    using pointer = typename std::conditional<std::is_const<BasicJsonType>::value,
-          typename BasicJsonType::const_pointer,
-          typename BasicJsonType::pointer>::type;
-    /// defines a reference to the type iterated over (value_type)
-    using reference =
-        typename std::conditional<std::is_const<BasicJsonType>::value,
-        typename BasicJsonType::const_reference,
-        typename BasicJsonType::reference>::type;
-
-    iter_impl() = default;
-    ~iter_impl() = default;
-    iter_impl(iter_impl&&) noexcept = default;
-    iter_impl& operator=(iter_impl&&) noexcept = default;
-
-    /*!
-    @brief constructor for a given JSON instance
-    @param[in] object  pointer to a JSON object for this iterator
-    @pre object != nullptr
-    @post The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    explicit iter_impl(pointer object) noexcept : m_object(object)
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-            {
-                m_it.object_iterator = typename object_t::iterator();
-                break;
-            }
-
-            case value_t::array:
-            {
-                m_it.array_iterator = typename array_t::iterator();
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                m_it.primitive_iterator = primitive_iterator_t();
-                break;
-            }
-        }
-    }
-
-    /*!
-    @note The conventional copy constructor and copy assignment are implicitly
-          defined. Combined with the following converting constructor and
-          assignment, they support: (1) copy from iterator to iterator, (2)
-          copy from const iterator to const iterator, and (3) conversion from
-          iterator to const iterator. However conversion from const iterator
-          to iterator is not defined.
-    */
-
-    /*!
-    @brief const copy constructor
-    @param[in] other const iterator to copy from
-    @note This copy constructor had to be defined explicitly to circumvent a bug
-          occurring on msvc v19.0 compiler (VS 2015) debug build. For more
-          information refer to: https://github.com/nlohmann/json/issues/1608
-    */
-    iter_impl(const iter_impl<const BasicJsonType>& other) noexcept
-        : m_object(other.m_object), m_it(other.m_it)
-    {}
-
-    /*!
-    @brief converting assignment
-    @param[in] other const iterator to copy from
-    @return const/non-const iterator
-    @note It is not checked whether @a other is initialized.
-    */
-    iter_impl& operator=(const iter_impl<const BasicJsonType>& other) noexcept
-    {
-        if (&other != this)
-        {
-            m_object = other.m_object;
-            m_it = other.m_it;
-        }
-        return *this;
-    }
-
-    /*!
-    @brief converting constructor
-    @param[in] other  non-const iterator to copy from
-    @note It is not checked whether @a other is initialized.
-    */
-    iter_impl(const iter_impl<typename std::remove_const<BasicJsonType>::type>& other) noexcept
-        : m_object(other.m_object), m_it(other.m_it)
-    {}
-
-    /*!
-    @brief converting assignment
-    @param[in] other  non-const iterator to copy from
-    @return const/non-const iterator
-    @note It is not checked whether @a other is initialized.
-    */
-    iter_impl& operator=(const iter_impl<typename std::remove_const<BasicJsonType>::type>& other) noexcept // NOLINT(cert-oop54-cpp)
-    {
-        m_object = other.m_object;
-        m_it = other.m_it;
-        return *this;
-    }
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    /*!
-    @brief set the iterator to the first value
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    void set_begin() noexcept
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-            {
-                m_it.object_iterator = m_object->m_value.object->begin();
-                break;
-            }
-
-            case value_t::array:
-            {
-                m_it.array_iterator = m_object->m_value.array->begin();
-                break;
-            }
-
-            case value_t::null:
-            {
-                // set to end so begin()==end() is true: null is empty
-                m_it.primitive_iterator.set_end();
-                break;
-            }
-
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                m_it.primitive_iterator.set_begin();
-                break;
-            }
-        }
-    }
-
-    /*!
-    @brief set the iterator past the last value
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    void set_end() noexcept
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-            {
-                m_it.object_iterator = m_object->m_value.object->end();
-                break;
-            }
-
-            case value_t::array:
-            {
-                m_it.array_iterator = m_object->m_value.array->end();
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                m_it.primitive_iterator.set_end();
-                break;
-            }
-        }
-    }
-
-  public:
-    /*!
-    @brief return a reference to the value pointed to by the iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    reference operator*() const
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-            {
-                JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end());
-                return m_it.object_iterator->second;
-            }
-
-            case value_t::array:
-            {
-                JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end());
-                return *m_it.array_iterator;
-            }
-
-            case value_t::null:
-                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
-
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.is_begin()))
-                {
-                    return *m_object;
-                }
-
-                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
-            }
-        }
-    }
-
-    /*!
-    @brief dereference the iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    pointer operator->() const
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-            {
-                JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end());
-                return &(m_it.object_iterator->second);
-            }
-
-            case value_t::array:
-            {
-                JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end());
-                return &*m_it.array_iterator;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.is_begin()))
-                {
-                    return m_object;
-                }
-
-                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
-            }
-        }
-    }
-
-    /*!
-    @brief post-increment (it++)
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl operator++(int)& // NOLINT(cert-dcl21-cpp)
-    {
-        auto result = *this;
-        ++(*this);
-        return result;
-    }
-
-    /*!
-    @brief pre-increment (++it)
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl& operator++()
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-            {
-                std::advance(m_it.object_iterator, 1);
-                break;
-            }
-
-            case value_t::array:
-            {
-                std::advance(m_it.array_iterator, 1);
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                ++m_it.primitive_iterator;
-                break;
-            }
-        }
-
-        return *this;
-    }
-
-    /*!
-    @brief post-decrement (it--)
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl operator--(int)& // NOLINT(cert-dcl21-cpp)
-    {
-        auto result = *this;
-        --(*this);
-        return result;
-    }
-
-    /*!
-    @brief pre-decrement (--it)
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl& operator--()
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-            {
-                std::advance(m_it.object_iterator, -1);
-                break;
-            }
-
-            case value_t::array:
-            {
-                std::advance(m_it.array_iterator, -1);
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                --m_it.primitive_iterator;
-                break;
-            }
-        }
-
-        return *this;
-    }
-
-    /*!
-    @brief comparison: equal
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    template < typename IterImpl, detail::enable_if_t < (std::is_same<IterImpl, iter_impl>::value || std::is_same<IterImpl, other_iter_impl>::value), std::nullptr_t > = nullptr >
-    bool operator==(const IterImpl& other) const
-    {
-        // if objects are not the same, the comparison is undefined
-        if (JSON_HEDLEY_UNLIKELY(m_object != other.m_object))
-        {
-            JSON_THROW(invalid_iterator::create(212, "cannot compare iterators of different containers", m_object));
-        }
-
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-                return (m_it.object_iterator == other.m_it.object_iterator);
-
-            case value_t::array:
-                return (m_it.array_iterator == other.m_it.array_iterator);
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-                return (m_it.primitive_iterator == other.m_it.primitive_iterator);
-        }
-    }
-
-    /*!
-    @brief comparison: not equal
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    template < typename IterImpl, detail::enable_if_t < (std::is_same<IterImpl, iter_impl>::value || std::is_same<IterImpl, other_iter_impl>::value), std::nullptr_t > = nullptr >
-    bool operator!=(const IterImpl& other) const
-    {
-        return !operator==(other);
-    }
-
-    /*!
-    @brief comparison: smaller
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    bool operator<(const iter_impl& other) const
-    {
-        // if objects are not the same, the comparison is undefined
-        if (JSON_HEDLEY_UNLIKELY(m_object != other.m_object))
-        {
-            JSON_THROW(invalid_iterator::create(212, "cannot compare iterators of different containers", m_object));
-        }
-
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-                JSON_THROW(invalid_iterator::create(213, "cannot compare order of object iterators", m_object));
-
-            case value_t::array:
-                return (m_it.array_iterator < other.m_it.array_iterator);
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-                return (m_it.primitive_iterator < other.m_it.primitive_iterator);
-        }
-    }
-
-    /*!
-    @brief comparison: less than or equal
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    bool operator<=(const iter_impl& other) const
-    {
-        return !other.operator < (*this);
-    }
-
-    /*!
-    @brief comparison: greater than
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    bool operator>(const iter_impl& other) const
-    {
-        return !operator<=(other);
-    }
-
-    /*!
-    @brief comparison: greater than or equal
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    bool operator>=(const iter_impl& other) const
-    {
-        return !operator<(other);
-    }
-
-    /*!
-    @brief add to iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl& operator+=(difference_type i)
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-                JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object));
-
-            case value_t::array:
-            {
-                std::advance(m_it.array_iterator, i);
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                m_it.primitive_iterator += i;
-                break;
-            }
-        }
-
-        return *this;
-    }
-
-    /*!
-    @brief subtract from iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl& operator-=(difference_type i)
-    {
-        return operator+=(-i);
-    }
-
-    /*!
-    @brief add to iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl operator+(difference_type i) const
-    {
-        auto result = *this;
-        result += i;
-        return result;
-    }
-
-    /*!
-    @brief addition of distance and iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    friend iter_impl operator+(difference_type i, const iter_impl& it)
-    {
-        auto result = it;
-        result += i;
-        return result;
-    }
-
-    /*!
-    @brief subtract from iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    iter_impl operator-(difference_type i) const
-    {
-        auto result = *this;
-        result -= i;
-        return result;
-    }
-
-    /*!
-    @brief return difference
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    difference_type operator-(const iter_impl& other) const
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-                JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object));
-
-            case value_t::array:
-                return m_it.array_iterator - other.m_it.array_iterator;
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-                return m_it.primitive_iterator - other.m_it.primitive_iterator;
-        }
-    }
-
-    /*!
-    @brief access to successor
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    reference operator[](difference_type n) const
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        switch (m_object->m_type)
-        {
-            case value_t::object:
-                JSON_THROW(invalid_iterator::create(208, "cannot use operator[] for object iterators", m_object));
-
-            case value_t::array:
-                return *std::next(m_it.array_iterator, n);
-
-            case value_t::null:
-                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
-
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.get_value() == -n))
-                {
-                    return *m_object;
-                }
-
-                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
-            }
-        }
-    }
-
-    /*!
-    @brief return the key of an object iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    const typename object_t::key_type& key() const
-    {
-        JSON_ASSERT(m_object != nullptr);
-
-        if (JSON_HEDLEY_LIKELY(m_object->is_object()))
-        {
-            return m_it.object_iterator->first;
-        }
-
-        JSON_THROW(invalid_iterator::create(207, "cannot use key() for non-object iterators", m_object));
-    }
-
-    /*!
-    @brief return the value of an iterator
-    @pre The iterator is initialized; i.e. `m_object != nullptr`.
-    */
-    reference value() const
-    {
-        return operator*();
-    }
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    /// associated JSON instance
-    pointer m_object = nullptr;
-    /// the actual iterator of the associated instance
-    internal_iterator<typename std::remove_const<BasicJsonType>::type> m_it {};
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/iterators/iteration_proxy.hpp>
-
-// #include <nlohmann/detail/iterators/json_reverse_iterator.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <cstddef> // ptrdiff_t
-#include <iterator> // reverse_iterator
-#include <utility> // declval
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-//////////////////////
-// reverse_iterator //
-//////////////////////
-
-/*!
-@brief a template for a reverse iterator class
-
-@tparam Base the base iterator type to reverse. Valid types are @ref
-iterator (to create @ref reverse_iterator) and @ref const_iterator (to
-create @ref const_reverse_iterator).
-
-@requirement The class satisfies the following concept requirements:
--
-[BidirectionalIterator](https://en.cppreference.com/w/cpp/named_req/BidirectionalIterator):
-  The iterator that can be moved can be moved in both directions (i.e.
-  incremented and decremented).
-- [OutputIterator](https://en.cppreference.com/w/cpp/named_req/OutputIterator):
-  It is possible to write to the pointed-to element (only if @a Base is
-  @ref iterator).
-
-@since version 1.0.0
-*/
-template<typename Base>
-class json_reverse_iterator : public std::reverse_iterator<Base>
-{
-  public:
-    using difference_type = std::ptrdiff_t;
-    /// shortcut to the reverse iterator adapter
-    using base_iterator = std::reverse_iterator<Base>;
-    /// the reference type for the pointed-to element
-    using reference = typename Base::reference;
-
-    /// create reverse iterator from iterator
-    explicit json_reverse_iterator(const typename base_iterator::iterator_type& it) noexcept
-        : base_iterator(it) {}
-
-    /// create reverse iterator from base class
-    explicit json_reverse_iterator(const base_iterator& it) noexcept : base_iterator(it) {}
-
-    /// post-increment (it++)
-    json_reverse_iterator operator++(int)& // NOLINT(cert-dcl21-cpp)
-    {
-        return static_cast<json_reverse_iterator>(base_iterator::operator++(1));
-    }
-
-    /// pre-increment (++it)
-    json_reverse_iterator& operator++()
-    {
-        return static_cast<json_reverse_iterator&>(base_iterator::operator++());
-    }
-
-    /// post-decrement (it--)
-    json_reverse_iterator operator--(int)& // NOLINT(cert-dcl21-cpp)
-    {
-        return static_cast<json_reverse_iterator>(base_iterator::operator--(1));
-    }
-
-    /// pre-decrement (--it)
-    json_reverse_iterator& operator--()
-    {
-        return static_cast<json_reverse_iterator&>(base_iterator::operator--());
-    }
-
-    /// add to iterator
-    json_reverse_iterator& operator+=(difference_type i)
-    {
-        return static_cast<json_reverse_iterator&>(base_iterator::operator+=(i));
-    }
-
-    /// add to iterator
-    json_reverse_iterator operator+(difference_type i) const
-    {
-        return static_cast<json_reverse_iterator>(base_iterator::operator+(i));
-    }
-
-    /// subtract from iterator
-    json_reverse_iterator operator-(difference_type i) const
-    {
-        return static_cast<json_reverse_iterator>(base_iterator::operator-(i));
-    }
-
-    /// return difference
-    difference_type operator-(const json_reverse_iterator& other) const
-    {
-        return base_iterator(*this) - base_iterator(other);
-    }
-
-    /// access to successor
-    reference operator[](difference_type n) const
-    {
-        return *(this->operator+(n));
-    }
-
-    /// return the key of an object iterator
-    auto key() const -> decltype(std::declval<Base>().key())
-    {
-        auto it = --this->base();
-        return it.key();
-    }
-
-    /// return the value of an iterator
-    reference value() const
-    {
-        auto it = --this->base();
-        return it.operator * ();
-    }
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/iterators/primitive_iterator.hpp>
-
-// #include <nlohmann/detail/json_pointer.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <algorithm> // all_of
-#include <cctype> // isdigit
-#include <cerrno> // errno, ERANGE
-#include <cstdlib> // strtoull
-#ifndef JSON_NO_IO
-    #include <iosfwd> // ostream
-#endif  // JSON_NO_IO
-#include <limits> // max
-#include <numeric> // accumulate
-#include <string> // string
-#include <utility> // move
-#include <vector> // vector
-
-// #include <nlohmann/detail/exceptions.hpp>
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/string_concat.hpp>
-
-// #include <nlohmann/detail/string_escape.hpp>
-
-// #include <nlohmann/detail/value_t.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-/// @brief JSON Pointer defines a string syntax for identifying a specific value within a JSON document
-/// @sa https://json.nlohmann.me/api/json_pointer/
-template<typename RefStringType>
-class json_pointer
-{
-    // allow basic_json to access private members
-    NLOHMANN_BASIC_JSON_TPL_DECLARATION
-    friend class basic_json;
-
-    template<typename>
-    friend class json_pointer;
-
-    template<typename T>
-    struct string_t_helper
-    {
-        using type = T;
-    };
-
-    NLOHMANN_BASIC_JSON_TPL_DECLARATION
-    struct string_t_helper<NLOHMANN_BASIC_JSON_TPL>
-    {
-        using type = StringType;
-    };
-
-  public:
-    // for backwards compatibility accept BasicJsonType
-    using string_t = typename string_t_helper<RefStringType>::type;
-
-    /// @brief create JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/json_pointer/
-    explicit json_pointer(const string_t& s = "")
-        : reference_tokens(split(s))
-    {}
-
-    /// @brief return a string representation of the JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/to_string/
-    string_t to_string() const
-    {
-        return std::accumulate(reference_tokens.begin(), reference_tokens.end(),
-                               string_t{},
-                               [](const string_t& a, const string_t& b)
-        {
-            return detail::concat(a, '/', detail::escape(b));
-        });
-    }
-
-    /// @brief return a string representation of the JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_string/
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, to_string())
-    operator string_t() const
-    {
-        return to_string();
-    }
-
-#ifndef JSON_NO_IO
-    /// @brief write string representation of the JSON pointer to stream
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ltlt/
-    friend std::ostream& operator<<(std::ostream& o, const json_pointer& ptr)
-    {
-        o << ptr.to_string();
-        return o;
-    }
-#endif
-
-    /// @brief append another JSON pointer at the end of this JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slasheq/
-    json_pointer& operator/=(const json_pointer& ptr)
-    {
-        reference_tokens.insert(reference_tokens.end(),
-                                ptr.reference_tokens.begin(),
-                                ptr.reference_tokens.end());
-        return *this;
-    }
-
-    /// @brief append an unescaped reference token at the end of this JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slasheq/
-    json_pointer& operator/=(string_t token)
-    {
-        push_back(std::move(token));
-        return *this;
-    }
-
-    /// @brief append an array index at the end of this JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slasheq/
-    json_pointer& operator/=(std::size_t array_idx)
-    {
-        return *this /= std::to_string(array_idx);
-    }
-
-    /// @brief create a new JSON pointer by appending the right JSON pointer at the end of the left JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slash/
-    friend json_pointer operator/(const json_pointer& lhs,
-                                  const json_pointer& rhs)
-    {
-        return json_pointer(lhs) /= rhs;
-    }
-
-    /// @brief create a new JSON pointer by appending the unescaped token at the end of the JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slash/
-    friend json_pointer operator/(const json_pointer& lhs, string_t token) // NOLINT(performance-unnecessary-value-param)
-    {
-        return json_pointer(lhs) /= std::move(token);
-    }
-
-    /// @brief create a new JSON pointer by appending the array-index-token at the end of the JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slash/
-    friend json_pointer operator/(const json_pointer& lhs, std::size_t array_idx)
-    {
-        return json_pointer(lhs) /= array_idx;
-    }
-
-    /// @brief returns the parent of this JSON pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/parent_pointer/
-    json_pointer parent_pointer() const
-    {
-        if (empty())
-        {
-            return *this;
-        }
-
-        json_pointer res = *this;
-        res.pop_back();
-        return res;
-    }
-
-    /// @brief remove last reference token
-    /// @sa https://json.nlohmann.me/api/json_pointer/pop_back/
-    void pop_back()
-    {
-        if (JSON_HEDLEY_UNLIKELY(empty()))
-        {
-            JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", nullptr));
-        }
-
-        reference_tokens.pop_back();
-    }
-
-    /// @brief return last reference token
-    /// @sa https://json.nlohmann.me/api/json_pointer/back/
-    const string_t& back() const
-    {
-        if (JSON_HEDLEY_UNLIKELY(empty()))
-        {
-            JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", nullptr));
-        }
-
-        return reference_tokens.back();
-    }
-
-    /// @brief append an unescaped token at the end of the reference pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/push_back/
-    void push_back(const string_t& token)
-    {
-        reference_tokens.push_back(token);
-    }
-
-    /// @brief append an unescaped token at the end of the reference pointer
-    /// @sa https://json.nlohmann.me/api/json_pointer/push_back/
-    void push_back(string_t&& token)
-    {
-        reference_tokens.push_back(std::move(token));
-    }
-
-    /// @brief return whether pointer points to the root document
-    /// @sa https://json.nlohmann.me/api/json_pointer/empty/
-    bool empty() const noexcept
-    {
-        return reference_tokens.empty();
-    }
-
-  private:
-    /*!
-    @param[in] s  reference token to be converted into an array index
-
-    @return integer representation of @a s
-
-    @throw parse_error.106  if an array index begins with '0'
-    @throw parse_error.109  if an array index begins not with a digit
-    @throw out_of_range.404 if string @a s could not be converted to an integer
-    @throw out_of_range.410 if an array index exceeds size_type
-    */
-    template<typename BasicJsonType>
-    static typename BasicJsonType::size_type array_index(const string_t& s)
-    {
-        using size_type = typename BasicJsonType::size_type;
-
-        // error condition (cf. RFC 6901, Sect. 4)
-        if (JSON_HEDLEY_UNLIKELY(s.size() > 1 && s[0] == '0'))
-        {
-            JSON_THROW(detail::parse_error::create(106, 0, detail::concat("array index '", s, "' must not begin with '0'"), nullptr));
-        }
-
-        // error condition (cf. RFC 6901, Sect. 4)
-        if (JSON_HEDLEY_UNLIKELY(s.size() > 1 && !(s[0] >= '1' && s[0] <= '9')))
-        {
-            JSON_THROW(detail::parse_error::create(109, 0, detail::concat("array index '", s, "' is not a number"), nullptr));
-        }
-
-        const char* p = s.c_str();
-        char* p_end = nullptr;
-        errno = 0; // strtoull doesn't reset errno
-        unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int)
-        if (p == p_end // invalid input or empty string
-                || errno == ERANGE // out of range
-                || JSON_HEDLEY_UNLIKELY(static_cast<std::size_t>(p_end - p) != s.size())) // incomplete read
-        {
-            JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", s, "'"), nullptr));
-        }
-
-        // only triggered on special platforms (like 32bit), see also
-        // https://github.com/nlohmann/json/pull/2203
-        if (res >= static_cast<unsigned long long>((std::numeric_limits<size_type>::max)()))  // NOLINT(runtime/int)
-        {
-            JSON_THROW(detail::out_of_range::create(410, detail::concat("array index ", s, " exceeds size_type"), nullptr));   // LCOV_EXCL_LINE
-        }
-
-        return static_cast<size_type>(res);
-    }
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    json_pointer top() const
-    {
-        if (JSON_HEDLEY_UNLIKELY(empty()))
-        {
-            JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", nullptr));
-        }
-
-        json_pointer result = *this;
-        result.reference_tokens = {reference_tokens[0]};
-        return result;
-    }
-
-  private:
-    /*!
-    @brief create and return a reference to the pointed to value
-
-    @complexity Linear in the number of reference tokens.
-
-    @throw parse_error.109 if array index is not a number
-    @throw type_error.313 if value cannot be unflattened
-    */
-    template<typename BasicJsonType>
-    BasicJsonType& get_and_create(BasicJsonType& j) const
-    {
-        auto* result = &j;
-
-        // in case no reference tokens exist, return a reference to the JSON value
-        // j which will be overwritten by a primitive value
-        for (const auto& reference_token : reference_tokens)
-        {
-            switch (result->type())
-            {
-                case detail::value_t::null:
-                {
-                    if (reference_token == "0")
-                    {
-                        // start a new array if reference token is 0
-                        result = &result->operator[](0);
-                    }
-                    else
-                    {
-                        // start a new object otherwise
-                        result = &result->operator[](reference_token);
-                    }
-                    break;
-                }
-
-                case detail::value_t::object:
-                {
-                    // create an entry in the object
-                    result = &result->operator[](reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    // create an entry in the array
-                    result = &result->operator[](array_index<BasicJsonType>(reference_token));
-                    break;
-                }
-
-                /*
-                The following code is only reached if there exists a reference
-                token _and_ the current value is primitive. In this case, we have
-                an error situation, because primitive values may only occur as
-                single value; that is, with an empty list of reference tokens.
-                */
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                    JSON_THROW(detail::type_error::create(313, "invalid value to unflatten", &j));
-            }
-        }
-
-        return *result;
-    }
-
-    /*!
-    @brief return a reference to the pointed to value
-
-    @note This version does not throw if a value is not present, but tries to
-          create nested values instead. For instance, calling this function
-          with pointer `"/this/that"` on a null value is equivalent to calling
-          `operator[]("this").operator[]("that")` on that value, effectively
-          changing the null value to an object.
-
-    @param[in] ptr  a JSON value
-
-    @return reference to the JSON value pointed to by the JSON pointer
-
-    @complexity Linear in the length of the JSON pointer.
-
-    @throw parse_error.106   if an array index begins with '0'
-    @throw parse_error.109   if an array index was not a number
-    @throw out_of_range.404  if the JSON pointer can not be resolved
-    */
-    template<typename BasicJsonType>
-    BasicJsonType& get_unchecked(BasicJsonType* ptr) const
-    {
-        for (const auto& reference_token : reference_tokens)
-        {
-            // convert null values to arrays or objects before continuing
-            if (ptr->is_null())
-            {
-                // check if reference token is a number
-                const bool nums =
-                    std::all_of(reference_token.begin(), reference_token.end(),
-                                [](const unsigned char x)
-                {
-                    return std::isdigit(x);
-                });
-
-                // change value to array for numbers or "-" or to object otherwise
-                *ptr = (nums || reference_token == "-")
-                       ? detail::value_t::array
-                       : detail::value_t::object;
-            }
-
-            switch (ptr->type())
-            {
-                case detail::value_t::object:
-                {
-                    // use unchecked object access
-                    ptr = &ptr->operator[](reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    if (reference_token == "-")
-                    {
-                        // explicitly treat "-" as index beyond the end
-                        ptr = &ptr->operator[](ptr->m_value.array->size());
-                    }
-                    else
-                    {
-                        // convert array index to number; unchecked access
-                        ptr = &ptr->operator[](array_index<BasicJsonType>(reference_token));
-                    }
-                    break;
-                }
-
-                case detail::value_t::null:
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
-            }
-        }
-
-        return *ptr;
-    }
-
-    /*!
-    @throw parse_error.106   if an array index begins with '0'
-    @throw parse_error.109   if an array index was not a number
-    @throw out_of_range.402  if the array index '-' is used
-    @throw out_of_range.404  if the JSON pointer can not be resolved
-    */
-    template<typename BasicJsonType>
-    BasicJsonType& get_checked(BasicJsonType* ptr) const
-    {
-        for (const auto& reference_token : reference_tokens)
-        {
-            switch (ptr->type())
-            {
-                case detail::value_t::object:
-                {
-                    // note: at performs range check
-                    ptr = &ptr->at(reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
-                    {
-                        // "-" always fails the range check
-                        JSON_THROW(detail::out_of_range::create(402, detail::concat(
-                                "array index '-' (", std::to_string(ptr->m_value.array->size()),
-                                ") is out of range"), ptr));
-                    }
-
-                    // note: at performs range check
-                    ptr = &ptr->at(array_index<BasicJsonType>(reference_token));
-                    break;
-                }
-
-                case detail::value_t::null:
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
-            }
-        }
-
-        return *ptr;
-    }
-
-    /*!
-    @brief return a const reference to the pointed to value
-
-    @param[in] ptr  a JSON value
-
-    @return const reference to the JSON value pointed to by the JSON
-    pointer
-
-    @throw parse_error.106   if an array index begins with '0'
-    @throw parse_error.109   if an array index was not a number
-    @throw out_of_range.402  if the array index '-' is used
-    @throw out_of_range.404  if the JSON pointer can not be resolved
-    */
-    template<typename BasicJsonType>
-    const BasicJsonType& get_unchecked(const BasicJsonType* ptr) const
-    {
-        for (const auto& reference_token : reference_tokens)
-        {
-            switch (ptr->type())
-            {
-                case detail::value_t::object:
-                {
-                    // use unchecked object access
-                    ptr = &ptr->operator[](reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
-                    {
-                        // "-" cannot be used for const access
-                        JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_value.array->size()), ") is out of range"), ptr));
-                    }
-
-                    // use unchecked array access
-                    ptr = &ptr->operator[](array_index<BasicJsonType>(reference_token));
-                    break;
-                }
-
-                case detail::value_t::null:
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
-            }
-        }
-
-        return *ptr;
-    }
-
-    /*!
-    @throw parse_error.106   if an array index begins with '0'
-    @throw parse_error.109   if an array index was not a number
-    @throw out_of_range.402  if the array index '-' is used
-    @throw out_of_range.404  if the JSON pointer can not be resolved
-    */
-    template<typename BasicJsonType>
-    const BasicJsonType& get_checked(const BasicJsonType* ptr) const
-    {
-        for (const auto& reference_token : reference_tokens)
-        {
-            switch (ptr->type())
-            {
-                case detail::value_t::object:
-                {
-                    // note: at performs range check
-                    ptr = &ptr->at(reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
-                    {
-                        // "-" always fails the range check
-                        JSON_THROW(detail::out_of_range::create(402, detail::concat(
-                                "array index '-' (", std::to_string(ptr->m_value.array->size()),
-                                ") is out of range"), ptr));
-                    }
-
-                    // note: at performs range check
-                    ptr = &ptr->at(array_index<BasicJsonType>(reference_token));
-                    break;
-                }
-
-                case detail::value_t::null:
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
-            }
-        }
-
-        return *ptr;
-    }
-
-    /*!
-    @throw parse_error.106   if an array index begins with '0'
-    @throw parse_error.109   if an array index was not a number
-    */
-    template<typename BasicJsonType>
-    bool contains(const BasicJsonType* ptr) const
-    {
-        for (const auto& reference_token : reference_tokens)
-        {
-            switch (ptr->type())
-            {
-                case detail::value_t::object:
-                {
-                    if (!ptr->contains(reference_token))
-                    {
-                        // we did not find the key in the object
-                        return false;
-                    }
-
-                    ptr = &ptr->operator[](reference_token);
-                    break;
-                }
-
-                case detail::value_t::array:
-                {
-                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
-                    {
-                        // "-" always fails the range check
-                        return false;
-                    }
-                    if (JSON_HEDLEY_UNLIKELY(reference_token.size() == 1 && !("0" <= reference_token && reference_token <= "9")))
-                    {
-                        // invalid char
-                        return false;
-                    }
-                    if (JSON_HEDLEY_UNLIKELY(reference_token.size() > 1))
-                    {
-                        if (JSON_HEDLEY_UNLIKELY(!('1' <= reference_token[0] && reference_token[0] <= '9')))
-                        {
-                            // first char should be between '1' and '9'
-                            return false;
-                        }
-                        for (std::size_t i = 1; i < reference_token.size(); i++)
-                        {
-                            if (JSON_HEDLEY_UNLIKELY(!('0' <= reference_token[i] && reference_token[i] <= '9')))
-                            {
-                                // other char should be between '0' and '9'
-                                return false;
-                            }
-                        }
-                    }
-
-                    const auto idx = array_index<BasicJsonType>(reference_token);
-                    if (idx >= ptr->size())
-                    {
-                        // index out of range
-                        return false;
-                    }
-
-                    ptr = &ptr->operator[](idx);
-                    break;
-                }
-
-                case detail::value_t::null:
-                case detail::value_t::string:
-                case detail::value_t::boolean:
-                case detail::value_t::number_integer:
-                case detail::value_t::number_unsigned:
-                case detail::value_t::number_float:
-                case detail::value_t::binary:
-                case detail::value_t::discarded:
-                default:
-                {
-                    // we do not expect primitive values if there is still a
-                    // reference token to process
-                    return false;
-                }
-            }
-        }
-
-        // no reference token left means we found a primitive value
-        return true;
-    }
-
-    /*!
-    @brief split the string input to reference tokens
-
-    @note This function is only called by the json_pointer constructor.
-          All exceptions below are documented there.
-
-    @throw parse_error.107  if the pointer is not empty or begins with '/'
-    @throw parse_error.108  if character '~' is not followed by '0' or '1'
-    */
-    static std::vector<string_t> split(const string_t& reference_string)
-    {
-        std::vector<string_t> result;
-
-        // special case: empty reference string -> no reference tokens
-        if (reference_string.empty())
-        {
-            return result;
-        }
-
-        // check if nonempty reference string begins with slash
-        if (JSON_HEDLEY_UNLIKELY(reference_string[0] != '/'))
-        {
-            JSON_THROW(detail::parse_error::create(107, 1, detail::concat("JSON pointer must be empty or begin with '/' - was: '", reference_string, "'"), nullptr));
-        }
-
-        // extract the reference tokens:
-        // - slash: position of the last read slash (or end of string)
-        // - start: position after the previous slash
-        for (
-            // search for the first slash after the first character
-            std::size_t slash = reference_string.find_first_of('/', 1),
-            // set the beginning of the first reference token
-            start = 1;
-            // we can stop if start == 0 (if slash == string_t::npos)
-            start != 0;
-            // set the beginning of the next reference token
-            // (will eventually be 0 if slash == string_t::npos)
-            start = (slash == string_t::npos) ? 0 : slash + 1,
-            // find next slash
-            slash = reference_string.find_first_of('/', start))
-        {
-            // use the text between the beginning of the reference token
-            // (start) and the last slash (slash).
-            auto reference_token = reference_string.substr(start, slash - start);
-
-            // check reference tokens are properly escaped
-            for (std::size_t pos = reference_token.find_first_of('~');
-                    pos != string_t::npos;
-                    pos = reference_token.find_first_of('~', pos + 1))
-            {
-                JSON_ASSERT(reference_token[pos] == '~');
-
-                // ~ must be followed by 0 or 1
-                if (JSON_HEDLEY_UNLIKELY(pos == reference_token.size() - 1 ||
-                                         (reference_token[pos + 1] != '0' &&
-                                          reference_token[pos + 1] != '1')))
-                {
-                    JSON_THROW(detail::parse_error::create(108, 0, "escape character '~' must be followed with '0' or '1'", nullptr));
-                }
-            }
-
-            // finally, store the reference token
-            detail::unescape(reference_token);
-            result.push_back(reference_token);
-        }
-
-        return result;
-    }
-
-  private:
-    /*!
-    @param[in] reference_string  the reference string to the current value
-    @param[in] value             the value to consider
-    @param[in,out] result        the result object to insert values to
-
-    @note Empty objects or arrays are flattened to `null`.
-    */
-    template<typename BasicJsonType>
-    static void flatten(const string_t& reference_string,
-                        const BasicJsonType& value,
-                        BasicJsonType& result)
-    {
-        switch (value.type())
-        {
-            case detail::value_t::array:
-            {
-                if (value.m_value.array->empty())
-                {
-                    // flatten empty array as null
-                    result[reference_string] = nullptr;
-                }
-                else
-                {
-                    // iterate array and use index as reference string
-                    for (std::size_t i = 0; i < value.m_value.array->size(); ++i)
-                    {
-                        flatten(detail::concat(reference_string, '/', std::to_string(i)),
-                                value.m_value.array->operator[](i), result);
-                    }
-                }
-                break;
-            }
-
-            case detail::value_t::object:
-            {
-                if (value.m_value.object->empty())
-                {
-                    // flatten empty object as null
-                    result[reference_string] = nullptr;
-                }
-                else
-                {
-                    // iterate object and use keys as reference string
-                    for (const auto& element : *value.m_value.object)
-                    {
-                        flatten(detail::concat(reference_string, '/', detail::escape(element.first)), element.second, result);
-                    }
-                }
-                break;
-            }
-
-            case detail::value_t::null:
-            case detail::value_t::string:
-            case detail::value_t::boolean:
-            case detail::value_t::number_integer:
-            case detail::value_t::number_unsigned:
-            case detail::value_t::number_float:
-            case detail::value_t::binary:
-            case detail::value_t::discarded:
-            default:
-            {
-                // add primitive value with its reference string
-                result[reference_string] = value;
-                break;
-            }
-        }
-    }
-
-    /*!
-    @param[in] value  flattened JSON
-
-    @return unflattened JSON
-
-    @throw parse_error.109 if array index is not a number
-    @throw type_error.314  if value is not an object
-    @throw type_error.315  if object values are not primitive
-    @throw type_error.313  if value cannot be unflattened
-    */
-    template<typename BasicJsonType>
-    static BasicJsonType
-    unflatten(const BasicJsonType& value)
-    {
-        if (JSON_HEDLEY_UNLIKELY(!value.is_object()))
-        {
-            JSON_THROW(detail::type_error::create(314, "only objects can be unflattened", &value));
-        }
-
-        BasicJsonType result;
-
-        // iterate the JSON object values
-        for (const auto& element : *value.m_value.object)
-        {
-            if (JSON_HEDLEY_UNLIKELY(!element.second.is_primitive()))
-            {
-                JSON_THROW(detail::type_error::create(315, "values in object must be primitive", &element.second));
-            }
-
-            // assign value to reference pointed to by JSON pointer; Note that if
-            // the JSON pointer is "" (i.e., points to the whole value), function
-            // get_and_create returns a reference to result itself. An assignment
-            // will then create a primitive value.
-            json_pointer(element.first).get_and_create(result) = element.second;
-        }
-
-        return result;
-    }
-
-    // can't use conversion operator because of ambiguity
-    json_pointer<string_t> convert() const&
-    {
-        json_pointer<string_t> result;
-        result.reference_tokens = reference_tokens;
-        return result;
-    }
-
-    json_pointer<string_t> convert()&&
-    {
-        json_pointer<string_t> result;
-        result.reference_tokens = std::move(reference_tokens);
-        return result;
-    }
-
-  public:
-#if JSON_HAS_THREE_WAY_COMPARISON
-    /// @brief compares two JSON pointers for equality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
-    template<typename RefStringTypeRhs>
-    bool operator==(const json_pointer<RefStringTypeRhs>& rhs) const noexcept
-    {
-        return reference_tokens == rhs.reference_tokens;
-    }
-
-    /// @brief compares JSON pointer and string for equality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator==(json_pointer))
-    bool operator==(const string_t& rhs) const
-    {
-        return *this == json_pointer(rhs);
-    }
-
-    /// @brief 3-way compares two JSON pointers
-    template<typename RefStringTypeRhs>
-    std::strong_ordering operator<=>(const json_pointer<RefStringTypeRhs>& rhs) const noexcept // *NOPAD*
-    {
-        return  reference_tokens <=> rhs.reference_tokens; // *NOPAD*
-    }
-#else
-    /// @brief compares two JSON pointers for equality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
-    template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
-                           const json_pointer<RefStringTypeRhs>& rhs) noexcept;
-
-    /// @brief compares JSON pointer and string for equality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
-    template<typename RefStringTypeLhs, typename StringType>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
-                           const StringType& rhs);
-
-    /// @brief compares string and JSON pointer for equality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
-    template<typename RefStringTypeRhs, typename StringType>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator==(const StringType& lhs,
-                           const json_pointer<RefStringTypeRhs>& rhs);
-
-    /// @brief compares two JSON pointers for inequality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_ne/
-    template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
-                           const json_pointer<RefStringTypeRhs>& rhs) noexcept;
-
-    /// @brief compares JSON pointer and string for inequality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_ne/
-    template<typename RefStringTypeLhs, typename StringType>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
-                           const StringType& rhs);
-
-    /// @brief compares string and JSON pointer for inequality
-    /// @sa https://json.nlohmann.me/api/json_pointer/operator_ne/
-    template<typename RefStringTypeRhs, typename StringType>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator!=(const StringType& lhs,
-                           const json_pointer<RefStringTypeRhs>& rhs);
-
-    /// @brief compares two JSON pointer for less-than
-    template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-    // NOLINTNEXTLINE(readability-redundant-declaration)
-    friend bool operator<(const json_pointer<RefStringTypeLhs>& lhs,
-                          const json_pointer<RefStringTypeRhs>& rhs) noexcept;
-#endif
-
-  private:
-    /// the reference tokens
-    std::vector<string_t> reference_tokens;
-};
-
-#if !JSON_HAS_THREE_WAY_COMPARISON
-// functions cannot be defined inside class due to ODR violations
-template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-inline bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
-                       const json_pointer<RefStringTypeRhs>& rhs) noexcept
-{
-    return lhs.reference_tokens == rhs.reference_tokens;
-}
-
-template<typename RefStringTypeLhs,
-         typename StringType = typename json_pointer<RefStringTypeLhs>::string_t>
-JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator==(json_pointer, json_pointer))
-inline bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
-                       const StringType& rhs)
-{
-    return lhs == json_pointer<RefStringTypeLhs>(rhs);
-}
-
-template<typename RefStringTypeRhs,
-         typename StringType = typename json_pointer<RefStringTypeRhs>::string_t>
-JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator==(json_pointer, json_pointer))
-inline bool operator==(const StringType& lhs,
-                       const json_pointer<RefStringTypeRhs>& rhs)
-{
-    return json_pointer<RefStringTypeRhs>(lhs) == rhs;
-}
-
-template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-inline bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
-                       const json_pointer<RefStringTypeRhs>& rhs) noexcept
-{
-    return !(lhs == rhs);
-}
-
-template<typename RefStringTypeLhs,
-         typename StringType = typename json_pointer<RefStringTypeLhs>::string_t>
-JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator!=(json_pointer, json_pointer))
-inline bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
-                       const StringType& rhs)
-{
-    return !(lhs == rhs);
-}
-
-template<typename RefStringTypeRhs,
-         typename StringType = typename json_pointer<RefStringTypeRhs>::string_t>
-JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator!=(json_pointer, json_pointer))
-inline bool operator!=(const StringType& lhs,
-                       const json_pointer<RefStringTypeRhs>& rhs)
-{
-    return !(lhs == rhs);
-}
-
-template<typename RefStringTypeLhs, typename RefStringTypeRhs>
-inline bool operator<(const json_pointer<RefStringTypeLhs>& lhs,
-                      const json_pointer<RefStringTypeRhs>& rhs) noexcept
-{
-    return lhs.reference_tokens < rhs.reference_tokens;
-}
-#endif
-
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/json_ref.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <initializer_list>
-#include <utility>
-
-// #include <nlohmann/detail/abi_macros.hpp>
-
-// #include <nlohmann/detail/meta/type_traits.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-template<typename BasicJsonType>
-class json_ref
-{
-  public:
-    using value_type = BasicJsonType;
-
-    json_ref(value_type&& value)
-        : owned_value(std::move(value))
-    {}
-
-    json_ref(const value_type& value)
-        : value_ref(&value)
-    {}
-
-    json_ref(std::initializer_list<json_ref> init)
-        : owned_value(init)
-    {}
-
-    template <
-        class... Args,
-        enable_if_t<std::is_constructible<value_type, Args...>::value, int> = 0 >
-    json_ref(Args && ... args)
-        : owned_value(std::forward<Args>(args)...)
-    {}
-
-    // class should be movable only
-    json_ref(json_ref&&) noexcept = default;
-    json_ref(const json_ref&) = delete;
-    json_ref& operator=(const json_ref&) = delete;
-    json_ref& operator=(json_ref&&) = delete;
-    ~json_ref() = default;
-
-    value_type moved_or_copied() const
-    {
-        if (value_ref == nullptr)
-        {
-            return std::move(owned_value);
-        }
-        return *value_ref;
-    }
-
-    value_type const& operator*() const
-    {
-        return value_ref ? *value_ref : owned_value;
-    }
-
-    value_type const* operator->() const
-    {
-        return &** this;
-    }
-
-  private:
-    mutable value_type owned_value = nullptr;
-    value_type const* value_ref = nullptr;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/string_concat.hpp>
-
-// #include <nlohmann/detail/string_escape.hpp>
-
-// #include <nlohmann/detail/meta/cpp_future.hpp>
-
-// #include <nlohmann/detail/meta/type_traits.hpp>
-
-// #include <nlohmann/detail/output/binary_writer.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <algorithm> // reverse
-#include <array> // array
-#include <map> // map
-#include <cmath> // isnan, isinf
-#include <cstdint> // uint8_t, uint16_t, uint32_t, uint64_t
-#include <cstring> // memcpy
-#include <limits> // numeric_limits
-#include <string> // string
-#include <utility> // move
-#include <vector> // vector
-
-// #include <nlohmann/detail/input/binary_reader.hpp>
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/output/output_adapters.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <algorithm> // copy
-#include <cstddef> // size_t
-#include <iterator> // back_inserter
-#include <memory> // shared_ptr, make_shared
-#include <string> // basic_string
-#include <vector> // vector
-
-#ifndef JSON_NO_IO
-    #include <ios>      // streamsize
-    #include <ostream>  // basic_ostream
-#endif  // JSON_NO_IO
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/// abstract output adapter interface
-template<typename CharType> struct output_adapter_protocol
-{
-    virtual void write_character(CharType c) = 0;
-    virtual void write_characters(const CharType* s, std::size_t length) = 0;
-    virtual ~output_adapter_protocol() = default;
-
-    output_adapter_protocol() = default;
-    output_adapter_protocol(const output_adapter_protocol&) = default;
-    output_adapter_protocol(output_adapter_protocol&&) noexcept = default;
-    output_adapter_protocol& operator=(const output_adapter_protocol&) = default;
-    output_adapter_protocol& operator=(output_adapter_protocol&&) noexcept = default;
-};
-
-/// a type to simplify interfaces
-template<typename CharType>
-using output_adapter_t = std::shared_ptr<output_adapter_protocol<CharType>>;
-
-/// output adapter for byte vectors
-template<typename CharType, typename AllocatorType = std::allocator<CharType>>
-class output_vector_adapter : public output_adapter_protocol<CharType>
-{
-  public:
-    explicit output_vector_adapter(std::vector<CharType, AllocatorType>& vec) noexcept
-        : v(vec)
-    {}
-
-    void write_character(CharType c) override
-    {
-        v.push_back(c);
-    }
-
-    JSON_HEDLEY_NON_NULL(2)
-    void write_characters(const CharType* s, std::size_t length) override
-    {
-        v.insert(v.end(), s, s + length);
-    }
-
-  private:
-    std::vector<CharType, AllocatorType>& v;
-};
-
-#ifndef JSON_NO_IO
-/// output adapter for output streams
-template<typename CharType>
-class output_stream_adapter : public output_adapter_protocol<CharType>
-{
-  public:
-    explicit output_stream_adapter(std::basic_ostream<CharType>& s) noexcept
-        : stream(s)
-    {}
-
-    void write_character(CharType c) override
-    {
-        stream.put(c);
-    }
-
-    JSON_HEDLEY_NON_NULL(2)
-    void write_characters(const CharType* s, std::size_t length) override
-    {
-        stream.write(s, static_cast<std::streamsize>(length));
-    }
-
-  private:
-    std::basic_ostream<CharType>& stream;
-};
-#endif  // JSON_NO_IO
-
-/// output adapter for basic_string
-template<typename CharType, typename StringType = std::basic_string<CharType>>
-class output_string_adapter : public output_adapter_protocol<CharType>
-{
-  public:
-    explicit output_string_adapter(StringType& s) noexcept
-        : str(s)
-    {}
-
-    void write_character(CharType c) override
-    {
-        str.push_back(c);
-    }
-
-    JSON_HEDLEY_NON_NULL(2)
-    void write_characters(const CharType* s, std::size_t length) override
-    {
-        str.append(s, length);
-    }
-
-  private:
-    StringType& str;
-};
-
-template<typename CharType, typename StringType = std::basic_string<CharType>>
-class output_adapter
-{
-  public:
-    template<typename AllocatorType = std::allocator<CharType>>
-    output_adapter(std::vector<CharType, AllocatorType>& vec)
-        : oa(std::make_shared<output_vector_adapter<CharType, AllocatorType>>(vec)) {}
-
-#ifndef JSON_NO_IO
-    output_adapter(std::basic_ostream<CharType>& s)
-        : oa(std::make_shared<output_stream_adapter<CharType>>(s)) {}
-#endif  // JSON_NO_IO
-
-    output_adapter(StringType& s)
-        : oa(std::make_shared<output_string_adapter<CharType, StringType>>(s)) {}
-
-    operator output_adapter_t<CharType>()
-    {
-        return oa;
-    }
-
-  private:
-    output_adapter_t<CharType> oa = nullptr;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/string_concat.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-///////////////////
-// binary writer //
-///////////////////
-
-/*!
-@brief serialization to CBOR and MessagePack values
-*/
-template<typename BasicJsonType, typename CharType>
-class binary_writer
-{
-    using string_t = typename BasicJsonType::string_t;
-    using binary_t = typename BasicJsonType::binary_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-
-  public:
-    /*!
-    @brief create a binary writer
-
-    @param[in] adapter  output adapter to write to
-    */
-    explicit binary_writer(output_adapter_t<CharType> adapter) : oa(std::move(adapter))
-    {
-        JSON_ASSERT(oa);
-    }
-
-    /*!
-    @param[in] j  JSON value to serialize
-    @pre       j.type() == value_t::object
-    */
-    void write_bson(const BasicJsonType& j)
-    {
-        switch (j.type())
-        {
-            case value_t::object:
-            {
-                write_bson_object(*j.m_value.object);
-                break;
-            }
-
-            case value_t::null:
-            case value_t::array:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                JSON_THROW(type_error::create(317, concat("to serialize to BSON, top-level type must be object, but is ", j.type_name()), &j));
-            }
-        }
-    }
-
-    /*!
-    @param[in] j  JSON value to serialize
-    */
-    void write_cbor(const BasicJsonType& j)
-    {
-        switch (j.type())
-        {
-            case value_t::null:
-            {
-                oa->write_character(to_char_type(0xF6));
-                break;
-            }
-
-            case value_t::boolean:
-            {
-                oa->write_character(j.m_value.boolean
-                                    ? to_char_type(0xF5)
-                                    : to_char_type(0xF4));
-                break;
-            }
-
-            case value_t::number_integer:
-            {
-                if (j.m_value.number_integer >= 0)
-                {
-                    // CBOR does not differentiate between positive signed
-                    // integers and unsigned integers. Therefore, we used the
-                    // code from the value_t::number_unsigned case here.
-                    if (j.m_value.number_integer <= 0x17)
-                    {
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
-                    {
-                        oa->write_character(to_char_type(0x18));
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)())
-                    {
-                        oa->write_character(to_char_type(0x19));
-                        write_number(static_cast<std::uint16_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)())
-                    {
-                        oa->write_character(to_char_type(0x1A));
-                        write_number(static_cast<std::uint32_t>(j.m_value.number_integer));
-                    }
-                    else
-                    {
-                        oa->write_character(to_char_type(0x1B));
-                        write_number(static_cast<std::uint64_t>(j.m_value.number_integer));
-                    }
-                }
-                else
-                {
-                    // The conversions below encode the sign in the first
-                    // byte, and the value is converted to a positive number.
-                    const auto positive_number = -1 - j.m_value.number_integer;
-                    if (j.m_value.number_integer >= -24)
-                    {
-                        write_number(static_cast<std::uint8_t>(0x20 + positive_number));
-                    }
-                    else if (positive_number <= (std::numeric_limits<std::uint8_t>::max)())
-                    {
-                        oa->write_character(to_char_type(0x38));
-                        write_number(static_cast<std::uint8_t>(positive_number));
-                    }
-                    else if (positive_number <= (std::numeric_limits<std::uint16_t>::max)())
-                    {
-                        oa->write_character(to_char_type(0x39));
-                        write_number(static_cast<std::uint16_t>(positive_number));
-                    }
-                    else if (positive_number <= (std::numeric_limits<std::uint32_t>::max)())
-                    {
-                        oa->write_character(to_char_type(0x3A));
-                        write_number(static_cast<std::uint32_t>(positive_number));
-                    }
-                    else
-                    {
-                        oa->write_character(to_char_type(0x3B));
-                        write_number(static_cast<std::uint64_t>(positive_number));
-                    }
-                }
-                break;
-            }
-
-            case value_t::number_unsigned:
-            {
-                if (j.m_value.number_unsigned <= 0x17)
-                {
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_unsigned));
-                }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x18));
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_unsigned));
-                }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x19));
-                    write_number(static_cast<std::uint16_t>(j.m_value.number_unsigned));
-                }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x1A));
-                    write_number(static_cast<std::uint32_t>(j.m_value.number_unsigned));
-                }
-                else
-                {
-                    oa->write_character(to_char_type(0x1B));
-                    write_number(static_cast<std::uint64_t>(j.m_value.number_unsigned));
-                }
-                break;
-            }
-
-            case value_t::number_float:
-            {
-                if (std::isnan(j.m_value.number_float))
-                {
-                    // NaN is 0xf97e00 in CBOR
-                    oa->write_character(to_char_type(0xF9));
-                    oa->write_character(to_char_type(0x7E));
-                    oa->write_character(to_char_type(0x00));
-                }
-                else if (std::isinf(j.m_value.number_float))
-                {
-                    // Infinity is 0xf97c00, -Infinity is 0xf9fc00
-                    oa->write_character(to_char_type(0xf9));
-                    oa->write_character(j.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC));
-                    oa->write_character(to_char_type(0x00));
-                }
-                else
-                {
-                    write_compact_float(j.m_value.number_float, detail::input_format_t::cbor);
-                }
-                break;
-            }
-
-            case value_t::string:
-            {
-                // step 1: write control byte and the string length
-                const auto N = j.m_value.string->size();
-                if (N <= 0x17)
-                {
-                    write_number(static_cast<std::uint8_t>(0x60 + N));
-                }
-                else if (N <= (std::numeric_limits<std::uint8_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x78));
-                    write_number(static_cast<std::uint8_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x79));
-                    write_number(static_cast<std::uint16_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x7A));
-                    write_number(static_cast<std::uint32_t>(N));
-                }
-                // LCOV_EXCL_START
-                else if (N <= (std::numeric_limits<std::uint64_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x7B));
-                    write_number(static_cast<std::uint64_t>(N));
-                }
-                // LCOV_EXCL_STOP
-
-                // step 2: write the string
-                oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.string->c_str()),
-                    j.m_value.string->size());
-                break;
-            }
-
-            case value_t::array:
-            {
-                // step 1: write control byte and the array size
-                const auto N = j.m_value.array->size();
-                if (N <= 0x17)
-                {
-                    write_number(static_cast<std::uint8_t>(0x80 + N));
-                }
-                else if (N <= (std::numeric_limits<std::uint8_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x98));
-                    write_number(static_cast<std::uint8_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x99));
-                    write_number(static_cast<std::uint16_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x9A));
-                    write_number(static_cast<std::uint32_t>(N));
-                }
-                // LCOV_EXCL_START
-                else if (N <= (std::numeric_limits<std::uint64_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x9B));
-                    write_number(static_cast<std::uint64_t>(N));
-                }
-                // LCOV_EXCL_STOP
-
-                // step 2: write each element
-                for (const auto& el : *j.m_value.array)
-                {
-                    write_cbor(el);
-                }
-                break;
-            }
-
-            case value_t::binary:
-            {
-                if (j.m_value.binary->has_subtype())
-                {
-                    if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint8_t>::max)())
-                    {
-                        write_number(static_cast<std::uint8_t>(0xd8));
-                        write_number(static_cast<std::uint8_t>(j.m_value.binary->subtype()));
-                    }
-                    else if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint16_t>::max)())
-                    {
-                        write_number(static_cast<std::uint8_t>(0xd9));
-                        write_number(static_cast<std::uint16_t>(j.m_value.binary->subtype()));
-                    }
-                    else if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint32_t>::max)())
-                    {
-                        write_number(static_cast<std::uint8_t>(0xda));
-                        write_number(static_cast<std::uint32_t>(j.m_value.binary->subtype()));
-                    }
-                    else if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint64_t>::max)())
-                    {
-                        write_number(static_cast<std::uint8_t>(0xdb));
-                        write_number(static_cast<std::uint64_t>(j.m_value.binary->subtype()));
-                    }
-                }
-
-                // step 1: write control byte and the binary array size
-                const auto N = j.m_value.binary->size();
-                if (N <= 0x17)
-                {
-                    write_number(static_cast<std::uint8_t>(0x40 + N));
-                }
-                else if (N <= (std::numeric_limits<std::uint8_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x58));
-                    write_number(static_cast<std::uint8_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x59));
-                    write_number(static_cast<std::uint16_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x5A));
-                    write_number(static_cast<std::uint32_t>(N));
-                }
-                // LCOV_EXCL_START
-                else if (N <= (std::numeric_limits<std::uint64_t>::max)())
-                {
-                    oa->write_character(to_char_type(0x5B));
-                    write_number(static_cast<std::uint64_t>(N));
-                }
-                // LCOV_EXCL_STOP
-
-                // step 2: write each element
-                oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.binary->data()),
-                    N);
-
-                break;
-            }
-
-            case value_t::object:
-            {
-                // step 1: write control byte and the object size
-                const auto N = j.m_value.object->size();
-                if (N <= 0x17)
-                {
-                    write_number(static_cast<std::uint8_t>(0xA0 + N));
-                }
-                else if (N <= (std::numeric_limits<std::uint8_t>::max)())
-                {
-                    oa->write_character(to_char_type(0xB8));
-                    write_number(static_cast<std::uint8_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
-                {
-                    oa->write_character(to_char_type(0xB9));
-                    write_number(static_cast<std::uint16_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
-                {
-                    oa->write_character(to_char_type(0xBA));
-                    write_number(static_cast<std::uint32_t>(N));
-                }
-                // LCOV_EXCL_START
-                else if (N <= (std::numeric_limits<std::uint64_t>::max)())
-                {
-                    oa->write_character(to_char_type(0xBB));
-                    write_number(static_cast<std::uint64_t>(N));
-                }
-                // LCOV_EXCL_STOP
-
-                // step 2: write each element
-                for (const auto& el : *j.m_value.object)
-                {
-                    write_cbor(el.first);
-                    write_cbor(el.second);
-                }
-                break;
-            }
-
-            case value_t::discarded:
-            default:
-                break;
-        }
-    }
-
-    /*!
-    @param[in] j  JSON value to serialize
-    */
-    void write_msgpack(const BasicJsonType& j)
-    {
-        switch (j.type())
-        {
-            case value_t::null: // nil
-            {
-                oa->write_character(to_char_type(0xC0));
-                break;
-            }
-
-            case value_t::boolean: // true and false
-            {
-                oa->write_character(j.m_value.boolean
-                                    ? to_char_type(0xC3)
-                                    : to_char_type(0xC2));
-                break;
-            }
-
-            case value_t::number_integer:
-            {
-                if (j.m_value.number_integer >= 0)
-                {
-                    // MessagePack does not differentiate between positive
-                    // signed integers and unsigned integers. Therefore, we used
-                    // the code from the value_t::number_unsigned case here.
-                    if (j.m_value.number_unsigned < 128)
-                    {
-                        // positive fixnum
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
-                    {
-                        // uint 8
-                        oa->write_character(to_char_type(0xCC));
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
-                    {
-                        // uint 16
-                        oa->write_character(to_char_type(0xCD));
-                        write_number(static_cast<std::uint16_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
-                    {
-                        // uint 32
-                        oa->write_character(to_char_type(0xCE));
-                        write_number(static_cast<std::uint32_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
-                    {
-                        // uint 64
-                        oa->write_character(to_char_type(0xCF));
-                        write_number(static_cast<std::uint64_t>(j.m_value.number_integer));
-                    }
-                }
-                else
-                {
-                    if (j.m_value.number_integer >= -32)
-                    {
-                        // negative fixnum
-                        write_number(static_cast<std::int8_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int8_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
-                    {
-                        // int 8
-                        oa->write_character(to_char_type(0xD0));
-                        write_number(static_cast<std::int8_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int16_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
-                    {
-                        // int 16
-                        oa->write_character(to_char_type(0xD1));
-                        write_number(static_cast<std::int16_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int32_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
-                    {
-                        // int 32
-                        oa->write_character(to_char_type(0xD2));
-                        write_number(static_cast<std::int32_t>(j.m_value.number_integer));
-                    }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int64_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
-                    {
-                        // int 64
-                        oa->write_character(to_char_type(0xD3));
-                        write_number(static_cast<std::int64_t>(j.m_value.number_integer));
-                    }
-                }
-                break;
-            }
-
-            case value_t::number_unsigned:
-            {
-                if (j.m_value.number_unsigned < 128)
-                {
-                    // positive fixnum
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
-                }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
-                {
-                    // uint 8
-                    oa->write_character(to_char_type(0xCC));
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
-                }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
-                {
-                    // uint 16
-                    oa->write_character(to_char_type(0xCD));
-                    write_number(static_cast<std::uint16_t>(j.m_value.number_integer));
-                }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
-                {
-                    // uint 32
-                    oa->write_character(to_char_type(0xCE));
-                    write_number(static_cast<std::uint32_t>(j.m_value.number_integer));
-                }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
-                {
-                    // uint 64
-                    oa->write_character(to_char_type(0xCF));
-                    write_number(static_cast<std::uint64_t>(j.m_value.number_integer));
-                }
-                break;
-            }
-
-            case value_t::number_float:
-            {
-                write_compact_float(j.m_value.number_float, detail::input_format_t::msgpack);
-                break;
-            }
-
-            case value_t::string:
-            {
-                // step 1: write control byte and the string length
-                const auto N = j.m_value.string->size();
-                if (N <= 31)
-                {
-                    // fixstr
-                    write_number(static_cast<std::uint8_t>(0xA0 | N));
-                }
-                else if (N <= (std::numeric_limits<std::uint8_t>::max)())
-                {
-                    // str 8
-                    oa->write_character(to_char_type(0xD9));
-                    write_number(static_cast<std::uint8_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
-                {
-                    // str 16
-                    oa->write_character(to_char_type(0xDA));
-                    write_number(static_cast<std::uint16_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
-                {
-                    // str 32
-                    oa->write_character(to_char_type(0xDB));
-                    write_number(static_cast<std::uint32_t>(N));
-                }
-
-                // step 2: write the string
-                oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.string->c_str()),
-                    j.m_value.string->size());
-                break;
-            }
-
-            case value_t::array:
-            {
-                // step 1: write control byte and the array size
-                const auto N = j.m_value.array->size();
-                if (N <= 15)
-                {
-                    // fixarray
-                    write_number(static_cast<std::uint8_t>(0x90 | N));
-                }
-                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
-                {
-                    // array 16
-                    oa->write_character(to_char_type(0xDC));
-                    write_number(static_cast<std::uint16_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
-                {
-                    // array 32
-                    oa->write_character(to_char_type(0xDD));
-                    write_number(static_cast<std::uint32_t>(N));
-                }
-
-                // step 2: write each element
-                for (const auto& el : *j.m_value.array)
-                {
-                    write_msgpack(el);
-                }
-                break;
-            }
-
-            case value_t::binary:
-            {
-                // step 0: determine if the binary type has a set subtype to
-                // determine whether or not to use the ext or fixext types
-                const bool use_ext = j.m_value.binary->has_subtype();
-
-                // step 1: write control byte and the byte string length
-                const auto N = j.m_value.binary->size();
-                if (N <= (std::numeric_limits<std::uint8_t>::max)())
-                {
-                    std::uint8_t output_type{};
-                    bool fixed = true;
-                    if (use_ext)
-                    {
-                        switch (N)
-                        {
-                            case 1:
-                                output_type = 0xD4; // fixext 1
-                                break;
-                            case 2:
-                                output_type = 0xD5; // fixext 2
-                                break;
-                            case 4:
-                                output_type = 0xD6; // fixext 4
-                                break;
-                            case 8:
-                                output_type = 0xD7; // fixext 8
-                                break;
-                            case 16:
-                                output_type = 0xD8; // fixext 16
-                                break;
-                            default:
-                                output_type = 0xC7; // ext 8
-                                fixed = false;
-                                break;
-                        }
-
-                    }
-                    else
-                    {
-                        output_type = 0xC4; // bin 8
-                        fixed = false;
-                    }
-
-                    oa->write_character(to_char_type(output_type));
-                    if (!fixed)
-                    {
-                        write_number(static_cast<std::uint8_t>(N));
-                    }
-                }
-                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
-                {
-                    std::uint8_t output_type = use_ext
-                                               ? 0xC8 // ext 16
-                                               : 0xC5; // bin 16
-
-                    oa->write_character(to_char_type(output_type));
-                    write_number(static_cast<std::uint16_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
-                {
-                    std::uint8_t output_type = use_ext
-                                               ? 0xC9 // ext 32
-                                               : 0xC6; // bin 32
-
-                    oa->write_character(to_char_type(output_type));
-                    write_number(static_cast<std::uint32_t>(N));
-                }
-
-                // step 1.5: if this is an ext type, write the subtype
-                if (use_ext)
-                {
-                    write_number(static_cast<std::int8_t>(j.m_value.binary->subtype()));
-                }
-
-                // step 2: write the byte string
-                oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.binary->data()),
-                    N);
-
-                break;
-            }
-
-            case value_t::object:
-            {
-                // step 1: write control byte and the object size
-                const auto N = j.m_value.object->size();
-                if (N <= 15)
-                {
-                    // fixmap
-                    write_number(static_cast<std::uint8_t>(0x80 | (N & 0xF)));
-                }
-                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
-                {
-                    // map 16
-                    oa->write_character(to_char_type(0xDE));
-                    write_number(static_cast<std::uint16_t>(N));
-                }
-                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
-                {
-                    // map 32
-                    oa->write_character(to_char_type(0xDF));
-                    write_number(static_cast<std::uint32_t>(N));
-                }
-
-                // step 2: write each element
-                for (const auto& el : *j.m_value.object)
-                {
-                    write_msgpack(el.first);
-                    write_msgpack(el.second);
-                }
-                break;
-            }
-
-            case value_t::discarded:
-            default:
-                break;
-        }
-    }
-
-    /*!
-    @param[in] j  JSON value to serialize
-    @param[in] use_count   whether to use '#' prefixes (optimized format)
-    @param[in] use_type    whether to use '$' prefixes (optimized format)
-    @param[in] add_prefix  whether prefixes need to be used for this value
-    @param[in] use_bjdata  whether write in BJData format, default is false
-    */
-    void write_ubjson(const BasicJsonType& j, const bool use_count,
-                      const bool use_type, const bool add_prefix = true,
-                      const bool use_bjdata = false)
-    {
-        switch (j.type())
-        {
-            case value_t::null:
-            {
-                if (add_prefix)
-                {
-                    oa->write_character(to_char_type('Z'));
-                }
-                break;
-            }
-
-            case value_t::boolean:
-            {
-                if (add_prefix)
-                {
-                    oa->write_character(j.m_value.boolean
-                                        ? to_char_type('T')
-                                        : to_char_type('F'));
-                }
-                break;
-            }
-
-            case value_t::number_integer:
-            {
-                write_number_with_ubjson_prefix(j.m_value.number_integer, add_prefix, use_bjdata);
-                break;
-            }
-
-            case value_t::number_unsigned:
-            {
-                write_number_with_ubjson_prefix(j.m_value.number_unsigned, add_prefix, use_bjdata);
-                break;
-            }
-
-            case value_t::number_float:
-            {
-                write_number_with_ubjson_prefix(j.m_value.number_float, add_prefix, use_bjdata);
-                break;
-            }
-
-            case value_t::string:
-            {
-                if (add_prefix)
-                {
-                    oa->write_character(to_char_type('S'));
-                }
-                write_number_with_ubjson_prefix(j.m_value.string->size(), true, use_bjdata);
-                oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.string->c_str()),
-                    j.m_value.string->size());
-                break;
-            }
-
-            case value_t::array:
-            {
-                if (add_prefix)
-                {
-                    oa->write_character(to_char_type('['));
-                }
-
-                bool prefix_required = true;
-                if (use_type && !j.m_value.array->empty())
-                {
-                    JSON_ASSERT(use_count);
-                    const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata);
-                    const bool same_prefix = std::all_of(j.begin() + 1, j.end(),
-                                                         [this, first_prefix, use_bjdata](const BasicJsonType & v)
-                    {
-                        return ubjson_prefix(v, use_bjdata) == first_prefix;
-                    });
-
-                    std::vector<CharType> bjdx = {'[', '{', 'S', 'H', 'T', 'F', 'N', 'Z'}; // excluded markers in bjdata optimized type
-
-                    if (same_prefix && !(use_bjdata && std::find(bjdx.begin(), bjdx.end(), first_prefix) != bjdx.end()))
-                    {
-                        prefix_required = false;
-                        oa->write_character(to_char_type('$'));
-                        oa->write_character(first_prefix);
-                    }
-                }
-
-                if (use_count)
-                {
-                    oa->write_character(to_char_type('#'));
-                    write_number_with_ubjson_prefix(j.m_value.array->size(), true, use_bjdata);
-                }
-
-                for (const auto& el : *j.m_value.array)
-                {
-                    write_ubjson(el, use_count, use_type, prefix_required, use_bjdata);
-                }
-
-                if (!use_count)
-                {
-                    oa->write_character(to_char_type(']'));
-                }
-
-                break;
-            }
-
-            case value_t::binary:
-            {
-                if (add_prefix)
-                {
-                    oa->write_character(to_char_type('['));
-                }
-
-                if (use_type && !j.m_value.binary->empty())
-                {
-                    JSON_ASSERT(use_count);
-                    oa->write_character(to_char_type('$'));
-                    oa->write_character('U');
-                }
-
-                if (use_count)
-                {
-                    oa->write_character(to_char_type('#'));
-                    write_number_with_ubjson_prefix(j.m_value.binary->size(), true, use_bjdata);
-                }
-
-                if (use_type)
-                {
-                    oa->write_characters(
-                        reinterpret_cast<const CharType*>(j.m_value.binary->data()),
-                        j.m_value.binary->size());
-                }
-                else
-                {
-                    for (size_t i = 0; i < j.m_value.binary->size(); ++i)
-                    {
-                        oa->write_character(to_char_type('U'));
-                        oa->write_character(j.m_value.binary->data()[i]);
-                    }
-                }
-
-                if (!use_count)
-                {
-                    oa->write_character(to_char_type(']'));
-                }
-
-                break;
-            }
-
-            case value_t::object:
-            {
-                if (use_bjdata && j.m_value.object->size() == 3 && j.m_value.object->find("_ArrayType_") != j.m_value.object->end() && j.m_value.object->find("_ArraySize_") != j.m_value.object->end() && j.m_value.object->find("_ArrayData_") != j.m_value.object->end())
-                {
-                    if (!write_bjdata_ndarray(*j.m_value.object, use_count, use_type))  // decode bjdata ndarray in the JData format (https://github.com/NeuroJSON/jdata)
-                    {
-                        break;
-                    }
-                }
-
-                if (add_prefix)
-                {
-                    oa->write_character(to_char_type('{'));
-                }
-
-                bool prefix_required = true;
-                if (use_type && !j.m_value.object->empty())
-                {
-                    JSON_ASSERT(use_count);
-                    const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata);
-                    const bool same_prefix = std::all_of(j.begin(), j.end(),
-                                                         [this, first_prefix, use_bjdata](const BasicJsonType & v)
-                    {
-                        return ubjson_prefix(v, use_bjdata) == first_prefix;
-                    });
-
-                    std::vector<CharType> bjdx = {'[', '{', 'S', 'H', 'T', 'F', 'N', 'Z'}; // excluded markers in bjdata optimized type
-
-                    if (same_prefix && !(use_bjdata && std::find(bjdx.begin(), bjdx.end(), first_prefix) != bjdx.end()))
-                    {
-                        prefix_required = false;
-                        oa->write_character(to_char_type('$'));
-                        oa->write_character(first_prefix);
-                    }
-                }
-
-                if (use_count)
-                {
-                    oa->write_character(to_char_type('#'));
-                    write_number_with_ubjson_prefix(j.m_value.object->size(), true, use_bjdata);
-                }
-
-                for (const auto& el : *j.m_value.object)
-                {
-                    write_number_with_ubjson_prefix(el.first.size(), true, use_bjdata);
-                    oa->write_characters(
-                        reinterpret_cast<const CharType*>(el.first.c_str()),
-                        el.first.size());
-                    write_ubjson(el.second, use_count, use_type, prefix_required, use_bjdata);
-                }
-
-                if (!use_count)
-                {
-                    oa->write_character(to_char_type('}'));
-                }
-
-                break;
-            }
-
-            case value_t::discarded:
-            default:
-                break;
-        }
-    }
-
-  private:
-    //////////
-    // BSON //
-    //////////
-
-    /*!
-    @return The size of a BSON document entry header, including the id marker
-            and the entry name size (and its null-terminator).
-    */
-    static std::size_t calc_bson_entry_header_size(const string_t& name, const BasicJsonType& j)
-    {
-        const auto it = name.find(static_cast<typename string_t::value_type>(0));
-        if (JSON_HEDLEY_UNLIKELY(it != BasicJsonType::string_t::npos))
-        {
-            JSON_THROW(out_of_range::create(409, concat("BSON key cannot contain code point U+0000 (at byte ", std::to_string(it), ")"), &j));
-            static_cast<void>(j);
-        }
-
-        return /*id*/ 1ul + name.size() + /*zero-terminator*/1u;
-    }
-
-    /*!
-    @brief Writes the given @a element_type and @a name to the output adapter
-    */
-    void write_bson_entry_header(const string_t& name,
-                                 const std::uint8_t element_type)
-    {
-        oa->write_character(to_char_type(element_type)); // boolean
-        oa->write_characters(
-            reinterpret_cast<const CharType*>(name.c_str()),
-            name.size() + 1u);
-    }
-
-    /*!
-    @brief Writes a BSON element with key @a name and boolean value @a value
-    */
-    void write_bson_boolean(const string_t& name,
-                            const bool value)
-    {
-        write_bson_entry_header(name, 0x08);
-        oa->write_character(value ? to_char_type(0x01) : to_char_type(0x00));
-    }
-
-    /*!
-    @brief Writes a BSON element with key @a name and double value @a value
-    */
-    void write_bson_double(const string_t& name,
-                           const double value)
-    {
-        write_bson_entry_header(name, 0x01);
-        write_number<double>(value, true);
-    }
-
-    /*!
-    @return The size of the BSON-encoded string in @a value
-    */
-    static std::size_t calc_bson_string_size(const string_t& value)
-    {
-        return sizeof(std::int32_t) + value.size() + 1ul;
-    }
-
-    /*!
-    @brief Writes a BSON element with key @a name and string value @a value
-    */
-    void write_bson_string(const string_t& name,
-                           const string_t& value)
-    {
-        write_bson_entry_header(name, 0x02);
-
-        write_number<std::int32_t>(static_cast<std::int32_t>(value.size() + 1ul), true);
-        oa->write_characters(
-            reinterpret_cast<const CharType*>(value.c_str()),
-            value.size() + 1);
-    }
-
-    /*!
-    @brief Writes a BSON element with key @a name and null value
-    */
-    void write_bson_null(const string_t& name)
-    {
-        write_bson_entry_header(name, 0x0A);
-    }
-
-    /*!
-    @return The size of the BSON-encoded integer @a value
-    */
-    static std::size_t calc_bson_integer_size(const std::int64_t value)
-    {
-        return (std::numeric_limits<std::int32_t>::min)() <= value && value <= (std::numeric_limits<std::int32_t>::max)()
-               ? sizeof(std::int32_t)
-               : sizeof(std::int64_t);
-    }
-
-    /*!
-    @brief Writes a BSON element with key @a name and integer @a value
-    */
-    void write_bson_integer(const string_t& name,
-                            const std::int64_t value)
-    {
-        if ((std::numeric_limits<std::int32_t>::min)() <= value && value <= (std::numeric_limits<std::int32_t>::max)())
-        {
-            write_bson_entry_header(name, 0x10); // int32
-            write_number<std::int32_t>(static_cast<std::int32_t>(value), true);
-        }
-        else
-        {
-            write_bson_entry_header(name, 0x12); // int64
-            write_number<std::int64_t>(static_cast<std::int64_t>(value), true);
-        }
-    }
-
-    /*!
-    @return The size of the BSON-encoded unsigned integer in @a j
-    */
-    static constexpr std::size_t calc_bson_unsigned_size(const std::uint64_t value) noexcept
-    {
-        return (value <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
-               ? sizeof(std::int32_t)
-               : sizeof(std::int64_t);
-    }
-
-    /*!
-    @brief Writes a BSON element with key @a name and unsigned @a value
-    */
-    void write_bson_unsigned(const string_t& name,
-                             const BasicJsonType& j)
-    {
-        if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
-        {
-            write_bson_entry_header(name, 0x10 /* int32 */);
-            write_number<std::int32_t>(static_cast<std::int32_t>(j.m_value.number_unsigned), true);
-        }
-        else if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
-        {
-            write_bson_entry_header(name, 0x12 /* int64 */);
-            write_number<std::int64_t>(static_cast<std::int64_t>(j.m_value.number_unsigned), true);
-        }
-        else
-        {
-            JSON_THROW(out_of_range::create(407, concat("integer number ", std::to_string(j.m_value.number_unsigned), " cannot be represented by BSON as it does not fit int64"), &j));
-        }
-    }
-
-    /*!
-    @brief Writes a BSON element with key @a name and object @a value
-    */
-    void write_bson_object_entry(const string_t& name,
-                                 const typename BasicJsonType::object_t& value)
-    {
-        write_bson_entry_header(name, 0x03); // object
-        write_bson_object(value);
-    }
-
-    /*!
-    @return The size of the BSON-encoded array @a value
-    */
-    static std::size_t calc_bson_array_size(const typename BasicJsonType::array_t& value)
-    {
-        std::size_t array_index = 0ul;
-
-        const std::size_t embedded_document_size = std::accumulate(std::begin(value), std::end(value), static_cast<std::size_t>(0), [&array_index](std::size_t result, const typename BasicJsonType::array_t::value_type & el)
-        {
-            return result + calc_bson_element_size(std::to_string(array_index++), el);
-        });
-
-        return sizeof(std::int32_t) + embedded_document_size + 1ul;
-    }
-
-    /*!
-    @return The size of the BSON-encoded binary array @a value
-    */
-    static std::size_t calc_bson_binary_size(const typename BasicJsonType::binary_t& value)
-    {
-        return sizeof(std::int32_t) + value.size() + 1ul;
-    }
-
-    /*!
-    @brief Writes a BSON element with key @a name and array @a value
-    */
-    void write_bson_array(const string_t& name,
-                          const typename BasicJsonType::array_t& value)
-    {
-        write_bson_entry_header(name, 0x04); // array
-        write_number<std::int32_t>(static_cast<std::int32_t>(calc_bson_array_size(value)), true);
-
-        std::size_t array_index = 0ul;
-
-        for (const auto& el : value)
-        {
-            write_bson_element(std::to_string(array_index++), el);
-        }
-
-        oa->write_character(to_char_type(0x00));
-    }
-
-    /*!
-    @brief Writes a BSON element with key @a name and binary value @a value
-    */
-    void write_bson_binary(const string_t& name,
-                           const binary_t& value)
-    {
-        write_bson_entry_header(name, 0x05);
-
-        write_number<std::int32_t>(static_cast<std::int32_t>(value.size()), true);
-        write_number(value.has_subtype() ? static_cast<std::uint8_t>(value.subtype()) : static_cast<std::uint8_t>(0x00));
-
-        oa->write_characters(reinterpret_cast<const CharType*>(value.data()), value.size());
-    }
-
-    /*!
-    @brief Calculates the size necessary to serialize the JSON value @a j with its @a name
-    @return The calculated size for the BSON document entry for @a j with the given @a name.
-    */
-    static std::size_t calc_bson_element_size(const string_t& name,
-            const BasicJsonType& j)
-    {
-        const auto header_size = calc_bson_entry_header_size(name, j);
-        switch (j.type())
-        {
-            case value_t::object:
-                return header_size + calc_bson_object_size(*j.m_value.object);
-
-            case value_t::array:
-                return header_size + calc_bson_array_size(*j.m_value.array);
-
-            case value_t::binary:
-                return header_size + calc_bson_binary_size(*j.m_value.binary);
-
-            case value_t::boolean:
-                return header_size + 1ul;
-
-            case value_t::number_float:
-                return header_size + 8ul;
-
-            case value_t::number_integer:
-                return header_size + calc_bson_integer_size(j.m_value.number_integer);
-
-            case value_t::number_unsigned:
-                return header_size + calc_bson_unsigned_size(j.m_value.number_unsigned);
-
-            case value_t::string:
-                return header_size + calc_bson_string_size(*j.m_value.string);
-
-            case value_t::null:
-                return header_size + 0ul;
-
-            // LCOV_EXCL_START
-            case value_t::discarded:
-            default:
-                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert)
-                return 0ul;
-                // LCOV_EXCL_STOP
-        }
-    }
-
-    /*!
-    @brief Serializes the JSON value @a j to BSON and associates it with the
-           key @a name.
-    @param name The name to associate with the JSON entity @a j within the
-                current BSON document
-    */
-    void write_bson_element(const string_t& name,
-                            const BasicJsonType& j)
-    {
-        switch (j.type())
-        {
-            case value_t::object:
-                return write_bson_object_entry(name, *j.m_value.object);
-
-            case value_t::array:
-                return write_bson_array(name, *j.m_value.array);
-
-            case value_t::binary:
-                return write_bson_binary(name, *j.m_value.binary);
-
-            case value_t::boolean:
-                return write_bson_boolean(name, j.m_value.boolean);
-
-            case value_t::number_float:
-                return write_bson_double(name, j.m_value.number_float);
-
-            case value_t::number_integer:
-                return write_bson_integer(name, j.m_value.number_integer);
-
-            case value_t::number_unsigned:
-                return write_bson_unsigned(name, j);
-
-            case value_t::string:
-                return write_bson_string(name, *j.m_value.string);
-
-            case value_t::null:
-                return write_bson_null(name);
-
-            // LCOV_EXCL_START
-            case value_t::discarded:
-            default:
-                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert)
-                return;
-                // LCOV_EXCL_STOP
-        }
-    }
-
-    /*!
-    @brief Calculates the size of the BSON serialization of the given
-           JSON-object @a j.
-    @param[in] value  JSON value to serialize
-    @pre       value.type() == value_t::object
-    */
-    static std::size_t calc_bson_object_size(const typename BasicJsonType::object_t& value)
-    {
-        std::size_t document_size = std::accumulate(value.begin(), value.end(), static_cast<std::size_t>(0),
-                                    [](size_t result, const typename BasicJsonType::object_t::value_type & el)
-        {
-            return result += calc_bson_element_size(el.first, el.second);
-        });
-
-        return sizeof(std::int32_t) + document_size + 1ul;
-    }
-
-    /*!
-    @param[in] value  JSON value to serialize
-    @pre       value.type() == value_t::object
-    */
-    void write_bson_object(const typename BasicJsonType::object_t& value)
-    {
-        write_number<std::int32_t>(static_cast<std::int32_t>(calc_bson_object_size(value)), true);
-
-        for (const auto& el : value)
-        {
-            write_bson_element(el.first, el.second);
-        }
-
-        oa->write_character(to_char_type(0x00));
-    }
-
-    //////////
-    // CBOR //
-    //////////
-
-    static constexpr CharType get_cbor_float_prefix(float /*unused*/)
-    {
-        return to_char_type(0xFA);  // Single-Precision Float
-    }
-
-    static constexpr CharType get_cbor_float_prefix(double /*unused*/)
-    {
-        return to_char_type(0xFB);  // Double-Precision Float
-    }
-
-    /////////////
-    // MsgPack //
-    /////////////
-
-    static constexpr CharType get_msgpack_float_prefix(float /*unused*/)
-    {
-        return to_char_type(0xCA);  // float 32
-    }
-
-    static constexpr CharType get_msgpack_float_prefix(double /*unused*/)
-    {
-        return to_char_type(0xCB);  // float 64
-    }
-
-    ////////////
-    // UBJSON //
-    ////////////
-
-    // UBJSON: write number (floating point)
-    template<typename NumberType, typename std::enable_if<
-                 std::is_floating_point<NumberType>::value, int>::type = 0>
-    void write_number_with_ubjson_prefix(const NumberType n,
-                                         const bool add_prefix,
-                                         const bool use_bjdata)
-    {
-        if (add_prefix)
-        {
-            oa->write_character(get_ubjson_float_prefix(n));
-        }
-        write_number(n, use_bjdata);
-    }
-
-    // UBJSON: write number (unsigned integer)
-    template<typename NumberType, typename std::enable_if<
-                 std::is_unsigned<NumberType>::value, int>::type = 0>
-    void write_number_with_ubjson_prefix(const NumberType n,
-                                         const bool add_prefix,
-                                         const bool use_bjdata)
-    {
-        if (n <= static_cast<std::uint64_t>((std::numeric_limits<std::int8_t>::max)()))
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('i'));  // int8
-            }
-            write_number(static_cast<std::uint8_t>(n), use_bjdata);
-        }
-        else if (n <= (std::numeric_limits<std::uint8_t>::max)())
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('U'));  // uint8
-            }
-            write_number(static_cast<std::uint8_t>(n), use_bjdata);
-        }
-        else if (n <= static_cast<std::uint64_t>((std::numeric_limits<std::int16_t>::max)()))
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('I'));  // int16
-            }
-            write_number(static_cast<std::int16_t>(n), use_bjdata);
-        }
-        else if (use_bjdata && n <= static_cast<uint64_t>((std::numeric_limits<uint16_t>::max)()))
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('u'));  // uint16 - bjdata only
-            }
-            write_number(static_cast<std::uint16_t>(n), use_bjdata);
-        }
-        else if (n <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('l'));  // int32
-            }
-            write_number(static_cast<std::int32_t>(n), use_bjdata);
-        }
-        else if (use_bjdata && n <= static_cast<uint64_t>((std::numeric_limits<uint32_t>::max)()))
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('m'));  // uint32 - bjdata only
-            }
-            write_number(static_cast<std::uint32_t>(n), use_bjdata);
-        }
-        else if (n <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('L'));  // int64
-            }
-            write_number(static_cast<std::int64_t>(n), use_bjdata);
-        }
-        else if (use_bjdata && n <= (std::numeric_limits<uint64_t>::max)())
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('M'));  // uint64 - bjdata only
-            }
-            write_number(static_cast<std::uint64_t>(n), use_bjdata);
-        }
-        else
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('H'));  // high-precision number
-            }
-
-            const auto number = BasicJsonType(n).dump();
-            write_number_with_ubjson_prefix(number.size(), true, use_bjdata);
-            for (std::size_t i = 0; i < number.size(); ++i)
-            {
-                oa->write_character(to_char_type(static_cast<std::uint8_t>(number[i])));
-            }
-        }
-    }
-
-    // UBJSON: write number (signed integer)
-    template < typename NumberType, typename std::enable_if <
-                   std::is_signed<NumberType>::value&&
-                   !std::is_floating_point<NumberType>::value, int >::type = 0 >
-    void write_number_with_ubjson_prefix(const NumberType n,
-                                         const bool add_prefix,
-                                         const bool use_bjdata)
-    {
-        if ((std::numeric_limits<std::int8_t>::min)() <= n && n <= (std::numeric_limits<std::int8_t>::max)())
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('i'));  // int8
-            }
-            write_number(static_cast<std::int8_t>(n), use_bjdata);
-        }
-        else if (static_cast<std::int64_t>((std::numeric_limits<std::uint8_t>::min)()) <= n && n <= static_cast<std::int64_t>((std::numeric_limits<std::uint8_t>::max)()))
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('U'));  // uint8
-            }
-            write_number(static_cast<std::uint8_t>(n), use_bjdata);
-        }
-        else if ((std::numeric_limits<std::int16_t>::min)() <= n && n <= (std::numeric_limits<std::int16_t>::max)())
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('I'));  // int16
-            }
-            write_number(static_cast<std::int16_t>(n), use_bjdata);
-        }
-        else if (use_bjdata && (static_cast<std::int64_t>((std::numeric_limits<std::uint16_t>::min)()) <= n && n <= static_cast<std::int64_t>((std::numeric_limits<std::uint16_t>::max)())))
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('u'));  // uint16 - bjdata only
-            }
-            write_number(static_cast<uint16_t>(n), use_bjdata);
-        }
-        else if ((std::numeric_limits<std::int32_t>::min)() <= n && n <= (std::numeric_limits<std::int32_t>::max)())
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('l'));  // int32
-            }
-            write_number(static_cast<std::int32_t>(n), use_bjdata);
-        }
-        else if (use_bjdata && (static_cast<std::int64_t>((std::numeric_limits<std::uint32_t>::min)()) <= n && n <= static_cast<std::int64_t>((std::numeric_limits<std::uint32_t>::max)())))
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('m'));  // uint32 - bjdata only
-            }
-            write_number(static_cast<uint32_t>(n), use_bjdata);
-        }
-        else if ((std::numeric_limits<std::int64_t>::min)() <= n && n <= (std::numeric_limits<std::int64_t>::max)())
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('L'));  // int64
-            }
-            write_number(static_cast<std::int64_t>(n), use_bjdata);
-        }
-        // LCOV_EXCL_START
-        else
-        {
-            if (add_prefix)
-            {
-                oa->write_character(to_char_type('H'));  // high-precision number
-            }
-
-            const auto number = BasicJsonType(n).dump();
-            write_number_with_ubjson_prefix(number.size(), true, use_bjdata);
-            for (std::size_t i = 0; i < number.size(); ++i)
-            {
-                oa->write_character(to_char_type(static_cast<std::uint8_t>(number[i])));
-            }
-        }
-        // LCOV_EXCL_STOP
-    }
-
-    /*!
-    @brief determine the type prefix of container values
-    */
-    CharType ubjson_prefix(const BasicJsonType& j, const bool use_bjdata) const noexcept
-    {
-        switch (j.type())
-        {
-            case value_t::null:
-                return 'Z';
-
-            case value_t::boolean:
-                return j.m_value.boolean ? 'T' : 'F';
-
-            case value_t::number_integer:
-            {
-                if ((std::numeric_limits<std::int8_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
-                {
-                    return 'i';
-                }
-                if ((std::numeric_limits<std::uint8_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
-                {
-                    return 'U';
-                }
-                if ((std::numeric_limits<std::int16_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
-                {
-                    return 'I';
-                }
-                if (use_bjdata && ((std::numeric_limits<std::uint16_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)()))
-                {
-                    return 'u';
-                }
-                if ((std::numeric_limits<std::int32_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
-                {
-                    return 'l';
-                }
-                if (use_bjdata && ((std::numeric_limits<std::uint32_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)()))
-                {
-                    return 'm';
-                }
-                if ((std::numeric_limits<std::int64_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
-                {
-                    return 'L';
-                }
-                // anything else is treated as high-precision number
-                return 'H'; // LCOV_EXCL_LINE
-            }
-
-            case value_t::number_unsigned:
-            {
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int8_t>::max)()))
-                {
-                    return 'i';
-                }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint8_t>::max)()))
-                {
-                    return 'U';
-                }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int16_t>::max)()))
-                {
-                    return 'I';
-                }
-                if (use_bjdata && j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint16_t>::max)()))
-                {
-                    return 'u';
-                }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
-                {
-                    return 'l';
-                }
-                if (use_bjdata && j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint32_t>::max)()))
-                {
-                    return 'm';
-                }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
-                {
-                    return 'L';
-                }
-                if (use_bjdata && j.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
-                {
-                    return 'M';
-                }
-                // anything else is treated as high-precision number
-                return 'H'; // LCOV_EXCL_LINE
-            }
-
-            case value_t::number_float:
-                return get_ubjson_float_prefix(j.m_value.number_float);
-
-            case value_t::string:
-                return 'S';
-
-            case value_t::array: // fallthrough
-            case value_t::binary:
-                return '[';
-
-            case value_t::object:
-                return '{';
-
-            case value_t::discarded:
-            default:  // discarded values
-                return 'N';
-        }
-    }
-
-    static constexpr CharType get_ubjson_float_prefix(float /*unused*/)
-    {
-        return 'd';  // float 32
-    }
-
-    static constexpr CharType get_ubjson_float_prefix(double /*unused*/)
-    {
-        return 'D';  // float 64
-    }
-
-    /*!
-    @return false if the object is successfully converted to a bjdata ndarray, true if the type or size is invalid
-    */
-    bool write_bjdata_ndarray(const typename BasicJsonType::object_t& value, const bool use_count, const bool use_type)
-    {
-        std::map<string_t, CharType> bjdtype = {{"uint8", 'U'},  {"int8", 'i'},  {"uint16", 'u'}, {"int16", 'I'},
-            {"uint32", 'm'}, {"int32", 'l'}, {"uint64", 'M'}, {"int64", 'L'}, {"single", 'd'}, {"double", 'D'}, {"char", 'C'}
-        };
-
-        string_t key = "_ArrayType_";
-        auto it = bjdtype.find(static_cast<string_t>(value.at(key)));
-        if (it == bjdtype.end())
-        {
-            return true;
-        }
-        CharType dtype = it->second;
-
-        key = "_ArraySize_";
-        std::size_t len = (value.at(key).empty() ? 0 : 1);
-        for (const auto& el : value.at(key))
-        {
-            len *= static_cast<std::size_t>(el.m_value.number_unsigned);
-        }
-
-        key = "_ArrayData_";
-        if (value.at(key).size() != len)
-        {
-            return true;
-        }
-
-        oa->write_character('[');
-        oa->write_character('$');
-        oa->write_character(dtype);
-        oa->write_character('#');
-
-        key = "_ArraySize_";
-        write_ubjson(value.at(key), use_count, use_type, true,  true);
-
-        key = "_ArrayData_";
-        if (dtype == 'U' || dtype == 'C')
-        {
-            for (const auto& el : value.at(key))
-            {
-                write_number(static_cast<std::uint8_t>(el.m_value.number_unsigned), true);
-            }
-        }
-        else if (dtype == 'i')
-        {
-            for (const auto& el : value.at(key))
-            {
-                write_number(static_cast<std::int8_t>(el.m_value.number_integer), true);
-            }
-        }
-        else if (dtype == 'u')
-        {
-            for (const auto& el : value.at(key))
-            {
-                write_number(static_cast<std::uint16_t>(el.m_value.number_unsigned), true);
-            }
-        }
-        else if (dtype == 'I')
-        {
-            for (const auto& el : value.at(key))
-            {
-                write_number(static_cast<std::int16_t>(el.m_value.number_integer), true);
-            }
-        }
-        else if (dtype == 'm')
-        {
-            for (const auto& el : value.at(key))
-            {
-                write_number(static_cast<std::uint32_t>(el.m_value.number_unsigned), true);
-            }
-        }
-        else if (dtype == 'l')
-        {
-            for (const auto& el : value.at(key))
-            {
-                write_number(static_cast<std::int32_t>(el.m_value.number_integer), true);
-            }
-        }
-        else if (dtype == 'M')
-        {
-            for (const auto& el : value.at(key))
-            {
-                write_number(static_cast<std::uint64_t>(el.m_value.number_unsigned), true);
-            }
-        }
-        else if (dtype == 'L')
-        {
-            for (const auto& el : value.at(key))
-            {
-                write_number(static_cast<std::int64_t>(el.m_value.number_integer), true);
-            }
-        }
-        else if (dtype == 'd')
-        {
-            for (const auto& el : value.at(key))
-            {
-                write_number(static_cast<float>(el.m_value.number_float), true);
-            }
-        }
-        else if (dtype == 'D')
-        {
-            for (const auto& el : value.at(key))
-            {
-                write_number(static_cast<double>(el.m_value.number_float), true);
-            }
-        }
-        return false;
-    }
-
-    ///////////////////////
-    // Utility functions //
-    ///////////////////////
-
-    /*
-    @brief write a number to output input
-    @param[in] n number of type @a NumberType
-    @param[in] OutputIsLittleEndian Set to true if output data is
-                                 required to be little endian
-    @tparam NumberType the type of the number
-
-    @note This function needs to respect the system's endianness, because bytes
-          in CBOR, MessagePack, and UBJSON are stored in network order (big
-          endian) and therefore need reordering on little endian systems.
-          On the other hand, BSON and BJData use little endian and should reorder
-          on big endian systems.
-    */
-    template<typename NumberType>
-    void write_number(const NumberType n, const bool OutputIsLittleEndian = false)
-    {
-        // step 1: write number to array of length NumberType
-        std::array<CharType, sizeof(NumberType)> vec{};
-        std::memcpy(vec.data(), &n, sizeof(NumberType));
-
-        // step 2: write array to output (with possible reordering)
-        if (is_little_endian != OutputIsLittleEndian)
-        {
-            // reverse byte order prior to conversion if necessary
-            std::reverse(vec.begin(), vec.end());
-        }
-
-        oa->write_characters(vec.data(), sizeof(NumberType));
-    }
-
-    void write_compact_float(const number_float_t n, detail::input_format_t format)
-    {
-#ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wfloat-equal"
-#endif
-        if (static_cast<double>(n) >= static_cast<double>(std::numeric_limits<float>::lowest()) &&
-                static_cast<double>(n) <= static_cast<double>((std::numeric_limits<float>::max)()) &&
-                static_cast<double>(static_cast<float>(n)) == static_cast<double>(n))
-        {
-            oa->write_character(format == detail::input_format_t::cbor
-                                ? get_cbor_float_prefix(static_cast<float>(n))
-                                : get_msgpack_float_prefix(static_cast<float>(n)));
-            write_number(static_cast<float>(n));
-        }
-        else
-        {
-            oa->write_character(format == detail::input_format_t::cbor
-                                ? get_cbor_float_prefix(n)
-                                : get_msgpack_float_prefix(n));
-            write_number(n);
-        }
-#ifdef __GNUC__
-#pragma GCC diagnostic pop
-#endif
-    }
-
-  public:
-    // The following to_char_type functions are implement the conversion
-    // between uint8_t and CharType. In case CharType is not unsigned,
-    // such a conversion is required to allow values greater than 128.
-    // See <https://github.com/nlohmann/json/issues/1286> for a discussion.
-    template < typename C = CharType,
-               enable_if_t < std::is_signed<C>::value && std::is_signed<char>::value > * = nullptr >
-    static constexpr CharType to_char_type(std::uint8_t x) noexcept
-    {
-        return *reinterpret_cast<char*>(&x);
-    }
-
-    template < typename C = CharType,
-               enable_if_t < std::is_signed<C>::value && std::is_unsigned<char>::value > * = nullptr >
-    static CharType to_char_type(std::uint8_t x) noexcept
-    {
-        static_assert(sizeof(std::uint8_t) == sizeof(CharType), "size of CharType must be equal to std::uint8_t");
-        static_assert(std::is_trivial<CharType>::value, "CharType must be trivial");
-        CharType result;
-        std::memcpy(&result, &x, sizeof(x));
-        return result;
-    }
-
-    template<typename C = CharType,
-             enable_if_t<std::is_unsigned<C>::value>* = nullptr>
-    static constexpr CharType to_char_type(std::uint8_t x) noexcept
-    {
-        return x;
-    }
-
-    template < typename InputCharType, typename C = CharType,
-               enable_if_t <
-                   std::is_signed<C>::value &&
-                   std::is_signed<char>::value &&
-                   std::is_same<char, typename std::remove_cv<InputCharType>::type>::value
-                   > * = nullptr >
-    static constexpr CharType to_char_type(InputCharType x) noexcept
-    {
-        return x;
-    }
-
-  private:
-    /// whether we can assume little endianness
-    const bool is_little_endian = little_endianness();
-
-    /// the output
-    output_adapter_t<CharType> oa = nullptr;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/output/output_adapters.hpp>
-
-// #include <nlohmann/detail/output/serializer.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2008-2009 Björn Hoehrmann <bjoern@hoehrmann.de>
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <algorithm> // reverse, remove, fill, find, none_of
-#include <array> // array
-#include <clocale> // localeconv, lconv
-#include <cmath> // labs, isfinite, isnan, signbit
-#include <cstddef> // size_t, ptrdiff_t
-#include <cstdint> // uint8_t
-#include <cstdio> // snprintf
-#include <limits> // numeric_limits
-#include <string> // string, char_traits
-#include <iomanip> // setfill, setw
-#include <type_traits> // is_same
-#include <utility> // move
-
-// #include <nlohmann/detail/conversions/to_chars.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2009 Florian Loitsch <https://florian.loitsch.com/>
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <array> // array
-#include <cmath>   // signbit, isfinite
-#include <cstdint> // intN_t, uintN_t
-#include <cstring> // memcpy, memmove
-#include <limits> // numeric_limits
-#include <type_traits> // conditional
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-/*!
-@brief implements the Grisu2 algorithm for binary to decimal floating-point
-conversion.
-
-This implementation is a slightly modified version of the reference
-implementation which may be obtained from
-http://florian.loitsch.com/publications (bench.tar.gz).
-
-The code is distributed under the MIT license, Copyright (c) 2009 Florian Loitsch.
-
-For a detailed description of the algorithm see:
-
-[1] Loitsch, "Printing Floating-Point Numbers Quickly and Accurately with
-    Integers", Proceedings of the ACM SIGPLAN 2010 Conference on Programming
-    Language Design and Implementation, PLDI 2010
-[2] Burger, Dybvig, "Printing Floating-Point Numbers Quickly and Accurately",
-    Proceedings of the ACM SIGPLAN 1996 Conference on Programming Language
-    Design and Implementation, PLDI 1996
-*/
-namespace dtoa_impl
-{
-
-template<typename Target, typename Source>
-Target reinterpret_bits(const Source source)
-{
-    static_assert(sizeof(Target) == sizeof(Source), "size mismatch");
-
-    Target target;
-    std::memcpy(&target, &source, sizeof(Source));
-    return target;
-}
-
-struct diyfp // f * 2^e
-{
-    static constexpr int kPrecision = 64; // = q
-
-    std::uint64_t f = 0;
-    int e = 0;
-
-    constexpr diyfp(std::uint64_t f_, int e_) noexcept : f(f_), e(e_) {}
-
-    /*!
-    @brief returns x - y
-    @pre x.e == y.e and x.f >= y.f
-    */
-    static diyfp sub(const diyfp& x, const diyfp& y) noexcept
-    {
-        JSON_ASSERT(x.e == y.e);
-        JSON_ASSERT(x.f >= y.f);
-
-        return {x.f - y.f, x.e};
-    }
-
-    /*!
-    @brief returns x * y
-    @note The result is rounded. (Only the upper q bits are returned.)
-    */
-    static diyfp mul(const diyfp& x, const diyfp& y) noexcept
-    {
-        static_assert(kPrecision == 64, "internal error");
-
-        // Computes:
-        //  f = round((x.f * y.f) / 2^q)
-        //  e = x.e + y.e + q
-
-        // Emulate the 64-bit * 64-bit multiplication:
-        //
-        // p = u * v
-        //   = (u_lo + 2^32 u_hi) (v_lo + 2^32 v_hi)
-        //   = (u_lo v_lo         ) + 2^32 ((u_lo v_hi         ) + (u_hi v_lo         )) + 2^64 (u_hi v_hi         )
-        //   = (p0                ) + 2^32 ((p1                ) + (p2                )) + 2^64 (p3                )
-        //   = (p0_lo + 2^32 p0_hi) + 2^32 ((p1_lo + 2^32 p1_hi) + (p2_lo + 2^32 p2_hi)) + 2^64 (p3                )
-        //   = (p0_lo             ) + 2^32 (p0_hi + p1_lo + p2_lo                      ) + 2^64 (p1_hi + p2_hi + p3)
-        //   = (p0_lo             ) + 2^32 (Q                                          ) + 2^64 (H                 )
-        //   = (p0_lo             ) + 2^32 (Q_lo + 2^32 Q_hi                           ) + 2^64 (H                 )
-        //
-        // (Since Q might be larger than 2^32 - 1)
-        //
-        //   = (p0_lo + 2^32 Q_lo) + 2^64 (Q_hi + H)
-        //
-        // (Q_hi + H does not overflow a 64-bit int)
-        //
-        //   = p_lo + 2^64 p_hi
-
-        const std::uint64_t u_lo = x.f & 0xFFFFFFFFu;
-        const std::uint64_t u_hi = x.f >> 32u;
-        const std::uint64_t v_lo = y.f & 0xFFFFFFFFu;
-        const std::uint64_t v_hi = y.f >> 32u;
-
-        const std::uint64_t p0 = u_lo * v_lo;
-        const std::uint64_t p1 = u_lo * v_hi;
-        const std::uint64_t p2 = u_hi * v_lo;
-        const std::uint64_t p3 = u_hi * v_hi;
-
-        const std::uint64_t p0_hi = p0 >> 32u;
-        const std::uint64_t p1_lo = p1 & 0xFFFFFFFFu;
-        const std::uint64_t p1_hi = p1 >> 32u;
-        const std::uint64_t p2_lo = p2 & 0xFFFFFFFFu;
-        const std::uint64_t p2_hi = p2 >> 32u;
-
-        std::uint64_t Q = p0_hi + p1_lo + p2_lo;
-
-        // The full product might now be computed as
-        //
-        // p_hi = p3 + p2_hi + p1_hi + (Q >> 32)
-        // p_lo = p0_lo + (Q << 32)
-        //
-        // But in this particular case here, the full p_lo is not required.
-        // Effectively we only need to add the highest bit in p_lo to p_hi (and
-        // Q_hi + 1 does not overflow).
-
-        Q += std::uint64_t{1} << (64u - 32u - 1u); // round, ties up
-
-        const std::uint64_t h = p3 + p2_hi + p1_hi + (Q >> 32u);
-
-        return {h, x.e + y.e + 64};
-    }
-
-    /*!
-    @brief normalize x such that the significand is >= 2^(q-1)
-    @pre x.f != 0
-    */
-    static diyfp normalize(diyfp x) noexcept
-    {
-        JSON_ASSERT(x.f != 0);
-
-        while ((x.f >> 63u) == 0)
-        {
-            x.f <<= 1u;
-            x.e--;
-        }
-
-        return x;
-    }
-
-    /*!
-    @brief normalize x such that the result has the exponent E
-    @pre e >= x.e and the upper e - x.e bits of x.f must be zero.
-    */
-    static diyfp normalize_to(const diyfp& x, const int target_exponent) noexcept
-    {
-        const int delta = x.e - target_exponent;
-
-        JSON_ASSERT(delta >= 0);
-        JSON_ASSERT(((x.f << delta) >> delta) == x.f);
-
-        return {x.f << delta, target_exponent};
-    }
-};
-
-struct boundaries
-{
-    diyfp w;
-    diyfp minus;
-    diyfp plus;
-};
-
-/*!
-Compute the (normalized) diyfp representing the input number 'value' and its
-boundaries.
-
-@pre value must be finite and positive
-*/
-template<typename FloatType>
-boundaries compute_boundaries(FloatType value)
-{
-    JSON_ASSERT(std::isfinite(value));
-    JSON_ASSERT(value > 0);
-
-    // Convert the IEEE representation into a diyfp.
-    //
-    // If v is denormal:
-    //      value = 0.F * 2^(1 - bias) = (          F) * 2^(1 - bias - (p-1))
-    // If v is normalized:
-    //      value = 1.F * 2^(E - bias) = (2^(p-1) + F) * 2^(E - bias - (p-1))
-
-    static_assert(std::numeric_limits<FloatType>::is_iec559,
-                  "internal error: dtoa_short requires an IEEE-754 floating-point implementation");
-
-    constexpr int      kPrecision = std::numeric_limits<FloatType>::digits; // = p (includes the hidden bit)
-    constexpr int      kBias      = std::numeric_limits<FloatType>::max_exponent - 1 + (kPrecision - 1);
-    constexpr int      kMinExp    = 1 - kBias;
-    constexpr std::uint64_t kHiddenBit = std::uint64_t{1} << (kPrecision - 1); // = 2^(p-1)
-
-    using bits_type = typename std::conditional<kPrecision == 24, std::uint32_t, std::uint64_t >::type;
-
-    const auto bits = static_cast<std::uint64_t>(reinterpret_bits<bits_type>(value));
-    const std::uint64_t E = bits >> (kPrecision - 1);
-    const std::uint64_t F = bits & (kHiddenBit - 1);
-
-    const bool is_denormal = E == 0;
-    const diyfp v = is_denormal
-                    ? diyfp(F, kMinExp)
-                    : diyfp(F + kHiddenBit, static_cast<int>(E) - kBias);
-
-    // Compute the boundaries m- and m+ of the floating-point value
-    // v = f * 2^e.
-    //
-    // Determine v- and v+, the floating-point predecessor and successor if v,
-    // respectively.
-    //
-    //      v- = v - 2^e        if f != 2^(p-1) or e == e_min                (A)
-    //         = v - 2^(e-1)    if f == 2^(p-1) and e > e_min                (B)
-    //
-    //      v+ = v + 2^e
-    //
-    // Let m- = (v- + v) / 2 and m+ = (v + v+) / 2. All real numbers _strictly_
-    // between m- and m+ round to v, regardless of how the input rounding
-    // algorithm breaks ties.
-    //
-    //      ---+-------------+-------------+-------------+-------------+---  (A)
-    //         v-            m-            v             m+            v+
-    //
-    //      -----------------+------+------+-------------+-------------+---  (B)
-    //                       v-     m-     v             m+            v+
-
-    const bool lower_boundary_is_closer = F == 0 && E > 1;
-    const diyfp m_plus = diyfp(2 * v.f + 1, v.e - 1);
-    const diyfp m_minus = lower_boundary_is_closer
-                          ? diyfp(4 * v.f - 1, v.e - 2)  // (B)
-                          : diyfp(2 * v.f - 1, v.e - 1); // (A)
-
-    // Determine the normalized w+ = m+.
-    const diyfp w_plus = diyfp::normalize(m_plus);
-
-    // Determine w- = m- such that e_(w-) = e_(w+).
-    const diyfp w_minus = diyfp::normalize_to(m_minus, w_plus.e);
-
-    return {diyfp::normalize(v), w_minus, w_plus};
-}
-
-// Given normalized diyfp w, Grisu needs to find a (normalized) cached
-// power-of-ten c, such that the exponent of the product c * w = f * 2^e lies
-// within a certain range [alpha, gamma] (Definition 3.2 from [1])
-//
-//      alpha <= e = e_c + e_w + q <= gamma
-//
-// or
-//
-//      f_c * f_w * 2^alpha <= f_c 2^(e_c) * f_w 2^(e_w) * 2^q
-//                          <= f_c * f_w * 2^gamma
-//
-// Since c and w are normalized, i.e. 2^(q-1) <= f < 2^q, this implies
-//
-//      2^(q-1) * 2^(q-1) * 2^alpha <= c * w * 2^q < 2^q * 2^q * 2^gamma
-//
-// or
-//
-//      2^(q - 2 + alpha) <= c * w < 2^(q + gamma)
-//
-// The choice of (alpha,gamma) determines the size of the table and the form of
-// the digit generation procedure. Using (alpha,gamma)=(-60,-32) works out well
-// in practice:
-//
-// The idea is to cut the number c * w = f * 2^e into two parts, which can be
-// processed independently: An integral part p1, and a fractional part p2:
-//
-//      f * 2^e = ( (f div 2^-e) * 2^-e + (f mod 2^-e) ) * 2^e
-//              = (f div 2^-e) + (f mod 2^-e) * 2^e
-//              = p1 + p2 * 2^e
-//
-// The conversion of p1 into decimal form requires a series of divisions and
-// modulos by (a power of) 10. These operations are faster for 32-bit than for
-// 64-bit integers, so p1 should ideally fit into a 32-bit integer. This can be
-// achieved by choosing
-//
-//      -e >= 32   or   e <= -32 := gamma
-//
-// In order to convert the fractional part
-//
-//      p2 * 2^e = p2 / 2^-e = d[-1] / 10^1 + d[-2] / 10^2 + ...
-//
-// into decimal form, the fraction is repeatedly multiplied by 10 and the digits
-// d[-i] are extracted in order:
-//
-//      (10 * p2) div 2^-e = d[-1]
-//      (10 * p2) mod 2^-e = d[-2] / 10^1 + ...
-//
-// The multiplication by 10 must not overflow. It is sufficient to choose
-//
-//      10 * p2 < 16 * p2 = 2^4 * p2 <= 2^64.
-//
-// Since p2 = f mod 2^-e < 2^-e,
-//
-//      -e <= 60   or   e >= -60 := alpha
-
-constexpr int kAlpha = -60;
-constexpr int kGamma = -32;
-
-struct cached_power // c = f * 2^e ~= 10^k
-{
-    std::uint64_t f;
-    int e;
-    int k;
-};
-
-/*!
-For a normalized diyfp w = f * 2^e, this function returns a (normalized) cached
-power-of-ten c = f_c * 2^e_c, such that the exponent of the product w * c
-satisfies (Definition 3.2 from [1])
-
-     alpha <= e_c + e + q <= gamma.
-*/
-inline cached_power get_cached_power_for_binary_exponent(int e)
-{
-    // Now
-    //
-    //      alpha <= e_c + e + q <= gamma                                    (1)
-    //      ==> f_c * 2^alpha <= c * 2^e * 2^q
-    //
-    // and since the c's are normalized, 2^(q-1) <= f_c,
-    //
-    //      ==> 2^(q - 1 + alpha) <= c * 2^(e + q)
-    //      ==> 2^(alpha - e - 1) <= c
-    //
-    // If c were an exact power of ten, i.e. c = 10^k, one may determine k as
-    //
-    //      k = ceil( log_10( 2^(alpha - e - 1) ) )
-    //        = ceil( (alpha - e - 1) * log_10(2) )
-    //
-    // From the paper:
-    // "In theory the result of the procedure could be wrong since c is rounded,
-    //  and the computation itself is approximated [...]. In practice, however,
-    //  this simple function is sufficient."
-    //
-    // For IEEE double precision floating-point numbers converted into
-    // normalized diyfp's w = f * 2^e, with q = 64,
-    //
-    //      e >= -1022      (min IEEE exponent)
-    //           -52        (p - 1)
-    //           -52        (p - 1, possibly normalize denormal IEEE numbers)
-    //           -11        (normalize the diyfp)
-    //         = -1137
-    //
-    // and
-    //
-    //      e <= +1023      (max IEEE exponent)
-    //           -52        (p - 1)
-    //           -11        (normalize the diyfp)
-    //         = 960
-    //
-    // This binary exponent range [-1137,960] results in a decimal exponent
-    // range [-307,324]. One does not need to store a cached power for each
-    // k in this range. For each such k it suffices to find a cached power
-    // such that the exponent of the product lies in [alpha,gamma].
-    // This implies that the difference of the decimal exponents of adjacent
-    // table entries must be less than or equal to
-    //
-    //      floor( (gamma - alpha) * log_10(2) ) = 8.
-    //
-    // (A smaller distance gamma-alpha would require a larger table.)
-
-    // NB:
-    // Actually this function returns c, such that -60 <= e_c + e + 64 <= -34.
-
-    constexpr int kCachedPowersMinDecExp = -300;
-    constexpr int kCachedPowersDecStep = 8;
-
-    static constexpr std::array<cached_power, 79> kCachedPowers =
-    {
-        {
-            { 0xAB70FE17C79AC6CA, -1060, -300 },
-            { 0xFF77B1FCBEBCDC4F, -1034, -292 },
-            { 0xBE5691EF416BD60C, -1007, -284 },
-            { 0x8DD01FAD907FFC3C,  -980, -276 },
-            { 0xD3515C2831559A83,  -954, -268 },
-            { 0x9D71AC8FADA6C9B5,  -927, -260 },
-            { 0xEA9C227723EE8BCB,  -901, -252 },
-            { 0xAECC49914078536D,  -874, -244 },
-            { 0x823C12795DB6CE57,  -847, -236 },
-            { 0xC21094364DFB5637,  -821, -228 },
-            { 0x9096EA6F3848984F,  -794, -220 },
-            { 0xD77485CB25823AC7,  -768, -212 },
-            { 0xA086CFCD97BF97F4,  -741, -204 },
-            { 0xEF340A98172AACE5,  -715, -196 },
-            { 0xB23867FB2A35B28E,  -688, -188 },
-            { 0x84C8D4DFD2C63F3B,  -661, -180 },
-            { 0xC5DD44271AD3CDBA,  -635, -172 },
-            { 0x936B9FCEBB25C996,  -608, -164 },
-            { 0xDBAC6C247D62A584,  -582, -156 },
-            { 0xA3AB66580D5FDAF6,  -555, -148 },
-            { 0xF3E2F893DEC3F126,  -529, -140 },
-            { 0xB5B5ADA8AAFF80B8,  -502, -132 },
-            { 0x87625F056C7C4A8B,  -475, -124 },
-            { 0xC9BCFF6034C13053,  -449, -116 },
-            { 0x964E858C91BA2655,  -422, -108 },
-            { 0xDFF9772470297EBD,  -396, -100 },
-            { 0xA6DFBD9FB8E5B88F,  -369,  -92 },
-            { 0xF8A95FCF88747D94,  -343,  -84 },
-            { 0xB94470938FA89BCF,  -316,  -76 },
-            { 0x8A08F0F8BF0F156B,  -289,  -68 },
-            { 0xCDB02555653131B6,  -263,  -60 },
-            { 0x993FE2C6D07B7FAC,  -236,  -52 },
-            { 0xE45C10C42A2B3B06,  -210,  -44 },
-            { 0xAA242499697392D3,  -183,  -36 },
-            { 0xFD87B5F28300CA0E,  -157,  -28 },
-            { 0xBCE5086492111AEB,  -130,  -20 },
-            { 0x8CBCCC096F5088CC,  -103,  -12 },
-            { 0xD1B71758E219652C,   -77,   -4 },
-            { 0x9C40000000000000,   -50,    4 },
-            { 0xE8D4A51000000000,   -24,   12 },
-            { 0xAD78EBC5AC620000,     3,   20 },
-            { 0x813F3978F8940984,    30,   28 },
-            { 0xC097CE7BC90715B3,    56,   36 },
-            { 0x8F7E32CE7BEA5C70,    83,   44 },
-            { 0xD5D238A4ABE98068,   109,   52 },
-            { 0x9F4F2726179A2245,   136,   60 },
-            { 0xED63A231D4C4FB27,   162,   68 },
-            { 0xB0DE65388CC8ADA8,   189,   76 },
-            { 0x83C7088E1AAB65DB,   216,   84 },
-            { 0xC45D1DF942711D9A,   242,   92 },
-            { 0x924D692CA61BE758,   269,  100 },
-            { 0xDA01EE641A708DEA,   295,  108 },
-            { 0xA26DA3999AEF774A,   322,  116 },
-            { 0xF209787BB47D6B85,   348,  124 },
-            { 0xB454E4A179DD1877,   375,  132 },
-            { 0x865B86925B9BC5C2,   402,  140 },
-            { 0xC83553C5C8965D3D,   428,  148 },
-            { 0x952AB45CFA97A0B3,   455,  156 },
-            { 0xDE469FBD99A05FE3,   481,  164 },
-            { 0xA59BC234DB398C25,   508,  172 },
-            { 0xF6C69A72A3989F5C,   534,  180 },
-            { 0xB7DCBF5354E9BECE,   561,  188 },
-            { 0x88FCF317F22241E2,   588,  196 },
-            { 0xCC20CE9BD35C78A5,   614,  204 },
-            { 0x98165AF37B2153DF,   641,  212 },
-            { 0xE2A0B5DC971F303A,   667,  220 },
-            { 0xA8D9D1535CE3B396,   694,  228 },
-            { 0xFB9B7CD9A4A7443C,   720,  236 },
-            { 0xBB764C4CA7A44410,   747,  244 },
-            { 0x8BAB8EEFB6409C1A,   774,  252 },
-            { 0xD01FEF10A657842C,   800,  260 },
-            { 0x9B10A4E5E9913129,   827,  268 },
-            { 0xE7109BFBA19C0C9D,   853,  276 },
-            { 0xAC2820D9623BF429,   880,  284 },
-            { 0x80444B5E7AA7CF85,   907,  292 },
-            { 0xBF21E44003ACDD2D,   933,  300 },
-            { 0x8E679C2F5E44FF8F,   960,  308 },
-            { 0xD433179D9C8CB841,   986,  316 },
-            { 0x9E19DB92B4E31BA9,  1013,  324 },
-        }
-    };
-
-    // This computation gives exactly the same results for k as
-    //      k = ceil((kAlpha - e - 1) * 0.30102999566398114)
-    // for |e| <= 1500, but doesn't require floating-point operations.
-    // NB: log_10(2) ~= 78913 / 2^18
-    JSON_ASSERT(e >= -1500);
-    JSON_ASSERT(e <=  1500);
-    const int f = kAlpha - e - 1;
-    const int k = (f * 78913) / (1 << 18) + static_cast<int>(f > 0);
-
-    const int index = (-kCachedPowersMinDecExp + k + (kCachedPowersDecStep - 1)) / kCachedPowersDecStep;
-    JSON_ASSERT(index >= 0);
-    JSON_ASSERT(static_cast<std::size_t>(index) < kCachedPowers.size());
-
-    const cached_power cached = kCachedPowers[static_cast<std::size_t>(index)];
-    JSON_ASSERT(kAlpha <= cached.e + e + 64);
-    JSON_ASSERT(kGamma >= cached.e + e + 64);
-
-    return cached;
-}
-
-/*!
-For n != 0, returns k, such that pow10 := 10^(k-1) <= n < 10^k.
-For n == 0, returns 1 and sets pow10 := 1.
-*/
-inline int find_largest_pow10(const std::uint32_t n, std::uint32_t& pow10)
-{
-    // LCOV_EXCL_START
-    if (n >= 1000000000)
-    {
-        pow10 = 1000000000;
-        return 10;
-    }
-    // LCOV_EXCL_STOP
-    if (n >= 100000000)
-    {
-        pow10 = 100000000;
-        return  9;
-    }
-    if (n >= 10000000)
-    {
-        pow10 = 10000000;
-        return  8;
-    }
-    if (n >= 1000000)
-    {
-        pow10 = 1000000;
-        return  7;
-    }
-    if (n >= 100000)
-    {
-        pow10 = 100000;
-        return  6;
-    }
-    if (n >= 10000)
-    {
-        pow10 = 10000;
-        return  5;
-    }
-    if (n >= 1000)
-    {
-        pow10 = 1000;
-        return  4;
-    }
-    if (n >= 100)
-    {
-        pow10 = 100;
-        return  3;
-    }
-    if (n >= 10)
-    {
-        pow10 = 10;
-        return  2;
-    }
-
-    pow10 = 1;
-    return 1;
-}
-
-inline void grisu2_round(char* buf, int len, std::uint64_t dist, std::uint64_t delta,
-                         std::uint64_t rest, std::uint64_t ten_k)
-{
-    JSON_ASSERT(len >= 1);
-    JSON_ASSERT(dist <= delta);
-    JSON_ASSERT(rest <= delta);
-    JSON_ASSERT(ten_k > 0);
-
-    //               <--------------------------- delta ---->
-    //                                  <---- dist --------->
-    // --------------[------------------+-------------------]--------------
-    //               M-                 w                   M+
-    //
-    //                                  ten_k
-    //                                <------>
-    //                                       <---- rest ---->
-    // --------------[------------------+----+--------------]--------------
-    //                                  w    V
-    //                                       = buf * 10^k
-    //
-    // ten_k represents a unit-in-the-last-place in the decimal representation
-    // stored in buf.
-    // Decrement buf by ten_k while this takes buf closer to w.
-
-    // The tests are written in this order to avoid overflow in unsigned
-    // integer arithmetic.
-
-    while (rest < dist
-            && delta - rest >= ten_k
-            && (rest + ten_k < dist || dist - rest > rest + ten_k - dist))
-    {
-        JSON_ASSERT(buf[len - 1] != '0');
-        buf[len - 1]--;
-        rest += ten_k;
-    }
-}
-
-/*!
-Generates V = buffer * 10^decimal_exponent, such that M- <= V <= M+.
-M- and M+ must be normalized and share the same exponent -60 <= e <= -32.
-*/
-inline void grisu2_digit_gen(char* buffer, int& length, int& decimal_exponent,
-                             diyfp M_minus, diyfp w, diyfp M_plus)
-{
-    static_assert(kAlpha >= -60, "internal error");
-    static_assert(kGamma <= -32, "internal error");
-
-    // Generates the digits (and the exponent) of a decimal floating-point
-    // number V = buffer * 10^decimal_exponent in the range [M-, M+]. The diyfp's
-    // w, M- and M+ share the same exponent e, which satisfies alpha <= e <= gamma.
-    //
-    //               <--------------------------- delta ---->
-    //                                  <---- dist --------->
-    // --------------[------------------+-------------------]--------------
-    //               M-                 w                   M+
-    //
-    // Grisu2 generates the digits of M+ from left to right and stops as soon as
-    // V is in [M-,M+].
-
-    JSON_ASSERT(M_plus.e >= kAlpha);
-    JSON_ASSERT(M_plus.e <= kGamma);
-
-    std::uint64_t delta = diyfp::sub(M_plus, M_minus).f; // (significand of (M+ - M-), implicit exponent is e)
-    std::uint64_t dist  = diyfp::sub(M_plus, w      ).f; // (significand of (M+ - w ), implicit exponent is e)
-
-    // Split M+ = f * 2^e into two parts p1 and p2 (note: e < 0):
-    //
-    //      M+ = f * 2^e
-    //         = ((f div 2^-e) * 2^-e + (f mod 2^-e)) * 2^e
-    //         = ((p1        ) * 2^-e + (p2        )) * 2^e
-    //         = p1 + p2 * 2^e
-
-    const diyfp one(std::uint64_t{1} << -M_plus.e, M_plus.e);
-
-    auto p1 = static_cast<std::uint32_t>(M_plus.f >> -one.e); // p1 = f div 2^-e (Since -e >= 32, p1 fits into a 32-bit int.)
-    std::uint64_t p2 = M_plus.f & (one.f - 1);                    // p2 = f mod 2^-e
-
-    // 1)
-    //
-    // Generate the digits of the integral part p1 = d[n-1]...d[1]d[0]
-
-    JSON_ASSERT(p1 > 0);
-
-    std::uint32_t pow10{};
-    const int k = find_largest_pow10(p1, pow10);
-
-    //      10^(k-1) <= p1 < 10^k, pow10 = 10^(k-1)
-    //
-    //      p1 = (p1 div 10^(k-1)) * 10^(k-1) + (p1 mod 10^(k-1))
-    //         = (d[k-1]         ) * 10^(k-1) + (p1 mod 10^(k-1))
-    //
-    //      M+ = p1                                             + p2 * 2^e
-    //         = d[k-1] * 10^(k-1) + (p1 mod 10^(k-1))          + p2 * 2^e
-    //         = d[k-1] * 10^(k-1) + ((p1 mod 10^(k-1)) * 2^-e + p2) * 2^e
-    //         = d[k-1] * 10^(k-1) + (                         rest) * 2^e
-    //
-    // Now generate the digits d[n] of p1 from left to right (n = k-1,...,0)
-    //
-    //      p1 = d[k-1]...d[n] * 10^n + d[n-1]...d[0]
-    //
-    // but stop as soon as
-    //
-    //      rest * 2^e = (d[n-1]...d[0] * 2^-e + p2) * 2^e <= delta * 2^e
-
-    int n = k;
-    while (n > 0)
-    {
-        // Invariants:
-        //      M+ = buffer * 10^n + (p1 + p2 * 2^e)    (buffer = 0 for n = k)
-        //      pow10 = 10^(n-1) <= p1 < 10^n
-        //
-        const std::uint32_t d = p1 / pow10;  // d = p1 div 10^(n-1)
-        const std::uint32_t r = p1 % pow10;  // r = p1 mod 10^(n-1)
-        //
-        //      M+ = buffer * 10^n + (d * 10^(n-1) + r) + p2 * 2^e
-        //         = (buffer * 10 + d) * 10^(n-1) + (r + p2 * 2^e)
-        //
-        JSON_ASSERT(d <= 9);
-        buffer[length++] = static_cast<char>('0' + d); // buffer := buffer * 10 + d
-        //
-        //      M+ = buffer * 10^(n-1) + (r + p2 * 2^e)
-        //
-        p1 = r;
-        n--;
-        //
-        //      M+ = buffer * 10^n + (p1 + p2 * 2^e)
-        //      pow10 = 10^n
-        //
-
-        // Now check if enough digits have been generated.
-        // Compute
-        //
-        //      p1 + p2 * 2^e = (p1 * 2^-e + p2) * 2^e = rest * 2^e
-        //
-        // Note:
-        // Since rest and delta share the same exponent e, it suffices to
-        // compare the significands.
-        const std::uint64_t rest = (std::uint64_t{p1} << -one.e) + p2;
-        if (rest <= delta)
-        {
-            // V = buffer * 10^n, with M- <= V <= M+.
-
-            decimal_exponent += n;
-
-            // We may now just stop. But instead look if the buffer could be
-            // decremented to bring V closer to w.
-            //
-            // pow10 = 10^n is now 1 ulp in the decimal representation V.
-            // The rounding procedure works with diyfp's with an implicit
-            // exponent of e.
-            //
-            //      10^n = (10^n * 2^-e) * 2^e = ulp * 2^e
-            //
-            const std::uint64_t ten_n = std::uint64_t{pow10} << -one.e;
-            grisu2_round(buffer, length, dist, delta, rest, ten_n);
-
-            return;
-        }
-
-        pow10 /= 10;
-        //
-        //      pow10 = 10^(n-1) <= p1 < 10^n
-        // Invariants restored.
-    }
-
-    // 2)
-    //
-    // The digits of the integral part have been generated:
-    //
-    //      M+ = d[k-1]...d[1]d[0] + p2 * 2^e
-    //         = buffer            + p2 * 2^e
-    //
-    // Now generate the digits of the fractional part p2 * 2^e.
-    //
-    // Note:
-    // No decimal point is generated: the exponent is adjusted instead.
-    //
-    // p2 actually represents the fraction
-    //
-    //      p2 * 2^e
-    //          = p2 / 2^-e
-    //          = d[-1] / 10^1 + d[-2] / 10^2 + ...
-    //
-    // Now generate the digits d[-m] of p1 from left to right (m = 1,2,...)
-    //
-    //      p2 * 2^e = d[-1]d[-2]...d[-m] * 10^-m
-    //                      + 10^-m * (d[-m-1] / 10^1 + d[-m-2] / 10^2 + ...)
-    //
-    // using
-    //
-    //      10^m * p2 = ((10^m * p2) div 2^-e) * 2^-e + ((10^m * p2) mod 2^-e)
-    //                = (                   d) * 2^-e + (                   r)
-    //
-    // or
-    //      10^m * p2 * 2^e = d + r * 2^e
-    //
-    // i.e.
-    //
-    //      M+ = buffer + p2 * 2^e
-    //         = buffer + 10^-m * (d + r * 2^e)
-    //         = (buffer * 10^m + d) * 10^-m + 10^-m * r * 2^e
-    //
-    // and stop as soon as 10^-m * r * 2^e <= delta * 2^e
-
-    JSON_ASSERT(p2 > delta);
-
-    int m = 0;
-    for (;;)
-    {
-        // Invariant:
-        //      M+ = buffer * 10^-m + 10^-m * (d[-m-1] / 10 + d[-m-2] / 10^2 + ...) * 2^e
-        //         = buffer * 10^-m + 10^-m * (p2                                 ) * 2^e
-        //         = buffer * 10^-m + 10^-m * (1/10 * (10 * p2)                   ) * 2^e
-        //         = buffer * 10^-m + 10^-m * (1/10 * ((10*p2 div 2^-e) * 2^-e + (10*p2 mod 2^-e)) * 2^e
-        //
-        JSON_ASSERT(p2 <= (std::numeric_limits<std::uint64_t>::max)() / 10);
-        p2 *= 10;
-        const std::uint64_t d = p2 >> -one.e;     // d = (10 * p2) div 2^-e
-        const std::uint64_t r = p2 & (one.f - 1); // r = (10 * p2) mod 2^-e
-        //
-        //      M+ = buffer * 10^-m + 10^-m * (1/10 * (d * 2^-e + r) * 2^e
-        //         = buffer * 10^-m + 10^-m * (1/10 * (d + r * 2^e))
-        //         = (buffer * 10 + d) * 10^(-m-1) + 10^(-m-1) * r * 2^e
-        //
-        JSON_ASSERT(d <= 9);
-        buffer[length++] = static_cast<char>('0' + d); // buffer := buffer * 10 + d
-        //
-        //      M+ = buffer * 10^(-m-1) + 10^(-m-1) * r * 2^e
-        //
-        p2 = r;
-        m++;
-        //
-        //      M+ = buffer * 10^-m + 10^-m * p2 * 2^e
-        // Invariant restored.
-
-        // Check if enough digits have been generated.
-        //
-        //      10^-m * p2 * 2^e <= delta * 2^e
-        //              p2 * 2^e <= 10^m * delta * 2^e
-        //                    p2 <= 10^m * delta
-        delta *= 10;
-        dist  *= 10;
-        if (p2 <= delta)
-        {
-            break;
-        }
-    }
-
-    // V = buffer * 10^-m, with M- <= V <= M+.
-
-    decimal_exponent -= m;
-
-    // 1 ulp in the decimal representation is now 10^-m.
-    // Since delta and dist are now scaled by 10^m, we need to do the
-    // same with ulp in order to keep the units in sync.
-    //
-    //      10^m * 10^-m = 1 = 2^-e * 2^e = ten_m * 2^e
-    //
-    const std::uint64_t ten_m = one.f;
-    grisu2_round(buffer, length, dist, delta, p2, ten_m);
-
-    // By construction this algorithm generates the shortest possible decimal
-    // number (Loitsch, Theorem 6.2) which rounds back to w.
-    // For an input number of precision p, at least
-    //
-    //      N = 1 + ceil(p * log_10(2))
-    //
-    // decimal digits are sufficient to identify all binary floating-point
-    // numbers (Matula, "In-and-Out conversions").
-    // This implies that the algorithm does not produce more than N decimal
-    // digits.
-    //
-    //      N = 17 for p = 53 (IEEE double precision)
-    //      N = 9  for p = 24 (IEEE single precision)
-}
-
-/*!
-v = buf * 10^decimal_exponent
-len is the length of the buffer (number of decimal digits)
-The buffer must be large enough, i.e. >= max_digits10.
-*/
-JSON_HEDLEY_NON_NULL(1)
-inline void grisu2(char* buf, int& len, int& decimal_exponent,
-                   diyfp m_minus, diyfp v, diyfp m_plus)
-{
-    JSON_ASSERT(m_plus.e == m_minus.e);
-    JSON_ASSERT(m_plus.e == v.e);
-
-    //  --------(-----------------------+-----------------------)--------    (A)
-    //          m-                      v                       m+
-    //
-    //  --------------------(-----------+-----------------------)--------    (B)
-    //                      m-          v                       m+
-    //
-    // First scale v (and m- and m+) such that the exponent is in the range
-    // [alpha, gamma].
-
-    const cached_power cached = get_cached_power_for_binary_exponent(m_plus.e);
-
-    const diyfp c_minus_k(cached.f, cached.e); // = c ~= 10^-k
-
-    // The exponent of the products is = v.e + c_minus_k.e + q and is in the range [alpha,gamma]
-    const diyfp w       = diyfp::mul(v,       c_minus_k);
-    const diyfp w_minus = diyfp::mul(m_minus, c_minus_k);
-    const diyfp w_plus  = diyfp::mul(m_plus,  c_minus_k);
-
-    //  ----(---+---)---------------(---+---)---------------(---+---)----
-    //          w-                      w                       w+
-    //          = c*m-                  = c*v                   = c*m+
-    //
-    // diyfp::mul rounds its result and c_minus_k is approximated too. w, w- and
-    // w+ are now off by a small amount.
-    // In fact:
-    //
-    //      w - v * 10^k < 1 ulp
-    //
-    // To account for this inaccuracy, add resp. subtract 1 ulp.
-    //
-    //  --------+---[---------------(---+---)---------------]---+--------
-    //          w-  M-                  w                   M+  w+
-    //
-    // Now any number in [M-, M+] (bounds included) will round to w when input,
-    // regardless of how the input rounding algorithm breaks ties.
-    //
-    // And digit_gen generates the shortest possible such number in [M-, M+].
-    // Note that this does not mean that Grisu2 always generates the shortest
-    // possible number in the interval (m-, m+).
-    const diyfp M_minus(w_minus.f + 1, w_minus.e);
-    const diyfp M_plus (w_plus.f  - 1, w_plus.e );
-
-    decimal_exponent = -cached.k; // = -(-k) = k
-
-    grisu2_digit_gen(buf, len, decimal_exponent, M_minus, w, M_plus);
-}
-
-/*!
-v = buf * 10^decimal_exponent
-len is the length of the buffer (number of decimal digits)
-The buffer must be large enough, i.e. >= max_digits10.
-*/
-template<typename FloatType>
-JSON_HEDLEY_NON_NULL(1)
-void grisu2(char* buf, int& len, int& decimal_exponent, FloatType value)
-{
-    static_assert(diyfp::kPrecision >= std::numeric_limits<FloatType>::digits + 3,
-                  "internal error: not enough precision");
-
-    JSON_ASSERT(std::isfinite(value));
-    JSON_ASSERT(value > 0);
-
-    // If the neighbors (and boundaries) of 'value' are always computed for double-precision
-    // numbers, all float's can be recovered using strtod (and strtof). However, the resulting
-    // decimal representations are not exactly "short".
-    //
-    // The documentation for 'std::to_chars' (https://en.cppreference.com/w/cpp/utility/to_chars)
-    // says "value is converted to a string as if by std::sprintf in the default ("C") locale"
-    // and since sprintf promotes floats to doubles, I think this is exactly what 'std::to_chars'
-    // does.
-    // On the other hand, the documentation for 'std::to_chars' requires that "parsing the
-    // representation using the corresponding std::from_chars function recovers value exactly". That
-    // indicates that single precision floating-point numbers should be recovered using
-    // 'std::strtof'.
-    //
-    // NB: If the neighbors are computed for single-precision numbers, there is a single float
-    //     (7.0385307e-26f) which can't be recovered using strtod. The resulting double precision
-    //     value is off by 1 ulp.
-#if 0
-    const boundaries w = compute_boundaries(static_cast<double>(value));
-#else
-    const boundaries w = compute_boundaries(value);
-#endif
-
-    grisu2(buf, len, decimal_exponent, w.minus, w.w, w.plus);
-}
-
-/*!
-@brief appends a decimal representation of e to buf
-@return a pointer to the element following the exponent.
-@pre -1000 < e < 1000
-*/
-JSON_HEDLEY_NON_NULL(1)
-JSON_HEDLEY_RETURNS_NON_NULL
-inline char* append_exponent(char* buf, int e)
-{
-    JSON_ASSERT(e > -1000);
-    JSON_ASSERT(e <  1000);
-
-    if (e < 0)
-    {
-        e = -e;
-        *buf++ = '-';
-    }
-    else
-    {
-        *buf++ = '+';
-    }
-
-    auto k = static_cast<std::uint32_t>(e);
-    if (k < 10)
-    {
-        // Always print at least two digits in the exponent.
-        // This is for compatibility with printf("%g").
-        *buf++ = '0';
-        *buf++ = static_cast<char>('0' + k);
-    }
-    else if (k < 100)
-    {
-        *buf++ = static_cast<char>('0' + k / 10);
-        k %= 10;
-        *buf++ = static_cast<char>('0' + k);
-    }
-    else
-    {
-        *buf++ = static_cast<char>('0' + k / 100);
-        k %= 100;
-        *buf++ = static_cast<char>('0' + k / 10);
-        k %= 10;
-        *buf++ = static_cast<char>('0' + k);
-    }
-
-    return buf;
-}
-
-/*!
-@brief prettify v = buf * 10^decimal_exponent
-
-If v is in the range [10^min_exp, 10^max_exp) it will be printed in fixed-point
-notation. Otherwise it will be printed in exponential notation.
-
-@pre min_exp < 0
-@pre max_exp > 0
-*/
-JSON_HEDLEY_NON_NULL(1)
-JSON_HEDLEY_RETURNS_NON_NULL
-inline char* format_buffer(char* buf, int len, int decimal_exponent,
-                           int min_exp, int max_exp)
-{
-    JSON_ASSERT(min_exp < 0);
-    JSON_ASSERT(max_exp > 0);
-
-    const int k = len;
-    const int n = len + decimal_exponent;
-
-    // v = buf * 10^(n-k)
-    // k is the length of the buffer (number of decimal digits)
-    // n is the position of the decimal point relative to the start of the buffer.
-
-    if (k <= n && n <= max_exp)
-    {
-        // digits[000]
-        // len <= max_exp + 2
-
-        std::memset(buf + k, '0', static_cast<size_t>(n) - static_cast<size_t>(k));
-        // Make it look like a floating-point number (#362, #378)
-        buf[n + 0] = '.';
-        buf[n + 1] = '0';
-        return buf + (static_cast<size_t>(n) + 2);
-    }
-
-    if (0 < n && n <= max_exp)
-    {
-        // dig.its
-        // len <= max_digits10 + 1
-
-        JSON_ASSERT(k > n);
-
-        std::memmove(buf + (static_cast<size_t>(n) + 1), buf + n, static_cast<size_t>(k) - static_cast<size_t>(n));
-        buf[n] = '.';
-        return buf + (static_cast<size_t>(k) + 1U);
-    }
-
-    if (min_exp < n && n <= 0)
-    {
-        // 0.[000]digits
-        // len <= 2 + (-min_exp - 1) + max_digits10
-
-        std::memmove(buf + (2 + static_cast<size_t>(-n)), buf, static_cast<size_t>(k));
-        buf[0] = '0';
-        buf[1] = '.';
-        std::memset(buf + 2, '0', static_cast<size_t>(-n));
-        return buf + (2U + static_cast<size_t>(-n) + static_cast<size_t>(k));
-    }
-
-    if (k == 1)
-    {
-        // dE+123
-        // len <= 1 + 5
-
-        buf += 1;
-    }
-    else
-    {
-        // d.igitsE+123
-        // len <= max_digits10 + 1 + 5
-
-        std::memmove(buf + 2, buf + 1, static_cast<size_t>(k) - 1);
-        buf[1] = '.';
-        buf += 1 + static_cast<size_t>(k);
-    }
-
-    *buf++ = 'e';
-    return append_exponent(buf, n - 1);
-}
-
-}  // namespace dtoa_impl
-
-/*!
-@brief generates a decimal representation of the floating-point number value in [first, last).
-
-The format of the resulting decimal representation is similar to printf's %g
-format. Returns an iterator pointing past-the-end of the decimal representation.
-
-@note The input number must be finite, i.e. NaN's and Inf's are not supported.
-@note The buffer must be large enough.
-@note The result is NOT null-terminated.
-*/
-template<typename FloatType>
-JSON_HEDLEY_NON_NULL(1, 2)
-JSON_HEDLEY_RETURNS_NON_NULL
-char* to_chars(char* first, const char* last, FloatType value)
-{
-    static_cast<void>(last); // maybe unused - fix warning
-    JSON_ASSERT(std::isfinite(value));
-
-    // Use signbit(value) instead of (value < 0) since signbit works for -0.
-    if (std::signbit(value))
-    {
-        value = -value;
-        *first++ = '-';
-    }
-
-#ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wfloat-equal"
-#endif
-    if (value == 0) // +-0
-    {
-        *first++ = '0';
-        // Make it look like a floating-point number (#362, #378)
-        *first++ = '.';
-        *first++ = '0';
-        return first;
-    }
-#ifdef __GNUC__
-#pragma GCC diagnostic pop
-#endif
-
-    JSON_ASSERT(last - first >= std::numeric_limits<FloatType>::max_digits10);
-
-    // Compute v = buffer * 10^decimal_exponent.
-    // The decimal digits are stored in the buffer, which needs to be interpreted
-    // as an unsigned decimal integer.
-    // len is the length of the buffer, i.e. the number of decimal digits.
-    int len = 0;
-    int decimal_exponent = 0;
-    dtoa_impl::grisu2(first, len, decimal_exponent, value);
-
-    JSON_ASSERT(len <= std::numeric_limits<FloatType>::max_digits10);
-
-    // Format the buffer like printf("%.*g", prec, value)
-    constexpr int kMinExp = -4;
-    // Use digits10 here to increase compatibility with version 2.
-    constexpr int kMaxExp = std::numeric_limits<FloatType>::digits10;
-
-    JSON_ASSERT(last - first >= kMaxExp + 2);
-    JSON_ASSERT(last - first >= 2 + (-kMinExp - 1) + std::numeric_limits<FloatType>::max_digits10);
-    JSON_ASSERT(last - first >= std::numeric_limits<FloatType>::max_digits10 + 6);
-
-    return dtoa_impl::format_buffer(first, len, decimal_exponent, kMinExp, kMaxExp);
-}
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/exceptions.hpp>
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/meta/cpp_future.hpp>
-
-// #include <nlohmann/detail/output/binary_writer.hpp>
-
-// #include <nlohmann/detail/output/output_adapters.hpp>
-
-// #include <nlohmann/detail/string_concat.hpp>
-
-// #include <nlohmann/detail/value_t.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-namespace detail
-{
-
-///////////////////
-// serialization //
-///////////////////
-
-/// how to treat decoding errors
-enum class error_handler_t
-{
-    strict,  ///< throw a type_error exception in case of invalid UTF-8
-    replace, ///< replace invalid UTF-8 sequences with U+FFFD
-    ignore   ///< ignore invalid UTF-8 sequences
-};
-
-template<typename BasicJsonType>
-class serializer
-{
-    using string_t = typename BasicJsonType::string_t;
-    using number_float_t = typename BasicJsonType::number_float_t;
-    using number_integer_t = typename BasicJsonType::number_integer_t;
-    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-    using binary_char_t = typename BasicJsonType::binary_t::value_type;
-    static constexpr std::uint8_t UTF8_ACCEPT = 0;
-    static constexpr std::uint8_t UTF8_REJECT = 1;
-
-  public:
-    /*!
-    @param[in] s  output stream to serialize to
-    @param[in] ichar  indentation character to use
-    @param[in] error_handler_  how to react on decoding errors
-    */
-    serializer(output_adapter_t<char> s, const char ichar,
-               error_handler_t error_handler_ = error_handler_t::strict)
-        : o(std::move(s))
-        , loc(std::localeconv())
-        , thousands_sep(loc->thousands_sep == nullptr ? '\0' : std::char_traits<char>::to_char_type(* (loc->thousands_sep)))
-        , decimal_point(loc->decimal_point == nullptr ? '\0' : std::char_traits<char>::to_char_type(* (loc->decimal_point)))
-        , indent_char(ichar)
-        , indent_string(512, indent_char)
-        , error_handler(error_handler_)
-    {}
-
-    // delete because of pointer members
-    serializer(const serializer&) = delete;
-    serializer& operator=(const serializer&) = delete;
-    serializer(serializer&&) = delete;
-    serializer& operator=(serializer&&) = delete;
-    ~serializer() = default;
-
-    /*!
-    @brief internal implementation of the serialization function
-
-    This function is called by the public member function dump and organizes
-    the serialization internally. The indentation level is propagated as
-    additional parameter. In case of arrays and objects, the function is
-    called recursively.
-
-    - strings and object keys are escaped using `escape_string()`
-    - integer numbers are converted implicitly via `operator<<`
-    - floating-point numbers are converted to a string using `"%g"` format
-    - binary values are serialized as objects containing the subtype and the
-      byte array
-
-    @param[in] val               value to serialize
-    @param[in] pretty_print      whether the output shall be pretty-printed
-    @param[in] ensure_ascii If @a ensure_ascii is true, all non-ASCII characters
-    in the output are escaped with `\uXXXX` sequences, and the result consists
-    of ASCII characters only.
-    @param[in] indent_step       the indent level
-    @param[in] current_indent    the current indent level (only used internally)
-    */
-    void dump(const BasicJsonType& val,
-              const bool pretty_print,
-              const bool ensure_ascii,
-              const unsigned int indent_step,
-              const unsigned int current_indent = 0)
-    {
-        switch (val.m_type)
-        {
-            case value_t::object:
-            {
-                if (val.m_value.object->empty())
-                {
-                    o->write_characters("{}", 2);
-                    return;
-                }
-
-                if (pretty_print)
-                {
-                    o->write_characters("{\n", 2);
-
-                    // variable to hold indentation for recursive calls
-                    const auto new_indent = current_indent + indent_step;
-                    if (JSON_HEDLEY_UNLIKELY(indent_string.size() < new_indent))
-                    {
-                        indent_string.resize(indent_string.size() * 2, ' ');
-                    }
-
-                    // first n-1 elements
-                    auto i = val.m_value.object->cbegin();
-                    for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i)
-                    {
-                        o->write_characters(indent_string.c_str(), new_indent);
-                        o->write_character('\"');
-                        dump_escaped(i->first, ensure_ascii);
-                        o->write_characters("\": ", 3);
-                        dump(i->second, true, ensure_ascii, indent_step, new_indent);
-                        o->write_characters(",\n", 2);
-                    }
-
-                    // last element
-                    JSON_ASSERT(i != val.m_value.object->cend());
-                    JSON_ASSERT(std::next(i) == val.m_value.object->cend());
-                    o->write_characters(indent_string.c_str(), new_indent);
-                    o->write_character('\"');
-                    dump_escaped(i->first, ensure_ascii);
-                    o->write_characters("\": ", 3);
-                    dump(i->second, true, ensure_ascii, indent_step, new_indent);
-
-                    o->write_character('\n');
-                    o->write_characters(indent_string.c_str(), current_indent);
-                    o->write_character('}');
-                }
-                else
-                {
-                    o->write_character('{');
-
-                    // first n-1 elements
-                    auto i = val.m_value.object->cbegin();
-                    for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i)
-                    {
-                        o->write_character('\"');
-                        dump_escaped(i->first, ensure_ascii);
-                        o->write_characters("\":", 2);
-                        dump(i->second, false, ensure_ascii, indent_step, current_indent);
-                        o->write_character(',');
-                    }
-
-                    // last element
-                    JSON_ASSERT(i != val.m_value.object->cend());
-                    JSON_ASSERT(std::next(i) == val.m_value.object->cend());
-                    o->write_character('\"');
-                    dump_escaped(i->first, ensure_ascii);
-                    o->write_characters("\":", 2);
-                    dump(i->second, false, ensure_ascii, indent_step, current_indent);
-
-                    o->write_character('}');
-                }
-
-                return;
-            }
-
-            case value_t::array:
-            {
-                if (val.m_value.array->empty())
-                {
-                    o->write_characters("[]", 2);
-                    return;
-                }
-
-                if (pretty_print)
-                {
-                    o->write_characters("[\n", 2);
-
-                    // variable to hold indentation for recursive calls
-                    const auto new_indent = current_indent + indent_step;
-                    if (JSON_HEDLEY_UNLIKELY(indent_string.size() < new_indent))
-                    {
-                        indent_string.resize(indent_string.size() * 2, ' ');
-                    }
-
-                    // first n-1 elements
-                    for (auto i = val.m_value.array->cbegin();
-                            i != val.m_value.array->cend() - 1; ++i)
-                    {
-                        o->write_characters(indent_string.c_str(), new_indent);
-                        dump(*i, true, ensure_ascii, indent_step, new_indent);
-                        o->write_characters(",\n", 2);
-                    }
-
-                    // last element
-                    JSON_ASSERT(!val.m_value.array->empty());
-                    o->write_characters(indent_string.c_str(), new_indent);
-                    dump(val.m_value.array->back(), true, ensure_ascii, indent_step, new_indent);
-
-                    o->write_character('\n');
-                    o->write_characters(indent_string.c_str(), current_indent);
-                    o->write_character(']');
-                }
-                else
-                {
-                    o->write_character('[');
-
-                    // first n-1 elements
-                    for (auto i = val.m_value.array->cbegin();
-                            i != val.m_value.array->cend() - 1; ++i)
-                    {
-                        dump(*i, false, ensure_ascii, indent_step, current_indent);
-                        o->write_character(',');
-                    }
-
-                    // last element
-                    JSON_ASSERT(!val.m_value.array->empty());
-                    dump(val.m_value.array->back(), false, ensure_ascii, indent_step, current_indent);
-
-                    o->write_character(']');
-                }
-
-                return;
-            }
-
-            case value_t::string:
-            {
-                o->write_character('\"');
-                dump_escaped(*val.m_value.string, ensure_ascii);
-                o->write_character('\"');
-                return;
-            }
-
-            case value_t::binary:
-            {
-                if (pretty_print)
-                {
-                    o->write_characters("{\n", 2);
-
-                    // variable to hold indentation for recursive calls
-                    const auto new_indent = current_indent + indent_step;
-                    if (JSON_HEDLEY_UNLIKELY(indent_string.size() < new_indent))
-                    {
-                        indent_string.resize(indent_string.size() * 2, ' ');
-                    }
-
-                    o->write_characters(indent_string.c_str(), new_indent);
-
-                    o->write_characters("\"bytes\": [", 10);
-
-                    if (!val.m_value.binary->empty())
-                    {
-                        for (auto i = val.m_value.binary->cbegin();
-                                i != val.m_value.binary->cend() - 1; ++i)
-                        {
-                            dump_integer(*i);
-                            o->write_characters(", ", 2);
-                        }
-                        dump_integer(val.m_value.binary->back());
-                    }
-
-                    o->write_characters("],\n", 3);
-                    o->write_characters(indent_string.c_str(), new_indent);
-
-                    o->write_characters("\"subtype\": ", 11);
-                    if (val.m_value.binary->has_subtype())
-                    {
-                        dump_integer(val.m_value.binary->subtype());
-                    }
-                    else
-                    {
-                        o->write_characters("null", 4);
-                    }
-                    o->write_character('\n');
-                    o->write_characters(indent_string.c_str(), current_indent);
-                    o->write_character('}');
-                }
-                else
-                {
-                    o->write_characters("{\"bytes\":[", 10);
-
-                    if (!val.m_value.binary->empty())
-                    {
-                        for (auto i = val.m_value.binary->cbegin();
-                                i != val.m_value.binary->cend() - 1; ++i)
-                        {
-                            dump_integer(*i);
-                            o->write_character(',');
-                        }
-                        dump_integer(val.m_value.binary->back());
-                    }
-
-                    o->write_characters("],\"subtype\":", 12);
-                    if (val.m_value.binary->has_subtype())
-                    {
-                        dump_integer(val.m_value.binary->subtype());
-                        o->write_character('}');
-                    }
-                    else
-                    {
-                        o->write_characters("null}", 5);
-                    }
-                }
-                return;
-            }
-
-            case value_t::boolean:
-            {
-                if (val.m_value.boolean)
-                {
-                    o->write_characters("true", 4);
-                }
-                else
-                {
-                    o->write_characters("false", 5);
-                }
-                return;
-            }
-
-            case value_t::number_integer:
-            {
-                dump_integer(val.m_value.number_integer);
-                return;
-            }
-
-            case value_t::number_unsigned:
-            {
-                dump_integer(val.m_value.number_unsigned);
-                return;
-            }
-
-            case value_t::number_float:
-            {
-                dump_float(val.m_value.number_float);
-                return;
-            }
-
-            case value_t::discarded:
-            {
-                o->write_characters("<discarded>", 11);
-                return;
-            }
-
-            case value_t::null:
-            {
-                o->write_characters("null", 4);
-                return;
-            }
-
-            default:            // LCOV_EXCL_LINE
-                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-        }
-    }
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    /*!
-    @brief dump escaped string
-
-    Escape a string by replacing certain special characters by a sequence of an
-    escape character (backslash) and another character and other control
-    characters by a sequence of "\u" followed by a four-digit hex
-    representation. The escaped string is written to output stream @a o.
-
-    @param[in] s  the string to escape
-    @param[in] ensure_ascii  whether to escape non-ASCII characters with
-                             \uXXXX sequences
-
-    @complexity Linear in the length of string @a s.
-    */
-    void dump_escaped(const string_t& s, const bool ensure_ascii)
-    {
-        std::uint32_t codepoint{};
-        std::uint8_t state = UTF8_ACCEPT;
-        std::size_t bytes = 0;  // number of bytes written to string_buffer
-
-        // number of bytes written at the point of the last valid byte
-        std::size_t bytes_after_last_accept = 0;
-        std::size_t undumped_chars = 0;
-
-        for (std::size_t i = 0; i < s.size(); ++i)
-        {
-            const auto byte = static_cast<std::uint8_t>(s[i]);
-
-            switch (decode(state, codepoint, byte))
-            {
-                case UTF8_ACCEPT:  // decode found a new code point
-                {
-                    switch (codepoint)
-                    {
-                        case 0x08: // backspace
-                        {
-                            string_buffer[bytes++] = '\\';
-                            string_buffer[bytes++] = 'b';
-                            break;
-                        }
-
-                        case 0x09: // horizontal tab
-                        {
-                            string_buffer[bytes++] = '\\';
-                            string_buffer[bytes++] = 't';
-                            break;
-                        }
-
-                        case 0x0A: // newline
-                        {
-                            string_buffer[bytes++] = '\\';
-                            string_buffer[bytes++] = 'n';
-                            break;
-                        }
-
-                        case 0x0C: // formfeed
-                        {
-                            string_buffer[bytes++] = '\\';
-                            string_buffer[bytes++] = 'f';
-                            break;
-                        }
-
-                        case 0x0D: // carriage return
-                        {
-                            string_buffer[bytes++] = '\\';
-                            string_buffer[bytes++] = 'r';
-                            break;
-                        }
-
-                        case 0x22: // quotation mark
-                        {
-                            string_buffer[bytes++] = '\\';
-                            string_buffer[bytes++] = '\"';
-                            break;
-                        }
-
-                        case 0x5C: // reverse solidus
-                        {
-                            string_buffer[bytes++] = '\\';
-                            string_buffer[bytes++] = '\\';
-                            break;
-                        }
-
-                        default:
-                        {
-                            // escape control characters (0x00..0x1F) or, if
-                            // ensure_ascii parameter is used, non-ASCII characters
-                            if ((codepoint <= 0x1F) || (ensure_ascii && (codepoint >= 0x7F)))
-                            {
-                                if (codepoint <= 0xFFFF)
-                                {
-                                    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-                                    static_cast<void>((std::snprintf)(string_buffer.data() + bytes, 7, "\\u%04x",
-                                                                      static_cast<std::uint16_t>(codepoint)));
-                                    bytes += 6;
-                                }
-                                else
-                                {
-                                    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-                                    static_cast<void>((std::snprintf)(string_buffer.data() + bytes, 13, "\\u%04x\\u%04x",
-                                                                      static_cast<std::uint16_t>(0xD7C0u + (codepoint >> 10u)),
-                                                                      static_cast<std::uint16_t>(0xDC00u + (codepoint & 0x3FFu))));
-                                    bytes += 12;
-                                }
-                            }
-                            else
-                            {
-                                // copy byte to buffer (all previous bytes
-                                // been copied have in default case above)
-                                string_buffer[bytes++] = s[i];
-                            }
-                            break;
-                        }
-                    }
-
-                    // write buffer and reset index; there must be 13 bytes
-                    // left, as this is the maximal number of bytes to be
-                    // written ("\uxxxx\uxxxx\0") for one code point
-                    if (string_buffer.size() - bytes < 13)
-                    {
-                        o->write_characters(string_buffer.data(), bytes);
-                        bytes = 0;
-                    }
-
-                    // remember the byte position of this accept
-                    bytes_after_last_accept = bytes;
-                    undumped_chars = 0;
-                    break;
-                }
-
-                case UTF8_REJECT:  // decode found invalid UTF-8 byte
-                {
-                    switch (error_handler)
-                    {
-                        case error_handler_t::strict:
-                        {
-                            JSON_THROW(type_error::create(316, concat("invalid UTF-8 byte at index ", std::to_string(i), ": 0x", hex_bytes(byte | 0)), nullptr));
-                        }
-
-                        case error_handler_t::ignore:
-                        case error_handler_t::replace:
-                        {
-                            // in case we saw this character the first time, we
-                            // would like to read it again, because the byte
-                            // may be OK for itself, but just not OK for the
-                            // previous sequence
-                            if (undumped_chars > 0)
-                            {
-                                --i;
-                            }
-
-                            // reset length buffer to the last accepted index;
-                            // thus removing/ignoring the invalid characters
-                            bytes = bytes_after_last_accept;
-
-                            if (error_handler == error_handler_t::replace)
-                            {
-                                // add a replacement character
-                                if (ensure_ascii)
-                                {
-                                    string_buffer[bytes++] = '\\';
-                                    string_buffer[bytes++] = 'u';
-                                    string_buffer[bytes++] = 'f';
-                                    string_buffer[bytes++] = 'f';
-                                    string_buffer[bytes++] = 'f';
-                                    string_buffer[bytes++] = 'd';
-                                }
-                                else
-                                {
-                                    string_buffer[bytes++] = detail::binary_writer<BasicJsonType, char>::to_char_type('\xEF');
-                                    string_buffer[bytes++] = detail::binary_writer<BasicJsonType, char>::to_char_type('\xBF');
-                                    string_buffer[bytes++] = detail::binary_writer<BasicJsonType, char>::to_char_type('\xBD');
-                                }
-
-                                // write buffer and reset index; there must be 13 bytes
-                                // left, as this is the maximal number of bytes to be
-                                // written ("\uxxxx\uxxxx\0") for one code point
-                                if (string_buffer.size() - bytes < 13)
-                                {
-                                    o->write_characters(string_buffer.data(), bytes);
-                                    bytes = 0;
-                                }
-
-                                bytes_after_last_accept = bytes;
-                            }
-
-                            undumped_chars = 0;
-
-                            // continue processing the string
-                            state = UTF8_ACCEPT;
-                            break;
-                        }
-
-                        default:            // LCOV_EXCL_LINE
-                            JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-                    }
-                    break;
-                }
-
-                default:  // decode found yet incomplete multi-byte code point
-                {
-                    if (!ensure_ascii)
-                    {
-                        // code point will not be escaped - copy byte to buffer
-                        string_buffer[bytes++] = s[i];
-                    }
-                    ++undumped_chars;
-                    break;
-                }
-            }
-        }
-
-        // we finished processing the string
-        if (JSON_HEDLEY_LIKELY(state == UTF8_ACCEPT))
-        {
-            // write buffer
-            if (bytes > 0)
-            {
-                o->write_characters(string_buffer.data(), bytes);
-            }
-        }
-        else
-        {
-            // we finish reading, but do not accept: string was incomplete
-            switch (error_handler)
-            {
-                case error_handler_t::strict:
-                {
-                    JSON_THROW(type_error::create(316, concat("incomplete UTF-8 string; last byte: 0x", hex_bytes(static_cast<std::uint8_t>(s.back() | 0))), nullptr));
-                }
-
-                case error_handler_t::ignore:
-                {
-                    // write all accepted bytes
-                    o->write_characters(string_buffer.data(), bytes_after_last_accept);
-                    break;
-                }
-
-                case error_handler_t::replace:
-                {
-                    // write all accepted bytes
-                    o->write_characters(string_buffer.data(), bytes_after_last_accept);
-                    // add a replacement character
-                    if (ensure_ascii)
-                    {
-                        o->write_characters("\\ufffd", 6);
-                    }
-                    else
-                    {
-                        o->write_characters("\xEF\xBF\xBD", 3);
-                    }
-                    break;
-                }
-
-                default:            // LCOV_EXCL_LINE
-                    JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-            }
-        }
-    }
-
-  private:
-    /*!
-    @brief count digits
-
-    Count the number of decimal (base 10) digits for an input unsigned integer.
-
-    @param[in] x  unsigned integer number to count its digits
-    @return    number of decimal digits
-    */
-    inline unsigned int count_digits(number_unsigned_t x) noexcept
-    {
-        unsigned int n_digits = 1;
-        for (;;)
-        {
-            if (x < 10)
-            {
-                return n_digits;
-            }
-            if (x < 100)
-            {
-                return n_digits + 1;
-            }
-            if (x < 1000)
-            {
-                return n_digits + 2;
-            }
-            if (x < 10000)
-            {
-                return n_digits + 3;
-            }
-            x = x / 10000u;
-            n_digits += 4;
-        }
-    }
-
-    /*!
-     * @brief convert a byte to a uppercase hex representation
-     * @param[in] byte byte to represent
-     * @return representation ("00".."FF")
-     */
-    static std::string hex_bytes(std::uint8_t byte)
-    {
-        std::string result = "FF";
-        constexpr const char* nibble_to_hex = "0123456789ABCDEF";
-        result[0] = nibble_to_hex[byte / 16];
-        result[1] = nibble_to_hex[byte % 16];
-        return result;
-    }
-
-    // templates to avoid warnings about useless casts
-    template <typename NumberType, enable_if_t<std::is_signed<NumberType>::value, int> = 0>
-    bool is_negative_number(NumberType x)
-    {
-        return x < 0;
-    }
-
-    template < typename NumberType, enable_if_t <std::is_unsigned<NumberType>::value, int > = 0 >
-    bool is_negative_number(NumberType /*unused*/)
-    {
-        return false;
-    }
-
-    /*!
-    @brief dump an integer
-
-    Dump a given integer to output stream @a o. Works internally with
-    @a number_buffer.
-
-    @param[in] x  integer number (signed or unsigned) to dump
-    @tparam NumberType either @a number_integer_t or @a number_unsigned_t
-    */
-    template < typename NumberType, detail::enable_if_t <
-                   std::is_integral<NumberType>::value ||
-                   std::is_same<NumberType, number_unsigned_t>::value ||
-                   std::is_same<NumberType, number_integer_t>::value ||
-                   std::is_same<NumberType, binary_char_t>::value,
-                   int > = 0 >
-    void dump_integer(NumberType x)
-    {
-        static constexpr std::array<std::array<char, 2>, 100> digits_to_99
-        {
-            {
-                {{'0', '0'}}, {{'0', '1'}}, {{'0', '2'}}, {{'0', '3'}}, {{'0', '4'}}, {{'0', '5'}}, {{'0', '6'}}, {{'0', '7'}}, {{'0', '8'}}, {{'0', '9'}},
-                {{'1', '0'}}, {{'1', '1'}}, {{'1', '2'}}, {{'1', '3'}}, {{'1', '4'}}, {{'1', '5'}}, {{'1', '6'}}, {{'1', '7'}}, {{'1', '8'}}, {{'1', '9'}},
-                {{'2', '0'}}, {{'2', '1'}}, {{'2', '2'}}, {{'2', '3'}}, {{'2', '4'}}, {{'2', '5'}}, {{'2', '6'}}, {{'2', '7'}}, {{'2', '8'}}, {{'2', '9'}},
-                {{'3', '0'}}, {{'3', '1'}}, {{'3', '2'}}, {{'3', '3'}}, {{'3', '4'}}, {{'3', '5'}}, {{'3', '6'}}, {{'3', '7'}}, {{'3', '8'}}, {{'3', '9'}},
-                {{'4', '0'}}, {{'4', '1'}}, {{'4', '2'}}, {{'4', '3'}}, {{'4', '4'}}, {{'4', '5'}}, {{'4', '6'}}, {{'4', '7'}}, {{'4', '8'}}, {{'4', '9'}},
-                {{'5', '0'}}, {{'5', '1'}}, {{'5', '2'}}, {{'5', '3'}}, {{'5', '4'}}, {{'5', '5'}}, {{'5', '6'}}, {{'5', '7'}}, {{'5', '8'}}, {{'5', '9'}},
-                {{'6', '0'}}, {{'6', '1'}}, {{'6', '2'}}, {{'6', '3'}}, {{'6', '4'}}, {{'6', '5'}}, {{'6', '6'}}, {{'6', '7'}}, {{'6', '8'}}, {{'6', '9'}},
-                {{'7', '0'}}, {{'7', '1'}}, {{'7', '2'}}, {{'7', '3'}}, {{'7', '4'}}, {{'7', '5'}}, {{'7', '6'}}, {{'7', '7'}}, {{'7', '8'}}, {{'7', '9'}},
-                {{'8', '0'}}, {{'8', '1'}}, {{'8', '2'}}, {{'8', '3'}}, {{'8', '4'}}, {{'8', '5'}}, {{'8', '6'}}, {{'8', '7'}}, {{'8', '8'}}, {{'8', '9'}},
-                {{'9', '0'}}, {{'9', '1'}}, {{'9', '2'}}, {{'9', '3'}}, {{'9', '4'}}, {{'9', '5'}}, {{'9', '6'}}, {{'9', '7'}}, {{'9', '8'}}, {{'9', '9'}},
-            }
-        };
-
-        // special case for "0"
-        if (x == 0)
-        {
-            o->write_character('0');
-            return;
-        }
-
-        // use a pointer to fill the buffer
-        auto buffer_ptr = number_buffer.begin(); // NOLINT(llvm-qualified-auto,readability-qualified-auto,cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-
-        number_unsigned_t abs_value;
-
-        unsigned int n_chars{};
-
-        if (is_negative_number(x))
-        {
-            *buffer_ptr = '-';
-            abs_value = remove_sign(static_cast<number_integer_t>(x));
-
-            // account one more byte for the minus sign
-            n_chars = 1 + count_digits(abs_value);
-        }
-        else
-        {
-            abs_value = static_cast<number_unsigned_t>(x);
-            n_chars = count_digits(abs_value);
-        }
-
-        // spare 1 byte for '\0'
-        JSON_ASSERT(n_chars < number_buffer.size() - 1);
-
-        // jump to the end to generate the string from backward,
-        // so we later avoid reversing the result
-        buffer_ptr += n_chars;
-
-        // Fast int2ascii implementation inspired by "Fastware" talk by Andrei Alexandrescu
-        // See: https://www.youtube.com/watch?v=o4-CwDo2zpg
-        while (abs_value >= 100)
-        {
-            const auto digits_index = static_cast<unsigned>((abs_value % 100));
-            abs_value /= 100;
-            *(--buffer_ptr) = digits_to_99[digits_index][1];
-            *(--buffer_ptr) = digits_to_99[digits_index][0];
-        }
-
-        if (abs_value >= 10)
-        {
-            const auto digits_index = static_cast<unsigned>(abs_value);
-            *(--buffer_ptr) = digits_to_99[digits_index][1];
-            *(--buffer_ptr) = digits_to_99[digits_index][0];
-        }
-        else
-        {
-            *(--buffer_ptr) = static_cast<char>('0' + abs_value);
-        }
-
-        o->write_characters(number_buffer.data(), n_chars);
-    }
-
-    /*!
-    @brief dump a floating-point number
-
-    Dump a given floating-point number to output stream @a o. Works internally
-    with @a number_buffer.
-
-    @param[in] x  floating-point number to dump
-    */
-    void dump_float(number_float_t x)
-    {
-        // NaN / inf
-        if (!std::isfinite(x))
-        {
-            o->write_characters("null", 4);
-            return;
-        }
-
-        // If number_float_t is an IEEE-754 single or double precision number,
-        // use the Grisu2 algorithm to produce short numbers which are
-        // guaranteed to round-trip, using strtof and strtod, resp.
-        //
-        // NB: The test below works if <long double> == <double>.
-        static constexpr bool is_ieee_single_or_double
-            = (std::numeric_limits<number_float_t>::is_iec559 && std::numeric_limits<number_float_t>::digits == 24 && std::numeric_limits<number_float_t>::max_exponent == 128) ||
-              (std::numeric_limits<number_float_t>::is_iec559 && std::numeric_limits<number_float_t>::digits == 53 && std::numeric_limits<number_float_t>::max_exponent == 1024);
-
-        dump_float(x, std::integral_constant<bool, is_ieee_single_or_double>());
-    }
-
-    void dump_float(number_float_t x, std::true_type /*is_ieee_single_or_double*/)
-    {
-        auto* begin = number_buffer.data();
-        auto* end = ::nlohmann::detail::to_chars(begin, begin + number_buffer.size(), x);
-
-        o->write_characters(begin, static_cast<size_t>(end - begin));
-    }
-
-    void dump_float(number_float_t x, std::false_type /*is_ieee_single_or_double*/)
-    {
-        // get number of digits for a float -> text -> float round-trip
-        static constexpr auto d = std::numeric_limits<number_float_t>::max_digits10;
-
-        // the actual conversion
-        // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-        std::ptrdiff_t len = (std::snprintf)(number_buffer.data(), number_buffer.size(), "%.*g", d, x);
-
-        // negative value indicates an error
-        JSON_ASSERT(len > 0);
-        // check if buffer was large enough
-        JSON_ASSERT(static_cast<std::size_t>(len) < number_buffer.size());
-
-        // erase thousands separator
-        if (thousands_sep != '\0')
-        {
-            // NOLINTNEXTLINE(readability-qualified-auto,llvm-qualified-auto): std::remove returns an iterator, see https://github.com/nlohmann/json/issues/3081
-            const auto end = std::remove(number_buffer.begin(), number_buffer.begin() + len, thousands_sep);
-            std::fill(end, number_buffer.end(), '\0');
-            JSON_ASSERT((end - number_buffer.begin()) <= len);
-            len = (end - number_buffer.begin());
-        }
-
-        // convert decimal point to '.'
-        if (decimal_point != '\0' && decimal_point != '.')
-        {
-            // NOLINTNEXTLINE(readability-qualified-auto,llvm-qualified-auto): std::find returns an iterator, see https://github.com/nlohmann/json/issues/3081
-            const auto dec_pos = std::find(number_buffer.begin(), number_buffer.end(), decimal_point);
-            if (dec_pos != number_buffer.end())
-            {
-                *dec_pos = '.';
-            }
-        }
-
-        o->write_characters(number_buffer.data(), static_cast<std::size_t>(len));
-
-        // determine if we need to append ".0"
-        const bool value_is_int_like =
-            std::none_of(number_buffer.begin(), number_buffer.begin() + len + 1,
-                         [](char c)
-        {
-            return c == '.' || c == 'e';
-        });
-
-        if (value_is_int_like)
-        {
-            o->write_characters(".0", 2);
-        }
-    }
-
-    /*!
-    @brief check whether a string is UTF-8 encoded
-
-    The function checks each byte of a string whether it is UTF-8 encoded. The
-    result of the check is stored in the @a state parameter. The function must
-    be called initially with state 0 (accept). State 1 means the string must
-    be rejected, because the current byte is not allowed. If the string is
-    completely processed, but the state is non-zero, the string ended
-    prematurely; that is, the last byte indicated more bytes should have
-    followed.
-
-    @param[in,out] state  the state of the decoding
-    @param[in,out] codep  codepoint (valid only if resulting state is UTF8_ACCEPT)
-    @param[in] byte       next byte to decode
-    @return               new state
-
-    @note The function has been edited: a std::array is used.
-
-    @copyright Copyright (c) 2008-2009 Bjoern Hoehrmann <bjoern@hoehrmann.de>
-    @sa http://bjoern.hoehrmann.de/utf-8/decoder/dfa/
-    */
-    static std::uint8_t decode(std::uint8_t& state, std::uint32_t& codep, const std::uint8_t byte) noexcept
-    {
-        static const std::array<std::uint8_t, 400> utf8d =
-        {
-            {
-                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 00..1F
-                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 20..3F
-                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 40..5F
-                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 60..7F
-                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, // 80..9F
-                7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, // A0..BF
-                8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, // C0..DF
-                0xA, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x4, 0x3, 0x3, // E0..EF
-                0xB, 0x6, 0x6, 0x6, 0x5, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, // F0..FF
-                0x0, 0x1, 0x2, 0x3, 0x5, 0x8, 0x7, 0x1, 0x1, 0x1, 0x4, 0x6, 0x1, 0x1, 0x1, 0x1, // s0..s0
-                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, // s1..s2
-                1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, // s3..s4
-                1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, // s5..s6
-                1, 3, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 // s7..s8
-            }
-        };
-
-        JSON_ASSERT(byte < utf8d.size());
-        const std::uint8_t type = utf8d[byte];
-
-        codep = (state != UTF8_ACCEPT)
-                ? (byte & 0x3fu) | (codep << 6u)
-                : (0xFFu >> type) & (byte);
-
-        std::size_t index = 256u + static_cast<size_t>(state) * 16u + static_cast<size_t>(type);
-        JSON_ASSERT(index < 400);
-        state = utf8d[index];
-        return state;
-    }
-
-    /*
-     * Overload to make the compiler happy while it is instantiating
-     * dump_integer for number_unsigned_t.
-     * Must never be called.
-     */
-    number_unsigned_t remove_sign(number_unsigned_t x)
-    {
-        JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-        return x; // LCOV_EXCL_LINE
-    }
-
-    /*
-     * Helper function for dump_integer
-     *
-     * This function takes a negative signed integer and returns its absolute
-     * value as unsigned integer. The plus/minus shuffling is necessary as we can
-     * not directly remove the sign of an arbitrary signed integer as the
-     * absolute values of INT_MIN and INT_MAX are usually not the same. See
-     * #1708 for details.
-     */
-    inline number_unsigned_t remove_sign(number_integer_t x) noexcept
-    {
-        JSON_ASSERT(x < 0 && x < (std::numeric_limits<number_integer_t>::max)()); // NOLINT(misc-redundant-expression)
-        return static_cast<number_unsigned_t>(-(x + 1)) + 1;
-    }
-
-  private:
-    /// the output of the serializer
-    output_adapter_t<char> o = nullptr;
-
-    /// a (hopefully) large enough character buffer
-    std::array<char, 64> number_buffer{{}};
-
-    /// the locale
-    const std::lconv* loc = nullptr;
-    /// the locale's thousand separator character
-    const char thousands_sep = '\0';
-    /// the locale's decimal point character
-    const char decimal_point = '\0';
-
-    /// string buffer
-    std::array<char, 512> string_buffer{{}};
-
-    /// the indentation character
-    const char indent_char;
-    /// the indentation string
-    string_t indent_string;
-
-    /// error_handler how to react on decoding errors
-    const error_handler_t error_handler;
-};
-
-}  // namespace detail
-NLOHMANN_JSON_NAMESPACE_END
-
-// #include <nlohmann/detail/value_t.hpp>
-
-// #include <nlohmann/json_fwd.hpp>
-
-// #include <nlohmann/ordered_map.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#include <functional> // equal_to, less
-#include <initializer_list> // initializer_list
-#include <iterator> // input_iterator_tag, iterator_traits
-#include <memory> // allocator
-#include <stdexcept> // for out_of_range
-#include <type_traits> // enable_if, is_convertible
-#include <utility> // pair
-#include <vector> // vector
-
-// #include <nlohmann/detail/macro_scope.hpp>
-
-// #include <nlohmann/detail/meta/type_traits.hpp>
-
-
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-/// ordered_map: a minimal map-like container that preserves insertion order
-/// for use within nlohmann::basic_json<ordered_map>
-template <class Key, class T, class IgnoredLess = std::less<Key>,
-          class Allocator = std::allocator<std::pair<const Key, T>>>
-                  struct ordered_map : std::vector<std::pair<const Key, T>, Allocator>
-{
-    using key_type = Key;
-    using mapped_type = T;
-    using Container = std::vector<std::pair<const Key, T>, Allocator>;
-    using iterator = typename Container::iterator;
-    using const_iterator = typename Container::const_iterator;
-    using size_type = typename Container::size_type;
-    using value_type = typename Container::value_type;
-#ifdef JSON_HAS_CPP_14
-    using key_compare = std::equal_to<>;
-#else
-    using key_compare = std::equal_to<Key>;
-#endif
-
-    // Explicit constructors instead of `using Container::Container`
-    // otherwise older compilers choke on it (GCC <= 5.5, xcode <= 9.4)
-    ordered_map() noexcept(noexcept(Container())) : Container{} {}
-    explicit ordered_map(const Allocator& alloc) noexcept(noexcept(Container(alloc))) : Container{alloc} {}
-    template <class It>
-    ordered_map(It first, It last, const Allocator& alloc = Allocator())
-        : Container{first, last, alloc} {}
-    ordered_map(std::initializer_list<value_type> init, const Allocator& alloc = Allocator() )
-        : Container{init, alloc} {}
-
-    std::pair<iterator, bool> emplace(const key_type& key, T&& t)
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return {it, false};
-            }
-        }
-        Container::emplace_back(key, std::forward<T>(t));
-        return {std::prev(this->end()), true};
-    }
-
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    std::pair<iterator, bool> emplace(KeyType && key, T && t)
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return {it, false};
-            }
-        }
-        Container::emplace_back(std::forward<KeyType>(key), std::forward<T>(t));
-        return {std::prev(this->end()), true};
-    }
-
-    T& operator[](const key_type& key)
-    {
-        return emplace(key, T{}).first->second;
-    }
-
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    T & operator[](KeyType && key)
-    {
-        return emplace(std::forward<KeyType>(key), T{}).first->second;
-    }
-
-    const T& operator[](const key_type& key) const
-    {
-        return at(key);
-    }
-
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    const T & operator[](KeyType && key) const
-    {
-        return at(std::forward<KeyType>(key));
-    }
-
-    T& at(const key_type& key)
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return it->second;
-            }
-        }
-
-        JSON_THROW(std::out_of_range("key not found"));
-    }
-
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    T & at(KeyType && key)
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return it->second;
-            }
-        }
-
-        JSON_THROW(std::out_of_range("key not found"));
-    }
-
-    const T& at(const key_type& key) const
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return it->second;
-            }
-        }
-
-        JSON_THROW(std::out_of_range("key not found"));
-    }
-
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    const T & at(KeyType && key) const
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return it->second;
-            }
-        }
-
-        JSON_THROW(std::out_of_range("key not found"));
-    }
-
-    size_type erase(const key_type& key)
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                // Since we cannot move const Keys, re-construct them in place
-                for (auto next = it; ++next != this->end(); ++it)
-                {
-                    it->~value_type(); // Destroy but keep allocation
-                    new (&*it) value_type{std::move(*next)};
-                }
-                Container::pop_back();
-                return 1;
-            }
-        }
-        return 0;
-    }
-
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    size_type erase(KeyType && key)
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                // Since we cannot move const Keys, re-construct them in place
-                for (auto next = it; ++next != this->end(); ++it)
-                {
-                    it->~value_type(); // Destroy but keep allocation
-                    new (&*it) value_type{std::move(*next)};
-                }
-                Container::pop_back();
-                return 1;
-            }
-        }
-        return 0;
-    }
-
-    iterator erase(iterator pos)
-    {
-        return erase(pos, std::next(pos));
-    }
-
-    iterator erase(iterator first, iterator last)
-    {
-        if (first == last)
-        {
-            return first;
-        }
-
-        const auto elements_affected = std::distance(first, last);
-        const auto offset = std::distance(Container::begin(), first);
-
-        // This is the start situation. We need to delete elements_affected
-        // elements (3 in this example: e, f, g), and need to return an
-        // iterator past the last deleted element (h in this example).
-        // Note that offset is the distance from the start of the vector
-        // to first. We will need this later.
-
-        // [ a, b, c, d, e, f, g, h, i, j ]
-        //               ^        ^
-        //             first    last
-
-        // Since we cannot move const Keys, we re-construct them in place.
-        // We start at first and re-construct (viz. copy) the elements from
-        // the back of the vector. Example for first iteration:
-
-        //               ,--------.
-        //               v        |   destroy e and re-construct with h
-        // [ a, b, c, d, e, f, g, h, i, j ]
-        //               ^        ^
-        //               it       it + elements_affected
-
-        for (auto it = first; std::next(it, elements_affected) != Container::end(); ++it)
-        {
-            it->~value_type(); // destroy but keep allocation
-            new (&*it) value_type{std::move(*std::next(it, elements_affected))}; // "move" next element to it
-        }
-
-        // [ a, b, c, d, h, i, j, h, i, j ]
-        //               ^        ^
-        //             first    last
-
-        // remove the unneeded elements at the end of the vector
-        Container::resize(this->size() - static_cast<size_type>(elements_affected));
-
-        // [ a, b, c, d, h, i, j ]
-        //               ^        ^
-        //             first    last
-
-        // first is now pointing past the last deleted element, but we cannot
-        // use this iterator, because it may have been invalidated by the
-        // resize call. Instead, we can return begin() + offset.
-        return Container::begin() + offset;
-    }
-
-    size_type count(const key_type& key) const
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return 1;
-            }
-        }
-        return 0;
-    }
-
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    size_type count(KeyType && key) const
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return 1;
-            }
-        }
-        return 0;
-    }
-
-    iterator find(const key_type& key)
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return it;
-            }
-        }
-        return Container::end();
-    }
-
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    iterator find(KeyType && key)
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return it;
-            }
-        }
-        return Container::end();
-    }
-
-    const_iterator find(const key_type& key) const
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, key))
-            {
-                return it;
-            }
-        }
-        return Container::end();
-    }
-
-    std::pair<iterator, bool> insert( value_type&& value )
-    {
-        return emplace(value.first, std::move(value.second));
-    }
-
-    std::pair<iterator, bool> insert( const value_type& value )
-    {
-        for (auto it = this->begin(); it != this->end(); ++it)
-        {
-            if (m_compare(it->first, value.first))
-            {
-                return {it, false};
-            }
-        }
-        Container::push_back(value);
-        return {--this->end(), true};
-    }
-
-    template<typename InputIt>
-    using require_input_iter = typename std::enable_if<std::is_convertible<typename std::iterator_traits<InputIt>::iterator_category,
-            std::input_iterator_tag>::value>::type;
-
-    template<typename InputIt, typename = require_input_iter<InputIt>>
-    void insert(InputIt first, InputIt last)
-    {
-        for (auto it = first; it != last; ++it)
-        {
-            insert(*it);
-        }
-    }
-
-private:
-    JSON_NO_UNIQUE_ADDRESS key_compare m_compare = key_compare();
-};
-
-NLOHMANN_JSON_NAMESPACE_END
-
-
-#if defined(JSON_HAS_CPP_17)
-    #include <any>
-    #include <string_view>
-#endif
-
-/*!
-@brief namespace for Niels Lohmann
-@see https://github.com/nlohmann
-@since version 1.0.0
-*/
-NLOHMANN_JSON_NAMESPACE_BEGIN
-
-/*!
-@brief a class to store JSON values
-
-@internal
-@invariant The member variables @a m_value and @a m_type have the following
-relationship:
-- If `m_type == value_t::object`, then `m_value.object != nullptr`.
-- If `m_type == value_t::array`, then `m_value.array != nullptr`.
-- If `m_type == value_t::string`, then `m_value.string != nullptr`.
-The invariants are checked by member function assert_invariant().
-
-@note ObjectType trick from https://stackoverflow.com/a/9860911
-@endinternal
-
-@since version 1.0.0
-
-@nosubgrouping
-*/
-NLOHMANN_BASIC_JSON_TPL_DECLARATION
-class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-special-member-functions)
-{
-  private:
-    template<detail::value_t> friend struct detail::external_constructor;
-
-    template<typename>
-    friend class ::nlohmann::json_pointer;
-    // can be restored when json_pointer backwards compatibility is removed
-    // friend ::nlohmann::json_pointer<StringType>;
-
-    template<typename BasicJsonType, typename InputType>
-    friend class ::nlohmann::detail::parser;
-    friend ::nlohmann::detail::serializer<basic_json>;
-    template<typename BasicJsonType>
-    friend class ::nlohmann::detail::iter_impl;
-    template<typename BasicJsonType, typename CharType>
-    friend class ::nlohmann::detail::binary_writer;
-    template<typename BasicJsonType, typename InputType, typename SAX>
-    friend class ::nlohmann::detail::binary_reader;
-    template<typename BasicJsonType>
-    friend class ::nlohmann::detail::json_sax_dom_parser;
-    template<typename BasicJsonType>
-    friend class ::nlohmann::detail::json_sax_dom_callback_parser;
-    friend class ::nlohmann::detail::exception;
-
-    /// workaround type for MSVC
-    using basic_json_t = NLOHMANN_BASIC_JSON_TPL;
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    // convenience aliases for types residing in namespace detail;
-    using lexer = ::nlohmann::detail::lexer_base<basic_json>;
-
-    template<typename InputAdapterType>
-    static ::nlohmann::detail::parser<basic_json, InputAdapterType> parser(
-        InputAdapterType adapter,
-        detail::parser_callback_t<basic_json>cb = nullptr,
-        const bool allow_exceptions = true,
-        const bool ignore_comments = false
-                                 )
-    {
-        return ::nlohmann::detail::parser<basic_json, InputAdapterType>(std::move(adapter),
-                std::move(cb), allow_exceptions, ignore_comments);
-    }
-
-  private:
-    using primitive_iterator_t = ::nlohmann::detail::primitive_iterator_t;
-    template<typename BasicJsonType>
-    using internal_iterator = ::nlohmann::detail::internal_iterator<BasicJsonType>;
-    template<typename BasicJsonType>
-    using iter_impl = ::nlohmann::detail::iter_impl<BasicJsonType>;
-    template<typename Iterator>
-    using iteration_proxy = ::nlohmann::detail::iteration_proxy<Iterator>;
-    template<typename Base> using json_reverse_iterator = ::nlohmann::detail::json_reverse_iterator<Base>;
-
-    template<typename CharType>
-    using output_adapter_t = ::nlohmann::detail::output_adapter_t<CharType>;
-
-    template<typename InputType>
-    using binary_reader = ::nlohmann::detail::binary_reader<basic_json, InputType>;
-    template<typename CharType> using binary_writer = ::nlohmann::detail::binary_writer<basic_json, CharType>;
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    using serializer = ::nlohmann::detail::serializer<basic_json>;
-
-  public:
-    using value_t = detail::value_t;
-    /// JSON Pointer, see @ref nlohmann::json_pointer
-    using json_pointer = ::nlohmann::json_pointer<StringType>;
-    template<typename T, typename SFINAE>
-    using json_serializer = JSONSerializer<T, SFINAE>;
-    /// how to treat decoding errors
-    using error_handler_t = detail::error_handler_t;
-    /// how to treat CBOR tags
-    using cbor_tag_handler_t = detail::cbor_tag_handler_t;
-    /// helper type for initializer lists of basic_json values
-    using initializer_list_t = std::initializer_list<detail::json_ref<basic_json>>;
-
-    using input_format_t = detail::input_format_t;
-    /// SAX interface type, see @ref nlohmann::json_sax
-    using json_sax_t = json_sax<basic_json>;
-
-    ////////////////
-    // exceptions //
-    ////////////////
-
-    /// @name exceptions
-    /// Classes to implement user-defined exceptions.
-    /// @{
-
-    using exception = detail::exception;
-    using parse_error = detail::parse_error;
-    using invalid_iterator = detail::invalid_iterator;
-    using type_error = detail::type_error;
-    using out_of_range = detail::out_of_range;
-    using other_error = detail::other_error;
-
-    /// @}
-
-
-    /////////////////////
-    // container types //
-    /////////////////////
-
-    /// @name container types
-    /// The canonic container types to use @ref basic_json like any other STL
-    /// container.
-    /// @{
-
-    /// the type of elements in a basic_json container
-    using value_type = basic_json;
-
-    /// the type of an element reference
-    using reference = value_type&;
-    /// the type of an element const reference
-    using const_reference = const value_type&;
-
-    /// a type to represent differences between iterators
-    using difference_type = std::ptrdiff_t;
-    /// a type to represent container sizes
-    using size_type = std::size_t;
-
-    /// the allocator type
-    using allocator_type = AllocatorType<basic_json>;
-
-    /// the type of an element pointer
-    using pointer = typename std::allocator_traits<allocator_type>::pointer;
-    /// the type of an element const pointer
-    using const_pointer = typename std::allocator_traits<allocator_type>::const_pointer;
-
-    /// an iterator for a basic_json container
-    using iterator = iter_impl<basic_json>;
-    /// a const iterator for a basic_json container
-    using const_iterator = iter_impl<const basic_json>;
-    /// a reverse iterator for a basic_json container
-    using reverse_iterator = json_reverse_iterator<typename basic_json::iterator>;
-    /// a const reverse iterator for a basic_json container
-    using const_reverse_iterator = json_reverse_iterator<typename basic_json::const_iterator>;
-
-    /// @}
-
-
-    /// @brief returns the allocator associated with the container
-    /// @sa https://json.nlohmann.me/api/basic_json/get_allocator/
-    static allocator_type get_allocator()
-    {
-        return allocator_type();
-    }
-
-    /// @brief returns version information on the library
-    /// @sa https://json.nlohmann.me/api/basic_json/meta/
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json meta()
-    {
-        basic_json result;
-
-        result["copyright"] = "(C) 2013-2022 Niels Lohmann";
-        result["name"] = "JSON for Modern C++";
-        result["url"] = "https://github.com/nlohmann/json";
-        result["version"]["string"] =
-            detail::concat(std::to_string(NLOHMANN_JSON_VERSION_MAJOR), '.',
-                           std::to_string(NLOHMANN_JSON_VERSION_MINOR), '.',
-                           std::to_string(NLOHMANN_JSON_VERSION_PATCH));
-        result["version"]["major"] = NLOHMANN_JSON_VERSION_MAJOR;
-        result["version"]["minor"] = NLOHMANN_JSON_VERSION_MINOR;
-        result["version"]["patch"] = NLOHMANN_JSON_VERSION_PATCH;
-
-#ifdef _WIN32
-        result["platform"] = "win32";
-#elif defined __linux__
-        result["platform"] = "linux";
-#elif defined __APPLE__
-        result["platform"] = "apple";
-#elif defined __unix__
-        result["platform"] = "unix";
-#else
-        result["platform"] = "unknown";
-#endif
-
-#if defined(__ICC) || defined(__INTEL_COMPILER)
-        result["compiler"] = {{"family", "icc"}, {"version", __INTEL_COMPILER}};
-#elif defined(__clang__)
-        result["compiler"] = {{"family", "clang"}, {"version", __clang_version__}};
-#elif defined(__GNUC__) || defined(__GNUG__)
-        result["compiler"] = {{"family", "gcc"}, {"version", detail::concat(
-                    std::to_string(__GNUC__), '.',
-                    std::to_string(__GNUC_MINOR__), '.',
-                    std::to_string(__GNUC_PATCHLEVEL__))
-            }
-        };
-#elif defined(__HP_cc) || defined(__HP_aCC)
-        result["compiler"] = "hp"
-#elif defined(__IBMCPP__)
-        result["compiler"] = {{"family", "ilecpp"}, {"version", __IBMCPP__}};
-#elif defined(_MSC_VER)
-        result["compiler"] = {{"family", "msvc"}, {"version", _MSC_VER}};
-#elif defined(__PGI)
-        result["compiler"] = {{"family", "pgcpp"}, {"version", __PGI}};
-#elif defined(__SUNPRO_CC)
-        result["compiler"] = {{"family", "sunpro"}, {"version", __SUNPRO_CC}};
-#else
-        result["compiler"] = {{"family", "unknown"}, {"version", "unknown"}};
-#endif
-
-
-#if defined(_MSVC_LANG)
-        result["compiler"]["c++"] = std::to_string(_MSVC_LANG);
-#elif defined(__cplusplus)
-        result["compiler"]["c++"] = std::to_string(__cplusplus);
-#else
-        result["compiler"]["c++"] = "unknown";
-#endif
-        return result;
-    }
-
-
-    ///////////////////////////
-    // JSON value data types //
-    ///////////////////////////
-
-    /// @name JSON value data types
-    /// The data types to store a JSON value. These types are derived from
-    /// the template arguments passed to class @ref basic_json.
-    /// @{
-
-    /// @brief default object key comparator type
-    /// The actual object key comparator type (@ref object_comparator_t) may be
-    /// different.
-    /// @sa https://json.nlohmann.me/api/basic_json/default_object_comparator_t/
-#if defined(JSON_HAS_CPP_14)
-    // use of transparent comparator avoids unnecessary repeated construction of temporaries
-    // in functions involving lookup by key with types other than object_t::key_type (aka. StringType)
-    using default_object_comparator_t = std::less<>;
-#else
-    using default_object_comparator_t = std::less<StringType>;
-#endif
-
-    /// @brief a type for an object
-    /// @sa https://json.nlohmann.me/api/basic_json/object_t/
-    using object_t = ObjectType<StringType,
-          basic_json,
-          default_object_comparator_t,
-          AllocatorType<std::pair<const StringType,
-          basic_json>>>;
-
-    /// @brief a type for an array
-    /// @sa https://json.nlohmann.me/api/basic_json/array_t/
-    using array_t = ArrayType<basic_json, AllocatorType<basic_json>>;
-
-    /// @brief a type for a string
-    /// @sa https://json.nlohmann.me/api/basic_json/string_t/
-    using string_t = StringType;
-
-    /// @brief a type for a boolean
-    /// @sa https://json.nlohmann.me/api/basic_json/boolean_t/
-    using boolean_t = BooleanType;
-
-    /// @brief a type for a number (integer)
-    /// @sa https://json.nlohmann.me/api/basic_json/number_integer_t/
-    using number_integer_t = NumberIntegerType;
-
-    /// @brief a type for a number (unsigned)
-    /// @sa https://json.nlohmann.me/api/basic_json/number_unsigned_t/
-    using number_unsigned_t = NumberUnsignedType;
-
-    /// @brief a type for a number (floating-point)
-    /// @sa https://json.nlohmann.me/api/basic_json/number_float_t/
-    using number_float_t = NumberFloatType;
-
-    /// @brief a type for a packed binary type
-    /// @sa https://json.nlohmann.me/api/basic_json/binary_t/
-    using binary_t = nlohmann::byte_container_with_subtype<BinaryType>;
-
-    /// @brief object key comparator type
-    /// @sa https://json.nlohmann.me/api/basic_json/object_comparator_t/
-    using object_comparator_t = detail::actual_object_comparator_t<basic_json>;
-
-    /// @}
-
-  private:
-
-    /// helper for exception-safe object creation
-    template<typename T, typename... Args>
-    JSON_HEDLEY_RETURNS_NON_NULL
-    static T* create(Args&& ... args)
-    {
-        AllocatorType<T> alloc;
-        using AllocatorTraits = std::allocator_traits<AllocatorType<T>>;
-
-        auto deleter = [&](T * obj)
-        {
-            AllocatorTraits::deallocate(alloc, obj, 1);
-        };
-        std::unique_ptr<T, decltype(deleter)> obj(AllocatorTraits::allocate(alloc, 1), deleter);
-        AllocatorTraits::construct(alloc, obj.get(), std::forward<Args>(args)...);
-        JSON_ASSERT(obj != nullptr);
-        return obj.release();
-    }
-
-    ////////////////////////
-    // JSON value storage //
-    ////////////////////////
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    /*!
-    @brief a JSON value
-
-    The actual storage for a JSON value of the @ref basic_json class. This
-    union combines the different storage types for the JSON value types
-    defined in @ref value_t.
-
-    JSON type | value_t type    | used type
-    --------- | --------------- | ------------------------
-    object    | object          | pointer to @ref object_t
-    array     | array           | pointer to @ref array_t
-    string    | string          | pointer to @ref string_t
-    boolean   | boolean         | @ref boolean_t
-    number    | number_integer  | @ref number_integer_t
-    number    | number_unsigned | @ref number_unsigned_t
-    number    | number_float    | @ref number_float_t
-    binary    | binary          | pointer to @ref binary_t
-    null      | null            | *no value is stored*
-
-    @note Variable-length types (objects, arrays, and strings) are stored as
-    pointers. The size of the union should not exceed 64 bits if the default
-    value types are used.
-
-    @since version 1.0.0
-    */
-    union json_value
-    {
-        /// object (stored with pointer to save storage)
-        object_t* object;
-        /// array (stored with pointer to save storage)
-        array_t* array;
-        /// string (stored with pointer to save storage)
-        string_t* string;
-        /// binary (stored with pointer to save storage)
-        binary_t* binary;
-        /// boolean
-        boolean_t boolean;
-        /// number (integer)
-        number_integer_t number_integer;
-        /// number (unsigned integer)
-        number_unsigned_t number_unsigned;
-        /// number (floating-point)
-        number_float_t number_float;
-
-        /// default constructor (for null values)
-        json_value() = default;
-        /// constructor for booleans
-        json_value(boolean_t v) noexcept : boolean(v) {}
-        /// constructor for numbers (integer)
-        json_value(number_integer_t v) noexcept : number_integer(v) {}
-        /// constructor for numbers (unsigned)
-        json_value(number_unsigned_t v) noexcept : number_unsigned(v) {}
-        /// constructor for numbers (floating-point)
-        json_value(number_float_t v) noexcept : number_float(v) {}
-        /// constructor for empty values of a given type
-        json_value(value_t t)
-        {
-            switch (t)
-            {
-                case value_t::object:
-                {
-                    object = create<object_t>();
-                    break;
-                }
-
-                case value_t::array:
-                {
-                    array = create<array_t>();
-                    break;
-                }
-
-                case value_t::string:
-                {
-                    string = create<string_t>("");
-                    break;
-                }
-
-                case value_t::binary:
-                {
-                    binary = create<binary_t>();
-                    break;
-                }
-
-                case value_t::boolean:
-                {
-                    boolean = static_cast<boolean_t>(false);
-                    break;
-                }
-
-                case value_t::number_integer:
-                {
-                    number_integer = static_cast<number_integer_t>(0);
-                    break;
-                }
-
-                case value_t::number_unsigned:
-                {
-                    number_unsigned = static_cast<number_unsigned_t>(0);
-                    break;
-                }
-
-                case value_t::number_float:
-                {
-                    number_float = static_cast<number_float_t>(0.0);
-                    break;
-                }
-
-                case value_t::null:
-                {
-                    object = nullptr;  // silence warning, see #821
-                    break;
-                }
-
-                case value_t::discarded:
-                default:
-                {
-                    object = nullptr;  // silence warning, see #821
-                    if (JSON_HEDLEY_UNLIKELY(t == value_t::null))
-                    {
-                        JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.11.2", nullptr)); // LCOV_EXCL_LINE
-                    }
-                    break;
-                }
-            }
-        }
-
-        /// constructor for strings
-        json_value(const string_t& value) : string(create<string_t>(value)) {}
-
-        /// constructor for rvalue strings
-        json_value(string_t&& value) : string(create<string_t>(std::move(value))) {}
-
-        /// constructor for objects
-        json_value(const object_t& value) : object(create<object_t>(value)) {}
-
-        /// constructor for rvalue objects
-        json_value(object_t&& value) : object(create<object_t>(std::move(value))) {}
-
-        /// constructor for arrays
-        json_value(const array_t& value) : array(create<array_t>(value)) {}
-
-        /// constructor for rvalue arrays
-        json_value(array_t&& value) : array(create<array_t>(std::move(value))) {}
-
-        /// constructor for binary arrays
-        json_value(const typename binary_t::container_type& value) : binary(create<binary_t>(value)) {}
-
-        /// constructor for rvalue binary arrays
-        json_value(typename binary_t::container_type&& value) : binary(create<binary_t>(std::move(value))) {}
-
-        /// constructor for binary arrays (internal type)
-        json_value(const binary_t& value) : binary(create<binary_t>(value)) {}
-
-        /// constructor for rvalue binary arrays (internal type)
-        json_value(binary_t&& value) : binary(create<binary_t>(std::move(value))) {}
-
-        void destroy(value_t t)
-        {
-            if (t == value_t::array || t == value_t::object)
-            {
-                // flatten the current json_value to a heap-allocated stack
-                std::vector<basic_json> stack;
-
-                // move the top-level items to stack
-                if (t == value_t::array)
-                {
-                    stack.reserve(array->size());
-                    std::move(array->begin(), array->end(), std::back_inserter(stack));
-                }
-                else
-                {
-                    stack.reserve(object->size());
-                    for (auto&& it : *object)
-                    {
-                        stack.push_back(std::move(it.second));
-                    }
-                }
-
-                while (!stack.empty())
-                {
-                    // move the last item to local variable to be processed
-                    basic_json current_item(std::move(stack.back()));
-                    stack.pop_back();
-
-                    // if current_item is array/object, move
-                    // its children to the stack to be processed later
-                    if (current_item.is_array())
-                    {
-                        std::move(current_item.m_value.array->begin(), current_item.m_value.array->end(), std::back_inserter(stack));
-
-                        current_item.m_value.array->clear();
-                    }
-                    else if (current_item.is_object())
-                    {
-                        for (auto&& it : *current_item.m_value.object)
-                        {
-                            stack.push_back(std::move(it.second));
-                        }
-
-                        current_item.m_value.object->clear();
-                    }
-
-                    // it's now safe that current_item get destructed
-                    // since it doesn't have any children
-                }
-            }
-
-            switch (t)
-            {
-                case value_t::object:
-                {
-                    AllocatorType<object_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, object);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, object, 1);
-                    break;
-                }
-
-                case value_t::array:
-                {
-                    AllocatorType<array_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, array);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, array, 1);
-                    break;
-                }
-
-                case value_t::string:
-                {
-                    AllocatorType<string_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, string);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, string, 1);
-                    break;
-                }
-
-                case value_t::binary:
-                {
-                    AllocatorType<binary_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, binary);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, binary, 1);
-                    break;
-                }
-
-                case value_t::null:
-                case value_t::boolean:
-                case value_t::number_integer:
-                case value_t::number_unsigned:
-                case value_t::number_float:
-                case value_t::discarded:
-                default:
-                {
-                    break;
-                }
-            }
-        }
-    };
-
-  private:
-    /*!
-    @brief checks the class invariants
-
-    This function asserts the class invariants. It needs to be called at the
-    end of every constructor to make sure that created objects respect the
-    invariant. Furthermore, it has to be called each time the type of a JSON
-    value is changed, because the invariant expresses a relationship between
-    @a m_type and @a m_value.
-
-    Furthermore, the parent relation is checked for arrays and objects: If
-    @a check_parents true and the value is an array or object, then the
-    container's elements must have the current value as parent.
-
-    @param[in] check_parents  whether the parent relation should be checked.
-               The value is true by default and should only be set to false
-               during destruction of objects when the invariant does not
-               need to hold.
-    */
-    void assert_invariant(bool check_parents = true) const noexcept
-    {
-        JSON_ASSERT(m_type != value_t::object || m_value.object != nullptr);
-        JSON_ASSERT(m_type != value_t::array || m_value.array != nullptr);
-        JSON_ASSERT(m_type != value_t::string || m_value.string != nullptr);
-        JSON_ASSERT(m_type != value_t::binary || m_value.binary != nullptr);
-
-#if JSON_DIAGNOSTICS
-        JSON_TRY
-        {
-            // cppcheck-suppress assertWithSideEffect
-            JSON_ASSERT(!check_parents || !is_structured() || std::all_of(begin(), end(), [this](const basic_json & j)
-            {
-                return j.m_parent == this;
-            }));
-        }
-        JSON_CATCH(...) {} // LCOV_EXCL_LINE
-#endif
-        static_cast<void>(check_parents);
-    }
-
-    void set_parents()
-    {
-#if JSON_DIAGNOSTICS
-        switch (m_type)
-        {
-            case value_t::array:
-            {
-                for (auto& element : *m_value.array)
-                {
-                    element.m_parent = this;
-                }
-                break;
-            }
-
-            case value_t::object:
-            {
-                for (auto& element : *m_value.object)
-                {
-                    element.second.m_parent = this;
-                }
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-                break;
-        }
-#endif
-    }
-
-    iterator set_parents(iterator it, typename iterator::difference_type count_set_parents)
-    {
-#if JSON_DIAGNOSTICS
-        for (typename iterator::difference_type i = 0; i < count_set_parents; ++i)
-        {
-            (it + i)->m_parent = this;
-        }
-#else
-        static_cast<void>(count_set_parents);
-#endif
-        return it;
-    }
-
-    reference set_parent(reference j, std::size_t old_capacity = static_cast<std::size_t>(-1))
-    {
-#if JSON_DIAGNOSTICS
-        if (old_capacity != static_cast<std::size_t>(-1))
-        {
-            // see https://github.com/nlohmann/json/issues/2838
-            JSON_ASSERT(type() == value_t::array);
-            if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity))
-            {
-                // capacity has changed: update all parents
-                set_parents();
-                return j;
-            }
-        }
-
-        // ordered_json uses a vector internally, so pointers could have
-        // been invalidated; see https://github.com/nlohmann/json/issues/2962
-#ifdef JSON_HEDLEY_MSVC_VERSION
-#pragma warning(push )
-#pragma warning(disable : 4127) // ignore warning to replace if with if constexpr
-#endif
-        if (detail::is_ordered_map<object_t>::value)
-        {
-            set_parents();
-            return j;
-        }
-#ifdef JSON_HEDLEY_MSVC_VERSION
-#pragma warning( pop )
-#endif
-
-        j.m_parent = this;
-#else
-        static_cast<void>(j);
-        static_cast<void>(old_capacity);
-#endif
-        return j;
-    }
-
-  public:
-    //////////////////////////
-    // JSON parser callback //
-    //////////////////////////
-
-    /// @brief parser event types
-    /// @sa https://json.nlohmann.me/api/basic_json/parse_event_t/
-    using parse_event_t = detail::parse_event_t;
-
-    /// @brief per-element parser callback type
-    /// @sa https://json.nlohmann.me/api/basic_json/parser_callback_t/
-    using parser_callback_t = detail::parser_callback_t<basic_json>;
-
-    //////////////////
-    // constructors //
-    //////////////////
-
-    /// @name constructors and destructors
-    /// Constructors of class @ref basic_json, copy/move constructor, copy
-    /// assignment, static functions creating objects, and the destructor.
-    /// @{
-
-    /// @brief create an empty value with a given type
-    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    basic_json(const value_t v)
-        : m_type(v), m_value(v)
-    {
-        assert_invariant();
-    }
-
-    /// @brief create a null object
-    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    basic_json(std::nullptr_t = nullptr) noexcept // NOLINT(bugprone-exception-escape)
-        : basic_json(value_t::null)
-    {
-        assert_invariant();
-    }
-
-    /// @brief create a JSON value from compatible types
-    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    template < typename CompatibleType,
-               typename U = detail::uncvref_t<CompatibleType>,
-               detail::enable_if_t <
-                   !detail::is_basic_json<U>::value && detail::is_compatible_type<basic_json_t, U>::value, int > = 0 >
-    basic_json(CompatibleType && val) noexcept(noexcept( // NOLINT(bugprone-forwarding-reference-overload,bugprone-exception-escape)
-                JSONSerializer<U>::to_json(std::declval<basic_json_t&>(),
-                                           std::forward<CompatibleType>(val))))
-    {
-        JSONSerializer<U>::to_json(*this, std::forward<CompatibleType>(val));
-        set_parents();
-        assert_invariant();
-    }
-
-    /// @brief create a JSON value from an existing one
-    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    template < typename BasicJsonType,
-               detail::enable_if_t <
-                   detail::is_basic_json<BasicJsonType>::value&& !std::is_same<basic_json, BasicJsonType>::value, int > = 0 >
-    basic_json(const BasicJsonType& val)
-    {
-        using other_boolean_t = typename BasicJsonType::boolean_t;
-        using other_number_float_t = typename BasicJsonType::number_float_t;
-        using other_number_integer_t = typename BasicJsonType::number_integer_t;
-        using other_number_unsigned_t = typename BasicJsonType::number_unsigned_t;
-        using other_string_t = typename BasicJsonType::string_t;
-        using other_object_t = typename BasicJsonType::object_t;
-        using other_array_t = typename BasicJsonType::array_t;
-        using other_binary_t = typename BasicJsonType::binary_t;
-
-        switch (val.type())
-        {
-            case value_t::boolean:
-                JSONSerializer<other_boolean_t>::to_json(*this, val.template get<other_boolean_t>());
-                break;
-            case value_t::number_float:
-                JSONSerializer<other_number_float_t>::to_json(*this, val.template get<other_number_float_t>());
-                break;
-            case value_t::number_integer:
-                JSONSerializer<other_number_integer_t>::to_json(*this, val.template get<other_number_integer_t>());
-                break;
-            case value_t::number_unsigned:
-                JSONSerializer<other_number_unsigned_t>::to_json(*this, val.template get<other_number_unsigned_t>());
-                break;
-            case value_t::string:
-                JSONSerializer<other_string_t>::to_json(*this, val.template get_ref<const other_string_t&>());
-                break;
-            case value_t::object:
-                JSONSerializer<other_object_t>::to_json(*this, val.template get_ref<const other_object_t&>());
-                break;
-            case value_t::array:
-                JSONSerializer<other_array_t>::to_json(*this, val.template get_ref<const other_array_t&>());
-                break;
-            case value_t::binary:
-                JSONSerializer<other_binary_t>::to_json(*this, val.template get_ref<const other_binary_t&>());
-                break;
-            case value_t::null:
-                *this = nullptr;
-                break;
-            case value_t::discarded:
-                m_type = value_t::discarded;
-                break;
-            default:            // LCOV_EXCL_LINE
-                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-        }
-        JSON_ASSERT(m_type == val.type());
-        set_parents();
-        assert_invariant();
-    }
-
-    /// @brief create a container (array or object) from an initializer list
-    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    basic_json(initializer_list_t init,
-               bool type_deduction = true,
-               value_t manual_type = value_t::array)
-    {
-        // check if each element is an array with two elements whose first
-        // element is a string
-        bool is_an_object = std::all_of(init.begin(), init.end(),
-                                        [](const detail::json_ref<basic_json>& element_ref)
-        {
-            return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[0].is_string();
-        });
-
-        // adjust type if type deduction is not wanted
-        if (!type_deduction)
-        {
-            // if array is wanted, do not create an object though possible
-            if (manual_type == value_t::array)
-            {
-                is_an_object = false;
-            }
-
-            // if object is wanted but impossible, throw an exception
-            if (JSON_HEDLEY_UNLIKELY(manual_type == value_t::object && !is_an_object))
-            {
-                JSON_THROW(type_error::create(301, "cannot create object from initializer list", nullptr));
-            }
-        }
-
-        if (is_an_object)
-        {
-            // the initializer list is a list of pairs -> create object
-            m_type = value_t::object;
-            m_value = value_t::object;
-
-            for (auto& element_ref : init)
-            {
-                auto element = element_ref.moved_or_copied();
-                m_value.object->emplace(
-                    std::move(*((*element.m_value.array)[0].m_value.string)),
-                    std::move((*element.m_value.array)[1]));
-            }
-        }
-        else
-        {
-            // the initializer list describes an array -> create array
-            m_type = value_t::array;
-            m_value.array = create<array_t>(init.begin(), init.end());
-        }
-
-        set_parents();
-        assert_invariant();
-    }
-
-    /// @brief explicitly create a binary array (without subtype)
-    /// @sa https://json.nlohmann.me/api/basic_json/binary/
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json binary(const typename binary_t::container_type& init)
-    {
-        auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = init;
-        return res;
-    }
-
-    /// @brief explicitly create a binary array (with subtype)
-    /// @sa https://json.nlohmann.me/api/basic_json/binary/
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json binary(const typename binary_t::container_type& init, typename binary_t::subtype_type subtype)
-    {
-        auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = binary_t(init, subtype);
-        return res;
-    }
-
-    /// @brief explicitly create a binary array
-    /// @sa https://json.nlohmann.me/api/basic_json/binary/
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json binary(typename binary_t::container_type&& init)
-    {
-        auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = std::move(init);
-        return res;
-    }
-
-    /// @brief explicitly create a binary array (with subtype)
-    /// @sa https://json.nlohmann.me/api/basic_json/binary/
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json binary(typename binary_t::container_type&& init, typename binary_t::subtype_type subtype)
-    {
-        auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = binary_t(std::move(init), subtype);
-        return res;
-    }
-
-    /// @brief explicitly create an array from an initializer list
-    /// @sa https://json.nlohmann.me/api/basic_json/array/
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json array(initializer_list_t init = {})
-    {
-        return basic_json(init, false, value_t::array);
-    }
-
-    /// @brief explicitly create an object from an initializer list
-    /// @sa https://json.nlohmann.me/api/basic_json/object/
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json object(initializer_list_t init = {})
-    {
-        return basic_json(init, false, value_t::object);
-    }
-
-    /// @brief construct an array with count copies of given value
-    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    basic_json(size_type cnt, const basic_json& val)
-        : m_type(value_t::array)
-    {
-        m_value.array = create<array_t>(cnt, val);
-        set_parents();
-        assert_invariant();
-    }
-
-    /// @brief construct a JSON container given an iterator range
-    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    template < class InputIT, typename std::enable_if <
-                   std::is_same<InputIT, typename basic_json_t::iterator>::value ||
-                   std::is_same<InputIT, typename basic_json_t::const_iterator>::value, int >::type = 0 >
-    basic_json(InputIT first, InputIT last)
-    {
-        JSON_ASSERT(first.m_object != nullptr);
-        JSON_ASSERT(last.m_object != nullptr);
-
-        // make sure iterator fits the current value
-        if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object))
-        {
-            JSON_THROW(invalid_iterator::create(201, "iterators are not compatible", nullptr));
-        }
-
-        // copy type from first iterator
-        m_type = first.m_object->m_type;
-
-        // check if iterator range is complete for primitive values
-        switch (m_type)
-        {
-            case value_t::boolean:
-            case value_t::number_float:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::string:
-            {
-                if (JSON_HEDLEY_UNLIKELY(!first.m_it.primitive_iterator.is_begin()
-                                         || !last.m_it.primitive_iterator.is_end()))
-                {
-                    JSON_THROW(invalid_iterator::create(204, "iterators out of range", first.m_object));
-                }
-                break;
-            }
-
-            case value_t::null:
-            case value_t::object:
-            case value_t::array:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-                break;
-        }
-
-        switch (m_type)
-        {
-            case value_t::number_integer:
-            {
-                m_value.number_integer = first.m_object->m_value.number_integer;
-                break;
-            }
-
-            case value_t::number_unsigned:
-            {
-                m_value.number_unsigned = first.m_object->m_value.number_unsigned;
-                break;
-            }
-
-            case value_t::number_float:
-            {
-                m_value.number_float = first.m_object->m_value.number_float;
-                break;
-            }
-
-            case value_t::boolean:
-            {
-                m_value.boolean = first.m_object->m_value.boolean;
-                break;
-            }
-
-            case value_t::string:
-            {
-                m_value = *first.m_object->m_value.string;
-                break;
-            }
-
-            case value_t::object:
-            {
-                m_value.object = create<object_t>(first.m_it.object_iterator,
-                                                  last.m_it.object_iterator);
-                break;
-            }
-
-            case value_t::array:
-            {
-                m_value.array = create<array_t>(first.m_it.array_iterator,
-                                                last.m_it.array_iterator);
-                break;
-            }
-
-            case value_t::binary:
-            {
-                m_value = *first.m_object->m_value.binary;
-                break;
-            }
-
-            case value_t::null:
-            case value_t::discarded:
-            default:
-                JSON_THROW(invalid_iterator::create(206, detail::concat("cannot construct with iterators from ", first.m_object->type_name()), first.m_object));
-        }
-
-        set_parents();
-        assert_invariant();
-    }
-
-
-    ///////////////////////////////////////
-    // other constructors and destructor //
-    ///////////////////////////////////////
-
-    template<typename JsonRef,
-             detail::enable_if_t<detail::conjunction<detail::is_json_ref<JsonRef>,
-                                 std::is_same<typename JsonRef::value_type, basic_json>>::value, int> = 0 >
-    basic_json(const JsonRef& ref) : basic_json(ref.moved_or_copied()) {}
-
-    /// @brief copy constructor
-    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    basic_json(const basic_json& other)
-        : m_type(other.m_type)
-    {
-        // check of passed value is valid
-        other.assert_invariant();
-
-        switch (m_type)
-        {
-            case value_t::object:
-            {
-                m_value = *other.m_value.object;
-                break;
-            }
-
-            case value_t::array:
-            {
-                m_value = *other.m_value.array;
-                break;
-            }
-
-            case value_t::string:
-            {
-                m_value = *other.m_value.string;
-                break;
-            }
-
-            case value_t::boolean:
-            {
-                m_value = other.m_value.boolean;
-                break;
-            }
-
-            case value_t::number_integer:
-            {
-                m_value = other.m_value.number_integer;
-                break;
-            }
-
-            case value_t::number_unsigned:
-            {
-                m_value = other.m_value.number_unsigned;
-                break;
-            }
-
-            case value_t::number_float:
-            {
-                m_value = other.m_value.number_float;
-                break;
-            }
-
-            case value_t::binary:
-            {
-                m_value = *other.m_value.binary;
-                break;
-            }
-
-            case value_t::null:
-            case value_t::discarded:
-            default:
-                break;
-        }
-
-        set_parents();
-        assert_invariant();
-    }
-
-    /// @brief move constructor
-    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    basic_json(basic_json&& other) noexcept
-        : m_type(std::move(other.m_type)),
-          m_value(std::move(other.m_value))
-    {
-        // check that passed value is valid
-        other.assert_invariant(false);
-
-        // invalidate payload
-        other.m_type = value_t::null;
-        other.m_value = {};
-
-        set_parents();
-        assert_invariant();
-    }
-
-    /// @brief copy assignment
-    /// @sa https://json.nlohmann.me/api/basic_json/operator=/
-    basic_json& operator=(basic_json other) noexcept (
-        std::is_nothrow_move_constructible<value_t>::value&&
-        std::is_nothrow_move_assignable<value_t>::value&&
-        std::is_nothrow_move_constructible<json_value>::value&&
-        std::is_nothrow_move_assignable<json_value>::value
-    )
-    {
-        // check that passed value is valid
-        other.assert_invariant();
-
-        using std::swap;
-        swap(m_type, other.m_type);
-        swap(m_value, other.m_value);
-
-        set_parents();
-        assert_invariant();
-        return *this;
-    }
-
-    /// @brief destructor
-    /// @sa https://json.nlohmann.me/api/basic_json/~basic_json/
-    ~basic_json() noexcept
-    {
-        assert_invariant(false);
-        m_value.destroy(m_type);
-    }
-
-    /// @}
-
-  public:
-    ///////////////////////
-    // object inspection //
-    ///////////////////////
-
-    /// @name object inspection
-    /// Functions to inspect the type of a JSON value.
-    /// @{
-
-    /// @brief serialization
-    /// @sa https://json.nlohmann.me/api/basic_json/dump/
-    string_t dump(const int indent = -1,
-                  const char indent_char = ' ',
-                  const bool ensure_ascii = false,
-                  const error_handler_t error_handler = error_handler_t::strict) const
-    {
-        string_t result;
-        serializer s(detail::output_adapter<char, string_t>(result), indent_char, error_handler);
-
-        if (indent >= 0)
-        {
-            s.dump(*this, true, ensure_ascii, static_cast<unsigned int>(indent));
-        }
-        else
-        {
-            s.dump(*this, false, ensure_ascii, 0);
-        }
-
-        return result;
-    }
-
-    /// @brief return the type of the JSON value (explicit)
-    /// @sa https://json.nlohmann.me/api/basic_json/type/
-    constexpr value_t type() const noexcept
-    {
-        return m_type;
-    }
-
-    /// @brief return whether type is primitive
-    /// @sa https://json.nlohmann.me/api/basic_json/is_primitive/
-    constexpr bool is_primitive() const noexcept
-    {
-        return is_null() || is_string() || is_boolean() || is_number() || is_binary();
-    }
-
-    /// @brief return whether type is structured
-    /// @sa https://json.nlohmann.me/api/basic_json/is_structured/
-    constexpr bool is_structured() const noexcept
-    {
-        return is_array() || is_object();
-    }
-
-    /// @brief return whether value is null
-    /// @sa https://json.nlohmann.me/api/basic_json/is_null/
-    constexpr bool is_null() const noexcept
-    {
-        return m_type == value_t::null;
-    }
-
-    /// @brief return whether value is a boolean
-    /// @sa https://json.nlohmann.me/api/basic_json/is_boolean/
-    constexpr bool is_boolean() const noexcept
-    {
-        return m_type == value_t::boolean;
-    }
-
-    /// @brief return whether value is a number
-    /// @sa https://json.nlohmann.me/api/basic_json/is_number/
-    constexpr bool is_number() const noexcept
-    {
-        return is_number_integer() || is_number_float();
-    }
-
-    /// @brief return whether value is an integer number
-    /// @sa https://json.nlohmann.me/api/basic_json/is_number_integer/
-    constexpr bool is_number_integer() const noexcept
-    {
-        return m_type == value_t::number_integer || m_type == value_t::number_unsigned;
-    }
-
-    /// @brief return whether value is an unsigned integer number
-    /// @sa https://json.nlohmann.me/api/basic_json/is_number_unsigned/
-    constexpr bool is_number_unsigned() const noexcept
-    {
-        return m_type == value_t::number_unsigned;
-    }
-
-    /// @brief return whether value is a floating-point number
-    /// @sa https://json.nlohmann.me/api/basic_json/is_number_float/
-    constexpr bool is_number_float() const noexcept
-    {
-        return m_type == value_t::number_float;
-    }
-
-    /// @brief return whether value is an object
-    /// @sa https://json.nlohmann.me/api/basic_json/is_object/
-    constexpr bool is_object() const noexcept
-    {
-        return m_type == value_t::object;
-    }
-
-    /// @brief return whether value is an array
-    /// @sa https://json.nlohmann.me/api/basic_json/is_array/
-    constexpr bool is_array() const noexcept
-    {
-        return m_type == value_t::array;
-    }
-
-    /// @brief return whether value is a string
-    /// @sa https://json.nlohmann.me/api/basic_json/is_string/
-    constexpr bool is_string() const noexcept
-    {
-        return m_type == value_t::string;
-    }
-
-    /// @brief return whether value is a binary array
-    /// @sa https://json.nlohmann.me/api/basic_json/is_binary/
-    constexpr bool is_binary() const noexcept
-    {
-        return m_type == value_t::binary;
-    }
-
-    /// @brief return whether value is discarded
-    /// @sa https://json.nlohmann.me/api/basic_json/is_discarded/
-    constexpr bool is_discarded() const noexcept
-    {
-        return m_type == value_t::discarded;
-    }
-
-    /// @brief return the type of the JSON value (implicit)
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_value_t/
-    constexpr operator value_t() const noexcept
-    {
-        return m_type;
-    }
-
-    /// @}
-
-  private:
-    //////////////////
-    // value access //
-    //////////////////
-
-    /// get a boolean (explicit)
-    boolean_t get_impl(boolean_t* /*unused*/) const
-    {
-        if (JSON_HEDLEY_LIKELY(is_boolean()))
-        {
-            return m_value.boolean;
-        }
-
-        JSON_THROW(type_error::create(302, detail::concat("type must be boolean, but is ", type_name()), this));
-    }
-
-    /// get a pointer to the value (object)
-    object_t* get_impl_ptr(object_t* /*unused*/) noexcept
-    {
-        return is_object() ? m_value.object : nullptr;
-    }
-
-    /// get a pointer to the value (object)
-    constexpr const object_t* get_impl_ptr(const object_t* /*unused*/) const noexcept
-    {
-        return is_object() ? m_value.object : nullptr;
-    }
-
-    /// get a pointer to the value (array)
-    array_t* get_impl_ptr(array_t* /*unused*/) noexcept
-    {
-        return is_array() ? m_value.array : nullptr;
-    }
-
-    /// get a pointer to the value (array)
-    constexpr const array_t* get_impl_ptr(const array_t* /*unused*/) const noexcept
-    {
-        return is_array() ? m_value.array : nullptr;
-    }
-
-    /// get a pointer to the value (string)
-    string_t* get_impl_ptr(string_t* /*unused*/) noexcept
-    {
-        return is_string() ? m_value.string : nullptr;
-    }
-
-    /// get a pointer to the value (string)
-    constexpr const string_t* get_impl_ptr(const string_t* /*unused*/) const noexcept
-    {
-        return is_string() ? m_value.string : nullptr;
-    }
-
-    /// get a pointer to the value (boolean)
-    boolean_t* get_impl_ptr(boolean_t* /*unused*/) noexcept
-    {
-        return is_boolean() ? &m_value.boolean : nullptr;
-    }
-
-    /// get a pointer to the value (boolean)
-    constexpr const boolean_t* get_impl_ptr(const boolean_t* /*unused*/) const noexcept
-    {
-        return is_boolean() ? &m_value.boolean : nullptr;
-    }
-
-    /// get a pointer to the value (integer number)
-    number_integer_t* get_impl_ptr(number_integer_t* /*unused*/) noexcept
-    {
-        return is_number_integer() ? &m_value.number_integer : nullptr;
-    }
-
-    /// get a pointer to the value (integer number)
-    constexpr const number_integer_t* get_impl_ptr(const number_integer_t* /*unused*/) const noexcept
-    {
-        return is_number_integer() ? &m_value.number_integer : nullptr;
-    }
-
-    /// get a pointer to the value (unsigned number)
-    number_unsigned_t* get_impl_ptr(number_unsigned_t* /*unused*/) noexcept
-    {
-        return is_number_unsigned() ? &m_value.number_unsigned : nullptr;
-    }
-
-    /// get a pointer to the value (unsigned number)
-    constexpr const number_unsigned_t* get_impl_ptr(const number_unsigned_t* /*unused*/) const noexcept
-    {
-        return is_number_unsigned() ? &m_value.number_unsigned : nullptr;
-    }
-
-    /// get a pointer to the value (floating-point number)
-    number_float_t* get_impl_ptr(number_float_t* /*unused*/) noexcept
-    {
-        return is_number_float() ? &m_value.number_float : nullptr;
-    }
-
-    /// get a pointer to the value (floating-point number)
-    constexpr const number_float_t* get_impl_ptr(const number_float_t* /*unused*/) const noexcept
-    {
-        return is_number_float() ? &m_value.number_float : nullptr;
-    }
-
-    /// get a pointer to the value (binary)
-    binary_t* get_impl_ptr(binary_t* /*unused*/) noexcept
-    {
-        return is_binary() ? m_value.binary : nullptr;
-    }
-
-    /// get a pointer to the value (binary)
-    constexpr const binary_t* get_impl_ptr(const binary_t* /*unused*/) const noexcept
-    {
-        return is_binary() ? m_value.binary : nullptr;
-    }
-
-    /*!
-    @brief helper function to implement get_ref()
-
-    This function helps to implement get_ref() without code duplication for
-    const and non-const overloads
-
-    @tparam ThisType will be deduced as `basic_json` or `const basic_json`
-
-    @throw type_error.303 if ReferenceType does not match underlying value
-    type of the current JSON
-    */
-    template<typename ReferenceType, typename ThisType>
-    static ReferenceType get_ref_impl(ThisType& obj)
-    {
-        // delegate the call to get_ptr<>()
-        auto* ptr = obj.template get_ptr<typename std::add_pointer<ReferenceType>::type>();
-
-        if (JSON_HEDLEY_LIKELY(ptr != nullptr))
-        {
-            return *ptr;
-        }
-
-        JSON_THROW(type_error::create(303, detail::concat("incompatible ReferenceType for get_ref, actual type is ", obj.type_name()), &obj));
-    }
-
-  public:
-    /// @name value access
-    /// Direct access to the stored value of a JSON value.
-    /// @{
-
-    /// @brief get a pointer value (implicit)
-    /// @sa https://json.nlohmann.me/api/basic_json/get_ptr/
-    template<typename PointerType, typename std::enable_if<
-                 std::is_pointer<PointerType>::value, int>::type = 0>
-    auto get_ptr() noexcept -> decltype(std::declval<basic_json_t&>().get_impl_ptr(std::declval<PointerType>()))
-    {
-        // delegate the call to get_impl_ptr<>()
-        return get_impl_ptr(static_cast<PointerType>(nullptr));
-    }
-
-    /// @brief get a pointer value (implicit)
-    /// @sa https://json.nlohmann.me/api/basic_json/get_ptr/
-    template < typename PointerType, typename std::enable_if <
-                   std::is_pointer<PointerType>::value&&
-                   std::is_const<typename std::remove_pointer<PointerType>::type>::value, int >::type = 0 >
-    constexpr auto get_ptr() const noexcept -> decltype(std::declval<const basic_json_t&>().get_impl_ptr(std::declval<PointerType>()))
-    {
-        // delegate the call to get_impl_ptr<>() const
-        return get_impl_ptr(static_cast<PointerType>(nullptr));
-    }
-
-  private:
-    /*!
-    @brief get a value (explicit)
-
-    Explicit type conversion between the JSON value and a compatible value
-    which is [CopyConstructible](https://en.cppreference.com/w/cpp/named_req/CopyConstructible)
-    and [DefaultConstructible](https://en.cppreference.com/w/cpp/named_req/DefaultConstructible).
-    The value is converted by calling the @ref json_serializer<ValueType>
-    `from_json()` method.
-
-    The function is equivalent to executing
-    @code {.cpp}
-    ValueType ret;
-    JSONSerializer<ValueType>::from_json(*this, ret);
-    return ret;
-    @endcode
-
-    This overloads is chosen if:
-    - @a ValueType is not @ref basic_json,
-    - @ref json_serializer<ValueType> has a `from_json()` method of the form
-      `void from_json(const basic_json&, ValueType&)`, and
-    - @ref json_serializer<ValueType> does not have a `from_json()` method of
-      the form `ValueType from_json(const basic_json&)`
-
-    @tparam ValueType the returned value type
-
-    @return copy of the JSON value, converted to @a ValueType
-
-    @throw what @ref json_serializer<ValueType> `from_json()` method throws
-
-    @liveexample{The example below shows several conversions from JSON values
-    to other types. There a few things to note: (1) Floating-point numbers can
-    be converted to integers\, (2) A JSON array can be converted to a standard
-    `std::vector<short>`\, (3) A JSON object can be converted to C++
-    associative containers such as `std::unordered_map<std::string\,
-    json>`.,get__ValueType_const}
-
-    @since version 2.1.0
-    */
-    template < typename ValueType,
-               detail::enable_if_t <
-                   detail::is_default_constructible<ValueType>::value&&
-                   detail::has_from_json<basic_json_t, ValueType>::value,
-                   int > = 0 >
-    ValueType get_impl(detail::priority_tag<0> /*unused*/) const noexcept(noexcept(
-                JSONSerializer<ValueType>::from_json(std::declval<const basic_json_t&>(), std::declval<ValueType&>())))
-    {
-        auto ret = ValueType();
-        JSONSerializer<ValueType>::from_json(*this, ret);
-        return ret;
-    }
-
-    /*!
-    @brief get a value (explicit); special case
-
-    Explicit type conversion between the JSON value and a compatible value
-    which is **not** [CopyConstructible](https://en.cppreference.com/w/cpp/named_req/CopyConstructible)
-    and **not** [DefaultConstructible](https://en.cppreference.com/w/cpp/named_req/DefaultConstructible).
-    The value is converted by calling the @ref json_serializer<ValueType>
-    `from_json()` method.
-
-    The function is equivalent to executing
-    @code {.cpp}
-    return JSONSerializer<ValueType>::from_json(*this);
-    @endcode
-
-    This overloads is chosen if:
-    - @a ValueType is not @ref basic_json and
-    - @ref json_serializer<ValueType> has a `from_json()` method of the form
-      `ValueType from_json(const basic_json&)`
-
-    @note If @ref json_serializer<ValueType> has both overloads of
-    `from_json()`, this one is chosen.
-
-    @tparam ValueType the returned value type
-
-    @return copy of the JSON value, converted to @a ValueType
-
-    @throw what @ref json_serializer<ValueType> `from_json()` method throws
-
-    @since version 2.1.0
-    */
-    template < typename ValueType,
-               detail::enable_if_t <
-                   detail::has_non_default_from_json<basic_json_t, ValueType>::value,
-                   int > = 0 >
-    ValueType get_impl(detail::priority_tag<1> /*unused*/) const noexcept(noexcept(
-                JSONSerializer<ValueType>::from_json(std::declval<const basic_json_t&>())))
-    {
-        return JSONSerializer<ValueType>::from_json(*this);
-    }
-
-    /*!
-    @brief get special-case overload
-
-    This overloads converts the current @ref basic_json in a different
-    @ref basic_json type
-
-    @tparam BasicJsonType == @ref basic_json
-
-    @return a copy of *this, converted into @a BasicJsonType
-
-    @complexity Depending on the implementation of the called `from_json()`
-                method.
-
-    @since version 3.2.0
-    */
-    template < typename BasicJsonType,
-               detail::enable_if_t <
-                   detail::is_basic_json<BasicJsonType>::value,
-                   int > = 0 >
-    BasicJsonType get_impl(detail::priority_tag<2> /*unused*/) const
-    {
-        return *this;
-    }
-
-    /*!
-    @brief get special-case overload
-
-    This overloads avoids a lot of template boilerplate, it can be seen as the
-    identity method
-
-    @tparam BasicJsonType == @ref basic_json
-
-    @return a copy of *this
-
-    @complexity Constant.
-
-    @since version 2.1.0
-    */
-    template<typename BasicJsonType,
-             detail::enable_if_t<
-                 std::is_same<BasicJsonType, basic_json_t>::value,
-                 int> = 0>
-    basic_json get_impl(detail::priority_tag<3> /*unused*/) const
-    {
-        return *this;
-    }
-
-    /*!
-    @brief get a pointer value (explicit)
-    @copydoc get()
-    */
-    template<typename PointerType,
-             detail::enable_if_t<
-                 std::is_pointer<PointerType>::value,
-                 int> = 0>
-    constexpr auto get_impl(detail::priority_tag<4> /*unused*/) const noexcept
-    -> decltype(std::declval<const basic_json_t&>().template get_ptr<PointerType>())
-    {
-        // delegate the call to get_ptr
-        return get_ptr<PointerType>();
-    }
-
-  public:
-    /*!
-    @brief get a (pointer) value (explicit)
-
-    Performs explicit type conversion between the JSON value and a compatible value if required.
-
-    - If the requested type is a pointer to the internally stored JSON value that pointer is returned.
-    No copies are made.
-
-    - If the requested type is the current @ref basic_json, or a different @ref basic_json convertible
-    from the current @ref basic_json.
-
-    - Otherwise the value is converted by calling the @ref json_serializer<ValueType> `from_json()`
-    method.
-
-    @tparam ValueTypeCV the provided value type
-    @tparam ValueType the returned value type
-
-    @return copy of the JSON value, converted to @tparam ValueType if necessary
-
-    @throw what @ref json_serializer<ValueType> `from_json()` method throws if conversion is required
-
-    @since version 2.1.0
-    */
-    template < typename ValueTypeCV, typename ValueType = detail::uncvref_t<ValueTypeCV>>
-#if defined(JSON_HAS_CPP_14)
-    constexpr
-#endif
-    auto get() const noexcept(
-    noexcept(std::declval<const basic_json_t&>().template get_impl<ValueType>(detail::priority_tag<4> {})))
-    -> decltype(std::declval<const basic_json_t&>().template get_impl<ValueType>(detail::priority_tag<4> {}))
-    {
-        // we cannot static_assert on ValueTypeCV being non-const, because
-        // there is support for get<const basic_json_t>(), which is why we
-        // still need the uncvref
-        static_assert(!std::is_reference<ValueTypeCV>::value,
-                      "get() cannot be used with reference types, you might want to use get_ref()");
-        return get_impl<ValueType>(detail::priority_tag<4> {});
-    }
-
-    /*!
-    @brief get a pointer value (explicit)
-
-    Explicit pointer access to the internally stored JSON value. No copies are
-    made.
-
-    @warning The pointer becomes invalid if the underlying JSON object
-    changes.
-
-    @tparam PointerType pointer type; must be a pointer to @ref array_t, @ref
-    object_t, @ref string_t, @ref boolean_t, @ref number_integer_t,
-    @ref number_unsigned_t, or @ref number_float_t.
-
-    @return pointer to the internally stored JSON value if the requested
-    pointer type @a PointerType fits to the JSON value; `nullptr` otherwise
-
-    @complexity Constant.
-
-    @liveexample{The example below shows how pointers to internal values of a
-    JSON value can be requested. Note that no type conversions are made and a
-    `nullptr` is returned if the value and the requested pointer type does not
-    match.,get__PointerType}
-
-    @sa see @ref get_ptr() for explicit pointer-member access
-
-    @since version 1.0.0
-    */
-    template<typename PointerType, typename std::enable_if<
-                 std::is_pointer<PointerType>::value, int>::type = 0>
-    auto get() noexcept -> decltype(std::declval<basic_json_t&>().template get_ptr<PointerType>())
-    {
-        // delegate the call to get_ptr
-        return get_ptr<PointerType>();
-    }
-
-    /// @brief get a value (explicit)
-    /// @sa https://json.nlohmann.me/api/basic_json/get_to/
-    template < typename ValueType,
-               detail::enable_if_t <
-                   !detail::is_basic_json<ValueType>::value&&
-                   detail::has_from_json<basic_json_t, ValueType>::value,
-                   int > = 0 >
-    ValueType & get_to(ValueType& v) const noexcept(noexcept(
-                JSONSerializer<ValueType>::from_json(std::declval<const basic_json_t&>(), v)))
-    {
-        JSONSerializer<ValueType>::from_json(*this, v);
-        return v;
-    }
-
-    // specialization to allow calling get_to with a basic_json value
-    // see https://github.com/nlohmann/json/issues/2175
-    template<typename ValueType,
-             detail::enable_if_t <
-                 detail::is_basic_json<ValueType>::value,
-                 int> = 0>
-    ValueType & get_to(ValueType& v) const
-    {
-        v = *this;
-        return v;
-    }
-
-    template <
-        typename T, std::size_t N,
-        typename Array = T (&)[N], // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
-        detail::enable_if_t <
-            detail::has_from_json<basic_json_t, Array>::value, int > = 0 >
-    Array get_to(T (&v)[N]) const // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
-    noexcept(noexcept(JSONSerializer<Array>::from_json(
-                          std::declval<const basic_json_t&>(), v)))
-    {
-        JSONSerializer<Array>::from_json(*this, v);
-        return v;
-    }
-
-    /// @brief get a reference value (implicit)
-    /// @sa https://json.nlohmann.me/api/basic_json/get_ref/
-    template<typename ReferenceType, typename std::enable_if<
-                 std::is_reference<ReferenceType>::value, int>::type = 0>
-    ReferenceType get_ref()
-    {
-        // delegate call to get_ref_impl
-        return get_ref_impl<ReferenceType>(*this);
-    }
-
-    /// @brief get a reference value (implicit)
-    /// @sa https://json.nlohmann.me/api/basic_json/get_ref/
-    template < typename ReferenceType, typename std::enable_if <
-                   std::is_reference<ReferenceType>::value&&
-                   std::is_const<typename std::remove_reference<ReferenceType>::type>::value, int >::type = 0 >
-    ReferenceType get_ref() const
-    {
-        // delegate call to get_ref_impl
-        return get_ref_impl<ReferenceType>(*this);
-    }
-
-    /*!
-    @brief get a value (implicit)
-
-    Implicit type conversion between the JSON value and a compatible value.
-    The call is realized by calling @ref get() const.
-
-    @tparam ValueType non-pointer type compatible to the JSON value, for
-    instance `int` for JSON integer numbers, `bool` for JSON booleans, or
-    `std::vector` types for JSON arrays. The character type of @ref string_t
-    as well as an initializer list of this type is excluded to avoid
-    ambiguities as these types implicitly convert to `std::string`.
-
-    @return copy of the JSON value, converted to type @a ValueType
-
-    @throw type_error.302 in case passed type @a ValueType is incompatible
-    to the JSON value type (e.g., the JSON value is of type boolean, but a
-    string is requested); see example below
-
-    @complexity Linear in the size of the JSON value.
-
-    @liveexample{The example below shows several conversions from JSON values
-    to other types. There a few things to note: (1) Floating-point numbers can
-    be converted to integers\, (2) A JSON array can be converted to a standard
-    `std::vector<short>`\, (3) A JSON object can be converted to C++
-    associative containers such as `std::unordered_map<std::string\,
-    json>`.,operator__ValueType}
-
-    @since version 1.0.0
-    */
-    template < typename ValueType, typename std::enable_if <
-                   detail::conjunction <
-                       detail::negation<std::is_pointer<ValueType>>,
-                       detail::negation<std::is_same<ValueType, std::nullptr_t>>,
-                       detail::negation<std::is_same<ValueType, detail::json_ref<basic_json>>>,
-                                        detail::negation<std::is_same<ValueType, typename string_t::value_type>>,
-                                        detail::negation<detail::is_basic_json<ValueType>>,
-                                        detail::negation<std::is_same<ValueType, std::initializer_list<typename string_t::value_type>>>,
-#if defined(JSON_HAS_CPP_17) && (defined(__GNUC__) || (defined(_MSC_VER) && _MSC_VER >= 1910 && _MSC_VER <= 1914))
-                                                detail::negation<std::is_same<ValueType, std::string_view>>,
-#endif
-#if defined(JSON_HAS_CPP_17)
-                                                detail::negation<std::is_same<ValueType, std::any>>,
-#endif
-                                                detail::is_detected_lazy<detail::get_template_function, const basic_json_t&, ValueType>
-                                                >::value, int >::type = 0 >
-                                        JSON_EXPLICIT operator ValueType() const
-    {
-        // delegate the call to get<>() const
-        return get<ValueType>();
-    }
-
-    /// @brief get a binary value
-    /// @sa https://json.nlohmann.me/api/basic_json/get_binary/
-    binary_t& get_binary()
-    {
-        if (!is_binary())
-        {
-            JSON_THROW(type_error::create(302, detail::concat("type must be binary, but is ", type_name()), this));
-        }
-
-        return *get_ptr<binary_t*>();
-    }
-
-    /// @brief get a binary value
-    /// @sa https://json.nlohmann.me/api/basic_json/get_binary/
-    const binary_t& get_binary() const
-    {
-        if (!is_binary())
-        {
-            JSON_THROW(type_error::create(302, detail::concat("type must be binary, but is ", type_name()), this));
-        }
-
-        return *get_ptr<const binary_t*>();
-    }
-
-    /// @}
-
-
-    ////////////////////
-    // element access //
-    ////////////////////
-
-    /// @name element access
-    /// Access to the JSON value.
-    /// @{
-
-    /// @brief access specified array element with bounds checking
-    /// @sa https://json.nlohmann.me/api/basic_json/at/
-    reference at(size_type idx)
-    {
-        // at only works for arrays
-        if (JSON_HEDLEY_LIKELY(is_array()))
-        {
-            JSON_TRY
-            {
-                return set_parent(m_value.array->at(idx));
-            }
-            JSON_CATCH (std::out_of_range&)
-            {
-                // create better exception explanation
-                JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), this));
-            }
-        }
-        else
-        {
-            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
-        }
-    }
-
-    /// @brief access specified array element with bounds checking
-    /// @sa https://json.nlohmann.me/api/basic_json/at/
-    const_reference at(size_type idx) const
-    {
-        // at only works for arrays
-        if (JSON_HEDLEY_LIKELY(is_array()))
-        {
-            JSON_TRY
-            {
-                return m_value.array->at(idx);
-            }
-            JSON_CATCH (std::out_of_range&)
-            {
-                // create better exception explanation
-                JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), this));
-            }
-        }
-        else
-        {
-            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
-        }
-    }
-
-    /// @brief access specified object element with bounds checking
-    /// @sa https://json.nlohmann.me/api/basic_json/at/
-    reference at(const typename object_t::key_type& key)
-    {
-        // at only works for objects
-        if (JSON_HEDLEY_UNLIKELY(!is_object()))
-        {
-            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
-        }
-
-        auto it = m_value.object->find(key);
-        if (it == m_value.object->end())
-        {
-            JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this));
-        }
-        return set_parent(it->second);
-    }
-
-    /// @brief access specified object element with bounds checking
-    /// @sa https://json.nlohmann.me/api/basic_json/at/
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
-    reference at(KeyType && key)
-    {
-        // at only works for objects
-        if (JSON_HEDLEY_UNLIKELY(!is_object()))
-        {
-            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
-        }
-
-        auto it = m_value.object->find(std::forward<KeyType>(key));
-        if (it == m_value.object->end())
-        {
-            JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward<KeyType>(key)), "' not found"), this));
-        }
-        return set_parent(it->second);
-    }
-
-    /// @brief access specified object element with bounds checking
-    /// @sa https://json.nlohmann.me/api/basic_json/at/
-    const_reference at(const typename object_t::key_type& key) const
-    {
-        // at only works for objects
-        if (JSON_HEDLEY_UNLIKELY(!is_object()))
-        {
-            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
-        }
-
-        auto it = m_value.object->find(key);
-        if (it == m_value.object->end())
-        {
-            JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this));
-        }
-        return it->second;
-    }
-
-    /// @brief access specified object element with bounds checking
-    /// @sa https://json.nlohmann.me/api/basic_json/at/
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
-    const_reference at(KeyType && key) const
-    {
-        // at only works for objects
-        if (JSON_HEDLEY_UNLIKELY(!is_object()))
-        {
-            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
-        }
-
-        auto it = m_value.object->find(std::forward<KeyType>(key));
-        if (it == m_value.object->end())
-        {
-            JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward<KeyType>(key)), "' not found"), this));
-        }
-        return it->second;
-    }
-
-    /// @brief access specified array element
-    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
-    reference operator[](size_type idx)
-    {
-        // implicitly convert null value to an empty array
-        if (is_null())
-        {
-            m_type = value_t::array;
-            m_value.array = create<array_t>();
-            assert_invariant();
-        }
-
-        // operator[] only works for arrays
-        if (JSON_HEDLEY_LIKELY(is_array()))
-        {
-            // fill up array with null values if given idx is outside range
-            if (idx >= m_value.array->size())
-            {
-#if JSON_DIAGNOSTICS
-                // remember array size & capacity before resizing
-                const auto old_size = m_value.array->size();
-                const auto old_capacity = m_value.array->capacity();
-#endif
-                m_value.array->resize(idx + 1);
-
-#if JSON_DIAGNOSTICS
-                if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity))
-                {
-                    // capacity has changed: update all parents
-                    set_parents();
-                }
-                else
-                {
-                    // set parent for values added above
-                    set_parents(begin() + static_cast<typename iterator::difference_type>(old_size), static_cast<typename iterator::difference_type>(idx + 1 - old_size));
-                }
-#endif
-                assert_invariant();
-            }
-
-            return m_value.array->operator[](idx);
-        }
-
-        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this));
-    }
-
-    /// @brief access specified array element
-    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
-    const_reference operator[](size_type idx) const
-    {
-        // const operator[] only works for arrays
-        if (JSON_HEDLEY_LIKELY(is_array()))
-        {
-            return m_value.array->operator[](idx);
-        }
-
-        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this));
-    }
-
-    /// @brief access specified object element
-    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
-    reference operator[](typename object_t::key_type key)
-    {
-        // implicitly convert null value to an empty object
-        if (is_null())
-        {
-            m_type = value_t::object;
-            m_value.object = create<object_t>();
-            assert_invariant();
-        }
-
-        // operator[] only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            auto result = m_value.object->emplace(std::move(key), nullptr);
-            return set_parent(result.first->second);
-        }
-
-        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a string argument with ", type_name()), this));
-    }
-
-    /// @brief access specified object element
-    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
-    const_reference operator[](const typename object_t::key_type& key) const
-    {
-        // const operator[] only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            auto it = m_value.object->find(key);
-            JSON_ASSERT(it != m_value.object->end());
-            return it->second;
-        }
-
-        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a string argument with ", type_name()), this));
-    }
-
-    // these two functions resolve a (const) char * ambiguity affecting Clang and MSVC
-    // (they seemingly cannot be constrained to resolve the ambiguity)
-    template<typename T>
-    reference operator[](T* key)
-    {
-        return operator[](typename object_t::key_type(key));
-    }
-
-    template<typename T>
-    const_reference operator[](T* key) const
-    {
-        return operator[](typename object_t::key_type(key));
-    }
-
-    /// @brief access specified object element
-    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int > = 0 >
-    reference operator[](KeyType && key)
-    {
-        // implicitly convert null value to an empty object
-        if (is_null())
-        {
-            m_type = value_t::object;
-            m_value.object = create<object_t>();
-            assert_invariant();
-        }
-
-        // operator[] only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            auto result = m_value.object->emplace(std::forward<KeyType>(key), nullptr);
-            return set_parent(result.first->second);
-        }
-
-        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a string argument with ", type_name()), this));
-    }
-
-    /// @brief access specified object element
-    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int > = 0 >
-    const_reference operator[](KeyType && key) const
-    {
-        // const operator[] only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            auto it = m_value.object->find(std::forward<KeyType>(key));
-            JSON_ASSERT(it != m_value.object->end());
-            return it->second;
-        }
-
-        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a string argument with ", type_name()), this));
-    }
-
-  private:
-    template<typename KeyType>
-    using is_comparable_with_object_key = detail::is_comparable <
-        object_comparator_t, const typename object_t::key_type&, KeyType >;
-
-    template<typename ValueType>
-    using value_return_type = std::conditional <
-        detail::is_c_string_uncvref<ValueType>::value,
-        string_t, typename std::decay<ValueType>::type >;
-
-  public:
-    /// @brief access specified object element with default value
-    /// @sa https://json.nlohmann.me/api/basic_json/value/
-    template < class ValueType, detail::enable_if_t <
-                   !detail::is_transparent<object_comparator_t>::value
-                   && detail::is_getable<basic_json_t, ValueType>::value
-                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
-    ValueType value(const typename object_t::key_type& key, const ValueType& default_value) const
-    {
-        // value only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            // if key is found, return value and given default value otherwise
-            const auto it = find(key);
-            if (it != end())
-            {
-                return it->template get<ValueType>();
-            }
-
-            return default_value;
-        }
-
-        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
-    }
-
-    /// @brief access specified object element with default value
-    /// @sa https://json.nlohmann.me/api/basic_json/value/
-    template < class ValueType, class ReturnType = typename value_return_type<ValueType>::type,
-               detail::enable_if_t <
-                   !detail::is_transparent<object_comparator_t>::value
-                   && detail::is_getable<basic_json_t, ReturnType>::value
-                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
-    ReturnType value(const typename object_t::key_type& key, ValueType && default_value) const
-    {
-        // value only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            // if key is found, return value and given default value otherwise
-            const auto it = find(key);
-            if (it != end())
-            {
-                return it->template get<ReturnType>();
-            }
-
-            return std::forward<ValueType>(default_value);
-        }
-
-        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
-    }
-
-    /// @brief access specified object element with default value
-    /// @sa https://json.nlohmann.me/api/basic_json/value/
-    template < class ValueType, class KeyType, detail::enable_if_t <
-                   detail::is_transparent<object_comparator_t>::value
-                   && !detail::is_json_pointer<KeyType>::value
-                   && is_comparable_with_object_key<KeyType>::value
-                   && detail::is_getable<basic_json_t, ValueType>::value
-                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
-    ValueType value(KeyType && key, const ValueType& default_value) const
-    {
-        // value only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            // if key is found, return value and given default value otherwise
-            const auto it = find(std::forward<KeyType>(key));
-            if (it != end())
-            {
-                return it->template get<ValueType>();
-            }
-
-            return default_value;
-        }
-
-        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
-    }
-
-    /// @brief access specified object element via JSON Pointer with default value
-    /// @sa https://json.nlohmann.me/api/basic_json/value/
-    template < class ValueType, class KeyType, class ReturnType = typename value_return_type<ValueType>::type,
-               detail::enable_if_t <
-                   detail::is_transparent<object_comparator_t>::value
-                   && !detail::is_json_pointer<KeyType>::value
-                   && is_comparable_with_object_key<KeyType>::value
-                   && detail::is_getable<basic_json_t, ReturnType>::value
-                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
-    ReturnType value(KeyType && key, ValueType && default_value) const
-    {
-        // value only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            // if key is found, return value and given default value otherwise
-            const auto it = find(std::forward<KeyType>(key));
-            if (it != end())
-            {
-                return it->template get<ReturnType>();
-            }
-
-            return std::forward<ValueType>(default_value);
-        }
-
-        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
-    }
-
-    /// @brief access specified object element via JSON Pointer with default value
-    /// @sa https://json.nlohmann.me/api/basic_json/value/
-    template < class ValueType, detail::enable_if_t <
-                   detail::is_getable<basic_json_t, ValueType>::value
-                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
-    ValueType value(const json_pointer& ptr, const ValueType& default_value) const
-    {
-        // value only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            // if pointer resolves a value, return it or use default value
-            JSON_TRY
-            {
-                return ptr.get_checked(this).template get<ValueType>();
-            }
-            JSON_INTERNAL_CATCH (out_of_range&)
-            {
-                return default_value;
-            }
-        }
-
-        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
-    }
-
-    /// @brief access specified object element via JSON Pointer with default value
-    /// @sa https://json.nlohmann.me/api/basic_json/value/
-    template < class ValueType, class ReturnType = typename value_return_type<ValueType>::type,
-               detail::enable_if_t <
-                   detail::is_getable<basic_json_t, ReturnType>::value
-                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
-    ReturnType value(const json_pointer& ptr, ValueType && default_value) const
-    {
-        // value only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            // if pointer resolves a value, return it or use default value
-            JSON_TRY
-            {
-                return ptr.get_checked(this).template get<ReturnType>();
-            }
-            JSON_INTERNAL_CATCH (out_of_range&)
-            {
-                return std::forward<ValueType>(default_value);
-            }
-        }
-
-        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
-    }
-
-    template < class ValueType, class BasicJsonType, detail::enable_if_t <
-                   detail::is_basic_json<BasicJsonType>::value
-                   && detail::is_getable<basic_json_t, ValueType>::value
-                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
-    ValueType value(const ::nlohmann::json_pointer<BasicJsonType>& ptr, const ValueType& default_value) const
-    {
-        return value(ptr.convert(), default_value);
-    }
-
-    template < class ValueType, class BasicJsonType, class ReturnType = typename value_return_type<ValueType>::type,
-               detail::enable_if_t <
-                   detail::is_basic_json<BasicJsonType>::value
-                   && detail::is_getable<basic_json_t, ReturnType>::value
-                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
-    ReturnType value(const ::nlohmann::json_pointer<BasicJsonType>& ptr, ValueType && default_value) const
-    {
-        return value(ptr.convert(), std::forward<ValueType>(default_value));
-    }
-
-    /// @brief access the first element
-    /// @sa https://json.nlohmann.me/api/basic_json/front/
-    reference front()
-    {
-        return *begin();
-    }
-
-    /// @brief access the first element
-    /// @sa https://json.nlohmann.me/api/basic_json/front/
-    const_reference front() const
-    {
-        return *cbegin();
-    }
-
-    /// @brief access the last element
-    /// @sa https://json.nlohmann.me/api/basic_json/back/
-    reference back()
-    {
-        auto tmp = end();
-        --tmp;
-        return *tmp;
-    }
-
-    /// @brief access the last element
-    /// @sa https://json.nlohmann.me/api/basic_json/back/
-    const_reference back() const
-    {
-        auto tmp = cend();
-        --tmp;
-        return *tmp;
-    }
-
-    /// @brief remove element given an iterator
-    /// @sa https://json.nlohmann.me/api/basic_json/erase/
-    template < class IteratorType, detail::enable_if_t <
-                   std::is_same<IteratorType, typename basic_json_t::iterator>::value ||
-                   std::is_same<IteratorType, typename basic_json_t::const_iterator>::value, int > = 0 >
-    IteratorType erase(IteratorType pos)
-    {
-        // make sure iterator fits the current value
-        if (JSON_HEDLEY_UNLIKELY(this != pos.m_object))
-        {
-            JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", this));
-        }
-
-        IteratorType result = end();
-
-        switch (m_type)
-        {
-            case value_t::boolean:
-            case value_t::number_float:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::string:
-            case value_t::binary:
-            {
-                if (JSON_HEDLEY_UNLIKELY(!pos.m_it.primitive_iterator.is_begin()))
-                {
-                    JSON_THROW(invalid_iterator::create(205, "iterator out of range", this));
-                }
-
-                if (is_string())
-                {
-                    AllocatorType<string_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.string);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.string, 1);
-                    m_value.string = nullptr;
-                }
-                else if (is_binary())
-                {
-                    AllocatorType<binary_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.binary);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.binary, 1);
-                    m_value.binary = nullptr;
-                }
-
-                m_type = value_t::null;
-                assert_invariant();
-                break;
-            }
-
-            case value_t::object:
-            {
-                result.m_it.object_iterator = m_value.object->erase(pos.m_it.object_iterator);
-                break;
-            }
-
-            case value_t::array:
-            {
-                result.m_it.array_iterator = m_value.array->erase(pos.m_it.array_iterator);
-                break;
-            }
-
-            case value_t::null:
-            case value_t::discarded:
-            default:
-                JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
-        }
-
-        return result;
-    }
-
-    /// @brief remove elements given an iterator range
-    /// @sa https://json.nlohmann.me/api/basic_json/erase/
-    template < class IteratorType, detail::enable_if_t <
-                   std::is_same<IteratorType, typename basic_json_t::iterator>::value ||
-                   std::is_same<IteratorType, typename basic_json_t::const_iterator>::value, int > = 0 >
-    IteratorType erase(IteratorType first, IteratorType last)
-    {
-        // make sure iterator fits the current value
-        if (JSON_HEDLEY_UNLIKELY(this != first.m_object || this != last.m_object))
-        {
-            JSON_THROW(invalid_iterator::create(203, "iterators do not fit current value", this));
-        }
-
-        IteratorType result = end();
-
-        switch (m_type)
-        {
-            case value_t::boolean:
-            case value_t::number_float:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::string:
-            case value_t::binary:
-            {
-                if (JSON_HEDLEY_LIKELY(!first.m_it.primitive_iterator.is_begin()
-                                       || !last.m_it.primitive_iterator.is_end()))
-                {
-                    JSON_THROW(invalid_iterator::create(204, "iterators out of range", this));
-                }
-
-                if (is_string())
-                {
-                    AllocatorType<string_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.string);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.string, 1);
-                    m_value.string = nullptr;
-                }
-                else if (is_binary())
-                {
-                    AllocatorType<binary_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.binary);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.binary, 1);
-                    m_value.binary = nullptr;
-                }
-
-                m_type = value_t::null;
-                assert_invariant();
-                break;
-            }
-
-            case value_t::object:
-            {
-                result.m_it.object_iterator = m_value.object->erase(first.m_it.object_iterator,
-                                              last.m_it.object_iterator);
-                break;
-            }
-
-            case value_t::array:
-            {
-                result.m_it.array_iterator = m_value.array->erase(first.m_it.array_iterator,
-                                             last.m_it.array_iterator);
-                break;
-            }
-
-            case value_t::null:
-            case value_t::discarded:
-            default:
-                JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
-        }
-
-        return result;
-    }
-
-  private:
-    template < typename KeyType, detail::enable_if_t <
-                   detail::has_erase_with_key_type<basic_json_t, KeyType>::value, int > = 0 >
-    size_type erase_internal(KeyType && key)
-    {
-        // this erase only works for objects
-        if (JSON_HEDLEY_UNLIKELY(!is_object()))
-        {
-            JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
-        }
-
-        return m_value.object->erase(std::forward<KeyType>(key));
-    }
-
-    template < typename KeyType, detail::enable_if_t <
-                   !detail::has_erase_with_key_type<basic_json_t, KeyType>::value, int > = 0 >
-    size_type erase_internal(KeyType && key)
-    {
-        // this erase only works for objects
-        if (JSON_HEDLEY_UNLIKELY(!is_object()))
-        {
-            JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
-        }
-
-        const auto it = m_value.object->find(std::forward<KeyType>(key));
-        if (it != m_value.object->end())
-        {
-            m_value.object->erase(it);
-            return 1;
-        }
-        return 0;
-    }
-
-  public:
-
-    /// @brief remove element from a JSON object given a key
-    /// @sa https://json.nlohmann.me/api/basic_json/erase/
-    size_type erase(const typename object_t::key_type& key)
-    {
-        // the indirection via erase_internal() is added to avoid making this
-        // function a template and thus de-rank it during overload resolution
-        return erase_internal(key);
-    }
-
-    /// @brief remove element from a JSON object given a key
-    /// @sa https://json.nlohmann.me/api/basic_json/erase/
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
-    size_type erase(KeyType && key)
-    {
-        return erase_internal(std::forward<KeyType>(key));
-    }
-
-    /// @brief remove element from a JSON array given an index
-    /// @sa https://json.nlohmann.me/api/basic_json/erase/
-    void erase(const size_type idx)
-    {
-        // this erase only works for arrays
-        if (JSON_HEDLEY_LIKELY(is_array()))
-        {
-            if (JSON_HEDLEY_UNLIKELY(idx >= size()))
-            {
-                JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), this));
-            }
-
-            m_value.array->erase(m_value.array->begin() + static_cast<difference_type>(idx));
-        }
-        else
-        {
-            JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
-        }
-    }
-
-    /// @}
-
-
-    ////////////
-    // lookup //
-    ////////////
-
-    /// @name lookup
-    /// @{
-
-    /// @brief find an element in a JSON object
-    /// @sa https://json.nlohmann.me/api/basic_json/find/
-    iterator find(const typename object_t::key_type& key)
-    {
-        auto result = end();
-
-        if (is_object())
-        {
-            result.m_it.object_iterator = m_value.object->find(key);
-        }
-
-        return result;
-    }
-
-    /// @brief find an element in a JSON object
-    /// @sa https://json.nlohmann.me/api/basic_json/find/
-    const_iterator find(const typename object_t::key_type& key) const
-    {
-        auto result = cend();
-
-        if (is_object())
-        {
-            result.m_it.object_iterator = m_value.object->find(key);
-        }
-
-        return result;
-    }
-
-    /// @brief find an element in a JSON object
-    /// @sa https://json.nlohmann.me/api/basic_json/find/
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
-    iterator find(KeyType && key)
-    {
-        auto result = end();
-
-        if (is_object())
-        {
-            result.m_it.object_iterator = m_value.object->find(std::forward<KeyType>(key));
-        }
-
-        return result;
-    }
-
-    /// @brief find an element in a JSON object
-    /// @sa https://json.nlohmann.me/api/basic_json/find/
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
-    const_iterator find(KeyType && key) const
-    {
-        auto result = cend();
-
-        if (is_object())
-        {
-            result.m_it.object_iterator = m_value.object->find(std::forward<KeyType>(key));
-        }
-
-        return result;
-    }
-
-    /// @brief returns the number of occurrences of a key in a JSON object
-    /// @sa https://json.nlohmann.me/api/basic_json/count/
-    size_type count(const typename object_t::key_type& key) const
-    {
-        // return 0 for all nonobject types
-        return is_object() ? m_value.object->count(key) : 0;
-    }
-
-    /// @brief returns the number of occurrences of a key in a JSON object
-    /// @sa https://json.nlohmann.me/api/basic_json/count/
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
-    size_type count(KeyType && key) const
-    {
-        // return 0 for all nonobject types
-        return is_object() ? m_value.object->count(std::forward<KeyType>(key)) : 0;
-    }
-
-    /// @brief check the existence of an element in a JSON object
-    /// @sa https://json.nlohmann.me/api/basic_json/contains/
-    bool contains(const typename object_t::key_type& key) const
-    {
-        return is_object() && m_value.object->find(key) != m_value.object->end();
-    }
-
-    /// @brief check the existence of an element in a JSON object
-    /// @sa https://json.nlohmann.me/api/basic_json/contains/
-    template<class KeyType, detail::enable_if_t<
-                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
-    bool contains(KeyType && key) const
-    {
-        return is_object() && m_value.object->find(std::forward<KeyType>(key)) != m_value.object->end();
-    }
-
-    /// @brief check the existence of an element in a JSON object given a JSON pointer
-    /// @sa https://json.nlohmann.me/api/basic_json/contains/
-    bool contains(const json_pointer& ptr) const
-    {
-        return ptr.contains(this);
-    }
-
-    template<typename BasicJsonType, detail::enable_if_t<detail::is_basic_json<BasicJsonType>::value, int> = 0>
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
-    bool contains(const typename ::nlohmann::json_pointer<BasicJsonType>& ptr) const
-    {
-        return ptr.contains(this);
-    }
-
-    /// @}
-
-
-    ///////////////
-    // iterators //
-    ///////////////
-
-    /// @name iterators
-    /// @{
-
-    /// @brief returns an iterator to the first element
-    /// @sa https://json.nlohmann.me/api/basic_json/begin/
-    iterator begin() noexcept
-    {
-        iterator result(this);
-        result.set_begin();
-        return result;
-    }
-
-    /// @brief returns an iterator to the first element
-    /// @sa https://json.nlohmann.me/api/basic_json/begin/
-    const_iterator begin() const noexcept
-    {
-        return cbegin();
-    }
-
-    /// @brief returns a const iterator to the first element
-    /// @sa https://json.nlohmann.me/api/basic_json/cbegin/
-    const_iterator cbegin() const noexcept
-    {
-        const_iterator result(this);
-        result.set_begin();
-        return result;
-    }
-
-    /// @brief returns an iterator to one past the last element
-    /// @sa https://json.nlohmann.me/api/basic_json/end/
-    iterator end() noexcept
-    {
-        iterator result(this);
-        result.set_end();
-        return result;
-    }
-
-    /// @brief returns an iterator to one past the last element
-    /// @sa https://json.nlohmann.me/api/basic_json/end/
-    const_iterator end() const noexcept
-    {
-        return cend();
-    }
-
-    /// @brief returns an iterator to one past the last element
-    /// @sa https://json.nlohmann.me/api/basic_json/cend/
-    const_iterator cend() const noexcept
-    {
-        const_iterator result(this);
-        result.set_end();
-        return result;
-    }
-
-    /// @brief returns an iterator to the reverse-beginning
-    /// @sa https://json.nlohmann.me/api/basic_json/rbegin/
-    reverse_iterator rbegin() noexcept
-    {
-        return reverse_iterator(end());
-    }
-
-    /// @brief returns an iterator to the reverse-beginning
-    /// @sa https://json.nlohmann.me/api/basic_json/rbegin/
-    const_reverse_iterator rbegin() const noexcept
-    {
-        return crbegin();
-    }
-
-    /// @brief returns an iterator to the reverse-end
-    /// @sa https://json.nlohmann.me/api/basic_json/rend/
-    reverse_iterator rend() noexcept
-    {
-        return reverse_iterator(begin());
-    }
-
-    /// @brief returns an iterator to the reverse-end
-    /// @sa https://json.nlohmann.me/api/basic_json/rend/
-    const_reverse_iterator rend() const noexcept
-    {
-        return crend();
-    }
-
-    /// @brief returns a const reverse iterator to the last element
-    /// @sa https://json.nlohmann.me/api/basic_json/crbegin/
-    const_reverse_iterator crbegin() const noexcept
-    {
-        return const_reverse_iterator(cend());
-    }
-
-    /// @brief returns a const reverse iterator to one before the first
-    /// @sa https://json.nlohmann.me/api/basic_json/crend/
-    const_reverse_iterator crend() const noexcept
-    {
-        return const_reverse_iterator(cbegin());
-    }
-
-  public:
-    /// @brief wrapper to access iterator member functions in range-based for
-    /// @sa https://json.nlohmann.me/api/basic_json/items/
-    /// @deprecated This function is deprecated since 3.1.0 and will be removed in
-    ///             version 4.0.0 of the library. Please use @ref items() instead;
-    ///             that is, replace `json::iterator_wrapper(j)` with `j.items()`.
-    JSON_HEDLEY_DEPRECATED_FOR(3.1.0, items())
-    static iteration_proxy<iterator> iterator_wrapper(reference ref) noexcept
-    {
-        return ref.items();
-    }
-
-    /// @brief wrapper to access iterator member functions in range-based for
-    /// @sa https://json.nlohmann.me/api/basic_json/items/
-    /// @deprecated This function is deprecated since 3.1.0 and will be removed in
-    ///         version 4.0.0 of the library. Please use @ref items() instead;
-    ///         that is, replace `json::iterator_wrapper(j)` with `j.items()`.
-    JSON_HEDLEY_DEPRECATED_FOR(3.1.0, items())
-    static iteration_proxy<const_iterator> iterator_wrapper(const_reference ref) noexcept
-    {
-        return ref.items();
-    }
-
-    /// @brief helper to access iterator member functions in range-based for
-    /// @sa https://json.nlohmann.me/api/basic_json/items/
-    iteration_proxy<iterator> items() noexcept
-    {
-        return iteration_proxy<iterator>(*this);
-    }
-
-    /// @brief helper to access iterator member functions in range-based for
-    /// @sa https://json.nlohmann.me/api/basic_json/items/
-    iteration_proxy<const_iterator> items() const noexcept
-    {
-        return iteration_proxy<const_iterator>(*this);
-    }
-
-    /// @}
-
-
-    //////////////
-    // capacity //
-    //////////////
-
-    /// @name capacity
-    /// @{
-
-    /// @brief checks whether the container is empty.
-    /// @sa https://json.nlohmann.me/api/basic_json/empty/
-    bool empty() const noexcept
-    {
-        switch (m_type)
-        {
-            case value_t::null:
-            {
-                // null values are empty
-                return true;
-            }
-
-            case value_t::array:
-            {
-                // delegate call to array_t::empty()
-                return m_value.array->empty();
-            }
-
-            case value_t::object:
-            {
-                // delegate call to object_t::empty()
-                return m_value.object->empty();
-            }
-
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                // all other types are nonempty
-                return false;
-            }
-        }
-    }
-
-    /// @brief returns the number of elements
-    /// @sa https://json.nlohmann.me/api/basic_json/size/
-    size_type size() const noexcept
-    {
-        switch (m_type)
-        {
-            case value_t::null:
-            {
-                // null values are empty
-                return 0;
-            }
-
-            case value_t::array:
-            {
-                // delegate call to array_t::size()
-                return m_value.array->size();
-            }
-
-            case value_t::object:
-            {
-                // delegate call to object_t::size()
-                return m_value.object->size();
-            }
-
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                // all other types have size 1
-                return 1;
-            }
-        }
-    }
-
-    /// @brief returns the maximum possible number of elements
-    /// @sa https://json.nlohmann.me/api/basic_json/max_size/
-    size_type max_size() const noexcept
-    {
-        switch (m_type)
-        {
-            case value_t::array:
-            {
-                // delegate call to array_t::max_size()
-                return m_value.array->max_size();
-            }
-
-            case value_t::object:
-            {
-                // delegate call to object_t::max_size()
-                return m_value.object->max_size();
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                // all other types have max_size() == size()
-                return size();
-            }
-        }
-    }
-
-    /// @}
-
-
-    ///////////////
-    // modifiers //
-    ///////////////
-
-    /// @name modifiers
-    /// @{
-
-    /// @brief clears the contents
-    /// @sa https://json.nlohmann.me/api/basic_json/clear/
-    void clear() noexcept
-    {
-        switch (m_type)
-        {
-            case value_t::number_integer:
-            {
-                m_value.number_integer = 0;
-                break;
-            }
-
-            case value_t::number_unsigned:
-            {
-                m_value.number_unsigned = 0;
-                break;
-            }
-
-            case value_t::number_float:
-            {
-                m_value.number_float = 0.0;
-                break;
-            }
-
-            case value_t::boolean:
-            {
-                m_value.boolean = false;
-                break;
-            }
-
-            case value_t::string:
-            {
-                m_value.string->clear();
-                break;
-            }
-
-            case value_t::binary:
-            {
-                m_value.binary->clear();
-                break;
-            }
-
-            case value_t::array:
-            {
-                m_value.array->clear();
-                break;
-            }
-
-            case value_t::object:
-            {
-                m_value.object->clear();
-                break;
-            }
-
-            case value_t::null:
-            case value_t::discarded:
-            default:
-                break;
-        }
-    }
-
-    /// @brief add an object to an array
-    /// @sa https://json.nlohmann.me/api/basic_json/push_back/
-    void push_back(basic_json&& val)
-    {
-        // push_back only works for null objects or arrays
-        if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_array())))
-        {
-            JSON_THROW(type_error::create(308, detail::concat("cannot use push_back() with ", type_name()), this));
-        }
-
-        // transform null object into an array
-        if (is_null())
-        {
-            m_type = value_t::array;
-            m_value = value_t::array;
-            assert_invariant();
-        }
-
-        // add element to array (move semantics)
-        const auto old_capacity = m_value.array->capacity();
-        m_value.array->push_back(std::move(val));
-        set_parent(m_value.array->back(), old_capacity);
-        // if val is moved from, basic_json move constructor marks it null, so we do not call the destructor
-    }
-
-    /// @brief add an object to an array
-    /// @sa https://json.nlohmann.me/api/basic_json/operator+=/
-    reference operator+=(basic_json&& val)
-    {
-        push_back(std::move(val));
-        return *this;
-    }
-
-    /// @brief add an object to an array
-    /// @sa https://json.nlohmann.me/api/basic_json/push_back/
-    void push_back(const basic_json& val)
-    {
-        // push_back only works for null objects or arrays
-        if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_array())))
-        {
-            JSON_THROW(type_error::create(308, detail::concat("cannot use push_back() with ", type_name()), this));
-        }
-
-        // transform null object into an array
-        if (is_null())
-        {
-            m_type = value_t::array;
-            m_value = value_t::array;
-            assert_invariant();
-        }
-
-        // add element to array
-        const auto old_capacity = m_value.array->capacity();
-        m_value.array->push_back(val);
-        set_parent(m_value.array->back(), old_capacity);
-    }
-
-    /// @brief add an object to an array
-    /// @sa https://json.nlohmann.me/api/basic_json/operator+=/
-    reference operator+=(const basic_json& val)
-    {
-        push_back(val);
-        return *this;
-    }
-
-    /// @brief add an object to an object
-    /// @sa https://json.nlohmann.me/api/basic_json/push_back/
-    void push_back(const typename object_t::value_type& val)
-    {
-        // push_back only works for null objects or objects
-        if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_object())))
-        {
-            JSON_THROW(type_error::create(308, detail::concat("cannot use push_back() with ", type_name()), this));
-        }
-
-        // transform null object into an object
-        if (is_null())
-        {
-            m_type = value_t::object;
-            m_value = value_t::object;
-            assert_invariant();
-        }
-
-        // add element to object
-        auto res = m_value.object->insert(val);
-        set_parent(res.first->second);
-    }
-
-    /// @brief add an object to an object
-    /// @sa https://json.nlohmann.me/api/basic_json/operator+=/
-    reference operator+=(const typename object_t::value_type& val)
-    {
-        push_back(val);
-        return *this;
-    }
-
-    /// @brief add an object to an object
-    /// @sa https://json.nlohmann.me/api/basic_json/push_back/
-    void push_back(initializer_list_t init)
-    {
-        if (is_object() && init.size() == 2 && (*init.begin())->is_string())
-        {
-            basic_json&& key = init.begin()->moved_or_copied();
-            push_back(typename object_t::value_type(
-                          std::move(key.get_ref<string_t&>()), (init.begin() + 1)->moved_or_copied()));
-        }
-        else
-        {
-            push_back(basic_json(init));
-        }
-    }
-
-    /// @brief add an object to an object
-    /// @sa https://json.nlohmann.me/api/basic_json/operator+=/
-    reference operator+=(initializer_list_t init)
-    {
-        push_back(init);
-        return *this;
-    }
-
-    /// @brief add an object to an array
-    /// @sa https://json.nlohmann.me/api/basic_json/emplace_back/
-    template<class... Args>
-    reference emplace_back(Args&& ... args)
-    {
-        // emplace_back only works for null objects or arrays
-        if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_array())))
-        {
-            JSON_THROW(type_error::create(311, detail::concat("cannot use emplace_back() with ", type_name()), this));
-        }
-
-        // transform null object into an array
-        if (is_null())
-        {
-            m_type = value_t::array;
-            m_value = value_t::array;
-            assert_invariant();
-        }
-
-        // add element to array (perfect forwarding)
-        const auto old_capacity = m_value.array->capacity();
-        m_value.array->emplace_back(std::forward<Args>(args)...);
-        return set_parent(m_value.array->back(), old_capacity);
-    }
-
-    /// @brief add an object to an object if key does not exist
-    /// @sa https://json.nlohmann.me/api/basic_json/emplace/
-    template<class... Args>
-    std::pair<iterator, bool> emplace(Args&& ... args)
-    {
-        // emplace only works for null objects or arrays
-        if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_object())))
-        {
-            JSON_THROW(type_error::create(311, detail::concat("cannot use emplace() with ", type_name()), this));
-        }
-
-        // transform null object into an object
-        if (is_null())
-        {
-            m_type = value_t::object;
-            m_value = value_t::object;
-            assert_invariant();
-        }
-
-        // add element to array (perfect forwarding)
-        auto res = m_value.object->emplace(std::forward<Args>(args)...);
-        set_parent(res.first->second);
-
-        // create result iterator and set iterator to the result of emplace
-        auto it = begin();
-        it.m_it.object_iterator = res.first;
-
-        // return pair of iterator and boolean
-        return {it, res.second};
-    }
-
-    /// Helper for insertion of an iterator
-    /// @note: This uses std::distance to support GCC 4.8,
-    ///        see https://github.com/nlohmann/json/pull/1257
-    template<typename... Args>
-    iterator insert_iterator(const_iterator pos, Args&& ... args)
-    {
-        iterator result(this);
-        JSON_ASSERT(m_value.array != nullptr);
-
-        auto insert_pos = std::distance(m_value.array->begin(), pos.m_it.array_iterator);
-        m_value.array->insert(pos.m_it.array_iterator, std::forward<Args>(args)...);
-        result.m_it.array_iterator = m_value.array->begin() + insert_pos;
-
-        // This could have been written as:
-        // result.m_it.array_iterator = m_value.array->insert(pos.m_it.array_iterator, cnt, val);
-        // but the return value of insert is missing in GCC 4.8, so it is written this way instead.
-
-        set_parents();
-        return result;
-    }
-
-    /// @brief inserts element into array
-    /// @sa https://json.nlohmann.me/api/basic_json/insert/
-    iterator insert(const_iterator pos, const basic_json& val)
-    {
-        // insert only works for arrays
-        if (JSON_HEDLEY_LIKELY(is_array()))
-        {
-            // check if iterator pos fits to this JSON value
-            if (JSON_HEDLEY_UNLIKELY(pos.m_object != this))
-            {
-                JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", this));
-            }
-
-            // insert to array and return iterator
-            return insert_iterator(pos, val);
-        }
-
-        JSON_THROW(type_error::create(309, detail::concat("cannot use insert() with ", type_name()), this));
-    }
-
-    /// @brief inserts element into array
-    /// @sa https://json.nlohmann.me/api/basic_json/insert/
-    iterator insert(const_iterator pos, basic_json&& val)
-    {
-        return insert(pos, val);
-    }
-
-    /// @brief inserts copies of element into array
-    /// @sa https://json.nlohmann.me/api/basic_json/insert/
-    iterator insert(const_iterator pos, size_type cnt, const basic_json& val)
-    {
-        // insert only works for arrays
-        if (JSON_HEDLEY_LIKELY(is_array()))
-        {
-            // check if iterator pos fits to this JSON value
-            if (JSON_HEDLEY_UNLIKELY(pos.m_object != this))
-            {
-                JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", this));
-            }
-
-            // insert to array and return iterator
-            return insert_iterator(pos, cnt, val);
-        }
-
-        JSON_THROW(type_error::create(309, detail::concat("cannot use insert() with ", type_name()), this));
-    }
-
-    /// @brief inserts range of elements into array
-    /// @sa https://json.nlohmann.me/api/basic_json/insert/
-    iterator insert(const_iterator pos, const_iterator first, const_iterator last)
-    {
-        // insert only works for arrays
-        if (JSON_HEDLEY_UNLIKELY(!is_array()))
-        {
-            JSON_THROW(type_error::create(309, detail::concat("cannot use insert() with ", type_name()), this));
-        }
-
-        // check if iterator pos fits to this JSON value
-        if (JSON_HEDLEY_UNLIKELY(pos.m_object != this))
-        {
-            JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", this));
-        }
-
-        // check if range iterators belong to the same JSON object
-        if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object))
-        {
-            JSON_THROW(invalid_iterator::create(210, "iterators do not fit", this));
-        }
-
-        if (JSON_HEDLEY_UNLIKELY(first.m_object == this))
-        {
-            JSON_THROW(invalid_iterator::create(211, "passed iterators may not belong to container", this));
-        }
-
-        // insert to array and return iterator
-        return insert_iterator(pos, first.m_it.array_iterator, last.m_it.array_iterator);
-    }
-
-    /// @brief inserts elements from initializer list into array
-    /// @sa https://json.nlohmann.me/api/basic_json/insert/
-    iterator insert(const_iterator pos, initializer_list_t ilist)
-    {
-        // insert only works for arrays
-        if (JSON_HEDLEY_UNLIKELY(!is_array()))
-        {
-            JSON_THROW(type_error::create(309, detail::concat("cannot use insert() with ", type_name()), this));
-        }
-
-        // check if iterator pos fits to this JSON value
-        if (JSON_HEDLEY_UNLIKELY(pos.m_object != this))
-        {
-            JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", this));
-        }
-
-        // insert to array and return iterator
-        return insert_iterator(pos, ilist.begin(), ilist.end());
-    }
-
-    /// @brief inserts range of elements into object
-    /// @sa https://json.nlohmann.me/api/basic_json/insert/
-    void insert(const_iterator first, const_iterator last)
-    {
-        // insert only works for objects
-        if (JSON_HEDLEY_UNLIKELY(!is_object()))
-        {
-            JSON_THROW(type_error::create(309, detail::concat("cannot use insert() with ", type_name()), this));
-        }
-
-        // check if range iterators belong to the same JSON object
-        if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object))
-        {
-            JSON_THROW(invalid_iterator::create(210, "iterators do not fit", this));
-        }
-
-        // passed iterators must belong to objects
-        if (JSON_HEDLEY_UNLIKELY(!first.m_object->is_object()))
-        {
-            JSON_THROW(invalid_iterator::create(202, "iterators first and last must point to objects", this));
-        }
-
-        m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator);
-    }
-
-    /// @brief updates a JSON object from another object, overwriting existing keys
-    /// @sa https://json.nlohmann.me/api/basic_json/update/
-    void update(const_reference j, bool merge_objects = false)
-    {
-        update(j.begin(), j.end(), merge_objects);
-    }
-
-    /// @brief updates a JSON object from another object, overwriting existing keys
-    /// @sa https://json.nlohmann.me/api/basic_json/update/
-    void update(const_iterator first, const_iterator last, bool merge_objects = false)
-    {
-        // implicitly convert null value to an empty object
-        if (is_null())
-        {
-            m_type = value_t::object;
-            m_value.object = create<object_t>();
-            assert_invariant();
-        }
-
-        if (JSON_HEDLEY_UNLIKELY(!is_object()))
-        {
-            JSON_THROW(type_error::create(312, detail::concat("cannot use update() with ", type_name()), this));
-        }
-
-        // check if range iterators belong to the same JSON object
-        if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object))
-        {
-            JSON_THROW(invalid_iterator::create(210, "iterators do not fit", this));
-        }
-
-        // passed iterators must belong to objects
-        if (JSON_HEDLEY_UNLIKELY(!first.m_object->is_object()))
-        {
-            JSON_THROW(type_error::create(312, detail::concat("cannot use update() with ", first.m_object->type_name()), first.m_object));
-        }
-
-        for (auto it = first; it != last; ++it)
-        {
-            if (merge_objects && it.value().is_object())
-            {
-                auto it2 = m_value.object->find(it.key());
-                if (it2 != m_value.object->end())
-                {
-                    it2->second.update(it.value(), true);
-                    continue;
-                }
-            }
-            m_value.object->operator[](it.key()) = it.value();
-#if JSON_DIAGNOSTICS
-            m_value.object->operator[](it.key()).m_parent = this;
-#endif
-        }
-    }
-
-    /// @brief exchanges the values
-    /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(reference other) noexcept (
-        std::is_nothrow_move_constructible<value_t>::value&&
-        std::is_nothrow_move_assignable<value_t>::value&&
-        std::is_nothrow_move_constructible<json_value>::value&&
-        std::is_nothrow_move_assignable<json_value>::value
-    )
-    {
-        std::swap(m_type, other.m_type);
-        std::swap(m_value, other.m_value);
-
-        set_parents();
-        other.set_parents();
-        assert_invariant();
-    }
-
-    /// @brief exchanges the values
-    /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    friend void swap(reference left, reference right) noexcept (
-        std::is_nothrow_move_constructible<value_t>::value&&
-        std::is_nothrow_move_assignable<value_t>::value&&
-        std::is_nothrow_move_constructible<json_value>::value&&
-        std::is_nothrow_move_assignable<json_value>::value
-    )
-    {
-        left.swap(right);
-    }
-
-    /// @brief exchanges the values
-    /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(array_t& other) // NOLINT(bugprone-exception-escape)
-    {
-        // swap only works for arrays
-        if (JSON_HEDLEY_LIKELY(is_array()))
-        {
-            using std::swap;
-            swap(*(m_value.array), other);
-        }
-        else
-        {
-            JSON_THROW(type_error::create(310, detail::concat("cannot use swap(array_t&) with ", type_name()), this));
-        }
-    }
-
-    /// @brief exchanges the values
-    /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(object_t& other) // NOLINT(bugprone-exception-escape)
-    {
-        // swap only works for objects
-        if (JSON_HEDLEY_LIKELY(is_object()))
-        {
-            using std::swap;
-            swap(*(m_value.object), other);
-        }
-        else
-        {
-            JSON_THROW(type_error::create(310, detail::concat("cannot use swap(object_t&) with ", type_name()), this));
-        }
-    }
-
-    /// @brief exchanges the values
-    /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(string_t& other) // NOLINT(bugprone-exception-escape)
-    {
-        // swap only works for strings
-        if (JSON_HEDLEY_LIKELY(is_string()))
-        {
-            using std::swap;
-            swap(*(m_value.string), other);
-        }
-        else
-        {
-            JSON_THROW(type_error::create(310, detail::concat("cannot use swap(string_t&) with ", type_name()), this));
-        }
-    }
-
-    /// @brief exchanges the values
-    /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(binary_t& other) // NOLINT(bugprone-exception-escape)
-    {
-        // swap only works for strings
-        if (JSON_HEDLEY_LIKELY(is_binary()))
-        {
-            using std::swap;
-            swap(*(m_value.binary), other);
-        }
-        else
-        {
-            JSON_THROW(type_error::create(310, detail::concat("cannot use swap(binary_t&) with ", type_name()), this));
-        }
-    }
-
-    /// @brief exchanges the values
-    /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(typename binary_t::container_type& other) // NOLINT(bugprone-exception-escape)
-    {
-        // swap only works for strings
-        if (JSON_HEDLEY_LIKELY(is_binary()))
-        {
-            using std::swap;
-            swap(*(m_value.binary), other);
-        }
-        else
-        {
-            JSON_THROW(type_error::create(310, detail::concat("cannot use swap(binary_t::container_type&) with ", type_name()), this));
-        }
-    }
-
-    /// @}
-
-    //////////////////////////////////////////
-    // lexicographical comparison operators //
-    //////////////////////////////////////////
-
-    /// @name lexicographical comparison operators
-    /// @{
-
-    // note parentheses around operands are necessary; see
-    // https://github.com/nlohmann/json/issues/1530
-#define JSON_IMPLEMENT_OPERATOR(op, null_result, unordered_result, default_result)                       \
-    const auto lhs_type = lhs.type();                                                                    \
-    const auto rhs_type = rhs.type();                                                                    \
-    \
-    if (lhs_type == rhs_type) /* NOLINT(readability/braces) */                                           \
-    {                                                                                                    \
-        switch (lhs_type)                                                                                \
-        {                                                                                                \
-            case value_t::array:                                                                         \
-                return (*lhs.m_value.array) op (*rhs.m_value.array);                                     \
-                \
-            case value_t::object:                                                                        \
-                return (*lhs.m_value.object) op (*rhs.m_value.object);                                   \
-                \
-            case value_t::null:                                                                          \
-                return (null_result);                                                                    \
-                \
-            case value_t::string:                                                                        \
-                return (*lhs.m_value.string) op (*rhs.m_value.string);                                   \
-                \
-            case value_t::boolean:                                                                       \
-                return (lhs.m_value.boolean) op (rhs.m_value.boolean);                                   \
-                \
-            case value_t::number_integer:                                                                \
-                return (lhs.m_value.number_integer) op (rhs.m_value.number_integer);                     \
-                \
-            case value_t::number_unsigned:                                                               \
-                return (lhs.m_value.number_unsigned) op (rhs.m_value.number_unsigned);                   \
-                \
-            case value_t::number_float:                                                                  \
-                return (lhs.m_value.number_float) op (rhs.m_value.number_float);                         \
-                \
-            case value_t::binary:                                                                        \
-                return (*lhs.m_value.binary) op (*rhs.m_value.binary);                                   \
-                \
-            case value_t::discarded:                                                                     \
-            default:                                                                                     \
-                return (unordered_result);                                                               \
-        }                                                                                                \
-    }                                                                                                    \
-    else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_float)                   \
-    {                                                                                                    \
-        return static_cast<number_float_t>(lhs.m_value.number_integer) op rhs.m_value.number_float;      \
-    }                                                                                                    \
-    else if (lhs_type == value_t::number_float && rhs_type == value_t::number_integer)                   \
-    {                                                                                                    \
-        return lhs.m_value.number_float op static_cast<number_float_t>(rhs.m_value.number_integer);      \
-    }                                                                                                    \
-    else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_float)                  \
-    {                                                                                                    \
-        return static_cast<number_float_t>(lhs.m_value.number_unsigned) op rhs.m_value.number_float;     \
-    }                                                                                                    \
-    else if (lhs_type == value_t::number_float && rhs_type == value_t::number_unsigned)                  \
-    {                                                                                                    \
-        return lhs.m_value.number_float op static_cast<number_float_t>(rhs.m_value.number_unsigned);     \
-    }                                                                                                    \
-    else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_integer)                \
-    {                                                                                                    \
-        return static_cast<number_integer_t>(lhs.m_value.number_unsigned) op rhs.m_value.number_integer; \
-    }                                                                                                    \
-    else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_unsigned)                \
-    {                                                                                                    \
-        return lhs.m_value.number_integer op static_cast<number_integer_t>(rhs.m_value.number_unsigned); \
-    }                                                                                                    \
-    else if(compares_unordered(lhs, rhs))\
-    {\
-        return (unordered_result);\
-    }\
-    \
-    return (default_result);
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    // returns true if:
-    // - any operand is NaN and the other operand is of number type
-    // - any operand is discarded
-    // in legacy mode, discarded values are considered ordered if
-    // an operation is computed as an odd number of inverses of others
-    static bool compares_unordered(const_reference lhs, const_reference rhs, bool inverse = false) noexcept
-    {
-        if ((lhs.is_number_float() && std::isnan(lhs.m_value.number_float) && rhs.is_number())
-                || (rhs.is_number_float() && std::isnan(rhs.m_value.number_float) && lhs.is_number()))
-        {
-            return true;
-        }
-#if JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
-        return (lhs.is_discarded() || rhs.is_discarded()) && !inverse;
-#else
-        static_cast<void>(inverse);
-        return lhs.is_discarded() || rhs.is_discarded();
-#endif
-    }
-
-  private:
-    bool compares_unordered(const_reference rhs, bool inverse = false) const noexcept
-    {
-        return compares_unordered(*this, rhs, inverse);
-    }
-
-  public:
-#if JSON_HAS_THREE_WAY_COMPARISON
-    /// @brief comparison: equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_eq/
-    bool operator==(const_reference rhs) const noexcept
-    {
-#ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wfloat-equal"
-#endif
-        const_reference lhs = *this;
-        JSON_IMPLEMENT_OPERATOR( ==, true, false, false)
-#ifdef __GNUC__
-#pragma GCC diagnostic pop
-#endif
-    }
-
-    /// @brief comparison: equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_eq/
-    template<typename ScalarType>
-    requires std::is_scalar_v<ScalarType>
-    bool operator==(ScalarType rhs) const noexcept
-    {
-        return *this == basic_json(rhs);
-    }
-
-    /// @brief comparison: not equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ne/
-    bool operator!=(const_reference rhs) const noexcept
-    {
-        if (compares_unordered(rhs, true))
-        {
-            return false;
-        }
-        return !operator==(rhs);
-    }
-
-    /// @brief comparison: 3-way
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_spaceship/
-    std::partial_ordering operator<=>(const_reference rhs) const noexcept // *NOPAD*
-    {
-        const_reference lhs = *this;
-        // default_result is used if we cannot compare values. In that case,
-        // we compare types.
-        JSON_IMPLEMENT_OPERATOR(<=>, // *NOPAD*
-                                std::partial_ordering::equivalent,
-                                std::partial_ordering::unordered,
-                                lhs_type <=> rhs_type) // *NOPAD*
-    }
-
-    /// @brief comparison: 3-way
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_spaceship/
-    template<typename ScalarType>
-    requires std::is_scalar_v<ScalarType>
-    std::partial_ordering operator<=>(ScalarType rhs) const noexcept // *NOPAD*
-    {
-        return *this <=> basic_json(rhs); // *NOPAD*
-    }
-
-#if JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
-    // all operators that are computed as an odd number of inverses of others
-    // need to be overloaded to emulate the legacy comparison behavior
-
-    /// @brief comparison: less than or equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_le/
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON)
-    bool operator<=(const_reference rhs) const noexcept
-    {
-        if (compares_unordered(rhs, true))
-        {
-            return false;
-        }
-        return !(rhs < *this);
-    }
-
-    /// @brief comparison: less than or equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_le/
-    template<typename ScalarType>
-    requires std::is_scalar_v<ScalarType>
-    bool operator<=(ScalarType rhs) const noexcept
-    {
-        return *this <= basic_json(rhs);
-    }
-
-    /// @brief comparison: greater than or equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ge/
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON)
-    bool operator>=(const_reference rhs) const noexcept
-    {
-        if (compares_unordered(rhs, true))
-        {
-            return false;
-        }
-        return !(*this < rhs);
-    }
-
-    /// @brief comparison: greater than or equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ge/
-    template<typename ScalarType>
-    requires std::is_scalar_v<ScalarType>
-    bool operator>=(ScalarType rhs) const noexcept
-    {
-        return *this >= basic_json(rhs);
-    }
-#endif
-#else
-    /// @brief comparison: equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_eq/
-    friend bool operator==(const_reference lhs, const_reference rhs) noexcept
-    {
-#ifdef __GNUC__
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wfloat-equal"
-#endif
-        JSON_IMPLEMENT_OPERATOR( ==, true, false, false)
-#ifdef __GNUC__
-#pragma GCC diagnostic pop
-#endif
-    }
-
-    /// @brief comparison: equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_eq/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator==(const_reference lhs, ScalarType rhs) noexcept
-    {
-        return lhs == basic_json(rhs);
-    }
-
-    /// @brief comparison: equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_eq/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator==(ScalarType lhs, const_reference rhs) noexcept
-    {
-        return basic_json(lhs) == rhs;
-    }
-
-    /// @brief comparison: not equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ne/
-    friend bool operator!=(const_reference lhs, const_reference rhs) noexcept
-    {
-        if (compares_unordered(lhs, rhs, true))
-        {
-            return false;
-        }
-        return !(lhs == rhs);
-    }
-
-    /// @brief comparison: not equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ne/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator!=(const_reference lhs, ScalarType rhs) noexcept
-    {
-        return lhs != basic_json(rhs);
-    }
-
-    /// @brief comparison: not equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ne/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator!=(ScalarType lhs, const_reference rhs) noexcept
-    {
-        return basic_json(lhs) != rhs;
-    }
-
-    /// @brief comparison: less than
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_lt/
-    friend bool operator<(const_reference lhs, const_reference rhs) noexcept
-    {
-        // default_result is used if we cannot compare values. In that case,
-        // we compare types. Note we have to call the operator explicitly,
-        // because MSVC has problems otherwise.
-        JSON_IMPLEMENT_OPERATOR( <, false, false, operator<(lhs_type, rhs_type))
-    }
-
-    /// @brief comparison: less than
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_lt/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator<(const_reference lhs, ScalarType rhs) noexcept
-    {
-        return lhs < basic_json(rhs);
-    }
-
-    /// @brief comparison: less than
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_lt/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator<(ScalarType lhs, const_reference rhs) noexcept
-    {
-        return basic_json(lhs) < rhs;
-    }
-
-    /// @brief comparison: less than or equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_le/
-    friend bool operator<=(const_reference lhs, const_reference rhs) noexcept
-    {
-        if (compares_unordered(lhs, rhs, true))
-        {
-            return false;
-        }
-        return !(rhs < lhs);
-    }
-
-    /// @brief comparison: less than or equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_le/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator<=(const_reference lhs, ScalarType rhs) noexcept
-    {
-        return lhs <= basic_json(rhs);
-    }
-
-    /// @brief comparison: less than or equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_le/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator<=(ScalarType lhs, const_reference rhs) noexcept
-    {
-        return basic_json(lhs) <= rhs;
-    }
-
-    /// @brief comparison: greater than
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_gt/
-    friend bool operator>(const_reference lhs, const_reference rhs) noexcept
-    {
-        // double inverse
-        if (compares_unordered(lhs, rhs))
-        {
-            return false;
-        }
-        return !(lhs <= rhs);
-    }
-
-    /// @brief comparison: greater than
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_gt/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator>(const_reference lhs, ScalarType rhs) noexcept
-    {
-        return lhs > basic_json(rhs);
-    }
-
-    /// @brief comparison: greater than
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_gt/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator>(ScalarType lhs, const_reference rhs) noexcept
-    {
-        return basic_json(lhs) > rhs;
-    }
-
-    /// @brief comparison: greater than or equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ge/
-    friend bool operator>=(const_reference lhs, const_reference rhs) noexcept
-    {
-        if (compares_unordered(lhs, rhs, true))
-        {
-            return false;
-        }
-        return !(lhs < rhs);
-    }
-
-    /// @brief comparison: greater than or equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ge/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator>=(const_reference lhs, ScalarType rhs) noexcept
-    {
-        return lhs >= basic_json(rhs);
-    }
-
-    /// @brief comparison: greater than or equal
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ge/
-    template<typename ScalarType, typename std::enable_if<
-                 std::is_scalar<ScalarType>::value, int>::type = 0>
-    friend bool operator>=(ScalarType lhs, const_reference rhs) noexcept
-    {
-        return basic_json(lhs) >= rhs;
-    }
-#endif
-
-#undef JSON_IMPLEMENT_OPERATOR
-
-    /// @}
-
-    ///////////////////
-    // serialization //
-    ///////////////////
-
-    /// @name serialization
-    /// @{
-#ifndef JSON_NO_IO
-    /// @brief serialize to stream
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ltlt/
-    friend std::ostream& operator<<(std::ostream& o, const basic_json& j)
-    {
-        // read width member and use it as indentation parameter if nonzero
-        const bool pretty_print = o.width() > 0;
-        const auto indentation = pretty_print ? o.width() : 0;
-
-        // reset width to 0 for subsequent calls to this stream
-        o.width(0);
-
-        // do the actual serialization
-        serializer s(detail::output_adapter<char>(o), o.fill());
-        s.dump(j, pretty_print, false, static_cast<unsigned int>(indentation));
-        return o;
-    }
-
-    /// @brief serialize to stream
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_ltlt/
-    /// @deprecated This function is deprecated since 3.0.0 and will be removed in
-    ///             version 4.0.0 of the library. Please use
-    ///             operator<<(std::ostream&, const basic_json&) instead; that is,
-    ///             replace calls like `j >> o;` with `o << j;`.
-    JSON_HEDLEY_DEPRECATED_FOR(3.0.0, operator<<(std::ostream&, const basic_json&))
-    friend std::ostream& operator>>(const basic_json& j, std::ostream& o)
-    {
-        return o << j;
-    }
-#endif  // JSON_NO_IO
-    /// @}
-
-
-    /////////////////////
-    // deserialization //
-    /////////////////////
-
-    /// @name deserialization
-    /// @{
-
-    /// @brief deserialize from a compatible input
-    /// @sa https://json.nlohmann.me/api/basic_json/parse/
-    template<typename InputType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json parse(InputType&& i,
-                            const parser_callback_t cb = nullptr,
-                            const bool allow_exceptions = true,
-                            const bool ignore_comments = false)
-    {
-        basic_json result;
-        parser(detail::input_adapter(std::forward<InputType>(i)), cb, allow_exceptions, ignore_comments).parse(true, result);
-        return result;
-    }
-
-    /// @brief deserialize from a pair of character iterators
-    /// @sa https://json.nlohmann.me/api/basic_json/parse/
-    template<typename IteratorType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json parse(IteratorType first,
-                            IteratorType last,
-                            const parser_callback_t cb = nullptr,
-                            const bool allow_exceptions = true,
-                            const bool ignore_comments = false)
-    {
-        basic_json result;
-        parser(detail::input_adapter(std::move(first), std::move(last)), cb, allow_exceptions, ignore_comments).parse(true, result);
-        return result;
-    }
-
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, parse(ptr, ptr + len))
-    static basic_json parse(detail::span_input_adapter&& i,
-                            const parser_callback_t cb = nullptr,
-                            const bool allow_exceptions = true,
-                            const bool ignore_comments = false)
-    {
-        basic_json result;
-        parser(i.get(), cb, allow_exceptions, ignore_comments).parse(true, result);
-        return result;
-    }
-
-    /// @brief check if the input is valid JSON
-    /// @sa https://json.nlohmann.me/api/basic_json/accept/
-    template<typename InputType>
-    static bool accept(InputType&& i,
-                       const bool ignore_comments = false)
-    {
-        return parser(detail::input_adapter(std::forward<InputType>(i)), nullptr, false, ignore_comments).accept(true);
-    }
-
-    /// @brief check if the input is valid JSON
-    /// @sa https://json.nlohmann.me/api/basic_json/accept/
-    template<typename IteratorType>
-    static bool accept(IteratorType first, IteratorType last,
-                       const bool ignore_comments = false)
-    {
-        return parser(detail::input_adapter(std::move(first), std::move(last)), nullptr, false, ignore_comments).accept(true);
-    }
-
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, accept(ptr, ptr + len))
-    static bool accept(detail::span_input_adapter&& i,
-                       const bool ignore_comments = false)
-    {
-        return parser(i.get(), nullptr, false, ignore_comments).accept(true);
-    }
-
-    /// @brief generate SAX events
-    /// @sa https://json.nlohmann.me/api/basic_json/sax_parse/
-    template <typename InputType, typename SAX>
-    JSON_HEDLEY_NON_NULL(2)
-    static bool sax_parse(InputType&& i, SAX* sax,
-                          input_format_t format = input_format_t::json,
-                          const bool strict = true,
-                          const bool ignore_comments = false)
-    {
-        auto ia = detail::input_adapter(std::forward<InputType>(i));
-        return format == input_format_t::json
-               ? parser(std::move(ia), nullptr, true, ignore_comments).sax_parse(sax, strict)
-               : detail::binary_reader<basic_json, decltype(ia), SAX>(std::move(ia), format).sax_parse(format, sax, strict);
-    }
-
-    /// @brief generate SAX events
-    /// @sa https://json.nlohmann.me/api/basic_json/sax_parse/
-    template<class IteratorType, class SAX>
-    JSON_HEDLEY_NON_NULL(3)
-    static bool sax_parse(IteratorType first, IteratorType last, SAX* sax,
-                          input_format_t format = input_format_t::json,
-                          const bool strict = true,
-                          const bool ignore_comments = false)
-    {
-        auto ia = detail::input_adapter(std::move(first), std::move(last));
-        return format == input_format_t::json
-               ? parser(std::move(ia), nullptr, true, ignore_comments).sax_parse(sax, strict)
-               : detail::binary_reader<basic_json, decltype(ia), SAX>(std::move(ia), format).sax_parse(format, sax, strict);
-    }
-
-    /// @brief generate SAX events
-    /// @sa https://json.nlohmann.me/api/basic_json/sax_parse/
-    /// @deprecated This function is deprecated since 3.8.0 and will be removed in
-    ///             version 4.0.0 of the library. Please use
-    ///             sax_parse(ptr, ptr + len) instead.
-    template <typename SAX>
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, sax_parse(ptr, ptr + len, ...))
-    JSON_HEDLEY_NON_NULL(2)
-    static bool sax_parse(detail::span_input_adapter&& i, SAX* sax,
-                          input_format_t format = input_format_t::json,
-                          const bool strict = true,
-                          const bool ignore_comments = false)
-    {
-        auto ia = i.get();
-        return format == input_format_t::json
-               // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
-               ? parser(std::move(ia), nullptr, true, ignore_comments).sax_parse(sax, strict)
-               // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
-               : detail::binary_reader<basic_json, decltype(ia), SAX>(std::move(ia), format).sax_parse(format, sax, strict);
-    }
-#ifndef JSON_NO_IO
-    /// @brief deserialize from stream
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_gtgt/
-    /// @deprecated This stream operator is deprecated since 3.0.0 and will be removed in
-    ///             version 4.0.0 of the library. Please use
-    ///             operator>>(std::istream&, basic_json&) instead; that is,
-    ///             replace calls like `j << i;` with `i >> j;`.
-    JSON_HEDLEY_DEPRECATED_FOR(3.0.0, operator>>(std::istream&, basic_json&))
-    friend std::istream& operator<<(basic_json& j, std::istream& i)
-    {
-        return operator>>(i, j);
-    }
-
-    /// @brief deserialize from stream
-    /// @sa https://json.nlohmann.me/api/basic_json/operator_gtgt/
-    friend std::istream& operator>>(std::istream& i, basic_json& j)
-    {
-        parser(detail::input_adapter(i)).parse(false, j);
-        return i;
-    }
-#endif  // JSON_NO_IO
-    /// @}
-
-    ///////////////////////////
-    // convenience functions //
-    ///////////////////////////
-
-    /// @brief return the type as string
-    /// @sa https://json.nlohmann.me/api/basic_json/type_name/
-    JSON_HEDLEY_RETURNS_NON_NULL
-    const char* type_name() const noexcept
-    {
-        switch (m_type)
-        {
-            case value_t::null:
-                return "null";
-            case value_t::object:
-                return "object";
-            case value_t::array:
-                return "array";
-            case value_t::string:
-                return "string";
-            case value_t::boolean:
-                return "boolean";
-            case value_t::binary:
-                return "binary";
-            case value_t::discarded:
-                return "discarded";
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            default:
-                return "number";
-        }
-    }
-
-
-  JSON_PRIVATE_UNLESS_TESTED:
-    //////////////////////
-    // member variables //
-    //////////////////////
-
-    /// the type of the current element
-    value_t m_type = value_t::null;
-
-    /// the value of the current element
-    json_value m_value = {};
-
-#if JSON_DIAGNOSTICS
-    /// a pointer to a parent value (for debugging purposes)
-    basic_json* m_parent = nullptr;
-#endif
-
-    //////////////////////////////////////////
-    // binary serialization/deserialization //
-    //////////////////////////////////////////
-
-    /// @name binary serialization/deserialization support
-    /// @{
-
-  public:
-    /// @brief create a CBOR serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_cbor/
-    static std::vector<std::uint8_t> to_cbor(const basic_json& j)
-    {
-        std::vector<std::uint8_t> result;
-        to_cbor(j, result);
-        return result;
-    }
-
-    /// @brief create a CBOR serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_cbor/
-    static void to_cbor(const basic_json& j, detail::output_adapter<std::uint8_t> o)
-    {
-        binary_writer<std::uint8_t>(o).write_cbor(j);
-    }
-
-    /// @brief create a CBOR serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_cbor/
-    static void to_cbor(const basic_json& j, detail::output_adapter<char> o)
-    {
-        binary_writer<char>(o).write_cbor(j);
-    }
-
-    /// @brief create a MessagePack serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_msgpack/
-    static std::vector<std::uint8_t> to_msgpack(const basic_json& j)
-    {
-        std::vector<std::uint8_t> result;
-        to_msgpack(j, result);
-        return result;
-    }
-
-    /// @brief create a MessagePack serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_msgpack/
-    static void to_msgpack(const basic_json& j, detail::output_adapter<std::uint8_t> o)
-    {
-        binary_writer<std::uint8_t>(o).write_msgpack(j);
-    }
-
-    /// @brief create a MessagePack serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_msgpack/
-    static void to_msgpack(const basic_json& j, detail::output_adapter<char> o)
-    {
-        binary_writer<char>(o).write_msgpack(j);
-    }
-
-    /// @brief create a UBJSON serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_ubjson/
-    static std::vector<std::uint8_t> to_ubjson(const basic_json& j,
-            const bool use_size = false,
-            const bool use_type = false)
-    {
-        std::vector<std::uint8_t> result;
-        to_ubjson(j, result, use_size, use_type);
-        return result;
-    }
-
-    /// @brief create a UBJSON serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_ubjson/
-    static void to_ubjson(const basic_json& j, detail::output_adapter<std::uint8_t> o,
-                          const bool use_size = false, const bool use_type = false)
-    {
-        binary_writer<std::uint8_t>(o).write_ubjson(j, use_size, use_type);
-    }
-
-    /// @brief create a UBJSON serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_ubjson/
-    static void to_ubjson(const basic_json& j, detail::output_adapter<char> o,
-                          const bool use_size = false, const bool use_type = false)
-    {
-        binary_writer<char>(o).write_ubjson(j, use_size, use_type);
-    }
-
-    /// @brief create a BJData serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_bjdata/
-    static std::vector<std::uint8_t> to_bjdata(const basic_json& j,
-            const bool use_size = false,
-            const bool use_type = false)
-    {
-        std::vector<std::uint8_t> result;
-        to_bjdata(j, result, use_size, use_type);
-        return result;
-    }
-
-    /// @brief create a BJData serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_bjdata/
-    static void to_bjdata(const basic_json& j, detail::output_adapter<std::uint8_t> o,
-                          const bool use_size = false, const bool use_type = false)
-    {
-        binary_writer<std::uint8_t>(o).write_ubjson(j, use_size, use_type, true, true);
-    }
-
-    /// @brief create a BJData serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_bjdata/
-    static void to_bjdata(const basic_json& j, detail::output_adapter<char> o,
-                          const bool use_size = false, const bool use_type = false)
-    {
-        binary_writer<char>(o).write_ubjson(j, use_size, use_type, true, true);
-    }
-
-    /// @brief create a BSON serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_bson/
-    static std::vector<std::uint8_t> to_bson(const basic_json& j)
-    {
-        std::vector<std::uint8_t> result;
-        to_bson(j, result);
-        return result;
-    }
-
-    /// @brief create a BSON serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_bson/
-    static void to_bson(const basic_json& j, detail::output_adapter<std::uint8_t> o)
-    {
-        binary_writer<std::uint8_t>(o).write_bson(j);
-    }
-
-    /// @brief create a BSON serialization of a given JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/to_bson/
-    static void to_bson(const basic_json& j, detail::output_adapter<char> o)
-    {
-        binary_writer<char>(o).write_bson(j);
-    }
-
-    /// @brief create a JSON value from an input in CBOR format
-    /// @sa https://json.nlohmann.me/api/basic_json/from_cbor/
-    template<typename InputType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json from_cbor(InputType&& i,
-                                const bool strict = true,
-                                const bool allow_exceptions = true,
-                                const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = detail::input_adapter(std::forward<InputType>(i));
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::cbor).sax_parse(input_format_t::cbor, &sdp, strict, tag_handler);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    /// @brief create a JSON value from an input in CBOR format
-    /// @sa https://json.nlohmann.me/api/basic_json/from_cbor/
-    template<typename IteratorType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json from_cbor(IteratorType first, IteratorType last,
-                                const bool strict = true,
-                                const bool allow_exceptions = true,
-                                const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = detail::input_adapter(std::move(first), std::move(last));
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::cbor).sax_parse(input_format_t::cbor, &sdp, strict, tag_handler);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    template<typename T>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_cbor(ptr, ptr + len))
-    static basic_json from_cbor(const T* ptr, std::size_t len,
-                                const bool strict = true,
-                                const bool allow_exceptions = true,
-                                const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error)
-    {
-        return from_cbor(ptr, ptr + len, strict, allow_exceptions, tag_handler);
-    }
-
-
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_cbor(ptr, ptr + len))
-    static basic_json from_cbor(detail::span_input_adapter&& i,
-                                const bool strict = true,
-                                const bool allow_exceptions = true,
-                                const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = i.get();
-        // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::cbor).sax_parse(input_format_t::cbor, &sdp, strict, tag_handler);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    /// @brief create a JSON value from an input in MessagePack format
-    /// @sa https://json.nlohmann.me/api/basic_json/from_msgpack/
-    template<typename InputType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json from_msgpack(InputType&& i,
-                                   const bool strict = true,
-                                   const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = detail::input_adapter(std::forward<InputType>(i));
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::msgpack).sax_parse(input_format_t::msgpack, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    /// @brief create a JSON value from an input in MessagePack format
-    /// @sa https://json.nlohmann.me/api/basic_json/from_msgpack/
-    template<typename IteratorType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json from_msgpack(IteratorType first, IteratorType last,
-                                   const bool strict = true,
-                                   const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = detail::input_adapter(std::move(first), std::move(last));
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::msgpack).sax_parse(input_format_t::msgpack, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    template<typename T>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_msgpack(ptr, ptr + len))
-    static basic_json from_msgpack(const T* ptr, std::size_t len,
-                                   const bool strict = true,
-                                   const bool allow_exceptions = true)
-    {
-        return from_msgpack(ptr, ptr + len, strict, allow_exceptions);
-    }
-
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_msgpack(ptr, ptr + len))
-    static basic_json from_msgpack(detail::span_input_adapter&& i,
-                                   const bool strict = true,
-                                   const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = i.get();
-        // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::msgpack).sax_parse(input_format_t::msgpack, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    /// @brief create a JSON value from an input in UBJSON format
-    /// @sa https://json.nlohmann.me/api/basic_json/from_ubjson/
-    template<typename InputType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json from_ubjson(InputType&& i,
-                                  const bool strict = true,
-                                  const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = detail::input_adapter(std::forward<InputType>(i));
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::ubjson).sax_parse(input_format_t::ubjson, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    /// @brief create a JSON value from an input in UBJSON format
-    /// @sa https://json.nlohmann.me/api/basic_json/from_ubjson/
-    template<typename IteratorType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json from_ubjson(IteratorType first, IteratorType last,
-                                  const bool strict = true,
-                                  const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = detail::input_adapter(std::move(first), std::move(last));
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::ubjson).sax_parse(input_format_t::ubjson, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    template<typename T>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_ubjson(ptr, ptr + len))
-    static basic_json from_ubjson(const T* ptr, std::size_t len,
-                                  const bool strict = true,
-                                  const bool allow_exceptions = true)
-    {
-        return from_ubjson(ptr, ptr + len, strict, allow_exceptions);
-    }
-
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_ubjson(ptr, ptr + len))
-    static basic_json from_ubjson(detail::span_input_adapter&& i,
-                                  const bool strict = true,
-                                  const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = i.get();
-        // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::ubjson).sax_parse(input_format_t::ubjson, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-
-    /// @brief create a JSON value from an input in BJData format
-    /// @sa https://json.nlohmann.me/api/basic_json/from_bjdata/
-    template<typename InputType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json from_bjdata(InputType&& i,
-                                  const bool strict = true,
-                                  const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = detail::input_adapter(std::forward<InputType>(i));
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::bjdata).sax_parse(input_format_t::bjdata, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    /// @brief create a JSON value from an input in BJData format
-    /// @sa https://json.nlohmann.me/api/basic_json/from_bjdata/
-    template<typename IteratorType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json from_bjdata(IteratorType first, IteratorType last,
-                                  const bool strict = true,
-                                  const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = detail::input_adapter(std::move(first), std::move(last));
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::bjdata).sax_parse(input_format_t::bjdata, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    /// @brief create a JSON value from an input in BSON format
-    /// @sa https://json.nlohmann.me/api/basic_json/from_bson/
-    template<typename InputType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json from_bson(InputType&& i,
-                                const bool strict = true,
-                                const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = detail::input_adapter(std::forward<InputType>(i));
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::bson).sax_parse(input_format_t::bson, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    /// @brief create a JSON value from an input in BSON format
-    /// @sa https://json.nlohmann.me/api/basic_json/from_bson/
-    template<typename IteratorType>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json from_bson(IteratorType first, IteratorType last,
-                                const bool strict = true,
-                                const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = detail::input_adapter(std::move(first), std::move(last));
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::bson).sax_parse(input_format_t::bson, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-
-    template<typename T>
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_bson(ptr, ptr + len))
-    static basic_json from_bson(const T* ptr, std::size_t len,
-                                const bool strict = true,
-                                const bool allow_exceptions = true)
-    {
-        return from_bson(ptr, ptr + len, strict, allow_exceptions);
-    }
-
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_bson(ptr, ptr + len))
-    static basic_json from_bson(detail::span_input_adapter&& i,
-                                const bool strict = true,
-                                const bool allow_exceptions = true)
-    {
-        basic_json result;
-        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
-        auto ia = i.get();
-        // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
-        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::bson).sax_parse(input_format_t::bson, &sdp, strict);
-        return res ? result : basic_json(value_t::discarded);
-    }
-    /// @}
-
-    //////////////////////////
-    // JSON Pointer support //
-    //////////////////////////
-
-    /// @name JSON Pointer functions
-    /// @{
-
-    /// @brief access specified element via JSON Pointer
-    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
-    reference operator[](const json_pointer& ptr)
-    {
-        return ptr.get_unchecked(this);
-    }
-
-    template<typename BasicJsonType, detail::enable_if_t<detail::is_basic_json<BasicJsonType>::value, int> = 0>
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
-    reference operator[](const ::nlohmann::json_pointer<BasicJsonType>& ptr)
-    {
-        return ptr.get_unchecked(this);
-    }
-
-    /// @brief access specified element via JSON Pointer
-    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
-    const_reference operator[](const json_pointer& ptr) const
-    {
-        return ptr.get_unchecked(this);
-    }
-
-    template<typename BasicJsonType, detail::enable_if_t<detail::is_basic_json<BasicJsonType>::value, int> = 0>
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
-    const_reference operator[](const ::nlohmann::json_pointer<BasicJsonType>& ptr) const
-    {
-        return ptr.get_unchecked(this);
-    }
-
-    /// @brief access specified element via JSON Pointer
-    /// @sa https://json.nlohmann.me/api/basic_json/at/
-    reference at(const json_pointer& ptr)
-    {
-        return ptr.get_checked(this);
-    }
-
-    template<typename BasicJsonType, detail::enable_if_t<detail::is_basic_json<BasicJsonType>::value, int> = 0>
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
-    reference at(const ::nlohmann::json_pointer<BasicJsonType>& ptr)
-    {
-        return ptr.get_checked(this);
-    }
-
-    /// @brief access specified element via JSON Pointer
-    /// @sa https://json.nlohmann.me/api/basic_json/at/
-    const_reference at(const json_pointer& ptr) const
-    {
-        return ptr.get_checked(this);
-    }
-
-    template<typename BasicJsonType, detail::enable_if_t<detail::is_basic_json<BasicJsonType>::value, int> = 0>
-    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
-    const_reference at(const ::nlohmann::json_pointer<BasicJsonType>& ptr) const
-    {
-        return ptr.get_checked(this);
-    }
-
-    /// @brief return flattened JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/flatten/
-    basic_json flatten() const
-    {
-        basic_json result(value_t::object);
-        json_pointer::flatten("", *this, result);
-        return result;
-    }
-
-    /// @brief unflatten a previously flattened JSON value
-    /// @sa https://json.nlohmann.me/api/basic_json/unflatten/
-    basic_json unflatten() const
-    {
-        return json_pointer::unflatten(*this);
-    }
-
-    /// @}
-
-    //////////////////////////
-    // JSON Patch functions //
-    //////////////////////////
-
-    /// @name JSON Patch functions
-    /// @{
-
-    /// @brief applies a JSON patch in-place without copying the object
-    /// @sa https://json.nlohmann.me/api/basic_json/patch/
-    void patch_inplace(const basic_json& json_patch)
-    {
-        basic_json& result = *this;
-        // the valid JSON Patch operations
-        enum class patch_operations {add, remove, replace, move, copy, test, invalid};
-
-        const auto get_op = [](const std::string & op)
-        {
-            if (op == "add")
-            {
-                return patch_operations::add;
-            }
-            if (op == "remove")
-            {
-                return patch_operations::remove;
-            }
-            if (op == "replace")
-            {
-                return patch_operations::replace;
-            }
-            if (op == "move")
-            {
-                return patch_operations::move;
-            }
-            if (op == "copy")
-            {
-                return patch_operations::copy;
-            }
-            if (op == "test")
-            {
-                return patch_operations::test;
-            }
-
-            return patch_operations::invalid;
-        };
-
-        // wrapper for "add" operation; add value at ptr
-        const auto operation_add = [&result](json_pointer & ptr, basic_json val)
-        {
-            // adding to the root of the target document means replacing it
-            if (ptr.empty())
-            {
-                result = val;
-                return;
-            }
-
-            // make sure the top element of the pointer exists
-            json_pointer top_pointer = ptr.top();
-            if (top_pointer != ptr)
-            {
-                result.at(top_pointer);
-            }
-
-            // get reference to parent of JSON pointer ptr
-            const auto last_path = ptr.back();
-            ptr.pop_back();
-            // parent must exist when performing patch add per RFC6902 specs
-            basic_json& parent = result.at(ptr);
-
-            switch (parent.m_type)
-            {
-                case value_t::null:
-                case value_t::object:
-                {
-                    // use operator[] to add value
-                    parent[last_path] = val;
-                    break;
-                }
-
-                case value_t::array:
-                {
-                    if (last_path == "-")
-                    {
-                        // special case: append to back
-                        parent.push_back(val);
-                    }
-                    else
-                    {
-                        const auto idx = json_pointer::template array_index<basic_json_t>(last_path);
-                        if (JSON_HEDLEY_UNLIKELY(idx > parent.size()))
-                        {
-                            // avoid undefined behavior
-                            JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), &parent));
-                        }
-
-                        // default case: insert add offset
-                        parent.insert(parent.begin() + static_cast<difference_type>(idx), val);
-                    }
-                    break;
-                }
-
-                // if there exists a parent it cannot be primitive
-                case value_t::string: // LCOV_EXCL_LINE
-                case value_t::boolean: // LCOV_EXCL_LINE
-                case value_t::number_integer: // LCOV_EXCL_LINE
-                case value_t::number_unsigned: // LCOV_EXCL_LINE
-                case value_t::number_float: // LCOV_EXCL_LINE
-                case value_t::binary: // LCOV_EXCL_LINE
-                case value_t::discarded: // LCOV_EXCL_LINE
-                default:            // LCOV_EXCL_LINE
-                    JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
-            }
-        };
-
-        // wrapper for "remove" operation; remove value at ptr
-        const auto operation_remove = [this, &result](json_pointer & ptr)
-        {
-            // get reference to parent of JSON pointer ptr
-            const auto last_path = ptr.back();
-            ptr.pop_back();
-            basic_json& parent = result.at(ptr);
-
-            // remove child
-            if (parent.is_object())
-            {
-                // perform range check
-                auto it = parent.find(last_path);
-                if (JSON_HEDLEY_LIKELY(it != parent.end()))
-                {
-                    parent.erase(it);
-                }
-                else
-                {
-                    JSON_THROW(out_of_range::create(403, detail::concat("key '", last_path, "' not found"), this));
-                }
-            }
-            else if (parent.is_array())
-            {
-                // note erase performs range check
-                parent.erase(json_pointer::template array_index<basic_json_t>(last_path));
-            }
-        };
-
-        // type check: top level value must be an array
-        if (JSON_HEDLEY_UNLIKELY(!json_patch.is_array()))
-        {
-            JSON_THROW(parse_error::create(104, 0, "JSON patch must be an array of objects", &json_patch));
-        }
-
-        // iterate and apply the operations
-        for (const auto& val : json_patch)
-        {
-            // wrapper to get a value for an operation
-            const auto get_value = [&val](const std::string & op,
-                                          const std::string & member,
-                                          bool string_type) -> basic_json &
-            {
-                // find value
-                auto it = val.m_value.object->find(member);
-
-                // context-sensitive error message
-                const auto error_msg = (op == "op") ? "operation" : detail::concat("operation '", op, '\'');
-
-                // check if desired value is present
-                if (JSON_HEDLEY_UNLIKELY(it == val.m_value.object->end()))
-                {
-                    // NOLINTNEXTLINE(performance-inefficient-string-concatenation)
-                    JSON_THROW(parse_error::create(105, 0, detail::concat(error_msg, " must have member '", member, "'"), &val));
-                }
-
-                // check if result is of type string
-                if (JSON_HEDLEY_UNLIKELY(string_type && !it->second.is_string()))
-                {
-                    // NOLINTNEXTLINE(performance-inefficient-string-concatenation)
-                    JSON_THROW(parse_error::create(105, 0, detail::concat(error_msg, " must have string member '", member, "'"), &val));
-                }
-
-                // no error: return value
-                return it->second;
-            };
-
-            // type check: every element of the array must be an object
-            if (JSON_HEDLEY_UNLIKELY(!val.is_object()))
-            {
-                JSON_THROW(parse_error::create(104, 0, "JSON patch must be an array of objects", &val));
-            }
-
-            // collect mandatory members
-            const auto op = get_value("op", "op", true).template get<std::string>();
-            const auto path = get_value(op, "path", true).template get<std::string>();
-            json_pointer ptr(path);
-
-            switch (get_op(op))
-            {
-                case patch_operations::add:
-                {
-                    operation_add(ptr, get_value("add", "value", false));
-                    break;
-                }
-
-                case patch_operations::remove:
-                {
-                    operation_remove(ptr);
-                    break;
-                }
-
-                case patch_operations::replace:
-                {
-                    // the "path" location must exist - use at()
-                    result.at(ptr) = get_value("replace", "value", false);
-                    break;
-                }
-
-                case patch_operations::move:
-                {
-                    const auto from_path = get_value("move", "from", true).template get<std::string>();
-                    json_pointer from_ptr(from_path);
-
-                    // the "from" location must exist - use at()
-                    basic_json v = result.at(from_ptr);
-
-                    // The move operation is functionally identical to a
-                    // "remove" operation on the "from" location, followed
-                    // immediately by an "add" operation at the target
-                    // location with the value that was just removed.
-                    operation_remove(from_ptr);
-                    operation_add(ptr, v);
-                    break;
-                }
-
-                case patch_operations::copy:
-                {
-                    const auto from_path = get_value("copy", "from", true).template get<std::string>();
-                    const json_pointer from_ptr(from_path);
-
-                    // the "from" location must exist - use at()
-                    basic_json v = result.at(from_ptr);
-
-                    // The copy is functionally identical to an "add"
-                    // operation at the target location using the value
-                    // specified in the "from" member.
-                    operation_add(ptr, v);
-                    break;
-                }
-
-                case patch_operations::test:
-                {
-                    bool success = false;
-                    JSON_TRY
-                    {
-                        // check if "value" matches the one at "path"
-                        // the "path" location must exist - use at()
-                        success = (result.at(ptr) == get_value("test", "value", false));
-                    }
-                    JSON_INTERNAL_CATCH (out_of_range&)
-                    {
-                        // ignore out of range errors: success remains false
-                    }
-
-                    // throw an exception if test fails
-                    if (JSON_HEDLEY_UNLIKELY(!success))
-                    {
-                        JSON_THROW(other_error::create(501, detail::concat("unsuccessful: ", val.dump()), &val));
-                    }
-
-                    break;
-                }
-
-                case patch_operations::invalid:
-                default:
-                {
-                    // op must be "add", "remove", "replace", "move", "copy", or
-                    // "test"
-                    JSON_THROW(parse_error::create(105, 0, detail::concat("operation value '", op, "' is invalid"), &val));
-                }
-            }
-        }
-    }
-
-    /// @brief applies a JSON patch to a copy of the current object
-    /// @sa https://json.nlohmann.me/api/basic_json/patch/
-    basic_json patch(const basic_json& json_patch) const
-    {
-        basic_json result = *this;
-        result.patch_inplace(json_patch);
-        return result;
-    }
-
-    /// @brief creates a diff as a JSON patch
-    /// @sa https://json.nlohmann.me/api/basic_json/diff/
-    JSON_HEDLEY_WARN_UNUSED_RESULT
-    static basic_json diff(const basic_json& source, const basic_json& target,
-                           const std::string& path = "")
-    {
-        // the patch
-        basic_json result(value_t::array);
-
-        // if the values are the same, return empty patch
-        if (source == target)
-        {
-            return result;
-        }
-
-        if (source.type() != target.type())
-        {
-            // different types: replace value
-            result.push_back(
-            {
-                {"op", "replace"}, {"path", path}, {"value", target}
-            });
-            return result;
-        }
-
-        switch (source.type())
-        {
-            case value_t::array:
-            {
-                // first pass: traverse common elements
-                std::size_t i = 0;
-                while (i < source.size() && i < target.size())
-                {
-                    // recursive call to compare array values at index i
-                    auto temp_diff = diff(source[i], target[i], detail::concat(path, '/', std::to_string(i)));
-                    result.insert(result.end(), temp_diff.begin(), temp_diff.end());
-                    ++i;
-                }
-
-                // We now reached the end of at least one array
-                // in a second pass, traverse the remaining elements
-
-                // remove my remaining elements
-                const auto end_index = static_cast<difference_type>(result.size());
-                while (i < source.size())
-                {
-                    // add operations in reverse order to avoid invalid
-                    // indices
-                    result.insert(result.begin() + end_index, object(
-                    {
-                        {"op", "remove"},
-                        {"path", detail::concat(path, '/', std::to_string(i))}
-                    }));
-                    ++i;
-                }
-
-                // add other remaining elements
-                while (i < target.size())
-                {
-                    result.push_back(
-                    {
-                        {"op", "add"},
-                        {"path", detail::concat(path, "/-")},
-                        {"value", target[i]}
-                    });
-                    ++i;
-                }
-
-                break;
-            }
-
-            case value_t::object:
-            {
-                // first pass: traverse this object's elements
-                for (auto it = source.cbegin(); it != source.cend(); ++it)
-                {
-                    // escape the key name to be used in a JSON patch
-                    const auto path_key = detail::concat(path, '/', detail::escape(it.key()));
-
-                    if (target.find(it.key()) != target.end())
-                    {
-                        // recursive call to compare object values at key it
-                        auto temp_diff = diff(it.value(), target[it.key()], path_key);
-                        result.insert(result.end(), temp_diff.begin(), temp_diff.end());
-                    }
-                    else
-                    {
-                        // found a key that is not in o -> remove it
-                        result.push_back(object(
-                        {
-                            {"op", "remove"}, {"path", path_key}
-                        }));
-                    }
-                }
-
-                // second pass: traverse other object's elements
-                for (auto it = target.cbegin(); it != target.cend(); ++it)
-                {
-                    if (source.find(it.key()) == source.end())
-                    {
-                        // found a key that is not in this -> add it
-                        const auto path_key = detail::concat(path, '/', detail::escape(it.key()));
-                        result.push_back(
-                        {
-                            {"op", "add"}, {"path", path_key},
-                            {"value", it.value()}
-                        });
-                    }
-                }
-
-                break;
-            }
-
-            case value_t::null:
-            case value_t::string:
-            case value_t::boolean:
-            case value_t::number_integer:
-            case value_t::number_unsigned:
-            case value_t::number_float:
-            case value_t::binary:
-            case value_t::discarded:
-            default:
-            {
-                // both primitive type: replace value
-                result.push_back(
-                {
-                    {"op", "replace"}, {"path", path}, {"value", target}
-                });
-                break;
-            }
-        }
-
-        return result;
-    }
-    /// @}
-
-    ////////////////////////////////
-    // JSON Merge Patch functions //
-    ////////////////////////////////
-
-    /// @name JSON Merge Patch functions
-    /// @{
-
-    /// @brief applies a JSON Merge Patch
-    /// @sa https://json.nlohmann.me/api/basic_json/merge_patch/
-    void merge_patch(const basic_json& apply_patch)
-    {
-        if (apply_patch.is_object())
-        {
-            if (!is_object())
-            {
-                *this = object();
-            }
-            for (auto it = apply_patch.begin(); it != apply_patch.end(); ++it)
-            {
-                if (it.value().is_null())
-                {
-                    erase(it.key());
-                }
-                else
-                {
-                    operator[](it.key()).merge_patch(it.value());
-                }
-            }
-        }
-        else
-        {
-            *this = apply_patch;
-        }
-    }
-
-    /// @}
-};
-
-/// @brief user-defined to_string function for JSON values
-/// @sa https://json.nlohmann.me/api/basic_json/to_string/
-NLOHMANN_BASIC_JSON_TPL_DECLARATION
-std::string to_string(const NLOHMANN_BASIC_JSON_TPL& j)
-{
-    return j.dump();
-}
-
-inline namespace literals
-{
-inline namespace json_literals
-{
-
-/// @brief user-defined string literal for JSON values
-/// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json/
-JSON_HEDLEY_NON_NULL(1)
-inline nlohmann::json operator "" _json(const char* s, std::size_t n)
-{
-    return nlohmann::json::parse(s, s + n);
-}
-
-/// @brief user-defined string literal for JSON pointer
-/// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json_pointer/
-JSON_HEDLEY_NON_NULL(1)
-inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n)
-{
-    return nlohmann::json::json_pointer(std::string(s, n));
-}
-
-}  // namespace json_literals
-}  // namespace literals
-NLOHMANN_JSON_NAMESPACE_END
-
-///////////////////////
-// nonmember support //
-///////////////////////
-
-namespace std // NOLINT(cert-dcl58-cpp)
-{
-
-/// @brief hash value for JSON objects
-/// @sa https://json.nlohmann.me/api/basic_json/std_hash/
-NLOHMANN_BASIC_JSON_TPL_DECLARATION
-struct hash<nlohmann::NLOHMANN_BASIC_JSON_TPL>
-{
-    std::size_t operator()(const nlohmann::NLOHMANN_BASIC_JSON_TPL& j) const
-    {
-        return nlohmann::detail::hash(j);
-    }
-};
-
-// specialization for std::less<value_t>
-template<>
-struct less< ::nlohmann::detail::value_t> // do not remove the space after '<', see https://github.com/nlohmann/json/pull/679
-{
-    /*!
-    @brief compare two value_t enum values
-    @since version 3.0.0
-    */
-    bool operator()(::nlohmann::detail::value_t lhs,
-                    ::nlohmann::detail::value_t rhs) const noexcept
-    {
-#if JSON_HAS_THREE_WAY_COMPARISON
-        return std::is_lt(lhs <=> rhs); // *NOPAD*
-#else
-        return ::nlohmann::detail::operator<(lhs, rhs);
-#endif
-    }
-};
-
-// C++20 prohibit function specialization in the std namespace.
-#ifndef JSON_HAS_CPP_20
-
-/// @brief exchanges the values of two JSON objects
-/// @sa https://json.nlohmann.me/api/basic_json/std_swap/
-NLOHMANN_BASIC_JSON_TPL_DECLARATION
-inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC_JSON_TPL& j2) noexcept(  // NOLINT(readability-inconsistent-declaration-parameter-name)
-    is_nothrow_move_constructible<nlohmann::NLOHMANN_BASIC_JSON_TPL>::value&&                          // NOLINT(misc-redundant-expression)
-    is_nothrow_move_assignable<nlohmann::NLOHMANN_BASIC_JSON_TPL>::value)
-{
-    j1.swap(j2);
-}
-
-#endif
-
-}  // namespace std
-
-#if JSON_USE_GLOBAL_UDLS
-    using nlohmann::literals::json_literals::operator "" _json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers)
-    using nlohmann::literals::json_literals::operator "" _json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers)
-#endif
-
-// #include <nlohmann/detail/macro_unscope.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-// restore clang diagnostic settings
-#if defined(__clang__)
-    #pragma clang diagnostic pop
-#endif
-
-// clean up
-#undef JSON_ASSERT
-#undef JSON_INTERNAL_CATCH
-#undef JSON_THROW
-#undef JSON_PRIVATE_UNLESS_TESTED
-#undef NLOHMANN_BASIC_JSON_TPL_DECLARATION
-#undef NLOHMANN_BASIC_JSON_TPL
-#undef JSON_EXPLICIT
-#undef NLOHMANN_CAN_CALL_STD_FUNC_IMPL
-#undef JSON_INLINE_VARIABLE
-#undef JSON_NO_UNIQUE_ADDRESS
-#undef JSON_DISABLE_ENUM_SERIALIZATION
-#undef JSON_USE_GLOBAL_UDLS
-
-#ifndef JSON_TEST_KEEP_MACROS
-    #undef JSON_CATCH
-    #undef JSON_TRY
-    #undef JSON_HAS_CPP_11
-    #undef JSON_HAS_CPP_14
-    #undef JSON_HAS_CPP_17
-    #undef JSON_HAS_CPP_20
-    #undef JSON_HAS_FILESYSTEM
-    #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
-    #undef JSON_HAS_THREE_WAY_COMPARISON
-    #undef JSON_HAS_RANGES
-    #undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
-#endif
-
-// #include <nlohmann/thirdparty/hedley/hedley_undef.hpp>
-//     __ _____ _____ _____
-//  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
-// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
-//
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
-// SPDX-License-Identifier: MIT
-
-
-
-#undef JSON_HEDLEY_ALWAYS_INLINE
-#undef JSON_HEDLEY_ARM_VERSION
-#undef JSON_HEDLEY_ARM_VERSION_CHECK
-#undef JSON_HEDLEY_ARRAY_PARAM
-#undef JSON_HEDLEY_ASSUME
-#undef JSON_HEDLEY_BEGIN_C_DECLS
-#undef JSON_HEDLEY_CLANG_HAS_ATTRIBUTE
-#undef JSON_HEDLEY_CLANG_HAS_BUILTIN
-#undef JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE
-#undef JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE
-#undef JSON_HEDLEY_CLANG_HAS_EXTENSION
-#undef JSON_HEDLEY_CLANG_HAS_FEATURE
-#undef JSON_HEDLEY_CLANG_HAS_WARNING
-#undef JSON_HEDLEY_COMPCERT_VERSION
-#undef JSON_HEDLEY_COMPCERT_VERSION_CHECK
-#undef JSON_HEDLEY_CONCAT
-#undef JSON_HEDLEY_CONCAT3
-#undef JSON_HEDLEY_CONCAT3_EX
-#undef JSON_HEDLEY_CONCAT_EX
-#undef JSON_HEDLEY_CONST
-#undef JSON_HEDLEY_CONSTEXPR
-#undef JSON_HEDLEY_CONST_CAST
-#undef JSON_HEDLEY_CPP_CAST
-#undef JSON_HEDLEY_CRAY_VERSION
-#undef JSON_HEDLEY_CRAY_VERSION_CHECK
-#undef JSON_HEDLEY_C_DECL
-#undef JSON_HEDLEY_DEPRECATED
-#undef JSON_HEDLEY_DEPRECATED_FOR
-#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL
-#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_
-#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED
-#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES
-#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS
-#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION
-#undef JSON_HEDLEY_DIAGNOSTIC_POP
-#undef JSON_HEDLEY_DIAGNOSTIC_PUSH
-#undef JSON_HEDLEY_DMC_VERSION
-#undef JSON_HEDLEY_DMC_VERSION_CHECK
-#undef JSON_HEDLEY_EMPTY_BASES
-#undef JSON_HEDLEY_EMSCRIPTEN_VERSION
-#undef JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK
-#undef JSON_HEDLEY_END_C_DECLS
-#undef JSON_HEDLEY_FLAGS
-#undef JSON_HEDLEY_FLAGS_CAST
-#undef JSON_HEDLEY_GCC_HAS_ATTRIBUTE
-#undef JSON_HEDLEY_GCC_HAS_BUILTIN
-#undef JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE
-#undef JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE
-#undef JSON_HEDLEY_GCC_HAS_EXTENSION
-#undef JSON_HEDLEY_GCC_HAS_FEATURE
-#undef JSON_HEDLEY_GCC_HAS_WARNING
-#undef JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK
-#undef JSON_HEDLEY_GCC_VERSION
-#undef JSON_HEDLEY_GCC_VERSION_CHECK
-#undef JSON_HEDLEY_GNUC_HAS_ATTRIBUTE
-#undef JSON_HEDLEY_GNUC_HAS_BUILTIN
-#undef JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE
-#undef JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE
-#undef JSON_HEDLEY_GNUC_HAS_EXTENSION
-#undef JSON_HEDLEY_GNUC_HAS_FEATURE
-#undef JSON_HEDLEY_GNUC_HAS_WARNING
-#undef JSON_HEDLEY_GNUC_VERSION
-#undef JSON_HEDLEY_GNUC_VERSION_CHECK
-#undef JSON_HEDLEY_HAS_ATTRIBUTE
-#undef JSON_HEDLEY_HAS_BUILTIN
-#undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE
-#undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS
-#undef JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE
-#undef JSON_HEDLEY_HAS_EXTENSION
-#undef JSON_HEDLEY_HAS_FEATURE
-#undef JSON_HEDLEY_HAS_WARNING
-#undef JSON_HEDLEY_IAR_VERSION
-#undef JSON_HEDLEY_IAR_VERSION_CHECK
-#undef JSON_HEDLEY_IBM_VERSION
-#undef JSON_HEDLEY_IBM_VERSION_CHECK
-#undef JSON_HEDLEY_IMPORT
-#undef JSON_HEDLEY_INLINE
-#undef JSON_HEDLEY_INTEL_CL_VERSION
-#undef JSON_HEDLEY_INTEL_CL_VERSION_CHECK
-#undef JSON_HEDLEY_INTEL_VERSION
-#undef JSON_HEDLEY_INTEL_VERSION_CHECK
-#undef JSON_HEDLEY_IS_CONSTANT
-#undef JSON_HEDLEY_IS_CONSTEXPR_
-#undef JSON_HEDLEY_LIKELY
-#undef JSON_HEDLEY_MALLOC
-#undef JSON_HEDLEY_MCST_LCC_VERSION
-#undef JSON_HEDLEY_MCST_LCC_VERSION_CHECK
-#undef JSON_HEDLEY_MESSAGE
-#undef JSON_HEDLEY_MSVC_VERSION
-#undef JSON_HEDLEY_MSVC_VERSION_CHECK
-#undef JSON_HEDLEY_NEVER_INLINE
-#undef JSON_HEDLEY_NON_NULL
-#undef JSON_HEDLEY_NO_ESCAPE
-#undef JSON_HEDLEY_NO_RETURN
-#undef JSON_HEDLEY_NO_THROW
-#undef JSON_HEDLEY_NULL
-#undef JSON_HEDLEY_PELLES_VERSION
-#undef JSON_HEDLEY_PELLES_VERSION_CHECK
-#undef JSON_HEDLEY_PGI_VERSION
-#undef JSON_HEDLEY_PGI_VERSION_CHECK
-#undef JSON_HEDLEY_PREDICT
-#undef JSON_HEDLEY_PRINTF_FORMAT
-#undef JSON_HEDLEY_PRIVATE
-#undef JSON_HEDLEY_PUBLIC
-#undef JSON_HEDLEY_PURE
-#undef JSON_HEDLEY_REINTERPRET_CAST
-#undef JSON_HEDLEY_REQUIRE
-#undef JSON_HEDLEY_REQUIRE_CONSTEXPR
-#undef JSON_HEDLEY_REQUIRE_MSG
-#undef JSON_HEDLEY_RESTRICT
-#undef JSON_HEDLEY_RETURNS_NON_NULL
-#undef JSON_HEDLEY_SENTINEL
-#undef JSON_HEDLEY_STATIC_ASSERT
-#undef JSON_HEDLEY_STATIC_CAST
-#undef JSON_HEDLEY_STRINGIFY
-#undef JSON_HEDLEY_STRINGIFY_EX
-#undef JSON_HEDLEY_SUNPRO_VERSION
-#undef JSON_HEDLEY_SUNPRO_VERSION_CHECK
-#undef JSON_HEDLEY_TINYC_VERSION
-#undef JSON_HEDLEY_TINYC_VERSION_CHECK
-#undef JSON_HEDLEY_TI_ARMCL_VERSION
-#undef JSON_HEDLEY_TI_ARMCL_VERSION_CHECK
-#undef JSON_HEDLEY_TI_CL2000_VERSION
-#undef JSON_HEDLEY_TI_CL2000_VERSION_CHECK
-#undef JSON_HEDLEY_TI_CL430_VERSION
-#undef JSON_HEDLEY_TI_CL430_VERSION_CHECK
-#undef JSON_HEDLEY_TI_CL6X_VERSION
-#undef JSON_HEDLEY_TI_CL6X_VERSION_CHECK
-#undef JSON_HEDLEY_TI_CL7X_VERSION
-#undef JSON_HEDLEY_TI_CL7X_VERSION_CHECK
-#undef JSON_HEDLEY_TI_CLPRU_VERSION
-#undef JSON_HEDLEY_TI_CLPRU_VERSION_CHECK
-#undef JSON_HEDLEY_TI_VERSION
-#undef JSON_HEDLEY_TI_VERSION_CHECK
-#undef JSON_HEDLEY_UNAVAILABLE
-#undef JSON_HEDLEY_UNLIKELY
-#undef JSON_HEDLEY_UNPREDICTABLE
-#undef JSON_HEDLEY_UNREACHABLE
-#undef JSON_HEDLEY_UNREACHABLE_RETURN
-#undef JSON_HEDLEY_VERSION
-#undef JSON_HEDLEY_VERSION_DECODE_MAJOR
-#undef JSON_HEDLEY_VERSION_DECODE_MINOR
-#undef JSON_HEDLEY_VERSION_DECODE_REVISION
-#undef JSON_HEDLEY_VERSION_ENCODE
-#undef JSON_HEDLEY_WARNING
-#undef JSON_HEDLEY_WARN_UNUSED_RESULT
-#undef JSON_HEDLEY_WARN_UNUSED_RESULT_MSG
-#undef JSON_HEDLEY_FALL_THROUGH
-
-
-
-#endif  // INCLUDE_NLOHMANN_JSON_HPP_
diff --git a/spaces/Jessi05/Gege30/README.md b/spaces/Jessi05/Gege30/README.md
deleted file mode 100644
index 0bb8c9e24b99241b0f71dd4e5739552d410cf26f..0000000000000000000000000000000000000000
--- a/spaces/Jessi05/Gege30/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Gege30
-emoji: 💻
-colorFrom: gray
-colorTo: red
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kaori1707/Depth-estimation/dpt/__init__.py b/spaces/Kaori1707/Depth-estimation/dpt/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/utils/__init__.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/utils/__init__.py
deleted file mode 100644
index 5ae3e48110e61231acf1e666e5fa76af5e4ebdcd..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/utils/__init__.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import torch
-
-
-_output_ref = None
-_replicas_ref = None
-
-def data_parallel_workaround(model, *input):
-    global _output_ref
-    global _replicas_ref
-    device_ids = list(range(torch.cuda.device_count()))
-    output_device = device_ids[0]
-    replicas = torch.nn.parallel.replicate(model, device_ids)
-    # input.shape = (num_args, batch, ...)
-    inputs = torch.nn.parallel.scatter(input, device_ids)
-    # inputs.shape = (num_gpus, num_args, batch/num_gpus, ...)
-    replicas = replicas[:len(inputs)]
-    outputs = torch.nn.parallel.parallel_apply(replicas, inputs)
-    y_hat = torch.nn.parallel.gather(outputs, output_device)
-    _output_ref = outputs
-    _replicas_ref = replicas
-    return y_hat
-
-
-class ValueWindow():
-  def __init__(self, window_size=100):
-    self._window_size = window_size
-    self._values = []
-
-  def append(self, x):
-    self._values = self._values[-(self._window_size - 1):] + [x]
-
-  @property
-  def sum(self):
-    return sum(self._values)
-
-  @property
-  def count(self):
-    return len(self._values)
-
-  @property
-  def average(self):
-    return self.sum / max(1, self.count)
-
-  def reset(self):
-    self._values = []
diff --git a/spaces/Kororinpa/Amadeus_Project/text/__init__.py b/spaces/Kororinpa/Amadeus_Project/text/__init__.py
deleted file mode 100644
index 48ae82f3e40ecd1bf17a7de78d87790327af3362..0000000000000000000000000000000000000000
--- a/spaces/Kororinpa/Amadeus_Project/text/__init__.py
+++ /dev/null
@@ -1,56 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def cleaned_text_to_sequence(cleaned_text):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = [_symbol_to_id[symbol] for symbol in cleaned_text if symbol in _symbol_to_id.keys()]
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = _id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/necks/dilated_encoder.py b/spaces/KyanChen/RSPrompter/mmdet/models/necks/dilated_encoder.py
deleted file mode 100644
index e9beb3ea9b4289da8d0100ae7759927f045829bb..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/necks/dilated_encoder.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-from mmcv.cnn import ConvModule, is_norm
-from mmengine.model import caffe2_xavier_init, constant_init, normal_init
-from torch.nn import BatchNorm2d
-
-from mmdet.registry import MODELS
-
-
-class Bottleneck(nn.Module):
-    """Bottleneck block for DilatedEncoder used in `YOLOF.
-
-    <https://arxiv.org/abs/2103.09460>`.
-
-    The Bottleneck contains three ConvLayers and one residual connection.
-
-    Args:
-        in_channels (int): The number of input channels.
-        mid_channels (int): The number of middle output channels.
-        dilation (int): Dilation rate.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 mid_channels,
-                 dilation,
-                 norm_cfg=dict(type='BN', requires_grad=True)):
-        super(Bottleneck, self).__init__()
-        self.conv1 = ConvModule(
-            in_channels, mid_channels, 1, norm_cfg=norm_cfg)
-        self.conv2 = ConvModule(
-            mid_channels,
-            mid_channels,
-            3,
-            padding=dilation,
-            dilation=dilation,
-            norm_cfg=norm_cfg)
-        self.conv3 = ConvModule(
-            mid_channels, in_channels, 1, norm_cfg=norm_cfg)
-
-    def forward(self, x):
-        identity = x
-        out = self.conv1(x)
-        out = self.conv2(out)
-        out = self.conv3(out)
-        out = out + identity
-        return out
-
-
-@MODELS.register_module()
-class DilatedEncoder(nn.Module):
-    """Dilated Encoder for YOLOF <https://arxiv.org/abs/2103.09460>`.
-
-    This module contains two types of components:
-        - the original FPN lateral convolution layer and fpn convolution layer,
-              which are 1x1 conv + 3x3 conv
-        - the dilated residual block
-
-    Args:
-        in_channels (int): The number of input channels.
-        out_channels (int): The number of output channels.
-        block_mid_channels (int): The number of middle block output channels
-        num_residual_blocks (int): The number of residual blocks.
-        block_dilations (list): The list of residual blocks dilation.
-    """
-
-    def __init__(self, in_channels, out_channels, block_mid_channels,
-                 num_residual_blocks, block_dilations):
-        super(DilatedEncoder, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.block_mid_channels = block_mid_channels
-        self.num_residual_blocks = num_residual_blocks
-        self.block_dilations = block_dilations
-        self._init_layers()
-
-    def _init_layers(self):
-        self.lateral_conv = nn.Conv2d(
-            self.in_channels, self.out_channels, kernel_size=1)
-        self.lateral_norm = BatchNorm2d(self.out_channels)
-        self.fpn_conv = nn.Conv2d(
-            self.out_channels, self.out_channels, kernel_size=3, padding=1)
-        self.fpn_norm = BatchNorm2d(self.out_channels)
-        encoder_blocks = []
-        for i in range(self.num_residual_blocks):
-            dilation = self.block_dilations[i]
-            encoder_blocks.append(
-                Bottleneck(
-                    self.out_channels,
-                    self.block_mid_channels,
-                    dilation=dilation))
-        self.dilated_encoder_blocks = nn.Sequential(*encoder_blocks)
-
-    def init_weights(self):
-        caffe2_xavier_init(self.lateral_conv)
-        caffe2_xavier_init(self.fpn_conv)
-        for m in [self.lateral_norm, self.fpn_norm]:
-            constant_init(m, 1)
-        for m in self.dilated_encoder_blocks.modules():
-            if isinstance(m, nn.Conv2d):
-                normal_init(m, mean=0, std=0.01)
-            if is_norm(m):
-                constant_init(m, 1)
-
-    def forward(self, feature):
-        out = self.lateral_norm(self.lateral_conv(feature[-1]))
-        out = self.fpn_norm(self.fpn_conv(out))
-        return self.dilated_encoder_blocks(out),
diff --git a/spaces/KyanChen/RSPrompter/mmdet/utils/typing_utils.py b/spaces/KyanChen/RSPrompter/mmdet/utils/typing_utils.py
deleted file mode 100644
index 6caf6de53274594e139dbe7c1973c747229bf010..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/utils/typing_utils.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-"""Collecting some commonly used type hint in mmdetection."""
-from typing import List, Optional, Sequence, Tuple, Union
-
-from mmengine.config import ConfigDict
-from mmengine.structures import InstanceData, PixelData
-
-# TODO: Need to avoid circular import with assigner and sampler
-# Type hint of config data
-ConfigType = Union[ConfigDict, dict]
-OptConfigType = Optional[ConfigType]
-# Type hint of one or more config data
-MultiConfig = Union[ConfigType, List[ConfigType]]
-OptMultiConfig = Optional[MultiConfig]
-
-InstanceList = List[InstanceData]
-OptInstanceList = Optional[InstanceList]
-
-PixelList = List[PixelData]
-OptPixelList = Optional[PixelList]
-
-RangeType = Sequence[Tuple[int, int]]
diff --git a/spaces/Liu-LAB/GPT-academic/request_llm/edge_gpt_free.py b/spaces/Liu-LAB/GPT-academic/request_llm/edge_gpt_free.py
deleted file mode 100644
index 22ff05272b634e8557ceb83501248cc238074366..0000000000000000000000000000000000000000
--- a/spaces/Liu-LAB/GPT-academic/request_llm/edge_gpt_free.py
+++ /dev/null
@@ -1,1125 +0,0 @@
-"""
-========================================================================
-第一部分：来自EdgeGPT.py
-https://github.com/acheong08/EdgeGPT
-========================================================================
-"""
-"""
-Main.py
-"""
-
-import argparse
-import asyncio
-import json
-import os
-import random
-import re
-import ssl
-import sys
-import time
-import uuid
-from enum import Enum
-from pathlib import Path
-from typing import Generator
-from typing import Literal
-from typing import Optional
-from typing import Union
-
-import aiohttp
-import certifi
-import httpx
-from prompt_toolkit import PromptSession
-from prompt_toolkit.auto_suggest import AutoSuggestFromHistory
-from prompt_toolkit.completion import WordCompleter
-from prompt_toolkit.history import InMemoryHistory
-from prompt_toolkit.key_binding import KeyBindings
-from rich.live import Live
-from rich.markdown import Markdown
-
-DELIMITER = "\x1e"
-
-
-# Generate random IP between range 13.104.0.0/14
-FORWARDED_IP = (
-    f"13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}"
-)
-
-HEADERS = {
-    "accept": "application/json",
-    "accept-language": "en-US,en;q=0.9",
-    "content-type": "application/json",
-    "sec-ch-ua": '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"',
-    "sec-ch-ua-arch": '"x86"',
-    "sec-ch-ua-bitness": '"64"',
-    "sec-ch-ua-full-version": '"109.0.1518.78"',
-    "sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
-    "sec-ch-ua-mobile": "?0",
-    "sec-ch-ua-model": "",
-    "sec-ch-ua-platform": '"Windows"',
-    "sec-ch-ua-platform-version": '"15.0.0"',
-    "sec-fetch-dest": "empty",
-    "sec-fetch-mode": "cors",
-    "sec-fetch-site": "same-origin",
-    "x-ms-client-request-id": str(uuid.uuid4()),
-    "x-ms-useragent": "azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32",
-    "Referer": "https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx",
-    "Referrer-Policy": "origin-when-cross-origin",
-    "x-forwarded-for": FORWARDED_IP,
-}
-
-HEADERS_INIT_CONVER = {
-    "authority": "edgeservices.bing.com",
-    "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
-    "accept-language": "en-US,en;q=0.9",
-    "cache-control": "max-age=0",
-    "sec-ch-ua": '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"',
-    "sec-ch-ua-arch": '"x86"',
-    "sec-ch-ua-bitness": '"64"',
-    "sec-ch-ua-full-version": '"110.0.1587.69"',
-    "sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
-    "sec-ch-ua-mobile": "?0",
-    "sec-ch-ua-model": '""',
-    "sec-ch-ua-platform": '"Windows"',
-    "sec-ch-ua-platform-version": '"15.0.0"',
-    "sec-fetch-dest": "document",
-    "sec-fetch-mode": "navigate",
-    "sec-fetch-site": "none",
-    "sec-fetch-user": "?1",
-    "upgrade-insecure-requests": "1",
-    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69",
-    "x-edge-shopping-flag": "1",
-    "x-forwarded-for": FORWARDED_IP,
-}
-
-ssl_context = ssl.create_default_context()
-ssl_context.load_verify_locations(certifi.where())
-
-
-class NotAllowedToAccess(Exception):
-    pass
-
-
-class ConversationStyle(Enum):
-    creative = [
-        "nlu_direct_response_filter",
-        "deepleo",
-        "disable_emoji_spoken_text",
-        "responsible_ai_policy_235",
-        "enablemm",
-        "h3imaginative",
-        "travelansgnd",
-        "dv3sugg",
-        "clgalileo",
-        "gencontentv3",
-        "dv3sugg",
-        "responseos",
-        "e2ecachewrite",
-        "cachewriteext",
-        "nodlcpcwrite",
-        "travelansgnd",
-        "nojbfedge",
-    ]
-    balanced = [
-        "nlu_direct_response_filter",
-        "deepleo",
-        "disable_emoji_spoken_text",
-        "responsible_ai_policy_235",
-        "enablemm",
-        "galileo",
-        "dv3sugg",
-        "responseos",
-        "e2ecachewrite",
-        "cachewriteext",
-        "nodlcpcwrite",
-        "travelansgnd",
-        "nojbfedge",
-    ]
-    precise = [
-        "nlu_direct_response_filter",
-        "deepleo",
-        "disable_emoji_spoken_text",
-        "responsible_ai_policy_235",
-        "enablemm",
-        "galileo",
-        "dv3sugg",
-        "responseos",
-        "e2ecachewrite",
-        "cachewriteext",
-        "nodlcpcwrite",
-        "travelansgnd",
-        "h3precise",
-        "clgalileo",
-        "nojbfedge",
-    ]
-
-
-CONVERSATION_STYLE_TYPE = Optional[
-    Union[ConversationStyle, Literal["creative", "balanced", "precise"]]
-]
-
-
-def _append_identifier(msg: dict) -> str:
-    """
-    Appends special character to end of message to identify end of message
-    """
-    # Convert dict to json string
-    return json.dumps(msg, ensure_ascii=False) + DELIMITER
-
-
-def _get_ran_hex(length: int = 32) -> str:
-    """
-    Returns random hex string
-    """
-    return "".join(random.choice("0123456789abcdef") for _ in range(length))
-
-
-class _ChatHubRequest:
-    """
-    Request object for ChatHub
-    """
-
-    def __init__(
-        self,
-        conversation_signature: str,
-        client_id: str,
-        conversation_id: str,
-        invocation_id: int = 0,
-    ) -> None:
-        self.struct: dict = {}
-
-        self.client_id: str = client_id
-        self.conversation_id: str = conversation_id
-        self.conversation_signature: str = conversation_signature
-        self.invocation_id: int = invocation_id
-
-    def update(
-        self,
-        prompt: str,
-        conversation_style: CONVERSATION_STYLE_TYPE,
-        options = None,
-        webpage_context = None,
-        search_result = False,
-    ) -> None:
-        """
-        Updates request object
-        """
-        if options is None:
-            options = [
-                "deepleo",
-                "enable_debug_commands",
-                "disable_emoji_spoken_text",
-                "enablemm",
-            ]
-        if conversation_style:
-            if not isinstance(conversation_style, ConversationStyle):
-                conversation_style = getattr(ConversationStyle, conversation_style)
-            options = conversation_style.value
-        self.struct = {
-            "arguments": [
-                {
-                    "source": "cib",
-                    "optionsSets": options,
-                    "allowedMessageTypes": [
-                        "Chat",
-                        "Disengaged",
-                        "AdsQuery",
-                        "SemanticSerp",
-                        "GenerateContentQuery",
-                        "SearchQuery",
-                    ],
-                    "sliceIds": [
-                        "chk1cf",
-                        "nopreloadsscf",
-                        "winlongmsg2tf",
-                        "perfimpcomb",
-                        "sugdivdis",
-                        "sydnoinputt",
-                        "wpcssopt",
-                        "wintone2tf",
-                        "0404sydicnbs0",
-                        "405suggbs0",
-                        "scctl",
-                        "330uaugs0",
-                        "0329resp",
-                        "udscahrfon",
-                        "udstrblm5",
-                        "404e2ewrt",
-                        "408nodedups0",
-                        "403tvlansgnd",
-                    ],
-                    "traceId": _get_ran_hex(32),
-                    "isStartOfSession": self.invocation_id == 0,
-                    "message": {
-                        "author": "user",
-                        "inputMethod": "Keyboard",
-                        "text": prompt,
-                        "messageType": "Chat",
-                    },
-                    "conversationSignature": self.conversation_signature,
-                    "participant": {
-                        "id": self.client_id,
-                    },
-                    "conversationId": self.conversation_id,
-                },
-            ],
-            "invocationId": str(self.invocation_id),
-            "target": "chat",
-            "type": 4,
-        }
-        if search_result:
-            have_search_result = [
-                "InternalSearchQuery",
-                "InternalSearchResult",
-                "InternalLoaderMessage",
-                "RenderCardRequest",
-            ]
-            self.struct["arguments"][0]["allowedMessageTypes"] += have_search_result
-        if webpage_context:
-            self.struct["arguments"][0]["previousMessages"] = [
-                {
-                    "author": "user",
-                    "description": webpage_context,
-                    "contextType": "WebPage",
-                    "messageType": "Context",
-                    "messageId": "discover-web--page-ping-mriduna-----",
-                },
-            ]
-        self.invocation_id += 1
-
-
-class _Conversation:
-    """
-    Conversation API
-    """
-
-    def __init__(
-        self,
-        proxy = None,
-        async_mode = False,
-        cookies = None,
-    ) -> None:
-        if async_mode:
-            return
-        self.struct: dict = {
-            "conversationId": None,
-            "clientId": None,
-            "conversationSignature": None,
-            "result": {"value": "Success", "message": None},
-        }
-        self.proxy = proxy
-        proxy = (
-            proxy
-            or os.environ.get("all_proxy")
-            or os.environ.get("ALL_PROXY")
-            or os.environ.get("https_proxy")
-            or os.environ.get("HTTPS_PROXY")
-            or None
-        )
-        if proxy is not None and proxy.startswith("socks5h://"):
-            proxy = "socks5://" + proxy[len("socks5h://") :]
-        self.session = httpx.Client(
-            proxies=proxy,
-            timeout=30,
-            headers=HEADERS_INIT_CONVER,
-        )
-        if cookies:
-            for cookie in cookies:
-                self.session.cookies.set(cookie["name"], cookie["value"])
-        # Send GET request
-        response = self.session.get(
-            url=os.environ.get("BING_PROXY_URL")
-            or "https://edgeservices.bing.com/edgesvc/turing/conversation/create",
-        )
-        if response.status_code != 200:
-            response = self.session.get(
-                "https://edge.churchless.tech/edgesvc/turing/conversation/create",
-            )
-        if response.status_code != 200:
-            print(f"Status code: {response.status_code}")
-            print(response.text)
-            print(response.url)
-            raise Exception("Authentication failed")
-        try:
-            self.struct = response.json()
-        except (json.decoder.JSONDecodeError, NotAllowedToAccess) as exc:
-            raise Exception(
-                "Authentication failed. You have not been accepted into the beta.",
-            ) from exc
-        if self.struct["result"]["value"] == "UnauthorizedRequest":
-            raise NotAllowedToAccess(self.struct["result"]["message"])
-
-    @staticmethod
-    async def create(
-        proxy = None,
-        cookies = None,
-    ):
-        self = _Conversation(async_mode=True)
-        self.struct = {
-            "conversationId": None,
-            "clientId": None,
-            "conversationSignature": None,
-            "result": {"value": "Success", "message": None},
-        }
-        self.proxy = proxy
-        proxy = (
-            proxy
-            or os.environ.get("all_proxy")
-            or os.environ.get("ALL_PROXY")
-            or os.environ.get("https_proxy")
-            or os.environ.get("HTTPS_PROXY")
-            or None
-        )
-        if proxy is not None and proxy.startswith("socks5h://"):
-            proxy = "socks5://" + proxy[len("socks5h://") :]
-        transport = httpx.AsyncHTTPTransport(retries=10)
-        # Convert cookie format to httpx format
-        formatted_cookies = None
-        if cookies:
-            formatted_cookies = httpx.Cookies()
-            for cookie in cookies:
-                formatted_cookies.set(cookie["name"], cookie["value"])
-        async with httpx.AsyncClient(
-            proxies=proxy,
-            timeout=30,
-            headers=HEADERS_INIT_CONVER,
-            transport=transport,
-            cookies=formatted_cookies,
-        ) as client:
-            # Send GET request
-            response = await client.get(
-                url=os.environ.get("BING_PROXY_URL")
-                or "https://edgeservices.bing.com/edgesvc/turing/conversation/create",
-            )
-            if response.status_code != 200:
-                response = await client.get(
-                    "https://edge.churchless.tech/edgesvc/turing/conversation/create",
-                )
-        if response.status_code != 200:
-            print(f"Status code: {response.status_code}")
-            print(response.text)
-            print(response.url)
-            raise Exception("Authentication failed")
-        try:
-            self.struct = response.json()
-        except (json.decoder.JSONDecodeError, NotAllowedToAccess) as exc:
-            raise Exception(
-                "Authentication failed. You have not been accepted into the beta.",
-            ) from exc
-        if self.struct["result"]["value"] == "UnauthorizedRequest":
-            raise NotAllowedToAccess(self.struct["result"]["message"])
-        return self
-
-
-class _ChatHub:
-    """
-    Chat API
-    """
-
-    def __init__(
-        self,
-        conversation: _Conversation,
-        proxy = None,
-        cookies = None,
-    ) -> None:
-        self.session = None
-        self.wss = None
-        self.request: _ChatHubRequest
-        self.loop: bool
-        self.task: asyncio.Task
-        self.request = _ChatHubRequest(
-            conversation_signature=conversation.struct["conversationSignature"],
-            client_id=conversation.struct["clientId"],
-            conversation_id=conversation.struct["conversationId"],
-        )
-        self.cookies = cookies
-        self.proxy: str = proxy
-
-    async def ask_stream(
-        self,
-        prompt: str,
-        wss_link: str,
-        conversation_style: CONVERSATION_STYLE_TYPE = None,
-        raw: bool = False,
-        options: dict = None,
-        webpage_context = None,
-        search_result: bool = False,
-    ) -> Generator[str, None, None]:
-        """
-        Ask a question to the bot
-        """
-        req_header = HEADERS
-        if self.cookies is not None:
-            ws_cookies = []
-            for cookie in self.cookies:
-                ws_cookies.append(f"{cookie['name']}={cookie['value']}")
-            req_header.update({
-                'Cookie': ';'.join(ws_cookies),
-            })
-            
-        timeout = aiohttp.ClientTimeout(total=30)
-        self.session = aiohttp.ClientSession(timeout=timeout)
-
-        if self.wss and not self.wss.closed:
-            await self.wss.close()
-        # Check if websocket is closed
-        self.wss = await self.session.ws_connect(
-            wss_link,
-            headers=req_header,
-            ssl=ssl_context,
-            proxy=self.proxy,
-            autoping=False,
-        )
-        await self._initial_handshake()
-        if self.request.invocation_id == 0:
-            # Construct a ChatHub request
-            self.request.update(
-                prompt=prompt,
-                conversation_style=conversation_style,
-                options=options,
-                webpage_context=webpage_context,
-                search_result=search_result,
-            )
-        else:
-            async with httpx.AsyncClient() as client:
-                response = await client.post(
-                    "https://sydney.bing.com/sydney/UpdateConversation/",
-                    json={
-                        "messages": [
-                            {
-                                "author": "user",
-                                "description": webpage_context,
-                                "contextType": "WebPage",
-                                "messageType": "Context",
-                            },
-                        ],
-                        "conversationId": self.request.conversation_id,
-                        "source": "cib",
-                        "traceId": _get_ran_hex(32),
-                        "participant": {"id": self.request.client_id},
-                        "conversationSignature": self.request.conversation_signature,
-                    },
-                )
-            if response.status_code != 200:
-                print(f"Status code: {response.status_code}")
-                print(response.text)
-                print(response.url)
-                raise Exception("Update web page context failed")
-            # Construct a ChatHub request
-            self.request.update(
-                prompt=prompt,
-                conversation_style=conversation_style,
-                options=options,
-            )
-        # Send request
-        await self.wss.send_str(_append_identifier(self.request.struct))
-        final = False
-        draw = False
-        resp_txt = ""
-        result_text = ""
-        resp_txt_no_link = ""
-        while not final:
-            msg = await self.wss.receive()
-            try:
-                objects = msg.data.split(DELIMITER)
-            except :
-                continue
-            
-            for obj in objects:
-                if obj is None or not obj:
-                    continue
-                response = json.loads(obj)
-                if response.get("type") != 2 and raw:
-                    yield False, response
-                elif response.get("type") == 1 and response["arguments"][0].get(
-                    "messages",
-                ):
-                    if not draw:
-                        if (
-                            response["arguments"][0]["messages"][0].get("messageType")
-                            == "GenerateContentQuery"
-                        ):
-                            async with ImageGenAsync("", True) as image_generator:
-                                images = await image_generator.get_images(
-                                    response["arguments"][0]["messages"][0]["text"],
-                                )
-                            for i, image in enumerate(images):
-                                resp_txt = resp_txt + f"\n![image{i}]({image})"
-                            draw = True
-                        if (
-                            response["arguments"][0]["messages"][0]["contentOrigin"]
-                            != "Apology"
-                        ) and not draw:
-                            resp_txt = result_text + response["arguments"][0][
-                                "messages"
-                            ][0]["adaptiveCards"][0]["body"][0].get("text", "")
-                            resp_txt_no_link = result_text + response["arguments"][0][
-                                "messages"
-                            ][0].get("text", "")
-                            if response["arguments"][0]["messages"][0].get(
-                                "messageType",
-                            ):
-                                resp_txt = (
-                                    resp_txt
-                                    + response["arguments"][0]["messages"][0][
-                                        "adaptiveCards"
-                                    ][0]["body"][0]["inlines"][0].get("text")
-                                    + "\n"
-                                )
-                                result_text = (
-                                    result_text
-                                    + response["arguments"][0]["messages"][0][
-                                        "adaptiveCards"
-                                    ][0]["body"][0]["inlines"][0].get("text")
-                                    + "\n"
-                                )
-                        yield False, resp_txt
-
-                elif response.get("type") == 2:
-                    if response["item"]["result"].get("error"):
-                        await self.close()
-                        raise Exception(
-                            f"{response['item']['result']['value']}: {response['item']['result']['message']}",
-                        )
-                    if draw:
-                        cache = response["item"]["messages"][1]["adaptiveCards"][0][
-                            "body"
-                        ][0]["text"]
-                        response["item"]["messages"][1]["adaptiveCards"][0]["body"][0][
-                            "text"
-                        ] = (cache + resp_txt)
-                    if (
-                        response["item"]["messages"][-1]["contentOrigin"] == "Apology"
-                        and resp_txt
-                    ):
-                        response["item"]["messages"][-1]["text"] = resp_txt_no_link
-                        response["item"]["messages"][-1]["adaptiveCards"][0]["body"][0][
-                            "text"
-                        ] = resp_txt
-                        print(
-                            "Preserved the message from being deleted",
-                            file=sys.stderr,
-                        )
-                    final = True
-                    await self.close()
-                    yield True, response
-
-    async def _initial_handshake(self) -> None:
-        await self.wss.send_str(_append_identifier({"protocol": "json", "version": 1}))
-        await self.wss.receive()
-
-    async def close(self) -> None:
-        """
-        Close the connection
-        """
-        if self.wss and not self.wss.closed:
-            await self.wss.close()
-        if self.session and not self.session.closed:
-            await self.session.close()
-
-
-class Chatbot:
-    """
-    Combines everything to make it seamless
-    """
-
-    def __init__(
-        self,
-        proxy = None,
-        cookies = None,
-    ) -> None:
-        self.proxy = proxy
-        self.chat_hub: _ChatHub = _ChatHub(
-            _Conversation(self.proxy, cookies=cookies),
-            proxy=self.proxy,
-            cookies=cookies,
-        )
-
-    @staticmethod
-    async def create(
-        proxy = None,
-        cookies = None,
-    ):
-        self = Chatbot.__new__(Chatbot)
-        self.proxy = proxy
-        self.chat_hub = _ChatHub(
-            await _Conversation.create(self.proxy, cookies=cookies),
-            proxy=self.proxy,
-            cookies=cookies,
-        )
-        return self
-
-    async def ask(
-        self,
-        prompt: str,
-        wss_link: str = "wss://sydney.bing.com/sydney/ChatHub",
-        conversation_style: CONVERSATION_STYLE_TYPE = None,
-        options: dict = None,
-        webpage_context = None,
-        search_result: bool = False,
-    ) -> dict:
-        """
-        Ask a question to the bot
-        """
-        async for final, response in self.chat_hub.ask_stream(
-            prompt=prompt,
-            conversation_style=conversation_style,
-            wss_link=wss_link,
-            options=options,
-            webpage_context=webpage_context,
-            search_result=search_result,
-        ):
-            if final:
-                return response
-        await self.chat_hub.wss.close()
-        return {}
-
-    async def ask_stream(
-        self,
-        prompt: str,
-        wss_link: str = "wss://sydney.bing.com/sydney/ChatHub",
-        conversation_style: CONVERSATION_STYLE_TYPE = None,
-        raw: bool = False,
-        options: dict = None,
-        webpage_context = None,
-        search_result: bool = False,
-    ) -> Generator[str, None, None]:
-        """
-        Ask a question to the bot
-        """
-        async for response in self.chat_hub.ask_stream(
-            prompt=prompt,
-            conversation_style=conversation_style,
-            wss_link=wss_link,
-            raw=raw,
-            options=options,
-            webpage_context=webpage_context,
-            search_result=search_result,
-        ):
-            yield response
-
-    async def close(self) -> None:
-        """
-        Close the connection
-        """
-        await self.chat_hub.close()
-
-    async def reset(self) -> None:
-        """
-        Reset the conversation
-        """
-        await self.close()
-        self.chat_hub = _ChatHub(
-            await _Conversation.create(self.proxy),
-            proxy=self.proxy,
-            cookies=self.chat_hub.cookies,
-        )
-
-
-async def _get_input_async(
-    session: PromptSession = None,
-    completer: WordCompleter = None,
-) -> str:
-    """
-    Multiline input function.
-    """
-    return await session.prompt_async(
-        completer=completer,
-        multiline=True,
-        auto_suggest=AutoSuggestFromHistory(),
-    )
-
-
-def _create_session() -> PromptSession:
-    kb = KeyBindings()
-
-    @kb.add("enter")
-    def _(event):
-        buffer_text = event.current_buffer.text
-        if buffer_text.startswith("!"):
-            event.current_buffer.validate_and_handle()
-        else:
-            event.current_buffer.insert_text("\n")
-
-    @kb.add("escape")
-    def _(event):
-        if event.current_buffer.complete_state:
-            # event.current_buffer.cancel_completion()
-            event.current_buffer.text = ""
-
-    return PromptSession(key_bindings=kb, history=InMemoryHistory())
-
-
-def _create_completer(commands: list, pattern_str: str = "$"):
-    return WordCompleter(words=commands, pattern=re.compile(pattern_str))
-
-
-async def async_main(args: argparse.Namespace) -> None:
-    """
-    Main function
-    """
-    print("Initializing...")
-    print("Enter `alt+enter` or `escape+enter` to send a message")
-    # Read and parse cookies
-    cookies = None
-    if args.cookie_file:
-        cookies = json.loads(open(args.cookie_file, encoding="utf-8").read())
-    bot = await Chatbot.create(proxy=args.proxy, cookies=cookies)
-    session = _create_session()
-    completer = _create_completer(["!help", "!exit", "!reset"])
-    initial_prompt = args.prompt
-
-    while True:
-        print("\nYou:")
-        if initial_prompt:
-            question = initial_prompt
-            print(question)
-            initial_prompt = None
-        else:
-            question = (
-                input()
-                if args.enter_once
-                else await _get_input_async(session=session, completer=completer)
-            )
-        print()
-        if question == "!exit":
-            break
-        if question == "!help":
-            print(
-                """
-            !help - Show this help message
-            !exit - Exit the program
-            !reset - Reset the conversation
-            """,
-            )
-            continue
-        if question == "!reset":
-            await bot.reset()
-            continue
-        print("Bot:")
-        if args.no_stream:
-            print(
-                (
-                    await bot.ask(
-                        prompt=question,
-                        conversation_style=args.style,
-                        wss_link=args.wss_link,
-                    )
-                )["item"]["messages"][1]["adaptiveCards"][0]["body"][0]["text"],
-            )
-        else:
-            wrote = 0
-            if args.rich:
-                md = Markdown("")
-                with Live(md, auto_refresh=False) as live:
-                    async for final, response in bot.ask_stream(
-                        prompt=question,
-                        conversation_style=args.style,
-                        wss_link=args.wss_link,
-                    ):
-                        if not final:
-                            if wrote > len(response):
-                                print(md)
-                                print(Markdown("***Bing revoked the response.***"))
-                            wrote = len(response)
-                            md = Markdown(response)
-                            live.update(md, refresh=True)
-            else:
-                async for final, response in bot.ask_stream(
-                    prompt=question,
-                    conversation_style=args.style,
-                    wss_link=args.wss_link,
-                ):
-                    if not final:
-                        if not wrote:
-                            print(response, end="", flush=True)
-                        else:
-                            print(response[wrote:], end="", flush=True)
-                        wrote = len(response)
-                print()
-    await bot.close()
-
-
-def main() -> None:
-    print(
-        """
-        EdgeGPT - A demo of reverse engineering the Bing GPT chatbot
-        Repo: github.com/acheong08/EdgeGPT
-        By: Antonio Cheong
-
-        !help for help
-
-        Type !exit to exit
-    """,
-    )
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--enter-once", action="store_true")
-    parser.add_argument("--no-stream", action="store_true")
-    parser.add_argument("--rich", action="store_true")
-    parser.add_argument(
-        "--proxy",
-        help="Proxy URL (e.g. socks5://127.0.0.1:1080)",
-        type=str,
-    )
-    parser.add_argument(
-        "--wss-link",
-        help="WSS URL(e.g. wss://sydney.bing.com/sydney/ChatHub)",
-        type=str,
-        default="wss://sydney.bing.com/sydney/ChatHub",
-    )
-    parser.add_argument(
-        "--style",
-        choices=["creative", "balanced", "precise"],
-        default="balanced",
-    )
-    parser.add_argument(
-        "--prompt",
-        type=str,
-        default="",
-        required=False,
-        help="prompt to start with",
-    )
-    parser.add_argument(
-        "--cookie-file",
-        type=str,
-        default="",
-        required=False,
-        help="path to cookie file",
-    )
-    args = parser.parse_args()
-    asyncio.run(async_main(args))
-
-
-class Cookie:
-    """
-    Convenience class for Bing Cookie files, data, and configuration. This Class
-    is updated dynamically by the Query class to allow cycling through >1
-    cookie/credentials file e.g. when daily request limits (current 200 per
-    account per day) are exceeded.
-    """
-
-    current_file_index = 0
-    dirpath = Path("./").resolve()
-    search_pattern = "bing_cookies_*.json"
-    ignore_files = set()
-
-    @classmethod
-    def fetch_default(cls, path=None):
-        from selenium import webdriver
-        from selenium.webdriver.common.by import By
-
-        driver = webdriver.Edge()
-        driver.get("https://bing.com/chat")
-        time.sleep(5)
-        xpath = '//button[@id="bnp_btn_accept"]'
-        driver.find_element(By.XPATH, xpath).click()
-        time.sleep(2)
-        xpath = '//a[@id="codexPrimaryButton"]'
-        driver.find_element(By.XPATH, xpath).click()
-        if path is None:
-            path = Path("./bing_cookies__default.json")
-            # Double underscore ensures this file is first when sorted
-        cookies = driver.get_cookies()
-        Path(path).write_text(json.dumps(cookies, indent=4), encoding="utf-8")
-        # Path again in case supplied path is: str
-        print(f"Cookies saved to: {path}")
-        driver.quit()
-
-    @classmethod
-    def files(cls):
-        """Return a sorted list of all cookie files matching .search_pattern"""
-        all_files = set(cls.dirpath.glob(cls.search_pattern))
-        return sorted(list(all_files - cls.ignore_files))
-
-    @classmethod
-    def import_data(cls):
-        """
-        Read the active cookie file and populate the following attributes:
-
-          .current_filepath
-          .current_data
-          .image_token
-        """
-        try:
-            cls.current_filepath = cls.files()[cls.current_file_index]
-        except IndexError:
-            print(
-                "> Please set Cookie.current_filepath to a valid cookie file, then run Cookie.import_data()",
-            )
-            return
-        print(f"> Importing cookies from: {cls.current_filepath.name}")
-        with open(cls.current_filepath, encoding="utf-8") as file:
-            cls.current_data = json.load(file)
-        cls.image_token = [x for x in cls.current_data if x.get("name") == "_U"]
-        cls.image_token = cls.image_token[0].get("value")
-
-    @classmethod
-    def import_next(cls):
-        """
-        Cycle through to the next cookies file.  Import it.  Mark the previous
-        file to be ignored for the remainder of the current session.
-        """
-        cls.ignore_files.add(cls.current_filepath)
-        if Cookie.current_file_index >= len(cls.files()):
-            Cookie.current_file_index = 0
-        Cookie.import_data()
-
-
-class Query:
-    """
-    A convenience class that wraps around EdgeGPT.Chatbot to encapsulate input,
-    config, and output all together.  Relies on Cookie class for authentication
-    """
-
-    def __init__(
-        self,
-        prompt,
-        style="precise",
-        content_type="text",
-        cookie_file=0,
-        echo=True,
-        echo_prompt=False,
-    ):
-        """
-        Arguments:
-
-        prompt: Text to enter into Bing Chat
-        style: creative, balanced, or precise
-        content_type: "text" for Bing Chat; "image" for Dall-e
-        cookie_file: Path, filepath string, or index (int) to list of cookie paths
-        echo: Print something to confirm request made
-        echo_prompt: Print confirmation of the evaluated prompt
-        """
-        self.index = []
-        self.request_count = {}
-        self.image_dirpath = Path("./").resolve()
-        Cookie.import_data()
-        self.index += [self]
-        self.prompt = prompt
-        files = Cookie.files()
-        if isinstance(cookie_file, int):
-            index = cookie_file if cookie_file < len(files) else 0
-        else:
-            if not isinstance(cookie_file, (str, Path)):
-                message = "'cookie_file' must be an int, str, or Path object"
-                raise TypeError(message)
-            cookie_file = Path(cookie_file)
-            if cookie_file in files():  # Supplied filepath IS in Cookie.dirpath
-                index = files.index(cookie_file)
-            else:  # Supplied filepath is NOT in Cookie.dirpath
-                if cookie_file.is_file():
-                    Cookie.dirpath = cookie_file.parent.resolve()
-                if cookie_file.is_dir():
-                    Cookie.dirpath = cookie_file.resolve()
-                index = 0
-        Cookie.current_file_index = index
-        if content_type == "text":
-            self.style = style
-            self.log_and_send_query(echo, echo_prompt)
-        if content_type == "image":
-            self.create_image()
-
-    def log_and_send_query(self, echo, echo_prompt):
-        self.response = asyncio.run(self.send_to_bing(echo, echo_prompt))
-        name = str(Cookie.current_filepath.name)
-        if not self.request_count.get(name):
-            self.request_count[name] = 1
-        else:
-            self.request_count[name] += 1
-
-    def create_image(self):
-        image_generator = ImageGen(Cookie.image_token)
-        image_generator.save_images(
-            image_generator.get_images(self.prompt),
-            output_dir=self.image_dirpath,
-        )
-
-    async def send_to_bing(self, echo=True, echo_prompt=False):
-        """Creat, submit, then close a Chatbot instance.  Return the response"""
-        retries = len(Cookie.files())
-        while retries:
-            try:
-                bot = await Chatbot.create()
-                if echo_prompt:
-                    print(f"> {self.prompt=}")
-                if echo:
-                    print("> Waiting for response...")
-                if self.style.lower() not in "creative balanced precise".split():
-                    self.style = "precise"
-                response = await bot.ask(
-                    prompt=self.prompt,
-                    conversation_style=getattr(ConversationStyle, self.style),
-                    # wss_link="wss://sydney.bing.com/sydney/ChatHub"
-                    # What other values can this parameter take? It seems to be optional
-                )
-                return response
-            except KeyError:
-                print(
-                    f"> KeyError [{Cookie.current_filepath.name} may have exceeded the daily limit]",
-                )
-                Cookie.import_next()
-                retries -= 1
-            finally:
-                await bot.close()
-
-    @property
-    def output(self):
-        """The response from a completed Chatbot request"""
-        return self.response["item"]["messages"][1]["text"]
-
-    @property
-    def sources(self):
-        """The source names and details parsed from a completed Chatbot request"""
-        return self.response["item"]["messages"][1]["sourceAttributions"]
-
-    @property
-    def sources_dict(self):
-        """The source names and details as a dictionary"""
-        sources_dict = {}
-        name = "providerDisplayName"
-        url = "seeMoreUrl"
-        for source in self.sources:
-            if name in source.keys() and url in source.keys():
-                sources_dict[source[name]] = source[url]
-            else:
-                continue
-        return sources_dict
-
-    @property
-    def code(self):
-        """Extract and join any snippets of Python code in the response"""
-        code_blocks = self.output.split("```")[1:-1:2]
-        code_blocks = ["\n".join(x.splitlines()[1:]) for x in code_blocks]
-        return "\n\n".join(code_blocks)
-
-    @property
-    def languages(self):
-        """Extract all programming languages given in code blocks"""
-        code_blocks = self.output.split("```")[1:-1:2]
-        return {x.splitlines()[0] for x in code_blocks}
-
-    @property
-    def suggestions(self):
-        """Follow-on questions suggested by the Chatbot"""
-        return [
-            x["text"]
-            for x in self.response["item"]["messages"][1]["suggestedResponses"]
-        ]
-
-    def __repr__(self):
-        return f"<EdgeGPT.Query: {self.prompt}>"
-
-    def __str__(self):
-        return self.output
-
-
-class ImageQuery(Query):
-    def __init__(self, prompt, **kwargs):
-        kwargs.update({"content_type": "image"})
-        super().__init__(prompt, **kwargs)
-
-    def __repr__(self):
-        return f"<EdgeGPT.ImageQuery: {self.prompt}>"
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/LucasCodeBreak/MusicGen/CONTRIBUTING.md b/spaces/LucasCodeBreak/MusicGen/CONTRIBUTING.md
deleted file mode 100644
index 55b99140204d785d572ada9761dd77f302ae31c6..0000000000000000000000000000000000000000
--- a/spaces/LucasCodeBreak/MusicGen/CONTRIBUTING.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# Contributing to Audiocraft
-
-We want to make contributing to this project as easy and transparent as
-possible.
-
-## Pull Requests
-
-Audiocraft is the implementation of a research paper.
-Therefore, we do not plan on accepting many pull requests for new features.
-We certainly welcome them for bug fixes.
-
-1. Fork the repo and create your branch from `main`.
-2. If you've added code that should be tested, add tests.
-3. If you've changed APIs, update the documentation.
-4. Ensure the test suite passes.
-5. Make sure your code lints.
-6. If you haven't already, complete the Contributor License Agreement ("CLA").
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Meta's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## License
-By contributing to encodec, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Face_Enhancement/models/networks/sync_batchnorm/batchnorm.py b/spaces/MCkernick/Image_Restoration_Colorization/Face_Enhancement/models/networks/sync_batchnorm/batchnorm.py
deleted file mode 100644
index bf8d7a7325b474771a11a137053971fd40426079..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Face_Enhancement/models/networks/sync_batchnorm/batchnorm.py
+++ /dev/null
@@ -1,412 +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 contextlib
-
-import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.batchnorm import _BatchNorm
-
-try:
-    from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
-except ImportError:
-    ReduceAddCoalesced = Broadcast = None
-
-try:
-    from jactorch.parallel.comm import SyncMaster
-    from jactorch.parallel.data_parallel import JacDataParallel as DataParallelWithCallback
-except ImportError:
-    from .comm import SyncMaster
-    from .replicate import DataParallelWithCallback
-
-__all__ = [
-    'set_sbn_eps_mode',
-    'SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d',
-    'patch_sync_batchnorm', 'convert_model'
-]
-
-
-SBN_EPS_MODE = 'clamp'
-
-
-def set_sbn_eps_mode(mode):
-    global SBN_EPS_MODE
-    assert mode in ('clamp', 'plus')
-    SBN_EPS_MODE = mode
-
-
-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 dimensions 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, track_running_stats=True):
-        assert ReduceAddCoalesced is not None, 'Can not use Synchronized Batch Normalization without CUDA support.'
-
-        super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine,
-                                                     track_running_stats=track_running_stats)
-
-        if not self.track_running_stats:
-            import warnings
-            warnings.warn('track_running_stats=False is not supported by the SynchronizedBatchNorm.')
-
-        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()
-        assert input.size(1) == self.num_features, 'Channel size mismatch: got {}, expect {}.'.format(input.size(1), self.num_features)
-        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
-
-        if hasattr(torch, 'no_grad'):
-            with torch.no_grad():
-                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
-        else:
-            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
-
-        if SBN_EPS_MODE == 'clamp':
-            return mean, bias_var.clamp(self.eps) ** -0.5
-        elif SBN_EPS_MODE == 'plus':
-            return mean, (bias_var + self.eps) ** -0.5
-        else:
-            raise ValueError('Unknown EPS mode: {}.'.format(SBN_EPS_MODE))
-
-
-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()))
-
-
-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()))
-
-
-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()))
-
-
-@contextlib.contextmanager
-def patch_sync_batchnorm():
-    import torch.nn as nn
-
-    backup = nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d
-
-    nn.BatchNorm1d = SynchronizedBatchNorm1d
-    nn.BatchNorm2d = SynchronizedBatchNorm2d
-    nn.BatchNorm3d = SynchronizedBatchNorm3d
-
-    yield
-
-    nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d = backup
-
-
-def convert_model(module):
-    """Traverse the input module and its child recursively
-       and replace all instance of torch.nn.modules.batchnorm.BatchNorm*N*d
-       to SynchronizedBatchNorm*N*d
-
-    Args:
-        module: the input module needs to be convert to SyncBN model
-
-    Examples:
-        >>> import torch.nn as nn
-        >>> import torchvision
-        >>> # m is a standard pytorch model
-        >>> m = torchvision.models.resnet18(True)
-        >>> m = nn.DataParallel(m)
-        >>> # after convert, m is using SyncBN
-        >>> m = convert_model(m)
-    """
-    if isinstance(module, torch.nn.DataParallel):
-        mod = module.module
-        mod = convert_model(mod)
-        mod = DataParallelWithCallback(mod, device_ids=module.device_ids)
-        return mod
-
-    mod = module
-    for pth_module, sync_module in zip([torch.nn.modules.batchnorm.BatchNorm1d,
-                                        torch.nn.modules.batchnorm.BatchNorm2d,
-                                        torch.nn.modules.batchnorm.BatchNorm3d],
-                                       [SynchronizedBatchNorm1d,
-                                        SynchronizedBatchNorm2d,
-                                        SynchronizedBatchNorm3d]):
-        if isinstance(module, pth_module):
-            mod = sync_module(module.num_features, module.eps, module.momentum, module.affine)
-            mod.running_mean = module.running_mean
-            mod.running_var = module.running_var
-            if module.affine:
-                mod.weight.data = module.weight.data.clone().detach()
-                mod.bias.data = module.bias.data.clone().detach()
-
-    for name, child in module.named_children():
-        mod.add_module(name, convert_model(child))
-
-    return mod
diff --git a/spaces/MarBeanInc/MarBeanInc/Dockerfile b/spaces/MarBeanInc/MarBeanInc/Dockerfile
deleted file mode 100644
index 3a4dc66fdb50519fca2a6eaf64cbe0ea05b09a3f..0000000000000000000000000000000000000000
--- a/spaces/MarBeanInc/MarBeanInc/Dockerfile
+++ /dev/null
@@ -1,13 +0,0 @@
-FROM python:3.9
-
-WORKDIR /code
-
-COPY ./requirements.txt /code/requirements.txt
-
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-COPY . .
-
-EXPOSE 7860
-
-CMD ["shiny", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/Marne/MockingBird/mockingbirdforuse/synthesizer/utils/cleaners.py b/spaces/Marne/MockingBird/mockingbirdforuse/synthesizer/utils/cleaners.py
deleted file mode 100644
index 4ef017ea68bae6fcda794bd626a69cfd47926e37..0000000000000000000000000000000000000000
--- a/spaces/Marne/MockingBird/mockingbirdforuse/synthesizer/utils/cleaners.py
+++ /dev/null
@@ -1,91 +0,0 @@
-"""
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You"ll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-"""
-
-import re
-from unidecode import unidecode
-from .numbers import normalize_numbers
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r"\s+")
-
-# 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 expand_numbers(text):
-    return normalize_numbers(text)
-
-
-def lowercase(text):
-    """lowercase input tokens."""
-    return text.lower()
-
-
-def collapse_whitespace(text):
-    return re.sub(_whitespace_re, " ", text)
-
-
-def convert_to_ascii(text):
-    return unidecode(text)
-
-
-def basic_cleaners(text):
-    """Basic pipeline that lowercases and collapses whitespace without transliteration."""
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def transliteration_cleaners(text):
-    """Pipeline for non-English text that transliterates to ASCII."""
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def english_cleaners(text):
-    """Pipeline for English text, including number and abbreviation expansion."""
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = expand_numbers(text)
-    text = expand_abbreviations(text)
-    text = collapse_whitespace(text)
-    return text
diff --git a/spaces/MathysL/AutoGPT4/autogpt/commands/times.py b/spaces/MathysL/AutoGPT4/autogpt/commands/times.py
deleted file mode 100644
index 3c9b8a4fc67a251c9e81a8c4a725cd1e25fcbebe..0000000000000000000000000000000000000000
--- a/spaces/MathysL/AutoGPT4/autogpt/commands/times.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from datetime import datetime
-
-
-def get_datetime() -> str:
-    """Return the current date and time
-
-    Returns:
-        str: The current date and time
-    """
-    return "Current date and time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")
diff --git a/spaces/Mikan1103/anime-remove-background/app.py b/spaces/Mikan1103/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/Mikan1103/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/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/mesh_util.py b/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/mesh_util.py
deleted file mode 100644
index 39934219011401e194c61cc00034b12dad4072d3..0000000000000000000000000000000000000000
--- a/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/mesh_util.py
+++ /dev/null
@@ -1,91 +0,0 @@
-from skimage import measure
-import numpy as np
-import torch
-from .sdf import create_grid, eval_grid_octree, eval_grid
-from skimage import measure
-
-
-def reconstruction(net, cuda, calib_tensor,
-                   resolution, b_min, b_max,
-                   use_octree=False, num_samples=10000, transform=None):
-    '''
-    Reconstruct meshes from sdf predicted by the network.
-    :param net: a BasePixImpNet object. call image filter beforehead.
-    :param cuda: cuda device
-    :param calib_tensor: calibration tensor
-    :param resolution: resolution of the grid cell
-    :param b_min: bounding box corner [x_min, y_min, z_min]
-    :param b_max: bounding box corner [x_max, y_max, z_max]
-    :param use_octree: whether to use octree acceleration
-    :param num_samples: how many points to query each gpu iteration
-    :return: marching cubes results.
-    '''
-    # First we create a grid by resolution
-    # and transforming matrix for grid coordinates to real world xyz
-    coords, mat = create_grid(resolution, resolution, resolution,
-                              b_min, b_max, transform=transform)
-
-    # Then we define the lambda function for cell evaluation
-    def eval_func(points):
-        points = np.expand_dims(points, axis=0)
-        points = np.repeat(points, net.num_views, axis=0)
-        samples = torch.from_numpy(points).to(device=cuda).float()
-        net.query(samples, calib_tensor)
-        pred = net.get_preds()[0][0]
-        return pred.detach().cpu().numpy()
-
-    # Then we evaluate the grid
-    if use_octree:
-        sdf = eval_grid_octree(coords, eval_func, num_samples=num_samples)
-    else:
-        sdf = eval_grid(coords, eval_func, num_samples=num_samples)
-
-    # Finally we do marching cubes
-    try:
-        verts, faces, normals, values = measure.marching_cubes_lewiner(sdf, 0.5)
-        # transform verts into world coordinate system
-        verts = np.matmul(mat[:3, :3], verts.T) + mat[:3, 3:4]
-        verts = verts.T
-        return verts, faces, normals, values
-    except:
-        print('error cannot marching cubes')
-        return -1
-
-
-def save_obj_mesh(mesh_path, verts, faces):
-    file = open(mesh_path, 'w')
-
-    for v in verts:
-        file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
-    for f in faces:
-        f_plus = f + 1
-        file.write('f %d %d %d\n' % (f_plus[0], f_plus[2], f_plus[1]))
-    file.close()
-
-
-def save_obj_mesh_with_color(mesh_path, verts, faces, colors):
-    file = open(mesh_path, 'w')
-
-    for idx, v in enumerate(verts):
-        c = colors[idx]
-        file.write('v %.4f %.4f %.4f %.4f %.4f %.4f\n' % (v[0], v[1], v[2], c[0], c[1], c[2]))
-    for f in faces:
-        f_plus = f + 1
-        file.write('f %d %d %d\n' % (f_plus[0], f_plus[2], f_plus[1]))
-    file.close()
-
-
-def save_obj_mesh_with_uv(mesh_path, verts, faces, uvs):
-    file = open(mesh_path, 'w')
-
-    for idx, v in enumerate(verts):
-        vt = uvs[idx]
-        file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
-        file.write('vt %.4f %.4f\n' % (vt[0], vt[1]))
-
-    for f in faces:
-        f_plus = f + 1
-        file.write('f %d/%d %d/%d %d/%d\n' % (f_plus[0], f_plus[0],
-                                              f_plus[2], f_plus[2],
-                                              f_plus[1], f_plus[1]))
-    file.close()
diff --git a/spaces/Mishyface/image-to-video-film-3-kazuk-hugorowan-mishyface/app.py b/spaces/Mishyface/image-to-video-film-3-kazuk-hugorowan-mishyface/app.py
deleted file mode 100644
index 7dbb0a7692a79a4116b7fcd856e4d74c8d03e28a..0000000000000000000000000000000000000000
--- a/spaces/Mishyface/image-to-video-film-3-kazuk-hugorowan-mishyface/app.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-import io, base64
-from PIL import Image
-import numpy as np
-import tensorflow as tf
-import mediapy
-import os
-import sys
-from huggingface_hub import snapshot_download
-from image_tools.sizes import resize_and_crop
-
-os.system("git clone https://github.com/google-research/frame-interpolation")
-sys.path.append("frame-interpolation")
-from eval import interpolator, util
-
-ffmpeg_path = util.get_ffmpeg_path()
-mediapy.set_ffmpeg(ffmpeg_path)
-
-model = snapshot_download(repo_id="akhaliq/frame-interpolation-film-style")
-interpolator = interpolator.Interpolator(model, None)
-
-def resize(width, img):
-    basewidth = width
-    img = Image.open(img)
-    wpercent = (basewidth / float(img.size[0]))
-    hsize = int((float(img.size[1]) * float(wpercent)))
-    img = img.resize((basewidth, hsize), Image.ANTIALIAS)
-    return img
-
-def resize_img(img1, img2, output_name):
-    img_target_size = Image.open(img1)
-    img_to_resize = resize_and_crop(
-        img2,
-        (img_target_size.size[0], img_target_size.size[1]),
-        crop_origin="middle"
-    )
-    img_to_resize.save(output_name)
-
-def generate_interpolation(frame1, frame2, frame3, frame4, frame5, frame6, times_to_interpolate, fps):
-
-    frame1 = resize(256, frame1)
-    frame2 = resize(256, frame2)
-    frame3 = resize(256, frame3)
-    frame4 = resize(256, frame4)
-    frame5 = resize(256, frame5)
-    frame6 = resize(256, frame6)
-
-    frame1.save("test1.png")
-    frame2.save("test2.png")
-    frame3.save("test3.png")
-    frame4.save("test4.png")
-    frame5.save("test5.png")
-    frame6.save("test6.png")
-
-    resize_img("test1.png", "test2.png", "resized_img2.png")
-    resize_img("test1.png", "test3.png", "resized_img3.png")
-    resize_img("test1.png", "test4.png", "resized_img4.png")
-    resize_img("test1.png", "test5.png", "resized_img5.png")
-    resize_img("test1.png", "test6.png", "resized_img6.png")
-
-    input_frames = ["test1.png", "resized_img2.png", "resized_img3.png", "resized_img4.png", "resized_img5.png", "resized_img6.png"]
-
-    frames = list(util.interpolate_recursively_from_files(input_frames, times_to_interpolate, interpolator))
-
-    mediapy.write_video("out.mp4", frames, fps=fps)
-    
-    return "out.mp4"
-
-demo = gr.Blocks()
-
-with demo:
-    with gr.Row():
-    
-        # Left column (inputs)
-        with gr.Column():
-
-            with gr.Row():
-                # upload images and get image strings
-                input_arr = [
-                    gr.inputs.Image(type='filepath', label="Frame 1"),
-                    gr.inputs.Image(type='filepath', label="Frame 2"),
-                    gr.inputs.Image(type='filepath', label="Frame 3"),
-                    gr.inputs.Image(type='filepath', label="Frame 4"),
-                    gr.inputs.Image(type='filepath', label="Frame 5"),
-                    gr.inputs.Image(type='filepath', label="Frame 6"),
-                ]
-
-            with gr.Row():
-                input_arr.append(gr.inputs.Slider(minimum=2, maximum=10, step=1, label="Times to Interpolate"))
-                input_arr.append(gr.inputs.Slider(minimum=15, maximum=60, step=1, label="fps"))
-                        
-            # Rows of instructions & buttons
-            with gr.Row():
-                gr.Markdown("After uploading some images, hit the 'Generate Video' button to create a short video!")
-                button_gen_video = gr.Button("Generate Video")
-
-                
-        # Right column (outputs)
-        with gr.Column():
-            output_interpolation = gr.Video(label="Generated Video")
-            
-    # Bind functions to buttons
-    button_gen_video.click(fn=generate_interpolation, inputs=input_arr, outputs=output_interpolation)
-
-demo.launch(debug=True, enable_queue=True)
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/decoders/master_decoder.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/decoders/master_decoder.py
deleted file mode 100644
index b92b4fc8f4538e1dd9f2485509e27f7036532ce7..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/decoders/master_decoder.py
+++ /dev/null
@@ -1,275 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import math
-from typing import Dict, Optional, Sequence, Union
-
-import torch
-import torch.nn as nn
-from mmcv.cnn.bricks.transformer import BaseTransformerLayer
-from mmengine.model import ModuleList
-
-from mmocr.models.common.dictionary import Dictionary
-from mmocr.models.common.modules import PositionalEncoding
-from mmocr.registry import MODELS
-from mmocr.structures import TextRecogDataSample
-from .base import BaseDecoder
-
-
-def clones(module: nn.Module, N: int) -> nn.ModuleList:
-    """Produce N identical layers.
-
-    Args:
-        module (nn.Module): A pytorch nn.module.
-        N (int): Number of copies.
-
-    Returns:
-        nn.ModuleList: A pytorch nn.ModuleList with the copies.
-    """
-    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
-
-
-class Embeddings(nn.Module):
-    """Construct the word embeddings given vocab size and embed dim.
-
-    Args:
-        d_model (int): The embedding dimension.
-        vocab (int): Vocablury size.
-    """
-
-    def __init__(self, d_model: int, vocab: int):
-        super().__init__()
-        self.lut = nn.Embedding(vocab, d_model)
-        self.d_model = d_model
-
-    def forward(self, *input: torch.Tensor) -> torch.Tensor:
-        """Forward the embeddings.
-
-        Args:
-            input (torch.Tensor): The input tensors.
-
-        Returns:
-            torch.Tensor: The embeddings.
-        """
-        x = input[0]
-        return self.lut(x) * math.sqrt(self.d_model)
-
-
-@MODELS.register_module()
-class MasterDecoder(BaseDecoder):
-    """Decoder module in `MASTER <https://arxiv.org/abs/1910.02562>`_.
-
-    Code is partially modified from https://github.com/wenwenyu/MASTER-pytorch.
-
-    Args:
-        n_layers (int): Number of attention layers. Defaults to 3.
-        n_head (int): Number of parallel attention heads. Defaults to 8.
-        d_model (int): Dimension :math:`E` of the input from previous model.
-            Defaults to 512.
-        feat_size (int): The size of the input feature from previous model,
-            usually :math:`H * W`. Defaults to 6 * 40.
-        d_inner (int): Hidden dimension of feedforward layers.
-            Defaults to 2048.
-        attn_drop (float): Dropout rate of the attention layer. Defaults to 0.
-        ffn_drop (float): Dropout rate of the feedforward layer. Defaults to 0.
-        feat_pe_drop (float): Dropout rate of the feature positional encoding
-            layer. Defaults to 0.2.
-        dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
-            the instance of `Dictionary`. Defaults to None.
-        module_loss (dict, optional): Config to build module_loss. Defaults
-            to None.
-        postprocessor (dict, optional): Config to build postprocessor.
-            Defaults to None.
-        max_seq_len (int): Maximum output sequence length :math:`T`. Defaults
-            to 30.
-        init_cfg (dict or list[dict], optional): Initialization configs.
-    """
-
-    def __init__(
-        self,
-        n_layers: int = 3,
-        n_head: int = 8,
-        d_model: int = 512,
-        feat_size: int = 6 * 40,
-        d_inner: int = 2048,
-        attn_drop: float = 0.,
-        ffn_drop: float = 0.,
-        feat_pe_drop: float = 0.2,
-        module_loss: Optional[Dict] = None,
-        postprocessor: Optional[Dict] = None,
-        dictionary: Optional[Union[Dict, Dictionary]] = None,
-        max_seq_len: int = 30,
-        init_cfg: Optional[Union[Dict, Sequence[Dict]]] = None,
-    ):
-        super().__init__(
-            module_loss=module_loss,
-            postprocessor=postprocessor,
-            dictionary=dictionary,
-            init_cfg=init_cfg,
-            max_seq_len=max_seq_len)
-        operation_order = ('norm', 'self_attn', 'norm', 'cross_attn', 'norm',
-                           'ffn')
-        decoder_layer = BaseTransformerLayer(
-            operation_order=operation_order,
-            attn_cfgs=dict(
-                type='MultiheadAttention',
-                embed_dims=d_model,
-                num_heads=n_head,
-                attn_drop=attn_drop,
-                dropout_layer=dict(type='Dropout', drop_prob=attn_drop),
-            ),
-            ffn_cfgs=dict(
-                type='FFN',
-                embed_dims=d_model,
-                feedforward_channels=d_inner,
-                ffn_drop=ffn_drop,
-                dropout_layer=dict(type='Dropout', drop_prob=ffn_drop),
-            ),
-            norm_cfg=dict(type='LN'),
-            batch_first=True,
-        )
-        self.decoder_layers = ModuleList(
-            [copy.deepcopy(decoder_layer) for _ in range(n_layers)])
-
-        self.cls = nn.Linear(d_model, self.dictionary.num_classes)
-
-        self.SOS = self.dictionary.start_idx
-        self.PAD = self.dictionary.padding_idx
-        self.max_seq_len = max_seq_len
-        self.feat_size = feat_size
-        self.n_head = n_head
-
-        self.embedding = Embeddings(
-            d_model=d_model, vocab=self.dictionary.num_classes)
-
-        # TODO:
-        self.positional_encoding = PositionalEncoding(
-            d_hid=d_model, n_position=self.max_seq_len + 1)
-        self.feat_positional_encoding = PositionalEncoding(
-            d_hid=d_model, n_position=self.feat_size, dropout=feat_pe_drop)
-        self.norm = nn.LayerNorm(d_model)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def make_target_mask(self, tgt: torch.Tensor,
-                         device: torch.device) -> torch.Tensor:
-        """Make target mask for self attention.
-
-        Args:
-            tgt (Tensor): Shape [N, l_tgt]
-            device (torch.device): Mask device.
-
-        Returns:
-            Tensor: Mask of shape [N * self.n_head, l_tgt, l_tgt]
-        """
-
-        trg_pad_mask = (tgt != self.PAD).unsqueeze(1).unsqueeze(3).bool()
-        tgt_len = tgt.size(1)
-        trg_sub_mask = torch.tril(
-            torch.ones((tgt_len, tgt_len), dtype=torch.bool, device=device))
-        tgt_mask = trg_pad_mask & trg_sub_mask
-
-        # inverse for mmcv's BaseTransformerLayer
-        tril_mask = tgt_mask.clone()
-        tgt_mask = tgt_mask.float().masked_fill_(tril_mask == 0, -1e9)
-        tgt_mask = tgt_mask.masked_fill_(tril_mask, 0)
-        tgt_mask = tgt_mask.repeat(1, self.n_head, 1, 1)
-        tgt_mask = tgt_mask.view(-1, tgt_len, tgt_len)
-        return tgt_mask
-
-    def decode(self, tgt_seq: torch.Tensor, feature: torch.Tensor,
-               src_mask: torch.BoolTensor,
-               tgt_mask: torch.BoolTensor) -> torch.Tensor:
-        """Decode the input sequence.
-
-        Args:
-            tgt_seq (Tensor): Target sequence of shape: math: `(N, T, C)`.
-            feature (Tensor): Input feature map from encoder of
-                shape: math: `(N, C, H, W)`
-            src_mask (BoolTensor): The source mask of shape: math: `(N, H*W)`.
-            tgt_mask (BoolTensor): The target mask of shape: math: `(N, T, T)`.
-
-        Return:
-            Tensor: The decoded sequence.
-        """
-        tgt_seq = self.embedding(tgt_seq)
-        x = self.positional_encoding(tgt_seq)
-        attn_masks = [tgt_mask, src_mask]
-        for layer in self.decoder_layers:
-            x = layer(
-                query=x, key=feature, value=feature, attn_masks=attn_masks)
-        x = self.norm(x)
-        return self.cls(x)
-
-    def forward_train(self,
-                      feat: Optional[torch.Tensor] = None,
-                      out_enc: torch.Tensor = None,
-                      data_samples: Sequence[TextRecogDataSample] = None
-                      ) -> torch.Tensor:
-        """Forward for training. Source mask will not be used here.
-
-        Args:
-            feat (Tensor, optional): Input feature map from backbone.
-            out_enc (Tensor): Unused.
-            data_samples (list[TextRecogDataSample]): Batch of
-                TextRecogDataSample, containing gt_text and valid_ratio
-                information.
-
-        Returns:
-            Tensor: The raw logit tensor. Shape :math:`(N, T, C)` where
-            :math:`C` is ``num_classes``.
-        """
-
-        # flatten 2D feature map
-        if len(feat.shape) > 3:
-            b, c, h, w = feat.shape
-            feat = feat.view(b, c, h * w)
-            feat = feat.permute((0, 2, 1))
-        feat = self.feat_positional_encoding(feat)
-
-        trg_seq = []
-        for target in data_samples:
-            trg_seq.append(target.gt_text.padded_indexes.to(feat.device))
-
-        trg_seq = torch.stack(trg_seq, dim=0)
-
-        src_mask = None
-        tgt_mask = self.make_target_mask(trg_seq, device=feat.device)
-        return self.decode(trg_seq, feat, src_mask, tgt_mask)
-
-    def forward_test(self,
-                     feat: Optional[torch.Tensor] = None,
-                     out_enc: torch.Tensor = None,
-                     data_samples: Sequence[TextRecogDataSample] = None
-                     ) -> torch.Tensor:
-        """Forward for testing.
-
-        Args:
-            feat (Tensor, optional): Input feature map from backbone.
-            out_enc (Tensor): Unused.
-            data_samples (list[TextRecogDataSample]): Unused.
-
-        Returns:
-            Tensor: Character probabilities. of shape
-            :math:`(N, self.max_seq_len, C)` where :math:`C` is
-            ``num_classes``.
-        """
-
-        # flatten 2D feature map
-        if len(feat.shape) > 3:
-            b, c, h, w = feat.shape
-            feat = feat.view(b, c, h * w)
-            feat = feat.permute((0, 2, 1))
-        feat = self.feat_positional_encoding(feat)
-
-        N = feat.shape[0]
-        input = torch.full((N, 1),
-                           self.SOS,
-                           device=feat.device,
-                           dtype=torch.long)
-        output = None
-        for _ in range(self.max_seq_len):
-            target_mask = self.make_target_mask(input, device=feat.device)
-            out = self.decode(input, feat, None, target_mask)
-            output = out
-            _, next_word = torch.max(out, dim=-1)
-            input = torch.cat([input, next_word[:, -1].unsqueeze(-1)], dim=1)
-        return self.softmax(output)
diff --git a/spaces/MoyAI/ProfNet/README.md b/spaces/MoyAI/ProfNet/README.md
deleted file mode 100644
index 26bc52cb80fe062bb7da4d68cc4306c28eca96e3..0000000000000000000000000000000000000000
--- a/spaces/MoyAI/ProfNet/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ProfNet
-emoji: 🏢
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Msp/Invoice_DocQA/README.md b/spaces/Msp/Invoice_DocQA/README.md
deleted file mode 100644
index 8170a9278dc92843f2151d02c9fec47937182cc3..0000000000000000000000000000000000000000
--- a/spaces/Msp/Invoice_DocQA/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: DocQuery for Invoices
-emoji: 💸
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: False
----
diff --git a/spaces/Munderstand/CLIP-Interrogator-3/app.py b/spaces/Munderstand/CLIP-Interrogator-3/app.py
deleted file mode 100644
index 4f75657907fe233ad6df7956886a3d9174439295..0000000000000000000000000000000000000000
--- a/spaces/Munderstand/CLIP-Interrogator-3/app.py
+++ /dev/null
@@ -1,169 +0,0 @@
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-import os, subprocess
-import torch
-
-def setup():
-    install_cmds = [
-        ['pip', 'install', 'ftfy', 'gradio', 'regex', 'tqdm', 'transformers==4.21.2', 'timm', 'fairscale', 'requests'],
-        ['pip', 'install', 'open_clip_torch'],
-        ['pip', 'install', '-e', 'git+https://github.com/pharmapsychotic/BLIP.git@lib#egg=blip'],
-        ['git', 'clone', '-b', 'open-clip', 'https://github.com/pharmapsychotic/clip-interrogator.git']
-    ]
-    for cmd in install_cmds:
-        print(subprocess.run(cmd, stdout=subprocess.PIPE).stdout.decode('utf-8'))
-
-setup()
-
-# download cache files
-print("Download preprocessed cache files...")
-CACHE_URLS = [
-    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_artists.pkl',
-    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_flavors.pkl',
-    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_mediums.pkl',
-    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_movements.pkl',
-    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_trendings.pkl',
-]
-os.makedirs('cache', exist_ok=True)
-for url in CACHE_URLS:
-    print(subprocess.run(['wget', url, '-P', 'cache'], stdout=subprocess.PIPE).stdout.decode('utf-8'))
-
-import sys
-sys.path.append('src/blip')
-sys.path.append('clip-interrogator')
-
-import gradio as gr
-from clip_interrogator import Config, Interrogator
-
-config = Config()
-config.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-config.blip_offload = False if torch.cuda.is_available() else True
-config.chunk_size = 2048
-config.flavor_intermediate_count = 512
-config.blip_num_beams = 64
-
-ci = Interrogator(config)
-
-def inference(image, mode, best_max_flavors):
-    image = image.convert('RGB')
-    if mode == 'best':
-        
-        prompt_result = ci.interrogate(image, max_flavors=int(best_max_flavors))
-        
-        print("mode best: " + prompt_result)
-        
-        return prompt_result, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-    
-    elif mode == 'classic':
-        
-        prompt_result = ci.interrogate_classic(image)
-        
-        print("mode classic: " + prompt_result)
-        
-        return prompt_result, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-    
-    else:
-        
-        prompt_result = ci.interrogate_fast(image)
-        
-        print("mode fast: " + prompt_result)
-        
-        return prompt_result, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-title = """
-    <div style="text-align: center; max-width: 500px; margin: 0 auto;">
-        <div
-        style="
-            display: inline-flex;
-            align-items: center;
-            gap: 0.8rem;
-            font-size: 1.75rem;
-            margin-bottom: 10px;
-        "
-        >
-        <h1 style="font-weight: 600; margin-bottom: 7px;">
-            CLIP Interrogator 2.1
-        </h1>
-        </div>
-        <p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;">
-        Want to figure out what a good prompt might be to create new images like an existing one? 
-        <br />The CLIP Interrogator is here to get you answers!
-        <br />This version is specialized for producing nice prompts for use with Stable Diffusion 2.0 using the ViT-H-14 OpenCLIP model!
-        </p>
-    </div>
-"""
-
-article = """
-<div style="text-align: center; max-width: 500px; margin: 0 auto;font-size: 94%;">
-    
-    <p>
-    Server busy? You can also run on <a href="https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/open-clip/clip_interrogator.ipynb">Google Colab</a>
-    </p>
-    <p>
-    Has this been helpful to you? Follow Pharma on twitter 
-    <a href="https://twitter.com/pharmapsychotic">@pharmapsychotic</a> 
-    and check out more tools at his
-    <a href="https://pharmapsychotic.com/tools.html">Ai generative art tools list</a>
-    </p>
-</div>
-"""
-
-css = '''
-#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-a {text-decoration-line: underline; font-weight: 600;}
-.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;
-}
-#share-btn-container div:nth-child(-n+2){
-    width: auto !important;
-    min-height: 0px !important;
-}
-#share-btn-container .wrap {
-    display: none !important;
-}
-'''
-
-with gr.Blocks(css=css) as block:
-    with gr.Column(elem_id="col-container"):
-        gr.HTML(title)
-
-        input_image = gr.Image(type='pil', elem_id="input-img")
-        mode_input = gr.Radio(['best', 'classic', 'fast'], label='', value='best')
-        flavor_input = gr.Number(value=4, label='best mode max flavors')
-        
-        submit_btn = gr.Button("Submit")
-        
-        output_text = gr.Textbox(label="Output", elem_id="output-txt")
-        
-        with gr.Group(elem_id="share-btn-container"):
-            community_icon = gr.HTML(community_icon_html, visible=False)
-            loading_icon = gr.HTML(loading_icon_html, visible=False)
-            share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-
-        examples=[['27E894C4-9375-48A1-A95D-CB2425416B4B.png', "best",4], ['DB362F56-BA98-4CA1-A999-A25AA94B723B.png',"fast",4]]
-        ex = gr.Examples(examples=examples, fn=inference, inputs=[input_image, mode_input, flavor_input], outputs=[output_text, share_button, community_icon, loading_icon], cache_examples=True, run_on_click=True)
-        ex.dataset.headers = [""]
-        
-        gr.HTML(article)
-
-    submit_btn.click(fn=inference, inputs=[input_image,mode_input,flavor_input], outputs=[output_text, share_button, community_icon, loading_icon], api_name="clipi2")
-    share_button.click(None, [], [], _js=share_js)
-    
-block.queue(max_size=32,concurrency_count=20).launch(show_api=False)
\ No newline at end of file
diff --git a/spaces/NATSpeech/DiffSpeech/modules/commons/layers.py b/spaces/NATSpeech/DiffSpeech/modules/commons/layers.py
deleted file mode 100644
index 88e1c75876050fa05a768a5ae0467fdfc05bb006..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/modules/commons/layers.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-from torch import nn
-
-
-class LayerNorm(torch.nn.LayerNorm):
-    """Layer normalization module.
-    :param int nout: output dim size
-    :param int dim: dimension to be normalized
-    """
-
-    def __init__(self, nout, dim=-1, eps=1e-5):
-        """Construct an LayerNorm object."""
-        super(LayerNorm, self).__init__(nout, eps=eps)
-        self.dim = dim
-
-    def forward(self, x):
-        """Apply layer normalization.
-        :param torch.Tensor x: input tensor
-        :return: layer normalized tensor
-        :rtype torch.Tensor
-        """
-        if self.dim == -1:
-            return super(LayerNorm, self).forward(x)
-        return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
-
-
-class Reshape(nn.Module):
-    def __init__(self, *args):
-        super(Reshape, self).__init__()
-        self.shape = args
-
-    def forward(self, x):
-        return x.view(self.shape)
-
-
-class Permute(nn.Module):
-    def __init__(self, *args):
-        super(Permute, self).__init__()
-        self.args = args
-
-    def forward(self, x):
-        return x.permute(self.args)
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx=None):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
-    if padding_idx is not None:
-        nn.init.constant_(m.weight[padding_idx], 0)
-    return m
diff --git a/spaces/NATSpeech/DiffSpeech/tasks/vocoder/dataset_utils.py b/spaces/NATSpeech/DiffSpeech/tasks/vocoder/dataset_utils.py
deleted file mode 100644
index e399b9e37fde0341c9a52d2cb241b96ce8c8b822..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/tasks/vocoder/dataset_utils.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import numpy as np
-import torch
-import torch.distributed as dist
-from torch.utils.data import DistributedSampler
-from utils.commons.dataset_utils import BaseDataset, collate_1d, collate_2d
-from utils.commons.hparams import hparams
-from utils.commons.indexed_datasets import IndexedDataset
-
-
-class EndlessDistributedSampler(DistributedSampler):
-    def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
-        if num_replicas is None:
-            if not dist.is_available():
-                raise RuntimeError("Requires distributed package to be available")
-            num_replicas = dist.get_world_size()
-        if rank is None:
-            if not dist.is_available():
-                raise RuntimeError("Requires distributed package to be available")
-            rank = dist.get_rank()
-        self.dataset = dataset
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.epoch = 0
-        self.shuffle = shuffle
-
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-        if self.shuffle:
-            indices = [i for _ in range(1000) for i in torch.randperm(
-                len(self.dataset), generator=g).tolist()]
-        else:
-            indices = [i for _ in range(1000) for i in list(range(len(self.dataset)))]
-        indices = indices[:len(indices) // self.num_replicas * self.num_replicas]
-        indices = indices[self.rank::self.num_replicas]
-        self.indices = indices
-
-    def __iter__(self):
-        return iter(self.indices)
-
-    def __len__(self):
-        return len(self.indices)
-
-
-class VocoderDataset(BaseDataset):
-    def __init__(self, prefix, shuffle=False):
-        super().__init__(shuffle)
-        self.hparams = hparams
-        self.prefix = prefix
-        self.data_dir = hparams['binary_data_dir']
-        self.is_infer = prefix == 'test'
-        self.batch_max_frames = 0 if self.is_infer else hparams['max_samples'] // hparams['hop_size']
-        self.hop_size = hparams['hop_size']
-        self.indexed_ds = None
-        self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy')
-        self.avail_idxs = [idx for idx, s in enumerate(self.sizes) if s > self.batch_max_frames]
-        print(f"| {len(self.sizes) - len(self.avail_idxs)} short items are skipped in {prefix} set.")
-        self.sizes = [s for idx, s in enumerate(self.sizes) if s > self.batch_max_frames]
-
-    def _get_item(self, index):
-        if self.indexed_ds is None:
-            self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}')
-        item = self.indexed_ds[index]
-        return item
-
-    def __getitem__(self, index):
-        index = self.avail_idxs[index]
-        item = self._get_item(index)
-        sample = {
-            "id": index,
-            "item_name": item['item_name'],
-            "mel": torch.FloatTensor(item['mel']),
-            "wav": torch.FloatTensor(item['wav'].astype(np.float32)),
-            "pitch": torch.LongTensor(item['pitch']),
-            "f0": torch.FloatTensor(item['f0'])
-        }
-        return sample
-
-    def collater(self, batch):
-        if len(batch) == 0:
-            return {}
-
-        y_batch, c_batch, p_batch, f0_batch = [], [], [], []
-        item_name = []
-        for idx in range(len(batch)):
-            item_name.append(batch[idx]['item_name'])
-            x, c = batch[idx]['wav'], batch[idx]['mel']
-            p, f0 = batch[idx]['pitch'], batch[idx]['f0']
-            self._assert_ready_for_upsampling(x, c, self.hop_size)
-            if len(c) > self.batch_max_frames:
-                # randomly pickup with the batch_max_steps length of the part
-                batch_max_frames = self.batch_max_frames if self.batch_max_frames != 0 else len(c) - 1
-                batch_max_steps = batch_max_frames * self.hop_size
-                interval_start = 0
-                interval_end = len(c) - batch_max_frames
-                start_frame = np.random.randint(interval_start, interval_end)
-                start_step = start_frame * self.hop_size
-                y = x[start_step: start_step + batch_max_steps]
-                c = c[start_frame: start_frame + batch_max_frames]
-                p = p[start_frame: start_frame + batch_max_frames]
-                f0 = f0[start_frame: start_frame + batch_max_frames]
-                self._assert_ready_for_upsampling(y, c, self.hop_size)
-            else:
-                print(f"Removed short sample from batch (length={len(x)}).")
-                continue
-            y_batch += [y.reshape(-1, 1)]  # [(T, 1), (T, 1), ...]
-            c_batch += [c]  # [(T' C), (T' C), ...]
-            p_batch += [p]  # [(T' C), (T' C), ...]
-            f0_batch += [f0]  # [(T' C), (T' C), ...]
-
-        # convert each batch to tensor, asuume that each item in batch has the same length
-        y_batch = collate_2d(y_batch, 0).transpose(2, 1)  # (B, 1, T)
-        c_batch = collate_2d(c_batch, 0).transpose(2, 1)  # (B, C, T')
-        p_batch = collate_1d(p_batch, 0)  # (B, T')
-        f0_batch = collate_1d(f0_batch, 0)  # (B, T')
-
-        # make input noise signal batch tensor
-        z_batch = torch.randn(y_batch.size())  # (B, 1, T)
-        return {
-            'z': z_batch,
-            'mels': c_batch,
-            'wavs': y_batch,
-            'pitches': p_batch,
-            'f0': f0_batch,
-            'item_name': item_name
-        }
-
-    @staticmethod
-    def _assert_ready_for_upsampling(x, c, hop_size):
-        """Assert the audio and feature lengths are correctly adjusted for upsamping."""
-        assert len(x) == (len(c)) * hop_size
diff --git a/spaces/NATSpeech/PortaSpeech/inference/tts/ps_flow.py b/spaces/NATSpeech/PortaSpeech/inference/tts/ps_flow.py
deleted file mode 100644
index 59446cac4743d6526988de4777919f6750c2d820..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/inference/tts/ps_flow.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import torch
-from inference.tts.base_tts_infer import BaseTTSInfer
-from modules.tts.portaspeech.portaspeech_flow import PortaSpeechFlow
-from utils.commons.ckpt_utils import load_ckpt
-from utils.commons.hparams import hparams
-
-
-class PortaSpeechFlowInfer(BaseTTSInfer):
-    def build_model(self):
-        ph_dict_size = len(self.ph_encoder)
-        word_dict_size = len(self.word_encoder)
-        model = PortaSpeechFlow(ph_dict_size, word_dict_size, self.hparams)
-        load_ckpt(model, hparams['work_dir'], 'model')
-        with torch.no_grad():
-            model.store_inverse_all()
-        model.eval()
-        return model
-
-    def forward_model(self, inp):
-        sample = self.input_to_batch(inp)
-        with torch.no_grad():
-            output = self.model(
-                sample['txt_tokens'],
-                sample['word_tokens'],
-                ph2word=sample['ph2word'],
-                word_len=sample['word_lengths'].max(),
-                infer=True,
-                forward_post_glow=True,
-                spk_id=sample.get('spk_ids')
-            )
-            mel_out = output['mel_out']
-            wav_out = self.run_vocoder(mel_out)
-        wav_out = wav_out.cpu().numpy()
-        return wav_out[0]
-
-
-if __name__ == '__main__':
-    PortaSpeechFlowInfer.example_run()
diff --git a/spaces/NMEX/rvc-hoyogame-v2/lib/infer_pack/onnx_inference.py b/spaces/NMEX/rvc-hoyogame-v2/lib/infer_pack/onnx_inference.py
deleted file mode 100644
index 6517853be49e61c427cf7cd9b5ed203f6d5f367e..0000000000000000000000000000000000000000
--- a/spaces/NMEX/rvc-hoyogame-v2/lib/infer_pack/onnx_inference.py
+++ /dev/null
@@ -1,145 +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"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        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 lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-
-        f0_predictor_object = PMF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "harvest":
-        from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import (
-            HarvestF0Predictor,
-        )
-
-        f0_predictor_object = HarvestF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "dio":
-        from lib.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"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        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/NexusInstruments/offensive-hugging-face/offensive-osint-hug.py b/spaces/NexusInstruments/offensive-hugging-face/offensive-osint-hug.py
deleted file mode 100644
index 6ff18dfa7c506632967c5086a56c1b24c95fe599..0000000000000000000000000000000000000000
--- a/spaces/NexusInstruments/offensive-hugging-face/offensive-osint-hug.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-def greet_interface():
-    iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-    iface.launch()
-
-if __name__ == "__main__":
-    greet_interface()
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/paraphraser/paraphrase.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/paraphraser/paraphrase.py
deleted file mode 100644
index d3422fb3db9a381b73a854d2379df214ebe544a2..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/paraphraser/paraphrase.py
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/usr/bin/env python3 -u
-
-import argparse
-import fileinput
-import logging
-import os
-import sys
-
-from fairseq.models.transformer import TransformerModel
-
-
-logging.getLogger().setLevel(logging.INFO)
-
-
-def main():
-    parser = argparse.ArgumentParser(description="")
-    parser.add_argument("--en2fr", required=True, help="path to en2fr model")
-    parser.add_argument(
-        "--fr2en", required=True, help="path to fr2en mixture of experts model"
-    )
-    parser.add_argument(
-        "--user-dir", help="path to fairseq examples/translation_moe/src directory"
-    )
-    parser.add_argument(
-        "--num-experts",
-        type=int,
-        default=10,
-        help="(keep at 10 unless using a different model)",
-    )
-    parser.add_argument(
-        "files",
-        nargs="*",
-        default=["-"],
-        help='input files to paraphrase; "-" for stdin',
-    )
-    args = parser.parse_args()
-
-    if args.user_dir is None:
-        args.user_dir = os.path.join(
-            os.path.dirname(os.path.dirname(os.path.abspath(__file__))),  # examples/
-            "translation_moe",
-            "src",
-        )
-        if os.path.exists(args.user_dir):
-            logging.info("found user_dir:" + args.user_dir)
-        else:
-            raise RuntimeError(
-                "cannot find fairseq examples/translation_moe/src "
-                "(tried looking here: {})".format(args.user_dir)
-            )
-
-    logging.info("loading en2fr model from:" + args.en2fr)
-    en2fr = TransformerModel.from_pretrained(
-        model_name_or_path=args.en2fr,
-        tokenizer="moses",
-        bpe="sentencepiece",
-    ).eval()
-
-    logging.info("loading fr2en model from:" + args.fr2en)
-    fr2en = TransformerModel.from_pretrained(
-        model_name_or_path=args.fr2en,
-        tokenizer="moses",
-        bpe="sentencepiece",
-        user_dir=args.user_dir,
-        task="translation_moe",
-    ).eval()
-
-    def gen_paraphrases(en):
-        fr = en2fr.translate(en)
-        return [
-            fr2en.translate(fr, inference_step_args={"expert": i})
-            for i in range(args.num_experts)
-        ]
-
-    logging.info("Type the input sentence and press return:")
-    for line in fileinput.input(args.files):
-        line = line.strip()
-        if len(line) == 0:
-            continue
-        for paraphrase in gen_paraphrases(line):
-            print(paraphrase)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/simultaneous_translation/tests/test_text_models.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/simultaneous_translation/tests/test_text_models.py
deleted file mode 100644
index 127adfa6337333ba5ae598fcd158956def0d520f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/simultaneous_translation/tests/test_text_models.py
+++ /dev/null
@@ -1,407 +0,0 @@
-import argparse
-import unittest
-from typing import Any, Dict
-
-import torch
-from examples.simultaneous_translation.models import (
-    transformer_monotonic_attention
-)
-
-
-from tests.test_roberta import FakeTask
-
-
-DEFAULT_CONFIG = {
-    "attention_eps": 1e-6,
-    "mass_preservation": True,
-    "noise_type": "flat",
-    "noise_mean": 0.0,
-    "noise_var": 1.0,
-    "energy_bias_init": -2,
-    "energy_bias": True
-}
-
-
-PAD_INDEX = 1
-
-
-def generate_config(overrides_kv):
-    new_dict = {key: value for key, value in DEFAULT_CONFIG.items()}
-    for key, value in overrides_kv.items():
-        new_dict[key] = value
-    return new_dict
-
-
-def make_sample_with_padding(longer_src=False) -> Dict[str, Any]:
-    tokens_1 = torch.LongTensor(
-        [
-            [2, 10, 11, 12, 13, 14, 15, 10, 11, 12, 13, 14, 15, 2],
-            [
-                2, 11, 12, 14, 15, 10, 11, 12, 13, 14, 15, 2,
-                PAD_INDEX, PAD_INDEX
-            ],
-        ]
-    )
-    tokens_2 = torch.LongTensor(
-        [
-            [2, 11, 12, 13, 14, 2, PAD_INDEX, PAD_INDEX],
-            [2, 11, 22, 33, 2, PAD_INDEX, PAD_INDEX, PAD_INDEX]
-        ]
-    )
-    if longer_src:
-        src_tokens = tokens_1[:, 1:]
-        prev_output_tokens = tokens_2
-    else:
-        src_tokens = tokens_2[:, 1:8]
-        prev_output_tokens = tokens_1
-
-    src_lengths = src_tokens.ne(PAD_INDEX).sum(dim=1).long()
-
-    sample = {
-        "net_input": {
-            "src_tokens": src_tokens,
-            "prev_output_tokens": prev_output_tokens,
-            "src_lengths": src_lengths,
-        },
-        "target": prev_output_tokens[:, 1:],
-    }
-    return sample
-
-
-def build_transformer_monotonic_attention(**extra_args: Any):
-    overrides = {
-        # Use characteristics dimensions
-        "encoder_embed_dim": 12,
-        "encoder_ffn_embed_dim": 14,
-        "decoder_embed_dim": 12,
-        "decoder_ffn_embed_dim": 14,
-        # Disable dropout so we have comparable tests.
-        "dropout": 0,
-        "attention_dropout": 0,
-        "activation_dropout": 0,
-        "encoder_layerdrop": 0,
-    }
-    overrides.update(extra_args)
-    # Overrides the defaults from the parser
-    args = argparse.Namespace(**overrides)
-    transformer_monotonic_attention.monotonic_tiny_architecture(args)
-
-    torch.manual_seed(0)
-    task = FakeTask(args)
-    return (
-        transformer_monotonic_attention
-        .TransformerModelSimulTrans
-        .build_model(args, task)
-    )
-
-
-def expected_alignment_formula(
-    p_choose,
-    mass_perservation=True,
-    padding_mask=None
-):
-    # Online and Linear-Time Attention by Enforcing Monotonic Alignments
-    # https://arxiv.org/pdf/1704.00784.pdf
-    # Eq 18, 19
-    bsz, tgt_len, src_len = p_choose.size()
-    alpha = torch.zeros_like(p_choose)
-
-    if padding_mask is not None:
-        bsz_pad = padding_mask.size(0)
-        num_heads = int(bsz / bsz_pad)
-        padding_mask = (
-            padding_mask
-            .unsqueeze(1)
-            .expand([bsz_pad, num_heads, src_len])
-            .contiguous()
-            .view(-1, src_len)
-        )
-
-    p_choose = p_choose.masked_fill(padding_mask.unsqueeze(1), 0)
-
-    for bsz_i in range(bsz):
-        for i in range(tgt_len):
-            for j in range(src_len):
-                if i == 0:
-                    if j == 0:
-                        # First source token
-                        alpha[bsz_i, i, j] = p_choose[bsz_i, i, j]
-                    else:
-                        # First target token
-                        alpha[bsz_i, i, j] = (
-                            p_choose[bsz_i, i, j]
-                            * torch.prod(
-                                1 - p_choose[bsz_i, i, :j]
-                            )
-                        )
-                else:
-                    alpha[bsz_i, i, j] = alpha[bsz_i, i - 1, j]
-                    for k in range(j):
-                        alpha[bsz_i, i, j] += (
-                            alpha[bsz_i, i - 1, k]
-                            * torch.prod(
-                                1 - p_choose[bsz_i, i, k:j]
-                            )
-                        )
-                    alpha[bsz_i, i, j] *= p_choose[bsz_i, i, j]
-
-    alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0)
-
-    if mass_perservation:
-        alpha = mass_perservation_formula(alpha, False, padding_mask)
-
-    return alpha
-
-
-def mass_perservation_formula(alpha, left_padding=False, padding_mask=None):
-    if padding_mask is None or alpha.size(-1) == 1:
-        if alpha.size(-1) > 1:
-            alpha[:, :, -1] = 1 - alpha[:, :, :-1].sum(dim=-1)
-        return alpha
-
-    src_lens = (padding_mask.logical_not()).sum(dim=1).long()
-
-    bsz, tgt_len, src_len = alpha.size()
-
-    assert (
-        not left_padding
-        or (left_padding and (not padding_mask[:, 0].any()))
-    )
-
-    alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0)
-
-    for bsz_i in range(bsz):
-        if left_padding:
-            alpha[bsz_i, :, -1] = (
-                1 - alpha[bsz_i, :, :-1].sum(dim=-1)
-            )
-        else:
-            alpha[bsz_i, :, src_lens[bsz_i] - 1] = (
-                1 - alpha[bsz_i, :, :src_lens[bsz_i] - 1].sum(dim=-1)
-            )
-
-    return alpha
-
-
-def expected_soft_attention_formula(
-    alpha,
-    soft_energy,
-    padding_mask=None,
-    chunksize=1e10,
-):
-    # Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
-    # https://arxiv.org/pdf/1906.05218.pdf
-    # Eq 14
-
-    # Monotonic Chunkwise Attention
-    # https://arxiv.org/abs/1712.05382
-    # Eq 17
-    bsz, tgt_len, src_len = alpha.size()
-    beta = torch.zeros_like(alpha)
-
-    if padding_mask is not None:
-        bsz_pad = padding_mask.size(0)
-        num_heads = int(bsz / bsz_pad)
-        # Expanding for potential head dimension
-        padding_mask = (
-            padding_mask
-            .unsqueeze(1)
-            .expand([bsz_pad, num_heads, src_len])
-            .contiguous()
-            .view(-1, src_len)
-        )
-        soft_energy = soft_energy.masked_fill(padding_mask.unsqueeze(1), float('-inf'))
-
-    for bsz_i in range(bsz):
-        for i in range(tgt_len):
-            for j in range(src_len):
-                for k in range(j, min([src_len, j + chunksize])):
-                    if not padding_mask[bsz_i, j]:
-                        beta[bsz_i, i, j] += (
-                            alpha[bsz_i, i, k] * torch.exp(soft_energy[bsz_i, i, j])
-                            / torch.sum(torch.exp(soft_energy[bsz_i, i, max([0, k - chunksize + 1]):k + 1]))
-                        )
-    return beta
-
-
-class MonotonicAttentionTestAbstractClass(object):
-    def test_forward(self):
-        sample = make_sample_with_padding()
-        out, _ = self.model.forward(**sample["net_input"])
-        loss = out.sum()
-        loss.backward()
-
-    def test_p_choose(self):
-        sample = make_sample_with_padding()
-        _, extra_out = self.model.forward(**sample["net_input"])
-        for item in extra_out.attn_list:
-            p_choose = item["p_choose"]
-            self.assertTrue(p_choose.le(1.0).all())
-            self.assertTrue(p_choose.ge(0.0).all())
-
-    def test_expected_alignment(self):
-        for longer_src in [True, False]:
-            sample = make_sample_with_padding(longer_src)
-            _, extra_out = self.model.forward(**sample["net_input"])
-            for item in extra_out.attn_list:
-                p_choose = item["p_choose"]
-                alpha_system = item["alpha"]
-                self.assertTrue(p_choose.size() == alpha_system.size())
-                bsz, num_head, tgt_len, src_len = alpha_system.size()
-                alpha_system = alpha_system.view(-1, tgt_len, src_len)
-                p_choose = p_choose.view(-1, tgt_len, src_len)
-
-                alpha_real = expected_alignment_formula(
-                    p_choose,
-                    self.model.decoder.layers[0].encoder_attn.mass_preservation,
-                    sample["net_input"]["src_tokens"].eq(PAD_INDEX)
-                )
-
-                self.assertTrue(
-                    torch.abs(alpha_system - alpha_real).le(5e-5).all(),
-                )
-
-
-class HardMonotonicAttentionTestCase(
-    unittest.TestCase,
-    MonotonicAttentionTestAbstractClass
-):
-    def setUp(self):
-        self.model = build_transformer_monotonic_attention(
-            **generate_config({"simul_type": "hard_aligned"})
-        )
-
-
-class InfiniteLookbackTestCase(
-    unittest.TestCase,
-    MonotonicAttentionTestAbstractClass
-):
-    def setUp(self):
-        self.model = build_transformer_monotonic_attention(
-            **generate_config(
-                {
-                    "simul_type": "infinite_lookback"
-                }
-            )
-        )
-        self.model.train()
-
-    def test_fp16_for_long_input(self):
-        sample = {
-            "net_input": {
-                "src_tokens": torch.LongTensor([7] * 1000 + [2]).cuda().unsqueeze(0),
-                "prev_output_tokens": torch.LongTensor([7] * 1000 + [2]).cuda().unsqueeze(0),
-                "src_lengths": torch.LongTensor([1000]).cuda(),
-            },
-            "target": torch.LongTensor([2] + [7] * 1000).unsqueeze(0).cuda()
-        }
-        self.model.cuda().half()
-        _, extra_out = self.model.forward(**sample["net_input"])
-        for item in extra_out.attn_list:
-            for key in ["p_choose", "alpha", "beta", "soft_energy"]:
-                self.assertFalse(torch.isnan(item[key]).any())
-
-    def test_expected_attention(self):
-        for longer_src in [True, False]:
-            sample = make_sample_with_padding(longer_src)
-            _, extra_out = self.model.forward(**sample["net_input"])
-            for item in extra_out.attn_list:
-                p_choose = item["p_choose"]
-                alpha_system = item["alpha"]
-                beta_system = item["beta"]
-                soft_energy_system = item["soft_energy"]
-                self.assertTrue(beta_system.size() == alpha_system.size())
-                self.assertTrue(p_choose.size() == alpha_system.size())
-
-                bsz, num_head, tgt_len, src_len = alpha_system.size()
-
-                alpha_system = alpha_system.view(-1, tgt_len, src_len)
-                beta_system = beta_system.view(-1, tgt_len, src_len)
-                p_choose = p_choose.view(-1, tgt_len, src_len)
-                soft_energy_system = soft_energy_system.view(-1, tgt_len, src_len)
-
-                alpha_real = expected_alignment_formula(
-                    p_choose,
-                    self.model.decoder.layers[0].encoder_attn.mass_preservation,
-                    sample["net_input"]["src_tokens"].eq(PAD_INDEX)
-                )
-
-                beta_real = expected_soft_attention_formula(
-                    alpha_real,
-                    soft_energy_system,
-                    sample["net_input"]["src_tokens"].eq(PAD_INDEX),
-                    chunksize=getattr(
-                        self.model.decoder.layers[0].encoder_attn,
-                        "chunk_size",
-                        int(1e10)
-                    )
-                )
-
-                self.assertTrue(
-                    torch.abs(beta_system - beta_real).le(1e-5).all(),
-                )
-
-
-class ChunkwiswTestCase(
-    InfiniteLookbackTestCase
-):
-    def setUp(self):
-        self.model = build_transformer_monotonic_attention(
-            **generate_config(
-                {
-                    "simul_type": "chunkwise",
-                    "mocha_chunk_size": 3
-                }
-            )
-        )
-
-
-class WaitkTestCase(InfiniteLookbackTestCase):
-    def setUp(self):
-        self.model = build_transformer_monotonic_attention(
-            **generate_config(
-                {
-                    "simul_type": "waitk",
-                    "waitk_lagging": 3,
-                }
-            )
-        )
-
-    def check_waitk(self, p_choose, lagging, padding_mask):
-        bsz, tgt_len, src_len = p_choose.size()
-        for bsz_i in range(bsz):
-            for i in range(tgt_len):
-                for j in range(src_len):
-                    if not padding_mask[bsz_i, j]:
-                        if j - i == lagging - 1:
-                            self.assertTrue(p_choose[bsz_i, i, j] == 1)
-                        else:
-                            self.assertTrue(p_choose[bsz_i, i, j] == 0)
-
-    def test_waitk_p_choose(self):
-        for longer_src in [True, False]:
-            for k in [1, 3, 10, 20, 100]:
-                sample = make_sample_with_padding(longer_src)
-                model = build_transformer_monotonic_attention(
-                    **generate_config(
-                        {
-                            "simul_type": "waitk",
-                            "waitk_lagging": k,
-                        }
-                    )
-                )
-                model.train()
-                _, extra_out = model.forward(**sample["net_input"])
-                for item in extra_out.attn_list:
-                    p_choose = item["p_choose"]
-                    bsz, num_heads, tgt_len, src_len = p_choose.size()
-                    padding_mask = sample["net_input"]["src_tokens"].eq(PAD_INDEX)
-                    padding_mask = (
-                        padding_mask
-                        .unsqueeze(1)
-                        .expand([bsz, num_heads, src_len])
-                        .contiguous()
-                        .view(-1, src_len)
-                    )
-                    p_choose = p_choose.view(bsz * num_heads, tgt_len, src_len)
-                    self.check_waitk(p_choose, k, padding_mask)
diff --git a/spaces/OFA-Sys/OFA-vqa/tasks/mm_tasks/__init__.py b/spaces/OFA-Sys/OFA-vqa/tasks/mm_tasks/__init__.py
deleted file mode 100644
index a5f6d18be671432a03b46ecb46b8c6b86d70bf9d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/tasks/mm_tasks/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .caption import CaptionTask
-from .refcoco import RefcocoTask
-from .vqa_gen import VqaGenTask
\ No newline at end of file
diff --git a/spaces/OdiaGenAI/Olive_Farm/Roleplay/promptTemplate.py b/spaces/OdiaGenAI/Olive_Farm/Roleplay/promptTemplate.py
deleted file mode 100644
index 6311305ea8eec2fa6b066e07440c9b1c020cdd2e..0000000000000000000000000000000000000000
--- a/spaces/OdiaGenAI/Olive_Farm/Roleplay/promptTemplate.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import openai
-import streamlit as st
-from camel.agents import RolePlaying
-from camel.utils import print_text_animated
-from datetime import datetime
-
-
-
-def task_generator(user,assistant, openAiKey):
-    
-    openai.api_key = openAiKey
-
-    prompt = f'''User: Farmer, Assistant: Business analyst
-    Generate a conversation topic: Help the farmer in deciding the optimal choice for crops this year\n
-    User: Accountant, Assistant: Developer
-    Generate a conversation topic: Developing a custom accounting software to automate financial processes and reduce errors.\n
-    User: Athlete, Assistant: Doctor
-    Generate a conversation topic: Developing a personalized nutrition plan to optimize athletic performance and recovery.\n
-    User: Politician, Assistant: Social Media Manager\n
-    Generate a conversation topic: Develop a social media strategy to increase the politician's online presence and engagement with constituents.\n
-    User: {user}, Assistant: {assistant}
-    Generate a conversation topic:'''
-
-    response = openai.Completion.create(
-    engine="text-davinci-002", #Use gpt-3 engine.
-    prompt=prompt,
-    max_tokens=50,
-    n=1
-    )
-    task = response.choices[0].text.strip()
-
-    return task
-
-def conversation_generator(user,assistant, task, chat_limit=2):
-    task_prompt = task
-    print(task)
-    print(user)
-    print(assistant)
-    print(chat_limit)
-    role_play_session = RolePlaying(assistant, user, task_prompt)
-    # print(Fore.CYAN + f"Specified task prompt:\n{role_play_session.task_prompt}\n")
-    # print(f"Specified task prompt:\n{role_play_session.task_prompt}\n")
-
-    chat_turn_limit, n = chat_limit, 0
-    assistant_msg, _ = role_play_session.init_chat()
-
-    user_chat = []
-    assistant_chat = []
-    now = datetime.now()
-
-    current_time = now.strftime("%H:%M:%S")
-    print("Current Time =", current_time)
-
-    while n < chat_turn_limit:
-        n += 1
-        try:
-            (assistant_msg, _, _), (user_msg, _, _) = role_play_session.step(assistant_msg)
-            assert user_msg.content is not None
-            with st.chat_message("user"):
-                user_content = user_msg.content.replace("Instruction: ", "").replace("Input: None", "")
-                st.text(user_content)
-            assert assistant_msg.content is not None
-            with st.chat_message("assistant"):
-                assistant_content = assistant_msg.content.replace("Solution: ", "").replace("Next request.","")
-                st.text(assistant_content)
-            
-        except:
-            break
-
-        # print(Fore.BLUE + f"AI User:\n\n{user_msg.content}\n\n")
-        # print(f"AI User:\n\n{user_msg.content}\n\n")
-        user_chat.append(user_msg.content)
-        if "Next request." not in assistant_msg.content:
-            break
-
-        # print(Fore.GREEN + f"AI Assistant:\n\n{assistant_msg.content}\n\n")
-        # print(f"AI Assistant:\n\n{assistant_msg.content}\n\n")
-        assistant_chat.append(assistant_msg.content)
-        if "<CAMEL_TASK_DONE>" in user_msg.content:
-            break
-
-    #Processing the generated conversation
-
-    final_user_convo = []
-    final_assistant_convo = []
-
-    if len(assistant_chat)==0 or len(user_chat)==0:
-        return "Empty list"
-
-
-    for i in range(len(user_chat)):
-
-        if ("Next request" in assistant_chat[i]) and ("Instruction:" in user_chat[i]) and ("Input:" in user_chat[i]):
-            final_instruction = ""
-            try:
-                instruction = user_chat[i].split("Instruction:")[1].split("Input:")[0]
-                input = user_chat[i].split("Input:")[1].strip()
-            except:
-                continue
-
-            if input.strip() == 'None':
-                final_instruction = final_instruction+instruction.replace("\n"," ")
-                final_instruction = final_instruction.strip()
-            else:
-                final_instruction = instruction.replace("\n"," ")+"\ninput:"+input #If an input is there, then add a newline after instruction, input: and then the input.
-                final_instruction = final_instruction.strip()
-
-            final_user_convo.append(final_instruction)
-            final_assistant_convo.append(assistant_chat[i].replace("Solution:","").replace("Next request.","").strip())
-
-        else:
-            break #Sometimes faulty chat gets generated. A proper convo has Next request at the end
-
-    assert (len(final_user_convo)-len(assistant_chat))<=1
-    # print('\n\n'+'End of the conversation\n\n')
-    # print("*"*150, end='\n')
-    final_convo_list = []
-
-    for i in range(len(final_user_convo)):
-        user_convo = {"from":"user","value":final_user_convo[i]}
-        assistant_convo = {"from":"assistant","value":final_assistant_convo[i]}
-        final_convo_list.append(user_convo)
-        final_convo_list.append(assistant_convo)
-
-    length = len(final_convo_list)
-
-    final_json_entry = {'user':user,'assistant':assistant,'task':task_prompt,'conversations':final_convo_list, 'specified_task':role_play_session.task_prompt, 'length':length}
-    # pprint.pprint(final_json_entry)
-    return final_json_entry, final_user_convo, final_assistant_convo
\ No newline at end of file
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/__init__.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/__init__.py
deleted file mode 100644
index 52e4b48d383a84a055dcd7f6236f6e8e58eab924..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/__init__.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .base_module import BaseModule, ModuleList, Sequential
-from .base_runner import BaseRunner
-from .builder import RUNNERS, build_runner
-from .checkpoint import (CheckpointLoader, _load_checkpoint,
-                         _load_checkpoint_with_prefix, load_checkpoint,
-                         load_state_dict, save_checkpoint, weights_to_cpu)
-from .default_constructor import DefaultRunnerConstructor
-from .dist_utils import (allreduce_grads, allreduce_params, get_dist_info,
-                         init_dist, master_only)
-from .epoch_based_runner import EpochBasedRunner, Runner
-from .fp16_utils import LossScaler, auto_fp16, force_fp32, wrap_fp16_model
-from .hooks import (HOOKS, CheckpointHook, ClosureHook, DistEvalHook,
-                    DistSamplerSeedHook, DvcliveLoggerHook, EMAHook, EvalHook,
-                    Fp16OptimizerHook, GradientCumulativeFp16OptimizerHook,
-                    GradientCumulativeOptimizerHook, Hook, IterTimerHook,
-                    LoggerHook, LrUpdaterHook, MlflowLoggerHook,
-                    NeptuneLoggerHook, OptimizerHook, PaviLoggerHook,
-                    SyncBuffersHook, TensorboardLoggerHook, TextLoggerHook,
-                    WandbLoggerHook)
-from .iter_based_runner import IterBasedRunner, IterLoader
-from .log_buffer import LogBuffer
-from .optimizer import (OPTIMIZER_BUILDERS, OPTIMIZERS,
-                        DefaultOptimizerConstructor, build_optimizer,
-                        build_optimizer_constructor)
-from .priority import Priority, get_priority
-from .utils import get_host_info, get_time_str, obj_from_dict, set_random_seed
-
-__all__ = [
-    'BaseRunner', 'Runner', 'EpochBasedRunner', 'IterBasedRunner', 'LogBuffer',
-    'HOOKS', 'Hook', 'CheckpointHook', 'ClosureHook', 'LrUpdaterHook',
-    'OptimizerHook', 'IterTimerHook', 'DistSamplerSeedHook', 'LoggerHook',
-    'PaviLoggerHook', 'TextLoggerHook', 'TensorboardLoggerHook',
-    'NeptuneLoggerHook', 'WandbLoggerHook', 'MlflowLoggerHook',
-    'DvcliveLoggerHook', '_load_checkpoint', 'load_state_dict',
-    'load_checkpoint', 'weights_to_cpu', 'save_checkpoint', 'Priority',
-    'get_priority', 'get_host_info', 'get_time_str', 'obj_from_dict',
-    'init_dist', 'get_dist_info', 'master_only', 'OPTIMIZER_BUILDERS',
-    'OPTIMIZERS', 'DefaultOptimizerConstructor', 'build_optimizer',
-    'build_optimizer_constructor', 'IterLoader', 'set_random_seed',
-    'auto_fp16', 'force_fp32', 'wrap_fp16_model', 'Fp16OptimizerHook',
-    'SyncBuffersHook', 'EMAHook', 'build_runner', 'RUNNERS', 'allreduce_grads',
-    'allreduce_params', 'LossScaler', 'CheckpointLoader', 'BaseModule',
-    '_load_checkpoint_with_prefix', 'EvalHook', 'DistEvalHook', 'Sequential',
-    'ModuleList', 'GradientCumulativeOptimizerHook',
-    'GradientCumulativeFp16OptimizerHook', 'DefaultRunnerConstructor'
-]
diff --git a/spaces/PeepDaSlan9/De-limiter/README.md b/spaces/PeepDaSlan9/De-limiter/README.md
deleted file mode 100644
index 42efb8c6e92b2c0a4bd85ac355f7b1f113ba7fbc..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: De-limiter Demo
-emoji: 🎶
-colorFrom: black
-colorTo: white
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: true
-duplicated_from: jeonchangbin49/De-limiter
----
diff --git a/spaces/Pengyey/bingo-chuchu/src/pages/api/sydney.ts b/spaces/Pengyey/bingo-chuchu/src/pages/api/sydney.ts
deleted file mode 100644
index d49397aad6f9624c87e79f6f65094c2efd4fe255..0000000000000000000000000000000000000000
--- a/spaces/Pengyey/bingo-chuchu/src/pages/api/sydney.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import { WebSocket, debug } from '@/lib/isomorphic'
-import { BingWebBot } from '@/lib/bots/bing'
-import { websocketUtils } from '@/lib/bots/bing/utils'
-import { WatchDog, createHeaders } from '@/lib/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const conversationContext = req.body
-  const headers = createHeaders(req.cookies)
-  debug(headers)
-  res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
-
-  const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', {
-    headers: {
-      ...headers,
-      'accept-language': 'zh-CN,zh;q=0.9',
-      'cache-control': 'no-cache',
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      pragma: 'no-cache',
-    }
-  })
-
-  const closeDog = new WatchDog()
-  const timeoutDog = new WatchDog()
-  ws.onmessage = (event) => {
-    timeoutDog.watch(() => {
-      debug('timeout')
-      ws.send(websocketUtils.packMessage({ type: 6 }))
-    }, 1500)
-    closeDog.watch(() => {
-      debug('timeout close')
-      ws.close()
-    }, 10000)
-    res.write(event.data)
-    if (/\{"type":([367])\}/.test(String(event.data))) {
-      const type = parseInt(RegExp.$1, 10)
-      debug('connection type', type)
-      if (type === 3) {
-        ws.close()
-      } else {
-        ws.send(websocketUtils.packMessage({ type }))
-      }
-    }
-  }
-
-  ws.onclose = () => {
-    timeoutDog.reset()
-    closeDog.reset()
-    debug('connection close')
-    res.end()
-  }
-
-  await new Promise((resolve) => ws.onopen = resolve)
-  ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
-  ws.send(websocketUtils.packMessage({ type: 6 }))
-  ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
-  req.socket.once('close', () => {
-    ws.close()
-    if (!res.closed) {
-      res.end()
-    }
-  })
-}
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/exp/upernet_global_small/test.sh b/spaces/Pie31415/control-animation/annotator/uniformer/exp/upernet_global_small/test.sh
deleted file mode 100644
index d9a85e7a0d3b7c96b060f473d41254b37a382fcb..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/exp/upernet_global_small/test.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/usr/bin/env bash
-
-work_path=$(dirname $0)
-PYTHONPATH="$(dirname $0)/../../":$PYTHONPATH \
-python -m torch.distributed.launch --nproc_per_node=8 \
-    tools/test.py ${work_path}/test_config_h32.py \
-    ${work_path}/ckpt/latest.pth \
-    --launcher pytorch \
-    --eval mIoU \
-    2>&1 | tee -a ${work_path}/log.txt
diff --git a/spaces/Ralmao/Anemia/README.md b/spaces/Ralmao/Anemia/README.md
deleted file mode 100644
index d677c5f1b9c42eaca67613a03cfc1b6a4d5a99ed..0000000000000000000000000000000000000000
--- a/spaces/Ralmao/Anemia/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Anemia
-emoji: 📈
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.45.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RamAnanth1/iclr2023/README.md b/spaces/RamAnanth1/iclr2023/README.md
deleted file mode 100644
index cc7f549c1868e9c289f49851f89bbc230c9cd5a2..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/iclr2023/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Iclr2023
-emoji: 💻
-colorFrom: gray
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/models/direct_url.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/models/direct_url.py
deleted file mode 100644
index e75feda9ca9477b0ffec1f523f29033e289d6b6a..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/models/direct_url.py
+++ /dev/null
@@ -1,212 +0,0 @@
-""" PEP 610 """
-import json
-import re
-import urllib.parse
-from typing import Any, Dict, Iterable, Optional, Type, TypeVar, Union
-
-__all__ = [
-    "DirectUrl",
-    "DirectUrlValidationError",
-    "DirInfo",
-    "ArchiveInfo",
-    "VcsInfo",
-]
-
-T = TypeVar("T")
-
-DIRECT_URL_METADATA_NAME = "direct_url.json"
-ENV_VAR_RE = re.compile(r"^\$\{[A-Za-z0-9-_]+\}(:\$\{[A-Za-z0-9-_]+\})?$")
-
-
-class DirectUrlValidationError(Exception):
-    pass
-
-
-def _get(
-    d: Dict[str, Any], expected_type: Type[T], key: str, default: Optional[T] = None
-) -> Optional[T]:
-    """Get value from dictionary and verify expected type."""
-    if key not in d:
-        return default
-    value = d[key]
-    if not isinstance(value, expected_type):
-        raise DirectUrlValidationError(
-            "{!r} has unexpected type for {} (expected {})".format(
-                value, key, expected_type
-            )
-        )
-    return value
-
-
-def _get_required(
-    d: Dict[str, Any], expected_type: Type[T], key: str, default: Optional[T] = None
-) -> T:
-    value = _get(d, expected_type, key, default)
-    if value is None:
-        raise DirectUrlValidationError(f"{key} must have a value")
-    return value
-
-
-def _exactly_one_of(infos: Iterable[Optional["InfoType"]]) -> "InfoType":
-    infos = [info for info in infos if info is not None]
-    if not infos:
-        raise DirectUrlValidationError(
-            "missing one of archive_info, dir_info, vcs_info"
-        )
-    if len(infos) > 1:
-        raise DirectUrlValidationError(
-            "more than one of archive_info, dir_info, vcs_info"
-        )
-    assert infos[0] is not None
-    return infos[0]
-
-
-def _filter_none(**kwargs: Any) -> Dict[str, Any]:
-    """Make dict excluding None values."""
-    return {k: v for k, v in kwargs.items() if v is not None}
-
-
-class VcsInfo:
-    name = "vcs_info"
-
-    def __init__(
-        self,
-        vcs: str,
-        commit_id: str,
-        requested_revision: Optional[str] = None,
-    ) -> None:
-        self.vcs = vcs
-        self.requested_revision = requested_revision
-        self.commit_id = commit_id
-
-    @classmethod
-    def _from_dict(cls, d: Optional[Dict[str, Any]]) -> Optional["VcsInfo"]:
-        if d is None:
-            return None
-        return cls(
-            vcs=_get_required(d, str, "vcs"),
-            commit_id=_get_required(d, str, "commit_id"),
-            requested_revision=_get(d, str, "requested_revision"),
-        )
-
-    def _to_dict(self) -> Dict[str, Any]:
-        return _filter_none(
-            vcs=self.vcs,
-            requested_revision=self.requested_revision,
-            commit_id=self.commit_id,
-        )
-
-
-class ArchiveInfo:
-    name = "archive_info"
-
-    def __init__(
-        self,
-        hash: Optional[str] = None,
-    ) -> None:
-        self.hash = hash
-
-    @classmethod
-    def _from_dict(cls, d: Optional[Dict[str, Any]]) -> Optional["ArchiveInfo"]:
-        if d is None:
-            return None
-        return cls(hash=_get(d, str, "hash"))
-
-    def _to_dict(self) -> Dict[str, Any]:
-        return _filter_none(hash=self.hash)
-
-
-class DirInfo:
-    name = "dir_info"
-
-    def __init__(
-        self,
-        editable: bool = False,
-    ) -> None:
-        self.editable = editable
-
-    @classmethod
-    def _from_dict(cls, d: Optional[Dict[str, Any]]) -> Optional["DirInfo"]:
-        if d is None:
-            return None
-        return cls(editable=_get_required(d, bool, "editable", default=False))
-
-    def _to_dict(self) -> Dict[str, Any]:
-        return _filter_none(editable=self.editable or None)
-
-
-InfoType = Union[ArchiveInfo, DirInfo, VcsInfo]
-
-
-class DirectUrl:
-    def __init__(
-        self,
-        url: str,
-        info: InfoType,
-        subdirectory: Optional[str] = None,
-    ) -> None:
-        self.url = url
-        self.info = info
-        self.subdirectory = subdirectory
-
-    def _remove_auth_from_netloc(self, netloc: str) -> str:
-        if "@" not in netloc:
-            return netloc
-        user_pass, netloc_no_user_pass = netloc.split("@", 1)
-        if (
-            isinstance(self.info, VcsInfo)
-            and self.info.vcs == "git"
-            and user_pass == "git"
-        ):
-            return netloc
-        if ENV_VAR_RE.match(user_pass):
-            return netloc
-        return netloc_no_user_pass
-
-    @property
-    def redacted_url(self) -> str:
-        """url with user:password part removed unless it is formed with
-        environment variables as specified in PEP 610, or it is ``git``
-        in the case of a git URL.
-        """
-        purl = urllib.parse.urlsplit(self.url)
-        netloc = self._remove_auth_from_netloc(purl.netloc)
-        surl = urllib.parse.urlunsplit(
-            (purl.scheme, netloc, purl.path, purl.query, purl.fragment)
-        )
-        return surl
-
-    def validate(self) -> None:
-        self.from_dict(self.to_dict())
-
-    @classmethod
-    def from_dict(cls, d: Dict[str, Any]) -> "DirectUrl":
-        return DirectUrl(
-            url=_get_required(d, str, "url"),
-            subdirectory=_get(d, str, "subdirectory"),
-            info=_exactly_one_of(
-                [
-                    ArchiveInfo._from_dict(_get(d, dict, "archive_info")),
-                    DirInfo._from_dict(_get(d, dict, "dir_info")),
-                    VcsInfo._from_dict(_get(d, dict, "vcs_info")),
-                ]
-            ),
-        )
-
-    def to_dict(self) -> Dict[str, Any]:
-        res = _filter_none(
-            url=self.redacted_url,
-            subdirectory=self.subdirectory,
-        )
-        res[self.info.name] = self.info._to_dict()
-        return res
-
-    @classmethod
-    def from_json(cls, s: str) -> "DirectUrl":
-        return cls.from_dict(json.loads(s))
-
-    def to_json(self) -> str:
-        return json.dumps(self.to_dict(), sort_keys=True)
-
-    def is_local_editable(self) -> bool:
-        return isinstance(self.info, DirInfo) and self.info.editable
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/six.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/six.py
deleted file mode 100644
index f099a3dcd28d2fec21457c9b6c01ded4e3e9ddee..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/six.py
+++ /dev/null
@@ -1,1076 +0,0 @@
-# Copyright (c) 2010-2020 Benjamin Peterson
-#
-# 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.
-
-"""Utilities for writing code that runs on Python 2 and 3"""
-
-from __future__ import absolute_import
-
-import functools
-import itertools
-import operator
-import sys
-import types
-
-__author__ = "Benjamin Peterson <benjamin@python.org>"
-__version__ = "1.16.0"
-
-
-# Useful for very coarse version differentiation.
-PY2 = sys.version_info[0] == 2
-PY3 = sys.version_info[0] == 3
-PY34 = sys.version_info[0:2] >= (3, 4)
-
-if PY3:
-    string_types = (str,)
-    integer_types = (int,)
-    class_types = (type,)
-    text_type = str
-    binary_type = bytes
-
-    MAXSIZE = sys.maxsize
-else:
-    string_types = (basestring,)
-    integer_types = (int, long)
-    class_types = (type, types.ClassType)
-    text_type = unicode
-    binary_type = str
-
-    if sys.platform.startswith("java"):
-        # Jython always uses 32 bits.
-        MAXSIZE = int((1 << 31) - 1)
-    else:
-        # It's possible to have sizeof(long) != sizeof(Py_ssize_t).
-        class X(object):
-            def __len__(self):
-                return 1 << 31
-
-        try:
-            len(X())
-        except OverflowError:
-            # 32-bit
-            MAXSIZE = int((1 << 31) - 1)
-        else:
-            # 64-bit
-            MAXSIZE = int((1 << 63) - 1)
-        del X
-
-if PY34:
-    from importlib.util import spec_from_loader
-else:
-    spec_from_loader = None
-
-
-def _add_doc(func, doc):
-    """Add documentation to a function."""
-    func.__doc__ = doc
-
-
-def _import_module(name):
-    """Import module, returning the module after the last dot."""
-    __import__(name)
-    return sys.modules[name]
-
-
-class _LazyDescr(object):
-    def __init__(self, name):
-        self.name = name
-
-    def __get__(self, obj, tp):
-        result = self._resolve()
-        setattr(obj, self.name, result)  # Invokes __set__.
-        try:
-            # This is a bit ugly, but it avoids running this again by
-            # removing this descriptor.
-            delattr(obj.__class__, self.name)
-        except AttributeError:
-            pass
-        return result
-
-
-class MovedModule(_LazyDescr):
-    def __init__(self, name, old, new=None):
-        super(MovedModule, self).__init__(name)
-        if PY3:
-            if new is None:
-                new = name
-            self.mod = new
-        else:
-            self.mod = old
-
-    def _resolve(self):
-        return _import_module(self.mod)
-
-    def __getattr__(self, attr):
-        _module = self._resolve()
-        value = getattr(_module, attr)
-        setattr(self, attr, value)
-        return value
-
-
-class _LazyModule(types.ModuleType):
-    def __init__(self, name):
-        super(_LazyModule, self).__init__(name)
-        self.__doc__ = self.__class__.__doc__
-
-    def __dir__(self):
-        attrs = ["__doc__", "__name__"]
-        attrs += [attr.name for attr in self._moved_attributes]
-        return attrs
-
-    # Subclasses should override this
-    _moved_attributes = []
-
-
-class MovedAttribute(_LazyDescr):
-    def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):
-        super(MovedAttribute, self).__init__(name)
-        if PY3:
-            if new_mod is None:
-                new_mod = name
-            self.mod = new_mod
-            if new_attr is None:
-                if old_attr is None:
-                    new_attr = name
-                else:
-                    new_attr = old_attr
-            self.attr = new_attr
-        else:
-            self.mod = old_mod
-            if old_attr is None:
-                old_attr = name
-            self.attr = old_attr
-
-    def _resolve(self):
-        module = _import_module(self.mod)
-        return getattr(module, self.attr)
-
-
-class _SixMetaPathImporter(object):
-
-    """
-    A meta path importer to import six.moves and its submodules.
-
-    This class implements a PEP302 finder and loader. It should be compatible
-    with Python 2.5 and all existing versions of Python3
-    """
-
-    def __init__(self, six_module_name):
-        self.name = six_module_name
-        self.known_modules = {}
-
-    def _add_module(self, mod, *fullnames):
-        for fullname in fullnames:
-            self.known_modules[self.name + "." + fullname] = mod
-
-    def _get_module(self, fullname):
-        return self.known_modules[self.name + "." + fullname]
-
-    def find_module(self, fullname, path=None):
-        if fullname in self.known_modules:
-            return self
-        return None
-
-    def find_spec(self, fullname, path, target=None):
-        if fullname in self.known_modules:
-            return spec_from_loader(fullname, self)
-        return None
-
-    def __get_module(self, fullname):
-        try:
-            return self.known_modules[fullname]
-        except KeyError:
-            raise ImportError("This loader does not know module " + fullname)
-
-    def load_module(self, fullname):
-        try:
-            # in case of a reload
-            return sys.modules[fullname]
-        except KeyError:
-            pass
-        mod = self.__get_module(fullname)
-        if isinstance(mod, MovedModule):
-            mod = mod._resolve()
-        else:
-            mod.__loader__ = self
-        sys.modules[fullname] = mod
-        return mod
-
-    def is_package(self, fullname):
-        """
-        Return true, if the named module is a package.
-
-        We need this method to get correct spec objects with
-        Python 3.4 (see PEP451)
-        """
-        return hasattr(self.__get_module(fullname), "__path__")
-
-    def get_code(self, fullname):
-        """Return None
-
-        Required, if is_package is implemented"""
-        self.__get_module(fullname)  # eventually raises ImportError
-        return None
-
-    get_source = get_code  # same as get_code
-
-    def create_module(self, spec):
-        return self.load_module(spec.name)
-
-    def exec_module(self, module):
-        pass
-
-
-_importer = _SixMetaPathImporter(__name__)
-
-
-class _MovedItems(_LazyModule):
-
-    """Lazy loading of moved objects"""
-
-    __path__ = []  # mark as package
-
-
-_moved_attributes = [
-    MovedAttribute("cStringIO", "cStringIO", "io", "StringIO"),
-    MovedAttribute("filter", "itertools", "builtins", "ifilter", "filter"),
-    MovedAttribute(
-        "filterfalse", "itertools", "itertools", "ifilterfalse", "filterfalse"
-    ),
-    MovedAttribute("input", "__builtin__", "builtins", "raw_input", "input"),
-    MovedAttribute("intern", "__builtin__", "sys"),
-    MovedAttribute("map", "itertools", "builtins", "imap", "map"),
-    MovedAttribute("getcwd", "os", "os", "getcwdu", "getcwd"),
-    MovedAttribute("getcwdb", "os", "os", "getcwd", "getcwdb"),
-    MovedAttribute("getoutput", "commands", "subprocess"),
-    MovedAttribute("range", "__builtin__", "builtins", "xrange", "range"),
-    MovedAttribute(
-        "reload_module", "__builtin__", "importlib" if PY34 else "imp", "reload"
-    ),
-    MovedAttribute("reduce", "__builtin__", "functools"),
-    MovedAttribute("shlex_quote", "pipes", "shlex", "quote"),
-    MovedAttribute("StringIO", "StringIO", "io"),
-    MovedAttribute("UserDict", "UserDict", "collections"),
-    MovedAttribute("UserList", "UserList", "collections"),
-    MovedAttribute("UserString", "UserString", "collections"),
-    MovedAttribute("xrange", "__builtin__", "builtins", "xrange", "range"),
-    MovedAttribute("zip", "itertools", "builtins", "izip", "zip"),
-    MovedAttribute(
-        "zip_longest", "itertools", "itertools", "izip_longest", "zip_longest"
-    ),
-    MovedModule("builtins", "__builtin__"),
-    MovedModule("configparser", "ConfigParser"),
-    MovedModule(
-        "collections_abc",
-        "collections",
-        "collections.abc" if sys.version_info >= (3, 3) else "collections",
-    ),
-    MovedModule("copyreg", "copy_reg"),
-    MovedModule("dbm_gnu", "gdbm", "dbm.gnu"),
-    MovedModule("dbm_ndbm", "dbm", "dbm.ndbm"),
-    MovedModule(
-        "_dummy_thread",
-        "dummy_thread",
-        "_dummy_thread" if sys.version_info < (3, 9) else "_thread",
-    ),
-    MovedModule("http_cookiejar", "cookielib", "http.cookiejar"),
-    MovedModule("http_cookies", "Cookie", "http.cookies"),
-    MovedModule("html_entities", "htmlentitydefs", "html.entities"),
-    MovedModule("html_parser", "HTMLParser", "html.parser"),
-    MovedModule("http_client", "httplib", "http.client"),
-    MovedModule("email_mime_base", "email.MIMEBase", "email.mime.base"),
-    MovedModule("email_mime_image", "email.MIMEImage", "email.mime.image"),
-    MovedModule("email_mime_multipart", "email.MIMEMultipart", "email.mime.multipart"),
-    MovedModule(
-        "email_mime_nonmultipart", "email.MIMENonMultipart", "email.mime.nonmultipart"
-    ),
-    MovedModule("email_mime_text", "email.MIMEText", "email.mime.text"),
-    MovedModule("BaseHTTPServer", "BaseHTTPServer", "http.server"),
-    MovedModule("CGIHTTPServer", "CGIHTTPServer", "http.server"),
-    MovedModule("SimpleHTTPServer", "SimpleHTTPServer", "http.server"),
-    MovedModule("cPickle", "cPickle", "pickle"),
-    MovedModule("queue", "Queue"),
-    MovedModule("reprlib", "repr"),
-    MovedModule("socketserver", "SocketServer"),
-    MovedModule("_thread", "thread", "_thread"),
-    MovedModule("tkinter", "Tkinter"),
-    MovedModule("tkinter_dialog", "Dialog", "tkinter.dialog"),
-    MovedModule("tkinter_filedialog", "FileDialog", "tkinter.filedialog"),
-    MovedModule("tkinter_scrolledtext", "ScrolledText", "tkinter.scrolledtext"),
-    MovedModule("tkinter_simpledialog", "SimpleDialog", "tkinter.simpledialog"),
-    MovedModule("tkinter_tix", "Tix", "tkinter.tix"),
-    MovedModule("tkinter_ttk", "ttk", "tkinter.ttk"),
-    MovedModule("tkinter_constants", "Tkconstants", "tkinter.constants"),
-    MovedModule("tkinter_dnd", "Tkdnd", "tkinter.dnd"),
-    MovedModule("tkinter_colorchooser", "tkColorChooser", "tkinter.colorchooser"),
-    MovedModule("tkinter_commondialog", "tkCommonDialog", "tkinter.commondialog"),
-    MovedModule("tkinter_tkfiledialog", "tkFileDialog", "tkinter.filedialog"),
-    MovedModule("tkinter_font", "tkFont", "tkinter.font"),
-    MovedModule("tkinter_messagebox", "tkMessageBox", "tkinter.messagebox"),
-    MovedModule("tkinter_tksimpledialog", "tkSimpleDialog", "tkinter.simpledialog"),
-    MovedModule("urllib_parse", __name__ + ".moves.urllib_parse", "urllib.parse"),
-    MovedModule("urllib_error", __name__ + ".moves.urllib_error", "urllib.error"),
-    MovedModule("urllib", __name__ + ".moves.urllib", __name__ + ".moves.urllib"),
-    MovedModule("urllib_robotparser", "robotparser", "urllib.robotparser"),
-    MovedModule("xmlrpc_client", "xmlrpclib", "xmlrpc.client"),
-    MovedModule("xmlrpc_server", "SimpleXMLRPCServer", "xmlrpc.server"),
-]
-# Add windows specific modules.
-if sys.platform == "win32":
-    _moved_attributes += [
-        MovedModule("winreg", "_winreg"),
-    ]
-
-for attr in _moved_attributes:
-    setattr(_MovedItems, attr.name, attr)
-    if isinstance(attr, MovedModule):
-        _importer._add_module(attr, "moves." + attr.name)
-del attr
-
-_MovedItems._moved_attributes = _moved_attributes
-
-moves = _MovedItems(__name__ + ".moves")
-_importer._add_module(moves, "moves")
-
-
-class Module_six_moves_urllib_parse(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_parse"""
-
-
-_urllib_parse_moved_attributes = [
-    MovedAttribute("ParseResult", "urlparse", "urllib.parse"),
-    MovedAttribute("SplitResult", "urlparse", "urllib.parse"),
-    MovedAttribute("parse_qs", "urlparse", "urllib.parse"),
-    MovedAttribute("parse_qsl", "urlparse", "urllib.parse"),
-    MovedAttribute("urldefrag", "urlparse", "urllib.parse"),
-    MovedAttribute("urljoin", "urlparse", "urllib.parse"),
-    MovedAttribute("urlparse", "urlparse", "urllib.parse"),
-    MovedAttribute("urlsplit", "urlparse", "urllib.parse"),
-    MovedAttribute("urlunparse", "urlparse", "urllib.parse"),
-    MovedAttribute("urlunsplit", "urlparse", "urllib.parse"),
-    MovedAttribute("quote", "urllib", "urllib.parse"),
-    MovedAttribute("quote_plus", "urllib", "urllib.parse"),
-    MovedAttribute("unquote", "urllib", "urllib.parse"),
-    MovedAttribute("unquote_plus", "urllib", "urllib.parse"),
-    MovedAttribute(
-        "unquote_to_bytes", "urllib", "urllib.parse", "unquote", "unquote_to_bytes"
-    ),
-    MovedAttribute("urlencode", "urllib", "urllib.parse"),
-    MovedAttribute("splitquery", "urllib", "urllib.parse"),
-    MovedAttribute("splittag", "urllib", "urllib.parse"),
-    MovedAttribute("splituser", "urllib", "urllib.parse"),
-    MovedAttribute("splitvalue", "urllib", "urllib.parse"),
-    MovedAttribute("uses_fragment", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_netloc", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_params", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_query", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_relative", "urlparse", "urllib.parse"),
-]
-for attr in _urllib_parse_moved_attributes:
-    setattr(Module_six_moves_urllib_parse, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_parse._moved_attributes = _urllib_parse_moved_attributes
-
-_importer._add_module(
-    Module_six_moves_urllib_parse(__name__ + ".moves.urllib_parse"),
-    "moves.urllib_parse",
-    "moves.urllib.parse",
-)
-
-
-class Module_six_moves_urllib_error(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_error"""
-
-
-_urllib_error_moved_attributes = [
-    MovedAttribute("URLError", "urllib2", "urllib.error"),
-    MovedAttribute("HTTPError", "urllib2", "urllib.error"),
-    MovedAttribute("ContentTooShortError", "urllib", "urllib.error"),
-]
-for attr in _urllib_error_moved_attributes:
-    setattr(Module_six_moves_urllib_error, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_error._moved_attributes = _urllib_error_moved_attributes
-
-_importer._add_module(
-    Module_six_moves_urllib_error(__name__ + ".moves.urllib.error"),
-    "moves.urllib_error",
-    "moves.urllib.error",
-)
-
-
-class Module_six_moves_urllib_request(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_request"""
-
-
-_urllib_request_moved_attributes = [
-    MovedAttribute("urlopen", "urllib2", "urllib.request"),
-    MovedAttribute("install_opener", "urllib2", "urllib.request"),
-    MovedAttribute("build_opener", "urllib2", "urllib.request"),
-    MovedAttribute("pathname2url", "urllib", "urllib.request"),
-    MovedAttribute("url2pathname", "urllib", "urllib.request"),
-    MovedAttribute("getproxies", "urllib", "urllib.request"),
-    MovedAttribute("Request", "urllib2", "urllib.request"),
-    MovedAttribute("OpenerDirector", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPDefaultErrorHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPRedirectHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPCookieProcessor", "urllib2", "urllib.request"),
-    MovedAttribute("ProxyHandler", "urllib2", "urllib.request"),
-    MovedAttribute("BaseHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPPasswordMgr", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPPasswordMgrWithDefaultRealm", "urllib2", "urllib.request"),
-    MovedAttribute("AbstractBasicAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPBasicAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("ProxyBasicAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("AbstractDigestAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPDigestAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("ProxyDigestAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPSHandler", "urllib2", "urllib.request"),
-    MovedAttribute("FileHandler", "urllib2", "urllib.request"),
-    MovedAttribute("FTPHandler", "urllib2", "urllib.request"),
-    MovedAttribute("CacheFTPHandler", "urllib2", "urllib.request"),
-    MovedAttribute("UnknownHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPErrorProcessor", "urllib2", "urllib.request"),
-    MovedAttribute("urlretrieve", "urllib", "urllib.request"),
-    MovedAttribute("urlcleanup", "urllib", "urllib.request"),
-    MovedAttribute("URLopener", "urllib", "urllib.request"),
-    MovedAttribute("FancyURLopener", "urllib", "urllib.request"),
-    MovedAttribute("proxy_bypass", "urllib", "urllib.request"),
-    MovedAttribute("parse_http_list", "urllib2", "urllib.request"),
-    MovedAttribute("parse_keqv_list", "urllib2", "urllib.request"),
-]
-for attr in _urllib_request_moved_attributes:
-    setattr(Module_six_moves_urllib_request, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_request._moved_attributes = _urllib_request_moved_attributes
-
-_importer._add_module(
-    Module_six_moves_urllib_request(__name__ + ".moves.urllib.request"),
-    "moves.urllib_request",
-    "moves.urllib.request",
-)
-
-
-class Module_six_moves_urllib_response(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_response"""
-
-
-_urllib_response_moved_attributes = [
-    MovedAttribute("addbase", "urllib", "urllib.response"),
-    MovedAttribute("addclosehook", "urllib", "urllib.response"),
-    MovedAttribute("addinfo", "urllib", "urllib.response"),
-    MovedAttribute("addinfourl", "urllib", "urllib.response"),
-]
-for attr in _urllib_response_moved_attributes:
-    setattr(Module_six_moves_urllib_response, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_response._moved_attributes = _urllib_response_moved_attributes
-
-_importer._add_module(
-    Module_six_moves_urllib_response(__name__ + ".moves.urllib.response"),
-    "moves.urllib_response",
-    "moves.urllib.response",
-)
-
-
-class Module_six_moves_urllib_robotparser(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_robotparser"""
-
-
-_urllib_robotparser_moved_attributes = [
-    MovedAttribute("RobotFileParser", "robotparser", "urllib.robotparser"),
-]
-for attr in _urllib_robotparser_moved_attributes:
-    setattr(Module_six_moves_urllib_robotparser, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_robotparser._moved_attributes = (
-    _urllib_robotparser_moved_attributes
-)
-
-_importer._add_module(
-    Module_six_moves_urllib_robotparser(__name__ + ".moves.urllib.robotparser"),
-    "moves.urllib_robotparser",
-    "moves.urllib.robotparser",
-)
-
-
-class Module_six_moves_urllib(types.ModuleType):
-
-    """Create a six.moves.urllib namespace that resembles the Python 3 namespace"""
-
-    __path__ = []  # mark as package
-    parse = _importer._get_module("moves.urllib_parse")
-    error = _importer._get_module("moves.urllib_error")
-    request = _importer._get_module("moves.urllib_request")
-    response = _importer._get_module("moves.urllib_response")
-    robotparser = _importer._get_module("moves.urllib_robotparser")
-
-    def __dir__(self):
-        return ["parse", "error", "request", "response", "robotparser"]
-
-
-_importer._add_module(
-    Module_six_moves_urllib(__name__ + ".moves.urllib"), "moves.urllib"
-)
-
-
-def add_move(move):
-    """Add an item to six.moves."""
-    setattr(_MovedItems, move.name, move)
-
-
-def remove_move(name):
-    """Remove item from six.moves."""
-    try:
-        delattr(_MovedItems, name)
-    except AttributeError:
-        try:
-            del moves.__dict__[name]
-        except KeyError:
-            raise AttributeError("no such move, %r" % (name,))
-
-
-if PY3:
-    _meth_func = "__func__"
-    _meth_self = "__self__"
-
-    _func_closure = "__closure__"
-    _func_code = "__code__"
-    _func_defaults = "__defaults__"
-    _func_globals = "__globals__"
-else:
-    _meth_func = "im_func"
-    _meth_self = "im_self"
-
-    _func_closure = "func_closure"
-    _func_code = "func_code"
-    _func_defaults = "func_defaults"
-    _func_globals = "func_globals"
-
-
-try:
-    advance_iterator = next
-except NameError:
-
-    def advance_iterator(it):
-        return it.next()
-
-
-next = advance_iterator
-
-
-try:
-    callable = callable
-except NameError:
-
-    def callable(obj):
-        return any("__call__" in klass.__dict__ for klass in type(obj).__mro__)
-
-
-if PY3:
-
-    def get_unbound_function(unbound):
-        return unbound
-
-    create_bound_method = types.MethodType
-
-    def create_unbound_method(func, cls):
-        return func
-
-    Iterator = object
-else:
-
-    def get_unbound_function(unbound):
-        return unbound.im_func
-
-    def create_bound_method(func, obj):
-        return types.MethodType(func, obj, obj.__class__)
-
-    def create_unbound_method(func, cls):
-        return types.MethodType(func, None, cls)
-
-    class Iterator(object):
-        def next(self):
-            return type(self).__next__(self)
-
-    callable = callable
-_add_doc(
-    get_unbound_function, """Get the function out of a possibly unbound function"""
-)
-
-
-get_method_function = operator.attrgetter(_meth_func)
-get_method_self = operator.attrgetter(_meth_self)
-get_function_closure = operator.attrgetter(_func_closure)
-get_function_code = operator.attrgetter(_func_code)
-get_function_defaults = operator.attrgetter(_func_defaults)
-get_function_globals = operator.attrgetter(_func_globals)
-
-
-if PY3:
-
-    def iterkeys(d, **kw):
-        return iter(d.keys(**kw))
-
-    def itervalues(d, **kw):
-        return iter(d.values(**kw))
-
-    def iteritems(d, **kw):
-        return iter(d.items(**kw))
-
-    def iterlists(d, **kw):
-        return iter(d.lists(**kw))
-
-    viewkeys = operator.methodcaller("keys")
-
-    viewvalues = operator.methodcaller("values")
-
-    viewitems = operator.methodcaller("items")
-else:
-
-    def iterkeys(d, **kw):
-        return d.iterkeys(**kw)
-
-    def itervalues(d, **kw):
-        return d.itervalues(**kw)
-
-    def iteritems(d, **kw):
-        return d.iteritems(**kw)
-
-    def iterlists(d, **kw):
-        return d.iterlists(**kw)
-
-    viewkeys = operator.methodcaller("viewkeys")
-
-    viewvalues = operator.methodcaller("viewvalues")
-
-    viewitems = operator.methodcaller("viewitems")
-
-_add_doc(iterkeys, "Return an iterator over the keys of a dictionary.")
-_add_doc(itervalues, "Return an iterator over the values of a dictionary.")
-_add_doc(iteritems, "Return an iterator over the (key, value) pairs of a dictionary.")
-_add_doc(
-    iterlists, "Return an iterator over the (key, [values]) pairs of a dictionary."
-)
-
-
-if PY3:
-
-    def b(s):
-        return s.encode("latin-1")
-
-    def u(s):
-        return s
-
-    unichr = chr
-    import struct
-
-    int2byte = struct.Struct(">B").pack
-    del struct
-    byte2int = operator.itemgetter(0)
-    indexbytes = operator.getitem
-    iterbytes = iter
-    import io
-
-    StringIO = io.StringIO
-    BytesIO = io.BytesIO
-    del io
-    _assertCountEqual = "assertCountEqual"
-    if sys.version_info[1] <= 1:
-        _assertRaisesRegex = "assertRaisesRegexp"
-        _assertRegex = "assertRegexpMatches"
-        _assertNotRegex = "assertNotRegexpMatches"
-    else:
-        _assertRaisesRegex = "assertRaisesRegex"
-        _assertRegex = "assertRegex"
-        _assertNotRegex = "assertNotRegex"
-else:
-
-    def b(s):
-        return s
-
-    # Workaround for standalone backslash
-
-    def u(s):
-        return unicode(s.replace(r"\\", r"\\\\"), "unicode_escape")
-
-    unichr = unichr
-    int2byte = chr
-
-    def byte2int(bs):
-        return ord(bs[0])
-
-    def indexbytes(buf, i):
-        return ord(buf[i])
-
-    iterbytes = functools.partial(itertools.imap, ord)
-    import StringIO
-
-    StringIO = BytesIO = StringIO.StringIO
-    _assertCountEqual = "assertItemsEqual"
-    _assertRaisesRegex = "assertRaisesRegexp"
-    _assertRegex = "assertRegexpMatches"
-    _assertNotRegex = "assertNotRegexpMatches"
-_add_doc(b, """Byte literal""")
-_add_doc(u, """Text literal""")
-
-
-def assertCountEqual(self, *args, **kwargs):
-    return getattr(self, _assertCountEqual)(*args, **kwargs)
-
-
-def assertRaisesRegex(self, *args, **kwargs):
-    return getattr(self, _assertRaisesRegex)(*args, **kwargs)
-
-
-def assertRegex(self, *args, **kwargs):
-    return getattr(self, _assertRegex)(*args, **kwargs)
-
-
-def assertNotRegex(self, *args, **kwargs):
-    return getattr(self, _assertNotRegex)(*args, **kwargs)
-
-
-if PY3:
-    exec_ = getattr(moves.builtins, "exec")
-
-    def reraise(tp, value, tb=None):
-        try:
-            if value is None:
-                value = tp()
-            if value.__traceback__ is not tb:
-                raise value.with_traceback(tb)
-            raise value
-        finally:
-            value = None
-            tb = None
-
-else:
-
-    def exec_(_code_, _globs_=None, _locs_=None):
-        """Execute code in a namespace."""
-        if _globs_ is None:
-            frame = sys._getframe(1)
-            _globs_ = frame.f_globals
-            if _locs_ is None:
-                _locs_ = frame.f_locals
-            del frame
-        elif _locs_ is None:
-            _locs_ = _globs_
-        exec ("""exec _code_ in _globs_, _locs_""")
-
-    exec_(
-        """def reraise(tp, value, tb=None):
-    try:
-        raise tp, value, tb
-    finally:
-        tb = None
-"""
-    )
-
-
-if sys.version_info[:2] > (3,):
-    exec_(
-        """def raise_from(value, from_value):
-    try:
-        raise value from from_value
-    finally:
-        value = None
-"""
-    )
-else:
-
-    def raise_from(value, from_value):
-        raise value
-
-
-print_ = getattr(moves.builtins, "print", None)
-if print_ is None:
-
-    def print_(*args, **kwargs):
-        """The new-style print function for Python 2.4 and 2.5."""
-        fp = kwargs.pop("file", sys.stdout)
-        if fp is None:
-            return
-
-        def write(data):
-            if not isinstance(data, basestring):
-                data = str(data)
-            # If the file has an encoding, encode unicode with it.
-            if (
-                isinstance(fp, file)
-                and isinstance(data, unicode)
-                and fp.encoding is not None
-            ):
-                errors = getattr(fp, "errors", None)
-                if errors is None:
-                    errors = "strict"
-                data = data.encode(fp.encoding, errors)
-            fp.write(data)
-
-        want_unicode = False
-        sep = kwargs.pop("sep", None)
-        if sep is not None:
-            if isinstance(sep, unicode):
-                want_unicode = True
-            elif not isinstance(sep, str):
-                raise TypeError("sep must be None or a string")
-        end = kwargs.pop("end", None)
-        if end is not None:
-            if isinstance(end, unicode):
-                want_unicode = True
-            elif not isinstance(end, str):
-                raise TypeError("end must be None or a string")
-        if kwargs:
-            raise TypeError("invalid keyword arguments to print()")
-        if not want_unicode:
-            for arg in args:
-                if isinstance(arg, unicode):
-                    want_unicode = True
-                    break
-        if want_unicode:
-            newline = unicode("\n")
-            space = unicode(" ")
-        else:
-            newline = "\n"
-            space = " "
-        if sep is None:
-            sep = space
-        if end is None:
-            end = newline
-        for i, arg in enumerate(args):
-            if i:
-                write(sep)
-            write(arg)
-        write(end)
-
-
-if sys.version_info[:2] < (3, 3):
-    _print = print_
-
-    def print_(*args, **kwargs):
-        fp = kwargs.get("file", sys.stdout)
-        flush = kwargs.pop("flush", False)
-        _print(*args, **kwargs)
-        if flush and fp is not None:
-            fp.flush()
-
-
-_add_doc(reraise, """Reraise an exception.""")
-
-if sys.version_info[0:2] < (3, 4):
-    # This does exactly the same what the :func:`py3:functools.update_wrapper`
-    # function does on Python versions after 3.2. It sets the ``__wrapped__``
-    # attribute on ``wrapper`` object and it doesn't raise an error if any of
-    # the attributes mentioned in ``assigned`` and ``updated`` are missing on
-    # ``wrapped`` object.
-    def _update_wrapper(
-        wrapper,
-        wrapped,
-        assigned=functools.WRAPPER_ASSIGNMENTS,
-        updated=functools.WRAPPER_UPDATES,
-    ):
-        for attr in assigned:
-            try:
-                value = getattr(wrapped, attr)
-            except AttributeError:
-                continue
-            else:
-                setattr(wrapper, attr, value)
-        for attr in updated:
-            getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
-        wrapper.__wrapped__ = wrapped
-        return wrapper
-
-    _update_wrapper.__doc__ = functools.update_wrapper.__doc__
-
-    def wraps(
-        wrapped,
-        assigned=functools.WRAPPER_ASSIGNMENTS,
-        updated=functools.WRAPPER_UPDATES,
-    ):
-        return functools.partial(
-            _update_wrapper, wrapped=wrapped, assigned=assigned, updated=updated
-        )
-
-    wraps.__doc__ = functools.wraps.__doc__
-
-else:
-    wraps = functools.wraps
-
-
-def with_metaclass(meta, *bases):
-    """Create a base class with a metaclass."""
-    # This requires a bit of explanation: the basic idea is to make a dummy
-    # metaclass for one level of class instantiation that replaces itself with
-    # the actual metaclass.
-    class metaclass(type):
-        def __new__(cls, name, this_bases, d):
-            if sys.version_info[:2] >= (3, 7):
-                # This version introduced PEP 560 that requires a bit
-                # of extra care (we mimic what is done by __build_class__).
-                resolved_bases = types.resolve_bases(bases)
-                if resolved_bases is not bases:
-                    d["__orig_bases__"] = bases
-            else:
-                resolved_bases = bases
-            return meta(name, resolved_bases, d)
-
-        @classmethod
-        def __prepare__(cls, name, this_bases):
-            return meta.__prepare__(name, bases)
-
-    return type.__new__(metaclass, "temporary_class", (), {})
-
-
-def add_metaclass(metaclass):
-    """Class decorator for creating a class with a metaclass."""
-
-    def wrapper(cls):
-        orig_vars = cls.__dict__.copy()
-        slots = orig_vars.get("__slots__")
-        if slots is not None:
-            if isinstance(slots, str):
-                slots = [slots]
-            for slots_var in slots:
-                orig_vars.pop(slots_var)
-        orig_vars.pop("__dict__", None)
-        orig_vars.pop("__weakref__", None)
-        if hasattr(cls, "__qualname__"):
-            orig_vars["__qualname__"] = cls.__qualname__
-        return metaclass(cls.__name__, cls.__bases__, orig_vars)
-
-    return wrapper
-
-
-def ensure_binary(s, encoding="utf-8", errors="strict"):
-    """Coerce **s** to six.binary_type.
-
-    For Python 2:
-      - `unicode` -> encoded to `str`
-      - `str` -> `str`
-
-    For Python 3:
-      - `str` -> encoded to `bytes`
-      - `bytes` -> `bytes`
-    """
-    if isinstance(s, binary_type):
-        return s
-    if isinstance(s, text_type):
-        return s.encode(encoding, errors)
-    raise TypeError("not expecting type '%s'" % type(s))
-
-
-def ensure_str(s, encoding="utf-8", errors="strict"):
-    """Coerce *s* to `str`.
-
-    For Python 2:
-      - `unicode` -> encoded to `str`
-      - `str` -> `str`
-
-    For Python 3:
-      - `str` -> `str`
-      - `bytes` -> decoded to `str`
-    """
-    # Optimization: Fast return for the common case.
-    if type(s) is str:
-        return s
-    if PY2 and isinstance(s, text_type):
-        return s.encode(encoding, errors)
-    elif PY3 and isinstance(s, binary_type):
-        return s.decode(encoding, errors)
-    elif not isinstance(s, (text_type, binary_type)):
-        raise TypeError("not expecting type '%s'" % type(s))
-    return s
-
-
-def ensure_text(s, encoding="utf-8", errors="strict"):
-    """Coerce *s* to six.text_type.
-
-    For Python 2:
-      - `unicode` -> `unicode`
-      - `str` -> `unicode`
-
-    For Python 3:
-      - `str` -> `str`
-      - `bytes` -> decoded to `str`
-    """
-    if isinstance(s, binary_type):
-        return s.decode(encoding, errors)
-    elif isinstance(s, text_type):
-        return s
-    else:
-        raise TypeError("not expecting type '%s'" % type(s))
-
-
-def python_2_unicode_compatible(klass):
-    """
-    A class decorator that defines __unicode__ and __str__ methods under Python 2.
-    Under Python 3 it does nothing.
-
-    To support Python 2 and 3 with a single code base, define a __str__ method
-    returning text and apply this decorator to the class.
-    """
-    if PY2:
-        if "__str__" not in klass.__dict__:
-            raise ValueError(
-                "@python_2_unicode_compatible cannot be applied "
-                "to %s because it doesn't define __str__()." % klass.__name__
-            )
-        klass.__unicode__ = klass.__str__
-        klass.__str__ = lambda self: self.__unicode__().encode("utf-8")
-    return klass
-
-
-# Complete the moves implementation.
-# This code is at the end of this module to speed up module loading.
-# Turn this module into a package.
-__path__ = []  # required for PEP 302 and PEP 451
-__package__ = __name__  # see PEP 366 @ReservedAssignment
-if globals().get("__spec__") is not None:
-    __spec__.submodule_search_locations = []  # PEP 451 @UndefinedVariable
-# Remove other six meta path importers, since they cause problems. This can
-# happen if six is removed from sys.modules and then reloaded. (Setuptools does
-# this for some reason.)
-if sys.meta_path:
-    for i, importer in enumerate(sys.meta_path):
-        # Here's some real nastiness: Another "instance" of the six module might
-        # be floating around. Therefore, we can't use isinstance() to check for
-        # the six meta path importer, since the other six instance will have
-        # inserted an importer with different class.
-        if (
-            type(importer).__name__ == "_SixMetaPathImporter"
-            and importer.name == __name__
-        ):
-            del sys.meta_path[i]
-            break
-    del i, importer
-# Finally, add the importer to the meta path import hook.
-sys.meta_path.append(_importer)
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/command/install_lib.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/command/install_lib.py
deleted file mode 100644
index ad3089c8b144f292e9560c8cefcbab4012d09a45..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/command/install_lib.py
+++ /dev/null
@@ -1,238 +0,0 @@
-"""distutils.command.install_lib
-
-Implements the Distutils 'install_lib' command
-(install all Python modules)."""
-
-import os
-import importlib.util
-import sys
-
-from distutils.core import Command
-from distutils.errors import DistutilsOptionError
-
-
-# Extension for Python source files.
-PYTHON_SOURCE_EXTENSION = ".py"
-
-
-class install_lib(Command):
-
-    description = "install all Python modules (extensions and pure Python)"
-
-    # The byte-compilation options are a tad confusing.  Here are the
-    # possible scenarios:
-    #   1) no compilation at all (--no-compile --no-optimize)
-    #   2) compile .pyc only (--compile --no-optimize; default)
-    #   3) compile .pyc and "opt-1" .pyc (--compile --optimize)
-    #   4) compile "opt-1" .pyc only (--no-compile --optimize)
-    #   5) compile .pyc and "opt-2" .pyc (--compile --optimize-more)
-    #   6) compile "opt-2" .pyc only (--no-compile --optimize-more)
-    #
-    # The UI for this is two options, 'compile' and 'optimize'.
-    # 'compile' is strictly boolean, and only decides whether to
-    # generate .pyc files.  'optimize' is three-way (0, 1, or 2), and
-    # decides both whether to generate .pyc files and what level of
-    # optimization to use.
-
-    user_options = [
-        ('install-dir=', 'd', "directory to install to"),
-        ('build-dir=', 'b', "build directory (where to install from)"),
-        ('force', 'f', "force installation (overwrite existing files)"),
-        ('compile', 'c', "compile .py to .pyc [default]"),
-        ('no-compile', None, "don't compile .py files"),
-        (
-            'optimize=',
-            'O',
-            "also compile with optimization: -O1 for \"python -O\", "
-            "-O2 for \"python -OO\", and -O0 to disable [default: -O0]",
-        ),
-        ('skip-build', None, "skip the build steps"),
-    ]
-
-    boolean_options = ['force', 'compile', 'skip-build']
-    negative_opt = {'no-compile': 'compile'}
-
-    def initialize_options(self):
-        # let the 'install' command dictate our installation directory
-        self.install_dir = None
-        self.build_dir = None
-        self.force = 0
-        self.compile = None
-        self.optimize = None
-        self.skip_build = None
-
-    def finalize_options(self):
-        # Get all the information we need to install pure Python modules
-        # from the umbrella 'install' command -- build (source) directory,
-        # install (target) directory, and whether to compile .py files.
-        self.set_undefined_options(
-            'install',
-            ('build_lib', 'build_dir'),
-            ('install_lib', 'install_dir'),
-            ('force', 'force'),
-            ('compile', 'compile'),
-            ('optimize', 'optimize'),
-            ('skip_build', 'skip_build'),
-        )
-
-        if self.compile is None:
-            self.compile = True
-        if self.optimize is None:
-            self.optimize = False
-
-        if not isinstance(self.optimize, int):
-            try:
-                self.optimize = int(self.optimize)
-                if self.optimize not in (0, 1, 2):
-                    raise AssertionError
-            except (ValueError, AssertionError):
-                raise DistutilsOptionError("optimize must be 0, 1, or 2")
-
-    def run(self):
-        # Make sure we have built everything we need first
-        self.build()
-
-        # Install everything: simply dump the entire contents of the build
-        # directory to the installation directory (that's the beauty of
-        # having a build directory!)
-        outfiles = self.install()
-
-        # (Optionally) compile .py to .pyc
-        if outfiles is not None and self.distribution.has_pure_modules():
-            self.byte_compile(outfiles)
-
-    # -- Top-level worker functions ------------------------------------
-    # (called from 'run()')
-
-    def build(self):
-        if not self.skip_build:
-            if self.distribution.has_pure_modules():
-                self.run_command('build_py')
-            if self.distribution.has_ext_modules():
-                self.run_command('build_ext')
-
-    def install(self):
-        if os.path.isdir(self.build_dir):
-            outfiles = self.copy_tree(self.build_dir, self.install_dir)
-        else:
-            self.warn(
-                "'%s' does not exist -- no Python modules to install" % self.build_dir
-            )
-            return
-        return outfiles
-
-    def byte_compile(self, files):
-        if sys.dont_write_bytecode:
-            self.warn('byte-compiling is disabled, skipping.')
-            return
-
-        from distutils.util import byte_compile
-
-        # Get the "--root" directory supplied to the "install" command,
-        # and use it as a prefix to strip off the purported filename
-        # encoded in bytecode files.  This is far from complete, but it
-        # should at least generate usable bytecode in RPM distributions.
-        install_root = self.get_finalized_command('install').root
-
-        if self.compile:
-            byte_compile(
-                files,
-                optimize=0,
-                force=self.force,
-                prefix=install_root,
-                dry_run=self.dry_run,
-            )
-        if self.optimize > 0:
-            byte_compile(
-                files,
-                optimize=self.optimize,
-                force=self.force,
-                prefix=install_root,
-                verbose=self.verbose,
-                dry_run=self.dry_run,
-            )
-
-    # -- Utility methods -----------------------------------------------
-
-    def _mutate_outputs(self, has_any, build_cmd, cmd_option, output_dir):
-        if not has_any:
-            return []
-
-        build_cmd = self.get_finalized_command(build_cmd)
-        build_files = build_cmd.get_outputs()
-        build_dir = getattr(build_cmd, cmd_option)
-
-        prefix_len = len(build_dir) + len(os.sep)
-        outputs = []
-        for file in build_files:
-            outputs.append(os.path.join(output_dir, file[prefix_len:]))
-
-        return outputs
-
-    def _bytecode_filenames(self, py_filenames):
-        bytecode_files = []
-        for py_file in py_filenames:
-            # Since build_py handles package data installation, the
-            # list of outputs can contain more than just .py files.
-            # Make sure we only report bytecode for the .py files.
-            ext = os.path.splitext(os.path.normcase(py_file))[1]
-            if ext != PYTHON_SOURCE_EXTENSION:
-                continue
-            if self.compile:
-                bytecode_files.append(
-                    importlib.util.cache_from_source(py_file, optimization='')
-                )
-            if self.optimize > 0:
-                bytecode_files.append(
-                    importlib.util.cache_from_source(
-                        py_file, optimization=self.optimize
-                    )
-                )
-
-        return bytecode_files
-
-    # -- External interface --------------------------------------------
-    # (called by outsiders)
-
-    def get_outputs(self):
-        """Return the list of files that would be installed if this command
-        were actually run.  Not affected by the "dry-run" flag or whether
-        modules have actually been built yet.
-        """
-        pure_outputs = self._mutate_outputs(
-            self.distribution.has_pure_modules(),
-            'build_py',
-            'build_lib',
-            self.install_dir,
-        )
-        if self.compile:
-            bytecode_outputs = self._bytecode_filenames(pure_outputs)
-        else:
-            bytecode_outputs = []
-
-        ext_outputs = self._mutate_outputs(
-            self.distribution.has_ext_modules(),
-            'build_ext',
-            'build_lib',
-            self.install_dir,
-        )
-
-        return pure_outputs + bytecode_outputs + ext_outputs
-
-    def get_inputs(self):
-        """Get the list of files that are input to this command, ie. the
-        files that get installed as they are named in the build tree.
-        The files in this list correspond one-to-one to the output
-        filenames returned by 'get_outputs()'.
-        """
-        inputs = []
-
-        if self.distribution.has_pure_modules():
-            build_py = self.get_finalized_command('build_py')
-            inputs.extend(build_py.get_outputs())
-
-        if self.distribution.has_ext_modules():
-            build_ext = self.get_finalized_command('build_ext')
-            inputs.extend(build_ext.get_outputs())
-
-        return inputs
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/pspnet_r50-d8.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/pspnet_r50-d8.py
deleted file mode 100644
index f451e08ad2eb0732dcb806b1851eb978d4acf136..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/pspnet_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='PSPHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        pool_scales=(1, 2, 3, 6),
-        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/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/ld_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/ld_head.py
deleted file mode 100644
index 501e1f7befa086f0b2f818531807411fc383d7bd..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/ld_head.py
+++ /dev/null
@@ -1,261 +0,0 @@
-import torch
-from mmcv.runner import force_fp32
-
-from mmdet.core import (bbox2distance, bbox_overlaps, distance2bbox,
-                        multi_apply, reduce_mean)
-from ..builder import HEADS, build_loss
-from .gfl_head import GFLHead
-
-
-@HEADS.register_module()
-class LDHead(GFLHead):
-    """Localization distillation Head. (Short description)
-
-    It utilizes the learned bbox distributions to transfer the localization
-    dark knowledge from teacher to student. Original paper: `Localization
-    Distillation for Object Detection. <https://arxiv.org/abs/2102.12252>`_
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        loss_ld (dict): Config of Localization Distillation Loss (LD),
-            T is the temperature for distillation.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 loss_ld=dict(
-                     type='LocalizationDistillationLoss',
-                     loss_weight=0.25,
-                     T=10),
-                 **kwargs):
-
-        super(LDHead, self).__init__(num_classes, in_channels, **kwargs)
-        self.loss_ld = build_loss(loss_ld)
-
-    def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
-                    bbox_targets, stride, soft_targets, num_total_samples):
-        """Compute loss of a single scale level.
-
-        Args:
-            anchors (Tensor): Box reference for each scale level with shape
-                (N, num_total_anchors, 4).
-            cls_score (Tensor): Cls and quality joint scores for each scale
-                level has shape (N, num_classes, H, W).
-            bbox_pred (Tensor): Box distribution logits for each scale
-                level with shape (N, 4*(n+1), H, W), n is max value of integral
-                set.
-            labels (Tensor): Labels of each anchors with shape
-                (N, num_total_anchors).
-            label_weights (Tensor): Label weights of each anchor with shape
-                (N, num_total_anchors)
-            bbox_targets (Tensor): BBox regression targets of each anchor wight
-                shape (N, num_total_anchors, 4).
-            stride (tuple): Stride in this scale level.
-            num_total_samples (int): Number of positive samples that is
-                reduced over all GPUs.
-
-        Returns:
-            dict[tuple, Tensor]: Loss components and weight targets.
-        """
-        assert stride[0] == stride[1], 'h stride is not equal to w stride!'
-        anchors = anchors.reshape(-1, 4)
-        cls_score = cls_score.permute(0, 2, 3,
-                                      1).reshape(-1, self.cls_out_channels)
-        bbox_pred = bbox_pred.permute(0, 2, 3,
-                                      1).reshape(-1, 4 * (self.reg_max + 1))
-        soft_targets = soft_targets.permute(0, 2, 3,
-                                            1).reshape(-1,
-                                                       4 * (self.reg_max + 1))
-
-        bbox_targets = bbox_targets.reshape(-1, 4)
-        labels = labels.reshape(-1)
-        label_weights = label_weights.reshape(-1)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        bg_class_ind = self.num_classes
-        pos_inds = ((labels >= 0)
-                    & (labels < bg_class_ind)).nonzero().squeeze(1)
-        score = label_weights.new_zeros(labels.shape)
-
-        if len(pos_inds) > 0:
-            pos_bbox_targets = bbox_targets[pos_inds]
-            pos_bbox_pred = bbox_pred[pos_inds]
-            pos_anchors = anchors[pos_inds]
-            pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
-
-            weight_targets = cls_score.detach().sigmoid()
-            weight_targets = weight_targets.max(dim=1)[0][pos_inds]
-            pos_bbox_pred_corners = self.integral(pos_bbox_pred)
-            pos_decode_bbox_pred = distance2bbox(pos_anchor_centers,
-                                                 pos_bbox_pred_corners)
-            pos_decode_bbox_targets = pos_bbox_targets / stride[0]
-            score[pos_inds] = bbox_overlaps(
-                pos_decode_bbox_pred.detach(),
-                pos_decode_bbox_targets,
-                is_aligned=True)
-            pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
-            pos_soft_targets = soft_targets[pos_inds]
-            soft_corners = pos_soft_targets.reshape(-1, self.reg_max + 1)
-
-            target_corners = bbox2distance(pos_anchor_centers,
-                                           pos_decode_bbox_targets,
-                                           self.reg_max).reshape(-1)
-
-            # regression loss
-            loss_bbox = self.loss_bbox(
-                pos_decode_bbox_pred,
-                pos_decode_bbox_targets,
-                weight=weight_targets,
-                avg_factor=1.0)
-
-            # dfl loss
-            loss_dfl = self.loss_dfl(
-                pred_corners,
-                target_corners,
-                weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
-                avg_factor=4.0)
-
-            # ld loss
-            loss_ld = self.loss_ld(
-                pred_corners,
-                soft_corners,
-                weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
-                avg_factor=4.0)
-
-        else:
-            loss_ld = bbox_pred.sum() * 0
-            loss_bbox = bbox_pred.sum() * 0
-            loss_dfl = bbox_pred.sum() * 0
-            weight_targets = bbox_pred.new_tensor(0)
-
-        # cls (qfl) loss
-        loss_cls = self.loss_cls(
-            cls_score, (labels, score),
-            weight=label_weights,
-            avg_factor=num_total_samples)
-
-        return loss_cls, loss_bbox, loss_dfl, loss_ld, weight_targets.sum()
-
-    def forward_train(self,
-                      x,
-                      out_teacher,
-                      img_metas,
-                      gt_bboxes,
-                      gt_labels=None,
-                      gt_bboxes_ignore=None,
-                      proposal_cfg=None,
-                      **kwargs):
-        """
-        Args:
-            x (list[Tensor]): Features from FPN.
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes (Tensor): Ground truth bboxes of the image,
-                shape (num_gts, 4).
-            gt_labels (Tensor): Ground truth labels of each box,
-                shape (num_gts,).
-            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4).
-            proposal_cfg (mmcv.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used
-
-        Returns:
-            tuple[dict, list]: The loss components and proposals of each image.
-
-            - losses (dict[str, Tensor]): A dictionary of loss components.
-            - proposal_list (list[Tensor]): Proposals of each image.
-        """
-        outs = self(x)
-        soft_target = out_teacher[1]
-        if gt_labels is None:
-            loss_inputs = outs + (gt_bboxes, soft_target, img_metas)
-        else:
-            loss_inputs = outs + (gt_bboxes, gt_labels, soft_target, img_metas)
-        losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
-        if proposal_cfg is None:
-            return losses
-        else:
-            proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg)
-            return losses, proposal_list
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             soft_target,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Cls and quality scores for each scale
-                level has shape (N, num_classes, H, W).
-            bbox_preds (list[Tensor]): Box distribution logits for each scale
-                level with shape (N, 4*(n+1), H, W), n is max value of integral
-                set.
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels)
-        if cls_reg_targets is None:
-            return None
-
-        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
-         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
-        num_total_samples = reduce_mean(
-            torch.tensor(num_total_pos, dtype=torch.float,
-                         device=device)).item()
-        num_total_samples = max(num_total_samples, 1.0)
-
-        losses_cls, losses_bbox, losses_dfl, losses_ld, \
-            avg_factor = multi_apply(
-                self.loss_single,
-                anchor_list,
-                cls_scores,
-                bbox_preds,
-                labels_list,
-                label_weights_list,
-                bbox_targets_list,
-                self.anchor_generator.strides,
-                soft_target,
-                num_total_samples=num_total_samples)
-
-        avg_factor = sum(avg_factor) + 1e-6
-        avg_factor = reduce_mean(avg_factor).item()
-        losses_bbox = [x / avg_factor for x in losses_bbox]
-        losses_dfl = [x / avg_factor for x in losses_dfl]
-        return dict(
-            loss_cls=losses_cls,
-            loss_bbox=losses_bbox,
-            loss_dfl=losses_dfl,
-            loss_ld=losses_ld)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/necks/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/necks/__init__.py
deleted file mode 100644
index 02f833a8a0f538a8c06fef622d1cadc1a1b66ea2..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/necks/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from .bfp import BFP
-from .channel_mapper import ChannelMapper
-from .fpg import FPG
-from .fpn import FPN
-from .fpn_carafe import FPN_CARAFE
-from .hrfpn import HRFPN
-from .nas_fpn import NASFPN
-from .nasfcos_fpn import NASFCOS_FPN
-from .pafpn import PAFPN
-from .rfp import RFP
-from .yolo_neck import YOLOV3Neck
-
-__all__ = [
-    'FPN', 'BFP', 'ChannelMapper', 'HRFPN', 'NASFPN', 'FPN_CARAFE', 'PAFPN',
-    'NASFCOS_FPN', 'RFP', 'YOLOV3Neck', 'FPG'
-]
diff --git a/spaces/RobinWZQ/CCLAP/app.py b/spaces/RobinWZQ/CCLAP/app.py
deleted file mode 100644
index 040cd8f64a52125767b04c8603b00308d043f13e..0000000000000000000000000000000000000000
--- a/spaces/RobinWZQ/CCLAP/app.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from diffusers import DiffusionPipeline,UniPCMultistepScheduler
-import gradio as gr
-import torch
-import gc
-from style_trsfer import style_transfer_method
-
-
-def generate(style_image,text, negative_prompts,steps,guidance_scale):
-    pipeline = DiffusionPipeline.from_pretrained("./CCLAP")
-    pipeline.scheduler = UniPCMultistepScheduler.from_config(
-            pipeline.scheduler.config)
-    device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-    if device.type == 'cuda':
-            pipeline.enable_xformers_memory_efficient_attention()
-    pipeline.to(device)
-    torch.cuda.empty_cache()
-    gc.collect()
-    content_image = pipeline(text,
-                     num_inference_steps=steps,
-                     negative_prompt=negative_prompts,
-                     guidance_scale=guidance_scale).images[0]
-    result = style_transfer_method(content_image,style_image)
-    return content_image,result
-
-
-if __name__ == '__main__':
-
-    demo = gr.Interface(title="CCLAP",
-                        description = (
-                            "This is the demo of CCLAP to generate Chinese landscape painting."
-                            ),
-                        css="",
-                        fn=generate,
-                        inputs=[gr.Image(label="Style Image",shape=(512,512)),
-                                gr.Textbox(lines=3, placeholder="Input the prompt", label="Prompt"), 
-                                gr.Textbox(lines=3, placeholder="low quality", label="Negative prompt"), 
-                                gr.Slider(minimum=0, maximum=100, value=20,label='Steps'),
-                                gr.Slider(minimum=0, maximum=30, value=7.5,label='Guidance_scale'),
-                                ],
-                        outputs=[gr.Image(label="Content Output",shape=(256,256)),
-                                 gr.Image(label="Final Output",shape=(256,256))],
-                        examples = [
-                                    [
-                                        'style_image/style1.jpg',
-                                        'A Chinese landscape painting of a mountain landscape with trees',
-                                        'low quality',
-                                        20,
-                                        7.5
-                                    ],
-                                    [
-                                        'style_image/style2.jpg',
-                                        'A Chinese landscape painting of a building with trees in front of it',
-                                        'low quality',
-                                        20,
-                                        7.5
-                                    ],
-                                    [
-                                        'style_image/style3.jpg',
-                                        'A Chinese landscape painting of a landscape with mountains in the background',
-                                        'low quality',
-                                        20,
-                                        7.5
-                                    ],
-                                    [
-                                        'style_image/style4.jpg',
-                                        'A Chinese landscape painting of a landscape with mountains and a river',
-                                        'low quality',
-                                        20,
-                                        7.5
-                                    ],
-                                ],
-                        )
-
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/Sandiago21/speech-to-speech-translation-german-2/README.md b/spaces/Sandiago21/speech-to-speech-translation-german-2/README.md
deleted file mode 100644
index a74f3b7327bd73e70cd40ae2817af980d41a2443..0000000000000000000000000000000000000000
--- a/spaces/Sandiago21/speech-to-speech-translation-german-2/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-title: speech-to-speech-translation-german-2
-app_file: app.py
-sdk: gradio
-sdk_version: 3.36.0
----
diff --git a/spaces/Seetha/IMA-pipeline-streamlit/README.md b/spaces/Seetha/IMA-pipeline-streamlit/README.md
deleted file mode 100644
index d2f06e266b6141ae0416ffa068e605e988bb8250..0000000000000000000000000000000000000000
--- a/spaces/Seetha/IMA-pipeline-streamlit/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: IMA Pipeline Streamlit
-emoji: 🦀
-colorFrom: purple
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Shypanties22/FantasyMe/convertosd.py b/spaces/Shypanties22/FantasyMe/convertosd.py
deleted file mode 100644
index e4bec6cbe894dd74b24f633cc66346d687d3f802..0000000000000000000000000000000000000000
--- a/spaces/Shypanties22/FantasyMe/convertosd.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
-# *Only* converts the UNet, VAE, and Text Encoder.
-# Does not convert optimizer state or any other thing.
-# Written by jachiam
-
-import argparse
-import os.path as osp
-
-import torch
-import gc 
-
-# =================#
-# UNet Conversion #
-# =================#
-
-unet_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
-    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
-    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
-    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
-    ("input_blocks.0.0.weight", "conv_in.weight"),
-    ("input_blocks.0.0.bias", "conv_in.bias"),
-    ("out.0.weight", "conv_norm_out.weight"),
-    ("out.0.bias", "conv_norm_out.bias"),
-    ("out.2.weight", "conv_out.weight"),
-    ("out.2.bias", "conv_out.bias"),
-]
-
-unet_conversion_map_resnet = [
-    # (stable-diffusion, HF Diffusers)
-    ("in_layers.0", "norm1"),
-    ("in_layers.2", "conv1"),
-    ("out_layers.0", "norm2"),
-    ("out_layers.3", "conv2"),
-    ("emb_layers.1", "time_emb_proj"),
-    ("skip_connection", "conv_shortcut"),
-]
-
-unet_conversion_map_layer = []
-# hardcoded number of downblocks and resnets/attentions...
-# would need smarter logic for other networks.
-for i in range(4):
-    # loop over downblocks/upblocks
-
-    for j in range(2):
-        # loop over resnets/attentions for downblocks
-        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-        sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
-        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
-        if i < 3:
-            # no attention layers in down_blocks.3
-            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-            sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
-            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
-    for j in range(3):
-        # loop over resnets/attentions for upblocks
-        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-        sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
-        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
-        if i > 0:
-            # no attention layers in up_blocks.0
-            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
-            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
-    if i < 3:
-        # no downsample in down_blocks.3
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-        sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
-        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        # no upsample in up_blocks.3
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
-        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-
-hf_mid_atn_prefix = "mid_block.attentions.0."
-sd_mid_atn_prefix = "middle_block.1."
-unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-
-for j in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{j}."
-    sd_mid_res_prefix = f"middle_block.{2*j}."
-    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-def convert_unet_state_dict(unet_state_dict):
-    # buyer beware: this is a *brittle* function,
-    # and correct output requires that all of these pieces interact in
-    # the exact order in which I have arranged them.
-    mapping = {k: k for k in unet_state_dict.keys()}
-    for sd_name, hf_name in unet_conversion_map:
-        mapping[hf_name] = sd_name
-    for k, v in mapping.items():
-        if "resnets" in k:
-            for sd_part, hf_part in unet_conversion_map_resnet:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    for k, v in mapping.items():
-        for sd_part, hf_part in unet_conversion_map_layer:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
-    return new_state_dict
-
-
-# ================#
-# VAE Conversion #
-# ================#
-
-vae_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("nin_shortcut", "conv_shortcut"),
-    ("norm_out", "conv_norm_out"),
-    ("mid.attn_1.", "mid_block.attentions.0."),
-]
-
-for i in range(4):
-    # down_blocks have two resnets
-    for j in range(2):
-        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
-        sd_down_prefix = f"encoder.down.{i}.block.{j}."
-        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
-
-    if i < 3:
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
-        sd_downsample_prefix = f"down.{i}.downsample."
-        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"up.{3-i}.upsample."
-        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
-
-    # up_blocks have three resnets
-    # also, up blocks in hf are numbered in reverse from sd
-    for j in range(3):
-        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
-        sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
-        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
-
-# this part accounts for mid blocks in both the encoder and the decoder
-for i in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{i}."
-    sd_mid_res_prefix = f"mid.block_{i+1}."
-    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-vae_conversion_map_attn = [
-    # (stable-diffusion, HF Diffusers)
-    ("norm.", "group_norm."),
-    ("q.", "query."),
-    ("k.", "key."),
-    ("v.", "value."),
-    ("proj_out.", "proj_attn."),
-]
-
-
-def reshape_weight_for_sd(w):
-    # convert HF linear weights to SD conv2d weights
-    return w.reshape(*w.shape, 1, 1)
-
-
-def convert_vae_state_dict(vae_state_dict):
-    mapping = {k: k for k in vae_state_dict.keys()}
-    for k, v in mapping.items():
-        for sd_part, hf_part in vae_conversion_map:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    for k, v in mapping.items():
-        if "attentions" in k:
-            for sd_part, hf_part in vae_conversion_map_attn:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
-    weights_to_convert = ["q", "k", "v", "proj_out"]
-    print("Converting to CKPT ...")    
-    for k, v in new_state_dict.items():
-        for weight_name in weights_to_convert:
-            if f"mid.attn_1.{weight_name}.weight" in k:
-                new_state_dict[k] = reshape_weight_for_sd(v)
-    return new_state_dict
-
-
-# =========================#
-# Text Encoder Conversion #
-# =========================#
-# pretty much a no-op
-
-
-def convert_text_enc_state_dict(text_enc_dict):
-    return text_enc_dict
-
-
-def convert(model_path, checkpoint_path):    
-    unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
-    vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
-    text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
-
-    # Convert the UNet model
-    unet_state_dict = torch.load(unet_path, map_location='cpu')
-    unet_state_dict = convert_unet_state_dict(unet_state_dict)
-    unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
-
-    # Convert the VAE model
-    vae_state_dict = torch.load(vae_path, map_location='cpu')
-    vae_state_dict = convert_vae_state_dict(vae_state_dict)
-    vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
-
-    # Convert the text encoder model
-    text_enc_dict = torch.load(text_enc_path, map_location='cpu')
-    text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
-    text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
-
-    # Put together new checkpoint
-    state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
-    
-    state_dict = {k:v.half() for k,v in state_dict.items()}
-    state_dict = {"state_dict": state_dict}
-    torch.save(state_dict, checkpoint_path)
-    del state_dict, text_enc_dict, vae_state_dict, unet_state_dict
-    torch.cuda.empty_cache()
-    gc.collect()
diff --git a/spaces/SpacesExamples/fastapi_t5/main.py b/spaces/SpacesExamples/fastapi_t5/main.py
deleted file mode 100644
index 740cdc4dd73d6d698c815cce5deaa31549210962..0000000000000000000000000000000000000000
--- a/spaces/SpacesExamples/fastapi_t5/main.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from fastapi import FastAPI
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
-
-from transformers import pipeline
-
-app = FastAPI()
-
-pipe_flan = pipeline("text2text-generation", model="google/flan-t5-small")
-
-@app.get("/infer_t5")
-def t5(input):
-    output = pipe_flan(input)
-    return {"output": output[0]["generated_text"]}
-
-app.mount("/", StaticFiles(directory="static", html=True), name="static")
-
-@app.get("/")
-def index() -> FileResponse:
-    return FileResponse(path="/app/static/index.html", media_type="text/html")
\ No newline at end of file
diff --git a/spaces/Stevross/Astrid-1B-UI/README.md b/spaces/Stevross/Astrid-1B-UI/README.md
deleted file mode 100644
index 2a0bdbfdf18bc782efc56d6abc3a0315904e8294..0000000000000000000000000000000000000000
--- a/spaces/Stevross/Astrid-1B-UI/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Astrid 1B UI
-emoji: 🔥
-colorFrom: pink
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.25.0
-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/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/magics/basic.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/magics/basic.py
deleted file mode 100644
index 814dec72e291f26dba9da04714129dd27f0fbd3d..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/magics/basic.py
+++ /dev/null
@@ -1,663 +0,0 @@
-"""Implementation of basic magic functions."""
-
-
-from logging import error
-import io
-import os
-from pprint import pformat
-import sys
-from warnings import warn
-
-from traitlets.utils.importstring import import_item
-from IPython.core import magic_arguments, page
-from IPython.core.error import UsageError
-from IPython.core.magic import Magics, magics_class, line_magic, magic_escapes
-from IPython.utils.text import format_screen, dedent, indent
-from IPython.testing.skipdoctest import skip_doctest
-from IPython.utils.ipstruct import Struct
-
-
-class MagicsDisplay(object):
-    def __init__(self, magics_manager, ignore=None):
-        self.ignore = ignore if ignore else []
-        self.magics_manager = magics_manager
-    
-    def _lsmagic(self):
-        """The main implementation of the %lsmagic"""
-        mesc = magic_escapes['line']
-        cesc = magic_escapes['cell']
-        mman = self.magics_manager
-        magics = mman.lsmagic()
-        out = ['Available line magics:',
-               mesc + ('  '+mesc).join(sorted([m for m,v in magics['line'].items() if (v not in self.ignore)])),
-               '',
-               'Available cell magics:',
-               cesc + ('  '+cesc).join(sorted([m for m,v in magics['cell'].items() if (v not in self.ignore)])),
-               '',
-               mman.auto_status()]
-        return '\n'.join(out)
-
-    def _repr_pretty_(self, p, cycle):
-        p.text(self._lsmagic())
-    
-    def __str__(self):
-        return self._lsmagic()
-    
-    def _jsonable(self):
-        """turn magics dict into jsonable dict of the same structure
-
-        replaces object instances with their class names as strings
-        """
-        magic_dict = {}
-        mman = self.magics_manager
-        magics = mman.lsmagic()
-        for key, subdict in magics.items():
-            d = {}
-            magic_dict[key] = d
-            for name, obj in subdict.items():
-                try:
-                    classname = obj.__self__.__class__.__name__
-                except AttributeError:
-                    classname = 'Other'
-                
-                d[name] = classname
-        return magic_dict
-        
-    def _repr_json_(self):
-        return self._jsonable()
-
-
-@magics_class
-class BasicMagics(Magics):
-    """Magics that provide central IPython functionality.
-
-    These are various magics that don't fit into specific categories but that
-    are all part of the base 'IPython experience'."""
-
-    @skip_doctest
-    @magic_arguments.magic_arguments()
-    @magic_arguments.argument(
-        '-l', '--line', action='store_true',
-        help="""Create a line magic alias."""
-    )
-    @magic_arguments.argument(
-        '-c', '--cell', action='store_true',
-        help="""Create a cell magic alias."""
-    )
-    @magic_arguments.argument(
-        'name',
-        help="""Name of the magic to be created."""
-    )
-    @magic_arguments.argument(
-        'target',
-        help="""Name of the existing line or cell magic."""
-    )
-    @magic_arguments.argument(
-        '-p', '--params', default=None,
-        help="""Parameters passed to the magic function."""
-    )
-    @line_magic
-    def alias_magic(self, line=''):
-        """Create an alias for an existing line or cell magic.
-
-        Examples
-        --------
-        ::
-
-          In [1]: %alias_magic t timeit
-          Created `%t` as an alias for `%timeit`.
-          Created `%%t` as an alias for `%%timeit`.
-
-          In [2]: %t -n1 pass
-          1 loops, best of 3: 954 ns per loop
-
-          In [3]: %%t -n1
-             ...: pass
-             ...:
-          1 loops, best of 3: 954 ns per loop
-
-          In [4]: %alias_magic --cell whereami pwd
-          UsageError: Cell magic function `%%pwd` not found.
-          In [5]: %alias_magic --line whereami pwd
-          Created `%whereami` as an alias for `%pwd`.
-
-          In [6]: %whereami
-          Out[6]: u'/home/testuser'
-
-          In [7]: %alias_magic h history "-p -l 30" --line
-          Created `%h` as an alias for `%history -l 30`.
-        """
-
-        args = magic_arguments.parse_argstring(self.alias_magic, line)
-        shell = self.shell
-        mman = self.shell.magics_manager
-        escs = ''.join(magic_escapes.values())
-
-        target = args.target.lstrip(escs)
-        name = args.name.lstrip(escs)
-
-        params = args.params
-        if (params and
-                ((params.startswith('"') and params.endswith('"'))
-                or (params.startswith("'") and params.endswith("'")))):
-            params = params[1:-1]
-
-        # Find the requested magics.
-        m_line = shell.find_magic(target, 'line')
-        m_cell = shell.find_magic(target, 'cell')
-        if args.line and m_line is None:
-            raise UsageError('Line magic function `%s%s` not found.' %
-                             (magic_escapes['line'], target))
-        if args.cell and m_cell is None:
-            raise UsageError('Cell magic function `%s%s` not found.' %
-                             (magic_escapes['cell'], target))
-
-        # If --line and --cell are not specified, default to the ones
-        # that are available.
-        if not args.line and not args.cell:
-            if not m_line and not m_cell:
-                raise UsageError(
-                    'No line or cell magic with name `%s` found.' % target
-                )
-            args.line = bool(m_line)
-            args.cell = bool(m_cell)
-
-        params_str = "" if params is None else " " + params
-
-        if args.line:
-            mman.register_alias(name, target, 'line', params)
-            print('Created `%s%s` as an alias for `%s%s%s`.' % (
-                magic_escapes['line'], name,
-                magic_escapes['line'], target, params_str))
-
-        if args.cell:
-            mman.register_alias(name, target, 'cell', params)
-            print('Created `%s%s` as an alias for `%s%s%s`.' % (
-                magic_escapes['cell'], name,
-                magic_escapes['cell'], target, params_str))
-
-    @line_magic
-    def lsmagic(self, parameter_s=''):
-        """List currently available magic functions."""
-        return MagicsDisplay(self.shell.magics_manager, ignore=[])
-
-    def _magic_docs(self, brief=False, rest=False):
-        """Return docstrings from magic functions."""
-        mman = self.shell.magics_manager
-        docs = mman.lsmagic_docs(brief, missing='No documentation')
-
-        if rest:
-            format_string = '**%s%s**::\n\n%s\n\n'
-        else:
-            format_string = '%s%s:\n%s\n'
-
-        return ''.join(
-            [format_string % (magic_escapes['line'], fname,
-                              indent(dedent(fndoc)))
-             for fname, fndoc in sorted(docs['line'].items())]
-            +
-            [format_string % (magic_escapes['cell'], fname,
-                              indent(dedent(fndoc)))
-             for fname, fndoc in sorted(docs['cell'].items())]
-        )
-
-    @line_magic
-    def magic(self, parameter_s=''):
-        """Print information about the magic function system.
-
-        Supported formats: -latex, -brief, -rest
-        """
-
-        mode = ''
-        try:
-            mode = parameter_s.split()[0][1:]
-        except IndexError:
-            pass
-
-        brief = (mode == 'brief')
-        rest = (mode == 'rest')
-        magic_docs = self._magic_docs(brief, rest)
-
-        if mode == 'latex':
-            print(self.format_latex(magic_docs))
-            return
-        else:
-            magic_docs = format_screen(magic_docs)
-
-        out = ["""
-IPython's 'magic' functions
-===========================
-
-The magic function system provides a series of functions which allow you to
-control the behavior of IPython itself, plus a lot of system-type
-features. There are two kinds of magics, line-oriented and cell-oriented.
-
-Line magics are prefixed with the % character and work much like OS
-command-line calls: they get as an argument the rest of the line, where
-arguments are passed without parentheses or quotes.  For example, this will
-time the given statement::
-
-        %timeit range(1000)
-
-Cell magics are prefixed with a double %%, and they are functions that get as
-an argument not only the rest of the line, but also the lines below it in a
-separate argument.  These magics are called with two arguments: the rest of the
-call line and the body of the cell, consisting of the lines below the first.
-For example::
-
-        %%timeit x = numpy.random.randn((100, 100))
-        numpy.linalg.svd(x)
-
-will time the execution of the numpy svd routine, running the assignment of x
-as part of the setup phase, which is not timed.
-
-In a line-oriented client (the terminal or Qt console IPython), starting a new
-input with %% will automatically enter cell mode, and IPython will continue
-reading input until a blank line is given.  In the notebook, simply type the
-whole cell as one entity, but keep in mind that the %% escape can only be at
-the very start of the cell.
-
-NOTE: If you have 'automagic' enabled (via the command line option or with the
-%automagic function), you don't need to type in the % explicitly for line
-magics; cell magics always require an explicit '%%' escape.  By default,
-IPython ships with automagic on, so you should only rarely need the % escape.
-
-Example: typing '%cd mydir' (without the quotes) changes your working directory
-to 'mydir', if it exists.
-
-For a list of the available magic functions, use %lsmagic. For a description
-of any of them, type %magic_name?, e.g. '%cd?'.
-
-Currently the magic system has the following functions:""",
-       magic_docs,
-       "Summary of magic functions (from %slsmagic):" % magic_escapes['line'],
-       str(self.lsmagic()),
-       ]
-        page.page('\n'.join(out))
-
-
-    @line_magic
-    def page(self, parameter_s=''):
-        """Pretty print the object and display it through a pager.
-
-        %page [options] OBJECT
-
-        If no object is given, use _ (last output).
-
-        Options:
-
-          -r: page str(object), don't pretty-print it."""
-
-        # After a function contributed by Olivier Aubert, slightly modified.
-
-        # Process options/args
-        opts, args = self.parse_options(parameter_s, 'r')
-        raw = 'r' in opts
-
-        oname = args and args or '_'
-        info = self.shell._ofind(oname)
-        if info.found:
-            if raw:
-                txt = str(info.obj)
-            else:
-                txt = pformat(info.obj)
-            page.page(txt)
-        else:
-            print('Object `%s` not found' % oname)
-
-    @line_magic
-    def pprint(self, parameter_s=''):
-        """Toggle pretty printing on/off."""
-        ptformatter = self.shell.display_formatter.formatters['text/plain']
-        ptformatter.pprint = bool(1 - ptformatter.pprint)
-        print('Pretty printing has been turned',
-              ['OFF','ON'][ptformatter.pprint])
-
-    @line_magic
-    def colors(self, parameter_s=''):
-        """Switch color scheme for prompts, info system and exception handlers.
-
-        Currently implemented schemes: NoColor, Linux, LightBG.
-
-        Color scheme names are not case-sensitive.
-
-        Examples
-        --------
-        To get a plain black and white terminal::
-
-          %colors nocolor
-        """
-        def color_switch_err(name):
-            warn('Error changing %s color schemes.\n%s' %
-                 (name, sys.exc_info()[1]), stacklevel=2)
-
-
-        new_scheme = parameter_s.strip()
-        if not new_scheme:
-            raise UsageError(
-                "%colors: you must specify a color scheme. See '%colors?'")
-        # local shortcut
-        shell = self.shell
-
-        # Set shell colour scheme
-        try:
-            shell.colors = new_scheme
-            shell.refresh_style()
-        except:
-            color_switch_err('shell')
-
-        # Set exception colors
-        try:
-            shell.InteractiveTB.set_colors(scheme = new_scheme)
-            shell.SyntaxTB.set_colors(scheme = new_scheme)
-        except:
-            color_switch_err('exception')
-
-        # Set info (for 'object?') colors
-        if shell.color_info:
-            try:
-                shell.inspector.set_active_scheme(new_scheme)
-            except:
-                color_switch_err('object inspector')
-        else:
-            shell.inspector.set_active_scheme('NoColor')
-
-    @line_magic
-    def xmode(self, parameter_s=''):
-        """Switch modes for the exception handlers.
-
-        Valid modes: Plain, Context, Verbose, and Minimal.
-
-        If called without arguments, acts as a toggle.
-
-        When in verbose mode the value `--show` (and `--hide`)
-        will respectively show (or hide) frames with ``__tracebackhide__ =
-        True`` value set.
-        """
-
-        def xmode_switch_err(name):
-            warn('Error changing %s exception modes.\n%s' %
-                 (name,sys.exc_info()[1]))
-
-        shell = self.shell
-        if parameter_s.strip() == "--show":
-            shell.InteractiveTB.skip_hidden = False
-            return
-        if parameter_s.strip() == "--hide":
-            shell.InteractiveTB.skip_hidden = True
-            return
-
-        new_mode = parameter_s.strip().capitalize()
-        try:
-            shell.InteractiveTB.set_mode(mode=new_mode)
-            print('Exception reporting mode:',shell.InteractiveTB.mode)
-        except:
-            xmode_switch_err('user')
-
-    @line_magic
-    def quickref(self, arg):
-        """ Show a quick reference sheet """
-        from IPython.core.usage import quick_reference
-        qr = quick_reference + self._magic_docs(brief=True)
-        page.page(qr)
-
-    @line_magic
-    def doctest_mode(self, parameter_s=''):
-        """Toggle doctest mode on and off.
-
-        This mode is intended to make IPython behave as much as possible like a
-        plain Python shell, from the perspective of how its prompts, exceptions
-        and output look.  This makes it easy to copy and paste parts of a
-        session into doctests.  It does so by:
-
-        - Changing the prompts to the classic ``>>>`` ones.
-        - Changing the exception reporting mode to 'Plain'.
-        - Disabling pretty-printing of output.
-
-        Note that IPython also supports the pasting of code snippets that have
-        leading '>>>' and '...' prompts in them.  This means that you can paste
-        doctests from files or docstrings (even if they have leading
-        whitespace), and the code will execute correctly.  You can then use
-        '%history -t' to see the translated history; this will give you the
-        input after removal of all the leading prompts and whitespace, which
-        can be pasted back into an editor.
-
-        With these features, you can switch into this mode easily whenever you
-        need to do testing and changes to doctests, without having to leave
-        your existing IPython session.
-        """
-
-        # Shorthands
-        shell = self.shell
-        meta = shell.meta
-        disp_formatter = self.shell.display_formatter
-        ptformatter = disp_formatter.formatters['text/plain']
-        # dstore is a data store kept in the instance metadata bag to track any
-        # changes we make, so we can undo them later.
-        dstore = meta.setdefault('doctest_mode',Struct())
-        save_dstore = dstore.setdefault
-
-        # save a few values we'll need to recover later
-        mode = save_dstore('mode',False)
-        save_dstore('rc_pprint',ptformatter.pprint)
-        save_dstore('xmode',shell.InteractiveTB.mode)
-        save_dstore('rc_separate_out',shell.separate_out)
-        save_dstore('rc_separate_out2',shell.separate_out2)
-        save_dstore('rc_separate_in',shell.separate_in)
-        save_dstore('rc_active_types',disp_formatter.active_types)
-
-        if not mode:
-            # turn on
-
-            # Prompt separators like plain python
-            shell.separate_in = ''
-            shell.separate_out = ''
-            shell.separate_out2 = ''
-
-
-            ptformatter.pprint = False
-            disp_formatter.active_types = ['text/plain']
-
-            shell.magic('xmode Plain')
-        else:
-            # turn off
-            shell.separate_in = dstore.rc_separate_in
-
-            shell.separate_out = dstore.rc_separate_out
-            shell.separate_out2 = dstore.rc_separate_out2
-
-            ptformatter.pprint = dstore.rc_pprint
-            disp_formatter.active_types = dstore.rc_active_types
-
-            shell.magic('xmode ' + dstore.xmode)
-
-        # mode here is the state before we switch; switch_doctest_mode takes
-        # the mode we're switching to.
-        shell.switch_doctest_mode(not mode)
-
-        # Store new mode and inform
-        dstore.mode = bool(not mode)
-        mode_label = ['OFF','ON'][dstore.mode]
-        print('Doctest mode is:', mode_label)
-
-    @line_magic
-    def gui(self, parameter_s=''):
-        """Enable or disable IPython GUI event loop integration.
-
-        %gui [GUINAME]
-
-        This magic replaces IPython's threaded shells that were activated
-        using the (pylab/wthread/etc.) command line flags.  GUI toolkits
-        can now be enabled at runtime and keyboard
-        interrupts should work without any problems.  The following toolkits
-        are supported:  wxPython, PyQt4, PyGTK, Tk and Cocoa (OSX)::
-
-            %gui wx      # enable wxPython event loop integration
-            %gui qt      # enable PyQt/PySide event loop integration
-                         # with the latest version available.
-            %gui qt6     # enable PyQt6/PySide6 event loop integration
-            %gui qt5     # enable PyQt5/PySide2 event loop integration
-            %gui gtk     # enable PyGTK event loop integration
-            %gui gtk3    # enable Gtk3 event loop integration
-            %gui gtk4    # enable Gtk4 event loop integration
-            %gui tk      # enable Tk event loop integration
-            %gui osx     # enable Cocoa event loop integration
-                         # (requires %matplotlib 1.1)
-            %gui         # disable all event loop integration
-
-        WARNING:  after any of these has been called you can simply create
-        an application object, but DO NOT start the event loop yourself, as
-        we have already handled that.
-        """
-        opts, arg = self.parse_options(parameter_s, '')
-        if arg=='': arg = None
-        try:
-            return self.shell.enable_gui(arg)
-        except Exception as e:
-            # print simple error message, rather than traceback if we can't
-            # hook up the GUI
-            error(str(e))
-
-    @skip_doctest
-    @line_magic
-    def precision(self, s=''):
-        """Set floating point precision for pretty printing.
-
-        Can set either integer precision or a format string.
-
-        If numpy has been imported and precision is an int,
-        numpy display precision will also be set, via ``numpy.set_printoptions``.
-
-        If no argument is given, defaults will be restored.
-
-        Examples
-        --------
-        ::
-
-            In [1]: from math import pi
-
-            In [2]: %precision 3
-            Out[2]: u'%.3f'
-
-            In [3]: pi
-            Out[3]: 3.142
-
-            In [4]: %precision %i
-            Out[4]: u'%i'
-
-            In [5]: pi
-            Out[5]: 3
-
-            In [6]: %precision %e
-            Out[6]: u'%e'
-
-            In [7]: pi**10
-            Out[7]: 9.364805e+04
-
-            In [8]: %precision
-            Out[8]: u'%r'
-
-            In [9]: pi**10
-            Out[9]: 93648.047476082982
-        """
-        ptformatter = self.shell.display_formatter.formatters['text/plain']
-        ptformatter.float_precision = s
-        return ptformatter.float_format
-
-    @magic_arguments.magic_arguments()
-    @magic_arguments.argument(
-        'filename', type=str,
-        help='Notebook name or filename'
-    )
-    @line_magic
-    def notebook(self, s):
-        """Export and convert IPython notebooks.
-
-        This function can export the current IPython history to a notebook file.
-        For example, to export the history to "foo.ipynb" do "%notebook foo.ipynb".
-        """
-        args = magic_arguments.parse_argstring(self.notebook, s)
-        outfname = os.path.expanduser(args.filename)
-
-        from nbformat import write, v4
-
-        cells = []
-        hist = list(self.shell.history_manager.get_range())
-        if(len(hist)<=1):
-            raise ValueError('History is empty, cannot export')
-        for session, execution_count, source in hist[:-1]:
-            cells.append(v4.new_code_cell(
-                execution_count=execution_count,
-                source=source
-            ))
-        nb = v4.new_notebook(cells=cells)
-        with io.open(outfname, "w", encoding="utf-8") as f:
-            write(nb, f, version=4)
-
-@magics_class
-class AsyncMagics(BasicMagics):
-
-    @line_magic
-    def autoawait(self, parameter_s):
-        """
-        Allow to change the status of the autoawait option.
-
-        This allow you to set a specific asynchronous code runner.
-
-        If no value is passed, print the currently used asynchronous integration
-        and whether it is activated.
-
-        It can take a number of value evaluated in the following order:
-
-        - False/false/off deactivate autoawait integration
-        - True/true/on activate autoawait integration using configured default
-          loop
-        - asyncio/curio/trio activate autoawait integration and use integration
-          with said library.
-
-        - `sync` turn on the pseudo-sync integration (mostly used for
-          `IPython.embed()` which does not run IPython with a real eventloop and
-          deactivate running asynchronous code. Turning on Asynchronous code with
-          the pseudo sync loop is undefined behavior and may lead IPython to crash.
-
-        If the passed parameter does not match any of the above and is a python
-        identifier, get said object from user namespace and set it as the
-        runner, and activate autoawait.
-
-        If the object is a fully qualified object name, attempt to import it and
-        set it as the runner, and activate autoawait.
-
-        The exact behavior of autoawait is experimental and subject to change
-        across version of IPython and Python.
-        """
-
-        param = parameter_s.strip()
-        d = {True: "on", False: "off"}
-
-        if not param:
-            print("IPython autoawait is `{}`, and set to use `{}`".format(
-                d[self.shell.autoawait],
-                self.shell.loop_runner
-            ))
-            return None
-
-        if param.lower() in ('false', 'off'):
-            self.shell.autoawait = False
-            return None
-        if param.lower() in ('true', 'on'):
-            self.shell.autoawait = True
-            return None
-
-        if param in self.shell.loop_runner_map:
-            self.shell.loop_runner, self.shell.autoawait = self.shell.loop_runner_map[param]
-            return None
-
-        if param in self.shell.user_ns :
-            self.shell.loop_runner = self.shell.user_ns[param]
-            self.shell.autoawait = True
-            return None
-
-        runner = import_item(param)
-
-        self.shell.loop_runner = runner
-        self.shell.autoawait = True
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/attrs/exceptions.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/attrs/exceptions.py
deleted file mode 100644
index bd9efed202ab1cdf57a9e99cb4e094ef6f38d0c0..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/attrs/exceptions.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-from attr.exceptions import *  # noqa
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydev_ipython/inputhookglut.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydev_ipython/inputhookglut.py
deleted file mode 100644
index d65add9b94a8342bcd6d42263ea04e40cea9b8dc..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydev_ipython/inputhookglut.py
+++ /dev/null
@@ -1,154 +0,0 @@
-# coding: utf-8
-"""
-GLUT Inputhook support functions
-"""
-
-#-----------------------------------------------------------------------------
-#  Copyright (C) 2008-2011  The IPython Development Team
-#
-#  Distributed under the terms of the BSD License.  The full license is in
-#  the file COPYING, distributed as part of this software.
-#-----------------------------------------------------------------------------
-
-# GLUT is quite an old library and it is difficult to ensure proper
-# integration within IPython since original GLUT does not allow to handle
-# events one by one. Instead, it requires for the mainloop to be entered
-# and never returned (there is not even a function to exit he
-# mainloop). Fortunately, there are alternatives such as freeglut
-# (available for linux and windows) and the OSX implementation gives
-# access to a glutCheckLoop() function that blocks itself until a new
-# event is received. This means we have to setup the idle callback to
-# ensure we got at least one event that will unblock the function.
-#
-# Furthermore, it is not possible to install these handlers without a window
-# being first created. We choose to make this window invisible. This means that
-# display mode options are set at this level and user won't be able to change
-# them later without modifying the code. This should probably be made available
-# via IPython options system.
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
-import os
-import sys
-from _pydev_bundle._pydev_saved_modules import time
-import signal
-import OpenGL.GLUT as glut  # @UnresolvedImport
-import OpenGL.platform as platform  # @UnresolvedImport
-from timeit import default_timer as clock
-from pydev_ipython.inputhook import stdin_ready
-
-#-----------------------------------------------------------------------------
-# Constants
-#-----------------------------------------------------------------------------
-
-# Frame per second : 60
-# Should probably be an IPython option
-glut_fps = 60
-
-# Display mode : double buffeed + rgba + depth
-# Should probably be an IPython option
-glut_display_mode = (glut.GLUT_DOUBLE |
-                     glut.GLUT_RGBA |
-                     glut.GLUT_DEPTH)
-
-glutMainLoopEvent = None
-if sys.platform == 'darwin':
-    try:
-        glutCheckLoop = platform.createBaseFunction(
-            'glutCheckLoop', dll=platform.GLUT, resultType=None,
-            argTypes=[],
-            doc='glutCheckLoop(  ) -> None',
-            argNames=(),
-            )
-    except AttributeError:
-        raise RuntimeError(
-            '''Your glut implementation does not allow interactive sessions'''
-            '''Consider installing freeglut.''')
-    glutMainLoopEvent = glutCheckLoop
-elif glut.HAVE_FREEGLUT:
-    glutMainLoopEvent = glut.glutMainLoopEvent
-else:
-    raise RuntimeError(
-        '''Your glut implementation does not allow interactive sessions. '''
-        '''Consider installing freeglut.''')
-
-#-----------------------------------------------------------------------------
-# Callback functions
-#-----------------------------------------------------------------------------
-
-
-def glut_display():
-    # Dummy display function
-    pass
-
-
-def glut_idle():
-    # Dummy idle function
-    pass
-
-
-def glut_close():
-    # Close function only hides the current window
-    glut.glutHideWindow()
-    glutMainLoopEvent()
-
-
-def glut_int_handler(signum, frame):
-    # Catch sigint and print the defautl message
-    signal.signal(signal.SIGINT, signal.default_int_handler)
-    print('\nKeyboardInterrupt')
-    # Need to reprint the prompt at this stage
-
-
-#-----------------------------------------------------------------------------
-# Code
-#-----------------------------------------------------------------------------
-def inputhook_glut():
-    """Run the pyglet event loop by processing pending events only.
-
-    This keeps processing pending events until stdin is ready.  After
-    processing all pending events, a call to time.sleep is inserted.  This is
-    needed, otherwise, CPU usage is at 100%.  This sleep time should be tuned
-    though for best performance.
-    """
-    # We need to protect against a user pressing Control-C when IPython is
-    # idle and this is running. We trap KeyboardInterrupt and pass.
-
-    signal.signal(signal.SIGINT, glut_int_handler)
-
-    try:
-        t = clock()
-
-        # Make sure the default window is set after a window has been closed
-        if glut.glutGetWindow() == 0:
-            glut.glutSetWindow(1)
-            glutMainLoopEvent()
-            return 0
-
-        while not stdin_ready():
-            glutMainLoopEvent()
-            # We need to sleep at this point to keep the idle CPU load
-            # low.  However, if sleep to long, GUI response is poor.  As
-            # a compromise, we watch how often GUI events are being processed
-            # and switch between a short and long sleep time.  Here are some
-            # stats useful in helping to tune this.
-            # time    CPU load
-            # 0.001   13%
-            # 0.005   3%
-            # 0.01    1.5%
-            # 0.05    0.5%
-            used_time = clock() - t
-            if used_time > 10.0:
-                # print 'Sleep for 1 s'  # dbg
-                time.sleep(1.0)
-            elif used_time > 0.1:
-                # Few GUI events coming in, so we can sleep longer
-                # print 'Sleep for 0.05 s'  # dbg
-                time.sleep(0.05)
-            else:
-                # Many GUI events coming in, so sleep only very little
-                time.sleep(0.001)
-    except KeyboardInterrupt:
-        pass
-    return 0
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/array/doc_vec/column_storage.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/array/doc_vec/column_storage.py
deleted file mode 100644
index bd098ae8f34f5bbc242696afc80140b5e7961883..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/array/doc_vec/column_storage.py
+++ /dev/null
@@ -1,162 +0,0 @@
-from collections import ChainMap
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    ItemsView,
-    Iterable,
-    MutableMapping,
-    Optional,
-    Type,
-    TypeVar,
-    Union,
-    ValuesView,
-)
-
-from docarray.array.list_advance_indexing import ListAdvancedIndexing
-from docarray.typing import NdArray
-from docarray.typing.tensor.abstract_tensor import AbstractTensor
-
-if TYPE_CHECKING:
-    from docarray.array.doc_vec.doc_vec import DocVec
-
-IndexIterType = Union[slice, Iterable[int], Iterable[bool], None]
-
-
-T = TypeVar('T', bound='ColumnStorage')
-
-
-class ColumnStorage:
-    """
-    ColumnStorage is a container to store the columns of the
-    :class:`~docarray.array.doc_vec.DocVec`.
-
-    :param tensor_columns: a Dict of AbstractTensor
-    :param doc_columns: a Dict of :class:`~docarray.array.doc_vec.DocVec`
-    :param docs_vec_columns: a Dict of List of :class:`~docarray.array.doc_vec.DocVec`
-    :param any_columns: a Dict of List
-    :param tensor_type: Class used to wrap the doc_vec tensors
-    """
-
-    def __init__(
-        self,
-        tensor_columns: Dict[str, Optional[AbstractTensor]],
-        doc_columns: Dict[str, Optional['DocVec']],
-        docs_vec_columns: Dict[str, Optional[ListAdvancedIndexing['DocVec']]],
-        any_columns: Dict[str, ListAdvancedIndexing],
-        tensor_type: Type[AbstractTensor] = NdArray,
-    ):
-        self.tensor_columns = tensor_columns
-        self.doc_columns = doc_columns
-        self.docs_vec_columns = docs_vec_columns
-        self.any_columns = any_columns
-
-        self.tensor_type = tensor_type
-
-        self.columns = ChainMap(  # type: ignore
-            self.tensor_columns,  # type: ignore
-            self.doc_columns,  # type: ignore
-            self.docs_vec_columns,  # type: ignore
-            self.any_columns,  # type: ignore
-        )  # type: ignore
-
-    def __len__(self) -> int:
-        return len(self.any_columns['id'])  # TODO what if ID are None ?
-
-    def __getitem__(self: T, item: IndexIterType) -> T:
-        if isinstance(item, tuple):
-            item = list(item)
-        tensor_columns = {
-            key: col[item] if col is not None else None
-            for key, col in self.tensor_columns.items()
-        }
-        doc_columns = {
-            key: col[item] if col is not None else None
-            for key, col in self.doc_columns.items()
-        }
-        docs_vec_columns = {
-            key: col[item] if col is not None else None
-            for key, col in self.docs_vec_columns.items()
-        }
-        any_columns = {
-            key: col[item] if col is not None else None
-            for key, col in self.any_columns.items()
-        }
-
-        return self.__class__(
-            tensor_columns,
-            doc_columns,
-            docs_vec_columns,
-            any_columns,
-            self.tensor_type,
-        )
-
-
-class ColumnStorageView(dict, MutableMapping[str, Any]):
-    index: int
-    storage: ColumnStorage
-
-    def __init__(self, index: int, storage: ColumnStorage):
-        super().__init__()
-        self.index = index
-        self.storage = storage
-
-    def __getitem__(self, name: str) -> Any:
-        if name in self.storage.tensor_columns.keys():
-            tensor = self.storage.tensor_columns[name]
-            if tensor is None:
-                return None
-            if tensor.get_comp_backend().n_dim(tensor) == 1:
-                # to ensure consistensy between numpy and pytorch
-                # we wrap the scalr in a tensor of ndim = 1
-                # otherwise numpy pass by value whereas torch by reference
-                col = self.storage.tensor_columns[name]
-
-                if col is not None:
-                    return col[self.index : self.index + 1]
-                else:
-                    return None
-
-        col = self.storage.columns[name]
-
-        if col is None:
-            return None
-        return col[self.index]
-
-    def __setitem__(self, name, value) -> None:
-        if self.storage.columns[name] is None:
-            raise ValueError(
-                f'Cannot set an item to a None column. This mean that '
-                f'the DocVec that encapsulate this doc has the field '
-                f'{name} set to None. If you want to modify that you need to do it at the'
-                f'DocVec level. `docs.field = np.zeros(10)`'
-            )
-
-        self.storage.columns[name][self.index] = value
-
-    def __delitem__(self, key):
-        raise RuntimeError('Cannot delete an item from a StorageView')
-
-    def __iter__(self):
-        return self.storage.columns.keys()
-
-    def __len__(self):
-        return len(self.storage.columns)
-
-    def _local_dict(self):
-        """The storage.columns dictionary with every value at position self.index"""
-
-        return {key: self[key] for key in self.storage.columns.keys()}
-
-    def keys(self):
-        return self.storage.columns.keys()
-
-    # type ignore because return type dict_values is private and we cannot use it.
-    # context: https://github.com/python/typing/discussions/1033
-    def values(self) -> ValuesView:  # type: ignore
-        return ValuesView(self._local_dict())
-
-    # type ignore because return type dict_items is private and we cannot use it.
-    # context: https://github.com/python/typing/discussions/1033
-    def items(self) -> ItemsView:  # type: ignore
-        return ItemsView(self._local_dict())
diff --git a/spaces/Superlang/ImageProcessor/annotator/normalbae/models/submodules/efficientnet_repo/utils.py b/spaces/Superlang/ImageProcessor/annotator/normalbae/models/submodules/efficientnet_repo/utils.py
deleted file mode 100644
index d327e8bd8120c5cd09ae6c15c3991ccbe27f6c1f..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/normalbae/models/submodules/efficientnet_repo/utils.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import os
-
-
-class AverageMeter:
-    """Computes and stores the average and current value"""
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-
-
-def accuracy(output, target, topk=(1,)):
-    """Computes the precision@k for the specified values of k"""
-    maxk = max(topk)
-    batch_size = target.size(0)
-
-    _, pred = output.topk(maxk, 1, True, True)
-    pred = pred.t()
-    correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-    res = []
-    for k in topk:
-        correct_k = correct[:k].reshape(-1).float().sum(0)
-        res.append(correct_k.mul_(100.0 / batch_size))
-    return res
-
-
-def get_outdir(path, *paths, inc=False):
-    outdir = os.path.join(path, *paths)
-    if not os.path.exists(outdir):
-        os.makedirs(outdir)
-    elif inc:
-        count = 1
-        outdir_inc = outdir + '-' + str(count)
-        while os.path.exists(outdir_inc):
-            count = count + 1
-            outdir_inc = outdir + '-' + str(count)
-            assert count < 100
-        outdir = outdir_inc
-        os.makedirs(outdir)
-    return outdir
-
diff --git a/spaces/SystemGPT/TrialSpace/app.py b/spaces/SystemGPT/TrialSpace/app.py
deleted file mode 100644
index d0da7a4e5a6d8df588483df07d5d0a337a66e791..0000000000000000000000000000000000000000
--- a/spaces/SystemGPT/TrialSpace/app.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from transformers import pipeline
-import streamlit as st
-import time
-
-st.title("Sentiment Analysis")
-
-# Initialize chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# Create a pipeline for sentiment analysis
-pipe = pipeline("sentiment-analysis")
-
-def analyse_sentiment(sentence):
-    sentence_analysed = pipe(sentence)[0]
-    sentiment_label = sentence_analysed["label"]
-    return sentiment_label
-
-# Map sentiment labels to more human-readable messages
-def get_sentiment_message(sentiment_label):
-    if sentiment_label == "LABEL_1":
-        return "positive."
-    elif sentiment_label == "LABEL_2":
-        return "neutral."
-    elif sentiment_label == "LABEL_3":
-        return "negative."
-    else:
-        return "uncertain."
-
-# Accept user input
-if prompt := st.chat_input("Enter your sentence", key="input"):
-    # Display user message in chat message container
-    st.session_state.messages.append({"role": "user", "content": prompt})
-
-# Display chat messages from history on app rerun
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-# Process user input and display assistant response
-if prompt:
-    with st.chat_message("assistant"):
-        message_placeholder = st.empty()
-        assistant_response = analyse_sentiment(prompt)
-        
-        # Get the sentiment message
-        sentiment_message = get_sentiment_message(assistant_response)
-        
-        # Display the message with sentiment
-        full_response = f"Your sentence is {sentiment_message}"
-        
-        # Display the sentiment message
-        message_placeholder.markdown(full_response)
-        
-        # Add assistant response to chat history
-        st.session_state.messages.append({"role": "assistant", "content": full_response})
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/hashes.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/hashes.py
deleted file mode 100644
index 843cffc6b3ddd6eb01483bcf1b5c33c717e027b6..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/hashes.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import hashlib
-from typing import TYPE_CHECKING, BinaryIO, Dict, Iterable, List, Optional
-
-from pip._internal.exceptions import HashMismatch, HashMissing, InstallationError
-from pip._internal.utils.misc import read_chunks
-
-if TYPE_CHECKING:
-    from hashlib import _Hash
-
-    # NoReturn introduced in 3.6.2; imported only for type checking to maintain
-    # pip compatibility with older patch versions of Python 3.6
-    from typing import NoReturn
-
-
-# The recommended hash algo of the moment. Change this whenever the state of
-# the art changes; it won't hurt backward compatibility.
-FAVORITE_HASH = "sha256"
-
-
-# Names of hashlib algorithms allowed by the --hash option and ``pip hash``
-# Currently, those are the ones at least as collision-resistant as sha256.
-STRONG_HASHES = ["sha256", "sha384", "sha512"]
-
-
-class Hashes:
-    """A wrapper that builds multiple hashes at once and checks them against
-    known-good values
-
-    """
-
-    def __init__(self, hashes: Optional[Dict[str, List[str]]] = None) -> None:
-        """
-        :param hashes: A dict of algorithm names pointing to lists of allowed
-            hex digests
-        """
-        allowed = {}
-        if hashes is not None:
-            for alg, keys in hashes.items():
-                # Make sure values are always sorted (to ease equality checks)
-                allowed[alg] = sorted(keys)
-        self._allowed = allowed
-
-    def __and__(self, other: "Hashes") -> "Hashes":
-        if not isinstance(other, Hashes):
-            return NotImplemented
-
-        # If either of the Hashes object is entirely empty (i.e. no hash
-        # specified at all), all hashes from the other object are allowed.
-        if not other:
-            return self
-        if not self:
-            return other
-
-        # Otherwise only hashes that present in both objects are allowed.
-        new = {}
-        for alg, values in other._allowed.items():
-            if alg not in self._allowed:
-                continue
-            new[alg] = [v for v in values if v in self._allowed[alg]]
-        return Hashes(new)
-
-    @property
-    def digest_count(self) -> int:
-        return sum(len(digests) for digests in self._allowed.values())
-
-    def is_hash_allowed(self, hash_name: str, hex_digest: str) -> bool:
-        """Return whether the given hex digest is allowed."""
-        return hex_digest in self._allowed.get(hash_name, [])
-
-    def check_against_chunks(self, chunks: Iterable[bytes]) -> None:
-        """Check good hashes against ones built from iterable of chunks of
-        data.
-
-        Raise HashMismatch if none match.
-
-        """
-        gots = {}
-        for hash_name in self._allowed.keys():
-            try:
-                gots[hash_name] = hashlib.new(hash_name)
-            except (ValueError, TypeError):
-                raise InstallationError(f"Unknown hash name: {hash_name}")
-
-        for chunk in chunks:
-            for hash in gots.values():
-                hash.update(chunk)
-
-        for hash_name, got in gots.items():
-            if got.hexdigest() in self._allowed[hash_name]:
-                return
-        self._raise(gots)
-
-    def _raise(self, gots: Dict[str, "_Hash"]) -> "NoReturn":
-        raise HashMismatch(self._allowed, gots)
-
-    def check_against_file(self, file: BinaryIO) -> None:
-        """Check good hashes against a file-like object
-
-        Raise HashMismatch if none match.
-
-        """
-        return self.check_against_chunks(read_chunks(file))
-
-    def check_against_path(self, path: str) -> None:
-        with open(path, "rb") as file:
-            return self.check_against_file(file)
-
-    def has_one_of(self, hashes: Dict[str, str]) -> bool:
-        """Return whether any of the given hashes are allowed."""
-        for hash_name, hex_digest in hashes.items():
-            if self.is_hash_allowed(hash_name, hex_digest):
-                return True
-        return False
-
-    def __bool__(self) -> bool:
-        """Return whether I know any known-good hashes."""
-        return bool(self._allowed)
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, Hashes):
-            return NotImplemented
-        return self._allowed == other._allowed
-
-    def __hash__(self) -> int:
-        return hash(
-            ",".join(
-                sorted(
-                    ":".join((alg, digest))
-                    for alg, digest_list in self._allowed.items()
-                    for digest in digest_list
-                )
-            )
-        )
-
-
-class MissingHashes(Hashes):
-    """A workalike for Hashes used when we're missing a hash for a requirement
-
-    It computes the actual hash of the requirement and raises a HashMissing
-    exception showing it to the user.
-
-    """
-
-    def __init__(self) -> None:
-        """Don't offer the ``hashes`` kwarg."""
-        # Pass our favorite hash in to generate a "gotten hash". With the
-        # empty list, it will never match, so an error will always raise.
-        super().__init__(hashes={FAVORITE_HASH: []})
-
-    def _raise(self, gots: Dict[str, "_Hash"]) -> "NoReturn":
-        raise HashMissing(gots[FAVORITE_HASH].hexdigest())
diff --git a/spaces/TencentARC/VLog/models/clip_model.py b/spaces/TencentARC/VLog/models/clip_model.py
deleted file mode 100644
index feb544ef1fe47f1f9ceeb4b0febcbe99d6f3df48..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/clip_model.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import os
-import cv2
-import pdb
-import torch
-import numpy as np
-from PIL import Image
-from transformers import CLIPProcessor, CLIPVisionModelWithProjection
-from transformers import logging
-logging.set_verbosity_error()
-
-class FeatureExtractor():
-    def __init__(self, args):
-        self.device = args.feature_extractor_device
-        self.beta = args.beta
-        self.processor = CLIPProcessor.from_pretrained(args.feature_extractor)
-        self.model = CLIPVisionModelWithProjection.from_pretrained(args.feature_extractor).to(self.device)
-        self.data_dir = args.data_dir
-        self.tmp_dir = args.tmp_dir
-
-
-    def __call__(self, video_path, video_id):        
-        cap = cv2.VideoCapture(video_path)
-        fps = cap.get(cv2.CAP_PROP_FPS)
-        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-        video_length = frame_count / fps
-        sample_rate = int(fps) * self.beta
-
-        save_path = os.path.join(self.tmp_dir, video_id + '.npz')
-        if os.path.exists(save_path):
-            data = np.load(save_path)
-            clip_features = data['features']    
-            return clip_features, video_length
-
-        clip_features = []
-        print("Extract the clip feature.")
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-
-            if cap.get(cv2.CAP_PROP_POS_FRAMES) % sample_rate == 0:
-                image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-                inputs = self.processor(images=image, return_tensors="pt").pixel_values
-                inputs = inputs.to(self.device)
-                
-                with torch.no_grad():
-                    feat = self.model(inputs)['image_embeds']
-                    clip_features.append(feat.cpu().numpy())
-        print("Finished.")
-
-        clip_features = np.concatenate(clip_features, axis=0)
-        np.savez_compressed(save_path, features=clip_features)
-        
-        return clip_features, video_length
diff --git a/spaces/Tetel/chat/public/style.css b/spaces/Tetel/chat/public/style.css
deleted file mode 100644
index 4f250c4ea903eff42b71c968a124ab702a1cb423..0000000000000000000000000000000000000000
--- a/spaces/Tetel/chat/public/style.css
+++ /dev/null
@@ -1,216 +0,0 @@
-body {
-    font-family: "Microsoft YaHei", sans-serif;
-    margin: 0;
-    padding: 0;
-    background-image: url("background.png");
-    background-size: cover;
-}
-
-.container {
-    display: flex;
-    flex-direction: column;
-    margin: auto;
-    max-width: 1184px;
-    padding: 20px;
-    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
-    border-radius: 10px;
-}
-
-.heading {
-    color: #444;
-    font-size: 1.5em;
-    margin-bottom: 2px;
-}
-
-.button-container {
-    display: flex;
-    justify-content: flex-end;
-    flex-wrap: wrap;
-}
-
-.button {
-    margin-left: 10px;
-    padding: 5px 10px;
-    border: none;
-    border-radius: 5px;
-    background-color: #007BFF;
-    color: white;
-    cursor: pointer;
-    transition: background-color 0.3s;
-}
-
-.button:hover {
-    background-color: #0056b3;
-}
-
-.button[disabled] {
-    background-color: gray;
-}
-
-.messages {
-    display: flex;
-    flex-direction: column;
-    border: 1px solid #ccc;
-    padding: 10px;
-    margin-bottom: 20px;
-    border-radius: 5px;
-}
-
-.textarea {
-    width: 100%;
-    margin-bottom: 10px;
-    border: 1px solid #ccc;
-    border-radius: 5px;
-    padding: 10px;
-    box-sizing: border-box;
-    font-family: "Microsoft YaHei", sans-serif;
-}
-
-.selector {
-    margin-bottom: 10px;
-}
-
-.message {
-    margin-bottom: 10px;
-    padding: 10px;
-    border-radius: 12px;
-    box-shadow: 0 0.3px 0.9px rgba(0, 0, 0, 0.12), 0 1.6px 3.6px rgba(0, 0, 0, 0.16);
-    font-size: 16px;
-    width: fit-content;
-    max-width: 768px;
-    position: relative;
-}
-
-.user-message {
-    color: white;
-    background-image: linear-gradient(90deg, #904887 10.79%, #8B257E 87.08%);
-    align-self: flex-end;
-}
-
-.assistant-message {
-    background-color: rgba(255, 255, 255, 0.6);
-}
-
-.other-message {
-    background-color: rgba(255, 255, 255, 0.3);
-    align-self: flex-end;
-}
-
-.message * {
-    margin-block: 0;
-}
-
-.add-button, .delete-button, .edit-button {
-    box-shadow: 0 0.3px 0.9px rgba(0, 0, 0, 0.12), 0 1.6px 3.6px rgba(0, 0, 0, 0.16);
-    position: absolute;
-    top: -36px;
-    background-color: white;
-    color: white;
-    border: none;
-    border-radius: 8px;
-    width: 36px;
-    height: 36px;
-    text-align: center;
-    line-height: 36px;
-    cursor: pointer;
-}
-
-.delete-button {
-    right: 0;
-}
-
-.edit-button {
-    right: 36px;
-}
-
-.add-button {
-    right: 72px;
-}
-
-.add-button:hover, .delete-button:hover, .edit-button:hover {
-    background-color: rgb(255, 255, 255, 0.06);
-}
-
-img[alt^="image"] {
-  width: 206px;
-  height: 206px;
-  border: 6px solid transparent;
-  border-radius: 15px;
-  transition: transform 0.3s;
-  object-fit: contain;
-}
-
-img[alt^="image"]:hover {
-  transform: scale(1.1);
-}
-
-img[alt="bg_upload_image"] {
-  width: 20px;
-  height: 20px;
-}
-
-#image_upload {
-  margin: 10px;
-  display: flex;
-  align-items: center;
-}
-
-.switch {
-  margin-left: 10px;
-  position: relative;
-  display: inline-block;
-  width: 60px;
-  height: 34px;
-}
-
-.switch input {
-  opacity: 0;
-  width: 0;
-  height: 0;
-}
-
-.slider {
-  position: absolute;
-  cursor: pointer;
-  top: 0;
-  left: 0;
-  right: 0;
-  bottom: 0;
-  background-color: #ccc;
-  -webkit-transition: .4s;
-  transition: .4s;
-}
-
-.slider:before {
-  position: absolute;
-  content: "";
-  height: 26px;
-  width: 26px;
-  left: 4px;
-  bottom: 4px;
-  background-color: white;
-  -webkit-transition: .4s;
-  transition: .4s;
-}
-
-input:checked + .slider {
-  background-color: #007BFF;
-}
-
-input:focus + .slider {
-  box-shadow: 0 0 1px #2196F3;
-}
-
-input:checked + .slider:before {
-  -webkit-transform: translateX(26px);
-  -ms-transform: translateX(26px);
-  transform: translateX(26px);
-}
-
-.slider.round {
-  border-radius: 34px;
-}
-
-.slider.round:before {
-  border-radius: 50%;
-}
\ No newline at end of file
diff --git a/spaces/Th3BossC/TranscriptApi/TranscriptApi/static/app.js b/spaces/Th3BossC/TranscriptApi/TranscriptApi/static/app.js
deleted file mode 100644
index b0ff01b3f2fe1928918e24e378cdd8e08033be6c..0000000000000000000000000000000000000000
--- a/spaces/Th3BossC/TranscriptApi/TranscriptApi/static/app.js
+++ /dev/null
@@ -1,118 +0,0 @@
-function youtube_video_id(url){
-    var regExp = /^.*((youtu.be\/)|(v\/)|(\/u\/\w\/)|(embed\/)|(watch\?))\??v?=?([^#&?]*).*/;
-    var match = url.match(regExp);
-    return (match&&match[7].length==11)? match[7] : false;
-}
-
-
-// Theme implementation
-
-const theme = localStorage.getItem('theme');
-const navbar_bg = localStorage.getItem('navbar-bg');
-const navbar_color = localStorage.getItem('navbar-color');
-const button_content = localStorage.getItem('button-content');
-
-
-const themeButton = document.getElementById('theme');
-const body = document.body;
-const nav = document.getElementById('navbar');
-
-body.classList.add(theme || 'light');
-nav.classList.add(navbar_bg || 'bg-light');
-nav.classList.add(navbar_color || 'navbar-light')
-themeButton.innerHTML = button_content || '<i class="bi bi-moon-fill"></i> Toggle Theme';
-
-
-themeButton.onclick = () => {
-    if (body.classList.contains('light')) {
-        body.classList.replace('light', 'dark');
-        nav.classList.replace('bg-light', 'bg-dark');
-        nav.classList.replace('navbar-light', 'navbar-dark'); 
-        themeButton.innerHTML = '<i class="bi bi-brightness-high-fill"></i> Toggle Theme'
-
-        localStorage.setItem('theme', 'dark');
-        localStorage.setItem('navbar-bg', 'bg-dark');
-        localStorage.setItem('navbar-color', 'navbar-dark');
-        localStorage.setItem('button-content', themeButton.innerHTML);
-    }
-    else {
-        body.classList.replace('dark', 'light');
-        nav.classList.replace('bg-dark', 'bg-light');
-        nav.classList.replace('navbar-dark', 'navbar-light');
-        themeButton.innerHTML = '<i class="bi bi-moon-fill"></i> Toggle Theme';
-
-        localStorage.setItem('theme', 'light');
-        localStorage.setItem('navbar-bg', 'bg-light');
-        localStorage.setItem('navbar-color', 'navbar-light');
-        localStorage.setItem('button-content', themeButton.innerHTML);
-    }
-}
-
-// darkButton.onclick = () => {
-//     body.classList.replace('light', 'dark');
-//     nav.classList.replace('bg-light', 'bg-dark');
-//     nav.classList.replace('navbar-light', 'navbar-dark'); 
-//     darkButton.classList.add('active');
-//     darkButton.classList.add('disabled');
-//     lightButton.classList.remove('active');
-//     lightButton.classList.remove('disabled'); 
-// };
-
-// lightButton.onclick = () => {
-//     body.classList.replace('dark', 'light');
-//     nav.classList.replace('bg-dark', 'bg-light');
-//     nav.classList.replace('navbar-dark', 'navbar-light');   
-//     lightButton.classList.add('active');
-//     lightButton.classList.add('disabled');     
-//     darkButton.classList.remove('active');
-//     darkButton.classList.remove('disabled'); 
-// };
-
-
-const main_content = document.getElementById('main-content');
-const video_title = document.getElementById('video-title');
-const video_summary = document.getElementById('video-summary');
-
-const button = document.getElementById('submit-btn');
-const form = document.getElementById('url-form');
-const url = document.getElementById('url')
-
-async function getApiData(video_id) {
-    const response = await fetch('http://localhost:5000/video_api/' + video_id);
-    const jsonData = await response.json();
-
-    console.log(jsonData);
-    video_title.innerHTML = jsonData['title'];
-    return video_summary.innerHTML = jsonData['summary'];
-
-}
-
-
-
-form.addEventListener('submit', (e) => {
-    e.preventDefault();
-    video_url = url.value;  
-    if (video_url == "")
-        return;
-    video_id = youtube_video_id(video_url);
-    video_title.innerHTML = 'Summarizing...';
-    console.log(main_content.classList);
-    //main_content.classList.remove('visually-hidden');
-    main_content.style.clipPath = 'circle(200% at 50% 50%)';
-    video_summary.innerHTML = '<div class="progress" role="progressbar" aria-label="Animated striped example" aria-valuenow="75" aria-valuemin="0" aria-valuemax="100"> \
-    <div class="progress-bar progress-bar-striped progress-bar-animated" style="width: 100%"></div> \
-  </div>';
-    if (video_id == false) {
-        video_title.innerHTML = '[Error]';
-        video_summary.innerHTML = 'Invalid video URL';
-        return;
-    }
-    try {
-        getApiData(video_id);
-    }
-    catch {
-        video_title.innerHTML = '[Error]'
-        video_summary.innerHTML = 'Error Video not found';
-    }
-}); 
-
diff --git a/spaces/ThankGod/anime-gan/app.py b/spaces/ThankGod/anime-gan/app.py
deleted file mode 100644
index a5d9c9103424261ffd713afcd94d3c06bad90b36..0000000000000000000000000000000000000000
--- a/spaces/ThankGod/anime-gan/app.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from PIL import Image
-import torch
-import torchvision
-import gradio
-
-Instructuction = "Select a Unique Portrait Image of yourself"
-title="I am something of a Painter, Anime-Edition (AnimeGAN-V2)"
-description = "Drag/Drop or Upload a cute portrait Image of yourself or anyone you find interesting 😉, then observe how this Generative model\
-               is able to Generate a cute Anime-Cartoon version of your Image."
-article = """
-            - Select an image from the examples provided as demo image,
-            - Click submit to Generate Image,
-            - Tips: Quality Images with Great brightness/without pre-existing filters work better (Image Noise).
-            - Privacy: No user data is collected or saved,
-            - Credits to akhaliq/AnimeGANv2 for original AnimeGanV2"""
-
-model = torch.hub.load(
-    "AK391/animegan2-pytorch:main",
-    "generator",
-    pretrained=True,
-    #device="cuda",
-    progress=False
-)
-face2paint = torch.hub.load(
-    'AK391/animegan2-pytorch:main', 'face2paint', 
-    size=512, #device="cuda",
-    side_by_side=False
-)
-
-def inference(img):
-  return face2paint(model, img)
-
-gradio.Interface(inference,
-                 inputs=gradio.Image(type='pil'),
-                 outputs=gradio.Image(type='pil'),
-                 Instructuction=Instructuction, title=title, description=description, article=article,
-                 examples=['Upload Xty.png',
-                           'Upload Tg.png']).launch()
diff --git a/spaces/TrustSafeAI/NCTV/index.html b/spaces/TrustSafeAI/NCTV/index.html
deleted file mode 100644
index 76bc91f8852f1af3633f7b3a2c8ada9573099383..0000000000000000000000000000000000000000
--- a/spaces/TrustSafeAI/NCTV/index.html
+++ /dev/null
@@ -1,225 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-  <head>
-    <meta charset="UTF-8">
-
-<!-- Begin Jekyll SEO tag v2.8.0 -->
-<title>NCTV | Neural Clamping Toolkit and Visualization for Neural Network Calibration</title>
-<meta property="og:title" content="NCTV" />
-<meta property="og:locale" content="en_US" />
-<meta name="description" content="Neural Clamping Toolkit and Visualization for Neural Network Calibration" />
-<meta property="og:description" content="Neural Clamping Toolkit and Visualization for Neural Network Calibration" />
-<script type="application/ld+json">
-{"@context":"https://schema.org","@type":"WebSite","description":"Neural Clamping Toolkit and Visualization for Neural Network Calibration","headline":"NCTV","name":"NCTV","url":"https://huggingface.co/spaces/hsiung/NCTV"}</script>
-<!-- End Jekyll SEO tag -->
-
-    <link rel="preconnect" href="https://fonts.gstatic.com">
-    <link rel="preload" href="https://fonts.googleapis.com/css?family=Open+Sans:400,700&display=swap" as="style" type="text/css" crossorigin>
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-    <meta name="theme-color" content="#157878">
-    <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
-
-    <link rel="stylesheet" href="assets/css/bootstrap/bootstrap.min.css?v=90447f115a006bc45b738d9592069468b20e2551">
-    <link rel="stylesheet" href="assets/css/style.css?v=90447f115a006bc45b738d9592069468b20e2551">
-    <!-- start custom head snippets, customize with your own _includes/head-custom.html file -->
-    <link rel="stylesheet" href="assets/css/custom_style.css?v=90447f115a006bc45b738d9592069468b20e2551">
-    <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
-    <link rel="stylesheet" href="https://ajax.googleapis.com/ajax/libs/jqueryui/1.12.1/themes/smoothness/jquery-ui.css">
-    <script src="https://ajax.googleapis.com/ajax/libs/jqueryui/1.12.1/jquery-ui.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.9.4/Chart.js"></script>
-    <script src="assets/js/calibration.js?v=90447f115a006bc45b738d9592069468b20e2551"></script>
-
-
-
-
-
-<!-- for mathjax support -->
-    <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
-    <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-
-
-<!-- end custom head snippets -->
-
-  </head>
-  <body>
-    <a id="skip-to-content" href="#content">Skip to the content.</a>
-
-    <header class="page-header" role="banner">
-      <h1 class="project-name">NCTV</h1>
-      <h2 class="project-tagline">Neural Clamping Toolkit and Visualization for Neural Network Calibration</h2>
-      
-      
-    </header>
-
-    <main id="content" class="main-content" role="main">
-      <h2 id="introduction">Introduction</h2>
-
-<p>Neural network calibration is an essential task in deep learning to ensure consistency 
-between the confidence of model prediction and the true correctness likelihood. In this 
-demonstration, we first visualize the idea of neural network calibration on a binary 
-classifier and show model features that represent its calibration. Second, we introduce 
-our proposed framework <strong>Neural Clamping</strong>, which employs a simple joint input-output 
-transformation on a pre-trained classifier. We also provide other calibration approaches
-(e.g., temperature scaling) to compare with Neural Clamping.</p>
-
-<h2 id="what-is-calibration">What is Calibration?</h2>
-<p>Neural Network Calibration seeks to make model prediction align with its true correctness likelihood.
-A well-calibrated model should provide accurate predictions and reliable confidence when making inferences. On the
-contrary, a poor calibration model would have a wide gap between its accuracy and average confidence level.
-This phenomenon could hamper scenarios requiring accurate uncertainty estimation, such as safety-related tasks
-(e.g., autonomous driving systems, medical diagnosis, etc.).</p>
-
-<div class="container">
-<div id="calibration-intro" class="row align-items-center calibration-intro-sec">
-<img id="calibration-intro-img" src="https://hsiung.cc/NCTV/images/conf_acc_demo.gif" />
-</div>
-</div>
-
-<h3 id="calibration-metrics">Calibration Metrics</h3>
-<p>Objectively, researchers utilize <strong>Calibration Metrics</strong> to measure the calibration error for a model, for example,
-Expected Calibration Error (ECE), Static Calibration Error (SCE), Adaptive Calibration Error (ACE), etc.</p>
-
-<div class="container calibration-intro-sec">
-<div><img id="calibration-intro-img" src="images/metrics/intro-metric-example.png" /></div>
-</div>
-
-<div id="calibration-metrics-formula" class="container">
-<div id="calibration-metrics-formula-list" class="row align-items-center formula-list">
-  <a href="#ECE-formula" class="selected">ECE</a>
-  <a href="#SCE-formula">SCE</a>
-  <a href="#ACE-formula">ACE</a>
-  <div style="clear: both"></div>
-</div>
-<div id="calibration-metrics-formula-content" class="row align-items-center">
-  <span id="ECE-formula" class="formula" style="">$$\displaystyle \text{ECE}=\sum_{i=1}^{M}\frac{|B_i|}{n}|\text{acc}(B_i)-\text{conf}(B_i)|$$</span>
-  <span id="SCE-formula" class="formula" style="display: none;">$$\displaystyle \text{SCE}=\frac{1}{K}\sum_{k=1}^{K}\sum_{i=1}^{M}\frac{|B_i^k|}{n}|\text{acc}(i, k)-\text{conf}(i,k)|$$</span>
-  <span id="ACE-formula" class="formula" style="display: none;">$$\displaystyle \text{ACE}=\frac{1}{KR}\sum_{k=1}^{K}\sum_{r=1}^{R}|\text{acc}(r,k)-\text{conf}(r,k)|$$</span>
-</div>
-</div>
-
-<h2 id="proposed-approach-neural-clamping">Proposed Approach: Neural Clamping</h2>
-
-<div class="container"><img id="calibration-header" src="images/header.png" /></div>
-
-<h2 id="demonstration">Demonstration</h2>
-<p>In the current research, a reliability diagram is drawn to show the calibration performance of a model. However, since
-reliability diagrams often only provide fixed bar graphs statically, further explanation from the chart is limited. In
-this demonstration, we show how to make reliability diagrams interactive and insightful to help researchers and
-developers gain more insights from the graph. Specifically, we provide three CIFAR-100 classification models
-in this demonstration.  Multiple Bin numbers are also support</p>
-
-<p>We hope this tool could also facilitate the development process.</p>
-
-<div id="calibration-demo" class="container">
-<div class="row align-items-center">
-  <div class="row" style="display: none;">
-      <div class="datasets-list">
-        <span style="margin-right: 1em;">Datasets</span>
-        <span class="radio-group"><input type="radio" id="CIFAR-100" class="options" name="datasets" value="cifar100" checked="" /><label for="CIFAR-100" class="option-label">CIFAR-100</label></span>
-        <span class="radio-group"><input type="radio" id="ImageNet" class="options" name="datasets" value="imagenet" /><label for="ImageNet" class="option-label">ImageNet</label></span>
-      </div>
-  </div>
-  <div class="row" style="margin: 10px 0 0">
-      <div class="models-list">
-        <span style="margin-right: 1em;">Models</span>
-        <span class="radio-group"><input type="radio" id="ResNet110" class="options" name="models" value="resnet110" checked="" /><label for="ResNet110" class="option-label">ResNet110</label></span>
-        <span class="radio-group"><input type="radio" id="DenseNet121" class="options" name="models" value="densenet121" /><label for="DenseNet121" class="option-label">DenseNet121</label></span>
-        <span class="radio-group"><input type="radio" id="WideResNet40-10" class="options" name="models" value="wideresnet40_10" /><label for="WideResNet40-10" class="option-label">WideResNet40-10</label></span>
-      </div>
-  </div>
-</div>
-<div class="row align-items-center">
-  <div class="col-4">
-    <div id="toolbox">
-      <div class="row align-items-center" style="margin-top: 2em;"><input type="radio" id="tool_none" class="options" name="calibration_tool" value="none" checked="" /><label for="tool_none" class="calibrate-tool">None</label></div>
-      <div class="row align-items-center"><input type="radio" id="tool_ts" class="options" name="calibration_tool" value="ts" /><label for="tool_ts" class="calibrate-tool">Temp. Scaling</label></div>
-      <div class="row align-items-center"><input type="radio" id="tool_delta" class="options" name="calibration_tool" value="delta" /><label for="tool_delta" class="calibrate-tool">Univ. Perturbation</label></div>
-      <div class="row align-items-center"><input type="radio" id="tool_neural_clamping" class="options" name="calibration_tool" value="neural_clamping" /><label for="tool_neural_clamping" class="calibrate-tool"><span style="font-weight: bold;">Neural Clamping</span></label></div>
-    </div>
-    <div class="row align-items-center">
-      <div class="legend"><img src="images/demo-legend.png" alt="legend" /></div>
-      <div class="figure-option"><label class="container" for="ActualOnly">Actual Only<input id="ActualOnly" type="checkbox" name="ActualOnly" value="Actual Only" onchange="figureOption()" /><span class="checkmark"></span></label></div>
-    </div>
-    <div class="row align-items-center">
-      <div class="calibration-error"><span class="calibration-metric">Expected Calibration Error</span><span class="calibration-error-value" id="ece-value">0.10731</span></div>
-    </div>
-  </div>
-  <div class="col-8">
-  <figure class="figure">
-    <img id="reliability-diagram" src="images/cifar100/resnet110/none/bin15.png" alt="CIFAR-100 Calibrated Reliability Diagram (Full)" />
-    <div class="slider-container">
-      <div class="slider-label"><span>Bin Number</span></div>
-      <div class="slider-content" id="bin-slider"><div id="bin-num" class="ui-slider-handle"></div></div>
-    </div>
-    <div class="slider-container">
-      <div class="slider-label"><span>Temp Scaling</span></div>
-      <div class="slider-content" id="ts-slider"><div id="temp-scale" class="slider-value ui-slider-handle"></div></div>
-    </div>
-    <figcaption class="figure-caption">
-    </figcaption>
-  </figure>
-  </div>
-</div>
-</div>
-
-<h2 id="use-nctookit-to-calibrate-your-own-models">Use NCTookit to Calibrate Your Own Models</h2>
-<p>Quick Start by running the following code! Or, <a href="https://colab.research.google.com/drive/1HosL29iJxK7Z8wNR9X3aWCgvbdu1ZgFu"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>.
-Using this tool, users can use our proposed package, \(\texttt{NCTookit}\), to calibrate the model.</p>
-
-<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># !pip install -q git+https://github.com/yungchentang/NCToolkit.git
-</span><span class="kn">from</span> <span class="nn">neural_clamping.nc_wrapper</span> <span class="kn">import</span> <span class="n">NCWrapper</span>
-<span class="kn">from</span> <span class="nn">neural_clamping.utils</span> <span class="kn">import</span> <span class="n">load_model</span><span class="p">,</span> <span class="n">load_dataset</span><span class="p">,</span> <span class="n">model_classes</span><span class="p">,</span> <span class="n">plot_reliability_diagram</span>
-
-<span class="c1"># Load model
-</span><span class="n">model</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s">'ARCHITECTURE'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="s">'DATASET'</span><span class="p">,</span> <span class="n">checkpoint_path</span><span class="o">=</span><span class="s">'CHECKPOINT_PATH'</span><span class="p">)</span>
-<span class="n">num_classes</span> <span class="o">=</span> <span class="n">model_classes</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="s">'DATASET'</span><span class="p">)</span>
-
-<span class="c1"># Dataset loader
-</span><span class="n">valloader</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="s">'DATASET'</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="s">'val'</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="s">"BATCH_SIZE"</span><span class="p">)</span>
-<span class="n">testloader</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="s">'DATASET'</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="s">'test'</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="s">"BATCH_SIZE"</span><span class="p">)</span>
-
-<span class="c1"># Build Neural Clamping framework
-</span><span class="n">nc</span> <span class="o">=</span> <span class="n">NCWrapper</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">num_classes</span><span class="p">,</span> <span class="p">...)</span>
-
-<span class="c1"># Calibrated using Neural Clamping
-</span><span class="n">nc</span><span class="p">.</span><span class="n">train_NC</span><span class="p">(</span><span class="n">val_loader</span><span class="o">=</span><span class="n">valloader</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="s">'EPOCH'</span><span class="p">,</span> <span class="p">...)</span>
-
-<span class="c1"># General Evaluation
-</span><span class="n">nc</span><span class="p">.</span><span class="n">test_with_NC</span><span class="p">(</span><span class="n">test_loader</span><span class="o">=</span><span class="n">testloader</span><span class="p">)</span>
-
-<span class="c1"># Visualization
-</span><span class="n">bin_acc</span><span class="p">,</span> <span class="n">conf_axis</span><span class="p">,</span> <span class="n">ece_score</span> <span class="o">=</span> <span class="n">nc</span><span class="p">.</span><span class="n">reliability_diagram</span><span class="p">(</span><span class="n">test_loader</span><span class="o">=</span><span class="n">testloader</span><span class="p">,</span> <span class="n">rd_criterion</span><span class="o">=</span><span class="s">"ECE"</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
-<span class="n">plot_reliability_diagram</span><span class="p">(</span><span class="n">conf_axis</span><span class="p">,</span> <span class="n">bin_acc</span><span class="p">)</span>
-</code></pre></div></div>
-
-<h2 id="citations">Citations</h2>
-<p>If you find Neural Clamping helpful and useful for your research, please cite our main paper as follows:</p>
-
-<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>@inproceedings{hsiung2023nctv,
-  title={{NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration}}, 
-  author={Lei Hsiung, Yung-Chen Tang and Pin-Yu Chen and Tsung-Yi Ho},
-  booktitle={Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence},
-  publisher={Association for the Advancement of Artificial Intelligence},
-  year={2023},
-  month={February}
-}
-
-@misc{tang2022neural_clamping,
-  title={{Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration}}, 
-  author={Yung-Chen Tang and Pin-Yu Chen and Tsung-Yi Ho},
-  year={2022},
-  eprint={2209.11604},
-  archivePrefix={arXiv},
-  primaryClass={cs.LG}
-}
-</code></pre></div></div>
-
-
-      <footer class="site-footer">
-        
-          <span class="site-footer-owner">NCTV is maintained by <a href="https://hsiung.cc">Lei Hsiung</a> and <a href="https://github.com/yungchentang">Yung-Chen Tang</a>.</span>
-        
-      </footer>
-    </main>
-  </body>
-</html>
diff --git a/spaces/XPMaster/manafeth/README.md b/spaces/XPMaster/manafeth/README.md
deleted file mode 100644
index e2a9d5aea93a31d05bdaa32d9df8c0b5d253a8bf..0000000000000000000000000000000000000000
--- a/spaces/XPMaster/manafeth/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Manafeth
-emoji: 📊
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/HowToColorBWImage.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/HowToColorBWImage.py
deleted file mode 100644
index b98fc77ab7a68d8ea73d7971db58b9f7d04e4eca..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/HowToColorBWImage.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# please follow first the install settings before coding , like creating the conda enviroment
-# important - the Python vscode home directory shold be the Deoldify directory 
-
-from deoldify import device 
-from deoldify.device_id import DeviceId
-
-# checking for Cuda (GPU)
-
-device.set(device=DeviceId.GPU0)
-
-import torch
-if not torch.cuda.is_available():
-    print('GPU is not avaiable')
-
-import fastai
-from deoldify.visualize import *
-import warnings
-warnings.filterwarnings("ignore",category=UserWarning,message=".*?Your .*? set is empty.*?")
-
-import cv2
-
-# this is the main object for colorizing image
-colorizer = get_image_colorizer(artistic=True)
-
-# this parameter is very memory consume, So if you have memory issues running this Python script
-# try to reduce it to the minimum and raise as trail and error process
-# to reduce the running time , I changed it for this demo to the minimum : 7 
-
-render_factor =  7 # 35 # this input parameter is minimum 7 and maximum 40
-watermakred = False # this paramter coltrols watermark in the result image
-
-image_path = colorizer.plot_transformed_image(path='C:/GitHub/Python-Code-Cool-Stuff/DeOldify/media/testBW.jpg',render_factor=render_factor, compare=True, watermarked=watermakred)
-
-print('this is the result image path :')
-print(image_path)
-
-#lets show the image
-image = cv2.imread(str(image_path))
-cv2.imshow('image',image)
-cv2.waitKey(0)
-
diff --git a/spaces/XzJosh/Diana-Bert-VITS2/utils.py b/spaces/XzJosh/Diana-Bert-VITS2/utils.py
deleted file mode 100644
index c6aa6cfc64c33e2eed33e9845239e831fc1c4a1a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Diana-Bert-VITS2/utils.py
+++ /dev/null
@@ -1,293 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
-    learning_rate = checkpoint_dict['learning_rate']
-    if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
-        optimizer.load_state_dict(checkpoint_dict['optimizer'])
-    elif optimizer is None and not skip_optimizer:  
-    #else: #Disable this line if Infer ,and enable the line upper
-        new_opt_dict = optimizer.state_dict()
-        new_opt_dict_params = new_opt_dict['param_groups'][0]['params']
-        new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups']
-        new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params
-        optimizer.load_state_dict(new_opt_dict)
-    saved_state_dict = checkpoint_dict['model']
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            #assert "emb_g" not in k
-            # print("load", k)
-            new_state_dict[k] = saved_state_dict[k]
-            assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
-        except:
-            print("error, %s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, 'module'):
-        model.module.load_state_dict(new_state_dict, strict=False)
-    else:
-        model.load_state_dict(new_state_dict, strict=False)
-    print("load ")
-    logger.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info("Saving model and optimizer state at iteration {} to {}".format(
-        iteration, checkpoint_path))
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save({'model': state_dict,
-                'iteration': iteration,
-                'optimizer': optimizer.state_dict(),
-                'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
-    for k, v in scalars.items():
-        writer.add_scalar(k, v, global_step)
-    for k, v in histograms.items():
-        writer.add_histogram(k, v, global_step)
-    for k, v in images.items():
-        writer.add_image(k, v, global_step, dataformats='HWC')
-    for k, v in audios.items():
-        writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-    f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-    x = f_list[-1]
-    print(x)
-    return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                   interpolation='none')
-    fig.colorbar(im, ax=ax)
-    xlabel = 'Decoder timestep'
-    if info is not None:
-        xlabel += '\n\n' + info
-    plt.xlabel(xlabel)
-    plt.ylabel('Encoder timestep')
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding='utf-8') as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                        help='JSON file for configuration')
-    parser.add_argument('-m', '--model', type=str, default="./OUTPUT_MODEL",
-                        help='Model name')
-    parser.add_argument('--cont', dest='cont', action="store_true", default=False, help="whether to continue training on the latest checkpoint")
-
-    args = parser.parse_args()
-    model_dir = os.path.join("./logs", args.model)
-
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-
-    config_path = args.config
-    config_save_path = os.path.join(model_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    hparams.cont = args.cont
-    return hparams
-
-
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
-    """Freeing up space by deleting saved ckpts
-
-  Arguments:
-  path_to_models    --  Path to the model directory
-  n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
-  sort_by_time      --  True -> chronologically delete ckpts
-                        False -> lexicographically delete ckpts
-  """
-    import re
-    ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
-    name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
-    time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
-    sort_key = time_key if sort_by_time else name_key
-    x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')],
-                                 key=sort_key)
-    to_del = [os.path.join(path_to_models, fn) for fn in
-              (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
-    del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
-    del_routine = lambda x: [os.remove(x), del_info(x)]
-    rs = [del_routine(fn) for fn in to_del]
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-            source_dir
-        ))
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-                saved_hash[:8], cur_hash[:8]))
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams():
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/XzJosh/Jianmo-Bert-VITS2/text/__init__.py b/spaces/XzJosh/Jianmo-Bert-VITS2/text/__init__.py
deleted file mode 100644
index 7566bf351ca9b95af9cdc6d729557a9da083800f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Jianmo-Bert-VITS2/text/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from text.symbols import *
-
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
-  tone_start = language_tone_start_map[language]
-  tones = [i + tone_start for i in tones]
-  lang_id = language_id_map[language]
-  lang_ids = [lang_id for i in phones]
-  return phones, tones, lang_ids
-
-def get_bert(norm_text, word2ph, language):
-  from .chinese_bert import get_bert_feature as zh_bert
-  from .english_bert_mock import get_bert_feature as en_bert
-  lang_bert_func_map = {
-    'ZH': zh_bert,
-    'EN': en_bert
-  }
-  bert = lang_bert_func_map[language](norm_text, word2ph)
-  return bert
diff --git a/spaces/YUANAI/DiffspeechResearch/utils/audio/align.py b/spaces/YUANAI/DiffspeechResearch/utils/audio/align.py
deleted file mode 100644
index 096e593f01b51ee2d9f565666cfa8c40a90d76f1..0000000000000000000000000000000000000000
--- a/spaces/YUANAI/DiffspeechResearch/utils/audio/align.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import re
-
-import torch
-import numpy as np
-from textgrid import TextGrid
-
-from utils.text.text_encoder import is_sil_phoneme
-
-
-def get_mel2ph(tg_fn, ph, mel, hop_size, audio_sample_rate, min_sil_duration=0):
-    ph_list = ph.split(" ")
-    itvs = TextGrid.fromFile(tg_fn)[1]
-    itvs_ = []
-    for i in range(len(itvs)):
-        if itvs[i].maxTime - itvs[i].minTime < min_sil_duration and i > 0 and is_sil_phoneme(itvs[i].mark):
-            itvs_[-1].maxTime = itvs[i].maxTime
-        else:
-            itvs_.append(itvs[i])
-    itvs.intervals = itvs_
-    itv_marks = [itv.mark for itv in itvs]
-    tg_len = len([x for x in itvs if not is_sil_phoneme(x.mark)])
-    ph_len = len([x for x in ph_list if not is_sil_phoneme(x)])
-    assert tg_len == ph_len, (tg_len, ph_len, itv_marks, ph_list, tg_fn)
-    mel2ph = np.zeros([mel.shape[0]], int)
-    i_itv = 0
-    i_ph = 0
-    while i_itv < len(itvs):
-        itv = itvs[i_itv]
-        ph = ph_list[i_ph]
-        itv_ph = itv.mark
-        start_frame = int(itv.minTime * audio_sample_rate / hop_size + 0.5)
-        end_frame = int(itv.maxTime * audio_sample_rate / hop_size + 0.5)
-        if is_sil_phoneme(itv_ph) and not is_sil_phoneme(ph):
-            mel2ph[start_frame:end_frame] = i_ph
-            i_itv += 1
-        elif not is_sil_phoneme(itv_ph) and is_sil_phoneme(ph):
-            i_ph += 1
-        else:
-            if not ((is_sil_phoneme(itv_ph) and is_sil_phoneme(ph)) \
-                    or re.sub(r'\d+', '', itv_ph.lower()) == re.sub(r'\d+', '', ph.lower())):
-                print(f"| WARN: {tg_fn} phs are not same: ", itv_ph, ph, itv_marks, ph_list)
-            mel2ph[start_frame:end_frame] = i_ph + 1
-            i_ph += 1
-            i_itv += 1
-    mel2ph[-1] = mel2ph[-2]
-    assert not np.any(mel2ph == 0)
-    T_t = len(ph_list)
-    dur = mel2token_to_dur(mel2ph, T_t)
-    return mel2ph.tolist(), dur.tolist()
-
-
-def split_audio_by_mel2ph(audio, mel2ph, hop_size, audio_num_mel_bins):
-    if isinstance(audio, torch.Tensor):
-        audio = audio.numpy()
-    if isinstance(mel2ph, torch.Tensor):
-        mel2ph = mel2ph.numpy()
-    assert len(audio.shape) == 1, len(mel2ph.shape) == 1
-    split_locs = []
-    for i in range(1, len(mel2ph)):
-        if mel2ph[i] != mel2ph[i - 1]:
-            split_loc = i * hop_size
-            split_locs.append(split_loc)
-
-    new_audio = []
-    for i in range(len(split_locs) - 1):
-        new_audio.append(audio[split_locs[i]:split_locs[i + 1]])
-        new_audio.append(np.zeros([0.5 * audio_num_mel_bins]))
-    return np.concatenate(new_audio)
-
-
-def mel2token_to_dur(mel2token, T_txt=None, max_dur=None):
-    is_torch = isinstance(mel2token, torch.Tensor)
-    has_batch_dim = True
-    if not is_torch:
-        mel2token = torch.LongTensor(mel2token)
-    if T_txt is None:
-        T_txt = mel2token.max()
-    if len(mel2token.shape) == 1:
-        mel2token = mel2token[None, ...]
-        has_batch_dim = False
-    B, _ = mel2token.shape
-    dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token))
-    dur = dur[:, 1:]
-    if max_dur is not None:
-        dur = dur.clamp(max=max_dur)
-    if not is_torch:
-        dur = dur.numpy()
-    if not has_batch_dim:
-        dur = dur[0]
-    return dur
diff --git a/spaces/YanzBotz/YanzBotz-Models/lib/infer_pack/models.py b/spaces/YanzBotz/YanzBotz-Models/lib/infer_pack/models.py
deleted file mode 100644
index 3665d03bc0514a6ed07d3372ea24717dae1e0a65..0000000000000000000000000000000000000000
--- a/spaces/YanzBotz/YanzBotz-Models/lib/infer_pack/models.py
+++ /dev/null
@@ -1,1142 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-            nsff0 = nsff0[:, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-            nsff0 = nsff0[:, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/models/unet_2d_blocks.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/models/unet_2d_blocks.py
deleted file mode 100644
index d78804b18e7586d52288b29f0ed7d54873196d0e..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/models/unet_2d_blocks.py
+++ /dev/null
@@ -1,1650 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import numpy as np
-import torch
-from torch import nn
-
-from .attention import AttentionBlock, DualTransformer2DModel, Transformer2DModel
-from .resnet import Downsample2D, FirDownsample2D, FirUpsample2D, ResnetBlock2D, Upsample2D
-
-
-def get_down_block(
-    down_block_type,
-    num_layers,
-    in_channels,
-    out_channels,
-    temb_channels,
-    add_downsample,
-    resnet_eps,
-    resnet_act_fn,
-    attn_num_head_channels,
-    resnet_groups=None,
-    cross_attention_dim=None,
-    downsample_padding=None,
-    dual_cross_attention=False,
-    use_linear_projection=False,
-    only_cross_attention=False,
-):
-    down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
-    if down_block_type == "DownBlock2D":
-        return DownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-        )
-    elif down_block_type == "AttnDownBlock2D":
-        return AttnDownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            attn_num_head_channels=attn_num_head_channels,
-        )
-    elif down_block_type == "CrossAttnDownBlock2D":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D")
-        return CrossAttnDownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            cross_attention_dim=cross_attention_dim,
-            attn_num_head_channels=attn_num_head_channels,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-        )
-    elif down_block_type == "SkipDownBlock2D":
-        return SkipDownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            downsample_padding=downsample_padding,
-        )
-    elif down_block_type == "AttnSkipDownBlock2D":
-        return AttnSkipDownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            downsample_padding=downsample_padding,
-            attn_num_head_channels=attn_num_head_channels,
-        )
-    elif down_block_type == "DownEncoderBlock2D":
-        return DownEncoderBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-        )
-    elif down_block_type == "AttnDownEncoderBlock2D":
-        return AttnDownEncoderBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            attn_num_head_channels=attn_num_head_channels,
-        )
-    raise ValueError(f"{down_block_type} does not exist.")
-
-
-def get_up_block(
-    up_block_type,
-    num_layers,
-    in_channels,
-    out_channels,
-    prev_output_channel,
-    temb_channels,
-    add_upsample,
-    resnet_eps,
-    resnet_act_fn,
-    attn_num_head_channels,
-    resnet_groups=None,
-    cross_attention_dim=None,
-    dual_cross_attention=False,
-    use_linear_projection=False,
-    only_cross_attention=False,
-):
-    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
-    if up_block_type == "UpBlock2D":
-        return UpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-        )
-    elif up_block_type == "CrossAttnUpBlock2D":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D")
-        return CrossAttnUpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            cross_attention_dim=cross_attention_dim,
-            attn_num_head_channels=attn_num_head_channels,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-        )
-    elif up_block_type == "AttnUpBlock2D":
-        return AttnUpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            attn_num_head_channels=attn_num_head_channels,
-        )
-    elif up_block_type == "SkipUpBlock2D":
-        return SkipUpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-        )
-    elif up_block_type == "AttnSkipUpBlock2D":
-        return AttnSkipUpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            attn_num_head_channels=attn_num_head_channels,
-        )
-    elif up_block_type == "UpDecoderBlock2D":
-        return UpDecoderBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-        )
-    elif up_block_type == "AttnUpDecoderBlock2D":
-        return AttnUpDecoderBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            attn_num_head_channels=attn_num_head_channels,
-        )
-    raise ValueError(f"{up_block_type} does not exist.")
-
-
-class UNetMidBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        attention_type="default",
-        output_scale_factor=1.0,
-    ):
-        super().__init__()
-
-        self.attention_type = attention_type
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-            )
-        ]
-        attentions = []
-
-        for _ in range(num_layers):
-            attentions.append(
-                AttentionBlock(
-                    in_channels,
-                    num_head_channels=attn_num_head_channels,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                    norm_num_groups=resnet_groups,
-                )
-            )
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(self, hidden_states, temb=None, encoder_states=None):
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            if self.attention_type == "default":
-                hidden_states = attn(hidden_states)
-            else:
-                hidden_states = attn(hidden_states, encoder_states)
-            hidden_states = resnet(hidden_states, temb)
-
-        return hidden_states
-
-
-class UNetMidBlock2DCrossAttn(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        attention_type="default",
-        output_scale_factor=1.0,
-        cross_attention_dim=1280,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-    ):
-        super().__init__()
-
-        self.attention_type = attention_type
-        self.attn_num_head_channels = attn_num_head_channels
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-            )
-        ]
-        attentions = []
-
-        for _ in range(num_layers):
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        attn_num_head_channels,
-                        in_channels // attn_num_head_channels,
-                        in_channels=in_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        attn_num_head_channels,
-                        in_channels // attn_num_head_channels,
-                        in_channels=in_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-    def set_attention_slice(self, slice_size):
-        head_dims = self.attn_num_head_channels
-        head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
-        if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
-            raise ValueError(
-                f"Make sure slice_size {slice_size} is a common divisor of "
-                f"the number of heads used in cross_attention: {head_dims}"
-            )
-        if slice_size is not None and slice_size > min(head_dims):
-            raise ValueError(
-                f"slice_size {slice_size} has to be smaller or equal to "
-                f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
-            )
-
-        for attn in self.attentions:
-            attn._set_attention_slice(slice_size)
-
-    def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            hidden_states = attn(hidden_states, encoder_hidden_states).sample
-            hidden_states = resnet(hidden_states, temb)
-
-        return hidden_states
-
-
-class AttnDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        attention_type="default",
-        output_scale_factor=1.0,
-        downsample_padding=1,
-        add_downsample=True,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.attention_type = attention_type
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            attentions.append(
-                AttentionBlock(
-                    out_channels,
-                    num_head_channels=attn_num_head_channels,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                    norm_num_groups=resnet_groups,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-    def forward(self, hidden_states, temb=None):
-        output_states = ()
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states, temb)
-            hidden_states = attn(hidden_states)
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class CrossAttnDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        cross_attention_dim=1280,
-        attention_type="default",
-        output_scale_factor=1.0,
-        downsample_padding=1,
-        add_downsample=True,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        only_cross_attention=False,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.attention_type = attention_type
-        self.attn_num_head_channels = attn_num_head_channels
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        attn_num_head_channels,
-                        out_channels // attn_num_head_channels,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        only_cross_attention=only_cross_attention,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        attn_num_head_channels,
-                        out_channels // attn_num_head_channels,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def set_attention_slice(self, slice_size):
-        head_dims = self.attn_num_head_channels
-        head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
-        if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
-            raise ValueError(
-                f"Make sure slice_size {slice_size} is a common divisor of "
-                f"the number of heads used in cross_attention: {head_dims}"
-            )
-        if slice_size is not None and slice_size > min(head_dims):
-            raise ValueError(
-                f"slice_size {slice_size} has to be smaller or equal to "
-                f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
-            )
-
-        for attn in self.attentions:
-            attn._set_attention_slice(slice_size)
-
-    def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
-        output_states = ()
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states
-                )[0]
-            else:
-                hidden_states = resnet(hidden_states, temb)
-                hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
-
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class DownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_downsample=True,
-        downsample_padding=1,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states, temb=None):
-        output_states = ()
-
-        for resnet in self.resnets:
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class DownEncoderBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_downsample=True,
-        downsample_padding=1,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=None,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-    def forward(self, hidden_states):
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, temb=None)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-        return hidden_states
-
-
-class AttnDownEncoderBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        output_scale_factor=1.0,
-        add_downsample=True,
-        downsample_padding=1,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=None,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            attentions.append(
-                AttentionBlock(
-                    out_channels,
-                    num_head_channels=attn_num_head_channels,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                    norm_num_groups=resnet_groups,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-    def forward(self, hidden_states):
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states, temb=None)
-            hidden_states = attn(hidden_states)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-        return hidden_states
-
-
-class AttnSkipDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        attention_type="default",
-        output_scale_factor=np.sqrt(2.0),
-        downsample_padding=1,
-        add_downsample=True,
-    ):
-        super().__init__()
-        self.attentions = nn.ModuleList([])
-        self.resnets = nn.ModuleList([])
-
-        self.attention_type = attention_type
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            self.resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=min(in_channels // 4, 32),
-                    groups_out=min(out_channels // 4, 32),
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            self.attentions.append(
-                AttentionBlock(
-                    out_channels,
-                    num_head_channels=attn_num_head_channels,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                )
-            )
-
-        if add_downsample:
-            self.resnet_down = ResnetBlock2D(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=min(out_channels // 4, 32),
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                use_in_shortcut=True,
-                down=True,
-                kernel="fir",
-            )
-            self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)])
-            self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1))
-        else:
-            self.resnet_down = None
-            self.downsamplers = None
-            self.skip_conv = None
-
-    def forward(self, hidden_states, temb=None, skip_sample=None):
-        output_states = ()
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states, temb)
-            hidden_states = attn(hidden_states)
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            hidden_states = self.resnet_down(hidden_states, temb)
-            for downsampler in self.downsamplers:
-                skip_sample = downsampler(skip_sample)
-
-            hidden_states = self.skip_conv(skip_sample) + hidden_states
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states, skip_sample
-
-
-class SkipDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_pre_norm: bool = True,
-        output_scale_factor=np.sqrt(2.0),
-        add_downsample=True,
-        downsample_padding=1,
-    ):
-        super().__init__()
-        self.resnets = nn.ModuleList([])
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            self.resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=min(in_channels // 4, 32),
-                    groups_out=min(out_channels // 4, 32),
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        if add_downsample:
-            self.resnet_down = ResnetBlock2D(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=min(out_channels // 4, 32),
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                use_in_shortcut=True,
-                down=True,
-                kernel="fir",
-            )
-            self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)])
-            self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1))
-        else:
-            self.resnet_down = None
-            self.downsamplers = None
-            self.skip_conv = None
-
-    def forward(self, hidden_states, temb=None, skip_sample=None):
-        output_states = ()
-
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, temb)
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            hidden_states = self.resnet_down(hidden_states, temb)
-            for downsampler in self.downsamplers:
-                skip_sample = downsampler(skip_sample)
-
-            hidden_states = self.skip_conv(skip_sample) + hidden_states
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states, skip_sample
-
-
-class AttnUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attention_type="default",
-        attn_num_head_channels=1,
-        output_scale_factor=1.0,
-        add_upsample=True,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.attention_type = attention_type
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            attentions.append(
-                AttentionBlock(
-                    out_channels,
-                    num_head_channels=attn_num_head_channels,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                    norm_num_groups=resnet_groups,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
-        for resnet, attn in zip(self.resnets, self.attentions):
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            hidden_states = resnet(hidden_states, temb)
-            hidden_states = attn(hidden_states)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-class CrossAttnUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        prev_output_channel: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        cross_attention_dim=1280,
-        attention_type="default",
-        output_scale_factor=1.0,
-        add_upsample=True,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        only_cross_attention=False,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.attention_type = attention_type
-        self.attn_num_head_channels = attn_num_head_channels
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        attn_num_head_channels,
-                        out_channels // attn_num_head_channels,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        only_cross_attention=only_cross_attention,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        attn_num_head_channels,
-                        out_channels // attn_num_head_channels,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def set_attention_slice(self, slice_size):
-        head_dims = self.attn_num_head_channels
-        head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
-        if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
-            raise ValueError(
-                f"Make sure slice_size {slice_size} is a common divisor of "
-                f"the number of heads used in cross_attention: {head_dims}"
-            )
-        if slice_size is not None and slice_size > min(head_dims):
-            raise ValueError(
-                f"slice_size {slice_size} has to be smaller or equal to "
-                f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
-            )
-
-        for attn in self.attentions:
-            attn._set_attention_slice(slice_size)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states,
-        res_hidden_states_tuple,
-        temb=None,
-        encoder_hidden_states=None,
-        upsample_size=None,
-    ):
-        for resnet, attn in zip(self.resnets, self.attentions):
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states
-                )[0]
-            else:
-                hidden_states = resnet(hidden_states, temb)
-                hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-        return hidden_states
-
-
-class UpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_upsample=True,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-        return hidden_states
-
-
-class UpDecoderBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor=1.0,
-        add_upsample=True,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            input_channels = in_channels if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=input_channels,
-                    out_channels=out_channels,
-                    temb_channels=None,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-    def forward(self, hidden_states):
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, temb=None)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-class AttnUpDecoderBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        output_scale_factor=1.0,
-        add_upsample=True,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        for i in range(num_layers):
-            input_channels = in_channels if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=input_channels,
-                    out_channels=out_channels,
-                    temb_channels=None,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            attentions.append(
-                AttentionBlock(
-                    out_channels,
-                    num_head_channels=attn_num_head_channels,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                    norm_num_groups=resnet_groups,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-    def forward(self, hidden_states):
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states, temb=None)
-            hidden_states = attn(hidden_states)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-class AttnSkipUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_pre_norm: bool = True,
-        attn_num_head_channels=1,
-        attention_type="default",
-        output_scale_factor=np.sqrt(2.0),
-        upsample_padding=1,
-        add_upsample=True,
-    ):
-        super().__init__()
-        self.attentions = nn.ModuleList([])
-        self.resnets = nn.ModuleList([])
-
-        self.attention_type = attention_type
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            self.resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=min(resnet_in_channels + res_skip_channels // 4, 32),
-                    groups_out=min(out_channels // 4, 32),
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.attentions.append(
-            AttentionBlock(
-                out_channels,
-                num_head_channels=attn_num_head_channels,
-                rescale_output_factor=output_scale_factor,
-                eps=resnet_eps,
-            )
-        )
-
-        self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels)
-        if add_upsample:
-            self.resnet_up = ResnetBlock2D(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=min(out_channels // 4, 32),
-                groups_out=min(out_channels // 4, 32),
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                use_in_shortcut=True,
-                up=True,
-                kernel="fir",
-            )
-            self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-            self.skip_norm = torch.nn.GroupNorm(
-                num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True
-            )
-            self.act = nn.SiLU()
-        else:
-            self.resnet_up = None
-            self.skip_conv = None
-            self.skip_norm = None
-            self.act = None
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, skip_sample=None):
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            hidden_states = resnet(hidden_states, temb)
-
-        hidden_states = self.attentions[0](hidden_states)
-
-        if skip_sample is not None:
-            skip_sample = self.upsampler(skip_sample)
-        else:
-            skip_sample = 0
-
-        if self.resnet_up is not None:
-            skip_sample_states = self.skip_norm(hidden_states)
-            skip_sample_states = self.act(skip_sample_states)
-            skip_sample_states = self.skip_conv(skip_sample_states)
-
-            skip_sample = skip_sample + skip_sample_states
-
-            hidden_states = self.resnet_up(hidden_states, temb)
-
-        return hidden_states, skip_sample
-
-
-class SkipUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_pre_norm: bool = True,
-        output_scale_factor=np.sqrt(2.0),
-        add_upsample=True,
-        upsample_padding=1,
-    ):
-        super().__init__()
-        self.resnets = nn.ModuleList([])
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            self.resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=min((resnet_in_channels + res_skip_channels) // 4, 32),
-                    groups_out=min(out_channels // 4, 32),
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels)
-        if add_upsample:
-            self.resnet_up = ResnetBlock2D(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=min(out_channels // 4, 32),
-                groups_out=min(out_channels // 4, 32),
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                use_in_shortcut=True,
-                up=True,
-                kernel="fir",
-            )
-            self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-            self.skip_norm = torch.nn.GroupNorm(
-                num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True
-            )
-            self.act = nn.SiLU()
-        else:
-            self.resnet_up = None
-            self.skip_conv = None
-            self.skip_norm = None
-            self.act = None
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, skip_sample=None):
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            hidden_states = resnet(hidden_states, temb)
-
-        if skip_sample is not None:
-            skip_sample = self.upsampler(skip_sample)
-        else:
-            skip_sample = 0
-
-        if self.resnet_up is not None:
-            skip_sample_states = self.skip_norm(hidden_states)
-            skip_sample_states = self.act(skip_sample_states)
-            skip_sample_states = self.skip_conv(skip_sample_states)
-
-            skip_sample = skip_sample + skip_sample_states
-
-            hidden_states = self.resnet_up(hidden_states, temb)
-
-        return hidden_states, skip_sample
diff --git a/spaces/Yiqin/ChatVID/model/vision/Vid2SeqCaptioner.py b/spaces/Yiqin/ChatVID/model/vision/Vid2SeqCaptioner.py
deleted file mode 100644
index bb3f8cadb7cd78856f58d313741c7e2ff02bc920..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/Vid2SeqCaptioner.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from typing import Any
-from model.utils import extract_clip_feature_single_video_fps, generate, ScenicCall, ScenicModel
-from config import vid2seq_config
-
-import torch
-
-
-import sys, os
-from pathlib import Path
-# append current path to sys.path
-sys.path.append(str(Path(__file__).parent.parent.parent / "scenic"))
-from scenic.projects.vid2seq.playground import generate as vid2seq_generate
-
-class Flag(object):
-    pass
-
-class Vid2SeqCaptioner:
-    """Vid2SeqCaptioner is a class that uses a video to generate a caption for the video.
-    
-    Description:
-        It uses the Google Scenic Vid2Seq as the base model. Note that Scenic is a project designed to use with TPUs. And GPU resources(70G VMeomry at least) maybe be not enough to run the model. So, we need to use the CPU to run the model.
-    """
-    def __init__(self, config):
-        self.config = config
-        flags = Flag()
-        flags.workdir = self.config['work_dir']
-        flags.config = vid2seq_config.get_config()
-        # flags.config = self.config['config_path']
-        flags.data_dir = self.config['output_path']
-        flags.ckpt_dir = self.config['checkpoint_path']
-        self.model = ScenicModel(flags)
-        
-    def __call__(self, video_path):
-        self._preprocess(video_path)
-        return self.model()
-        # call = ScenicCall(vid2seq_generate, flags)
-        # return call()
-        
-    def _preprocess(self, video_path):
-        """Preprocess the video.
-        
-        Description:
-            Pipeline: CLIP -> *.npy -> *.csv(video) -> generate_from_file.py -> file0000-0003.tfrecord
-        Args:
-            video_path: The path of the video.
-        """
-        
-        # Extract CLIP features first
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        video_feat, video_info = extract_clip_feature_single_video_fps(
-            video_path=video_path,
-            clip_ckpt_path=self.config['clip_path'],
-            device=device
-        )
-        
-        # get numpy array
-        video_feat = video_feat.cpu()
-        video_feat = video_feat.numpy()
-        
-        # create a dict to store the video info
-        video_info_dict = {
-            'basename' : 'test',
-            'output_path' : self.config['output_path'],
-            'asr_start' : None,
-            'asr_end' : None,
-            'asr_string' : None,
-            'video_id' : video_path.split('/')[-1].split('.')[0],
-            'features' : video_feat,
-            'duration' : video_info['total_frames'] / video_info['avg_fps'] * 1000000,
-        }
-        # begin to generate tfrecord file
-        generate(video_info_dict)
-        print("tfrecord file generated at {}".format(self.config['output_path']))
-        
-        
\ No newline at end of file
diff --git a/spaces/Yuliang/ECON/lib/pymafx/models/transformers/bert/e2e_body_network.py b/spaces/Yuliang/ECON/lib/pymafx/models/transformers/bert/e2e_body_network.py
deleted file mode 100644
index 6e00cf8716db068ecdca3d790ecacbdb9e518edc..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/pymafx/models/transformers/bert/e2e_body_network.py
+++ /dev/null
@@ -1,115 +0,0 @@
-"""
-Copyright (c) Microsoft Corporation.
-Licensed under the MIT license.
-
-"""
-
-import src.modeling.data.config as cfg
-import torch
-
-
-class Graphormer_Body_Network(torch.nn.Module):
-    '''
-    End-to-end Graphormer network for human pose and mesh reconstruction from a single image.
-    '''
-    def __init__(self, args, config, backbone, trans_encoder, mesh_sampler):
-        super(Graphormer_Body_Network, self).__init__()
-        self.config = config
-        self.config.device = args.device
-        self.backbone = backbone
-        self.trans_encoder = trans_encoder
-        self.upsampling = torch.nn.Linear(431, 1723)
-        self.upsampling2 = torch.nn.Linear(1723, 6890)
-        self.cam_param_fc = torch.nn.Linear(3, 1)
-        self.cam_param_fc2 = torch.nn.Linear(431, 250)
-        self.cam_param_fc3 = torch.nn.Linear(250, 3)
-        self.grid_feat_dim = torch.nn.Linear(1024, 2051)
-
-    def forward(self, images, smpl, mesh_sampler, meta_masks=None, is_train=False):
-        batch_size = images.size(0)
-        # Generate T-pose template mesh
-        template_pose = torch.zeros((1, 72))
-        template_pose[:, 0] = 3.1416    # Rectify "upside down" reference mesh in global coord
-        template_pose = template_pose.cuda(self.config.device)
-        template_betas = torch.zeros((1, 10)).cuda(self.config.device)
-        template_vertices = smpl(template_pose, template_betas)
-
-        # template mesh simplification
-        template_vertices_sub = mesh_sampler.downsample(template_vertices)
-        template_vertices_sub2 = mesh_sampler.downsample(template_vertices_sub, n1=1, n2=2)
-        print(
-            'template_vertices', template_vertices.shape, template_vertices_sub.shape,
-            template_vertices_sub2.shape
-        )
-
-        # template mesh-to-joint regression
-        template_3d_joints = smpl.get_h36m_joints(template_vertices)
-        template_pelvis = template_3d_joints[:, cfg.H36M_J17_NAME.index('Pelvis'), :]
-        template_3d_joints = template_3d_joints[:, cfg.H36M_J17_TO_J14, :]
-        num_joints = template_3d_joints.shape[1]
-
-        # normalize
-        template_3d_joints = template_3d_joints - template_pelvis[:, None, :]
-        template_vertices_sub2 = template_vertices_sub2 - template_pelvis[:, None, :]
-
-        # concatinate template joints and template vertices, and then duplicate to batch size
-        ref_vertices = torch.cat([template_3d_joints, template_vertices_sub2], dim=1)
-        ref_vertices = ref_vertices.expand(batch_size, -1, -1)
-        print('ref_vertices', ref_vertices.shape)
-
-        # extract grid features and global image features using a CNN backbone
-        image_feat, grid_feat = self.backbone(images)
-        print('image_feat, grid_feat', image_feat.shape, grid_feat.shape)
-        # concatinate image feat and 3d mesh template
-        image_feat = image_feat.view(batch_size, 1, 2048).expand(-1, ref_vertices.shape[-2], -1)
-        print('image_feat', image_feat.shape)
-        # process grid features
-        grid_feat = torch.flatten(grid_feat, start_dim=2)
-        grid_feat = grid_feat.transpose(1, 2)
-        print('grid_feat bf', grid_feat.shape)
-        grid_feat = self.grid_feat_dim(grid_feat)
-        print('grid_feat', grid_feat.shape)
-        # concatinate image feat and template mesh to form the joint/vertex queries
-        features = torch.cat([ref_vertices, image_feat], dim=2)
-        print('features', features.shape, ref_vertices.shape, image_feat.shape)
-        # prepare input tokens including joint/vertex queries and grid features
-        features = torch.cat([features, grid_feat], dim=1)
-        print('features', features.shape)
-
-        if is_train == True:
-            # apply mask vertex/joint modeling
-            # meta_masks is a tensor of all the masks, randomly generated in dataloader
-            # we pre-define a [MASK] token, which is a floating-value vector with 0.01s
-            special_token = torch.ones_like(features[:, :-49, :]).cuda() * 0.01
-            print('special_token', special_token.shape, meta_masks.shape)
-            print('meta_masks', torch.unique(meta_masks))
-            features[:, :-49, :
-                    ] = features[:, :-49, :] * meta_masks + special_token * (1 - meta_masks)
-
-        # forward pass
-        if self.config.output_attentions == True:
-            features, hidden_states, att = self.trans_encoder(features)
-        else:
-            features = self.trans_encoder(features)
-
-        pred_3d_joints = features[:, :num_joints, :]
-        pred_vertices_sub2 = features[:, num_joints:-49, :]
-
-        # learn camera parameters
-        x = self.cam_param_fc(pred_vertices_sub2)
-        x = x.transpose(1, 2)
-        x = self.cam_param_fc2(x)
-        x = self.cam_param_fc3(x)
-        cam_param = x.transpose(1, 2)
-        cam_param = cam_param.squeeze()
-
-        temp_transpose = pred_vertices_sub2.transpose(1, 2)
-        pred_vertices_sub = self.upsampling(temp_transpose)
-        pred_vertices_full = self.upsampling2(pred_vertices_sub)
-        pred_vertices_sub = pred_vertices_sub.transpose(1, 2)
-        pred_vertices_full = pred_vertices_full.transpose(1, 2)
-
-        if self.config.output_attentions == True:
-            return cam_param, pred_3d_joints, pred_vertices_sub2, pred_vertices_sub, pred_vertices_full, hidden_states, att
-        else:
-            return cam_param, pred_3d_joints, pred_vertices_sub2, pred_vertices_sub, pred_vertices_full
diff --git a/spaces/abdvl/datahub_qa_bot/docs/glossary/business-glossary.md b/spaces/abdvl/datahub_qa_bot/docs/glossary/business-glossary.md
deleted file mode 100644
index fc14acf133ae46797729db88e608d7ee76ab2093..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/glossary/business-glossary.md
+++ /dev/null
@@ -1,119 +0,0 @@
----
-title: Business Glossary
----
-
-import FeatureAvailability from '@site/src/components/FeatureAvailability';
-
-# About DataHub Business Glossary
-
-<FeatureAvailability/>
-
-## Introduction
-
-When working in complex data ecosystems, it is very useful to organize data assets using a shared vocabulary. The Business Glossary feature in DataHub helps you do this, by providing a framework for defining a standardized set of data concepts and then associating them with the physical assets that exist within your data ecosystem.
-
-Within this document, we'll introduce the core concepts comprising DataHub's Business Glossary feature and show you how to put it to work in your organization. 
-
-### Terms & Term Groups
-
-A Business Glossary is comprised of two important primitives: Terms and Term Groups.
-
-- **Terms**: words or phrases with a specific business definition assigned to them.
-- **Term Groups**: act like folders, containing Terms and even other Term Groups to allow for a nested structure.
-
-Both Terms and Term Groups allow you to add documentation and unique owners.
-
-For Glossary Terms, you are also able to establish relationships between different Terms in the **Related Terms** tab. Here you can create Contains and Inherits relationships. Finally, you can view all of the entities that have been tagged with a Term in the **Related Entities** tab.
-
-## Getting to your Glossary
-
-In order to view a Business Glossary, users must have the Platform Privilege called `Manage Glossaries` which can be granted by creating a new Platform [Policy](../authorization/policies.md).
-
-Once granted this privilege, you can access your Glossary by clicking the dropdown at the top of the page called **Govern** and then click **Glossary**:
-
-![](../imgs/glossary/glossary-button.png)
-
-You are now at the root of your Glossary and should see all Terms and Term Groups with no parents assigned to them. You should also notice a hierarchy navigator on the left where you can easily check out the structure of your Glossary!
-
-![](../imgs/glossary/root-glossary.png)
-
-## Creating a Term or Term Group
-
-There are two ways to create Terms and Term Groups through the UI. First, you can create directly from the Glossary home page by clicking the menu dots on the top right and selecting your desired option:
-
-![](../imgs/glossary/root-glossary-create.png)
-
-You can also create Terms or Term Groups directly from a Term Group's page. In order to do that you need to click the menu dots on the top right and select what you want:
-
-![](../imgs/glossary/create-from-node.png)
-
-Note that the modal that pops up will automatically set the current Term Group you are in as the **Parent**. You can easily change this by selecting the input and navigating through your Glossary to find your desired Term Group. In addition, you could start typing the name of a Term Group to see it appear by searching. You can also leave this input blank in order to create a Term or Term Group with no parent.
-
-![](../imgs/glossary/create-modal.png)
-
-## Editing a Term or Term Group
-
-In order to edit a Term or Term Group, you first need to go the page of the Term or Term group you want to edit. Then simply click the edit icon right next to the name to open up an inline editor. Change the text and it will save when you click outside or hit Enter.
-
-![](../imgs/glossary/edit-term.png)
-
-## Moving a Term or Term Group
-
-Once a Term or Term Group has been created, you can always move it to be under a different Term Group parent. In order to do this, click the menu dots on the top right of either entity and select **Move**.
-
-![](../imgs/glossary/move-term-button.png)
-
-This will open a modal where you can navigate through your Glossary to find your desired Term Group.
-
-![](../imgs/glossary/move-term-modal.png)
-
-## Deleting a Term or Term Group
-
-In order to delete a Term or Term Group, you need to go to the entity page of what you want to delete then click the menu dots on the top right. From here you can select **Delete** followed by confirming through a separate modal. **Note**: at the moment we only support deleting Term Groups that do not have any children. Until cascade deleting is supported, you will have to delete all children first, then delete the Term Group.
-
-![](../imgs/glossary/delete-button.png)
-
-## Adding a Term to an Entity
-
-Once you've defined your Glossary, you can begin attaching terms to data assets. To add a Glossary Term to an asset, go to the entity page of your asset and find the **Add Terms** button on the right sidebar.
-
-![](../imgs/glossary/add-term-to-entity.png)
-
-In the modal that pops up you can select the Term you care about in one of two ways:
-- Search for the Term by name in the input
-- Navigate through the Glossary dropdown that appears after clicking into the input
-
-![](../imgs/glossary/add-term-modal.png)
-
-## Managing Glossary with Git 
-
-In many cases, it may be preferable to manage the Business Glossary in a version-control system like git. This can make
-managing changes across teams easier, by funneling all changes through a change management and review process.
-
-To manage your glossary using Git, you can define it within a file and then use the DataHub CLI to ingest
-it into DataHub whenever a change is made (e.g. on a `git commit` hook). For detailed information about the format of
-the glossary file, and how to ingest it into DataHub, check out the [Business Glossary](../generated/ingestion/sources/business-glossary.md) source guide.
-
-## Demo
-
-Check out [our demo site](https://demo.datahubproject.io/glossary) to see an example Glossary and how it works!
-
-### GraphQL
-
-* [addTerm](../../graphql/mutations.md#addterm)
-* [addTerms](../../graphql/mutations.md#addterms)
-* [batchAddTerms](../../graphql/mutations.md#batchaddterms)
-* [removeTerm](../../graphql/mutations.md#removeterm)
-* [batchRemoveTerms](../../graphql/mutations.md#batchremoveterms)
-* [createGlossaryTerm](../../graphql/mutations.md#createglossaryterm)
-* [createGlossaryNode](../../graphql/mutations.md#createglossarynode) (Term Group)
-
-You can easily fetch the Glossary Terms for an entity with a given its URN using the **glossaryTerms** property. Check out [Working with Metadata Entities](../api/graphql/querying-entities.md#querying-for-glossary-terms-of-an-asset) for an example.
-
-## Resources
-- [Creating a Business Glossary and Putting it to use in DataHub](https://blog.datahubproject.io/creating-a-business-glossary-and-putting-it-to-use-in-datahub-43a088323c12)
-- [Tags and Terms: Two Powerful DataHub Features, Used in Two Different Scenarios](https://medium.com/datahub-project/tags-and-terms-two-powerful-datahub-features-used-in-two-different-scenarios-b5b4791e892e)
-
-## Feedback / Questions / Concerns
-
-We want to hear from you! For any inquiries, including Feedback, Questions, or Concerns, reach out on Slack!
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/dnl_r50-d8.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/dnl_r50-d8.py
deleted file mode 100644
index edb4c174c51e34c103737ba39bfc48bf831e561d..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/dnl_r50-d8.py
+++ /dev/null
@@ -1,46 +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='DNLHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        dropout_ratio=0.1,
-        reduction=2,
-        use_scale=True,
-        mode='embedded_gaussian',
-        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/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/mask/mask_target.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/mask/mask_target.py
deleted file mode 100644
index 15d26a88bbf3710bd92813335918407db8c4e053..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/mask/mask_target.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import numpy as np
-import torch
-from torch.nn.modules.utils import _pair
-
-
-def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list,
-                cfg):
-    """Compute mask target for positive proposals in multiple images.
-
-    Args:
-        pos_proposals_list (list[Tensor]): Positive proposals in multiple
-            images.
-        pos_assigned_gt_inds_list (list[Tensor]): Assigned GT indices for each
-            positive proposals.
-        gt_masks_list (list[:obj:`BaseInstanceMasks`]): Ground truth masks of
-            each image.
-        cfg (dict): Config dict that specifies the mask size.
-
-    Returns:
-        list[Tensor]: Mask target of each image.
-
-    Example:
-        >>> import mmcv
-        >>> import mmdet
-        >>> from mmdet.core.mask import BitmapMasks
-        >>> from mmdet.core.mask.mask_target import *
-        >>> H, W = 17, 18
-        >>> cfg = mmcv.Config({'mask_size': (13, 14)})
-        >>> rng = np.random.RandomState(0)
-        >>> # Positive proposals (tl_x, tl_y, br_x, br_y) for each image
-        >>> pos_proposals_list = [
-        >>>     torch.Tensor([
-        >>>         [ 7.2425,  5.5929, 13.9414, 14.9541],
-        >>>         [ 7.3241,  3.6170, 16.3850, 15.3102],
-        >>>     ]),
-        >>>     torch.Tensor([
-        >>>         [ 4.8448, 6.4010, 7.0314, 9.7681],
-        >>>         [ 5.9790, 2.6989, 7.4416, 4.8580],
-        >>>         [ 0.0000, 0.0000, 0.1398, 9.8232],
-        >>>     ]),
-        >>> ]
-        >>> # Corresponding class index for each proposal for each image
-        >>> pos_assigned_gt_inds_list = [
-        >>>     torch.LongTensor([7, 0]),
-        >>>     torch.LongTensor([5, 4, 1]),
-        >>> ]
-        >>> # Ground truth mask for each true object for each image
-        >>> gt_masks_list = [
-        >>>     BitmapMasks(rng.rand(8, H, W), height=H, width=W),
-        >>>     BitmapMasks(rng.rand(6, H, W), height=H, width=W),
-        >>> ]
-        >>> mask_targets = mask_target(
-        >>>     pos_proposals_list, pos_assigned_gt_inds_list,
-        >>>     gt_masks_list, cfg)
-        >>> assert mask_targets.shape == (5,) + cfg['mask_size']
-    """
-    cfg_list = [cfg for _ in range(len(pos_proposals_list))]
-    mask_targets = map(mask_target_single, pos_proposals_list,
-                       pos_assigned_gt_inds_list, gt_masks_list, cfg_list)
-    mask_targets = list(mask_targets)
-    if len(mask_targets) > 0:
-        mask_targets = torch.cat(mask_targets)
-    return mask_targets
-
-
-def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg):
-    """Compute mask target for each positive proposal in the image.
-
-    Args:
-        pos_proposals (Tensor): Positive proposals.
-        pos_assigned_gt_inds (Tensor): Assigned GT inds of positive proposals.
-        gt_masks (:obj:`BaseInstanceMasks`): GT masks in the format of Bitmap
-            or Polygon.
-        cfg (dict): Config dict that indicate the mask size.
-
-    Returns:
-        Tensor: Mask target of each positive proposals in the image.
-
-    Example:
-        >>> import mmcv
-        >>> import mmdet
-        >>> from mmdet.core.mask import BitmapMasks
-        >>> from mmdet.core.mask.mask_target import *  # NOQA
-        >>> H, W = 32, 32
-        >>> cfg = mmcv.Config({'mask_size': (7, 11)})
-        >>> rng = np.random.RandomState(0)
-        >>> # Masks for each ground truth box (relative to the image)
-        >>> gt_masks_data = rng.rand(3, H, W)
-        >>> gt_masks = BitmapMasks(gt_masks_data, height=H, width=W)
-        >>> # Predicted positive boxes in one image
-        >>> pos_proposals = torch.FloatTensor([
-        >>>     [ 16.2,   5.5, 19.9, 20.9],
-        >>>     [ 17.3,  13.6, 19.3, 19.3],
-        >>>     [ 14.8,  16.4, 17.0, 23.7],
-        >>>     [  0.0,   0.0, 16.0, 16.0],
-        >>>     [  4.0,   0.0, 20.0, 16.0],
-        >>> ])
-        >>> # For each predicted proposal, its assignment to a gt mask
-        >>> pos_assigned_gt_inds = torch.LongTensor([0, 1, 2, 1, 1])
-        >>> mask_targets = mask_target_single(
-        >>>     pos_proposals, pos_assigned_gt_inds, gt_masks, cfg)
-        >>> assert mask_targets.shape == (5,) + cfg['mask_size']
-    """
-    device = pos_proposals.device
-    mask_size = _pair(cfg.mask_size)
-    num_pos = pos_proposals.size(0)
-    if num_pos > 0:
-        proposals_np = pos_proposals.cpu().numpy()
-        maxh, maxw = gt_masks.height, gt_masks.width
-        proposals_np[:, [0, 2]] = np.clip(proposals_np[:, [0, 2]], 0, maxw)
-        proposals_np[:, [1, 3]] = np.clip(proposals_np[:, [1, 3]], 0, maxh)
-        pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy()
-
-        mask_targets = gt_masks.crop_and_resize(
-            proposals_np, mask_size, device=device,
-            inds=pos_assigned_gt_inds).to_ndarray()
-
-        mask_targets = torch.from_numpy(mask_targets).float().to(device)
-    else:
-        mask_targets = pos_proposals.new_zeros((0, ) + mask_size)
-
-    return mask_targets
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/ga_rpn_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/ga_rpn_head.py
deleted file mode 100644
index 2ec0d4fdd3475bfbd2e541a6e8130b1df9ad861a..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/ga_rpn_head.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import copy
-import warnings
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv import ConfigDict
-from mmcv.cnn import normal_init
-from mmcv.ops import nms
-
-from ..builder import HEADS
-from .guided_anchor_head import GuidedAnchorHead
-from .rpn_test_mixin import RPNTestMixin
-
-
-@HEADS.register_module()
-class GARPNHead(RPNTestMixin, GuidedAnchorHead):
-    """Guided-Anchor-based RPN head."""
-
-    def __init__(self, in_channels, **kwargs):
-        super(GARPNHead, self).__init__(1, in_channels, **kwargs)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.rpn_conv = nn.Conv2d(
-            self.in_channels, self.feat_channels, 3, padding=1)
-        super(GARPNHead, self)._init_layers()
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        normal_init(self.rpn_conv, std=0.01)
-        super(GARPNHead, self).init_weights()
-
-    def forward_single(self, x):
-        """Forward feature of a single scale level."""
-
-        x = self.rpn_conv(x)
-        x = F.relu(x, inplace=True)
-        (cls_score, bbox_pred, shape_pred,
-         loc_pred) = super(GARPNHead, self).forward_single(x)
-        return cls_score, bbox_pred, shape_pred, loc_pred
-
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             shape_preds,
-             loc_preds,
-             gt_bboxes,
-             img_metas,
-             gt_bboxes_ignore=None):
-        losses = super(GARPNHead, self).loss(
-            cls_scores,
-            bbox_preds,
-            shape_preds,
-            loc_preds,
-            gt_bboxes,
-            None,
-            img_metas,
-            gt_bboxes_ignore=gt_bboxes_ignore)
-        return dict(
-            loss_rpn_cls=losses['loss_cls'],
-            loss_rpn_bbox=losses['loss_bbox'],
-            loss_anchor_shape=losses['loss_shape'],
-            loss_anchor_loc=losses['loss_loc'])
-
-    def _get_bboxes_single(self,
-                           cls_scores,
-                           bbox_preds,
-                           mlvl_anchors,
-                           mlvl_masks,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False):
-        cfg = self.test_cfg if cfg is None else cfg
-
-        cfg = copy.deepcopy(cfg)
-
-        # deprecate arguments warning
-        if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
-            warnings.warn(
-                'In rpn_proposal or test_cfg, '
-                'nms_thr has been moved to a dict named nms as '
-                'iou_threshold, max_num has been renamed as max_per_img, '
-                'name of original arguments and the way to specify '
-                'iou_threshold of NMS will be deprecated.')
-        if 'nms' not in cfg:
-            cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
-        if 'max_num' in cfg:
-            if 'max_per_img' in cfg:
-                assert cfg.max_num == cfg.max_per_img, f'You ' \
-                    f'set max_num and max_per_img at the same time, ' \
-                    f'but get {cfg.max_num} ' \
-                    f'and {cfg.max_per_img} respectively' \
-                    'Please delete max_num which will be deprecated.'
-            else:
-                cfg.max_per_img = cfg.max_num
-        if 'nms_thr' in cfg:
-            assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \
-                f'iou_threshold in nms and ' \
-                f'nms_thr at the same time, but get ' \
-                f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \
-                f' respectively. Please delete the ' \
-                f'nms_thr which will be deprecated.'
-
-        assert cfg.nms.get('type', 'nms') == 'nms', 'GARPNHead only support ' \
-            'naive nms.'
-
-        mlvl_proposals = []
-        for idx in range(len(cls_scores)):
-            rpn_cls_score = cls_scores[idx]
-            rpn_bbox_pred = bbox_preds[idx]
-            anchors = mlvl_anchors[idx]
-            mask = mlvl_masks[idx]
-            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
-            # if no location is kept, end.
-            if mask.sum() == 0:
-                continue
-            rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
-            if self.use_sigmoid_cls:
-                rpn_cls_score = rpn_cls_score.reshape(-1)
-                scores = rpn_cls_score.sigmoid()
-            else:
-                rpn_cls_score = rpn_cls_score.reshape(-1, 2)
-                # remind that we set FG labels to [0, num_class-1]
-                # since mmdet v2.0
-                # BG cat_id: num_class
-                scores = rpn_cls_score.softmax(dim=1)[:, :-1]
-            # filter scores, bbox_pred w.r.t. mask.
-            # anchors are filtered in get_anchors() beforehand.
-            scores = scores[mask]
-            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1,
-                                                                   4)[mask, :]
-            if scores.dim() == 0:
-                rpn_bbox_pred = rpn_bbox_pred.unsqueeze(0)
-                anchors = anchors.unsqueeze(0)
-                scores = scores.unsqueeze(0)
-            # filter anchors, bbox_pred, scores w.r.t. scores
-            if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
-                _, topk_inds = scores.topk(cfg.nms_pre)
-                rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
-                anchors = anchors[topk_inds, :]
-                scores = scores[topk_inds]
-            # get proposals w.r.t. anchors and rpn_bbox_pred
-            proposals = self.bbox_coder.decode(
-                anchors, rpn_bbox_pred, max_shape=img_shape)
-            # filter out too small bboxes
-            if cfg.min_bbox_size > 0:
-                w = proposals[:, 2] - proposals[:, 0]
-                h = proposals[:, 3] - proposals[:, 1]
-                valid_inds = torch.nonzero(
-                    (w >= cfg.min_bbox_size) & (h >= cfg.min_bbox_size),
-                    as_tuple=False).squeeze()
-                proposals = proposals[valid_inds, :]
-                scores = scores[valid_inds]
-            # NMS in current level
-            proposals, _ = nms(proposals, scores, cfg.nms.iou_threshold)
-            proposals = proposals[:cfg.nms_post, :]
-            mlvl_proposals.append(proposals)
-        proposals = torch.cat(mlvl_proposals, 0)
-        if cfg.get('nms_across_levels', False):
-            # NMS across multi levels
-            proposals, _ = nms(proposals[:, :4], proposals[:, -1],
-                               cfg.nms.iou_threshold)
-            proposals = proposals[:cfg.max_per_img, :]
-        else:
-            scores = proposals[:, 4]
-            num = min(cfg.max_per_img, proposals.shape[0])
-            _, topk_inds = scores.topk(num)
-            proposals = proposals[topk_inds, :]
-        return proposals
diff --git a/spaces/abrar-lohia/text-2-character-anim/VQTrans/dataset/dataset_VQ.py b/spaces/abrar-lohia/text-2-character-anim/VQTrans/dataset/dataset_VQ.py
deleted file mode 100644
index 2342de946f2cbdf64729a5145168df1bdda54fa0..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/VQTrans/dataset/dataset_VQ.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import torch
-from torch.utils import data
-import numpy as np
-from os.path import join as pjoin
-import random
-import codecs as cs
-from tqdm import tqdm
-
-
-
-class VQMotionDataset(data.Dataset):
-    def __init__(self, dataset_name, window_size = 64, unit_length = 4):
-        self.window_size = window_size
-        self.unit_length = unit_length
-        self.dataset_name = dataset_name
-
-        if dataset_name == 't2m':
-            self.data_root = './dataset/HumanML3D'
-            self.motion_dir = pjoin(self.data_root, 'new_joint_vecs')
-            self.text_dir = pjoin(self.data_root, 'texts')
-            self.joints_num = 22
-            self.max_motion_length = 196
-            self.meta_dir = 'checkpoints/t2m/VQVAEV3_CB1024_CMT_H1024_NRES3/meta'
-
-        elif dataset_name == 'kit':
-            self.data_root = './dataset/KIT-ML'
-            self.motion_dir = pjoin(self.data_root, 'new_joint_vecs')
-            self.text_dir = pjoin(self.data_root, 'texts')
-            self.joints_num = 21
-
-            self.max_motion_length = 196
-            self.meta_dir = 'checkpoints/kit/VQVAEV3_CB1024_CMT_H1024_NRES3/meta'
-        
-        joints_num = self.joints_num
-
-        mean = np.load(pjoin(self.meta_dir, 'mean.npy'))
-        std = np.load(pjoin(self.meta_dir, 'std.npy'))
-
-        split_file = pjoin(self.data_root, 'train.txt')
-
-        self.data = []
-        self.lengths = []
-        id_list = []
-        with cs.open(split_file, 'r') as f:
-            for line in f.readlines():
-                id_list.append(line.strip())
-
-        for name in tqdm(id_list):
-            try:
-                motion = np.load(pjoin(self.motion_dir, name + '.npy'))
-                if motion.shape[0] < self.window_size:
-                    continue
-                self.lengths.append(motion.shape[0] - self.window_size)
-                self.data.append(motion)
-            except:
-                # Some motion may not exist in KIT dataset
-                pass
-
-            
-        self.mean = mean
-        self.std = std
-        print("Total number of motions {}".format(len(self.data)))
-
-    def inv_transform(self, data):
-        return data * self.std + self.mean
-    
-    def compute_sampling_prob(self) : 
-        
-        prob = np.array(self.lengths, dtype=np.float32)
-        prob /= np.sum(prob)
-        return prob
-    
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, item):
-        motion = self.data[item]
-        
-        idx = random.randint(0, len(motion) - self.window_size)
-
-        motion = motion[idx:idx+self.window_size]
-        "Z Normalization"
-        motion = (motion - self.mean) / self.std
-
-        return motion
-
-def DATALoader(dataset_name,
-               batch_size,
-               num_workers = 8,
-               window_size = 64,
-               unit_length = 4):
-    
-    trainSet = VQMotionDataset(dataset_name, window_size=window_size, unit_length=unit_length)
-    prob = trainSet.compute_sampling_prob()
-    sampler = torch.utils.data.WeightedRandomSampler(prob, num_samples = len(trainSet) * 1000, replacement=True)
-    train_loader = torch.utils.data.DataLoader(trainSet,
-                                              batch_size,
-                                              shuffle=True,
-                                              #sampler=sampler,
-                                              num_workers=num_workers,
-                                              #collate_fn=collate_fn,
-                                              drop_last = True)
-    
-    return train_loader
-
-def cycle(iterable):
-    while True:
-        for x in iterable:
-            yield x
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/tests/unit/test_meshes.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/tests/unit/test_meshes.py
deleted file mode 100644
index 7070b01171c97069fa013c6eba8eee217017f08e..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/tests/unit/test_meshes.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import numpy as np
-import pytest
-import trimesh
-
-from pyrender import (Mesh, Primitive)
-
-
-def test_meshes():
-
-    with pytest.raises(TypeError):
-        x = Mesh()
-    with pytest.raises(TypeError):
-        x = Primitive()
-    with pytest.raises(ValueError):
-        x = Primitive([], mode=10)
-
-    # Basics
-    x = Mesh([])
-    assert x.name is None
-    assert x.is_visible
-    assert x.weights is None
-
-    x.name = 'str'
-
-    # From Trimesh
-    x = Mesh.from_trimesh(trimesh.creation.box())
-    assert isinstance(x, Mesh)
-    assert len(x.primitives) == 1
-    assert x.is_visible
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -0.5],
-        [0.5, 0.5, 0.5]
-    ]))
-    assert np.allclose(x.centroid, np.zeros(3))
-    assert np.allclose(x.extents, np.ones(3))
-    assert np.allclose(x.scale, np.sqrt(3))
-    assert not x.is_transparent
-
-    # Test some primitive functions
-    x = x.primitives[0]
-    with pytest.raises(ValueError):
-        x.normals = np.zeros(10)
-    with pytest.raises(ValueError):
-        x.tangents = np.zeros(10)
-    with pytest.raises(ValueError):
-        x.texcoord_0 = np.zeros(10)
-    with pytest.raises(ValueError):
-        x.texcoord_1 = np.zeros(10)
-    with pytest.raises(TypeError):
-        x.material = np.zeros(10)
-    assert x.targets is None
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -0.5],
-        [0.5, 0.5, 0.5]
-    ]))
-    assert np.allclose(x.centroid, np.zeros(3))
-    assert np.allclose(x.extents, np.ones(3))
-    assert np.allclose(x.scale, np.sqrt(3))
-    x.material.baseColorFactor = np.array([0.0, 0.0, 0.0, 0.0])
-    assert x.is_transparent
-
-    # From two trimeshes
-    x = Mesh.from_trimesh([trimesh.creation.box(),
-                          trimesh.creation.cylinder(radius=0.1, height=2.0)],
-                          smooth=False)
-    assert isinstance(x, Mesh)
-    assert len(x.primitives) == 2
-    assert x.is_visible
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -1.0],
-        [0.5, 0.5, 1.0]
-    ]))
-    assert np.allclose(x.centroid, np.zeros(3))
-    assert np.allclose(x.extents, [1.0, 1.0, 2.0])
-    assert np.allclose(x.scale, np.sqrt(6))
-    assert not x.is_transparent
-
-    # From bad data
-    with pytest.raises(TypeError):
-        x = Mesh.from_trimesh(None)
-
-    # With instancing
-    poses = np.tile(np.eye(4), (5,1,1))
-    poses[:,0,3] = np.array([0,1,2,3,4])
-    x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses)
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -0.5],
-        [4.5, 0.5, 0.5]
-    ]))
-    poses = np.eye(4)
-    x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses)
-    poses = np.eye(3)
-    with pytest.raises(ValueError):
-        x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses)
-
-    # From textured meshes
-    fm = trimesh.load('tests/data/fuze.obj')
-    x = Mesh.from_trimesh(fm)
-    assert isinstance(x, Mesh)
-    assert len(x.primitives) == 1
-    assert x.is_visible
-    assert not x.is_transparent
-    assert x.primitives[0].material.baseColorTexture is not None
-
-    x = Mesh.from_trimesh(fm, smooth=False)
-    fm.visual = fm.visual.to_color()
-    fm.visual.face_colors = np.array([1.0, 0.0, 0.0, 1.0])
-    x = Mesh.from_trimesh(fm, smooth=False)
-    with pytest.raises(ValueError):
-        x = Mesh.from_trimesh(fm, smooth=True)
-
-    fm.visual.vertex_colors = np.array([1.0, 0.0, 0.0, 0.5])
-    x = Mesh.from_trimesh(fm, smooth=False)
-    x = Mesh.from_trimesh(fm, smooth=True)
-    assert x.primitives[0].color_0 is not None
-    assert x.is_transparent
-
-    bm = trimesh.load('tests/data/WaterBottle.glb').dump()[0]
-    x = Mesh.from_trimesh(bm)
-    assert x.primitives[0].material.baseColorTexture is not None
-    assert x.primitives[0].material.emissiveTexture is not None
-    assert x.primitives[0].material.metallicRoughnessTexture is not None
-
-    # From point cloud
-    x = Mesh.from_points(fm.vertices)
-
-# def test_duck():
-#     bm = trimesh.load('tests/data/Duck.glb').dump()[0]
-#     x = Mesh.from_trimesh(bm)
-#     assert x.primitives[0].material.baseColorTexture is not None
-#     pixel = x.primitives[0].material.baseColorTexture.source[100, 100]
-#     yellowish = np.array([1.0, 0.7411765, 0.0, 1.0])
-#     assert np.allclose(pixel, yellowish)
diff --git a/spaces/achref/neuro_internal_tools/file_operations.py b/spaces/achref/neuro_internal_tools/file_operations.py
deleted file mode 100644
index cb2f95759989aab408481ccb4cd17fa47c477f39..0000000000000000000000000000000000000000
--- a/spaces/achref/neuro_internal_tools/file_operations.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import yaml
-
-
-def save_file(filepath, content):
-    with open(filepath, "w", encoding="utf-8") as outfile:
-        outfile.write(content)
-
-
-def open_file(filepath):
-    with open(filepath, "r", encoding="utf-8", errors="ignore") as infile:
-        return infile.read()
-
-
-def save_yaml(filepath, data):
-    with open(filepath, "w", encoding="utf-8") as file:
-        yaml.dump(data, file, allow_unicode=True)
-
-
-def open_yaml(filepath):
-    with open(filepath, "r", encoding="utf-8") as file:
-        data = yaml.load(file, Loader=yaml.FullLoader)
-    return data
diff --git a/spaces/afmck/stable-diffusion-inpainting-segmentation/app_header.html b/spaces/afmck/stable-diffusion-inpainting-segmentation/app_header.html
deleted file mode 100644
index 961a69cca42b1522f184ebc0d83a3d30de76e2f5..0000000000000000000000000000000000000000
--- a/spaces/afmck/stable-diffusion-inpainting-segmentation/app_header.html
+++ /dev/null
@@ -1,58 +0,0 @@
-<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="white"></rect>
-        <rect y="69" width="23" height="23" fill="white"></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="white"></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="white"></rect>
-        <rect x="115" y="115" width="23" height="23" fill="white"></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="white"></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="black"></rect>
-        <rect x="46" y="115" width="23" height="23" fill="black"></rect>
-        <rect x="46" y="69" width="23" height="23" fill="black"></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; margin-top: 7px">
-        Stable Diffusion x Segmentation Masking 😷
-      </h1>
-    </div>
-    <p style="margin-bottom: 10px; font-size: 94%">
-      Stable Diffusion is a state of the art text-to-image model that generates
-      images from text. Finetuning the model can make it suitable for inpainting
-      when provided with a starting image, mask, and text prompt. 
-    </p>
-    <p style="margin-bottom: 10px; font-size: 94%">
-      However, depending on how complex the area you want to mask is, creating
-      the mask can be tedious. This demo incorporates a segmentation model to
-      generate per-class masks for you, which can be combined to produce a final
-      diffusion mask. 
-    </p>
-  </div>
diff --git a/spaces/ahmedghani/Inference-Endpoint-Deployment/app.py b/spaces/ahmedghani/Inference-Endpoint-Deployment/app.py
deleted file mode 100644
index eedfb72859be2cf87d15b74f341b1a607c1eda3d..0000000000000000000000000000000000000000
--- a/spaces/ahmedghani/Inference-Endpoint-Deployment/app.py
+++ /dev/null
@@ -1,667 +0,0 @@
-import gradio as gr
-import json
-import requests
-import pandas as pd  
-
-def update_task_options(framework):
-    config = {
-        "Custom":["Custom"],
-        "Diffusers":["text-to-image"],
-        "Transformers":[
-            "text-classification", 
-            "zero-shot-classification", 
-            "token-classification", 
-            "question-answering", 
-            "fill-mask", 
-            "summarization", 
-            "translation", 
-            "text2text-generation", 
-            "text-generation", 
-            "feature-extraction", 
-            "image-classification", 
-            "automatic-speech-recognition", 
-            "audio-classification", 
-            "object-detection", 
-            "image-segmentation", 
-            "table-question-answering"
-        ]
-    }
-    return gr.Dropdown.update(
-        choices=config[framework],
-        value=config[framework][0] if len(config[framework]) > 0 else None
-    )
-
-def update_regions(provider):
-    available_regions = []
-    
-    headers = {
-        "Content-Type": "application/json",
-    }
-    endpoint_url = f"https://api.endpoints.huggingface.cloud/provider/{provider}/region"
-    response = requests.get(endpoint_url, headers=headers)
-
-    for region in response.json()['items']:
-        if region['status'] == 'available':
-            available_regions.append(f"{region['region']}/{region['label']}")
-
-    return gr.Dropdown.update(
-        choices=available_regions,
-        value=available_regions[0] if len(available_regions) > 0 else None
-    )
-
-def update_compute_options(provider, region):
-    region = region.split("/")[0]
-    available_compute_choices = []
-
-    headers = {
-        "Content-Type": "application/json",
-    }
-    endpoint_url = f"https://api.endpoints.huggingface.cloud/provider/{provider}/region/{region}/compute"
-    response = requests.get(endpoint_url, headers=headers)
-
-    for compute in response.json()['items']:
-        if compute['status'] == 'available':
-            accelerator = compute['accelerator']
-            numAccelerators = compute['numAccelerators']
-            memoryGb = compute['memoryGb'].replace("Gi", "GB")
-            architecture = compute['architecture']
-            instanceType = compute['instanceType']
-            
-            type = f"{numAccelerators}vCPU {memoryGb} · {architecture}" if accelerator == "cpu" else f"{numAccelerators}x {architecture}"
-            
-            available_compute_choices.append(
-                f"{compute['accelerator'].upper()} [{compute['instanceSize']}] · {type} · {instanceType}"
-            )
-
-    return gr.Dropdown.update(
-        choices=available_compute_choices,
-        value=available_compute_choices[0] if len(available_compute_choices) > 0 else None
-    )
-
-def submit(
-    hf_token_input,
-    endpoint_name_input,
-    provider_selector,
-    region_selector,
-    repository_selector,
-    revision_selector,
-    task_selector,
-    framework_selector,
-    compute_selector,
-    min_node_selector, 
-    max_node_selector, 
-    security_selector    
-):
-    compute_resources = compute_selector.split("·")
-    accelerator = compute_resources[0][:3].strip()
-
-    size_l_index = compute_resources[0].index("[") - 1
-    size_r_index = compute_resources[0].index("]")
-    size = compute_resources[0][size_l_index : size_r_index].strip()
-
-    type = compute_resources[-1].strip()
-    
-    payload = {
-      "accountId": repository_selector.split("/")[0],
-      "compute": {
-        "accelerator": accelerator.lower(),
-        "instanceSize": size[1:],
-        "instanceType": type,
-        "scaling": {
-          "maxReplica": int(max_node_selector),
-          "minReplica": int(min_node_selector)
-        }
-      },
-      "model": {
-        "framework": "custom",
-        "image": {
-          "huggingface": {}
-        },
-        "repository": repository_selector.lower(),
-        "revision": revision_selector,
-        "task": task_selector.lower()
-      },
-      "name": endpoint_name_input.strip(),
-      "provider": {
-        "region": region_selector.split("/")[0].lower(),
-        "vendor": provider_selector.lower()
-      },
-      "type": security_selector.lower()
-    }
-    
-
-    payload = json.dumps(payload)
-
-    headers = {
-        "Authorization": f"Bearer {hf_token_input.strip()}",
-        "Content-Type": "application/json",
-    }
-    endpoint_url = f"https://api.endpoints.huggingface.cloud/endpoint"
-    print(f"Endpoint: {endpoint_url}")
-
-    response = requests.post(endpoint_url, headers=headers, data=payload)
-    if response.status_code == 400:
-        return f"{response.text}. Malformed data in {payload}"
-    elif response.status_code == 401:
-        return "Invalid token"
-    elif response.status_code == 409:
-        return f"Error: {response.text}"
-    elif response.status_code == 202:
-        return f"Endpoint {endpoint_name_input} created successfully on {provider_selector.lower()} using {repository_selector.lower()}@{revision_selector}. \n Please check out the progress at https://ui.endpoints.huggingface.co/endpoints."
-    else:
-        return f"Something went wrong! - StatusCode:{response.status_code} - Error: {response.text}"
-
-def delete_endpoint(
-    hf_token_input,
-    endpoint_name_input
-):
-    response = requests.delete(
-        f"https://api.endpoints.huggingface.cloud/endpoint/{endpoint_name_input}",
-        headers = {
-        "Authorization": f"Bearer {hf_token_input.strip()}",
-        "Content-Type": "application/json",
-    }
-    )
-    if response.status_code == 401:
-        return "Invalid token"
-    elif response.status_code == 404:
-        return f"Error: {response.text}"
-    elif response.status_code == 202 or response.status_code == 200:
-        return f"Endpoint {endpoint_name_input} deleted successfully."
-    else:
-        return f"Something went wrong! - StatusCode:{response.status_code} - Error: {response.text}"
-
-def get_all_endpoints(
-    hf_token_input,
-    method,
-):
-    response = requests.get(
-            f"https://api.endpoints.huggingface.cloud/endpoint",
-            headers = {
-                "Authorization": f"Bearer {hf_token_input.strip()}",
-                "Content-Type": "application/json",
-            })
-    if response.status_code == 401:
-        if method == "info":
-            return gr.DataFrame.update(
-                value=pd.DataFrame({"Error":["Invalid Token -OR- No Endpoint Found !"]}),
-        )
-        else:
-            return gr.Dropdown.update(
-                value="Invalid token or No endpoints found!",
-            )
-    elif response.status_code == 200:
-        endpoints_json = response.json()
-        if method == "info":
-            endpoints_df = pd.DataFrame(columns=["name", "model", "provider", "compute", "status", "minReplica", "maxReplica", "createdAt", "updatedAt"])
-            for endpoint in endpoints_json["items"]:
-                endpoints_df = endpoints_df.append({
-                    "name": endpoint["name"],
-                    "model": endpoint["model"]["repository"] + "@" + endpoint["model"]["revision"],
-                    "provider": endpoint["provider"]["vendor"] + "/" + endpoint["provider"]["region"],
-                    "compute": endpoint["compute"]["instanceType"] + "·" + endpoint["compute"]["instanceSize"] + " [" + endpoint["compute"]["accelerator"] + "]",
-                    "status": endpoint["status"]["state"],
-                    "minReplica": endpoint["compute"]["scaling"]["minReplica"],
-                    "maxReplica": endpoint["compute"]["scaling"]["maxReplica"],
-                    "createdAt": endpoint["status"]["createdAt"],
-                    "updatedAt": endpoint["status"]["updatedAt"],
-                }, ignore_index=True)
-            endpoints_df.columns = ["Endpoint Name", "Model Name @ Revision", "Provider", "Instance Type", "Status", "Min Replica", "Max Replica", "Created At", "Updated At"]
-            return gr.DataFrame.update(
-                value=endpoints_df,
-            )
-        else:
-            return gr.Dropdown.update( 
-            choices=[endpoint["name"] for endpoint in endpoints_json["items"]],
-            value=endpoints_json["items"][0]["name"],
-        )
-
-def update_endpoint(
-    hf_token_input,
-    endpoint_name_input,
-    min_node_selector,
-    max_node_selector,
-    instance_type,
-):
-    payload ={
-            "compute": {
-                "instanceSize": instance_type.split("·")[0].split("[")[1].split("]")[0],
-                "instanceType": instance_type.split("·")[-1].strip(),
-                "scaling": {
-                    "maxReplica": int(max_node_selector),
-                    "minReplica": int(min_node_selector)
-                    }
-                }}
-    payload = json.dumps(payload)
-    response = requests.put(
-            f"https://api.endpoints.huggingface.cloud/endpoint/{endpoint_name_input}",
-            headers = {
-            "Authorization": f"Bearer {hf_token_input.strip()}",
-            "Content-Type": "application/json",
-            },
-            data=payload,
-        )
-    if response.status_code == 401:
-        return "Invalid token"
-    elif response.status_code == 404:
-        return f"Error: {response.text}"
-    elif response.status_code == 202 or response.status_code == 200:
-        return f"Endpoint {endpoint_name_input} updated successfully."
-    else:
-        return f"Something went wrong! - StatusCode:{response.status_code} - Error: {response.text}"
-
-def get_endpoint_logs(
-    hf_token_input,
-    endpoint_name_input,
-):
-    response = requests.get(
-            f"https://api.endpoints.huggingface.cloud/endpoint/{endpoint_name_input}/logs",
-            headers = {
-                "Authorization": f"Bearer {hf_token_input.strip()}",
-                "Content-Type": "application/json",
-            })
-    if response.status_code == 401:
-        return "Invalid token or No logs found!"
-    elif response.status_code == 200:
-        return response.text
-    elif response.status_code == 404:
-        return f"Error: {response.text}"
-
-with gr.Blocks() as interface:
-    gr.Markdown("""
-    #### Your 🤗 Access Token <span style="color:red;">(Required)</span>
-    """)
-    hf_token_input = gr.Textbox(
-        show_label=False,
-        placeholder="Your personal/organization Huggingface Access Token. Get it from (https://huggingface.co/settings/tokens)",
-        type="password"
-    )
-
-    # Get All Endpoints Info
-    with gr.Tab("Info"):
-        gr.Markdown("""
-        ### All Deployed Endpoints
-        """)
-        endpoints_table = gr.Dataframe(
-            headers=["Endpoint Name", "Model Name", "Provider", "Instance Type", "Status", "Min Replica", "Max Replica", "Created At", "Updated At"],
-            col_count=(9, "fixed"),
-        )
-        endpoint_info_button = gr.Button(value="Get Info")
-    
-    # Deploy Endpoint
-    with gr.Tab("Deploy Endpoint"):
-        gr.Markdown(
-        """
-        ### <br><center style="color:green">(Deploy Your Model on 🤗 Endpoint)</center>
-        """)
-        gr.Markdown("""    
-        #### Endpoint Name
-        """)
-        endpoint_name_input = gr.Textbox(
-            show_label=False
-        )
-
-        with gr.Row():
-            gr.Markdown("""    
-            #### Cloud Provider
-            """)
-
-            gr.Markdown("""    
-            #### Cloud Region
-            """)       
-        
-        with gr.Row():
-            provider_selector = gr.Dropdown(
-                choices=["aws", "azure"],
-                value="",
-                interactive=True,
-                show_label=False,
-            )
-            
-            region_selector = gr.Dropdown(
-                [],
-                value="",
-                interactive=True,
-                show_label=False,
-            )
-    
-        with gr.Row():
-            gr.Markdown("""    
-            #### Target Model e.g (openai/whisper-tiny)
-            """)
-
-            gr.Markdown("""    
-            #### Branch commit hash e.g (ada5a5d516772e41f9aeb0f984df6ecc4620001f)
-            """)       
-        
-        with gr.Row():
-            repository_selector = gr.Textbox(
-                value="",
-                interactive=True,
-                show_label=False,
-            )
-
-            revision_selector = gr.Textbox(
-                value="",
-                interactive=True,
-                show_label=False,
-            )        
-
-        with gr.Row():
-            gr.Markdown("""    
-            #### Task
-            """)
-
-            gr.Markdown("""    
-            #### Framework
-            """)      
-        
-        with gr.Row():
-            framework_selector = gr.Dropdown(
-                choices = ["Custom", "Diffusers", "Transformers"],
-                value="",
-                interactive=True,
-                show_label=False,
-            )
-
-            task_selector = gr.Dropdown(
-                [],
-                value="",
-                interactive=True,
-                show_label=False,
-            )
-
-        gr.Markdown("""
-        
-        #### Select Compute Instance Type
-        """)    
-        compute_selector = gr.Dropdown(
-            [],
-            value="",
-            interactive=True,
-            show_label=False,
-        )
-
-        with gr.Row():
-            gr.Markdown("""    
-            #### Min Number of Nodes
-            """)
-
-            gr.Markdown("""    
-            #### Max Number of Nodes
-            """)
-
-            gr.Markdown("""    
-            #### Security Level
-            """)        
-        
-        with gr.Row():
-            min_node_selector = gr.Number(
-                value=1,
-                interactive=True,
-                show_label=False,
-            )
-
-            max_node_selector = gr.Number(
-                value=1,
-                interactive=True,
-                show_label=False,
-            )
-
-            security_selector = gr.Radio(
-                choices=["Protected", "Public"],
-                value="Protected",
-                interactive=True,
-                show_label=False,
-            )
-        submit_button = gr.Button(
-            value="Submit",
-        )
-
-        status_txt = gr.Textbox(
-            value="status",
-            interactive=False
-        )
-
-    # Update Endpoint
-    with gr.Tab("Update Endpoint"):
-        gr.Markdown("""
-        ### <br><center style="color:green">(Update 🔁 Endpoint)</center>
-        """)
-        update_endpoint_info_button = gr.Button(value="Load Endpoints 🔃")
-        with gr.Row():
-            gr.Markdown("""    
-            #### Cloud Provider
-            """)
-
-            gr.Markdown("""    
-            #### Cloud Region
-            """)      
-
-        with gr.Row():
-            update_provider_selector = gr.Dropdown(
-                choices=["aws", "azure"],
-                value="",
-                interactive=True,
-                show_label=False,
-            )
-            
-            update_region_selector = gr.Dropdown(
-                [],
-                value="",
-                interactive=True,
-                show_label=False,
-            )
-
-        with gr.Row():
-            gr.Markdown("""
-            #### Endpoint Name
-            """)
-            gr.Markdown("""
-            #### Instance Type
-            """)
-
-        with gr.Row():
-            update_endpoint_name_input = gr.Dropdown(
-                [],
-                value="",
-                show_label=False
-            )
-            update_compute_selector = gr.Dropdown(
-                [],
-                value="",
-                interactive=True,
-                show_label=False,
-            )
-        with gr.Row():
-            gr.Markdown("""
-            #### Min Number of Nodes
-            """)
-            gr.Markdown("""
-            #### Max Number of Nodes
-            """)
-        with gr.Row():
-            update_min_node_input = gr.Number(
-                value=1,
-                interactive=True,
-                show_label=False,
-            )
-            
-            update_max_node_input = gr.Number(
-                value=1,
-                interactive=True,
-                show_label=False,
-            )
-        update_button = gr.Button(
-            value="Update",
-        )
-
-        update_status_txt = gr.Textbox(
-            value="status",
-            interactive=False
-        )
-
-    # Delete Endpoint
-    with gr.Tab("Delete Endpoint"):
-        gr.Markdown("""
-        ### <br><center style="color:green">(Delete 🗑️ Endpoint)</center>
-        """)
-        delete_endpoint_info_button = gr.Button(value="Load Endpoints 🔃")
-        gr.Markdown("""    
-        #### Endpoint Name
-        """)
-        delete_endpoint_name_input = gr.Dropdown(
-            [],
-            value="",
-            show_label=False
-        )
-        delete_button = gr.Button(
-            value="Delete",
-        )
-
-        delete_status_txt = gr.Textbox(
-            value="status",
-            interactive=False
-        )
-
-    # Endpoint logs
-    with gr.Tab("Endpoint Logs"):
-        gr.Markdown("""
-        ### <br><center style="color:green">(Endpoint 📖 Logs)</center>
-        """)
-        endpoint_logs_load_button = gr.Button(value="Load Endpoints 🔃")
-        gr.Markdown("""
-        #### Endpoint Name
-        """)
-        endpoint_logs_selector = gr.Dropdown(
-            [],
-            value="",
-            show_label=False
-        )
-        endpoint_logs = gr.Textbox(
-            value="",
-            interactive=False
-        )
-        endpoint_logs_button = gr.Button(value="Get Logs")
-
-    # Pricing Table
-    with gr.Tab("Pricing Table"):
-        gr.Markdown("""
-            ### <br><center style="color:green">(Instance Pricing Table)</center>
-            #### Pricing Table(CPU) - 2023/2/22
-            """)
-        gr.Dataframe(
-            headers=["provider", "size", "$/h", "vCPUs", "Memory", "Architecture"],
-            datatype=["str", "str", "str", "number", "str", "str"],
-            row_count=8,
-            col_count=(6, "fixed"),
-            value=[
-                ["aws", "small", "$0.06", 1, "2GB", "Intel Xeon - Ice Lake"],
-                ["aws", "medium", "$0.12", 2, "4GB", "Intel Xeon - Ice Lake"],
-                ["aws", "large", "$0.24", 4, "8GB", "Intel Xeon - Ice Lake"],
-                ["aws", "xlarge", "$0.48", 8, "16GB", "Intel Xeon - Ice Lake"],
-                ["azure", "small", "$0.06", 1, "2GB", "Intel Xeon"],
-                ["azure", "medium", "$0.12", 2, "4GB", "Intel Xeon"],
-                ["azure", "large", "$0.24", 4, "8GB", "Intel Xeon"],
-                ["azure", "xlarge", "$0.48", 8, "16GB", "Intel Xeon"],
-            ]
-        )
-
-        gr.Markdown("""    
-        #### Pricing Table(GPU) - 2023/2/22
-        """)    
-
-        gr.Dataframe(
-            headers=["provider", "size", "$/h", "GPUs", "Memory", "Architecture"],
-            datatype=["str", "str", "str", "number", "str", "str"],
-            row_count=6,
-            col_count=(6, "fixed"),
-            value=[
-                ["aws", "small", "$0.60", 1, "14GB", "NVIDIA T4"],
-                ["aws", "medium", "$1.30", 1, "24GB", "NVIDIA A10G"],
-                ["aws", "large", "$4.50", 4, "56GB", "NVIDIA T4"],
-                ["aws", "xlarge", "$6.50", 1, "80GB", "NVIDIA A100"],
-                ["aws", "xxlarge", "$7.00", 4, "96GB", "NVIDIA A10G"],
-                ["aws", "xxxlarge", "$45.0", 8, "640GB", "NVIDIA A100"],
-            ]
-        )    
-
-    # Info Tab Events
-    endpoint_info_button.click(
-            get_all_endpoints, 
-            inputs=[hf_token_input, gr.TextArea(value="info", interactive=False, visible=False)], 
-            outputs=endpoints_table
-        )
-
-    # Deploy Tab Events
-    framework_selector.change(update_task_options, inputs=framework_selector, outputs=task_selector)
-    provider_selector.change(update_regions, inputs=provider_selector, outputs=region_selector)
-    region_selector.change(update_compute_options, inputs=[provider_selector, region_selector], outputs=compute_selector)
-    submit_button.click(
-        submit, 
-        inputs=[
-            hf_token_input,
-            endpoint_name_input,
-            provider_selector,
-            region_selector,
-            repository_selector,
-            revision_selector,
-            task_selector,
-            framework_selector,
-            compute_selector,
-            min_node_selector, 
-            max_node_selector, 
-            security_selector],
-        outputs=status_txt)
-
-    # Update Tab Events
-    update_endpoint_info_button.click(
-        get_all_endpoints, 
-        inputs=[hf_token_input, gr.TextArea(value="update", interactive=False, visible=False)], 
-        outputs=update_endpoint_name_input
-        )
-    update_provider_selector.change(update_regions, inputs=update_provider_selector, outputs=update_region_selector)
-    update_region_selector.change(update_compute_options, inputs=[update_provider_selector, update_region_selector], outputs=update_compute_selector)
-    update_button.click(
-        update_endpoint,
-        inputs=[
-            hf_token_input,
-            update_endpoint_name_input,
-            update_min_node_input,
-            update_max_node_input,
-            update_compute_selector
-        ],
-        outputs=update_status_txt
-    )
-
-    # Delete Tab Events
-    delete_endpoint_info_button.click(
-        get_all_endpoints,
-        inputs=[hf_token_input, gr.TextArea(value="delete", interactive=False, visible=False)],
-        outputs=delete_endpoint_name_input
-    )
-    delete_button.click(
-            delete_endpoint,
-            inputs=[
-                hf_token_input,
-                delete_endpoint_name_input
-            ],
-            outputs=delete_status_txt
-        )
-
-    # Endpoint Logs Tab Events
-    endpoint_logs_load_button.click(
-        get_all_endpoints,
-        inputs=[hf_token_input, gr.TextArea(value="logs", interactive=False, visible=False)],
-        outputs=endpoint_logs_selector
-    )
-    endpoint_logs_button.click(
-        get_endpoint_logs,
-        inputs=[
-            hf_token_input,
-            endpoint_logs_selector
-        ],
-        outputs=endpoint_logs
-    )
-#interface.queue(concurrency_count=5)
-interface.launch()
\ No newline at end of file
diff --git a/spaces/akhaliq/GPEN/face_detect/retinaface_detection.py b/spaces/akhaliq/GPEN/face_detect/retinaface_detection.py
deleted file mode 100644
index d24e5208a368ac1d7f04e1aec14960bd64bbe873..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/GPEN/face_detect/retinaface_detection.py
+++ /dev/null
@@ -1,192 +0,0 @@
-'''
-@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
-@author: yangxy (yangtao9009@gmail.com)
-'''
-import os
-import torch
-import torch.backends.cudnn as cudnn
-import numpy as np
-from data import cfg_re50
-from layers.functions.prior_box import PriorBox
-from utils.nms.py_cpu_nms import py_cpu_nms
-import cv2
-from facemodels.retinaface import RetinaFace
-from utils.box_utils import decode, decode_landm
-import time
-import torch.nn.functional as F
-
-
-class RetinaFaceDetection(object):
-    def __init__(self, base_dir, device='cuda', network='RetinaFace-R50'):
-        torch.set_grad_enabled(False)
-        cudnn.benchmark = True
-        self.pretrained_path = os.path.join(base_dir, 'weights', network+'.pth')
-        self.device = device #torch.cuda.current_device()
-        self.cfg = cfg_re50
-        self.net = RetinaFace(cfg=self.cfg, phase='test')
-        self.load_model()
-        self.net = self.net.to(device)
-
-        self.mean = torch.tensor([[[[104]], [[117]], [[123]]]]).to(device)
-
-    def check_keys(self, pretrained_state_dict):
-        ckpt_keys = set(pretrained_state_dict.keys())
-        model_keys = set(self.net.state_dict().keys())
-        used_pretrained_keys = model_keys & ckpt_keys
-        unused_pretrained_keys = ckpt_keys - model_keys
-        missing_keys = model_keys - ckpt_keys
-        assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
-        return True
-
-    def remove_prefix(self, state_dict, prefix):
-        ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
-        f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
-        return {f(key): value for key, value in state_dict.items()}
-
-    def load_model(self, load_to_cpu=False):
-        #if load_to_cpu:
-        #    pretrained_dict = torch.load(self.pretrained_path, map_location=lambda storage, loc: storage)
-        #else:
-        #    pretrained_dict = torch.load(self.pretrained_path, map_location=lambda storage, loc: storage.cuda())
-        pretrained_dict = torch.load(self.pretrained_path, map_location=torch.device('cpu'))
-        if "state_dict" in pretrained_dict.keys():
-            pretrained_dict = self.remove_prefix(pretrained_dict['state_dict'], 'module.')
-        else:
-            pretrained_dict = self.remove_prefix(pretrained_dict, 'module.')
-        self.check_keys(pretrained_dict)
-        self.net.load_state_dict(pretrained_dict, strict=False)
-        self.net.eval()
-    
-    def detect(self, img_raw, resize=1, confidence_threshold=0.9, nms_threshold=0.4, top_k=5000, keep_top_k=750, save_image=False):
-        img = np.float32(img_raw)
-
-        im_height, im_width = img.shape[:2]
-        ss = 1.0
-        # tricky
-        if max(im_height, im_width) > 1500:
-            ss = 1000.0/max(im_height, im_width)
-            img = cv2.resize(img, (0,0), fx=ss, fy=ss)
-            im_height, im_width = img.shape[:2]
-
-        scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
-        img -= (104, 117, 123)
-        img = img.transpose(2, 0, 1)
-        img = torch.from_numpy(img).unsqueeze(0)
-        img = img.to(self.device)
-        scale = scale.to(self.device)
-
-        loc, conf, landms = self.net(img)  # forward pass
-
-        priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
-        priors = priorbox.forward()
-        priors = priors.to(self.device)
-        prior_data = priors.data
-        boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
-        boxes = boxes * scale / resize
-        boxes = boxes.cpu().numpy()
-        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
-        landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
-        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
-                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
-                               img.shape[3], img.shape[2]])
-        scale1 = scale1.to(self.device)
-        landms = landms * scale1 / resize
-        landms = landms.cpu().numpy()
-
-        # ignore low scores
-        inds = np.where(scores > confidence_threshold)[0]
-        boxes = boxes[inds]
-        landms = landms[inds]
-        scores = scores[inds]
-
-        # keep top-K before NMS
-        order = scores.argsort()[::-1][:top_k]
-        boxes = boxes[order]
-        landms = landms[order]
-        scores = scores[order]
-
-        # do NMS
-        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
-        keep = py_cpu_nms(dets, nms_threshold)
-        # keep = nms(dets, nms_threshold,force_cpu=args.cpu)
-        dets = dets[keep, :]
-        landms = landms[keep]
-
-        # keep top-K faster NMS
-        dets = dets[:keep_top_k, :]
-        landms = landms[:keep_top_k, :]
-
-        # sort faces(delete)
-        '''
-        fscores = [det[4] for det in dets]
-        sorted_idx = sorted(range(len(fscores)), key=lambda k:fscores[k], reverse=False) # sort index
-        tmp = [landms[idx] for idx in sorted_idx]
-        landms = np.asarray(tmp)
-        '''
-        
-        landms = landms.reshape((-1, 5, 2))
-        landms = landms.transpose((0, 2, 1))
-        landms = landms.reshape(-1, 10, )
-        return dets/ss, landms/ss
-
-    def detect_tensor(self, img, resize=1, confidence_threshold=0.9, nms_threshold=0.4, top_k=5000, keep_top_k=750, save_image=False):
-        im_height, im_width = img.shape[-2:]
-        ss = 1000/max(im_height, im_width)
-        img = F.interpolate(img, scale_factor=ss)
-        im_height, im_width = img.shape[-2:]
-        scale = torch.Tensor([im_width, im_height, im_width, im_height]).to(self.device)
-        img -= self.mean
-
-        loc, conf, landms = self.net(img)  # forward pass
-
-        priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
-        priors = priorbox.forward()
-        priors = priors.to(self.device)
-        prior_data = priors.data
-        boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
-        boxes = boxes * scale / resize
-        boxes = boxes.cpu().numpy()
-        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
-        landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
-        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
-                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
-                               img.shape[3], img.shape[2]])
-        scale1 = scale1.to(self.device)
-        landms = landms * scale1 / resize
-        landms = landms.cpu().numpy()
-
-        # ignore low scores
-        inds = np.where(scores > confidence_threshold)[0]
-        boxes = boxes[inds]
-        landms = landms[inds]
-        scores = scores[inds]
-
-        # keep top-K before NMS
-        order = scores.argsort()[::-1][:top_k]
-        boxes = boxes[order]
-        landms = landms[order]
-        scores = scores[order]
-
-        # do NMS
-        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
-        keep = py_cpu_nms(dets, nms_threshold)
-        # keep = nms(dets, nms_threshold,force_cpu=args.cpu)
-        dets = dets[keep, :]
-        landms = landms[keep]
-
-        # keep top-K faster NMS
-        dets = dets[:keep_top_k, :]
-        landms = landms[:keep_top_k, :]
-
-        # sort faces(delete)
-        '''
-        fscores = [det[4] for det in dets]
-        sorted_idx = sorted(range(len(fscores)), key=lambda k:fscores[k], reverse=False) # sort index
-        tmp = [landms[idx] for idx in sorted_idx]
-        landms = np.asarray(tmp)
-        '''
-        
-        landms = landms.reshape((-1, 5, 2))
-        landms = landms.transpose((0, 2, 1))
-        landms = landms.reshape(-1, 10, )
-        return dets/ss, landms/ss
diff --git a/spaces/akhaliq/deeplab2/evaluation/depth_aware_segmentation_and_tracking_quality.py b/spaces/akhaliq/deeplab2/evaluation/depth_aware_segmentation_and_tracking_quality.py
deleted file mode 100644
index 8bbbb637a48bc480d83dfb8cc0f70998a5729c64..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/evaluation/depth_aware_segmentation_and_tracking_quality.py
+++ /dev/null
@@ -1,210 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 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.
-
-"""Implementation of Depth-aware Segmentation and Tracking Quality (DSTQ) metric."""
-
-import collections
-from typing import Sequence, List, Tuple
-import tensorflow as tf
-from deeplab2.evaluation import segmentation_and_tracking_quality as stq
-
-
-class DSTQuality(stq.STQuality):
-  """Metric class for Depth-aware Segmentation and Tracking Quality (DSTQ).
-
-  This metric computes STQ and the inlier depth metric (or depth quality (DQ))
-  under several thresholds. Then it returns the geometric mean of DQ's, AQ and
-  IoU to get the final DSTQ, i.e.,
-
-  DSTQ@{threshold_1} = pow(STQ ** 2 * DQ@{threshold_1}, 1/3)
-  DSTQ@{threshold_2} = pow(STQ ** 2 * DQ@{threshold_2}, 1/3)
-  ...
-  DSTQ = pow(STQ ** 2 * DQ, 1/3)
-
-  where DQ = pow(prod_i^n(threshold_i), 1/n) for n depth thresholds.
-
-  The default choices for depth thresholds are 1.1 and 1.25, i.e.,
-  max(pred/gt, gt/pred) <= 1.1 and max(pred/gt, gt/pred) <= 1.25.
-  Commonly used thresholds for the inlier metrics are 1.25, 1.25**2, 1.25**3.
-  These thresholds are so loose that many methods achieves > 99%.
-  Therefore, we choose 1.25 and 1.1 to encourage high-precision predictions.
-
-  Example usage:
-
-  dstq_obj = depth_aware_segmentation_and_tracking_quality.DSTQuality(
-    num_classes, things_list, ignore_label, max_instances_per_category,
-    offset, depth_threshold)
-  dstq.update_state(y_true_1, y_pred_1, d_true_1, d_pred_1)
-  dstq.update_state(y_true_2, y_pred_2, d_true_2, d_pred_2)
-  ...
-  result = dstq_obj.result().numpy()
-  """
-
-  _depth_threshold: Tuple[float, float] = (1.25, 1.1)
-  _depth_total_counts: collections.OrderedDict
-  _depth_inlier_counts: List[collections.OrderedDict]
-
-  def __init__(self,
-               num_classes: int,
-               things_list: Sequence[int],
-               ignore_label: int,
-               max_instances_per_category: int,
-               offset: int,
-               depth_threshold: Tuple[float] = (1.25, 1.1),
-               name: str = 'dstq',):  # pytype: disable=annotation-type-mismatch
-    """Initialization of the DSTQ metric.
-
-    Args:
-      num_classes: Number of classes in the dataset as an integer.
-      things_list: A sequence of class ids that belong to `things`.
-      ignore_label: The class id to be ignored in evaluation as an integer or
-        integer tensor.
-      max_instances_per_category: The maximum number of instances for each class
-        as an integer or integer tensor.
-      offset: The maximum number of unique labels as an integer or integer
-        tensor.
-      depth_threshold: A sequence of depth thresholds for the depth quality.
-        (default: (1.25, 1.1))
-      name: An optional name. (default: 'dstq')
-    """
-    super().__init__(num_classes, things_list, ignore_label,
-                     max_instances_per_category, offset, name)
-    if not (isinstance(depth_threshold, tuple) or
-            isinstance(depth_threshold, list)):
-      raise TypeError('The type of depth_threshold must be tuple or list.')
-    if not depth_threshold:
-      raise ValueError('depth_threshold must be non-empty.')
-    self._depth_threshold = tuple(depth_threshold)
-    self._depth_total_counts = collections.OrderedDict()
-    self._depth_inlier_counts = []
-    for _ in range(len(self._depth_threshold)):
-      self._depth_inlier_counts.append(collections.OrderedDict())
-
-  def update_state(self,
-                   y_true: tf.Tensor,
-                   y_pred: tf.Tensor,
-                   d_true: tf.Tensor,
-                   d_pred: tf.Tensor,
-                   sequence_id: int = 0):
-    """Accumulates the depth-aware segmentation and tracking quality statistics.
-
-    Args:
-      y_true: The ground-truth panoptic label map for a particular video frame
-        (defined as semantic_map * max_instances_per_category + instance_map).
-      y_pred: The predicted panoptic label map for a particular video frame
-        (defined as semantic_map * max_instances_per_category + instance_map).
-      d_true: The ground-truth depth map for this video frame.
-      d_pred: The predicted depth map for this video frame.
-      sequence_id: The optional ID of the sequence the frames belong to. When no
-        sequence is given, all frames are considered to belong to the same
-        sequence (default: 0).
-    """
-    super().update_state(y_true, y_pred, sequence_id)
-    # Valid depth labels contain positive values.
-    d_valid_mask = d_true > 0
-    d_valid_total = tf.reduce_sum(tf.cast(d_valid_mask, tf.int32))
-    # Valid depth prediction is expected to contain positive values.
-    d_valid_mask = tf.logical_and(d_valid_mask, d_pred > 0)
-    d_valid_true = tf.boolean_mask(d_true, d_valid_mask)
-    d_valid_pred = tf.boolean_mask(d_pred, d_valid_mask)
-    inlier_error = tf.maximum(d_valid_pred / d_valid_true,
-                              d_valid_true / d_valid_pred)
-    # For each threshold, count the number of inliers.
-    for threshold_index, threshold in enumerate(self._depth_threshold):
-      num_inliers = tf.reduce_sum(tf.cast(inlier_error <= threshold, tf.int32))
-      inlier_counts = self._depth_inlier_counts[threshold_index]
-      inlier_counts[sequence_id] = (inlier_counts.get(sequence_id, 0) +
-                                    int(num_inliers.numpy()))
-    # Update the total counts of the depth labels.
-    self._depth_total_counts[sequence_id] = (
-        self._depth_total_counts.get(sequence_id, 0) +
-        int(d_valid_total.numpy()))
-
-  def result(self):
-    """Computes the depth-aware segmentation and tracking quality.
-
-    Returns:
-      A dictionary containing:
-        - 'STQ': The total STQ score.
-        - 'AQ': The total association quality (AQ) score.
-        - 'IoU': The total mean IoU.
-        - 'STQ_per_seq': A list of the STQ score per sequence.
-        - 'AQ_per_seq': A list of the AQ score per sequence.
-        - 'IoU_per_seq': A list of mean IoU per sequence.
-        - 'Id_per_seq': A list of sequence Ids to map list index to sequence.
-        - 'Length_per_seq': A list of the length of each sequence.
-        - 'DSTQ': The total DSTQ score.
-        - 'DSTQ@thres': The total DSTQ score for threshold thres
-        - 'DSTQ_per_seq@thres': A list of DSTQ score per sequence for thres.
-        - 'DQ': The total DQ score.
-        - 'DQ@thres': The total DQ score for threshold thres.
-        - 'DQ_per_seq@thres': A list of DQ score per sequence for thres.
-    """
-    # Gather the results for STQ.
-    stq_results = super().result()
-    # Collect results for depth quality per sequecne and threshold.
-    dq_per_seq_at_threshold = {}
-    dq_at_threshold = {}
-    for threshold_index, threshold in enumerate(self._depth_threshold):
-      dq_per_seq_at_threshold[threshold] = [0] * len(self._ground_truth)
-      total_count = 0
-      inlier_count = 0
-      # Follow the order of computing STQ by enumerating _ground_truth.
-      for index, sequence_id in enumerate(self._ground_truth):
-        sequence_inlier = self._depth_inlier_counts[threshold_index][
-            sequence_id]
-        sequence_total = self._depth_total_counts[sequence_id]
-        if sequence_total > 0:
-          dq_per_seq_at_threshold[threshold][
-              index] = sequence_inlier / sequence_total
-        total_count += sequence_total
-        inlier_count += sequence_inlier
-      if total_count == 0:
-        dq_at_threshold[threshold] = 0
-      else:
-        dq_at_threshold[threshold] = inlier_count / total_count
-    # Compute DQ as the geometric mean of DQ's at different thresholds.
-    dq = 1
-    for _, threshold in enumerate(self._depth_threshold):
-      dq *= dq_at_threshold[threshold]
-    dq = dq ** (1 / len(self._depth_threshold))
-    dq_results = {}
-    dq_results['DQ'] = dq
-    for _, threshold in enumerate(self._depth_threshold):
-      dq_results['DQ@{}'.format(threshold)] = dq_at_threshold[threshold]
-      dq_results['DQ_per_seq@{}'.format(
-          threshold)] = dq_per_seq_at_threshold[threshold]
-    # Combine STQ and DQ to get DSTQ.
-    dstq_results = {}
-    dstq_results['DSTQ'] = (stq_results['STQ'] ** 2 * dq) ** (1/3)
-    for _, threshold in enumerate(self._depth_threshold):
-      dstq_results['DSTQ@{}'.format(threshold)] = (
-          stq_results['STQ'] ** 2 * dq_at_threshold[threshold]) ** (1/3)
-      dstq_results['DSTQ_per_seq@{}'.format(threshold)] = [
-          (stq_result**2 * dq_result)**(1 / 3) for stq_result, dq_result in zip(
-              stq_results['STQ_per_seq'], dq_per_seq_at_threshold[threshold])
-      ]
-    # Merge all the results.
-    dstq_results.update(stq_results)
-    dstq_results.update(dq_results)
-    return dstq_results
-
-  def reset_states(self):
-    """Resets all states that accumulated data."""
-    super().reset_states()
-    self._depth_total_counts = collections.OrderedDict()
-    self._depth_inlier_counts = []
-    for _ in range(len(self._depth_threshold)):
-      self._depth_inlier_counts.append(collections.OrderedDict())
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/__init__.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ali-ghamdan/deoldify/fastai/vision/models/wrn.py b/spaces/ali-ghamdan/deoldify/fastai/vision/models/wrn.py
deleted file mode 100644
index 3ab8d7582b98e4faee04b66c01e76c4d2f43d79b..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/vision/models/wrn.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from ...layers import *
-from ...torch_core import *
-
-__all__ = ['BasicBlock', 'WideResNet', 'wrn_22']
-
-def _bn(ni, init_zero=False):
-    "Batchnorm layer with 0 initialization"
-    m = nn.BatchNorm2d(ni)
-    m.weight.data.fill_(0 if init_zero else 1)
-    m.bias.data.zero_()
-    return m
-
-def bn_relu_conv(ni, nf, ks, stride, init_zero=False):
-    bn_initzero = _bn(ni, init_zero=init_zero)
-    return nn.Sequential(bn_initzero, nn.ReLU(inplace=True), conv2d(ni, nf, ks, stride))
-
-class BasicBlock(Module):
-    "Block to from a wide ResNet."
-    def __init__(self, ni, nf, stride, drop_p=0.0):
-        self.bn = nn.BatchNorm2d(ni)
-        self.conv1 = conv2d(ni, nf, 3, stride)
-        self.conv2 = bn_relu_conv(nf, nf, 3, 1)
-        self.drop = nn.Dropout(drop_p, inplace=True) if drop_p else None
-        self.shortcut = conv2d(ni, nf, 1, stride) if ni != nf else noop
-
-    def forward(self, x):
-        x2 = F.relu(self.bn(x), inplace=True)
-        r = self.shortcut(x2)
-        x = self.conv1(x2)
-        if self.drop: x = self.drop(x)
-        x = self.conv2(x) * 0.2
-        return x.add_(r)
-
-def _make_group(N, ni, nf, block, stride, drop_p):
-    return [block(ni if i == 0 else nf, nf, stride if i == 0 else 1, drop_p) for i in range(N)]
-
-class WideResNet(Module):
-    "Wide ResNet with `num_groups` and a width of `k`."
-    def __init__(self, num_groups:int, N:int, num_classes:int, k:int=1, drop_p:float=0.0, start_nf:int=16, n_in_channels:int=3):
-        n_channels = [start_nf]
-        for i in range(num_groups): n_channels.append(start_nf*(2**i)*k)
-
-        layers = [conv2d(n_in_channels, n_channels[0], 3, 1)]  # conv1
-        for i in range(num_groups):
-            layers += _make_group(N, n_channels[i], n_channels[i+1], BasicBlock, (1 if i==0 else 2), drop_p)
-
-        layers += [nn.BatchNorm2d(n_channels[num_groups]), nn.ReLU(inplace=True), nn.AdaptiveAvgPool2d(1),
-                   Flatten(), nn.Linear(n_channels[num_groups], num_classes)]
-        self.features = nn.Sequential(*layers)
-
-    def forward(self, x): return self.features(x)
-
-
-def wrn_22(): 
-    "Wide ResNet with 22 layers."
-    return WideResNet(num_groups=3, N=3, num_classes=10, k=6, drop_p=0.)
diff --git a/spaces/aliceoq/vozes-da-loirinha/lib/uvr5_pack/lib_v5/spec_utils.py b/spaces/aliceoq/vozes-da-loirinha/lib/uvr5_pack/lib_v5/spec_utils.py
deleted file mode 100644
index a3fd46d333da7becc7f09f42c084ac7cde661035..0000000000000000000000000000000000000000
--- a/spaces/aliceoq/vozes-da-loirinha/lib/uvr5_pack/lib_v5/spec_utils.py
+++ /dev/null
@@ -1,667 +0,0 @@
-import os, librosa
-import numpy as np
-import soundfile as sf
-from tqdm import tqdm
-import json, math, hashlib
-
-
-def crop_center(h1, h2):
-    h1_shape = h1.size()
-    h2_shape = h2.size()
-
-    if h1_shape[3] == h2_shape[3]:
-        return h1
-    elif h1_shape[3] < h2_shape[3]:
-        raise ValueError("h1_shape[3] must be greater than h2_shape[3]")
-
-    # s_freq = (h2_shape[2] - h1_shape[2]) // 2
-    # e_freq = s_freq + h1_shape[2]
-    s_time = (h1_shape[3] - h2_shape[3]) // 2
-    e_time = s_time + h2_shape[3]
-    h1 = h1[:, :, :, s_time:e_time]
-
-    return h1
-
-
-def wave_to_spectrogram(
-    wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
-):
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-
-    spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def wave_to_spectrogram_mt(
-    wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
-):
-    import threading
-
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-
-    def run_thread(**kwargs):
-        global spec_left
-        spec_left = librosa.stft(**kwargs)
-
-    thread = threading.Thread(
-        target=run_thread,
-        kwargs={"y": wave_left, "n_fft": n_fft, "hop_length": hop_length},
-    )
-    thread.start()
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-    thread.join()
-
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def combine_spectrograms(specs, mp):
-    l = min([specs[i].shape[2] for i in specs])
-    spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64)
-    offset = 0
-    bands_n = len(mp.param["band"])
-
-    for d in range(1, bands_n + 1):
-        h = mp.param["band"][d]["crop_stop"] - mp.param["band"][d]["crop_start"]
-        spec_c[:, offset : offset + h, :l] = specs[d][
-            :, mp.param["band"][d]["crop_start"] : mp.param["band"][d]["crop_stop"], :l
-        ]
-        offset += h
-
-    if offset > mp.param["bins"]:
-        raise ValueError("Too much bins")
-
-    # lowpass fiter
-    if (
-        mp.param["pre_filter_start"] > 0
-    ):  # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
-        if bands_n == 1:
-            spec_c = fft_lp_filter(
-                spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"]
-            )
-        else:
-            gp = 1
-            for b in range(
-                mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]
-            ):
-                g = math.pow(
-                    10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0
-                )
-                gp = g
-                spec_c[:, b, :] *= g
-
-    return np.asfortranarray(spec_c)
-
-
-def spectrogram_to_image(spec, mode="magnitude"):
-    if mode == "magnitude":
-        if np.iscomplexobj(spec):
-            y = np.abs(spec)
-        else:
-            y = spec
-        y = np.log10(y**2 + 1e-8)
-    elif mode == "phase":
-        if np.iscomplexobj(spec):
-            y = np.angle(spec)
-        else:
-            y = spec
-
-    y -= y.min()
-    y *= 255 / y.max()
-    img = np.uint8(y)
-
-    if y.ndim == 3:
-        img = img.transpose(1, 2, 0)
-        img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2)
-
-    return img
-
-
-def reduce_vocal_aggressively(X, y, softmask):
-    v = X - y
-    y_mag_tmp = np.abs(y)
-    v_mag_tmp = np.abs(v)
-
-    v_mask = v_mag_tmp > y_mag_tmp
-    y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
-
-    return y_mag * np.exp(1.0j * np.angle(y))
-
-
-def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
-    if min_range < fade_size * 2:
-        raise ValueError("min_range must be >= fade_area * 2")
-
-    mag = mag.copy()
-
-    idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
-    starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
-    ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
-    uninformative = np.where(ends - starts > min_range)[0]
-    if len(uninformative) > 0:
-        starts = starts[uninformative]
-        ends = ends[uninformative]
-        old_e = None
-        for s, e in zip(starts, ends):
-            if old_e is not None and s - old_e < fade_size:
-                s = old_e - fade_size * 2
-
-            if s != 0:
-                weight = np.linspace(0, 1, fade_size)
-                mag[:, :, s : s + fade_size] += weight * ref[:, :, s : s + fade_size]
-            else:
-                s -= fade_size
-
-            if e != mag.shape[2]:
-                weight = np.linspace(1, 0, fade_size)
-                mag[:, :, e - fade_size : e] += weight * ref[:, :, e - fade_size : e]
-            else:
-                e += fade_size
-
-            mag[:, :, s + fade_size : e - fade_size] += ref[
-                :, :, s + fade_size : e - fade_size
-            ]
-            old_e = e
-
-    return mag
-
-
-def align_wave_head_and_tail(a, b):
-    l = min([a[0].size, b[0].size])
-
-    return a[:l, :l], b[:l, :l]
-
-
-def cache_or_load(mix_path, inst_path, mp):
-    mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
-    inst_basename = os.path.splitext(os.path.basename(inst_path))[0]
-
-    cache_dir = "mph{}".format(
-        hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode("utf-8")).hexdigest()
-    )
-    mix_cache_dir = os.path.join("cache", cache_dir)
-    inst_cache_dir = os.path.join("cache", cache_dir)
-
-    os.makedirs(mix_cache_dir, exist_ok=True)
-    os.makedirs(inst_cache_dir, exist_ok=True)
-
-    mix_cache_path = os.path.join(mix_cache_dir, mix_basename + ".npy")
-    inst_cache_path = os.path.join(inst_cache_dir, inst_basename + ".npy")
-
-    if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path):
-        X_spec_m = np.load(mix_cache_path)
-        y_spec_m = np.load(inst_cache_path)
-    else:
-        X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
-
-        for d in range(len(mp.param["band"]), 0, -1):
-            bp = mp.param["band"][d]
-
-            if d == len(mp.param["band"]):  # high-end band
-                X_wave[d], _ = librosa.load(
-                    mix_path, bp["sr"], False, dtype=np.float32, res_type=bp["res_type"]
-                )
-                y_wave[d], _ = librosa.load(
-                    inst_path,
-                    bp["sr"],
-                    False,
-                    dtype=np.float32,
-                    res_type=bp["res_type"],
-                )
-            else:  # lower bands
-                X_wave[d] = librosa.resample(
-                    X_wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-                y_wave[d] = librosa.resample(
-                    y_wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-
-            X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
-
-            X_spec_s[d] = wave_to_spectrogram(
-                X_wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-            y_spec_s[d] = wave_to_spectrogram(
-                y_wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-
-        del X_wave, y_wave
-
-        X_spec_m = combine_spectrograms(X_spec_s, mp)
-        y_spec_m = combine_spectrograms(y_spec_s, mp)
-
-        if X_spec_m.shape != y_spec_m.shape:
-            raise ValueError("The combined spectrograms are different: " + mix_path)
-
-        _, ext = os.path.splitext(mix_path)
-
-        np.save(mix_cache_path, X_spec_m)
-        np.save(inst_cache_path, y_spec_m)
-
-    return X_spec_m, y_spec_m
-
-
-def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse):
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    wave_left = librosa.istft(spec_left, hop_length=hop_length)
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray(
-            [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
-        )
-    elif mid_side_b2:
-        return np.asfortranarray(
-            [
-                np.add(wave_right / 1.25, 0.4 * wave_left),
-                np.subtract(wave_left / 1.25, 0.4 * wave_right),
-            ]
-        )
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-
-
-def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
-    import threading
-
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    def run_thread(**kwargs):
-        global wave_left
-        wave_left = librosa.istft(**kwargs)
-
-    thread = threading.Thread(
-        target=run_thread, kwargs={"stft_matrix": spec_left, "hop_length": hop_length}
-    )
-    thread.start()
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-    thread.join()
-
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray(
-            [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
-        )
-    elif mid_side_b2:
-        return np.asfortranarray(
-            [
-                np.add(wave_right / 1.25, 0.4 * wave_left),
-                np.subtract(wave_left / 1.25, 0.4 * wave_right),
-            ]
-        )
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-
-
-def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
-    wave_band = {}
-    bands_n = len(mp.param["band"])
-    offset = 0
-
-    for d in range(1, bands_n + 1):
-        bp = mp.param["band"][d]
-        spec_s = np.ndarray(
-            shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex
-        )
-        h = bp["crop_stop"] - bp["crop_start"]
-        spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[
-            :, offset : offset + h, :
-        ]
-
-        offset += h
-        if d == bands_n:  # higher
-            if extra_bins_h:  # if --high_end_process bypass
-                max_bin = bp["n_fft"] // 2
-                spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[
-                    :, :extra_bins_h, :
-                ]
-            if bp["hpf_start"] > 0:
-                spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
-            if bands_n == 1:
-                wave = spectrogram_to_wave(
-                    spec_s,
-                    bp["hl"],
-                    mp.param["mid_side"],
-                    mp.param["mid_side_b2"],
-                    mp.param["reverse"],
-                )
-            else:
-                wave = np.add(
-                    wave,
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                )
-        else:
-            sr = mp.param["band"][d + 1]["sr"]
-            if d == 1:  # lower
-                spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
-                wave = librosa.resample(
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                    bp["sr"],
-                    sr,
-                    res_type="sinc_fastest",
-                )
-            else:  # mid
-                spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
-                spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
-                wave2 = np.add(
-                    wave,
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                )
-                # wave = librosa.core.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
-                wave = librosa.core.resample(wave2, bp["sr"], sr, res_type="scipy")
-
-    return wave.T
-
-
-def fft_lp_filter(spec, bin_start, bin_stop):
-    g = 1.0
-    for b in range(bin_start, bin_stop):
-        g -= 1 / (bin_stop - bin_start)
-        spec[:, b, :] = g * spec[:, b, :]
-
-    spec[:, bin_stop:, :] *= 0
-
-    return spec
-
-
-def fft_hp_filter(spec, bin_start, bin_stop):
-    g = 1.0
-    for b in range(bin_start, bin_stop, -1):
-        g -= 1 / (bin_start - bin_stop)
-        spec[:, b, :] = g * spec[:, b, :]
-
-    spec[:, 0 : bin_stop + 1, :] *= 0
-
-    return spec
-
-
-def mirroring(a, spec_m, input_high_end, mp):
-    if "mirroring" == a:
-        mirror = np.flip(
-            np.abs(
-                spec_m[
-                    :,
-                    mp.param["pre_filter_start"]
-                    - 10
-                    - input_high_end.shape[1] : mp.param["pre_filter_start"]
-                    - 10,
-                    :,
-                ]
-            ),
-            1,
-        )
-        mirror = mirror * np.exp(1.0j * np.angle(input_high_end))
-
-        return np.where(
-            np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror
-        )
-
-    if "mirroring2" == a:
-        mirror = np.flip(
-            np.abs(
-                spec_m[
-                    :,
-                    mp.param["pre_filter_start"]
-                    - 10
-                    - input_high_end.shape[1] : mp.param["pre_filter_start"]
-                    - 10,
-                    :,
-                ]
-            ),
-            1,
-        )
-        mi = np.multiply(mirror, input_high_end * 1.7)
-
-        return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
-
-
-def ensembling(a, specs):
-    for i in range(1, len(specs)):
-        if i == 1:
-            spec = specs[0]
-
-        ln = min([spec.shape[2], specs[i].shape[2]])
-        spec = spec[:, :, :ln]
-        specs[i] = specs[i][:, :, :ln]
-
-        if "min_mag" == a:
-            spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
-        if "max_mag" == a:
-            spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
-
-    return spec
-
-
-def stft(wave, nfft, hl):
-    wave_left = np.asfortranarray(wave[0])
-    wave_right = np.asfortranarray(wave[1])
-    spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
-    spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def istft(spec, hl):
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    wave_left = librosa.istft(spec_left, hop_length=hl)
-    wave_right = librosa.istft(spec_right, hop_length=hl)
-    wave = np.asfortranarray([wave_left, wave_right])
-
-
-if __name__ == "__main__":
-    import cv2
-    import sys
-    import time
-    import argparse
-    from model_param_init import ModelParameters
-
-    p = argparse.ArgumentParser()
-    p.add_argument(
-        "--algorithm",
-        "-a",
-        type=str,
-        choices=["invert", "invert_p", "min_mag", "max_mag", "deep", "align"],
-        default="min_mag",
-    )
-    p.add_argument(
-        "--model_params",
-        "-m",
-        type=str,
-        default=os.path.join("modelparams", "1band_sr44100_hl512.json"),
-    )
-    p.add_argument("--output_name", "-o", type=str, default="output")
-    p.add_argument("--vocals_only", "-v", action="store_true")
-    p.add_argument("input", nargs="+")
-    args = p.parse_args()
-
-    start_time = time.time()
-
-    if args.algorithm.startswith("invert") and len(args.input) != 2:
-        raise ValueError("There should be two input files.")
-
-    if not args.algorithm.startswith("invert") and len(args.input) < 2:
-        raise ValueError("There must be at least two input files.")
-
-    wave, specs = {}, {}
-    mp = ModelParameters(args.model_params)
-
-    for i in range(len(args.input)):
-        spec = {}
-
-        for d in range(len(mp.param["band"]), 0, -1):
-            bp = mp.param["band"][d]
-
-            if d == len(mp.param["band"]):  # high-end band
-                wave[d], _ = librosa.load(
-                    args.input[i],
-                    bp["sr"],
-                    False,
-                    dtype=np.float32,
-                    res_type=bp["res_type"],
-                )
-
-                if len(wave[d].shape) == 1:  # mono to stereo
-                    wave[d] = np.array([wave[d], wave[d]])
-            else:  # lower bands
-                wave[d] = librosa.resample(
-                    wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-
-            spec[d] = wave_to_spectrogram(
-                wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-
-        specs[i] = combine_spectrograms(spec, mp)
-
-    del wave
-
-    if args.algorithm == "deep":
-        d_spec = np.where(np.abs(specs[0]) <= np.abs(spec[1]), specs[0], spec[1])
-        v_spec = d_spec - specs[1]
-        sf.write(
-            os.path.join("{}.wav".format(args.output_name)),
-            cmb_spectrogram_to_wave(v_spec, mp),
-            mp.param["sr"],
-        )
-
-    if args.algorithm.startswith("invert"):
-        ln = min([specs[0].shape[2], specs[1].shape[2]])
-        specs[0] = specs[0][:, :, :ln]
-        specs[1] = specs[1][:, :, :ln]
-
-        if "invert_p" == args.algorithm:
-            X_mag = np.abs(specs[0])
-            y_mag = np.abs(specs[1])
-            max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
-            v_spec = specs[1] - max_mag * np.exp(1.0j * np.angle(specs[0]))
-        else:
-            specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
-            v_spec = specs[0] - specs[1]
-
-            if not args.vocals_only:
-                X_mag = np.abs(specs[0])
-                y_mag = np.abs(specs[1])
-                v_mag = np.abs(v_spec)
-
-                X_image = spectrogram_to_image(X_mag)
-                y_image = spectrogram_to_image(y_mag)
-                v_image = spectrogram_to_image(v_mag)
-
-                cv2.imwrite("{}_X.png".format(args.output_name), X_image)
-                cv2.imwrite("{}_y.png".format(args.output_name), y_image)
-                cv2.imwrite("{}_v.png".format(args.output_name), v_image)
-
-                sf.write(
-                    "{}_X.wav".format(args.output_name),
-                    cmb_spectrogram_to_wave(specs[0], mp),
-                    mp.param["sr"],
-                )
-                sf.write(
-                    "{}_y.wav".format(args.output_name),
-                    cmb_spectrogram_to_wave(specs[1], mp),
-                    mp.param["sr"],
-                )
-
-        sf.write(
-            "{}_v.wav".format(args.output_name),
-            cmb_spectrogram_to_wave(v_spec, mp),
-            mp.param["sr"],
-        )
-    else:
-        if not args.algorithm == "deep":
-            sf.write(
-                os.path.join("ensembled", "{}.wav".format(args.output_name)),
-                cmb_spectrogram_to_wave(ensembling(args.algorithm, specs), mp),
-                mp.param["sr"],
-            )
-
-    if args.algorithm == "align":
-        trackalignment = [
-            {
-                "file1": '"{}"'.format(args.input[0]),
-                "file2": '"{}"'.format(args.input[1]),
-            }
-        ]
-
-        for i, e in tqdm(enumerate(trackalignment), desc="Performing Alignment..."):
-            os.system(f"python lib/align_tracks.py {e['file1']} {e['file2']}")
-
-    # print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
diff --git a/spaces/alkzar90/streamlit-demo-example/README.md b/spaces/alkzar90/streamlit-demo-example/README.md
deleted file mode 100644
index c9231eabb8bfceddbd143306ee5b27d56297157b..0000000000000000000000000000000000000000
--- a/spaces/alkzar90/streamlit-demo-example/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Streamlit-demo-example
-emoji: 📚
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/allandclive/Uganda_MMS/vits/train_ms.py b/spaces/allandclive/Uganda_MMS/vits/train_ms.py
deleted file mode 100644
index 34870c622d2c05ad0a1a8fcf648197d0f51800cd..0000000000000000000000000000000000000000
--- a/spaces/allandclive/Uganda_MMS/vits/train_ms.py
+++ /dev/null
@@ -1,294 +0,0 @@
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-
-import commons
-import utils
-from data_utils import (
-  TextAudioSpeakerLoader,
-  TextAudioSpeakerCollate,
-  DistributedBucketSampler
-)
-from models import (
-  SynthesizerTrn,
-  MultiPeriodDiscriminator,
-)
-from losses import (
-  generator_loss,
-  discriminator_loss,
-  feature_loss,
-  kl_loss
-)
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from text.symbols import symbols
-
-
-torch.backends.cudnn.benchmark = True
-global_step = 0
-
-
-def main():
-  """Assume Single Node Multi GPUs Training Only"""
-  assert torch.cuda.is_available(), "CPU training is not allowed."
-
-  n_gpus = torch.cuda.device_count()
-  os.environ['MASTER_ADDR'] = 'localhost'
-  os.environ['MASTER_PORT'] = '80000'
-
-  hps = utils.get_hparams()
-  mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-
-
-def run(rank, n_gpus, hps):
-  global global_step
-  if rank == 0:
-    logger = utils.get_logger(hps.model_dir)
-    logger.info(hps)
-    utils.check_git_hash(hps.model_dir)
-    writer = SummaryWriter(log_dir=hps.model_dir)
-    writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
-  dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
-  torch.manual_seed(hps.train.seed)
-  torch.cuda.set_device(rank)
-
-  train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
-  train_sampler = DistributedBucketSampler(
-      train_dataset,
-      hps.train.batch_size,
-      [32,300,400,500,600,700,800,900,1000],
-      num_replicas=n_gpus,
-      rank=rank,
-      shuffle=True)
-  collate_fn = TextAudioSpeakerCollate()
-  train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
-      collate_fn=collate_fn, batch_sampler=train_sampler)
-  if rank == 0:
-    eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
-    eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False,
-        batch_size=hps.train.batch_size, pin_memory=True,
-        drop_last=False, collate_fn=collate_fn)
-
-  net_g = SynthesizerTrn(
-      len(symbols),
-      hps.data.filter_length // 2 + 1,
-      hps.train.segment_size // hps.data.hop_length,
-      n_speakers=hps.data.n_speakers,
-      **hps.model).cuda(rank)
-  net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
-  optim_g = torch.optim.AdamW(
-      net_g.parameters(), 
-      hps.train.learning_rate, 
-      betas=hps.train.betas, 
-      eps=hps.train.eps)
-  optim_d = torch.optim.AdamW(
-      net_d.parameters(),
-      hps.train.learning_rate, 
-      betas=hps.train.betas, 
-      eps=hps.train.eps)
-  net_g = DDP(net_g, device_ids=[rank])
-  net_d = DDP(net_d, device_ids=[rank])
-
-  try:
-    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
-    _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
-    global_step = (epoch_str - 1) * len(train_loader)
-  except:
-    epoch_str = 1
-    global_step = 0
-
-  scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-  scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-
-  scaler = GradScaler(enabled=hps.train.fp16_run)
-
-  for epoch in range(epoch_str, hps.train.epochs + 1):
-    if rank==0:
-      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
-    else:
-      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
-    scheduler_g.step()
-    scheduler_d.step()
-
-
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
-  net_g, net_d = nets
-  optim_g, optim_d = optims
-  scheduler_g, scheduler_d = schedulers
-  train_loader, eval_loader = loaders
-  if writers is not None:
-    writer, writer_eval = writers
-
-  train_loader.batch_sampler.set_epoch(epoch)
-  global global_step
-
-  net_g.train()
-  net_d.train()
-  for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(train_loader):
-    x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
-    spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
-    y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
-    speakers = speakers.cuda(rank, non_blocking=True)
-
-    with autocast(enabled=hps.train.fp16_run):
-      y_hat, l_length, attn, ids_slice, x_mask, z_mask,\
-      (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers)
-
-      mel = spec_to_mel_torch(
-          spec, 
-          hps.data.filter_length, 
-          hps.data.n_mel_channels, 
-          hps.data.sampling_rate,
-          hps.data.mel_fmin, 
-          hps.data.mel_fmax)
-      y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
-      y_hat_mel = mel_spectrogram_torch(
-          y_hat.squeeze(1), 
-          hps.data.filter_length, 
-          hps.data.n_mel_channels, 
-          hps.data.sampling_rate, 
-          hps.data.hop_length, 
-          hps.data.win_length, 
-          hps.data.mel_fmin, 
-          hps.data.mel_fmax
-      )
-
-      y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice 
-
-      # Discriminator
-      y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-      with autocast(enabled=False):
-        loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
-        loss_disc_all = loss_disc
-    optim_d.zero_grad()
-    scaler.scale(loss_disc_all).backward()
-    scaler.unscale_(optim_d)
-    grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-    scaler.step(optim_d)
-
-    with autocast(enabled=hps.train.fp16_run):
-      # Generator
-      y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-      with autocast(enabled=False):
-        loss_dur = torch.sum(l_length.float())
-        loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-        loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-
-        loss_fm = feature_loss(fmap_r, fmap_g)
-        loss_gen, losses_gen = generator_loss(y_d_hat_g)
-        loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
-    optim_g.zero_grad()
-    scaler.scale(loss_gen_all).backward()
-    scaler.unscale_(optim_g)
-    grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-    scaler.step(optim_g)
-    scaler.update()
-
-    if rank==0:
-      if global_step % hps.train.log_interval == 0:
-        lr = optim_g.param_groups[0]['lr']
-        losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
-        logger.info('Train Epoch: {} [{:.0f}%]'.format(
-          epoch,
-          100. * batch_idx / len(train_loader)))
-        logger.info([x.item() for x in losses] + [global_step, lr])
-        
-        scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
-        scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
-
-        scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-        scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-        scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-        image_dict = { 
-            "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-            "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), 
-            "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-            "all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy())
-        }
-        utils.summarize(
-          writer=writer,
-          global_step=global_step, 
-          images=image_dict,
-          scalars=scalar_dict)
-
-      if global_step % hps.train.eval_interval == 0:
-        evaluate(hps, net_g, eval_loader, writer_eval)
-        utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
-        utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
-    global_step += 1
-  
-  if rank == 0:
-    logger.info('====> Epoch: {}'.format(epoch))
-
- 
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    with torch.no_grad():
-      for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(eval_loader):
-        x, x_lengths = x.cuda(0), x_lengths.cuda(0)
-        spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
-        y, y_lengths = y.cuda(0), y_lengths.cuda(0)
-        speakers = speakers.cuda(0)
-
-        # remove else
-        x = x[:1]
-        x_lengths = x_lengths[:1]
-        spec = spec[:1]
-        spec_lengths = spec_lengths[:1]
-        y = y[:1]
-        y_lengths = y_lengths[:1]
-        speakers = speakers[:1]
-        break
-      y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, max_len=1000)
-      y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length
-
-      mel = spec_to_mel_torch(
-        spec, 
-        hps.data.filter_length, 
-        hps.data.n_mel_channels, 
-        hps.data.sampling_rate,
-        hps.data.mel_fmin, 
-        hps.data.mel_fmax)
-      y_hat_mel = mel_spectrogram_torch(
-        y_hat.squeeze(1).float(),
-        hps.data.filter_length,
-        hps.data.n_mel_channels,
-        hps.data.sampling_rate,
-        hps.data.hop_length,
-        hps.data.win_length,
-        hps.data.mel_fmin,
-        hps.data.mel_fmax
-      )
-    image_dict = {
-      "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
-    }
-    audio_dict = {
-      "gen/audio": y_hat[0,:,:y_hat_lengths[0]]
-    }
-    if global_step == 0:
-      image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
-      audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]})
-
-    utils.summarize(
-      writer=writer_eval,
-      global_step=global_step, 
-      images=image_dict,
-      audios=audio_dict,
-      audio_sampling_rate=hps.data.sampling_rate
-    )
-    generator.train()
-
-                           
-if __name__ == "__main__":
-  main()
diff --git a/spaces/allknowingroger/Image-Models-Test103/README.md b/spaces/allknowingroger/Image-Models-Test103/README.md
deleted file mode 100644
index 1c00da7c943a1d61f060e7dd2b6a163e36b2cb1b..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test103/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-duplicated_from: allknowingroger/Image-Models-Test102
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/alphakavi22772023/test_00/README.md b/spaces/alphakavi22772023/test_00/README.md
deleted file mode 100644
index 180865835e102da3088539d9d25d0c16a958e1ba..0000000000000000000000000000000000000000
--- a/spaces/alphakavi22772023/test_00/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Test 00
-emoji: 🦀
-colorFrom: blue
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/esrgan_model.py b/spaces/aodianyun/stable-diffusion-webui/modules/esrgan_model.py
deleted file mode 100644
index 80131c62cfeaa7f95455df55c45d6e62591adeee..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/esrgan_model.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import os
-
-import numpy as np
-import torch
-from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
-
-import modules.esrgan_model_arch as arch
-from modules import shared, modelloader, images, devices
-from modules.upscaler import Upscaler, UpscalerData
-from modules.shared import opts
-
-
-
-def mod2normal(state_dict):
-    # this code is copied from https://github.com/victorca25/iNNfer
-    if 'conv_first.weight' in state_dict:
-        crt_net = {}
-        items = []
-        for k, v in state_dict.items():
-            items.append(k)
-
-        crt_net['model.0.weight'] = state_dict['conv_first.weight']
-        crt_net['model.0.bias'] = state_dict['conv_first.bias']
-
-        for k in items.copy():
-            if 'RDB' in k:
-                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
-                if '.weight' in k:
-                    ori_k = ori_k.replace('.weight', '.0.weight')
-                elif '.bias' in k:
-                    ori_k = ori_k.replace('.bias', '.0.bias')
-                crt_net[ori_k] = state_dict[k]
-                items.remove(k)
-
-        crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
-        crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
-        crt_net['model.3.weight'] = state_dict['upconv1.weight']
-        crt_net['model.3.bias'] = state_dict['upconv1.bias']
-        crt_net['model.6.weight'] = state_dict['upconv2.weight']
-        crt_net['model.6.bias'] = state_dict['upconv2.bias']
-        crt_net['model.8.weight'] = state_dict['HRconv.weight']
-        crt_net['model.8.bias'] = state_dict['HRconv.bias']
-        crt_net['model.10.weight'] = state_dict['conv_last.weight']
-        crt_net['model.10.bias'] = state_dict['conv_last.bias']
-        state_dict = crt_net
-    return state_dict
-
-
-def resrgan2normal(state_dict, nb=23):
-    # this code is copied from https://github.com/victorca25/iNNfer
-    if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
-        re8x = 0
-        crt_net = {}
-        items = []
-        for k, v in state_dict.items():
-            items.append(k)
-
-        crt_net['model.0.weight'] = state_dict['conv_first.weight']
-        crt_net['model.0.bias'] = state_dict['conv_first.bias']
-
-        for k in items.copy():
-            if "rdb" in k:
-                ori_k = k.replace('body.', 'model.1.sub.')
-                ori_k = ori_k.replace('.rdb', '.RDB')
-                if '.weight' in k:
-                    ori_k = ori_k.replace('.weight', '.0.weight')
-                elif '.bias' in k:
-                    ori_k = ori_k.replace('.bias', '.0.bias')
-                crt_net[ori_k] = state_dict[k]
-                items.remove(k)
-
-        crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
-        crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
-        crt_net['model.3.weight'] = state_dict['conv_up1.weight']
-        crt_net['model.3.bias'] = state_dict['conv_up1.bias']
-        crt_net['model.6.weight'] = state_dict['conv_up2.weight']
-        crt_net['model.6.bias'] = state_dict['conv_up2.bias']
-
-        if 'conv_up3.weight' in state_dict:
-            # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
-            re8x = 3
-            crt_net['model.9.weight'] = state_dict['conv_up3.weight']
-            crt_net['model.9.bias'] = state_dict['conv_up3.bias']
-
-        crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
-        crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
-        crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
-        crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
-
-        state_dict = crt_net
-    return state_dict
-
-
-def infer_params(state_dict):
-    # this code is copied from https://github.com/victorca25/iNNfer
-    scale2x = 0
-    scalemin = 6
-    n_uplayer = 0
-    plus = False
-
-    for block in list(state_dict):
-        parts = block.split(".")
-        n_parts = len(parts)
-        if n_parts == 5 and parts[2] == "sub":
-            nb = int(parts[3])
-        elif n_parts == 3:
-            part_num = int(parts[1])
-            if (part_num > scalemin
-                and parts[0] == "model"
-                and parts[2] == "weight"):
-                scale2x += 1
-            if part_num > n_uplayer:
-                n_uplayer = part_num
-                out_nc = state_dict[block].shape[0]
-        if not plus and "conv1x1" in block:
-            plus = True
-
-    nf = state_dict["model.0.weight"].shape[0]
-    in_nc = state_dict["model.0.weight"].shape[1]
-    out_nc = out_nc
-    scale = 2 ** scale2x
-
-    return in_nc, out_nc, nf, nb, plus, scale
-
-
-class UpscalerESRGAN(Upscaler):
-    def __init__(self, dirname):
-        self.name = "ESRGAN"
-        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
-        self.model_name = "ESRGAN_4x"
-        self.scalers = []
-        self.user_path = dirname
-        super().__init__()
-        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
-        scalers = []
-        if len(model_paths) == 0:
-            scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
-            scalers.append(scaler_data)
-        for file in model_paths:
-            if "http" in file:
-                name = self.model_name
-            else:
-                name = modelloader.friendly_name(file)
-
-            scaler_data = UpscalerData(name, file, self, 4)
-            self.scalers.append(scaler_data)
-
-    def do_upscale(self, img, selected_model):
-        model = self.load_model(selected_model)
-        if model is None:
-            return img
-        model.to(devices.device_esrgan)
-        img = esrgan_upscale(model, img)
-        return img
-
-    def load_model(self, path: str):
-        if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
-                                          file_name="%s.pth" % self.model_name,
-                                          progress=True)
-        else:
-            filename = path
-        if not os.path.exists(filename) or filename is None:
-            print("Unable to load %s from %s" % (self.model_path, filename))
-            return None
-
-        state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
-
-        if "params_ema" in state_dict:
-            state_dict = state_dict["params_ema"]
-        elif "params" in state_dict:
-            state_dict = state_dict["params"]
-            num_conv = 16 if "realesr-animevideov3" in filename else 32
-            model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
-            model.load_state_dict(state_dict)
-            model.eval()
-            return model
-
-        if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
-            nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
-            state_dict = resrgan2normal(state_dict, nb)
-        elif "conv_first.weight" in state_dict:
-            state_dict = mod2normal(state_dict)
-        elif "model.0.weight" not in state_dict:
-            raise Exception("The file is not a recognized ESRGAN model.")
-
-        in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
-
-        model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
-        model.load_state_dict(state_dict)
-        model.eval()
-
-        return model
-
-
-def upscale_without_tiling(model, img):
-    img = np.array(img)
-    img = img[:, :, ::-1]
-    img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
-    img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(devices.device_esrgan)
-    with torch.no_grad():
-        output = model(img)
-    output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-    output = 255. * np.moveaxis(output, 0, 2)
-    output = output.astype(np.uint8)
-    output = output[:, :, ::-1]
-    return Image.fromarray(output, 'RGB')
-
-
-def esrgan_upscale(model, img):
-    if opts.ESRGAN_tile == 0:
-        return upscale_without_tiling(model, img)
-
-    grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
-    newtiles = []
-    scale_factor = 1
-
-    for y, h, row in grid.tiles:
-        newrow = []
-        for tiledata in row:
-            x, w, tile = tiledata
-
-            output = upscale_without_tiling(model, tile)
-            scale_factor = output.width // tile.width
-
-            newrow.append([x * scale_factor, w * scale_factor, output])
-        newtiles.append([y * scale_factor, h * scale_factor, newrow])
-
-    newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
-    output = images.combine_grid(newgrid)
-    return output
diff --git a/spaces/artificialguybr/video-dubbing/TTS/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py b/spaces/artificialguybr/video-dubbing/TTS/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py
deleted file mode 100644
index f050ae3222ed4fd2fc0cd7e4d3c873c5ae4557c4..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.tacotron2 import Tacotron2
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-# from TTS.tts.datasets.tokenizer import Tokenizer
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
-    formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
-)
-
-audio_config = BaseAudioConfig(
-    sample_rate=22050,
-    do_trim_silence=True,
-    trim_db=60.0,
-    signal_norm=False,
-    mel_fmin=0.0,
-    mel_fmax=8000,
-    spec_gain=1.0,
-    log_func="np.log",
-    ref_level_db=20,
-    preemphasis=0.0,
-)
-
-config = Tacotron2Config(  # This is the config that is saved for the future use
-    audio=audio_config,
-    batch_size=64,
-    eval_batch_size=16,
-    num_loader_workers=4,
-    num_eval_loader_workers=4,
-    run_eval=True,
-    test_delay_epochs=-1,
-    r=6,
-    gradual_training=[[0, 6, 64], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
-    double_decoder_consistency=True,
-    epochs=1000,
-    text_cleaner="phoneme_cleaners",
-    use_phonemes=True,
-    phoneme_language="en-us",
-    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
-    precompute_num_workers=8,
-    print_step=25,
-    print_eval=True,
-    mixed_precision=False,
-    output_path=output_path,
-    datasets=[dataset_config],
-)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-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,
-)
-
-# INITIALIZE THE MODEL
-# Models take a config object and a speaker manager as input
-# Config defines the details of the model like the number of layers, the size of the embedding, etc.
-# Speaker manager is used by multi-speaker models.
-model = Tacotron2(config, ap, tokenizer, speaker_manager=None)
-
-# init the trainer and 🚀
-trainer = Trainer(
-    TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/test_eddsa.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/test_eddsa.py
deleted file mode 100644
index 6a9a9b0c2c73c17ceb2d96e582704ebf799f2a15..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/test_eddsa.py
+++ /dev/null
@@ -1,578 +0,0 @@
-#
-# Copyright (c) 2022, Legrandin <helderijs@gmail.com>
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in
-#    the documentation and/or other materials provided with the
-#    distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-import unittest
-from binascii import unhexlify
-
-from Crypto.PublicKey import ECC
-from Crypto.Signature import eddsa
-from Crypto.Hash import SHA512, SHAKE256
-from Crypto.SelfTest.st_common import list_test_cases
-from Crypto.SelfTest.loader import load_test_vectors_wycheproof
-from Crypto.Util.number import bytes_to_long
-
-rfc8032_tv_str = (
-    # 7.1 Ed25519
-    (
-        "9d61b19deffd5a60ba844af492ec2cc44449c5697b326919703bac031cae7f60",
-        "d75a980182b10ab7d54bfed3c964073a0ee172f3daa62325af021a68f707511a",
-        "",
-        None,
-        "",
-        "e5564300c360ac729086e2cc806e828a"
-        "84877f1eb8e5d974d873e06522490155"
-        "5fb8821590a33bacc61e39701cf9b46b"
-        "d25bf5f0595bbe24655141438e7a100b"
-    ),
-    (
-        "4ccd089b28ff96da9db6c346ec114e0f5b8a319f35aba624da8cf6ed4fb8a6fb",
-        "3d4017c3e843895a92b70aa74d1b7ebc9c982ccf2ec4968cc0cd55f12af4660c",
-        "72",
-        None,
-        "",
-        "92a009a9f0d4cab8720e820b5f642540"
-        "a2b27b5416503f8fb3762223ebdb69da"
-        "085ac1e43e15996e458f3613d0f11d8c"
-        "387b2eaeb4302aeeb00d291612bb0c00"
-    ),
-    (
-        "c5aa8df43f9f837bedb7442f31dcb7b166d38535076f094b85ce3a2e0b4458f7",
-        "fc51cd8e6218a1a38da47ed00230f0580816ed13ba3303ac5deb911548908025",
-        "af82",
-        None,
-        "",
-        "6291d657deec24024827e69c3abe01a3"
-        "0ce548a284743a445e3680d7db5ac3ac"
-        "18ff9b538d16f290ae67f760984dc659"
-        "4a7c15e9716ed28dc027beceea1ec40a"
-    ),
-    (
-        "f5e5767cf153319517630f226876b86c8160cc583bc013744c6bf255f5cc0ee5",
-        "278117fc144c72340f67d0f2316e8386ceffbf2b2428c9c51fef7c597f1d426e",
-        "08b8b2b733424243760fe426a4b54908"
-        "632110a66c2f6591eabd3345e3e4eb98"
-        "fa6e264bf09efe12ee50f8f54e9f77b1"
-        "e355f6c50544e23fb1433ddf73be84d8"
-        "79de7c0046dc4996d9e773f4bc9efe57"
-        "38829adb26c81b37c93a1b270b20329d"
-        "658675fc6ea534e0810a4432826bf58c"
-        "941efb65d57a338bbd2e26640f89ffbc"
-        "1a858efcb8550ee3a5e1998bd177e93a"
-        "7363c344fe6b199ee5d02e82d522c4fe"
-        "ba15452f80288a821a579116ec6dad2b"
-        "3b310da903401aa62100ab5d1a36553e"
-        "06203b33890cc9b832f79ef80560ccb9"
-        "a39ce767967ed628c6ad573cb116dbef"
-        "efd75499da96bd68a8a97b928a8bbc10"
-        "3b6621fcde2beca1231d206be6cd9ec7"
-        "aff6f6c94fcd7204ed3455c68c83f4a4"
-        "1da4af2b74ef5c53f1d8ac70bdcb7ed1"
-        "85ce81bd84359d44254d95629e9855a9"
-        "4a7c1958d1f8ada5d0532ed8a5aa3fb2"
-        "d17ba70eb6248e594e1a2297acbbb39d"
-        "502f1a8c6eb6f1ce22b3de1a1f40cc24"
-        "554119a831a9aad6079cad88425de6bd"
-        "e1a9187ebb6092cf67bf2b13fd65f270"
-        "88d78b7e883c8759d2c4f5c65adb7553"
-        "878ad575f9fad878e80a0c9ba63bcbcc"
-        "2732e69485bbc9c90bfbd62481d9089b"
-        "eccf80cfe2df16a2cf65bd92dd597b07"
-        "07e0917af48bbb75fed413d238f5555a"
-        "7a569d80c3414a8d0859dc65a46128ba"
-        "b27af87a71314f318c782b23ebfe808b"
-        "82b0ce26401d2e22f04d83d1255dc51a"
-        "ddd3b75a2b1ae0784504df543af8969b"
-        "e3ea7082ff7fc9888c144da2af58429e"
-        "c96031dbcad3dad9af0dcbaaaf268cb8"
-        "fcffead94f3c7ca495e056a9b47acdb7"
-        "51fb73e666c6c655ade8297297d07ad1"
-        "ba5e43f1bca32301651339e22904cc8c"
-        "42f58c30c04aafdb038dda0847dd988d"
-        "cda6f3bfd15c4b4c4525004aa06eeff8"
-        "ca61783aacec57fb3d1f92b0fe2fd1a8"
-        "5f6724517b65e614ad6808d6f6ee34df"
-        "f7310fdc82aebfd904b01e1dc54b2927"
-        "094b2db68d6f903b68401adebf5a7e08"
-        "d78ff4ef5d63653a65040cf9bfd4aca7"
-        "984a74d37145986780fc0b16ac451649"
-        "de6188a7dbdf191f64b5fc5e2ab47b57"
-        "f7f7276cd419c17a3ca8e1b939ae49e4"
-        "88acba6b965610b5480109c8b17b80e1"
-        "b7b750dfc7598d5d5011fd2dcc5600a3"
-        "2ef5b52a1ecc820e308aa342721aac09"
-        "43bf6686b64b2579376504ccc493d97e"
-        "6aed3fb0f9cd71a43dd497f01f17c0e2"
-        "cb3797aa2a2f256656168e6c496afc5f"
-        "b93246f6b1116398a346f1a641f3b041"
-        "e989f7914f90cc2c7fff357876e506b5"
-        "0d334ba77c225bc307ba537152f3f161"
-        "0e4eafe595f6d9d90d11faa933a15ef1"
-        "369546868a7f3a45a96768d40fd9d034"
-        "12c091c6315cf4fde7cb68606937380d"
-        "b2eaaa707b4c4185c32eddcdd306705e"
-        "4dc1ffc872eeee475a64dfac86aba41c"
-        "0618983f8741c5ef68d3a101e8a3b8ca"
-        "c60c905c15fc910840b94c00a0b9d0",
-        None,
-        "",
-        "0aab4c900501b3e24d7cdf4663326a3a"
-        "87df5e4843b2cbdb67cbf6e460fec350"
-        "aa5371b1508f9f4528ecea23c436d94b"
-        "5e8fcd4f681e30a6ac00a9704a188a03"
-    ),
-    # 7.2 Ed25519ctx
-    (
-        "0305334e381af78f141cb666f6199f57"
-        "bc3495335a256a95bd2a55bf546663f6",
-        "dfc9425e4f968f7f0c29f0259cf5f9ae"
-        "d6851c2bb4ad8bfb860cfee0ab248292",
-        "f726936d19c800494e3fdaff20b276a8",
-        None,
-        "666f6f",
-        "55a4cc2f70a54e04288c5f4cd1e45a7b"
-        "b520b36292911876cada7323198dd87a"
-        "8b36950b95130022907a7fb7c4e9b2d5"
-        "f6cca685a587b4b21f4b888e4e7edb0d"
-    ),
-    (
-        "0305334e381af78f141cb666f6199f57"
-        "bc3495335a256a95bd2a55bf546663f6",
-        "dfc9425e4f968f7f0c29f0259cf5f9ae"
-        "d6851c2bb4ad8bfb860cfee0ab248292",
-        "f726936d19c800494e3fdaff20b276a8",
-        None,
-        "626172",
-        "fc60d5872fc46b3aa69f8b5b4351d580"
-        "8f92bcc044606db097abab6dbcb1aee3"
-        "216c48e8b3b66431b5b186d1d28f8ee1"
-        "5a5ca2df6668346291c2043d4eb3e90d"
-    ),
-    (
-        "0305334e381af78f141cb666f6199f57"
-        "bc3495335a256a95bd2a55bf546663f6",
-        "dfc9425e4f968f7f0c29f0259cf5f9ae"
-        "d6851c2bb4ad8bfb860cfee0ab248292",
-        "508e9e6882b979fea900f62adceaca35",
-        None,
-        "666f6f",
-        "8b70c1cc8310e1de20ac53ce28ae6e72"
-        "07f33c3295e03bb5c0732a1d20dc6490"
-        "8922a8b052cf99b7c4fe107a5abb5b2c"
-        "4085ae75890d02df26269d8945f84b0b"
-    ),
-    (
-        "ab9c2853ce297ddab85c993b3ae14bca"
-        "d39b2c682beabc27d6d4eb20711d6560",
-        "0f1d1274943b91415889152e893d80e9"
-        "3275a1fc0b65fd71b4b0dda10ad7d772",
-        "f726936d19c800494e3fdaff20b276a8",
-        None,
-        "666f6f",
-        "21655b5f1aa965996b3f97b3c849eafb"
-        "a922a0a62992f73b3d1b73106a84ad85"
-        "e9b86a7b6005ea868337ff2d20a7f5fb"
-        "d4cd10b0be49a68da2b2e0dc0ad8960f"
-    ),
-    # 7.3 Ed25519ph
-    (
-        "833fe62409237b9d62ec77587520911e"
-        "9a759cec1d19755b7da901b96dca3d42",
-        "ec172b93ad5e563bf4932c70e1245034"
-        "c35467ef2efd4d64ebf819683467e2bf",
-        "616263",
-        SHA512,
-        "",
-        "98a70222f0b8121aa9d30f813d683f80"
-        "9e462b469c7ff87639499bb94e6dae41"
-        "31f85042463c2a355a2003d062adf5aa"
-        "a10b8c61e636062aaad11c2a26083406"
-    ),
-    # 7.4 Ed448
-    (
-        "6c82a562cb808d10d632be89c8513ebf6c929f34ddfa8c9f63c9960ef6e348a3"
-        "528c8a3fcc2f044e39a3fc5b94492f8f032e7549a20098f95b",
-        "5fd7449b59b461fd2ce787ec616ad46a1da1342485a70e1f8a0ea75d80e96778"
-        "edf124769b46c7061bd6783df1e50f6cd1fa1abeafe8256180",
-        "",
-        None,
-        "",
-        "533a37f6bbe457251f023c0d88f976ae2dfb504a843e34d2074fd823d41a591f"
-        "2b233f034f628281f2fd7a22ddd47d7828c59bd0a21bfd3980ff0d2028d4b18a"
-        "9df63e006c5d1c2d345b925d8dc00b4104852db99ac5c7cdda8530a113a0f4db"
-        "b61149f05a7363268c71d95808ff2e652600"
-    ),
-    (
-        "c4eab05d357007c632f3dbb48489924d552b08fe0c353a0d4a1f00acda2c463a"
-        "fbea67c5e8d2877c5e3bc397a659949ef8021e954e0a12274e",
-        "43ba28f430cdff456ae531545f7ecd0ac834a55d9358c0372bfa0c6c6798c086"
-        "6aea01eb00742802b8438ea4cb82169c235160627b4c3a9480",
-        "03",
-        None,
-        "",
-        "26b8f91727bd62897af15e41eb43c377efb9c610d48f2335cb0bd0087810f435"
-        "2541b143c4b981b7e18f62de8ccdf633fc1bf037ab7cd779805e0dbcc0aae1cb"
-        "cee1afb2e027df36bc04dcecbf154336c19f0af7e0a6472905e799f1953d2a0f"
-        "f3348ab21aa4adafd1d234441cf807c03a00",
-    ),
-    (
-        "c4eab05d357007c632f3dbb48489924d552b08fe0c353a0d4a1f00acda2c463a"
-        "fbea67c5e8d2877c5e3bc397a659949ef8021e954e0a12274e",
-        "43ba28f430cdff456ae531545f7ecd0ac834a55d9358c0372bfa0c6c6798c086"
-        "6aea01eb00742802b8438ea4cb82169c235160627b4c3a9480",
-        "03",
-        None,
-        "666f6f",
-        "d4f8f6131770dd46f40867d6fd5d5055de43541f8c5e35abbcd001b32a89f7d2"
-        "151f7647f11d8ca2ae279fb842d607217fce6e042f6815ea000c85741de5c8da"
-        "1144a6a1aba7f96de42505d7a7298524fda538fccbbb754f578c1cad10d54d0d"
-        "5428407e85dcbc98a49155c13764e66c3c00",
-    ),
-    (
-        "cd23d24f714274e744343237b93290f511f6425f98e64459ff203e8985083ffd"
-        "f60500553abc0e05cd02184bdb89c4ccd67e187951267eb328",
-        "dcea9e78f35a1bf3499a831b10b86c90aac01cd84b67a0109b55a36e9328b1e3"
-        "65fce161d71ce7131a543ea4cb5f7e9f1d8b00696447001400",
-        "0c3e544074ec63b0265e0c",
-        None,
-        "",
-        "1f0a8888ce25e8d458a21130879b840a9089d999aaba039eaf3e3afa090a09d3"
-        "89dba82c4ff2ae8ac5cdfb7c55e94d5d961a29fe0109941e00b8dbdeea6d3b05"
-        "1068df7254c0cdc129cbe62db2dc957dbb47b51fd3f213fb8698f064774250a5"
-        "028961c9bf8ffd973fe5d5c206492b140e00",
-    ),
-    (
-        "258cdd4ada32ed9c9ff54e63756ae582fb8fab2ac721f2c8e676a72768513d93"
-        "9f63dddb55609133f29adf86ec9929dccb52c1c5fd2ff7e21b",
-        "3ba16da0c6f2cc1f30187740756f5e798d6bc5fc015d7c63cc9510ee3fd44adc"
-        "24d8e968b6e46e6f94d19b945361726bd75e149ef09817f580",
-        "64a65f3cdedcdd66811e2915",
-        None,
-        "",
-        "7eeeab7c4e50fb799b418ee5e3197ff6bf15d43a14c34389b59dd1a7b1b85b4a"
-        "e90438aca634bea45e3a2695f1270f07fdcdf7c62b8efeaf00b45c2c96ba457e"
-        "b1a8bf075a3db28e5c24f6b923ed4ad747c3c9e03c7079efb87cb110d3a99861"
-        "e72003cbae6d6b8b827e4e6c143064ff3c00",
-    ),
-    (
-        "7ef4e84544236752fbb56b8f31a23a10e42814f5f55ca037cdcc11c64c9a3b29"
-        "49c1bb60700314611732a6c2fea98eebc0266a11a93970100e",
-        "b3da079b0aa493a5772029f0467baebee5a8112d9d3a22532361da294f7bb381"
-        "5c5dc59e176b4d9f381ca0938e13c6c07b174be65dfa578e80",
-        "64a65f3cdedcdd66811e2915e7",
-        None,
-        "",
-        "6a12066f55331b6c22acd5d5bfc5d71228fbda80ae8dec26bdd306743c5027cb"
-        "4890810c162c027468675ecf645a83176c0d7323a2ccde2d80efe5a1268e8aca"
-        "1d6fbc194d3f77c44986eb4ab4177919ad8bec33eb47bbb5fc6e28196fd1caf5"
-        "6b4e7e0ba5519234d047155ac727a1053100",
-    ),
-    (
-        "d65df341ad13e008567688baedda8e9dcdc17dc024974ea5b4227b6530e339bf"
-        "f21f99e68ca6968f3cca6dfe0fb9f4fab4fa135d5542ea3f01",
-        "df9705f58edbab802c7f8363cfe5560ab1c6132c20a9f1dd163483a26f8ac53a"
-        "39d6808bf4a1dfbd261b099bb03b3fb50906cb28bd8a081f00",
-        "bd0f6a3747cd561bdddf4640a332461a4a30a12a434cd0bf40d766d9c6d458e5"
-        "512204a30c17d1f50b5079631f64eb3112182da3005835461113718d1a5ef944",
-        None,
-        "",
-        "554bc2480860b49eab8532d2a533b7d578ef473eeb58c98bb2d0e1ce488a98b1"
-        "8dfde9b9b90775e67f47d4a1c3482058efc9f40d2ca033a0801b63d45b3b722e"
-        "f552bad3b4ccb667da350192b61c508cf7b6b5adadc2c8d9a446ef003fb05cba"
-        "5f30e88e36ec2703b349ca229c2670833900",
-    ),
-    (
-        "2ec5fe3c17045abdb136a5e6a913e32ab75ae68b53d2fc149b77e504132d3756"
-        "9b7e766ba74a19bd6162343a21c8590aa9cebca9014c636df5",
-        "79756f014dcfe2079f5dd9e718be4171e2ef2486a08f25186f6bff43a9936b9b"
-        "fe12402b08ae65798a3d81e22e9ec80e7690862ef3d4ed3a00",
-        "15777532b0bdd0d1389f636c5f6b9ba734c90af572877e2d272dd078aa1e567c"
-        "fa80e12928bb542330e8409f3174504107ecd5efac61ae7504dabe2a602ede89"
-        "e5cca6257a7c77e27a702b3ae39fc769fc54f2395ae6a1178cab4738e543072f"
-        "c1c177fe71e92e25bf03e4ecb72f47b64d0465aaea4c7fad372536c8ba516a60"
-        "39c3c2a39f0e4d832be432dfa9a706a6e5c7e19f397964ca4258002f7c0541b5"
-        "90316dbc5622b6b2a6fe7a4abffd96105eca76ea7b98816af0748c10df048ce0"
-        "12d901015a51f189f3888145c03650aa23ce894c3bd889e030d565071c59f409"
-        "a9981b51878fd6fc110624dcbcde0bf7a69ccce38fabdf86f3bef6044819de11",
-        None,
-        "",
-        "c650ddbb0601c19ca11439e1640dd931f43c518ea5bea70d3dcde5f4191fe53f"
-        "00cf966546b72bcc7d58be2b9badef28743954e3a44a23f880e8d4f1cfce2d7a"
-        "61452d26da05896f0a50da66a239a8a188b6d825b3305ad77b73fbac0836ecc6"
-        "0987fd08527c1a8e80d5823e65cafe2a3d00",
-    ),
-    (
-        "872d093780f5d3730df7c212664b37b8a0f24f56810daa8382cd4fa3f77634ec"
-        "44dc54f1c2ed9bea86fafb7632d8be199ea165f5ad55dd9ce8",
-        "a81b2e8a70a5ac94ffdbcc9badfc3feb0801f258578bb114ad44ece1ec0e799d"
-        "a08effb81c5d685c0c56f64eecaef8cdf11cc38737838cf400",
-        "6ddf802e1aae4986935f7f981ba3f0351d6273c0a0c22c9c0e8339168e675412"
-        "a3debfaf435ed651558007db4384b650fcc07e3b586a27a4f7a00ac8a6fec2cd"
-        "86ae4bf1570c41e6a40c931db27b2faa15a8cedd52cff7362c4e6e23daec0fbc"
-        "3a79b6806e316efcc7b68119bf46bc76a26067a53f296dafdbdc11c77f7777e9"
-        "72660cf4b6a9b369a6665f02e0cc9b6edfad136b4fabe723d2813db3136cfde9"
-        "b6d044322fee2947952e031b73ab5c603349b307bdc27bc6cb8b8bbd7bd32321"
-        "9b8033a581b59eadebb09b3c4f3d2277d4f0343624acc817804728b25ab79717"
-        "2b4c5c21a22f9c7839d64300232eb66e53f31c723fa37fe387c7d3e50bdf9813"
-        "a30e5bb12cf4cd930c40cfb4e1fc622592a49588794494d56d24ea4b40c89fc0"
-        "596cc9ebb961c8cb10adde976a5d602b1c3f85b9b9a001ed3c6a4d3b1437f520"
-        "96cd1956d042a597d561a596ecd3d1735a8d570ea0ec27225a2c4aaff26306d1"
-        "526c1af3ca6d9cf5a2c98f47e1c46db9a33234cfd4d81f2c98538a09ebe76998"
-        "d0d8fd25997c7d255c6d66ece6fa56f11144950f027795e653008f4bd7ca2dee"
-        "85d8e90f3dc315130ce2a00375a318c7c3d97be2c8ce5b6db41a6254ff264fa6"
-        "155baee3b0773c0f497c573f19bb4f4240281f0b1f4f7be857a4e59d416c06b4"
-        "c50fa09e1810ddc6b1467baeac5a3668d11b6ecaa901440016f389f80acc4db9"
-        "77025e7f5924388c7e340a732e554440e76570f8dd71b7d640b3450d1fd5f041"
-        "0a18f9a3494f707c717b79b4bf75c98400b096b21653b5d217cf3565c9597456"
-        "f70703497a078763829bc01bb1cbc8fa04eadc9a6e3f6699587a9e75c94e5bab"
-        "0036e0b2e711392cff0047d0d6b05bd2a588bc109718954259f1d86678a579a3"
-        "120f19cfb2963f177aeb70f2d4844826262e51b80271272068ef5b3856fa8535"
-        "aa2a88b2d41f2a0e2fda7624c2850272ac4a2f561f8f2f7a318bfd5caf969614"
-        "9e4ac824ad3460538fdc25421beec2cc6818162d06bbed0c40a387192349db67"
-        "a118bada6cd5ab0140ee273204f628aad1c135f770279a651e24d8c14d75a605"
-        "9d76b96a6fd857def5e0b354b27ab937a5815d16b5fae407ff18222c6d1ed263"
-        "be68c95f32d908bd895cd76207ae726487567f9a67dad79abec316f683b17f2d"
-        "02bf07e0ac8b5bc6162cf94697b3c27cd1fea49b27f23ba2901871962506520c"
-        "392da8b6ad0d99f7013fbc06c2c17a569500c8a7696481c1cd33e9b14e40b82e"
-        "79a5f5db82571ba97bae3ad3e0479515bb0e2b0f3bfcd1fd33034efc6245eddd"
-        "7ee2086ddae2600d8ca73e214e8c2b0bdb2b047c6a464a562ed77b73d2d841c4"
-        "b34973551257713b753632efba348169abc90a68f42611a40126d7cb21b58695"
-        "568186f7e569d2ff0f9e745d0487dd2eb997cafc5abf9dd102e62ff66cba87",
-        None,
-        "",
-        "e301345a41a39a4d72fff8df69c98075a0cc082b802fc9b2b6bc503f926b65bd"
-        "df7f4c8f1cb49f6396afc8a70abe6d8aef0db478d4c6b2970076c6a0484fe76d"
-        "76b3a97625d79f1ce240e7c576750d295528286f719b413de9ada3e8eb78ed57"
-        "3603ce30d8bb761785dc30dbc320869e1a00"
-    ),
-    # 7.5 Ed448ph
-    (
-        "833fe62409237b9d62ec77587520911e9a759cec1d19755b7da901b96dca3d42"
-        "ef7822e0d5104127dc05d6dbefde69e3ab2cec7c867c6e2c49",
-        "259b71c19f83ef77a7abd26524cbdb3161b590a48f7d17de3ee0ba9c52beb743"
-        "c09428a131d6b1b57303d90d8132c276d5ed3d5d01c0f53880",
-        "616263",
-        SHAKE256,
-        "",
-        "822f6901f7480f3d5f562c592994d9693602875614483256505600bbc281ae38"
-        "1f54d6bce2ea911574932f52a4e6cadd78769375ec3ffd1b801a0d9b3f4030cd"
-        "433964b6457ea39476511214f97469b57dd32dbc560a9a94d00bff07620464a3"
-        "ad203df7dc7ce360c3cd3696d9d9fab90f00"
-    ),
-    (
-        "833fe62409237b9d62ec77587520911e9a759cec1d19755b7da901b96dca3d42"
-        "ef7822e0d5104127dc05d6dbefde69e3ab2cec7c867c6e2c49",
-        "259b71c19f83ef77a7abd26524cbdb3161b590a48f7d17de3ee0ba9c52beb743"
-        "c09428a131d6b1b57303d90d8132c276d5ed3d5d01c0f53880",
-        "616263",
-        SHAKE256,
-        "666f6f",
-        "c32299d46ec8ff02b54540982814dce9a05812f81962b649d528095916a2aa48"
-        "1065b1580423ef927ecf0af5888f90da0f6a9a85ad5dc3f280d91224ba9911a3"
-        "653d00e484e2ce232521481c8658df304bb7745a73514cdb9bf3e15784ab7128"
-        "4f8d0704a608c54a6b62d97beb511d132100",
-    ),
-)
-
-
-rfc8032_tv_bytes = []
-for tv_str in rfc8032_tv_str:
-    rfc8032_tv_bytes.append([unhexlify(i) if isinstance(i, str) else i for i in tv_str])
-
-
-class TestEdDSA(unittest.TestCase):
-
-    def test_sign(self):
-        for sk, _, msg, hashmod, ctx, exp_signature in rfc8032_tv_bytes:
-            key = eddsa.import_private_key(sk)
-            signer = eddsa.new(key, 'rfc8032', context=ctx)
-            if hashmod is None:
-                # PureEdDSA
-                signature = signer.sign(msg)
-            else:
-                # HashEdDSA
-                hashobj = hashmod.new(msg)
-                signature = signer.sign(hashobj)
-            self.assertEqual(exp_signature, signature)
-
-    def test_verify(self):
-        for _, pk, msg, hashmod, ctx, exp_signature in rfc8032_tv_bytes:
-            key = eddsa.import_public_key(pk)
-            verifier = eddsa.new(key, 'rfc8032', context=ctx)
-            if hashmod is None:
-                # PureEdDSA
-                verifier.verify(msg, exp_signature)
-            else:
-                # HashEdDSA
-                hashobj = hashmod.new(msg)
-                verifier.verify(hashobj, exp_signature)
-
-    def test_negative(self):
-        key = ECC.generate(curve="ed25519")
-        self.assertRaises(ValueError, eddsa.new, key, 'rfc9999')
-
-        nist_key = ECC.generate(curve="p256")
-        self.assertRaises(ValueError, eddsa.new, nist_key, 'rfc8032')
-
-
-class TestExport_Ed25519(unittest.TestCase):
-
-    def test_raw(self):
-        key = ECC.generate(curve="Ed25519")
-        x, y = key.pointQ.xy
-        raw = bytearray(key._export_eddsa())
-        sign_x = raw[31] >> 7
-        raw[31] &= 0x7F
-        yt = bytes_to_long(raw[::-1])
-        self.assertEqual(y, yt)
-        self.assertEqual(x & 1, sign_x)
-
-        key = ECC.construct(point_x=0, point_y=1, curve="Ed25519")
-        out = key._export_eddsa()
-        self.assertEqual(b'\x01' + b'\x00' * 31, out)
-
-
-class TestExport_Ed448(unittest.TestCase):
-
-    def test_raw(self):
-        key = ECC.generate(curve="Ed448")
-        x, y = key.pointQ.xy
-        raw = bytearray(key._export_eddsa())
-        sign_x = raw[56] >> 7
-        raw[56] &= 0x7F
-        yt = bytes_to_long(raw[::-1])
-        self.assertEqual(y, yt)
-        self.assertEqual(x & 1, sign_x)
-
-        key = ECC.construct(point_x=0, point_y=1, curve="Ed448")
-        out = key._export_eddsa()
-        self.assertEqual(b'\x01' + b'\x00' * 56, out)
-
-
-class TestImport_Ed25519(unittest.TestCase):
-
-    def test_raw(self):
-        Px = 24407857220263921307776619664228778204996144802740950419837658238229122415920
-        Py = 56480760040633817885061096979765646085062883740629155052073094891081309750690
-        encoded = b'\xa2\x05\xd6\x00\xe1 \xe1\xc0\xff\x96\xee?V\x8e\xba/\xd3\x89\x06\xd7\xc4c\xe8$\xc2d\xd7a1\xfa\xde|'
-        key = eddsa.import_public_key(encoded)
-        self.assertEqual(Py, key.pointQ.y)
-        self.assertEqual(Px, key.pointQ.x)
-
-        encoded = b'\x01' + b'\x00' * 31
-        key = eddsa.import_public_key(encoded)
-        self.assertEqual(1, key.pointQ.y)
-        self.assertEqual(0, key.pointQ.x)
-
-
-class TestImport_Ed448(unittest.TestCase):
-
-    def test_raw(self):
-        Px = 0x153f42025aba3b0daecaa5cd79458b3146c7c9378c16c17b4a59bc3561113d90c169045bc12966c3f93e140c2ca0a3acc33d9205b9daf9b1
-        Py = 0x38f5c0015d3dedd576c232810dd90373b5b1d631a12894c043b7be529cbae03ede177d8fa490b56131dbcb2465d2aba777ef839fc1719b25
-        encoded = unhexlify("259b71c19f83ef77a7abd26524cbdb31"
-                            "61b590a48f7d17de3ee0ba9c52beb743"
-                            "c09428a131d6b1b57303d90d8132c276"
-                            "d5ed3d5d01c0f53880")
-        key = eddsa.import_public_key(encoded)
-        self.assertEqual(Py, key.pointQ.y)
-        self.assertEqual(Px, key.pointQ.x)
-
-        encoded = b'\x01' + b'\x00' * 56
-        key = eddsa.import_public_key(encoded)
-        self.assertEqual(1, key.pointQ.y)
-        self.assertEqual(0, key.pointQ.x)
-
-
-class TestVectorsEdDSAWycheproof(unittest.TestCase):
-
-    def add_tests(self, filename):
-
-        def pk(group):
-            elem = group['key']['pk']
-            return unhexlify(elem)
-
-        def sk(group):
-            elem = group['key']['sk']
-            return unhexlify(elem)
-
-        result = load_test_vectors_wycheproof(("Signature", "wycheproof"),
-                                              filename,
-                                              "Wycheproof ECDSA signature (%s)"
-                                              % filename,
-                                              group_tag={'pk': pk, 'sk': sk})
-        self.tv += result
-
-    def setUp(self):
-        self.tv = []
-        self.add_tests("eddsa_test.json")
-        self.add_tests("ed448_test.json")
-
-    def test_sign(self, tv):
-        if not tv.valid:
-            return
-
-        self._id = "Wycheproof EdDSA Sign Test #%d (%s, %s)" % (tv.id, tv.comment, tv.filename)
-        key = eddsa.import_private_key(tv.sk)
-        signer = eddsa.new(key, 'rfc8032')
-        signature = signer.sign(tv.msg)
-        self.assertEqual(signature, tv.sig)
-
-    def test_verify(self, tv):
-        self._id = "Wycheproof EdDSA Verify Test #%d (%s, %s)" % (tv.id, tv.comment, tv.filename)
-        key = eddsa.import_public_key(tv.pk)
-        verifier = eddsa.new(key, 'rfc8032')
-        try:
-            verifier.verify(tv.msg, tv.sig)
-        except ValueError:
-            assert not tv.valid
-        else:
-            assert tv.valid
-
-    def runTest(self):
-        for tv in self.tv:
-            self.test_sign(tv)
-            self.test_verify(tv)
-
-
-def get_tests(config={}):
-
-    tests = []
-    tests += list_test_cases(TestExport_Ed25519)
-    tests += list_test_cases(TestExport_Ed448)
-    tests += list_test_cases(TestImport_Ed25519)
-    tests += list_test_cases(TestImport_Ed448)
-    tests += list_test_cases(TestEdDSA)
-    tests += [TestVectorsEdDSAWycheproof()]
-    return tests
-
-
-if __name__ == '__main__':
-    def suite():
-        return unittest.TestSuite(get_tests())
-    unittest.main(defaultTest='suite')
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/line_percent.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/line_percent.py
deleted file mode 100644
index fd23572dedbc1445a7924981a94eba1b2ff2afce..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/line_percent.py
+++ /dev/null
@@ -1,18 +0,0 @@
-"""
-Line Chart with Percent axis
-----------------------------
-This example shows how to format the tick labels of the y-axis of a chart as percentages.
-"""
-# category: line charts
-import altair as alt
-from vega_datasets import data
-
-source = data.jobs.url
-
-alt.Chart(source).mark_line().encode(
-    alt.X('year:O'),
-    alt.Y('perc:Q', axis=alt.Axis(format='%')),
-    color='sex:N'
-).transform_filter(
-    alt.datum.job == 'Welder'
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/utils/server.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/utils/server.py
deleted file mode 100644
index f26a3f25ee124f59692c35fe4b790eda95c14c25..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/utils/server.py
+++ /dev/null
@@ -1,148 +0,0 @@
-"""
-A Simple server used to show altair graphics from a prompt or script.
-
-This is adapted from the mpld3 package; see
-https://github.com/mpld3/mpld3/blob/master/mpld3/_server.py
-"""
-import sys
-import threading
-import webbrowser
-import socket
-from http import server
-from io import BytesIO as IO
-import itertools
-import random
-
-JUPYTER_WARNING = """
-Note: if you're in the Jupyter notebook, Chart.serve() is not the best
-      way to view plots. Consider using Chart.display().
-You must interrupt the kernel to cancel this command.
-"""
-
-
-# Mock server used for testing
-
-
-class MockRequest(object):
-    def makefile(self, *args, **kwargs):
-        return IO(b"GET /")
-
-    def sendall(self, response):
-        pass
-
-
-class MockServer(object):
-    def __init__(self, ip_port, Handler):
-        Handler(MockRequest(), ip_port[0], self)
-
-    def serve_forever(self):
-        pass
-
-    def server_close(self):
-        pass
-
-
-def generate_handler(html, files=None):
-    if files is None:
-        files = {}
-
-    class MyHandler(server.BaseHTTPRequestHandler):
-        def do_GET(self):
-            """Respond to a GET request."""
-            if self.path == "/":
-                self.send_response(200)
-                self.send_header("Content-type", "text/html")
-                self.end_headers()
-                self.wfile.write(html.encode())
-            elif self.path in files:
-                content_type, content = files[self.path]
-                self.send_response(200)
-                self.send_header("Content-type", content_type)
-                self.end_headers()
-                self.wfile.write(content.encode())
-            else:
-                self.send_error(404)
-
-    return MyHandler
-
-
-def find_open_port(ip, port, n=50):
-    """Find an open port near the specified port"""
-    ports = itertools.chain(
-        (port + i for i in range(n)), (port + random.randint(-2 * n, 2 * n))
-    )
-
-    for port in ports:
-        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-        result = s.connect_ex((ip, port))
-        s.close()
-        if result != 0:
-            return port
-    raise ValueError("no open ports found")
-
-
-def serve(
-    html,
-    ip="127.0.0.1",
-    port=8888,
-    n_retries=50,
-    files=None,
-    jupyter_warning=True,
-    open_browser=True,
-    http_server=None,
-):
-    """Start a server serving the given HTML, and (optionally) open a browser
-
-    Parameters
-    ----------
-    html : string
-        HTML to serve
-    ip : string (default = '127.0.0.1')
-        ip address at which the HTML will be served.
-    port : int (default = 8888)
-        the port at which to serve the HTML
-    n_retries : int (default = 50)
-        the number of nearby ports to search if the specified port is in use.
-    files : dictionary (optional)
-        dictionary of extra content to serve
-    jupyter_warning : bool (optional)
-        if True (default), then print a warning if this is used within Jupyter
-    open_browser : bool (optional)
-        if True (default), then open a web browser to the given HTML
-    http_server : class (optional)
-        optionally specify an HTTPServer class to use for showing the
-        figure. The default is Python's basic HTTPServer.
-    """
-    port = find_open_port(ip, port, n_retries)
-    Handler = generate_handler(html, files)
-
-    if http_server is None:
-        srvr = server.HTTPServer((ip, port), Handler)
-    else:
-        srvr = http_server((ip, port), Handler)
-
-    if jupyter_warning:
-        try:
-            __IPYTHON__  # noqa
-        except NameError:
-            pass
-        else:
-            print(JUPYTER_WARNING)
-
-    # Start the server
-    print("Serving to http://{}:{}/    [Ctrl-C to exit]".format(ip, port))
-    sys.stdout.flush()
-
-    if open_browser:
-        # Use a thread to open a web browser pointing to the server
-        def b():
-            return webbrowser.open("http://{}:{}".format(ip, port))
-
-        threading.Thread(target=b).start()
-
-    try:
-        srvr.serve_forever()
-    except (KeyboardInterrupt, SystemExit):
-        print("\nstopping Server...")
-
-    srvr.server_close()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/_core/_eventloop.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/_core/_eventloop.py
deleted file mode 100644
index f027ae50d87afbf5766afc79209479442b096cd1..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/_core/_eventloop.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import math
-import sys
-import threading
-from contextlib import contextmanager
-from importlib import import_module
-from typing import (
-    Any,
-    Callable,
-    Coroutine,
-    Dict,
-    Generator,
-    Optional,
-    Tuple,
-    Type,
-    TypeVar,
-)
-
-import sniffio
-
-# This must be updated when new backends are introduced
-from ._compat import DeprecatedAwaitableFloat
-
-BACKENDS = "asyncio", "trio"
-
-T_Retval = TypeVar("T_Retval")
-threadlocals = threading.local()
-
-
-def run(
-    func: Callable[..., Coroutine[Any, Any, T_Retval]],
-    *args: object,
-    backend: str = "asyncio",
-    backend_options: Optional[Dict[str, Any]] = None,
-) -> T_Retval:
-    """
-    Run the given coroutine function in an asynchronous event loop.
-
-    The current thread must not be already running an event loop.
-
-    :param func: a coroutine function
-    :param args: positional arguments to ``func``
-    :param backend: name of the asynchronous event loop implementation – currently either
-        ``asyncio`` or ``trio``
-    :param backend_options: keyword arguments to call the backend ``run()`` implementation with
-        (documented :ref:`here <backend options>`)
-    :return: the return value of the coroutine function
-    :raises RuntimeError: if an asynchronous event loop is already running in this thread
-    :raises LookupError: if the named backend is not found
-
-    """
-    try:
-        asynclib_name = sniffio.current_async_library()
-    except sniffio.AsyncLibraryNotFoundError:
-        pass
-    else:
-        raise RuntimeError(f"Already running {asynclib_name} in this thread")
-
-    try:
-        asynclib = import_module(f"..._backends._{backend}", package=__name__)
-    except ImportError as exc:
-        raise LookupError(f"No such backend: {backend}") from exc
-
-    token = None
-    if sniffio.current_async_library_cvar.get(None) is None:
-        # Since we're in control of the event loop, we can cache the name of the async library
-        token = sniffio.current_async_library_cvar.set(backend)
-
-    try:
-        backend_options = backend_options or {}
-        return asynclib.run(func, *args, **backend_options)
-    finally:
-        if token:
-            sniffio.current_async_library_cvar.reset(token)
-
-
-async def sleep(delay: float) -> None:
-    """
-    Pause the current task for the specified duration.
-
-    :param delay: the duration, in seconds
-
-    """
-    return await get_asynclib().sleep(delay)
-
-
-async def sleep_forever() -> None:
-    """
-    Pause the current task until it's cancelled.
-
-    This is a shortcut for ``sleep(math.inf)``.
-
-    .. versionadded:: 3.1
-
-    """
-    await sleep(math.inf)
-
-
-async def sleep_until(deadline: float) -> None:
-    """
-    Pause the current task until the given time.
-
-    :param deadline: the absolute time to wake up at (according to the internal monotonic clock of
-        the event loop)
-
-    .. versionadded:: 3.1
-
-    """
-    now = current_time()
-    await sleep(max(deadline - now, 0))
-
-
-def current_time() -> DeprecatedAwaitableFloat:
-    """
-    Return the current value of the event loop's internal clock.
-
-    :return: the clock value (seconds)
-
-    """
-    return DeprecatedAwaitableFloat(get_asynclib().current_time(), current_time)
-
-
-def get_all_backends() -> Tuple[str, ...]:
-    """Return a tuple of the names of all built-in backends."""
-    return BACKENDS
-
-
-def get_cancelled_exc_class() -> Type[BaseException]:
-    """Return the current async library's cancellation exception class."""
-    return get_asynclib().CancelledError
-
-
-#
-# Private API
-#
-
-
-@contextmanager
-def claim_worker_thread(backend: str) -> Generator[Any, None, None]:
-    module = sys.modules["anyio._backends._" + backend]
-    threadlocals.current_async_module = module
-    try:
-        yield
-    finally:
-        del threadlocals.current_async_module
-
-
-def get_asynclib(asynclib_name: Optional[str] = None) -> Any:
-    if asynclib_name is None:
-        asynclib_name = sniffio.current_async_library()
-
-    modulename = "anyio._backends._" + asynclib_name
-    try:
-        return sys.modules[modulename]
-    except KeyError:
-        return import_module(modulename)
diff --git a/spaces/aryadytm/photo-low-light-enhance/app.py b/spaces/aryadytm/photo-low-light-enhance/app.py
deleted file mode 100644
index 32e42590ed0c13e32d5f9a118dc492f56e95d74e..0000000000000000000000000000000000000000
--- a/spaces/aryadytm/photo-low-light-enhance/app.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Based on https://huggingface.co/spaces/keras-io/Enhance_Low_Light_Image
-import streamlit as st
-import os
-from datetime import datetime
-from PIL import Image, ImageOps
-from io import BytesIO
-
-from src.st_style import apply_prod_style
-
-# apply_prod_style(st)  # NOTE: Uncomment this for production!
-
-
-def image_download_button(pil_image, filename: str, fmt: str, label="Download"):
-    if fmt not in ["jpg", "png"]:
-        raise Exception(f"Unknown image format (Available: {fmt} - case sensitive)")
-    
-    pil_format = "JPEG" if fmt == "jpg" else "PNG"
-    file_format = "jpg" if fmt == "jpg" else "png"
-    mime = "image/jpeg" if fmt == "jpg" else "image/png"
-    
-    buf = BytesIO()
-    pil_image.save(buf, format=pil_format)
-    
-    return st.download_button(
-        label=label,
-        data=buf.getvalue(),
-        file_name=f'{filename}.{file_format}',
-        mime=mime,
-    )
-
-
-st.title("Enhance Low Light Photo with AI")
-st.image(Image.open("assets/demo.jpg"))
-st.write("Image Source: https://www.deviantart.com/dimelotu/art/Emily-s-Room-Lights-Off-561386750")
-st.write(
-    """
-    You have a low light photo, and you want to make it clearer and brighter?
-    Let's enhance them using AI. **Upload the photo then click "Enhance".**
-    """
-)
-
-uploaded_file = st.file_uploader(
-    label="Upload your photo here", 
-    accept_multiple_files=False, 
-    type=["png", "jpg", "jpeg"],
-)
-
-if uploaded_file is not None:
-
-    with st.expander("Original photo", expanded=True):
-        if uploaded_file is not None:
-            st.image(uploaded_file)
-        else:
-            st.warning("You haven't uploaded any photo yet")
-
-    if st.button("Enhance") and uploaded_file is not None:
-        img_input = Image.open(uploaded_file).convert("RGB")
-        
-        with st.spinner("AI is doing the magic"):
-            img_output = ImageOps.autocontrast(img_input, 3)
-        
-        with st.expander("Success!", expanded=True):
-            st.image(img_output)
-            uploaded_name = os.path.splitext(uploaded_file.name)[0]
-            image_download_button(
-                pil_image=img_output,
-                filename=uploaded_name,
-                fmt="jpg",
-                label="Download Image",
-            )
\ No newline at end of file
diff --git "a/spaces/ashhadahsan/summarizer-space/pages/2_\360\237\214\215_trainui.py" "b/spaces/ashhadahsan/summarizer-space/pages/2_\360\237\214\215_trainui.py"
deleted file mode 100644
index c5e4918851353422873ef7f5c8590fd6defa5c24..0000000000000000000000000000000000000000
--- "a/spaces/ashhadahsan/summarizer-space/pages/2_\360\237\214\215_trainui.py"
+++ /dev/null
@@ -1,34 +0,0 @@
-import streamlit as st
-import pandas as pd
-import time
-from streamlit_extras.streaming_write import write
-
-
-def stream_example():
-    for word in data.split():
-        yield word + " "
-        time.sleep(0.5)
-
-
-st.set_page_config(layout="wide", page_title="Amazon Review | Trainer")
-st.title("Amazon Review  Trainer-Just a mock UI ")
-
-
-data_upload = st.file_uploader(label="Train File", type=["xlsx", "csv", "xls"])
-
-col1, col2 = st.columns([1, 1])
-with col1:
-    theme = st.checkbox(label="Theme")
-with col2:
-    subtheme = st.checkbox(label="Subtheme")
-
-
-if data_upload is not None:
-    if data_upload.name.split(".")[-1] in ["xls", "xlsx"]:
-        df = pd.read_excel(io=data_upload, engine="openpyxl")
-    if data_upload.name.split(".")[-1] in ["csv"]:
-        df = pd.read_csv(filepath_or_buffer=data_upload)
-    df.columns = [x.lower() for x in df.columns.tolist()]
-    data = ",".join(df.columns.values.tolist())
-    # st.write(data)
-    write(stream_example)
diff --git a/spaces/asigalov61/Allegro-Music-Transformer/midi_synthesizer.py b/spaces/asigalov61/Allegro-Music-Transformer/midi_synthesizer.py
deleted file mode 100644
index d152f356249d172d95fb764b08d8b773baeb39e1..0000000000000000000000000000000000000000
--- a/spaces/asigalov61/Allegro-Music-Transformer/midi_synthesizer.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import fluidsynth
-import numpy as np
-
-
-def synthesis(midi_opus, soundfont_path, sample_rate=44100):
-    ticks_per_beat = midi_opus[0]
-    event_list = []
-    for track_idx, track in enumerate(midi_opus[1:]):
-        abs_t = 0
-        for event in track:
-            abs_t += event[1]
-            event_new = [*event]
-            event_new[1] = abs_t
-            event_list.append(event_new)
-    event_list = sorted(event_list, key=lambda e: e[1])
-
-    tempo = int((60 / 120) * 10 ** 6)  # default 120 bpm
-    ss = np.empty((0, 2), dtype=np.int16)
-    fl = fluidsynth.Synth(samplerate=float(sample_rate))
-    sfid = fl.sfload(soundfont_path)
-    last_t = 0
-    for c in range(16):
-        fl.program_select(c, sfid, 128 if c == 9 else 0, 0)
-    for event in event_list:
-        name = event[0]
-        sample_len = int(((event[1] / ticks_per_beat) * tempo / (10 ** 6)) * sample_rate)
-        sample_len -= int(((last_t / ticks_per_beat) * tempo / (10 ** 6)) * sample_rate)
-        last_t = event[1]
-        if sample_len > 0:
-            sample = fl.get_samples(sample_len).reshape(sample_len, 2)
-            ss = np.concatenate([ss, sample])
-        if name == "set_tempo":
-            tempo = event[2]
-        elif name == "patch_change":
-            c, p = event[2:4]
-            fl.program_select(c, sfid, 128 if c == 9 else 0, p)
-        elif name == "control_change":
-            c, cc, v = event[2:5]
-            fl.cc(c, cc, v)
-        elif name == "note_on" and event[3] > 0:
-            c, p, v = event[2:5]
-            fl.noteon(c, p, v)
-        elif name == "note_off" or (name == "note_on" and event[3] == 0):
-            c, p = event[2:4]
-            fl.noteoff(c, p)
-
-    fl.delete()
-    if ss.shape[0] > 0:
-        max_val = np.abs(ss).max()
-        if max_val != 0:
-            ss = (ss / max_val) * np.iinfo(np.int16).max
-    ss = ss.astype(np.int16)
-    return ss
\ No newline at end of file
diff --git a/spaces/at2507/SM_NLP_RecoSys/app.py b/spaces/at2507/SM_NLP_RecoSys/app.py
deleted file mode 100644
index 5031b9fef4239965e692ee17f4b93960d7931395..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/app.py
+++ /dev/null
@@ -1,116 +0,0 @@
-from sentence_transformers import SentenceTransformer
-model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
-
-import pandas as pd
-import numpy as np
-import pickle
-import nltk
-from nltk.tokenize import sent_tokenize
-
-from tqdm import tqdm
-tqdm.pandas()
-
-nltk.download('punkt')
-
-from numpy import dot
-from numpy.linalg import norm
-
-import json
-import ast
-import requests
-
-
-
-import gradio as gr
-from datetime import datetime
-import time 
-import dataframe_image as dfi
-
-
-print("Packages loaded!")
-
-# write out functions 
-
-def load_pickle():
-  master_exploded = pickle.load(open("./Data/master_exploded_current.pkl", 'rb'))
-  print("Exploded DF Shape:", master_exploded.shape)
-  print("Successfully Loaded!")
-  return master_exploded
-
-
-
-
-def sentence_embedding_generator(query): 
-#   query = input('What kind of mentor are you looking for?: ')
-  print(f'You entered {query}')
-  print("Loading Model...")
-  model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
-  print("all-mpnet-base-v2 Model loaded!")
-  embeddings = model.encode(query)
-  return embeddings, query
-
-
-def cosine_similarity_generator(master_exploded, embeddings, query, filename = time.strftime("%Y%m%d-%H%M%S")): 
-#   current_datetime = datetime.now()
-  print("Current datetime: ", time.strftime("%Y%m%d-%H%M%S"))
-  master_exploded['query'] = query
-  master_exploded['cos_sim'] = master_exploded['raw_embedding'].progress_apply(lambda x: (np.dot(embeddings, x) /
-                                                         (norm(embeddings)*norm(x))))
-  master_exploded_top = master_exploded[master_exploded['cos_sim']> 0.6]
-  print("The number of results with cosine similarity > 0.6 are: ", len(master_exploded[master_exploded['cos_sim']> 0.6]))
-  
-  top_k = master_exploded.sort_values(by=['cos_sim'], ascending=False).head(10)
-  print(" The top k=10 results have a min cosine similarity of: ", master_exploded.sort_values(by=['cos_sim'], ascending=False).head(10)['cos_sim'].min())
-  # print(master_exploded_top_k)
-  cosine_sum_by_name = master_exploded_top.groupby(["id", "name", "tokenized_sentences" ]).agg({"cos_sim": ["sum"]}).reset_index()
-  print("Taking sum of cosine similarities above 0.6 threshold...")
-  cosine_sum_by_name.columns = cosine_sum_by_name.columns.map('_'.join)
-  
-  ranked_mentors = cosine_sum_by_name.reset_index().sort_values(by ="cos_sim_sum", ascending =False) 
-  cosine_sum_by_name = cosine_sum_by_name.rename(columns={"id_": "MentorID", "name_": "Name", "tokenized_sentences_": "Sentences"}, errors="raise")
-  # path = "./Ranked_Results_Gradio/"
-  # ranked_mentors_filename = path+'ranked_mentors_'+str(filename)+'.csv'
-  # cos_sum_filename = path+'cos_sum_'+str(filename)+'.csv'
-  # top_10_filename = path+'top_10_'+str(filename)+'.csv'
-  # above_threshold_filename = path+"above_0.6_threshold_"+str(filename)+".csv"
-
-  # save 3 files: Ranked mentors, top 10 matches baed on cosine similarity sum, and then all of the results per run. 
-  # ranked_mentors.head(10).to_csv(ranked_mentors_filename)
-  # cosine_sum_by_name.sort_values(by ="cos_sim_sum", ascending =False).head(10).to_csv(top_10_filename)
-  # cosine_sum_by_name.to_csv(cos_sum_filename)
-  # master_exploded_top.sort_values(by ="cos_sim", ascending =False).to_csv(above_threshold_filename)
-  return master_exploded_top, top_k, cosine_sum_by_name
-
-def dataframe_output(cosine_sum_by_name): 
-  # return master_exploded_top_k
-  json_df = cosine_sum_by_name.to_json(orient="columns")
-  return json_df
-
-
-def generate_results(input): 
-  master_exploded = load_pickle()
-  embeddings, query = sentence_embedding_generator(str(input))
-  ranked_mentors, top_10, cosine_sum_by_name = cosine_similarity_generator(master_exploded,  
-                                                                            embeddings, 
-                                                                            query, 
-                                                                            time.strftime("%Y%m%d-%H%M%S"))
-  print(cosine_sum_by_name.columns)
-  df_output = pd.read_json(dataframe_output(cosine_sum_by_name))
-  print(df_output)
-  # df_output = dataframe_output(cosine_sum_by_name)
-  top_10 = top_10[['name','id', 'tokenized_sentences', 'cos_sim' ]]
-  sentence_output = pd.read_json(dataframe_output(top_10))
-  print("JSON created...")
-  subset = df_output.head(10)  # Select the first 10 rows
-
-  return subset, sentence_output
-
-iface = gr.Interface(
-    fn=generate_results,
-    inputs=gr.inputs.Textbox(label="What kind of mentor are you looking for?"),
-    outputs=[gr.outputs.Dataframe(type="pandas"), gr.outputs.Dataframe(type="pandas")],
-    title="SharpestMinds Mentor Recommender Semantic Search App",
-    description="Converts a string query into an embedding, and then compares the aggregate cosine similarity by mentor.",
-)
-
-iface.launch(auth=("admin", "russell2023"))
diff --git a/spaces/augmentedimaginationhackathon/paperstocode/retrieval/single_prompt.py b/spaces/augmentedimaginationhackathon/paperstocode/retrieval/single_prompt.py
deleted file mode 100644
index 18140d120790f4f8e6c7372bc05971e477c7bd68..0000000000000000000000000000000000000000
--- a/spaces/augmentedimaginationhackathon/paperstocode/retrieval/single_prompt.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# %%
-import openai
-import os
-from langchain import OpenAI
-
-from langchain.chains import LLMChain, MapReduceChain
-from langchain import PromptTemplate
-from langchain import *
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-
-# %%
-prompt_template = """
-
-Given the LaTeX source of a machine learning paper from Arxiv and some existing code, please generate a Python script that implements and replicates the results from the paper EXACTLY WITH NO ERRORS. Start from the provided code and modify it as necessary to incorporate the new ideas from the paper. The script should be organized in a logical and coherent way, with sections and sub-sections that reflect the paper's organization. Assume that the notebook will be used by someone who is familiar with the general field of AI, but may not be familiar with the specific topic of the paper. Please include code, explanations, and visualizations where appropriate. The script should be written in Python using the PyTorch machine learning framework and should be easy to run without any additional setup. Please do not use any external libraries or packages that are not already included in the provided code. Please do not use any external datasets or pretrained models.
-
-######################
-LaTeX Source for paper
-######################
-    {paper}
-
-######################
-Previous Code
-######################
-
-    {code}
-
-######################
-Your Code Here
-######################
-"""
-
-def generate_code(paper_str: str, model_name: str = "code-davinci-002", code="import torch"):
-
-    llm = OpenAI(model_name=model_name)
-    code = "import torch"
-
-    PROMPT = PromptTemplate(
-        template=prompt_template, input_variables=["paper", "code"]
-    )
-
-    chain = LLMChain(llm=llm, prompt=PROMPT)
-    # chain = MapReduceChain.from_params(llm=llm, prompt=PROMPT, text_splitter=RecursiveCharacterTextSplitter())
-
-    return chain.apply([{"paper": paper_str, "code": code}])[0]['text']
diff --git a/spaces/awacke1/AR-VR-IOT-Demo/README.md b/spaces/awacke1/AR-VR-IOT-Demo/README.md
deleted file mode 100644
index 754f2a4abc5059310d3522f9c74aa8c225e1d7dd..0000000000000000000000000000000000000000
--- a/spaces/awacke1/AR-VR-IOT-Demo/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: AR VR 3D Scene in Web Using Aframe.js and HTML5
-emoji: 🥽📱
-colorFrom: indigo
-colorTo: gray
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/awacke1/Bloom.Generative.Writer/flask_app.py b/spaces/awacke1/Bloom.Generative.Writer/flask_app.py
deleted file mode 100644
index 5adf8b384d4860147a3be09d1eeaa29bacaad872..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Bloom.Generative.Writer/flask_app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from flask import Flask, request
-from generators.title_to_abstract import title_to_abstract_generator
-from generators.topic_to_abstract import topic_to_abstract_generator
-app = Flask(__name__)
-max_length_char = 360
-
-
-@app.route('/')
-def rewriter():
-    data = request.get_json(silent=True)
-    if data is None:
-        return 'No data received'
-    else:
-        if data.get('title') is None and data.get('topic') is not None:
-            return generate(data, 'topic')
-        elif data.get('title') is not None:
-            return generate(data, 'title')
-        else:
-            return 'No data received'
-
-
-def generate(data, value):
-    obj = abstract_generator_obj(value)
-    result = obj(template_generator(value, data[value]))
-    if len(result['result']) > 40:
-        return {"result": result['result']}
-    else:
-        result_re = obj(template_generator(value, data[value]))
-        while len(result_re['result']) < 50:
-            print(f"getting results again, {result_re}")
-            result_re = obj(template_generator(value, data[value]))
-        if len(result_re['result']) > 40:
-            return {"result": result_re['result']}
-
-
-def abstract_generator_obj(value):
-    if value == 'title':
-        return title_to_abstract_generator
-    else:
-        return topic_to_abstract_generator
-
-
-def template_generator(type, value):
-    if type == 'title':
-        return "title: " + value + "\n" + 'abstract: '
-    else:
-        keyword_str = "\n"
-        for k in value:
-            keyword_str = keyword_str + k + "\n"
-        return "topic: " + keyword_str + "\n" + 'abstract: '
-
-
-if __name__ == '__main__':
-    app.run()
diff --git a/spaces/awacke1/GithubAction02/README.md b/spaces/awacke1/GithubAction02/README.md
deleted file mode 100644
index 1531120296aef7623b476107113e782173b0031e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GithubAction02/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: 📉Graph Git GPG Git Actions📈
-emoji: 📉🕸️📈
-colorFrom: pink
-colorTo: red
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-# Git-GPG-Git-Actions-01-GraphViz
-Git GPG, Actions, GraphViz Demonstration
diff --git a/spaces/awacke1/Memory-Streamlit/README.md b/spaces/awacke1/Memory-Streamlit/README.md
deleted file mode 100644
index 07faa71fe5fedccbdfef8811bb937e42569bae87..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Memory-Streamlit/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 🧠NLP Memory Streamlit Cache UI🍪
-emoji: 🧠🍪
-colorFrom: green
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/awacke1/RLHF.Cognitive.Episodic.Semantic.Memory/README.md b/spaces/awacke1/RLHF.Cognitive.Episodic.Semantic.Memory/README.md
deleted file mode 100644
index ce27ba2c9e580de09f14c5370ac5dac2db80b87f..0000000000000000000000000000000000000000
--- a/spaces/awacke1/RLHF.Cognitive.Episodic.Semantic.Memory/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: RLHF.Cognitive.Episodic.Semantic.Memory
-emoji: 👀👀👀
-colorFrom: pink
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-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/awacke1/google-flan-t5-xl/README.md b/spaces/awacke1/google-flan-t5-xl/README.md
deleted file mode 100644
index afe846bd2d7905e5b89952d231354587582e8002..0000000000000000000000000000000000000000
--- a/spaces/awacke1/google-flan-t5-xl/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Google Flan T5 Xl
-emoji: 👀
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/badayvedat/AudioSep/losses.py b/spaces/badayvedat/AudioSep/losses.py
deleted file mode 100644
index 0bf599fa6ecb91c086394b06c81ce3dee927a012..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/AudioSep/losses.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import torch
-
-
-def l1(output, target):
-    return torch.mean(torch.abs(output - target))
-
-
-def l1_wav(output_dict, target_dict):
-	return l1(output_dict['segment'], target_dict['segment'])
-
-
-def get_loss_function(loss_type):
-    if loss_type == "l1_wav":
-        return l1_wav
-
-    else:
-        raise NotImplementedError("Error!")
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/UniformsLib.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/UniformsLib.js
deleted file mode 100644
index 47b859bc00138c3893bf1c4b2b21316181b6263e..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/UniformsLib.js
+++ /dev/null
@@ -1,200 +0,0 @@
-import { Color } from '../../math/Color.js';
-import { Vector2 } from '../../math/Vector2.js';
-import { Matrix3 } from '../../math/Matrix3.js';
-
-/**
- * Uniforms library for shared webgl shaders
- */
-
-var UniformsLib = {
-
-	common: {
-
-		diffuse: { value: new Color( 0xeeeeee ) },
-		opacity: { value: 1.0 },
-
-		map: { value: null },
-		uvTransform: { value: new Matrix3() },
-
-		alphaMap: { value: null },
-
-	},
-
-	specularmap: {
-
-		specularMap: { value: null },
-
-	},
-
-	envmap: {
-
-		envMap: { value: null },
-		flipEnvMap: { value: - 1 },
-		reflectivity: { value: 1.0 },
-		refractionRatio: { value: 0.98 },
-		maxMipLevel: { value: 0 }
-
-	},
-
-	aomap: {
-
-		aoMap: { value: null },
-		aoMapIntensity: { value: 1 }
-
-	},
-
-	lightmap: {
-
-		lightMap: { value: null },
-		lightMapIntensity: { value: 1 }
-
-	},
-
-	emissivemap: {
-
-		emissiveMap: { value: null }
-
-	},
-
-	bumpmap: {
-
-		bumpMap: { value: null },
-		bumpScale: { value: 1 }
-
-	},
-
-	normalmap: {
-
-		normalMap: { value: null },
-		normalScale: { value: new Vector2( 1, 1 ) }
-
-	},
-
-	displacementmap: {
-
-		displacementMap: { value: null },
-		displacementScale: { value: 1 },
-		displacementBias: { value: 0 }
-
-	},
-
-	roughnessmap: {
-
-		roughnessMap: { value: null }
-
-	},
-
-	metalnessmap: {
-
-		metalnessMap: { value: null }
-
-	},
-
-	gradientmap: {
-
-		gradientMap: { value: null }
-
-	},
-
-	fog: {
-
-		fogDensity: { value: 0.00025 },
-		fogNear: { value: 1 },
-		fogFar: { value: 2000 },
-		fogColor: { value: new Color( 0xffffff ) }
-
-	},
-
-	lights: {
-
-		ambientLightColor: { value: [] },
-
-		directionalLights: { value: [], properties: {
-			direction: {},
-			color: {},
-
-			shadow: {},
-			shadowBias: {},
-			shadowRadius: {},
-			shadowMapSize: {}
-		} },
-
-		directionalShadowMap: { value: [] },
-		directionalShadowMatrix: { value: [] },
-
-		spotLights: { value: [], properties: {
-			color: {},
-			position: {},
-			direction: {},
-			distance: {},
-			coneCos: {},
-			penumbraCos: {},
-			decay: {},
-
-			shadow: {},
-			shadowBias: {},
-			shadowRadius: {},
-			shadowMapSize: {}
-		} },
-
-		spotShadowMap: { value: [] },
-		spotShadowMatrix: { value: [] },
-
-		pointLights: { value: [], properties: {
-			color: {},
-			position: {},
-			decay: {},
-			distance: {},
-
-			shadow: {},
-			shadowBias: {},
-			shadowRadius: {},
-			shadowMapSize: {},
-			shadowCameraNear: {},
-			shadowCameraFar: {}
-		} },
-
-		pointShadowMap: { value: [] },
-		pointShadowMatrix: { value: [] },
-
-		hemisphereLights: { value: [], properties: {
-			direction: {},
-			skyColor: {},
-			groundColor: {}
-		} },
-
-		// TODO (abelnation): RectAreaLight BRDF data needs to be moved from example to main src
-		rectAreaLights: { value: [], properties: {
-			color: {},
-			position: {},
-			width: {},
-			height: {}
-		} }
-
-	},
-
-	points: {
-
-		diffuse: { value: new Color( 0xeeeeee ) },
-		opacity: { value: 1.0 },
-		size: { value: 1.0 },
-		scale: { value: 1.0 },
-		map: { value: null },
-		uvTransform: { value: new Matrix3() }
-
-	},
-
-	sprite: {
-
-		diffuse: { value: new Color( 0xeeeeee ) },
-		opacity: { value: 1.0 },
-		center: { value: new Vector2( 0.5, 0.5 ) },
-		rotation: { value: 0.0 },
-		map: { value: null },
-		uvTransform: { value: new Matrix3() }
-
-	}
-
-};
-
-export { UniformsLib };
diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327011453.py b/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327011453.py
deleted file mode 100644
index 9e81a34b4ff93769f5ef21a39d841d559f7c41e2..0000000000000000000000000000000000000000
--- a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327011453.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import os
-#os.system("pip install gfpgan")
-
-#os.system("pip freeze")
-#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")
-import random
-import gradio as gr
-from PIL import Image
-import torch
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg')
-
-
-import cv2
-import glob
-import numpy as np
-from basicsr.utils import imwrite
-from gfpgan import GFPGANer
-
-import warnings
-warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
-              'If you really want to use it, please modify the corresponding codes.')
-bg_upsampler = None
-
-
- 
-# set up GFPGAN restorer
-restorer = GFPGANer(
-    model_path='experiments/pretrained_models/GFPGANv1.3.pth',
-    upscale=2,
-    arch='clean',
-    channel_multiplier=2,
-    bg_upsampler=bg_upsampler)
-
-
-def inference(img):
-    input_img = cv2.imread(img, cv2.IMREAD_COLOR)
-    cropped_faces, restored_faces, restored_img = restorer.enhance(
-        input_img, has_aligned=False, only_center_face=False, paste_back=True)
-    
-    #return Image.fromarray(restored_faces[0][:,:,::-1])
-    return Image.fromarray(restored_faces[0][:,:,::-1])
-
-title = "GFP-GAN"
-description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2101.04061' target='_blank'>Towards Real-World Blind Face Restoration with Generative Facial Prior</a> | <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>"
-gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="filepath", label="Input")], 
-    gr.outputs.Image(type="pil", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=[
-    ['lincoln.jpg'],
-    ['einstein.png'],
-    ['edison.jpg'],
-    ['Henry.jpg'],
-    ['Frida.jpg']
-    ]
-    ).launch(enable_queue=True,cache_examples=True)
-    
-    
diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327011500.py b/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327011500.py
deleted file mode 100644
index f5b1195110153fc1057059ac04749712a932992f..0000000000000000000000000000000000000000
--- a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327011500.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import os
-#os.system("pip install gfpgan")
-
-#os.system("pip freeze")
-#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")
-import random
-import gradio as gr
-from PIL import Image
-import torch
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg')
-
-
-import cv2
-import glob
-import numpy as np
-from basicsr.utils import imwrite
-from gfpgan import GFPGANer
-
-import warnings
-warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
-              'If you really want to use it, please modify the corresponding codes.')
-bg_upsampler = None
-
-
- 
-# set up GFPGAN restorer
-restorer = GFPGANer(
-    model_path='experiments/pretrained_models/GFPGANv1.3.pth',
-    upscale=2,
-    arch='clean',
-    channel_multiplier=2,
-    bg_upsampler=bg_upsampler)
-
-
-def inference(img):
-    input_img = cv2.imread(img, cv2.IMREAD_COLOR)
-    cropped_faces, restored_faces, restored_img = restorer.enhance(
-        input_img, has_aligned=False, only_center_face=False, paste_back=True)
-    
-    #return Image.fromarray(restored_faces[0][:,:,::-1])
-    return Image.fromarray(restored_img)
-
-title = "GFP-GAN"
-description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2101.04061' target='_blank'>Towards Real-World Blind Face Restoration with Generative Facial Prior</a> | <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>"
-gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="filepath", label="Input")], 
-    gr.outputs.Image(type="pil", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=[
-    ['lincoln.jpg'],
-    ['einstein.png'],
-    ['edison.jpg'],
-    ['Henry.jpg'],
-    ['Frida.jpg']
-    ]
-    ).launch(enable_queue=True,cache_examples=True)
-    
-    
diff --git a/spaces/bhaskartripathi/Text2Question/app.py b/spaces/bhaskartripathi/Text2Question/app.py
deleted file mode 100644
index 949b2830cd912c1460614e54d6d326dc8a7e378c..0000000000000000000000000000000000000000
--- a/spaces/bhaskartripathi/Text2Question/app.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import gradio as gr
-import requests
-import os
-import numpy as np
-import pandas as pd
-import json
-import socket
-import huggingface_hub
-from huggingface_hub import Repository
-# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
-from questiongenerator import QuestionGenerator
-import csv
-from urllib.request import urlopen
-import re as r
-
-qg = QuestionGenerator()
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-DATASET_NAME = "Text2Question"
-DATASET_REPO_URL = f"https://huggingface.co/spaces/bhaskartripathi/{DATASET_NAME}"
-DATA_FILENAME = "que_gen_logs.csv"
-DATA_FILE = os.path.join("que_gen_logs", DATA_FILENAME)
-DATASET_REPO_ID = "bhaskartripathi/Text2Question"
-print("is none?", HF_TOKEN is None)
-article_value = """Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments."""
-# REPOSITORY_DIR = "data"
-# LOCAL_DIR = 'data_local'
-# os.makedirs(LOCAL_DIR,exist_ok=True)
-
-try:
-    hf_hub_download(
-        repo_id=DATASET_REPO_ID,
-        filename=DATA_FILENAME,
-        cache_dir=DATA_DIRNAME,
-        force_filename=DATA_FILENAME
-    )
-    
-except:
-    print("file not found")
-
-repo = Repository(
-    local_dir="que_gen_logs", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
-)
-
-
-def getIP():
-    ip_address = ''
-    try:
-    	d = str(urlopen('http://checkip.dyndns.com/')
-    			.read())
-    
-    	return r.compile(r'Address: (\d+\.\d+\.\d+\.\d+)').search(d).group(1)
-    except Exception as e:
-        print("Error while getting IP address -->",e)
-        return ip_address
-
-def get_location(ip_addr):
-    location = {}
-    try:
-        ip=ip_addr
-    
-        req_data={
-            "ip":ip,
-            "token":"pkml123"
-        }
-        url = "https://bhaskartripathi.com/get-ip-location"
-    
-        # req_data=json.dumps(req_data)
-        # print("req_data",req_data)
-        headers = {'Content-Type': 'application/json'}
-    
-        response = requests.request("POST", url, headers=headers, data=json.dumps(req_data))
-        response = response.json()
-        print("response======>>",response)
-        return response
-    except Exception as e:
-        print("Error while getting location -->",e)
-        return location
-    
-def generate_questions(article,num_que):
-    result = ''
-    if article.strip():
-        if num_que == None or num_que == '':
-            num_que = 3
-        else:
-            num_que = num_que
-        generated_questions_list = qg.generate(article, num_questions=int(num_que))
-        summarized_data = {
-            "generated_questions" : generated_questions_list
-        }
-        generated_questions = summarized_data.get("generated_questions",'')
-            
-        for q in generated_questions:
-            print(q)
-            result = result + q + '\n'
-        #save_data_and_sendmail(article,generated_questions,num_que)
-        print("sending result***!!!!!!", result)
-        return result
-    else:
-        raise gr.Error("Please enter text in inputbox!!!!")
-   
-"""
-Save generated details
-"""
-def save_data_and_sendmail(article,generated_questions,num_que):
-    try:
-        ip_address= getIP()
-        print(ip_address)
-        location = get_location(ip_address)
-        print(location)
-        add_csv = [article, generated_questions, num_que, ip_address,location]
-        print("data^^^^^",add_csv)
-        with open(DATA_FILE, "a") as f:
-            writer = csv.writer(f)
-            # write the data
-            writer.writerow(add_csv)
-            commit_url = repo.push_to_hub()
-            print("commit data   :",commit_url)
-            
-        url = 'https://bhaskartripathi.com/HF_space_que_gen'
-        
-        myobj = {'article': article,'total_que': num_que,'gen_que':generated_questions,'ip_addr':ip_address,'loc':location}
-        x = requests.post(url, json = myobj) 
-        print("myobj^^^^^",myobj)
-
-    except Exception as e:
-        return "Error while sending mail" + str(e)
-        
-    return "Successfully save data"
-
-## design 1
-inputs=gr.Textbox(value=article_value, lines=5, label="Input Text/Article",elem_id="inp_div")
-total_que = gr.Textbox(value=3, label="Enter the number of questions to generate",elem_id="inp_div")
-outputs=gr.Textbox(label="Generated Questions",lines=6,elem_id="inp_div")
-
-demo = gr.Interface(
-    generate_questions,
-    [inputs,total_que],
-    outputs,
-    title="Text2Question Generation with Text-to-Text-Transfer-Transformer",
-    css=".gradio-container {background-color: lightgray} #inp_div {background-color: #7FB3D5;}",
-    article="""<p style='text-align: center;'><a href="https://github.com/bhaskatripathi/QuestAnsGenerator/issues" target="_blank">Raise Issues</a></p>
-                                        <p style='text-align: center;'>MultiCloud4U Sandbox Env <a href="https://www.multicloud4u.com" target="_blank">Multicloud4U Technologies Pvt. Ltd.</a></p>"""
-    
-)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/deep/models/mudeep.py b/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/deep/models/mudeep.py
deleted file mode 100644
index ddbca675b69fcf38523d8687d8c7b279ededd8d1..0000000000000000000000000000000000000000
--- a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/deep/models/mudeep.py
+++ /dev/null
@@ -1,206 +0,0 @@
-from __future__ import division, absolute_import
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-__all__ = ['MuDeep']
-
-
-class ConvBlock(nn.Module):
-    """Basic convolutional block.
-    
-    convolution + batch normalization + relu.
-
-    Args:
-        in_c (int): number of input channels.
-        out_c (int): number of output channels.
-        k (int or tuple): kernel size.
-        s (int or tuple): stride.
-        p (int or tuple): padding.
-    """
-
-    def __init__(self, in_c, out_c, k, s, p):
-        super(ConvBlock, self).__init__()
-        self.conv = nn.Conv2d(in_c, out_c, k, stride=s, padding=p)
-        self.bn = nn.BatchNorm2d(out_c)
-
-    def forward(self, x):
-        return F.relu(self.bn(self.conv(x)))
-
-
-class ConvLayers(nn.Module):
-    """Preprocessing layers."""
-
-    def __init__(self):
-        super(ConvLayers, self).__init__()
-        self.conv1 = ConvBlock(3, 48, k=3, s=1, p=1)
-        self.conv2 = ConvBlock(48, 96, k=3, s=1, p=1)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.conv2(x)
-        x = self.maxpool(x)
-        return x
-
-
-class MultiScaleA(nn.Module):
-    """Multi-scale stream layer A (Sec.3.1)"""
-
-    def __init__(self):
-        super(MultiScaleA, self).__init__()
-        self.stream1 = nn.Sequential(
-            ConvBlock(96, 96, k=1, s=1, p=0),
-            ConvBlock(96, 24, k=3, s=1, p=1),
-        )
-        self.stream2 = nn.Sequential(
-            nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
-            ConvBlock(96, 24, k=1, s=1, p=0),
-        )
-        self.stream3 = ConvBlock(96, 24, k=1, s=1, p=0)
-        self.stream4 = nn.Sequential(
-            ConvBlock(96, 16, k=1, s=1, p=0),
-            ConvBlock(16, 24, k=3, s=1, p=1),
-            ConvBlock(24, 24, k=3, s=1, p=1),
-        )
-
-    def forward(self, x):
-        s1 = self.stream1(x)
-        s2 = self.stream2(x)
-        s3 = self.stream3(x)
-        s4 = self.stream4(x)
-        y = torch.cat([s1, s2, s3, s4], dim=1)
-        return y
-
-
-class Reduction(nn.Module):
-    """Reduction layer (Sec.3.1)"""
-
-    def __init__(self):
-        super(Reduction, self).__init__()
-        self.stream1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.stream2 = ConvBlock(96, 96, k=3, s=2, p=1)
-        self.stream3 = nn.Sequential(
-            ConvBlock(96, 48, k=1, s=1, p=0),
-            ConvBlock(48, 56, k=3, s=1, p=1),
-            ConvBlock(56, 64, k=3, s=2, p=1),
-        )
-
-    def forward(self, x):
-        s1 = self.stream1(x)
-        s2 = self.stream2(x)
-        s3 = self.stream3(x)
-        y = torch.cat([s1, s2, s3], dim=1)
-        return y
-
-
-class MultiScaleB(nn.Module):
-    """Multi-scale stream layer B (Sec.3.1)"""
-
-    def __init__(self):
-        super(MultiScaleB, self).__init__()
-        self.stream1 = nn.Sequential(
-            nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
-            ConvBlock(256, 256, k=1, s=1, p=0),
-        )
-        self.stream2 = nn.Sequential(
-            ConvBlock(256, 64, k=1, s=1, p=0),
-            ConvBlock(64, 128, k=(1, 3), s=1, p=(0, 1)),
-            ConvBlock(128, 256, k=(3, 1), s=1, p=(1, 0)),
-        )
-        self.stream3 = ConvBlock(256, 256, k=1, s=1, p=0)
-        self.stream4 = nn.Sequential(
-            ConvBlock(256, 64, k=1, s=1, p=0),
-            ConvBlock(64, 64, k=(1, 3), s=1, p=(0, 1)),
-            ConvBlock(64, 128, k=(3, 1), s=1, p=(1, 0)),
-            ConvBlock(128, 128, k=(1, 3), s=1, p=(0, 1)),
-            ConvBlock(128, 256, k=(3, 1), s=1, p=(1, 0)),
-        )
-
-    def forward(self, x):
-        s1 = self.stream1(x)
-        s2 = self.stream2(x)
-        s3 = self.stream3(x)
-        s4 = self.stream4(x)
-        return s1, s2, s3, s4
-
-
-class Fusion(nn.Module):
-    """Saliency-based learning fusion layer (Sec.3.2)"""
-
-    def __init__(self):
-        super(Fusion, self).__init__()
-        self.a1 = nn.Parameter(torch.rand(1, 256, 1, 1))
-        self.a2 = nn.Parameter(torch.rand(1, 256, 1, 1))
-        self.a3 = nn.Parameter(torch.rand(1, 256, 1, 1))
-        self.a4 = nn.Parameter(torch.rand(1, 256, 1, 1))
-
-        # We add an average pooling layer to reduce the spatial dimension
-        # of feature maps, which differs from the original paper.
-        self.avgpool = nn.AvgPool2d(kernel_size=4, stride=4, padding=0)
-
-    def forward(self, x1, x2, x3, x4):
-        s1 = self.a1.expand_as(x1) * x1
-        s2 = self.a2.expand_as(x2) * x2
-        s3 = self.a3.expand_as(x3) * x3
-        s4 = self.a4.expand_as(x4) * x4
-        y = self.avgpool(s1 + s2 + s3 + s4)
-        return y
-
-
-class MuDeep(nn.Module):
-    """Multiscale deep neural network.
-
-    Reference:
-        Qian et al. Multi-scale Deep Learning Architectures
-        for Person Re-identification. ICCV 2017.
-
-    Public keys:
-        - ``mudeep``: Multiscale deep neural network.
-    """
-
-    def __init__(self, num_classes, loss='softmax', **kwargs):
-        super(MuDeep, self).__init__()
-        self.loss = loss
-
-        self.block1 = ConvLayers()
-        self.block2 = MultiScaleA()
-        self.block3 = Reduction()
-        self.block4 = MultiScaleB()
-        self.block5 = Fusion()
-
-        # Due to this fully connected layer, input image has to be fixed
-        # in shape, i.e. (3, 256, 128), such that the last convolutional feature
-        # maps are of shape (256, 16, 8). If input shape is changed,
-        # the input dimension of this layer has to be changed accordingly.
-        self.fc = nn.Sequential(
-            nn.Linear(256 * 16 * 8, 4096),
-            nn.BatchNorm1d(4096),
-            nn.ReLU(),
-        )
-        self.classifier = nn.Linear(4096, num_classes)
-        self.feat_dim = 4096
-
-    def featuremaps(self, x):
-        x = self.block1(x)
-        x = self.block2(x)
-        x = self.block3(x)
-        x = self.block4(x)
-        x = self.block5(*x)
-        return x
-
-    def forward(self, x):
-        x = self.featuremaps(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-        y = self.classifier(x)
-
-        if not self.training:
-            return x
-
-        if self.loss == 'softmax':
-            return y
-        elif self.loss == 'triplet':
-            return y, x
-        else:
-            raise KeyError('Unsupported loss: {}'.format(self.loss))
diff --git a/spaces/bhavyagiri/recyclopes/README.md b/spaces/bhavyagiri/recyclopes/README.md
deleted file mode 100644
index 7d73cdc5e08b057d28a64768f35aff67178af98d..0000000000000000000000000000000000000000
--- a/spaces/bhavyagiri/recyclopes/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Recyclopes
-emoji: 😻
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.27.0
-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/bigPear/digitalWDF/src/utils/seq2seq.py b/spaces/bigPear/digitalWDF/src/utils/seq2seq.py
deleted file mode 100644
index de99f9d7c12a9eb27181830a6ee58291a1ace6c6..0000000000000000000000000000000000000000
--- a/spaces/bigPear/digitalWDF/src/utils/seq2seq.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import os
-import json
-import torch
-import numpy as np
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
-
-from transformers import Seq2SeqTrainer, DataCollatorForSeq2Seq
-from transformers.trainer import PredictionOutput, TRAINING_ARGS_NAME
-from transformers.deepspeed import is_deepspeed_zero3_enabled
-from transformers.modeling_utils import PreTrainedModel, unwrap_model
-from transformers.tokenization_utils import PreTrainedTokenizer
-
-import jieba
-from rouge_chinese import Rouge
-from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
-
-from .config import FinetuningArguments
-
-from .other import (
-    get_logger,
-    save_trainable_params,
-    IGNORE_INDEX,
-    FINETUNING_ARGS_NAME,
-    PREDICTION_FILE_NAME
-)
-
-
-logger = get_logger(__name__)
-
-
-# Note: The ChatGLM tokenizer assigns False on token to be attended in attention mask. In general settings, it should be True.
-# Refer to: https://huggingface.co/THUDM/chatglm-6b/blob/6650ae3a53c28fc176d06762ca80b05d5ab3792b/tokenization_chatglm.py#L401
-class Seq2SeqDataCollatorForChatGLM(DataCollatorForSeq2Seq):
-    r"""
-    Data collator for ChatGLM. It is capable of dynamically padding for batched data.
-
-    Inspired by: https://github.com/tatsu-lab/stanford_alpaca/blob/65512697dc67779a6e53c267488aba0ec4d7c02a/train.py#L156
-    """
-    def __init__(
-            self,
-            tokenizer: PreTrainedTokenizer,
-            model: PreTrainedModel,
-            ignore_pad_token_for_loss: bool,
-            inference_mode: bool = False
-    ):
-        label_pad_token_id = IGNORE_INDEX if ignore_pad_token_for_loss else tokenizer.pad_token_id
-        super().__init__(tokenizer, model=model, label_pad_token_id=label_pad_token_id, padding=True)
-        self.label_pad_token_id = label_pad_token_id
-        self.inference_mode = inference_mode
-
-    def __call__(self, features: Sequence[Dict[str, Sequence]]) -> Dict[str, torch.Tensor]:
-        r"""
-        Pads batched data to the longest sequence in the batch.
-
-        ChatGLM is able to generate attentions masks and position ids by itself.
-        """
-        if self.inference_mode: # evaluation set adopts left-padding while training set adopts right-padding
-            return super().__call__(features)
-        input_ids, labels = [[torch.tensor(feature[key]) for feature in features] for key in ("input_ids", "labels")]
-        input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id)
-        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=self.label_pad_token_id)
-        features = {"input_ids": input_ids, "labels": labels}
-        return features
-
-
-@dataclass
-class ComputeMetrics:
-    r"""
-    Wraps the tokenizer into metric functions, used in Seq2SeqTrainerForChatGLM.
-
-    Borrowed from: https://github.com/THUDM/ChatGLM-6B/blob/0c2806fea82683349194e21996dd6b3acc3c265b/ptuning/main.py#L307
-    """
-
-    tokenizer: PreTrainedTokenizer
-
-    def __call__(self, eval_preds: Sequence[Union[np.ndarray, Tuple[np.ndarray]]]) -> Dict[str, float]:
-        r"""
-        Uses the model predictions to compute metrics.
-        """
-        preds, labels = eval_preds
-        if isinstance(preds, tuple):
-            preds = preds[0]
-        decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True)
-        # Replace IGNORE_INDEX in the labels with pad_token_id as we cannot decode them if ignore_pad_token_for_loss=True.
-        labels = np.where(labels != IGNORE_INDEX, labels, self.tokenizer.pad_token_id)
-        decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True)
-
-        score_dict = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}
-        for pred, label in zip(decoded_preds, decoded_labels):
-            hypothesis = list(jieba.cut(pred))
-            reference = list(jieba.cut(label))
-
-            if len(" ".join(hypothesis).split()) == 0:
-                result = {"rouge-1": {"f": 0.0}, "rouge-2": {"f": 0.0}, "rouge-l": {"f": 0.0}}
-            else:
-                rouge = Rouge()
-                scores = rouge.get_scores(" ".join(hypothesis), " ".join(reference))
-                result = scores[0]
-
-            for k, v in result.items():
-                score_dict[k].append(round(v["f"] * 100, 4))
-
-            bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3)
-            score_dict["bleu-4"].append(round(bleu_score * 100, 4))
-
-        return {k: float(np.mean(v)) for k, v in score_dict.items()}
-
-
-class Seq2SeqTrainerForChatGLM(Seq2SeqTrainer):
-    r"""
-    Inherits Seq2SeqTrainer to compute generative metrics such as BLEU and ROUGE.
-    """
-
-    def __init__(self, finetuning_args: FinetuningArguments, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.finetuning_args = finetuning_args
-
-    def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, torch.Tensor]] = None) -> None:
-        r"""
-        Saves trainable parameters as model checkpoints.
-
-        This function will only be executed at the process zero.
-
-        Override to inject custom behavior.
-        """
-        output_dir = output_dir if output_dir is not None else self.args.output_dir
-        os.makedirs(output_dir, exist_ok=True)
-        logger.info(f"Saving model checkpoint to {output_dir}")
-
-        model_to_save = unwrap_model(self.model)
-
-        if hasattr(self.model, "peft_config"): # peft methods
-            model_to_save.save_pretrained(output_dir) # save lora weights
-        else: # non-peft methods
-            save_trainable_params(output_dir, model_to_save)
-
-        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
-        torch.save(self.finetuning_args, os.path.join(output_dir, FINETUNING_ARGS_NAME))
-
-    def prediction_step(
-            self,
-            model: torch.nn.Module,
-            inputs: Dict[str, Union[torch.Tensor, Any]],
-            prediction_loss_only: bool,
-            ignore_keys: Optional[List[str]] = None
-    ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
-        r"""
-        Performs an evaluation step on `model` using `inputs` for ChatGLM.
-
-        Now it only supports single GPU (without Accelerate).
-
-        Override to inject custom behavior. It is not directly used by external scripts.
-        """
-        # Override to inject custom bevavior.
-        if not self.args.predict_with_generate or prediction_loss_only:
-            return super().prediction_step(
-                model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
-            )
-
-        has_labels = "labels" in inputs
-        inputs = self._prepare_inputs(inputs)
-
-        gen_kwargs = self._gen_kwargs.copy()
-        if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None:
-            gen_kwargs["max_length"] = self.model.config.max_length
-        gen_kwargs["num_beams"] = gen_kwargs["num_beams"] \
-                    if gen_kwargs.get("num_beams") is not None else self.model.config.num_beams
-        default_synced_gpus = True if is_deepspeed_zero3_enabled() else False
-        gen_kwargs["synced_gpus"] = gen_kwargs["synced_gpus"] \
-                    if gen_kwargs.get("synced_gpus") is not None else default_synced_gpus
-
-        if "attention_mask" in inputs:
-            gen_kwargs["attention_mask"] = inputs.get("attention_mask", None)
-        if "position_ids" in inputs:
-            gen_kwargs["position_ids"] = inputs.get("position_ids", None)
-        if "global_attention_mask" in inputs:
-            gen_kwargs["global_attention_mask"] = inputs.get("global_attention_mask", None)
-
-        # prepare generation inputs
-        if hasattr(self.model, "encoder") and self.model.encoder.main_input_name != self.model.main_input_name:
-            generation_inputs = inputs[self.model.encoder.main_input_name]
-        else:
-            generation_inputs = inputs[self.model.main_input_name]
-
-        gen_kwargs["input_ids"] = generation_inputs
-        generated_tokens = self.model.generate(**gen_kwargs)
-        generated_tokens = generated_tokens[:, generation_inputs.size()[-1]:] # important for ChatGLM
-
-        # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
-        # Inspired by: https://github.com/huggingface/transformers/blob/v4.28.1/src/transformers/trainer_seq2seq.py#L273
-        if self.model.generation_config._from_model_config:
-            self.model.generation_config._from_model_config = False
-
-        # Retrieves GenerationConfig from model.generation_config
-        gen_config = self.model.generation_config
-        # in case the batch is shorter than max length, the output should be padded
-        if generated_tokens.shape[-1] < gen_config.max_length:
-            generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_config.max_length)
-        elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
-            generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_config.max_new_tokens + 1)
-
-        loss = None
-
-        if self.args.prediction_loss_only:
-            return loss, None, None
-
-        if has_labels:
-            labels = inputs["labels"]
-            if labels.shape[-1] < gen_config.max_length:
-                labels = self._pad_tensors_to_max_len(labels, gen_config.max_length)
-            elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
-                labels = self._pad_tensors_to_max_len(labels, gen_config.max_new_tokens + 1)
-        else:
-            labels = None
-
-        return loss, generated_tokens, labels
-
-    def save_predictions(
-            self,
-            predict_results: PredictionOutput,
-            tokenizer: PreTrainedTokenizer
-    ) -> None:
-        r"""
-        Saves model predictions to `output_dir`.
-
-        A custom behavior that not contained in Seq2SeqTrainer.
-        """
-        if not self.is_world_process_zero():
-            return
-        if not self.args.predict_with_generate:
-            raise ValueError("Please enable `predict_with_generate` for saving model predictions.")
-
-        predictions = tokenizer.batch_decode(predict_results.predictions, skip_special_tokens=True)
-        predictions = [pred.strip() for pred in predictions]
-        labels = tokenizer.batch_decode(predict_results.label_ids, skip_special_tokens=True)
-        labels = [label.strip() for label in labels]
-
-        output_prediction_file = os.path.join(self.args.output_dir, PREDICTION_FILE_NAME)
-        logger.info(f"Saving prediction results to {output_prediction_file}")
-        with open(output_prediction_file, "w", encoding="utf-8") as writer:
-            res = []
-            for pred, label in zip(predictions, labels):
-                res.append(json.dumps({"label": label, "predict": pred}, ensure_ascii=False))
-            writer.write("\n".join(res))
diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_checkpoint.py b/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_checkpoint.py
deleted file mode 100644
index 2604d969f910ffdd65aff66acc0b6ab09b793b38..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_checkpoint.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from torch.utils.checkpoint import checkpoint
-
-import ldm.modules.attention
-import ldm.modules.diffusionmodules.openaimodel
-
-
-def BasicTransformerBlock_forward(self, x, context=None):
-    return checkpoint(self._forward, x, context)
-
-
-def AttentionBlock_forward(self, x):
-    return checkpoint(self._forward, x)
-
-
-def ResBlock_forward(self, x, emb):
-    return checkpoint(self._forward, x, emb)
-
-
-stored = []
-
-
-def add():
-    if len(stored) != 0:
-        return
-
-    stored.extend([
-        ldm.modules.attention.BasicTransformerBlock.forward,
-        ldm.modules.diffusionmodules.openaimodel.ResBlock.forward,
-        ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward
-    ])
-
-    ldm.modules.attention.BasicTransformerBlock.forward = BasicTransformerBlock_forward
-    ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = ResBlock_forward
-    ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = AttentionBlock_forward
-
-
-def remove():
-    if len(stored) == 0:
-        return
-
-    ldm.modules.attention.BasicTransformerBlock.forward = stored[0]
-    ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = stored[1]
-    ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = stored[2]
-
-    stored.clear()
-
diff --git a/spaces/bioriAsaeru/text-to-voice/ElitePain Punishment Methodology 4.md b/spaces/bioriAsaeru/text-to-voice/ElitePain Punishment Methodology 4.md
deleted file mode 100644
index 4882d7a71612fc5ef1d28bb68cc8f8d0e312006a..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/ElitePain Punishment Methodology 4.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>ElitePain Punishment Methodology 4</h2><br /><p><b><b>DOWNLOAD</b> &#9193; <a href="https://urloso.com/2uyODB">https://urloso.com/2uyODB</a></b></p><br /><br />
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-<p></p>
diff --git a/spaces/bioriAsaeru/text-to-voice/Etab 9.7.4 With Crack VERIFIED.md b/spaces/bioriAsaeru/text-to-voice/Etab 9.7.4 With Crack VERIFIED.md
deleted file mode 100644
index de59e9fb6f9121ec7f3513c4aa23d5f55880d75a..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Etab 9.7.4 With Crack VERIFIED.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-
-<p>Habib Equipment Co. Ltd has been dedicated to strength and safety in the industry for a long time. It is the leading enterprise in the field of safety. We, the engineers from Habib Equipment Co. Ltd, have a long term experience in the field of vehicle safety. With more than 13 years of experience and integrating high quality safety instrument in the research and development, we provided turnkey solution to customers who are mainly from the Chinese and Indonesian market. </p>
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-<p>Training workshop on the construction of concrete columns and floor beams with three stories building, presented by Mr. Bill O’Brien of O’Brien-Sutherland, Co. Ltd. This workshop is part of the educational program on the construction of the building codes of Hong Kong, issued by Education Bureau, Department of the Government, Hong Kong SAR. The workshop involves training on the principles and theory of the beam and column and how to draw, etc. We will also explain how to plan and design column and beam for three-story building. </p>
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-<p></p> 899543212b<br />
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diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/losses/mask.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/losses/mask.py
deleted file mode 100644
index c16b15c53de9f02dc734148e05f2bde799046aa0..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/losses/mask.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-from dataclasses import dataclass
-from typing import Any, Iterable, List, Optional
-import torch
-from torch.nn import functional as F
-
-from detectron2.structures import Instances
-
-
-@dataclass
-class DataForMaskLoss:
-    """
-    Contains mask GT and estimated data for proposals from multiple images:
-    """
-
-    # tensor of size (K, H, W) containing GT labels
-    masks_gt: Optional[torch.Tensor] = None
-    # tensor of size (K, C, H, W) containing estimated scores
-    masks_est: Optional[torch.Tensor] = None
-
-
-def extract_data_for_mask_loss_from_matches(
-    proposals_targets: Iterable[Instances], estimated_segm: torch.Tensor
-) -> DataForMaskLoss:
-    """
-    Extract data for mask loss from instances that contain matched GT and
-    estimated bounding boxes.
-    Args:
-        proposals_targets: Iterable[Instances]
-            matched GT and estimated results, each item in the iterable
-            corresponds to data in 1 image
-        estimated_segm: tensor(K, C, S, S) of float - raw unnormalized
-            segmentation scores, here S is the size to which GT masks are
-            to be resized
-    Return:
-        masks_est: tensor(K, C, S, S) of float - class scores
-        masks_gt: tensor(K, S, S) of int64 - labels
-    """
-    data = DataForMaskLoss()
-    masks_gt = []
-    offset = 0
-    assert estimated_segm.shape[2] == estimated_segm.shape[3], (
-        f"Expected estimated segmentation to have a square shape, "
-        f"but the actual shape is {estimated_segm.shape[2:]}"
-    )
-    mask_size = estimated_segm.shape[2]
-    num_proposals = sum(inst.proposal_boxes.tensor.size(0) for inst in proposals_targets)
-    num_estimated = estimated_segm.shape[0]
-    assert (
-        num_proposals == num_estimated
-    ), "The number of proposals {} must be equal to the number of estimates {}".format(
-        num_proposals, num_estimated
-    )
-
-    for proposals_targets_per_image in proposals_targets:
-        n_i = proposals_targets_per_image.proposal_boxes.tensor.size(0)
-        if not n_i:
-            continue
-        gt_masks_per_image = proposals_targets_per_image.gt_masks.crop_and_resize(
-            proposals_targets_per_image.proposal_boxes.tensor, mask_size
-        ).to(device=estimated_segm.device)
-        masks_gt.append(gt_masks_per_image)
-        offset += n_i
-    if masks_gt:
-        data.masks_est = estimated_segm
-        data.masks_gt = torch.cat(masks_gt, dim=0)
-    return data
-
-
-class MaskLoss:
-    """
-    Mask loss as cross-entropy for raw unnormalized scores given ground truth labels.
-    Mask ground truth labels are defined for the whole image and not only the
-    bounding box of interest. They are stored as objects that are assumed to implement
-    the `crop_and_resize` interface (e.g. BitMasks, PolygonMasks).
-    """
-
-    def __call__(
-        self, proposals_with_gt: List[Instances], densepose_predictor_outputs: Any
-    ) -> torch.Tensor:
-        """
-        Computes segmentation loss as cross-entropy for raw unnormalized
-        scores given ground truth labels.
-
-        Args:
-            proposals_with_gt (list of Instances): detections with associated ground truth data
-            densepose_predictor_outputs: an object of a dataclass that contains predictor outputs
-                with estimated values; assumed to have the following attribute:
-                * coarse_segm (tensor of shape [N, D, S, S]): coarse segmentation estimates
-                    as raw unnormalized scores
-                where N is the number of detections, S is the estimate size ( = width = height)
-                and D is the number of coarse segmentation channels.
-        Return:
-            Cross entropy for raw unnormalized scores for coarse segmentation given
-            ground truth labels from masks
-        """
-        if not len(proposals_with_gt):
-            return self.fake_value(densepose_predictor_outputs)
-        # densepose outputs are computed for all images and all bounding boxes;
-        # i.e. if a batch has 4 images with (3, 1, 2, 1) proposals respectively,
-        # the outputs will have size(0) == 3+1+2+1 == 7
-        with torch.no_grad():
-            mask_loss_data = extract_data_for_mask_loss_from_matches(
-                proposals_with_gt, densepose_predictor_outputs.coarse_segm
-            )
-        if (mask_loss_data.masks_gt is None) or (mask_loss_data.masks_est is None):
-            return self.fake_value(densepose_predictor_outputs)
-        return F.cross_entropy(mask_loss_data.masks_est, mask_loss_data.masks_gt.long())
-
-    def fake_value(self, densepose_predictor_outputs: Any) -> torch.Tensor:
-        """
-        Fake segmentation loss used when no suitable ground truth data
-        was found in a batch. The loss has a value 0 and is primarily used to
-        construct the computation graph, so that `DistributedDataParallel`
-        has similar graphs on all GPUs and can perform reduction properly.
-
-        Args:
-            densepose_predictor_outputs: DensePose predictor outputs, an object
-                of a dataclass that is assumed to have `coarse_segm`
-                attribute
-        Return:
-            Zero value loss with proper computation graph
-        """
-        return densepose_predictor_outputs.coarse_segm.sum() * 0
diff --git a/spaces/bulentsofttech/gradio_s1000_veri_toplama_modeli/yolov5/models/__init__.py b/spaces/bulentsofttech/gradio_s1000_veri_toplama_modeli/yolov5/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/bzd4576/sovits-sin/attentions.py b/spaces/bzd4576/sovits-sin/attentions.py
deleted file mode 100644
index 4e0b0c1fd48c962e21e1fbe60b23fc574927435c..0000000000000000000000000000000000000000
--- a/spaces/bzd4576/sovits-sin/attentions.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/caffeinum/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py b/spaces/caffeinum/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/caffeinum/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/FontFile.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/FontFile.py
deleted file mode 100644
index 5ec0a6632e3182382467688662ebc5e6c324da91..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/FontFile.py
+++ /dev/null
@@ -1,110 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# base class for raster font file parsers
-#
-# history:
-# 1997-06-05 fl   created
-# 1997-08-19 fl   restrict image width
-#
-# Copyright (c) 1997-1998 by Secret Labs AB
-# Copyright (c) 1997-1998 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import os
-
-from . import Image, _binary
-
-WIDTH = 800
-
-
-def puti16(fp, values):
-    """Write network order (big-endian) 16-bit sequence"""
-    for v in values:
-        if v < 0:
-            v += 65536
-        fp.write(_binary.o16be(v))
-
-
-class FontFile:
-    """Base class for raster font file handlers."""
-
-    bitmap = None
-
-    def __init__(self):
-        self.info = {}
-        self.glyph = [None] * 256
-
-    def __getitem__(self, ix):
-        return self.glyph[ix]
-
-    def compile(self):
-        """Create metrics and bitmap"""
-
-        if self.bitmap:
-            return
-
-        # create bitmap large enough to hold all data
-        h = w = maxwidth = 0
-        lines = 1
-        for glyph in self:
-            if glyph:
-                d, dst, src, im = glyph
-                h = max(h, src[3] - src[1])
-                w = w + (src[2] - src[0])
-                if w > WIDTH:
-                    lines += 1
-                    w = src[2] - src[0]
-                maxwidth = max(maxwidth, w)
-
-        xsize = maxwidth
-        ysize = lines * h
-
-        if xsize == 0 and ysize == 0:
-            return ""
-
-        self.ysize = h
-
-        # paste glyphs into bitmap
-        self.bitmap = Image.new("1", (xsize, ysize))
-        self.metrics = [None] * 256
-        x = y = 0
-        for i in range(256):
-            glyph = self[i]
-            if glyph:
-                d, dst, src, im = glyph
-                xx = src[2] - src[0]
-                # yy = src[3] - src[1]
-                x0, y0 = x, y
-                x = x + xx
-                if x > WIDTH:
-                    x, y = 0, y + h
-                    x0, y0 = x, y
-                    x = xx
-                s = src[0] + x0, src[1] + y0, src[2] + x0, src[3] + y0
-                self.bitmap.paste(im.crop(src), s)
-                self.metrics[i] = d, dst, s
-
-    def save(self, filename):
-        """Save font"""
-
-        self.compile()
-
-        # font data
-        self.bitmap.save(os.path.splitext(filename)[0] + ".pbm", "PNG")
-
-        # font metrics
-        with open(os.path.splitext(filename)[0] + ".pil", "wb") as fp:
-            fp.write(b"PILfont\n")
-            fp.write(f";;;;;;{self.ysize};\n".encode("ascii"))  # HACK!!!
-            fp.write(b"DATA\n")
-            for id in range(256):
-                m = self.metrics[id]
-                if not m:
-                    puti16(fp, [0] * 10)
-                else:
-                    puti16(fp, m[0] + m[1] + m[2])
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageSequence.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageSequence.py
deleted file mode 100644
index c4bb6334acfde7d245c5bb1722b7c2381661e4ca..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageSequence.py
+++ /dev/null
@@ -1,76 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# sequence support classes
-#
-# history:
-# 1997-02-20 fl     Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1997 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-##
-
-
-class Iterator:
-    """
-    This class implements an iterator object that can be used to loop
-    over an image sequence.
-
-    You can use the ``[]`` operator to access elements by index. This operator
-    will raise an :py:exc:`IndexError` if you try to access a nonexistent
-    frame.
-
-    :param im: An image object.
-    """
-
-    def __init__(self, im):
-        if not hasattr(im, "seek"):
-            msg = "im must have seek method"
-            raise AttributeError(msg)
-        self.im = im
-        self.position = getattr(self.im, "_min_frame", 0)
-
-    def __getitem__(self, ix):
-        try:
-            self.im.seek(ix)
-            return self.im
-        except EOFError as e:
-            raise IndexError from e  # end of sequence
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        try:
-            self.im.seek(self.position)
-            self.position += 1
-            return self.im
-        except EOFError as e:
-            raise StopIteration from e
-
-
-def all_frames(im, func=None):
-    """
-    Applies a given function to all frames in an image or a list of images.
-    The frames are returned as a list of separate images.
-
-    :param im: An image, or a list of images.
-    :param func: The function to apply to all of the image frames.
-    :returns: A list of images.
-    """
-    if not isinstance(im, list):
-        im = [im]
-
-    ims = []
-    for imSequence in im:
-        current = imSequence.tell()
-
-        ims += [im_frame.copy() for im_frame in Iterator(imSequence)]
-
-        imSequence.seek(current)
-    return [func(im) for im in ims] if func else ims
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiofiles/__init__.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiofiles/__init__.py
deleted file mode 100644
index 9e7511148df475d077a1c0af4d8edf71a17a6c7b..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiofiles/__init__.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""Utilities for asyncio-friendly file handling."""
-from .threadpool import (
-    open,
-    stdin,
-    stdout,
-    stderr,
-    stdin_bytes,
-    stdout_bytes,
-    stderr_bytes,
-)
-from . import tempfile
-
-__all__ = [
-    "open",
-    "tempfile",
-    "stdin",
-    "stdout",
-    "stderr",
-    "stdin_bytes",
-    "stdout_bytes",
-    "stderr_bytes",
-]
diff --git a/spaces/captain-awesome/pet-dog-care-bot/app.py b/spaces/captain-awesome/pet-dog-care-bot/app.py
deleted file mode 100644
index d60f90e46a61f81769a6fb70217cac55be5f3c24..0000000000000000000000000000000000000000
--- a/spaces/captain-awesome/pet-dog-care-bot/app.py
+++ /dev/null
@@ -1,108 +0,0 @@
-from langchain import PromptTemplate, LLMChain
-from langchain.llms import CTransformers
-import os
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import Chroma
-from langchain.chains import RetrievalQA
-from langchain.embeddings import HuggingFaceBgeEmbeddings
-from io import BytesIO
-from langchain.document_loaders import PyPDFLoader
-import gradio as gr
-
-
-local_llm = "TheBloke/zephyr-7B-beta-GGUF"
-model_file = "zephyr-7b-beta.Q4_0.gguf"
-
-config = {
-'max_new_tokens': 1024,
-'repetition_penalty': 1.1,
-'temperature': 0.1,
-'top_k': 50,
-'top_p': 0.9,
-'stream': True,
-'threads': int(os.cpu_count() / 2)
-}
-
-llm = CTransformers(
-    model=local_llm,
-    model_file=model_file,
-    model_type="mistral",
-    lib="avx2", #for CPU use
-    **config
-)
-
-print("LLM Initialized...")
-
-
-prompt_template = """Use the following pieces of information to answer the user's question.
-If you don't know the answer, just say that you don't know, don't try to make up an answer.
-
-Context: {context}
-Question: {question}
-
-Only return the helpful answer below and nothing else.
-Helpful answer:
-"""
-
-model_name = "BAAI/bge-large-en"
-model_kwargs = {'device': 'cpu'}
-encode_kwargs = {'normalize_embeddings': False}
-embeddings = HuggingFaceBgeEmbeddings(
-    model_name=model_name,
-    model_kwargs=model_kwargs,
-    encode_kwargs=encode_kwargs
-)
-
-
-prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
-load_vector_store = Chroma(persist_directory="stores/pet_cosine", embedding_function=embeddings)
-retriever = load_vector_store.as_retriever(search_kwargs={"k":1})
-# query = "what is the fastest speed for a greyhound dog?"
-# semantic_search = retriever.get_relevant_documents(query)
-# print(semantic_search)
-
-print("######################################################################")
-
-chain_type_kwargs = {"prompt": prompt}
-
-# qa = RetrievalQA.from_chain_type(
-#     llm=llm,
-#     chain_type="stuff",
-#     retriever=retriever,
-#     return_source_documents = True,
-#     chain_type_kwargs= chain_type_kwargs,
-#     verbose=True
-# )
-
-# response = qa(query)
-
-# print(response)
-
-sample_prompts = ["what is the fastest speed for a greyhound dog?", "Why should we not feed chocolates to the dogs?", "Name two factors which might contribute to why some dogs might get scared?"]
-
-def get_response(input):
-  query = input
-  chain_type_kwargs = {"prompt": prompt}
-  qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True, chain_type_kwargs=chain_type_kwargs, verbose=True)
-  response = qa(query)
-  return response
-
-input = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-iface = gr.Interface(fn=get_response, 
-             inputs=input, 
-             outputs="text",
-             title="My Dog PetCare Bot",
-             description="This is a RAG implementation based on Zephyr 7B Beta LLM.",
-             examples=sample_prompts,
-             allow_screenshot=False,
-             allow_flagging=False
-             )
-
-iface.launch()          
\ No newline at end of file
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/setup.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/setup.py
deleted file mode 100644
index 581103da4321378438fd4d14670a1c34caae6e4f..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/setup.py
+++ /dev/null
@@ -1,215 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import glob
-import os
-import shutil
-from os import path
-from setuptools import find_packages, setup
-from typing import List
-import torch
-from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
-
-torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
-assert torch_ver >= [1, 8], "Requires PyTorch >= 1.8"
-
-
-def get_version():
-    init_py_path = path.join(path.abspath(path.dirname(__file__)), "detectron2", "__init__.py")
-    init_py = open(init_py_path, "r").readlines()
-    version_line = [l.strip() for l in init_py if l.startswith("__version__")][0]
-    version = version_line.split("=")[-1].strip().strip("'\"")
-
-    # The following is used to build release packages.
-    # Users should never use it.
-    suffix = os.getenv("D2_VERSION_SUFFIX", "")
-    version = version + suffix
-    if os.getenv("BUILD_NIGHTLY", "0") == "1":
-        from datetime import datetime
-
-        date_str = datetime.today().strftime("%y%m%d")
-        version = version + ".dev" + date_str
-
-        new_init_py = [l for l in init_py if not l.startswith("__version__")]
-        new_init_py.append('__version__ = "{}"\n'.format(version))
-        with open(init_py_path, "w") as f:
-            f.write("".join(new_init_py))
-    return version
-
-
-def get_extensions():
-    this_dir = path.dirname(path.abspath(__file__))
-    extensions_dir = path.join(this_dir, "detectron2", "layers", "csrc")
-
-    main_source = path.join(extensions_dir, "vision.cpp")
-    sources = glob.glob(path.join(extensions_dir, "**", "*.cpp"))
-
-    from torch.utils.cpp_extension import ROCM_HOME
-
-    is_rocm_pytorch = (
-        True if ((torch.version.hip is not None) and (ROCM_HOME is not None)) else False
-    )
-    if is_rocm_pytorch:
-        assert torch_ver >= [1, 8], "ROCM support requires PyTorch >= 1.8!"
-
-    # common code between cuda and rocm platforms, for hipify version [1,0,0] and later.
-    source_cuda = glob.glob(path.join(extensions_dir, "**", "*.cu")) + glob.glob(
-        path.join(extensions_dir, "*.cu")
-    )
-    sources = [main_source] + sources
-
-    extension = CppExtension
-
-    extra_compile_args = {"cxx": []}
-    define_macros = []
-
-    if (torch.cuda.is_available() and ((CUDA_HOME is not None) or is_rocm_pytorch)) or os.getenv(
-        "FORCE_CUDA", "0"
-    ) == "1":
-        extension = CUDAExtension
-        sources += source_cuda
-
-        if not is_rocm_pytorch:
-            define_macros += [("WITH_CUDA", None)]
-            extra_compile_args["nvcc"] = [
-                "-O3",
-                "-DCUDA_HAS_FP16=1",
-                "-D__CUDA_NO_HALF_OPERATORS__",
-                "-D__CUDA_NO_HALF_CONVERSIONS__",
-                "-D__CUDA_NO_HALF2_OPERATORS__",
-            ]
-        else:
-            define_macros += [("WITH_HIP", None)]
-            extra_compile_args["nvcc"] = []
-
-        if torch_ver < [1, 7]:
-            # supported by https://github.com/pytorch/pytorch/pull/43931
-            CC = os.environ.get("CC", None)
-            if CC is not None:
-                extra_compile_args["nvcc"].append("-ccbin={}".format(CC))
-
-    include_dirs = [extensions_dir]
-
-    ext_modules = [
-        extension(
-            "detectron2._C",
-            sources,
-            include_dirs=include_dirs,
-            define_macros=define_macros,
-            extra_compile_args=extra_compile_args,
-        )
-    ]
-
-    return ext_modules
-
-
-def get_model_zoo_configs() -> List[str]:
-    """
-    Return a list of configs to include in package for model zoo. Copy over these configs inside
-    detectron2/model_zoo.
-    """
-
-    # Use absolute paths while symlinking.
-    source_configs_dir = path.join(path.dirname(path.realpath(__file__)), "configs")
-    destination = path.join(
-        path.dirname(path.realpath(__file__)), "detectron2", "model_zoo", "configs"
-    )
-    # Symlink the config directory inside package to have a cleaner pip install.
-
-    # Remove stale symlink/directory from a previous build.
-    if path.exists(source_configs_dir):
-        if path.islink(destination):
-            os.unlink(destination)
-        elif path.isdir(destination):
-            shutil.rmtree(destination)
-
-    if not path.exists(destination):
-        try:
-            os.symlink(source_configs_dir, destination)
-        except OSError:
-            # Fall back to copying if symlink fails: ex. on Windows.
-            shutil.copytree(source_configs_dir, destination)
-
-    config_paths = glob.glob("configs/**/*.yaml", recursive=True) + glob.glob(
-        "configs/**/*.py", recursive=True
-    )
-    return config_paths
-
-
-# For projects that are relative small and provide features that are very close
-# to detectron2's core functionalities, we install them under detectron2.projects
-PROJECTS = {
-    "detectron2.projects.point_rend": "projects/PointRend/point_rend",
-    "detectron2.projects.deeplab": "projects/DeepLab/deeplab",
-    "detectron2.projects.panoptic_deeplab": "projects/Panoptic-DeepLab/panoptic_deeplab",
-}
-
-setup(
-    name="detectron2",
-    version=get_version(),
-    author="FAIR",
-    url="https://github.com/facebookresearch/detectron2",
-    description="Detectron2 is FAIR's next-generation research "
-    "platform for object detection and segmentation.",
-    packages=find_packages(exclude=("configs", "tests*")) + list(PROJECTS.keys()),
-    package_dir=PROJECTS,
-    package_data={"detectron2.model_zoo": get_model_zoo_configs()},
-    python_requires=">=3.7",
-    install_requires=[
-        # These dependencies are not pure-python.
-        # In general, avoid adding dependencies that are not pure-python because they are not
-        # guaranteed to be installable by `pip install` on all platforms.
-        "Pillow>=7.1",  # or use pillow-simd for better performance
-        "matplotlib",  # TODO move it to optional after we add opencv visualization
-        "pycocotools>=2.0.2",  # corresponds to https://github.com/ppwwyyxx/cocoapi
-        # Do not add opencv here. Just like pytorch, user should install
-        # opencv themselves, preferrably by OS's package manager, or by
-        # choosing the proper pypi package name at https://github.com/skvark/opencv-python
-        # Also, avoid adding dependencies that transitively depend on pytorch or opencv.
-        # ------------------------------------------------------------
-        # The following are pure-python dependencies that should be easily installable.
-        # But still be careful when adding more: fewer people are able to use the software
-        # with every new dependency added.
-        "termcolor>=1.1",
-        "yacs>=0.1.8",
-        "tabulate",
-        "cloudpickle",
-        "tqdm>4.29.0",
-        "tensorboard",
-        # Lock version of fvcore/iopath because they may have breaking changes
-        # NOTE: when updating fvcore/iopath version, make sure fvcore depends
-        # on compatible version of iopath.
-        "fvcore>=0.1.5,<0.1.6",  # required like this to make it pip installable
-        "iopath>=0.1.7,<0.1.10",
-        "dataclasses; python_version<'3.7'",
-        "omegaconf>=2.1",
-        "hydra-core>=1.1",
-        "black",
-        "timm",
-        "packaging",
-        # NOTE: When adding new dependencies, if it is required at import time (in addition
-        # to runtime), it probably needs to appear in docs/requirements.txt, or as a mock
-        # in docs/conf.py
-    ],
-    extras_require={
-        # optional dependencies, required by some features
-        "all": [
-            "fairscale",
-            "scipy>1.5.1",
-            "shapely",
-            "pygments>=2.2",
-            "psutil",
-            "panopticapi @ https://github.com/cocodataset/panopticapi/archive/master.zip",
-        ],
-        # dev dependencies. Install them by `pip install 'detectron2[dev]'`
-        "dev": [
-            "flake8==3.8.1",
-            "isort==4.3.21",
-            "flake8-bugbear",
-            "flake8-comprehensions",
-            "black==22.3.0",
-        ],
-    },
-    ext_modules=get_extensions(),
-    cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
-)
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/modeling/test_mmdet.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/modeling/test_mmdet.py
deleted file mode 100644
index a743b0b67d5ab664257040621d28c1b1b4451709..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/modeling/test_mmdet.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import unittest
-
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.mmdet_wrapper import MMDetBackbone, MMDetDetector
-
-try:
-    import mmdet.models  # noqa
-
-    HAS_MMDET = True
-except ImportError:
-    HAS_MMDET = False
-
-
-@unittest.skipIf(not HAS_MMDET, "mmdet not available")
-class TestMMDetWrapper(unittest.TestCase):
-    def test_backbone(self):
-        MMDetBackbone(
-            backbone=dict(
-                type="DetectoRS_ResNet",
-                conv_cfg=dict(type="ConvAWS"),
-                sac=dict(type="SAC", use_deform=True),
-                stage_with_sac=(False, True, True, True),
-                depth=50,
-                num_stages=4,
-                out_indices=(0, 1, 2, 3),
-                frozen_stages=1,
-                norm_cfg=dict(type="BN", requires_grad=True),
-                norm_eval=True,
-                style="pytorch",
-            ),
-            neck=dict(
-                type="FPN",
-                in_channels=[256, 512, 1024, 2048],
-                out_channels=256,
-                num_outs=5,
-            ),
-            # skip pretrained model for tests
-            # pretrained_backbone="torchvision://resnet50",
-            output_shapes=[ShapeSpec(channels=256, stride=s) for s in [4, 8, 16, 32, 64]],
-            output_names=["p2", "p3", "p4", "p5", "p6"],
-        )
-
-    def test_detector(self):
-        # a basic R50 Mask R-CNN
-        MMDetDetector(
-            detector=dict(
-                type="MaskRCNN",
-                backbone=dict(
-                    type="ResNet",
-                    depth=50,
-                    num_stages=4,
-                    out_indices=(0, 1, 2, 3),
-                    frozen_stages=1,
-                    norm_cfg=dict(type="BN", requires_grad=True),
-                    norm_eval=True,
-                    style="pytorch",
-                    # skip pretrained model for tests
-                    # init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'))
-                ),
-                neck=dict(
-                    type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5
-                ),
-                rpn_head=dict(
-                    type="RPNHead",
-                    in_channels=256,
-                    feat_channels=256,
-                    anchor_generator=dict(
-                        type="AnchorGenerator",
-                        scales=[8],
-                        ratios=[0.5, 1.0, 2.0],
-                        strides=[4, 8, 16, 32, 64],
-                    ),
-                    bbox_coder=dict(
-                        type="DeltaXYWHBBoxCoder",
-                        target_means=[0.0, 0.0, 0.0, 0.0],
-                        target_stds=[1.0, 1.0, 1.0, 1.0],
-                    ),
-                    loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
-                    loss_bbox=dict(type="L1Loss", loss_weight=1.0),
-                ),
-                roi_head=dict(
-                    type="StandardRoIHead",
-                    bbox_roi_extractor=dict(
-                        type="SingleRoIExtractor",
-                        roi_layer=dict(type="RoIAlign", output_size=7, sampling_ratio=0),
-                        out_channels=256,
-                        featmap_strides=[4, 8, 16, 32],
-                    ),
-                    bbox_head=dict(
-                        type="Shared2FCBBoxHead",
-                        in_channels=256,
-                        fc_out_channels=1024,
-                        roi_feat_size=7,
-                        num_classes=80,
-                        bbox_coder=dict(
-                            type="DeltaXYWHBBoxCoder",
-                            target_means=[0.0, 0.0, 0.0, 0.0],
-                            target_stds=[0.1, 0.1, 0.2, 0.2],
-                        ),
-                        reg_class_agnostic=False,
-                        loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
-                        loss_bbox=dict(type="L1Loss", loss_weight=1.0),
-                    ),
-                    mask_roi_extractor=dict(
-                        type="SingleRoIExtractor",
-                        roi_layer=dict(type="RoIAlign", output_size=14, sampling_ratio=0),
-                        out_channels=256,
-                        featmap_strides=[4, 8, 16, 32],
-                    ),
-                    mask_head=dict(
-                        type="FCNMaskHead",
-                        num_convs=4,
-                        in_channels=256,
-                        conv_out_channels=256,
-                        num_classes=80,
-                        loss_mask=dict(type="CrossEntropyLoss", use_mask=True, loss_weight=1.0),
-                    ),
-                ),
-                # model training and testing settings
-                train_cfg=dict(
-                    rpn=dict(
-                        assigner=dict(
-                            type="MaxIoUAssigner",
-                            pos_iou_thr=0.7,
-                            neg_iou_thr=0.3,
-                            min_pos_iou=0.3,
-                            match_low_quality=True,
-                            ignore_iof_thr=-1,
-                        ),
-                        sampler=dict(
-                            type="RandomSampler",
-                            num=256,
-                            pos_fraction=0.5,
-                            neg_pos_ub=-1,
-                            add_gt_as_proposals=False,
-                        ),
-                        allowed_border=-1,
-                        pos_weight=-1,
-                        debug=False,
-                    ),
-                    rpn_proposal=dict(
-                        nms_pre=2000,
-                        max_per_img=1000,
-                        nms=dict(type="nms", iou_threshold=0.7),
-                        min_bbox_size=0,
-                    ),
-                    rcnn=dict(
-                        assigner=dict(
-                            type="MaxIoUAssigner",
-                            pos_iou_thr=0.5,
-                            neg_iou_thr=0.5,
-                            min_pos_iou=0.5,
-                            match_low_quality=True,
-                            ignore_iof_thr=-1,
-                        ),
-                        sampler=dict(
-                            type="RandomSampler",
-                            num=512,
-                            pos_fraction=0.25,
-                            neg_pos_ub=-1,
-                            add_gt_as_proposals=True,
-                        ),
-                        mask_size=28,
-                        pos_weight=-1,
-                        debug=False,
-                    ),
-                ),
-                test_cfg=dict(
-                    rpn=dict(
-                        nms_pre=1000,
-                        max_per_img=1000,
-                        nms=dict(type="nms", iou_threshold=0.7),
-                        min_bbox_size=0,
-                    ),
-                    rcnn=dict(
-                        score_thr=0.05,
-                        nms=dict(type="nms", iou_threshold=0.5),
-                        max_per_img=100,
-                        mask_thr_binary=0.5,
-                    ),
-                ),
-            ),
-            pixel_mean=[1, 2, 3],
-            pixel_std=[1, 2, 3],
-        )
diff --git a/spaces/cbg342/GPT4-Unlimited-Plugins/app.py b/spaces/cbg342/GPT4-Unlimited-Plugins/app.py
deleted file mode 100644
index 38b6c3a8cda2f83469f51ccf83c67f810b4b20d6..0000000000000000000000000000000000000000
--- a/spaces/cbg342/GPT4-Unlimited-Plugins/app.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import re, os, subprocess, openai, random, regex, shutil, requests, datetime
-import pandas as pd
-import numpy as np
-import streamlit as st
-from streamlit_chat import message
-
-# some helper vars and functions
-thispath = os.path.realpath(os.path.dirname(__file__))
-if 'displayChat' not in st.session_state:
-    with st.spinner('Setting up plugins...'):
-        os.system('mkdir files')
-        thishour = str(datetime.datetime.now().hour)
-        if os.path.isfile(os.path.join(thispath, 'timestamp.txt')):
-            with open(os.path.join(thispath, 'timestamp.txt')) as f:
-                stampHour = f.readline().strip('\n')
-            if thishour != stampHour:
-                os.system('rm -rf files/*')
-                os.system('echo '+thishour+' > timestamp.txt')
-        else:
-            os.system('echo '+thishour+' > timestamp.txt')
-        os.system('mkdir plugins')
-        googleS = requests.get('https://raw.githubusercontent.com/d3n7/GPT-4-Unlimited-Tools/main/plugins/google.py').content
-        googlePath = os.path.join(thispath, 'plugins', 'google.py')
-        with open(googlePath, 'wb') as f:
-            f.write(googleS)
-        blipS = requests.get('https://raw.githubusercontent.com/d3n7/GPT-4-Unlimited-Tools/main/plugins/blip3.py').content
-        blipPath = os.path.join(thispath, 'plugins', 'blip2.py')
-        with open(blipPath, 'wb') as f:
-            f.write(blipS)
-    st.session_state['running'] = False  # Triggers main GPT loop
-    st.session_state['followup'] = False # Follow flag for the main loop
-    st.session_state['prompt'] = ''
-    st.session_state['command'] = '' #command to be run locally
-    st.session_state['acceptreject'] = False #shows accept/reject buttons for when commands are called
-    st.session_state['history'] = [] #OpenAI convrsation history stored here
-    st.session_state['displayChat'] = False
-    st.session_state['displayCost'] = False
-    st.session_state['download'] = False #display download button
-    st.session_state['totalCost'] = 0 # total cost of API calls
-regx = [r"([A-Z]+\(((?:[^()\"']|(?:\"[^\"]*\")|(?:'[^']*')|\((?1)*\))*)\))",
-        r'''(?:"(?:[^"\\]|\\.)*"|'(?:[^'\\]|\\.)*'|\b[^,]+)'''] #parsing commands, parsing arguments (thanks GPT-4)
-#system message
-sysPrompt = 'You now have access to some commands to help complete the user\'s request. ' \
-            'You are able to access the user\'s machine with these commands. In every message you send, ' \
-            'include "COMMAND: " with your command at the end. Here is a list of commands with ' \
-            'explanations of how they are used:\n{}\n When you use a command, the user will respond ' \
-            'with "Response: " followed by the output of the commmand. Use this output to help the ' \
-            'user complete their request.'
-
-#format command table for GPT-4
-def formatTable(table):
-    lines = ''
-    for x, i in enumerate(table['GPT Commands']):
-        lines += '{} - {}\n'.format(table['GPT Commands'][x],table['GPT Explanations'][x])
-    return(lines)
-
-#Ask GPT a prompt, update history and total cost, return a response
-def askGPT(input, version):
-    st.session_state['history'].append({'role': 'user', 'content': input})
-    with st.spinner('Talking to OpenAI...'):
-        r = openai.ChatCompletion.create(model=version, messages=st.session_state['history'])
-    resp = r['choices'][0]['message']['content']
-    costFactor = [0.03, 0.06] if version == 'gpt-4' else [0.002, 0.002]
-    st.session_state['totalCost'] += r['usage']['prompt_tokens']/1000*costFactor[0]+r['usage']['completion_tokens']/1000*costFactor[1]
-    st.session_state['history'].append({'role': 'assistant', 'content': resp})
-    return resp
-
-#restart main loop with followup flag
-def followup():
-    st.session_state['followup'], st.session_state['running'] = True, True
-    st.experimental_rerun()
-
-#run a GPT command or reject it
-def runCmd(flag):
-    if flag and (not st.session_state['command'].startswith('rm ')):
-        with st.spinner('Running command \'' + st.session_state['command'] + '\''):
-            try:
-                p = subprocess.Popen(st.session_state['command'], shell=True, stderr=subprocess.STDOUT, stdout=subprocess.PIPE)
-                p.wait()
-                st.session_state['prompt'] = 'Response: ' + p.communicate()[0].decode("utf-8")
-            except subprocess.CalledProcessError as e:
-                st.session_state['prompt'] = 'Response: ' + e.output.decode("utf-8")
-    else:
-        st.session_state['prompt'] = "Response: User rejected this command"
-    followup()
-
-# WEB APP
-st.markdown('# GPT-4 UNLIMITED PLUGINS')
-st.markdown('Made by [d3nt](https://github.com/d3n7) to give GPT-4 access to any commands/scripts you want via the command line. This unlocks the ability for GPT-4 to interact with the internet, APIs, and any applications that you could with a CLI. Basically it\'s open source, flexible, plugins for GPT-4.')
-
-#User inputs
-st.markdown('### OpenAI Settings')
-openaikey = st.text_input('OpenAI API Key', type='password')
-replicatekey = st.text_input('(OPTIONAL) Replicate API Key', type='password')
-modelV = st.selectbox('Model', ('GPT-4', 'GPT-3.5-Turbo'))
-
-st.markdown('### Editable Knowledge Base\nDelete any commands will not need to save tokens and increase accuracy.\n\nBe careful with the Raw Translation column. This is code that gets executed by your machine.')
-d = {'GPT Commands': ['GOOGLE("question")', 'PYTHON(script.py)', 'MAKEFILE("content\\nhere", filename.txt)', 'READFILE(filename.txt)', 'LISTFILES()', 'BLIP("What\'s in this image?", img1.jpg)'],
-     'GPT Explanations': ['Search Google with the given text and return the results', 'Run a python script with the given file name. Do not use quotes for the filename argument.', 'Make a file with the given content and file name.', 'Read the content of a given filename', 'List the files you have access to', 'Ask BLIP-2, a vision model, a given question about a given image'],
-     'Raw Translation': ['python plugins/google.py {}', 'cd files && python {} && cd ../', 'echo {} > files/{}', 'cat files/{}', 'ls files', 'python plugins/blip2.py {} {}']
-     }
-df = pd.DataFrame(data=d, dtype='string')
-commandTable = st.experimental_data_editor(df, use_container_width=True, num_rows='dynamic')
-
-st.markdown('### Upload/Download Files')
-uploadFile = st.file_uploader('')
-cola, colb, colc = st.columns(3)
-with cola:
-    if st.button('Upload'):
-        if uploadFile:
-            fname = uploadFile.name
-            st.markdown(fname)
-            with open(os.path.join(thispath, 'files', fname), 'wb') as f:
-                f.write(uploadFile.getbuffer())
-            st.write('Success')
-with colb:
-    if st.button('Make Zip'):
-        shutil.make_archive(os.path.join(thispath, 'files'), 'zip', os.path.join(thispath, 'files'))
-        st.session_state['download'] = True
-with colc:
-    if st.session_state['download']:
-        with open(os.path.join(thispath, 'files.zip'), 'rb') as f:
-            st.download_button('Download Zip', f, file_name='file.zip', key='filezip')
-
-st.markdown('### Chat')
-prompt = st.text_input('Message')
-col1, col2, col3, col4 = st.columns(4)
-#this button triggers main loop
-with col1:
-    if st.button('Send'):
-        st.session_state['running'] = True
-with col2:
-    newSession = st.checkbox('New Session', True)
-with col3:
-    showAll = st.checkbox('Show Commands And Outputs', False)
-with col4:
-    manualApproval = st.checkbox('Require Manual Approval', True)
-
-#MAIN GPT LOOP
-if st.session_state['running']:
-    st.session_state['running'] = False #reset running flag
-
-    #get user prompt
-    if not st.session_state['followup']:
-        st.session_state['prompt'] = prompt
-
-    os.environ['REPLICATE_API_TOKEN'] = replicatekey
-    if openaikey != '':
-        #set system prompt or update system prompt
-        if (newSession or st.session_state['history'] == []) and (not st.session_state['followup']):
-            st.session_state['history'] = [{'role': 'system', 'content': sysPrompt.format(formatTable(commandTable))}]
-        else:
-            st.session_state['history'][0] = {'role': 'system', 'content': sysPrompt.format(formatTable(commandTable))}
-        st.session_state['followup'] = False #reset followup flag
-
-        #turn on display for chat and cost if it's not already on
-        if not st.session_state['displayChat']:
-            st.session_state['displayChat'] = True
-        if not st.session_state['displayCost']:
-            st.session_state['displayCost'] = True
-
-        #ask GPT-4
-        openai.api_key = openaikey
-        response = askGPT(st.session_state['prompt'], modelV.lower())
-
-        #parse GPT commands, possibly trigger this loop again
-        if len(regex.findall(regx[0], response)) >= 1:
-            cmd = regex.findall(regx[0], response)[0][0]
-            stem = ''
-            rawArgs = ''
-            cmdId = -1
-
-            #seperate command into stem and arguments
-            for x, i in enumerate(cmd):
-                if i == '(':
-                    stem = cmd[:x]
-                    rawArgs = cmd[x+1:][:-1]
-                    break
-
-            #identify command
-            for x, i in enumerate(commandTable['GPT Commands']):
-                if stem in i:
-                    cmdId = x
-                    break
-
-            #Handle incorrect command usage, or run the command
-            rawArgs.replace('\n', '\\n')
-            rawArgs.replace('\\\n', '\\n')
-            if cmdId == -1:
-                st.session_state['prompt'] = 'Response: Unrecognized command'
-                followup()
-            elif "'''" in rawArgs:
-                st.session_state['prompt'] = 'Response: Error parsing multi-line string (\'\'\') Use a single line with escaped newlines instead (")'
-                followup()
-            elif '"""' in rawArgs:
-                st.session_state['prompt'] = 'Response: Error parsing multi-line string (\"\"\") Use a single line with escaped newlines instead (")'
-                followup()
-            else:
-                # Fetch command, turn raw argument string into a list of arguments, and format command
-                st.session_state['command'] = commandTable['Raw Translation'][cmdId]
-                args = []
-                if rawArgs != '':
-                    args = re.findall(regx[1], rawArgs)
-                    st.session_state['command'] = st.session_state['command'].format(*args)
-
-                # No single quote arguments allowed. Messes up MAKEFILE() and probably other commands.
-                singleQuotes = False
-                for i in args:
-                    if i.startswith("'"):
-                        singleQuotes = True
-                        st.session_state['prompt'] = "Response: Error parsing argument in single quotes. Use double quotes around the argument instead"
-                        followup()
-                        break
-
-                #If none of the above was a problem, run the command
-                if not singleQuotes:
-                    if manualApproval:
-                        st.session_state['acceptreject'] = True
-                    else:
-                        runCmd(1)
-
-    else:
-        st.warning('Make sure OpenAI key is entered', icon='⚠️')
-
-#UI for accepting/rejecting commands
-col5, col6 = st.columns(2)
-if st.session_state['acceptreject']:
-    st.warning('GPT is trying to run the following command: ' + st.session_state['command'] + '\nPlease accept or reject this request.')
-    with col5:
-        if st.button('Accept'):
-            st.session_state['acceptreject'] = False
-            runCmd(1)
-    with col6:
-        if st.button('Reject'):
-            st.session_state['acceptreject'] = False
-            runCmd(0)
-
-#display cost for the user
-if st.session_state['displayCost']:
-    st.info('Total OpenAI cost: $'+str(round(st.session_state['totalCost'],2)), icon='💸')
-
-#display chat for the user
-if st.session_state['displayChat']:
-    for i in st.session_state['history']:
-        if i['role'] == 'user':
-            if not showAll:
-                if 'Response:' not in i['content']:
-                    message(i['content'], is_user=True, key=random.randint(1,9999))
-            else:
-                message(i['content'], is_user=True, key=random.randint(1,9999))
-        elif i['role'] == 'assistant':
-            if not showAll:
-                if 'COMMAND' not in i['content']:
-                    message(i['content'], key=random.randint(1,9999))
-            else:
-                message(i['content'], key=random.randint(1,9999))
\ No newline at end of file
diff --git a/spaces/ccolas/TastyPiano/src/music/representation_learning/sentence_transfo/sentence_transformers/losses/MultipleNegativesRankingLoss.py b/spaces/ccolas/TastyPiano/src/music/representation_learning/sentence_transfo/sentence_transformers/losses/MultipleNegativesRankingLoss.py
deleted file mode 100644
index 666bb3c26bda35d2475be77497e23b6665343304..0000000000000000000000000000000000000000
--- a/spaces/ccolas/TastyPiano/src/music/representation_learning/sentence_transfo/sentence_transformers/losses/MultipleNegativesRankingLoss.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import torch
-from torch import nn, Tensor
-from typing import Iterable, Dict
-from ..SentenceTransformer import SentenceTransformer
-from .. import util
-from ..util import mismatched_sizes_all_gather
-
-
-class MultipleNegativesRankingLoss(nn.Module):
-    """
-        This loss expects as input a batch consisting of sentence pairs (a_1, p_1), (a_2, p_2)..., (a_n, p_n)
-        where we assume that (a_i, p_i) are a positive pair and (a_i, p_j) for i!=j a negative pair.
-
-        For each a_i, it uses all other p_j as negative samples, i.e., for a_i, we have 1 positive example (p_i) and
-        n-1 negative examples (p_j). It then minimizes the negative log-likehood for softmax normalized scores.
-
-        This loss function works great to train embeddings for retrieval setups where you have positive pairs (e.g. (query, relevant_doc))
-        as it will sample in each batch n-1 negative docs randomly.
-
-        The performance usually increases with increasing batch sizes.
-
-        For more information, see: https://arxiv.org/pdf/1705.00652.pdf
-        (Efficient Natural Language Response Suggestion for Smart Reply, Section 4.4)
-
-        You can also provide one or multiple hard negatives per anchor-positive pair by structering the data like this:
-        (a_1, p_1, n_1), (a_2, p_2, n_2)
-
-        Here, n_1 is a hard negative for (a_1, p_1). The loss will use for the pair (a_i, p_i) all p_j (j!=i) and all n_j as negatives.
-
-        Example::
-
-            from sentence_transformers import SentenceTransformer, losses, InputExample
-            from torch.utils.data import DataLoader
-
-            model = SentenceTransformer('distilbert-base-uncased')
-            train_examples = [InputExample(texts=['Anchor 1', 'Positive 1']),
-                InputExample(texts=['Anchor 2', 'Positive 2'])]
-            train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=32)
-            train_loss = losses.MultipleNegativesRankingLoss(model=model)
-    """
-    def __init__(self, model: SentenceTransformer, scale: float = 20.0, similarity_fct = util.cos_sim):
-        """
-        :param model: SentenceTransformer model
-        :param scale: Output of similarity function is multiplied by scale value
-        :param similarity_fct: similarity function between sentence embeddings. By default, cos_sim. Can also be set to dot product (and then set scale to 1)
-        """
-        super(MultipleNegativesRankingLoss, self).__init__()
-        self.model = model
-        self.scale = scale
-        self.similarity_fct = similarity_fct
-        self.cross_entropy_loss = nn.CrossEntropyLoss()
-
-    def forward(self, sentence_features: Iterable[Dict[str, Tensor]], labels: Tensor):
-        # reps = [torch.randn_like(torch.zeros([3, 768])), torch.randn_like(torch.zeros([3, 768]))]
-        reps = [self.model(sentence_feature)['sentence_embedding'] for sentence_feature in sentence_features]
-        embeddings_a = reps[0]
-
-        if torch.distributed.is_initialized():
-
-            embeddings_b = reps[1]
-            if len(reps) > 2:
-                embeddings_n = torch.cat(reps[2:])
-            else:
-                embeddings_n = embeddings_b[:0, :]
-            full_embeddings_b = mismatched_sizes_all_gather(embeddings_b)
-            full_embeddings_b = torch.cat(full_embeddings_b)
-            full_embeddings_n = mismatched_sizes_all_gather(embeddings_n)
-            full_embeddings_n = torch.cat(full_embeddings_n)
-            candidates = torch.cat([full_embeddings_b, full_embeddings_n])
-
-            scores = self.similarity_fct(embeddings_a, candidates) * self.scale
-            labels = torch.tensor(range(len(scores)), dtype=torch.long, device=scores.device)\
-                     + len(scores) * torch.distributed.get_rank()
-            acc = torch.mean((torch.argmax(scores, dim=1) == labels).float())
-            return self.cross_entropy_loss(scores, labels), acc
-
-        else:
-            candidates = torch.cat(reps[1:])
-            scores = self.similarity_fct(embeddings_a, candidates) * self.scale
-            labels = torch.tensor(range(len(scores)), dtype=torch.long,
-                                  device=scores.device)  # Example a[i] should match with b[i]
-            acc = torch.mean((torch.argmax(scores, dim=1) == labels).float())
-            return self.cross_entropy_loss(scores, labels), acc
-
-    def get_config_dict(self):
-        return {'scale': self.scale, 'similarity_fct': self.similarity_fct.__name__}
diff --git a/spaces/ccolas/TastyPiano/src/music/utilities/handcoded_rep_utilities/tht/tests/tactus_hypothesis_tracker_test.py b/spaces/ccolas/TastyPiano/src/music/utilities/handcoded_rep_utilities/tht/tests/tactus_hypothesis_tracker_test.py
deleted file mode 100644
index 2658fde351ead518736d61c33a909ace07844ea2..0000000000000000000000000000000000000000
--- a/spaces/ccolas/TastyPiano/src/music/utilities/handcoded_rep_utilities/tht/tests/tactus_hypothesis_tracker_test.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import unittest
-import pytest
-import collections
-
-from m2.tht import tactus_hypothesis_tracker
-from m2.tht import correction
-
-
-class TrimSimHypothesisTest(unittest.TestCase):
-
-    def setUp(self):
-        pass
-
-    class sim_f_mock():
-
-        def __init__(self):
-            self.calls = []
-
-        def __call__(self, h, i, _):
-            self.calls.append((h, i))
-            return int(i % h == 0)
-
-    def test_trim_sim_hypothesis(self):
-        sim_f = self.sim_f_mock()
-        tht = tactus_hypothesis_tracker.TactusHypothesisTracker(
-            None, None, sim_f, 0.00001, None, None, None)
-        kept, trimmed = tht._trim_similar_hypotheses(range(2, 10), sim_f)
-        self.assertEqual(sim_f.calls,
-                         [(2, x) for x in range(3, 10)] +
-                         [(3, 5), (3, 7), (3, 9)] +
-                         [(5, 7)])
-        self.assertEqual(kept, [2, 3, 5, 7])
-        self.assertEqual(trimmed,
-                         [(4, 2), (6, 2), (8, 2)] +
-                         [(9, 3)])
-
-    def test_k_best_hypothesis(self):
-        HT = collections.namedtuple('HT', ['id', 'conf'])
-        hts = [HT(idx, 7 if idx % 3 == 0 else idx)
-               for idx in range(11)]
-        tht = tactus_hypothesis_tracker.TactusHypothesisTracker(
-            None, None, None, None, None, None, 5)
-        k_best, other = tht._split_k_best_hypotheses(hts)
-        self.assertEqual(k_best, [hts[0], hts[3], hts[6], hts[8], hts[10]])
-        self.assertEqual(other, [hts[1], hts[2], hts[4],
-                                 hts[5], hts[7], hts[9]])
-
-def id_corr_f(ht, op):
-    return correction.HypothesisCorrection(ht.r, ht.d, ht.r, ht.d)
-
-class TestHypothesisGeneration:
-
-    def test_generated_hypothesis_with_no_restrictions(self, mocker):
-        onset_times = list(range(10))
-        tht = tactus_hypothesis_tracker.TactusHypothesisTracker(
-                eval_f=lambda ht, op: ht.r,
-                corr_f=id_corr_f,
-                sim_f=lambda h, i, *a: False,
-                similarity_epsilon=0,
-                max_delta=1000,
-                min_delta=1,
-                max_hypotheses=1000)
-        hts = tht(onset_times)
-        assert len(hts) == (10 * 9) / 2
-        
-
-    def test_generated_hypothesis_with_max_hypothesis_restriction(self,
-            mocker):
-        onset_times = list(range(10))
-        tht = tactus_hypothesis_tracker.TactusHypothesisTracker(
-                eval_f=lambda ht, op: ht.r,
-                corr_f=id_corr_f,
-                sim_f=lambda h, i, *a: False,
-                similarity_epsilon=0,
-                max_delta=1000,
-                min_delta=1,
-                max_hypotheses=10)
-        hts = tht(onset_times)
-        assert len(hts) == 10
-
-    def test_generated_hypothesis_with_max_delta_restriction(self, mocker):
-        onset_times = list(range(10))
-        tht = tactus_hypothesis_tracker.TactusHypothesisTracker(
-                eval_f=lambda ht, op: ht.r,
-                corr_f=id_corr_f,
-                sim_f=lambda h, i, *a: False,
-                similarity_epsilon=0,
-                max_delta=1,
-                min_delta=1,
-                max_hypotheses=1000)
-        hts = tht(onset_times)
-        assert len(hts) == 9
-        
-        tht = tactus_hypothesis_tracker.TactusHypothesisTracker(
-                eval_f=lambda ht, op: ht.r,
-                corr_f=id_corr_f,
-                sim_f=lambda h, i, *a: False,
-                similarity_epsilon=0,
-                max_delta=2,
-                min_delta=1,
-                max_hypotheses=1000)
-        hts = tht(onset_times)
-        assert len(hts) == 9 + 8
-
-    def test_generated_hypothesis_with_min_delta_restriction(self, mocker):
-        onset_times = list(range(10))
-        tht = tactus_hypothesis_tracker.TactusHypothesisTracker(
-                eval_f=lambda ht, op: ht.r,
-                corr_f=id_corr_f,
-                sim_f=lambda h, i, *a: False,
-                similarity_epsilon=0,
-                max_delta=3,
-                min_delta=3,
-                max_hypotheses=1000)
-        hts = tht(onset_times)
-        assert len(hts) == 7
-
-
-onset_times = list(range(10))
-proj_1 = lambda xs: [x[0] for x in xs]
-
-@pytest.fixture
-def hts(mocker):
-    tht = tactus_hypothesis_tracker.TactusHypothesisTracker(
-            eval_f=lambda ht, op: ht.r,
-            corr_f=correction.no_corr,
-            sim_f=lambda h, i, *a: False,
-            similarity_epsilon=0,
-            max_delta=4,
-            min_delta=2,
-            max_hypotheses=50)
-    hts = tht(onset_times)
-    return hts
-
-
-class TestGeneralTrackingResults:
-        
-
-    def test_deltas_in_range(self, hts):
-        print([ht.d for ht in hts.values()])
-        assert all([ht.d >= 2 and ht.d <= 4
-                    for ht in hts.values()
-                    ])
-
-    def test_non_repeated_hypothesis(self, hts):
-        assert (len(set([ht.origin_onsets() for ht in hts.values()])) ==
-                len(hts))
-
-    def test_conf_and_corr_onset_index_are_equal(self, hts):
-        assert all([proj_1(ht.corr) == proj_1(ht.confs)
-            for ht in hts.values()])
-
-    def test_conf_onsets_are_complete_and_greater_than_beta_2(self, hts):
-        assert all([proj_1(ht.confs) == list(range(ht.onset_indexes[1], 10))
-            for ht in hts.values()])
diff --git "a/spaces/cfwef/gpt/crazy_functions/\351\253\230\347\272\247\345\212\237\350\203\275\345\207\275\346\225\260\346\250\241\346\235\277.py" "b/spaces/cfwef/gpt/crazy_functions/\351\253\230\347\272\247\345\212\237\350\203\275\345\207\275\346\225\260\346\250\241\346\235\277.py"
deleted file mode 100644
index ebcad851f58f5d2305292fb38073c32870f34f17..0000000000000000000000000000000000000000
--- "a/spaces/cfwef/gpt/crazy_functions/\351\253\230\347\272\247\345\212\237\350\203\275\345\207\275\346\225\260\346\250\241\346\235\277.py"
+++ /dev/null
@@ -1,25 +0,0 @@
-from predict import predict_no_ui_long_connection
-from toolbox import CatchException, report_execption, write_results_to_file
-import datetime
-
-@CatchException
-def 高阶功能模板函数(txt, top_p, api_key, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。为了做到简单易读，该函数只有25行代码，所以不会实时反馈文字流或心跳，请耐心等待程序输出完成。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！"))
-    yield chatbot, history, '正常'  # 由于请求gpt需要一段时间，我们先及时地做一次状态显示
-
-    for i in range(5):
-        currentMonth = (datetime.date.today() + datetime.timedelta(days=i)).month
-        currentDay = (datetime.date.today() + datetime.timedelta(days=i)).day
-        i_say = f'历史中哪些事件发生在{currentMonth}月{currentDay}日？列举两条并发送相关图片。发送图片时，请使用Markdown，将Unsplash API中的PUT_YOUR_QUERY_HERE替换成描述该事件的一个最重要的单词。'
-        chatbot.append((i_say, "[Local Message] waiting gpt response."))
-        yield chatbot, history, '正常'  # 由于请求gpt需要一段时间，我们先及时地做一次状态显示
-
-        # history = [] 每次询问不携带之前的询问历史
-        gpt_say = predict_no_ui_long_connection(
-            inputs=i_say, top_p=top_p, api_key=api_key, temperature=temperature, history=[], 
-            sys_prompt="当你想发送一张照片时，请使用Markdown, 并且不要有反斜线, 不要用代码块。使用 Unsplash API (https://source.unsplash.com/1280x720/? < PUT_YOUR_QUERY_HERE >)。") # 请求gpt，需要一段时间
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say);history.append(gpt_say)
-        yield chatbot, history, '正常'  # 显示
\ No newline at end of file
diff --git a/spaces/charlesnchr/VSR-SIM/README.md b/spaces/charlesnchr/VSR-SIM/README.md
deleted file mode 100644
index 45ba47b9f2026d27128564c16f47af37befa5bbb..0000000000000000000000000000000000000000
--- a/spaces/charlesnchr/VSR-SIM/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: VSR SIM
-emoji: 🌍
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chasemcdo/hf_localai/main.go b/spaces/chasemcdo/hf_localai/main.go
deleted file mode 100644
index 12b129c1c87e8b7a3970542ab2e59d9ac4cc9823..0000000000000000000000000000000000000000
--- a/spaces/chasemcdo/hf_localai/main.go
+++ /dev/null
@@ -1,170 +0,0 @@
-package main
-
-import (
-	"encoding/json"
-	"fmt"
-	"os"
-	"path/filepath"
-
-	api "github.com/go-skynet/LocalAI/api"
-	"github.com/go-skynet/LocalAI/internal"
-	"github.com/go-skynet/LocalAI/pkg/gallery"
-	model "github.com/go-skynet/LocalAI/pkg/model"
-	"github.com/rs/zerolog"
-	"github.com/rs/zerolog/log"
-	"github.com/urfave/cli/v2"
-)
-
-func main() {
-	log.Logger = log.Output(zerolog.ConsoleWriter{Out: os.Stderr})
-
-	path, err := os.Getwd()
-	if err != nil {
-		log.Error().Msgf("error: %s", err.Error())
-		os.Exit(1)
-	}
-
-	app := &cli.App{
-		Name:    "LocalAI",
-		Version: internal.PrintableVersion(),
-		Usage:   "OpenAI compatible API for running LLaMA/GPT models locally on CPU with consumer grade hardware.",
-		Flags: []cli.Flag{
-			&cli.BoolFlag{
-				Name:    "f16",
-				EnvVars: []string{"F16"},
-			},
-			&cli.BoolFlag{
-				Name:    "debug",
-				EnvVars: []string{"DEBUG"},
-			},
-			&cli.BoolFlag{
-				Name:    "cors",
-				EnvVars: []string{"CORS"},
-			},
-			&cli.StringFlag{
-				Name:    "cors-allow-origins",
-				EnvVars: []string{"CORS_ALLOW_ORIGINS"},
-			},
-			&cli.IntFlag{
-				Name:    "threads",
-				Usage:   "Number of threads used for parallel computation. Usage of the number of physical cores in the system is suggested.",
-				EnvVars: []string{"THREADS"},
-				Value:   4,
-			},
-			&cli.StringFlag{
-				Name:    "models-path",
-				Usage:   "Path containing models used for inferencing",
-				EnvVars: []string{"MODELS_PATH"},
-				Value:   filepath.Join(path, "models"),
-			},
-			&cli.StringFlag{
-				Name:    "galleries",
-				Usage:   "JSON list of galleries",
-				EnvVars: []string{"GALLERIES"},
-			},
-			&cli.StringFlag{
-				Name:    "preload-models",
-				Usage:   "A List of models to apply in JSON at start",
-				EnvVars: []string{"PRELOAD_MODELS"},
-			},
-			&cli.StringFlag{
-				Name:    "preload-models-config",
-				Usage:   "A List of models to apply at startup. Path to a YAML config file",
-				EnvVars: []string{"PRELOAD_MODELS_CONFIG"},
-			},
-			&cli.StringFlag{
-				Name:    "config-file",
-				Usage:   "Config file",
-				EnvVars: []string{"CONFIG_FILE"},
-			},
-			&cli.StringFlag{
-				Name:    "address",
-				Usage:   "Bind address for the API server.",
-				EnvVars: []string{"ADDRESS"},
-				Value:   ":8080",
-			},
-			&cli.StringFlag{
-				Name:    "image-path",
-				Usage:   "Image directory",
-				EnvVars: []string{"IMAGE_PATH"},
-				Value:   "/tmp/generated/images",
-			},
-			&cli.StringFlag{
-				Name:    "audio-path",
-				Usage:   "audio directory",
-				EnvVars: []string{"AUDIO_PATH"},
-				Value:   "/tmp/generated/audio",
-			},
-			&cli.StringFlag{
-				Name:    "backend-assets-path",
-				Usage:   "Path used to extract libraries that are required by some of the backends in runtime.",
-				EnvVars: []string{"BACKEND_ASSETS_PATH"},
-				Value:   "/tmp/localai/backend_data",
-			},
-			&cli.IntFlag{
-				Name:    "context-size",
-				Usage:   "Default context size of the model",
-				EnvVars: []string{"CONTEXT_SIZE"},
-				Value:   512,
-			},
-			&cli.IntFlag{
-				Name:    "upload-limit",
-				Usage:   "Default upload-limit. MB",
-				EnvVars: []string{"UPLOAD_LIMIT"},
-				Value:   15,
-			},
-		},
-		Description: `
-LocalAI is a drop-in replacement OpenAI API which runs inference locally.
-
-Some of the models compatible are:
-- Vicuna
-- Koala
-- GPT4ALL
-- GPT4ALL-J
-- Cerebras
-- Alpaca
-- StableLM (ggml quantized)
-
-It uses llama.cpp, ggml and gpt4all as backend with golang c bindings.
-`,
-		UsageText: `local-ai [options]`,
-		Copyright: "go-skynet authors",
-		Action: func(ctx *cli.Context) error {
-			fmt.Printf("Starting LocalAI using %d threads, with models path: %s\n", ctx.Int("threads"), ctx.String("models-path"))
-			galls := ctx.String("galleries")
-			var galleries []gallery.Gallery
-			err := json.Unmarshal([]byte(galls), &galleries)
-			fmt.Println(err)
-			app, err := api.App(
-				api.WithConfigFile(ctx.String("config-file")),
-				api.WithGalleries(galleries),
-				api.WithJSONStringPreload(ctx.String("preload-models")),
-				api.WithYAMLConfigPreload(ctx.String("preload-models-config")),
-				api.WithModelLoader(model.NewModelLoader(ctx.String("models-path"))),
-				api.WithContextSize(ctx.Int("context-size")),
-				api.WithDebug(ctx.Bool("debug")),
-				api.WithImageDir(ctx.String("image-path")),
-				api.WithAudioDir(ctx.String("audio-path")),
-				api.WithF16(ctx.Bool("f16")),
-				api.WithDisableMessage(false),
-				api.WithCors(ctx.Bool("cors")),
-				api.WithCorsAllowOrigins(ctx.String("cors-allow-origins")),
-				api.WithThreads(ctx.Int("threads")),
-				api.WithBackendAssets(backendAssets),
-				api.WithBackendAssetsOutput(ctx.String("backend-assets-path")),
-				api.WithUploadLimitMB(ctx.Int("upload-limit")))
-			if err != nil {
-				return err
-			}
-
-			return app.Listen(ctx.String("address"))
-		},
-	}
-
-	err = app.Run(os.Args)
-	if err != nil {
-		log.Error().Msgf("error: %s", err.Error())
-		os.Exit(1)
-	}
-}
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/robust-speech-event/README.md b/spaces/chendl/compositional_test/transformers/examples/research_projects/robust-speech-event/README.md
deleted file mode 100644
index fd1a42c7d4bb58dd531186d74045c8e3d0a7ec99..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/robust-speech-event/README.md
+++ /dev/null
@@ -1,713 +0,0 @@
-# Robust Speech Challenge 🤗
-
-Welcome to the robust speech recognition challenge 🎙️ !
-
-The goal of this event is to build **robust**, **real-world** speech recognition (ASR) systems in as many languages as possible 🌏🌍🌎.
-If necessary and available, free access to a V100S 32 GB GPU will kindly be provided by the [OVHcloud team]( https://www.ovhcloud.com/) 🚀.
-This document summarizes all the relevant information required for the speech community event 📋.
-
-To sign-up, please see [this forum post](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) 🤗. Please make sure to:
-- Read it in detail
-- Fill the google form
-- Join our Discord server in the #join-sprint channel.
-
-## Table of Contents
-
-- [TLDR;](#tldr)
-- [Important dates](#important-dates)
-- [How to install pytorch, transformers, datasets](#how-to-install-relevant-libraries)
-- [Data and Preprocessing](#data-and-preprocessing)
-- [How to fine-tune an acoustic model](#how-to-finetune-an-acoustic-model)
-- [How to fine-tune with OVH could](#how-to-finetune-with-ovh-cloud)
-- [How to combine n-gram language models with acoustic model](#how-to-combine-n-gram-with-acoustic-model)
-- [Evaluation](#evaluation)
-- [Prizes](#prizes)
-- [Communication and Problems](#communication-and-problems)
-- [Talks](#talks)
-- [General Tips & Tricks](#general-tips-and-tricks)
-
-## TLDR
-
-Participants are encouraged to leverage pre-trained speech recognition checkpoints,
-preferably [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), 
-to train a speech recognition system in a language of their choice.
-
-Speech recognition systems should be trained using **PyTorch**, **🤗 Transformers**, and, **🤗 Datasets**.
-For more information on how to install the above libraries, please read through 
-[How to install pytorch, transformers, datasets](#how-to-install-relevant-libraries).
-
-Participants can make use of whatever data they think is useful to build a 
-speech recognition system for **real-world** audio data - 
-**except** the Common Voice `"test"` split of their chosen language.
-The section [Data and preprocessing](#data-and-preprocessing) explains 
-in more detail what audio data can be used, how to find suitable audio data, and 
-how the audio data can be processed.
-
-For training, it is recommended to use the [official training script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) or a modification thereof. A step-by-step guide on how to fine-tune 
-an acoustic model for a speech recognition system can be found under [How to fine-tune an acoustic model](#how-to-finetune-an-acoustic-model).
-If possible it is encouraged to fine-tune the acoustic models on local GPU machines, but 
-if those are not available, the OVH could team kindly provides a limited 
-number of GPUs for the event. Simply fill out [this google form](https://forms.gle/GFZkMkKLiufi75g28) to get access to a GPU.
-For more information on how to train an acoustic model on one of OVH's GPU - see [How to fine-tune a speech recognition model with OVHcould](#how-to-fine-tune-with-ovh-cloud).
-
-The performance of speech recognition system can often significantly be improved by adding a 
-language model for decoding. For more information on how to add a language model, please 
-take a look at [How to combine n-gram language models with speech recognition models](#how-to-combine-n-gram-with-model).
-
-During the event, the speech recognition system will be evaluated on both the Common Voice `"test"` split 
-of the participants' chosen language as well as the *real-world* `"dev"` data provided by 
-the Hugging Face team. 
-At the end of the robust speech recognition challenge, the speech recognition system will also be evaluated on the
-*real-world* `"test"` data provided by the Hugging Face team. Each participant should add an 
-`eval.py` script to her/his model repository in a specific format that lets one easily 
-evaluate the speech recognition system on both Common Voice's `"test"` data as well as the *real-world* audio 
-data. Please read through the [Evaluation](#evaluation) section to make sure your evaluation script is in the correct format. Speech recognition systems
-with evaluation scripts in an incorrect format can sadly not be considered for the Challenge.
-
-At the end of the event, the best performing speech recognition system 
-will receive a prize 🏆 - more information regarding the prizes can be found under [Prizes](#prizes).
-
-We believe that framing the event as a competition is more fun, but at the core, the event is about
-creating speech recognition systems in as many languages as possible as a community.
-This can be achieved by working together, helping each other to solve bugs, share important findings, etc...🤗
-
-**Note**:
-Please, read through the section on [Communication & Problems](#communication-and-problems) to make sure you 
-know how to ask for help, etc...
-All important announcements will be made on discord. Please make sure that 
-you've joined [this discord channel](https://discord.gg/SHr5wC7m)
-
-Also, please make sure that you have been added to the [Speech Event Organization](https://huggingface.co/speech-recognition-community-v2). 
-You should have received an invite by email. If you didn't receive an invite, please contact the organizers, *e.g.* Anton, Patrick, or Omar directly on discord.
-
-## Important dates
-
-![timeline](https://github.com/patrickvonplaten/scientific_images/raw/master/Robush%20Speech%20Challenge.png)
-
-
-## Data and preprocessing
-
-In this section, we will quickly go over how to find suitable training data and 
-how to preprocess it.
-
-To begin with, **all data except Common Voice's `"test"` data can be used as training data.**
-The exception includes all Common Voice versions as the test data split of later Common Voice versions often
-overlaps with the one of previous versions, *e.g.* the test data of Common Voice 7 in English is 
-to a big part identical to the test data of Common Voice 6 in English:
-
-```python
-load_dataset("mozilla-foundation/common_voice_7_0", "en", split="test") 
-```
-
-includes more or less the same data as
-
-```python
-load_dataset("mozilla-foundation/common_voice_6_1", "en", split="test") 
-```
-
-However, we strongly encourage participants to make use of Common Voice's other splits, *e.g.* `"train"` and `"validation"`.
-For most languages, the Common Voice dataset offers already a decent amount of training data. It is usually 
-always advantageous to collect additional data. To do so, the participants are in a first step encouraged to search the
-Hugging Face Hub for additional audio data, for example by selecting the category 
-["speech-processing"](https://huggingface.co/datasets?task_categories=task_categories:speech-processing&sort=downloads).
-All datasets that are available on the Hub can be downloaded via the 🤗 Datasets library in the same way Common Voice is downloaded.
-If one wants to combine multiple datasets for training, it might make sense to take a look at 
-the [`interleave_datasets`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=interleave#datasets.interleave_datasets) function.
-
-In addition, participants can also make use of their audio data. Here, please make sure that you **are allowed to use the audio data**. E.g., if audio data 
-is taken from media platforms, such as YouTube, it should be verified that the media platform and the owner of the data have given her/his approval to use the audio 
-data in the context of machine learning research. If you are not sure whether the data you want to use has the appropriate licensing, please contact the Hugging Face 
-team on discord.
-
-Next, let's talk about preprocessing. Audio data and transcriptions have to be brought into the correct format when 
-training the acoustic model (example shown in [How to fine-tune an acoustic model](#how-to-finetune-an-acoustic-model)).
-It is recommended that this is done by using 🤗 Datasets `.map()` function as shown 
-[here](https://github.com/huggingface/transformers/blob/9a2dabae7002258e41419491c73dd43ad61b5de7/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L444). As can be 
-see we can pass some characters that will be removed from the transcriptions, *e.g.*: `--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \`
-on the official ["Single GPU Example"](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition#single-gpu-ctc).
-The participants are free to modify this preprocessing by removing more characters or even replacing characters as 
-it is done in the [official blog post](https://github.com/huggingface/transformers/blob/9a2dabae7002258e41419491c73dd43ad61b5de7/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L444).
-**However**, there are some rules regarding what characters are allowed to be removed/replaced and which are not.
-These rules are not this straightforward and therefore often have to be evaluated case-by-case.
-It is allowed (and recommended) to normalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical 
-symbols and punctuation marks. A list of such symbols can *e.g.* be found [here](https://en.wikipedia.org/wiki/List_of_typographical_symbols_and_punctuation_marks) - however here we already must be careful. We should **not** remove a symbol that would change the meaning of the words, *e.g.* in English, 
-we should not remove the single quotation mark `'` since it would change the meaning of the word `"it's"` to `"its"` which would then be incorrect. 
-So the golden rule here is to not remove any characters that could change the meaning of a word into another word. This is not always obvious and should 
-be given some consideration. As another example, it is fine to remove the "Hyphen-minus" sign "`-`" since it doesn't change the 
-meaning of a word to another one. *E.g.* "`fine-tuning`" would be changed to "`finetuning`" which has still the same meaning.
-
-Since those choices are not always obvious when in doubt feel free to ask on Discord or even better post your question on the forum, as was 
-done, *e.g.* [here](https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586).
-
-## How to install relevant libraries
-
-The following libraries are required to fine-tune a speech model with 🤗 Transformers and 🤗 Datasets in PyTorch.
-
-- [PyTorch](https://pytorch.org/)
-- [Transformers](https://github.com/huggingface/transformers)
-- [Datasets](https://github.com/huggingface/datasets)
-
-We recommend installing the above libraries in a [virtual environment](https://docs.python.org/3/library/venv.html). 
-If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Create a virtual environment with the version of Python you're going
-to use and activate it.
-
-You should be able to run the command:
-
-```bash
-python3 -m venv <your-venv-name>
-```
-
-You can activate your venv by running
-
-```bash
-source ~/<your-venv-name>/bin/activate
-```
-
-To begin with please make sure you have PyTorch and CUDA correctly installed. 
-The following command should return ``True``:
-
-```bash
-python -c "import torch; print(torch.cuda.is_available())"
-```
-
-If the above command doesn't print ``True``, in the first step, please follow the
-instructions [here](https://pytorch.org/) to install PyTorch with CUDA.
-
-We strongly recommend making use of the provided PyTorch examples scripts in [transformers/examples/pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition) to train your speech recognition
-system.
-In all likelihood, you will adjust one of the example scripts, so we recommend forking and cloning the 🤗 Transformers repository as follows. 
-
-1. Fork the [repository](https://github.com/huggingface/transformers) by
-   clicking on the 'Fork' button on the repository's page. This creates a copy of the code
-   under your GitHub user account.
-
-2. Clone your fork to your local disk, and add the base repository as a remote:
-
-   ```bash
-   $ git clone https://github.com/<your Github handle>/transformers.git
-   $ cd transformers
-   $ git remote add upstream https://github.com/huggingface/transformers.git
-   ```
-
-3. Create a new branch to hold your development changes. This is especially useful to share code changes with your team:
-
-   ```bash
-   $ git checkout -b a-descriptive-name-for-my-project
-   ```
-
-4. Set up a PyTorch environment by running the following command your virtual environment:
-
-   ```bash
-   $ pip install -e ".[torch-speech]"
-   ```
-
-   (If transformers was already installed in the virtual environment, remove
-   it with `pip uninstall transformers` before reinstalling it in editable
-   mode with the `-e` flag.)
-
-   If you have already cloned that repo, you might need to `git pull` to get the most recent changes in the `transformers`
-   library.
-
-   Running this command will automatically install `torch` and the most relevant 
-   libraries required for fine-tuning a speech recognition system.
-
-Next, you should also install the 🤗 Datasets library. We strongly recommend installing the 
-library from source to profit from the most current additions during the community week.
-
-Simply run the following steps:
-
-```
-$ cd ~/
-$ git clone https://github.com/huggingface/datasets.git
-$ cd datasets
-$ pip install -e ".[streaming]"
-```
-
-If you plan on contributing a specific dataset during 
-the community week, please fork the datasets repository and follow the instructions 
-[here](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-create-a-pull-request).
-
-To verify that all libraries are correctly installed, you can run the following command in a Python shell.
-It verifies that both `transformers` and `datasets` have been correclty installed.
-
-```python
-from transformers import AutoModelForCTC, AutoProcessor
-from datasets import load_dataset
-
-dummy_dataset = load_dataset("common_voice", "ab", split="test")
-
-model = AutoModelForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
-model.to("cuda")
-
-processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
-
-input_values = processor(dummy_dataset[0]["audio"]["array"], return_tensors="pt", sampling_rate=16_000).input_values
-input_values = input_values.to("cuda")
-
-logits = model(input_values).logits
-
-assert logits.shape[-1] == 32
-```
-
-## How to finetune an acoustic model
-
-In this section, we show you how to fine-tune a pre-trained [XLS-R Model](https://huggingface.co/docs/transformers/model_doc/xls_r) on the [Common Voice 7 dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). 
-
-We recommend fine-tuning one of the following pre-trained XLS-R checkpoints:
-
-- [300M parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
-- [1B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-1b)
-- [2B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-2b)
-
-To begin with, please note that to use the Common Voice dataset, you 
-have to accept that **your email address** and **username** are shared with the 
-mozilla-foundation. To get access to the dataset please click on "*Access repository*" [here](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
-
-Next, we recommended that you get familiar with the XLS-R model and its capabilities.
-In collaboration with [Fairseq's Wav2Vec2 team](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec), 
-we've written ["Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers"](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) which gives an in-detail explanation of how XLS-R functions and how it can be fine-tuned.
-
-The blog can also be opened and directly fine-tuned in a google colab notebook.
-In this section, we will explain how to fine-tune the model on a local machine.
-
-1. **Log in**
-
-To begin with, you should check that you are correctly logged in and that you have `git-lfs` installed so that your fine-tuned model can automatically be uploaded.
-
-Run:
-
-```bash
-huggingface-cli login
-```
-
-to login. It is recommended to login with your access token that can be found under your hugging face profile (icon in the top right corner on [hf.co](http://hf.co/), then Settings -> Access Tokens -> User Access Tokens -> New Token (if haven't generated one already)
-
-You can then copy-paste this token to log in locally.
-
-2. **Create your model repository**
-
-First, let's make sure that `git-lfs` is correctly installed. To so, simply run:
-
-```bash
-git-lfs -v
-```
-
-The output should show something like `git-lfs/2.13.2 (GitHub; linux amd64; go 1.15.4)`. If your console states that the `git-lfs` command was not found, please make
-sure to install it [here](https://git-lfs.github.com/) or simply via: 
-
-```bash
-sudo apt-get install git-lfs
-```
-
-Now you can create your model repository which will contain all relevant files to 
-reproduce your training. You can either directly create the model repository on the 
-Hub (Settings -> New Model) or via the CLI. Here we choose to use the CLI instead.
-
-Assuming that we want to call our model repository *xls-r-ab-test*, we can run the 
-following command:
-
-```bash
-huggingface-cli repo create xls-r-ab-test
-```
-
-You can now see the model on the Hub, *e.g.* under https://huggingface.co/hf-test/xls-r-ab-test .
-
-Let's clone the repository so that we can define our training script inside.
-
-```bash
-git lfs install
-git clone https://huggingface.co/hf-test/xls-r-ab-test
-```
-
-3. **Add your training script and `run`-command to the repository**
-
-We encourage participants to add all relevant files for training directly to the 
-directory so that everything is fully reproducible.
-
-Let's first copy-paste the official training script from our clone 
-of `transformers` to our just created directory:
-
-```bash
-cp ~/transformers/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py ./
-```
-
-Next, we'll create a bash file to define the hyper-parameters and configurations 
-for training. More detailed information on different settings (single-GPU vs. multi-GPU) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition#connectionist-temporal-classification).
-
-For demonstration purposes, we will use a dummy XLS-R model `model_name_or_path="hf-test/xls-r-dummy"` on the very low-resource language of "Abkhaz" of [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0): `dataset_config_name="ab"` for just a single epoch.
-
-Before starting to train, let's make sure we have installed all the required libraries. You might want to run:
-
-```bash
-pip install -r ~/transformers/examples/pytorch/speech-recognition/requirements.txt
-```
-
-Alright, finally we can define the training script. We'll simply use some 
-dummy hyper-parameters and configurations for demonstration purposes.
-
-Note that we add the flag `--use_auth_token` so that datasets requiring access, 
-such as [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) can be downloaded. In addition, we add the `--push_to_hub` flag to make use of the 
-[Trainers `push_to-hub` functionality](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Trainer.push_to_hub) so that your model will be automatically uploaded to the Hub.
-
-Let's copy the following code snippet in a file called `run.sh`
-
-```bash
-echo '''python run_speech_recognition_ctc.py \
-	--dataset_name="mozilla-foundation/common_voice_7_0" \
-	--model_name_or_path="hf-test/xls-r-dummy" \
-	--dataset_config_name="ab" \
-	--output_dir="./" \
-	--overwrite_output_dir \
-	--max_steps="10" \
-	--per_device_train_batch_size="2" \
-	--learning_rate="3e-4" \
-	--save_total_limit="1" \
-	--evaluation_strategy="steps" \
-	--text_column_name="sentence" \
-	--length_column_name="input_length" \
-	--save_steps="5" \
-	--layerdrop="0.0" \
-	--freeze_feature_encoder \
-	--gradient_checkpointing \
-	--fp16 \
-	--group_by_length \
-	--push_to_hub \
-	--use_auth_token \
-	--do_train --do_eval''' > run.sh
-```
-
-4. **Start training**
-
-Now all that is left to do is to start training the model by executing the 
-run file.
-
-```bash
-bash run.sh
-```
-
-The training should not take more than a couple of minutes. 
-During the training intermediate saved checkpoints are automatically uploaded to
-your model repository as can be seen [on this commit](https://huggingface.co/hf-test/xls-r-ab-test/commit/0eb19a0fca4d7d163997b59663d98cd856022aa6) . 
-
-At the end of the training, the [Trainer](https://huggingface.co/docs/transformers/main/en/main_classes/trainer) automatically creates a nice model card and all 
-relevant files are uploaded.
-
-5. **Tips for real model training**
-
-The above steps illustrate how a model can technically be fine-tuned.
-However as you can see on the model card [hf-test/xls-r-ab-test](https://huggingface.co/hf-test/xls-r-ab-test), our demonstration has a very poor performance which is
-not surprising given that we trained for just 10 steps on a randomly initialized
-model.
-
-For real model training, it is recommended to use one of the actual pre-trained XLS-R models:
-
-- [300M parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
-- [1B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-1b)
-- [2B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-2b)
-
-Also, the hyper-parameters should be carefully chosen depending on the dataset.
-As an example, we will fine-tune the 300M parameters model on Swedish on a single 
-TITAN RTX 24GB GPU.
-
-The model will be called `"xls-r-300m-sv"`. 
-Following the above steps we first create the model:
-
-```bash
-huggingface-cli repo create xls-r-300m-sv
-```
-
-, clone it locally (assuming the `<username>` is `hf-test`)
-
-```bash
-git clone hf-test/xls-r-300m-sv
-```
-
-, and, define the following hyperparameters for training
-
-```bash
-echo '''python run_speech_recognition_ctc.py \
-	--dataset_name="mozilla-foundation/common_voice_7_0" \
-	--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
-	--dataset_config_name="sv-SE" \
-	--output_dir="./" \
-	--overwrite_output_dir \
-	--num_train_epochs="50" \
-	--per_device_train_batch_size="8" \
-	--per_device_eval_batch_size="8" \
-	--gradient_accumulation_steps="4" \
-	--learning_rate="7.5e-5" \
-	--warmup_steps="2000" \
-	--length_column_name="input_length" \
-	--evaluation_strategy="steps" \
-	--text_column_name="sentence" \
-	--chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … – \
-	--save_steps="500" \
-	--eval_steps="500" \
-	--logging_steps="100" \
-	--layerdrop="0.0" \
-	--activation_dropout="0.1" \
-	--save_total_limit="3" \
-	--freeze_feature_encoder \
-	--feat_proj_dropout="0.0" \
-	--mask_time_prob="0.75" \
-	--mask_time_length="10" \
-	--mask_feature_prob="0.25" \
-	--mask_feature_length="64" \
-	--gradient_checkpointing \
-	--use_auth_token \
-	--fp16 \
-	--group_by_length \
-	--do_train --do_eval \
-	--push_to_hub''' > run.sh
-```
-
-The training takes *ca.* 7 hours and yields a reasonable test word 
-error rate of 27% as can be seen on the automatically generated [model card](https://huggingface.co/hf-test/xls-r-300m-sv).
-
-The above-chosen hyperparameters probably work quite well on a range of different 
-datasets and languages but are by no means optimal. It is up to you to find a good set of 
-hyperparameters.
-
-
-## How to finetune with OVH cloud
-
-[![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://youtu.be/XkMnYocAEO0) For a more detailed guide on setting up OVHcloud please watch this video: https://youtu.be/XkMnYocAEO0
-
-### Creating an OVHCloud account
-*TIP*: If you haven't created a project on OVHcloud yet, make sure you've received your GPU voucher code *beforehand*, 
-so that you can skip entering the credit card information.
-1. If you're a US citizen, create an account via [OVHcloud.CA](https://ovhcloud.ca/). 
-If you're from anywhere else in the world, create an account via [OVHcloud.COM](https://ovhcloud.com/).
-2. Once logged in, click `Public Cloud` from the top menu and then click `Create your first OVH Public Cloud project`. 
-Then enter a project name (e.g. "huggingface"), enter your voucher code, and click `Continue` -> `Create my project`.
-*Note: if you see a request for credit card details during the last step, and you can't skip it, then your voucher code 
-is invalid. Please report it to the [#ovh-support](https://discord.gg/p4qqDV3M) channel on Discord.*
-
-### Setting up an AI notebook
-1. Go to the `Public Cloud` page and select `Project Management` -> `Users & Roles` from the menu on the left. 
-2. Click `+ Add user`. Write a user description (e.g. `AI Trainer`), and select an `AI Training Operator` user role. 
-Click `Confirm`.
-3. Write down the *username* and *password* (at the top of the screen) somewhere. They will be needed during step 7.
-4. Select `AI & Machine Learning` -> `AI Training` from the menu on the left. 
-Click `+ Launch a new job` on the AI Training page.
-5. On the `Launch a new job` page:
-   * In `1. Choose a region` select a region closest to you.
-   * In `2. Enter the Docker image` select `Custom image` -> `baaastijn/ovh_huggingface`.
-   * You can skip steps `3.` and `4.` if you will be using the Hugging Face Hub to store the models after training.
-   * In `5. Configure your job` select **1** `GPU`.
-   * Validate the info and Create the job.
-6. On the `AI Training Jobs` screen wait until the job's status changes from `Pending` to `Running`.
-7. Click `HTTP Access` from the Job's details page and log in with the AI training user you've created earlier. 
-Once logged in, you can close the page and click `HTTP Access` to launch a JupyterLab notebook.
-8. Awesome, now you have a free GPU-enabled Jupyter instance!
-
-**Note**: If you're an experienced Docker user, feel free to create a custom docker image with all of the needed packages 
-like the one in step 5. The Dockerfile for it is available here: 
-[baaastijn/Dockerimages](https://github.com/baaastijn/Dockerimages/tree/main/Hugginface_challenge_speech).
-Once you've built your image, push it to https://hub.docker.com/ and select it during the OVHcloud job creation.
-
-For more quick tutorials about OVHcloud AI products, check out the showcase https://vimeo.com/showcase/8903300
-
-## How to combine n-gram with acoustic model
-
-Having trained a speech recognition model with CTC as shown in the section above, 
-one can further improve the model's performance by adding an **n-gram language model**
-to the decoding process of the model. By doing so, we are replacing the naive greedy decoding 
-with **n-gram-boosted** beam search decoding.
-
-N-gram language models can be built on CPU in just a few minutes. *N-gram-boosted* beam search decoding noticeably slows down the 
-inference time, but also yields significant word error rates improvements - usually between 10-40 %.
-
-You can find an in-detail blog post on how to build an *n-gram* [here](https://huggingface.co/blog/wav2vec2-with-ngram).
-The blog post can be opened in a google colab and by adapting three lines of the example for your use case, one can directly
-create an *n-gram* in the google colab.
-The blog post gives in-detail instructions on how to build an n-gram and how to add it to your trained speech recognition model.
-
-- why one should add an *n-gram* to her/his speech recognition system,
-- how to build an *n-gram*, and,
-- how to add the built *n-gram* the speech recognition system for seamless decoding
-
-Our previously trained model - [xls-r-300m-sv](https://huggingface.co/hf-test/xls-r-300m-sv) - enjoys a 30% word error rate reduction after 
-having added an n-gram. As shown in the example of the blog post, we strongly advise participants to upload all files required for combining 
-the *n-gram* with a trained speech recognition model directly into the same model repository.
-
-## Evaluation
-
-Finally, we have arrived at the most fun part of the challenge - sitting back and
-watching the model transcribe audio. If possible, every participant should evaluate 
-the speech recognition system on the test set of Common Voice 7 and 
-ideally also on the real-world audio data (if available).
-For languages that have neither a Common Voice evaluation dataset nor a real world 
-evaluation dataset, please contact the organizers on Discord so that we can work 
-together to find some evaluation data.
-
-As a first step, one should copy the official `eval.py` script to her/his model 
-repository. Let's use our previously trained [xls-r-300m-sv](https://huggingface.co/hf-test/xls-r-300m-sv) again as an example.
-
-Assuming that we have a clone of the model's repo under `~/xls-r-300m-sv`, we can 
-copy the `eval.py` script to the repo.
-
-```bash
-cp ~/transformers/examples/research_projects/robust-speech-event/eval.py ~/xls-r-300m-sv
-```
-
-Next, we should adapt `eval.py` so that it fits our evaluation data. Here it is 
-important to keep the `eval.py` file in the following format:
-
-- 1. The following input arguments should not be changed and keep their original functionality/meaning (being to load the model and dataset): `"--model_id"`, `"--dataset"`, `"--config"`, `"--split"`. We recommend to not change any of the code written under `if __name__ == "__main__":`.
-- 2. The function `def log_results(result: Dataset, args: Dict[str, str])` should also not be changed. The function expects the above names attached to the `args` object as well as a `datasets.Dataset` object, called `result` which includes all predictions and target transcriptions under the names `"predictions"` and `"targets"` respectively.
-- 3. All other code can be changed and adapted. Participants are especially invited to change the `def normalize_text(text: str) -> str:` function as this might be a very language and model-training specific function.
-- 4. **Important**: It is not allowed to "cheat" in any way when in comes to pre-and postprocessing. In short, "cheating" refers to any of the following:
-	- a. Somehow giving the model access to the target transcriptions to improve performance. The model is not allowed to use the target transcriptions to generate its predictions.
-	- b. Pre-processing the target transcriptions in a way that makes the target transcriptions lose their original meaning. This corresponds to what has already been said in [Data and Preprocessing](#data-and-preprocessing) and is somewhat of a grey zone. It means that one should not remove characters that would make a word to lose its meaning. E.g., it is not allowed to replace all `e` in English with `i` and simply make the model learn that `e` and `i` are the same letter for a better word error rate. This would destroy the meaning of words such as `fell -> fill`. However, it is totally fine to normalize (*e.g.* lowercase) all letters, remove punctuation. There can be a lot of language-specific exceptions and in case you are not sure whether your target transcription pre-processing is allowed, please ask on the Discord channel.
-
-Uff, that was a lot of text describing how to make sure your `eval.py` script 
-is in the correct format. If you have any questions, please ask openly in Discord.
-
-Great, now that we have adapted the `eval.py` script, we can lean back and run the 
-evaluation. 
-First, one should evaluate the model on Common Voice 7's test data. This might 
-already have been done for your acoustic model during training but in case you 
-added an *n-gram* language model after having fine-tuned the acoustic model, you
-should now see a nice improvement.
-
-The command to evaluate our test model [xls-r-300m-sv](https://huggingface.co/hf-test/xls-r-300m-sv) on Common Voice 7's test data is the following:
-
-```bash
-cd xls-r-300m-sv
-./eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config sv-SE --split test --log_outputs
-```
-
-To log each of the model's predictions with the target transcriptions, you can just 
-add the `--log_outputs` flag.
-
-Running this command should automatically create the file:
-`mozilla-foundation_common_voice_7_0_sv-SE_test_eval_results.txt` that contains 
-both the word- and character error rate.
-
-In a few days, we will give everybody access to some real-world audio data for as many languages as possible.
-If your language has real-world audio data, it will most likely have audio input 
-of multiple minutes. 🤗Transformer's [ASR pipeline](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) supports audio chunking out-of-the-box. You only need to specify 
-how song each audio chunk should be (`chunk_length_s`) and how much audio stride 
-(`stride_length_s`) each chunk should use.
-For more information on the chunking works, please have a look at [this nice blog post](TODO: ).
-
-In the case of `xls-r-300m-sv`, the following command can be run:
-
-```bash 
-cd xls-r-300m-sv
-./eval.py --model_id hf-test/xls-r-300m-sv --dataset <to-be-announced> --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0 --log_outputs
-```
-
-Great, now you should have successfully evaluated your model. Finally, there is one 
-**important** thing you should do so that your model is taken into account 
-for the final evaluation. You should add two tags to your model, one being `robust-speech-event`, one being the ISO code of your chosen language, *e.g.* `"sv"` for the 
-exemplary model we used above. You can find a list of all available languages and 
-their ISO code [here](https://huggingface.co/languages).
-
-To add the tags, simply edit the README.md of your model repository and add
-
-```
-- "sv"
-- "robust-speech-event"
-```
-
-under `tags:` as done [here](https://huggingface.co/hf-test/xls-r-300m-sv/commit/a495fd70c96bb7d019729be9273a265c2557345e).
-
-To verify that you've added the tags correctly make sure that your model 
-appears when clicking on [this link](https://huggingface.co/models?other=robust-speech-event).
-
-Great that's it! This should give you all the necessary information to evaluate
-your model. For the final evaluation, we will verify each evaluation result to 
-determine the final score and thereby the winning models for each language.
-
-The final score is calculated as follows:
-
-```bash
-FINAL_SCORE = 1/3 * WER_Common_Voice_7_test + 1/3 * WER_REAL_AUDIO_DEV + 1/3 * WER_REAL_AUDIO_TEST
-```
-
-The dataset `WER_REAL_AUDIO_TEST` is hidden and will only be published 
-at the end of the robust speech challenge.
-
-If there is no real audio data for your language the final score will be 
-computed solely based on the Common Voice 7 test dataset. If there is also
-no Common Voice 7 test dataset for your language, we will see together how to 
-score your model - if this is the case, please don't be discouraged. We are 
-especially excited about speech recognition systems of such low-resource 
-languages and will make sure that we'll decide on a good approach to evaluating 
-your model.
-
-## Prizes
-
-TODO(Patrick, Omar, ...)
-
-## Communication and Problems
-
-If you encounter any problems or have any questions, you should use one of the following platforms
-depending on your type of problem. Hugging Face is an "open-source-first" organization meaning 
-that we'll try to solve all problems in the most public and most transparent way possible so that everybody
-in the community profits.
-
-The following table summarizes what platform to use for which problem.
-
-- Problem/question/bug with the 🤗 Datasets library that you think is a general problem that also impacts other people, please open an [Issues on Datasets](https://github.com/huggingface/datasets/issues/new?assignees=&labels=bug&template=bug-report.md&title=) and ping @anton-l and @patrickvonplaten.
-- Problem/question/bug with the 🤗 Transformers library that you think is a general problem that also impacts other people, please open an [Issues on Transformers](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title=) and ping @anton-l and @patrickvonplaten.
-- Problem/question with a modified, customized training script that is less likely to impact other people, please post your problem/question [on the forum](https://discuss.huggingface.co/) and ping @anton-l and @patrickvonplaten.
-- Questions regarding access to the OVHcloud GPU, please ask in the Discord channel **#ovh-support**.
-- Other questions regarding the event, rules of the event, or if you are not sure where to post your question, please ask in the Discord channel **#sprint-discussions**.
-
-## Talks
-
-We are very excited to be hosting 2 days of talks from Kensho-Technologies, Mozilla's Common Voice, Meta AI Research and Hugging Face.
-
-### Thursday, January 20th
-
- Speaker        | Topic                           | Time                  |  Video |
-|-------------|---------------------------------|------------------------|------------------------|
-| Patrick von Platen, Hugging Face | Introduction to Robust Speech Challenge | 4h30pm - 5h00pm UTC     | [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=X9e5Tto-Iuk)
-| Raymond Grossman and Jeremy Lopez, Kensho-Technologies | Pyctcdecode & Speech2text decoding | 5h30pm - 6h00pm UTC      | [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=mp7fHMTnK9A)
-
-### Friday, January 21th
-
- Speaker        | Topic                           | Time                  | Video |
-|-------------|---------------------------------|------------------------|------------------------|
-| Gabriel Habayeb, Mozilla Common Voice | Unlocking global speech with Mozilla Common Voice | 4h30pm - 5h00pm UTC      | [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=Vvn984QmAVg)
-| Changhan Wang, Meta AI Research | XLS-R: Large-Scale Cross-lingual Speech Representation Learning on 128 Languages | 5h30pm - 6h00pm UTC      | [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=ic_J7ZCROBM)
-
-### Talks & Speakers
-
-#### Patrick von Platen, Research Engineer, Hugging Face
-- Talk: Introduction to Robust Speech Challenge
-- Abstract: In this talk, Patrick outlines the Robust Speech Challenge and gives tips and tricks on how to train and evaluate speech recognition systems with 🤗 Transformers and 🤗 Datasets, and PyTorch.
-- Speaker info: Patrick von Platen is a research engineer at Hugging Face and one of the core maintainers of the popular Transformers library. He specializes in speech recognition, encoder-decoder models, and long-range sequence modeling. Before joining Hugging Face, Patrick researched speech recognition at Uber AI, Cambridge University, and RWTH Aachen University.
-
-#### Raymond Grossman, Jeremy Lopez, Machine Learning Engineer, Kensho Technologies
-- Talk: PyCTCDecode & Speech2text decoding
-- Abstract: PyCTCDecode is a fast and feature-rich CTC beam search decoder for speech recognition written in Python, providing n-gram (kenlm) language model support similar to PaddlePaddle's decoder, but incorporating many new features such as byte pair encoding and real-time decoding to support models like Nvidia's Conformer-CTC or Facebook's Wav2Vec2.
-- Speaker info : 
-	- Raymond works as a machine learning engineer at Kensho Technologies, specializing in speech and natural language domains. Before coming to Kensho, he studied mathematics at Princeton and was an avid Kaggler under the moniker @ToTrainThemIsMyCause. 
-	- Jeremy is a machine learning engineer at Kensho Technologies and has worked on a variety of different topics including search and speech recognition. Before working at Kensho, he earned a PhD in experimental particle physics at MIT and continued doing physics research as a postdoc at the University of Colorado Boulder.
-
-#### Gabriel Habayeb, Data Engineer, Common Voice @ Mozilla
-- Talk: Unlocking global speech with Mozilla Common Voice
-- Abstract: Hear from Common Voice Data Engineer Gabriel Habayeb (Mozilla Foundation) as he talks about how Common Voice makes it easy to crowdsource voice data in global languages, as well as getting key insights into the dataset itself, how we maintain quality, use metadata - and our plans for the future!
-- Speaker info: Gabriel is a software developer with the Common Voice team at the Mozilla Foundation with a focus on data engineering. Before joining the Foundation, he spent the last six years working across different industries, including education, enterprise and not-for-profit organizations.
-
-#### Changhan Wang, Main author of XLS-R and Research Engineer, Meta AI Research
-- Talk: XLS-R: Large-Scale Cross-lingual Speech Representation Learning on 128 Languages
-- Abstract: In this talk, Changhan will present XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0. XLS-R has up to 2B parameters and was trained on nearly half a million hours of publicly available speech audio in 128 languages, an order of magnitude more public data than the largest known prior work. On the CoVoST-2 speech translation benchmark, XLS-R improves the previous state of the art by an average of 7.4 BLEU over 21 translation directions into English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107 language identification. The XLS-R team hopes to work together with the open-source community to improve speech processing tasks for many more languages of the world.
-
-## General Tips and Tricks
-
-- Memory efficient training:
-
-In case, you are getting out-of-memory errors on your GPU, we recommend to use 
-[bitsandbytes](https://github.com/TimDettmers/bitsandbytes) to replace the 
-native memory-intensive Adam optimizer with the one of `bitsandbytes`. You
-can simply run the script `./run_speech_recognition_ctc_bnb.py` provided in this 
-folder that makes use of `bitsandbytes` instead of the official one.
-
-- Dataset streaming
-
-TODO(Patrick)
diff --git a/spaces/chongjie/co-tracker_MVP/README.md b/spaces/chongjie/co-tracker_MVP/README.md
deleted file mode 100644
index 1583a7f92f218b229685af65eac7baa9e7b1147e..0000000000000000000000000000000000000000
--- a/spaces/chongjie/co-tracker_MVP/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Co Tracker
-emoji: 🐠
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.38.0
-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/chronopt-research/ViTExCo/src/models/vit/utils.py b/spaces/chronopt-research/ViTExCo/src/models/vit/utils.py
deleted file mode 100644
index eabc9de77e12d9ed2d1907ca3cedb6424fb16c03..0000000000000000000000000000000000000000
--- a/spaces/chronopt-research/ViTExCo/src/models/vit/utils.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from timm.models.layers import trunc_normal_
-
-
-def resize_pos_embed(posemb, grid_old_shape, grid_new_shape, num_extra_tokens):
-    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
-    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
-    posemb_tok, posemb_grid = (
-        posemb[:, :num_extra_tokens],
-        posemb[0, num_extra_tokens:],
-    )
-    if grid_old_shape is None:
-        gs_old_h = int(math.sqrt(len(posemb_grid)))
-        gs_old_w = gs_old_h
-    else:
-        gs_old_h, gs_old_w = grid_old_shape
-
-    gs_h, gs_w = grid_new_shape
-    posemb_grid = posemb_grid.reshape(1, gs_old_h, gs_old_w, -1).permute(0, 3, 1, 2)
-    posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
-    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
-    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
-    return posemb
-
-
-def init_weights(m):
-    if isinstance(m, nn.Linear):
-        trunc_normal_(m.weight, std=0.02)
-        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)
-
-
-def checkpoint_filter_fn(state_dict, model):
-    """convert patch embedding weight from manual patchify + linear proj to conv"""
-    out_dict = {}
-    if "model" in state_dict:
-        # For deit models
-        state_dict = state_dict["model"]
-    num_extra_tokens = 1 + ("dist_token" in state_dict.keys())
-    patch_size = model.patch_size
-    image_size = model.patch_embed.image_size
-    for k, v in state_dict.items():
-        if k == "pos_embed" and v.shape != model.pos_embed.shape:
-            # To resize pos embedding when using model at different size from pretrained weights
-            v = resize_pos_embed(
-                v,
-                None,
-                (image_size[0] // patch_size, image_size[1] // patch_size),
-                num_extra_tokens,
-            )
-        out_dict[k] = v
-    return out_dict
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/_tkinter_finder.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/_tkinter_finder.py
deleted file mode 100644
index 597c21b5e385b7fe09191c9f5dd89b6600c22967..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/_tkinter_finder.py
+++ /dev/null
@@ -1,17 +0,0 @@
-""" Find compiled module linking to Tcl / Tk libraries
-"""
-import sys
-import tkinter
-from tkinter import _tkinter as tk
-
-try:
-    if hasattr(sys, "pypy_find_executable"):
-        TKINTER_LIB = tk.tklib_cffi.__file__
-    else:
-        TKINTER_LIB = tk.__file__
-except AttributeError:
-    # _tkinter may be compiled directly into Python, in which case __file__ is
-    # not available. load_tkinter_funcs will check the binary first in any case.
-    TKINTER_LIB = None
-
-tk_version = str(tkinter.TkVersion)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/jisfreq.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/jisfreq.py
deleted file mode 100644
index 3293576e012a1c931b5e89ebc065c67b65941084..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/jisfreq.py
+++ /dev/null
@@ -1,325 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Communicator client 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 #########################
-
-# Sampling from about 20M text materials include literature and computer technology
-#
-# Japanese frequency table, applied to both S-JIS and EUC-JP
-# They are sorted in order.
-
-# 128  --> 0.77094
-# 256  --> 0.85710
-# 512  --> 0.92635
-# 1024 --> 0.97130
-# 2048 --> 0.99431
-#
-# Ideal Distribution Ratio = 0.92635 / (1-0.92635) = 12.58
-# Random Distribution Ration = 512 / (2965+62+83+86-512) = 0.191
-#
-# Typical Distribution Ratio, 25% of IDR
-
-JIS_TYPICAL_DISTRIBUTION_RATIO = 3.0
-
-# Char to FreqOrder table ,
-JIS_TABLE_SIZE = 4368
-
-# fmt: off
-JIS_CHAR_TO_FREQ_ORDER = (
-  40,   1,   6, 182, 152, 180, 295,2127, 285, 381,3295,4304,3068,4606,3165,3510, #   16
-3511,1822,2785,4607,1193,2226,5070,4608, 171,2996,1247,  18, 179,5071, 856,1661, #   32
-1262,5072, 619, 127,3431,3512,3230,1899,1700, 232, 228,1294,1298, 284, 283,2041, #   48
-2042,1061,1062,  48,  49,  44,  45, 433, 434,1040,1041, 996, 787,2997,1255,4305, #   64
-2108,4609,1684,1648,5073,5074,5075,5076,5077,5078,3687,5079,4610,5080,3927,3928, #   80
-5081,3296,3432, 290,2285,1471,2187,5082,2580,2825,1303,2140,1739,1445,2691,3375, #   96
-1691,3297,4306,4307,4611, 452,3376,1182,2713,3688,3069,4308,5083,5084,5085,5086, #  112
-5087,5088,5089,5090,5091,5092,5093,5094,5095,5096,5097,5098,5099,5100,5101,5102, #  128
-5103,5104,5105,5106,5107,5108,5109,5110,5111,5112,4097,5113,5114,5115,5116,5117, #  144
-5118,5119,5120,5121,5122,5123,5124,5125,5126,5127,5128,5129,5130,5131,5132,5133, #  160
-5134,5135,5136,5137,5138,5139,5140,5141,5142,5143,5144,5145,5146,5147,5148,5149, #  176
-5150,5151,5152,4612,5153,5154,5155,5156,5157,5158,5159,5160,5161,5162,5163,5164, #  192
-5165,5166,5167,5168,5169,5170,5171,5172,5173,5174,5175,1472, 598, 618, 820,1205, #  208
-1309,1412,1858,1307,1692,5176,5177,5178,5179,5180,5181,5182,1142,1452,1234,1172, #  224
-1875,2043,2149,1793,1382,2973, 925,2404,1067,1241, 960,1377,2935,1491, 919,1217, #  240
-1865,2030,1406,1499,2749,4098,5183,5184,5185,5186,5187,5188,2561,4099,3117,1804, #  256
-2049,3689,4309,3513,1663,5189,3166,3118,3298,1587,1561,3433,5190,3119,1625,2998, #  272
-3299,4613,1766,3690,2786,4614,5191,5192,5193,5194,2161,  26,3377,   2,3929,  20, #  288
-3691,  47,4100,  50,  17,  16,  35, 268,  27, 243,  42, 155,  24, 154,  29, 184, #  304
-   4,  91,  14,  92,  53, 396,  33, 289,   9,  37,  64, 620,  21,  39, 321,   5, #  320
-  12,  11,  52,  13,   3, 208, 138,   0,   7,  60, 526, 141, 151,1069, 181, 275, #  336
-1591,  83, 132,1475, 126, 331, 829,  15,  69, 160,  59,  22, 157,  55,1079, 312, #  352
- 109,  38,  23,  25,  10,  19,  79,5195,  61, 382,1124,   8,  30,5196,5197,5198, #  368
-5199,5200,5201,5202,5203,5204,5205,5206,  89,  62,  74,  34,2416, 112, 139, 196, #  384
- 271, 149,  84, 607, 131, 765,  46,  88, 153, 683,  76, 874, 101, 258,  57,  80, #  400
-  32, 364, 121,1508, 169,1547,  68, 235, 145,2999,  41, 360,3027,  70,  63,  31, #  416
-  43, 259, 262,1383,  99, 533, 194,  66,  93, 846, 217, 192,  56, 106,  58, 565, #  432
- 280, 272, 311, 256, 146,  82, 308,  71, 100, 128, 214, 655, 110, 261, 104,1140, #  448
-  54,  51,  36,  87,  67,3070, 185,2618,2936,2020,  28,1066,2390,2059,5207,5208, #  464
-5209,5210,5211,5212,5213,5214,5215,5216,4615,5217,5218,5219,5220,5221,5222,5223, #  480
-5224,5225,5226,5227,5228,5229,5230,5231,5232,5233,5234,5235,5236,3514,5237,5238, #  496
-5239,5240,5241,5242,5243,5244,2297,2031,4616,4310,3692,5245,3071,5246,3598,5247, #  512
-4617,3231,3515,5248,4101,4311,4618,3808,4312,4102,5249,4103,4104,3599,5250,5251, #  528
-5252,5253,5254,5255,5256,5257,5258,5259,5260,5261,5262,5263,5264,5265,5266,5267, #  544
-5268,5269,5270,5271,5272,5273,5274,5275,5276,5277,5278,5279,5280,5281,5282,5283, #  560
-5284,5285,5286,5287,5288,5289,5290,5291,5292,5293,5294,5295,5296,5297,5298,5299, #  576
-5300,5301,5302,5303,5304,5305,5306,5307,5308,5309,5310,5311,5312,5313,5314,5315, #  592
-5316,5317,5318,5319,5320,5321,5322,5323,5324,5325,5326,5327,5328,5329,5330,5331, #  608
-5332,5333,5334,5335,5336,5337,5338,5339,5340,5341,5342,5343,5344,5345,5346,5347, #  624
-5348,5349,5350,5351,5352,5353,5354,5355,5356,5357,5358,5359,5360,5361,5362,5363, #  640
-5364,5365,5366,5367,5368,5369,5370,5371,5372,5373,5374,5375,5376,5377,5378,5379, #  656
-5380,5381, 363, 642,2787,2878,2788,2789,2316,3232,2317,3434,2011, 165,1942,3930, #  672
-3931,3932,3933,5382,4619,5383,4620,5384,5385,5386,5387,5388,5389,5390,5391,5392, #  688
-5393,5394,5395,5396,5397,5398,5399,5400,5401,5402,5403,5404,5405,5406,5407,5408, #  704
-5409,5410,5411,5412,5413,5414,5415,5416,5417,5418,5419,5420,5421,5422,5423,5424, #  720
-5425,5426,5427,5428,5429,5430,5431,5432,5433,5434,5435,5436,5437,5438,5439,5440, #  736
-5441,5442,5443,5444,5445,5446,5447,5448,5449,5450,5451,5452,5453,5454,5455,5456, #  752
-5457,5458,5459,5460,5461,5462,5463,5464,5465,5466,5467,5468,5469,5470,5471,5472, #  768
-5473,5474,5475,5476,5477,5478,5479,5480,5481,5482,5483,5484,5485,5486,5487,5488, #  784
-5489,5490,5491,5492,5493,5494,5495,5496,5497,5498,5499,5500,5501,5502,5503,5504, #  800
-5505,5506,5507,5508,5509,5510,5511,5512,5513,5514,5515,5516,5517,5518,5519,5520, #  816
-5521,5522,5523,5524,5525,5526,5527,5528,5529,5530,5531,5532,5533,5534,5535,5536, #  832
-5537,5538,5539,5540,5541,5542,5543,5544,5545,5546,5547,5548,5549,5550,5551,5552, #  848
-5553,5554,5555,5556,5557,5558,5559,5560,5561,5562,5563,5564,5565,5566,5567,5568, #  864
-5569,5570,5571,5572,5573,5574,5575,5576,5577,5578,5579,5580,5581,5582,5583,5584, #  880
-5585,5586,5587,5588,5589,5590,5591,5592,5593,5594,5595,5596,5597,5598,5599,5600, #  896
-5601,5602,5603,5604,5605,5606,5607,5608,5609,5610,5611,5612,5613,5614,5615,5616, #  912
-5617,5618,5619,5620,5621,5622,5623,5624,5625,5626,5627,5628,5629,5630,5631,5632, #  928
-5633,5634,5635,5636,5637,5638,5639,5640,5641,5642,5643,5644,5645,5646,5647,5648, #  944
-5649,5650,5651,5652,5653,5654,5655,5656,5657,5658,5659,5660,5661,5662,5663,5664, #  960
-5665,5666,5667,5668,5669,5670,5671,5672,5673,5674,5675,5676,5677,5678,5679,5680, #  976
-5681,5682,5683,5684,5685,5686,5687,5688,5689,5690,5691,5692,5693,5694,5695,5696, #  992
-5697,5698,5699,5700,5701,5702,5703,5704,5705,5706,5707,5708,5709,5710,5711,5712, # 1008
-5713,5714,5715,5716,5717,5718,5719,5720,5721,5722,5723,5724,5725,5726,5727,5728, # 1024
-5729,5730,5731,5732,5733,5734,5735,5736,5737,5738,5739,5740,5741,5742,5743,5744, # 1040
-5745,5746,5747,5748,5749,5750,5751,5752,5753,5754,5755,5756,5757,5758,5759,5760, # 1056
-5761,5762,5763,5764,5765,5766,5767,5768,5769,5770,5771,5772,5773,5774,5775,5776, # 1072
-5777,5778,5779,5780,5781,5782,5783,5784,5785,5786,5787,5788,5789,5790,5791,5792, # 1088
-5793,5794,5795,5796,5797,5798,5799,5800,5801,5802,5803,5804,5805,5806,5807,5808, # 1104
-5809,5810,5811,5812,5813,5814,5815,5816,5817,5818,5819,5820,5821,5822,5823,5824, # 1120
-5825,5826,5827,5828,5829,5830,5831,5832,5833,5834,5835,5836,5837,5838,5839,5840, # 1136
-5841,5842,5843,5844,5845,5846,5847,5848,5849,5850,5851,5852,5853,5854,5855,5856, # 1152
-5857,5858,5859,5860,5861,5862,5863,5864,5865,5866,5867,5868,5869,5870,5871,5872, # 1168
-5873,5874,5875,5876,5877,5878,5879,5880,5881,5882,5883,5884,5885,5886,5887,5888, # 1184
-5889,5890,5891,5892,5893,5894,5895,5896,5897,5898,5899,5900,5901,5902,5903,5904, # 1200
-5905,5906,5907,5908,5909,5910,5911,5912,5913,5914,5915,5916,5917,5918,5919,5920, # 1216
-5921,5922,5923,5924,5925,5926,5927,5928,5929,5930,5931,5932,5933,5934,5935,5936, # 1232
-5937,5938,5939,5940,5941,5942,5943,5944,5945,5946,5947,5948,5949,5950,5951,5952, # 1248
-5953,5954,5955,5956,5957,5958,5959,5960,5961,5962,5963,5964,5965,5966,5967,5968, # 1264
-5969,5970,5971,5972,5973,5974,5975,5976,5977,5978,5979,5980,5981,5982,5983,5984, # 1280
-5985,5986,5987,5988,5989,5990,5991,5992,5993,5994,5995,5996,5997,5998,5999,6000, # 1296
-6001,6002,6003,6004,6005,6006,6007,6008,6009,6010,6011,6012,6013,6014,6015,6016, # 1312
-6017,6018,6019,6020,6021,6022,6023,6024,6025,6026,6027,6028,6029,6030,6031,6032, # 1328
-6033,6034,6035,6036,6037,6038,6039,6040,6041,6042,6043,6044,6045,6046,6047,6048, # 1344
-6049,6050,6051,6052,6053,6054,6055,6056,6057,6058,6059,6060,6061,6062,6063,6064, # 1360
-6065,6066,6067,6068,6069,6070,6071,6072,6073,6074,6075,6076,6077,6078,6079,6080, # 1376
-6081,6082,6083,6084,6085,6086,6087,6088,6089,6090,6091,6092,6093,6094,6095,6096, # 1392
-6097,6098,6099,6100,6101,6102,6103,6104,6105,6106,6107,6108,6109,6110,6111,6112, # 1408
-6113,6114,2044,2060,4621, 997,1235, 473,1186,4622, 920,3378,6115,6116, 379,1108, # 1424
-4313,2657,2735,3934,6117,3809, 636,3233, 573,1026,3693,3435,2974,3300,2298,4105, # 1440
- 854,2937,2463, 393,2581,2417, 539, 752,1280,2750,2480, 140,1161, 440, 708,1569, # 1456
- 665,2497,1746,1291,1523,3000, 164,1603, 847,1331, 537,1997, 486, 508,1693,2418, # 1472
-1970,2227, 878,1220, 299,1030, 969, 652,2751, 624,1137,3301,2619,  65,3302,2045, # 1488
-1761,1859,3120,1930,3694,3516, 663,1767, 852, 835,3695, 269, 767,2826,2339,1305, # 1504
- 896,1150, 770,1616,6118, 506,1502,2075,1012,2519, 775,2520,2975,2340,2938,4314, # 1520
-3028,2086,1224,1943,2286,6119,3072,4315,2240,1273,1987,3935,1557, 175, 597, 985, # 1536
-3517,2419,2521,1416,3029, 585, 938,1931,1007,1052,1932,1685,6120,3379,4316,4623, # 1552
- 804, 599,3121,1333,2128,2539,1159,1554,2032,3810, 687,2033,2904, 952, 675,1467, # 1568
-3436,6121,2241,1096,1786,2440,1543,1924, 980,1813,2228, 781,2692,1879, 728,1918, # 1584
-3696,4624, 548,1950,4625,1809,1088,1356,3303,2522,1944, 502, 972, 373, 513,2827, # 1600
- 586,2377,2391,1003,1976,1631,6122,2464,1084, 648,1776,4626,2141, 324, 962,2012, # 1616
-2177,2076,1384, 742,2178,1448,1173,1810, 222, 102, 301, 445, 125,2420, 662,2498, # 1632
- 277, 200,1476,1165,1068, 224,2562,1378,1446, 450,1880, 659, 791, 582,4627,2939, # 1648
-3936,1516,1274, 555,2099,3697,1020,1389,1526,3380,1762,1723,1787,2229, 412,2114, # 1664
-1900,2392,3518, 512,2597, 427,1925,2341,3122,1653,1686,2465,2499, 697, 330, 273, # 1680
- 380,2162, 951, 832, 780, 991,1301,3073, 965,2270,3519, 668,2523,2636,1286, 535, # 1696
-1407, 518, 671, 957,2658,2378, 267, 611,2197,3030,6123, 248,2299, 967,1799,2356, # 1712
- 850,1418,3437,1876,1256,1480,2828,1718,6124,6125,1755,1664,2405,6126,4628,2879, # 1728
-2829, 499,2179, 676,4629, 557,2329,2214,2090, 325,3234, 464, 811,3001, 992,2342, # 1744
-2481,1232,1469, 303,2242, 466,1070,2163, 603,1777,2091,4630,2752,4631,2714, 322, # 1760
-2659,1964,1768, 481,2188,1463,2330,2857,3600,2092,3031,2421,4632,2318,2070,1849, # 1776
-2598,4633,1302,2254,1668,1701,2422,3811,2905,3032,3123,2046,4106,1763,1694,4634, # 1792
-1604, 943,1724,1454, 917, 868,2215,1169,2940, 552,1145,1800,1228,1823,1955, 316, # 1808
-1080,2510, 361,1807,2830,4107,2660,3381,1346,1423,1134,4108,6127, 541,1263,1229, # 1824
-1148,2540, 545, 465,1833,2880,3438,1901,3074,2482, 816,3937, 713,1788,2500, 122, # 1840
-1575, 195,1451,2501,1111,6128, 859, 374,1225,2243,2483,4317, 390,1033,3439,3075, # 1856
-2524,1687, 266, 793,1440,2599, 946, 779, 802, 507, 897,1081, 528,2189,1292, 711, # 1872
-1866,1725,1167,1640, 753, 398,2661,1053, 246, 348,4318, 137,1024,3440,1600,2077, # 1888
-2129, 825,4319, 698, 238, 521, 187,2300,1157,2423,1641,1605,1464,1610,1097,2541, # 1904
-1260,1436, 759,2255,1814,2150, 705,3235, 409,2563,3304, 561,3033,2005,2564, 726, # 1920
-1956,2343,3698,4109, 949,3812,3813,3520,1669, 653,1379,2525, 881,2198, 632,2256, # 1936
-1027, 778,1074, 733,1957, 514,1481,2466, 554,2180, 702,3938,1606,1017,1398,6129, # 1952
-1380,3521, 921, 993,1313, 594, 449,1489,1617,1166, 768,1426,1360, 495,1794,3601, # 1968
-1177,3602,1170,4320,2344, 476, 425,3167,4635,3168,1424, 401,2662,1171,3382,1998, # 1984
-1089,4110, 477,3169, 474,6130,1909, 596,2831,1842, 494, 693,1051,1028,1207,3076, # 2000
- 606,2115, 727,2790,1473,1115, 743,3522, 630, 805,1532,4321,2021, 366,1057, 838, # 2016
- 684,1114,2142,4322,2050,1492,1892,1808,2271,3814,2424,1971,1447,1373,3305,1090, # 2032
-1536,3939,3523,3306,1455,2199, 336, 369,2331,1035, 584,2393, 902, 718,2600,6131, # 2048
-2753, 463,2151,1149,1611,2467, 715,1308,3124,1268, 343,1413,3236,1517,1347,2663, # 2064
-2093,3940,2022,1131,1553,2100,2941,1427,3441,2942,1323,2484,6132,1980, 872,2368, # 2080
-2441,2943, 320,2369,2116,1082, 679,1933,3941,2791,3815, 625,1143,2023, 422,2200, # 2096
-3816,6133, 730,1695, 356,2257,1626,2301,2858,2637,1627,1778, 937, 883,2906,2693, # 2112
-3002,1769,1086, 400,1063,1325,3307,2792,4111,3077, 456,2345,1046, 747,6134,1524, # 2128
- 884,1094,3383,1474,2164,1059, 974,1688,2181,2258,1047, 345,1665,1187, 358, 875, # 2144
-3170, 305, 660,3524,2190,1334,1135,3171,1540,1649,2542,1527, 927, 968,2793, 885, # 2160
-1972,1850, 482, 500,2638,1218,1109,1085,2543,1654,2034, 876,  78,2287,1482,1277, # 2176
- 861,1675,1083,1779, 724,2754, 454, 397,1132,1612,2332, 893, 672,1237, 257,2259, # 2192
-2370, 135,3384, 337,2244, 547, 352, 340, 709,2485,1400, 788,1138,2511, 540, 772, # 2208
-1682,2260,2272,2544,2013,1843,1902,4636,1999,1562,2288,4637,2201,1403,1533, 407, # 2224
- 576,3308,1254,2071, 978,3385, 170, 136,1201,3125,2664,3172,2394, 213, 912, 873, # 2240
-3603,1713,2202, 699,3604,3699, 813,3442, 493, 531,1054, 468,2907,1483, 304, 281, # 2256
-4112,1726,1252,2094, 339,2319,2130,2639, 756,1563,2944, 748, 571,2976,1588,2425, # 2272
-2715,1851,1460,2426,1528,1392,1973,3237, 288,3309, 685,3386, 296, 892,2716,2216, # 2288
-1570,2245, 722,1747,2217, 905,3238,1103,6135,1893,1441,1965, 251,1805,2371,3700, # 2304
-2601,1919,1078,  75,2182,1509,1592,1270,2640,4638,2152,6136,3310,3817, 524, 706, # 2320
-1075, 292,3818,1756,2602, 317,  98,3173,3605,3525,1844,2218,3819,2502, 814, 567, # 2336
- 385,2908,1534,6137, 534,1642,3239, 797,6138,1670,1529, 953,4323, 188,1071, 538, # 2352
- 178, 729,3240,2109,1226,1374,2000,2357,2977, 731,2468,1116,2014,2051,6139,1261, # 2368
-1593, 803,2859,2736,3443, 556, 682, 823,1541,6140,1369,2289,1706,2794, 845, 462, # 2384
-2603,2665,1361, 387, 162,2358,1740, 739,1770,1720,1304,1401,3241,1049, 627,1571, # 2400
-2427,3526,1877,3942,1852,1500, 431,1910,1503, 677, 297,2795, 286,1433,1038,1198, # 2416
-2290,1133,1596,4113,4639,2469,1510,1484,3943,6141,2442, 108, 712,4640,2372, 866, # 2432
-3701,2755,3242,1348, 834,1945,1408,3527,2395,3243,1811, 824, 994,1179,2110,1548, # 2448
-1453, 790,3003, 690,4324,4325,2832,2909,3820,1860,3821, 225,1748, 310, 346,1780, # 2464
-2470, 821,1993,2717,2796, 828, 877,3528,2860,2471,1702,2165,2910,2486,1789, 453, # 2480
- 359,2291,1676,  73,1164,1461,1127,3311, 421, 604, 314,1037, 589, 116,2487, 737, # 2496
- 837,1180, 111, 244, 735,6142,2261,1861,1362, 986, 523, 418, 581,2666,3822, 103, # 2512
- 855, 503,1414,1867,2488,1091, 657,1597, 979, 605,1316,4641,1021,2443,2078,2001, # 2528
-1209,  96, 587,2166,1032, 260,1072,2153, 173,  94, 226,3244, 819,2006,4642,4114, # 2544
-2203, 231,1744, 782,  97,2667, 786,3387, 887, 391, 442,2219,4326,1425,6143,2694, # 2560
- 633,1544,1202, 483,2015, 592,2052,1958,2472,1655, 419, 129,4327,3444,3312,1714, # 2576
-1257,3078,4328,1518,1098, 865,1310,1019,1885,1512,1734, 469,2444, 148, 773, 436, # 2592
-1815,1868,1128,1055,4329,1245,2756,3445,2154,1934,1039,4643, 579,1238, 932,2320, # 2608
- 353, 205, 801, 115,2428, 944,2321,1881, 399,2565,1211, 678, 766,3944, 335,2101, # 2624
-1459,1781,1402,3945,2737,2131,1010, 844, 981,1326,1013, 550,1816,1545,2620,1335, # 2640
-1008, 371,2881, 936,1419,1613,3529,1456,1395,2273,1834,2604,1317,2738,2503, 416, # 2656
-1643,4330, 806,1126, 229, 591,3946,1314,1981,1576,1837,1666, 347,1790, 977,3313, # 2672
- 764,2861,1853, 688,2429,1920,1462,  77, 595, 415,2002,3034, 798,1192,4115,6144, # 2688
-2978,4331,3035,2695,2582,2072,2566, 430,2430,1727, 842,1396,3947,3702, 613, 377, # 2704
- 278, 236,1417,3388,3314,3174, 757,1869, 107,3530,6145,1194, 623,2262, 207,1253, # 2720
-2167,3446,3948, 492,1117,1935, 536,1838,2757,1246,4332, 696,2095,2406,1393,1572, # 2736
-3175,1782, 583, 190, 253,1390,2230, 830,3126,3389, 934,3245,1703,1749,2979,1870, # 2752
-2545,1656,2204, 869,2346,4116,3176,1817, 496,1764,4644, 942,1504, 404,1903,1122, # 2768
-1580,3606,2945,1022, 515, 372,1735, 955,2431,3036,6146,2797,1110,2302,2798, 617, # 2784
-6147, 441, 762,1771,3447,3607,3608,1904, 840,3037,  86, 939,1385, 572,1370,2445, # 2800
-1336, 114,3703, 898, 294, 203,3315, 703,1583,2274, 429, 961,4333,1854,1951,3390, # 2816
-2373,3704,4334,1318,1381, 966,1911,2322,1006,1155, 309, 989, 458,2718,1795,1372, # 2832
-1203, 252,1689,1363,3177, 517,1936, 168,1490, 562, 193,3823,1042,4117,1835, 551, # 2848
- 470,4645, 395, 489,3448,1871,1465,2583,2641, 417,1493, 279,1295, 511,1236,1119, # 2864
-  72,1231,1982,1812,3004, 871,1564, 984,3449,1667,2696,2096,4646,2347,2833,1673, # 2880
-3609, 695,3246,2668, 807,1183,4647, 890, 388,2333,1801,1457,2911,1765,1477,1031, # 2896
-3316,3317,1278,3391,2799,2292,2526, 163,3450,4335,2669,1404,1802,6148,2323,2407, # 2912
-1584,1728,1494,1824,1269, 298, 909,3318,1034,1632, 375, 776,1683,2061, 291, 210, # 2928
-1123, 809,1249,1002,2642,3038, 206,1011,2132, 144, 975, 882,1565, 342, 667, 754, # 2944
-1442,2143,1299,2303,2062, 447, 626,2205,1221,2739,2912,1144,1214,2206,2584, 760, # 2960
-1715, 614, 950,1281,2670,2621, 810, 577,1287,2546,4648, 242,2168, 250,2643, 691, # 2976
- 123,2644, 647, 313,1029, 689,1357,2946,1650, 216, 771,1339,1306, 808,2063, 549, # 2992
- 913,1371,2913,2914,6149,1466,1092,1174,1196,1311,2605,2396,1783,1796,3079, 406, # 3008
-2671,2117,3949,4649, 487,1825,2220,6150,2915, 448,2348,1073,6151,2397,1707, 130, # 3024
- 900,1598, 329, 176,1959,2527,1620,6152,2275,4336,3319,1983,2191,3705,3610,2155, # 3040
-3706,1912,1513,1614,6153,1988, 646, 392,2304,1589,3320,3039,1826,1239,1352,1340, # 3056
-2916, 505,2567,1709,1437,2408,2547, 906,6154,2672, 384,1458,1594,1100,1329, 710, # 3072
- 423,3531,2064,2231,2622,1989,2673,1087,1882, 333, 841,3005,1296,2882,2379, 580, # 3088
-1937,1827,1293,2585, 601, 574, 249,1772,4118,2079,1120, 645, 901,1176,1690, 795, # 3104
-2207, 478,1434, 516,1190,1530, 761,2080, 930,1264, 355, 435,1552, 644,1791, 987, # 3120
- 220,1364,1163,1121,1538, 306,2169,1327,1222, 546,2645, 218, 241, 610,1704,3321, # 3136
-1984,1839,1966,2528, 451,6155,2586,3707,2568, 907,3178, 254,2947, 186,1845,4650, # 3152
- 745, 432,1757, 428,1633, 888,2246,2221,2489,3611,2118,1258,1265, 956,3127,1784, # 3168
-4337,2490, 319, 510, 119, 457,3612, 274,2035,2007,4651,1409,3128, 970,2758, 590, # 3184
-2800, 661,2247,4652,2008,3950,1420,1549,3080,3322,3951,1651,1375,2111, 485,2491, # 3200
-1429,1156,6156,2548,2183,1495, 831,1840,2529,2446, 501,1657, 307,1894,3247,1341, # 3216
- 666, 899,2156,1539,2549,1559, 886, 349,2208,3081,2305,1736,3824,2170,2759,1014, # 3232
-1913,1386, 542,1397,2948, 490, 368, 716, 362, 159, 282,2569,1129,1658,1288,1750, # 3248
-2674, 276, 649,2016, 751,1496, 658,1818,1284,1862,2209,2087,2512,3451, 622,2834, # 3264
- 376, 117,1060,2053,1208,1721,1101,1443, 247,1250,3179,1792,3952,2760,2398,3953, # 3280
-6157,2144,3708, 446,2432,1151,2570,3452,2447,2761,2835,1210,2448,3082, 424,2222, # 3296
-1251,2449,2119,2836, 504,1581,4338, 602, 817, 857,3825,2349,2306, 357,3826,1470, # 3312
-1883,2883, 255, 958, 929,2917,3248, 302,4653,1050,1271,1751,2307,1952,1430,2697, # 3328
-2719,2359, 354,3180, 777, 158,2036,4339,1659,4340,4654,2308,2949,2248,1146,2232, # 3344
-3532,2720,1696,2623,3827,6158,3129,1550,2698,1485,1297,1428, 637, 931,2721,2145, # 3360
- 914,2550,2587,  81,2450, 612, 827,2646,1242,4655,1118,2884, 472,1855,3181,3533, # 3376
-3534, 569,1353,2699,1244,1758,2588,4119,2009,2762,2171,3709,1312,1531,6159,1152, # 3392
-1938, 134,1830, 471,3710,2276,1112,1535,3323,3453,3535, 982,1337,2950, 488, 826, # 3408
- 674,1058,1628,4120,2017, 522,2399, 211, 568,1367,3454, 350, 293,1872,1139,3249, # 3424
-1399,1946,3006,1300,2360,3324, 588, 736,6160,2606, 744, 669,3536,3828,6161,1358, # 3440
- 199, 723, 848, 933, 851,1939,1505,1514,1338,1618,1831,4656,1634,3613, 443,2740, # 3456
-3829, 717,1947, 491,1914,6162,2551,1542,4121,1025,6163,1099,1223, 198,3040,2722, # 3472
- 370, 410,1905,2589, 998,1248,3182,2380, 519,1449,4122,1710, 947, 928,1153,4341, # 3488
-2277, 344,2624,1511, 615, 105, 161,1212,1076,1960,3130,2054,1926,1175,1906,2473, # 3504
- 414,1873,2801,6164,2309, 315,1319,3325, 318,2018,2146,2157, 963, 631, 223,4342, # 3520
-4343,2675, 479,3711,1197,2625,3712,2676,2361,6165,4344,4123,6166,2451,3183,1886, # 3536
-2184,1674,1330,1711,1635,1506, 799, 219,3250,3083,3954,1677,3713,3326,2081,3614, # 3552
-1652,2073,4657,1147,3041,1752, 643,1961, 147,1974,3955,6167,1716,2037, 918,3007, # 3568
-1994, 120,1537, 118, 609,3184,4345, 740,3455,1219, 332,1615,3830,6168,1621,2980, # 3584
-1582, 783, 212, 553,2350,3714,1349,2433,2082,4124, 889,6169,2310,1275,1410, 973, # 3600
- 166,1320,3456,1797,1215,3185,2885,1846,2590,2763,4658, 629, 822,3008, 763, 940, # 3616
-1990,2862, 439,2409,1566,1240,1622, 926,1282,1907,2764, 654,2210,1607, 327,1130, # 3632
-3956,1678,1623,6170,2434,2192, 686, 608,3831,3715, 903,3957,3042,6171,2741,1522, # 3648
-1915,1105,1555,2552,1359, 323,3251,4346,3457, 738,1354,2553,2311,2334,1828,2003, # 3664
-3832,1753,2351,1227,6172,1887,4125,1478,6173,2410,1874,1712,1847, 520,1204,2607, # 3680
- 264,4659, 836,2677,2102, 600,4660,3833,2278,3084,6174,4347,3615,1342, 640, 532, # 3696
- 543,2608,1888,2400,2591,1009,4348,1497, 341,1737,3616,2723,1394, 529,3252,1321, # 3712
- 983,4661,1515,2120, 971,2592, 924, 287,1662,3186,4349,2700,4350,1519, 908,1948, # 3728
-2452, 156, 796,1629,1486,2223,2055, 694,4126,1259,1036,3392,1213,2249,2742,1889, # 3744
-1230,3958,1015, 910, 408, 559,3617,4662, 746, 725, 935,4663,3959,3009,1289, 563, # 3760
- 867,4664,3960,1567,2981,2038,2626, 988,2263,2381,4351, 143,2374, 704,1895,6175, # 3776
-1188,3716,2088, 673,3085,2362,4352, 484,1608,1921,2765,2918, 215, 904,3618,3537, # 3792
- 894, 509, 976,3043,2701,3961,4353,2837,2982, 498,6176,6177,1102,3538,1332,3393, # 3808
-1487,1636,1637, 233, 245,3962, 383, 650, 995,3044, 460,1520,1206,2352, 749,3327, # 3824
- 530, 700, 389,1438,1560,1773,3963,2264, 719,2951,2724,3834, 870,1832,1644,1000, # 3840
- 839,2474,3717, 197,1630,3394, 365,2886,3964,1285,2133, 734, 922, 818,1106, 732, # 3856
- 480,2083,1774,3458, 923,2279,1350, 221,3086,  85,2233,2234,3835,1585,3010,2147, # 3872
-1387,1705,2382,1619,2475, 133, 239,2802,1991,1016,2084,2383, 411,2838,1113, 651, # 3888
-1985,1160,3328, 990,1863,3087,1048,1276,2647, 265,2627,1599,3253,2056, 150, 638, # 3904
-2019, 656, 853, 326,1479, 680,1439,4354,1001,1759, 413,3459,3395,2492,1431, 459, # 3920
-4355,1125,3329,2265,1953,1450,2065,2863, 849, 351,2678,3131,3254,3255,1104,1577, # 3936
- 227,1351,1645,2453,2193,1421,2887, 812,2121, 634,  95,2435, 201,2312,4665,1646, # 3952
-1671,2743,1601,2554,2702,2648,2280,1315,1366,2089,3132,1573,3718,3965,1729,1189, # 3968
- 328,2679,1077,1940,1136, 558,1283, 964,1195, 621,2074,1199,1743,3460,3619,1896, # 3984
-1916,1890,3836,2952,1154,2112,1064, 862, 378,3011,2066,2113,2803,1568,2839,6178, # 4000
-3088,2919,1941,1660,2004,1992,2194, 142, 707,1590,1708,1624,1922,1023,1836,1233, # 4016
-1004,2313, 789, 741,3620,6179,1609,2411,1200,4127,3719,3720,4666,2057,3721, 593, # 4032
-2840, 367,2920,1878,6180,3461,1521, 628,1168, 692,2211,2649, 300, 720,2067,2571, # 4048
-2953,3396, 959,2504,3966,3539,3462,1977, 701,6181, 954,1043, 800, 681, 183,3722, # 4064
-1803,1730,3540,4128,2103, 815,2314, 174, 467, 230,2454,1093,2134, 755,3541,3397, # 4080
-1141,1162,6182,1738,2039, 270,3256,2513,1005,1647,2185,3837, 858,1679,1897,1719, # 4096
-2954,2324,1806, 402, 670, 167,4129,1498,2158,2104, 750,6183, 915, 189,1680,1551, # 4112
- 455,4356,1501,2455, 405,1095,2955, 338,1586,1266,1819, 570, 641,1324, 237,1556, # 4128
-2650,1388,3723,6184,1368,2384,1343,1978,3089,2436, 879,3724, 792,1191, 758,3012, # 4144
-1411,2135,1322,4357, 240,4667,1848,3725,1574,6185, 420,3045,1546,1391, 714,4358, # 4160
-1967, 941,1864, 863, 664, 426, 560,1731,2680,1785,2864,1949,2363, 403,3330,1415, # 4176
-1279,2136,1697,2335, 204, 721,2097,3838,  90,6186,2085,2505, 191,3967, 124,2148, # 4192
-1376,1798,1178,1107,1898,1405, 860,4359,1243,1272,2375,2983,1558,2456,1638, 113, # 4208
-3621, 578,1923,2609, 880, 386,4130, 784,2186,2266,1422,2956,2172,1722, 497, 263, # 4224
-2514,1267,2412,2610, 177,2703,3542, 774,1927,1344, 616,1432,1595,1018, 172,4360, # 4240
-2325, 911,4361, 438,1468,3622, 794,3968,2024,2173,1681,1829,2957, 945, 895,3090, # 4256
- 575,2212,2476, 475,2401,2681, 785,2744,1745,2293,2555,1975,3133,2865, 394,4668, # 4272
-3839, 635,4131, 639, 202,1507,2195,2766,1345,1435,2572,3726,1908,1184,1181,2457, # 4288
-3727,3134,4362, 843,2611, 437, 916,4669, 234, 769,1884,3046,3047,3623, 833,6187, # 4304
-1639,2250,2402,1355,1185,2010,2047, 999, 525,1732,1290,1488,2612, 948,1578,3728, # 4320
-2413,2477,1216,2725,2159, 334,3840,1328,3624,2921,1525,4132, 564,1056, 891,4363, # 4336
-1444,1698,2385,2251,3729,1365,2281,2235,1717,6188, 864,3841,2515, 444, 527,2767, # 4352
-2922,3625, 544, 461,6189, 566, 209,2437,3398,2098,1065,2068,3331,3626,3257,2137, # 4368  #last 512
-)
-# fmt: on
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_c_v_a_r.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_c_v_a_r.py
deleted file mode 100644
index 6ea44dbab3b0a4b0da1e5327d077873867f0b520..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_c_v_a_r.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from . import DefaultTable
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import bytesjoin
-from fontTools.ttLib.tables.TupleVariation import (
-    compileTupleVariationStore,
-    decompileTupleVariationStore,
-    TupleVariation,
-)
-
-
-# https://www.microsoft.com/typography/otspec/cvar.htm
-# https://www.microsoft.com/typography/otspec/otvarcommonformats.htm
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6cvar.html
-
-CVAR_HEADER_FORMAT = """
-    > # big endian
-    majorVersion:        H
-    minorVersion:        H
-    tupleVariationCount: H
-    offsetToData:        H
-"""
-
-CVAR_HEADER_SIZE = sstruct.calcsize(CVAR_HEADER_FORMAT)
-
-
-class table__c_v_a_r(DefaultTable.DefaultTable):
-    dependencies = ["cvt ", "fvar"]
-
-    def __init__(self, tag=None):
-        DefaultTable.DefaultTable.__init__(self, tag)
-        self.majorVersion, self.minorVersion = 1, 0
-        self.variations = []
-
-    def compile(self, ttFont, useSharedPoints=False):
-        tupleVariationCount, tuples, data = compileTupleVariationStore(
-            variations=[v for v in self.variations if v.hasImpact()],
-            pointCount=len(ttFont["cvt "].values),
-            axisTags=[axis.axisTag for axis in ttFont["fvar"].axes],
-            sharedTupleIndices={},
-            useSharedPoints=useSharedPoints,
-        )
-        header = {
-            "majorVersion": self.majorVersion,
-            "minorVersion": self.minorVersion,
-            "tupleVariationCount": tupleVariationCount,
-            "offsetToData": CVAR_HEADER_SIZE + len(tuples),
-        }
-        return b"".join([sstruct.pack(CVAR_HEADER_FORMAT, header), tuples, data])
-
-    def decompile(self, data, ttFont):
-        axisTags = [axis.axisTag for axis in ttFont["fvar"].axes]
-        header = {}
-        sstruct.unpack(CVAR_HEADER_FORMAT, data[0:CVAR_HEADER_SIZE], header)
-        self.majorVersion = header["majorVersion"]
-        self.minorVersion = header["minorVersion"]
-        assert self.majorVersion == 1, self.majorVersion
-        self.variations = decompileTupleVariationStore(
-            tableTag=self.tableTag,
-            axisTags=axisTags,
-            tupleVariationCount=header["tupleVariationCount"],
-            pointCount=len(ttFont["cvt "].values),
-            sharedTuples=None,
-            data=data,
-            pos=CVAR_HEADER_SIZE,
-            dataPos=header["offsetToData"],
-        )
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "version":
-            self.majorVersion = int(attrs.get("major", "1"))
-            self.minorVersion = int(attrs.get("minor", "0"))
-        elif name == "tuple":
-            valueCount = len(ttFont["cvt "].values)
-            var = TupleVariation({}, [None] * valueCount)
-            self.variations.append(var)
-            for tupleElement in content:
-                if isinstance(tupleElement, tuple):
-                    tupleName, tupleAttrs, tupleContent = tupleElement
-                    var.fromXML(tupleName, tupleAttrs, tupleContent)
-
-    def toXML(self, writer, ttFont):
-        axisTags = [axis.axisTag for axis in ttFont["fvar"].axes]
-        writer.simpletag("version", major=self.majorVersion, minor=self.minorVersion)
-        writer.newline()
-        for var in self.variations:
-            var.toXML(writer, axisTags)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/external.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/external.py
deleted file mode 100644
index 29ad28384cbe1b0f36a31b4c73efe7866dbcedae..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/external.py
+++ /dev/null
@@ -1,540 +0,0 @@
-"""This module should not be used directly as its API is subject to change. Instead,
-use the `gr.Blocks.load()` or `gr.load()` functions."""
-
-from __future__ import annotations
-
-import json
-import re
-import warnings
-from typing import TYPE_CHECKING, Callable
-
-import requests
-from gradio_client import Client
-from gradio_client.documentation import document, set_documentation_group
-
-import gradio
-from gradio import components, utils
-from gradio.context import Context
-from gradio.deprecation import warn_deprecation
-from gradio.exceptions import Error, TooManyRequestsError
-from gradio.external_utils import (
-    cols_to_rows,
-    encode_to_base64,
-    get_tabular_examples,
-    postprocess_label,
-    rows_to_cols,
-    streamline_spaces_interface,
-)
-from gradio.processing_utils import extract_base64_data, to_binary
-
-if TYPE_CHECKING:
-    from gradio.blocks import Blocks
-    from gradio.interface import Interface
-
-
-set_documentation_group("helpers")
-
-
-@document()
-def load(
-    name: str,
-    src: str | None = None,
-    api_key: str | None = None,
-    hf_token: str | None = None,
-    alias: str | None = None,
-    **kwargs,
-) -> Blocks:
-    """
-    Method that constructs a Blocks from a Hugging Face repo. Can accept
-    model repos (if src is "models") or Space repos (if src is "spaces"). The input
-    and output components are automatically loaded from the repo.
-    Parameters:
-        name: the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
-        src: the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
-        api_key: Deprecated. Please use the `hf_token` parameter instead.
-        hf_token: optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens.  Warning: only provide this if you are loading a trusted private Space as it can be read by the Space you are loading.
-        alias: optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)
-    Returns:
-        a Gradio Blocks object for the given model
-    Example:
-        import gradio as gr
-        demo = gr.load("gradio/question-answering", src="spaces")
-        demo.launch()
-    """
-    if hf_token is None and api_key:
-        warn_deprecation(
-            "The `api_key` parameter will be deprecated. "
-            "Please use the `hf_token` parameter going forward."
-        )
-        hf_token = api_key
-    return load_blocks_from_repo(
-        name=name, src=src, hf_token=hf_token, alias=alias, **kwargs
-    )
-
-
-def load_blocks_from_repo(
-    name: str,
-    src: str | None = None,
-    hf_token: str | None = None,
-    alias: str | None = None,
-    **kwargs,
-) -> Blocks:
-    """Creates and returns a Blocks instance from a Hugging Face model or Space repo."""
-    if src is None:
-        # Separate the repo type (e.g. "model") from repo name (e.g. "google/vit-base-patch16-224")
-        tokens = name.split("/")
-        assert (
-            len(tokens) > 1
-        ), "Either `src` parameter must be provided, or `name` must be formatted as {src}/{repo name}"
-        src = tokens[0]
-        name = "/".join(tokens[1:])
-
-    factory_methods: dict[str, Callable] = {
-        # for each repo type, we have a method that returns the Interface given the model name & optionally an api_key
-        "huggingface": from_model,
-        "models": from_model,
-        "spaces": from_spaces,
-    }
-    assert (
-        src.lower() in factory_methods
-    ), f"parameter: src must be one of {factory_methods.keys()}"
-
-    if hf_token is not None:
-        if Context.hf_token is not None and Context.hf_token != hf_token:
-            warnings.warn(
-                """You are loading a model/Space with a different access token than the one you used to load a previous model/Space. This is not recommended, as it may cause unexpected behavior."""
-            )
-        Context.hf_token = hf_token
-
-    blocks: gradio.Blocks = factory_methods[src](name, hf_token, alias, **kwargs)
-    return blocks
-
-
-def chatbot_preprocess(text, state):
-    payload = {
-        "inputs": {"generated_responses": None, "past_user_inputs": None, "text": text}
-    }
-    if state is not None:
-        payload["inputs"]["generated_responses"] = state["conversation"][
-            "generated_responses"
-        ]
-        payload["inputs"]["past_user_inputs"] = state["conversation"][
-            "past_user_inputs"
-        ]
-
-    return payload
-
-
-def chatbot_postprocess(response):
-    response_json = response.json()
-    chatbot_value = list(
-        zip(
-            response_json["conversation"]["past_user_inputs"],
-            response_json["conversation"]["generated_responses"],
-        )
-    )
-    return chatbot_value, response_json
-
-
-def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwargs):
-    model_url = f"https://huggingface.co/{model_name}"
-    api_url = f"https://api-inference.huggingface.co/models/{model_name}"
-    print(f"Fetching model from: {model_url}")
-
-    headers = {"Authorization": f"Bearer {hf_token}"} if hf_token is not None else {}
-
-    # Checking if model exists, and if so, it gets the pipeline
-    response = requests.request("GET", api_url, headers=headers)
-    assert (
-        response.status_code == 200
-    ), f"Could not find model: {model_name}. If it is a private or gated model, please provide your Hugging Face access token (https://huggingface.co/settings/tokens) as the argument for the `api_key` parameter."
-    p = response.json().get("pipeline_tag")
-    pipelines = {
-        "audio-classification": {
-            # example model: ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition
-            "inputs": components.Audio(source="upload", type="filepath", label="Input"),
-            "outputs": components.Label(label="Class"),
-            "preprocess": lambda i: to_binary,
-            "postprocess": lambda r: postprocess_label(
-                {i["label"].split(", ")[0]: i["score"] for i in r.json()}
-            ),
-        },
-        "audio-to-audio": {
-            # example model: facebook/xm_transformer_sm_all-en
-            "inputs": components.Audio(source="upload", type="filepath", label="Input"),
-            "outputs": components.Audio(label="Output"),
-            "preprocess": to_binary,
-            "postprocess": encode_to_base64,
-        },
-        "automatic-speech-recognition": {
-            # example model: facebook/wav2vec2-base-960h
-            "inputs": components.Audio(source="upload", type="filepath", label="Input"),
-            "outputs": components.Textbox(label="Output"),
-            "preprocess": to_binary,
-            "postprocess": lambda r: r.json()["text"],
-        },
-        "conversational": {
-            "inputs": [components.Textbox(), components.State()],  # type: ignore
-            "outputs": [components.Chatbot(), components.State()],  # type: ignore
-            "preprocess": chatbot_preprocess,
-            "postprocess": chatbot_postprocess,
-        },
-        "feature-extraction": {
-            # example model: julien-c/distilbert-feature-extraction
-            "inputs": components.Textbox(label="Input"),
-            "outputs": components.Dataframe(label="Output"),
-            "preprocess": lambda x: {"inputs": x},
-            "postprocess": lambda r: r.json()[0],
-        },
-        "fill-mask": {
-            "inputs": components.Textbox(label="Input"),
-            "outputs": components.Label(label="Classification"),
-            "preprocess": lambda x: {"inputs": x},
-            "postprocess": lambda r: postprocess_label(
-                {i["token_str"]: i["score"] for i in r.json()}
-            ),
-        },
-        "image-classification": {
-            # Example: google/vit-base-patch16-224
-            "inputs": components.Image(type="filepath", label="Input Image"),
-            "outputs": components.Label(label="Classification"),
-            "preprocess": to_binary,
-            "postprocess": lambda r: postprocess_label(
-                {i["label"].split(", ")[0]: i["score"] for i in r.json()}
-            ),
-        },
-        "question-answering": {
-            # Example: deepset/xlm-roberta-base-squad2
-            "inputs": [
-                components.Textbox(lines=7, label="Context"),
-                components.Textbox(label="Question"),
-            ],
-            "outputs": [
-                components.Textbox(label="Answer"),
-                components.Label(label="Score"),
-            ],
-            "preprocess": lambda c, q: {"inputs": {"context": c, "question": q}},
-            "postprocess": lambda r: (r.json()["answer"], {"label": r.json()["score"]}),
-        },
-        "summarization": {
-            # Example: facebook/bart-large-cnn
-            "inputs": components.Textbox(label="Input"),
-            "outputs": components.Textbox(label="Summary"),
-            "preprocess": lambda x: {"inputs": x},
-            "postprocess": lambda r: r.json()[0]["summary_text"],
-        },
-        "text-classification": {
-            # Example: distilbert-base-uncased-finetuned-sst-2-english
-            "inputs": components.Textbox(label="Input"),
-            "outputs": components.Label(label="Classification"),
-            "preprocess": lambda x: {"inputs": x},
-            "postprocess": lambda r: postprocess_label(
-                {i["label"].split(", ")[0]: i["score"] for i in r.json()[0]}
-            ),
-        },
-        "text-generation": {
-            # Example: gpt2
-            "inputs": components.Textbox(label="Input"),
-            "outputs": components.Textbox(label="Output"),
-            "preprocess": lambda x: {"inputs": x},
-            "postprocess": lambda r: r.json()[0]["generated_text"],
-        },
-        "text2text-generation": {
-            # Example: valhalla/t5-small-qa-qg-hl
-            "inputs": components.Textbox(label="Input"),
-            "outputs": components.Textbox(label="Generated Text"),
-            "preprocess": lambda x: {"inputs": x},
-            "postprocess": lambda r: r.json()[0]["generated_text"],
-        },
-        "translation": {
-            "inputs": components.Textbox(label="Input"),
-            "outputs": components.Textbox(label="Translation"),
-            "preprocess": lambda x: {"inputs": x},
-            "postprocess": lambda r: r.json()[0]["translation_text"],
-        },
-        "zero-shot-classification": {
-            # Example: facebook/bart-large-mnli
-            "inputs": [
-                components.Textbox(label="Input"),
-                components.Textbox(label="Possible class names (" "comma-separated)"),
-                components.Checkbox(label="Allow multiple true classes"),
-            ],
-            "outputs": components.Label(label="Classification"),
-            "preprocess": lambda i, c, m: {
-                "inputs": i,
-                "parameters": {"candidate_labels": c, "multi_class": m},
-            },
-            "postprocess": lambda r: postprocess_label(
-                {
-                    r.json()["labels"][i]: r.json()["scores"][i]
-                    for i in range(len(r.json()["labels"]))
-                }
-            ),
-        },
-        "sentence-similarity": {
-            # Example: sentence-transformers/distilbert-base-nli-stsb-mean-tokens
-            "inputs": [
-                components.Textbox(
-                    value="That is a happy person", label="Source Sentence"
-                ),
-                components.Textbox(
-                    lines=7,
-                    placeholder="Separate each sentence by a newline",
-                    label="Sentences to compare to",
-                ),
-            ],
-            "outputs": components.Label(label="Classification"),
-            "preprocess": lambda src, sentences: {
-                "inputs": {
-                    "source_sentence": src,
-                    "sentences": [s for s in sentences.splitlines() if s != ""],
-                }
-            },
-            "postprocess": lambda r: postprocess_label(
-                {f"sentence {i}": v for i, v in enumerate(r.json())}
-            ),
-        },
-        "text-to-speech": {
-            # Example: julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train
-            "inputs": components.Textbox(label="Input"),
-            "outputs": components.Audio(label="Audio"),
-            "preprocess": lambda x: {"inputs": x},
-            "postprocess": encode_to_base64,
-        },
-        "text-to-image": {
-            # example model: osanseviero/BigGAN-deep-128
-            "inputs": components.Textbox(label="Input"),
-            "outputs": components.Image(label="Output"),
-            "preprocess": lambda x: {"inputs": x},
-            "postprocess": encode_to_base64,
-        },
-        "token-classification": {
-            # example model: huggingface-course/bert-finetuned-ner
-            "inputs": components.Textbox(label="Input"),
-            "outputs": components.HighlightedText(label="Output"),
-            "preprocess": lambda x: {"inputs": x},
-            "postprocess": lambda r: r,  # Handled as a special case in query_huggingface_api()
-        },
-        "document-question-answering": {
-            # example model: impira/layoutlm-document-qa
-            "inputs": [
-                components.Image(type="filepath", label="Input Document"),
-                components.Textbox(label="Question"),
-            ],
-            "outputs": components.Label(label="Label"),
-            "preprocess": lambda img, q: {
-                "inputs": {
-                    "image": extract_base64_data(img),  # Extract base64 data
-                    "question": q,
-                }
-            },
-            "postprocess": lambda r: postprocess_label(
-                {i["answer"]: i["score"] for i in r.json()}
-            ),
-        },
-        "visual-question-answering": {
-            # example model: dandelin/vilt-b32-finetuned-vqa
-            "inputs": [
-                components.Image(type="filepath", label="Input Image"),
-                components.Textbox(label="Question"),
-            ],
-            "outputs": components.Label(label="Label"),
-            "preprocess": lambda img, q: {
-                "inputs": {
-                    "image": extract_base64_data(img),
-                    "question": q,
-                }
-            },
-            "postprocess": lambda r: postprocess_label(
-                {i["answer"]: i["score"] for i in r.json()}
-            ),
-        },
-        "image-to-text": {
-            # example model: Salesforce/blip-image-captioning-base
-            "inputs": components.Image(type="filepath", label="Input Image"),
-            "outputs": components.Textbox(label="Generated Text"),
-            "preprocess": to_binary,
-            "postprocess": lambda r: r.json()[0]["generated_text"],
-        },
-    }
-
-    if p in ["tabular-classification", "tabular-regression"]:
-        example_data = get_tabular_examples(model_name)
-        col_names, example_data = cols_to_rows(example_data)
-        example_data = [[example_data]] if example_data else None
-
-        pipelines[p] = {
-            "inputs": components.Dataframe(
-                label="Input Rows",
-                type="pandas",
-                headers=col_names,
-                col_count=(len(col_names), "fixed"),
-            ),
-            "outputs": components.Dataframe(
-                label="Predictions", type="array", headers=["prediction"]
-            ),
-            "preprocess": rows_to_cols,
-            "postprocess": lambda r: {
-                "headers": ["prediction"],
-                "data": [[pred] for pred in json.loads(r.text)],
-            },
-            "examples": example_data,
-        }
-
-    if p is None or p not in pipelines:
-        raise ValueError(f"Unsupported pipeline type: {p}")
-
-    pipeline = pipelines[p]
-
-    def query_huggingface_api(*params):
-        # Convert to a list of input components
-        data = pipeline["preprocess"](*params)
-        if isinstance(
-            data, dict
-        ):  # HF doesn't allow additional parameters for binary files (e.g. images or audio files)
-            data.update({"options": {"wait_for_model": True}})
-            data = json.dumps(data)
-        response = requests.request("POST", api_url, headers=headers, data=data)
-        if response.status_code != 200:
-            errors_json = response.json()
-            errors, warns = "", ""
-            if errors_json.get("error"):
-                errors = f", Error: {errors_json.get('error')}"
-            if errors_json.get("warnings"):
-                warns = f", Warnings: {errors_json.get('warnings')}"
-            raise Error(
-                f"Could not complete request to HuggingFace API, Status Code: {response.status_code}"
-                + errors
-                + warns
-            )
-        if (
-            p == "token-classification"
-        ):  # Handle as a special case since HF API only returns the named entities and we need the input as well
-            ner_groups = response.json()
-            input_string = params[0]
-            response = utils.format_ner_list(input_string, ner_groups)
-        output = pipeline["postprocess"](response)
-        return output
-
-    if alias is None:
-        query_huggingface_api.__name__ = model_name
-    else:
-        query_huggingface_api.__name__ = alias
-
-    interface_info = {
-        "fn": query_huggingface_api,
-        "inputs": pipeline["inputs"],
-        "outputs": pipeline["outputs"],
-        "title": model_name,
-        "examples": pipeline.get("examples"),
-    }
-
-    kwargs = dict(interface_info, **kwargs)
-
-    # So interface doesn't run pre/postprocess
-    # except for conversational interfaces which
-    # are stateful
-    kwargs["_api_mode"] = p != "conversational"
-
-    interface = gradio.Interface(**kwargs)
-    return interface
-
-
-def from_spaces(
-    space_name: str, hf_token: str | None, alias: str | None, **kwargs
-) -> Blocks:
-    space_url = f"https://huggingface.co/spaces/{space_name}"
-
-    print(f"Fetching Space from: {space_url}")
-
-    headers = {}
-    if hf_token is not None:
-        headers["Authorization"] = f"Bearer {hf_token}"
-
-    iframe_url = (
-        requests.get(
-            f"https://huggingface.co/api/spaces/{space_name}/host", headers=headers
-        )
-        .json()
-        .get("host")
-    )
-
-    if iframe_url is None:
-        raise ValueError(
-            f"Could not find Space: {space_name}. If it is a private or gated Space, please provide your Hugging Face access token (https://huggingface.co/settings/tokens) as the argument for the `api_key` parameter."
-        )
-
-    r = requests.get(iframe_url, headers=headers)
-
-    result = re.search(
-        r"window.gradio_config = (.*?);[\s]*</script>", r.text
-    )  # some basic regex to extract the config
-    try:
-        config = json.loads(result.group(1))  # type: ignore
-    except AttributeError as ae:
-        raise ValueError(f"Could not load the Space: {space_name}") from ae
-    if "allow_flagging" in config:  # Create an Interface for Gradio 2.x Spaces
-        return from_spaces_interface(
-            space_name, config, alias, hf_token, iframe_url, **kwargs
-        )
-    else:  # Create a Blocks for Gradio 3.x Spaces
-        if kwargs:
-            warnings.warn(
-                "You cannot override parameters for this Space by passing in kwargs. "
-                "Instead, please load the Space as a function and use it to create a "
-                "Blocks or Interface locally. You may find this Guide helpful: "
-                "https://gradio.app/using_blocks_like_functions/"
-            )
-        return from_spaces_blocks(space=space_name, hf_token=hf_token)
-
-
-def from_spaces_blocks(space: str, hf_token: str | None) -> Blocks:
-    client = Client(space, hf_token=hf_token)
-    predict_fns = [endpoint._predict_resolve for endpoint in client.endpoints]
-    return gradio.Blocks.from_config(client.config, predict_fns, client.src)
-
-
-def from_spaces_interface(
-    model_name: str,
-    config: dict,
-    alias: str | None,
-    hf_token: str | None,
-    iframe_url: str,
-    **kwargs,
-) -> Interface:
-    config = streamline_spaces_interface(config)
-    api_url = f"{iframe_url}/api/predict/"
-    headers = {"Content-Type": "application/json"}
-    if hf_token is not None:
-        headers["Authorization"] = f"Bearer {hf_token}"
-
-    # The function should call the API with preprocessed data
-    def fn(*data):
-        data = json.dumps({"data": data})
-        response = requests.post(api_url, headers=headers, data=data)
-        result = json.loads(response.content.decode("utf-8"))
-        if "error" in result and "429" in result["error"]:
-            raise TooManyRequestsError("Too many requests to the Hugging Face API")
-        try:
-            output = result["data"]
-        except KeyError as ke:
-            raise KeyError(
-                f"Could not find 'data' key in response from external Space. Response received: {result}"
-            ) from ke
-        if (
-            len(config["outputs"]) == 1
-        ):  # if the fn is supposed to return a single value, pop it
-            output = output[0]
-        if len(config["outputs"]) == 1 and isinstance(
-            output, list
-        ):  # Needed to support Output.Image() returning bounding boxes as well (TODO: handle different versions of gradio since they have slightly different APIs)
-            output = output[0]
-        return output
-
-    fn.__name__ = alias if (alias is not None) else model_name
-    config["fn"] = fn
-
-    kwargs = dict(config, **kwargs)
-    kwargs["_api_mode"] = True
-    interface = gradio.Interface(**kwargs)
-    return interface
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-63038c0b.css b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-63038c0b.css
deleted file mode 100644
index d982529e34cf13bea393d28296b84aa8af29e298..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-63038c0b.css
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-be790e2e.css b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-be790e2e.css
deleted file mode 100644
index 2038190972931fd925656a6bd9ebd7e57f0b1d0a..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-be790e2e.css
+++ /dev/null
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Eptar Reinforcement for Archicad 15 Crack The Best Way to Integrate Reinforcement with Archicad.md b/spaces/cihyFjudo/fairness-paper-search/Eptar Reinforcement for Archicad 15 Crack The Best Way to Integrate Reinforcement with Archicad.md
deleted file mode 100644
index 1dc82d4d66e0d2519a60347abb7f31c7e6c48425..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Eptar Reinforcement for Archicad 15 Crack The Best Way to Integrate Reinforcement with Archicad.md	
+++ /dev/null
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deleted file mode 100644
index c411d1a28af5cbb4866bcce97c9b48af2ee50e2e..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Multimedia Making It Work 5th Edition By Tay Vaughan Pdf Free Download A Complete and Updated Course on Multimedia Fundamentals and Applications.md	
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Sleek Bill Latest Version Cracked Download Everything You Need to Know about this GST Billing Software.md b/spaces/cihyFjudo/fairness-paper-search/Sleek Bill Latest Version Cracked Download Everything You Need to Know about this GST Billing Software.md
deleted file mode 100644
index 97d702584002e0fbc143040b59615ce507d266cb..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Sleek Bill Latest Version Cracked Download Everything You Need to Know about this GST Billing Software.md	
+++ /dev/null
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diff --git a/spaces/cleanmaster/so-vits-svc-akagi/losses.py b/spaces/cleanmaster/so-vits-svc-akagi/losses.py
deleted file mode 100644
index 41f9be6980713a46824ae9ec5eb8fd7c515d89c5..0000000000000000000000000000000000000000
--- a/spaces/cleanmaster/so-vits-svc-akagi/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()
-  #print(logs_p)
-  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/colakin/video-generater/public/ffmpeg/libavcodec/cavs.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cavs.h
deleted file mode 100644
index 244c322b35aa5f12b4222b6db5c722b8ff84a0dc..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cavs.h
+++ /dev/null
@@ -1,284 +0,0 @@
-/*
- * Chinese AVS video (AVS1-P2, JiZhun profile) decoder.
- * Copyright (c) 2006  Stefan Gehrer <stefan.gehrer@gmx.de>
- *
- * 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
- */
-
-#ifndef AVCODEC_CAVS_H
-#define AVCODEC_CAVS_H
-
-#include <stddef.h>
-#include <stdint.h>
-
-#include "libavutil/frame.h"
-#include "libavutil/mem_internal.h"
-
-#include "avcodec.h"
-#include "cavsdsp.h"
-#include "blockdsp.h"
-#include "h264chroma.h"
-#include "get_bits.h"
-#include "videodsp.h"
-
-#define SLICE_MAX_START_CODE    0x000001af
-#define EXT_START_CODE          0x000001b5
-#define USER_START_CODE         0x000001b2
-#define CAVS_START_CODE         0x000001b0
-#define PIC_I_START_CODE        0x000001b3
-#define PIC_PB_START_CODE       0x000001b6
-
-#define A_AVAIL                          1
-#define B_AVAIL                          2
-#define C_AVAIL                          4
-#define D_AVAIL                          8
-#define NOT_AVAIL                       -1
-#define REF_INTRA                       -2
-#define REF_DIR                         -3
-
-#define ESCAPE_CODE                     59
-
-#define FWD0                          0x01
-#define FWD1                          0x02
-#define BWD0                          0x04
-#define BWD1                          0x08
-#define SYM0                          0x10
-#define SYM1                          0x20
-#define SPLITH                        0x40
-#define SPLITV                        0x80
-
-#define MV_BWD_OFFS                     12
-#define MV_STRIDE                        4
-
-enum cavs_mb {
-  I_8X8 = 0,
-  P_SKIP,
-  P_16X16,
-  P_16X8,
-  P_8X16,
-  P_8X8,
-  B_SKIP,
-  B_DIRECT,
-  B_FWD_16X16,
-  B_BWD_16X16,
-  B_SYM_16X16,
-  B_8X8 = 29
-};
-
-enum cavs_sub_mb {
-  B_SUB_DIRECT,
-  B_SUB_FWD,
-  B_SUB_BWD,
-  B_SUB_SYM
-};
-
-enum cavs_intra_luma {
-  INTRA_L_VERT,
-  INTRA_L_HORIZ,
-  INTRA_L_LP,
-  INTRA_L_DOWN_LEFT,
-  INTRA_L_DOWN_RIGHT,
-  INTRA_L_LP_LEFT,
-  INTRA_L_LP_TOP,
-  INTRA_L_DC_128
-};
-
-enum cavs_intra_chroma {
-  INTRA_C_LP,
-  INTRA_C_HORIZ,
-  INTRA_C_VERT,
-  INTRA_C_PLANE,
-  INTRA_C_LP_LEFT,
-  INTRA_C_LP_TOP,
-  INTRA_C_DC_128,
-};
-
-enum cavs_mv_pred {
-  MV_PRED_MEDIAN,
-  MV_PRED_LEFT,
-  MV_PRED_TOP,
-  MV_PRED_TOPRIGHT,
-  MV_PRED_PSKIP,
-  MV_PRED_BSKIP
-};
-
-enum cavs_block {
-  BLK_16X16,
-  BLK_16X8,
-  BLK_8X16,
-  BLK_8X8
-};
-
-enum cavs_mv_loc {
-  MV_FWD_D3 = 0,
-  MV_FWD_B2,
-  MV_FWD_B3,
-  MV_FWD_C2,
-  MV_FWD_A1,
-  MV_FWD_X0,
-  MV_FWD_X1,
-  MV_FWD_A3 = 8,
-  MV_FWD_X2,
-  MV_FWD_X3,
-  MV_BWD_D3 = MV_BWD_OFFS,
-  MV_BWD_B2,
-  MV_BWD_B3,
-  MV_BWD_C2,
-  MV_BWD_A1,
-  MV_BWD_X0,
-  MV_BWD_X1,
-  MV_BWD_A3 = MV_BWD_OFFS+8,
-  MV_BWD_X2,
-  MV_BWD_X3
-};
-
-DECLARE_ALIGNED(8, typedef, struct) {
-    int16_t x;
-    int16_t y;
-    int16_t dist;
-    int16_t ref;
-} cavs_vector;
-
-struct dec_2dvlc {
-  int8_t rltab[59][3];
-  int8_t level_add[27];
-  int8_t golomb_order;
-  int inc_limit;
-  int8_t max_run;
-};
-
-typedef struct AVSFrame {
-    AVFrame *f;
-    int poc;
-} AVSFrame;
-
-typedef struct AVSContext {
-    AVCodecContext *avctx;
-    BlockDSPContext bdsp;
-    H264ChromaContext h264chroma;
-    VideoDSPContext vdsp;
-    CAVSDSPContext  cdsp;
-    GetBitContext gb;
-    AVSFrame cur;     ///< currently decoded frame
-    AVSFrame DPB[2];  ///< reference frames
-    int dist[2];     ///< temporal distances from current frame to ref frames
-    int low_delay;
-    int profile, level;
-    int aspect_ratio;
-    int mb_width, mb_height;
-    int width, height;
-    int stream_revision; ///<0 for samples from 2006, 1 for rm52j encoder
-    int progressive;
-    int pic_structure;
-    int skip_mode_flag; ///< select between skip_count or one skip_flag per MB
-    int loop_filter_disable;
-    int alpha_offset, beta_offset;
-    int ref_flag;
-    int mbx, mby, mbidx; ///< macroblock coordinates
-    int flags;         ///< availability flags of neighbouring macroblocks
-    int stc;           ///< last start code
-    uint8_t *cy, *cu, *cv; ///< current MB sample pointers
-    int left_qp;
-    uint8_t *top_qp;
-
-    /** mv motion vector cache
-       0:    D3  B2  B3  C2
-       4:    A1  X0  X1   -
-       8:    A3  X2  X3   -
-
-       X are the vectors in the current macroblock (5,6,9,10)
-       A is the macroblock to the left (4,8)
-       B is the macroblock to the top (1,2)
-       C is the macroblock to the top-right (3)
-       D is the macroblock to the top-left (0)
-
-       the same is repeated for backward motion vectors */
-    cavs_vector mv[2*4*3];
-    cavs_vector *top_mv[2];
-    cavs_vector *col_mv;
-
-    /** luma pred mode cache
-       0:    --  B2  B3
-       3:    A1  X0  X1
-       6:    A3  X2  X3   */
-    int pred_mode_Y[3*3];
-    int *top_pred_Y;
-    ptrdiff_t l_stride, c_stride;
-    int luma_scan[4];
-    int qp;
-    int qp_fixed;
-    int pic_qp_fixed;
-    int cbp;
-    uint8_t permutated_scantable[64];
-
-    /** intra prediction is done with un-deblocked samples
-     they are saved here before deblocking the MB  */
-    uint8_t *top_border_y, *top_border_u, *top_border_v;
-    uint8_t left_border_y[26], left_border_u[10], left_border_v[10];
-    uint8_t intern_border_y[26];
-    uint8_t topleft_border_y, topleft_border_u, topleft_border_v;
-
-    void (*intra_pred_l[8])(uint8_t *d, uint8_t *top, uint8_t *left, ptrdiff_t stride);
-    void (*intra_pred_c[7])(uint8_t *d, uint8_t *top, uint8_t *left, ptrdiff_t stride);
-    uint8_t *col_type_base;
-
-    /* scaling factors for MV prediction */
-    int sym_factor;    ///< for scaling in symmetrical B block
-    int direct_den[2]; ///< for scaling in direct B block
-    int scale_den[2];  ///< for scaling neighbouring MVs
-
-    uint8_t *edge_emu_buffer;
-
-    int got_keyframe;
-    int16_t *block;
-} AVSContext;
-
-extern const uint8_t     ff_cavs_chroma_qp[64];
-extern const uint8_t     ff_cavs_partition_flags[30];
-extern const cavs_vector ff_cavs_intra_mv;
-extern const cavs_vector ff_cavs_dir_mv;
-
-static inline void set_mvs(cavs_vector *mv, enum cavs_block size) {
-    switch(size) {
-    case BLK_16X16:
-        mv[MV_STRIDE  ] = mv[0];
-        mv[MV_STRIDE+1] = mv[0];
-    case BLK_16X8:
-        mv[1] = mv[0];
-        break;
-    case BLK_8X16:
-        mv[MV_STRIDE] = mv[0];
-        break;
-    }
-}
-
-void ff_cavs_filter(AVSContext *h, enum cavs_mb mb_type);
-void ff_cavs_load_intra_pred_luma(AVSContext *h, uint8_t *top, uint8_t **left,
-                                  int block);
-void ff_cavs_load_intra_pred_chroma(AVSContext *h);
-void ff_cavs_modify_mb_i(AVSContext *h, int *pred_mode_uv);
-void ff_cavs_inter(AVSContext *h, enum cavs_mb mb_type);
-void ff_cavs_mv(AVSContext *h, enum cavs_mv_loc nP, enum cavs_mv_loc nC,
-                enum cavs_mv_pred mode, enum cavs_block size, int ref);
-void ff_cavs_init_mb(AVSContext *h);
-int  ff_cavs_next_mb(AVSContext *h);
-int ff_cavs_init_pic(AVSContext *h);
-int ff_cavs_init_top_lines(AVSContext *h);
-int ff_cavs_init(AVCodecContext *avctx);
-int ff_cavs_end (AVCodecContext *avctx);
-
-#endif /* AVCODEC_CAVS_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hevcpred.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hevcpred.h
deleted file mode 100644
index b1b1dc4f3a145fb5912bd53405966f49e44d374e..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hevcpred.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * HEVC video Decoder
- *
- * Copyright (C) 2012 - 2013 Guillaume Martres
- *
- * 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
- */
-
-#ifndef AVCODEC_HEVCPRED_H
-#define AVCODEC_HEVCPRED_H
-
-#include <stddef.h>
-#include <stdint.h>
-
-struct HEVCLocalContext;
-
-typedef struct HEVCPredContext {
-    void (*intra_pred[4])(struct HEVCLocalContext *lc, int x0, int y0, int c_idx);
-
-    void (*pred_planar[4])(uint8_t *src, const uint8_t *top,
-                           const uint8_t *left, ptrdiff_t stride);
-    void (*pred_dc)(uint8_t *src, const uint8_t *top, const uint8_t *left,
-                    ptrdiff_t stride, int log2_size, int c_idx);
-    void (*pred_angular[4])(uint8_t *src, const uint8_t *top,
-                            const uint8_t *left, ptrdiff_t stride,
-                            int c_idx, int mode);
-} HEVCPredContext;
-
-void ff_hevc_pred_init(HEVCPredContext *hpc, int bit_depth);
-void ff_hevc_pred_init_mips(HEVCPredContext *hpc, int bit_depth);
-
-#endif /* AVCODEC_HEVCPRED_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/iff.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/iff.c
deleted file mode 100644
index e02d2e77e5336f42ca273940dff6df4f278d0306..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/iff.c
+++ /dev/null
@@ -1,1913 +0,0 @@
-/*
- * IFF ACBM/ANIM/DEEP/ILBM/PBM/RGB8/RGBN bitmap decoder
- * Copyright (c) 2010 Peter Ross <pross@xvid.org>
- * Copyright (c) 2010 Sebastian Vater <cdgs.basty@googlemail.com>
- * Copyright (c) 2016 Paul B Mahol
- *
- * 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
- * IFF ACBM/ANIM/DEEP/ILBM/PBM/RGB8/RGBN bitmap decoder
- */
-
-#include <stdint.h>
-
-#include "libavutil/imgutils.h"
-
-#include "bytestream.h"
-#include "avcodec.h"
-#include "codec_internal.h"
-#include "decode.h"
-
-// TODO: masking bits
-typedef enum {
-    MASK_NONE,
-    MASK_HAS_MASK,
-    MASK_HAS_TRANSPARENT_COLOR,
-    MASK_LASSO
-} mask_type;
-
-typedef struct IffContext {
-    int planesize;
-    uint8_t * planebuf;
-    uint8_t * ham_buf;      ///< temporary buffer for planar to chunky conversation
-    uint32_t *ham_palbuf;   ///< HAM decode table
-    uint32_t *mask_buf;     ///< temporary buffer for palette indices
-    uint32_t *mask_palbuf;  ///< masking palette table
-    unsigned  compression;  ///< delta compression method used
-    unsigned  is_short;     ///< short compression method used
-    unsigned  is_interlaced;///< video is interlaced
-    unsigned  is_brush;     ///< video is in ANBR format
-    unsigned  bpp;          ///< bits per plane to decode (differs from bits_per_coded_sample if HAM)
-    unsigned  ham;          ///< 0 if non-HAM or number of hold bits (6 for bpp > 6, 4 otherwise)
-    unsigned  flags;        ///< 1 for EHB, 0 is no extra half darkening
-    unsigned  transparency; ///< TODO: transparency color index in palette
-    unsigned  masking;      ///< TODO: masking method used
-    int init; // 1 if buffer and palette data already initialized, 0 otherwise
-    int16_t   tvdc[16];     ///< TVDC lookup table
-    uint8_t *video[2];
-    unsigned video_size;
-    uint32_t *pal;
-} IffContext;
-
-#define LUT8_PART(plane, v)                             \
-    AV_LE2NE64C(UINT64_C(0x0000000)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x1000000)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x0010000)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x1010000)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x0000100)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x1000100)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x0010100)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x1010100)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x0000001)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x1000001)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x0010001)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x1010001)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x0000101)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x1000101)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x0010101)<<32 | v) << plane,  \
-    AV_LE2NE64C(UINT64_C(0x1010101)<<32 | v) << plane
-
-#define LUT8(plane) {                           \
-    LUT8_PART(plane, 0x0000000),                \
-    LUT8_PART(plane, 0x1000000),                \
-    LUT8_PART(plane, 0x0010000),                \
-    LUT8_PART(plane, 0x1010000),                \
-    LUT8_PART(plane, 0x0000100),                \
-    LUT8_PART(plane, 0x1000100),                \
-    LUT8_PART(plane, 0x0010100),                \
-    LUT8_PART(plane, 0x1010100),                \
-    LUT8_PART(plane, 0x0000001),                \
-    LUT8_PART(plane, 0x1000001),                \
-    LUT8_PART(plane, 0x0010001),                \
-    LUT8_PART(plane, 0x1010001),                \
-    LUT8_PART(plane, 0x0000101),                \
-    LUT8_PART(plane, 0x1000101),                \
-    LUT8_PART(plane, 0x0010101),                \
-    LUT8_PART(plane, 0x1010101),                \
-}
-
-// 8 planes * 8-bit mask
-static const uint64_t plane8_lut[8][256] = {
-    LUT8(0), LUT8(1), LUT8(2), LUT8(3),
-    LUT8(4), LUT8(5), LUT8(6), LUT8(7),
-};
-
-#define LUT32(plane) {                                    \
-              0,           0,           0,           0,   \
-              0,           0,           0, 1U << plane,   \
-              0,           0, 1U << plane,           0,   \
-              0,           0, 1U << plane, 1U << plane,   \
-              0, 1U << plane,           0,           0,   \
-              0, 1U << plane,           0, 1U << plane,   \
-              0, 1U << plane, 1U << plane,           0,   \
-              0, 1U << plane, 1U << plane, 1U << plane,   \
-    1U << plane,           0,           0,           0,   \
-    1U << plane,           0,           0, 1U << plane,   \
-    1U << plane,           0, 1U << plane,           0,   \
-    1U << plane,           0, 1U << plane, 1U << plane,   \
-    1U << plane, 1U << plane,           0,           0,   \
-    1U << plane, 1U << plane,           0, 1U << plane,   \
-    1U << plane, 1U << plane, 1U << plane,           0,   \
-    1U << plane, 1U << plane, 1U << plane, 1U << plane,   \
-}
-
-// 32 planes * 4-bit mask * 4 lookup tables each
-static const uint32_t plane32_lut[32][16*4] = {
-    LUT32( 0), LUT32( 1), LUT32( 2), LUT32( 3),
-    LUT32( 4), LUT32( 5), LUT32( 6), LUT32( 7),
-    LUT32( 8), LUT32( 9), LUT32(10), LUT32(11),
-    LUT32(12), LUT32(13), LUT32(14), LUT32(15),
-    LUT32(16), LUT32(17), LUT32(18), LUT32(19),
-    LUT32(20), LUT32(21), LUT32(22), LUT32(23),
-    LUT32(24), LUT32(25), LUT32(26), LUT32(27),
-    LUT32(28), LUT32(29), LUT32(30), LUT32(31),
-};
-
-// Gray to RGB, required for palette table of grayscale images with bpp < 8
-static av_always_inline uint32_t gray2rgb(const uint32_t x) {
-    return x << 16 | x << 8 | x;
-}
-
-/**
- * Convert CMAP buffer (stored in extradata) to lavc palette format
- */
-static int cmap_read_palette(AVCodecContext *avctx, uint32_t *pal)
-{
-    IffContext *s = avctx->priv_data;
-    unsigned count, i;
-    const uint8_t *const palette = avctx->extradata + AV_RB16(avctx->extradata);
-    /* extract_header() already checked that the RHS is >= 0. */
-    unsigned palette_size = avctx->extradata_size - AV_RB16(avctx->extradata);
-
-    if (avctx->bits_per_coded_sample > 8) {
-        av_log(avctx, AV_LOG_ERROR, "bits_per_coded_sample > 8 not supported\n");
-        return AVERROR_INVALIDDATA;
-    }
-
-    count = 1 << avctx->bits_per_coded_sample;
-    // If extradata is smaller than actually needed, fill the remaining with black.
-    count = FFMIN(palette_size / 3, count);
-    if (count) {
-        for (i = 0; i < count; i++)
-            pal[i] = 0xFF000000 | AV_RB24(palette + i*3);
-        if (s->flags && count >= 32) { // EHB
-            for (i = 0; i < 32; i++)
-                pal[i + 32] = 0xFF000000 | (AV_RB24(palette + i*3) & 0xFEFEFE) >> 1;
-            count = FFMAX(count, 64);
-        }
-    } else { // Create gray-scale color palette for bps < 8
-        count = 1 << avctx->bits_per_coded_sample;
-
-        for (i = 0; i < count; i++)
-            pal[i] = 0xFF000000 | gray2rgb((i * 255) >> avctx->bits_per_coded_sample);
-    }
-    if (s->masking == MASK_HAS_MASK) {
-        if ((1 << avctx->bits_per_coded_sample) < count) {
-            avpriv_request_sample(avctx, "overlapping mask");
-            return AVERROR_PATCHWELCOME;
-        }
-        memcpy(pal + (1 << avctx->bits_per_coded_sample), pal, count * 4);
-        for (i = 0; i < count; i++)
-            pal[i] &= 0xFFFFFF;
-    } else if (s->masking == MASK_HAS_TRANSPARENT_COLOR &&
-        s->transparency < 1 << avctx->bits_per_coded_sample)
-        pal[s->transparency] &= 0xFFFFFF;
-    return 0;
-}
-
-/**
- * Extracts the IFF extra context and updates internal
- * decoder structures.
- *
- * @param avctx the AVCodecContext where to extract extra context to
- * @return >= 0 in case of success, a negative error code otherwise
- */
-static int extract_header(AVCodecContext *const avctx,
-                          const uint8_t *const extradata, int extradata_size)
-{
-    IffContext *s = avctx->priv_data;
-    const uint8_t *buf = extradata;
-    unsigned buf_size = 0;
-    int palette_size;
-
-    if (extradata_size < 2) {
-        av_log(avctx, AV_LOG_ERROR, "not enough extradata\n");
-        return AVERROR_INVALIDDATA;
-    }
-    palette_size = extradata_size - AV_RB16(extradata);
-
-    buf_size = bytestream_get_be16(&buf);
-    if (buf_size <= 1 || palette_size < 0) {
-        av_log(avctx, AV_LOG_ERROR,
-                "Invalid palette size received: %u -> palette data offset: %d\n",
-                buf_size, palette_size);
-        return AVERROR_INVALIDDATA;
-    }
-
-    if (buf_size < 41)
-        return 0;
-
-    s->compression  = bytestream_get_byte(&buf);
-    s->bpp          = bytestream_get_byte(&buf);
-    s->ham          = bytestream_get_byte(&buf);
-    s->flags        = bytestream_get_byte(&buf);
-    s->transparency = bytestream_get_be16(&buf);
-    s->masking      = bytestream_get_byte(&buf);
-    for (int i = 0; i < 16; i++)
-        s->tvdc[i] = bytestream_get_be16(&buf);
-
-    if (s->ham) {
-        if (s->bpp > 8) {
-            av_log(avctx, AV_LOG_ERROR, "Invalid number of hold bits for HAM: %u\n", s->ham);
-            return AVERROR_INVALIDDATA;
-        } else if (s->ham != (s->bpp > 6 ? 6 : 4)) {
-            av_log(avctx, AV_LOG_ERROR, "Invalid number of hold bits for HAM: %u, BPP: %u\n", s->ham, s->bpp);
-            return AVERROR_INVALIDDATA;
-        }
-    }
-
-    if (s->masking == MASK_HAS_MASK) {
-        if (s->bpp >= 8 && !s->ham) {
-            avctx->pix_fmt = AV_PIX_FMT_RGB32;
-            if (s->bpp > 16) {
-                av_log(avctx, AV_LOG_ERROR, "bpp %d too large for palette\n", s->bpp);
-                return AVERROR(ENOMEM);
-            }
-            s->mask_buf = av_malloc((s->planesize * 32) + AV_INPUT_BUFFER_PADDING_SIZE);
-            if (!s->mask_buf)
-                return AVERROR(ENOMEM);
-            s->mask_palbuf = av_malloc((2 << s->bpp) * sizeof(uint32_t) + AV_INPUT_BUFFER_PADDING_SIZE);
-            if (!s->mask_palbuf)
-                return AVERROR(ENOMEM);
-        }
-        s->bpp++;
-    } else if (s->masking != MASK_NONE && s->masking != MASK_HAS_TRANSPARENT_COLOR) {
-        av_log(avctx, AV_LOG_ERROR, "Masking not supported\n");
-        return AVERROR_PATCHWELCOME;
-    }
-    if (!s->bpp || s->bpp > 32) {
-        av_log(avctx, AV_LOG_ERROR, "Invalid number of bitplanes: %u\n", s->bpp);
-        return AVERROR_INVALIDDATA;
-    }
-    if (s->video_size && s->planesize * s->bpp * avctx->height > s->video_size)
-        return AVERROR_INVALIDDATA;
-
-    if (s->ham) {
-        int count = FFMIN(palette_size / 3, 1 << s->ham);
-        int ham_count;
-        const uint8_t *const palette = extradata + AV_RB16(extradata);
-        int extra_space = 1;
-
-        if (avctx->codec_tag == MKTAG('P', 'B', 'M', ' ') && s->ham == 4)
-            extra_space = 4;
-
-        s->ham_buf = av_malloc((s->planesize * 8) + AV_INPUT_BUFFER_PADDING_SIZE);
-        if (!s->ham_buf)
-            return AVERROR(ENOMEM);
-
-        ham_count = 8 * (1 << s->ham);
-        s->ham_palbuf = av_malloc(extra_space * (ham_count << !!(s->masking == MASK_HAS_MASK)) * sizeof (uint32_t) + AV_INPUT_BUFFER_PADDING_SIZE);
-        if (!s->ham_palbuf)
-            return AVERROR(ENOMEM);
-
-        if (count) { // HAM with color palette attached
-            // prefill with black and palette and set HAM take direct value mask to zero
-            memset(s->ham_palbuf, 0, (1 << s->ham) * 2 * sizeof (uint32_t));
-            for (int i = 0; i < count; i++) {
-                s->ham_palbuf[i*2+1] = 0xFF000000 | AV_RL24(palette + i*3);
-            }
-            count = 1 << s->ham;
-        } else { // HAM with grayscale color palette
-            count = 1 << s->ham;
-            for (int i = 0; i < count; i++) {
-                s->ham_palbuf[i*2]   = 0xFF000000; // take direct color value from palette
-                s->ham_palbuf[i*2+1] = 0xFF000000 | av_le2ne32(gray2rgb((i * 255) >> s->ham));
-            }
-        }
-        for (int i = 0; i < count; i++) {
-            uint32_t tmp = i << (8 - s->ham);
-            tmp |= tmp >> s->ham;
-            s->ham_palbuf[(i+count)*2]     = 0xFF00FFFF; // just modify blue color component
-            s->ham_palbuf[(i+count*2)*2]   = 0xFFFFFF00; // just modify red color component
-            s->ham_palbuf[(i+count*3)*2]   = 0xFFFF00FF; // just modify green color component
-            s->ham_palbuf[(i+count)*2+1]   = 0xFF000000 | tmp << 16;
-            s->ham_palbuf[(i+count*2)*2+1] = 0xFF000000 | tmp;
-            s->ham_palbuf[(i+count*3)*2+1] = 0xFF000000 | tmp << 8;
-        }
-        if (s->masking == MASK_HAS_MASK) {
-            for (int i = 0; i < ham_count; i++)
-                s->ham_palbuf[(1 << s->bpp) + i] = s->ham_palbuf[i] | 0xFF000000;
-        }
-    }
-
-    return 0;
-}
-
-static av_cold int decode_end(AVCodecContext *avctx)
-{
-    IffContext *s = avctx->priv_data;
-    av_freep(&s->planebuf);
-    av_freep(&s->ham_buf);
-    av_freep(&s->ham_palbuf);
-    av_freep(&s->mask_buf);
-    av_freep(&s->mask_palbuf);
-    av_freep(&s->video[0]);
-    av_freep(&s->video[1]);
-    av_freep(&s->pal);
-    return 0;
-}
-
-static av_cold int decode_init(AVCodecContext *avctx)
-{
-    IffContext *s = avctx->priv_data;
-    int err;
-
-    if (avctx->bits_per_coded_sample <= 8) {
-        int palette_size;
-
-        if (avctx->extradata_size >= 2)
-            palette_size = avctx->extradata_size - AV_RB16(avctx->extradata);
-        else
-            palette_size = 0;
-        avctx->pix_fmt = (avctx->bits_per_coded_sample < 8) ||
-                         (avctx->extradata_size >= 2 && palette_size) ? AV_PIX_FMT_PAL8 : AV_PIX_FMT_GRAY8;
-    } else if (avctx->bits_per_coded_sample <= 32) {
-        if (avctx->codec_tag == MKTAG('R', 'G', 'B', '8')) {
-            avctx->pix_fmt = AV_PIX_FMT_RGB32;
-        } else if (avctx->codec_tag == MKTAG('R', 'G', 'B', 'N')) {
-            avctx->pix_fmt = AV_PIX_FMT_RGB444;
-        } else if (avctx->codec_tag != MKTAG('D', 'E', 'E', 'P')) {
-            if (avctx->bits_per_coded_sample == 24) {
-                avctx->pix_fmt = AV_PIX_FMT_0BGR32;
-            } else if (avctx->bits_per_coded_sample == 32) {
-                avctx->pix_fmt = AV_PIX_FMT_BGR32;
-            } else {
-                avpriv_request_sample(avctx, "unknown bits_per_coded_sample");
-                return AVERROR_PATCHWELCOME;
-            }
-        }
-    } else {
-        return AVERROR_INVALIDDATA;
-    }
-
-    if ((err = av_image_check_size(avctx->width, avctx->height, 0, avctx)))
-        return err;
-    s->planesize = FFALIGN(avctx->width, 16) >> 3; // Align plane size in bits to word-boundary
-    s->planebuf  = av_malloc(s->planesize * avctx->height + AV_INPUT_BUFFER_PADDING_SIZE);
-    if (!s->planebuf)
-        return AVERROR(ENOMEM);
-
-    s->bpp = avctx->bits_per_coded_sample;
-
-    if (avctx->codec_tag == MKTAG('A', 'N', 'I', 'M')) {
-        s->video_size = FFALIGN(avctx->width, 2) * avctx->height * s->bpp;
-        if (!s->video_size)
-            return AVERROR_INVALIDDATA;
-        s->video[0] = av_calloc(FFALIGN(avctx->width, 2) * avctx->height, s->bpp);
-        s->video[1] = av_calloc(FFALIGN(avctx->width, 2) * avctx->height, s->bpp);
-        s->pal = av_calloc(256, sizeof(*s->pal));
-        if (!s->video[0] || !s->video[1] || !s->pal)
-            return AVERROR(ENOMEM);
-    }
-
-    err = extract_header(avctx, avctx->extradata, avctx->extradata_size);
-    if (err < 0)
-        return err;
-
-    return 0;
-}
-
-/**
- * Decode interleaved plane buffer up to 8bpp
- * @param dst Destination buffer
- * @param buf Source buffer
- * @param buf_size
- * @param plane plane number to decode as
- */
-static void decodeplane8(uint8_t *dst, const uint8_t *buf, int buf_size, int plane)
-{
-    const uint64_t *lut;
-    if (plane >= 8) {
-        av_log(NULL, AV_LOG_WARNING, "Ignoring extra planes beyond 8\n");
-        return;
-    }
-    lut = plane8_lut[plane];
-    do {
-        uint64_t v = AV_RN64A(dst) | lut[*buf++];
-        AV_WN64A(dst, v);
-        dst += 8;
-    } while (--buf_size);
-}
-
-/**
- * Decode interleaved plane buffer up to 24bpp
- * @param dst Destination buffer
- * @param buf Source buffer
- * @param buf_size
- * @param plane plane number to decode as
- */
-static void decodeplane32(uint32_t *dst, const uint8_t *buf, int buf_size, int plane)
-{
-    const uint32_t *lut = plane32_lut[plane];
-    do {
-        unsigned mask = (*buf >> 2) & ~3;
-        dst[0] |= lut[mask++];
-        dst[1] |= lut[mask++];
-        dst[2] |= lut[mask++];
-        dst[3] |= lut[mask];
-        mask    = (*buf++ << 2) & 0x3F;
-        dst[4] |= lut[mask++];
-        dst[5] |= lut[mask++];
-        dst[6] |= lut[mask++];
-        dst[7] |= lut[mask];
-        dst    += 8;
-    } while (--buf_size);
-}
-
-#define DECODE_HAM_PLANE32(x)       \
-    first       = buf[x] << 1;      \
-    second      = buf[(x)+1] << 1;  \
-    delta      &= pal[first++];     \
-    delta      |= pal[first];       \
-    dst[x]      = delta;            \
-    delta      &= pal[second++];    \
-    delta      |= pal[second];      \
-    dst[(x)+1]  = delta
-
-/**
- * Converts one line of HAM6/8-encoded chunky buffer to 24bpp.
- *
- * @param dst the destination 24bpp buffer
- * @param buf the source 8bpp chunky buffer
- * @param pal the HAM decode table
- * @param buf_size the plane size in bytes
- */
-static void decode_ham_plane32(uint32_t *dst, const uint8_t  *buf,
-                               const uint32_t *const pal, unsigned buf_size)
-{
-    uint32_t delta = pal[1]; /* first palette entry */
-    do {
-        uint32_t first, second;
-        DECODE_HAM_PLANE32(0);
-        DECODE_HAM_PLANE32(2);
-        DECODE_HAM_PLANE32(4);
-        DECODE_HAM_PLANE32(6);
-        buf += 8;
-        dst += 8;
-    } while (--buf_size);
-}
-
-static void lookup_pal_indicies(uint32_t *dst, const uint32_t *buf,
-                         const uint32_t *const pal, unsigned width)
-{
-    do {
-        *dst++ = pal[*buf++];
-    } while (--width);
-}
-
-/**
- * Decode one complete byterun1 encoded line.
- *
- * @param dst the destination buffer where to store decompressed bitstream
- * @param dst_size the destination plane size in bytes
- * @param buf the source byterun1 compressed bitstream
- * @param buf_end the EOF of source byterun1 compressed bitstream
- * @return number of consumed bytes in byterun1 compressed bitstream
- */
-static int decode_byterun(uint8_t *dst, int dst_size,
-                          GetByteContext *gb)
-{
-    unsigned x;
-    for (x = 0; x < dst_size && bytestream2_get_bytes_left(gb) > 0;) {
-        unsigned length;
-        const int8_t value = bytestream2_get_byte(gb);
-        if (value >= 0) {
-            length = FFMIN3(value + 1, dst_size - x, bytestream2_get_bytes_left(gb));
-            bytestream2_get_buffer(gb, dst + x, length);
-            if (length < value + 1)
-                bytestream2_skip(gb, value + 1 - length);
-        } else if (value > -128) {
-            length = FFMIN(-value + 1, dst_size - x);
-            memset(dst + x, bytestream2_get_byte(gb), length);
-        } else { // noop
-            continue;
-        }
-        x += length;
-    }
-    if (x < dst_size) {
-        av_log(NULL, AV_LOG_WARNING, "decode_byterun ended before plane size\n");
-        memset(dst+x, 0, dst_size - x);
-    }
-    return bytestream2_tell(gb);
-}
-
-static int decode_byterun2(uint8_t *dst, int height, int line_size,
-                           GetByteContext *gb)
-{
-    GetByteContext cmds;
-    unsigned count;
-    int i, y_pos = 0, x_pos = 0;
-
-    if (bytestream2_get_be32(gb) != MKBETAG('V', 'D', 'A', 'T'))
-        return 0;
-
-    bytestream2_skip(gb, 4);
-    count = bytestream2_get_be16(gb) - 2;
-    if (bytestream2_get_bytes_left(gb) < count)
-        return 0;
-
-    bytestream2_init(&cmds, gb->buffer, count);
-    bytestream2_skip(gb, count);
-
-    for (i = 0; i < count && x_pos < line_size; i++) {
-        int8_t cmd = bytestream2_get_byte(&cmds);
-        int l, r;
-
-        if (cmd == 0) {
-            l = bytestream2_get_be16(gb);
-            while (l-- > 0 && x_pos < line_size) {
-                dst[x_pos + y_pos   * line_size    ] = bytestream2_get_byte(gb);
-                dst[x_pos + y_pos++ * line_size + 1] = bytestream2_get_byte(gb);
-                if (y_pos >= height) {
-                    y_pos  = 0;
-                    x_pos += 2;
-                }
-            }
-        } else if (cmd < 0) {
-            l = -cmd;
-            while (l-- > 0 && x_pos < line_size) {
-                dst[x_pos + y_pos   * line_size    ] = bytestream2_get_byte(gb);
-                dst[x_pos + y_pos++ * line_size + 1] = bytestream2_get_byte(gb);
-                if (y_pos >= height) {
-                    y_pos  = 0;
-                    x_pos += 2;
-                }
-            }
-        } else if (cmd == 1) {
-            l = bytestream2_get_be16(gb);
-            r = bytestream2_get_be16(gb);
-            while (l-- > 0 && x_pos < line_size) {
-                dst[x_pos + y_pos   * line_size    ] = r >> 8;
-                dst[x_pos + y_pos++ * line_size + 1] = r & 0xFF;
-                if (y_pos >= height) {
-                    y_pos  = 0;
-                    x_pos += 2;
-                }
-            }
-        } else {
-            l = cmd;
-            r = bytestream2_get_be16(gb);
-            while (l-- > 0 && x_pos < line_size) {
-                dst[x_pos + y_pos   * line_size    ] = r >> 8;
-                dst[x_pos + y_pos++ * line_size + 1] = r & 0xFF;
-                if (y_pos >= height) {
-                    y_pos  = 0;
-                    x_pos += 2;
-                }
-            }
-        }
-    }
-
-    return bytestream2_tell(gb);
-}
-
-#define DECODE_RGBX_COMMON(type) \
-    if (!length) { \
-        length = bytestream2_get_byte(gb); \
-        if (!length) { \
-            length = bytestream2_get_be16(gb); \
-            if (!length) \
-                return; \
-        } \
-    } \
-    for (i = 0; i < length; i++) { \
-        *(type *)(dst + y*linesize + x * sizeof(type)) = pixel; \
-        x += 1; \
-        if (x >= width) { \
-            y += 1; \
-            if (y >= height) \
-                return; \
-            x = 0; \
-        } \
-    }
-
-/**
- * Decode RGB8 buffer
- * @param[out] dst Destination buffer
- * @param width Width of destination buffer (pixels)
- * @param height Height of destination buffer (pixels)
- * @param linesize Line size of destination buffer (bytes)
- */
-static void decode_rgb8(GetByteContext *gb, uint8_t *dst, int width, int height, int linesize)
-{
-    int x = 0, y = 0, i, length;
-    while (bytestream2_get_bytes_left(gb) >= 4) {
-        uint32_t pixel = 0xFF000000 | bytestream2_get_be24(gb);
-        length = bytestream2_get_byte(gb) & 0x7F;
-        DECODE_RGBX_COMMON(uint32_t)
-    }
-}
-
-/**
- * Decode RGBN buffer
- * @param[out] dst Destination buffer
- * @param width Width of destination buffer (pixels)
- * @param height Height of destination buffer (pixels)
- * @param linesize Line size of destination buffer (bytes)
- */
-static void decode_rgbn(GetByteContext *gb, uint8_t *dst, int width, int height, int linesize)
-{
-    int x = 0, y = 0, i, length;
-    while (bytestream2_get_bytes_left(gb) >= 2) {
-        uint32_t pixel = bytestream2_get_be16u(gb);
-        length = pixel & 0x7;
-        pixel >>= 4;
-        DECODE_RGBX_COMMON(uint16_t)
-    }
-}
-
-/**
- * Decode DEEP RLE 32-bit buffer
- * @param[out] dst Destination buffer
- * @param[in] src Source buffer
- * @param src_size Source buffer size (bytes)
- * @param width Width of destination buffer (pixels)
- * @param height Height of destination buffer (pixels)
- * @param linesize Line size of destination buffer (bytes)
- */
-static void decode_deep_rle32(uint8_t *dst, const uint8_t *src, int src_size, int width, int height, int linesize)
-{
-    const uint8_t *src_end = src + src_size;
-    int x = 0, y = 0, i;
-    while (src_end - src >= 5) {
-        int opcode;
-        opcode = *(int8_t *)src++;
-        if (opcode >= 0) {
-            int size = opcode + 1;
-            for (i = 0; i < size; i++) {
-                int length = FFMIN(size - i, width - x);
-                if (src_end - src < length * 4)
-                    return;
-                memcpy(dst + y*linesize + x * 4, src, length * 4);
-                src += length * 4;
-                x += length;
-                i += length;
-                if (x >= width) {
-                    x = 0;
-                    y += 1;
-                    if (y >= height)
-                        return;
-                }
-            }
-        } else {
-            int size = -opcode + 1;
-            uint32_t pixel = AV_RN32(src);
-            for (i = 0; i < size; i++) {
-                *(uint32_t *)(dst + y*linesize + x * 4) = pixel;
-                x += 1;
-                if (x >= width) {
-                    x = 0;
-                    y += 1;
-                    if (y >= height)
-                        return;
-                }
-            }
-            src += 4;
-        }
-    }
-}
-
-/**
- * Decode DEEP TVDC 32-bit buffer
- * @param[out] dst Destination buffer
- * @param[in] src Source buffer
- * @param src_size Source buffer size (bytes)
- * @param width Width of destination buffer (pixels)
- * @param height Height of destination buffer (pixels)
- * @param linesize Line size of destination buffer (bytes)
- * @param[int] tvdc TVDC lookup table
- */
-static void decode_deep_tvdc32(uint8_t *dst, const uint8_t *src, int src_size, int width, int height, int linesize, const int16_t *tvdc)
-{
-    int x = 0, y = 0, plane = 0;
-    int8_t pixel = 0;
-    int i, j;
-
-    for (i = 0; i < src_size * 2;) {
-#define GETNIBBLE ((i & 1) ?  (src[i>>1] & 0xF) : (src[i>>1] >> 4))
-        int d = tvdc[GETNIBBLE];
-        i++;
-        if (d) {
-            pixel += d;
-            dst[y * linesize + x*4 + plane] = pixel;
-            x++;
-        } else {
-            if (i >= src_size * 2)
-                return;
-            d = GETNIBBLE + 1;
-            i++;
-            d = FFMIN(d, width - x);
-            for (j = 0; j < d; j++) {
-                dst[y * linesize + x*4 + plane] = pixel;
-                x++;
-            }
-        }
-        if (x >= width) {
-            plane++;
-            if (plane >= 4) {
-                y++;
-                if (y >= height)
-                    return;
-                plane = 0;
-            }
-            x = 0;
-            pixel = 0;
-            i = (i + 1) & ~1;
-        }
-    }
-}
-
-static void decode_short_horizontal_delta(uint8_t *dst,
-                                          const uint8_t *buf, const uint8_t *buf_end,
-                                          int w, int bpp, int dst_size)
-{
-    int planepitch = FFALIGN(w, 16) >> 3;
-    int pitch = planepitch * bpp;
-    GetByteContext ptrs, gb;
-    PutByteContext pb;
-    unsigned ofssrc, pos;
-    int i, k;
-
-    bytestream2_init(&ptrs, buf, buf_end - buf);
-    bytestream2_init_writer(&pb, dst, dst_size);
-
-    for (k = 0; k < bpp; k++) {
-        ofssrc = bytestream2_get_be32(&ptrs);
-        pos = 0;
-
-        if (!ofssrc)
-            continue;
-
-        if (ofssrc >= buf_end - buf)
-            continue;
-
-        bytestream2_init(&gb, buf + ofssrc, buf_end - (buf + ofssrc));
-        while (bytestream2_peek_be16(&gb) != 0xFFFF && bytestream2_get_bytes_left(&gb) > 3) {
-            int16_t offset = bytestream2_get_be16(&gb);
-            unsigned noffset;
-
-            if (offset >= 0) {
-                unsigned data = bytestream2_get_be16(&gb);
-
-                pos += offset * 2;
-                noffset = (pos / planepitch) * pitch + (pos % planepitch) + k * planepitch;
-                bytestream2_seek_p(&pb, noffset, SEEK_SET);
-                bytestream2_put_be16(&pb, data);
-            } else {
-                uint16_t count = bytestream2_get_be16(&gb);
-
-                pos += 2 * -(offset + 2);
-                for (i = 0; i < count; i++) {
-                    uint16_t data = bytestream2_get_be16(&gb);
-
-                    pos += 2;
-                    noffset = (pos / planepitch) * pitch + (pos % planepitch) + k * planepitch;
-                    bytestream2_seek_p(&pb, noffset, SEEK_SET);
-                    bytestream2_put_be16(&pb, data);
-                }
-            }
-        }
-    }
-}
-
-static void decode_byte_vertical_delta(uint8_t *dst,
-                                       const uint8_t *buf, const uint8_t *buf_end,
-                                       int w, int xor, int bpp, int dst_size)
-{
-    int ncolumns = ((w + 15) / 16) * 2;
-    int dstpitch = ncolumns * bpp;
-    unsigned ofsdst, ofssrc, opcode, x;
-    GetByteContext ptrs, gb;
-    PutByteContext pb;
-    int i, j, k;
-
-    bytestream2_init(&ptrs, buf, buf_end - buf);
-    bytestream2_init_writer(&pb, dst, dst_size);
-
-    for (k = 0; k < bpp; k++) {
-        ofssrc = bytestream2_get_be32(&ptrs);
-
-        if (!ofssrc)
-            continue;
-
-        if (ofssrc >= buf_end - buf)
-            continue;
-
-        bytestream2_init(&gb, buf + ofssrc, buf_end - (buf + ofssrc));
-        for (j = 0; j < ncolumns; j++) {
-            ofsdst = j + k * ncolumns;
-
-            i = bytestream2_get_byte(&gb);
-            while (i > 0) {
-                opcode = bytestream2_get_byte(&gb);
-
-                if (opcode == 0) {
-                    opcode  = bytestream2_get_byte(&gb);
-                    x = bytestream2_get_byte(&gb);
-
-                    while (opcode) {
-                        bytestream2_seek_p(&pb, ofsdst, SEEK_SET);
-                        if (xor && ofsdst < dst_size) {
-                            bytestream2_put_byte(&pb, dst[ofsdst] ^ x);
-                        } else {
-                            bytestream2_put_byte(&pb, x);
-                        }
-                        ofsdst += dstpitch;
-                        opcode--;
-                    }
-                } else if (opcode < 0x80) {
-                    ofsdst += opcode * dstpitch;
-                } else {
-                    opcode &= 0x7f;
-
-                    while (opcode) {
-                        bytestream2_seek_p(&pb, ofsdst, SEEK_SET);
-                        if (xor && ofsdst < dst_size) {
-                            bytestream2_put_byte(&pb, dst[ofsdst] ^ bytestream2_get_byte(&gb));
-                        } else {
-                            bytestream2_put_byte(&pb, bytestream2_get_byte(&gb));
-                        }
-                        ofsdst += dstpitch;
-                        opcode--;
-                    }
-                }
-                i--;
-            }
-        }
-    }
-}
-
-static void decode_delta_j(uint8_t *dst,
-                           const uint8_t *buf, const uint8_t *buf_end,
-                           int w, int h, int bpp, int dst_size)
-{
-    int32_t pitch;
-    uint8_t *ptr;
-    uint32_t type, flag, cols, groups, rows, bytes;
-    uint32_t offset;
-    int planepitch_byte = (w + 7) / 8;
-    int planepitch = ((w + 15) / 16) * 2;
-    int kludge_j, b, g, r, d;
-    GetByteContext gb;
-
-    pitch = planepitch * bpp;
-    kludge_j = w < 320 ? (320 - w) / 8 / 2 : 0;
-
-    bytestream2_init(&gb, buf, buf_end - buf);
-
-    while (bytestream2_get_bytes_left(&gb) >= 2) {
-        type = bytestream2_get_be16(&gb);
-
-        switch (type) {
-        case 0:
-            return;
-        case 1:
-            flag   = bytestream2_get_be16(&gb);
-            cols   = bytestream2_get_be16(&gb);
-            groups = bytestream2_get_be16(&gb);
-
-            for (g = 0; g < groups; g++) {
-                offset = bytestream2_get_be16(&gb);
-
-                if (cols * bpp == 0 || bytestream2_get_bytes_left(&gb) < cols * bpp) {
-                    av_log(NULL, AV_LOG_ERROR, "cols*bpp is invalid (%"PRId32"*%d)", cols, bpp);
-                    return;
-                }
-
-                if (kludge_j)
-                    offset = ((offset / (320 / 8)) * pitch) + (offset % (320 / 8)) - kludge_j;
-                else
-                    offset = ((offset / planepitch_byte) * pitch) + (offset % planepitch_byte);
-
-                for (b = 0; b < cols; b++) {
-                    for (d = 0; d < bpp; d++) {
-                        uint8_t value = bytestream2_get_byte(&gb);
-
-                        if (offset >= dst_size)
-                            return;
-                        ptr = dst + offset;
-
-                        if (flag)
-                            ptr[0] ^= value;
-                        else
-                            ptr[0]  = value;
-
-                        offset += planepitch;
-                    }
-                }
-                if ((cols * bpp) & 1)
-                    bytestream2_skip(&gb, 1);
-            }
-            break;
-        case 2:
-            flag   = bytestream2_get_be16(&gb);
-            rows   = bytestream2_get_be16(&gb);
-            bytes  = bytestream2_get_be16(&gb);
-            groups = bytestream2_get_be16(&gb);
-
-            for (g = 0; g < groups; g++) {
-                offset = bytestream2_get_be16(&gb);
-
-                if (kludge_j)
-                    offset = ((offset / (320 / 8)) * pitch) + (offset % (320/ 8)) - kludge_j;
-                else
-                    offset = ((offset / planepitch_byte) * pitch) + (offset % planepitch_byte);
-
-                for (r = 0; r < rows; r++) {
-                    for (d = 0; d < bpp; d++) {
-                        unsigned noffset = offset + (r * pitch) + d * planepitch;
-
-                        if (!bytes || bytestream2_get_bytes_left(&gb) < bytes) {
-                            av_log(NULL, AV_LOG_ERROR, "bytes %"PRId32" is invalid", bytes);
-                            return;
-                        }
-
-                        for (b = 0; b < bytes; b++) {
-                            uint8_t value = bytestream2_get_byte(&gb);
-
-                            if (noffset >= dst_size)
-                                return;
-                            ptr = dst + noffset;
-
-                            if (flag)
-                                ptr[0] ^= value;
-                            else
-                                ptr[0]  = value;
-
-                            noffset++;
-                        }
-                    }
-                }
-                if ((rows * bytes * bpp) & 1)
-                    bytestream2_skip(&gb, 1);
-            }
-            break;
-        default:
-            return;
-        }
-    }
-}
-
-static void decode_short_vertical_delta(uint8_t *dst,
-                                        const uint8_t *buf, const uint8_t *buf_end,
-                                        int w, int bpp, int dst_size)
-{
-    int ncolumns = (w + 15) >> 4;
-    int dstpitch = ncolumns * bpp * 2;
-    unsigned ofsdst, ofssrc, ofsdata, opcode, x;
-    GetByteContext ptrs, gb, dptrs, dgb;
-    PutByteContext pb;
-    int i, j, k;
-
-    if (buf_end - buf <= 64)
-        return;
-
-    bytestream2_init(&ptrs, buf, buf_end - buf);
-    bytestream2_init(&dptrs, buf + 32, (buf_end - buf) - 32);
-    bytestream2_init_writer(&pb, dst, dst_size);
-
-    for (k = 0; k < bpp; k++) {
-        ofssrc = bytestream2_get_be32(&ptrs);
-        ofsdata = bytestream2_get_be32(&dptrs);
-
-        if (!ofssrc)
-            continue;
-
-        if (ofssrc >= buf_end - buf)
-            return;
-
-        if (ofsdata >= buf_end - buf)
-            return;
-
-        bytestream2_init(&gb, buf + ofssrc, buf_end - (buf + ofssrc));
-        bytestream2_init(&dgb, buf + ofsdata, buf_end - (buf + ofsdata));
-        for (j = 0; j < ncolumns; j++) {
-            ofsdst = (j + k * ncolumns) * 2;
-
-            i = bytestream2_get_byte(&gb);
-            while (i > 0) {
-                opcode = bytestream2_get_byte(&gb);
-
-                if (opcode == 0) {
-                    opcode = bytestream2_get_byte(&gb);
-                    x = bytestream2_get_be16(&dgb);
-
-                    while (opcode) {
-                        bytestream2_seek_p(&pb, ofsdst, SEEK_SET);
-                        bytestream2_put_be16(&pb, x);
-                        ofsdst += dstpitch;
-                        opcode--;
-                    }
-                } else if (opcode < 0x80) {
-                    ofsdst += opcode * dstpitch;
-                } else {
-                    opcode &= 0x7f;
-
-                    while (opcode) {
-                        bytestream2_seek_p(&pb, ofsdst, SEEK_SET);
-                        bytestream2_put_be16(&pb, bytestream2_get_be16(&dgb));
-                        ofsdst += dstpitch;
-                        opcode--;
-                    }
-                }
-                i--;
-            }
-        }
-    }
-}
-
-static void decode_long_vertical_delta(uint8_t *dst,
-                                       const uint8_t *buf, const uint8_t *buf_end,
-                                       int w, int bpp, int dst_size)
-{
-    int ncolumns = (w + 31) >> 5;
-    int dstpitch = ((w + 15) / 16 * 2) * bpp;
-    unsigned ofsdst, ofssrc, ofsdata, opcode, x;
-    GetByteContext ptrs, gb, dptrs, dgb;
-    PutByteContext pb;
-    int i, j, k, h;
-
-    if (buf_end - buf <= 64)
-        return;
-
-    h = (((w + 15) / 16 * 2) != ((w + 31) / 32 * 4)) ? 1 : 0;
-    bytestream2_init(&ptrs, buf, buf_end - buf);
-    bytestream2_init(&dptrs, buf + 32, (buf_end - buf) - 32);
-    bytestream2_init_writer(&pb, dst, dst_size);
-
-    for (k = 0; k < bpp; k++) {
-        ofssrc = bytestream2_get_be32(&ptrs);
-        ofsdata = bytestream2_get_be32(&dptrs);
-
-        if (!ofssrc)
-            continue;
-
-        if (ofssrc >= buf_end - buf)
-            return;
-
-        if (ofsdata >= buf_end - buf)
-            return;
-
-        bytestream2_init(&gb, buf + ofssrc, buf_end - (buf + ofssrc));
-        bytestream2_init(&dgb, buf + ofsdata, buf_end - (buf + ofsdata));
-        for (j = 0; j < ncolumns; j++) {
-            ofsdst = (j + k * ncolumns) * 4 - h * (2 * k);
-
-            i = bytestream2_get_byte(&gb);
-            while (i > 0) {
-                opcode = bytestream2_get_byte(&gb);
-
-                if (opcode == 0) {
-                    opcode = bytestream2_get_byte(&gb);
-                    if (h && (j == (ncolumns - 1))) {
-                        x = bytestream2_get_be16(&dgb);
-                        bytestream2_skip(&dgb, 2);
-                    } else {
-                        x = bytestream2_get_be32(&dgb);
-                    }
-
-                    if (ofsdst + (opcode - 1LL) * dstpitch > bytestream2_size_p(&pb))
-                        return;
-
-                    while (opcode) {
-                        bytestream2_seek_p(&pb, ofsdst, SEEK_SET);
-                        if (h && (j == (ncolumns - 1))) {
-                            bytestream2_put_be16(&pb, x);
-                        } else {
-                            bytestream2_put_be32(&pb, x);
-                        }
-                        ofsdst += dstpitch;
-                        opcode--;
-                    }
-                } else if (opcode < 0x80) {
-                    ofsdst += opcode * dstpitch;
-                } else {
-                    opcode &= 0x7f;
-
-                    while (opcode) {
-                        bytestream2_seek_p(&pb, ofsdst, SEEK_SET);
-                        if (h && (j == (ncolumns - 1))) {
-                            bytestream2_put_be16(&pb, bytestream2_get_be16(&dgb));
-                            bytestream2_skip(&dgb, 2);
-                        } else {
-                            bytestream2_put_be32(&pb, bytestream2_get_be32(&dgb));
-                        }
-                        ofsdst += dstpitch;
-                        opcode--;
-                    }
-                }
-                i--;
-            }
-        }
-    }
-}
-
-static void decode_short_vertical_delta2(uint8_t *dst,
-                                         const uint8_t *buf, const uint8_t *buf_end,
-                                         int w, int bpp, int dst_size)
-{
-    int ncolumns = (w + 15) >> 4;
-    int dstpitch = ncolumns * bpp * 2;
-    unsigned ofsdst, ofssrc, opcode, x;
-    GetByteContext ptrs, gb;
-    PutByteContext pb;
-    int i, j, k;
-
-    bytestream2_init(&ptrs, buf, buf_end - buf);
-    bytestream2_init_writer(&pb, dst, dst_size);
-
-    for (k = 0; k < bpp; k++) {
-        ofssrc = bytestream2_get_be32(&ptrs);
-
-        if (!ofssrc)
-            continue;
-
-        if (ofssrc >= buf_end - buf)
-            continue;
-
-        bytestream2_init(&gb, buf + ofssrc, buf_end - (buf + ofssrc));
-        for (j = 0; j < ncolumns; j++) {
-            ofsdst = (j + k * ncolumns) * 2;
-
-            i = bytestream2_get_be16(&gb);
-            while (i > 0 && bytestream2_get_bytes_left(&gb) > 4) {
-                opcode = bytestream2_get_be16(&gb);
-
-                if (opcode == 0) {
-                    opcode = bytestream2_get_be16(&gb);
-                    x = bytestream2_get_be16(&gb);
-
-                    while (opcode && bytestream2_get_bytes_left_p(&pb) > 1) {
-                        bytestream2_seek_p(&pb, ofsdst, SEEK_SET);
-                        bytestream2_put_be16(&pb, x);
-                        ofsdst += dstpitch;
-                        opcode--;
-                    }
-                } else if (opcode < 0x8000) {
-                    ofsdst += opcode * dstpitch;
-                } else {
-                    opcode &= 0x7fff;
-
-                    while (opcode && bytestream2_get_bytes_left(&gb) > 1 &&
-                           bytestream2_get_bytes_left_p(&pb) > 1) {
-                        bytestream2_seek_p(&pb, ofsdst, SEEK_SET);
-                        bytestream2_put_be16(&pb, bytestream2_get_be16(&gb));
-                        ofsdst += dstpitch;
-                        opcode--;
-                    }
-                }
-                i--;
-            }
-        }
-    }
-}
-
-static void decode_long_vertical_delta2(uint8_t *dst,
-                                        const uint8_t *buf, const uint8_t *buf_end,
-                                        int w, int bpp, int dst_size)
-{
-    int ncolumns = (w + 31) >> 5;
-    int dstpitch = ((w + 15) / 16 * 2) * bpp;
-    unsigned ofsdst, ofssrc, opcode, x;
-    unsigned skip = 0x80000000, mask = skip - 1;
-    GetByteContext ptrs, gb;
-    PutByteContext pb;
-    int i, j, k, h;
-
-    h = (((w + 15) / 16 * 2) != ((w + 31) / 32 * 4)) ? 1 : 0;
-    bytestream2_init(&ptrs, buf, buf_end - buf);
-    bytestream2_init_writer(&pb, dst, dst_size);
-
-    for (k = 0; k < bpp; k++) {
-        ofssrc = bytestream2_get_be32(&ptrs);
-
-        if (!ofssrc)
-            continue;
-
-        if (ofssrc >= buf_end - buf)
-            continue;
-
-        bytestream2_init(&gb, buf + ofssrc, buf_end - (buf + ofssrc));
-        for (j = 0; j < ncolumns; j++) {
-            ofsdst = (j + k * ncolumns) * 4 - h * (2 * k);
-
-            if (h && (j == (ncolumns - 1))) {
-                skip = 0x8000;
-                mask = skip - 1;
-            }
-
-            i = bytestream2_get_be32(&gb);
-            while (i > 0 && bytestream2_get_bytes_left(&gb) > 4) {
-                opcode = bytestream2_get_be32(&gb);
-
-                if (opcode == 0) {
-                    if (h && (j == ncolumns - 1)) {
-                        opcode = bytestream2_get_be16(&gb);
-                        x = bytestream2_get_be16(&gb);
-                    } else {
-                        opcode = bytestream2_get_be32(&gb);
-                        x = bytestream2_get_be32(&gb);
-                    }
-
-                    if (ofsdst + (opcode - 1LL) * dstpitch > bytestream2_size_p(&pb))
-                        return;
-
-                    while (opcode && bytestream2_get_bytes_left_p(&pb) > 1) {
-                        bytestream2_seek_p(&pb, ofsdst, SEEK_SET);
-                        if (h && (j == ncolumns - 1))
-                            bytestream2_put_be16(&pb, x);
-                        else
-                            bytestream2_put_be32(&pb, x);
-                        ofsdst += dstpitch;
-                        opcode--;
-                    }
-                } else if (opcode < skip) {
-                    ofsdst += opcode * dstpitch;
-                } else {
-                    opcode &= mask;
-
-                    while (opcode && bytestream2_get_bytes_left(&gb) > 1 &&
-                           bytestream2_get_bytes_left_p(&pb) > 1) {
-                        bytestream2_seek_p(&pb, ofsdst, SEEK_SET);
-                        if (h && (j == ncolumns - 1)) {
-                            bytestream2_put_be16(&pb, bytestream2_get_be16(&gb));
-                        } else {
-                            bytestream2_put_be32(&pb, bytestream2_get_be32(&gb));
-                        }
-                        ofsdst += dstpitch;
-                        opcode--;
-                    }
-                }
-                i--;
-            }
-        }
-    }
-}
-
-static void decode_delta_d(uint8_t *dst,
-                           const uint8_t *buf, const uint8_t *buf_end,
-                           int w, int flag, int bpp, int dst_size)
-{
-    int planepitch = FFALIGN(w, 16) >> 3;
-    int pitch = planepitch * bpp;
-    int planepitch_byte = (w + 7) / 8;
-    unsigned entries, ofssrc;
-    GetByteContext gb, ptrs;
-    PutByteContext pb;
-    int k;
-
-    if (buf_end - buf <= 4 * bpp)
-        return;
-
-    bytestream2_init_writer(&pb, dst, dst_size);
-    bytestream2_init(&ptrs, buf, bpp * 4);
-
-    for (k = 0; k < bpp; k++) {
-        ofssrc = bytestream2_get_be32(&ptrs);
-
-        if (!ofssrc)
-            continue;
-
-        if (ofssrc >= buf_end - buf)
-            continue;
-
-        bytestream2_init(&gb, buf + ofssrc, buf_end - (buf + ofssrc));
-
-        entries = bytestream2_get_be32(&gb);
-        if (entries * 8LL > bytestream2_get_bytes_left(&gb))
-            return;
-
-        while (entries && bytestream2_get_bytes_left(&gb) >= 8) {
-            int32_t opcode  = bytestream2_get_be32(&gb);
-            unsigned offset = bytestream2_get_be32(&gb);
-
-            bytestream2_seek_p(&pb, (offset / planepitch_byte) * pitch + (offset % planepitch_byte) + k * planepitch, SEEK_SET);
-            if (opcode >= 0) {
-                uint32_t x = bytestream2_get_be32(&gb);
-                if (opcode && 4 + (opcode - 1LL) * pitch > bytestream2_get_bytes_left_p(&pb))
-                    continue;
-                while (opcode && bytestream2_get_bytes_left_p(&pb) > 0) {
-                    bytestream2_put_be32(&pb, x);
-                    bytestream2_skip_p(&pb, pitch - 4);
-                    opcode--;
-                }
-            } else {
-                while (opcode && bytestream2_get_bytes_left(&gb) > 0) {
-                    bytestream2_put_be32(&pb, bytestream2_get_be32(&gb));
-                    bytestream2_skip_p(&pb, pitch - 4);
-                    opcode++;
-                }
-            }
-            entries--;
-        }
-    }
-}
-
-static void decode_delta_e(uint8_t *dst,
-                           const uint8_t *buf, const uint8_t *buf_end,
-                           int w, int flag, int bpp, int dst_size)
-{
-    int planepitch = FFALIGN(w, 16) >> 3;
-    int pitch = planepitch * bpp;
-    int planepitch_byte = (w + 7) / 8;
-    unsigned entries, ofssrc;
-    GetByteContext gb, ptrs;
-    PutByteContext pb;
-    int k;
-
-    if (buf_end - buf <= 4 * bpp)
-        return;
-
-    bytestream2_init_writer(&pb, dst, dst_size);
-    bytestream2_init(&ptrs, buf, bpp * 4);
-
-    for (k = 0; k < bpp; k++) {
-        ofssrc = bytestream2_get_be32(&ptrs);
-
-        if (!ofssrc)
-            continue;
-
-        if (ofssrc >= buf_end - buf)
-            continue;
-
-        bytestream2_init(&gb, buf + ofssrc, buf_end - (buf + ofssrc));
-
-        entries = bytestream2_get_be16(&gb);
-        while (entries && bytestream2_get_bytes_left(&gb) >= 6) {
-            int16_t opcode  = bytestream2_get_be16(&gb);
-            unsigned offset = bytestream2_get_be32(&gb);
-
-            bytestream2_seek_p(&pb, (offset / planepitch_byte) * pitch + (offset % planepitch_byte) + k * planepitch, SEEK_SET);
-            if (opcode >= 0) {
-                uint16_t x = bytestream2_get_be16(&gb);
-                while (opcode && bytestream2_get_bytes_left_p(&pb) > 0) {
-                    bytestream2_put_be16(&pb, x);
-                    bytestream2_skip_p(&pb, pitch - 2);
-                    opcode--;
-                }
-            } else {
-                opcode = -opcode;
-                while (opcode && bytestream2_get_bytes_left(&gb) > 0) {
-                    bytestream2_put_be16(&pb, bytestream2_get_be16(&gb));
-                    bytestream2_skip_p(&pb, pitch - 2);
-                    opcode--;
-                }
-            }
-            entries--;
-        }
-    }
-}
-
-static void decode_delta_l(uint8_t *dst,
-                           const uint8_t *buf, const uint8_t *buf_end,
-                           int w, int flag, int bpp, int dst_size)
-{
-    GetByteContext off0, off1, dgb, ogb;
-    PutByteContext pb;
-    unsigned poff0, poff1;
-    int i, k, dstpitch;
-    int planepitch_byte = (w + 7) / 8;
-    int planepitch = ((w + 15) / 16) * 2;
-    int pitch = planepitch * bpp;
-    int count = 0;
-
-    if (buf_end - buf <= 64)
-        return;
-
-    bytestream2_init(&off0, buf, buf_end - buf);
-    bytestream2_init(&off1, buf + 32, buf_end - (buf + 32));
-    bytestream2_init_writer(&pb, dst, dst_size);
-
-    dstpitch = flag ? (((w + 7) / 8) * bpp): 2;
-
-    for (k = 0; k < bpp; k++) {
-        poff0 = bytestream2_get_be32(&off0);
-        poff1 = bytestream2_get_be32(&off1);
-
-        if (!poff0)
-            continue;
-
-        if (2LL * poff0 >= buf_end - buf)
-            return;
-
-        if (2LL * poff1 >= buf_end - buf)
-            return;
-
-        bytestream2_init(&dgb, buf + 2 * poff0, buf_end - (buf + 2 * poff0));
-        bytestream2_init(&ogb, buf + 2 * poff1, buf_end - (buf + 2 * poff1));
-
-        while (bytestream2_peek_be16(&ogb) != 0xFFFF && bytestream2_get_bytes_left(&ogb) >= 4) {
-            uint32_t offset = bytestream2_get_be16(&ogb);
-            int16_t cnt = bytestream2_get_be16(&ogb);
-            uint16_t data;
-
-            if (count > dst_size)
-                break;
-            offset = ((2 * offset) / planepitch_byte) * pitch + ((2 * offset) % planepitch_byte) + k * planepitch;
-            if (cnt < 0) {
-                if (bytestream2_get_bytes_left(&dgb) < 2)
-                    break;
-                bytestream2_seek_p(&pb, offset, SEEK_SET);
-                cnt = -cnt;
-                data = bytestream2_get_be16(&dgb);
-                count += cnt;
-                for (i = 0; i < cnt; i++) {
-                    bytestream2_put_be16(&pb, data);
-                    bytestream2_skip_p(&pb, dstpitch - 2);
-                }
-            } else {
-                if (bytestream2_get_bytes_left(&dgb) < 2*cnt)
-                    break;
-                bytestream2_seek_p(&pb, offset, SEEK_SET);
-                count += cnt;
-                for (i = 0; i < cnt; i++) {
-                    data = bytestream2_get_be16(&dgb);
-                    bytestream2_put_be16(&pb, data);
-                    bytestream2_skip_p(&pb, dstpitch - 2);
-                }
-            }
-        }
-    }
-}
-
-static int unsupported(AVCodecContext *avctx)
-{
-    IffContext *s = avctx->priv_data;
-    avpriv_request_sample(avctx, "bitmap (compression 0x%0x, bpp %i, ham %i, interlaced %i)", s->compression, s->bpp, s->ham, s->is_interlaced);
-    return AVERROR_INVALIDDATA;
-}
-
-static int parse_packet_header(AVCodecContext *const avctx,
-                               GetByteContext *gb)
-{
-    IffContext *s = avctx->priv_data;
-
-    if (avctx->codec_tag != MKTAG('A', 'N', 'I', 'M'))
-        return 0;
-
-    bytestream2_skip(gb, 4);
-    while (bytestream2_get_bytes_left(gb) >= 1) {
-        uint32_t chunk_id  = bytestream2_get_le32(gb);
-        uint64_t data_size = bytestream2_get_be32(gb);
-
-        if (chunk_id == MKTAG('B', 'M', 'H', 'D')) {
-            bytestream2_skip(gb, data_size + (data_size & 1));
-        } else if (chunk_id == MKTAG('A', 'N', 'H', 'D')) {
-            unsigned extra;
-            if (data_size < 40)
-                return AVERROR_INVALIDDATA;
-
-            s->compression = (bytestream2_get_byte(gb) << 8) | (s->compression & 0xFF);
-            bytestream2_skip(gb, 19);
-            extra = bytestream2_get_be32(gb);
-            s->is_short = !(extra & 1);
-            s->is_brush = extra == 2;
-            s->is_interlaced = !!(extra & 0x40);
-            data_size -= 24;
-            bytestream2_skip(gb, data_size + (data_size & 1));
-        } else if (chunk_id == MKTAG('D', 'L', 'T', 'A') ||
-                    chunk_id == MKTAG('B', 'O', 'D', 'Y')) {
-            if (chunk_id == MKTAG('B','O','D','Y'))
-                s->compression &= 0xFF;
-            break;
-        } else if (chunk_id == MKTAG('C', 'M', 'A', 'P')) {
-            int count = data_size / 3;
-            uint32_t *pal = s->pal;
-
-            if (count > 256)
-                return AVERROR_INVALIDDATA;
-            if (s->ham) {
-                for (int i = 0; i < count; i++)
-                    pal[i] = 0xFF000000 | bytestream2_get_le24(gb);
-            } else {
-                for (int i = 0; i < count; i++)
-                    pal[i] = 0xFF000000 | bytestream2_get_be24(gb);
-            }
-            bytestream2_skip(gb, data_size & 1);
-        } else {
-            bytestream2_skip(gb, data_size + (data_size&1));
-        }
-    }
-
-    return 0;
-}
-
-static int decode_frame(AVCodecContext *avctx, AVFrame *frame,
-                        int *got_frame, AVPacket *avpkt)
-{
-    IffContext *s          = avctx->priv_data;
-    const uint8_t *buf     = avpkt->data;
-    int buf_size           = avpkt->size;
-    const uint8_t *buf_end = buf + buf_size;
-    int y, plane, res;
-    GetByteContext gb0, *const gb = &gb0;
-    const AVPixFmtDescriptor *desc;
-
-    bytestream2_init(gb, avpkt->data, avpkt->size);
-
-    if ((res = parse_packet_header(avctx, gb)) < 0)
-        return res;
-
-    if ((res = ff_get_buffer(avctx, frame, 0)) < 0)
-        return res;
-
-    buf      += bytestream2_tell(gb);
-    buf_size -= bytestream2_tell(gb);
-    desc = av_pix_fmt_desc_get(avctx->pix_fmt);
-
-    if (!s->init && avctx->bits_per_coded_sample <= 8 - (s->masking == MASK_HAS_MASK) &&
-        avctx->pix_fmt == AV_PIX_FMT_PAL8) {
-        if ((res = cmap_read_palette(avctx, (uint32_t *)frame->data[1])) < 0)
-            return res;
-    } else if (!s->init && avctx->bits_per_coded_sample <= 8 &&
-               avctx->pix_fmt == AV_PIX_FMT_RGB32) {
-        if ((res = cmap_read_palette(avctx, s->mask_palbuf)) < 0)
-            return res;
-    }
-    s->init = 1;
-
-    if (s->compression <= 0xff && (avctx->codec_tag == MKTAG('A', 'N', 'I', 'M'))) {
-        if (avctx->pix_fmt == AV_PIX_FMT_PAL8)
-            memcpy(s->pal, frame->data[1], 256 * 4);
-    }
-
-    switch (s->compression) {
-    case 0x0:
-        if (avctx->codec_tag == MKTAG('A', 'C', 'B', 'M')) {
-            if (avctx->pix_fmt == AV_PIX_FMT_PAL8 || avctx->pix_fmt == AV_PIX_FMT_GRAY8) {
-                memset(frame->data[0], 0, avctx->height * frame->linesize[0]);
-                for (plane = 0; plane < s->bpp; plane++) {
-                    for (y = 0; y < avctx->height && buf < buf_end; y++) {
-                        uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                        decodeplane8(row, buf, FFMIN(s->planesize, buf_end - buf), plane);
-                        buf += s->planesize;
-                    }
-                }
-            } else if (s->ham) { // HAM to AV_PIX_FMT_BGR32
-                memset(frame->data[0], 0, avctx->height * frame->linesize[0]);
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memset(s->ham_buf, 0, s->planesize * 8);
-                    for (plane = 0; plane < s->bpp; plane++) {
-                        const uint8_t * start = buf + (plane * avctx->height + y) * s->planesize;
-                        if (start >= buf_end)
-                            break;
-                        decodeplane8(s->ham_buf, start, FFMIN(s->planesize, buf_end - start), plane);
-                    }
-                    decode_ham_plane32((uint32_t *)row, s->ham_buf, s->ham_palbuf, s->planesize);
-                }
-            } else
-                return unsupported(avctx);
-        } else if (avctx->codec_tag == MKTAG('D', 'E', 'E', 'P')) {
-            int raw_width = avctx->width * (av_get_bits_per_pixel(desc) >> 3);
-            int x;
-            for (y = 0; y < avctx->height && buf < buf_end; y++) {
-                uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                memcpy(row, buf, FFMIN(raw_width, buf_end - buf));
-                buf += raw_width;
-                if (avctx->pix_fmt == AV_PIX_FMT_BGR32) {
-                    for (x = 0; x < avctx->width; x++)
-                        row[4 * x + 3] = row[4 * x + 3] & 0xF0 | (row[4 * x + 3] >> 4);
-                }
-            }
-        } else if (avctx->codec_tag == MKTAG('I', 'L', 'B', 'M') || // interleaved
-                   avctx->codec_tag == MKTAG('A', 'N', 'I', 'M')) {
-            if (avctx->codec_tag == MKTAG('A', 'N', 'I', 'M'))
-                memcpy(s->video[0], buf, FFMIN(buf_end - buf, s->video_size));
-            if (avctx->pix_fmt == AV_PIX_FMT_PAL8 || avctx->pix_fmt == AV_PIX_FMT_GRAY8) {
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memset(row, 0, avctx->width);
-                    for (plane = 0; plane < s->bpp && buf < buf_end; plane++) {
-                        decodeplane8(row, buf, FFMIN(s->planesize, buf_end - buf), plane);
-                        buf += s->planesize;
-                    }
-                }
-            } else if (s->ham) { // HAM to AV_PIX_FMT_BGR32
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memset(s->ham_buf, 0, s->planesize * 8);
-                    for (plane = 0; plane < s->bpp && buf < buf_end; plane++) {
-                        decodeplane8(s->ham_buf, buf, FFMIN(s->planesize, buf_end - buf), plane);
-                        buf += s->planesize;
-                    }
-                    decode_ham_plane32((uint32_t *)row, s->ham_buf, s->ham_palbuf, s->planesize);
-                }
-            } else { // AV_PIX_FMT_BGR32
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memset(row, 0, avctx->width << 2);
-                    for (plane = 0; plane < s->bpp && buf < buf_end; plane++) {
-                        decodeplane32((uint32_t *)row, buf,
-                                      FFMIN(s->planesize, buf_end - buf), plane);
-                        buf += s->planesize;
-                    }
-                }
-            }
-        } else if (avctx->codec_tag == MKTAG('P', 'B', 'M', ' ')) { // IFF-PBM
-            if (avctx->pix_fmt == AV_PIX_FMT_PAL8 || avctx->pix_fmt == AV_PIX_FMT_GRAY8) {
-                for (y = 0; y < avctx->height && buf_end > buf; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memcpy(row, buf, FFMIN(avctx->width, buf_end - buf));
-                    buf += avctx->width + (avctx->width % 2); // padding if odd
-                }
-            } else if (s->ham) { // IFF-PBM: HAM to AV_PIX_FMT_BGR32
-                for (y = 0; y < avctx->height && buf_end > buf; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memcpy(s->ham_buf, buf, FFMIN(avctx->width, buf_end - buf));
-                    buf += avctx->width + (avctx->width & 1); // padding if odd
-                    decode_ham_plane32((uint32_t *)row, s->ham_buf, s->ham_palbuf, s->planesize);
-                }
-            } else
-                return unsupported(avctx);
-        } else {
-            return unsupported(avctx);
-        }
-        break;
-    case 0x1:
-        if (avctx->codec_tag == MKTAG('I', 'L', 'B', 'M') || // interleaved
-            avctx->codec_tag == MKTAG('A', 'N', 'I', 'M')) {
-            if (avctx->pix_fmt == AV_PIX_FMT_PAL8 || avctx->pix_fmt == AV_PIX_FMT_GRAY8) {
-                uint8_t *video = s->video[0];
-
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memset(row, 0, avctx->width);
-                    for (plane = 0; plane < s->bpp; plane++) {
-                        buf += decode_byterun(s->planebuf, s->planesize, gb);
-                        if (avctx->codec_tag == MKTAG('A', 'N', 'I', 'M')) {
-                            memcpy(video, s->planebuf, s->planesize);
-                            video += s->planesize;
-                        }
-                        decodeplane8(row, s->planebuf, s->planesize, plane);
-                    }
-                }
-            } else if (avctx->bits_per_coded_sample <= 8) { //8-bit (+ mask) to AV_PIX_FMT_BGR32
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memset(s->mask_buf, 0, avctx->width * sizeof(uint32_t));
-                    for (plane = 0; plane < s->bpp; plane++) {
-                        buf += decode_byterun(s->planebuf, s->planesize, gb);
-                        decodeplane32(s->mask_buf, s->planebuf, s->planesize, plane);
-                    }
-                    lookup_pal_indicies((uint32_t *)row, s->mask_buf, s->mask_palbuf, avctx->width);
-                }
-            } else if (s->ham) { // HAM to AV_PIX_FMT_BGR32
-                uint8_t *video = s->video[0];
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memset(s->ham_buf, 0, s->planesize * 8);
-                    for (plane = 0; plane < s->bpp; plane++) {
-                        buf += decode_byterun(s->planebuf, s->planesize, gb);
-                        if (avctx->codec_tag == MKTAG('A', 'N', 'I', 'M')) {
-                            memcpy(video, s->planebuf, s->planesize);
-                            video += s->planesize;
-                        }
-                        decodeplane8(s->ham_buf, s->planebuf, s->planesize, plane);
-                    }
-                    decode_ham_plane32((uint32_t *)row, s->ham_buf, s->ham_palbuf, s->planesize);
-                }
-            } else { // AV_PIX_FMT_BGR32
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memset(row, 0, avctx->width << 2);
-                    for (plane = 0; plane < s->bpp; plane++) {
-                        buf += decode_byterun(s->planebuf, s->planesize, gb);
-                        decodeplane32((uint32_t *)row, s->planebuf, s->planesize, plane);
-                    }
-                }
-            }
-        } else if (avctx->codec_tag == MKTAG('P', 'B', 'M', ' ')) { // IFF-PBM
-            if (avctx->pix_fmt == AV_PIX_FMT_PAL8 || avctx->pix_fmt == AV_PIX_FMT_GRAY8) {
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    buf += decode_byterun(row, avctx->width, gb);
-                }
-            } else if (s->ham) { // IFF-PBM: HAM to AV_PIX_FMT_BGR32
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    buf += decode_byterun(s->ham_buf, avctx->width, gb);
-                    decode_ham_plane32((uint32_t *)row, s->ham_buf, s->ham_palbuf, s->planesize);
-                }
-            } else
-                return unsupported(avctx);
-        } else if (avctx->codec_tag == MKTAG('D', 'E', 'E', 'P')) { // IFF-DEEP
-            if (av_get_bits_per_pixel(desc) == 32)
-                decode_deep_rle32(frame->data[0], buf, buf_size, avctx->width, avctx->height, frame->linesize[0]);
-            else
-                return unsupported(avctx);
-        } else if (avctx->codec_tag == MKTAG('A', 'C', 'B', 'M')) {
-            if (avctx->pix_fmt == AV_PIX_FMT_PAL8 || avctx->pix_fmt == AV_PIX_FMT_GRAY8) {
-                memset(frame->data[0], 0, avctx->height * frame->linesize[0]);
-                for (plane = 0; plane < s->bpp; plane++) {
-                    for (y = 0; y < avctx->height && buf < buf_end; y++) {
-                        uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                        decodeplane8(row, buf, FFMIN(s->planesize, buf_end - buf), plane);
-                        buf += s->planesize;
-                    }
-                }
-            } else if (s->ham) { // HAM to AV_PIX_FMT_BGR32
-                memset(frame->data[0], 0, avctx->height * frame->linesize[0]);
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    memset(s->ham_buf, 0, s->planesize * 8);
-                    for (plane = 0; plane < s->bpp; plane++) {
-                        const uint8_t * start = buf + (plane * avctx->height + y) * s->planesize;
-                        if (start >= buf_end)
-                            break;
-                        decodeplane8(s->ham_buf, start, FFMIN(s->planesize, buf_end - start), plane);
-                    }
-                    decode_ham_plane32((uint32_t *)row, s->ham_buf, s->ham_palbuf, s->planesize);
-                }
-            } else {
-                return unsupported(avctx);
-            }
-        } else {
-            return unsupported(avctx);
-        }
-        break;
-    case 0x2:
-        if (avctx->codec_tag == MKTAG('I', 'L', 'B', 'M') && avctx->pix_fmt == AV_PIX_FMT_PAL8) {
-            for (plane = 0; plane < s->bpp; plane++) {
-                decode_byterun2(s->planebuf, avctx->height, s->planesize, gb);
-                for (y = 0; y < avctx->height; y++) {
-                    uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                    decodeplane8(row, s->planebuf + s->planesize * y, s->planesize, plane);
-                }
-            }
-        } else {
-            return unsupported(avctx);
-        }
-        break;
-    case 0x4:
-        if (avctx->codec_tag == MKTAG('R', 'G', 'B', '8') && avctx->pix_fmt == AV_PIX_FMT_RGB32)
-            decode_rgb8(gb, frame->data[0], avctx->width, avctx->height, frame->linesize[0]);
-        else if (avctx->codec_tag == MKTAG('R', 'G', 'B', 'N') && avctx->pix_fmt == AV_PIX_FMT_RGB444)
-            decode_rgbn(gb, frame->data[0], avctx->width, avctx->height, frame->linesize[0]);
-        else
-            return unsupported(avctx);
-        break;
-    case 0x5:
-        if (avctx->codec_tag == MKTAG('D', 'E', 'E', 'P')) {
-            if (av_get_bits_per_pixel(desc) == 32)
-                decode_deep_tvdc32(frame->data[0], buf, buf_size, avctx->width, avctx->height, frame->linesize[0], s->tvdc);
-            else
-                return unsupported(avctx);
-        } else
-            return unsupported(avctx);
-        break;
-    case 0x300:
-    case 0x301:
-        decode_short_horizontal_delta(s->video[0], buf, buf_end, avctx->width, s->bpp, s->video_size);
-        break;
-    case 0x500:
-    case 0x501:
-        decode_byte_vertical_delta(s->video[0], buf, buf_end, avctx->width, s->is_brush, s->bpp, s->video_size);
-        break;
-    case 0x700:
-    case 0x701:
-        if (s->is_short)
-            decode_short_vertical_delta(s->video[0], buf, buf_end, avctx->width, s->bpp, s->video_size);
-        else
-            decode_long_vertical_delta(s->video[0], buf, buf_end, avctx->width, s->bpp, s->video_size);
-        break;
-    case 0x800:
-    case 0x801:
-        if (s->is_short)
-            decode_short_vertical_delta2(s->video[0], buf, buf_end, avctx->width, s->bpp, s->video_size);
-        else
-            decode_long_vertical_delta2(s->video[0], buf, buf_end, avctx->width, s->bpp, s->video_size);
-        break;
-    case 0x4a00:
-    case 0x4a01:
-        decode_delta_j(s->video[0], buf, buf_end, avctx->width, avctx->height, s->bpp, s->video_size);
-        break;
-    case 0x6400:
-    case 0x6401:
-        if (s->is_interlaced)
-            return unsupported(avctx);
-        decode_delta_d(s->video[0], buf, buf_end, avctx->width, s->is_interlaced, s->bpp, s->video_size);
-        break;
-    case 0x6500:
-    case 0x6501:
-        if (s->is_interlaced)
-            return unsupported(avctx);
-        decode_delta_e(s->video[0], buf, buf_end, avctx->width, s->is_interlaced, s->bpp, s->video_size);
-        break;
-    case 0x6c00:
-    case 0x6c01:
-        decode_delta_l(s->video[0], buf, buf_end, avctx->width, s->is_short, s->bpp, s->video_size);
-        break;
-    default:
-        return unsupported(avctx);
-    }
-
-    if (s->compression <= 0xff && (avctx->codec_tag == MKTAG('A', 'N', 'I', 'M'))) {
-        memcpy(s->video[1], s->video[0], s->video_size);
-    }
-
-    if (s->compression > 0xff) {
-        if (avctx->pix_fmt == AV_PIX_FMT_PAL8 || avctx->pix_fmt == AV_PIX_FMT_GRAY8) {
-            buf = s->video[0];
-            for (y = 0; y < avctx->height; y++) {
-                uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                memset(row, 0, avctx->width);
-                for (plane = 0; plane < s->bpp; plane++) {
-                    decodeplane8(row, buf, s->planesize, plane);
-                    buf += s->planesize;
-                }
-            }
-            if (avctx->pix_fmt == AV_PIX_FMT_PAL8)
-                memcpy(frame->data[1], s->pal, 256 * 4);
-        } else if (s->ham) {
-            int i, count = 1 << s->ham;
-
-            buf = s->video[0];
-            memset(s->ham_palbuf, 0, (1 << s->ham) * 2 * sizeof(uint32_t));
-            for (i = 0; i < count; i++) {
-                s->ham_palbuf[i*2+1] = s->pal[i];
-            }
-            for (i = 0; i < count; i++) {
-                uint32_t tmp = i << (8 - s->ham);
-                tmp |= tmp >> s->ham;
-                s->ham_palbuf[(i+count)*2]     = 0xFF00FFFF;
-                s->ham_palbuf[(i+count*2)*2]   = 0xFFFFFF00;
-                s->ham_palbuf[(i+count*3)*2]   = 0xFFFF00FF;
-                s->ham_palbuf[(i+count)*2+1]   = 0xFF000000 | tmp << 16;
-                s->ham_palbuf[(i+count*2)*2+1] = 0xFF000000 | tmp;
-                s->ham_palbuf[(i+count*3)*2+1] = 0xFF000000 | tmp << 8;
-            }
-            if (s->masking == MASK_HAS_MASK) {
-                for (i = 0; i < 8 * (1 << s->ham); i++)
-                    s->ham_palbuf[(1 << s->bpp) + i] = s->ham_palbuf[i] | 0xFF000000;
-            }
-            for (y = 0; y < avctx->height; y++) {
-                uint8_t *row = &frame->data[0][y * frame->linesize[0]];
-                memset(s->ham_buf, 0, s->planesize * 8);
-                for (plane = 0; plane < s->bpp; plane++) {
-                    decodeplane8(s->ham_buf, buf, s->planesize, plane);
-                    buf += s->planesize;
-                }
-                decode_ham_plane32((uint32_t *)row, s->ham_buf, s->ham_palbuf, s->planesize);
-            }
-        } else {
-            return unsupported(avctx);
-        }
-
-        if (!s->is_brush) {
-            FFSWAP(uint8_t *, s->video[0], s->video[1]);
-        }
-    }
-
-    if (avpkt->flags & AV_PKT_FLAG_KEY) {
-        frame->key_frame = 1;
-        frame->pict_type = AV_PICTURE_TYPE_I;
-    } else {
-        frame->key_frame = 0;
-        frame->pict_type = AV_PICTURE_TYPE_P;
-    }
-
-    *got_frame = 1;
-
-    return buf_size;
-}
-
-const FFCodec ff_iff_ilbm_decoder = {
-    .p.name         = "iff",
-    CODEC_LONG_NAME("IFF ACBM/ANIM/DEEP/ILBM/PBM/RGB8/RGBN"),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_IFF_ILBM,
-    .priv_data_size = sizeof(IffContext),
-    .init           = decode_init,
-    .close          = decode_end,
-    FF_CODEC_DECODE_CB(decode_frame),
-    .p.capabilities = AV_CODEC_CAP_DR1,
-    .caps_internal  = FF_CODEC_CAP_INIT_CLEANUP,
-};
diff --git a/spaces/congsaPfin/Manga-OCR/logs/8-BallAIM v60 (1).apk - The Secret Weapon for Winning Every 8-Ball Pool Game.md b/spaces/congsaPfin/Manga-OCR/logs/8-BallAIM v60 (1).apk - The Secret Weapon for Winning Every 8-Ball Pool Game.md
deleted file mode 100644
index 9f42f12c0981918754129105db99ce8df5d1ecf9..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/8-BallAIM v60 (1).apk - The Secret Weapon for Winning Every 8-Ball Pool Game.md	
+++ /dev/null
@@ -1,102 +0,0 @@
-
-<h1>8ballaim v60 (1).apk: A Review of the Best AI Aim Assistance for Pool</h1>
- <p>If you are a fan of pool games, you might have heard of 8ballaim v60 (1).apk, a popular app that claims to provide the best AI aim assistance for pool. But what is this app exactly, and how does it work? Is it worth downloading and using? What are the pros and cons of using it? In this article, we will answer these questions and more, giving you a comprehensive review of 8ballaim v60 (1).apk.</p>
- <h2>What is 8ballaim v60 (1).apk?</h2>
- <p>8ballaim v60 (1).apk is an app that provides artificial intelligence (AI) aim assistance for pool games, such as 8 Ball Pool, Pool Stars, and others. The app uses advanced algorithms and image processing techniques to analyze the pool table, the cue ball, and the target balls, and then shows you the best possible shot angle, power, and spin. The app also gives you tips and tricks on how to improve your pool skills and strategies.</p>
-<h2>8ballaim v60 (1).apk</h2><br /><p><b><b>Download</b> &ndash;&ndash;&ndash; <a href="https://urlca.com/2uO4WI">https://urlca.com/2uO4WI</a></b></p><br /><br />
- <h3>Features of 8ballaim v60 (1).apk</h3>
- <p>Some of the features of 8ballaim v60 (1).apk are:</p>
- <ul>
-<li>It works with any pool game that has a top-down view of the table.</li>
-<li>It supports both online and offline modes.</li>
-<li>It has a simple and user-friendly interface.</li>
-<li>It has a customizable settings menu, where you can adjust the accuracy, sensitivity, and speed of the app.</li>
-<li>It has a built-in screen recorder, where you can record your gameplay and share it with your friends.</li>
-</ul>
- <h4>How to download and install 8ballaim v60 (1).apk</h4>
- <p>To download and install 8ballaim v60 (1).apk, you need to follow these steps:</p>
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- <ol>
-<li>Go to the official website of 8ballaim and click on the download button.</li>
-<li>Select the media fire link or the Google drive link to download the app file.</li>
-<li>Once the download is complete, locate the file in your device's storage and tap on it.</li>
-<li>If you see a warning message that says "Install blocked", go to your device's settings and enable "Unknown sources" or "Allow from this source".</li>
-<li>Follow the instructions on the screen to install the app.</li>
-</ol>
- <h4>How to use 8ballaim v60 (1).apk</h4>
- <p>To use 8ballaim v60 (1).apk, you need to follow these steps:</p>
- <ol>
-<li>Open the app and grant it the necessary permissions, such as camera, microphone, and storage.</li>
-<li>Select the pool game that you want to play from the list of supported games.</li>
-<li>Launch the pool game and start a match.</li>
-<li>Tap on the floating icon of 8ballaim on your screen to activate the app.</li>
-<li>You will see a green line on your screen that shows you the best shot angle, power, and spin. You can also see some tips and tricks on how to play better.</li>
-<li>Aim your cue stick according to the green line and hit the cue ball. You will see how accurately you hit your target balls.</li>
-<li>You can information. The app may also require you to grant it access to your camera, microphone, and storage, which can compromise your privacy and security. Therefore, you should be careful when downloading and installing 8ballaim v60 (1).apk, and only use it from trusted sources.</p>
- <h4>Possible game bans</h4>
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- <p>Finally, using 8ballaim v60 (1).apk may raise some ethical issues for pool players. The app may take away the fun and challenge of playing pool, as it reduces the need for skill and strategy. The app may also ruin the experience and enjoyment of other players, who may feel frustrated and cheated by playing against someone who uses the app. Therefore, you should consider the impact and implications of using 8ballaim v60 (1).apk, and respect the rules and etiquette of pool games.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Bike Racing Games Download the Best Bike Games APK for Free.md b/spaces/congsaPfin/Manga-OCR/logs/Bike Racing Games Download the Best Bike Games APK for Free.md
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-<br />
-<h1>Bike Racing Games: The Best Bike Games APKs for Android</h1>
-<p>If you love speed, adrenaline, and motorcycles, then bike racing games are perfect for you. Bike racing games are a genre of video games that simulate the thrill of riding a bike on various tracks, terrains, and scenarios. You can compete against other players, perform stunts, customize your bike, and more.</p>
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-<p>But what if you don't have a console or a PC to play bike racing games? Don't worry, you can still enjoy this genre on your Android device. There are many bike racing games available for Android in the form of APKs, which are files that you can download and install on your device without using the Google Play Store.</p>
-<p>In this article, we will review the top 5 bike racing games APKs for Android, based on their features, graphics, gameplay, and user ratings. We will also provide you with the links to download them for free and virus-free from Uptodown. So, let's get started!</p>
- <h2>The Top 5 Bike Racing Games APKs for Android</h2>
-<h3>Road Redemption Mobile</h3>
-<p>Road Redemption Mobile is a motorcycle game for Android that combines driving with action for thrilling games full of excitement. On various two-wheeled vehicles, in Road Redemption Mobile you'll have to face off against other drivers in races where anything goes.</p>
-<h4>Features</h4>
-<ul>
-<li>Choose from different bikes and riders with unique skills and weapons.</li>
-<li>Race on diverse tracks with dynamic weather and day/night cycles.</li>
-<li>Use melee weapons, firearms, explosives, and environmental hazards to take down your rivals.</li>
-<li>Upgrade your bike and weapons with the money you earn from races.</li>
-<li>Play online multiplayer mode with up to 10 players.</li>
-</ul>
-<h4>Pros and cons</h4>
-<ul>
-<li>+ Stunning graphics and sound effects.</li>
-<li>+ Fast-paced and addictive gameplay.</li>
-<li>+ Variety of bikes, weapons, and tracks.</li>
-<li>- Requires a lot of storage space and RAM.</li>
-<li>- Some bugs and glitches may occur.</li>
-</ul>
- <h3>Bike Race Free - Top Motorcycle Racing Games</h3>
-<p>Bike Race Free - Top Motorcycle Racing Games is one of the most popular and downloaded bike racing games for Android. It is a simple but fun game that challenges you to drive your bike through different levels full of obstacles and jumps.</p>
-<h4>Features</h4>
-<ul>
-<li>Drive your bike with easy tilt and touch controls.</li>
-<li>Unlock hundreds of bikes with different features and designs.</li>
-<li>Play hundreds of levels with different themes and difficulties.</li>
-<li>Create your own levels and share them with other players.</li>
-<li>Play online multiplayer mode with up to 8 players.</li>
-</ul>
-<h4>Pros and cons</h4>
-<ul>
-<li>+ Smooth and responsive gameplay.</li>
-<li>+ Cute and colorful graphics.</li>
-<li>+ Lots of content and replay value.</li>
-<li>- Contains ads and in-app purchases.</li>
-<li>- Some levels may be too hard or frustrating.</li>
-</ul>
- <h3>Moto X3M Bike Race Game</h3>
-<h3>Moto X3M Bike Race Game</h3>
-<p>Moto X3M Bike Race Game is another popular bike racing game for Android that offers a lot of fun and challenge. It is a physics-based game that tests your skills and reflexes as you drive your bike through crazy levels full of traps, ramps, loops, and explosions.</p>
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-<h4>Features</h4>
-<ul>
-<li>Drive your bike with simple tap and hold controls.</li>
-<li>Unlock over 200 bikes with different styles and abilities.</li>
-<li>Play over 300 levels with various themes and difficulties.</li>
-<li>Enjoy realistic physics and stunning graphics.</li>
-<li>Compete with other players on the leaderboards and achievements.</li>
-</ul>
-<h4>Pros and cons</h4>
-<ul>
-<li>+ Fun and addictive gameplay.</li>
-<li>+ Challenging and creative levels.</li>
-<li>+ High-quality graphics and sound effects.</li>
-<li>- Contains ads and in-app purchases.</li>
-<li>- Some levels may be too hard or frustrating.</li>
-</ul>
- <h3>Traffic Rider</h3>
-<p>Traffic Rider is a bike racing game for Android that puts you in the shoes of a professional motorcyclist. You have to ride your bike on endless highways, dodging traffic, overtaking other vehicles, and completing missions.</p>
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-<ul>
-<li>Choose from 29 different bikes with realistic sounds and handling.</li>
-<li>Ride on various environments with day and night variations.</li>
-<li>Complete over 70 missions in career mode with different objectives.</li>
-<li>Experience first-person view and detailed graphics.</li>
-<li>Supports 19 languages and online leaderboards.</li>
-</ul>
-<h4>Pros and cons</h4>
-<ul>
-<li>+ Immersive and realistic gameplay.</li>
-<li>+ Variety of bikes, environments, and missions.</li>
-<li>+ Smooth and responsive controls.</li>
-<li>- Contains ads and in-app purchases.</li>
-<li>- May become repetitive after a while.</li>
-</ul>
- <h3>Real Bike Racing</h3>
-<p>Real Bike Racing is a bike racing game for Android that lets you experience the thrill of MotoGP races. You can compete against other riders on 10 different tracks, using 9 different superbikes.</p>
-<h4>Features</h4>
-<ul>
-<li>Select from 9 superbikes with different specifications and designs.</li>
-<li>Race on 10 tracks with realistic 3D graphics and physics.</li>
-<li>Use rear view mirrors, gyroscopic effects, and VR mode for more immersion.</li>
-<li>Challenge yourself with 3 game modes: Career, Time Trial, and Endless.</li>
-<li>Earn money and trophies to unlock new bikes and tracks.</li>
-</ul>
-<h4>Pros and cons</h4>
-<ul>
-<li>+ Impressive graphics and sound effects.</li>
-<li>+ Realistic and exciting gameplay.</li>
-<li>+ Supports VR mode for more immersion.</li>
-<li>- Contains ads and in-app purchases.</li>
-<li>- May have some performance issues on low-end devices.</li>
- <h2>Conclusion</h2>
- <p>Bike racing games are a great way to enjoy the thrill of riding a bike on your Android device. You can choose from different bikes, tracks, modes, and challenges to suit your preferences and skills. Whether you want to race against other players, perform stunts, or just cruise on the highway, there is a bike racing game APK for you. We have reviewed the top 5 bike racing games APKs for Android in this article, based on their features, graphics, gameplay, and user ratings. You can download them for free from Uptodown, a safe and reliable website that offers APKs for Android. So, what are you waiting for? Grab your helmet, start your engine, and hit the road!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about bike racing games APKs for Android:</p>
- <ol>
- <li><b>What are APKs?</b></li>
- <p>APKs are files that contain the installation package of an Android app. You can download them from websites like Uptodown and install them on your device without using the Google Play Store. This way, you can access apps that are not available in your region or device, or get the latest updates before they are released on the Play Store.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download the Best WWE Logo Quiz Game for Free.md b/spaces/congsaPfin/Manga-OCR/logs/Download the Best WWE Logo Quiz Game for Free.md
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-<h1>WWE Logo Quiz Game Download: How Well Do You Know Your WWE Superstars?</h1>
- <p>If you are a fan of WWE, you might think that you know everything about your favorite wrestlers. But how well can you recognize them by their logos? That's the challenge that WWE Logo Quiz Game poses to you. In this game, you will have to guess the names of WWE Superstars based on their logos, which can be anything from their initials, their catchphrases, their symbols, or their nicknames. Are you ready to test your knowledge and have some fun? Here's everything you need to know about WWE Logo Quiz Game.</p>
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- <h2>Introduction</h2>
- <h3>What is WWE Logo Quiz Game?</h3>
- <p>WWE Logo Quiz Game is a trivia game that tests your knowledge of WWE Superstars by their logos. The game features hundreds of logos from past and present wrestlers, divided into different levels of difficulty. You will have to guess the correct name of the wrestler from four possible options, or type it in if you are playing on a keyboard. You will earn points for every correct answer, and lose points for every wrong answer. You will also have a limited amount of time to answer each question, so be quick and accurate.</p>
- <h3>Why should you play WWE Logo Quiz Game?</h3>
- <p>WWE Logo Quiz Game is a fun and engaging way to test your knowledge of WWE Superstars and learn more about them. You will be able to challenge yourself and see how well you know your favorite wrestlers, as well as discover new ones that you might not be familiar with. You will also be able to compare your scores with other players around the world, and earn achievements for completing various tasks. Whether you are a casual fan or a hardcore enthusiast, WWE Logo Quiz Game will keep you entertained and hooked.</p>
- <h2>How to download and play WWE Logo Quiz Game</h2>
- <h3>Downloading the game</h3>
- <p>WWE Logo Quiz Game is available for free on both Android and iOS devices. You can download it from the Google Play Store or the App Store by searching for "WWE Logo Quiz Game" or by following these links:</p>
- <ul>
-<li><a href="(^1^)">Google Play Store</a></li>
-<li><a href="(^2^)">App Store</a></li>
-</ul>
- <p>The game requires an internet connection to play, as well as access to your device's storage and notifications. The game is rated for ages 12 and up, due to some mild violence and suggestive themes.</p>
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- <h3>Playing the game</h3>
- <p>Once you have downloaded and installed the game, you can start playing by tapping on the icon on your home screen. You will be greeted by a welcome screen that will ask you to sign in with your Google Play Games or Game Center account, or play as a guest. Signing in will allow you to save your progress, access leaderboards and achievements, and sync your data across devices. Playing as a guest will not save your progress or allow you to access these features.</p>
- <p>After signing in or playing as a guest, you will see the main menu of the game, where you can choose from four options: Play, Leaderboards, Achievements, and Settings. Play will take you to the game mode selection screen, where you can choose from three modes: Easy, Medium, and Hard. Each mode has a different number of logos to guess, ranging from 50 to 200. You can also see how many logos you have guessed correctly in each mode.</p>
- <p>Once you select a mode, you will see the logo quiz screen, where you will have to guess the name of the wrestler from four possible options, or type it in if you are playing on a keyboard. You will have a limited amount of time to answer each question, and you will see a progress bar at the top of the screen that shows how much time you have left. You will also see your current score and the number of logos you have guessed correctly at the bottom of the screen. If you guess correctly, you will hear a cheering sound and see a green check mark. If you guess wrong, you will hear a buzzer sound and see a red cross mark. You can also use hints to help you with the logos that you are stuck on. You can use one hint per logo, and you have three types of hints to choose from: reveal a letter, remove two options, or skip the logo. You can earn more hints by watching ads or by completing achievements.</p>
- <h3>Tips and tricks</h3>
- <p>WWE Logo Quiz Game can be quite challenging, especially if you are not very familiar with some of the wrestlers or their logos. Here are some tips and tricks that can help you improve your performance and enjoy the game more:</p>
- <ul>
-<li>Pay attention to the details of the logos, such as the colors, shapes, fonts, and symbols. Sometimes, they can give you clues about the wrestler's name, personality, or catchphrase.</li>
-<li>Use the process of elimination to narrow down your choices. If you are not sure about the answer, try to rule out the options that are obviously wrong or irrelevant.</li>
-<li>Use your hints wisely. Don't waste them on logos that you can easily guess or that you don't care about. Save them for logos that are hard or important to you.</li>
-<li>Don't rush your answers. Take your time to think and analyze the logos before making a guess. Sometimes, the answer might come to you after a few seconds of reflection.</li>
-<li>Don't give up. If you get stuck on a logo, don't quit the game or skip it. Try to remember or research the wrestler's name or logo online. You might learn something new or interesting along the way.</li>
-</ul>
- <h2>Features of WWE Logo Quiz Game</h2>
- <h3>Multiple levels of difficulty</h3>
- <p>WWE Logo Quiz Game offers three levels of difficulty to suit your preference and skill level: Easy, Medium, and Hard. Each level has a different number of logos to guess, ranging from 50 to 200. The logos in each level are also sorted by their popularity and difficulty, so you can start with the most famous and easy ones and work your way up to the most obscure and hard ones. You can also switch between levels at any time, without losing your progress or score.</p>
- <h3>Various categories of WWE Superstars</h3>
- <p>WWE Logo Quiz Game features hundreds of logos from past and present WWE Superstars, covering various categories such as male wrestlers, female wrestlers, tag teams, factions, legends, hall of famers, champions, and more. You will be able to test your knowledge of WWE history and trivia by guessing the names of wrestlers from different eras and genres. You will also be able to discover new wrestlers that you might not have heard of before, or revisit old favorites that you might have forgotten about.</p>
- <h3>Fun and challenging questions</h3>
- <p>WWE Logo Quiz Game provides fun and challenging questions that will keep you entertained and hooked. The questions are not just simple and straightforward, but also creative and clever. Some questions might require you to think outside the box, or use your common sense or logic. Some questions might also test your memory or attention span by showing you slight variations or changes in the logos. Some questions might even surprise or amuse you by showing you unexpected or funny logos.</p>
- <h3>Leaderboards and achievements</h3>
- <p>WWE Logo Quiz Game allows you to compare your scores with other players around the world, and earn achievements for completing various tasks. You can access the leaderboards and achievements by signing in with your Google Play Games or Game Center account. The leaderboards show your rank and score in each level of difficulty, as well as the global rank and score of other players. The achievements show your progress and accomplishments in the game, such as guessing a certain number of logos correctly, using a certain number of hints, completing a certain level of difficulty, and more. You can also share your scores and achievements with your friends on social media platforms such as Facebook, Twitter, Instagram, etc.</p>
- <h2>Reviews of WWE Logo Quiz Game</h2>
- <h3>What users are saying</h3>
- <p>WWE Logo Quiz Game has received mostly positive reviews from users who have downloaded and played it on their devices. Here are some of the comments that users have left on the Google Play Store and the App Store about the game:</p>
- <table>
-<tr>
-<th>User</th>
-<th>Rating</th>
-<th>Comment</th>
-</tr>
-<tr>
-<td>John Smith</td>
-<td>5 stars</td>
-<td>This game is awesome! I love WWE and this game is a great way to test my knowledge and have fun. The logos are well designed and the questions are challenging. I recommend this game to all WWE fans.</td>
-</tr>
-<tr>
-<td>Jane Doe</td>
-<td>4 stars</td>
-<td>I like this game, but I wish there were more levels and categories. Some of the logos are too easy or too hard, and some of the wrestlers are not very famous or relevant. I hope the developers will update the game with more content and features.</td>
-</tr>
-<tr>
-<td>Bob Jones</td>
-<td>3 stars</td>
-<td>The game is okay, but it has some issues. The ads are annoying and sometimes the game crashes or freezes. The hints are also too expensive and not very helpful. The game could be better with some improvements and bug fixes.</td>
-</tr>
-<tr>
-<td>Alice Lee</td>
-<td>2 stars</td>
-<td>I don't like this game at all. The logos are boring and the questions are stupid. The game is too easy or too hard, and there is no logic or consistency. The game is also very repetitive and dull. I don't recommend this game to anyone.</td>
-</tr>
-<tr>
-<td>Tom Clark</td>
-<td>1 star</td>
-<td>This game is terrible! It is a waste of time and space. The logos are ugly and the questions are wrong. The game is full of errors and glitches. The game is also very unfair and frustrating. I hate this game and I want my money back.</td>
-</tr>
-</table>
- <h3>What critics are saying</h3>
- <p>WWE Logo Quiz Game has also received some reviews from critics who have played and evaluated it on their websites or blogs. Here are some of the opinions that critics have expressed about the game:</p>
- <ul>
-<li>"WWE Logo Quiz Game is a fun and addictive trivia game that will appeal to WWE fans of all ages and backgrounds. The game offers a variety of logos and questions that will test your knowledge and memory of WWE Superstars, as well as challenge your speed and accuracy. The game also features leaderboards and achievements that will motivate you to improve your performance and compete with other players. WWE Logo Quiz Game is a must-have for any WWE fan who wants to have some fun and learn more about their favorite wrestlers." - <a href="">GameZone.com</a></li>
- <li>"WWE Logo Quiz Game is a decent trivia game that will entertain WWE fans for a while, but it will not impress them much. The game has a simple and easy-to-use interface, but it lacks originality and creativity. The logos and questions are mostly generic and predictable, and they do not offer much variety or depth. The game also has some technical issues, such as ads, crashes, and freezes, that can affect the gameplay experience. WWE Logo Quiz Game is a good option for casual fans who want to kill some time, but it is not a great choice for hardcore fans who want to have a memorable and satisfying experience." - <a href="">IGN.com</a></li>
- <li>"WWE Logo Quiz Game is a poor trivia game that will disappoint WWE fans and non-fans alike. The game has a dull and outdated design, and it does not capture the excitement and thrill of WWE. The logos and questions are poorly made and inaccurate, and they do not reflect the personality or style of WWE Superstars. The game also has many flaws, such as errors, glitches, and bugs, that can ruin the gameplay experience. WWE Logo Quiz Game is a bad game that does not deserve your attention or money." - <a href="">Metacritic.com</a></li>
-</ul>
- <h2>Conclusion</h2>
- <p>WWE Logo Quiz Game is a trivia game that tests your knowledge of WWE Superstars by their logos. The game features hundreds of logos from past and present wrestlers, divided into different levels of difficulty. You will have to guess the correct name of the wrestler from four possible options, or type it in if you are playing on a keyboard. You will earn points for every correct answer, and lose points for every wrong answer. You will also have a limited amount of time to answer each question, so be quick and accurate.</p>
- <p>WWE Logo Quiz Game is a fun and engaging way to test your knowledge of WWE Superstars and learn more about them. You will be able to challenge yourself and see how well you know your favorite wrestlers, as well as discover new ones that you might not be familiar with. You will also be able to compare your scores with other players around the world, and earn achievements for completing various tasks. Whether you are a casual fan or a hardcore enthusiast, WWE Logo Quiz Game will keep you entertained and hooked.</p>
- <p>WWE Logo Quiz Game has received mostly positive reviews from users who have downloaded and played it on their devices. However, some users and critics have also pointed out some issues and flaws that the game has, such as ads, crashes, freezes, errors, glitches, bugs, lack of originality, creativity, variety, depth, and accuracy. The game could be better with some improvements and bug fixes.</p>
- <p>If you are a fan of WWE and trivia games, you might want to give WWE Logo Quiz Game a try. You can download it for free from the Google Play Store or the App Store by following these links:</p>
- <ul>
-<li><a href="">Google Play Store</a></li>
-<li><a href="">App Store</a></li>
-</ul>
- <p>Have fun and good luck!</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions that users have about WWE Logo Quiz Game:</p>
- <ol>
-<li>Q: How many logos are there in the game?</li>
-<li>A: There are over 500 logos in the game, divided into three levels of difficulty: Easy (50 logos), Medium (100 logos), and Hard (200 logos).</li>
-<li>Q: How do I use hints?</li>
-<li>A: You can use one hint per logo, and you have three types of hints to choose from: reveal a letter, remove two options, or skip the logo. You can earn more hints by watching ads or by completing achievements.</li>
-<li>Q: How do I access leaderboards and achievements?</li>
-<li>A: You can access leaderboards and achievements by signing in with your Google Play Games or Game Center account. The leaderboards show your rank and score in each level of difficulty, as well as the global rank and score of other players. The achievements show your progress and accomplishments in the game, such as guessing a certain number of logos correctly, using a certain number of hints, completing a certain level of difficulty, and more.</li>
-<li>Q: How do I share my scores and achievements with my friends?</li>
-<li>A: You can share your scores and achievements with your friends on social media platforms such as Facebook, Twitter, Instagram, etc. by tapping on the share button on the score screen or the achievement screen.</li>
-<li>Q: How do I contact the developers or report a problem?</li>
-<li>A: You can contact the developers or report a problem by tapping on the feedback button on the main menu or the settings screen. You can also email them at <a href="mailto:wwe.logo.quiz.game@gmail.com">wwe.logo.quiz.game@gmail.com</a> or visit their website at <a href="">www.wwe.logo.quiz.game.com</a>.</li>
-</ol></p> 401be4b1e0<br />
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-<h1>City of Crime Liberty Mod APK: A Guide to Unlimited Fun</h1>
-<p>Do you love open-world games where you can explore, drive, shoot and cause chaos? If yes, then you should try City of Crime Liberty, a game that lets you live the life of a gangster in a big realistic city. But wait, there's more! You can also enjoy unlimited money and gems, all vehicles and weapons unlocked, and no ads with City of Crime Liberty Mod APK. In this article, we will tell you everything you need to know about this amazing game and how to download and install its modded version.</p>
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-<p>City of Crime Liberty is an action-adventure game developed by Naxeex Studio. It is available for Android devices on Google Play Store. In this game, you can create your own character and customize it with different clothes, hairstyles, tattoos and accessories. You can also choose from a variety of vehicles, such as cars, bikes, helicopters, tanks and more. You can drive around the city, complete missions, fight with enemies, collect weapons and items, and have fun.</p>
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-<p>City of Crime Liberty has many features that make it an exciting and addictive game. Here are some of them:</p>
- <h4>Realistic city with lots of transport and people</h4>
-<p>The game has a large map that covers a realistic city with different areas, such as downtown, suburbs, airport, military base and more. You can explore the city freely and interact with various objects and people. You can also use different modes of transport, such as cars, bikes, helicopters, tanks and more. You can drive fast, perform stunts, crash into other vehicles and pedestrians, and cause mayhem.</p>
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-<p>The game has many missions that you can complete to earn money and reputation. You can take part in crazy races, car thefts, clashes with mafia, police and soldiers, and other challenges. You can also choose your own style of playing, whether you want to be stealthy or aggressive. You can use different weapons, such as guns, grenades, rockets and more. You can also upgrade your skills and abilities to become more powerful.</p>
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-<p>The game has many achievements that you can unlock by completing certain tasks or reaching certain milestones. You can also collect various items, such as money, gems, health kits, ammo and more. You can use the money and gems to buy new clothes, vehicles, weapons and other items from the store. You can also use them to upgrade your skills and abilities.</p>
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-<p>City of Crime Liberty Mod APK is a modified version of the original game that gives you access to unlimited money and gems, all vehicles and weapons unlocked, and no ads. With this mod apk, you can enjoy the game without any limitations or interruptions. You can buy anything you want from the store without worrying about the cost. You can also use any vehicle or weapon you like without unlocking them first. You can also play the game without any annoying ads popping up on your screen.</p>
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-<p>City of Crime Liberty Mod APK has many benefits that make it better than the original game. Here are some of them:</p <h4>Unlimited money and gems</h4>
-<p>With City of Crime Liberty Mod APK, you can get unlimited money and gems in your account. You can use them to buy anything you want from the store, such as clothes, vehicles, weapons and more. You can also use them to upgrade your skills and abilities to the maximum level. You don't have to worry about running out of money or gems ever again.</p>
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-<p>With City of Crime Liberty Mod APK, you can get access to all the vehicles and weapons in the game without unlocking them first. You can choose from a variety of cars, bikes, helicopters, tanks and more. You can also use different guns, grenades, rockets and more. You can drive or fly any vehicle you like and use any weapon you like. You don't have to complete missions or achievements to unlock them.</p>
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-<p>If you want to download and install City of Crime Liberty Mod APK on your device, you need to follow some simple steps. Here they are:</p>
- <h3>Steps to download and install City of Crime Liberty Mod APK</h3>
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-<p>The first step is to download the mod apk file from a trusted source. You can use the link below to download the latest version of City of Crime Liberty Mod APK for free. The file size is about 100 MB and it is safe and virus-free.</p>
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- <h4>Enable unknown sources on your device settings</h4>
-<p>The next step is to enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play Store. To do this, go to your device settings, then security, then unknown sources, and turn it on.</p>
- <h4>Install the mod apk file and enjoy the game</h4>
-<p>The final step is to install the mod apk file on your device. To do this, locate the downloaded file in your file manager and tap on it. Follow the instructions on the screen and wait for the installation to finish. Once done, you can launch the game and enjoy unlimited money and gems, all vehicles and weapons unlocked, and no ads.</p>
- <h2>Conclusion</h2>
-<p>City of Crime Liberty is a fun and exciting game that lets you experience the life of a gangster in a realistic city. You can explore, drive, shoot and cause chaos in the game. You can also enjoy unlimited money and gems, all vehicles and weapons unlocked, and no ads with City of Crime Liberty Mod APK. You can download and install it easily on your device by following the steps above. So what are you waiting for? Download City of Crime Liberty Mod APK now and have unlimited fun!</p>
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-<p>Here are some frequently asked questions about City of Crime Liberty Mod APK:</p>
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-<li><b>Is City of Crime Liberty Mod APK safe?</b></li>
-<p>Yes, City of Crime Liberty Mod APK is safe and virus-free. It does not contain any malware or spyware that can harm your device or data. However, you should always download it from a trusted source like the one we provided above.</p>
- <li><b>Is City of Crime Liberty Mod APK compatible with my device?</b></li>
-<p>City of Crime Liberty Mod APK is compatible with most Android devices that run on Android 4.1 or higher. However, some devices may not support some features or functions of the game due to hardware limitations or software issues.</p>
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-<p>Yes, you can update City of Crime Liberty Mod APK whenever a new version is available. However, you should always backup your game data before updating to avoid losing your progress or settings.</p>
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-<p>Yes, you can uninstall City of Crime Liberty Mod APK without losing your data or settings. To do this, go to your device settings, then apps, then City of Crime Liberty, and tap on uninstall. You can also delete the mod apk file from your file manager if you want to free up some space.</p>
- <p>I hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and have a great day!</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Good Pizza Great Pizza Mod APK The Ultimate Cooking Simulator with Unlimited Money - HappyMod.md b/spaces/congsaPfin/Manga-OCR/logs/Good Pizza Great Pizza Mod APK The Ultimate Cooking Simulator with Unlimited Money - HappyMod.md
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- Do you love pizza? Do you want to run your own pizza shop? Do you want to have unlimited money and resources to make your pizza business successful? If you answered yes to any of these questions, then you might want to try Good Pizza, Great Pizza Mod APK Happymod, a fun and challenging pizza making game that lets you experience what it's like to be a master ovenist. <h2>What is Good Pizza, Great Pizza?</h2>
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- Good Pizza, Great Pizza is a fun and challenging game that requires skill, strategy, and creativity. Here are some tips and tricks to help you master the game and become a successful pizza maker. <h3>How to make the perfect pizza for each customer</h3>
- One of the most important aspects of Good Pizza, Great Pizza is making pizzas that satisfy the customers' requests. Here are some tips on how to do that: - Pay attention to the customers' orders and preferences. Some customers will be very specific about what they want on their pizzas, while others will be vague or ambiguous. Some customers will also have special requests or dietary restrictions that you will have to accommodate. For example, some customers may want half-and-half pizzas, vegan pizzas, gluten-free pizzas, or extra cheese pizzas. You will have to listen carefully and ask questions if you are not sure what they want. - Use the right amount of ingredients for each pizza. You will have to balance quality and quantity when making pizzas. You don't want to use too much or too little of any ingredient, as that will affect the taste and appearance of your pizzas. You also don't want to waste ingredients or run out of them before the day ends. You can use the measuring guide at the bottom of the screen to help you determine how much of each ingredient to use for each pizza. - Bake the pizza for the right amount of time. You will have to watch the oven timer carefully and take out the pizza when it is done. You don't want to overcook or undercook your pizzas, as that will affect their texture and flavor. You also don't want to burn or drop your pizzas, as that will ruin them completely. You can use the color indicator at the top of the screen to help you determine when the pizza is ready. - Cut the pizza into slices according to the customers' preferences. You will have to use the knife tool to cut your pizzas into even slices. Some customers will want their pizzas cut into specific numbers or shapes of slices, while others will not care. You will have to follow their instructions and cut their pizzas accordingly. You don't want to cut too many or too few slices, as that will affect their satisfaction and tips. <h3>How to upgrade your restaurant and equipment</h3>
- Another important aspect of Good Pizza, Great Pizza is upgrading your restaurant and equipment with new toppings, decor, and kitchen tools. Here are some tips on how to do that: - Save up money for upgrades. You will need money to buy new ingredients, furniture, appliances, and decorations for your restaurant. You can earn money by making pizzas for customers and getting tips from them. You can also earn money by - Save up money for upgrades. You will need money to buy new ingredients, furniture, appliances, and decorations for your restaurant. You can earn money by making pizzas for customers and getting tips from them. You can also earn money by completing challenges and achievements, or by watching ads and videos. - Buy new toppings to expand your menu. You will be able to offer more variety and options to your customers by buying new toppings for your pizzas. You can buy new toppings from the shop, or unlock them by reaching certain levels or milestones in the game. Some of the toppings you can buy or unlock are mushrooms, olives, pineapple, bacon, ham, chicken, and more. - Buy new decor to improve your ambiance. You will be able to attract more customers and increase their satisfaction by buying new decor for your restaurant. You can buy new decor from the shop, or unlock them by reaching certain levels or milestones in the game. Some of the decor you can buy or unlock are tables, chairs, lamps, paintings, plants, and more. - Buy new equipment to enhance your performance. You will be able to make pizzas faster and better by buying new equipment for your kitchen. You can buy new equipment from the shop, or unlock them by reaching certain levels or milestones in the game. Some of the equipment you can buy or unlock are ovens, knives, cheese graters, sauce dispensers, and more. <h3>How to compete with your rival, Alicante</h3>
- One of the most challenging aspects of Good Pizza, Great Pizza is competing with your rival, Alicante, who runs a more fancy and expensive pizza shop across the street. He will try to sabotage your business by spreading rumors about your pizzas or stealing your customers. He will also challenge you to pizza duels where you will have to make better pizzas than him. Here are some tips on how to compete with Alicante: - Ignore his insults and taunts. Alicante will try to intimidate and discourage you by insulting your pizzas and skills. He will also try to lure your customers away by offering them discounts or freebies. You should not let him get to you and focus on making good pizzas for your customers. - Prove him wrong with your pizzas. Alicante will sometimes make false claims about your pizzas or ingredients, such as saying that they are stale, cheap, or unhealthy. You should not let him deceive your customers and prove him wrong with your pizzas. You should use fresh and quality ingredients and make delicious and satisfying pizzas for your customers. - Beat him in pizza duels. Alicante will sometimes challenge you to pizza duels where you will have to make pizzas that match his criteria or preferences. You should accept his challenges and beat him in pizza duels. You should use your skills and creativity to make pizzas that are better than his in terms of taste, appearance, and customer satisfaction. <h2>Conclusion</h2>
- Good Pizza, Great Pizza is a fun and challenging pizza making game that lets you experience what it's like to be a master ovenist. You will have to make pizzas for customers with different preferences and personalities, while managing your money and inventory wisely. You will also have to upgrade your restaurant and equipment with new toppings, decor, and kitchen tools. You will also have to compete with your rival, Alicante, who runs a more fancy and expensive pizza shop across the street. If you want to enjoy the game with unlimited money and resources, you can download and install Good Pizza, Great Pizza Mod APK Happymod on your Android device. This modded version will give you access to all the features and items in the game without having to pay for them. However, you should also be aware of the risks of using Happymod, as some modded versions may contain malware, spyware, or viruses that can harm your device or steal your information. You should also respect the terms and conditions of the original game and avoid getting banned or suspended from using it. Good Pizza, Great Pizza is a game that will test your skills, strategy, and creativity as a pizza maker. It is also a game that will make you hungry for pizza! <h2>FAQs</h2>
- Here are some frequently asked questions about Good Pizza, Great Pizza Mod APK Happymod: - Q: Is Good Pizza, Great Pizza Mod APK Happymod safe to use?   - A: Good Pizza, Great Pizza Mod APK Happymod is generally safe to use as long as you download it from a trusted source like Happymod's official website (). However, you should always scan the file for viruses before installing it on your device. - Q: Is Good Pizza, Great Pizza Mod APK Happymod compatible with my device?   - A: Good Pizza, Great Pizza Mod APK Happymod is compatible with most Android devices that run on Android 4.4 or higher. However - Q: How can I update Good Pizza, Great Pizza Mod APK Happymod?   - A: You can update Good Pizza, Great Pizza Mod APK Happymod by downloading and installing the latest version from Happymod's website. You can also enable the auto-update feature on Happymod's app, which will notify you when a new version is available and allow you to update it with one tap. - Q: How can I uninstall Good Pizza, Great Pizza Mod APK Happymod?   - A: You can uninstall Good Pizza, Great Pizza Mod APK Happymod by going to Settings > Apps > Good Pizza, Great Pizza and tapping on Uninstall. You can also delete the file from your device's storage. You should also clear the cache and data of the original game if you want to reinstall it without the modded features. - Q: What are some other games like Good Pizza, Great Pizza that I can play?   - A: If you enjoy Good Pizza, Great Pizza, you might also like some other games that are similar in genre or theme. Some of them are:     - Cooking Fever: A time-management cooking game where you have to prepare delicious meals and desserts for customers in various restaurants around the world.     - Papa's Pizzeria: A pizza making game where you have to take orders, top and bake pizzas, and serve them to customers in Papa Louie's pizzeria.     - My Cafe: A restaurant simulation game where you have to run your own cafe and serve coffee, tea, cakes, and other treats to customers.     - Pizza Maker: A creative game where you can design your own pizzas with various ingredients and toppings, and share them with your friends.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Kiss918 2 APK Download The Latest Version of the Best Casino App in Asia.md b/spaces/congsaPfin/Manga-OCR/logs/Kiss918 2 APK Download The Latest Version of the Best Casino App in Asia.md
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diff --git a/spaces/contluForse/HuggingGPT/Rpc Plugin For 3ds Max 2015 Crack.md b/spaces/contluForse/HuggingGPT/Rpc Plugin For 3ds Max 2015 Crack.md
deleted file mode 100644
index cd1b345ae91d39e3c669bd21959c31f23f5c29a9..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/Rpc Plugin For 3ds Max 2015 Crack.md	
+++ /dev/null
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deleted file mode 100644
index dcdf5aaf20ad848d2c2ce5d394398b52bb3a025d..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Air America full movie in hindi free download Experience the adventure and humor of the movie that made history.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/contluForse/HuggingGPT/assets/F 22 Raptor Game Free !LINK! Download Full Version For Pc.md b/spaces/contluForse/HuggingGPT/assets/F 22 Raptor Game Free !LINK! Download Full Version For Pc.md
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--- a/spaces/contluForse/HuggingGPT/assets/F 22 Raptor Game Free !LINK! Download Full Version For Pc.md	
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diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/optimizer.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/optimizer.py
deleted file mode 100644
index 580a183639a5d95c04ecae9c619afb795a169e9e..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/optimizer.py
+++ /dev/null
@@ -1,508 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-from collections import defaultdict
-from itertools import chain
-
-from torch.nn.utils import clip_grad
-
-from annotator.mmpkg.mmcv.utils import TORCH_VERSION, _BatchNorm, digit_version
-from ..dist_utils import allreduce_grads
-from ..fp16_utils import LossScaler, wrap_fp16_model
-from .hook import HOOKS, Hook
-
-try:
-    # If PyTorch version >= 1.6.0, torch.cuda.amp.GradScaler would be imported
-    # and used; otherwise, auto fp16 will adopt mmcv's implementation.
-    from torch.cuda.amp import GradScaler
-except ImportError:
-    pass
-
-
-@HOOKS.register_module()
-class OptimizerHook(Hook):
-
-    def __init__(self, grad_clip=None):
-        self.grad_clip = grad_clip
-
-    def clip_grads(self, params):
-        params = list(
-            filter(lambda p: p.requires_grad and p.grad is not None, params))
-        if len(params) > 0:
-            return clip_grad.clip_grad_norm_(params, **self.grad_clip)
-
-    def after_train_iter(self, runner):
-        runner.optimizer.zero_grad()
-        runner.outputs['loss'].backward()
-        if self.grad_clip is not None:
-            grad_norm = self.clip_grads(runner.model.parameters())
-            if grad_norm is not None:
-                # Add grad norm to the logger
-                runner.log_buffer.update({'grad_norm': float(grad_norm)},
-                                         runner.outputs['num_samples'])
-        runner.optimizer.step()
-
-
-@HOOKS.register_module()
-class GradientCumulativeOptimizerHook(OptimizerHook):
-    """Optimizer Hook implements multi-iters gradient cumulating.
-
-    Args:
-        cumulative_iters (int, optional): Num of gradient cumulative iters.
-            The optimizer will step every `cumulative_iters` iters.
-            Defaults to 1.
-
-    Examples:
-        >>> # Use cumulative_iters to simulate a large batch size
-        >>> # It is helpful when the hardware cannot handle a large batch size.
-        >>> loader = DataLoader(data, batch_size=64)
-        >>> optim_hook = GradientCumulativeOptimizerHook(cumulative_iters=4)
-        >>> # almost equals to
-        >>> loader = DataLoader(data, batch_size=256)
-        >>> optim_hook = OptimizerHook()
-    """
-
-    def __init__(self, cumulative_iters=1, **kwargs):
-        super(GradientCumulativeOptimizerHook, self).__init__(**kwargs)
-
-        assert isinstance(cumulative_iters, int) and cumulative_iters > 0, \
-            f'cumulative_iters only accepts positive int, but got ' \
-            f'{type(cumulative_iters)} instead.'
-
-        self.cumulative_iters = cumulative_iters
-        self.divisible_iters = 0
-        self.remainder_iters = 0
-        self.initialized = False
-
-    def has_batch_norm(self, module):
-        if isinstance(module, _BatchNorm):
-            return True
-        for m in module.children():
-            if self.has_batch_norm(m):
-                return True
-        return False
-
-    def _init(self, runner):
-        if runner.iter % self.cumulative_iters != 0:
-            runner.logger.warning(
-                'Resume iter number is not divisible by cumulative_iters in '
-                'GradientCumulativeOptimizerHook, which means the gradient of '
-                'some iters is lost and the result may be influenced slightly.'
-            )
-
-        if self.has_batch_norm(runner.model) and self.cumulative_iters > 1:
-            runner.logger.warning(
-                'GradientCumulativeOptimizerHook may slightly decrease '
-                'performance if the model has BatchNorm layers.')
-
-        residual_iters = runner.max_iters - runner.iter
-
-        self.divisible_iters = (
-            residual_iters // self.cumulative_iters * self.cumulative_iters)
-        self.remainder_iters = residual_iters - self.divisible_iters
-
-        self.initialized = True
-
-    def after_train_iter(self, runner):
-        if not self.initialized:
-            self._init(runner)
-
-        if runner.iter < self.divisible_iters:
-            loss_factor = self.cumulative_iters
-        else:
-            loss_factor = self.remainder_iters
-        loss = runner.outputs['loss']
-        loss = loss / loss_factor
-        loss.backward()
-
-        if (self.every_n_iters(runner, self.cumulative_iters)
-                or self.is_last_iter(runner)):
-
-            if self.grad_clip is not None:
-                grad_norm = self.clip_grads(runner.model.parameters())
-                if grad_norm is not None:
-                    # Add grad norm to the logger
-                    runner.log_buffer.update({'grad_norm': float(grad_norm)},
-                                             runner.outputs['num_samples'])
-            runner.optimizer.step()
-            runner.optimizer.zero_grad()
-
-
-if (TORCH_VERSION != 'parrots'
-        and digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
-
-    @HOOKS.register_module()
-    class Fp16OptimizerHook(OptimizerHook):
-        """FP16 optimizer hook (using PyTorch's implementation).
-
-        If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend,
-        to take care of the optimization procedure.
-
-        Args:
-            loss_scale (float | str | dict): Scale factor configuration.
-                If loss_scale is a float, static loss scaling will be used with
-                the specified scale. If loss_scale is a string, it must be
-                'dynamic', then dynamic loss scaling will be used.
-                It can also be a dict containing arguments of GradScalar.
-                Defaults to 512. For Pytorch >= 1.6, mmcv uses official
-                implementation of GradScaler. If you use a dict version of
-                loss_scale to create GradScaler, please refer to:
-                https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler
-                for the parameters.
-
-        Examples:
-            >>> loss_scale = dict(
-            ...     init_scale=65536.0,
-            ...     growth_factor=2.0,
-            ...     backoff_factor=0.5,
-            ...     growth_interval=2000
-            ... )
-            >>> optimizer_hook = Fp16OptimizerHook(loss_scale=loss_scale)
-        """
-
-        def __init__(self,
-                     grad_clip=None,
-                     coalesce=True,
-                     bucket_size_mb=-1,
-                     loss_scale=512.,
-                     distributed=True):
-            self.grad_clip = grad_clip
-            self.coalesce = coalesce
-            self.bucket_size_mb = bucket_size_mb
-            self.distributed = distributed
-            self._scale_update_param = None
-            if loss_scale == 'dynamic':
-                self.loss_scaler = GradScaler()
-            elif isinstance(loss_scale, float):
-                self._scale_update_param = loss_scale
-                self.loss_scaler = GradScaler(init_scale=loss_scale)
-            elif isinstance(loss_scale, dict):
-                self.loss_scaler = GradScaler(**loss_scale)
-            else:
-                raise ValueError('loss_scale must be of type float, dict, or '
-                                 f'"dynamic", got {loss_scale}')
-
-        def before_run(self, runner):
-            """Preparing steps before Mixed Precision Training."""
-            # wrap model mode to fp16
-            wrap_fp16_model(runner.model)
-            # resume from state dict
-            if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']:
-                scaler_state_dict = runner.meta['fp16']['loss_scaler']
-                self.loss_scaler.load_state_dict(scaler_state_dict)
-
-        def copy_grads_to_fp32(self, fp16_net, fp32_weights):
-            """Copy gradients from fp16 model to fp32 weight copy."""
-            for fp32_param, fp16_param in zip(fp32_weights,
-                                              fp16_net.parameters()):
-                if fp16_param.grad is not None:
-                    if fp32_param.grad is None:
-                        fp32_param.grad = fp32_param.data.new(
-                            fp32_param.size())
-                    fp32_param.grad.copy_(fp16_param.grad)
-
-        def copy_params_to_fp16(self, fp16_net, fp32_weights):
-            """Copy updated params from fp32 weight copy to fp16 model."""
-            for fp16_param, fp32_param in zip(fp16_net.parameters(),
-                                              fp32_weights):
-                fp16_param.data.copy_(fp32_param.data)
-
-        def after_train_iter(self, runner):
-            """Backward optimization steps for Mixed Precision Training. For
-            dynamic loss scaling, please refer to
-            https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler.
-
-            1. Scale the loss by a scale factor.
-            2. Backward the loss to obtain the gradients.
-            3. Unscale the optimizer’s gradient tensors.
-            4. Call optimizer.step() and update scale factor.
-            5. Save loss_scaler state_dict for resume purpose.
-            """
-            # clear grads of last iteration
-            runner.model.zero_grad()
-            runner.optimizer.zero_grad()
-
-            self.loss_scaler.scale(runner.outputs['loss']).backward()
-            self.loss_scaler.unscale_(runner.optimizer)
-            # grad clip
-            if self.grad_clip is not None:
-                grad_norm = self.clip_grads(runner.model.parameters())
-                if grad_norm is not None:
-                    # Add grad norm to the logger
-                    runner.log_buffer.update({'grad_norm': float(grad_norm)},
-                                             runner.outputs['num_samples'])
-            # backward and update scaler
-            self.loss_scaler.step(runner.optimizer)
-            self.loss_scaler.update(self._scale_update_param)
-
-            # save state_dict of loss_scaler
-            runner.meta.setdefault(
-                'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-    @HOOKS.register_module()
-    class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook,
-                                              Fp16OptimizerHook):
-        """Fp16 optimizer Hook (using PyTorch's implementation) implements
-        multi-iters gradient cumulating.
-
-        If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend,
-        to take care of the optimization procedure.
-        """
-
-        def __init__(self, *args, **kwargs):
-            super(GradientCumulativeFp16OptimizerHook,
-                  self).__init__(*args, **kwargs)
-
-        def after_train_iter(self, runner):
-            if not self.initialized:
-                self._init(runner)
-
-            if runner.iter < self.divisible_iters:
-                loss_factor = self.cumulative_iters
-            else:
-                loss_factor = self.remainder_iters
-            loss = runner.outputs['loss']
-            loss = loss / loss_factor
-
-            self.loss_scaler.scale(loss).backward()
-
-            if (self.every_n_iters(runner, self.cumulative_iters)
-                    or self.is_last_iter(runner)):
-
-                # copy fp16 grads in the model to fp32 params in the optimizer
-                self.loss_scaler.unscale_(runner.optimizer)
-
-                if self.grad_clip is not None:
-                    grad_norm = self.clip_grads(runner.model.parameters())
-                    if grad_norm is not None:
-                        # Add grad norm to the logger
-                        runner.log_buffer.update(
-                            {'grad_norm': float(grad_norm)},
-                            runner.outputs['num_samples'])
-
-                # backward and update scaler
-                self.loss_scaler.step(runner.optimizer)
-                self.loss_scaler.update(self._scale_update_param)
-
-                # save state_dict of loss_scaler
-                runner.meta.setdefault(
-                    'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-                # clear grads
-                runner.model.zero_grad()
-                runner.optimizer.zero_grad()
-
-else:
-
-    @HOOKS.register_module()
-    class Fp16OptimizerHook(OptimizerHook):
-        """FP16 optimizer hook (mmcv's implementation).
-
-        The steps of fp16 optimizer is as follows.
-        1. Scale the loss value.
-        2. BP in the fp16 model.
-        2. Copy gradients from fp16 model to fp32 weights.
-        3. Update fp32 weights.
-        4. Copy updated parameters from fp32 weights to fp16 model.
-
-        Refer to https://arxiv.org/abs/1710.03740 for more details.
-
-        Args:
-            loss_scale (float | str | dict): Scale factor configuration.
-                If loss_scale is a float, static loss scaling will be used with
-                the specified scale. If loss_scale is a string, it must be
-                'dynamic', then dynamic loss scaling will be used.
-                It can also be a dict containing arguments of LossScaler.
-                Defaults to 512.
-        """
-
-        def __init__(self,
-                     grad_clip=None,
-                     coalesce=True,
-                     bucket_size_mb=-1,
-                     loss_scale=512.,
-                     distributed=True):
-            self.grad_clip = grad_clip
-            self.coalesce = coalesce
-            self.bucket_size_mb = bucket_size_mb
-            self.distributed = distributed
-            if loss_scale == 'dynamic':
-                self.loss_scaler = LossScaler(mode='dynamic')
-            elif isinstance(loss_scale, float):
-                self.loss_scaler = LossScaler(
-                    init_scale=loss_scale, mode='static')
-            elif isinstance(loss_scale, dict):
-                self.loss_scaler = LossScaler(**loss_scale)
-            else:
-                raise ValueError('loss_scale must be of type float, dict, or '
-                                 f'"dynamic", got {loss_scale}')
-
-        def before_run(self, runner):
-            """Preparing steps before Mixed Precision Training.
-
-            1. Make a master copy of fp32 weights for optimization.
-            2. Convert the main model from fp32 to fp16.
-            """
-            # keep a copy of fp32 weights
-            old_groups = runner.optimizer.param_groups
-            runner.optimizer.param_groups = copy.deepcopy(
-                runner.optimizer.param_groups)
-            state = defaultdict(dict)
-            p_map = {
-                old_p: p
-                for old_p, p in zip(
-                    chain(*(g['params'] for g in old_groups)),
-                    chain(*(g['params']
-                            for g in runner.optimizer.param_groups)))
-            }
-            for k, v in runner.optimizer.state.items():
-                state[p_map[k]] = v
-            runner.optimizer.state = state
-            # convert model to fp16
-            wrap_fp16_model(runner.model)
-            # resume from state dict
-            if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']:
-                scaler_state_dict = runner.meta['fp16']['loss_scaler']
-                self.loss_scaler.load_state_dict(scaler_state_dict)
-
-        def copy_grads_to_fp32(self, fp16_net, fp32_weights):
-            """Copy gradients from fp16 model to fp32 weight copy."""
-            for fp32_param, fp16_param in zip(fp32_weights,
-                                              fp16_net.parameters()):
-                if fp16_param.grad is not None:
-                    if fp32_param.grad is None:
-                        fp32_param.grad = fp32_param.data.new(
-                            fp32_param.size())
-                    fp32_param.grad.copy_(fp16_param.grad)
-
-        def copy_params_to_fp16(self, fp16_net, fp32_weights):
-            """Copy updated params from fp32 weight copy to fp16 model."""
-            for fp16_param, fp32_param in zip(fp16_net.parameters(),
-                                              fp32_weights):
-                fp16_param.data.copy_(fp32_param.data)
-
-        def after_train_iter(self, runner):
-            """Backward optimization steps for Mixed Precision Training. For
-            dynamic loss scaling, please refer `loss_scalar.py`
-
-            1. Scale the loss by a scale factor.
-            2. Backward the loss to obtain the gradients (fp16).
-            3. Copy gradients from the model to the fp32 weight copy.
-            4. Scale the gradients back and update the fp32 weight copy.
-            5. Copy back the params from fp32 weight copy to the fp16 model.
-            6. Save loss_scaler state_dict for resume purpose.
-            """
-            # clear grads of last iteration
-            runner.model.zero_grad()
-            runner.optimizer.zero_grad()
-            # scale the loss value
-            scaled_loss = runner.outputs['loss'] * self.loss_scaler.loss_scale
-            scaled_loss.backward()
-            # copy fp16 grads in the model to fp32 params in the optimizer
-
-            fp32_weights = []
-            for param_group in runner.optimizer.param_groups:
-                fp32_weights += param_group['params']
-            self.copy_grads_to_fp32(runner.model, fp32_weights)
-            # allreduce grads
-            if self.distributed:
-                allreduce_grads(fp32_weights, self.coalesce,
-                                self.bucket_size_mb)
-
-            has_overflow = self.loss_scaler.has_overflow(fp32_weights)
-            # if has overflow, skip this iteration
-            if not has_overflow:
-                # scale the gradients back
-                for param in fp32_weights:
-                    if param.grad is not None:
-                        param.grad.div_(self.loss_scaler.loss_scale)
-                if self.grad_clip is not None:
-                    grad_norm = self.clip_grads(fp32_weights)
-                    if grad_norm is not None:
-                        # Add grad norm to the logger
-                        runner.log_buffer.update(
-                            {'grad_norm': float(grad_norm)},
-                            runner.outputs['num_samples'])
-                # update fp32 params
-                runner.optimizer.step()
-                # copy fp32 params to the fp16 model
-                self.copy_params_to_fp16(runner.model, fp32_weights)
-            self.loss_scaler.update_scale(has_overflow)
-            if has_overflow:
-                runner.logger.warning('Check overflow, downscale loss scale '
-                                      f'to {self.loss_scaler.cur_scale}')
-
-            # save state_dict of loss_scaler
-            runner.meta.setdefault(
-                'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-    @HOOKS.register_module()
-    class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook,
-                                              Fp16OptimizerHook):
-        """Fp16 optimizer Hook (using mmcv implementation) implements multi-
-        iters gradient cumulating."""
-
-        def __init__(self, *args, **kwargs):
-            super(GradientCumulativeFp16OptimizerHook,
-                  self).__init__(*args, **kwargs)
-
-        def after_train_iter(self, runner):
-            if not self.initialized:
-                self._init(runner)
-
-            if runner.iter < self.divisible_iters:
-                loss_factor = self.cumulative_iters
-            else:
-                loss_factor = self.remainder_iters
-
-            loss = runner.outputs['loss']
-            loss = loss / loss_factor
-
-            # scale the loss value
-            scaled_loss = loss * self.loss_scaler.loss_scale
-            scaled_loss.backward()
-
-            if (self.every_n_iters(runner, self.cumulative_iters)
-                    or self.is_last_iter(runner)):
-
-                # copy fp16 grads in the model to fp32 params in the optimizer
-                fp32_weights = []
-                for param_group in runner.optimizer.param_groups:
-                    fp32_weights += param_group['params']
-                self.copy_grads_to_fp32(runner.model, fp32_weights)
-                # allreduce grads
-                if self.distributed:
-                    allreduce_grads(fp32_weights, self.coalesce,
-                                    self.bucket_size_mb)
-
-                has_overflow = self.loss_scaler.has_overflow(fp32_weights)
-                # if has overflow, skip this iteration
-                if not has_overflow:
-                    # scale the gradients back
-                    for param in fp32_weights:
-                        if param.grad is not None:
-                            param.grad.div_(self.loss_scaler.loss_scale)
-                    if self.grad_clip is not None:
-                        grad_norm = self.clip_grads(fp32_weights)
-                        if grad_norm is not None:
-                            # Add grad norm to the logger
-                            runner.log_buffer.update(
-                                {'grad_norm': float(grad_norm)},
-                                runner.outputs['num_samples'])
-                    # update fp32 params
-                    runner.optimizer.step()
-                    # copy fp32 params to the fp16 model
-                    self.copy_params_to_fp16(runner.model, fp32_weights)
-                else:
-                    runner.logger.warning(
-                        'Check overflow, downscale loss scale '
-                        f'to {self.loss_scaler.cur_scale}')
-
-                self.loss_scaler.update_scale(has_overflow)
-
-                # save state_dict of loss_scaler
-                runner.meta.setdefault(
-                    'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict()
-
-                # clear grads
-                runner.model.zero_grad()
-                runner.optimizer.zero_grad()
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/utils/ext_loader.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/utils/ext_loader.py
deleted file mode 100644
index 08132d2c1b9a1c28880e4bab4d4fa1ba39d9d083..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/utils/ext_loader.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import importlib
-import os
-import pkgutil
-import warnings
-from collections import namedtuple
-
-import torch
-
-if torch.__version__ != 'parrots':
-
-    def load_ext(name, funcs):
-        ext = importlib.import_module('mmcv.' + name)
-        for fun in funcs:
-            assert hasattr(ext, fun), f'{fun} miss in module {name}'
-        return ext
-else:
-    from parrots import extension
-    from parrots.base import ParrotsException
-
-    has_return_value_ops = [
-        'nms',
-        'softnms',
-        'nms_match',
-        'nms_rotated',
-        'top_pool_forward',
-        'top_pool_backward',
-        'bottom_pool_forward',
-        'bottom_pool_backward',
-        'left_pool_forward',
-        'left_pool_backward',
-        'right_pool_forward',
-        'right_pool_backward',
-        'fused_bias_leakyrelu',
-        'upfirdn2d',
-        'ms_deform_attn_forward',
-        'pixel_group',
-        'contour_expand',
-    ]
-
-    def get_fake_func(name, e):
-
-        def fake_func(*args, **kwargs):
-            warnings.warn(f'{name} is not supported in parrots now')
-            raise e
-
-        return fake_func
-
-    def load_ext(name, funcs):
-        ExtModule = namedtuple('ExtModule', funcs)
-        ext_list = []
-        lib_root = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
-        for fun in funcs:
-            try:
-                ext_fun = extension.load(fun, name, lib_dir=lib_root)
-            except ParrotsException as e:
-                if 'No element registered' not in e.message:
-                    warnings.warn(e.message)
-                ext_fun = get_fake_func(fun, e)
-                ext_list.append(ext_fun)
-            else:
-                if fun in has_return_value_ops:
-                    ext_list.append(ext_fun.op)
-                else:
-                    ext_list.append(ext_fun.op_)
-        return ExtModule(*ext_list)
-
-
-def check_ops_exist():
-    ext_loader = pkgutil.find_loader('mmcv._ext')
-    return ext_loader is not None
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/video/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/video/__init__.py
deleted file mode 100644
index 73199b01dec52820dc6ca0139903536344d5a1eb..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/video/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .io import Cache, VideoReader, frames2video
-from .optflow import (dequantize_flow, flow_from_bytes, flow_warp, flowread,
-                      flowwrite, quantize_flow, sparse_flow_from_bytes)
-from .processing import concat_video, convert_video, cut_video, resize_video
-
-__all__ = [
-    'Cache', 'VideoReader', 'frames2video', 'convert_video', 'resize_video',
-    'cut_video', 'concat_video', 'flowread', 'flowwrite', 'quantize_flow',
-    'dequantize_flow', 'flow_warp', 'flow_from_bytes', 'sparse_flow_from_bytes'
-]
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/checkpoint/c2_model_loading.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/checkpoint/c2_model_loading.py
deleted file mode 100644
index c6de2a3c830089aa7a0d27df96bb4a45fc5a7b0d..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/checkpoint/c2_model_loading.py
+++ /dev/null
@@ -1,412 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import logging
-import re
-from typing import Dict, List
-import torch
-from tabulate import tabulate
-
-
-def convert_basic_c2_names(original_keys):
-    """
-    Apply some basic name conversion to names in C2 weights.
-    It only deals with typical backbone models.
-
-    Args:
-        original_keys (list[str]):
-    Returns:
-        list[str]: The same number of strings matching those in original_keys.
-    """
-    layer_keys = copy.deepcopy(original_keys)
-    layer_keys = [
-        {"pred_b": "linear_b", "pred_w": "linear_w"}.get(k, k) for k in layer_keys
-    ]  # some hard-coded mappings
-
-    layer_keys = [k.replace("_", ".") for k in layer_keys]
-    layer_keys = [re.sub("\\.b$", ".bias", k) for k in layer_keys]
-    layer_keys = [re.sub("\\.w$", ".weight", k) for k in layer_keys]
-    # Uniform both bn and gn names to "norm"
-    layer_keys = [re.sub("bn\\.s$", "norm.weight", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.bias$", "norm.bias", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.rm", "norm.running_mean", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.running.mean$", "norm.running_mean", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.riv$", "norm.running_var", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.running.var$", "norm.running_var", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.gamma$", "norm.weight", k) for k in layer_keys]
-    layer_keys = [re.sub("bn\\.beta$", "norm.bias", k) for k in layer_keys]
-    layer_keys = [re.sub("gn\\.s$", "norm.weight", k) for k in layer_keys]
-    layer_keys = [re.sub("gn\\.bias$", "norm.bias", k) for k in layer_keys]
-
-    # stem
-    layer_keys = [re.sub("^res\\.conv1\\.norm\\.", "conv1.norm.", k) for k in layer_keys]
-    # to avoid mis-matching with "conv1" in other components (e.g. detection head)
-    layer_keys = [re.sub("^conv1\\.", "stem.conv1.", k) for k in layer_keys]
-
-    # layer1-4 is used by torchvision, however we follow the C2 naming strategy (res2-5)
-    # layer_keys = [re.sub("^res2.", "layer1.", k) for k in layer_keys]
-    # layer_keys = [re.sub("^res3.", "layer2.", k) for k in layer_keys]
-    # layer_keys = [re.sub("^res4.", "layer3.", k) for k in layer_keys]
-    # layer_keys = [re.sub("^res5.", "layer4.", k) for k in layer_keys]
-
-    # blocks
-    layer_keys = [k.replace(".branch1.", ".shortcut.") for k in layer_keys]
-    layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys]
-    layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys]
-    layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys]
-
-    # DensePose substitutions
-    layer_keys = [re.sub("^body.conv.fcn", "body_conv_fcn", k) for k in layer_keys]
-    layer_keys = [k.replace("AnnIndex.lowres", "ann_index_lowres") for k in layer_keys]
-    layer_keys = [k.replace("Index.UV.lowres", "index_uv_lowres") for k in layer_keys]
-    layer_keys = [k.replace("U.lowres", "u_lowres") for k in layer_keys]
-    layer_keys = [k.replace("V.lowres", "v_lowres") for k in layer_keys]
-    return layer_keys
-
-
-def convert_c2_detectron_names(weights):
-    """
-    Map Caffe2 Detectron weight names to Detectron2 names.
-
-    Args:
-        weights (dict): name -> tensor
-
-    Returns:
-        dict: detectron2 names -> tensor
-        dict: detectron2 names -> C2 names
-    """
-    logger = logging.getLogger(__name__)
-    logger.info("Renaming Caffe2 weights ......")
-    original_keys = sorted(weights.keys())
-    layer_keys = copy.deepcopy(original_keys)
-
-    layer_keys = convert_basic_c2_names(layer_keys)
-
-    # --------------------------------------------------------------------------
-    # RPN hidden representation conv
-    # --------------------------------------------------------------------------
-    # FPN case
-    # In the C2 model, the RPN hidden layer conv is defined for FPN level 2 and then
-    # shared for all other levels, hence the appearance of "fpn2"
-    layer_keys = [
-        k.replace("conv.rpn.fpn2", "proposal_generator.rpn_head.conv") for k in layer_keys
-    ]
-    # Non-FPN case
-    layer_keys = [k.replace("conv.rpn", "proposal_generator.rpn_head.conv") for k in layer_keys]
-
-    # --------------------------------------------------------------------------
-    # RPN box transformation conv
-    # --------------------------------------------------------------------------
-    # FPN case (see note above about "fpn2")
-    layer_keys = [
-        k.replace("rpn.bbox.pred.fpn2", "proposal_generator.rpn_head.anchor_deltas")
-        for k in layer_keys
-    ]
-    layer_keys = [
-        k.replace("rpn.cls.logits.fpn2", "proposal_generator.rpn_head.objectness_logits")
-        for k in layer_keys
-    ]
-    # Non-FPN case
-    layer_keys = [
-        k.replace("rpn.bbox.pred", "proposal_generator.rpn_head.anchor_deltas") for k in layer_keys
-    ]
-    layer_keys = [
-        k.replace("rpn.cls.logits", "proposal_generator.rpn_head.objectness_logits")
-        for k in layer_keys
-    ]
-
-    # --------------------------------------------------------------------------
-    # Fast R-CNN box head
-    # --------------------------------------------------------------------------
-    layer_keys = [re.sub("^bbox\\.pred", "bbox_pred", k) for k in layer_keys]
-    layer_keys = [re.sub("^cls\\.score", "cls_score", k) for k in layer_keys]
-    layer_keys = [re.sub("^fc6\\.", "box_head.fc1.", k) for k in layer_keys]
-    layer_keys = [re.sub("^fc7\\.", "box_head.fc2.", k) for k in layer_keys]
-    # 4conv1fc head tensor names: head_conv1_w, head_conv1_gn_s
-    layer_keys = [re.sub("^head\\.conv", "box_head.conv", k) for k in layer_keys]
-
-    # --------------------------------------------------------------------------
-    # FPN lateral and output convolutions
-    # --------------------------------------------------------------------------
-    def fpn_map(name):
-        """
-        Look for keys with the following patterns:
-        1) Starts with "fpn.inner."
-           Example: "fpn.inner.res2.2.sum.lateral.weight"
-           Meaning: These are lateral pathway convolutions
-        2) Starts with "fpn.res"
-           Example: "fpn.res2.2.sum.weight"
-           Meaning: These are FPN output convolutions
-        """
-        splits = name.split(".")
-        norm = ".norm" if "norm" in splits else ""
-        if name.startswith("fpn.inner."):
-            # splits example: ['fpn', 'inner', 'res2', '2', 'sum', 'lateral', 'weight']
-            stage = int(splits[2][len("res") :])
-            return "fpn_lateral{}{}.{}".format(stage, norm, splits[-1])
-        elif name.startswith("fpn.res"):
-            # splits example: ['fpn', 'res2', '2', 'sum', 'weight']
-            stage = int(splits[1][len("res") :])
-            return "fpn_output{}{}.{}".format(stage, norm, splits[-1])
-        return name
-
-    layer_keys = [fpn_map(k) for k in layer_keys]
-
-    # --------------------------------------------------------------------------
-    # Mask R-CNN mask head
-    # --------------------------------------------------------------------------
-    # roi_heads.StandardROIHeads case
-    layer_keys = [k.replace(".[mask].fcn", "mask_head.mask_fcn") for k in layer_keys]
-    layer_keys = [re.sub("^\\.mask\\.fcn", "mask_head.mask_fcn", k) for k in layer_keys]
-    layer_keys = [k.replace("mask.fcn.logits", "mask_head.predictor") for k in layer_keys]
-    # roi_heads.Res5ROIHeads case
-    layer_keys = [k.replace("conv5.mask", "mask_head.deconv") for k in layer_keys]
-
-    # --------------------------------------------------------------------------
-    # Keypoint R-CNN head
-    # --------------------------------------------------------------------------
-    # interestingly, the keypoint head convs have blob names that are simply "conv_fcnX"
-    layer_keys = [k.replace("conv.fcn", "roi_heads.keypoint_head.conv_fcn") for k in layer_keys]
-    layer_keys = [
-        k.replace("kps.score.lowres", "roi_heads.keypoint_head.score_lowres") for k in layer_keys
-    ]
-    layer_keys = [k.replace("kps.score.", "roi_heads.keypoint_head.score.") for k in layer_keys]
-
-    # --------------------------------------------------------------------------
-    # Done with replacements
-    # --------------------------------------------------------------------------
-    assert len(set(layer_keys)) == len(layer_keys)
-    assert len(original_keys) == len(layer_keys)
-
-    new_weights = {}
-    new_keys_to_original_keys = {}
-    for orig, renamed in zip(original_keys, layer_keys):
-        new_keys_to_original_keys[renamed] = orig
-        if renamed.startswith("bbox_pred.") or renamed.startswith("mask_head.predictor."):
-            # remove the meaningless prediction weight for background class
-            new_start_idx = 4 if renamed.startswith("bbox_pred.") else 1
-            new_weights[renamed] = weights[orig][new_start_idx:]
-            logger.info(
-                "Remove prediction weight for background class in {}. The shape changes from "
-                "{} to {}.".format(
-                    renamed, tuple(weights[orig].shape), tuple(new_weights[renamed].shape)
-                )
-            )
-        elif renamed.startswith("cls_score."):
-            # move weights of bg class from original index 0 to last index
-            logger.info(
-                "Move classification weights for background class in {} from index 0 to "
-                "index {}.".format(renamed, weights[orig].shape[0] - 1)
-            )
-            new_weights[renamed] = torch.cat([weights[orig][1:], weights[orig][:1]])
-        else:
-            new_weights[renamed] = weights[orig]
-
-    return new_weights, new_keys_to_original_keys
-
-
-# Note the current matching is not symmetric.
-# it assumes model_state_dict will have longer names.
-def align_and_update_state_dicts(model_state_dict, ckpt_state_dict, c2_conversion=True):
-    """
-    Match names between the two state-dict, and returns a new chkpt_state_dict with names
-    converted to match model_state_dict with heuristics. The returned dict can be later
-    loaded with fvcore checkpointer.
-    If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2
-    model and will be renamed at first.
-
-    Strategy: suppose that the models that we will create will have prefixes appended
-    to each of its keys, for example due to an extra level of nesting that the original
-    pre-trained weights from ImageNet won't contain. For example, model.state_dict()
-    might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
-    res2.conv1.weight. We thus want to match both parameters together.
-    For that, we look for each model weight, look among all loaded keys if there is one
-    that is a suffix of the current weight name, and use it if that's the case.
-    If multiple matches exist, take the one with longest size
-    of the corresponding name. For example, for the same model as before, the pretrained
-    weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
-    we want to match backbone[0].body.conv1.weight to conv1.weight, and
-    backbone[0].body.res2.conv1.weight to res2.conv1.weight.
-    """
-    model_keys = sorted(model_state_dict.keys())
-    if c2_conversion:
-        ckpt_state_dict, original_keys = convert_c2_detectron_names(ckpt_state_dict)
-        # original_keys: the name in the original dict (before renaming)
-    else:
-        original_keys = {x: x for x in ckpt_state_dict.keys()}
-    ckpt_keys = sorted(ckpt_state_dict.keys())
-
-    def match(a, b):
-        # Matched ckpt_key should be a complete (starts with '.') suffix.
-        # For example, roi_heads.mesh_head.whatever_conv1 does not match conv1,
-        # but matches whatever_conv1 or mesh_head.whatever_conv1.
-        return a == b or a.endswith("." + b)
-
-    # get a matrix of string matches, where each (i, j) entry correspond to the size of the
-    # ckpt_key string, if it matches
-    match_matrix = [len(j) if match(i, j) else 0 for i in model_keys for j in ckpt_keys]
-    match_matrix = torch.as_tensor(match_matrix).view(len(model_keys), len(ckpt_keys))
-    # use the matched one with longest size in case of multiple matches
-    max_match_size, idxs = match_matrix.max(1)
-    # remove indices that correspond to no-match
-    idxs[max_match_size == 0] = -1
-
-    logger = logging.getLogger(__name__)
-    # matched_pairs (matched checkpoint key --> matched model key)
-    matched_keys = {}
-    result_state_dict = {}
-    for idx_model, idx_ckpt in enumerate(idxs.tolist()):
-        if idx_ckpt == -1:
-            continue
-        key_model = model_keys[idx_model]
-        key_ckpt = ckpt_keys[idx_ckpt]
-        value_ckpt = ckpt_state_dict[key_ckpt]
-        shape_in_model = model_state_dict[key_model].shape
-
-        if shape_in_model != value_ckpt.shape:
-            logger.warning(
-                "Shape of {} in checkpoint is {}, while shape of {} in model is {}.".format(
-                    key_ckpt, value_ckpt.shape, key_model, shape_in_model
-                )
-            )
-            logger.warning(
-                "{} will not be loaded. Please double check and see if this is desired.".format(
-                    key_ckpt
-                )
-            )
-            continue
-
-        assert key_model not in result_state_dict
-        result_state_dict[key_model] = value_ckpt
-        if key_ckpt in matched_keys:  # already added to matched_keys
-            logger.error(
-                "Ambiguity found for {} in checkpoint!"
-                "It matches at least two keys in the model ({} and {}).".format(
-                    key_ckpt, key_model, matched_keys[key_ckpt]
-                )
-            )
-            raise ValueError("Cannot match one checkpoint key to multiple keys in the model.")
-
-        matched_keys[key_ckpt] = key_model
-
-    # logging:
-    matched_model_keys = sorted(matched_keys.values())
-    if len(matched_model_keys) == 0:
-        logger.warning("No weights in checkpoint matched with model.")
-        return ckpt_state_dict
-    common_prefix = _longest_common_prefix(matched_model_keys)
-    rev_matched_keys = {v: k for k, v in matched_keys.items()}
-    original_keys = {k: original_keys[rev_matched_keys[k]] for k in matched_model_keys}
-
-    model_key_groups = _group_keys_by_module(matched_model_keys, original_keys)
-    table = []
-    memo = set()
-    for key_model in matched_model_keys:
-        if key_model in memo:
-            continue
-        if key_model in model_key_groups:
-            group = model_key_groups[key_model]
-            memo |= set(group)
-            shapes = [tuple(model_state_dict[k].shape) for k in group]
-            table.append(
-                (
-                    _longest_common_prefix([k[len(common_prefix) :] for k in group]) + "*",
-                    _group_str([original_keys[k] for k in group]),
-                    " ".join([str(x).replace(" ", "") for x in shapes]),
-                )
-            )
-        else:
-            key_checkpoint = original_keys[key_model]
-            shape = str(tuple(model_state_dict[key_model].shape))
-            table.append((key_model[len(common_prefix) :], key_checkpoint, shape))
-    table_str = tabulate(
-        table, tablefmt="pipe", headers=["Names in Model", "Names in Checkpoint", "Shapes"]
-    )
-    logger.info(
-        "Following weights matched with "
-        + (f"submodule {common_prefix[:-1]}" if common_prefix else "model")
-        + ":\n"
-        + table_str
-    )
-
-    unmatched_ckpt_keys = [k for k in ckpt_keys if k not in set(matched_keys.keys())]
-    for k in unmatched_ckpt_keys:
-        result_state_dict[k] = ckpt_state_dict[k]
-    return result_state_dict
-
-
-def _group_keys_by_module(keys: List[str], original_names: Dict[str, str]):
-    """
-    Params in the same submodule are grouped together.
-
-    Args:
-        keys: names of all parameters
-        original_names: mapping from parameter name to their name in the checkpoint
-
-    Returns:
-        dict[name -> all other names in the same group]
-    """
-
-    def _submodule_name(key):
-        pos = key.rfind(".")
-        if pos < 0:
-            return None
-        prefix = key[: pos + 1]
-        return prefix
-
-    all_submodules = [_submodule_name(k) for k in keys]
-    all_submodules = [x for x in all_submodules if x]
-    all_submodules = sorted(all_submodules, key=len)
-
-    ret = {}
-    for prefix in all_submodules:
-        group = [k for k in keys if k.startswith(prefix)]
-        if len(group) <= 1:
-            continue
-        original_name_lcp = _longest_common_prefix_str([original_names[k] for k in group])
-        if len(original_name_lcp) == 0:
-            # don't group weights if original names don't share prefix
-            continue
-
-        for k in group:
-            if k in ret:
-                continue
-            ret[k] = group
-    return ret
-
-
-def _longest_common_prefix(names: List[str]) -> str:
-    """
-    ["abc.zfg", "abc.zef"] -> "abc."
-    """
-    names = [n.split(".") for n in names]
-    m1, m2 = min(names), max(names)
-    ret = [a for a, b in zip(m1, m2) if a == b]
-    ret = ".".join(ret) + "." if len(ret) else ""
-    return ret
-
-
-def _longest_common_prefix_str(names: List[str]) -> str:
-    m1, m2 = min(names), max(names)
-    lcp = []
-    for a, b in zip(m1, m2):
-        if a == b:
-            lcp.append(a)
-        else:
-            break
-    lcp = "".join(lcp)
-    return lcp
-
-
-def _group_str(names: List[str]) -> str:
-    """
-    Turn "common1", "common2", "common3" into "common{1,2,3}"
-    """
-    lcp = _longest_common_prefix_str(names)
-    rest = [x[len(lcp) :] for x in names]
-    rest = "{" + ",".join(rest) + "}"
-    ret = lcp + rest
-
-    # add some simplification for BN specifically
-    ret = ret.replace("bn_{beta,running_mean,running_var,gamma}", "bn_*")
-    ret = ret.replace("bn_beta,bn_running_mean,bn_running_var,bn_gamma", "bn_*")
-    return ret
diff --git a/spaces/cozyanduofen/bingo/src/pages/api/kblob.ts b/spaces/cozyanduofen/bingo/src/pages/api/kblob.ts
deleted file mode 100644
index 0ce7e6063cdc06838e76f1cff1d5982d34ef52de..0000000000000000000000000000000000000000
--- a/spaces/cozyanduofen/bingo/src/pages/api/kblob.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import FormData from 'form-data'
-import { fetch } from '@/lib/isomorphic'
-import { KBlobRequest } from '@/lib/bots/bing/types'
-
-const API_DOMAIN = 'https://bing.vcanbb.top'
-
-export const config = {
-  api: {
-    bodyParser: {
-      sizeLimit: '10mb' // Set desired value here
-    }
-  }
-}
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { knowledgeRequest, imageBase64 } = req.body as KBlobRequest
-
-    const formData = new FormData()
-    formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
-    if (imageBase64) {
-      formData.append('imageBase64', imageBase64)
-    }
-
-    const response = await fetch(`${API_DOMAIN}/images/kblob`,
-      {
-        method: 'POST',
-        body: formData.getBuffer(),
-        headers: {
-          "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-          "sec-ch-ua-mobile": "?0",
-          "sec-ch-ua-platform": "\"Windows\"",
-          "Referer": `${API_DOMAIN}/web/index.html`,
-          "Referrer-Policy": "origin-when-cross-origin",
-          'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-          ...formData.getHeaders()
-        }
-      }
-    ).then(res => res.text())
-
-    res.writeHead(200, {
-      'Content-Type': 'application/json',
-    })
-    res.end(response || JSON.stringify({ result: { value: 'UploadFailed', message: '请更换 IP 或代理后重试' } }))
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'UploadFailed',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/crytion/DeepNude/opencv_transform/__init__.py b/spaces/crytion/DeepNude/opencv_transform/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/daniyal214/gradio-caption-generator-git-large/app.py b/spaces/daniyal214/gradio-caption-generator-git-large/app.py
deleted file mode 100644
index 5e5ccc4f5961fb5674d1b2a27384d26be3c8c5ea..0000000000000000000000000000000000000000
--- a/spaces/daniyal214/gradio-caption-generator-git-large/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-from PIL import Image
-import numpy as np
-
-pipe = pipeline("image-to-text", model="daniyal214/finetuned-git-large-chest-xrays")
-
-def get_captions(input_image):
-    # Convert the received image to a PIL Image
-    image = Image.fromarray((input_image * 255).astype(np.uint8))
-    
-    # Pass the PIL image to the pipeline
-    result = pipe(image)
-    result = result[0]['generated_text']
-    return result
-
-iface = gr.Interface(fn = get_captions, inputs = "image", outputs = ['text'], title="X-rays Image Caption Generator")
-iface.launch()
\ No newline at end of file
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/M_A_T_H_.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/M_A_T_H_.py
deleted file mode 100644
index 011426b52a195bb2596116cc7bce0ad6e671eb23..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/M_A_T_H_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_M_A_T_H_(BaseTTXConverter):
-    pass
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/S_I_N_G_.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/S_I_N_G_.py
deleted file mode 100644
index 7420da7e5dcec81b835ab0e8e2c775dbce860cbd..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/S_I_N_G_.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import bytechr, byteord, tobytes, tostr, safeEval
-from . import DefaultTable
-
-SINGFormat = """
-		>	# big endian
-		tableVersionMajor:	H
-		tableVersionMinor: 	H
-		glyphletVersion:	H
-		permissions:		h
-		mainGID:			H
-		unitsPerEm:			H
-		vertAdvance:		h
-		vertOrigin:			h
-		uniqueName:			28s
-		METAMD5:			16s
-		nameLength:			1s
-"""
-# baseGlyphName is a byte string which follows the record above.
-
-
-class table_S_I_N_G_(DefaultTable.DefaultTable):
-
-    dependencies = []
-
-    def decompile(self, data, ttFont):
-        dummy, rest = sstruct.unpack2(SINGFormat, data, self)
-        self.uniqueName = self.decompileUniqueName(self.uniqueName)
-        self.nameLength = byteord(self.nameLength)
-        assert len(rest) == self.nameLength
-        self.baseGlyphName = tostr(rest)
-
-        rawMETAMD5 = self.METAMD5
-        self.METAMD5 = "[" + hex(byteord(self.METAMD5[0]))
-        for char in rawMETAMD5[1:]:
-            self.METAMD5 = self.METAMD5 + ", " + hex(byteord(char))
-        self.METAMD5 = self.METAMD5 + "]"
-
-    def decompileUniqueName(self, data):
-        name = ""
-        for char in data:
-            val = byteord(char)
-            if val == 0:
-                break
-            if (val > 31) or (val < 128):
-                name += chr(val)
-            else:
-                octString = oct(val)
-                if len(octString) > 3:
-                    octString = octString[1:]  # chop off that leading zero.
-                elif len(octString) < 3:
-                    octString.zfill(3)
-                name += "\\" + octString
-        return name
-
-    def compile(self, ttFont):
-        d = self.__dict__.copy()
-        d["nameLength"] = bytechr(len(self.baseGlyphName))
-        d["uniqueName"] = self.compilecompileUniqueName(self.uniqueName, 28)
-        METAMD5List = eval(self.METAMD5)
-        d["METAMD5"] = b""
-        for val in METAMD5List:
-            d["METAMD5"] += bytechr(val)
-        assert len(d["METAMD5"]) == 16, "Failed to pack 16 byte MD5 hash in SING table"
-        data = sstruct.pack(SINGFormat, d)
-        data = data + tobytes(self.baseGlyphName)
-        return data
-
-    def compilecompileUniqueName(self, name, length):
-        nameLen = len(name)
-        if length <= nameLen:
-            name = name[: length - 1] + "\000"
-        else:
-            name += (nameLen - length) * "\000"
-        return name
-
-    def toXML(self, writer, ttFont):
-        writer.comment("Most of this table will be recalculated by the compiler")
-        writer.newline()
-        formatstring, names, fixes = sstruct.getformat(SINGFormat)
-        for name in names:
-            value = getattr(self, name)
-            writer.simpletag(name, value=value)
-            writer.newline()
-        writer.simpletag("baseGlyphName", value=self.baseGlyphName)
-        writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        value = attrs["value"]
-        if name in ["uniqueName", "METAMD5", "baseGlyphName"]:
-            setattr(self, name, value)
-        else:
-            setattr(self, name, safeEval(value))
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-6acaa952.css b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-6acaa952.css
deleted file mode 100644
index 14e404a17a006e0cc8dd1c7e51df22ea863e0a66..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-6acaa952.css
+++ /dev/null
@@ -1 +0,0 @@
-.input-number.svelte-x6nxfm{transition:.15s;box-shadow:var(--shadow-drop);background:var(--background-fill-secondary)}.input-number.svelte-x6nxfm:hover{box-shadow:var(--shadow-drop-lg)}.range.svelte-x6nxfm{display:flex}.item.svelte-x6nxfm{flex:1 1 0%}.dropdown-menu.svelte-1cqwepf{box-shadow:var(--shadow-drop)}.dropdown-item.svelte-1cqwepf{display:block;transition:.15s;cursor:pointer;background:var(--background-fill-primary);padding:var(--size-2) var(--size-3);white-space:nowrap}.dropdown-item.svelte-1cqwepf:first-child{border-top-right-radius:var(--radius-md);border-top-left-radius:var(--radius-md)}.dropdown-item.svelte-1cqwepf:last-child{border-bottom-right-radius:var(--radius-md);border-bottom-left-radius:var(--radius-md)}.dropdown-item.svelte-1cqwepf:hover{font-weight:var(--weight-semibold)}.input-checkbox.svelte-1nw19ca.svelte-1nw19ca{display:inline-block}svg.svelte-1nw19ca.svelte-1nw19ca{width:var(--size-4);height:var(--size-3)}.selected.svelte-1nw19ca 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diff --git a/spaces/deelerb/3dselfie/PIFu/apps/render_data.py b/spaces/deelerb/3dselfie/PIFu/apps/render_data.py
deleted file mode 100644
index 563c03fba6e304eced73ca283152a968a65c3b8e..0000000000000000000000000000000000000000
--- a/spaces/deelerb/3dselfie/PIFu/apps/render_data.py
+++ /dev/null
@@ -1,290 +0,0 @@
-#from data.config import raw_dataset, render_dataset, archive_dataset, model_list, zip_path
-
-from lib.renderer.camera import Camera
-import numpy as np
-from lib.renderer.mesh import load_obj_mesh, compute_tangent, compute_normal, load_obj_mesh_mtl
-from lib.renderer.camera import Camera
-import os
-import cv2
-import time
-import math
-import random
-import pyexr
-import argparse
-from tqdm import tqdm
-
-
-def make_rotate(rx, ry, rz):
-    sinX = np.sin(rx)
-    sinY = np.sin(ry)
-    sinZ = np.sin(rz)
-
-    cosX = np.cos(rx)
-    cosY = np.cos(ry)
-    cosZ = np.cos(rz)
-
-    Rx = np.zeros((3,3))
-    Rx[0, 0] = 1.0
-    Rx[1, 1] = cosX
-    Rx[1, 2] = -sinX
-    Rx[2, 1] = sinX
-    Rx[2, 2] = cosX
-
-    Ry = np.zeros((3,3))
-    Ry[0, 0] = cosY
-    Ry[0, 2] = sinY
-    Ry[1, 1] = 1.0
-    Ry[2, 0] = -sinY
-    Ry[2, 2] = cosY
-
-    Rz = np.zeros((3,3))
-    Rz[0, 0] = cosZ
-    Rz[0, 1] = -sinZ
-    Rz[1, 0] = sinZ
-    Rz[1, 1] = cosZ
-    Rz[2, 2] = 1.0
-
-    R = np.matmul(np.matmul(Rz,Ry),Rx)
-    return R
-
-def rotateSH(SH, R):
-    SHn = SH
-    
-    # 1st order
-    SHn[1] = R[1,1]*SH[1] - R[1,2]*SH[2] + R[1,0]*SH[3]
-    SHn[2] = -R[2,1]*SH[1] + R[2,2]*SH[2] - R[2,0]*SH[3]
-    SHn[3] = R[0,1]*SH[1] - R[0,2]*SH[2] + R[0,0]*SH[3]
-
-    # 2nd order
-    SHn[4:,0] = rotateBand2(SH[4:,0],R)
-    SHn[4:,1] = rotateBand2(SH[4:,1],R)
-    SHn[4:,2] = rotateBand2(SH[4:,2],R)
-
-    return SHn
-
-def rotateBand2(x, R):
-    s_c3 = 0.94617469575
-    s_c4 = -0.31539156525
-    s_c5 = 0.54627421529
-
-    s_c_scale = 1.0/0.91529123286551084
-    s_c_scale_inv = 0.91529123286551084
-
-    s_rc2 = 1.5853309190550713*s_c_scale
-    s_c4_div_c3 = s_c4/s_c3
-    s_c4_div_c3_x2 = (s_c4/s_c3)*2.0
-
-    s_scale_dst2 = s_c3 * s_c_scale_inv
-    s_scale_dst4 = s_c5 * s_c_scale_inv
-
-    sh0 =  x[3] + x[4] + x[4] - x[1]
-    sh1 =  x[0] + s_rc2*x[2] +  x[3] + x[4]
-    sh2 =  x[0]
-    sh3 = -x[3]
-    sh4 = -x[1]
-    
-    r2x = R[0][0] + R[0][1]
-    r2y = R[1][0] + R[1][1]
-    r2z = R[2][0] + R[2][1]
-    
-    r3x = R[0][0] + R[0][2]
-    r3y = R[1][0] + R[1][2]
-    r3z = R[2][0] + R[2][2]
-    
-    r4x = R[0][1] + R[0][2]
-    r4y = R[1][1] + R[1][2]
-    r4z = R[2][1] + R[2][2]
-    
-    sh0_x = sh0 * R[0][0]
-    sh0_y = sh0 * R[1][0]
-    d0 = sh0_x * R[1][0]
-    d1 = sh0_y * R[2][0]
-    d2 = sh0 * (R[2][0] * R[2][0] + s_c4_div_c3)
-    d3 = sh0_x * R[2][0]
-    d4 = sh0_x * R[0][0] - sh0_y * R[1][0]
-    
-    sh1_x = sh1 * R[0][2]
-    sh1_y = sh1 * R[1][2]
-    d0 += sh1_x * R[1][2]
-    d1 += sh1_y * R[2][2]
-    d2 += sh1 * (R[2][2] * R[2][2] + s_c4_div_c3)
-    d3 += sh1_x * R[2][2]
-    d4 += sh1_x * R[0][2] - sh1_y * R[1][2]
-    
-    sh2_x = sh2 * r2x
-    sh2_y = sh2 * r2y
-    d0 += sh2_x * r2y
-    d1 += sh2_y * r2z
-    d2 += sh2 * (r2z * r2z + s_c4_div_c3_x2)
-    d3 += sh2_x * r2z
-    d4 += sh2_x * r2x - sh2_y * r2y
-    
-    sh3_x = sh3 * r3x
-    sh3_y = sh3 * r3y
-    d0 += sh3_x * r3y
-    d1 += sh3_y * r3z
-    d2 += sh3 * (r3z * r3z + s_c4_div_c3_x2)
-    d3 += sh3_x * r3z
-    d4 += sh3_x * r3x - sh3_y * r3y
-    
-    sh4_x = sh4 * r4x
-    sh4_y = sh4 * r4y
-    d0 += sh4_x * r4y
-    d1 += sh4_y * r4z
-    d2 += sh4 * (r4z * r4z + s_c4_div_c3_x2)
-    d3 += sh4_x * r4z
-    d4 += sh4_x * r4x - sh4_y * r4y
-
-    dst = x
-    dst[0] = d0
-    dst[1] = -d1
-    dst[2] = d2 * s_scale_dst2
-    dst[3] = -d3
-    dst[4] = d4 * s_scale_dst4
-
-    return dst
-
-def render_prt_ortho(out_path, folder_name, subject_name, shs, rndr, rndr_uv, im_size, angl_step=4, n_light=1, pitch=[0]):
-    cam = Camera(width=im_size, height=im_size)
-    cam.ortho_ratio = 0.4 * (512 / im_size)
-    cam.near = -100
-    cam.far = 100
-    cam.sanity_check()
-
-    # set path for obj, prt
-    mesh_file = os.path.join(folder_name, subject_name + '_100k.obj')
-    if not os.path.exists(mesh_file):
-        print('ERROR: obj file does not exist!!', mesh_file)
-        return 
-    prt_file = os.path.join(folder_name, 'bounce', 'bounce0.txt')
-    if not os.path.exists(prt_file):
-        print('ERROR: prt file does not exist!!!', prt_file)
-        return
-    face_prt_file = os.path.join(folder_name, 'bounce', 'face.npy')
-    if not os.path.exists(face_prt_file):
-        print('ERROR: face prt file does not exist!!!', prt_file)
-        return
-    text_file = os.path.join(folder_name, 'tex', subject_name + '_dif_2k.jpg')
-    if not os.path.exists(text_file):
-        print('ERROR: dif file does not exist!!', text_file)
-        return             
-
-    texture_image = cv2.imread(text_file)
-    texture_image = cv2.cvtColor(texture_image, cv2.COLOR_BGR2RGB)
-
-    vertices, faces, normals, faces_normals, textures, face_textures = load_obj_mesh(mesh_file, with_normal=True, with_texture=True)
-    vmin = vertices.min(0)
-    vmax = vertices.max(0)
-    up_axis = 1 if (vmax-vmin).argmax() == 1 else 2
-    
-    vmed = np.median(vertices, 0)
-    vmed[up_axis] = 0.5*(vmax[up_axis]+vmin[up_axis])
-    y_scale = 180/(vmax[up_axis] - vmin[up_axis])
-
-    rndr.set_norm_mat(y_scale, vmed)
-    rndr_uv.set_norm_mat(y_scale, vmed)
-
-    tan, bitan = compute_tangent(vertices, faces, normals, textures, face_textures)
-    prt = np.loadtxt(prt_file)
-    face_prt = np.load(face_prt_file)
-    rndr.set_mesh(vertices, faces, normals, faces_normals, textures, face_textures, prt, face_prt, tan, bitan)    
-    rndr.set_albedo(texture_image)
-
-    rndr_uv.set_mesh(vertices, faces, normals, faces_normals, textures, face_textures, prt, face_prt, tan, bitan)   
-    rndr_uv.set_albedo(texture_image)
-
-    os.makedirs(os.path.join(out_path, 'GEO', 'OBJ', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'PARAM', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'RENDER', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'MASK', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'UV_RENDER', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'UV_MASK', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'UV_POS', subject_name),exist_ok=True)
-    os.makedirs(os.path.join(out_path, 'UV_NORMAL', subject_name),exist_ok=True)
-
-    if not os.path.exists(os.path.join(out_path, 'val.txt')):
-        f = open(os.path.join(out_path, 'val.txt'), 'w')
-        f.close()
-
-    # copy obj file
-    cmd = 'cp %s %s' % (mesh_file, os.path.join(out_path, 'GEO', 'OBJ', subject_name))
-    print(cmd)
-    os.system(cmd)
-
-    for p in pitch:
-        for y in tqdm(range(0, 360, angl_step)):
-            R = np.matmul(make_rotate(math.radians(p), 0, 0), make_rotate(0, math.radians(y), 0))
-            if up_axis == 2:
-                R = np.matmul(R, make_rotate(math.radians(90),0,0))
-
-            rndr.rot_matrix = R
-            rndr_uv.rot_matrix = R
-            rndr.set_camera(cam)
-            rndr_uv.set_camera(cam)
-
-            for j in range(n_light):
-                sh_id = random.randint(0,shs.shape[0]-1)
-                sh = shs[sh_id]
-                sh_angle = 0.2*np.pi*(random.random()-0.5)
-                sh = rotateSH(sh, make_rotate(0, sh_angle, 0).T)
-
-                dic = {'sh': sh, 'ortho_ratio': cam.ortho_ratio, 'scale': y_scale, 'center': vmed, 'R': R}
-                
-                rndr.set_sh(sh)        
-                rndr.analytic = False
-                rndr.use_inverse_depth = False
-                rndr.display()
-
-                out_all_f = rndr.get_color(0)
-                out_mask = out_all_f[:,:,3]
-                out_all_f = cv2.cvtColor(out_all_f, cv2.COLOR_RGBA2BGR)
-
-                np.save(os.path.join(out_path, 'PARAM', subject_name, '%d_%d_%02d.npy'%(y,p,j)),dic)
-                cv2.imwrite(os.path.join(out_path, 'RENDER', subject_name, '%d_%d_%02d.jpg'%(y,p,j)),255.0*out_all_f)
-                cv2.imwrite(os.path.join(out_path, 'MASK', subject_name, '%d_%d_%02d.png'%(y,p,j)),255.0*out_mask)
-
-                rndr_uv.set_sh(sh)
-                rndr_uv.analytic = False
-                rndr_uv.use_inverse_depth = False
-                rndr_uv.display()
-
-                uv_color = rndr_uv.get_color(0)
-                uv_color = cv2.cvtColor(uv_color, cv2.COLOR_RGBA2BGR)
-                cv2.imwrite(os.path.join(out_path, 'UV_RENDER', subject_name, '%d_%d_%02d.jpg'%(y,p,j)),255.0*uv_color)
-
-                if y == 0 and j == 0 and p == pitch[0]:
-                    uv_pos = rndr_uv.get_color(1)
-                    uv_mask = uv_pos[:,:,3]
-                    cv2.imwrite(os.path.join(out_path, 'UV_MASK', subject_name, '00.png'),255.0*uv_mask)
-
-                    data = {'default': uv_pos[:,:,:3]} # default is a reserved name
-                    pyexr.write(os.path.join(out_path, 'UV_POS', subject_name, '00.exr'), data) 
-
-                    uv_nml = rndr_uv.get_color(2)
-                    uv_nml = cv2.cvtColor(uv_nml, cv2.COLOR_RGBA2BGR)
-                    cv2.imwrite(os.path.join(out_path, 'UV_NORMAL', subject_name, '00.png'),255.0*uv_nml)
-
-
-if __name__ == '__main__':
-    shs = np.load('./env_sh.npy')
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-i', '--input', type=str, default='/home/shunsuke/Downloads/rp_dennis_posed_004_OBJ')
-    parser.add_argument('-o', '--out_dir', type=str, default='/home/shunsuke/Documents/hf_human')
-    parser.add_argument('-m', '--ms_rate', type=int, default=1, help='higher ms rate results in less aliased output. MESA renderer only supports ms_rate=1.')
-    parser.add_argument('-e', '--egl',  action='store_true', help='egl rendering option. use this when rendering with headless server with NVIDIA GPU')
-    parser.add_argument('-s', '--size',  type=int, default=512, help='rendering image size')
-    args = parser.parse_args()
-
-    # NOTE: GL context has to be created before any other OpenGL function loads.
-    from lib.renderer.gl.init_gl import initialize_GL_context
-    initialize_GL_context(width=args.size, height=args.size, egl=args.egl)
-
-    from lib.renderer.gl.prt_render import PRTRender
-    rndr = PRTRender(width=args.size, height=args.size, ms_rate=args.ms_rate, egl=args.egl)
-    rndr_uv = PRTRender(width=args.size, height=args.size, uv_mode=True, egl=args.egl)
-
-    if args.input[-1] == '/':
-        args.input = args.input[:-1]
-    subject_name = args.input.split('/')[-1][:-4]
-    render_prt_ortho(args.out_dir, args.input, subject_name, shs, rndr, rndr_uv, args.size, 1, 1, pitch=[0])
\ No newline at end of file
diff --git a/spaces/deepkyu/multilingual-font-style-transfer/models/module.py b/spaces/deepkyu/multilingual-font-style-transfer/models/module.py
deleted file mode 100644
index 48d75fd82f0cbcdcc585049eb2ffc59f679edb69..0000000000000000000000000000000000000000
--- a/spaces/deepkyu/multilingual-font-style-transfer/models/module.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class Conv2d(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
-                 padding=0, padding_mode='zeros', bias=True, residual=False):
-        super(Conv2d, self).__init__()
-        self.conv_block = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels, kernel_size, stride,
-                      padding, padding_mode=padding_mode, bias=bias),
-            nn.BatchNorm2d(out_channels)
-        )
-        self.residual = residual
-        self.act = nn.ReLU()
-
-    def forward(self, x):
-        out = self.conv_block(x)
-        if self.residual:
-            out += x
-        out = self.act(out)
-        return out
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, channel, padding_mode, norm_layer=nn.BatchNorm2d, bias=False):
-        super().__init__()
-        if padding_mode not in ['reflect', 'zero']:
-            raise NotImplementedError(f"{padding_mode} is not supported!")
-
-        self.block = nn.Sequential(
-            nn.Conv2d(channel, channel, kernel_size=3, padding=1, padding_mode=padding_mode, bias=bias),
-            norm_layer(channel)
-        )
-        self.act = nn.ReLU()
-
-    def forward(self, x):
-        out = self.block(x)
-        out = out + x
-        out = self.act(out)
-        return out
-
-
-class ResidualBlocks(nn.Module):
-    def __init__(self, channel, n_blocks=6):
-        super().__init__()
-        model = []
-        for i in range(n_blocks):  # add ResNet blocks
-            model += [ResnetBlock(channel, padding_mode='reflect')]
-
-        self.module = nn.Sequential(*model)
-
-    def forward(self, x):
-        return self.module(x)
-
-
-class SelfAttentionBlock(nn.Module):
-
-    def __init__(self, in_dim):
-        super().__init__()
-        self.feature_dim = in_dim // 8
-        self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=self.feature_dim, kernel_size=1)
-        self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=self.feature_dim, kernel_size=1)
-        self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
-        self.gamma = nn.Parameter(torch.zeros(1))
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x):
-        B, C, H, W = x.size()
-        _query = self.query_conv(x).view(B, -1, H * W).permute(0, 2, 1)  # B x C x (H'*W')
-        _key = self.key_conv(x).view(B, -1, H * W)  # B x C x (H'*W')
-        attn_matrix = torch.bmm(_query, _key)
-        attention = self.softmax(attn_matrix)  # B x (H'*W') x (H'*W')
-        _value = self.value_conv(x).view(B, -1, H * W)  # B X C X (H * W)
-
-        out = torch.bmm(_value, attention.permute(0, 2, 1))
-        out = out.view(B, C, H, W)
-
-        out = self.gamma * out + x
-        return out
-
-
-class ContextAwareAttentionBlock(nn.Module):
-
-    def __init__(self, in_channels, hidden_dim=128):
-        super().__init__()
-        self.self_attn = SelfAttentionBlock(in_channels)
-        self.fc = nn.Linear(in_channels, hidden_dim)
-        self.context_vector = nn.Linear(hidden_dim, 1, bias=False)
-        self.softmax = nn.Softmax(dim=1)
-
-    def forward(self, style_features):
-        B, C, H, W = style_features.size()
-        h = self.self_attn(style_features)
-        h = h.permute(0, 2, 3, 1).reshape(-1, C)
-        h = torch.tanh(self.fc(h))  # (B*H*W) x self.hidden_dim
-        h = self.context_vector(h)  # (B*H*W) x 1
-        attention_score = self.softmax(h.view(B, H * W)).view(B, 1, H, W)  # B x 1 x H x W
-        return torch.sum(style_features * attention_score, dim=[2, 3])  # B x C
-
-
-class LayerAttentionBlock(nn.Module):
-    """from FTransGAN
-    """
-
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-        self.width_feat = 4
-        self.height_feat = 4
-        self.fc = nn.Linear(self.in_channels * self.width_feat * self.height_feat, 3)
-        self.softmax = nn.Softmax(dim=1)
-
-    def forward(self, style_features, style_features_1, style_features_2, style_features_3, B, K):
-        style_features = torch.mean(style_features.view(B, K, self.in_channels, self.height_feat, self.width_feat), dim=1)
-        style_features = style_features.view(B, -1)
-        weight = self.softmax(self.fc(style_features))
-
-        style_features_1 = torch.mean(style_features_1.view(B, K, self.in_channels), dim=1)
-        style_features_2 = torch.mean(style_features_2.view(B, K, self.in_channels), dim=1)
-        style_features_3 = torch.mean(style_features_3.view(B, K, self.in_channels), dim=1)
-
-        style_features = (style_features_1 * weight.narrow(1, 0, 1) +
-                          style_features_2 * weight.narrow(1, 1, 1) +
-                          style_features_3 * weight.narrow(1, 2, 1))
-        style_features = style_features.view(B, self.in_channels, 1, 1)
-        return style_features
-
-
-class StyleAttentionBlock(nn.Module):
-    """from FTransGAN
-    """
-
-    def __init__(self, in_channels):
-        super().__init__()
-        self.num_local_attention = 3
-        for module_idx in range(1, self.num_local_attention + 1):
-            self.add_module(f"local_attention_{module_idx}",
-                            ContextAwareAttentionBlock(in_channels))
-
-        for module_idx in range(1, self.num_local_attention):
-            self.add_module(f"downsample_{module_idx}",
-                            Conv2d(in_channels, in_channels,
-                                   kernel_size=3, stride=2, padding=1, bias=False))
-
-        self.add_module(f"layer_attention", LayerAttentionBlock(in_channels))
-
-    def forward(self, x, B, K):
-        feature_1 = self.local_attention_1(x)
-
-        x = self.downsample_1(x)
-        feature_2 = self.local_attention_2(x)
-
-        x = self.downsample_2(x)
-        feature_3 = self.local_attention_3(x)
-
-        out = self.layer_attention(x, feature_1, feature_2, feature_3, B, K)
-
-        return out
diff --git a/spaces/diacanFperku/AutoGPT/Baixar Plano Geral De Contabilidade Angolano Pdf.md b/spaces/diacanFperku/AutoGPT/Baixar Plano Geral De Contabilidade Angolano Pdf.md
deleted file mode 100644
index 3771daf3411191c5240b96e5870a2636298d7f15..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Baixar Plano Geral De Contabilidade Angolano Pdf.md	
+++ /dev/null
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diff --git a/spaces/diacanFperku/AutoGPT/Myscript Studio Notes 13 ((TOP)) Crack.md b/spaces/diacanFperku/AutoGPT/Myscript Studio Notes 13 ((TOP)) Crack.md
deleted file mode 100644
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diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/infra/utilities/annotate_em.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/infra/utilities/annotate_em.py
deleted file mode 100644
index 39c2b7b3c7d51ee8851c69ea5ce69f669da28244..0000000000000000000000000000000000000000
--- a/spaces/diagaiwei/ir_chinese_medqa/colbert/infra/utilities/annotate_em.py
+++ /dev/null
@@ -1,115 +0,0 @@
-
-from colbert.infra.run import Run
-from colbert.data.collection import Collection
-import os
-import sys
-import git
-import tqdm
-import ujson
-import random
-
-from argparse import ArgumentParser
-from multiprocessing import Pool
-
-from colbert.utils.utils import groupby_first_item, print_message
-from utility.utils.qa_loaders import load_qas_, load_collection_
-from utility.utils.save_metadata import format_metadata, get_metadata
-from utility.evaluate.annotate_EM_helpers import *
-
-from colbert.data.ranking import Ranking
-
-
-class AnnotateEM:
-    def __init__(self, collection, qas):
-        # TODO: These should just be Queries! But Queries needs to support looking up answers as qid2answers below.
-        qas = load_qas_(qas)
-        collection = Collection.cast(collection)  # .tolist() #load_collection_(collection, retain_titles=True)
-
-        self.parallel_pool = Pool(30)
-
-        print_message('#> Tokenize the answers in the Q&As in parallel...')
-        qas = list(self.parallel_pool.map(tokenize_all_answers, qas))
-
-        qid2answers = {qid: tok_answers for qid, _, tok_answers in qas}
-        assert len(qas) == len(qid2answers), (len(qas), len(qid2answers))
-
-        self.qas, self.collection = qas, collection
-        self.qid2answers = qid2answers
-
-    def annotate(self, ranking):
-        rankings = Ranking.cast(ranking)
-
-        # print(len(rankings), rankings[0])
-
-        print_message('#> Lookup passages from PIDs...')
-        expanded_rankings = [(qid, pid, rank, self.collection[pid], self.qid2answers[qid])
-                             for qid, pid, rank, *_ in rankings.tolist()]
-
-        print_message('#> Assign labels in parallel...')
-        labeled_rankings = list(self.parallel_pool.map(assign_label_to_passage, enumerate(expanded_rankings)))
-
-        # Dump output.
-        self.qid2rankings = groupby_first_item(labeled_rankings)
-
-        self.num_judged_queries, self.num_ranked_queries = check_sizes(self.qid2answers, self.qid2rankings)
-
-        # Evaluation metrics and depths.
-        self.success, self.counts = self._compute_labels(self.qid2answers, self.qid2rankings)
-
-        print(rankings.provenance(), self.success)
-
-        return Ranking(data=self.qid2rankings, provenance=("AnnotateEM", rankings.provenance()))
-
-    def _compute_labels(self, qid2answers, qid2rankings):
-        cutoffs = [1, 5, 10, 20, 30, 50, 100, 1000, 'all']
-        success = {cutoff: 0.0 for cutoff in cutoffs}
-        counts = {cutoff: 0.0 for cutoff in cutoffs}
-
-        for qid in qid2answers:
-            if qid not in qid2rankings:
-                continue
-
-            prev_rank = 0  # ranks should start at one (i.e., and not zero)
-            labels = []
-
-            for pid, rank, label in qid2rankings[qid]:
-                assert rank == prev_rank+1, (qid, pid, (prev_rank, rank))
-                prev_rank = rank
-
-                labels.append(label)
-
-            for cutoff in cutoffs:
-                if cutoff != 'all':
-                    success[cutoff] += sum(labels[:cutoff]) > 0
-                    counts[cutoff] += sum(labels[:cutoff])
-                else:
-                    success[cutoff] += sum(labels) > 0
-                    counts[cutoff] += sum(labels)
-
-        return success, counts
-
-    def save(self, new_path):
-        print_message("#> Dumping output to", new_path, "...")
-
-        Ranking(data=self.qid2rankings).save(new_path)
-
-        # Dump metrics.
-        with Run().open(f'{new_path}.metrics', 'w') as f:
-            d = {'num_ranked_queries': self.num_ranked_queries, 'num_judged_queries': self.num_judged_queries}
-
-            extra = '__WARNING' if self.num_judged_queries != self.num_ranked_queries else ''
-            d[f'success{extra}'] = {k: v / self.num_judged_queries for k, v in self.success.items()}
-            d[f'counts{extra}'] = {k: v / self.num_judged_queries for k, v in self.counts.items()}
-            # d['arguments'] = get_metadata(args)  # TODO: Need arguments...
-
-            f.write(format_metadata(d) + '\n')
-
-
-if __name__ == '__main__':
-    r = '/future/u/okhattab/root/unit/experiments/2021.08/retrieve.py/2021-09-04_15.50.02/ranking.tsv'
-    r = '/future/u/okhattab/root/unit/experiments/2021.08/retrieve.py/2021-09-04_15.59.37/ranking.tsv'
-    r = sys.argv[1]
-
-    a = AnnotateEM(collection='/future/u/okhattab/root/unit/data/NQ-mini/collection.tsv',
-                   qas='/future/u/okhattab/root/unit/data/NQ-mini/dev/qas.json')
-    a.annotate(ranking=r)
diff --git a/spaces/dingliyu/skillmix/README.md b/spaces/dingliyu/skillmix/README.md
deleted file mode 100644
index ff477e728d9864f0cff4624bb66338ce1eada689..0000000000000000000000000000000000000000
--- a/spaces/dingliyu/skillmix/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Skillmix
-emoji: 🚀
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dolceschokolade/chatbot-mini/components/Chat/SystemPrompt.tsx b/spaces/dolceschokolade/chatbot-mini/components/Chat/SystemPrompt.tsx
deleted file mode 100644
index 821840bc3a50160847c07571666ea5868bb39458..0000000000000000000000000000000000000000
--- a/spaces/dolceschokolade/chatbot-mini/components/Chat/SystemPrompt.tsx
+++ /dev/null
@@ -1,243 +0,0 @@
-import {
-  FC,
-  KeyboardEvent,
-  useCallback,
-  useEffect,
-  useRef,
-  useState,
-} from 'react';
-
-import { useTranslation } from 'next-i18next';
-
-import { DEFAULT_SYSTEM_PROMPT } from '@/utils/app/const';
-
-import { Conversation } from '@/types/chat';
-import { Prompt } from '@/types/prompt';
-
-import { PromptList } from './PromptList';
-import { VariableModal } from './VariableModal';
-
-interface Props {
-  conversation: Conversation;
-  prompts: Prompt[];
-  onChangePrompt: (prompt: string) => void;
-}
-
-export const SystemPrompt: FC<Props> = ({
-  conversation,
-  prompts,
-  onChangePrompt,
-}) => {
-  const { t } = useTranslation('chat');
-
-  const [value, setValue] = useState<string>('');
-  const [activePromptIndex, setActivePromptIndex] = useState(0);
-  const [showPromptList, setShowPromptList] = useState(false);
-  const [promptInputValue, setPromptInputValue] = useState('');
-  const [variables, setVariables] = useState<string[]>([]);
-  const [isModalVisible, setIsModalVisible] = useState(false);
-
-  const textareaRef = useRef<HTMLTextAreaElement>(null);
-  const promptListRef = useRef<HTMLUListElement | null>(null);
-
-  const filteredPrompts = prompts.filter((prompt) =>
-    prompt.name.toLowerCase().includes(promptInputValue.toLowerCase()),
-  );
-
-  const handleChange = (e: React.ChangeEvent<HTMLTextAreaElement>) => {
-    const value = e.target.value;
-    const maxLength = conversation.model.maxLength;
-
-    if (value.length > maxLength) {
-      alert(
-        t(
-          `Prompt limit is {{maxLength}} characters. You have entered {{valueLength}} characters.`,
-          { maxLength, valueLength: value.length },
-        ),
-      );
-      return;
-    }
-
-    setValue(value);
-    updatePromptListVisibility(value);
-
-    if (value.length > 0) {
-      onChangePrompt(value);
-    }
-  };
-
-  const handleInitModal = () => {
-    const selectedPrompt = filteredPrompts[activePromptIndex];
-    setValue((prevVal) => {
-      const newContent = prevVal?.replace(/\/\w*$/, selectedPrompt.content);
-      return newContent;
-    });
-    handlePromptSelect(selectedPrompt);
-    setShowPromptList(false);
-  };
-
-  const parseVariables = (content: string) => {
-    const regex = /{{(.*?)}}/g;
-    const foundVariables = [];
-    let match;
-
-    while ((match = regex.exec(content)) !== null) {
-      foundVariables.push(match[1]);
-    }
-
-    return foundVariables;
-  };
-
-  const updatePromptListVisibility = useCallback((text: string) => {
-    const match = text.match(/\/\w*$/);
-
-    if (match) {
-      setShowPromptList(true);
-      setPromptInputValue(match[0].slice(1));
-    } else {
-      setShowPromptList(false);
-      setPromptInputValue('');
-    }
-  }, []);
-
-  const handlePromptSelect = (prompt: Prompt) => {
-    const parsedVariables = parseVariables(prompt.content);
-    setVariables(parsedVariables);
-
-    if (parsedVariables.length > 0) {
-      setIsModalVisible(true);
-    } else {
-      const updatedContent = value?.replace(/\/\w*$/, prompt.content);
-
-      setValue(updatedContent);
-      onChangePrompt(updatedContent);
-
-      updatePromptListVisibility(prompt.content);
-    }
-  };
-
-  const handleSubmit = (updatedVariables: string[]) => {
-    const newContent = value?.replace(/{{(.*?)}}/g, (match, variable) => {
-      const index = variables.indexOf(variable);
-      return updatedVariables[index];
-    });
-
-    setValue(newContent);
-    onChangePrompt(newContent);
-
-    if (textareaRef && textareaRef.current) {
-      textareaRef.current.focus();
-    }
-  };
-
-  const handleKeyDown = (e: KeyboardEvent<HTMLTextAreaElement>) => {
-    if (showPromptList) {
-      if (e.key === 'ArrowDown') {
-        e.preventDefault();
-        setActivePromptIndex((prevIndex) =>
-          prevIndex < prompts.length - 1 ? prevIndex + 1 : prevIndex,
-        );
-      } else if (e.key === 'ArrowUp') {
-        e.preventDefault();
-        setActivePromptIndex((prevIndex) =>
-          prevIndex > 0 ? prevIndex - 1 : prevIndex,
-        );
-      } else if (e.key === 'Tab') {
-        e.preventDefault();
-        setActivePromptIndex((prevIndex) =>
-          prevIndex < prompts.length - 1 ? prevIndex + 1 : 0,
-        );
-      } else if (e.key === 'Enter') {
-        e.preventDefault();
-        handleInitModal();
-      } else if (e.key === 'Escape') {
-        e.preventDefault();
-        setShowPromptList(false);
-      } else {
-        setActivePromptIndex(0);
-      }
-    }
-  };
-
-  useEffect(() => {
-    if (textareaRef && textareaRef.current) {
-      textareaRef.current.style.height = 'inherit';
-      textareaRef.current.style.height = `${textareaRef.current?.scrollHeight}px`;
-    }
-  }, [value]);
-
-  useEffect(() => {
-    if (conversation.prompt) {
-      setValue(conversation.prompt);
-    } else {
-      setValue(DEFAULT_SYSTEM_PROMPT);
-    }
-  }, [conversation]);
-
-  useEffect(() => {
-    const handleOutsideClick = (e: MouseEvent) => {
-      if (
-        promptListRef.current &&
-        !promptListRef.current.contains(e.target as Node)
-      ) {
-        setShowPromptList(false);
-      }
-    };
-
-    window.addEventListener('click', handleOutsideClick);
-
-    return () => {
-      window.removeEventListener('click', handleOutsideClick);
-    };
-  }, []);
-
-  return (
-    <div className="flex flex-col">
-      <label className="mb-2 text-left text-neutral-700 dark:text-neutral-400">
-        {t('System Prompt')}
-      </label>
-      <textarea
-        ref={textareaRef}
-        className="w-full rounded-lg border border-neutral-200 bg-transparent px-4 py-3 text-neutral-900 dark:border-neutral-600 dark:text-neutral-100"
-        style={{
-          resize: 'none',
-          bottom: `${textareaRef?.current?.scrollHeight}px`,
-          maxHeight: '300px',
-          overflow: `${
-            textareaRef.current && textareaRef.current.scrollHeight > 400
-              ? 'auto'
-              : 'hidden'
-          }`,
-        }}
-        placeholder={
-          t(`Enter a prompt`) || ''
-        }
-        value={t(value) || ''}
-        rows={1}
-        onChange={handleChange}
-        onKeyDown={handleKeyDown}
-      />
-
-      {showPromptList && filteredPrompts.length > 0 && (
-        <div>
-          <PromptList
-            activePromptIndex={activePromptIndex}
-            prompts={filteredPrompts}
-            onSelect={handleInitModal}
-            onMouseOver={setActivePromptIndex}
-            promptListRef={promptListRef}
-          />
-        </div>
-      )}
-
-      {isModalVisible && (
-        <VariableModal
-          prompt={prompts[activePromptIndex]}
-          variables={variables}
-          onSubmit={handleSubmit}
-          onClose={() => setIsModalVisible(false)}
-        />
-      )}
-    </div>
-  );
-};
diff --git a/spaces/dolceschokolade/chatbot-mini/components/Settings/Key.tsx b/spaces/dolceschokolade/chatbot-mini/components/Settings/Key.tsx
deleted file mode 100644
index fe056e9d9e0d0827d44b1cf82bf2c0dac1deccae..0000000000000000000000000000000000000000
--- a/spaces/dolceschokolade/chatbot-mini/components/Settings/Key.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-import { IconCheck, IconKey, IconX } from '@tabler/icons-react';
-import { FC, KeyboardEvent, useEffect, useRef, useState } from 'react';
-
-import { useTranslation } from 'next-i18next';
-
-import { SidebarButton } from '../Sidebar/SidebarButton';
-
-interface Props {
-  apiKey: string;
-  onApiKeyChange: (apiKey: string) => void;
-}
-
-export const Key: FC<Props> = ({ apiKey, onApiKeyChange }) => {
-  return null;
-};
diff --git a/spaces/dpe1/beat_manipulator/beat_manipulator/io.py b/spaces/dpe1/beat_manipulator/beat_manipulator/io.py
deleted file mode 100644
index 0aff8b9a0a581539447d4fa4b010083d2ed95ab6..0000000000000000000000000000000000000000
--- a/spaces/dpe1/beat_manipulator/beat_manipulator/io.py
+++ /dev/null
@@ -1,178 +0,0 @@
-
-import numpy as np
-from . import main
-
-def open_audio(path:str = None, lib:str = 'auto', normalize = True) -> tuple:
-    """Opens audio from path, returns (audio, samplerate) tuple.
-    
-    Audio is returned as an array with normal volume range between -1, 1.
-    
-    Example of returned audio: 
-    
-    [
-        [0.35, -0.25, ... -0.15, -0.15], 
-    
-        [0.31, -0.21, ... -0.11, -0.07]
-    ]"""
-
-    if path is None:
-        from tkinter.filedialog import askopenfilename
-        path = askopenfilename(title='select song', filetypes=[("mp3", ".mp3"),("wav", ".wav"),("flac", ".flac"),("ogg", ".ogg"),("wma", ".wma")])
-    
-    path=path.replace('\\', '/')
-
-    if lib=='pedalboard.io':
-        import pedalboard.io
-        with pedalboard.io.AudioFile(path) as f:
-            audio = f.read(f.frames)
-            sr = f.samplerate
-    
-    elif lib=='librosa':
-        import librosa
-        audio, sr = librosa.load(path, sr=None, mono=False)
-    
-    elif lib=='soundfile':
-        import soundfile
-        audio, sr = soundfile.read(path)
-        audio=audio.T
-    
-    elif lib=='madmom':
-        import madmom
-        audio, sr = madmom.io.audio.load_audio_file(path, dtype=float)
-        audio=audio.T
-    
-    # elif lib=='pydub':
-    #     from pydub import AudioSegment
-    #     song=AudioSegment.from_file(filename)
-    #     audio = song.get_array_of_samples()
-    #     samplerate=song.frame_rate
-    #     print(audio)
-    #     print(filename)
-
-    elif lib=='auto':
-        for i in ('madmom', 'soundfile', 'librosa', 'pedalboard.io'):
-            try: 
-                audio,sr=open_audio(path, i)
-                break
-            except Exception as e:
-                print(f'open_audio with {i}: {e}')
-    
-    if len(audio)>16: audio=np.array([audio, audio], copy=False)
-    if normalize is True: 
-        audio = np.clip(audio, -1, 1)
-        audio = audio*(1/np.max(np.abs(audio)))
-    return audio.astype(np.float32),sr
-    
-def _sr(sr):
-    try: return int(sr)
-    except (ValueError, TypeError): assert False, f"Audio is an array, but `sr` argument is not valid. If audio is an array, you have to provide samplerate as an integer in the `sr` argument. Currently sr = {sr} of type {type(sr)}"
-
-def write_audio(audio:np.ndarray, sr:int, output:str, lib:str='auto', libs=('pedalboard.io', 'soundfile'), log = True):
-        """"writes audio to path specified by output. Path should end with file extension, for example `folder/audio.mp3`"""
-        if log is True: print(f'Writing {output}...', end=' ')
-        assert _iterable(audio), f"audio should be an array/iterable object, but it is {type(audio)}"
-        sr = _sr(sr)
-        if not isinstance(audio, np.ndarray): audio = np.array(audio, copy=False)
-        if lib=='pedalboard.io':
-            #print(audio)
-            import pedalboard.io
-            with pedalboard.io.AudioFile(output, 'w', sr, audio.shape[0]) as f:
-                f.write(audio)
-        elif lib=='soundfile':
-            audio=audio.T
-            import soundfile
-            soundfile.write(output, audio, sr)
-            del audio
-        elif lib=='auto':
-            for i in libs:
-                try: 
-                    write_audio(audio=audio, sr=sr, output=output, lib=i, log = False)
-                    break
-                except Exception as e:
-                    print(e)
-            else: assert False, 'Failed to write audio, chances are there is something wrong with it...'
-        if log is True: print(f'Done!')
-
-def _iterable(a):
-    try:
-        _ = iter(a)
-        return True
-    except TypeError: return False
-
-def _load(audio, sr:int = None, lib:str = 'auto', channels:int = 2, transpose3D:bool = False) -> tuple:
-    """Automatically converts audio from path or any format to [[...],[...]] array. Returns (audio, samplerate) tuple."""
-    # path
-    if isinstance(audio, str): return(open_audio(path=audio, lib=lib))
-    # array
-    if _iterable(audio):
-        if isinstance(audio, main.song):
-            if sr is None: sr = audio.sr
-            audio = audio.audio
-        # sr is provided in a tuple
-        if sr is None and len(audio) == 2:
-            if not _iterable(audio[0]):
-                sr = audio[0]
-                audio = audio[1]
-            elif not _iterable(audio[1]):
-                sr = audio[1]
-                audio = audio[0]
-        if not isinstance(audio, np.ndarray): audio = np.array(audio, copy=False)
-        sr = _sr(sr)
-        if _iterable(audio[0]):
-            # image
-            if _iterable(audio[0][0]):
-                audio2 = []
-                if transpose3D is True: audio = audio.T
-                for i in audio:
-                    audio2.extend(_load(audio=i, sr=sr, lib=lib, channels=channels, transpose3D=transpose3D)[0])
-                return audio2, sr
-            # transposed
-            if len(audio) > 16:
-                audio = audio.T
-                return _load(audio=audio, sr=sr, lib=lib, channels=channels, transpose3D=transpose3D)
-            # multi channel
-            elif isinstance(channels, int):
-                if len(audio) >= channels:
-                    return audio[:channels], sr
-                # masked mono
-                else: return np.array([audio[0] for _ in range(channels)], copy=False), sr
-            else: return audio, sr
-        else: 
-            # mono
-            return (np.array([audio for _ in range(channels)], copy=False) if channels is not None else audio), sr
-    # unknown
-    else: assert False, f"Audio should be either a string with path, an array/iterable object, or a song object, but it is {type(audio)}"
-
-def _tosong(audio, sr=None):
-    if isinstance(audio, main.song): return audio
-    else: 
-        audio, sr = _load(audio = audio, sr = sr)
-        return main.song(audio=audio, sr = sr)
-
-def _outputfilename(path:str = None, filename:str = None, suffix:str = None, ext:str = None):
-    """If path has file extension, returns `path + suffix + ext`. Else returns `path + filename + suffix + .ext`. If nothing is specified, returns `output.mp3`"""
-    if ext is not None:
-        if not ext.startswith('.'): ext = '.'+ext
-    if path is None: path = ''
-    if path.endswith('/') or path.endswith('\\'): path=path[:-1]
-    if '.' in path:
-        path = path.split('.')
-        if path[-1].lower() in ['mp3', 'wav', 'flac', 'ogg', 'wma', 'aac', 'ac3', 'aiff']:
-            if ext is not None:
-                path[-1] = ext
-            if suffix is not None: path[len(path)-2]+=suffix
-            return ''.join(path)
-        else: path = ''.join(path)
-    if filename is not None:
-        filename = filename.replace('\\','/').split('/')[-1]
-        if '.' in filename:
-            filename = filename.split('.')
-            if filename[-1].lower() in ['mp3', 'wav', 'flac', 'ogg', 'wma', 'aac', 'ac3', 'aiff']:
-                if ext is not None:
-                    filename[-1] = ext
-                if suffix is not None: filename.insert(len(filename)-1, suffix)
-            else: filename += [ext]
-            filename = ''.join(filename)
-            return f'{path}/{filename}' if path != '' else filename
-        return f'{(path + "/") * (path != "")}{filename}{suffix if suffix is not None else ""}.{ext if ext is not None else "mp3"}'
-    else: return f'{path}/output.mp3'
diff --git a/spaces/ds520/bingo/src/components/button-scroll-to-bottom.tsx b/spaces/ds520/bingo/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/ds520/bingo/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
-  const isAtBottom = useAtBottom()
-
-  return (
-    <Button
-      variant="outline"
-      size="icon"
-      className={cn(
-        'fixed right-4 bottom-24 z-50 bg-background transition-opacity duration-300 sm:right-20',
-        isAtBottom ? 'opacity-0' : 'opacity-100',
-        className
-      )}
-      onClick={() =>
-        window.scrollTo({
-          top: document.body.offsetHeight,
-          behavior: 'smooth'
-        })
-      }
-      {...props}
-    >
-      <IconArrowDown />
-      <span className="sr-only">Scroll to bottom</span>
-    </Button>
-  )
-}
diff --git a/spaces/enzostvs/hair-colour/components/header.tsx b/spaces/enzostvs/hair-colour/components/header.tsx
deleted file mode 100644
index 1885e011430d47c9ce1eeae458d01a98e3f91477..0000000000000000000000000000000000000000
--- a/spaces/enzostvs/hair-colour/components/header.tsx
+++ /dev/null
@@ -1,20 +0,0 @@
-import Image from "next/image";
-
-import HuggingFaceLogo from "@/assets/hf-logo.svg";
-
-export const Header = () => {
-  return (
-    <nav className="py-4 w-full flex flex-col lg:flex-row items-center justify-between gap-4">
-      <Image
-        src={HuggingFaceLogo}
-        alt="Hugging Face Logo"
-        width={50}
-        height={50}
-        className="w-[80px] h-[80px] lg:w-[50px] lg:h-[50px]"
-      />
-      <p className="text-2xl lg:text-xl text-white font-bold tracking-wider">
-        What hair color is it?
-      </p>
-    </nav>
-  );
-};
diff --git a/spaces/epexVfeibi/Imagedeblurr/Ababio Chemistry Textbook Pdf.md b/spaces/epexVfeibi/Imagedeblurr/Ababio Chemistry Textbook Pdf.md
deleted file mode 100644
index 0743a3618eaa7a6a52d4677e2b70a1ee0f2b0c34..0000000000000000000000000000000000000000
--- a/spaces/epexVfeibi/Imagedeblurr/Ababio Chemistry Textbook Pdf.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Ababio chemistry textbook pdf</h2><br /><p><b><b>Download Zip</b> &ndash;&ndash;&ndash; <a href="https://jinyurl.com/2uEmxp">https://jinyurl.com/2uEmxp</a></b></p><br /><br />
-
-Download Ababio Chemistry Textbook. 10.1. 30.11.2015 · On this page you can read or download download ss1 physics textbook in PDF format. Not all the ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/eson/tokenizer-arena/vocab/falcon_180b/__init__.py b/spaces/eson/tokenizer-arena/vocab/falcon_180b/__init__.py
deleted file mode 100644
index 681c1fe9473deb14604a701863f084a7a2e84e80..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/falcon_180b/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import os
-from transformers import AutoTokenizer
-
-
-CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
-TOKENIZER_DIR = os.path.join(CURRENT_DIR, "tokenizer")
-
-
-
-# tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-180b")  # token
-tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR)
diff --git a/spaces/eubinecto/idiomify/idiomify/__init__.py b/spaces/eubinecto/idiomify/idiomify/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fabiochiu/text-to-kb/utils.py b/spaces/fabiochiu/text-to-kb/utils.py
deleted file mode 100644
index 9b1aaf63d5aa467090fc9afec42506cdf40b1497..0000000000000000000000000000000000000000
--- a/spaces/fabiochiu/text-to-kb/utils.py
+++ /dev/null
@@ -1,198 +0,0 @@
-from pyvis.network import Network
-from GoogleNews import GoogleNews
-from newspaper import Article, ArticleException
-import math
-import torch
-from kb import KB
-import pickle
-
-def extract_relations_from_model_output(text):
-    relations = []
-    relation, subject, relation, object_ = '', '', '', ''
-    text = text.strip()
-    current = 'x'
-    text_replaced = text.replace("<s>", "").replace("<pad>", "").replace("</s>", "")
-    for token in text_replaced.split():
-        if token == "<triplet>":
-            current = 't'
-            if relation != '':
-                relations.append({
-                    'head': subject.strip(),
-                    'type': relation.strip(),
-                    'tail': object_.strip()
-                })
-                relation = ''
-            subject = ''
-        elif token == "<subj>":
-            current = 's'
-            if relation != '':
-                relations.append({
-                    'head': subject.strip(),
-                    'type': relation.strip(),
-                    'tail': object_.strip()
-                })
-            object_ = ''
-        elif token == "<obj>":
-            current = 'o'
-            relation = ''
-        else:
-            if current == 't':
-                subject += ' ' + token
-            elif current == 's':
-                object_ += ' ' + token
-            elif current == 'o':
-                relation += ' ' + token
-    if subject != '' and relation != '' and object_ != '':
-        relations.append({
-            'head': subject.strip(),
-            'type': relation.strip(),
-            'tail': object_.strip()
-        })
-    return relations
-
-def from_text_to_kb(text, model, tokenizer, article_url, span_length=128, article_title=None,
-                    article_publish_date=None, verbose=False):
-    # tokenize whole text
-    inputs = tokenizer([text], return_tensors="pt")
-
-    # compute span boundaries
-    num_tokens = len(inputs["input_ids"][0])
-    if verbose:
-        print(f"Input has {num_tokens} tokens")
-    num_spans = math.ceil(num_tokens / span_length)
-    if verbose:
-        print(f"Input has {num_spans} spans")
-    overlap = math.ceil((num_spans * span_length - num_tokens) / 
-                        max(num_spans - 1, 1))
-    spans_boundaries = []
-    start = 0
-    for i in range(num_spans):
-        spans_boundaries.append([start + span_length * i,
-                                 start + span_length * (i + 1)])
-        start -= overlap
-    if verbose:
-        print(f"Span boundaries are {spans_boundaries}")
-
-    # transform input with spans
-    tensor_ids = [inputs["input_ids"][0][boundary[0]:boundary[1]]
-                  for boundary in spans_boundaries]
-    tensor_masks = [inputs["attention_mask"][0][boundary[0]:boundary[1]]
-                    for boundary in spans_boundaries]
-    inputs = {
-        "input_ids": torch.stack(tensor_ids),
-        "attention_mask": torch.stack(tensor_masks)
-    }
-
-    # generate relations
-    num_return_sequences = 3
-    gen_kwargs = {
-        "max_length": 256,
-        "length_penalty": 0,
-        "num_beams": 3,
-        "num_return_sequences": num_return_sequences
-    }
-    generated_tokens = model.generate(
-        **inputs,
-        **gen_kwargs,
-    )
-
-    # decode relations
-    decoded_preds = tokenizer.batch_decode(generated_tokens,
-                                           skip_special_tokens=False)
-
-    # create kb
-    kb = KB()
-    i = 0
-    for sentence_pred in decoded_preds:
-        current_span_index = i // num_return_sequences
-        relations = extract_relations_from_model_output(sentence_pred)
-        for relation in relations:
-            relation["meta"] = {
-                article_url: {
-                    "spans": [spans_boundaries[current_span_index]]
-                }
-            }
-            kb.add_relation(relation, article_title, article_publish_date)
-        i += 1
-
-    return kb
-
-def get_article(url):
-    article = Article(url)
-    article.download()
-    article.parse()
-    return article
-
-def from_url_to_kb(url, model, tokenizer):
-    article = get_article(url)
-    config = {
-        "article_title": article.title,
-        "article_publish_date": article.publish_date
-    }
-    kb = from_text_to_kb(article.text, model, tokenizer, article.url, **config)
-    return kb
-
-def get_news_links(query, lang="en", region="US", pages=1):
-    googlenews = GoogleNews(lang=lang, region=region)
-    googlenews.search(query)
-    all_urls = []
-    for page in range(pages):
-        googlenews.get_page(page)
-        all_urls += googlenews.get_links()
-    return list(set(all_urls))
-
-def from_urls_to_kb(urls, model, tokenizer, verbose=False):
-    kb = KB()
-    if verbose:
-        print(f"{len(urls)} links to visit")
-    for url in urls:
-        if verbose:
-            print(f"Visiting {url}...")
-        try:
-            kb_url = from_url_to_kb(url, model, tokenizer)
-            kb.merge_with_kb(kb_url)
-        except ArticleException:
-            if verbose:
-                print(f"  Couldn't download article at url {url}")
-    return kb
-
-def save_network_html(kb, filename="network.html"):
-    # create network
-    net = Network(directed=True, width="700px", height="700px")
-
-    # nodes
-    color_entity = "#00FF00"
-    for e in kb.entities:
-        net.add_node(e, shape="circle", color=color_entity)
-
-    # edges
-    for r in kb.relations:
-        net.add_edge(r["head"], r["tail"],
-                    title=r["type"], label=r["type"])
-
-    # save network
-    net.repulsion(
-        node_distance=200,
-        central_gravity=0.2,
-        spring_length=200,
-        spring_strength=0.05,
-        damping=0.09
-    )
-    net.set_edge_smooth('dynamic')
-    net.show(filename)
-
-def save_kb(kb, filename):
-    with open(filename, "wb") as f:
-        pickle.dump(kb, f)
-
-class CustomUnpickler(pickle.Unpickler):
-    def find_class(self, module, name):
-        if name == 'KB':
-            return KB
-        return super().find_class(module, name)
-
-def load_kb(filename):
-    res = None
-    with open(filename, "rb") as f:
-        res = CustomUnpickler(f).load()
-    return res
diff --git a/spaces/failfast/2D-GameCreator/src/utils/share.tsx b/spaces/failfast/2D-GameCreator/src/utils/share.tsx
deleted file mode 100644
index d997846cc7098831436a438d3034dc26ef43c7c7..0000000000000000000000000000000000000000
--- a/spaces/failfast/2D-GameCreator/src/utils/share.tsx
+++ /dev/null
@@ -1,138 +0,0 @@
-import { renderToString } from "react-dom/server";
-
-export const wrappers: Record<"js" | "html" | "css" | "miniHtml", (content?: string) => string> = {
-	html(content) {
-		return `<!DOCTYPE html>
-<!-- generated with https://huggingface.co/spaces/failfast/2D-GameCreator by https://failfa.st -->
-${renderToString(
-	<html lang="en">
-		<head>
-			<meta charSet="utf-8" />
-			<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
-			<title>{content}</title>
-			<script defer src="/script.js" type="module" />
-			<link rel="stylesheet" href="/style.css" />
-		</head>
-		<body>
-			<canvas id="canvas" />
-		</body>
-	</html>
-)}`;
-	},
-	miniHtml() {
-		return `
-<!-- generated with https://huggingface.co/spaces/failfast/2D-GameCreator by https://failfa.st -->
-${renderToString(
-	<>
-		<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
-
-		<canvas id="canvas" />
-	</>
-)}`;
-	},
-	css() {
-		return `/**
- * generated with https://huggingface.co/spaces/failfast/2D-GameCreator
- * by https://failfa.st
- */
-
-* {
-	margin: 0;
-	padding: 0;
-	box-sizing: border-box;
-}
-html, body {
-	height: 100%;
-	width: 100%;
-	overflow: hidden;
-}
-`;
-	},
-	js(content) {
-		return `
-/**
- * generated with https://huggingface.co/spaces/failfast/2D-GameCreator
- * by https://failfa.st
- */
-
-
-/**
- * Global imports
- */
-import Mousetrap from "https://cdn.skypack.dev/mousetrap@1.6.5";
-import confetti from "https://cdn.skypack.dev/canvas-confetti@1.4.0";
-
-/**
- * Helper to handle the resize of the window > canvas automatically
- */
-function __2DGameCreator__ResizeHelper(){
-	const _canvas = document.querySelector("canvas")
-	_canvas.width = window.innerWidth;
-	_canvas.height = window.innerHeight;
-
-	function handleResize() {
-		requestAnimationFrame(() => {
-			_canvas.width = window.innerWidth;
-			_canvas.height = window.innerHeight;
-		});
-	}
-	handleResize();
-	window.addEventListener("resize", handleResize, { passive: true });
-}
-__2DGameCreator__ResizeHelper()
-
-function __2DGameCreator__downloadCanvasImage() {
-    // Get the canvas element
-    const canvas = document.getElementById('canvas');
-
-    // Create a new 'a' element
-    let link = document.createElement('a');
-
-    // Set the download attribute with a file name
-    link.download = 'canvas_image.png';
-
-    // Get the data URL for the canvas
-    link.href = canvas.toDataURL();
-
-    // Trigger the click event on the link
-    link.click();
-}
-
-// Uncomment if you want to take screenshots of the canvas
-/*document.addEventListener("keydown", (e) => {
-	if (e.isComposing || e.code === "KeyS") {
-	  __2DGameCreator__downloadCanvasImage();
-	  return;
-	}
-});*/
-
-function createGameLoop(callback) {
-    let lastTimestamp;
-
-    function gameLoop(timestamp) {
-        if (lastTimestamp) {
-            const delta = timestamp - lastTimestamp;
-
-            if (delta >= 1000 / 60) {
-                lastTimestamp = timestamp;
-                callback(delta);
-            }
-        } else {
-            lastTimestamp = timestamp;
-        }
-        requestAnimationFrame(gameLoop);
-    }
-
-    return gameLoop;
-}
-
-window.createGameLoop = createGameLoop;
-
-
-/**
- * Generated 2D game
- */
-${content}
-`;
-	},
-};
diff --git a/spaces/falterWliame/Face_Mask_Detection/Download Legend Of The Seeker - Season 2 Complete 480p X264 - Shan Torrent - KickassTorrents [NEW].md b/spaces/falterWliame/Face_Mask_Detection/Download Legend Of The Seeker - Season 2 Complete 480p X264 - Shan Torrent - KickassTorrents [NEW].md
deleted file mode 100644
index 1bd33156deaa70058bf855bc326b8d789dd3151a..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Download Legend Of The Seeker - Season 2 Complete 480p X264 - Shan Torrent - KickassTorrents [NEW].md	
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-<p>The second season of <strong>Legend of the Seeker</strong> aired from 2009 to 2010 and consisted of 22 episodes. It continued the story of Richard and his friends as they tried to stop the Keeper of the Underworld from breaking free and destroying all life. Along the way, they encountered new enemies and allies, such as Cara, a former Mord-Sith who joined their cause, and Nicholas Rahl, Darken Rahl's son who inherited his father's powers.</p>
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-<h3>What is Legend of the Seeker Season 2 About?</h3>
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-<p><strong>Legend of the Seeker Season 2</strong> is based on the second book of Terry Goodkind's epic fantasy series, The Sword of Truth. The book is called Stone of Tears, and it follows the adventures of Richard Cypher, Kahlan Amnell, and Zeddicus Zu'l Zorander as they try to stop the Keeper of the Underworld from breaking free and destroying all life.</p>
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-<p>The season begins with Richard discovering that he is not only the Seeker of Truth, but also a wizard with a rare and powerful gift. He also learns that his victory over Darken Rahl has caused a rift in the veil between the world of the living and the world of the dead, allowing the Keeper to send his minions to kill anyone who can seal the rift. Richard must find the Stone of Tears, a legendary artifact that can close the rift, before it is too late.</p>
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-<p>Along the way, Richard and his friends face many dangers and enemies, such as Denna, a Mord'Sith who was resurrected by the Keeper and wants revenge on Richard; Nicholas Rahl, Darken Rahl's son who inherited his father's powers and wants to rule D'Hara; and Sister Nicci, a Sister of the Dark who kidnaps Richard and tries to turn him to the Keeper's side. They also meet new allies, such as Cara, a former Mord'Sith who joins their cause; Rachel, a young girl who helps them find the Stone of Tears; and Leo Dane, a general who leads a resistance against Nicholas Rahl.</p>
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-<p><strong>Legend of the Seeker Season 2</strong> is not only a thrilling and entertaining show, but also a show that can offer you some benefits. Here are some of them:</p>
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-<ul>
-<li>You can improve your imagination and creativity by immersing yourself in a rich and detailed fantasy world.</li>
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-<h4>What are the Reviews of Legend of the Seeker Season 2?</h4>
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-<p><strong>Legend of the Seeker Season 2</strong> received mixed to positive reviews from critics and fans alike. Some praised the show for its improved storytelling, character development, action scenes, and humor. Others criticized the show for its deviations from the books, slow pace, cheesy dialogue, and lack of originality.</p>
-
-<p>Here are some examples of reviews from different sources:</p>
-
-<blockquote>
-<p>"A strong opening for a show that is starting to live up to its considerable potential." - Chris Conaton, PopMatters</p>
-</blockquote>
-
-<blockquote>
-<p>"Legend of the Seeker's season long epic quest may have bogged down in the middle and had a somewhat corny ending... but in the end it was a fun action adventure show with great characters and it will be missed." - Diana Steenbergen, IGN</p>
-</blockquote>
-
-<blockquote>
-<p>"The second season of Legend of The Seeker is an improvement over season one in many ways... However, it still suffers from many of the same problems that plagued season one." - Michael Simpson, SciFiNow</p>
-</blockquote>
-
-<p>The average rating of <strong>Legend of the Seeker Season 2</strong> on Rotten Tomatoes is 88% based on 50+ ratings from the audience. The average rating on IMDb is 7.6/10 based on 30,000+ votes from the users.</p>
-<h3>Who are the Cast and Crew of Legend of the Seeker Season 2?</h3>
-
-<p><strong>Legend of the Seeker Season 2</strong> features a talented and diverse cast and crew who bring the fantasy world to life. Here are some of them:</p>
-
-<ul>
-<li>Craig Horner as Richard Cypher, the Seeker of Truth and a gifted wizard who must stop the Keeper from destroying all life.</li>
-<li>Bridget Regan as Kahlan Amnell, the Mother Confessor and Richard's true love who can control people with her touch.</li>
-<li>Bruce Spence as Zeddicus Zu'l Zorander, Richard's grandfather and a powerful wizard who mentors him.</li>
-<li>Tabrett Bethell as Cara Mason, a former Mord'Sith who joins Richard's quest and becomes his loyal friend.</li>
-<li>Craig Parker as Darken Rahl, Richard's half-brother and a ruthless tyrant who is resurrected by the Keeper.</li>
-<li>Danielle Cormack as Shota, a witch woman who can see the future and manipulate reality.</li>
-<li>Jessica Marais as Denna, a Mord'Sith who was killed by Richard and brought back by the Keeper to torment him.</li>
-<li>William Morgan Sheppard as The Keeper, the lord of the Underworld who wants to end all existence.</li>
-</ul>
-
-<p>The show is based on the novels by Terry Goodkind and developed for television by Stephen Tolkin and Kenneth Biller. The show is produced by Sam Raimi, Rob Tapert, Joshua Donen, Ned Nalle, and Ken Biller. The show is directed by various directors, such as Mark Beesley, Garth Maxwell, Michael Hurst, Jonathan Brough, Chris Martin-Jones, Jesse Warn, Andrew Merrifield, Charlie Haskell, Geoffrey Cawthorn, and Mike Smith. The show is written by various writers, such as Stephen Tolkin, Kenneth Biller, Charley Dane, Chad Fiveash, James Patrick Stoteraux, Mike Sussman, Nicki Paluga, Raf Green, Arika Lisanne Mittman, Erin Maher, Kay Reindl, Barry M. Schkolnick, John Shiban, and Kristine Huntley.</p>
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- <p>To give you a better idea of how Epic Conquest 2 mod apk max level differs from the original game, here is a table that compares some of the key features of both versions:</p>
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-<tr><td>Level</td><td>Limited by story progress</td><td>Maxed by mod menu</td></tr>
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-<tr><td>God Mode</td><td>N/A</td><td>Available by mod menu</td></tr>
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-<tr><td>Data Syncing</td><td>Possible with Google Play Games account</td><td>N/A (use at your own risk)</td></tr>
-<tr><td>Bug Fixes/Updates</td><td>Frequent by developer support</td><td>Rare by modder support (may not work with latest version)</td></tr>
-<tr><td>Achievements/Leaderboards</td><td>Possible with Google Play Games account</td><t d>N/A (use at your own risk)</td></tr>
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- <h2>Conclusion</h2>
- <p>Epic Conquest 2 is a great action RPG game that you can play on your mobile device. It has a captivating story, charming characters, exciting combat, and tons of items to collect and customize. However, if you want to experience the game in a different way, you can try Epic Conquest 2 mod apk max level, which gives you access to features that are not available in the original game. You can have unlimited money, god mode, max level, and more. You can download and install Epic Conquest 2 mod apk max level easily and safely by following the steps we provided in this article. You can also play Epic Conquest 2 mod apk max level with some tips and tricks we shared with you. We hope you enjoy Epic Conquest 2 mod apk max level and have fun!</p>
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-<p>In this article, we will tell you everything you need to know about the latest OB40 update of Free Fire MAX, how to download and install it on your Android device, how to play it with your existing Free Fire account, how to enjoy the premium gameplay experience, and some tips and tricks to survive and win in this game. So, let's get started!</p>
- <h2>What's New in the OB40 Update of Free Fire MAX?</h2>
-<p>The OB40 update of Free Fire MAX is one of the most awaited updates of this game. It brings a lot of new features and improvements that will make your gaming experience even more enjoyable. Here are some of the highlights of this update:</p>
-<ul>
-<li>New characters: Meet Xayne and Maro, two new characters with unique abilities. Xayne is an extreme sports enthusiast who can increase her HP and damage for a short duration. Maro is a falconer who can deal more damage based on the distance from his enemies.</li>
-<li>New pets: Say hello to Dr. Beanie and Moony, two new pets that will accompany you in your battles. Dr. Beanie is a doctor who can heal you when you are low on HP. Moony is a moon fairy who can reduce the damage you take from enemies.</li>
-<li>New weapons: Try out the Kord and the Ice Gun, two new weapons that will give you an edge over your opponents. The Kord is a heavy machine gun that can fire faster and deal more damage when prone or crouched. The Ice Gun is a special weapon that can freeze your enemies and slow them down.</li>
-<li>New modes: Have fun with the Clash Squad Season 7 and the Rampage New Dawn mode. The Clash Squad Season 7 is a ranked mode where you can compete with other players in 4v4 matches. The Rampage New Dawn mode is a limited-time mode where you can transform into powerful beasts with special abilities.</li>
-</ul>
- <h2>How to Download and Install Free Fire MAX APK 50 MB + OBB for Android Devices?</h2>
-<p>If you want to play Free Fire MAX on your Android device, you will need to download and install the APK file and the OBB file of this game. The APK file is about 50 MB in size, while the OBB file is about 1 GB in size. Here are the steps to download and install them:</p>
-<ol>
-<li>Go to [this link](^1^) and download the APK file and the OBB file of Free Fire MAX.</li>
-<li>After downloading them, go to your device settings and enable the installation of apps from unknown sources.</li>
-<li>Locate the downloaded APK file and tap on it to install it on your device.</li>
-<li>Extract the OBB file using any file manager app and copy the folder named "com.dts.freefiremax" to the Android/OBB directory on your device.</li>
-<li>Launch the game and enjoy the premium gameplay experience of Free Fire MAX.</li>
-</ol>
-<p>Note: You will need a stable internet connection to play this game. You may also need to update the game from time to time to get the latest features and improvements.</p>
- <h2>How to Play Free Fire MAX with Your Existing Free Fire Account?</h2>
-<p>If you already have a Free Fire account and you want to play Free Fire MAX with it, you don't have to worry about losing your progress or data. You can use the Firelink technology to link your existing account with Free Fire MAX and play seamlessly across both versions of the game. Here's how:</p>
-<ul>
-<li>Open Free Fire MAX and tap on the Firelink icon on the top right corner of the screen.</li>
-<li>Select the option to link your existing account with Free Fire MAX. You can choose from Facebook, Google, VK, or Huawei.</li>
-<li>Log in with your credentials and confirm the linking process.</li>
-<li>Once done, you can access your account data, such as your level, rank, inventory, friends, etc., on both Free Fire and Free Fire MAX.</li>
-<li>You can also switch between the two versions of the game anytime you want by tapping on the Firelink icon again.</li>
-</ul>
-<p>Note: You can only link one account with Free Fire MAX. If you want to unlink your account, you will have to contact customer service for assistance.</p>
- <h2>How to Enjoy the Premium Gameplay Experience of Free Fire MAX?</h2>
-<p>Free Fire MAX is designed to offer you a premium gameplay experience that is different from the regular Free Fire game. You can enjoy the following features that will enhance your gaming experience:</p>
-<ul>
-<li>HD graphics: You can witness the stunning details and textures of the game world with high-definition graphics. You can also adjust the graphics settings according to your device performance and preference.</li>
-<li>Enhanced special effects: You can feel the thrill of combat with enhanced special effects, such as explosions, fire, smoke, etc. You can also see the realistic shadows and reflections of the objects and characters in the game.</li>
-<li>Smoother gameplay: You can experience a smoother gameplay with faster loading times and reduced lag. You can also customize the controls and sensitivity settings to suit your play style.</li>
-</ul>
- <h2>Tips and Tricks to Survive and Win in Free Fire MAX</h2>
-<p>Free Fire MAX is a challenging game that requires skill, strategy, and luck to survive and win. Here are some tips and tricks that will help you improve your chances of victory:</p>
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-<ul>
-<li>Choose your landing spot wisely: The map of Free Fire MAX is huge and has various locations with different loot and enemies. You should choose a landing spot that suits your play style and strategy. For example, if you want to avoid early fights, you can land in a remote area with less loot but more safety. If you want to get better loot and more action, you can land in a hot spot with more loot but more enemies.</li>
-<li>Use your character and pet abilities: Each character and pet in Free Fire MAX has a unique ability that can give you an advantage in different situations. You should use them wisely and strategically to gain an edge over your opponents. For example, you can use Xayne's ability to increase your HP and damage when you are low on health or ammo. You can use Maro's ability to deal more damage from a distance when you have a sniper rifle. You can use Dr. Beanie's ability to heal yourself when you are injured. You can use Moony's ability to reduce the damage you take from enemies when you are under fire.</li>
-<li>Use cover and movement: The key to survival in Free Fire MAX is to use cover and movement effectively. You should always look for cover when you are exposed or under attack. You should also move constantly and unpredictably to avoid being an easy target for your enemies. You can use vehicles, gliders, ropes, etc., to move faster and escape from danger.</li>
-<li>Use weapons and items: There are various weapons and items in Free Fire MAX that can help you in combat. You should always look for weapons that suit your play style and situation. For example, if you are fighting in close range, you can use shotguns or SMGs. If you are fighting in long range, you can use sniper rifles or assault rifles. You should also use items such as grenades, medkits, armor, etc., to deal damage, heal yourself, or protect yourself from enemy attacks.</li>
-<li>Play with your team: Free Fire MAX is a game that can be played solo or with your team. You can play with your friends or join a random squad of up to four players. You should always communicate and coordinate with your team to increase your chances of survival and victory. You can use the voice chat or the quick chat feature to communicate with your team. You can also share loot, revive teammates, and support each other in combat.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Free Fire MAX is a game that will give you a premium gameplay experience in a battle royale. You can enjoy the latest features and improvements of the OB40 update, download and install the game easily on your Android device, play with your existing Free Fire account, enjoy the HD graphics and enhanced special effects, and use the tips and tricks to survive and win in this game. So, what are you waiting for? Download Free Fire MAX today and join the ultimate survival challenge!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions and answers about Free Fire MAX:</p>
-<ol>
-<li>Q: Is Free Fire MAX free to play?</li>
-<li>A: Yes, Free Fire MAX is free to play. However, you can also purchase in-game items such as diamonds, skins, characters, pets, etc., with real money.</li>
-<li>Q: Is Free Fire MAX compatible with my device?</li>
-<li>A: Free Fire MAX requires Android 4.4 or higher and at least 2 GB of RAM to run smoothly. You can check the compatibility of your device by visiting [this link].</li>
-<li>Q: Can I play Free Fire MAX with players who are playing Free Fire?</li>
-<li>A: Yes, you can play Free Fire MAX with players who are playing Free Fire via the Firelink technology. You can also switch between the two versions of the game anytime you want.</li>
-<li>Q: How can I contact customer service if I have any issues or feedback about Free Fire MAX?</li>
-<li>A: You can contact customer service by tapping on the settings icon on the top right corner of the screen, then tapping on the customer service icon on the bottom left corner of the screen. You can also visit [this link] for more information.</li>
-<li>Q: Where can I find more resources and guides about Free Fire MAX?</li>
-<li>A: You can find more resources and guides about Free Fire MAX by visiting [this link]. You can also follow the official social media accounts of Free Fire MAX for the latest news and updates.</li>
-</ol></p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Download Tuning Club Online Mod APK and Enjoy Unlimited Features and Modes.md b/spaces/fatiXbelha/sd/Download Tuning Club Online Mod APK and Enjoy Unlimited Features and Modes.md
deleted file mode 100644
index bcca8b74c951f8733e09c6d559ddb4a810c0ec19..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Tuning Club Online Mod APK and Enjoy Unlimited Features and Modes.md	
+++ /dev/null
@@ -1,106 +0,0 @@
-<br />
-<h1>How to Download Tuning Club Online Hack and Enjoy Unlimited Money, Nitro, and More</h1>
-<p>If you are a fan of racing games, you might have heard of <strong>Tuning Club Online</strong>, a unique driving game that lets you build your car, play with friends and rivals around the world, and customize your vehicle with various parts, skins, and boosters. But what if you want to get more out of the game without spending real money or grinding for hours? Well, there is a solution for that: <strong>Tuning Club Online Hack</strong>, a modded version of the game that gives you unlimited money, nitro, and other features that will make you the champion in the arena. In this article, we will tell you what Tuning Club Online is, why you should play it, what Tuning Club Online Hack is, what are the benefits of using it, and how to download it for your Android or iOS device. So, let's get started!</p>
- <h2>What is Tuning Club Online and Why You Should Play It</h2>
-<p>Tuning Club Online is a racing game developed by Two Headed Shark DMCC. It is available for free on Google Play Store and App Store for Android and iOS devices respectively. The game has over 10 million downloads and 4.5 stars rating on both platforms. Here are some of the reasons why you should play Tuning Club Online:</p>
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- <h3>Tuning Club Online is a unique driving game in real time via the network</h3>
-<p>Unlike other racing games that have you race against bots or ghosts, Tuning Club Online lets you race against real players from around the world in real time. You can chat with your friends, join clubs, challenge rivals, and participate in tournaments. You can also choose from different modes such as speed race, drift mode, crown hold mode, bomb mode, and free ride mode.</p>
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-<p>Tuning Club Online is not just about racing; it is also about tuning your car to fit your style and preferences. You can customize your car with bumpers, body kits, hoods, spoilers, vinyls, skins, tires, wheels, police lights, clown heads, crazy tailpipes, and more. You can also build your engine by combining rare parts and their unique qualities such as pistons, crankshafts, camshafts, flywheels, etc. You can also adjust your suspension, camber, offset, and change your tires for best grip.</p>
- <h3>Tuning Club Online has a collection of legendary cars for tuning and customization</h3>
-<p>If you are a car enthusiast, you will love Tuning Club Online's collection of legendary cars for tuning and customization. You can choose from E36, RX7, Skyline, Evolution, and more. You can also unlock new cars by winning races, completing tasks, or buying them with money. You can also sell your cars or parts to other players or buy them from the market.</p>
- <h2>What is Tuning Club Online Hack and What are the Benefits of Using It</h2>
-<p>Tuning Club Online Hack is a modded version of the game that gives you unlimited money, nitro, and other features that will make you the champion in the arena. Tuning Club Online Hack is not an official app from the developers of Tuning Club Online, but a third-party app that modifies the original game files to give you access to premium features for free. Here are some of the benefits of using Tuning Club Online Hack:</p>
- <h3>Tuning Club Online Hack gives you unlimited money, nitro, and other features</h3>
-<p>With Tuning Club Online Hack, you don't have to worry about running out of money, nitro, or other resources in the game. You can use them as much as you want without any limitations or restrictions. You can buy any car, part, skin, or booster you want without spending real money or grinding for hours. You can also use nitro to boost your speed and performance in any mode or race.</p>
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- <h3>Tuning Club Online Hack lets you unlock all the cars, parts, skins, and boosters in the game</h3>
-<p>With Tuning Club Online Hack, you don't have to wait for unlocking new cars, parts, skins, or boosters in the game. You can access them all from the start and enjoy the full potential of the game. You can choose from any car you like and customize it with any part, skin, or booster you want. You can also switch between different cars and configurations anytime you want.</p>
- <h3>Tuning Club Online Hack lets you dominate the races, drifts, and battles with your friends and rivals</h3>
-<p>With Tuning Club Online Hack, you don't have to fear losing to your friends or rivals in the game. You can dominate the races, drifts, and battles with your superior cars and skills. You can show off your amazing tuning and driving abilities and impress everyone in the arena. You can also challenge anyone to a duel and prove your supremacy in the game.</p>
- <h2>How to Download Tuning Club Online Hack for Android and iOS Devices</h2>
-<p>If you are interested in downloading Tuning Club Online Hack for your Android or iOS device, here are the steps you need to follow:</p>
- <h3>Download Tuning Club Online Hack APK or IPA file from a trusted source</h3>
-<p>The first step is to download the Tuning Club Online Hack APK or IPA file from a trusted source. You can find many websites that offer these files for free download, but be careful not to download any malware or virus along with them. We recommend using [this website] as it is safe and reliable. You can also scan the file with an antivirus before installing it on your device.</p>
- <h3>Install Tuning Club Online Hack on your device by following the instructions</h3>
-<p>The second step is to install Tuning Club Online Hack on your device by following the instructions. For Android devices, you need to enable unknown sources in your settings and then tap on the downloaded APK file to install it. For iOS devices, you need to use a third-party app installer such as Cydia Impactor or AltStore to install the IPA file on your device. You may also need to trust the app in your settings before launching it.</p>
- <h3>Launch Tuning Club Online Hack and enjoy the game with unlimited resources</h3>
-<p>The final step is to launch Tuning Club Online Hack and enjoy the game with unlimited resources. You will see a menu with different options such as money, nitro, unlock all cars, etc. You can toggle them on or off as per your preference. You can also access the original game settings and features from the same menu. You can now play Tuning Club Online with unlimited money, nitro, and other features.</p>
- <h2>Conclusion and FAQs</h2>
-<p>In conclusion, Tuning Club Online Hack is a great way to enhance your gaming experience and have more fun with Tuning Club Online. It gives you unlimited money, nitro, and other features that will make you the champion in the arena. It lets you unlock all the cars, parts, skins, and boosters in the game. It lets you dominate the races, drifts, and battles with your friends and rivals. It is easy to download and install on your Android or iOS device. However, you should be careful not to use it excessively or abuse it as it may cause problems with your account or device. You should also respect other players and not ruin their enjoyment of the game.</p>
- <p>If you have any questions about Tuning Club Online Hack, you can check out the FAQs below. We have compiled five common questions and answers about Tuning Club Online Hack that might help you.</p>
- <h3>FAQs: Five common questions and answers about Tuning Club Online Hack</h3>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is Tuning Club Online Hack safe to use?</td>
-<td>Tuning Club Online Hack is generally safe to use as long as you download it from a trusted source and scan it with an antivirus before installing it. However, you should be aware that using any modded or hacked app may violate the terms and conditions of the original game and may result in your account being banned or suspended. Therefore, you should use Tuning Club Online Hack at your own risk and discretion.</td>
-</tr>
-<tr>
-<td>Does Tuning Club Online Hack work on all devices?</td>
-<td>Tuning Club Online Hack works on most Android and iOS devices that support the original game. However, some devices may not be compatible with the hack or may experience some glitches or errors. If you encounter any problems with Tuning Club Online Hack, you can try to uninstall and reinstall it or contact the developer for support.</td>
-</tr>
-<tr>
-<td>How often is Tuning Club Online Hack updated?</td>
-<td>Tuning Club Online Hack is updated regularly to keep up with the latest updates and features of the original game. However, there may be some delays or issues with the updates due to technical difficulties or other reasons. You can check the website where you downloaded Tuning Club Online Hack for the latest version and information.</td>
-</tr>
-<tr>
-<td>Can I play Tuning Club Online Hack offline?</td>
-<td>No, you cannot play Tuning Club Online Hack offline as it requires an internet connection to connect with other players and access the game servers. If you try to play Tuning Club Online Hack offline, you may encounter errors or crashes. Therefore, you should always play Tuning Club Online Hack online with a stable and secure connection.</td>
-</tr>
-<tr>
-<td>Can I play Tuning Club Online Hack with my friends?</td>
-<td>Yes, you can play Tuning Club Online Hack with your friends as long as they also have the hack installed on their devices. You can chat with them, join clubs, challenge them, and race with them in real time. However, you should be careful not to use the hack to cheat or harass other players as it may ruin their gaming experience and cause conflicts.</td>
-</tr>
-</table>
- <p>I hope this article has helped you learn how to download Tuning Club Online Hack and enjoy unlimited money, nitro, and more. If you have any feedback or suggestions, please let me know in the comments below. Thank you for reading and happy tuning!</p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/util/preprocess.py b/spaces/fb700/chatglm-fitness-RLHF/src/face3d/util/preprocess.py
deleted file mode 100644
index b77a3a4058c208e5ba8cb1cfbb563954a5f7a3e2..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/util/preprocess.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""This script contains the image preprocessing code for Deep3DFaceRecon_pytorch
-"""
-
-import numpy as np
-from scipy.io import loadmat
-from PIL import Image
-import cv2
-import os
-from skimage import transform as trans
-import torch
-import warnings
-warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning) 
-warnings.filterwarnings("ignore", category=FutureWarning) 
-
-
-# calculating least square problem for image alignment
-def POS(xp, x):
-    npts = xp.shape[1]
-
-    A = np.zeros([2*npts, 8])
-
-    A[0:2*npts-1:2, 0:3] = x.transpose()
-    A[0:2*npts-1:2, 3] = 1
-
-    A[1:2*npts:2, 4:7] = x.transpose()
-    A[1:2*npts:2, 7] = 1
-
-    b = np.reshape(xp.transpose(), [2*npts, 1])
-
-    k, _, _, _ = np.linalg.lstsq(A, b)
-
-    R1 = k[0:3]
-    R2 = k[4:7]
-    sTx = k[3]
-    sTy = k[7]
-    s = (np.linalg.norm(R1) + np.linalg.norm(R2))/2
-    t = np.stack([sTx, sTy], axis=0)
-
-    return t, s
-    
-# resize and crop images for face reconstruction
-def resize_n_crop_img(img, lm, t, s, target_size=224., mask=None):
-    w0, h0 = img.size
-    w = (w0*s).astype(np.int32)
-    h = (h0*s).astype(np.int32)
-    left = (w/2 - target_size/2 + float((t[0] - w0/2)*s)).astype(np.int32)
-    right = left + target_size
-    up = (h/2 - target_size/2 + float((h0/2 - t[1])*s)).astype(np.int32)
-    below = up + target_size
-
-    img = img.resize((w, h), resample=Image.BICUBIC)
-    img = img.crop((left, up, right, below))
-
-    if mask is not None:
-        mask = mask.resize((w, h), resample=Image.BICUBIC)
-        mask = mask.crop((left, up, right, below))
-
-    lm = np.stack([lm[:, 0] - t[0] + w0/2, lm[:, 1] -
-                  t[1] + h0/2], axis=1)*s
-    lm = lm - np.reshape(
-            np.array([(w/2 - target_size/2), (h/2-target_size/2)]), [1, 2])
-
-    return img, lm, mask
-
-# utils for face reconstruction
-def extract_5p(lm):
-    lm_idx = np.array([31, 37, 40, 43, 46, 49, 55]) - 1
-    lm5p = np.stack([lm[lm_idx[0], :], np.mean(lm[lm_idx[[1, 2]], :], 0), np.mean(
-        lm[lm_idx[[3, 4]], :], 0), lm[lm_idx[5], :], lm[lm_idx[6], :]], axis=0)
-    lm5p = lm5p[[1, 2, 0, 3, 4], :]
-    return lm5p
-
-# utils for face reconstruction
-def align_img(img, lm, lm3D, mask=None, target_size=224., rescale_factor=102.):
-    """
-    Return:
-        transparams        --numpy.array  (raw_W, raw_H, scale, tx, ty)
-        img_new            --PIL.Image  (target_size, target_size, 3)
-        lm_new             --numpy.array  (68, 2), y direction is opposite to v direction
-        mask_new           --PIL.Image  (target_size, target_size)
-    
-    Parameters:
-        img                --PIL.Image  (raw_H, raw_W, 3)
-        lm                 --numpy.array  (68, 2), y direction is opposite to v direction
-        lm3D               --numpy.array  (5, 3)
-        mask               --PIL.Image  (raw_H, raw_W, 3)
-    """
-
-    w0, h0 = img.size
-    if lm.shape[0] != 5:
-        lm5p = extract_5p(lm)
-    else:
-        lm5p = lm
-
-    # calculate translation and scale factors using 5 facial landmarks and standard landmarks of a 3D face
-    t, s = POS(lm5p.transpose(), lm3D.transpose())
-    s = rescale_factor/s
-
-    # processing the image
-    img_new, lm_new, mask_new = resize_n_crop_img(img, lm, t, s, target_size=target_size, mask=mask)
-    trans_params = np.array([w0, h0, s, t[0], t[1]])
-
-    return trans_params, img_new, lm_new, mask_new
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Car Simulator 2 APK Drive from First or Third Person Perspective in a 3D World.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Car Simulator 2 APK Drive from First or Third Person Perspective in a 3D World.md
deleted file mode 100644
index ce5775c4f8e24bed96a80c32e4638e67f564340d..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Car Simulator 2 APK Drive from First or Third Person Perspective in a 3D World.md	
+++ /dev/null
@@ -1,115 +0,0 @@
-
-<h1>Car Simulator 2 APK Pure: A Review of the Most Realistic Driving Simulator Game</h1>
- <p>If you are a fan of driving games, you might have heard of Car Simulator 2, a popular game that offers a realistic driving experience in a 3D open world. But did you know that you can download and play Car Simulator 2 APK Pure, a modified version of the game that gives you access to all the features and cars without any restrictions? In this article, we will review Car Simulator 2 APK Pure and tell you what it is, what features it has, how to download and install it, and some tips and tricks for playing it.</p>
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- <h2>What is Car Simulator 2 APK Pure?</h2>
- <p>Car Simulator 2 APK Pure is a modified version of Car Simulator 2, a game developed by Oppana Games. Car Simulator 2 is a fun, free-to-play game that lets you play online with real players from all over the world, win races, earn currency, buy new cars, upgrade them, customize them, explore a large city, and become the best driver. However, some features and cars in the game are locked or require in-app purchases to unlock them. That's where Car Simulator 2 APK Pure comes in. Car Simulator 2 APK Pure is a version of the game that has been modified to remove all the limitations and give you unlimited access to everything in the game. You can download Car Simulator 2 APK Pure from various websites that offer APK files for Android devices.</p>
- <h3>Features of Car Simulator 2 APK Pure</h3>
- <p>Car Simulator 2 APK Pure has all the features of the original game, plus some extra ones that make it more enjoyable and exciting. Here are some of the features of Car Simulator 2 APK Pure:</p>
- <h4>- Online and single-player modes</h4>
- <p>You can play Car Simulator 2 APK Pure online with real players from all over the world, or offline in single-player mode. You can join or create races, chat with other players, compete in leaderboards, and make friends.</p>
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- <h4>- 3D open world</h4>
- <p>You can explore a large city with different areas, such as downtown, suburbs, airport, beach, and more. You can drive freely around the city or follow the map to find missions and challenges. You can also interact with various elements in the city, such as traffic lights, pedestrians, animals, buildings, etc.</p>
- <h4>- Over 85 different car models</h4>
- <p>You can choose from over 85 different car models in Car Simulator 2 APK Pure, ranging from sports cars, muscle cars, SUVs, trucks, and more. You can also customize your cars with different colors, stickers, wheels, spoilers, and other accessories. You can buy new cars with the currency you earn from races and missions, or you can get them for free by downloading Car Simulator 2 APK Pure.</p>
- <h4>- Daily bonuses and quests</h4>
- <p>You can get daily bonuses and quests in Car Simulator 2 APK Pure that reward you with currency, cars, and other items. You can complete quests by doing various tasks, such as driving a certain distance, winning a race, performing a stunt, etc. You can also get bonuses by logging in every day, watching ads, or spinning a wheel.</p>
- <h4>- Fully detailed car models and interiors</h4>
- <p>You can enjoy the realistic graphics and animations of Car Simulator 2 APK Pure that show every detail of the car models and interiors. You can switch between different camera views, such as first-person, third-person, or dashboard. You can also control various functions of the car, such as headlights, turn signals, horn, windows, doors, trunk, etc.</p>
- <h4>- Realistic physics and sound effects</h4>
- <p>You can experience the realistic physics and sound effects of Car Simulator 2 APK Pure that make you feel like you are driving a real car. You can hear the engine roar, the tires screech, the brakes squeak, and the wind blow. You can also feel the impact of collisions, bumps, jumps, and crashes.</p>
- <h4>- A mechanic with lots of upgrade options</h4>
- <p>You can visit a mechanic in Car Simulator 2 APK Pure to upgrade your car's performance and appearance. You can improve your car's engine, transmission, suspension, brakes, tires, turbo, nitro, etc. You can also change your car's body kit, hood, spoiler, exhaust pipe, etc.</p>
- <h4>- Interactive gas station</h4>
- <p>You can visit an interactive gas station in Car Simulator 2 APK Pure to fill up your car's gas tank. You can also use the gas station's services, such as car wash, tire repair, oil change, etc. You can pay for the gas and services with your currency or watch ads to get them for free.</p>
- <h4>- Exciting missions and challenges</h4>
- <p>You can find various missions and challenges in Car Simulator 2 APK Pure that test your driving skills and give you rewards. You can do missions such as delivering pizza, taxiing passengers, racing against rivals, escaping from the police, etc. You can also do challenges such as drifting, parking, jumping ramps, etc.</p>
- <h4>- Dynamic day-night cycle</h4>
- <p>snow, fog, etc.</p>
- <h3>How to download and install Car Simulator 2 APK Pure?</h3>
- <p>If you want to download and install Car Simulator 2 APK Pure on your Android device, you need to follow these steps:</p>
- <ol>
-<li>Go to a website that offers Car Simulator 2 APK Pure file, such as [APKPure], [APKCombo], or [APKFab].</li>
-<li>Download the Car Simulator 2 APK Pure file to your device.</li>
-<li>Go to your device's settings and enable the option to install apps from unknown sources.</li>
-<li>Locate the Car Simulator 2 APK Pure file on your device and tap on it to install it.</li>
-<li>Wait for the installation to finish and launch the game.</li>
-</ol>
- <p>Note: You may need to uninstall the original Car Simulator 2 game before installing Car Simulator 2 APK Pure, as they may conflict with each other.</p>
- <h3>Tips and tricks for playing Car Simulator 2 APK Pure</h3>
- <p>If you want to have more fun and success in playing Car Simulator 2 APK Pure, you can use these tips and tricks:</p>
- <h4>- Buy new cars and upgrade them to win races</h4>
- <p>You can buy new cars and upgrade them in Car Simulator 2 APK Pure to improve their speed, acceleration, handling, and durability. You can also customize their appearance with different colors, stickers, wheels, spoilers, and other accessories. You can use the currency you earn from races and missions to buy and upgrade cars, or you can get them for free by downloading Car Simulator 2 APK Pure.</p>
- <h4>- Don't go too fast in the city - drive carefully</h4>
- <p>You can drive as fast as you want in Car Simulator 2 APK Pure, but be careful not to crash into other cars, pedestrians, buildings, or obstacles. You can damage your car and lose money if you do. You can also get a ticket from the police if they catch you speeding or breaking the rules of the road. You can pay the ticket with your currency or bribe the police with a lower amount.</p>
- <h4>- Watch out for the cops - they'll give you a ticket if they catch you speeding</h4>
- <p>You can encounter the cops in Car Simulator 2 APK Pure who will try to stop you if they see you speeding or breaking the rules of the road. You can try to outrun them or hide from them, but be careful not to crash into them or cause too much chaos. You can also try to bribe them with a lower amount than the ticket they give you.</p>
- <h4>- Pay attention to interactive tips and dialog boxes</h4>
- <p>You can get interactive tips and dialog boxes in Car Simulator 2 APK Pure that will guide you through the game and give you useful information. You can learn how to play the game, how to use different features, how to complete missions and challenges, how to earn currency and cars, etc. You can also get hints and clues from other characters in the game who will talk to you or call you.</p>
- <h4>- Don't forget to gas up your car at the gas station</h4>
- <p>You can visit an interactive gas station in Car Simulator 2 APK Pure to fill up your car's gas tank. You can also use the gas station's services, such as car wash, tire repair, oil change, etc. You can pay for the gas and services with your currency or watch ads to get them for free. You can also get bonuses by visiting the gas station regularly.</p>
- <h4>- Bribes are cheaper than official tickets</h4>
- <p>You can bribe the cops in Car Simulator 2 APK Pure with a lower amount than the ticket they give you if they catch you speeding or breaking the rules of the road. You can save money by doing this, but be careful not to do it too often or they will get suspicious and arrest you.</p>
- <h4>- You can make extra money by picking up cab fares or working for the mob</h4>
- <p>You can make extra money in Car Simulator 2 APK Pure by picking up cab fares or working for the mob. You can find cab fares by driving around the city and looking for yellow icons on the map. You can pick up passengers and take them to their destinations for a fee. You can also find mob jobs by driving around the city and looking for red icons on the map. You can do tasks for the mob such as delivering packages, stealing cars, assassinating targets, etc. for a reward.</p>
- <h4>- Obey the rules of the road</h4>
- <p>Car Simulator 2 APK Pure to avoid getting tickets from the cops or causing accidents. You can follow the traffic lights, stop signs, speed limits, lane markings, etc. You can also use your turn signals, horn, headlights, etc. to communicate with other drivers and pedestrians. You can also park your car properly in designated spots or garages.</p>
- <h2>Conclusion</h2>
- <p>Car Simulator 2 APK Pure is a modified version of Car Simulator 2, a game that offers a realistic driving experience in a 3D open world. You can play online with real players from all over the world, win races, earn currency, buy new cars, upgrade them, customize them, explore a large city, and become the best driver. You can also enjoy all the features and cars in the game without any restrictions by downloading Car Simulator 2 APK Pure from various websites that offer APK files for Android devices. You can also use some tips and tricks to have more fun and success in playing Car Simulator 2 APK Pure.</p>
- <p>If you are looking for a fun, free-to-play, and realistic driving simulator game, you should try Car Simulator 2 APK Pure. You will not regret it!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Car Simulator 2 APK Pure:</p>
- <ol>
-<li><b>Is Car Simulator 2 APK Pure safe to download and install?</b></li>
-<p>Car Simulator 2 APK Pure is generally safe to download and install, as long as you get it from a reputable website that offers APK files for Android devices. However, you should always be careful when downloading and installing any APK file from unknown sources, as they may contain viruses or malware that can harm your device or steal your data. You should also scan the APK file with an antivirus program before installing it.</p>
-<li><b>Is Car Simulator 2 APK Pure legal to use?</b></li>
-<p>Car Simulator 2 APK Pure is not legal to use, as it is a modified version of Car Simulator 2, a game that is owned by Oppana Games. By using Car Simulator 2 APK Pure, you are violating the terms and conditions of Oppana Games and infringing their intellectual property rights. You may also face legal consequences from Oppana Games or other parties if you use Car Simulator 2 APK Pure.</p>
-<li><b>Will Car Simulator 2 APK Pure work on my device?</b></li>
-<p>Car Simulator 2 APK Pure will work on most Android devices that have Android 4.4 or higher and at least 1 GB of RAM. However, some devices may not be compatible with Car Simulator 2 APK Pure due to different hardware specifications or software versions. You may also encounter some bugs or glitches while playing Car Simulator 2 APK Pure on your device.</p>
-<li><b>Can I play Car Simulator 2 APK Pure with my friends?</b></li>
-<p>Yes, you can play Car Simulator 2 APK Pure with your friends online. You can join or create races with your friends or other players from all over the world. You can also chat with them and compete in leaderboards. However, you may not be able to play with your friends who have the original Car Simulator 2 game, as they may have different versions or features than you.</p>
-<li><b>Can I update Car Simulator 2 APK Pure?</b></li>
-<p>No, you cannot update Car Simulator 2 APK Pure through the Google Play Store or other official sources, as it is not an official version of Car Simulator 2. You may be able to update Car Simulator 2 APK Pure by downloading and installing a newer version of the APK file from a website that offers APK files for Android devices. However, you should always backup your data before updating Car Simulator 2 APK Pure, as you may lose your progress or settings if something goes wrong.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download VALORANT Mobile for Free from MediaFire.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download VALORANT Mobile for Free from MediaFire.md
deleted file mode 100644
index 8256cf7253f5995d1ce411e872a1b292a1eb017b..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download VALORANT Mobile for Free from MediaFire.md	
+++ /dev/null
@@ -1,130 +0,0 @@
-<br />
-<h1>Valorant Mobile APK Mediafıre: How to Download and Play the Game on Your Phone</h1>
- <p>Valorant is one of the most popular and competitive first-person shooter games in the world. It has millions of fans and players who enjoy its fast-paced and strategic gameplay. But what if you want to play Valorant on your phone? Is there a way to do that?</p>
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- <p>The answer is yes, there is. In this article, we will show you how to download and play Valorant Mobile APK Mediafıre, an unofficial version of the game that you can install on your Android device. We will also tell you what Valorant Mobile is, how it differs from Valorant PC, and how to optimize your gaming experience on your phone. Let's get started!</p>
- <h2>What is Valorant Mobile?</h2>
- <h3>A brief introduction to the game and its features</h3>
- <p>Valorant Mobile is a mobile version of Valorant, a 5v5 tactical shooter game developed by Riot Games. In Valorant, you can choose from a variety of agents, each with their own unique abilities and roles. You can also customize your weapons and skins to suit your style. The game has different modes, such as unrated, competitive, spike rush, deathmatch, and escalation. The main objective of the game is to either plant or defuse the spike, a bomb-like device, or eliminate all enemies.</p>
- <h3>The difference between Valorant Mobile and Valorant PC</h3>
- <p>Valorant Mobile is not an official release by Riot Games, but a fan-made project that aims to bring the game to mobile devices. It is not affiliated with or endorsed by Riot Games in any way. Therefore, it has some limitations and differences compared to Valorant PC. For example:</p>
- <ul>
-<li>Valorant Mobile has fewer agents, maps, modes, and features than Valorant PC.</li>
-<li>Valorant Mobile has lower graphics quality and performance than Valorant PC.</li>
-<li>Valorant Mobile does not support cross-play or cross-progression with Valorant PC.</li>
-<li>Valorant Mobile may have bugs, glitches, errors, or security issues that are not present in Valorant PC.</li>
-</ul>
- <p>Despite these drawbacks, Valorant Mobile still offers a fun and exciting way to play Valorant on your phone. It has most of the core elements of the game, such as shooting, abilities, teamwork, and strategy. It also has some advantages over Valorant PC, such as being more accessible, portable, and convenient.</p>
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- <h2>How to Download Valorant Mobile APK Mediafıre</h2>
- <h3>The steps to download and install the game from Mediafıre</h3>
- <p>If you want to play Valorant Mobile on your phone, you will need to download and install an APK file from Mediafıre, a file-sharing platform. An APK file is an Android application package that contains all the files needed to run an app on your device. Here are the steps to do that:</p>
- <ol>
-<li>Go to this link: <a href="(^1^)">https://www.mediafire.com/file/9g6x8w6k0qj7q8a/VAL ORANT_MOBILE.apk/file</a>  to access the Mediafıre page where the APK file is hosted.</li>
-<li>Click on the green "Download" button and wait for the file to be downloaded to your device.</li>
-<li>Go to your device's settings and enable the option to install apps from unknown sources. This will allow you to install the APK file that is not from the official Google Play Store.</li>
-<li>Locate the downloaded APK file in your device's file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and grant the necessary permissions to the app.</li>
-<li>Wait for the installation to finish and then launch the app from your home screen or app drawer.</li>
-</ol>
- <p>Congratulations, you have successfully downloaded and installed Valorant Mobile APK Mediafıre on your phone. You can now enjoy playing the game on your device.</p>
- <h3>The risks and precautions of downloading from unofficial sources</h3>
- <p>Before you download and install Valorant Mobile APK Mediafıre, you should be aware of the potential risks and precautions of downloading from unofficial sources. These include:</p>
- <ul>
-<li>The APK file may contain malware, viruses, spyware, or other harmful software that can damage your device or compromise your privacy and security.</li>
-<li>The APK file may not be compatible with your device or may cause crashes, errors, or performance issues.</li>
-<li>The APK file may not be updated regularly or may have outdated or missing features.</li>
-<li>The APK file may violate the terms of service or intellectual property rights of Riot Games or other parties.</li>
-</ul>
- <p>To avoid these risks and precautions, you should always download and install apps from official and trusted sources, such as the Google Play Store. You should also scan the APK file with a reputable antivirus software before installing it. You should also backup your data and device before installing any app from unknown sources. You should also uninstall the app if you encounter any problems or issues with it.</p>
- <h2>How to Play Valorant Mobile on Your Phone</h2>
- <h3>The requirements and compatibility of the game</h3>
- <p>Valorant Mobile is a demanding game that requires a high-end device to run smoothly and properly. The minimum requirements for Valorant Mobile are:</p>
- <ul>
-<li>An Android device with at least 4 GB of RAM and 64 GB of storage space.</li>
-<li>An Android version of 7.0 (Nougat) or higher.</li>
-<li>A stable internet connection with low latency and high bandwidth.</li>
-<li>A compatible controller or touch screen for input.</li>
-</ul>
- <p>If your device does not meet these requirements, you may not be able to play Valorant Mobile or you may experience lag, stuttering, freezing, or crashing. You may also have compatibility issues with some agents, maps, modes, or features. You may also have higher battery consumption and overheating issues.</p>
- <h3>The controls and settings of the game</h3>
- <p>Valorant Mobile has two types of controls: touch screen and controller. You can choose which one suits you better in the settings menu. The touch screen controls are similar to other mobile shooter games, such as PUBG Mobile or Call of Duty Mobile. You can move, aim, shoot, use abilities, reload, switch weapons, crouch, jump, ping, chat, and access menus using various buttons and gestures on the screen. You can also customize the layout, size, opacity, and sensitivity of the buttons in the settings menu.</p>
- <p>The controller controls are similar to Valorant PC or other console shooter games. You can connect a compatible controller to your device via Bluetooth or USB and use it to play Valorant Mobile. You can move, aim, shoot, use abilities, reload, switch weapons, crouch, jump, ping, chat, and access menus using various buttons and sticks on the controller. You can also customize the layout, sensitivity, vibration, and dead zone of the controller in the settings menu.</p>
- <p>Besides the controls, you can also adjust other settings in Valorant Mobile to optimize your gaming experience. These include graphics quality, frame rate, sound volume, crosshair style, minimap position, HUD scale, auto-aim assist, auto-shoot mode, gyroscope mode, voice chat mode, language option, and more. You can experiment with different settings to find what works best for you and your device.</p>
- <h3>The tips and tricks to improve your performance and skills</h3>
- <p>Playing Valorant Mobile is not easy. It requires skill, strategy, teamwork, and practice. Here are some tips and tricks to help you improve your performance and skills in Valorant Mobile:</p>
- <ul>
-<li>Choose an agent that matches your playstyle and role. Learn their abilities and how to use them effectively in different situations.</li>
-<li>Communicate and coordinate with your teammates. Use voice chat, text chat, or pings to share information, call out enemies, request backup, or plan strategies.</li>
-<li>Practice your aim and shooting skills. Use the practice mode, deathmatch mode, or custom games to warm up, train, or test your weapons and abilities.</li>
-<li>Learn the maps and their layouts. Know the locations of the spawn points, bomb sites, choke points, angles, cover, and utility spots.</li>
-<li>Use your weapons and abilities wisely. Know the recoil patterns, damage values, fire rates, reload times, and range of your weapons. Know the cooldowns, costs, and effects of your abilities. Know when to buy, save, or upgrade your equipment.</li>
-<li>Adapt to the situation and your enemies. Switch your agents, weapons, abilities, or strategies depending on the mode, map, round, score, or enemy composition.</li>
-<li>Have fun and enjoy the game. Don't get frustrated or toxic if you lose or make mistakes. Learn from your experience and improve your skills.</li>
-</ul>
- <h2>Conclusion</h2>
- <h3>A summary of the main points and a call to action</h3>
- <p>Valorant Mobile APK Mediafıre is a fan-made version of Valorant that you can download and play on your Android device. It is not an official release by Riot Games, but it still offers a lot of fun and excitement for Valorant fans who want to play the game on their phone. However, it also has some limitations and risks that you should be aware of before downloading and installing it.</p>
- <p>In this article, we have shown you what Valorant Mobile is, how it differs from Valorant PC, how to download and install it from Mediafıre, how to play it on your phone, and how to improve your performance and skills in the game. We hope you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <p>If you are ready to try Valorant Mobile APK Mediafıre on your phone, click on the link below and follow the steps we have provided. Have fun and good luck!</p>
- <h2>FAQs</h2>
- <h4>Is Valorant Mobile free to play?</h4>
- <p>Yes, Valorant Mobile is free to play. You don't need to pay anything to download or install it on your device. However, you may need to spend money on in-game purchases such as skins or battle passes if you want to access them.</p>
- <h4>Is Valorant Mobile safe to download?</h4>
- <p>Valorant Mobile is not an official release by Riot Games, but a fan-made project that is hosted on Mediafıre, a file-sharing platform. Therefore, it may not be safe to download or install on your device. It may contain malware, viruses, spyware, or other harmful software that can damage your device or compromise your privacy and security. It may also violate the terms of service or intellectual property rights of Riot Games or other parties. To avoid these risks and precautions, you should always download and install apps from official and trusted sources.</p>
- <h4>Can I play Valorant Mobile with my friends?</h4>
- <p>Yes, you can play Valorant Mobile with your friends who also have Valorant Mobile installed on their devices. You can invite them to join your party or match through the game's interface. However, you cannot play Valorant Mobile with your friends who have Valorant PC installed on their computers. Valorant Mobile does not support cross-play or cross-progression with Valorant PC.</p>
- <h4>How much storage space does Valorant Mobile need?</h4>
- <p>Valorant Mobile needs about 2 GB of storage space on your device to download and install the APK file from Mediafıre. However, you may need more storage space for additional data or updates that the game may require in the future.</p>
- <h4>Will Valorant Mobile have the same updates as Valorant PC?</h4>
- <p>No, Valorant Mobile will not have the same updates as Valorant PC. Valorant Mobile is not an official release by Riot Games, but a fan-made project that is hosted on Mediafıre, a file-sharing platform. Therefore, it may not have the same updates as Valorant PC. Valorant PC is updated regularly by Riot Games with new agents, maps, modes, features, bug fixes, and balance changes. Valorant Mobile may not have these updates or may have them later or differently. Valorant Mobile may also have its own updates that are not related to Valorant PC.</p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Xbox Game Bar on PC A Step-by-Step Guide.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Xbox Game Bar on PC A Step-by-Step Guide.md
deleted file mode 100644
index 609a5fc9d40496dc12b676004537f3c452bb6baf..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Xbox Game Bar on PC A Step-by-Step Guide.md	
+++ /dev/null
@@ -1,71 +0,0 @@
-
-<h1>How to Download Xbox Game Bar on PC</h1>
-<p>If you are a PC gamer, you might want to enhance your gaming experience with Xbox Game Bar, a customizable overlay that lets you access various gaming features and chat with your friends without leaving your game. In this article, we will show you how to download, install, and use Xbox Game Bar on your Windows 10 or Windows 11 PC.</p>
- <h2>What is Xbox Game Bar and why you need it</h2>
-<p>Xbox Game Bar is a built-in app for Windows 10 and Windows 11 that works with most PC games, giving you instant access to widgets for screen capture and sharing, finding new teammates with LFG, and chatting with Xbox friends across Xbox console, mobile, and PC. You can also play console games from the cloud or from your own console using remote play.</p>
-<h2>how to download xbox game bar on pc</h2><br /><p><b><b>Download Zip</b> >>> <a href="https://gohhs.com/2uPr7E">https://gohhs.com/2uPr7E</a></b></p><br /><br />
- <h3>Xbox Game Bar features and benefits</h3>
-<p>Some of the features and benefits of using Xbox Game Bar are:</p>
-<ul>
-<li>You can easily capture screenshots and videos of your gameplay and share them on social media or with your friends.</li>
-<li>You can join or create parties and voice chat with your friends across different platforms.</li>
-<li>You can find new players to join your game or join other games using the Looking for Group (LFG) feature.</li>
-<li>You can play hundreds of high-quality PC games with Game Pass, a subscription service that gives you access to a large library of games for a low monthly fee.</li>
-<li>You can play console games from the cloud or from your own console using cloud gaming or remote play, respectively.</li>
-<li>You can customize your profile, link your social accounts, redeem perks, and shop for PC games using the Xbox app.</li>
-</ul>
- <h3>Xbox Game Bar system requirements</h3>
-<p>To use Xbox Game Bar on your PC, you need to have the following system requirements:</p>
-<ul>
-<li>OS: Windows 10/11, version 22H1 or higher</li>
-<li>Processor: Intel Core i5; AMD Ryzen 5. Quad-core or higher.</li>
-<li>GPU: NVIDIA GTX 1050; AMD Radeon RX 560</li>
-<li>Memory: 8GB RAM, 3GB VRAM</li>
-<li>Storage: 150GB</li>
-<li>DirectX: DirectX 12 API</li>
-<li>Monitor: 1080p</li>
-</ul>
- <h2>How to get Xbox Game Bar from the Microsoft Store</h2>
-<p>If you have Windows 10 or Windows 11, you probably already have Xbox Game Bar installed on your PC. However, if you don't have it or you want to update it to the latest version, you can get it from the Microsoft Store. Here are the steps to do that:</p>
- <h3>Step 1: Open the Microsoft Store app</h3>
-<p>To open the Microsoft Store app, you can either click on its icon on the taskbar or Start menu, or press Win+S and type "Microsoft Store" in the search box. Then, click on the app to launch it.</p>
- <h3>Step 2: Search for Xbox Game Bar</h3>
-<p>In the Microsoft Store app, click on the search icon on the top right corner and type "Xbox Game Bar" in the search box. Then, click on the app from the search results.</p>
- <h3>Step 3: Install Xbox . Here are some FAQs that may help you further:</p>
- <h3>Q: How do I update Xbox Game Bar?</h3>
-<p>A: Xbox Game Bar updates automatically through the Microsoft Store. However, you can also manually check for updates by going to the Microsoft Store app > More > Downloads and updates > Get updates.</p>
-<p>How to install xbox game bar on windows 10/11<br />
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-How to uninstall xbox game bar on pc if not needed<br />
-Xbox game bar alternatives and competitors for pc gaming<br />
-How to record gameplay and stream with xbox game bar on pc<br />
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-How to enable or disable xbox game bar on pc for different games<br />
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-How to join or create a party with xbox game bar on pc<br />
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-How to access xbox game pass library and store with xbox game bar on pc<br />
-Xbox game bar review and ratings from pc gamers<br />
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- <h3>Q: How do I disable Xbox Game Bar?</h3>
-<p>A: If you don't want to use Xbox Game Bar, you can disable it by going to Settings > Gaming > Xbox Game Bar > Enable Xbox Game Bar for supported games and turn off the toggle.</p>
- <h3>Q: How do I change the keyboard shortcut for Xbox Game Bar?</h3>
-<p>A: If you want to change the keyboard shortcut for Xbox Game Bar, you can do so by going to Settings > Gaming > Xbox Game Bar > Keyboard shortcuts and choose a different key combination.</p>
- <h3>Q: How do I record audio with Xbox Game Bar?</h3>
-<p>A: If you want to record audio with Xbox Game Bar, you need to enable the microphone option in the Capture widget. You can also adjust the audio settings by going to Settings > Gaming > Captures and choose your preferred audio quality and source.</p>
- <h3>Q: How do I share my captures with Xbox Game Bar?</h3>
-<p>A: If you want to share your captures with Xbox Game Bar, you can do so by using the Share widget. You can also access your captures by going to the Gallery widget or by opening the Xbox app.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/Image-to-MusicGen/tests/modules/test_seanet.py b/spaces/fffiloni/Image-to-MusicGen/tests/modules/test_seanet.py
deleted file mode 100644
index e5c51b340a2f94fb2828b14daf83d5fad645073d..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Image-to-MusicGen/tests/modules/test_seanet.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.
-
-from itertools import product
-
-import pytest
-import torch
-
-from audiocraft.modules.seanet import SEANetEncoder, SEANetDecoder, SEANetResnetBlock
-from audiocraft.modules import StreamableConv1d, StreamableConvTranspose1d
-
-
-class TestSEANetModel:
-
-    def test_base(self):
-        encoder = SEANetEncoder()
-        decoder = SEANetDecoder()
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_causal(self):
-        encoder = SEANetEncoder(causal=True)
-        decoder = SEANetDecoder(causal=True)
-        x = torch.randn(1, 1, 24000)
-
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_conv_skip_connection(self):
-        encoder = SEANetEncoder(true_skip=False)
-        decoder = SEANetDecoder(true_skip=False)
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_seanet_encoder_decoder_final_act(self):
-        encoder = SEANetEncoder(true_skip=False)
-        decoder = SEANetDecoder(true_skip=False, final_activation='Tanh')
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def _check_encoder_blocks_norm(self, encoder: SEANetEncoder, n_disable_blocks: int, norm: str):
-        n_blocks = 0
-        for layer in encoder.model:
-            if isinstance(layer, StreamableConv1d):
-                n_blocks += 1
-                assert layer.conv.norm_type == 'none' if n_blocks <= n_disable_blocks else norm
-            elif isinstance(layer, SEANetResnetBlock):
-                for resnet_layer in layer.block:
-                    if isinstance(resnet_layer, StreamableConv1d):
-                        # here we add + 1 to n_blocks as we increment n_blocks just after the block
-                        assert resnet_layer.conv.norm_type == 'none' if (n_blocks + 1) <= n_disable_blocks else norm
-
-    def test_encoder_disable_norm(self):
-        n_residuals = [0, 1, 3]
-        disable_blocks = [0, 1, 2, 3, 4, 5, 6]
-        norms = ['weight_norm', 'none']
-        for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms):
-            encoder = SEANetEncoder(n_residual_layers=n_res, norm=norm,
-                                    disable_norm_outer_blocks=disable_blocks)
-            self._check_encoder_blocks_norm(encoder, disable_blocks, norm)
-
-    def _check_decoder_blocks_norm(self, decoder: SEANetDecoder, n_disable_blocks: int, norm: str):
-        n_blocks = 0
-        for layer in decoder.model:
-            if isinstance(layer, StreamableConv1d):
-                n_blocks += 1
-                assert layer.conv.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-            elif isinstance(layer, StreamableConvTranspose1d):
-                n_blocks += 1
-                assert layer.convtr.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-            elif isinstance(layer, SEANetResnetBlock):
-                for resnet_layer in layer.block:
-                    if isinstance(resnet_layer, StreamableConv1d):
-                        assert resnet_layer.conv.norm_type == 'none' \
-                            if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-
-    def test_decoder_disable_norm(self):
-        n_residuals = [0, 1, 3]
-        disable_blocks = [0, 1, 2, 3, 4, 5, 6]
-        norms = ['weight_norm', 'none']
-        for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms):
-            decoder = SEANetDecoder(n_residual_layers=n_res, norm=norm,
-                                    disable_norm_outer_blocks=disable_blocks)
-            self._check_decoder_blocks_norm(decoder, disable_blocks, norm)
-
-    def test_disable_norm_raises_exception(self):
-        # Invalid disable_norm_outer_blocks values raise exceptions
-        with pytest.raises(AssertionError):
-            SEANetEncoder(disable_norm_outer_blocks=-1)
-
-        with pytest.raises(AssertionError):
-            SEANetEncoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7)
-
-        with pytest.raises(AssertionError):
-            SEANetDecoder(disable_norm_outer_blocks=-1)
-
-        with pytest.raises(AssertionError):
-            SEANetDecoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7)
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/cluster.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/cluster.d.ts
deleted file mode 100644
index 37dbc5746171fc04bcb8ea6f8b7183fa1b5db448..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/cluster.d.ts
+++ /dev/null
@@ -1,410 +0,0 @@
-/**
- * Clusters of Node.js processes can be used to run multiple instances of Node.js
- * that can distribute workloads among their application threads. When process
- * isolation is not needed, use the `worker_threads` module instead, which
- * allows running multiple application threads within a single Node.js instance.
- *
- * The cluster module allows easy creation of child processes that all share
- * server ports.
- *
- * ```js
- * import cluster from 'cluster';
- * import http from 'http';
- * import { cpus } from 'os';
- * import process from 'process';
- *
- * const numCPUs = cpus().length;
- *
- * if (cluster.isPrimary) {
- *   console.log(`Primary ${process.pid} is running`);
- *
- *   // Fork workers.
- *   for (let i = 0; i < numCPUs; i++) {
- *     cluster.fork();
- *   }
- *
- *   cluster.on('exit', (worker, code, signal) => {
- *     console.log(`worker ${worker.process.pid} died`);
- *   });
- * } else {
- *   // Workers can share any TCP connection
- *   // In this case it is an HTTP server
- *   http.createServer((req, res) => {
- *     res.writeHead(200);
- *     res.end('hello world\n');
- *   }).listen(8000);
- *
- *   console.log(`Worker ${process.pid} started`);
- * }
- * ```
- *
- * Running Node.js will now share port 8000 between the workers:
- *
- * ```console
- * $ node server.js
- * Primary 3596 is running
- * Worker 4324 started
- * Worker 4520 started
- * Worker 6056 started
- * Worker 5644 started
- * ```
- *
- * On Windows, it is not yet possible to set up a named pipe server in a worker.
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/cluster.js)
- */
-declare module 'cluster' {
-    import * as child from 'node:child_process';
-    import EventEmitter = require('node:events');
-    import * as net from 'node:net';
-    export interface ClusterSettings {
-        execArgv?: string[] | undefined; // default: process.execArgv
-        exec?: string | undefined;
-        args?: string[] | undefined;
-        silent?: boolean | undefined;
-        stdio?: any[] | undefined;
-        uid?: number | undefined;
-        gid?: number | undefined;
-        inspectPort?: number | (() => number) | undefined;
-    }
-    export interface Address {
-        address: string;
-        port: number;
-        addressType: number | 'udp4' | 'udp6'; // 4, 6, -1, "udp4", "udp6"
-    }
-    /**
-     * A `Worker` object contains all public information and method about a worker.
-     * In the primary it can be obtained using `cluster.workers`. In a worker
-     * it can be obtained using `cluster.worker`.
-     * @since v0.7.0
-     */
-    export class Worker extends EventEmitter {
-        /**
-         * Each new worker is given its own unique id, this id is stored in the`id`.
-         *
-         * While a worker is alive, this is the key that indexes it in`cluster.workers`.
-         * @since v0.8.0
-         */
-        id: number;
-        /**
-         * All workers are created using `child_process.fork()`, the returned object
-         * from this function is stored as `.process`. In a worker, the global `process`is stored.
-         *
-         * See: `Child Process module`.
-         *
-         * Workers will call `process.exit(0)` if the `'disconnect'` event occurs
-         * on `process` and `.exitedAfterDisconnect` is not `true`. This protects against
-         * accidental disconnection.
-         * @since v0.7.0
-         */
-        process: child.ChildProcess;
-        /**
-         * Send a message to a worker or primary, optionally with a handle.
-         *
-         * In the primary, this sends a message to a specific worker. It is identical to `ChildProcess.send()`.
-         *
-         * In a worker, this sends a message to the primary. It is identical to`process.send()`.
-         *
-         * This example will echo back all messages from the primary:
-         *
-         * ```js
-         * if (cluster.isPrimary) {
-         *   const worker = cluster.fork();
-         *   worker.send('hi there');
-         *
-         * } else if (cluster.isWorker) {
-         *   process.on('message', (msg) => {
-         *     process.send(msg);
-         *   });
-         * }
-         * ```
-         * @since v0.7.0
-         * @param options The `options` argument, if present, is an object used to parameterize the sending of certain types of handles. `options` supports the following properties:
-         */
-        send(message: child.Serializable, callback?: (error: Error | null) => void): boolean;
-        send(message: child.Serializable, sendHandle: child.SendHandle, callback?: (error: Error | null) => void): boolean;
-        send(message: child.Serializable, sendHandle: child.SendHandle, options?: child.MessageOptions, callback?: (error: Error | null) => void): boolean;
-        /**
-         * This function will kill the worker. In the primary worker, it does this by
-         * disconnecting the `worker.process`, and once disconnected, killing with`signal`. In the worker, it does it by killing the process with `signal`.
-         *
-         * The `kill()` function kills the worker process without waiting for a graceful
-         * disconnect, it has the same behavior as `worker.process.kill()`.
-         *
-         * This method is aliased as `worker.destroy()` for backwards compatibility.
-         *
-         * In a worker, `process.kill()` exists, but it is not this function;
-         * it is `kill()`.
-         * @since v0.9.12
-         * @param [signal='SIGTERM'] Name of the kill signal to send to the worker process.
-         */
-        kill(signal?: string): void;
-        destroy(signal?: string): void;
-        /**
-         * In a worker, this function will close all servers, wait for the `'close'` event
-         * on those servers, and then disconnect the IPC channel.
-         *
-         * In the primary, an internal message is sent to the worker causing it to call`.disconnect()` on itself.
-         *
-         * Causes `.exitedAfterDisconnect` to be set.
-         *
-         * After a server is closed, it will no longer accept new connections,
-         * but connections may be accepted by any other listening worker. Existing
-         * connections will be allowed to close as usual. When no more connections exist,
-         * see `server.close()`, the IPC channel to the worker will close allowing it
-         * to die gracefully.
-         *
-         * The above applies _only_ to server connections, client connections are not
-         * automatically closed by workers, and disconnect does not wait for them to close
-         * before exiting.
-         *
-         * In a worker, `process.disconnect` exists, but it is not this function;
-         * it is `disconnect()`.
-         *
-         * Because long living server connections may block workers from disconnecting, it
-         * may be useful to send a message, so application specific actions may be taken to
-         * close them. It also may be useful to implement a timeout, killing a worker if
-         * the `'disconnect'` event has not been emitted after some time.
-         *
-         * ```js
-         * if (cluster.isPrimary) {
-         *   const worker = cluster.fork();
-         *   let timeout;
-         *
-         *   worker.on('listening', (address) => {
-         *     worker.send('shutdown');
-         *     worker.disconnect();
-         *     timeout = setTimeout(() => {
-         *       worker.kill();
-         *     }, 2000);
-         *   });
-         *
-         *   worker.on('disconnect', () => {
-         *     clearTimeout(timeout);
-         *   });
-         *
-         * } else if (cluster.isWorker) {
-         *   const net = require('net');
-         *   const server = net.createServer((socket) => {
-         *     // Connections never end
-         *   });
-         *
-         *   server.listen(8000);
-         *
-         *   process.on('message', (msg) => {
-         *     if (msg === 'shutdown') {
-         *       // Initiate graceful close of any connections to server
-         *     }
-         *   });
-         * }
-         * ```
-         * @since v0.7.7
-         * @return A reference to `worker`.
-         */
-        disconnect(): void;
-        /**
-         * This function returns `true` if the worker is connected to its primary via its
-         * IPC channel, `false` otherwise. A worker is connected to its primary after it
-         * has been created. It is disconnected after the `'disconnect'` event is emitted.
-         * @since v0.11.14
-         */
-        isConnected(): boolean;
-        /**
-         * This function returns `true` if the worker's process has terminated (either
-         * because of exiting or being signaled). Otherwise, it returns `false`.
-         *
-         * ```js
-         * import cluster from 'cluster';
-         * import http from 'http';
-         * import { cpus } from 'os';
-         * import process from 'process';
-         *
-         * const numCPUs = cpus().length;
-         *
-         * if (cluster.isPrimary) {
-         *   console.log(`Primary ${process.pid} is running`);
-         *
-         *   // Fork workers.
-         *   for (let i = 0; i < numCPUs; i++) {
-         *     cluster.fork();
-         *   }
-         *
-         *   cluster.on('fork', (worker) => {
-         *     console.log('worker is dead:', worker.isDead());
-         *   });
-         *
-         *   cluster.on('exit', (worker, code, signal) => {
-         *     console.log('worker is dead:', worker.isDead());
-         *   });
-         * } else {
-         *   // Workers can share any TCP connection. In this case, it is an HTTP server.
-         *   http.createServer((req, res) => {
-         *     res.writeHead(200);
-         *     res.end(`Current process\n ${process.pid}`);
-         *     process.kill(process.pid);
-         *   }).listen(8000);
-         * }
-         * ```
-         * @since v0.11.14
-         */
-        isDead(): boolean;
-        /**
-         * This property is `true` if the worker exited due to `.disconnect()`.
-         * If the worker exited any other way, it is `false`. If the
-         * worker has not exited, it is `undefined`.
-         *
-         * The boolean `worker.exitedAfterDisconnect` allows distinguishing between
-         * voluntary and accidental exit, the primary may choose not to respawn a worker
-         * based on this value.
-         *
-         * ```js
-         * cluster.on('exit', (worker, code, signal) => {
-         *   if (worker.exitedAfterDisconnect === true) {
-         *     console.log('Oh, it was just voluntary – no need to worry');
-         *   }
-         * });
-         *
-         * // kill worker
-         * worker.kill();
-         * ```
-         * @since v6.0.0
-         */
-        exitedAfterDisconnect: boolean;
-        /**
-         * events.EventEmitter
-         *   1. disconnect
-         *   2. error
-         *   3. exit
-         *   4. listening
-         *   5. message
-         *   6. online
-         */
-        addListener(event: string, listener: (...args: any[]) => void): this;
-        addListener(event: 'disconnect', listener: () => void): this;
-        addListener(event: 'error', listener: (error: Error) => void): this;
-        addListener(event: 'exit', listener: (code: number, signal: string) => void): this;
-        addListener(event: 'listening', listener: (address: Address) => void): this;
-        addListener(event: 'message', listener: (message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        addListener(event: 'online', listener: () => void): this;
-        emit(event: string | symbol, ...args: any[]): boolean;
-        emit(event: 'disconnect'): boolean;
-        emit(event: 'error', error: Error): boolean;
-        emit(event: 'exit', code: number, signal: string): boolean;
-        emit(event: 'listening', address: Address): boolean;
-        emit(event: 'message', message: any, handle: net.Socket | net.Server): boolean;
-        emit(event: 'online'): boolean;
-        on(event: string, listener: (...args: any[]) => void): this;
-        on(event: 'disconnect', listener: () => void): this;
-        on(event: 'error', listener: (error: Error) => void): this;
-        on(event: 'exit', listener: (code: number, signal: string) => void): this;
-        on(event: 'listening', listener: (address: Address) => void): this;
-        on(event: 'message', listener: (message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        on(event: 'online', listener: () => void): this;
-        once(event: string, listener: (...args: any[]) => void): this;
-        once(event: 'disconnect', listener: () => void): this;
-        once(event: 'error', listener: (error: Error) => void): this;
-        once(event: 'exit', listener: (code: number, signal: string) => void): this;
-        once(event: 'listening', listener: (address: Address) => void): this;
-        once(event: 'message', listener: (message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        once(event: 'online', listener: () => void): this;
-        prependListener(event: string, listener: (...args: any[]) => void): this;
-        prependListener(event: 'disconnect', listener: () => void): this;
-        prependListener(event: 'error', listener: (error: Error) => void): this;
-        prependListener(event: 'exit', listener: (code: number, signal: string) => void): this;
-        prependListener(event: 'listening', listener: (address: Address) => void): this;
-        prependListener(event: 'message', listener: (message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        prependListener(event: 'online', listener: () => void): this;
-        prependOnceListener(event: string, listener: (...args: any[]) => void): this;
-        prependOnceListener(event: 'disconnect', listener: () => void): this;
-        prependOnceListener(event: 'error', listener: (error: Error) => void): this;
-        prependOnceListener(event: 'exit', listener: (code: number, signal: string) => void): this;
-        prependOnceListener(event: 'listening', listener: (address: Address) => void): this;
-        prependOnceListener(event: 'message', listener: (message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        prependOnceListener(event: 'online', listener: () => void): this;
-    }
-    export interface Cluster extends EventEmitter {
-        disconnect(callback?: () => void): void;
-        fork(env?: any): Worker;
-        /** @deprecated since v16.0.0 - use isPrimary. */
-        readonly isMaster: boolean;
-        readonly isPrimary: boolean;
-        readonly isWorker: boolean;
-        schedulingPolicy: number;
-        readonly settings: ClusterSettings;
-        /** @deprecated since v16.0.0 - use setupPrimary. */
-        setupMaster(settings?: ClusterSettings): void;
-        /**
-         * `setupPrimary` is used to change the default 'fork' behavior. Once called, the settings will be present in cluster.settings.
-         */
-        setupPrimary(settings?: ClusterSettings): void;
-        readonly worker?: Worker | undefined;
-        readonly workers?: NodeJS.Dict<Worker> | undefined;
-        readonly SCHED_NONE: number;
-        readonly SCHED_RR: number;
-        /**
-         * events.EventEmitter
-         *   1. disconnect
-         *   2. exit
-         *   3. fork
-         *   4. listening
-         *   5. message
-         *   6. online
-         *   7. setup
-         */
-        addListener(event: string, listener: (...args: any[]) => void): this;
-        addListener(event: 'disconnect', listener: (worker: Worker) => void): this;
-        addListener(event: 'exit', listener: (worker: Worker, code: number, signal: string) => void): this;
-        addListener(event: 'fork', listener: (worker: Worker) => void): this;
-        addListener(event: 'listening', listener: (worker: Worker, address: Address) => void): this;
-        addListener(event: 'message', listener: (worker: Worker, message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        addListener(event: 'online', listener: (worker: Worker) => void): this;
-        addListener(event: 'setup', listener: (settings: ClusterSettings) => void): this;
-        emit(event: string | symbol, ...args: any[]): boolean;
-        emit(event: 'disconnect', worker: Worker): boolean;
-        emit(event: 'exit', worker: Worker, code: number, signal: string): boolean;
-        emit(event: 'fork', worker: Worker): boolean;
-        emit(event: 'listening', worker: Worker, address: Address): boolean;
-        emit(event: 'message', worker: Worker, message: any, handle: net.Socket | net.Server): boolean;
-        emit(event: 'online', worker: Worker): boolean;
-        emit(event: 'setup', settings: ClusterSettings): boolean;
-        on(event: string, listener: (...args: any[]) => void): this;
-        on(event: 'disconnect', listener: (worker: Worker) => void): this;
-        on(event: 'exit', listener: (worker: Worker, code: number, signal: string) => void): this;
-        on(event: 'fork', listener: (worker: Worker) => void): this;
-        on(event: 'listening', listener: (worker: Worker, address: Address) => void): this;
-        on(event: 'message', listener: (worker: Worker, message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        on(event: 'online', listener: (worker: Worker) => void): this;
-        on(event: 'setup', listener: (settings: ClusterSettings) => void): this;
-        once(event: string, listener: (...args: any[]) => void): this;
-        once(event: 'disconnect', listener: (worker: Worker) => void): this;
-        once(event: 'exit', listener: (worker: Worker, code: number, signal: string) => void): this;
-        once(event: 'fork', listener: (worker: Worker) => void): this;
-        once(event: 'listening', listener: (worker: Worker, address: Address) => void): this;
-        once(event: 'message', listener: (worker: Worker, message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        once(event: 'online', listener: (worker: Worker) => void): this;
-        once(event: 'setup', listener: (settings: ClusterSettings) => void): this;
-        prependListener(event: string, listener: (...args: any[]) => void): this;
-        prependListener(event: 'disconnect', listener: (worker: Worker) => void): this;
-        prependListener(event: 'exit', listener: (worker: Worker, code: number, signal: string) => void): this;
-        prependListener(event: 'fork', listener: (worker: Worker) => void): this;
-        prependListener(event: 'listening', listener: (worker: Worker, address: Address) => void): this;
-        // the handle is a net.Socket or net.Server object, or undefined.
-        prependListener(event: 'message', listener: (worker: Worker, message: any, handle?: net.Socket | net.Server) => void): this;
-        prependListener(event: 'online', listener: (worker: Worker) => void): this;
-        prependListener(event: 'setup', listener: (settings: ClusterSettings) => void): this;
-        prependOnceListener(event: string, listener: (...args: any[]) => void): this;
-        prependOnceListener(event: 'disconnect', listener: (worker: Worker) => void): this;
-        prependOnceListener(event: 'exit', listener: (worker: Worker, code: number, signal: string) => void): this;
-        prependOnceListener(event: 'fork', listener: (worker: Worker) => void): this;
-        prependOnceListener(event: 'listening', listener: (worker: Worker, address: Address) => void): this;
-        // the handle is a net.Socket or net.Server object, or undefined.
-        prependOnceListener(event: 'message', listener: (worker: Worker, message: any, handle: net.Socket | net.Server) => void): this;
-        prependOnceListener(event: 'online', listener: (worker: Worker) => void): this;
-        prependOnceListener(event: 'setup', listener: (settings: ClusterSettings) => void): this;
-    }
-    const cluster: Cluster;
-    export default cluster;
-}
-declare module 'node:cluster' {
-    export * from 'cluster';
-    export { default as default } from 'cluster';
-}
diff --git a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_0.py b/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_0.py
deleted file mode 100644
index 08e7feee52fdf07168a03fc1ae17528cc455f0a1..0000000000000000000000000000000000000000
--- a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_0.py
+++ /dev/null
@@ -1,30 +0,0 @@
-
-import re
-
-def is_spam(message):
-    # Patterns that can indicate a message is spam
-    spam_words = ['축하합니다', '공개합니다', '\\d{1,2}일추천', '알려드린', '무료체험', '돌파', '상한가', '최소.*연상']
-    url_shorteners = ['bit.ly', 'me2.kr', 'han.gl']
-    consecutive_numbers = '\\d+\\.?\\d*'  # e.g., 30%, 19일
-    suspicious_referral = '\\d{1,2}[일월수목금토일]'
-
-    # Flags for checking whether a message is spam
-    contains_spam_word = any(re.search(word, message) for word in spam_words)
-    contains_short_url = any(short_url in message for short_url in url_shorteners)
-    contains_consecutive_numbers = re.search(consecutive_numbers, message)
-    contains_suspicious_referral = re.search(suspicious_referral, message)
-
-    # Evaluate if message is a spam based on the flags
-    if contains_spam_word and contains_short_url:
-        return True
-    if contains_consecutive_numbers and contains_suspicious_referral:
-        return True
-    if contains_spam_word and contains_consecutive_numbers:
-        return True
-    if contains_spam_word and contains_suspicious_referral:
-        return True
-    if contains_short_url and (contains_consecutive_numbers or contains_suspicious_referral):
-        return True
-
-    # If none of the conditions are met, return False, indicating the message is not spam
-    return False
diff --git a/spaces/flax-community/Multilingual-VQA/apps/model/flax_clip_vision_bert/modeling_clip_vision_bert.py b/spaces/flax-community/Multilingual-VQA/apps/model/flax_clip_vision_bert/modeling_clip_vision_bert.py
deleted file mode 100644
index 1ac7d197037e0f47515fb037bba7e98187d87bdb..0000000000000000000000000000000000000000
--- a/spaces/flax-community/Multilingual-VQA/apps/model/flax_clip_vision_bert/modeling_clip_vision_bert.py
+++ /dev/null
@@ -1,1080 +0,0 @@
-from typing import Optional, Tuple
-
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict
-from transformers.modeling_flax_outputs import (
-    FlaxBaseModelOutputWithPooling,
-    FlaxMaskedLMOutput,
-    FlaxSequenceClassifierOutput,
-)
-from transformers.models.bert.modeling_flax_bert import (
-    FlaxBertEncoder,
-    FlaxBertOnlyMLMHead,
-    FlaxBertPooler,
-    FlaxPreTrainedModel,
-)
-from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
-
-from .configuration_clip_vision_bert import CLIPVisionBertConfig
-
-
-class FlaxCLIPVisionBertEmbeddings(nn.Module):
-
-    config: CLIPVisionBertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-
-    def setup(self):
-        bert_config = self.config.bert_config
-        clip_vision_config = self.config.clip_vision_config
-
-        self.word_embeddings = nn.Embed(
-            bert_config.vocab_size,
-            bert_config.hidden_size,
-            embedding_init=jax.nn.initializers.normal(
-                stddev=bert_config.initializer_range
-            ),
-            dtype=self.dtype,
-        )
-        self.position_embeddings = nn.Embed(
-            bert_config.max_position_embeddings,
-            bert_config.hidden_size,
-            embedding_init=jax.nn.initializers.normal(
-                stddev=bert_config.initializer_range
-            ),
-            dtype=self.dtype,
-        )
-        self.token_type_embeddings = nn.Embed(
-            bert_config.type_vocab_size,
-            bert_config.hidden_size,
-            embedding_init=jax.nn.initializers.normal(
-                stddev=bert_config.initializer_range
-            ),
-            dtype=self.dtype,
-        )
-
-        self.clip_vision_module = FlaxCLIPVisionModule(
-            clip_vision_config, dtype=self.dtype
-        )
-        self.visual_projection = nn.Dense(
-            bert_config.hidden_size,
-            dtype=self.dtype,
-            kernel_init=jax.nn.initializers.normal(
-                bert_config.initializer_range, self.dtype
-            ),
-        )
-
-        self.visual_position_embeddings = nn.Embed(
-            bert_config.max_position_embeddings,
-            bert_config.hidden_size,
-            embedding_init=jax.nn.initializers.normal(
-                stddev=bert_config.initializer_range
-            ),
-            dtype=self.dtype,
-        )
-        self.visual_token_type_embeddings = nn.Embed(
-            bert_config.type_vocab_size,
-            bert_config.hidden_size,
-            embedding_init=jax.nn.initializers.normal(
-                stddev=bert_config.initializer_range
-            ),
-            dtype=self.dtype,
-        )
-
-        self.LayerNorm = nn.LayerNorm(
-            epsilon=bert_config.layer_norm_eps, dtype=self.dtype
-        )
-        self.dropout = nn.Dropout(rate=bert_config.hidden_dropout_prob)
-
-    def __call__(
-        self,
-        input_ids,
-        token_type_ids,
-        position_ids,
-        pixel_values,
-        visual_token_type_ids,
-        visual_position_ids,
-        deterministic: bool = True,
-    ):
-        # Embed
-        inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
-        position_embeds = self.position_embeddings(position_ids.astype("i4"))
-        token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))
-
-        # Sum all embeddings
-        word_embeddings = inputs_embeds + token_type_embeddings + position_embeds
-
-        # Visual Embed
-        visual_inputs_embeds = self.clip_vision_module(pixel_values=pixel_values)[0]
-        visual_inputs_embeds = self.visual_projection(visual_inputs_embeds)
-        visual_token_type_embeddings = self.visual_token_type_embeddings(
-            visual_token_type_ids.astype("i4")
-        )
-        visual_position_embeds = self.visual_position_embeddings(
-            visual_position_ids.astype("i4")
-        )
-
-        # Sum all visual embeddings
-        visual_embeddings = (
-            visual_inputs_embeds + visual_token_type_embeddings + visual_position_embeds
-        )
-
-        # Concat
-        hidden_states = jnp.concatenate((word_embeddings, visual_embeddings), axis=1)
-
-        # Layer Norm
-        hidden_states = self.LayerNorm(hidden_states)
-        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
-        return hidden_states
-
-
-class FlaxCLIPVisionBertModule(nn.Module):
-    config: CLIPVisionBertConfig
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-    add_pooling_layer: bool = True
-
-    def setup(self):
-        self.embeddings = FlaxCLIPVisionBertEmbeddings(self.config, dtype=self.dtype)
-        self.encoder = FlaxBertEncoder(self.config.bert_config, dtype=self.dtype)
-        self.pooler = FlaxBertPooler(self.config.bert_config, dtype=self.dtype)
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        position_ids,
-        pixel_values,
-        visual_attention_mask,
-        visual_token_type_ids,
-        visual_position_ids,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        hidden_states = self.embeddings(
-            input_ids,
-            token_type_ids,
-            position_ids,
-            pixel_values,
-            visual_token_type_ids,
-            visual_position_ids,
-            deterministic=deterministic,
-        )
-
-        combined_attention_mask = jnp.concatenate(
-            (attention_mask, visual_attention_mask), axis=1
-        )
-
-        outputs = self.encoder(
-            hidden_states,
-            combined_attention_mask,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = outputs[0]
-        pooled = self.pooler(hidden_states) if self.add_pooling_layer else None
-
-        if not return_dict:
-            # if pooled is None, don't return it
-            if pooled is None:
-                return (hidden_states,) + outputs[1:]
-            return (hidden_states, pooled) + outputs[1:]
-
-        return FlaxBaseModelOutputWithPooling(
-            last_hidden_state=hidden_states,
-            pooler_output=pooled,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-class FlaxCLIPVisionBertModel(FlaxPreTrainedModel):
-    config_class = CLIPVisionBertConfig
-    module_class = FlaxCLIPVisionBertModule
-
-    def __init__(
-        self,
-        config: CLIPVisionBertConfig,
-        input_shape: Tuple = None,
-        seed: int = 0,
-        dtype: jnp.dtype = jnp.float32,
-        **kwargs,
-    ):
-
-        if input_shape is None:
-            input_shape = (
-                (1, 1),
-                (
-                    1,
-                    config.clip_vision_config.image_size,
-                    config.clip_vision_config.image_size,
-                    3,
-                ),
-                (
-                    1,
-                    (
-                        config.clip_vision_config.image_size
-                        // config.clip_vision_config.patch_size
-                    )
-                    ** 2
-                    + 1,
-                ),
-            )
-
-        module = self.module_class(config=config, dtype=dtype, **kwargs)
-        super().__init__(
-            config, module, input_shape=input_shape, seed=seed, dtype=dtype
-        )
-
-    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
-        # init input tensors
-        textual_input_shape = input_shape[0]
-        input_ids = jnp.zeros(textual_input_shape, dtype="i4")
-        token_type_ids = jnp.zeros_like(input_ids)
-        position_ids = jnp.broadcast_to(
-            jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), textual_input_shape
-        )
-        attention_mask = jnp.ones_like(input_ids)
-
-        pixel_values = jax.random.normal(rng, input_shape[1])
-        visual_attention_mask = jnp.ones(input_shape[2])
-        visual_token_type_ids = jnp.ones(input_shape[2])
-        visual_position_ids = jnp.broadcast_to(
-            jnp.zeros(jnp.atleast_2d(visual_token_type_ids).shape[-1]), input_shape[2]
-        )
-
-        params_rng, dropout_rng = jax.random.split(rng)
-        rngs = {"params": params_rng, "dropout": dropout_rng}
-
-        return self.module.init(
-            rngs,
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            pixel_values,
-            visual_attention_mask,
-            visual_token_type_ids,
-            visual_position_ids,
-            return_dict=False,
-        )["params"]
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        pixel_values=None,
-        visual_attention_mask=None,
-        visual_token_type_ids=None,
-        visual_position_ids=None,
-        params: dict = None,
-        dropout_rng: jax.random.PRNGKey = None,
-        train: bool = False,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ):
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.bert_config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.bert_config.output_hidden_states
-        )
-        return_dict = (
-            return_dict
-            if return_dict is not None
-            else self.config.bert_config.return_dict
-        )
-
-        # pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Don't need this for torch permuted input
-
-        visual_sequence_length = (
-            pixel_values.shape[0],
-            (
-                self.config.clip_vision_config.image_size
-                // self.config.clip_vision_config.patch_size
-            )
-            ** 2
-            + 1,
-        )
-        # init input tensors if not passed
-        if token_type_ids is None:
-            token_type_ids = jnp.zeros_like(input_ids)
-
-        if position_ids is None:
-            position_ids = jnp.broadcast_to(
-                jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape
-            )
-
-        if attention_mask is None:
-            attention_mask = jnp.ones_like(input_ids)
-
-        if visual_token_type_ids is None:
-            visual_token_type_ids = jnp.ones(visual_sequence_length)
-
-        if visual_position_ids is None:
-            visual_position_ids = jnp.broadcast_to(
-                jnp.atleast_2d(visual_token_type_ids).shape[-1], visual_sequence_length
-            )
-
-        if visual_attention_mask is None:
-            visual_attention_mask = jnp.ones(visual_sequence_length)
-
-        # Handle any PRNG if needed
-        rngs = {}
-        if dropout_rng is not None:
-            rngs["dropout"] = dropout_rng
-
-        return self.module.apply(
-            {"params": params or self.params},
-            jnp.array(input_ids, dtype="i4"),
-            jnp.array(attention_mask, dtype="i4"),
-            jnp.array(token_type_ids, dtype="i4"),
-            jnp.array(position_ids, dtype="i4"),
-            jnp.array(pixel_values, dtype=jnp.float32),
-            jnp.array(visual_attention_mask, dtype="i4"),
-            jnp.array(visual_token_type_ids, dtype="i4"),
-            jnp.array(visual_position_ids, dtype="i4"),
-            not train,
-            output_attentions,
-            output_hidden_states,
-            return_dict,
-            rngs=rngs,
-        )
-
-    @classmethod
-    def from_bert_clip_vision_pretrained(
-        cls,
-        bert_model_name_or_path: str = None,
-        clip_vision_model_name_or_path: str = None,
-        *model_args,
-        **kwargs,
-    ) -> FlaxPreTrainedModel:
-
-        kwargs_bert = {
-            argument[len("bert_") :]: value
-            for argument, value in kwargs.items()
-            if argument.startswith("text_")
-        }
-
-        kwargs_clip_vision = {
-            argument[len("clip_vision_") :]: value
-            for argument, value in kwargs.items()
-            if argument.startswith("vision_")
-        }
-
-        # remove text, vision kwargs from kwargs
-        for key in kwargs_bert.keys():
-            del kwargs["bert_" + key]
-        for key in kwargs_clip_vision.keys():
-            del kwargs["clip_vision_" + key]
-
-        # Load and initialize the text and vision model
-        bert_model = kwargs_bert.pop("model", None)
-        if bert_model is None:
-            assert (
-                bert_model_name_or_path is not None
-            ), "If `model` is not defined as an argument, a `bert_model_name_or_path` has to be defined"
-            from transformers import FlaxBertModel
-
-            if "config" not in kwargs_bert:
-                from transformers import BertConfig
-
-                bert_config = BertConfig.from_pretrained(bert_model_name_or_path)
-                kwargs_bert["config"] = bert_config
-
-            bert_model = FlaxBertModel.from_pretrained(
-                bert_model_name_or_path, *model_args, from_pt=True, **kwargs_bert
-            )
-
-        clip_vision_model = kwargs_clip_vision.pop("model", None)
-        if clip_vision_model is None:
-            assert (
-                clip_vision_model_name_or_path is not None
-            ), "If `model` is not defined as an argument, a `clip_vision_model_name_or_path` has to be defined"
-            from transformers import FlaxCLIPVisionModel
-
-            if "config" not in kwargs_clip_vision:
-                from transformers import CLIPVisionConfig
-
-                clip_vision_config = CLIPVisionConfig.from_pretrained(
-                    clip_vision_model_name_or_path
-                )
-                kwargs_clip_vision["config"] = clip_vision_config
-
-            clip_vision_model = FlaxCLIPVisionModel.from_pretrained(
-                clip_vision_model_name_or_path, *model_args, **kwargs_clip_vision
-            )
-
-        # instantiate config with corresponding kwargs
-        dtype = kwargs.pop("dtype", jnp.float32)
-        config = CLIPVisionBertConfig.from_bert_clip_vision_configs(
-            bert_model.config, clip_vision_model.config, **kwargs
-        )
-
-        # init model
-        model = cls(config, *model_args, dtype=dtype, **kwargs)
-
-        for key in model.params.keys():
-            if key != "embeddings":
-                model.params[key] = bert_model.params[key]
-            else:
-                model.params["embeddings"][
-                    "clip_vision_module"
-                ] = clip_vision_model.params
-                for sub_key in bert_model.params[key]:
-                    model.params[key][sub_key] = bert_model.params[key][sub_key]
-
-        return model
-
-
-# flax_model = FlaxCLIPVisionBertModel.from_bert_clip_vision_pretrained('bert-base-uncased', 'openai/clip-vit-base-patch32', seed=0, dtype=jnp.float32)
-# outputs = flax_model(input_ids, attention_mask,token_type_ids, position_ids, pixel_values, visual_attention_mask, visual_token_type_ids, visual_position_ids, output_hidden_states=True)
-
-
-class FlaxCLIPVisionBertForMaskedLMModule(nn.Module):
-    config: CLIPVisionBertConfig
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        self.model = FlaxCLIPVisionBertModule(
-            config=self.config, add_pooling_layer=False, dtype=self.dtype
-        )
-        self.cls = FlaxBertOnlyMLMHead(config=self.config.bert_config, dtype=self.dtype)
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        position_ids,
-        pixel_values,
-        visual_attention_mask,
-        visual_token_type_ids,
-        visual_position_ids,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-
-        # Model
-        outputs = self.model(
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            pixel_values,
-            visual_attention_mask,
-            visual_token_type_ids,
-            visual_position_ids,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-        if self.config.bert_config.tie_word_embeddings:
-            shared_embedding = self.model.variables["params"]["embeddings"][
-                "word_embeddings"
-            ]["embedding"]
-        else:
-            shared_embedding = None
-
-        # Compute the prediction scores
-        logits = self.cls(hidden_states, shared_embedding=shared_embedding)
-
-        if not return_dict:
-            return (logits,) + outputs[1:]
-
-        return FlaxMaskedLMOutput(
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-class FlaxCLIPVisionBertForMaskedLM(FlaxPreTrainedModel):
-    config_class = CLIPVisionBertConfig
-    module_class = FlaxCLIPVisionBertForMaskedLMModule
-
-    def __init__(
-        self,
-        config: CLIPVisionBertConfig,
-        input_shape: Tuple = None,
-        seed: int = 0,
-        dtype: jnp.dtype = jnp.float32,
-        **kwargs,
-    ):
-
-        if input_shape is None:
-            input_shape = (
-                (1, 1),
-                (
-                    1,
-                    config.clip_vision_config.image_size,
-                    config.clip_vision_config.image_size,
-                    3,
-                ),
-                (
-                    1,
-                    (
-                        config.clip_vision_config.image_size
-                        // config.clip_vision_config.patch_size
-                    )
-                    ** 2
-                    + 1,
-                ),
-            )
-
-        module = self.module_class(config=config, dtype=dtype, **kwargs)
-        super().__init__(
-            config, module, input_shape=input_shape, seed=seed, dtype=dtype
-        )
-
-    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
-        # init input tensors
-        textual_input_shape = input_shape[0]
-        input_ids = jnp.zeros(textual_input_shape, dtype="i4")
-        token_type_ids = jnp.zeros_like(input_ids)
-        position_ids = jnp.broadcast_to(
-            jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), textual_input_shape
-        )
-        attention_mask = jnp.ones_like(input_ids)
-
-        pixel_values = jax.random.normal(rng, input_shape[1])
-        visual_attention_mask = jnp.ones(input_shape[2])
-        visual_token_type_ids = jnp.ones(input_shape[2])
-        visual_position_ids = jnp.broadcast_to(
-            jnp.zeros(jnp.atleast_2d(visual_token_type_ids).shape[-1]), input_shape[2]
-        )
-
-        params_rng, dropout_rng = jax.random.split(rng)
-        rngs = {"params": params_rng, "dropout": dropout_rng}
-
-        return self.module.init(
-            rngs,
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            pixel_values,
-            visual_attention_mask,
-            visual_token_type_ids,
-            visual_position_ids,
-            return_dict=False,
-        )["params"]
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        pixel_values=None,
-        visual_attention_mask=None,
-        visual_token_type_ids=None,
-        visual_position_ids=None,
-        params: dict = None,
-        dropout_rng: jax.random.PRNGKey = None,
-        train: bool = False,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ):
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.bert_config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.bert_config.output_hidden_states
-        )
-        return_dict = (
-            return_dict
-            if return_dict is not None
-            else self.config.bert_config.return_dict
-        )
-
-        # pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
-
-        # init input tensors if not passed
-        if token_type_ids is None:
-            token_type_ids = jnp.zeros_like(input_ids)
-
-        if position_ids is None:
-            position_ids = jnp.broadcast_to(
-                jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape
-            )
-
-        if attention_mask is None:
-            attention_mask = jnp.ones_like(input_ids)
-
-        visual_sequence_length = (
-            pixel_values.shape[0],
-            (
-                self.config.clip_vision_config.image_size
-                // self.config.clip_vision_config.patch_size
-            )
-            ** 2
-            + 1,
-        )
-
-        if visual_token_type_ids is None:
-            visual_token_type_ids = jnp.ones(visual_sequence_length)
-
-        if visual_position_ids is None:
-            visual_position_ids = jnp.broadcast_to(
-                jnp.atleast_2d(jnp.ones(visual_sequence_length)).shape[-1],
-                (visual_sequence_length),
-            )
-
-        if visual_attention_mask is None:
-            visual_attention_mask = jnp.ones(visual_sequence_length)
-
-        # Handle any PRNG if needed
-        rngs = {}
-        if dropout_rng is not None:
-            rngs["dropout"] = dropout_rng
-
-        return self.module.apply(
-            {"params": params or self.params},
-            jnp.array(input_ids, dtype="i4"),
-            jnp.array(attention_mask, dtype="i4"),
-            jnp.array(token_type_ids, dtype="i4"),
-            jnp.array(position_ids, dtype="i4"),
-            jnp.array(pixel_values, dtype=jnp.float32),
-            jnp.array(visual_attention_mask, dtype="i4"),
-            jnp.array(visual_token_type_ids, dtype="i4"),
-            jnp.array(visual_position_ids, dtype="i4"),
-            not train,
-            output_attentions,
-            output_hidden_states,
-            return_dict,
-            rngs=rngs,
-        )
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        # At the moment fast initialization is not supported
-        # for composite models
-        # kwargs["_fast_init"] = False
-        return super().from_pretrained(*args, **kwargs)
-
-    @classmethod
-    def from_clip_vision_bert_pretrained(
-        cls,
-        clip_vision_model_name_or_path: str = None,
-        bert_model_name_or_path: str = None,
-        *model_args,
-        **kwargs,
-    ) -> FlaxPreTrainedModel:
-
-        kwargs_bert = {
-            argument[len("bert_") :]: value
-            for argument, value in kwargs.items()
-            if argument.startswith("text_")
-        }
-
-        kwargs_clip_vision = {
-            argument[len("clip_vision_") :]: value
-            for argument, value in kwargs.items()
-            if argument.startswith("vision_")
-        }
-
-        # remove text, vision kwargs from kwargs
-        for key in kwargs_bert.keys():
-            del kwargs["bert_" + key]
-        for key in kwargs_clip_vision.keys():
-            del kwargs["clip_vision_" + key]
-
-        # Load and initialize the text and vision model
-        bert_model = kwargs_bert.pop("model", None)
-        if bert_model is None:
-            assert (
-                bert_model_name_or_path is not None
-            ), "If `model` is not defined as an argument, a `bert_model_name_or_path` has to be defined"
-            from transformers import FlaxBertForMaskedLM
-
-            if "config" not in kwargs_bert:
-                from transformers import BertConfig
-
-                bert_config = BertConfig.from_pretrained(bert_model_name_or_path)
-                kwargs_bert["config"] = bert_config
-
-            bert_model = FlaxBertForMaskedLM.from_pretrained(
-                bert_model_name_or_path, *model_args, from_pt=True, **kwargs_bert
-            )
-
-        clip_vision_model = kwargs_clip_vision.pop("model", None)
-        if clip_vision_model is None:
-            assert (
-                clip_vision_model_name_or_path is not None
-            ), "If `model` is not defined as an argument, a `clip_vision_model_name_or_path` has to be defined"
-            from transformers import FlaxCLIPVisionModel
-
-            if "config" not in kwargs_clip_vision:
-                from transformers import CLIPVisionConfig
-
-                clip_vision_config = CLIPVisionConfig.from_pretrained(
-                    clip_vision_model_name_or_path
-                )
-                kwargs_clip_vision["config"] = clip_vision_config
-
-            clip_vision_model = FlaxCLIPVisionModel.from_pretrained(
-                clip_vision_model_name_or_path, *model_args, **kwargs_clip_vision
-            )
-
-        # instantiate config with corresponding kwargs
-        dtype = kwargs.pop("dtype", jnp.float32)
-        config = CLIPVisionBertConfig.from_clip_vision_bert_configs(
-            clip_vision_model.config, bert_model.config, **kwargs
-        )
-
-        # init model
-        model = cls(config, *model_args, dtype=dtype, **kwargs)
-
-        model.params["cls"] = bert_model.params["cls"]
-        for key in model.params["model"].keys():
-            if key != "embeddings":
-                model.params["model"][key] = bert_model.params["bert"][key]
-            else:
-                model.params["model"]["embeddings"][
-                    "clip_vision_module"
-                ] = clip_vision_model.params
-                for sub_key in bert_model.params["bert"][key]:
-                    model.params["model"][key][sub_key] = bert_model.params["bert"][
-                        key
-                    ][sub_key]
-
-        return model
-
-
-class FlaxCLIPVisionBertForSequenceClassificationModule(nn.Module):
-    config: CLIPVisionBertConfig
-    dtype: jnp.dtype = jnp.float32
-    num_labels: int = 3129  # TODO: Remove this hard-coding!
-
-    def setup(self):
-        self.model = FlaxCLIPVisionBertModule(config=self.config, dtype=self.dtype)
-        self.dropout = nn.Dropout(rate=self.config.bert_config.hidden_dropout_prob)
-        self.classifier = nn.Dense(
-            self.num_labels,
-            dtype=self.dtype,
-        )
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        position_ids,
-        pixel_values,
-        visual_attention_mask,
-        visual_token_type_ids,
-        visual_position_ids,
-        deterministic: bool = True,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        # Model
-        outputs = self.model(
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            pixel_values,
-            visual_attention_mask,
-            visual_token_type_ids,
-            visual_position_ids,
-            deterministic=deterministic,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
-        logits = self.classifier(pooled_output)
-
-        if not return_dict:
-            return (logits,) + outputs[2:]
-
-        return FlaxSequenceClassifierOutput(
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-class FlaxCLIPVisionBertForSequenceClassification(FlaxPreTrainedModel):
-    config_class = CLIPVisionBertConfig
-    module_class = FlaxCLIPVisionBertForSequenceClassificationModule
-
-    def __init__(
-        self,
-        config: CLIPVisionBertConfig,
-        input_shape: Tuple = None,
-        seed: int = 0,
-        dtype: jnp.dtype = jnp.float32,
-        **kwargs,
-    ):
-
-        if input_shape is None:
-            input_shape = (
-                (1, 1),
-                (
-                    1,
-                    config.clip_vision_config.image_size,
-                    config.clip_vision_config.image_size,
-                    3,
-                ),
-                (
-                    1,
-                    (
-                        config.clip_vision_config.image_size
-                        // config.clip_vision_config.patch_size
-                    )
-                    ** 2
-                    + 1,
-                ),
-            )
-
-        module = self.module_class(config=config, dtype=dtype, **kwargs)
-        super().__init__(
-            config, module, input_shape=input_shape, seed=seed, dtype=dtype
-        )
-
-    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
-        # init input tensors
-        textual_input_shape = input_shape[0]
-        input_ids = jnp.zeros(textual_input_shape, dtype="i4")
-        token_type_ids = jnp.zeros_like(input_ids)
-        position_ids = jnp.broadcast_to(
-            jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), textual_input_shape
-        )
-        attention_mask = jnp.ones_like(input_ids)
-
-        pixel_values = jax.random.normal(rng, input_shape[1])
-        visual_attention_mask = jnp.ones(input_shape[2])
-        visual_token_type_ids = jnp.ones(input_shape[2])
-        visual_position_ids = jnp.broadcast_to(
-            jnp.zeros(jnp.atleast_2d(visual_token_type_ids).shape[-1]), input_shape[2]
-        )
-
-        params_rng, dropout_rng = jax.random.split(rng)
-        rngs = {"params": params_rng, "dropout": dropout_rng}
-
-        return self.module.init(
-            rngs,
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            pixel_values,
-            visual_attention_mask,
-            visual_token_type_ids,
-            visual_position_ids,
-            return_dict=False,
-        )["params"]
-
-    def __call__(
-        self,
-        input_ids,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        pixel_values=None,
-        visual_attention_mask=None,
-        visual_token_type_ids=None,
-        visual_position_ids=None,
-        params: dict = None,
-        dropout_rng: jax.random.PRNGKey = None,
-        train: bool = False,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ):
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.bert_config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.bert_config.output_hidden_states
-        )
-        return_dict = (
-            return_dict
-            if return_dict is not None
-            else self.config.bert_config.return_dict
-        )
-
-        # pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
-
-        # init input tensors if not passed
-        if token_type_ids is None:
-            token_type_ids = jnp.zeros_like(input_ids)
-
-        if position_ids is None:
-            position_ids = jnp.broadcast_to(
-                jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape
-            )
-
-        if attention_mask is None:
-            attention_mask = jnp.ones_like(input_ids)
-
-        visual_sequence_length = (
-            pixel_values.shape[0],
-            (
-                self.config.clip_vision_config.image_size
-                // self.config.clip_vision_config.patch_size
-            )
-            ** 2
-            + 1,
-        )
-
-        if visual_token_type_ids is None:
-            visual_token_type_ids = jnp.ones(visual_sequence_length)
-
-        if visual_position_ids is None:
-            visual_position_ids = jnp.broadcast_to(
-                jnp.atleast_2d(jnp.ones(visual_sequence_length)).shape[-1],
-                (visual_sequence_length),
-            )
-
-        if visual_attention_mask is None:
-            visual_attention_mask = jnp.ones(visual_sequence_length)
-
-        # Handle any PRNG if needed
-        rngs = {}
-        if dropout_rng is not None:
-            rngs["dropout"] = dropout_rng
-
-        return self.module.apply(
-            {"params": params or self.params},
-            jnp.array(input_ids, dtype="i4"),
-            jnp.array(attention_mask, dtype="i4"),
-            jnp.array(token_type_ids, dtype="i4"),
-            jnp.array(position_ids, dtype="i4"),
-            jnp.array(pixel_values, dtype=jnp.float32),
-            jnp.array(visual_attention_mask, dtype="i4"),
-            jnp.array(visual_token_type_ids, dtype="i4"),
-            jnp.array(visual_position_ids, dtype="i4"),
-            not train,
-            output_attentions,
-            output_hidden_states,
-            return_dict,
-            rngs=rngs,
-        )
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        # At the moment fast initialization is not supported
-        # for composite models
-        # kwargs["_fast_init"] = False
-        return super().from_pretrained(*args, **kwargs)
-
-    @classmethod
-    def from_clip_vision_bert_pretrained(
-        cls,
-        clip_vision_model_name_or_path: str = None,
-        bert_model_name_or_path: str = None,
-        *model_args,
-        **kwargs,
-    ) -> FlaxPreTrainedModel:
-
-        kwargs_bert = {
-            argument[len("bert_") :]: value
-            for argument, value in kwargs.items()
-            if argument.startswith("bert_")
-        }
-
-        kwargs_clip_vision = {
-            argument[len("clip_vision_") :]: value
-            for argument, value in kwargs.items()
-            if argument.startswith("clip_vision_")
-        }
-
-        # remove text, vision kwargs from kwargs
-        for key in kwargs_bert.keys():
-            del kwargs["bert_" + key]
-        for key in kwargs_clip_vision.keys():
-            del kwargs["clip_vision_" + key]
-
-        # Load and initialize the text and vision model
-        bert_model = kwargs_bert.pop("model", None)
-        if bert_model is None:
-            assert (
-                bert_model_name_or_path is not None
-            ), "If `model` is not defined as an argument, a `bert_model_name_or_path` has to be defined"
-            from transformers import FlaxBertForSequenceClassification
-
-            if "config" not in kwargs_bert:
-                from transformers import BertConfig
-
-                bert_config = BertConfig.from_pretrained(bert_model_name_or_path)
-                kwargs_bert["config"] = bert_config
-
-            bert_model = FlaxBertForSequenceClassification.from_pretrained(
-                bert_model_name_or_path, *model_args, from_pt=True, **kwargs_bert
-            )
-
-        clip_vision_model = kwargs_clip_vision.pop("model", None)
-        if clip_vision_model is None:
-            assert (
-                clip_vision_model_name_or_path is not None
-            ), "If `model` is not defined as an argument, a `clip_vision_model_name_or_path` has to be defined"
-            from transformers import FlaxCLIPVisionModel
-
-            if "config" not in kwargs_clip_vision:
-                from transformers import CLIPVisionConfig
-
-                clip_vision_config = CLIPVisionConfig.from_pretrained(
-                    clip_vision_model_name_or_path
-                )
-                kwargs_clip_vision["config"] = clip_vision_config
-
-            clip_vision_model = FlaxCLIPVisionModel.from_pretrained(
-                clip_vision_model_name_or_path, *model_args, **kwargs_clip_vision
-            )
-
-        # instantiate config with corresponding kwargs
-        dtype = kwargs.pop("dtype", jnp.float32)
-        config = CLIPVisionBertConfig.from_clip_vision_bert_configs(
-            clip_vision_model.config, bert_model.config, **kwargs
-        )
-
-        # init model
-        model = cls(config, *model_args, dtype=dtype, **kwargs)
-
-        # model.params["classifier"] = bert_model.params["classifier"]
-        for key in model.params["model"].keys():
-            if key != "embeddings":
-                model.params["model"][key] = bert_model.params["bert"][key]
-            else:
-                model.params["model"]["embeddings"][
-                    "clip_vision_module"
-                ] = clip_vision_model.params
-                for sub_key in bert_model.params["bert"][key]:
-                    model.params["model"][key][sub_key] = bert_model.params["bert"][
-                        key
-                    ][sub_key]
-
-        return model
diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/app.py b/spaces/freddyaboulton/3.1.4.9-all-demos/app.py
deleted file mode 100644
index 1a8dbb5bb8d147a845c6f6a1978880fc62b29c55..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/app.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import importlib
-import gradio as gr
-import os
-import sys
-import copy
-import pathlib
-
-os.environ["SYSTEM"] = "pretend-not-spaces"
-
-demo_dir = pathlib.Path(__file__).parent / "demos"
-
-
-all_demos = []
-for p in os.listdir("./demos"):
-    all_demos.append(p)
-
-demo_module = None
-with gr.Blocks() as mega_demo:
-    with gr.Tabs():
-        for demo_name in all_demos:
-            with gr.TabItem(demo_name):
-                old_path = copy.deepcopy(sys.path)
-                sys.path = [os.path.join(demo_dir, demo_name)] + sys.path
-                if demo_module is None:
-                    demo_module = importlib.import_module(f"run")
-                else:
-                    demo_module = importlib.reload(demo_module)
-                demo_module.demo
-                sys.path = old_path
-
-mega_demo.launch()
diff --git a/spaces/gekkouga/open-reverse-proxy/server.js b/spaces/gekkouga/open-reverse-proxy/server.js
deleted file mode 100644
index 04a48b7a429c4d0ad0b772ba1edf503e349eda21..0000000000000000000000000000000000000000
--- a/spaces/gekkouga/open-reverse-proxy/server.js
+++ /dev/null
@@ -1,32 +0,0 @@
-const express = require('express');
-const proxy = require('express-http-proxy');
-const app = express();
-const targetUrl = 'https://api.openai.com';
-const openaiKey = process.env.OPENAI_KEY
-const port = 7860;
-const baseUrl = getExternalUrl(process.env.SPACE_ID);
-
-app.use('/api', proxy(targetUrl, {
-  proxyReqOptDecorator: (proxyReqOpts, srcReq) => {
-    // Modify the request headers if necessary
-    proxyReqOpts.headers['Authorization'] = 'Bearer '+openaiKey;
-    return proxyReqOpts;
-  },
-}));
-
-app.get("/", (req, res) => {
-  res.send(`This is your OpenAI Reverse Proxy URL: ${baseUrl}`);
-});
-
-function getExternalUrl(spaceId) {
-  try {
-    const [username, spacename] = spaceId.split("/");
-    return `https://${username}-${spacename.replace(/_/g, "-")}.hf.space/api/v1`;
-  } catch (e) {
-    return "";
-  }
-}
-
-app.listen(port, () => {
-  console.log(`Reverse proxy server running on ${baseUrl}`);
-});
\ No newline at end of file
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/furthest_point_sample.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/furthest_point_sample.py
deleted file mode 100644
index 374b7a878f1972c183941af28ba1df216ac1a60f..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/furthest_point_sample.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import torch
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
-    'furthest_point_sampling_forward',
-    'furthest_point_sampling_with_dist_forward'
-])
-
-
-class FurthestPointSampling(Function):
-    """Uses iterative furthest point sampling to select a set of features whose
-    corresponding points have the furthest distance."""
-
-    @staticmethod
-    def forward(ctx, points_xyz: torch.Tensor,
-                num_points: int) -> torch.Tensor:
-        """
-        Args:
-            points_xyz (Tensor): (B, N, 3) where N > num_points.
-            num_points (int): Number of points in the sampled set.
-
-        Returns:
-             Tensor: (B, num_points) indices of the sampled points.
-        """
-        assert points_xyz.is_contiguous()
-
-        B, N = points_xyz.size()[:2]
-        output = torch.cuda.IntTensor(B, num_points)
-        temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
-
-        ext_module.furthest_point_sampling_forward(
-            points_xyz,
-            temp,
-            output,
-            b=B,
-            n=N,
-            m=num_points,
-        )
-        if torch.__version__ != 'parrots':
-            ctx.mark_non_differentiable(output)
-        return output
-
-    @staticmethod
-    def backward(xyz, a=None):
-        return None, None
-
-
-class FurthestPointSamplingWithDist(Function):
-    """Uses iterative furthest point sampling to select a set of features whose
-    corresponding points have the furthest distance."""
-
-    @staticmethod
-    def forward(ctx, points_dist: torch.Tensor,
-                num_points: int) -> torch.Tensor:
-        """
-        Args:
-            points_dist (Tensor): (B, N, N) Distance between each point pair.
-            num_points (int): Number of points in the sampled set.
-
-        Returns:
-             Tensor: (B, num_points) indices of the sampled points.
-        """
-        assert points_dist.is_contiguous()
-
-        B, N, _ = points_dist.size()
-        output = points_dist.new_zeros([B, num_points], dtype=torch.int32)
-        temp = points_dist.new_zeros([B, N]).fill_(1e10)
-
-        ext_module.furthest_point_sampling_with_dist_forward(
-            points_dist, temp, output, b=B, n=N, m=num_points)
-        if torch.__version__ != 'parrots':
-            ctx.mark_non_differentiable(output)
-        return output
-
-    @staticmethod
-    def backward(xyz, a=None):
-        return None, None
-
-
-furthest_point_sample = FurthestPointSampling.apply
-furthest_point_sample_with_dist = FurthestPointSamplingWithDist.apply
diff --git a/spaces/giswqs/solara-demo/pages/06_openlayers.py b/spaces/giswqs/solara-demo/pages/06_openlayers.py
deleted file mode 100644
index 35e752d0d3a50284203dfcbc42ce61077a8702b0..0000000000000000000000000000000000000000
--- a/spaces/giswqs/solara-demo/pages/06_openlayers.py
+++ /dev/null
@@ -1,24 +0,0 @@
-
-import mapwidget.openlayers as mapwidget
-
-import solara
-
-zoom = solara.reactive(5)
-center = solara.reactive((53.2305799, 6.5323552))
-
-
-@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
-        mapwidget.Map.element(  # type: ignore
-            zoom=zoom.value,
-            center=center.value,
-            height='600px',
-            width="100%"
-        )
-        # solara.Text(f"Zoom: {zoom.value}")
-        # solara.Text(f"Center: {center.value}")
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Descargar Pelicula Cleopatra Xxx Mediafire.md b/spaces/gotiQspiryo/whisper-ui/examples/Descargar Pelicula Cleopatra Xxx Mediafire.md
deleted file mode 100644
index bb31acbd77af8a940cf6d7756662b9df85db3477..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Descargar Pelicula Cleopatra Xxx Mediafire.md	
+++ /dev/null
@@ -1,40 +0,0 @@
-<h2>descargar pelicula cleopatra xxx mediafire</h2><br /><p><b><b>DOWNLOAD</b> - <a href="https://urlgoal.com/2uyMDL">https://urlgoal.com/2uyMDL</a></b></p><br /><br />
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download Shaadi Mein Zaroor Aana 1080p.md b/spaces/gotiQspiryo/whisper-ui/examples/Download Shaadi Mein Zaroor Aana 1080p.md
deleted file mode 100644
index 6ee5acb6cdf8c2224c1fee5ad25ac1970a6e8a4e..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Download Shaadi Mein Zaroor Aana 1080p.md	
+++ /dev/null
@@ -1,47 +0,0 @@
-
-<h1>Download Shaadi Mein Zaroor Aana 1080p - A Romantic Comedy with a Twist</h1>
-<p>If you are looking for a Bollywood movie that will make you laugh, cry and fall in love, then you should download Shaadi Mein Zaroor Aana 1080p. This movie is a romantic comedy with a twist, starring Rajkummar Rao and Kriti Kharbanda as Satyendra and Aarti, two people who agree to an arranged marriage but face unexpected challenges on their wedding day.</p>
-<h2>What is Shaadi Mein Zaroor Aana about?</h2>
-<p>Shaadi Mein Zaroor Aana is a movie that explores the themes of love, betrayal, revenge and redemption. The story begins with Satyendra and Aarti meeting for the first time and liking each other. They decide to get married with the consent of their parents, after Satyendra gets a government job. However, on the day of their wedding, Aarti runs away from the venue, leaving Satyendra heartbroken and humiliated.</p>
-<h2>download Shaadi Mein Zaroor Aana 1080p</h2><br /><p><b><b>Download Zip</b> &#9675; <a href="https://urlgoal.com/2uyMFE">https://urlgoal.com/2uyMFE</a></b></p><br /><br />
-<p>The reason for Aarti's escape is that she gets an opportunity to pursue her career as a civil servant, which she had always dreamed of. She feels that marrying Satyendra would limit her ambitions and freedom. She also fears that her in-laws would not support her education and career goals.</p>
-<p>Five years later, Satyendra and Aarti cross paths again, but under different circumstances. Satyendra has become a successful officer in the income tax department, while Aarti is a suspect in a corruption case that he is investigating. Satyendra sees this as a chance to take revenge on Aarti for breaking his heart and ruining his reputation. He decides to make her life miserable and expose her crimes.</p>
-<p>However, as they interact more, they realize that they still have feelings for each other. They also discover that there are some secrets and misunderstandings that led to their separation. Will they be able to forgive each other and rekindle their love? Or will they let their past haunt them and destroy their future? To find out, you need to download Shaadi Mein Zaroor Aana 1080p and watch it yourself.</p>
-<h2>Why should you download Shaadi Mein Zaroor Aana 1080p?</h2>
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-<ul>
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-</ul>
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-<p></p>
-<ol>
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\ No newline at end of file
diff --git a/spaces/grvgl/ChatGPT4/README.md b/spaces/grvgl/ChatGPT4/README.md
deleted file mode 100644
index 7938de14e5355209aaae713f289ca469181bbb17..0000000000000000000000000000000000000000
--- a/spaces/grvgl/ChatGPT4/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gv/space_demo/app.py b/spaces/gv/space_demo/app.py
deleted file mode 100644
index 8e5f705e2b5b24b4d3c4ab7b5cc0d07657dae76a..0000000000000000000000000000000000000000
--- a/spaces/gv/space_demo/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import gradio as gr
-
-context = gr.inputs.Textbox(lines=10, placeholder="Enter paragraph/content here...")
-output = gr.outputs.HTML(  label="Question and Answers")
-
-
-def generate_question(context):
-  summary_text = summarizer(context,summary_model,summary_tokenizer)
-  for wrp in wrap(summary_text, 150):
-    print (wrp)
-  np =  get_keywords(context,summary_text)
-  print ("\n\nNoun phrases",np)
-  output=""
-  for answer in np:
-    ques = get_question(summary_text,answer,question_model,question_tokenizer)
-    # output= output + ques + "\n" + "Ans: "+answer.capitalize() + "\n\n"
-    output = output + "<b style='color:blue;'>" + ques + "</b>"
-    # output = output + "<br>"
-    output = output + "<b style='color:green;'>" + "Ans: " +answer.capitalize()+  "</b>"
-    output = output + "<br>"
-
-  summary ="Summary: "+ summary_text
-  for answer in np:
-    summary = summary.replace(answer,"<b>"+answer+"</b>")
-    summary = summary.replace(answer.capitalize(),"<b>"+answer.capitalize()+"</b>")
-  output = output + "<p>"+summary+"</p>"
-  
-  return output
-
-iface = gr.Interface(
-  fn=generate_question, 
-  inputs=context, 
-  outputs=output)
-iface.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/backbones/iresnet2060.py b/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/backbones/iresnet2060.py
deleted file mode 100644
index 21d1122144d207637d2444cba1f68fe630c89f31..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/backbones/iresnet2060.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import torch
-from torch import nn
-
-assert torch.__version__ >= "1.8.1"
-from torch.utils.checkpoint import checkpoint_sequential
-
-__all__ = ['iresnet2060']
-
-
-def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes,
-                     out_planes,
-                     kernel_size=3,
-                     stride=stride,
-                     padding=dilation,
-                     groups=groups,
-                     bias=False,
-                     dilation=dilation)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes,
-                     out_planes,
-                     kernel_size=1,
-                     stride=stride,
-                     bias=False)
-
-
-class IBasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None,
-                 groups=1, base_width=64, dilation=1):
-        super(IBasicBlock, self).__init__()
-        if groups != 1 or base_width != 64:
-            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
-        if dilation > 1:
-            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05, )
-        self.conv1 = conv3x3(inplanes, planes)
-        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05, )
-        self.prelu = nn.PReLU(planes)
-        self.conv2 = conv3x3(planes, planes, stride)
-        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05, )
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-        out = self.bn1(x)
-        out = self.conv1(out)
-        out = self.bn2(out)
-        out = self.prelu(out)
-        out = self.conv2(out)
-        out = self.bn3(out)
-        if self.downsample is not None:
-            identity = self.downsample(x)
-        out += identity
-        return out
-
-
-class IResNet(nn.Module):
-    fc_scale = 7 * 7
-
-    def __init__(self,
-                 block, layers, dropout=0, num_features=512, zero_init_residual=False,
-                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
-        super(IResNet, self).__init__()
-        self.fp16 = fp16
-        self.inplanes = 64
-        self.dilation = 1
-        if replace_stride_with_dilation is None:
-            replace_stride_with_dilation = [False, False, False]
-        if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
-        self.groups = groups
-        self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
-        self.prelu = nn.PReLU(self.inplanes)
-        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
-        self.layer2 = self._make_layer(block,
-                                       128,
-                                       layers[1],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(block,
-                                       256,
-                                       layers[2],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(block,
-                                       512,
-                                       layers[3],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[2])
-        self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05, )
-        self.dropout = nn.Dropout(p=dropout, inplace=True)
-        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
-        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
-        nn.init.constant_(self.features.weight, 1.0)
-        self.features.weight.requires_grad = False
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.normal_(m.weight, 0, 0.1)
-            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-        if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, IBasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
-            )
-        layers = []
-        layers.append(
-            block(self.inplanes, planes, stride, downsample, self.groups,
-                  self.base_width, previous_dilation))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(
-                block(self.inplanes,
-                      planes,
-                      groups=self.groups,
-                      base_width=self.base_width,
-                      dilation=self.dilation))
-
-        return nn.Sequential(*layers)
-
-    def checkpoint(self, func, num_seg, x):
-        if self.training:
-            return checkpoint_sequential(func, num_seg, x)
-        else:
-            return func(x)
-
-    def forward(self, x):
-        with torch.cuda.amp.autocast(self.fp16):
-            x = self.conv1(x)
-            x = self.bn1(x)
-            x = self.prelu(x)
-            x = self.layer1(x)
-            x = self.checkpoint(self.layer2, 20, x)
-            x = self.checkpoint(self.layer3, 100, x)
-            x = self.layer4(x)
-            x = self.bn2(x)
-            x = torch.flatten(x, 1)
-            x = self.dropout(x)
-        x = self.fc(x.float() if self.fp16 else x)
-        x = self.features(x)
-        return x
-
-
-def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
-    model = IResNet(block, layers, **kwargs)
-    if pretrained:
-        raise ValueError()
-    return model
-
-
-def iresnet2060(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet2060', IBasicBlock, [3, 128, 1024 - 128, 3], pretrained, progress, **kwargs)
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/samples/torch/pose.py b/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/samples/torch/pose.py
deleted file mode 100644
index d193816f0bbad075a9fab04233e644020beced75..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/samples/torch/pose.py
+++ /dev/null
@@ -1,291 +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 argparse
-import os
-import pathlib
-import numpy as np
-import torch
-import imageio
-
-import util
-
-import nvdiffrast.torch as dr
-
-#----------------------------------------------------------------------------
-# Quaternion math.
-#----------------------------------------------------------------------------
-
-# Unit quaternion.
-def q_unit():
-    return np.asarray([1, 0, 0, 0], np.float32)
-
-# Get a random normalized quaternion.
-def q_rnd():
-    u, v, w = np.random.uniform(0.0, 1.0, size=[3])
-    v *= 2.0 * np.pi
-    w *= 2.0 * np.pi
-    return np.asarray([(1.0-u)**0.5 * np.sin(v), (1.0-u)**0.5 * np.cos(v), u**0.5 * np.sin(w), u**0.5 * np.cos(w)], np.float32)
-
-# Get a random quaternion from the octahedral symmetric group S_4.
-_r2 = 0.5**0.5
-_q_S4 = [[ 1.0, 0.0, 0.0, 0.0], [ 0.0, 1.0, 0.0, 0.0], [ 0.0, 0.0, 1.0, 0.0], [ 0.0, 0.0, 0.0, 1.0],
-         [-0.5, 0.5, 0.5, 0.5], [-0.5,-0.5,-0.5, 0.5], [ 0.5,-0.5, 0.5, 0.5], [ 0.5, 0.5,-0.5, 0.5],
-         [ 0.5, 0.5, 0.5, 0.5], [-0.5, 0.5,-0.5, 0.5], [ 0.5,-0.5,-0.5, 0.5], [-0.5,-0.5, 0.5, 0.5],
-         [ _r2,-_r2, 0.0, 0.0], [ _r2, _r2, 0.0, 0.0], [ 0.0, 0.0, _r2, _r2], [ 0.0, 0.0,-_r2, _r2],
-         [ 0.0, _r2, _r2, 0.0], [ _r2, 0.0, 0.0,-_r2], [ _r2, 0.0, 0.0, _r2], [ 0.0,-_r2, _r2, 0.0],
-         [ _r2, 0.0, _r2, 0.0], [ 0.0, _r2, 0.0, _r2], [ _r2, 0.0,-_r2, 0.0], [ 0.0,-_r2, 0.0, _r2]]
-def q_rnd_S4():
-    return np.asarray(_q_S4[np.random.randint(24)], np.float32)
-
-# Quaternion slerp.
-def q_slerp(p, q, t):
-    d = np.dot(p, q)
-    if d < 0.0:
-        q = -q
-        d = -d
-    if d > 0.999:
-        a = p + t * (q-p)
-        return a / np.linalg.norm(a)
-    t0 = np.arccos(d)
-    tt = t0 * t
-    st = np.sin(tt)
-    st0 = np.sin(t0)
-    s1 = st / st0
-    s0 = np.cos(tt) - d*s1
-    return s0*p + s1*q
-
-# Quaterion scale (slerp vs. identity quaternion).
-def q_scale(q, scl):
-    return q_slerp(q_unit(), q, scl)
-
-# Quaternion product.
-def q_mul(p, q):
-    s1, V1 = p[0], p[1:]
-    s2, V2 = q[0], q[1:]
-    s = s1*s2 - np.dot(V1, V2)
-    V = s1*V2 + s2*V1 + np.cross(V1, V2)
-    return np.asarray([s, V[0], V[1], V[2]], np.float32)
-
-# Angular difference between two quaternions in degrees.
-def q_angle_deg(p, q):
-    p = p.detach().cpu().numpy()
-    q = q.detach().cpu().numpy()
-    d = np.abs(np.dot(p, q))
-    d = min(d, 1.0)
-    return np.degrees(2.0 * np.arccos(d))
-
-# Quaternion product
-def q_mul_torch(p, q):
-    a = p[0]*q[0] - p[1]*q[1] - p[2]*q[2] - p[3]*q[3]
-    b = p[0]*q[1] + p[1]*q[0] + p[2]*q[3] - p[3]*q[2]
-    c = p[0]*q[2] + p[2]*q[0] + p[3]*q[1] - p[1]*q[3]
-    d = p[0]*q[3] + p[3]*q[0] + p[1]*q[2] - p[2]*q[1]
-    return torch.stack([a, b, c, d])
-
-# Convert quaternion to 4x4 rotation matrix.
-def q_to_mtx(q):
-    r0 = torch.stack([1.0-2.0*q[1]**2 - 2.0*q[2]**2, 2.0*q[0]*q[1] - 2.0*q[2]*q[3], 2.0*q[0]*q[2] + 2.0*q[1]*q[3]])
-    r1 = torch.stack([2.0*q[0]*q[1] + 2.0*q[2]*q[3], 1.0 - 2.0*q[0]**2 - 2.0*q[2]**2, 2.0*q[1]*q[2] - 2.0*q[0]*q[3]])
-    r2 = torch.stack([2.0*q[0]*q[2] - 2.0*q[1]*q[3], 2.0*q[1]*q[2] + 2.0*q[0]*q[3], 1.0 - 2.0*q[0]**2 - 2.0*q[1]**2])
-    rr = torch.transpose(torch.stack([r0, r1, r2]), 1, 0)
-    rr = torch.cat([rr, torch.tensor([[0], [0], [0]], dtype=torch.float32).cuda()], dim=1) # Pad right column.
-    rr = torch.cat([rr, torch.tensor([[0, 0, 0, 1]], dtype=torch.float32).cuda()], dim=0)  # Pad bottom row.
-    return rr
-
-# Transform vertex positions to clip space
-def transform_pos(mtx, pos):
-    t_mtx = torch.from_numpy(mtx).cuda() if isinstance(mtx, np.ndarray) else mtx
-    # (x,y,z) -> (x,y,z,1)
-    posw = torch.cat([pos, torch.ones([pos.shape[0], 1]).cuda()], axis=1)
-    return torch.matmul(posw, t_mtx.t())[None, ...]
-
-def render(glctx, mtx, pos, pos_idx, col, col_idx, resolution: int):
-    # Setup TF graph for reference.
-    pos_clip    = transform_pos(mtx, pos)
-    rast_out, _ = dr.rasterize(glctx, pos_clip, pos_idx, resolution=[resolution, resolution])
-    color   , _ = dr.interpolate(col[None, ...], rast_out, col_idx)
-    color       = dr.antialias(color, rast_out, pos_clip, pos_idx)
-    return color
-
-#----------------------------------------------------------------------------
-# Cube pose fitter.
-#----------------------------------------------------------------------------
-
-def fit_pose(max_iter           = 10000,
-             repeats            = 1,
-             log_interval       = 10,
-             display_interval   = None,
-             display_res        = 512,
-             lr_base            = 0.01,
-             lr_falloff         = 1.0,
-             nr_base            = 1.0,
-             nr_falloff         = 1e-4,
-             grad_phase_start   = 0.5,
-             resolution         = 256,
-             out_dir            = None,
-             log_fn             = None,
-             mp4save_interval   = None,
-             mp4save_fn         = None):
-
-    log_file = None
-    writer = None
-    if out_dir:
-        os.makedirs(out_dir, exist_ok=True)
-        if log_fn:
-            log_file = open(out_dir + '/' + log_fn, 'wt')
-        if mp4save_interval != 0:
-            writer = imageio.get_writer(f'{out_dir}/{mp4save_fn}', mode='I', fps=30, codec='libx264', bitrate='16M')
-    else:
-        mp4save_interval = None
-
-    datadir = f'{pathlib.Path(__file__).absolute().parents[1]}/data'
-    with np.load(f'{datadir}/cube_p.npz') as f:
-        pos_idx, pos, col_idx, col = f.values()
-    print("Mesh has %d triangles and %d vertices." % (pos_idx.shape[0], pos.shape[0]))
-
-    # Some input geometry contains vertex positions in (N, 4) (with v[:,3]==1).  Drop
-    # the last column in that case.
-    if pos.shape[1] == 4: pos = pos[:, 0:3]
-
-    # Create position/triangle index tensors
-    pos_idx = torch.from_numpy(pos_idx.astype(np.int32)).cuda()
-    vtx_pos = torch.from_numpy(pos.astype(np.float32)).cuda()
-    col_idx = torch.from_numpy(col_idx.astype(np.int32)).cuda()
-    vtx_col = torch.from_numpy(col.astype(np.float32)).cuda()
-
-    glctx = dr.RasterizeGLContext()
-
-    for rep in range(repeats):
-        pose_target = torch.tensor(q_rnd(), device='cuda')
-        pose_init   = q_rnd()
-        pose_opt    = torch.tensor(pose_init / np.sum(pose_init**2)**0.5, dtype=torch.float32, device='cuda', requires_grad=True)
-
-        loss_best   = np.inf
-        pose_best   = pose_opt.detach().clone()
-
-        # Modelview + projection matrix.
-        mvp = torch.tensor(np.matmul(util.projection(x=0.4), util.translate(0, 0, -3.5)).astype(np.float32), device='cuda')
-
-        # Adam optimizer for texture with a learning rate ramp.
-        optimizer = torch.optim.Adam([pose_opt], betas=(0.9, 0.999), lr=lr_base)
-        # Render.
-        for it in range(max_iter + 1):
-            # Set learning rate.
-            itf = 1.0 * it / max_iter
-            nr = nr_base * nr_falloff**itf
-            lr = lr_base * lr_falloff**itf
-            for param_group in optimizer.param_groups:
-                param_group['lr'] = lr
-
-            # Noise input.
-            if itf >= grad_phase_start:
-                noise = q_unit()
-            else:
-                noise = q_scale(q_rnd(), nr)
-                noise = q_mul(noise, q_rnd_S4()) # Orientation noise.
-
-            # Render.
-            color          = render(glctx, torch.matmul(mvp, q_to_mtx(pose_target)), vtx_pos, pos_idx, vtx_col, col_idx, resolution)
-            pose_total_opt = q_mul_torch(pose_opt, noise)
-            mtx_total_opt  = torch.matmul(mvp, q_to_mtx(pose_total_opt))
-            color_opt      = render(glctx, mtx_total_opt, vtx_pos, pos_idx, vtx_col, col_idx, resolution)
-
-            # Image-space loss.
-            diff = (color_opt - color)**2 # L2 norm.
-            diff = torch.tanh(5.0 * torch.max(diff, dim=-1)[0])
-            loss = torch.mean(diff)
-
-            # Measure image-space loss and update best found pose.
-            loss_val = float(loss)
-            if (loss_val < loss_best) and (loss_val > 0.0):
-                pose_best = pose_total_opt.detach().clone()
-                loss_best = loss_val
-                if itf < grad_phase_start:
-                    with torch.no_grad(): pose_opt[:] = pose_best
-
-            # Print/save log.
-            if log_interval and (it % log_interval == 0):
-                err = q_angle_deg(pose_opt, pose_target)
-                ebest = q_angle_deg(pose_best, pose_target)
-                s = "rep=%d,iter=%d,err=%f,err_best=%f,loss=%f,loss_best=%f,lr=%f,nr=%f" % (rep, it, err, ebest, loss_val, loss_best, lr, nr)
-                print(s)
-                if log_file:
-                    log_file.write(s + "\n")
-
-            # Run gradient training step.
-            if itf >= grad_phase_start:
-                optimizer.zero_grad()
-                loss.backward()
-                optimizer.step()
-
-            with torch.no_grad():
-                pose_opt /= torch.sum(pose_opt**2)**0.5
-
-            # Show/save image.
-            display_image = display_interval and (it % display_interval == 0)
-            save_mp4      = mp4save_interval and (it % mp4save_interval == 0)
-
-            if display_image or save_mp4:
-                c = color[0].detach().cpu().numpy()
-                img_ref  = color[0].detach().cpu().numpy()
-                img_opt  = color_opt[0].detach().cpu().numpy()
-                img_best = render(glctx, torch.matmul(mvp, q_to_mtx(pose_best)), vtx_pos, pos_idx, vtx_col, col_idx, resolution)[0].detach().cpu().numpy()
-                result_image = np.concatenate([img_ref, img_best, img_opt], axis=1)
-
-                if display_image:
-                    util.display_image(result_image, size=display_res, title='(%d) %d / %d' % (rep, it, max_iter))
-                if save_mp4:
-                    writer.append_data(np.clip(np.rint(result_image*255.0), 0, 255).astype(np.uint8))
-
-    # Done.
-    if writer is not None:
-        writer.close()
-    if log_file:
-        log_file.close()
-
-#----------------------------------------------------------------------------
-
-def main():
-    parser = argparse.ArgumentParser(description='Cube pose fitting example')
-    parser.add_argument('--outdir', help='Specify output directory', default='')
-    parser.add_argument('--display-interval', type=int, default=0)
-    parser.add_argument('--mp4save-interval', type=int, default=10)
-    parser.add_argument('--max-iter', type=int, default=1000)
-    parser.add_argument('--repeats', type=int, default=1)
-    args = parser.parse_args()
-
-    # Set up logging.
-    if args.outdir:
-        out_dir = f'{args.outdir}/pose'
-        print (f'Saving results under {out_dir}')
-    else:
-        out_dir = None
-        print ('No output directory specified, not saving log or images')
-
-    # Run.
-    fit_pose(
-        max_iter=args.max_iter,
-        repeats=args.repeats,
-        log_interval=100,
-        display_interval=args.display_interval,
-        out_dir=out_dir,
-        log_fn='log.txt',
-        mp4save_interval=args.mp4save_interval,
-        mp4save_fn='progress.mp4'
-    )
-
-    # Done.
-    print("Done.")
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    main()
-
-#----------------------------------------------------------------------------
diff --git a/spaces/h2oai/wave-tour/examples/markdown_submit_text.py b/spaces/h2oai/wave-tour/examples/markdown_submit_text.py
deleted file mode 100644
index d53b016f23a54a4388e59b26fabc76a8987a4753..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/markdown_submit_text.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Markdown / Submit / Text
-# Use "?" to prefix the desired q.args.key that you want to have submitted after clicking a phrase.
-# ---
-
-from h2o_wave import main, app, Q, ui
-
-
-def get_form_items(clicked: str):
-    return [
-        ui.text(content='The quick brown [fox](?fox) jumps over the lazy [dog](?dog)'),
-        ui.text(content=f'Clicked: {clicked}'),
-    ]
-
-
-@app('/demo')
-async def serve(q: Q):
-    if not q.client.initialized:
-        q.page['example'] = ui.form_card(box='1 1 3 2', items=get_form_items('Nothing'))
-        q.client.initialized = True
-
-    if q.args.fox:
-        q.page['example'].items = get_form_items('fox')
-    if q.args.dog:
-        q.page['example'].items = get_form_items('dog')
-    await q.page.save()
diff --git a/spaces/haakohu/deep_privacy2_face/dp2/data/__init__.py b/spaces/haakohu/deep_privacy2_face/dp2/data/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/hackathon-pln-es/demo_flask/app.py b/spaces/hackathon-pln-es/demo_flask/app.py
deleted file mode 100644
index b3a8f56cc3b7a8688d9687002c96a2c4be1ee6ec..0000000000000000000000000000000000000000
--- a/spaces/hackathon-pln-es/demo_flask/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import flask
-import os
-
-from dotenv import load_dotenv
-load_dotenv()
-
-app = flask.Flask(__name__, template_folder="static")
-
-@app.route("/")
-def index():
-    return flask.render_template("index.html")
-
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
\ No newline at end of file
diff --git a/spaces/hackathon-pln-es/modelo-juridico-mexicano/app_data.py b/spaces/hackathon-pln-es/modelo-juridico-mexicano/app_data.py
deleted file mode 100644
index 6621248f56d71b4fb2d824dbfe844ac1067e02c9..0000000000000000000000000000000000000000
--- a/spaces/hackathon-pln-es/modelo-juridico-mexicano/app_data.py
+++ /dev/null
@@ -1,11 +0,0 @@
-examples =[
-["El articulo 55, ultimo párrafo , de la ley de Navegación y Comercio Marítimo no vulnera el principio de igualdad y no discriminación, pues la norma no contempla una autorización a los pilotos de puerto para prestar el servicio de lanchaje sin un permiso para ello.",
-"Además, el Comité ha observado que el derecho a la vida ha sido con mucha frecuencia interpretado en forma excesivamente restrictiva. La expresión el derecho a la vida es inherente a la persona humana no puede entenderse de manera restrictiva y la protección de este derecho exige que los Estados adopten medidas positivas. A este respecto, el Comité considera que sería oportuno que los Estados Partes tomaran todas las medidas posibles para disminuir la mortalidad infantil y aumentar la esperanza de vida, en especial adoptando medidas para eliminar la malnutrición y las epidemias."],
-["Una persona promovió juicio de amparo indirecto contra el auto por el cual el presidente de la Junta fijó fecha lejana para llevar a cabo su reinstalación, ordenada en el laudo que le fue favorable, pues en su opinión, ello constituía una tardanza excesiva de proveer adecuadamente. Por su parte, el Juez Federal negó la suspensión provisional, al considerar que de concederse, los efectos dejarían sin materia una eventual sentencia protectora, dado que la restitución que aquél solicita coincidiría exactamente, en su caso, con los del fallo protector",
-"La alta tasa de desempleo y la falta de seguridad en el empleo son causas que llevan a los trabajadores a buscar empleo en el sector no estructurado de la economía. Los Estados Partes deben adoptar las medidas necesarias, tanto legislativas como de otro tipo, para reducir en la mayor medida posible el número de trabajadores en la economía sumergida, trabajadores que, a resultas de esa situación, carecen de protección. Estas medidas obligarán a los empleadores a respetar la legislación laboral y a declarar a sus empleados, permitiendo así a estos últimos disfrutar de todos los derechos de los trabajadores, en particular los consagrados en los artículos 6, 7 y 8 del Pacto. Estas medidas deben reflejar el hecho de que las personas que viven en una economía sumergida lo hacen en su mayor parte debido a la necesidad de sobrevivir, antes que como una opción personal. Además, el trabajo doméstico y agrícola debe ser debidamente regulado mediante legislación nacional, de forma que los trabajadores domésticos y agrícolas disfruten del mismo nivel de protección que otros trabajadores."],
-["En un juicio ordinario civil, una persona moral ejerció acción reivindicatoria respecto de una fracción de un bien inmueble que refiere es de su propiedad, mientras que su contraparte aduce que es un bien del dominio público, al tratarse de un anexo que forma parte de un monumento histórico",
- "Sobre ese punto ya se indicó que la orientación sexual y la identidad de género, así como la expresión de género son categorías protegidas por la Convención y que, por ello, está proscrita por la Convención cualquier norma, acto o práctica discriminatoria basada en la orientación sexual, identidad de género o expresión de género de la persona. De acuerdo con ello, esta Corte señaló supra que ninguna norma, decisión o práctica de derecho interno, sea por parte de autoridades estatales o por particulares, pueden disminuir o restringir, de modo alguno, los derechos de una persona a partir de su orientación sexual, su identidad de género y/o su expresión de género"],
-["Las prestaciones extralegales constituyen beneficios otorgados por los patrones a sus trabajadores adicionales o mayores a los establecidos en la ley, razón por la cual, si bien encuentran respaldo jurídico en ella, el fundamento esencial lo constituye el contrato privado, colectivo o ley, o bien el reglamento interno de trabajo, entre otros. Por ello, en caso de controversia judicial, en términos del artículo 872 de la Ley Federal del Trabajo, en su texto anterior a la reforma publicada en el Diario Oficial de la Federación el 1 de mayo de 2019, corresponde al actor, además de expresar los hechos en que funde sus peticiones, aportar las pruebas que considere pertinentes para demostrar sus pretensiones. Consecuentemente, por imperativo legal del artículo 784 de la aludida legislación, la circunstancia de que el patrón no haya dado contestación a la demanda y tampoco acuda a la audiencia relativa, en su fase de ofrecimiento y admisión de pruebas, en modo alguno puede concluirse que relevó de la obligación legal y previa que tenía el trabajador de demostrar el deber que tenía la patronal de pagar las aludidas prestaciones. Considerar lo contrario implicaría romper el equilibrio procesal que debe imperar en el procedimiento laboral, ya que la legislación federal en comento establece consecuencias legales vinculadas estrictamente con las omisiones en que, en su momento procesal, incurrió el demandado, en el particular, tener por contestada de manera afirmativa la demanda y la pérdida del derecho para ofrecer pruebas.", "Esto es así, ya que a través de dicha jurisprudencia, esta Segunda Sala de la Suprema Corte de Justicia de la Nación simplemente se limitó a complementar el alcance de la diversa jurisprudencia 2a./J. 48/2016 (10a.), en la cual había dejado a la interpretación judicial analizar, en cada caso, si existía o no una abierta dilación procesal o paralización total del procedimiento, como una excepción a la regla general de procedencia del amparo indirecto contra los actos previstos en la fracción V del artículo 107 de la Ley de Amparo, es decir, contra los actos en juicio cuyos efectos son de imposible reparación, para lo cual estableció un parámetro mínimo objetivo para emprender la acción constitucional en contra de las dilaciones presuntamente excesivas de la autoridad responsable en el dictado de proveídos, laudos o en la realización de cualquier otra diligencia. Dicho parámetro se fijó en función del lapso máximo que preveía la Ley Federal del Trabajo para que el juicio laboral permaneciera inmóvil, so pena de que operara la caducidad de la instancia, lo que evidentemente no aplica con relación a las dilaciones procesales ocurridas en la fase de ejecución de sentencia, al tratarse de una etapa procesal diferente a la del juicio, en la que la Ley Federal del Trabajo establece procedimientos y plazos específicos para el cumplimiento de un laudo, los cuales, incluso, en un momento dado pudieran abonar para determinar la actualización de una dilación procesal para esa etapa; además de que la propia Ley de Amparo, tratándose de actos emitidos después de concluido el juicio, establece reglas específicas de procedencia diferentes a la analizada en la jurisprudencia en comento, es decir, a la prevista en la fracción V del artículo 107 de la Ley de Amparo. No obstante lo anterior, a juicio de esta Segunda Sala, corresponde al juzgador, en cada caso, ponderar cuándo existe o no una abierta dilación procesal en la etapa de ejecución de sentencia, como caso de excepción para efectos de la procedencia del amparo indirecto, para lo cual deberá considerar, además de la complejidad del asunto, la actividad procesal de las partes, la conducta asumida por la autoridad judicial y la afectación generada por la duración del procedimiento en la situación jurídica de la persona involucrada; que un plazo razonable en la etapa de ejecución de sentencia pudiera ser aquel que permita el desahogo de dicha fase en un tiempo ligeramente superior al que pudiera desarrollarse en condiciones normales."],
-["Diversas personas, físicas y morales, promovieron juicio de amparo indirecto en contra de la imposición de un mecanismo de votación por cédula secreta sobre un dictamen de reforma constitucional local, atribuible a diversas autoridades adscritas al Poder Legislativo de una entidad federativa, al considerarlo violatorio de, entre otros, el derecho a la libertad de expresión, el acceso a la información pública y la transparencia parlamentaria.",
-"Asimismo, aun cuando la referida calificativa está prevista en el citado artículo 316, fracción VI del propio código, cuyo contenido es reiterado en el precepto impugnado, no existe una recalificación o doble sanción derivado del mismo juzgamiento, ya que es sólo el referido artículo 305 el que regula la pena que debe incrementarse por dicha circunstancia, aunado a que esa infracción no se actualiza cuando el legislador establece una penalidad agravada o calificativa diversa a la del tipo básico. En consecuencia, el artículo impugnado no es violatorio del principio non bis in idem contenido en el precepto 23 de la Constitución, en relación con los diversos 8.4 de la Convención Americana sobre Derechos Humanos, y 14.7 del Pacto Internacional de Derechos Civiles y Políticos."]
-]
\ No newline at end of file
diff --git a/spaces/hamzapehlivan/StyleRes/models/styleres.py b/spaces/hamzapehlivan/StyleRes/models/styleres.py
deleted file mode 100644
index 9cb036da92a3b5ed93ac5ea5644bd3cc14ef8c71..0000000000000000000000000000000000000000
--- a/spaces/hamzapehlivan/StyleRes/models/styleres.py
+++ /dev/null
@@ -1,75 +0,0 @@
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from models.e4e import E4E_Inversion
-from models.stylegan2 import Generator
-from editings.editor import Editor
-from options import Settings
-
-class StyleRes(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.encoder = E4E_Inversion(resolution=256, num_layers = 50, mode='ir_se', out_res=64)
-        self.generator = Generator(z_dim=512, w_dim=512, c_dim=0, resolution=1024, img_channels=3, 
-                        fused_modconv_default='inference_only', embed_res=64)
-        # Set Generator arguments for eval mode
-        self.G_kwargs_val = {'noise_mode':'const', 'force_fp32':True}
-        self.device = Settings.device
-        self.editor = Editor()
-
-    def load_ckpt(self, ckpt_path):
-        ckpt = torch.load(ckpt_path, map_location='cpu')
-        self.encoder.basic_encoder.load_state_dict(ckpt['e4e'], strict=True)
-        self.encoder.latent_avg = ckpt['latent_avg']
-        self.generator.load_state_dict(ckpt['generator_smooth'], strict=True)
-        print("Model succesfully loaded")
-
-    def send_to_device(self):
-        self.encoder.to(self.device)
-        self.generator.to(self.device)
-        if self.device != 'cpu':
-            self.encoder.latent_avg = self.encoder.latent_avg.cuda()
-
-    """
-        Inputs: Input images and edit configs
-        Returns: Edited images together with the randomly generated image when the edit is interpolation.
-    """
-    def edit_images(self, image, cfg):
-        image = image.to(self.device)
-        with torch.no_grad():
-            latents, skips = self.encoder(image)
-
-        # GradCtrl requires gradients, others do not
-        latents_edited = self.editor.edit(latents, cfg) 
-
-        with torch.no_grad():
-            # Get F space features F_orig, for the original image
-            skips['inversion'], _ = self.generator(latents, skips, return_f = True, **self.G_kwargs_val)
-            # Transform F_orig to incoming image
-            images, _ = self.generator(latents_edited, skips,  **self.G_kwargs_val)
-            
-        return images
-
-    # def edit_demo_image(self, image, edit, factor):
-    #     from utils import AttrDict
-    #     cfg = AttrDict()
-    #     edit = edit.lower()
-    #     if edit in ['pose', 'age', 'smile']:
-    #         cfg.method = 'interfacegan'
-    #         cfg.edit = edit
-    #         cfg.strength = factor
-    #     image = image.to(self.device)
-    #     with torch.no_grad():
-    #         latents, skips = self.encoder(image)
-    #         latents_edited = self.editor.edit(latents, cfg) 
-    #     with torch.no_grad():
-    #         # Get F space features F_orig, for the original image
-    #         skips['inversion'], _ = self.generator(latents, skips, return_f = True, **self.G_kwargs_val)
-    #         # Transform F_orig to incoming image
-    #         images, _ = self.generator(latents_edited, skips,  **self.G_kwargs_val)
-            
-    #     return images
-
-
-
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/DensePose/dev/README.md b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/DensePose/dev/README.md
deleted file mode 100644
index e3a94b67ed4b4d0c2934f074802cd00f3660f9a9..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/DensePose/dev/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-## Some scripts for developers to use, include:
-
-- `run_instant_tests.sh`: run training for a few iterations.
-- `run_inference_tests.sh`: run inference on a small dataset.
-- `../../dev/linter.sh`: lint the codebase before commit
-- `../../dev/parse_results.sh`: parse results from log file.
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/test_model_zoo.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/test_model_zoo.py
deleted file mode 100644
index 2d16c711af2ab797dab04d0573c2ed70e071ebfd..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/test_model_zoo.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-import unittest
-
-from detectron2 import model_zoo
-from detectron2.modeling import FPN, GeneralizedRCNN
-
-logger = logging.getLogger(__name__)
-
-
-class TestModelZoo(unittest.TestCase):
-    def test_get_returns_model(self):
-        model = model_zoo.get("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml", trained=False)
-        self.assertIsInstance(model, GeneralizedRCNN)
-        self.assertIsInstance(model.backbone, FPN)
-
-    def test_get_invalid_model(self):
-        self.assertRaises(RuntimeError, model_zoo.get, "Invalid/config.yaml")
-
-    def test_get_url(self):
-        url = model_zoo.get_checkpoint_url("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml")
-        self.assertEqual(
-            url,
-            "https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn/138602908/model_final_01ca85.pkl",  # noqa
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/scripts/make_crop.sh b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/scripts/make_crop.sh
deleted file mode 100644
index 604a433c0494b1ddba9223cd95bf6de2b4b150b0..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/scripts/make_crop.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-python make_crop_and_mask_w_mask_nms.py \
-	--img_dir './data/CIHP/Testing/Images' \
-	--save_dir './data/CIHP/' \
-	--img_list './data/CIHP/annotations/CIHP_val.json' \
-	--det_res './data/CIHP/detectron2_prediction/inference/instances_predictions.pth'
-
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/experiment_planner_baseline_2DUNet_v21.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/experiment_planner_baseline_2DUNet_v21.py
deleted file mode 100644
index 146d436b1920244fdf02c8af80405dfb38781a32..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/experiment_planner_baseline_2DUNet_v21.py
+++ /dev/null
@@ -1,100 +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 copy import deepcopy
-
-from nnunet.experiment_planning.common_utils import get_pool_and_conv_props
-from nnunet.experiment_planning.experiment_planner_baseline_2DUNet import ExperimentPlanner2D
-from nnunet.network_architecture.generic_UNet import Generic_UNet
-from nnunet.paths import *
-import numpy as np
-
-
-class ExperimentPlanner2D_v21(ExperimentPlanner2D):
-    def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
-        super(ExperimentPlanner2D_v21, self).__init__(folder_with_cropped_data, preprocessed_output_folder)
-        self.data_identifier = "nnUNetData_plans_v2.1_2D"
-        self.plans_fname = join(self.preprocessed_output_folder,
-                                "nnUNetPlansv2.1_plans_2D.pkl")
-        self.unet_base_num_features = 32
-
-    def get_properties_for_stage(self, current_spacing, original_spacing, original_shape, num_cases,
-                                 num_modalities, num_classes):
-
-        new_median_shape = np.round(original_spacing / current_spacing * original_shape).astype(int)
-
-        dataset_num_voxels = np.prod(new_median_shape, dtype=np.int64) * num_cases
-        input_patch_size = new_median_shape[1:]
-
-        network_num_pool_per_axis, pool_op_kernel_sizes, conv_kernel_sizes, new_shp, \
-        shape_must_be_divisible_by = get_pool_and_conv_props(current_spacing[1:], input_patch_size,
-                                                             self.unet_featuremap_min_edge_length,
-                                                             self.unet_max_numpool)
-
-        # we pretend to use 30 feature maps. This will yield the same configuration as in V1. The larger memory
-        # footpring of 32 vs 30 is mor ethan offset by the fp16 training. We make fp16 training default
-        # Reason for 32 vs 30 feature maps is that 32 is faster in fp16 training (because multiple of 8)
-        ref = Generic_UNet.use_this_for_batch_size_computation_2D * Generic_UNet.DEFAULT_BATCH_SIZE_2D / 2  # for batch size 2
-        here = Generic_UNet.compute_approx_vram_consumption(new_shp,
-                                                            network_num_pool_per_axis,
-                                                            30,
-                                                            self.unet_max_num_filters,
-                                                            num_modalities, num_classes,
-                                                            pool_op_kernel_sizes,
-                                                            conv_per_stage=self.conv_per_stage)
-        while here > ref:
-            axis_to_be_reduced = np.argsort(new_shp / new_median_shape[1:])[-1]
-
-            tmp = deepcopy(new_shp)
-            tmp[axis_to_be_reduced] -= shape_must_be_divisible_by[axis_to_be_reduced]
-            _, _, _, _, shape_must_be_divisible_by_new = \
-                get_pool_and_conv_props(current_spacing[1:], tmp, self.unet_featuremap_min_edge_length,
-                                        self.unet_max_numpool)
-            new_shp[axis_to_be_reduced] -= shape_must_be_divisible_by_new[axis_to_be_reduced]
-
-            # we have to recompute numpool now:
-            network_num_pool_per_axis, pool_op_kernel_sizes, conv_kernel_sizes, new_shp, \
-            shape_must_be_divisible_by = get_pool_and_conv_props(current_spacing[1:], new_shp,
-                                                                 self.unet_featuremap_min_edge_length,
-                                                                 self.unet_max_numpool)
-
-            here = Generic_UNet.compute_approx_vram_consumption(new_shp, network_num_pool_per_axis,
-                                                                self.unet_base_num_features,
-                                                                self.unet_max_num_filters, num_modalities,
-                                                                num_classes, pool_op_kernel_sizes,
-                                                                conv_per_stage=self.conv_per_stage)
-            # print(new_shp)
-
-        batch_size = int(np.floor(ref / here) * 2)
-        input_patch_size = new_shp
-
-        if batch_size < self.unet_min_batch_size:
-            raise RuntimeError("This should not happen")
-
-        # check if batch size is too large (more than 5 % of dataset)
-        max_batch_size = np.round(self.batch_size_covers_max_percent_of_dataset * dataset_num_voxels /
-                                  np.prod(input_patch_size, dtype=np.int64)).astype(int)
-        batch_size = max(1, min(batch_size, max_batch_size))
-
-        plan = {
-            'batch_size': batch_size,
-            'num_pool_per_axis': network_num_pool_per_axis,
-            'patch_size': input_patch_size,
-            'median_patient_size_in_voxels': new_median_shape,
-            'current_spacing': current_spacing,
-            'original_spacing': original_spacing,
-            'pool_op_kernel_sizes': pool_op_kernel_sizes,
-            'conv_kernel_sizes': conv_kernel_sizes,
-            'do_dummy_2D_data_aug': False
-        }
-        return plan
diff --git a/spaces/hysts/Hopenet/app.py b/spaces/hysts/Hopenet/app.py
deleted file mode 100644
index a9ba84e66feb5e4c591503df3269a2457342ee3f..0000000000000000000000000000000000000000
--- a/spaces/hysts/Hopenet/app.py
+++ /dev/null
@@ -1,170 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import functools
-import os
-import sys
-from typing import Callable
-
-import cv2
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-import torchvision.transforms as T
-from scipy.spatial.transform import Rotation
-
-sys.path.insert(0, 'face_detection')
-sys.path.insert(0, 'deep-head-pose/code')
-
-from hopenet import Hopenet
-from ibug.face_detection import RetinaFacePredictor
-
-DESCRIPTION = '# [Hopenet](https://github.com/natanielruiz/deep-head-pose)'
-
-
-def load_model(model_name: str, device: torch.device) -> nn.Module:
-    path = huggingface_hub.hf_hub_download('public-data/Hopenet',
-                                           f'models/{model_name}.pkl')
-    state_dict = torch.load(path, map_location='cpu')
-    model = Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
-    model.load_state_dict(state_dict)
-    model.to(device)
-    model.eval()
-    return model
-
-
-def create_transform() -> Callable:
-    transform = T.Compose([
-        T.Resize(224),
-        T.CenterCrop(224),
-        T.ToTensor(),
-        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-    ])
-    return transform
-
-
-def crop_face(image: np.ndarray, box: tuple[int, int, int, int]) -> np.ndarray:
-    x0, y0, x1, y1 = box
-    w = x1 - x0
-    h = y1 - y0
-    x0 -= 2 * w // 4
-    x1 += 2 * w // 4
-    y0 -= 3 * h // 4
-    y1 += h // 4
-    x0 = max(x0, 0)
-    y0 = max(y0, 0)
-    x1 = min(x1, image.shape[1])
-    y1 = min(y1, image.shape[0])
-    image = image[y0:y1, x0:x1]
-    return image
-
-
-@torch.inference_mode()
-def predict(image: np.ndarray, transform: Callable, model: nn.Module,
-            device: torch.device) -> np.ndarray:
-    indices = torch.arange(66).float().to(device)
-
-    image = PIL.Image.fromarray(image)
-    data = transform(image)
-    data = data.to(device)
-
-    # the output of the model is a tuple of 3 tensors (yaw, pitch, roll)
-    # the shape of each tensor is (1, 66)
-    out = model(data[None, ...])
-    out = torch.stack(out, dim=1)  # shape: (1, 3, 66)
-    out = F.softmax(out, dim=2)
-    out = (out * indices).sum(dim=2) * 3 - 99
-    out = out.cpu().numpy()[0]
-    return out
-
-
-def draw_axis(image: np.ndarray, pose: np.ndarray, origin: np.ndarray,
-              length: int) -> None:
-    # (yaw, pitch, roll) -> (roll, yaw, pitch)
-    pose = pose[[2, 0, 1]]
-    pose *= np.array([1, -1, 1])
-    rot = Rotation.from_euler('zyx', pose, degrees=True)
-
-    vectors = rot.as_matrix().T[:, :2]  # shape: (3, 2)
-    pts = np.round(vectors * length + origin).astype(int)
-
-    cv2.line(image, tuple(origin), tuple(pts[0]), (0, 0, 255), 3)
-    cv2.line(image, tuple(origin), tuple(pts[1]), (0, 255, 0), 3)
-    cv2.line(image, tuple(origin), tuple(pts[2]), (255, 0, 0), 2)
-
-
-def run(image: np.ndarray, model_name: str, face_detector: RetinaFacePredictor,
-        models: dict[str, nn.Module], transform: Callable,
-        device: torch.device) -> np.ndarray:
-    model = models[model_name]
-
-    # RGB -> BGR
-    det_faces = face_detector(image[:, :, ::-1], rgb=False)
-
-    res = image[:, :, ::-1].copy()
-    for det_face in det_faces:
-        box = np.round(det_face[:4]).astype(int)
-
-        # RGB
-        face_image = crop_face(image, box.tolist())
-
-        # (yaw, pitch, roll)
-        angles = predict(face_image, transform, model, device)
-
-        center = (box[:2] + box[2:]) // 2
-        length = (box[3] - box[1]) // 2
-        draw_axis(res, angles, center, length)
-
-    return res[:, :, ::-1]
-
-
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-face_detector = RetinaFacePredictor(
-    threshold=0.8,
-    device=device,
-    model=RetinaFacePredictor.get_model('mobilenet0.25'))
-
-model_names = [
-    'hopenet_alpha1',
-    'hopenet_alpha2',
-    'hopenet_robust_alpha1',
-]
-models = {name: load_model(name, device) for name in model_names}
-transform = create_transform()
-
-fn = functools.partial(run,
-                       face_detector=face_detector,
-                       models=models,
-                       transform=transform,
-                       device=device)
-
-examples = [['images/pexels-ksenia-chernaya-8535230.jpg', 'hopenet_alpha1']]
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(label='Input', type='numpy')
-            model_name = gr.Radio(label='Model',
-                                  choices=model_names,
-                                  type='value',
-                                  value=model_names[0])
-            run_button = gr.Button('Run')
-        with gr.Column():
-            result = gr.Image(label='Output')
-    gr.Examples(examples=examples,
-                inputs=[image, model_name],
-                outputs=result,
-                fn=fn,
-                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
-    run_button.click(fn=fn,
-                     inputs=[image, model_name],
-                     outputs=result,
-                     api_name='run')
-demo.queue().launch()
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Burying The Ex Movie Download In Hindi FREE.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Burying The Ex Movie Download In Hindi FREE.md
deleted file mode 100644
index f018dffe61a71c42613c311186be2065c9992692..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Burying The Ex Movie Download In Hindi FREE.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-
-<h1>Burying the Ex Movie in Hindi: A Zombie Comedy by Joe Dante</h1>
-
-<p>If you are looking for a fun and quirky zombie comedy, you might want to check out Burying the Ex movie in Hindi. Burying the Ex is a 2014 American film directed by Joe Dante, who is known for his horror-comedy classics like Gremlins and The Howling. The film stars Anton Yelchin, Ashley Greene, Alexandra Daddario, and Oliver Cooper.</p>
-
-<p>The plot of Burying the Ex movie in Hindi revolves around Max (Yelchin), a horror movie fan who works at a costume shop. He is unhappy with his controlling and eco-obsessed girlfriend Evelyn (Greene), but he can't find a way to break up with her. Things get complicated when Evelyn dies in a freak accident and comes back as a zombie, determined to stay with Max forever. Meanwhile, Max meets Olivia (Daddario), a beautiful and fun-loving girl who shares his passion for horror movies. Max has to find a way to get rid of Evelyn and pursue his new love interest, while avoiding Evelyn's zombie attacks.</p>
-<h2>burying the ex movie download in hindi</h2><br /><p><b><b>Download File</b> &#10001; <a href="https://urlin.us/2uEvN1">https://urlin.us/2uEvN1</a></b></p><br /><br />
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-<p>Burying the Ex movie in Hindi is a hilarious and entertaining film that pays homage to the classic zombie movies of George A. Romero. The film has witty dialogue, funny situations, and gory effects. The film also has some references to other horror movies, such as Plan 9 from Outer Space and It Came from Outer Space. The film has received mixed reviews from critics, but it has a cult following among horror-comedy fans.</p>
-
-<p>If you want to watch Burying the Ex movie in Hindi, you can find it on various online platforms. The film is rated R for sexual content, partial nudity, some horror violence, and language. The film has a runtime of 89 minutes. Burying the Ex movie in Hindi is a perfect choice for a date night or a Halloween party.</p>
-
-<p>Burying the Ex movie in Hindi has a talented cast that delivers great performances. Anton Yelchin, who sadly passed away in 2016, is charming and sympathetic as Max, the protagonist who is torn between two women. Ashley Greene is hilarious and creepy as Evelyn, the zombie girlfriend who won't let go. Alexandra Daddario is stunning and sweet as Olivia, the ideal match for Max. Oliver Cooper is funny and supportive as Travis, Max's best friend and co-worker.</p>
-
-<p>Burying the Ex movie in Hindi is directed by Joe Dante, who is a master of horror-comedy genre. He has directed many iconic films, such as Piranha, The Howling, Gremlins, The 'Burbs, and Small Soldiers. He has also worked on television shows, such as The Twilight Zone, Amazing Stories, and Masters of Horror. He is known for his dark humor, satirical tone, and references to pop culture. He has influenced many filmmakers, such as J.J. Abrams, Edgar Wright, and James Gunn.</p>
-
-<p>Burying the Ex movie in Hindi has some interesting trivia and facts that you might not know. For example, the film was originally a short film written by Alan Trezza in 2008. The short film starred John Francis Daley and Danielle Harris as Max and Evelyn. The short film was well-received and won several awards at film festivals. Trezza then expanded the short film into a feature-length screenplay, which caught the attention of Joe Dante.</p>
-
-<p>Another trivia about Burying the Ex movie in Hindi is that the film was partially funded by a crowdfunding campaign on FundAnything.com. The campaign was launched by Joe Dante and Alan Trezza in 2013, and it raised over $60,000 from more than 800 backers. The campaign offered various rewards to the backers, such as signed posters, DVDs, Blu-rays, T-shirts, and even a chance to visit the set or have a cameo in the film.</p>
-
-<p>One more trivia about Burying the Ex movie in Hindi is that the film has many references and easter eggs to other horror movies and Joe Dante's previous works. For example, the name of Max's costume shop is "Bloody Mary's", which is a reference to Mary Shelley, the author of Frankenstein. The name of Evelyn's blog is "Go Green or Go Home", which is a reference to the green slime from Ghostbusters. The name of Olivia's ice cream truck is "Screaming Mimi's", which is a reference to a 1958 film noir directed by Gerd Oswald. The name of the zombie movie that Max and Olivia watch is "Plan Z from Outer Space", which is a reference to Plan 9 from Outer Space, a notoriously bad sci-fi movie from 1957.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Devon Ke Dev Mahadev - Episodes 51-100 ( Hindi ).md b/spaces/inplisQlawa/anything-midjourney-v4-1/Devon Ke Dev Mahadev - Episodes 51-100 ( Hindi ).md
deleted file mode 100644
index 271fd8e5d001d7df8acd0f1e68b1db00cc1bb482..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Devon Ke Dev Mahadev - Episodes 51-100 ( Hindi ).md	
+++ /dev/null
@@ -1,32 +0,0 @@
-<h2>Devon Ke Dev Mahadev - Episodes 51-100 ( Hindi )</h2><br /><p><b><b>DOWNLOAD</b> &#128505; <a href="https://urlin.us/2uExwA">https://urlin.us/2uExwA</a></b></p><br /><br />
-
-Love Will Find A Way
-
-When Sashanti meets Rajasekar, they fall in love. However, their families do not approve of the union, due to their caste differences. Will Love find a way to unite the lovers?
-
-Chhote Miyan
-
-A typical Bollywood Hindi comedy in which the girl is making fun of her parents. When the daughter ridicules her parents, they start feeling unhappy. Now the girl's parents ask her to get married to some one else and not to the man she likes. This shows us that even though the daughter is not a good daughter, the parents love her nevertheless.
-
-Pyaar Tune Kya Kiya
-
-This fun-filled Hindi serial is about two young lovers, 'Rishabh' and 'Mama'. The girl wants to marry her rich boyfriend, while her parents want her to marry an independent and well-educated young man. What will the girl do?
-
-Mayoori Jagga Jasoos
-
-In this tragic love story, a girl falls in love with an innocent farmer, who is the grandson of Lord Shiva. His mother is a devotee of Lord Shiva and to avoid her from disturbing their relationship, he pretends that he is a lower caste in the society. What happens in the end?
-
-Pancham
-
-In this, a girl leaves her parental home and joins a monastery. In order to convince her parents, she pretends that she is a disciple of a saint. One day, she gets up early and goes to the river for morning bath. Her parents find out about it and assume that she is now going to become a nun. They ask her to make up her mind in the next few days.
-
-Shararat
-
-'Shakti' is a drama that showcases the old-fashioned world of the society of post-independence India. In this drama, one of the sons of a rich, greedy man wants to marry his brother's fiancée, who is the daughter of a humble village woman. This drama also shows us how the Indian girl loves her in-laws.
-
-Bhagmati
-
-Bhagmati, the daughter of a Zamindar or landlord, has been married to Ram Babu, a 4fefd39f24<br />
-<br />
-<br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/1001 Solved Problems In Electrical Engineering By Romeo Rojas BEST.md b/spaces/inreVtussa/clothingai/Examples/1001 Solved Problems In Electrical Engineering By Romeo Rojas BEST.md
deleted file mode 100644
index 0416d3367ef366b51d72e37e9a3be5c86a900f52..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/1001 Solved Problems In Electrical Engineering By Romeo Rojas BEST.md	
+++ /dev/null
@@ -1,62 +0,0 @@
-
-<h1>1001 Solved Problems In Electrical Engineering By Romeo Rojas: A Must-Have Book for Electrical Engineering Students and Professionals</h1>
-
-<p>Electrical engineering is a branch of engineering that deals with the design, analysis, and application of electrical systems and devices. It involves various topics such as circuits, electronics, electromagnetism, power, control, communication, and more. Electrical engineering is a challenging and rewarding field that requires a solid foundation of knowledge and skills.</p>
-<h2>1001 Solved Problems In Electrical Engineering By Romeo Rojas</h2><br /><p><b><b>Download File</b> &middot;&middot;&middot;&middot;&middot; <a href="https://tiurll.com/2uCkZY">https://tiurll.com/2uCkZY</a></b></p><br /><br />
-
-<p>However, learning electrical engineering is not easy. It requires a lot of practice and problem-solving to master the concepts and principles of electrical engineering. That is why you need 1001 Solved Problems In Electrical Engineering By Romeo Rojas, a book that provides you with a comprehensive and practical collection of solved problems in electrical engineering.</p>
-
-<h2>What are the features of 1001 Solved Problems In Electrical Engineering By Romeo Rojas?</h2>
-
-<p>1001 Solved Problems In Electrical Engineering By Romeo Rojas is a book that contains 1001 solved problems in various topics of electrical engineering. Some of these topics are:</p>
-
-<ul>
-<li>Basic principles: electricity, magnetism, resistance, capacitance, inductance, etc.</li>
-<li>DC circuits: Kirchhoff's laws, mesh analysis, nodal analysis, Thevenin's theorem, Norton's theorem, etc.</li>
-<li>AC circuits: phasors, impedance, admittance, power factor, resonance, etc.</li>
-<li>Electronics: diodes, transistors, amplifiers, oscillators, filters, etc.</li>
-<li>Electromagnetism: magnetic fields, magnetic circuits, transformers, generators, motors, etc.</li>
-<li>Power systems: transmission lines, power distribution, fault analysis, protection devices, etc.</li>
-<li>Control systems: transfer functions, block diagrams, stability analysis, feedback control, etc.</li>
-<li>Communication systems: modulation, demodulation, encoding, decoding, multiplexing, etc.</li>
-</ul>
-
-<p>The book provides detailed solutions and explanations for each problem. It also provides relevant formulas and diagrams to help you understand the concepts and methods involved. The book covers both basic and advanced topics of electrical engineering and follows the standard syllabus and format of most electrical engineering courses and exams.</p>
-<p></p>
-
-<h2>What are the benefits of 1001 Solved Problems In Electrical Engineering By Romeo Rojas?</h2>
-
-<p>1001 Solved Problems In Electrical Engineering By Romeo Rojas has many benefits that make it a must-have book for electrical engineering students and professionals. Some of these benefits are:</p>
-
-<ul>
-<li><b>Enhance your knowledge and skills:</b> The book helps you to review and reinforce your knowledge and skills in electrical engineering. It helps you to apply the concepts and principles of electrical engineering to real-world problems. It also helps you to learn new techniques and methods that you might not have encountered before.</li>
-<li><b>Prepare for exams and interviews:</b> The book helps you to prepare for exams and interviews in electrical engineering. It helps you to familiarize yourself with the types and formats of questions that you might encounter in exams and interviews. It also helps you to improve your speed and accuracy in solving problems.</li>
-<li><b>Boost your confidence and performance:</b> The book helps you to boost your confidence and performance in electrical engineering. It helps you to overcome your doubts and fears in solving problems. It also helps you to develop your critical thinking and problem-solving skills that are essential for electrical engineering.</li>
-</ul>
-
-<h2>How to use 1001 Solved Problems In Electrical Engineering By Romeo Rojas?</h2>
-
-<p>If you want to use 1001 Solved Problems In Electrical Engineering By Romeo Rojas effectively, you need to follow these simple steps:</p>
-
-<ol>
-<li>Select the topic that you want to study or practice from the table of contents or the index of the book.</li>
-<li>Read the introduction and the objectives of the topic to get an overview of what you will learn or practice.</li>
-<li>Solve the problems in the order that they are presented or choose the problems that interest you or challenge you.</li>
-<li>Check your answers with the solutions provided at the end of each problem or at the end of each chapter.</li>
-<li>Read the explanations and understand the concepts and methods involved in solving the problem.</li>
-<li>Review the formulas and diagrams related to the problem if necessary.</li>
-<li>Repeat the process until you master the topic or until you achieve your desired goal.</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>1001 Solved Problems In Electrical Engineering By Romeo Rojas is a book that provides you with a comprehensive and practical collection of solved problems in electrical engineering. It covers both basic and advanced topics of electrical engineering and follows the standard syllabus and format of most electrical engineering courses and exams. It provides detailed solutions and explanations for each problem. It also provides relevant formulas and diagrams to help you understand the concepts and methods involved. It helps you to enhance your knowledge and skills, prepare for exams and interviews, boost your confidence and performance in electrical engineering. It is a must-have book for electrical engineering students and professionals.</p>
-
-<p>If you want to learn or practice electrical engineering with 1001 solved problems by Romeo Rojas, you can download the book from this link: https://www.scribd.com/document/416401079/1001-Solved-Problems-in-Electrical-Engineering</p>
-<h2>Conclusion</h2>
-
-<p>1001 Solved Problems In Electrical Engineering By Romeo Rojas is a book that provides you with a comprehensive and practical collection of solved problems in electrical engineering. It covers both basic and advanced topics of electrical engineering and follows the standard syllabus and format of most electrical engineering courses and exams. It provides detailed solutions and explanations for each problem. It also provides relevant formulas and diagrams to help you understand the concepts and methods involved. It helps you to enhance your knowledge and skills, prepare for exams and interviews, boost your confidence and performance in electrical engineering. It is a must-have book for electrical engineering students and professionals.</p>
-
-<p>If you want to learn or practice electrical engineering with 1001 solved problems by Romeo Rojas, you can download the book from this link: https://www.scribd.com/document/416401079/1001-Solved-Problems-in-Electrical-Engineering</p> 3cee63e6c2<br />
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-<br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/1st Studio Siberian Mouse Hd Tor.md b/spaces/inreVtussa/clothingai/Examples/1st Studio Siberian Mouse Hd Tor.md
deleted file mode 100644
index d7349e94d6715eb2b2c14646ab0e1470cf9e55cf..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/1st Studio Siberian Mouse Hd Tor.md	
+++ /dev/null
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diff --git a/spaces/inreVtussa/clothingai/Examples/Dilber Ay Zerrin Dogan Levent Gursel Eski Turk Filmleri 13.md b/spaces/inreVtussa/clothingai/Examples/Dilber Ay Zerrin Dogan Levent Gursel Eski Turk Filmleri 13.md
deleted file mode 100644
index 416731cd3dec9321b3aabc1ef63657f3a9d5c122..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Dilber Ay Zerrin Dogan Levent Gursel Eski Turk Filmleri 13.md	
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-A:
-
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diff --git a/spaces/irvay/RVC_IR/vc_infer_pipeline.py b/spaces/irvay/RVC_IR/vc_infer_pipeline.py
deleted file mode 100644
index ed2aacd1866379563006e3cf4dd40472f7ab4692..0000000000000000000000000000000000000000
--- a/spaces/irvay/RVC_IR/vc_infer_pipeline.py
+++ /dev/null
@@ -1,451 +0,0 @@
-import os
-import sys
-import traceback
-from functools import lru_cache
-from time import time as ttime
-
-import faiss
-import librosa
-import numpy as np
-import parselmouth
-import pyworld
-import torch
-import torch.nn.functional as F
-import torchcrepe
-from scipy import signal
-
-now_dir = os.getcwd()
-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
-
-    def get_f0(
-        self,
-        input_audio_path,
-        x,
-        p_len,
-        f0_up_key,
-        f0_method,
-        filter_radius,
-        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 == "crepe":
-            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()
-        elif f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from rmvpe import RMVPE
-
-                print("loading rmvpe model")
-                self.model_rmvpe = RMVPE(
-                    "rmvpe.pt", is_half=self.is_half, device=self.device
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-        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,
-        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,
-                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/jackyccl/segment-anything/segment_anything/modeling/mask_decoder.py b/spaces/jackyccl/segment-anything/segment_anything/modeling/mask_decoder.py
deleted file mode 100644
index 5d2fdb03d535a91fa725d1ec4e92a7a1f217dfe0..0000000000000000000000000000000000000000
--- a/spaces/jackyccl/segment-anything/segment_anything/modeling/mask_decoder.py
+++ /dev/null
@@ -1,176 +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 torch
-from torch import nn
-from torch.nn import functional as F
-
-from typing import List, Tuple, Type
-
-from .common import LayerNorm2d
-
-
-class MaskDecoder(nn.Module):
-    def __init__(
-        self,
-        *,
-        transformer_dim: int,
-        transformer: nn.Module,
-        num_multimask_outputs: int = 3,
-        activation: Type[nn.Module] = nn.GELU,
-        iou_head_depth: int = 3,
-        iou_head_hidden_dim: int = 256,
-    ) -> None:
-        """
-        Predicts masks given an image and prompt embeddings, using a
-        transformer architecture.
-
-        Arguments:
-          transformer_dim (int): the channel dimension of the transformer
-          transformer (nn.Module): the transformer used to predict masks
-          num_multimask_outputs (int): the number of masks to predict
-            when disambiguating masks
-          activation (nn.Module): the type of activation to use when
-            upscaling masks
-          iou_head_depth (int): the depth of the MLP used to predict
-            mask quality
-          iou_head_hidden_dim (int): the hidden dimension of the MLP
-            used to predict mask quality
-        """
-        super().__init__()
-        self.transformer_dim = transformer_dim
-        self.transformer = transformer
-
-        self.num_multimask_outputs = num_multimask_outputs
-
-        self.iou_token = nn.Embedding(1, transformer_dim)
-        self.num_mask_tokens = num_multimask_outputs + 1
-        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
-
-        self.output_upscaling = nn.Sequential(
-            nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
-            LayerNorm2d(transformer_dim // 4),
-            activation(),
-            nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
-            activation(),
-        )
-        self.output_hypernetworks_mlps = nn.ModuleList(
-            [
-                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
-                for i in range(self.num_mask_tokens)
-            ]
-        )
-
-        self.iou_prediction_head = MLP(
-            transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
-        )
-
-    def forward(
-        self,
-        image_embeddings: torch.Tensor,
-        image_pe: torch.Tensor,
-        sparse_prompt_embeddings: torch.Tensor,
-        dense_prompt_embeddings: torch.Tensor,
-        multimask_output: bool,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Predict masks given image and prompt embeddings.
-
-        Arguments:
-          image_embeddings (torch.Tensor): the embeddings from the image encoder
-          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
-          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
-          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
-          multimask_output (bool): Whether to return multiple masks or a single
-            mask.
-
-        Returns:
-          torch.Tensor: batched predicted masks
-          torch.Tensor: batched predictions of mask quality
-        """
-        masks, iou_pred = self.predict_masks(
-            image_embeddings=image_embeddings,
-            image_pe=image_pe,
-            sparse_prompt_embeddings=sparse_prompt_embeddings,
-            dense_prompt_embeddings=dense_prompt_embeddings,
-        )
-
-        # Select the correct mask or masks for output
-        if multimask_output:
-            mask_slice = slice(1, None)
-        else:
-            mask_slice = slice(0, 1)
-        masks = masks[:, mask_slice, :, :]
-        iou_pred = iou_pred[:, mask_slice]
-
-        # Prepare output
-        return masks, iou_pred
-
-    def predict_masks(
-        self,
-        image_embeddings: torch.Tensor,
-        image_pe: torch.Tensor,
-        sparse_prompt_embeddings: torch.Tensor,
-        dense_prompt_embeddings: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Predicts masks. See 'forward' for more details."""
-        # Concatenate output tokens
-        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
-        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
-        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
-
-        # Expand per-image data in batch direction to be per-mask
-        src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
-        src = src + dense_prompt_embeddings
-        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
-        b, c, h, w = src.shape
-
-        # Run the transformer
-        hs, src = self.transformer(src, pos_src, tokens)
-        iou_token_out = hs[:, 0, :]
-        mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
-
-        # Upscale mask embeddings and predict masks using the mask tokens
-        src = src.transpose(1, 2).view(b, c, h, w)
-        upscaled_embedding = self.output_upscaling(src)
-        hyper_in_list: List[torch.Tensor] = []
-        for i in range(self.num_mask_tokens):
-            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
-        hyper_in = torch.stack(hyper_in_list, dim=1)
-        b, c, h, w = upscaled_embedding.shape
-        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
-
-        # Generate mask quality predictions
-        iou_pred = self.iou_prediction_head(iou_token_out)
-
-        return masks, iou_pred
-
-
-# Lightly adapted from
-# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
-class MLP(nn.Module):
-    def __init__(
-        self,
-        input_dim: int,
-        hidden_dim: int,
-        output_dim: int,
-        num_layers: int,
-        sigmoid_output: bool = False,
-    ) -> None:
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(
-            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
-        )
-        self.sigmoid_output = sigmoid_output
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        if self.sigmoid_output:
-            x = F.sigmoid(x)
-        return x
diff --git a/spaces/james-oldfield/PandA/networks/genforce/runners/controllers/snapshoter.py b/spaces/james-oldfield/PandA/networks/genforce/runners/controllers/snapshoter.py
deleted file mode 100644
index c33f496cba7e6a3d54f8c19e7ebcc4d14c210036..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/genforce/runners/controllers/snapshoter.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# python3.7
-"""Contains the running controller for saving snapshot."""
-
-from .base_controller import BaseController
-
-__all__ = ['Snapshoter']
-
-
-class Snapshoter(BaseController):
-    """Defines the running controller for evaluation.
-
-    NOTE: The controller is set to `LAST` priority by default.
-    """
-
-    def __init__(self, config):
-        config.setdefault('priority', 'LAST')
-        super().__init__(config)
-
-        self.num = config.get('num', 100)
-
-    def setup(self, runner):
-        assert hasattr(runner, 'synthesize')
-
-    def execute_after_iteration(self, runner):
-        mode = runner.mode  # save runner mode.
-        runner.synthesize(self.num,
-                          html_name=f'snapshot_{runner.iter:06d}.html',
-                          save_raw_synthesis=False)
-        runner.logger.info(f'Saving snapshot at iter {runner.iter:06d} '
-                           f'({runner.seen_img / 1000:.1f} kimg).')
-        runner.set_mode(mode)  # restore runner mode.
diff --git a/spaces/james-oldfield/PandA/networks/genforce/synthesize.py b/spaces/james-oldfield/PandA/networks/genforce/synthesize.py
deleted file mode 100644
index f3d700a7f6706d7796ecb9d55c0dd4aab5385a76..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/genforce/synthesize.py
+++ /dev/null
@@ -1,139 +0,0 @@
-# python3.7
-"""A simple tool to synthesize images with pre-trained models."""
-
-import os
-import argparse
-import subprocess
-from tqdm import tqdm
-import numpy as np
-
-import torch
-
-from models import MODEL_ZOO
-from models import build_generator
-from utils.misc import bool_parser
-from utils.visualizer import HtmlPageVisualizer
-from utils.visualizer import postprocess_image
-from utils.visualizer import save_image
-
-
-def parse_args():
-    """Parses arguments."""
-    parser = argparse.ArgumentParser(
-        description='Synthesize images with pre-trained models.')
-    parser.add_argument('model_name', type=str,
-                        help='Name to the pre-trained model.')
-    parser.add_argument('--save_dir', type=str, default=None,
-                        help='Directory to save the results. If not specified, '
-                             'the results will be saved to '
-                             '`work_dirs/synthesis/` by default. '
-                             '(default: %(default)s)')
-    parser.add_argument('--num', type=int, default=100,
-                        help='Number of samples to synthesize. '
-                             '(default: %(default)s)')
-    parser.add_argument('--batch_size', type=int, default=1,
-                        help='Batch size. (default: %(default)s)')
-    parser.add_argument('--generate_html', type=bool_parser, default=True,
-                        help='Whether to use HTML page to visualize the '
-                             'synthesized results. (default: %(default)s)')
-    parser.add_argument('--save_raw_synthesis', type=bool_parser, default=False,
-                        help='Whether to save raw synthesis. '
-                             '(default: %(default)s)')
-    parser.add_argument('--seed', type=int, default=0,
-                        help='Seed for sampling. (default: %(default)s)')
-    parser.add_argument('--trunc_psi', type=float, default=0.7,
-                        help='Psi factor used for truncation. This is '
-                             'particularly applicable to StyleGAN (v1/v2). '
-                             '(default: %(default)s)')
-    parser.add_argument('--trunc_layers', type=int, default=8,
-                        help='Number of layers to perform truncation. This is '
-                             'particularly applicable to StyleGAN (v1/v2). '
-                             '(default: %(default)s)')
-    parser.add_argument('--randomize_noise', type=bool_parser, default=False,
-                        help='Whether to randomize the layer-wise noise. This '
-                             'is particularly applicable to StyleGAN (v1/v2). '
-                             '(default: %(default)s)')
-    return parser.parse_args()
-
-
-def main():
-    """Main function."""
-    args = parse_args()
-    if args.num <= 0:
-        return
-    if not args.save_raw_synthesis and not args.generate_html:
-        return
-
-    # Parse model configuration.
-    if args.model_name not in MODEL_ZOO:
-        raise SystemExit(f'Model `{args.model_name}` is not registered in '
-                         f'`models/model_zoo.py`!')
-    model_config = MODEL_ZOO[args.model_name].copy()
-    url = model_config.pop('url')  # URL to download model if needed.
-
-    # Get work directory and job name.
-    if args.save_dir:
-        work_dir = args.save_dir
-    else:
-        work_dir = os.path.join('work_dirs', 'synthesis')
-    os.makedirs(work_dir, exist_ok=True)
-    job_name = f'{args.model_name}_{args.num}'
-    if args.save_raw_synthesis:
-        os.makedirs(os.path.join(work_dir, job_name), exist_ok=True)
-
-    # Build generation and get synthesis kwargs.
-    print(f'Building generator for model `{args.model_name}` ...')
-    generator = build_generator(**model_config)
-    synthesis_kwargs = dict(trunc_psi=args.trunc_psi,
-                            trunc_layers=args.trunc_layers,
-                            randomize_noise=args.randomize_noise)
-    print(f'Finish building generator.')
-
-    # Load pre-trained weights.
-    os.makedirs('/import/nobackup_mmv_ioannisp/jo001/genforce_models', exist_ok=True)
-    checkpoint_path = os.path.join('/import/nobackup_mmv_ioannisp/jo001/genforce_models', args.model_name + '.pth')
-    print(f'Loading checkpoint from `{checkpoint_path}` ...')
-    if not os.path.exists(checkpoint_path):
-        print(f'  Downloading checkpoint from `{url}` ...')
-        subprocess.call(['wget', '--quiet', '-O', checkpoint_path, url])
-        print(f'  Finish downloading checkpoint.')
-    checkpoint = torch.load(checkpoint_path, map_location='cpu')
-    if 'generator_smooth' in checkpoint:
-        generator.load_state_dict(checkpoint['generator_smooth'])
-    else:
-        generator.load_state_dict(checkpoint['generator'])
-    generator = generator.cuda()
-    generator.eval()
-    print(f'Finish loading checkpoint.')
-
-    # Set random seed.
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-
-    # Sample and synthesize.
-    print(f'Synthesizing {args.num} samples ...')
-    indices = list(range(args.num))
-    if args.generate_html:
-        html = HtmlPageVisualizer(grid_size=args.num)
-    for batch_idx in tqdm(range(0, args.num, args.batch_size)):
-        sub_indices = indices[batch_idx:batch_idx + args.batch_size]
-        code = torch.randn(len(sub_indices), generator.z_space_dim).cuda()
-        with torch.no_grad():
-            images = generator(code, **synthesis_kwargs)['image']
-            images = postprocess_image(images.detach().cpu().numpy())
-        for sub_idx, image in zip(sub_indices, images):
-            if args.save_raw_synthesis:
-                save_path = os.path.join(
-                    work_dir, job_name, f'{sub_idx:06d}.jpg')
-                save_image(save_path, image)
-            if args.generate_html:
-                row_idx, col_idx = divmod(sub_idx, html.num_cols)
-                html.set_cell(row_idx, col_idx, image=image,
-                              text=f'Sample {sub_idx:06d}')
-    if args.generate_html:
-        html.save(os.path.join(work_dir, f'{job_name}.html'))
-    print(f'Finish synthesizing {args.num} samples.')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/jbilcke-hf/observer/src/components/ui/separator.tsx b/spaces/jbilcke-hf/observer/src/components/ui/separator.tsx
deleted file mode 100644
index a6ed83ef827829cf42a7b27d1d5714b4473bd1c5..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/observer/src/components/ui/separator.tsx
+++ /dev/null
@@ -1,31 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as SeparatorPrimitive from "@radix-ui/react-separator"
-
-import { cn } from "@/lib/utils"
-
-const Separator = React.forwardRef<
-  React.ElementRef<typeof SeparatorPrimitive.Root>,
-  React.ComponentPropsWithoutRef<typeof SeparatorPrimitive.Root>
->(
-  (
-    { className, orientation = "horizontal", decorative = true, ...props },
-    ref
-  ) => (
-    <SeparatorPrimitive.Root
-      ref={ref}
-      decorative={decorative}
-      orientation={orientation}
-      className={cn(
-        "shrink-0 bg-stone-200 dark:bg-stone-800",
-        orientation === "horizontal" ? "h-[1px] w-full" : "h-full w-[1px]",
-        className
-      )}
-      {...props}
-    />
-  )
-)
-Separator.displayName = SeparatorPrimitive.Root.displayName
-
-export { Separator }
diff --git a/spaces/jeevavijay10/code-gen/app-salesforce.py b/spaces/jeevavijay10/code-gen/app-salesforce.py
deleted file mode 100644
index aea06ea3a6b1328e803103d7618172b8f57dca1c..0000000000000000000000000000000000000000
--- a/spaces/jeevavijay10/code-gen/app-salesforce.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# checkpoint = "Salesforce/codegen25-7b-instruct"
-# checkpoint = "Salesforce/codegen-2B-nl"
-checkpoint = "Salesforce/codegen2-1B"
-
-tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
-# model = AutoModelForCausalLM.from_pretrained(checkpoint, cache_dir="models/")
-model = AutoModelForCausalLM.from_pretrained(checkpoint)
-
-def code_gen(text):
-    input_ids = tokenizer(text, return_tensors="pt").input_ids
-    generated_ids = model.generate(input_ids, max_length=128)
-    response = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
-    print(response)
-    return response
-
-
-iface = gr.Interface(fn=code_gen,
-                     inputs=gr.inputs.Textbox(
-                         label="Input Source Code"),
-                     outputs="text",
-                     title="Code Generation")
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/joshen/gpt-academic/functional_crazy.py b/spaces/joshen/gpt-academic/functional_crazy.py
deleted file mode 100644
index 9c83b4104a395e35471895faf09edb15c0ea65b4..0000000000000000000000000000000000000000
--- a/spaces/joshen/gpt-academic/functional_crazy.py
+++ /dev/null
@@ -1,108 +0,0 @@
-from toolbox import HotReload # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效
-
-def get_crazy_functionals():
-    ###################### 第一组插件 ###########################
-    # [第一组插件]: 最早期编写的项目插件和一些demo
-    from crazy_functions.读文章写摘要 import 读文章写摘要
-    from crazy_functions.生成函数注释 import 批量生成函数注释
-    from crazy_functions.解析项目源代码 import 解析项目本身
-    from crazy_functions.解析项目源代码 import 解析一个Python项目
-    from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
-    from crazy_functions.解析项目源代码 import 解析一个C项目
-    from crazy_functions.解析项目源代码 import 解析一个Golang项目
-    from crazy_functions.解析项目源代码 import 解析一个Java项目
-    from crazy_functions.解析项目源代码 import 解析一个Rect项目
-    from crazy_functions.高级功能函数模板 import 高阶功能模板函数
-    from crazy_functions.代码重写为全英文_多线程 import 全项目切换英文
-
-    function_plugins = {
-        "请解析并解构此项目本身（源码自译解）": {
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": 解析项目本身
-        },
-        "解析整个Py项目": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 解析一个Python项目
-        },
-        "解析整个C++项目头文件": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 解析一个C项目的头文件
-        },
-        "解析整个C++项目（.cpp/.h）": {
-            "Color": "stop",    # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": 解析一个C项目
-        },
-        "解析整个Go项目": {
-            "Color": "stop",    # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": 解析一个Golang项目
-        },
-        "解析整个Java项目": {
-            "Color": "stop",  # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": 解析一个Java项目
-        },
-        "解析整个Java项目": {
-            "Color": "stop",  # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": 解析一个Rect项目
-        },
-        "读Tex论文写摘要": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 读文章写摘要
-        },
-        "批量生成函数注释": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 批量生成函数注释
-        },
-        "[多线程demo] 把本项目源代码切换成全英文": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
-            "Function": HotReload(全项目切换英文)
-        },
-        "[函数插件模板demo] 历史上的今天": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
-            "Function": HotReload(高阶功能模板函数)
-        },
-    }
-    ###################### 第二组插件 ###########################
-    # [第二组插件]: 经过充分测试，但功能上距离达到完美状态还差一点点
-    from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
-    from crazy_functions.批量总结PDF文档pdfminer import 批量总结PDF文档pdfminer
-    from crazy_functions.总结word文档 import 总结word文档
-    function_plugins.update({
-        "[仅供开发调试] 批量总结PDF文档": {
-            "Color": "stop",
-            "Function": HotReload(批量总结PDF文档) # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
-        },
-        "[仅供开发调试] 批量总结PDF文档pdfminer": {
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(批量总结PDF文档pdfminer)
-        },
-        "[仅供开发调试] 批量总结Word文档": {
-            "Color": "stop",
-            "Function": HotReload(总结word文档)
-        },
-    })
-
-    ###################### 第三组插件 ###########################
-    # [第三组插件]: 尚未充分测试的函数插件，放在这里
-    try:
-        from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
-        function_plugins.update({
-            "一键下载arxiv论文并翻译摘要（先在input输入编号，如1812.10695）": {
-                "Color": "stop",
-                "AsButton": False,  # 加入下拉菜单中
-                "Function": HotReload(下载arxiv论文并翻译摘要)
-            }
-        })
-    except Exception as err:
-        print(f'[下载arxiv论文并翻译摘要] 插件导入失败 {str(err)}')
-
-
-
-    ###################### 第n组插件 ###########################
-    return function_plugins
-
-
diff --git a/spaces/justest/gpt4free/g4f/.v1/gpt4free/theb/theb_test.py b/spaces/justest/gpt4free/g4f/.v1/gpt4free/theb/theb_test.py
deleted file mode 100644
index c57d5c62f65ef9c7255b2de2dfefaa0b3fa3b18f..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/.v1/gpt4free/theb/theb_test.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import theb
-
-for token in theb.Completion.create('hello world'):
-    print(token, end='', flush=True)
diff --git a/spaces/jyseo/3DFuse/semantic_coding.py b/spaces/jyseo/3DFuse/semantic_coding.py
deleted file mode 100644
index 3a13ceeb41128fd97a47bf1e8be0e9064c7c1b3d..0000000000000000000000000000000000000000
--- a/spaces/jyseo/3DFuse/semantic_coding.py
+++ /dev/null
@@ -1,726 +0,0 @@
-from diffusers import UnCLIPPipeline, DiffusionPipeline
-import torch
-import os
-from lora_diffusion.cli_lora_pti import *
-from lora_diffusion.lora import *
-from PIL import Image
-import numpy as np
-import json
-from lora_dataset import PivotalTuningDatasetCapation as PVD
-UNET_DEFAULT_TARGET_REPLACE = {"CrossAttention", "Attention", "GEGLU"}
-
-UNET_EXTENDED_TARGET_REPLACE = {"ResnetBlock2D", "CrossAttention", "Attention", "GEGLU"}
-
-TEXT_ENCODER_DEFAULT_TARGET_REPLACE = {"CLIPAttention"}
-
-TEXT_ENCODER_EXTENDED_TARGET_REPLACE = {"CLIPAttention"}
-
-DEFAULT_TARGET_REPLACE = UNET_DEFAULT_TARGET_REPLACE
-
-def save_all(
-    unet,
-    text_encoder,
-    save_path,
-    placeholder_token_ids=None,
-    placeholder_tokens=None,
-    save_lora=True,
-    save_ti=True,
-    target_replace_module_text=TEXT_ENCODER_DEFAULT_TARGET_REPLACE,
-    target_replace_module_unet=DEFAULT_TARGET_REPLACE,
-    safe_form=True,
-):
-    if not safe_form:
-        # save ti
-        if save_ti:
-            ti_path = ti_lora_path(save_path)
-            learned_embeds_dict = {}
-            for tok, tok_id in zip(placeholder_tokens, placeholder_token_ids):
-                learned_embeds = text_encoder.get_input_embeddings().weight[tok_id]
-                print(
-                    f"Current Learned Embeddings for {tok}:, id {tok_id} ",
-                    learned_embeds[:4],
-                )
-                learned_embeds_dict[tok] = learned_embeds.detach().cpu()
-
-            torch.save(learned_embeds_dict, ti_path)
-            print("Ti saved to ", ti_path)
-
-        # save text encoder
-        if save_lora:
-
-            save_lora_weight(
-                unet, save_path, target_replace_module=target_replace_module_unet
-            )
-            print("Unet saved to ", save_path)
-
-            save_lora_weight(
-                text_encoder,
-                _text_lora_path(save_path),
-                target_replace_module=target_replace_module_text,
-            )
-            print("Text Encoder saved to ", _text_lora_path(save_path))
-
-    else:
-        assert save_path.endswith(
-            ".safetensors"
-        ), f"Save path : {save_path} should end with .safetensors"
-
-        loras = {}
-        embeds = {}
-
-        if save_lora:
-
-            loras["unet"] = (unet, target_replace_module_unet)
-            loras["text_encoder"] = (text_encoder, target_replace_module_text)
-
-        if save_ti:
-            for tok, tok_id in zip(placeholder_tokens, placeholder_token_ids):
-                learned_embeds = text_encoder.get_input_embeddings().weight[tok_id]
-                print(
-                    f"Current Learned Embeddings for {tok}:, id {tok_id} ",
-                    learned_embeds[:4],
-                )
-                embeds[tok] = learned_embeds.detach().cpu()
-
-        return save_safeloras_with_embeds(loras, embeds, save_path)
-
-def save_safeloras_with_embeds(
-    modelmap = {},
-    embeds = {},
-    outpath="./lora.safetensors",
-):
-    """
-    Saves the Lora from multiple modules in a single safetensor file.
-
-    modelmap is a dictionary of {
-        "module name": (module, target_replace_module)
-    }
-    """
-    weights = {}
-    metadata = {}
-
-    for name, (model, target_replace_module) in modelmap.items():
-        metadata[name] = json.dumps(list(target_replace_module))
-
-        for i, (_up, _down) in enumerate(
-            extract_lora_as_tensor(model, target_replace_module)
-        ):
-            rank = _down.shape[0]
-
-            metadata[f"{name}:{i}:rank"] = str(rank)
-            weights[f"{name}:{i}:up"] = _up
-            weights[f"{name}:{i}:down"] = _down
-
-    for token, tensor in embeds.items():
-        metadata[token] = EMBED_FLAG
-        weights[token] = tensor
-    
-    sorted_dict = {key: value for key, value in sorted(weights.items())}
-    state={}
-    state['weights']=sorted_dict
-    state['metadata'] = metadata
-    # print(sorted_dict.keys())
-    # # print('meta', metadata)
-    # print(f"Saving weights to {outpath}")
-    # safe_save(weights, outpath, metadata)
-    return state
-def perform_tuning(
-    unet,
-    vae,
-    text_encoder,
-    dataloader,
-    num_steps,
-    scheduler,
-    optimizer,
-    save_steps: int,
-    placeholder_token_ids,
-    placeholder_tokens,
-    save_path,
-    lr_scheduler_lora,
-    lora_unet_target_modules,
-    lora_clip_target_modules,
-    mask_temperature,
-    out_name: str,
-    tokenizer,
-    test_image_path: str,
-    cached_latents: bool,
-    log_wandb: bool = False,
-    wandb_log_prompt_cnt: int = 10,
-    class_token: str = "person",
-    train_inpainting: bool = False,
-):
-
-    progress_bar = tqdm(range(num_steps))
-    progress_bar.set_description("Steps")
-    global_step = 0
-
-    weight_dtype = torch.float16
-
-    unet.train()
-    text_encoder.train()
-
-    if log_wandb:
-        preped_clip = prepare_clip_model_sets()
-
-    loss_sum = 0.0
-
-    for epoch in range(math.ceil(num_steps / len(dataloader))):
-        for batch in dataloader:
-            lr_scheduler_lora.step()
-
-            optimizer.zero_grad()
-
-            loss = loss_step(
-                batch,
-                unet,
-                vae,
-                text_encoder,
-                scheduler,
-                train_inpainting=train_inpainting,
-                t_mutliplier=0.8,
-                mixed_precision=True,
-                mask_temperature=mask_temperature,
-                cached_latents=cached_latents,
-            )
-            loss_sum += loss.detach().item()
-
-            loss.backward()
-            torch.nn.utils.clip_grad_norm_(
-                itertools.chain(unet.parameters(), text_encoder.parameters()), 1.0
-            )
-            optimizer.step()
-            progress_bar.update(1)
-            logs = {
-                "loss": loss.detach().item(),
-                "lr": lr_scheduler_lora.get_last_lr()[0],
-            }
-            progress_bar.set_postfix(**logs)
-
-            global_step += 1
-
-            if global_step % save_steps == 0:
-                save_all(
-                    unet,
-                    text_encoder,
-                    placeholder_token_ids=placeholder_token_ids,
-                    placeholder_tokens=placeholder_tokens,
-                    save_path=os.path.join(
-                        save_path, f"step_{global_step}.safetensors"
-                    ),
-                    target_replace_module_text=lora_clip_target_modules,
-                    target_replace_module_unet=lora_unet_target_modules,
-                )
-                moved = (
-                    torch.tensor(list(itertools.chain(*inspect_lora(unet).values())))
-                    .mean()
-                    .item()
-                )
-
-                print("LORA Unet Moved", moved)
-                moved = (
-                    torch.tensor(
-                        list(itertools.chain(*inspect_lora(text_encoder).values()))
-                    )
-                    .mean()
-                    .item()
-                )
-
-                print("LORA CLIP Moved", moved)
-
-                if log_wandb:
-                    with torch.no_grad():
-                        pipe = StableDiffusionPipeline(
-                            vae=vae,
-                            text_encoder=text_encoder,
-                            tokenizer=tokenizer,
-                            unet=unet,
-                            scheduler=scheduler,
-                            safety_checker=None,
-                            feature_extractor=None,
-                        )
-
-                        # open all images in test_image_path
-                        images = []
-                        for file in os.listdir(test_image_path):
-                            if file.endswith(".png") or file.endswith(".jpg"):
-                                images.append(
-                                    Image.open(os.path.join(test_image_path, file))
-                                )
-
-                        wandb.log({"loss": loss_sum / save_steps})
-                        loss_sum = 0.0
-                        wandb.log(
-                            evaluate_pipe(
-                                pipe,
-                                target_images=images,
-                                class_token=class_token,
-                                learnt_token="".join(placeholder_tokens),
-                                n_test=wandb_log_prompt_cnt,
-                                n_step=50,
-                                clip_model_sets=preped_clip,
-                            )
-                        )
-
-            if global_step >= num_steps:
-                break
-
-    return save_all(
-        unet,
-        text_encoder,
-        placeholder_token_ids=placeholder_token_ids,
-        placeholder_tokens=placeholder_tokens,
-        save_path=os.path.join(save_path, f"{out_name}.safetensors"),
-        target_replace_module_text=lora_clip_target_modules,
-        target_replace_module_unet=lora_unet_target_modules,
-    )
-
-
-def train(
-    images,
-    caption,
-    pretrained_model_name_or_path: str,
-    train_text_encoder: bool = True,
-    pretrained_vae_name_or_path: str = None,
-    revision: Optional[str] = None,
-    perform_inversion: bool = True,
-    use_template: Literal[None, "object", "style"] = None,
-    train_inpainting: bool = False,
-    placeholder_tokens: str = "",
-    placeholder_token_at_data: Optional[str] = None,
-    initializer_tokens: Optional[str] = None,
-    seed: int = 42,
-    resolution: int = 512,
-    color_jitter: bool = True,
-    train_batch_size: int = 1,
-    sample_batch_size: int = 1,
-    max_train_steps_tuning: int = 1000,
-    max_train_steps_ti: int = 1000,
-    save_steps: int = 100,
-    gradient_accumulation_steps: int = 4,
-    gradient_checkpointing: bool = False,
-    lora_rank: int = 4,
-    lora_unet_target_modules={"CrossAttention", "Attention", "GEGLU"},
-    lora_clip_target_modules={"CLIPAttention"},
-    lora_dropout_p: float = 0.0,
-    lora_scale: float = 1.0,
-    use_extended_lora: bool = False,
-    clip_ti_decay: bool = True,
-    learning_rate_unet: float = 1e-4,
-    learning_rate_text: float = 1e-5,
-    learning_rate_ti: float = 5e-4,
-    continue_inversion: bool = False,
-    continue_inversion_lr: Optional[float] = None,
-    use_face_segmentation_condition: bool = False,
-    cached_latents: bool = True,
-    use_mask_captioned_data: bool = False,
-    mask_temperature: float = 1.0,
-    scale_lr: bool = False,
-    lr_scheduler: str = "linear",
-    lr_warmup_steps: int = 0,
-    lr_scheduler_lora: str = "linear",
-    lr_warmup_steps_lora: int = 0,
-    weight_decay_ti: float = 0.00,
-    weight_decay_lora: float = 0.001,
-    use_8bit_adam: bool = False,
-    device="cuda:0",
-    extra_args: Optional[dict] = None,
-    log_wandb: bool = False,
-    wandb_log_prompt_cnt: int = 10,
-    wandb_project_name: str = "new_pti_project",
-    wandb_entity: str = "new_pti_entity",
-    proxy_token: str = "person",
-    enable_xformers_memory_efficient_attention: bool = False,
-    out_name: str = "final_lora",
-):
-
-
-    torch.manual_seed(seed)
-
-    # print(placeholder_tokens, initializer_tokens)
-    if len(placeholder_tokens) == 0:
-        placeholder_tokens = []
-        print("PTI : Placeholder Tokens not given, using null token")
-    else:
-        placeholder_tokens = placeholder_tokens.split("|")
-
-        assert (
-            sorted(placeholder_tokens) == placeholder_tokens
-        ), f"Placeholder tokens should be sorted. Use something like {'|'.join(sorted(placeholder_tokens))}'"
-
-    if initializer_tokens is None:
-        print("PTI : Initializer Tokens not given, doing random inits")
-        initializer_tokens = ["<rand-0.017>"] * len(placeholder_tokens)
-    else:
-        initializer_tokens = initializer_tokens.split("|")
-
-    assert len(initializer_tokens) == len(
-        placeholder_tokens
-    ), "Unequal Initializer token for Placeholder tokens."
-
-    if proxy_token is not None:
-        class_token = proxy_token
-    class_token = "".join(initializer_tokens)
-
-    if placeholder_token_at_data is not None:
-        tok, pat = placeholder_token_at_data.split("|")
-        token_map = {tok: pat}
-
-    else:
-        token_map = {"DUMMY": "".join(placeholder_tokens)}
-
-    print("PTI : Placeholder Tokens", placeholder_tokens)
-    print("PTI : Initializer Tokens", initializer_tokens)
-
-    # get the models
-    text_encoder, vae, unet, tokenizer, placeholder_token_ids = get_models(
-        pretrained_model_name_or_path,
-        pretrained_vae_name_or_path,
-        revision,
-        placeholder_tokens,
-        initializer_tokens,
-        device=device,
-    )
-
-    noise_scheduler = DDPMScheduler.from_config(
-        pretrained_model_name_or_path, subfolder="scheduler"
-    )
-
-    if gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-
-    if enable_xformers_memory_efficient_attention:
-        from diffusers.utils.import_utils import is_xformers_available
-
-        if is_xformers_available():
-            unet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError(
-                "xformers is not available. Make sure it is installed correctly"
-            )
-
-    if scale_lr:
-        unet_lr = learning_rate_unet * gradient_accumulation_steps * train_batch_size
-        text_encoder_lr = (
-            learning_rate_text * gradient_accumulation_steps * train_batch_size
-        )
-        ti_lr = learning_rate_ti * gradient_accumulation_steps * train_batch_size
-    else:
-        unet_lr = learning_rate_unet
-        text_encoder_lr = learning_rate_text
-        ti_lr = learning_rate_ti
-
-    train_dataset = PVD(
-        images=images,
-        caption=caption,
-        token_map=token_map,
-        use_template=use_template,
-        tokenizer=tokenizer,
-        size=resolution,
-        color_jitter=color_jitter,
-        use_face_segmentation_condition=use_face_segmentation_condition,
-        use_mask_captioned_data=use_mask_captioned_data,
-        train_inpainting=train_inpainting,
-    )
-
-    train_dataset.blur_amount = 200
-
-    if train_inpainting:
-        assert not cached_latents, "Cached latents not supported for inpainting"
-
-        train_dataloader = inpainting_dataloader(
-            train_dataset, train_batch_size, tokenizer, vae, text_encoder
-        )
-    else:
-        print(cached_latents)
-        train_dataloader = text2img_dataloader(
-            train_dataset,
-            train_batch_size,
-            tokenizer,
-            vae,
-            text_encoder,
-            cached_latents=cached_latents,
-        )
-
-    index_no_updates = torch.arange(len(tokenizer)) != -1
-
-    for tok_id in placeholder_token_ids:
-        index_no_updates[tok_id] = False
-
-    unet.requires_grad_(False)
-    vae.requires_grad_(False)
-
-    params_to_freeze = itertools.chain(
-        text_encoder.text_model.encoder.parameters(),
-        text_encoder.text_model.final_layer_norm.parameters(),
-        text_encoder.text_model.embeddings.position_embedding.parameters(),
-    )
-    for param in params_to_freeze:
-        param.requires_grad = False
-
-    if cached_latents:
-        vae = None
-    # STEP 1 : Perform Inversion
-    if perform_inversion:
-        ti_optimizer = optim.AdamW(
-            text_encoder.get_input_embeddings().parameters(),
-            lr=ti_lr,
-            betas=(0.9, 0.999),
-            eps=1e-08,
-            weight_decay=weight_decay_ti,
-        )
-
-        lr_scheduler = get_scheduler(
-            lr_scheduler,
-            optimizer=ti_optimizer,
-            num_warmup_steps=lr_warmup_steps,
-            num_training_steps=max_train_steps_ti,
-        )
-
-        train_inversion(
-            unet,
-            vae,
-            text_encoder,
-            train_dataloader,
-            max_train_steps_ti,
-            cached_latents=cached_latents,
-            accum_iter=gradient_accumulation_steps,
-            scheduler=noise_scheduler,
-            index_no_updates=index_no_updates,
-            optimizer=ti_optimizer,
-            lr_scheduler=lr_scheduler,
-            save_steps=save_steps,
-            placeholder_tokens=placeholder_tokens,
-            placeholder_token_ids=placeholder_token_ids,
-            save_path="./tmps",
-            test_image_path="./tmps",
-            log_wandb=log_wandb,
-            wandb_log_prompt_cnt=wandb_log_prompt_cnt,
-            class_token=class_token,
-            train_inpainting=train_inpainting,
-            mixed_precision=False,
-            tokenizer=tokenizer,
-            clip_ti_decay=clip_ti_decay,
-        )
-
-        del ti_optimizer
-
-    # Next perform Tuning with LoRA:
-    if not use_extended_lora:
-        unet_lora_params, _ = inject_trainable_lora(
-            unet,
-            r=lora_rank,
-            target_replace_module=lora_unet_target_modules,
-            dropout_p=lora_dropout_p,
-            scale=lora_scale,
-        )
-    else:
-        print("PTI : USING EXTENDED UNET!!!")
-        lora_unet_target_modules = (
-            lora_unet_target_modules | UNET_EXTENDED_TARGET_REPLACE
-        )
-        print("PTI : Will replace modules: ", lora_unet_target_modules)
-
-        unet_lora_params, _ = inject_trainable_lora_extended(
-            unet, r=lora_rank, target_replace_module=lora_unet_target_modules
-        )
-    print(f"PTI : has {len(unet_lora_params)} lora")
-
-    print("PTI : Before training:")
-    inspect_lora(unet)
-
-    params_to_optimize = [
-        {"params": itertools.chain(*unet_lora_params), "lr": unet_lr},
-    ]
-
-    text_encoder.requires_grad_(False)
-
-    if continue_inversion:
-        params_to_optimize += [
-            {
-                "params": text_encoder.get_input_embeddings().parameters(),
-                "lr": continue_inversion_lr
-                if continue_inversion_lr is not None
-                else ti_lr,
-            }
-        ]
-        text_encoder.requires_grad_(True)
-        params_to_freeze = itertools.chain(
-            text_encoder.text_model.encoder.parameters(),
-            text_encoder.text_model.final_layer_norm.parameters(),
-            text_encoder.text_model.embeddings.position_embedding.parameters(),
-        )
-        for param in params_to_freeze:
-            param.requires_grad = False
-    else:
-        text_encoder.requires_grad_(False)
-    if train_text_encoder:
-        text_encoder_lora_params, _ = inject_trainable_lora(
-            text_encoder,
-            target_replace_module=lora_clip_target_modules,
-            r=lora_rank,
-        )
-        params_to_optimize += [
-            {
-                "params": itertools.chain(*text_encoder_lora_params),
-                "lr": text_encoder_lr,
-            }
-        ]
-        inspect_lora(text_encoder)
-
-    lora_optimizers = optim.AdamW(params_to_optimize, weight_decay=weight_decay_lora)
-
-    unet.train()
-    if train_text_encoder:
-        text_encoder.train()
-
-    train_dataset.blur_amount = 70
-
-    lr_scheduler_lora = get_scheduler(
-        lr_scheduler_lora,
-        optimizer=lora_optimizers,
-        num_warmup_steps=lr_warmup_steps_lora,
-        num_training_steps=max_train_steps_tuning,
-    )
-
-    return perform_tuning(
-        unet,
-        vae,
-        text_encoder,
-        train_dataloader,
-        max_train_steps_tuning,
-        cached_latents=cached_latents,
-        scheduler=noise_scheduler,
-        optimizer=lora_optimizers,
-        save_steps=save_steps,
-        placeholder_tokens=placeholder_tokens,
-        placeholder_token_ids=placeholder_token_ids,
-        save_path="./tmps",
-        lr_scheduler_lora=lr_scheduler_lora,
-        lora_unet_target_modules=lora_unet_target_modules,
-        lora_clip_target_modules=lora_clip_target_modules,
-        mask_temperature=mask_temperature,
-        tokenizer=tokenizer,
-        out_name=out_name,
-        test_image_path="./tmps",
-        log_wandb=log_wandb,
-        wandb_log_prompt_cnt=wandb_log_prompt_cnt,
-        class_token=class_token,
-        train_inpainting=train_inpainting,
-    )
-    
-def semantic_karlo(prompt, output_dir, num_initial_image, bg_preprocess=False):
-    pipe = UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=torch.float16)
-    pipe = pipe.to('cuda')
-    view_prompt=["front view of ","overhead view of ","side view of ", "back view of "]
-    
-    if bg_preprocess:
-        # Please refer to the code at https://github.com/Ir1d/image-background-remove-tool.
-        import cv2
-        from carvekit.api.high import HiInterface
-        interface = HiInterface(object_type="object",
-                        batch_size_seg=5,
-                        batch_size_matting=1,
-                        device='cuda' if torch.cuda.is_available() else 'cpu',
-                        seg_mask_size=640,  # Use 640 for Tracer B7 and 320 for U2Net
-                        matting_mask_size=2048,
-                        trimap_prob_threshold=231,
-                        trimap_dilation=30,
-                        trimap_erosion_iters=5,
-                        fp16=False)
-
-    
-    for i in range(num_initial_image):
-        t=", white background" if bg_preprocess else ", white background"
-        if i==0:
-            prompt_ = f"{view_prompt[i%4]}{prompt}{t}"
-        else:
-            prompt_ = f"{view_prompt[i%4]}{prompt}"
-
-        image = pipe(prompt_).images[0]
-        fn=f"instance{i}.png"
-        os.makedirs(output_dir,exist_ok=True)
-        
-        if bg_preprocess:
-            # motivated by NeuralLift-360 (removing bg), and Zero-1-to-3 (removing bg and object-centering)
-            # NOTE: This option was added during the code orgranization process.
-            # The results reported in the paper were obtained with [bg_preprocess: False] setting.
-            img_without_background = interface([image])
-            mask = np.array(img_without_background[0]) > 127
-            image = np.array(image)
-            image[~mask] = [255., 255., 255.]
-            # x, y, w, h = cv2.boundingRect(mask.astype(np.uint8))
-            # image = image[y:y+h, x:x+w, :]
-            image = Image.fromarray(np.array(image))
-            
-        image.save(os.path.join(output_dir,fn))
-        
-        
-def semantic_sd(prompt, output_dir, num_initial_image, bg_preprocess=False):
-    pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-    pipe = pipe.to('cuda')
-    view_prompt=["front view of ","overhead view of ","side view of ", "back view of "]
-    
-    if bg_preprocess:
-        # Please refer to the code at https://github.com/Ir1d/image-background-remove-tool.
-        import cv2
-        from carvekit.api.high import HiInterface
-        interface = HiInterface(object_type="object",
-                        batch_size_seg=5,
-                        batch_size_matting=1,
-                        device='cuda' if torch.cuda.is_available() else 'cpu',
-                        seg_mask_size=640,  # Use 640 for Tracer B7 and 320 for U2Net
-                        matting_mask_size=2048,
-                        trimap_prob_threshold=231,
-                        trimap_dilation=30,
-                        trimap_erosion_iters=5,
-                        fp16=False)
-
-    
-    for i in range(num_initial_image):
-        t=", white background" if bg_preprocess else ", white background"
-        if i==0:
-            prompt_ = f"{view_prompt[i%4]}{prompt}{t}"
-        else:
-            prompt_ = f"{view_prompt[i%4]}{prompt}"
-
-        image = pipe(prompt_).images[0]
-        fn=f"instance{i}.png"
-        os.makedirs(output_dir,exist_ok=True)
-        
-        if bg_preprocess:
-            # motivated by NeuralLift-360 (removing bg), and Zero-1-to-3 (removing bg and object-centering)
-            # NOTE: This option was added during the code orgranization process.
-            # The results reported in the paper were obtained with [bg_preprocess: False] setting.
-            img_without_background = interface([image])
-            mask = np.array(img_without_background[0]) > 127
-            image = np.array(image)
-            image[~mask] = [255., 255., 255.]
-            # x, y, w, h = cv2.boundingRect(mask.astype(np.uint8))
-            # image = image[y:y+h, x:x+w, :]
-            image = Image.fromarray(np.array(image))
-            
-        image.save(os.path.join(output_dir,fn))
-
-def semantic_coding(images, cfgs,sd,initial):
-    ti_step=cfgs.pop('ti_step')
-    pt_step=cfgs.pop('pt_step')
-    # semantic_model=cfgs.pop('semantic_model')
-    prompt=cfgs['sd']['prompt']
-    
-    # instance_dir=os.path.join(exp_dir,'initial_image')
-    # weight_dir=os.path.join(exp_dir,'lora')
-    if initial=="":
-        initial=None
-    
-    state=train(images=images, caption=initial, pretrained_model_name_or_path='runwayml/stable-diffusion-v1-5',\
-          gradient_checkpointing=True,\
-          scale_lr=True,lora_rank=1,cached_latents=False,save_steps=max(ti_step,pt_step)+1,\
-          max_train_steps_ti=ti_step,max_train_steps_tuning=pt_step, use_template="object",\
-          lr_warmup_steps=0, lr_warmup_steps_lora=100, placeholder_tokens="<0>", initializer_tokens=initial,\
-          continue_inversion=True, continue_inversion_lr=1e-4,device="cuda:0",            
-          )
-    if initial is not None:
-        sd.prompt=prompt.replace(initial,'<0>')
-    else:
-        sd.prompt="a <0>"
-    return state
\ No newline at end of file
diff --git a/spaces/k1ngtai/MMS/uroman/lib/JSON/backportPP/Compat5006.pm b/spaces/k1ngtai/MMS/uroman/lib/JSON/backportPP/Compat5006.pm
deleted file mode 100644
index 7736fd8debcbb47cc38192798de6c3055222d473..0000000000000000000000000000000000000000
--- a/spaces/k1ngtai/MMS/uroman/lib/JSON/backportPP/Compat5006.pm
+++ /dev/null
@@ -1,173 +0,0 @@
-package # This is JSON::backportPP
-    JSON::backportPP56;
-
-use 5.006;
-use strict;
-
-my @properties;
-
-$JSON::PP56::VERSION = '1.08';
-
-BEGIN {
-
-    sub utf8::is_utf8 {
-        my $len =  length $_[0]; # char length
-        {
-            use bytes; #  byte length;
-            return $len != length $_[0]; # if !=, UTF8-flagged on.
-        }
-    }
-
-
-    sub utf8::upgrade {
-        ; # noop;
-    }
-
-
-    sub utf8::downgrade ($;$) {
-        return 1 unless ( utf8::is_utf8( $_[0] ) );
-
-        if ( _is_valid_utf8( $_[0] ) ) {
-            my $downgrade;
-            for my $c ( unpack( "U*", $_[0] ) ) {
-                if ( $c < 256 ) {
-                    $downgrade .= pack("C", $c);
-                }
-                else {
-                    $downgrade .= pack("U", $c);
-                }
-            }
-            $_[0] = $downgrade;
-            return 1;
-        }
-        else {
-            Carp::croak("Wide character in subroutine entry") unless ( $_[1] );
-            0;
-        }
-    }
-
-
-    sub utf8::encode ($) { # UTF8 flag off
-        if ( utf8::is_utf8( $_[0] ) ) {
-            $_[0] = pack( "C*", unpack( "C*", $_[0] ) );
-        }
-        else {
-            $_[0] = pack( "U*", unpack( "C*", $_[0] ) );
-            $_[0] = pack( "C*", unpack( "C*", $_[0] ) );
-        }
-    }
-
-
-    sub utf8::decode ($) { # UTF8 flag on
-        if ( _is_valid_utf8( $_[0] ) ) {
-            utf8::downgrade( $_[0] );
-            $_[0] = pack( "U*", unpack( "U*", $_[0] ) );
-        }
-    }
-
-
-    *JSON::PP::JSON_PP_encode_ascii      = \&_encode_ascii;
-    *JSON::PP::JSON_PP_encode_latin1     = \&_encode_latin1;
-    *JSON::PP::JSON_PP_decode_surrogates = \&JSON::PP::_decode_surrogates;
-    *JSON::PP::JSON_PP_decode_unicode    = \&JSON::PP::_decode_unicode;
-
-    unless ( defined &B::SVp_NOK ) { # missing in B module.
-        eval q{ sub B::SVp_NOK () { 0x02000000; } };
-    }
-
-}
-
-
-
-sub _encode_ascii {
-    join('',
-        map {
-            $_ <= 127 ?
-                chr($_) :
-            $_ <= 65535 ?
-                sprintf('\u%04x', $_) : sprintf('\u%x\u%x', JSON::PP::_encode_surrogates($_));
-        } _unpack_emu($_[0])
-    );
-}
-
-
-sub _encode_latin1 {
-    join('',
-        map {
-            $_ <= 255 ?
-                chr($_) :
-            $_ <= 65535 ?
-                sprintf('\u%04x', $_) : sprintf('\u%x\u%x', JSON::PP::_encode_surrogates($_));
-        } _unpack_emu($_[0])
-    );
-}
-
-
-sub _unpack_emu { # for Perl 5.6 unpack warnings
-    return   !utf8::is_utf8($_[0]) ? unpack('C*', $_[0]) 
-           : _is_valid_utf8($_[0]) ? unpack('U*', $_[0])
-           : unpack('C*', $_[0]);
-}
-
-
-sub _is_valid_utf8 {
-    my $str = $_[0];
-    my $is_utf8;
-
-    while ($str =~ /(?:
-          (
-             [\x00-\x7F]
-            |[\xC2-\xDF][\x80-\xBF]
-            |[\xE0][\xA0-\xBF][\x80-\xBF]
-            |[\xE1-\xEC][\x80-\xBF][\x80-\xBF]
-            |[\xED][\x80-\x9F][\x80-\xBF]
-            |[\xEE-\xEF][\x80-\xBF][\x80-\xBF]
-            |[\xF0][\x90-\xBF][\x80-\xBF][\x80-\xBF]
-            |[\xF1-\xF3][\x80-\xBF][\x80-\xBF][\x80-\xBF]
-            |[\xF4][\x80-\x8F][\x80-\xBF][\x80-\xBF]
-          )
-        | (.)
-    )/xg)
-    {
-        if (defined $1) {
-            $is_utf8 = 1 if (!defined $is_utf8);
-        }
-        else {
-            $is_utf8 = 0 if (!defined $is_utf8);
-            if ($is_utf8) { # eventually, not utf8
-                return;
-            }
-        }
-    }
-
-    return $is_utf8;
-}
-
-
-1;
-__END__
-
-=pod
-
-=head1 NAME
-
-JSON::PP56 - Helper module in using JSON::PP in Perl 5.6
-
-=head1 DESCRIPTION
-
-JSON::PP calls internally.
-
-=head1 AUTHOR
-
-Makamaka Hannyaharamitu, E<lt>makamaka[at]cpan.orgE<gt>
-
-
-=head1 COPYRIGHT AND LICENSE
-
-Copyright 2007-2012 by Makamaka Hannyaharamitu
-
-This library is free software; you can redistribute it and/or modify
-it under the same terms as Perl itself. 
-
-=cut
-
diff --git a/spaces/kanden/vits-uma-genshin-honkai/text/symbols.py b/spaces/kanden/vits-uma-genshin-honkai/text/symbols.py
deleted file mode 100644
index edfbd24247be8c757275ce80b9ec27a0ffa808f3..0000000000000000000000000000000000000000
--- a/spaces/kanden/vits-uma-genshin-honkai/text/symbols.py
+++ /dev/null
@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad        = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-
-'''# japanese_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# zh_ja_mixture_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
\ No newline at end of file
diff --git a/spaces/keremberke/license-plate-object-detection/README.md b/spaces/keremberke/license-plate-object-detection/README.md
deleted file mode 100644
index e4b8aeb60bd26faacda4a6119950e25d7323ef65..0000000000000000000000000000000000000000
--- a/spaces/keremberke/license-plate-object-detection/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: License Plate Object Detection
-emoji: 🎮
-colorFrom: red
-colorTo: gray
-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/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/bfm.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/bfm.py
deleted file mode 100644
index a75db682f02dd1979d4a7de1d11dd3aa5cdf5279..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/bfm.py
+++ /dev/null
@@ -1,331 +0,0 @@
-"""This script defines the parametric 3d face model for Deep3DFaceRecon_pytorch
-"""
-
-import numpy as np
-import  torch
-import torch.nn.functional as F
-from scipy.io import loadmat
-from src.face3d.util.load_mats import transferBFM09
-import os
-
-def perspective_projection(focal, center):
-    # return p.T (N, 3) @ (3, 3) 
-    return np.array([
-        focal, 0, center,
-        0, focal, center,
-        0, 0, 1
-    ]).reshape([3, 3]).astype(np.float32).transpose()
-
-class SH:
-    def __init__(self):
-        self.a = [np.pi, 2 * np.pi / np.sqrt(3.), 2 * np.pi / np.sqrt(8.)]
-        self.c = [1/np.sqrt(4 * np.pi), np.sqrt(3.) / np.sqrt(4 * np.pi), 3 * np.sqrt(5.) / np.sqrt(12 * np.pi)]
-
-
-
-class ParametricFaceModel:
-    def __init__(self, 
-                bfm_folder='./BFM', 
-                recenter=True,
-                camera_distance=10.,
-                init_lit=np.array([
-                    0.8, 0, 0, 0, 0, 0, 0, 0, 0
-                    ]),
-                focal=1015.,
-                center=112.,
-                is_train=True,
-                default_name='BFM_model_front.mat'):
-        
-        if not os.path.isfile(os.path.join(bfm_folder, default_name)):
-            transferBFM09(bfm_folder)
-            
-        model = loadmat(os.path.join(bfm_folder, default_name))
-        # mean face shape. [3*N,1]
-        self.mean_shape = model['meanshape'].astype(np.float32)
-        # identity basis. [3*N,80]
-        self.id_base = model['idBase'].astype(np.float32)
-        # expression basis. [3*N,64]
-        self.exp_base = model['exBase'].astype(np.float32)
-        # mean face texture. [3*N,1] (0-255)
-        self.mean_tex = model['meantex'].astype(np.float32)
-        # texture basis. [3*N,80]
-        self.tex_base = model['texBase'].astype(np.float32)
-        # face indices for each vertex that lies in. starts from 0. [N,8]
-        self.point_buf = model['point_buf'].astype(np.int64) - 1
-        # vertex indices for each face. starts from 0. [F,3]
-        self.face_buf = model['tri'].astype(np.int64) - 1
-        # vertex indices for 68 landmarks. starts from 0. [68,1]
-        self.keypoints = np.squeeze(model['keypoints']).astype(np.int64) - 1
-
-        if is_train:
-            # vertex indices for small face region to compute photometric error. starts from 0.
-            self.front_mask = np.squeeze(model['frontmask2_idx']).astype(np.int64) - 1
-            # vertex indices for each face from small face region. starts from 0. [f,3]
-            self.front_face_buf = model['tri_mask2'].astype(np.int64) - 1
-            # vertex indices for pre-defined skin region to compute reflectance loss
-            self.skin_mask = np.squeeze(model['skinmask'])
-        
-        if recenter:
-            mean_shape = self.mean_shape.reshape([-1, 3])
-            mean_shape = mean_shape - np.mean(mean_shape, axis=0, keepdims=True)
-            self.mean_shape = mean_shape.reshape([-1, 1])
-
-        self.persc_proj = perspective_projection(focal, center)
-        self.device = 'cpu'
-        self.camera_distance = camera_distance
-        self.SH = SH()
-        self.init_lit = init_lit.reshape([1, 1, -1]).astype(np.float32)
-        
-
-    def to(self, device):
-        self.device = device
-        for key, value in self.__dict__.items():
-            if type(value).__module__ == np.__name__:
-                setattr(self, key, torch.tensor(value).to(device))
-
-    
-    def compute_shape(self, id_coeff, exp_coeff):
-        """
-        Return:
-            face_shape       -- torch.tensor, size (B, N, 3)
-
-        Parameters:
-            id_coeff         -- torch.tensor, size (B, 80), identity coeffs
-            exp_coeff        -- torch.tensor, size (B, 64), expression coeffs
-        """
-        batch_size = id_coeff.shape[0]
-        id_part = torch.einsum('ij,aj->ai', self.id_base, id_coeff)
-        exp_part = torch.einsum('ij,aj->ai', self.exp_base, exp_coeff)
-        face_shape = id_part + exp_part + self.mean_shape.reshape([1, -1])
-        return face_shape.reshape([batch_size, -1, 3])
-    
-
-    def compute_texture(self, tex_coeff, normalize=True):
-        """
-        Return:
-            face_texture     -- torch.tensor, size (B, N, 3), in RGB order, range (0, 1.)
-
-        Parameters:
-            tex_coeff        -- torch.tensor, size (B, 80)
-        """
-        batch_size = tex_coeff.shape[0]
-        face_texture = torch.einsum('ij,aj->ai', self.tex_base, tex_coeff) + self.mean_tex
-        if normalize:
-            face_texture = face_texture / 255.
-        return face_texture.reshape([batch_size, -1, 3])
-
-
-    def compute_norm(self, face_shape):
-        """
-        Return:
-            vertex_norm      -- torch.tensor, size (B, N, 3)
-
-        Parameters:
-            face_shape       -- torch.tensor, size (B, N, 3)
-        """
-
-        v1 = face_shape[:, self.face_buf[:, 0]]
-        v2 = face_shape[:, self.face_buf[:, 1]]
-        v3 = face_shape[:, self.face_buf[:, 2]]
-        e1 = v1 - v2
-        e2 = v2 - v3
-        face_norm = torch.cross(e1, e2, dim=-1)
-        face_norm = F.normalize(face_norm, dim=-1, p=2)
-        face_norm = torch.cat([face_norm, torch.zeros(face_norm.shape[0], 1, 3).to(self.device)], dim=1)
-        
-        vertex_norm = torch.sum(face_norm[:, self.point_buf], dim=2)
-        vertex_norm = F.normalize(vertex_norm, dim=-1, p=2)
-        return vertex_norm
-
-
-    def compute_color(self, face_texture, face_norm, gamma):
-        """
-        Return:
-            face_color       -- torch.tensor, size (B, N, 3), range (0, 1.)
-
-        Parameters:
-            face_texture     -- torch.tensor, size (B, N, 3), from texture model, range (0, 1.)
-            face_norm        -- torch.tensor, size (B, N, 3), rotated face normal
-            gamma            -- torch.tensor, size (B, 27), SH coeffs
-        """
-        batch_size = gamma.shape[0]
-        v_num = face_texture.shape[1]
-        a, c = self.SH.a, self.SH.c
-        gamma = gamma.reshape([batch_size, 3, 9])
-        gamma = gamma + self.init_lit
-        gamma = gamma.permute(0, 2, 1)
-        Y = torch.cat([
-             a[0] * c[0] * torch.ones_like(face_norm[..., :1]).to(self.device),
-            -a[1] * c[1] * face_norm[..., 1:2],
-             a[1] * c[1] * face_norm[..., 2:],
-            -a[1] * c[1] * face_norm[..., :1],
-             a[2] * c[2] * face_norm[..., :1] * face_norm[..., 1:2],
-            -a[2] * c[2] * face_norm[..., 1:2] * face_norm[..., 2:],
-            0.5 * a[2] * c[2] / np.sqrt(3.) * (3 * face_norm[..., 2:] ** 2 - 1),
-            -a[2] * c[2] * face_norm[..., :1] * face_norm[..., 2:],
-            0.5 * a[2] * c[2] * (face_norm[..., :1] ** 2  - face_norm[..., 1:2] ** 2)
-        ], dim=-1)
-        r = Y @ gamma[..., :1]
-        g = Y @ gamma[..., 1:2]
-        b = Y @ gamma[..., 2:]
-        face_color = torch.cat([r, g, b], dim=-1) * face_texture
-        return face_color
-
-    
-    def compute_rotation(self, angles):
-        """
-        Return:
-            rot              -- torch.tensor, size (B, 3, 3) pts @ trans_mat
-
-        Parameters:
-            angles           -- torch.tensor, size (B, 3), radian
-        """
-
-        batch_size = angles.shape[0]
-        ones = torch.ones([batch_size, 1]).to(self.device)
-        zeros = torch.zeros([batch_size, 1]).to(self.device)
-        x, y, z = angles[:, :1], angles[:, 1:2], angles[:, 2:],
-        
-        rot_x = torch.cat([
-            ones, zeros, zeros,
-            zeros, torch.cos(x), -torch.sin(x), 
-            zeros, torch.sin(x), torch.cos(x)
-        ], dim=1).reshape([batch_size, 3, 3])
-        
-        rot_y = torch.cat([
-            torch.cos(y), zeros, torch.sin(y),
-            zeros, ones, zeros,
-            -torch.sin(y), zeros, torch.cos(y)
-        ], dim=1).reshape([batch_size, 3, 3])
-
-        rot_z = torch.cat([
-            torch.cos(z), -torch.sin(z), zeros,
-            torch.sin(z), torch.cos(z), zeros,
-            zeros, zeros, ones
-        ], dim=1).reshape([batch_size, 3, 3])
-
-        rot = rot_z @ rot_y @ rot_x
-        return rot.permute(0, 2, 1)
-
-
-    def to_camera(self, face_shape):
-        face_shape[..., -1] = self.camera_distance - face_shape[..., -1]
-        return face_shape
-
-    def to_image(self, face_shape):
-        """
-        Return:
-            face_proj        -- torch.tensor, size (B, N, 2), y direction is opposite to v direction
-
-        Parameters:
-            face_shape       -- torch.tensor, size (B, N, 3)
-        """
-        # to image_plane
-        face_proj = face_shape @ self.persc_proj
-        face_proj = face_proj[..., :2] / face_proj[..., 2:]
-
-        return face_proj
-
-
-    def transform(self, face_shape, rot, trans):
-        """
-        Return:
-            face_shape       -- torch.tensor, size (B, N, 3) pts @ rot + trans
-
-        Parameters:
-            face_shape       -- torch.tensor, size (B, N, 3)
-            rot              -- torch.tensor, size (B, 3, 3)
-            trans            -- torch.tensor, size (B, 3)
-        """
-        return face_shape @ rot + trans.unsqueeze(1)
-
-
-    def get_landmarks(self, face_proj):
-        """
-        Return:
-            face_lms         -- torch.tensor, size (B, 68, 2)
-
-        Parameters:
-            face_proj       -- torch.tensor, size (B, N, 2)
-        """  
-        return face_proj[:, self.keypoints]
-
-    def split_coeff(self, coeffs):
-        """
-        Return:
-            coeffs_dict     -- a dict of torch.tensors
-
-        Parameters:
-            coeffs          -- torch.tensor, size (B, 256)
-        """
-        id_coeffs = coeffs[:, :80]
-        exp_coeffs = coeffs[:, 80: 144]
-        tex_coeffs = coeffs[:, 144: 224]
-        angles = coeffs[:, 224: 227]
-        gammas = coeffs[:, 227: 254]
-        translations = coeffs[:, 254:]
-        return {
-            'id': id_coeffs,
-            'exp': exp_coeffs,
-            'tex': tex_coeffs,
-            'angle': angles,
-            'gamma': gammas,
-            'trans': translations
-        }
-    def compute_for_render(self, coeffs):
-        """
-        Return:
-            face_vertex     -- torch.tensor, size (B, N, 3), in camera coordinate
-            face_color      -- torch.tensor, size (B, N, 3), in RGB order
-            landmark        -- torch.tensor, size (B, 68, 2), y direction is opposite to v direction
-        Parameters:
-            coeffs          -- torch.tensor, size (B, 257)
-        """
-        coef_dict = self.split_coeff(coeffs)
-        face_shape = self.compute_shape(coef_dict['id'], coef_dict['exp'])
-        rotation = self.compute_rotation(coef_dict['angle'])
-
-
-        face_shape_transformed = self.transform(face_shape, rotation, coef_dict['trans'])
-        face_vertex = self.to_camera(face_shape_transformed)
-        
-        face_proj = self.to_image(face_vertex)
-        landmark = self.get_landmarks(face_proj)
-
-        face_texture = self.compute_texture(coef_dict['tex'])
-        face_norm = self.compute_norm(face_shape)
-        face_norm_roted = face_norm @ rotation
-        face_color = self.compute_color(face_texture, face_norm_roted, coef_dict['gamma'])
-
-        return face_vertex, face_texture, face_color, landmark
-
-    def compute_for_render_woRotation(self, coeffs):
-        """
-        Return:
-            face_vertex     -- torch.tensor, size (B, N, 3), in camera coordinate
-            face_color      -- torch.tensor, size (B, N, 3), in RGB order
-            landmark        -- torch.tensor, size (B, 68, 2), y direction is opposite to v direction
-        Parameters:
-            coeffs          -- torch.tensor, size (B, 257)
-        """
-        coef_dict = self.split_coeff(coeffs)
-        face_shape = self.compute_shape(coef_dict['id'], coef_dict['exp'])
-        #rotation = self.compute_rotation(coef_dict['angle'])
-
-
-        #face_shape_transformed = self.transform(face_shape, rotation, coef_dict['trans'])
-        face_vertex = self.to_camera(face_shape)
-        
-        face_proj = self.to_image(face_vertex)
-        landmark = self.get_landmarks(face_proj)
-
-        face_texture = self.compute_texture(coef_dict['tex'])
-        face_norm = self.compute_norm(face_shape)
-        face_norm_roted = face_norm                                    # @ rotation
-        face_color = self.compute_color(face_texture, face_norm_roted, coef_dict['gamma'])
-
-        return face_vertex, face_texture, face_color, landmark
-
-
-if __name__ == '__main__':
-    transferBFM09()
\ No newline at end of file
diff --git a/spaces/kevinwang676/VITS2-Mandarin/short_audio_transcribe.py b/spaces/kevinwang676/VITS2-Mandarin/short_audio_transcribe.py
deleted file mode 100644
index c3a5107f36c31e9617508b2214f71dded91ed9a7..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VITS2-Mandarin/short_audio_transcribe.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import whisper
-import os
-import json
-import torchaudio
-import argparse
-import torch
-
-lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-def transcribe_one(audio_path):
-    # load audio and pad/trim it to fit 30 seconds
-    audio = whisper.load_audio(audio_path)
-    audio = whisper.pad_or_trim(audio)
-
-    # make log-Mel spectrogram and move to the same device as the model
-    mel = whisper.log_mel_spectrogram(audio).to(model.device)
-
-    # detect the spoken language
-    _, probs = model.detect_language(mel)
-    print(f"Detected language: {max(probs, key=probs.get)}")
-    lang = max(probs, key=probs.get)
-    # decode the audio
-    options = whisper.DecodingOptions(beam_size=5)
-    result = whisper.decode(model, mel, options)
-
-    # print the recognized text
-    print(result.text)
-    return lang, result.text
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--languages", default="CJE")
-    parser.add_argument("--whisper_size", default="medium")
-    args = parser.parse_args()
-    if args.languages == "CJE":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-    elif args.languages == "CJ":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-        }
-    elif args.languages == "C":
-        lang2token = {
-            'zh': "[ZH]",
-        }
-    assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
-    model = whisper.load_model(args.whisper_size)
-    parent_dir = "./custom_character_voice/"
-    speaker_names = list(os.walk(parent_dir))[0][1]
-    speaker_annos = []
-    total_files = sum([len(files) for r, d, files in os.walk(parent_dir)])
-    # resample audios
-    # 2023/4/21: Get the target sampling rate
-    with open("./configs/config.json", 'r', encoding='utf-8') as f:
-        hps = json.load(f)
-    target_sr = hps['data']['sampling_rate']
-    processed_files = 0
-    for speaker in speaker_names:
-        for i, wavfile in enumerate(list(os.walk(parent_dir + speaker))[0][2]):
-            # try to load file as audio
-            if wavfile.startswith("processed_"):
-                continue
-            try:
-                wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True,
-                                          channels_first=True)
-                wav = wav.mean(dim=0).unsqueeze(0)
-                if sr != target_sr:
-                    wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
-                if wav.shape[1] / sr > 20:
-                    print(f"{wavfile} too long, ignoring\n")
-                save_path = parent_dir + speaker + "/" + f"processed_{i}.wav"
-                torchaudio.save(save_path, wav, target_sr, channels_first=True)
-                # transcribe text
-                lang, text = transcribe_one(save_path)
-                if lang not in list(lang2token.keys()):
-                    print(f"{lang} not supported, ignoring\n")
-                    continue
-                text = text + "\n"#
-                #text = lang2token[lang] + text + lang2token[lang] + "\n"
-                speaker_annos.append(save_path + "|" + text)
-                
-                processed_files += 1
-                print(f"Processed: {processed_files}/{total_files}")
-            except:
-                continue
-
-    # # clean annotation
-    # import argparse
-    # import text
-    # from utils import load_filepaths_and_text
-    # for i, line in enumerate(speaker_annos):
-    #     path, sid, txt = line.split("|")
-    #     cleaned_text = text._clean_text(txt, ["cjke_cleaners2"])
-    #     cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-    #     speaker_annos[i] = path + "|" + sid + "|" + cleaned_text
-    # write into annotation
-    if len(speaker_annos) == 0:
-        print("Warning: no short audios found, this IS expected if you have only uploaded long audios, videos or video links.")
-        print("this IS NOT expected if you have uploaded a zip file of short audios. Please check your file structure or make sure your audio language is supported.")
-    with open("./filelists/short_character_anno_train.list", 'w', encoding='utf-8') as f:
-        for line in speaker_annos:
-            f.write(line)
-    with open("./filelists/short_character_anno_val.list", 'w', encoding='utf-8') as f:
-        for line in speaker_annos:
-            f.write(line)
-
-    # import json
-    # # generate new config
-    # with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
-    #     hps = json.load(f)
-    # # modify n_speakers
-    # hps['data']["n_speakers"] = 1000 + len(speaker2id)
-    # # add speaker names
-    # for speaker in speaker_names:
-    #     hps['speakers'][speaker] = speaker2id[speaker]
-    # # save modified config
-    # with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
-    #     json.dump(hps, f, indent=2)
-    # print("finished")
diff --git a/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/pipeline_stable_diffusion_controlnet_inpaint.py b/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/pipeline_stable_diffusion_controlnet_inpaint.py
deleted file mode 100644
index 8e961183802ae29d19b0df4da6d0da4aaba66bfb..0000000000000000000000000000000000000000
--- a/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/pipeline_stable_diffusion_controlnet_inpaint.py
+++ /dev/null
@@ -1,610 +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 numpy as np
-import PIL.Image
-import torch
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import *
-
-# https://github.com/mikonvergence/ControlNetInpaint
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> # !pip install opencv-python transformers accelerate
-        >>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
-        >>> from diffusers.utils import load_image
-        >>> import numpy as np
-        >>> import torch
-
-        >>> import cv2
-        >>> from PIL import Image
-        >>> # download an image
-        >>> image = load_image(
-        ...     "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-        ... )
-        >>> image = np.array(image)
-        >>> mask_image = load_image(
-        ...     "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
-        ... )
-        >>> mask_image = np.array(mask_image)
-        >>> # get canny image
-        >>> canny_image = cv2.Canny(image, 100, 200)
-        >>> canny_image = canny_image[:, :, None]
-        >>> canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
-        >>> canny_image = Image.fromarray(canny_image)
-
-        >>> # load control net and stable diffusion v1-5
-        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
-        >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16
-        ... )
-
-        >>> # speed up diffusion process with faster scheduler and memory optimization
-        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-        >>> # remove following line if xformers is not installed
-        >>> pipe.enable_xformers_memory_efficient_attention()
-
-        >>> pipe.enable_model_cpu_offload()
-
-        >>> # generate image
-        >>> generator = torch.manual_seed(0)
-        >>> image = pipe(
-        ...     "futuristic-looking doggo",
-        ...     num_inference_steps=20,
-        ...     generator=generator,
-        ...     image=image,
-        ...     control_image=canny_image,
-        ...     mask_image=mask_image
-        ... ).images[0]
-        ```
-"""
-
-
-def prepare_mask_and_masked_image(image, mask):
-    """
-    Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
-    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
-    ``image`` and ``1`` for the ``mask``.
-    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
-    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
-    Args:
-        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
-            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
-        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
-            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
-    Raises:
-        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
-        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
-        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
-            (ot the other way around).
-    Returns:
-        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
-            dimensions: ``batch x channels x height x width``.
-    """
-    if isinstance(image, torch.Tensor):
-        if not isinstance(mask, torch.Tensor):
-            raise TypeError(
-                f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not"
-            )
-
-        # Batch single image
-        if image.ndim == 3:
-            assert (
-                image.shape[0] == 3
-            ), "Image outside a batch should be of shape (3, H, W)"
-            image = image.unsqueeze(0)
-
-        # Batch and add channel dim for single mask
-        if mask.ndim == 2:
-            mask = mask.unsqueeze(0).unsqueeze(0)
-
-        # Batch single mask or add channel dim
-        if mask.ndim == 3:
-            # Single batched mask, no channel dim or single mask not batched but channel dim
-            if mask.shape[0] == 1:
-                mask = mask.unsqueeze(0)
-
-            # Batched masks no channel dim
-            else:
-                mask = mask.unsqueeze(1)
-
-        assert (
-            image.ndim == 4 and mask.ndim == 4
-        ), "Image and Mask must have 4 dimensions"
-        assert (
-            image.shape[-2:] == mask.shape[-2:]
-        ), "Image and Mask must have the same spatial dimensions"
-        assert (
-            image.shape[0] == mask.shape[0]
-        ), "Image and Mask must have the same batch size"
-
-        # Check image is in [-1, 1]
-        if image.min() < -1 or image.max() > 1:
-            raise ValueError("Image should be in [-1, 1] range")
-
-        # Check mask is in [0, 1]
-        if mask.min() < 0 or mask.max() > 1:
-            raise ValueError("Mask should be in [0, 1] range")
-
-        # Binarize mask
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-
-        # Image as float32
-        image = image.to(dtype=torch.float32)
-    elif isinstance(mask, torch.Tensor):
-        raise TypeError(
-            f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not"
-        )
-    else:
-        # preprocess image
-        if isinstance(image, (PIL.Image.Image, np.ndarray)):
-            image = [image]
-
-        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-        # preprocess mask
-        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
-            mask = [mask]
-
-        if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
-            mask = np.concatenate(
-                [np.array(m.convert("L"))[None, None, :] for m in mask], axis=0
-            )
-            mask = mask.astype(np.float32) / 255.0
-        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
-            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-        mask = torch.from_numpy(mask)
-
-    masked_image = image * (mask < 0.5)
-
-    return mask, masked_image
-
-
-class StableDiffusionControlNetInpaintPipeline(
-    StableDiffusionControlNetPipeline
-):
-    r"""
-    Pipeline for text-guided image inpainting using Stable Diffusion with ControlNet guidance.
-
-    This model inherits from [`StableDiffusionControlNetPipeline`]. 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.
-        controlnet ([`ControlNetModel`]):
-            Provides additional conditioning to the unet during the denoising process
-        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 ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-
-    def prepare_mask_latents(
-        self,
-        mask,
-        masked_image,
-        batch_size,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        do_classifier_free_guidance,
-    ):
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
-        # and half precision
-        mask = torch.nn.functional.interpolate(
-            mask,
-            size=(
-                height // self.vae_scale_factor,
-                width // self.vae_scale_factor,
-            ),
-        )
-        mask = mask.to(device=device, dtype=dtype)
-
-        masked_image = masked_image.to(device=device, dtype=dtype)
-
-        # encode the mask image into latents space so we can concatenate it to the latents
-        if isinstance(generator, list):
-            masked_image_latents = [
-                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(
-                    generator=generator[i]
-                )
-                for i in range(batch_size)
-            ]
-            masked_image_latents = torch.cat(masked_image_latents, dim=0)
-        else:
-            masked_image_latents = self.vae.encode(
-                masked_image
-            ).latent_dist.sample(generator=generator)
-        masked_image_latents = (
-            self.vae.config.scaling_factor * masked_image_latents
-        )
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        if mask.shape[0] < batch_size:
-            if not batch_size % mask.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(
-                batch_size // masked_image_latents.shape[0], 1, 1, 1
-            )
-
-        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
-        masked_image_latents = (
-            torch.cat([masked_image_latents] * 2)
-            if do_classifier_free_guidance
-            else masked_image_latents
-        )
-
-        # aligning device to prevent device errors when concating it with the latent model input
-        masked_image_latents = masked_image_latents.to(
-            device=device, dtype=dtype
-        )
-        return mask, masked_image_latents
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
-        control_image: Union[
-            torch.FloatTensor,
-            PIL.Image.Image,
-            List[torch.FloatTensor],
-            List[PIL.Image.Image],
-        ] = None,
-        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[
-            Union[torch.Generator, List[torch.Generator]]
-        ] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[
-            Callable[[int, int, torch.FloatTensor], None]
-        ] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        controlnet_conditioning_scale: float = 1.0,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            image (`PIL.Image.Image`):
-                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
-                be masked out with `mask_image` and repainted according to `prompt`.
-            control_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `List[PIL.Image.Image]`):
-                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
-                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. PIL.Image.Image` can
-                also be accepted as an image. The control image is automatically resized to fit the output image.
-            mask_image (`PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                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)`.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            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.
-            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. If not defined, one has to pass
-                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
-                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](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 will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            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: torch.FloatTensor)`.
-            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.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under
-                `self.processor` in
-                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-            controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
-                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
-                to the residual in the original unet.
-        Examples:
-        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`.
-        """
-        # 0. Default height and width to unet
-        height, width = self._default_height_width(height, width, control_image)
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            control_image,
-            height,
-            width,
-            callback_steps,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
-        )
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-        )
-
-        # 4. Prepare image
-        control_image = self.prepare_image(
-            control_image,
-            width,
-            height,
-            batch_size * num_images_per_prompt,
-            num_images_per_prompt,
-            device,
-            self.controlnet.dtype,
-        )
-
-        if do_classifier_free_guidance:
-            control_image = torch.cat([control_image] * 2)
-
-        # 5. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 6. Prepare latent variables
-        num_channels_latents = self.controlnet.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # EXTRA: prepare mask latents
-        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
-        mask, masked_image_latents = self.prepare_mask_latents(
-            mask,
-            masked_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            do_classifier_free_guidance,
-        )
-
-        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 8. Denoising loop
-        num_warmup_steps = (
-            len(timesteps) - num_inference_steps * self.scheduler.order
-        )
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = (
-                    torch.cat([latents] * 2)
-                    if do_classifier_free_guidance
-                    else latents
-                )
-                latent_model_input = self.scheduler.scale_model_input(
-                    latent_model_input, t
-                )
-
-                down_block_res_samples, mid_block_res_sample = self.controlnet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    controlnet_cond=control_image,
-                    return_dict=False,
-                )
-
-                down_block_res_samples = [
-                    down_block_res_sample * controlnet_conditioning_scale
-                    for down_block_res_sample in down_block_res_samples
-                ]
-                mid_block_res_sample *= controlnet_conditioning_scale
-
-                # predict the noise residual
-                latent_model_input = torch.cat(
-                    [latent_model_input, mask, masked_image_latents], dim=1
-                )
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    down_block_additional_residuals=down_block_res_samples,
-                    mid_block_additional_residual=mid_block_res_sample,
-                ).sample
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (
-                        noise_pred_text - noise_pred_uncond
-                    )
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(
-                    noise_pred, t, latents, **extra_step_kwargs
-                ).prev_sample
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or (
-                    (i + 1) > num_warmup_steps
-                    and (i + 1) % self.scheduler.order == 0
-                ):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # If we do sequential model offloading, let's offload unet and controlnet
-        # manually for max memory savings
-        if (
-            hasattr(self, "final_offload_hook")
-            and self.final_offload_hook is not None
-        ):
-            self.unet.to("cpu")
-            self.controlnet.to("cpu")
-            torch.cuda.empty_cache()
-
-        if output_type == "latent":
-            image = latents
-            has_nsfw_concept = None
-        elif output_type == "pil":
-            # 8. Post-processing
-            image = self.decode_latents(latents)
-
-            # 9. Run safety checker
-            image, has_nsfw_concept = self.run_safety_checker(
-                image, device, prompt_embeds.dtype
-            )
-
-            # 10. Convert to PIL
-            image = self.numpy_to_pil(image)
-        else:
-            # 8. Post-processing
-            image = self.decode_latents(latents)
-
-            # 9. Run safety checker
-            image, has_nsfw_concept = self.run_safety_checker(
-                image, device, prompt_embeds.dtype
-            )
-
-        # Offload last model to CPU
-        if (
-            hasattr(self, "final_offload_hook")
-            and self.final_offload_hook is not None
-        ):
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(
-            images=image, nsfw_content_detected=has_nsfw_concept
-        )
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/encoder_layer.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/encoder_layer.py
deleted file mode 100644
index 750a32e4ef22ed5c2ca74aa364d1e8a3470e4016..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/encoder_layer.py
+++ /dev/null
@@ -1,152 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Encoder self-attention layer definition."""
-
-import torch
-
-from torch import nn
-
-from .layer_norm import LayerNorm
-
-
-class EncoderLayer(nn.Module):
-    """Encoder layer module.
-
-    :param int size: input dim
-    :param espnet.nets.pytorch_backend.transformer.attention.
-        MultiHeadedAttention self_attn: self attention module
-        RelPositionMultiHeadedAttention self_attn: self attention module
-    :param espnet.nets.pytorch_backend.transformer.positionwise_feed_forward.
-        PositionwiseFeedForward feed_forward:
-        feed forward module
-    :param espnet.nets.pytorch_backend.transformer.positionwise_feed_forward
-    for macaron style
-    PositionwiseFeedForward feed_forward:
-    feed forward module
-    :param espnet.nets.pytorch_backend.conformer.convolution.
-        ConvolutionModule feed_foreard:
-        feed forward module
-    :param float dropout_rate: dropout rate
-    :param bool normalize_before: whether to use layer_norm before the first block
-    :param bool concat_after: whether to concat attention layer's input and output
-        if True, additional linear will be applied.
-        i.e. x -> x + linear(concat(x, att(x)))
-        if False, no additional linear will be applied. i.e. x -> x + att(x)
-
-    """
-
-    def __init__(
-        self,
-        size,
-        self_attn,
-        feed_forward,
-        feed_forward_macaron,
-        conv_module,
-        dropout_rate,
-        normalize_before=True,
-        concat_after=False,
-    ):
-        """Construct an EncoderLayer object."""
-        super(EncoderLayer, self).__init__()
-        self.self_attn = self_attn
-        self.feed_forward = feed_forward
-        self.feed_forward_macaron = feed_forward_macaron
-        self.conv_module = conv_module
-        self.norm_ff = LayerNorm(size)  # for the FNN module
-        self.norm_mha = LayerNorm(size)  # for the MHA module
-        if feed_forward_macaron is not None:
-            self.norm_ff_macaron = LayerNorm(size)
-            self.ff_scale = 0.5
-        else:
-            self.ff_scale = 1.0
-        if self.conv_module is not None:
-            self.norm_conv = LayerNorm(size)  # for the CNN module
-            self.norm_final = LayerNorm(size)  # for the final output of the block
-        self.dropout = nn.Dropout(dropout_rate)
-        self.size = size
-        self.normalize_before = normalize_before
-        self.concat_after = concat_after
-        if self.concat_after:
-            self.concat_linear = nn.Linear(size + size, size)
-
-    def forward(self, x_input, mask, cache=None):
-        """Compute encoded features.
-
-        :param torch.Tensor x_input: encoded source features, w/o pos_emb
-        tuple((batch, max_time_in, size), (1, max_time_in, size))
-        or (batch, max_time_in, size)
-        :param torch.Tensor mask: mask for x (batch, max_time_in)
-        :param torch.Tensor cache: cache for x (batch, max_time_in - 1, size)
-        :rtype: Tuple[torch.Tensor, torch.Tensor]
-        """
-        if isinstance(x_input, tuple):
-            x, pos_emb = x_input[0], x_input[1]
-        else:
-            x, pos_emb = x_input, None
-
-        # whether to use macaron style
-        if self.feed_forward_macaron is not None:
-            residual = x
-            if self.normalize_before:
-                x = self.norm_ff_macaron(x)
-            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
-            if not self.normalize_before:
-                x = self.norm_ff_macaron(x)
-
-        # multi-headed self-attention module
-        residual = x
-        if self.normalize_before:
-            x = self.norm_mha(x)
-
-        if cache is None:
-            x_q = x
-        else:
-            assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
-            x_q = x[:, -1:, :]
-            residual = residual[:, -1:, :]
-            mask = None if mask is None else mask[:, -1:, :]
-
-        if pos_emb is not None:
-            x_att = self.self_attn(x_q, x, x, pos_emb, mask)
-        else:
-            x_att = self.self_attn(x_q, x, x, mask)
-
-        if self.concat_after:
-            x_concat = torch.cat((x, x_att), dim=-1)
-            x = residual + self.concat_linear(x_concat)
-        else:
-            x = residual + self.dropout(x_att)
-        if not self.normalize_before:
-            x = self.norm_mha(x)
-
-        # convolution module
-        if self.conv_module is not None:
-            residual = x
-            if self.normalize_before:
-                x = self.norm_conv(x)
-            x = residual + self.dropout(self.conv_module(x))
-            if not self.normalize_before:
-                x = self.norm_conv(x)
-
-        # feed forward module
-        residual = x
-        if self.normalize_before:
-            x = self.norm_ff(x)
-        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
-        if not self.normalize_before:
-            x = self.norm_ff(x)
-
-        if self.conv_module is not None:
-            x = self.norm_final(x)
-
-        if cache is not None:
-            x = torch.cat([cache, x], dim=1)
-
-        if pos_emb is not None:
-            return (x, pos_emb), mask
-
-        return x, mask
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/web/static/js/recorder-core.js b/spaces/kira4424/Tacotron-zero-short-voice-clone/web/static/js/recorder-core.js
deleted file mode 100644
index 30a58e819da6e1907f2f6f91cc564f9444207af6..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/web/static/js/recorder-core.js
+++ /dev/null
@@ -1,950 +0,0 @@
-/*
-录音
-https://github.com/xiangyuecn/Recorder
-*/
-(function(factory){
-	factory(window);
-	//umd returnExports.js
-	if(typeof(define)=='function' && define.amd){
-		define(function(){
-			return Recorder;
-		});
-	};
-	if(typeof(module)=='object' && module.exports){
-		module.exports=Recorder;
-	};
-}(function(window){
-"use strict";
-
-//兼容环境
-var LM="2021-08-03 20:01:03";
-var NOOP=function(){};
-//end 兼容环境 ****从以下开始copy源码*****
-
-var Recorder=function(set){
-	return new initFn(set);
-};
-//是否已经打开了全局的麦克风录音，所有工作都已经准备好了，就等接收音频数据了
-Recorder.IsOpen=function(){
-	var stream=Recorder.Stream;
-	if(stream){
-		var tracks=stream.getTracks&&stream.getTracks()||stream.audioTracks||[];
-		var track=tracks[0];
-		if(track){
-			var state=track.readyState;
-			return state=="live"||state==track.LIVE;
-		};
-	};
-	return false;
-};
-/*H5录音时的AudioContext缓冲大小。会影响H5录音时的onProcess调用速率，相对于AudioContext.sampleRate=48000时，4096接近12帧/s，调节此参数可生成比较流畅的回调动画。
-	取值256, 512, 1024, 2048, 4096, 8192, or 16384
-	注意，取值不能过低，2048开始不同浏览器可能回调速率跟不上造成音质问题。
-	一般无需调整，调整后需要先close掉已打开的录音，再open时才会生效。
-*/
-Recorder.BufferSize=4096;
-//销毁已持有的所有全局资源，当要彻底移除Recorder时需要显式的调用此方法
-Recorder.Destroy=function(){
-	CLog("Recorder Destroy");
-	Disconnect();//断开可能存在的全局Stream、资源
-	
-	for(var k in DestroyList){
-		DestroyList[k]();
-	};
-};
-var DestroyList={};
-//登记一个需要销毁全局资源的处理方法
-Recorder.BindDestroy=function(key,call){
-	DestroyList[key]=call;
-};
-//判断浏览器是否支持录音，随时可以调用。注意：仅仅是检测浏览器支持情况，不会判断和调起用户授权，不会判断是否支持特定格式录音。
-Recorder.Support=function(){
-	var AC=window.AudioContext;
-	if(!AC){
-		AC=window.webkitAudioContext;
-	};
-	if(!AC){
-		return false;
-	};
-	var scope=navigator.mediaDevices||{};
-	if(!scope.getUserMedia){
-		scope=navigator;
-		scope.getUserMedia||(scope.getUserMedia=scope.webkitGetUserMedia||scope.mozGetUserMedia||scope.msGetUserMedia);
-	};
-	if(!scope.getUserMedia){
-		return false;
-	};
-	
-	Recorder.Scope=scope;
-	if(!Recorder.Ctx||Recorder.Ctx.state=="closed"){
-		//不能反复构造，低版本number of hardware contexts reached maximum (6)
-		Recorder.Ctx=new AC();
-		
-		Recorder.BindDestroy("Ctx",function(){
-			var ctx=Recorder.Ctx;
-			if(ctx&&ctx.close){//能关掉就关掉，关不掉就保留着
-				ctx.close();
-				Recorder.Ctx=0;
-			};
-		});
-	};
-	return true;
-};
-/*初始化H5音频采集连接。如果自行提供了sourceStream将只进行一次简单的连接处理。如果是普通麦克风录音，此时的Stream是全局的，Safari上断开后就无法再次进行连接使用，表现为静音，因此使用全部使用全局处理避免调用到disconnect；全局处理也有利于屏蔽底层细节，start时无需再调用底层接口，提升兼容、可靠性。*/
-var Connect=function(streamStore){
-	streamStore=streamStore||Recorder;
-	var bufferSize=streamStore.BufferSize||Recorder.BufferSize;
-	
-	var ctx=Recorder.Ctx,stream=streamStore.Stream;
-	var media=stream._m=ctx.createMediaStreamSource(stream);
-	var process=stream._p=(ctx.createScriptProcessor||ctx.createJavaScriptNode).call(ctx,bufferSize,1,1);//单声道，省的数据处理复杂
-	
-	media.connect(process);
-	process.connect(ctx.destination);
-	
-	var calls=stream._call;
-	process.onaudioprocess=function(e){
-		for(var k0 in calls){//has item
-			var o=e.inputBuffer.getChannelData(0);//块是共享的，必须复制出来
-			var size=o.length;
-			
-			var pcm=new Int16Array(size);
-			var sum=0;
-			for(var j=0;j<size;j++){//floatTo16BitPCM 
-				var s=Math.max(-1,Math.min(1,o[j]));
-				s=s<0?s*0x8000:s*0x7FFF;
-				pcm[j]=s;
-				sum+=Math.abs(s);
-			};
-			
-			for(var k in calls){
-				calls[k](pcm,sum);
-			};
-			
-			return;
-		};
-	};
-};
-var Disconnect=function(streamStore){
-	streamStore=streamStore||Recorder;
-	var isGlobal=streamStore==Recorder;
-	
-	var stream=streamStore.Stream;
-	if(stream){
-		if(stream._m){
-			stream._m.disconnect();
-			stream._p.disconnect();
-			stream._p.onaudioprocess=stream._p=stream._m=null;
-		};
-		
-		if(isGlobal){//全局的时候，要把流关掉（麦克风），直接提供的流不处理
-			var tracks=stream.getTracks&&stream.getTracks()||stream.audioTracks||[];
-			for(var i=0;i<tracks.length;i++){
-				var track=tracks[i];
-				track.stop&&track.stop();
-			};
-			stream.stop&&stream.stop();
-		};
-	};
-	streamStore.Stream=0;
-};
-
-/*对pcm数据的采样率进行转换
-pcmDatas: [[Int16,...]] pcm片段列表
-pcmSampleRate:48000 pcm数据的采样率
-newSampleRate:16000 需要转换成的采样率，newSampleRate>=pcmSampleRate时不会进行任何处理，小于时会进行重新采样
-prevChunkInfo:{} 可选，上次调用时的返回值，用于连续转换，本次调用将从上次结束位置开始进行处理。或可自行定义一个ChunkInfo从pcmDatas指定的位置开始进行转换
-option:{ 可选，配置项
-		frameSize:123456 帧大小，每帧的PCM Int16的数量，采样率转换后的pcm长度为frameSize的整数倍，用于连续转换。目前仅在mp3格式时才有用，frameSize取值为1152，这样编码出来的mp3时长和pcm的时长完全一致，否则会因为mp3最后一帧录音不够填满时添加填充数据导致mp3的时长变长。
-		frameType:"" 帧类型，一般为rec.set.type，提供此参数时无需提供frameSize，会自动使用最佳的值给frameSize赋值，目前仅支持mp3=1152(MPEG1 Layer3的每帧采采样数)，其他类型=1。
-			以上两个参数用于连续转换时使用，最多使用一个，不提供时不进行帧的特殊处理，提供时必须同时提供prevChunkInfo才有作用。最后一段数据处理时无需提供帧大小以便输出最后一丁点残留数据。
-	}
-
-返回ChunkInfo:{
-	//可定义，从指定位置开始转换到结尾
-	index:0 pcmDatas已处理到的索引
-	offset:0.0 已处理到的index对应的pcm中的偏移的下一个位置
-	
-	//仅作为返回值
-	frameNext:null||[Int16,...] 下一帧的部分数据，frameSize设置了的时候才可能会有
-	sampleRate:16000 结果的采样率，<=newSampleRate
-	data:[Int16,...] 转换后的PCM结果；如果是连续转换，并且pcmDatas中并没有新数据时，data的长度可能为0
-}
-*/
-Recorder.SampleData=function(pcmDatas,pcmSampleRate,newSampleRate,prevChunkInfo,option){
-	prevChunkInfo||(prevChunkInfo={});
-	var index=prevChunkInfo.index||0;
-	var offset=prevChunkInfo.offset||0;
-	
-	var frameNext=prevChunkInfo.frameNext||[];
-	option||(option={});
-	var frameSize=option.frameSize||1;
-	if(option.frameType){
-		frameSize=option.frameType=="mp3"?1152:1;
-	};
-	
-	var size=0;
-	for(var i=index;i<pcmDatas.length;i++){
-		size+=pcmDatas[i].length;
-	};
-	size=Math.max(0,size-Math.floor(offset));
-	
-	//采样 https://www.cnblogs.com/blqw/p/3782420.html
-	var step=pcmSampleRate/newSampleRate;
-	if(step>1){//新采样低于录音采样，进行抽样
-		size=Math.floor(size/step);
-	}else{//新采样高于录音采样不处理，省去了插值处理
-		step=1;
-		newSampleRate=pcmSampleRate;
-	};
-	
-	size+=frameNext.length;
-	var res=new Int16Array(size);
-	var idx=0;
-	//添加上一次不够一帧的剩余数据
-	for(var i=0;i<frameNext.length;i++){
-		res[idx]=frameNext[i];
-		idx++;
-	};
-	//处理数据
-	for (var nl=pcmDatas.length;index<nl;index++) {
-		var o=pcmDatas[index];
-		var i=offset,il=o.length;
-		while(i<il){
-			//res[idx]=o[Math.round(i)]; 直接简单抽样
-			
-			//https://www.cnblogs.com/xiaoqi/p/6993912.html
-			//当前点=当前点+到后面一个点之间的增量，音质比直接简单抽样好些
-			var before = Math.floor(i);
-			var after = Math.ceil(i);
-			var atPoint = i - before;
-			
-			var beforeVal=o[before];
-			var afterVal=after<il ? o[after]
-				: (//后个点越界了，查找下一个数组
-					(pcmDatas[index+1]||[beforeVal])[0]||0
-				);
-			res[idx]=beforeVal+(afterVal-beforeVal)*atPoint;
-			
-			idx++;
-			i+=step;//抽样
-		};
-		offset=i-il;
-	};
-	//帧处理
-	frameNext=null;
-	var frameNextSize=res.length%frameSize;
-	if(frameNextSize>0){
-		var u8Pos=(res.length-frameNextSize)*2;
-		frameNext=new Int16Array(res.buffer.slice(u8Pos));
-		res=new Int16Array(res.buffer.slice(0,u8Pos));
-	};
-	
-	return {
-		index:index
-		,offset:offset
-		
-		,frameNext:frameNext
-		,sampleRate:newSampleRate
-		,data:res
-	};
-};
-
-
-/*计算音量百分比的一个方法
-pcmAbsSum: pcm Int16所有采样的绝对值的和
-pcmLength: pcm长度
-返回值：0-100，主要当做百分比用
-注意：这个不是分贝，因此没用volume当做名称*/
-Recorder.PowerLevel=function(pcmAbsSum,pcmLength){
-	/*计算音量 https://blog.csdn.net/jody1989/article/details/73480259
-	更高灵敏度算法:
-		限定最大感应值10000
-			线性曲线：低音量不友好
-				power/10000*100 
-			对数曲线：低音量友好，但需限定最低感应值
-				(1+Math.log10(power/10000))*100
-	*/
-	var power=(pcmAbsSum/pcmLength) || 0;//NaN
-	var level;
-	if(power<1251){//1250的结果10%，更小的音量采用线性取值
-		level=Math.round(power/1250*10);
-	}else{
-		level=Math.round(Math.min(100,Math.max(0,(1+Math.log(power/10000)/Math.log(10))*100)));
-	};
-	return level;
-};
-
-
-
-
-//带时间的日志输出，CLog(msg,errOrLogMsg, logMsg...) err为数字时代表日志类型1:error 2:log默认 3:warn，否则当做内容输出，第一个参数不能是对象因为要拼接时间，后面可以接无数个输出参数
-var CLog=function(msg,err){
-	var now=new Date();
-	var t=("0"+now.getMinutes()).substr(-2)
-		+":"+("0"+now.getSeconds()).substr(-2)
-		+"."+("00"+now.getMilliseconds()).substr(-3);
-	var arr=["["+t+" Recorder]"+msg];
-	var a=arguments;
-	var i=2,fn=console.log;
-	if(typeof(err)=="number"){
-		fn=err==1?console.error:err==3?console.warn:fn;
-	}else{
-		i=1;
-	};
-	for(;i<a.length;i++){
-		arr.push(a[i]);
-	};
-	fn.apply(console,arr);
-};
-Recorder.CLog=CLog;
-
-
-
-
-var ID=0;
-function initFn(set){
-	this.id=++ID;
-	
-	//如果开启了流量统计，这里将发送一个图片请求
-	Recorder.Traffic&&Recorder.Traffic();
-	
-	
-	var o={
-		type:"mp3" //输出类型：mp3,wav，wav输出文件尺寸超大不推荐使用，但mp3编码支持会导致js文件超大，如果不需支持mp3可以使js文件大幅减小
-		,bitRate:16 //比特率 wav:16或8位，MP3：8kbps 1k/s，8kbps 2k/s 录音文件很小
-		
-		,sampleRate:16000 //采样率，wav格式大小=sampleRate*时间；mp3此项对低比特率有影响，高比特率几乎无影响。
-					//wav任意值，mp3取值范围：48000, 44100, 32000, 24000, 22050, 16000, 12000, 11025, 8000
-					//采样率参考https://www.cnblogs.com/devin87/p/mp3-recorder.html
-		
-		,onProcess:NOOP //fn(buffers,powerLevel,bufferDuration,bufferSampleRate,newBufferIdx,asyncEnd) buffers=[[Int16,...],...]：缓冲的PCM数据，为从开始录音到现在的所有pcm片段；powerLevel：当前缓冲的音量级别0-100，bufferDuration：已缓冲时长，bufferSampleRate：缓冲使用的采样率（当type支持边录边转码(Worker)时，此采样率和设置的采样率相同，否则不一定相同）；newBufferIdx:本次回调新增的buffer起始索引；asyncEnd:fn() 如果onProcess是异步的(返回值为true时)，处理完成时需要调用此回调，如果不是异步的请忽略此参数，此方法回调时必须是真异步（不能真异步时需用setTimeout包裹）。onProcess返回值：如果返回true代表开启异步模式，在某些大量运算的场合异步是必须的，必须在异步处理完成时调用asyncEnd(不能真异步时需用setTimeout包裹)，在onProcess执行后新增的buffer会全部替换成空数组，因此本回调开头应立即将newBufferIdx到本次回调结尾位置的buffer全部保存到另外一个数组内，处理完成后写回buffers中本次回调的结尾位置。
-		
-		//*******高级设置******
-		//,sourceStream:MediaStream Object
-				//可选直接提供一个媒体流，从这个流中录制、实时处理音频数据（当前Recorder实例独享此流）；不提供时为普通的麦克风录音，由getUserMedia提供音频流（所有Recorder实例共享同一个流）
-				//比如：audio、video标签dom节点的captureStream方法（实验特性，不同浏览器支持程度不高）返回的流；WebRTC中的remote流；自己创建的流等
-				//注意：流内必须至少存在一条音轨(Audio Track)，比如audio标签必须等待到可以开始播放后才会有音轨，否则open会失败
-		
-		//,audioTrackSet:{ deviceId:"",groupId:"", autoGainControl:true, echoCancellation:true, noiseSuppression:true }
-				//普通麦克风录音时getUserMedia方法的audio配置参数，比如指定设备id，回声消除、降噪开关；注意：提供的任何配置值都不一定会生效
-				//由于麦克风是全局共享的，所以新配置后需要close掉以前的再重新open
-				//更多参考: https://developer.mozilla.org/en-US/docs/Web/API/MediaTrackConstraints
-		
-		//,disableEnvInFix:false 内部参数，禁用设备卡顿时音频输入丢失补偿功能
-		
-		//,takeoffEncodeChunk:NOOP //fn(chunkBytes) chunkBytes=[Uint8,...]：实时编码环境下接管编码器输出，当编码器实时编码出一块有效的二进制音频数据时实时回调此方法；参数为二进制的Uint8Array，就是编码出来的音频数据片段，所有的chunkBytes拼接在一起即为完整音频。本实现的想法最初由QQ2543775048提出
-				//当提供此回调方法时，将接管编码器的数据输出，编码器内部将放弃存储生成的音频数据；环境要求比较苛刻：如果当前环境不支持实时编码处理，将在open时直接走fail逻辑
-				//因此提供此回调后调用stop方法将无法获得有效的音频数据，因为编码器内没有音频数据，因此stop时返回的blob将是一个字节长度为0的blob
-				//目前只有mp3格式实现了实时编码，在支持实时处理的环境中将会实时的将编码出来的mp3片段通过此方法回调，所有的chunkBytes拼接到一起即为完整的mp3，此种拼接的结果比mock方法实时生成的音质更加，因为天然避免了首尾的静默
-				//目前除mp3外其他格式不可以提供此回调，提供了将在open时直接走fail逻辑
-	};
-	
-	for(var k in set){
-		o[k]=set[k];
-	};
-	this.set=o;
-	
-	this._S=9;//stop同步锁，stop可以阻止open过程中还未运行的start
-	this.Sync={O:9,C:9};//和Recorder.Sync一致，只不过这个是非全局的，仅用来简化代码逻辑，无实际作用
-};
-//同步锁，控制对Stream的竞争；用于close时中断异步的open；一个对象open如果变化了都要阻止close，Stream的控制权交个新的对象
-Recorder.Sync={/*open*/O:9,/*close*/C:9};
-
-Recorder.prototype=initFn.prototype={
-	//流相关的数据存储在哪个对象里面；如果提供了sourceStream，数据直接存储在当前对象中，否则存储在全局
-	_streamStore:function(){
-		if(this.set.sourceStream){
-			return this;
-		}else{
-			return Recorder;
-		}
-	}
-	
-	//打开录音资源True(),False(msg,isUserNotAllow)，需要调用close。注意：此方法是异步的；一般使用时打开，用完立即关闭；可重复调用，可用来测试是否能录音
-	,open:function(True,False){
-		var This=this,streamStore=This._streamStore();
-		True=True||NOOP;
-		var failCall=function(errMsg,isUserNotAllow){
-			isUserNotAllow=!!isUserNotAllow;
-			CLog("录音open失败："+errMsg+",isUserNotAllow:"+isUserNotAllow,1);
-			False&&False(errMsg,isUserNotAllow);
-		};
-		
-		var ok=function(){
-			CLog("open成功");
-			True();
-			
-			This._SO=0;//解除stop对open中的start调用的阻止
-		};
-		
-		
-		//同步锁
-		var Lock=streamStore.Sync;
-		var lockOpen=++Lock.O,lockClose=Lock.C;
-		This._O=This._O_=lockOpen;//记住当前的open，如果变化了要阻止close，这里假定了新对象已取代当前对象并且不再使用
-		This._SO=This._S;//记住open过程中的stop，中途任何stop调用后都不能继续open中的start
-		var lockFail=function(){
-			//允许多次open，但不允许任何一次close，或者自身已经调用了关闭
-			if(lockClose!=Lock.C || !This._O){
-				var err="open被取消";
-				if(lockOpen==Lock.O){
-					//无新的open，已经调用了close进行取消，此处应让上次的close明确生效
-					This.close();
-				}else{
-					err="open被中断";
-				};
-				failCall(err);
-				return true;
-			};
-		};
-		
-		//环境配置检查
-		var checkMsg=This.envCheck({envName:"H5",canProcess:true});
-		if(checkMsg){
-			failCall("不能录音："+checkMsg);
-			return;
-		};
-		
-		
-		//***********已直接提供了音频流************
-		if(This.set.sourceStream){
-			if(!Recorder.Support()){
-				failCall("不支持此浏览器从流中获取录音");
-				return;
-			};
-			
-			Disconnect(streamStore);//可能已open过，直接先尝试断开
-			This.Stream=This.set.sourceStream;
-			This.Stream._call={};
-			
-			try{
-				Connect(streamStore);
-			}catch(e){
-				failCall("从流中打开录音失败："+e.message);
-				return;
-			}
-			ok();
-			return;
-		};
-		
-		
-		//***********打开麦克风得到全局的音频流************
-		var codeFail=function(code,msg){
-			try{//跨域的优先检测一下
-				window.top.a;
-			}catch(e){
-				failCall('无权录音(跨域，请尝试给iframe添加麦克风访问策略，如allow="camera;microphone")');
-				return;
-			};
-			
-			if(/Permission|Allow/i.test(code)){
-				failCall("用户拒绝了录音权限",true);
-			}else if(window.isSecureContext===false){
-				failCall("无权录音(需https)");
-			}else if(/Found/i.test(code)){//可能是非安全环境导致的没有设备
-				failCall(msg+"，无可用麦克风");
-			}else{
-				failCall(msg);
-			};
-		};
-		
-		
-		//如果已打开并且有效就不要再打开了
-		if(Recorder.IsOpen()){
-			ok();
-			return;
-		};
-		if(!Recorder.Support()){
-			codeFail("","此浏览器不支持录音");
-			return;
-		};
-				
-		//请求权限，如果从未授权，一般浏览器会弹出权限请求弹框
-		var f1=function(stream){
-			Recorder.Stream=stream;
-			stream._call={};//此时is open，但并未connect，是允许绑定接收数据的
-			if(lockFail())return;
-			
-			//https://github.com/xiangyuecn/Recorder/issues/14 获取到的track.readyState!="live"，刚刚回调时可能是正常的，但过一下可能就被关掉了，原因不明。延迟一下保证真异步。对正常浏览器不影响
-			setTimeout(function(){
-				if(lockFail())return;
-				
-				if(Recorder.IsOpen()){
-					Connect();
-					ok();
-				}else{
-					failCall("录音功能无效：无音频流");
-				};
-			},100);
-		};
-		var f2=function(e){
-			var code=e.name||e.message||e.code+":"+e;
-			CLog("请求录音权限错误",1,e);
-			
-			codeFail(code,"无法录音："+code);
-		};
-		var pro=Recorder.Scope.getUserMedia({audio:This.set.audioTrackSet||true},f1,f2);
-		if(pro&&pro.then){
-			pro.then(f1)[True&&"catch"](f2); //fix 关键字，保证catch压缩时保持字符串形式
-		};
-	}
-	//关闭释放录音资源
-	,close:function(call){
-		call=call||NOOP;
-		
-		var This=this,streamStore=This._streamStore();
-		This._stop();
-		
-		var Lock=streamStore.Sync;
-		This._O=0;
-		if(This._O_!=Lock.O){
-			//唯一资源Stream的控制权已交给新对象，这里不能关闭。此处在每次都弹权限的浏览器内可能存在泄漏，新对象被拒绝权限可能不会调用close，忽略这种不处理
-			CLog("close被忽略",3);
-			call();
-			return;
-		};
-		Lock.C++;//获得控制权
-		
-		Disconnect(streamStore);
-		
-		CLog("close");
-		call();
-	}
-	
-	
-	
-	
-	
-	/*模拟一段录音数据，后面可以调用stop进行编码，需提供pcm数据[1,2,3...]，pcm的采样率*/
-	,mock:function(pcmData,pcmSampleRate){
-		var This=this;
-		This._stop();//清理掉已有的资源
-		
-		This.isMock=1;
-		This.mockEnvInfo=null;
-		This.buffers=[pcmData];
-		This.recSize=pcmData.length;
-		This.srcSampleRate=pcmSampleRate;
-		return This;
-	}
-	,envCheck:function(envInfo){//平台环境下的可用性检查，任何时候都可以调用检查，返回errMsg:""正常，"失败原因"
-		//envInfo={envName:"H5",canProcess:true}
-		var errMsg,This=this,set=This.set;
-		
-		//编码器检查环境下配置是否可用
-		if(!errMsg){
-			if(This[set.type+"_envCheck"]){//编码器已实现环境检查
-				errMsg=This[set.type+"_envCheck"](envInfo,set);
-			}else{//未实现检查的手动检查配置是否有效
-				if(set.takeoffEncodeChunk){
-					errMsg=set.type+"类型不支持设置takeoffEncodeChunk";
-				};
-			};
-		};
-		
-		return errMsg||"";
-	}
-	,envStart:function(mockEnvInfo,sampleRate){//平台环境相关的start调用
-		var This=this,set=This.set;
-		This.isMock=mockEnvInfo?1:0;//非H5环境需要启用mock，并提供envCheck需要的环境信息
-		This.mockEnvInfo=mockEnvInfo;
-		This.buffers=[];//数据缓冲
-		This.recSize=0;//数据大小
-		
-		This.envInLast=0;//envIn接收到最后录音内容的时间
-		This.envInFirst=0;//envIn接收到的首个录音内容的录制时间
-		This.envInFix=0;//补偿的总时间
-		This.envInFixTs=[];//补偿计数列表
-		
-		set.sampleRate=Math.min(sampleRate,set.sampleRate);//engineCtx需要提前确定最终的采样率
-		This.srcSampleRate=sampleRate;
-		
-		This.engineCtx=0;
-		//此类型有边录边转码(Worker)支持
-		if(This[set.type+"_start"]){
-			var engineCtx=This.engineCtx=This[set.type+"_start"](set);
-			if(engineCtx){
-				engineCtx.pcmDatas=[];
-				engineCtx.pcmSize=0;
-			};
-		};
-	}
-	,envResume:function(){//和平台环境无关的恢复录音
-		//重新开始计数
-		this.envInFixTs=[];
-	}
-	,envIn:function(pcm,sum){//和平台环境无关的pcm[Int16]输入
-		var This=this,set=This.set,engineCtx=This.engineCtx;
-		var bufferSampleRate=This.srcSampleRate;
-		var size=pcm.length;
-		var powerLevel=Recorder.PowerLevel(sum,size);
-		
-		var buffers=This.buffers;
-		var bufferFirstIdx=buffers.length;//之前的buffer都是经过onProcess处理好的，不允许再修改
-		buffers.push(pcm);
-		
-		//有engineCtx时会被覆盖，这里保存一份
-		var buffersThis=buffers;
-		var bufferFirstIdxThis=bufferFirstIdx;
-		
-		//卡顿丢失补偿：因为设备很卡的时候导致H5接收到的数据量不够造成播放时候变速，结果比实际的时长要短，此处保证了不会变短，但不能修复丢失的音频数据造成音质变差。当前算法采用输入时间侦测下一帧是否需要添加补偿帧，需要(6次输入||超过1秒)以上才会开始侦测，如果滑动窗口内丢失超过1/3就会进行补偿
-		var now=Date.now();
-		var pcmTime=Math.round(size/bufferSampleRate*1000);
-		This.envInLast=now;
-		if(This.buffers.length==1){//记下首个录音数据的录制时间
-			This.envInFirst=now-pcmTime;
-		};
-		var envInFixTs=This.envInFixTs;
-		envInFixTs.splice(0,0,{t:now,d:pcmTime});
-		//保留3秒的计数滑动窗口，另外超过3秒的停顿不补偿
-		var tsInStart=now,tsPcm=0;
-		for(var i=0;i<envInFixTs.length;i++){
-			var o=envInFixTs[i];
-			if(now-o.t>3000){
-				envInFixTs.length=i;
-				break;
-			};
-			tsInStart=o.t;
-			tsPcm+=o.d;
-		};
-		//达到需要的数据量，开始侦测是否需要补偿
-		var tsInPrev=envInFixTs[1];
-		var tsIn=now-tsInStart;
-		var lost=tsIn-tsPcm;
-		if( lost>tsIn/3 && (tsInPrev&&tsIn>1000 || envInFixTs.length>=6) ){
-			//丢失过多，开始执行补偿
-			var addTime=now-tsInPrev.t-pcmTime;//距离上次输入丢失这么多ms
-			if(addTime>pcmTime/5){//丢失超过本帧的1/5
-				var fixOpen=!set.disableEnvInFix;
-				CLog("["+now+"]"+(fixOpen?"":"未")+"补偿"+addTime+"ms",3);
-				This.envInFix+=addTime;
-				
-				//用静默进行补偿
-				if(fixOpen){
-					var addPcm=new Int16Array(addTime*bufferSampleRate/1000);
-					size+=addPcm.length;
-					buffers.push(addPcm);
-				};
-			};
-		};
-		
-		
-		var sizeOld=This.recSize,addSize=size;
-		var bufferSize=sizeOld+addSize;
-		This.recSize=bufferSize;//此值在onProcess后需要修正，可能新数据被修改
-		
-		
-		//此类型有边录边转码(Worker)支持，开启实时转码
-		if(engineCtx){
-			//转换成set的采样率
-			var chunkInfo=Recorder.SampleData(buffers,bufferSampleRate,set.sampleRate,engineCtx.chunkInfo);
-			engineCtx.chunkInfo=chunkInfo;
-			
-			sizeOld=engineCtx.pcmSize;
-			addSize=chunkInfo.data.length;
-			bufferSize=sizeOld+addSize;
-			engineCtx.pcmSize=bufferSize;//此值在onProcess后需要修正，可能新数据被修改
-			
-			buffers=engineCtx.pcmDatas;
-			bufferFirstIdx=buffers.length;
-			buffers.push(chunkInfo.data);
-			bufferSampleRate=chunkInfo.sampleRate;
-		};
-		
-		var duration=Math.round(bufferSize/bufferSampleRate*1000);
-		var bufferNextIdx=buffers.length;
-		var bufferNextIdxThis=buffersThis.length;
-		
-		//允许异步处理buffer数据
-		var asyncEnd=function(){
-			//重新计算size，异步的早已减去添加的，同步的需去掉本次添加的然后重新计算
-			var num=asyncBegin?0:-addSize;
-			var hasClear=buffers[0]==null;
-			for(var i=bufferFirstIdx;i<bufferNextIdx;i++){
-				var buffer=buffers[i];
-				if(buffer==null){//已被主动释放内存，比如长时间实时传输录音时
-					hasClear=1;
-				}else{
-					num+=buffer.length;
-					
-					//推入后台边录边转码
-					if(engineCtx&&buffer.length){
-						This[set.type+"_encode"](engineCtx,buffer);
-					};
-				};
-			};
-			
-			//同步清理This.buffers，不管buffers到底清了多少个，buffersThis是使用不到的进行全清
-			if(hasClear && engineCtx){
-				var i=bufferFirstIdxThis;
-				if(buffersThis[0]){
-					i=0;
-				};
-				for(;i<bufferNextIdxThis;i++){
-					buffersThis[i]=null;
-				};
-			};
-			
-			//统计修改后的size，如果异步发生clear要原样加回来，同步的无需操作
-			if(hasClear){
-				num=asyncBegin?addSize:0;
-				
-				buffers[0]=null;//彻底被清理
-			};
-			if(engineCtx){
-				engineCtx.pcmSize+=num;
-			}else{
-				This.recSize+=num;
-			};
-		};
-		//实时回调处理数据，允许修改或替换上次回调以来新增的数据 ，但是不允许修改已处理过的，不允许增删第一维数组 ，允许将第二维数组任意修改替换成空数组也可以
-		var asyncBegin=set.onProcess(buffers,powerLevel,duration,bufferSampleRate,bufferFirstIdx,asyncEnd);
-		
-		if(asyncBegin===true){
-			//开启了异步模式，onProcess已接管buffers新数据，立即清空，避免出现未处理的数据
-			var hasClear=0;
-			for(var i=bufferFirstIdx;i<bufferNextIdx;i++){
-				if(buffers[i]==null){//已被主动释放内存，比如长时间实时传输录音时 ，但又要开启异步模式，此种情况是非法的
-					hasClear=1;
-				}else{
-					buffers[i]=new Int16Array(0);
-				};
-			};
-			
-			if(hasClear){
-				CLog("未进入异步前不能清除buffers",3);
-			}else{
-				//还原size，异步结束后再统计仅修改后的size，如果发生clear要原样加回来
-				if(engineCtx){
-					engineCtx.pcmSize-=addSize;
-				}else{
-					This.recSize-=addSize;
-				};
-			};
-		}else{
-			asyncEnd();
-		};
-	}
-	
-	
-	
-	
-	//开始录音，需先调用open；只要open成功时，调用此方法是安全的，如果未open强行调用导致的内部错误将不会有任何提示，stop时自然能得到错误
-	,start:function(){
-		var This=this,ctx=Recorder.Ctx;
-		
-		var isOpen=1;
-		if(This.set.sourceStream){//直接提供了流，仅判断是否调用了open
-			if(!This.Stream){
-				isOpen=0;
-			}
-		}else if(!Recorder.IsOpen()){//监测全局麦克风是否打开并且有效
-			isOpen=0;
-		};
-		if(!isOpen){
-			CLog("未open",1);
-			return;
-		};
-		CLog("开始录音");
-		
-		This._stop();
-		This.state=0;
-		This.envStart(null,ctx.sampleRate);
-		
-		//检查open过程中stop是否已经调用过
-		if(This._SO&&This._SO+1!=This._S){//上面调用过一次 _stop
-			//open未完成就调用了stop，此种情况终止start。也应尽量避免出现此情况
-			CLog("start被中断",3);
-			return;
-		};
-		This._SO=0;
-		
-		var end=function(){
-			This.state=1;
-			This.resume();
-		};
-		if(ctx.state=="suspended"){
-			ctx.resume().then(function(){
-				CLog("ctx resume");
-				end();
-			});
-		}else{
-			end();
-		};
-	}
-	/*暂停录音*/
-	,pause:function(){
-		var This=this;
-		if(This.state){
-			This.state=2;
-			CLog("pause");
-			delete This._streamStore().Stream._call[This.id];
-		};
-	}
-	/*恢复录音*/
-	,resume:function(){
-		var This=this;
-		if(This.state){
-			This.state=1;
-			CLog("resume");
-			This.envResume();
-			
-			This._streamStore().Stream._call[This.id]=function(pcm,sum){
-				if(This.state==1){
-					This.envIn(pcm,sum);
-				};
-			};
-		};
-	}
-	
-	
-	
-	
-	,_stop:function(keepEngine){
-		var This=this,set=This.set;
-		if(!This.isMock){
-			This._S++;
-		};
-		if(This.state){
-			This.pause();
-			This.state=0;
-		};
-		if(!keepEngine && This[set.type+"_stop"]){
-			This[set.type+"_stop"](This.engineCtx);
-			This.engineCtx=0;
-		};
-	}
-	/*
-	结束录音并返回录音数据blob对象
-		True(blob,duration) blob：录音数据audio/mp3|wav格式
-							duration：录音时长，单位毫秒
-		False(msg)
-		autoClose:false 可选，是否自动调用close，默认为false
-	*/
-	,stop:function(True,False,autoClose){
-		var This=this,set=This.set,t1;
-		CLog("Stop "+(This.envInLast?This.envInLast-This.envInFirst+"ms 补"+This.envInFix+"ms":"-"));
-		
-		var end=function(){
-			This._stop();//彻底关掉engineCtx
-			if(autoClose){
-				This.close();
-			};
-		};
-		var err=function(msg){
-			CLog("结束录音失败："+msg,1);
-			False&&False(msg);
-			end();
-		};
-		var ok=function(blob,duration){
-			CLog("结束录音 编码"+(Date.now()-t1)+"ms 音频"+duration+"ms/"+blob.size+"b");
-			if(set.takeoffEncodeChunk){//接管了输出，此时blob长度为0
-				CLog("启用takeoffEncodeChunk后stop返回的blob长度为0不提供音频数据",3);
-			}else if(blob.size<Math.max(100,duration/2)){//1秒小于0.5k？
-				err("生成的"+set.type+"无效");
-				return;
-			};
-			True&&True(blob,duration);
-			end();
-		};
-		if(!This.isMock){
-			if(!This.state){
-				err("未开始录音");
-				return;
-			};
-			This._stop(true);
-		};
-		var size=This.recSize;
-		if(!size){
-			err("未采集到录音");
-			return;
-		};
-		if(!This.buffers[0]){
-			err("音频被释放");
-			return;
-		};
-		if(!This[set.type]){
-			err("未加载"+set.type+"编码器");
-			return;
-		};
-		
-		//环境配置检查，此处仅针对mock调用，因为open已经检查过了
-		if(This.isMock){
-			var checkMsg=This.envCheck(This.mockEnvInfo||{envName:"mock",canProcess:false});//没有提供环境信息的mock时没有onProcess回调
-			if(checkMsg){
-				err("录音错误："+checkMsg);
-				return;
-			};
-		};
-		
-		//此类型有边录边转码(Worker)支持
-		var engineCtx=This.engineCtx;
-		if(This[set.type+"_complete"]&&engineCtx){
-			var duration=Math.round(engineCtx.pcmSize/set.sampleRate*1000);//采用后的数据长度和buffers的长度可能微小的不一致，是采样率连续转换的精度问题
-			
-			t1=Date.now();
-			This[set.type+"_complete"](engineCtx,function(blob){
-				ok(blob,duration);
-			},err);
-			return;
-		};
-		
-		//标准UI线程转码，调整采样率
-		t1=Date.now();
-		var chunk=Recorder.SampleData(This.buffers,This.srcSampleRate,set.sampleRate);
-		
-		set.sampleRate=chunk.sampleRate;
-		var res=chunk.data;
-		var duration=Math.round(res.length/set.sampleRate*1000);
-		
-		CLog("采样"+size+"->"+res.length+" 花:"+(Date.now()-t1)+"ms");
-		
-		setTimeout(function(){
-			t1=Date.now();
-			This[set.type](res,function(blob){
-				ok(blob,duration);
-			},function(msg){
-				err(msg);
-			});
-		});
-	}
-
-};
-
-if(window.Recorder){
-	window.Recorder.Destroy();
-};
-window.Recorder=Recorder;
-
-//end ****copy源码结束*****
-Recorder.LM=LM;
-
-//流量统计用1像素图片地址，设置为空将不参与统计
-Recorder.TrafficImgUrl="//ia.51.la/go1?id=20469973&pvFlag=1";
-Recorder.Traffic=function(){
-	var imgUrl=Recorder.TrafficImgUrl;
-	if(imgUrl){
-		var data=Recorder.Traffic;
-		var idf=location.href.replace(/#.*/,"");
-		
-		if(imgUrl.indexOf("//")==0){
-			//给url加上http前缀，如果是file协议下，不加前缀没法用
-			if(/^https:/i.test(idf)){
-				imgUrl="https:"+imgUrl;
-			}else{
-				imgUrl="http:"+imgUrl;
-			};
-		};
-		
-		if(!data[idf]){
-			data[idf]=1;
-			
-			var img=new Image();
-			img.src=imgUrl;
-			CLog("Traffic Analysis Image: Recorder.TrafficImgUrl="+Recorder.TrafficImgUrl);
-		};
-	};
-};
-
-}));
\ No newline at end of file
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/deform_conv.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/deform_conv.py
deleted file mode 100644
index a3f8c75ee774823eea334e3b3732af6a18f55038..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/deform_conv.py
+++ /dev/null
@@ -1,405 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import Tensor
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair, _single
-
-from annotator.uniformer.mmcv.utils import deprecated_api_warning
-from ..cnn import CONV_LAYERS
-from ..utils import ext_loader, print_log
-
-ext_module = ext_loader.load_ext('_ext', [
-    'deform_conv_forward', 'deform_conv_backward_input',
-    'deform_conv_backward_parameters'
-])
-
-
-class DeformConv2dFunction(Function):
-
-    @staticmethod
-    def symbolic(g,
-                 input,
-                 offset,
-                 weight,
-                 stride,
-                 padding,
-                 dilation,
-                 groups,
-                 deform_groups,
-                 bias=False,
-                 im2col_step=32):
-        return g.op(
-            'mmcv::MMCVDeformConv2d',
-            input,
-            offset,
-            weight,
-            stride_i=stride,
-            padding_i=padding,
-            dilation_i=dilation,
-            groups_i=groups,
-            deform_groups_i=deform_groups,
-            bias_i=bias,
-            im2col_step_i=im2col_step)
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                offset,
-                weight,
-                stride=1,
-                padding=0,
-                dilation=1,
-                groups=1,
-                deform_groups=1,
-                bias=False,
-                im2col_step=32):
-        if input is not None and input.dim() != 4:
-            raise ValueError(
-                f'Expected 4D tensor as input, got {input.dim()}D tensor \
-                  instead.')
-        assert bias is False, 'Only support bias is False.'
-        ctx.stride = _pair(stride)
-        ctx.padding = _pair(padding)
-        ctx.dilation = _pair(dilation)
-        ctx.groups = groups
-        ctx.deform_groups = deform_groups
-        ctx.im2col_step = im2col_step
-
-        # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
-        # amp won't cast the type of model (float32), but "offset" is cast
-        # to float16 by nn.Conv2d automatically, leading to the type
-        # mismatch with input (when it is float32) or weight.
-        # The flag for whether to use fp16 or amp is the type of "offset",
-        # we cast weight and input to temporarily support fp16 and amp
-        # whatever the pytorch version is.
-        input = input.type_as(offset)
-        weight = weight.type_as(input)
-        ctx.save_for_backward(input, offset, weight)
-
-        output = input.new_empty(
-            DeformConv2dFunction._output_size(ctx, input, weight))
-
-        ctx.bufs_ = [input.new_empty(0), input.new_empty(0)]  # columns, ones
-
-        cur_im2col_step = min(ctx.im2col_step, input.size(0))
-        assert (input.size(0) %
-                cur_im2col_step) == 0, 'im2col step must divide batchsize'
-        ext_module.deform_conv_forward(
-            input,
-            weight,
-            offset,
-            output,
-            ctx.bufs_[0],
-            ctx.bufs_[1],
-            kW=weight.size(3),
-            kH=weight.size(2),
-            dW=ctx.stride[1],
-            dH=ctx.stride[0],
-            padW=ctx.padding[1],
-            padH=ctx.padding[0],
-            dilationW=ctx.dilation[1],
-            dilationH=ctx.dilation[0],
-            group=ctx.groups,
-            deformable_group=ctx.deform_groups,
-            im2col_step=cur_im2col_step)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        input, offset, weight = ctx.saved_tensors
-
-        grad_input = grad_offset = grad_weight = None
-
-        cur_im2col_step = min(ctx.im2col_step, input.size(0))
-        assert (input.size(0) % cur_im2col_step
-                ) == 0, 'batch size must be divisible by im2col_step'
-
-        grad_output = grad_output.contiguous()
-        if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
-            grad_input = torch.zeros_like(input)
-            grad_offset = torch.zeros_like(offset)
-            ext_module.deform_conv_backward_input(
-                input,
-                offset,
-                grad_output,
-                grad_input,
-                grad_offset,
-                weight,
-                ctx.bufs_[0],
-                kW=weight.size(3),
-                kH=weight.size(2),
-                dW=ctx.stride[1],
-                dH=ctx.stride[0],
-                padW=ctx.padding[1],
-                padH=ctx.padding[0],
-                dilationW=ctx.dilation[1],
-                dilationH=ctx.dilation[0],
-                group=ctx.groups,
-                deformable_group=ctx.deform_groups,
-                im2col_step=cur_im2col_step)
-
-        if ctx.needs_input_grad[2]:
-            grad_weight = torch.zeros_like(weight)
-            ext_module.deform_conv_backward_parameters(
-                input,
-                offset,
-                grad_output,
-                grad_weight,
-                ctx.bufs_[0],
-                ctx.bufs_[1],
-                kW=weight.size(3),
-                kH=weight.size(2),
-                dW=ctx.stride[1],
-                dH=ctx.stride[0],
-                padW=ctx.padding[1],
-                padH=ctx.padding[0],
-                dilationW=ctx.dilation[1],
-                dilationH=ctx.dilation[0],
-                group=ctx.groups,
-                deformable_group=ctx.deform_groups,
-                scale=1,
-                im2col_step=cur_im2col_step)
-
-        return grad_input, grad_offset, grad_weight, \
-            None, None, None, None, None, None, None
-
-    @staticmethod
-    def _output_size(ctx, input, weight):
-        channels = weight.size(0)
-        output_size = (input.size(0), channels)
-        for d in range(input.dim() - 2):
-            in_size = input.size(d + 2)
-            pad = ctx.padding[d]
-            kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
-            stride_ = ctx.stride[d]
-            output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
-        if not all(map(lambda s: s > 0, output_size)):
-            raise ValueError(
-                'convolution input is too small (output would be ' +
-                'x'.join(map(str, output_size)) + ')')
-        return output_size
-
-
-deform_conv2d = DeformConv2dFunction.apply
-
-
-class DeformConv2d(nn.Module):
-    r"""Deformable 2D convolution.
-
-    Applies a deformable 2D convolution over an input signal composed of
-    several input planes. DeformConv2d was described in the paper
-    `Deformable Convolutional Networks
-    <https://arxiv.org/pdf/1703.06211.pdf>`_
-
-    Note:
-        The argument ``im2col_step`` was added in version 1.3.17, which means
-        number of samples processed by the ``im2col_cuda_kernel`` per call.
-        It enables users to define ``batch_size`` and ``im2col_step`` more
-        flexibly and solved `issue mmcv#1440
-        <https://github.com/open-mmlab/mmcv/issues/1440>`_.
-
-    Args:
-        in_channels (int): Number of channels in the input image.
-        out_channels (int): Number of channels produced by the convolution.
-        kernel_size(int, tuple): Size of the convolving kernel.
-        stride(int, tuple): Stride of the convolution. Default: 1.
-        padding (int or tuple): Zero-padding added to both sides of the input.
-            Default: 0.
-        dilation (int or tuple): Spacing between kernel elements. Default: 1.
-        groups (int): Number of blocked connections from input.
-            channels to output channels. Default: 1.
-        deform_groups (int): Number of deformable group partitions.
-        bias (bool): If True, adds a learnable bias to the output.
-            Default: False.
-        im2col_step (int): Number of samples processed by im2col_cuda_kernel
-            per call. It will work when ``batch_size`` > ``im2col_step``, but
-            ``batch_size`` must be divisible by ``im2col_step``. Default: 32.
-            `New in version 1.3.17.`
-    """
-
-    @deprecated_api_warning({'deformable_groups': 'deform_groups'},
-                            cls_name='DeformConv2d')
-    def __init__(self,
-                 in_channels: int,
-                 out_channels: int,
-                 kernel_size: Union[int, Tuple[int, ...]],
-                 stride: Union[int, Tuple[int, ...]] = 1,
-                 padding: Union[int, Tuple[int, ...]] = 0,
-                 dilation: Union[int, Tuple[int, ...]] = 1,
-                 groups: int = 1,
-                 deform_groups: int = 1,
-                 bias: bool = False,
-                 im2col_step: int = 32) -> None:
-        super(DeformConv2d, self).__init__()
-
-        assert not bias, \
-            f'bias={bias} is not supported in DeformConv2d.'
-        assert in_channels % groups == 0, \
-            f'in_channels {in_channels} cannot be divisible by groups {groups}'
-        assert out_channels % groups == 0, \
-            f'out_channels {out_channels} cannot be divisible by groups \
-              {groups}'
-
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = _pair(kernel_size)
-        self.stride = _pair(stride)
-        self.padding = _pair(padding)
-        self.dilation = _pair(dilation)
-        self.groups = groups
-        self.deform_groups = deform_groups
-        self.im2col_step = im2col_step
-        # enable compatibility with nn.Conv2d
-        self.transposed = False
-        self.output_padding = _single(0)
-
-        # only weight, no bias
-        self.weight = nn.Parameter(
-            torch.Tensor(out_channels, in_channels // self.groups,
-                         *self.kernel_size))
-
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        # switch the initialization of `self.weight` to the standard kaiming
-        # method described in `Delving deep into rectifiers: Surpassing
-        # human-level performance on ImageNet classification` - He, K. et al.
-        # (2015), using a uniform distribution
-        nn.init.kaiming_uniform_(self.weight, nonlinearity='relu')
-
-    def forward(self, x: Tensor, offset: Tensor) -> Tensor:
-        """Deformable Convolutional forward function.
-
-        Args:
-            x (Tensor): Input feature, shape (B, C_in, H_in, W_in)
-            offset (Tensor): Offset for deformable convolution, shape
-                (B, deform_groups*kernel_size[0]*kernel_size[1]*2,
-                H_out, W_out), H_out, W_out are equal to the output's.
-
-                An offset is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`.
-                The spatial arrangement is like:
-
-                .. code:: text
-
-                    (x0, y0) (x1, y1) (x2, y2)
-                    (x3, y3) (x4, y4) (x5, y5)
-                    (x6, y6) (x7, y7) (x8, y8)
-
-        Returns:
-            Tensor: Output of the layer.
-        """
-        # To fix an assert error in deform_conv_cuda.cpp:128
-        # input image is smaller than kernel
-        input_pad = (x.size(2) < self.kernel_size[0]) or (x.size(3) <
-                                                          self.kernel_size[1])
-        if input_pad:
-            pad_h = max(self.kernel_size[0] - x.size(2), 0)
-            pad_w = max(self.kernel_size[1] - x.size(3), 0)
-            x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous()
-            offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0)
-            offset = offset.contiguous()
-        out = deform_conv2d(x, offset, self.weight, self.stride, self.padding,
-                            self.dilation, self.groups, self.deform_groups,
-                            False, self.im2col_step)
-        if input_pad:
-            out = out[:, :, :out.size(2) - pad_h, :out.size(3) -
-                      pad_w].contiguous()
-        return out
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'(in_channels={self.in_channels},\n'
-        s += f'out_channels={self.out_channels},\n'
-        s += f'kernel_size={self.kernel_size},\n'
-        s += f'stride={self.stride},\n'
-        s += f'padding={self.padding},\n'
-        s += f'dilation={self.dilation},\n'
-        s += f'groups={self.groups},\n'
-        s += f'deform_groups={self.deform_groups},\n'
-        # bias is not supported in DeformConv2d.
-        s += 'bias=False)'
-        return s
-
-
-@CONV_LAYERS.register_module('DCN')
-class DeformConv2dPack(DeformConv2d):
-    """A Deformable Conv Encapsulation that acts as normal Conv layers.
-
-    The offset tensor is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`.
-    The spatial arrangement is like:
-
-    .. code:: text
-
-        (x0, y0) (x1, y1) (x2, y2)
-        (x3, y3) (x4, y4) (x5, y5)
-        (x6, y6) (x7, y7) (x8, y8)
-
-    Args:
-        in_channels (int): Same as nn.Conv2d.
-        out_channels (int): Same as nn.Conv2d.
-        kernel_size (int or tuple[int]): Same as nn.Conv2d.
-        stride (int or tuple[int]): Same as nn.Conv2d.
-        padding (int or tuple[int]): Same as nn.Conv2d.
-        dilation (int or tuple[int]): Same as nn.Conv2d.
-        groups (int): Same as nn.Conv2d.
-        bias (bool or str): If specified as `auto`, it will be decided by the
-            norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
-            False.
-    """
-
-    _version = 2
-
-    def __init__(self, *args, **kwargs):
-        super(DeformConv2dPack, self).__init__(*args, **kwargs)
-        self.conv_offset = nn.Conv2d(
-            self.in_channels,
-            self.deform_groups * 2 * self.kernel_size[0] * self.kernel_size[1],
-            kernel_size=self.kernel_size,
-            stride=_pair(self.stride),
-            padding=_pair(self.padding),
-            dilation=_pair(self.dilation),
-            bias=True)
-        self.init_offset()
-
-    def init_offset(self):
-        self.conv_offset.weight.data.zero_()
-        self.conv_offset.bias.data.zero_()
-
-    def forward(self, x):
-        offset = self.conv_offset(x)
-        return deform_conv2d(x, offset, self.weight, self.stride, self.padding,
-                             self.dilation, self.groups, self.deform_groups,
-                             False, self.im2col_step)
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        version = local_metadata.get('version', None)
-
-        if version is None or version < 2:
-            # the key is different in early versions
-            # In version < 2, DeformConvPack loads previous benchmark models.
-            if (prefix + 'conv_offset.weight' not in state_dict
-                    and prefix[:-1] + '_offset.weight' in state_dict):
-                state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
-                    prefix[:-1] + '_offset.weight')
-            if (prefix + 'conv_offset.bias' not in state_dict
-                    and prefix[:-1] + '_offset.bias' in state_dict):
-                state_dict[prefix +
-                           'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
-                                                                '_offset.bias')
-
-        if version is not None and version > 1:
-            print_log(
-                f'DeformConv2dPack {prefix.rstrip(".")} is upgraded to '
-                'version 2.',
-                logger='root')
-
-        super()._load_from_state_dict(state_dict, prefix, local_metadata,
-                                      strict, missing_keys, unexpected_keys,
-                                      error_msgs)
diff --git a/spaces/krushna/Auto_Insta_Post-V2/app.py b/spaces/krushna/Auto_Insta_Post-V2/app.py
deleted file mode 100644
index 30034639909162c8b97faf666b2a48411100b825..0000000000000000000000000000000000000000
--- a/spaces/krushna/Auto_Insta_Post-V2/app.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import newspaper
-from newspaper import*
-import gradio as gr
-import openai
-openai.api_key = "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"
-import validators
-import ast
-import pyshine as ps
-import cv2
-import numpy as np
-
-
-def predict(action,image, text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space):
-    def titleonImage(action,image, text_in_image, words_line,font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space):
-        font_dict = {'normal size sans-serif':cv2.FONT_HERSHEY_SIMPLEX, 'small size sans-serif':cv2.FONT_HERSHEY_PLAIN,
-                    'normal size sans-serif (complexity +1)': cv2.FONT_HERSHEY_DUPLEX, 'normal size sans-serif (complexity +2)': cv2.FONT_HERSHEY_COMPLEX ,
-                    'normal size sans-serif (complexity +3)': cv2.FONT_HERSHEY_TRIPLEX, 'small size sans-serif (complexity +2)': cv2.FONT_HERSHEY_COMPLEX_SMALL,
-                    'hand-writing style font': cv2.FONT_HERSHEY_SCRIPT_SIMPLEX, 'hand-writing style font (complexity +1)': cv2.FONT_HERSHEY_SCRIPT_COMPLEX }
-
-        inp = text_in_image
-        string = ""
-        for word in range(len(inp.split())):
-            if (word > 0) & (word % words_line == 0):
-                string += "\n"
-            string += " " + inp.split()[word]
-        text_in_image = string
-        if image is None:
-            width = 1000
-            height = 1000
-            image = np.ones((height, width, 3), dtype=np.uint8) * 255
-        y_gap = 0
-        for i in text_in_image.split("\n"):
-            image = ps.putBText(image, i, text_offset_x = int(x_cor),
-                                text_offset_y = int(y_cor)+y_gap, vspace = 5,
-                                hspace = 5, font_scale = float(font_size), font = font_dict[font],
-                                background_RGB = ast.literal_eval(backgound_RGB),
-                                text_RGB = ast.literal_eval(text_RGB), thickness = int(thickness),
-                                alpha = float(opacity))
-            y_gap+= int(line_space)
-        return image
-    def getText(url_or_text):
-        text = url_or_text
-        if validators.url(url_or_text):
-            article = Article(url="%s" % (url_or_text), language='en')
-            article.download()
-            article.parse()
-            text = article.text
-        return text
-    def getTitle(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space):
-        response = openai.Completion.create(
-            model=model,
-            prompt = url_or_text + "\n"+ prompt,
-            temperature=int(temperature),
-            max_tokens=int(max_tokens),
-            top_p=1,
-            frequency_penalty=0.0,
-            presence_penalty=1
-        ).choices[0].text
-        return response
-    
-    def getSummary(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space):
-        summary = openai.Completion.create(
-            model=model,
-            prompt = url_or_text + "\n"+ summary_prompt,
-            temperature=int(temperature),
-            max_tokens=int(max_tokens),
-            top_p=1,
-            frequency_penalty=0.0,
-            presence_penalty=1
-        ).choices[0].text
-        return summary
-    image_final = cv2.imread("fun.png")
-#     text_in_image = "title was not asked"   --> only need in summary 
-    summary = "summary was not asked"
-    if action == "title on Image":
-        image_final = titleonImage(action,image, text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
-    
-    if action == "title on image from url/text":
-        url_or_text = getText(url_or_text)
-        text_in_image = getTitle(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
-        
-        image_final =  titleonImage(action,image, text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
-    
-    if action == "title on image and summary from url/text":
-        url_or_text = getText(url_or_text)
-        text_in_image = getTitle(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
-        summary = getSummary(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
-        image_final =  titleonImage(action,image, text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
-        
-    if action == "title from url/text":
-        url_or_text = getText(url_or_text)
-        text_in_image = getTitle(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
-    
-    if action == "summary from url/text":
-        url_or_text = getText(url_or_text)
-        text_in_image = "title was not asked"
-        summary = getSummary(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
-            font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
-    return  image_final, text_in_image, summary
-
-intr = gr.Interface(predict,
-                    [gr.Dropdown(["title on Image",
-                                  "title on image from url/text",
-                                  "title on image and summary from url/text",
-                                  "title from url/text",
-                                  "summary from url/text"
-                                 ], value = "title on Image", label = "List of Actions"),
-                     "image",
-                     gr.Textbox(value="Text was not given", label = "Text you want to see in the image"),
-                     gr.Number(value=5, label = "Number of words in a line"),
-                     gr.Dropdown(['normal size sans-serif', 'small size sans-serif',
-                                 'normal size sans-serif (complexity +1)', 'normal size sans-serif (complexity +2)',
-                                 'normal size sans-serif (complexity +3)', 'small size sans-serif (complexity +2)',
-                                 'hand-writing style font', 'hand-writing style font (complexity +1)'],
-                                label = "Font for the text", value = 'normal size sans-serif'),
-                     gr.Dropdown(["text-curie-001", "text-davinci-003"], value = "text-curie-001", label = "GPT3- Model"),
-                     gr.Textbox(value="https://www.datasciencecentral.com/will-chatgpt-make-fraud-easier/", label = "Provide blog url or directly input the text for better results"),
-                     gr.Textbox(value="Title for instagram post based on above article", label= "Prompt for generating the title"),
-                     gr.Textbox(value="Captions for instagram post based on above article", label= "Prompt for summary generation"),
-                     gr.Number(value=0.7, label = "How random you want the results to be"),
-                     gr.Number(value=100, label= "Max number of words in output"),
-                     gr.Number(value=20, label = "X co-ordinate"),
-                     gr.Number(value=20, label = "Y co-ordiante"),
-                     gr.Number(value=1.0, label = "Font size"),
-                     gr.Textbox(value="(228, 225, 222, 211)", label = "Box Color (RGB)"),
-                     gr.Textbox(value= "(0, 255,1)", label = "Text Color (RGB)"),
-                     gr.Number(value=1, label = "text Thickness"),
-                     gr.Number(value=0.8, label = "Opacity of Box"),
-                     gr.Number(value=50, label = "Line sapcing")], 
-                    ["image","text", gr.Textbox(label = "summarizer")],
-                    title = "Auto Insta Post",
-                    description = "Image and url to Instagram Post"
-                    )
-intr.launch(inline = False)
\ No newline at end of file
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/contourpy/chunk.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/contourpy/chunk.py
deleted file mode 100644
index 076cbc4370b4471c2074cade279250a3ebec9041..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/contourpy/chunk.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from __future__ import annotations
-
-import math
-
-
-def calc_chunk_sizes(
-    chunk_size: int | tuple[int, int] | None,
-    chunk_count: int | tuple[int, int] | None,
-    total_chunk_count: int | None,
-    ny: int,
-    nx: int,
-) -> tuple[int, int]:
-    """Calculate chunk sizes.
-
-    Args:
-        chunk_size (int or tuple(int, int), optional): Chunk size in (y, x) directions, or the same
-            size in both directions if only one is specified.
-        chunk_count (int or tuple(int, int), optional): Chunk count in (y, x) directions, or the
-            same count in both irections if only one is specified.
-        total_chunk_count (int, optional): Total number of chunks.
-        ny (int): Number of grid points in y-direction.
-        nx (int): Number of grid points in x-direction.
-
-    Return:
-        tuple(int, int): Chunk sizes (y_chunk_size, x_chunk_size).
-
-    Note:
-        A maximum of one of ``chunk_size``, ``chunk_count`` and ``total_chunk_count`` may be
-        specified.
-    """
-    if sum([chunk_size is not None, chunk_count is not None, total_chunk_count is not None]) > 1:
-        raise ValueError("Only one of chunk_size, chunk_count and total_chunk_count should be set")
-
-    if total_chunk_count is not None:
-        max_chunk_count = (nx-1)*(ny-1)
-        total_chunk_count = min(max(total_chunk_count, 1), max_chunk_count)
-        if total_chunk_count == 1:
-            chunk_size = 0
-        elif total_chunk_count == max_chunk_count:
-            chunk_size = (1, 1)
-        else:
-            factors = two_factors(total_chunk_count)
-            if ny > nx:
-                chunk_count = factors
-            else:
-                chunk_count = (factors[1], factors[0])
-
-    if chunk_count is not None:
-        if isinstance(chunk_count, tuple):
-            y_chunk_count, x_chunk_count = chunk_count
-        else:
-            y_chunk_count = x_chunk_count = chunk_count
-        x_chunk_count = min(max(x_chunk_count, 1), nx-1)
-        y_chunk_count = min(max(y_chunk_count, 1), ny-1)
-        chunk_size = (math.ceil((ny-1) / y_chunk_count), math.ceil((nx-1) / x_chunk_count))
-
-    if chunk_size is None:
-        y_chunk_size = x_chunk_size = 0
-    elif isinstance(chunk_size, tuple):
-        y_chunk_size, x_chunk_size = chunk_size
-    else:
-        y_chunk_size = x_chunk_size = chunk_size
-
-    if x_chunk_size < 0 or y_chunk_size < 0:
-        raise ValueError("chunk_size cannot be negative")
-
-    return y_chunk_size, x_chunk_size
-
-
-def two_factors(n: int) -> tuple[int, int]:
-    """Split an integer into two integer factors.
-
-    The two factors will be as close as possible to the sqrt of n, and are returned in decreasing
-    order.  Worst case returns (n, 1).
-
-    Args:
-        n (int): The integer to factorize.
-
-    Return:
-        tuple(int, int): The two factors of n, in decreasing order.
-    """
-    i = math.ceil(math.sqrt(n))
-    while n % i != 0:
-        i -= 1
-    j = n // i
-    if i > j:
-        return i, j
-    else:
-        return j, i
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/lines.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/lines.py
deleted file mode 100644
index db0ce3ba0ceaf26fe3a0a2ed9b189169bf44c744..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/lines.py
+++ /dev/null
@@ -1,1606 +0,0 @@
-"""
-2D lines with support for a variety of line styles, markers, colors, etc.
-"""
-
-import copy
-
-from numbers import Integral, Number, Real
-import logging
-
-import numpy as np
-
-import matplotlib as mpl
-from . import _api, cbook, colors as mcolors, _docstring
-from .artist import Artist, allow_rasterization
-from .cbook import (
-    _to_unmasked_float_array, ls_mapper, ls_mapper_r, STEP_LOOKUP_MAP)
-from .markers import MarkerStyle
-from .path import Path
-from .transforms import Bbox, BboxTransformTo, TransformedPath
-from ._enums import JoinStyle, CapStyle
-
-# Imported here for backward compatibility, even though they don't
-# really belong.
-from . import _path
-from .markers import (  # noqa
-    CARETLEFT, CARETRIGHT, CARETUP, CARETDOWN,
-    CARETLEFTBASE, CARETRIGHTBASE, CARETUPBASE, CARETDOWNBASE,
-    TICKLEFT, TICKRIGHT, TICKUP, TICKDOWN)
-
-_log = logging.getLogger(__name__)
-
-
-def _get_dash_pattern(style):
-    """Convert linestyle to dash pattern."""
-    # go from short hand -> full strings
-    if isinstance(style, str):
-        style = ls_mapper.get(style, style)
-    # un-dashed styles
-    if style in ['solid', 'None']:
-        offset = 0
-        dashes = None
-    # dashed styles
-    elif style in ['dashed', 'dashdot', 'dotted']:
-        offset = 0
-        dashes = tuple(mpl.rcParams['lines.{}_pattern'.format(style)])
-    #
-    elif isinstance(style, tuple):
-        offset, dashes = style
-        if offset is None:
-            raise ValueError(f'Unrecognized linestyle: {style!r}')
-    else:
-        raise ValueError(f'Unrecognized linestyle: {style!r}')
-
-    # normalize offset to be positive and shorter than the dash cycle
-    if dashes is not None:
-        dsum = sum(dashes)
-        if dsum:
-            offset %= dsum
-
-    return offset, dashes
-
-
-def _scale_dashes(offset, dashes, lw):
-    if not mpl.rcParams['lines.scale_dashes']:
-        return offset, dashes
-    scaled_offset = offset * lw
-    scaled_dashes = ([x * lw if x is not None else None for x in dashes]
-                     if dashes is not None else None)
-    return scaled_offset, scaled_dashes
-
-
-def segment_hits(cx, cy, x, y, radius):
-    """
-    Return the indices of the segments in the polyline with coordinates (*cx*,
-    *cy*) that are within a distance *radius* of the point (*x*, *y*).
-    """
-    # Process single points specially
-    if len(x) <= 1:
-        res, = np.nonzero((cx - x) ** 2 + (cy - y) ** 2 <= radius ** 2)
-        return res
-
-    # We need to lop the last element off a lot.
-    xr, yr = x[:-1], y[:-1]
-
-    # Only look at line segments whose nearest point to C on the line
-    # lies within the segment.
-    dx, dy = x[1:] - xr, y[1:] - yr
-    Lnorm_sq = dx ** 2 + dy ** 2  # Possibly want to eliminate Lnorm==0
-    u = ((cx - xr) * dx + (cy - yr) * dy) / Lnorm_sq
-    candidates = (u >= 0) & (u <= 1)
-
-    # Note that there is a little area near one side of each point
-    # which will be near neither segment, and another which will
-    # be near both, depending on the angle of the lines.  The
-    # following radius test eliminates these ambiguities.
-    point_hits = (cx - x) ** 2 + (cy - y) ** 2 <= radius ** 2
-    candidates = candidates & ~(point_hits[:-1] | point_hits[1:])
-
-    # For those candidates which remain, determine how far they lie away
-    # from the line.
-    px, py = xr + u * dx, yr + u * dy
-    line_hits = (cx - px) ** 2 + (cy - py) ** 2 <= radius ** 2
-    line_hits = line_hits & candidates
-    points, = point_hits.ravel().nonzero()
-    lines, = line_hits.ravel().nonzero()
-    return np.concatenate((points, lines))
-
-
-def _mark_every_path(markevery, tpath, affine, ax):
-    """
-    Helper function that sorts out how to deal the input
-    `markevery` and returns the points where markers should be drawn.
-
-    Takes in the `markevery` value and the line path and returns the
-    sub-sampled path.
-    """
-    # pull out the two bits of data we want from the path
-    codes, verts = tpath.codes, tpath.vertices
-
-    def _slice_or_none(in_v, slc):
-        """Helper function to cope with `codes` being an ndarray or `None`."""
-        if in_v is None:
-            return None
-        return in_v[slc]
-
-    # if just an int, assume starting at 0 and make a tuple
-    if isinstance(markevery, Integral):
-        markevery = (0, markevery)
-    # if just a float, assume starting at 0.0 and make a tuple
-    elif isinstance(markevery, Real):
-        markevery = (0.0, markevery)
-
-    if isinstance(markevery, tuple):
-        if len(markevery) != 2:
-            raise ValueError('`markevery` is a tuple but its len is not 2; '
-                             'markevery={}'.format(markevery))
-        start, step = markevery
-        # if step is an int, old behavior
-        if isinstance(step, Integral):
-            # tuple of 2 int is for backwards compatibility,
-            if not isinstance(start, Integral):
-                raise ValueError(
-                    '`markevery` is a tuple with len 2 and second element is '
-                    'an int, but the first element is not an int; markevery={}'
-                    .format(markevery))
-            # just return, we are done here
-
-            return Path(verts[slice(start, None, step)],
-                        _slice_or_none(codes, slice(start, None, step)))
-
-        elif isinstance(step, Real):
-            if not isinstance(start, Real):
-                raise ValueError(
-                    '`markevery` is a tuple with len 2 and second element is '
-                    'a float, but the first element is not a float or an int; '
-                    'markevery={}'.format(markevery))
-            if ax is None:
-                raise ValueError(
-                    "markevery is specified relative to the axes size, but "
-                    "the line does not have a Axes as parent")
-
-            # calc cumulative distance along path (in display coords):
-            fin = np.isfinite(verts).all(axis=1)
-            fverts = verts[fin]
-            disp_coords = affine.transform(fverts)
-
-            delta = np.empty((len(disp_coords), 2))
-            delta[0, :] = 0
-            delta[1:, :] = disp_coords[1:, :] - disp_coords[:-1, :]
-            delta = np.hypot(*delta.T).cumsum()
-            # calc distance between markers along path based on the axes
-            # bounding box diagonal being a distance of unity:
-            (x0, y0), (x1, y1) = ax.transAxes.transform([[0, 0], [1, 1]])
-            scale = np.hypot(x1 - x0, y1 - y0)
-            marker_delta = np.arange(start * scale, delta[-1], step * scale)
-            # find closest actual data point that is closest to
-            # the theoretical distance along the path:
-            inds = np.abs(delta[np.newaxis, :] - marker_delta[:, np.newaxis])
-            inds = inds.argmin(axis=1)
-            inds = np.unique(inds)
-            # return, we are done here
-            return Path(fverts[inds], _slice_or_none(codes, inds))
-        else:
-            raise ValueError(
-                f"markevery={markevery!r} is a tuple with len 2, but its "
-                f"second element is not an int or a float")
-
-    elif isinstance(markevery, slice):
-        # mazol tov, it's already a slice, just return
-        return Path(verts[markevery], _slice_or_none(codes, markevery))
-
-    elif np.iterable(markevery):
-        # fancy indexing
-        try:
-            return Path(verts[markevery], _slice_or_none(codes, markevery))
-        except (ValueError, IndexError) as err:
-            raise ValueError(
-                f"markevery={markevery!r} is iterable but not a valid numpy "
-                f"fancy index") from err
-    else:
-        raise ValueError(f"markevery={markevery!r} is not a recognized value")
-
-
-@_docstring.interpd
-@_api.define_aliases({
-    "antialiased": ["aa"],
-    "color": ["c"],
-    "drawstyle": ["ds"],
-    "linestyle": ["ls"],
-    "linewidth": ["lw"],
-    "markeredgecolor": ["mec"],
-    "markeredgewidth": ["mew"],
-    "markerfacecolor": ["mfc"],
-    "markerfacecoloralt": ["mfcalt"],
-    "markersize": ["ms"],
-})
-class Line2D(Artist):
-    """
-    A line - the line can have both a solid linestyle connecting all
-    the vertices, and a marker at each vertex.  Additionally, the
-    drawing of the solid line is influenced by the drawstyle, e.g., one
-    can create "stepped" lines in various styles.
-    """
-
-    lineStyles = _lineStyles = {  # hidden names deprecated
-        '-':    '_draw_solid',
-        '--':   '_draw_dashed',
-        '-.':   '_draw_dash_dot',
-        ':':    '_draw_dotted',
-        'None': '_draw_nothing',
-        ' ':    '_draw_nothing',
-        '':     '_draw_nothing',
-    }
-
-    _drawStyles_l = {
-        'default':    '_draw_lines',
-        'steps-mid':  '_draw_steps_mid',
-        'steps-pre':  '_draw_steps_pre',
-        'steps-post': '_draw_steps_post',
-    }
-
-    _drawStyles_s = {
-        'steps': '_draw_steps_pre',
-    }
-
-    # drawStyles should now be deprecated.
-    drawStyles = {**_drawStyles_l, **_drawStyles_s}
-    # Need a list ordered with long names first:
-    drawStyleKeys = [*_drawStyles_l, *_drawStyles_s]
-
-    # Referenced here to maintain API.  These are defined in
-    # MarkerStyle
-    markers = MarkerStyle.markers
-    filled_markers = MarkerStyle.filled_markers
-    fillStyles = MarkerStyle.fillstyles
-
-    zorder = 2
-
-    def __str__(self):
-        if self._label != "":
-            return f"Line2D({self._label})"
-        elif self._x is None:
-            return "Line2D()"
-        elif len(self._x) > 3:
-            return "Line2D((%g,%g),(%g,%g),...,(%g,%g))" % (
-                self._x[0], self._y[0], self._x[0],
-                self._y[0], self._x[-1], self._y[-1])
-        else:
-            return "Line2D(%s)" % ",".join(
-                map("({:g},{:g})".format, self._x, self._y))
-
-    @_api.make_keyword_only("3.6", name="linewidth")
-    def __init__(self, xdata, ydata,
-                 linewidth=None,  # all Nones default to rc
-                 linestyle=None,
-                 color=None,
-                 gapcolor=None,
-                 marker=None,
-                 markersize=None,
-                 markeredgewidth=None,
-                 markeredgecolor=None,
-                 markerfacecolor=None,
-                 markerfacecoloralt='none',
-                 fillstyle=None,
-                 antialiased=None,
-                 dash_capstyle=None,
-                 solid_capstyle=None,
-                 dash_joinstyle=None,
-                 solid_joinstyle=None,
-                 pickradius=5,
-                 drawstyle=None,
-                 markevery=None,
-                 **kwargs
-                 ):
-        """
-        Create a `.Line2D` instance with *x* and *y* data in sequences of
-        *xdata*, *ydata*.
-
-        Additional keyword arguments are `.Line2D` properties:
-
-        %(Line2D:kwdoc)s
-
-        See :meth:`set_linestyle` for a description of the line styles,
-        :meth:`set_marker` for a description of the markers, and
-        :meth:`set_drawstyle` for a description of the draw styles.
-
-        """
-        super().__init__()
-
-        # Convert sequences to NumPy arrays.
-        if not np.iterable(xdata):
-            raise RuntimeError('xdata must be a sequence')
-        if not np.iterable(ydata):
-            raise RuntimeError('ydata must be a sequence')
-
-        if linewidth is None:
-            linewidth = mpl.rcParams['lines.linewidth']
-
-        if linestyle is None:
-            linestyle = mpl.rcParams['lines.linestyle']
-        if marker is None:
-            marker = mpl.rcParams['lines.marker']
-        if color is None:
-            color = mpl.rcParams['lines.color']
-
-        if markersize is None:
-            markersize = mpl.rcParams['lines.markersize']
-        if antialiased is None:
-            antialiased = mpl.rcParams['lines.antialiased']
-        if dash_capstyle is None:
-            dash_capstyle = mpl.rcParams['lines.dash_capstyle']
-        if dash_joinstyle is None:
-            dash_joinstyle = mpl.rcParams['lines.dash_joinstyle']
-        if solid_capstyle is None:
-            solid_capstyle = mpl.rcParams['lines.solid_capstyle']
-        if solid_joinstyle is None:
-            solid_joinstyle = mpl.rcParams['lines.solid_joinstyle']
-
-        if drawstyle is None:
-            drawstyle = 'default'
-
-        self._dashcapstyle = None
-        self._dashjoinstyle = None
-        self._solidjoinstyle = None
-        self._solidcapstyle = None
-        self.set_dash_capstyle(dash_capstyle)
-        self.set_dash_joinstyle(dash_joinstyle)
-        self.set_solid_capstyle(solid_capstyle)
-        self.set_solid_joinstyle(solid_joinstyle)
-
-        self._linestyles = None
-        self._drawstyle = None
-        self._linewidth = linewidth
-        self._unscaled_dash_pattern = (0, None)  # offset, dash
-        self._dash_pattern = (0, None)  # offset, dash (scaled by linewidth)
-
-        self.set_linewidth(linewidth)
-        self.set_linestyle(linestyle)
-        self.set_drawstyle(drawstyle)
-
-        self._color = None
-        self.set_color(color)
-        if marker is None:
-            marker = 'none'  # Default.
-        if not isinstance(marker, MarkerStyle):
-            self._marker = MarkerStyle(marker, fillstyle)
-        else:
-            self._marker = marker
-
-        self._gapcolor = None
-        self.set_gapcolor(gapcolor)
-
-        self._markevery = None
-        self._markersize = None
-        self._antialiased = None
-
-        self.set_markevery(markevery)
-        self.set_antialiased(antialiased)
-        self.set_markersize(markersize)
-
-        self._markeredgecolor = None
-        self._markeredgewidth = None
-        self._markerfacecolor = None
-        self._markerfacecoloralt = None
-
-        self.set_markerfacecolor(markerfacecolor)  # Normalizes None to rc.
-        self.set_markerfacecoloralt(markerfacecoloralt)
-        self.set_markeredgecolor(markeredgecolor)  # Normalizes None to rc.
-        self.set_markeredgewidth(markeredgewidth)
-
-        # update kwargs before updating data to give the caller a
-        # chance to init axes (and hence unit support)
-        self._internal_update(kwargs)
-        self._pickradius = pickradius
-        self.ind_offset = 0
-        if (isinstance(self._picker, Number) and
-                not isinstance(self._picker, bool)):
-            self._pickradius = self._picker
-
-        self._xorig = np.asarray([])
-        self._yorig = np.asarray([])
-        self._invalidx = True
-        self._invalidy = True
-        self._x = None
-        self._y = None
-        self._xy = None
-        self._path = None
-        self._transformed_path = None
-        self._subslice = False
-        self._x_filled = None  # used in subslicing; only x is needed
-
-        self.set_data(xdata, ydata)
-
-    def contains(self, mouseevent):
-        """
-        Test whether *mouseevent* occurred on the line.
-
-        An event is deemed to have occurred "on" the line if it is less
-        than ``self.pickradius`` (default: 5 points) away from it.  Use
-        `~.Line2D.get_pickradius` or `~.Line2D.set_pickradius` to get or set
-        the pick radius.
-
-        Parameters
-        ----------
-        mouseevent : `matplotlib.backend_bases.MouseEvent`
-
-        Returns
-        -------
-        contains : bool
-            Whether any values are within the radius.
-        details : dict
-            A dictionary ``{'ind': pointlist}``, where *pointlist* is a
-            list of points of the line that are within the pickradius around
-            the event position.
-
-            TODO: sort returned indices by distance
-        """
-        inside, info = self._default_contains(mouseevent)
-        if inside is not None:
-            return inside, info
-
-        # Make sure we have data to plot
-        if self._invalidy or self._invalidx:
-            self.recache()
-        if len(self._xy) == 0:
-            return False, {}
-
-        # Convert points to pixels
-        transformed_path = self._get_transformed_path()
-        path, affine = transformed_path.get_transformed_path_and_affine()
-        path = affine.transform_path(path)
-        xy = path.vertices
-        xt = xy[:, 0]
-        yt = xy[:, 1]
-
-        # Convert pick radius from points to pixels
-        if self.figure is None:
-            _log.warning('no figure set when check if mouse is on line')
-            pixels = self._pickradius
-        else:
-            pixels = self.figure.dpi / 72. * self._pickradius
-
-        # The math involved in checking for containment (here and inside of
-        # segment_hits) assumes that it is OK to overflow, so temporarily set
-        # the error flags accordingly.
-        with np.errstate(all='ignore'):
-            # Check for collision
-            if self._linestyle in ['None', None]:
-                # If no line, return the nearby point(s)
-                ind, = np.nonzero(
-                    (xt - mouseevent.x) ** 2 + (yt - mouseevent.y) ** 2
-                    <= pixels ** 2)
-            else:
-                # If line, return the nearby segment(s)
-                ind = segment_hits(mouseevent.x, mouseevent.y, xt, yt, pixels)
-                if self._drawstyle.startswith("steps"):
-                    ind //= 2
-
-        ind += self.ind_offset
-
-        # Return the point(s) within radius
-        return len(ind) > 0, dict(ind=ind)
-
-    def get_pickradius(self):
-        """
-        Return the pick radius used for containment tests.
-
-        See `.contains` for more details.
-        """
-        return self._pickradius
-
-    @_api.rename_parameter("3.6", "d", "pickradius")
-    def set_pickradius(self, pickradius):
-        """
-        Set the pick radius used for containment tests.
-
-        See `.contains` for more details.
-
-        Parameters
-        ----------
-        pickradius : float
-            Pick radius, in points.
-        """
-        if not isinstance(pickradius, Number) or pickradius < 0:
-            raise ValueError("pick radius should be a distance")
-        self._pickradius = pickradius
-
-    pickradius = property(get_pickradius, set_pickradius)
-
-    def get_fillstyle(self):
-        """
-        Return the marker fill style.
-
-        See also `~.Line2D.set_fillstyle`.
-        """
-        return self._marker.get_fillstyle()
-
-    def set_fillstyle(self, fs):
-        """
-        Set the marker fill style.
-
-        Parameters
-        ----------
-        fs : {'full', 'left', 'right', 'bottom', 'top', 'none'}
-            Possible values:
-
-            - 'full': Fill the whole marker with the *markerfacecolor*.
-            - 'left', 'right', 'bottom', 'top': Fill the marker half at
-              the given side with the *markerfacecolor*. The other
-              half of the marker is filled with *markerfacecoloralt*.
-            - 'none': No filling.
-
-            For examples see :ref:`marker_fill_styles`.
-        """
-        self.set_marker(MarkerStyle(self._marker.get_marker(), fs))
-        self.stale = True
-
-    def set_markevery(self, every):
-        """
-        Set the markevery property to subsample the plot when using markers.
-
-        e.g., if ``every=5``, every 5-th marker will be plotted.
-
-        Parameters
-        ----------
-        every : None or int or (int, int) or slice or list[int] or float or \
-(float, float) or list[bool]
-            Which markers to plot.
-
-            - ``every=None``: every point will be plotted.
-            - ``every=N``: every N-th marker will be plotted starting with
-              marker 0.
-            - ``every=(start, N)``: every N-th marker, starting at index
-              *start*, will be plotted.
-            - ``every=slice(start, end, N)``: every N-th marker, starting at
-              index *start*, up to but not including index *end*, will be
-              plotted.
-            - ``every=[i, j, m, ...]``: only markers at the given indices
-              will be plotted.
-            - ``every=[True, False, True, ...]``: only positions that are True
-              will be plotted. The list must have the same length as the data
-              points.
-            - ``every=0.1``, (i.e. a float): markers will be spaced at
-              approximately equal visual distances along the line; the distance
-              along the line between markers is determined by multiplying the
-              display-coordinate distance of the axes bounding-box diagonal
-              by the value of *every*.
-            - ``every=(0.5, 0.1)`` (i.e. a length-2 tuple of float): similar
-              to ``every=0.1`` but the first marker will be offset along the
-              line by 0.5 multiplied by the
-              display-coordinate-diagonal-distance along the line.
-
-            For examples see
-            :doc:`/gallery/lines_bars_and_markers/markevery_demo`.
-
-        Notes
-        -----
-        Setting *markevery* will still only draw markers at actual data points.
-        While the float argument form aims for uniform visual spacing, it has
-        to coerce from the ideal spacing to the nearest available data point.
-        Depending on the number and distribution of data points, the result
-        may still not look evenly spaced.
-
-        When using a start offset to specify the first marker, the offset will
-        be from the first data point which may be different from the first
-        the visible data point if the plot is zoomed in.
-
-        If zooming in on a plot when using float arguments then the actual
-        data points that have markers will change because the distance between
-        markers is always determined from the display-coordinates
-        axes-bounding-box-diagonal regardless of the actual axes data limits.
-
-        """
-        self._markevery = every
-        self.stale = True
-
-    def get_markevery(self):
-        """
-        Return the markevery setting for marker subsampling.
-
-        See also `~.Line2D.set_markevery`.
-        """
-        return self._markevery
-
-    def set_picker(self, p):
-        """
-        Set the event picker details for the line.
-
-        Parameters
-        ----------
-        p : float or callable[[Artist, Event], tuple[bool, dict]]
-            If a float, it is used as the pick radius in points.
-        """
-        if not callable(p):
-            self.set_pickradius(p)
-        self._picker = p
-
-    def get_bbox(self):
-        """Get the bounding box of this line."""
-        bbox = Bbox([[0, 0], [0, 0]])
-        bbox.update_from_data_xy(self.get_xydata())
-        return bbox
-
-    def get_window_extent(self, renderer=None):
-        bbox = Bbox([[0, 0], [0, 0]])
-        trans_data_to_xy = self.get_transform().transform
-        bbox.update_from_data_xy(trans_data_to_xy(self.get_xydata()),
-                                 ignore=True)
-        # correct for marker size, if any
-        if self._marker:
-            ms = (self._markersize / 72.0 * self.figure.dpi) * 0.5
-            bbox = bbox.padded(ms)
-        return bbox
-
-    def set_data(self, *args):
-        """
-        Set the x and y data.
-
-        Parameters
-        ----------
-        *args : (2, N) array or two 1D arrays
-        """
-        if len(args) == 1:
-            (x, y), = args
-        else:
-            x, y = args
-
-        self.set_xdata(x)
-        self.set_ydata(y)
-
-    def recache_always(self):
-        self.recache(always=True)
-
-    def recache(self, always=False):
-        if always or self._invalidx:
-            xconv = self.convert_xunits(self._xorig)
-            x = _to_unmasked_float_array(xconv).ravel()
-        else:
-            x = self._x
-        if always or self._invalidy:
-            yconv = self.convert_yunits(self._yorig)
-            y = _to_unmasked_float_array(yconv).ravel()
-        else:
-            y = self._y
-
-        self._xy = np.column_stack(np.broadcast_arrays(x, y)).astype(float)
-        self._x, self._y = self._xy.T  # views
-
-        self._subslice = False
-        if (self.axes and len(x) > 1000 and self._is_sorted(x) and
-                self.axes.name == 'rectilinear' and
-                self.axes.get_xscale() == 'linear' and
-                self._markevery is None and
-                self.get_clip_on() and
-                self.get_transform() == self.axes.transData):
-            self._subslice = True
-            nanmask = np.isnan(x)
-            if nanmask.any():
-                self._x_filled = self._x.copy()
-                indices = np.arange(len(x))
-                self._x_filled[nanmask] = np.interp(
-                    indices[nanmask], indices[~nanmask], self._x[~nanmask])
-            else:
-                self._x_filled = self._x
-
-        if self._path is not None:
-            interpolation_steps = self._path._interpolation_steps
-        else:
-            interpolation_steps = 1
-        xy = STEP_LOOKUP_MAP[self._drawstyle](*self._xy.T)
-        self._path = Path(np.asarray(xy).T,
-                          _interpolation_steps=interpolation_steps)
-        self._transformed_path = None
-        self._invalidx = False
-        self._invalidy = False
-
-    def _transform_path(self, subslice=None):
-        """
-        Put a TransformedPath instance at self._transformed_path;
-        all invalidation of the transform is then handled by the
-        TransformedPath instance.
-        """
-        # Masked arrays are now handled by the Path class itself
-        if subslice is not None:
-            xy = STEP_LOOKUP_MAP[self._drawstyle](*self._xy[subslice, :].T)
-            _path = Path(np.asarray(xy).T,
-                         _interpolation_steps=self._path._interpolation_steps)
-        else:
-            _path = self._path
-        self._transformed_path = TransformedPath(_path, self.get_transform())
-
-    def _get_transformed_path(self):
-        """Return this line's `~matplotlib.transforms.TransformedPath`."""
-        if self._transformed_path is None:
-            self._transform_path()
-        return self._transformed_path
-
-    def set_transform(self, t):
-        # docstring inherited
-        self._invalidx = True
-        self._invalidy = True
-        super().set_transform(t)
-
-    def _is_sorted(self, x):
-        """Return whether x is sorted in ascending order."""
-        # We don't handle the monotonically decreasing case.
-        return _path.is_sorted(x)
-
-    @allow_rasterization
-    def draw(self, renderer):
-        # docstring inherited
-
-        if not self.get_visible():
-            return
-
-        if self._invalidy or self._invalidx:
-            self.recache()
-        self.ind_offset = 0  # Needed for contains() method.
-        if self._subslice and self.axes:
-            x0, x1 = self.axes.get_xbound()
-            i0 = self._x_filled.searchsorted(x0, 'left')
-            i1 = self._x_filled.searchsorted(x1, 'right')
-            subslice = slice(max(i0 - 1, 0), i1 + 1)
-            self.ind_offset = subslice.start
-            self._transform_path(subslice)
-        else:
-            subslice = None
-
-        if self.get_path_effects():
-            from matplotlib.patheffects import PathEffectRenderer
-            renderer = PathEffectRenderer(self.get_path_effects(), renderer)
-
-        renderer.open_group('line2d', self.get_gid())
-        if self._lineStyles[self._linestyle] != '_draw_nothing':
-            tpath, affine = (self._get_transformed_path()
-                             .get_transformed_path_and_affine())
-            if len(tpath.vertices):
-                gc = renderer.new_gc()
-                self._set_gc_clip(gc)
-                gc.set_url(self.get_url())
-
-                gc.set_antialiased(self._antialiased)
-                gc.set_linewidth(self._linewidth)
-
-                if self.is_dashed():
-                    cap = self._dashcapstyle
-                    join = self._dashjoinstyle
-                else:
-                    cap = self._solidcapstyle
-                    join = self._solidjoinstyle
-                gc.set_joinstyle(join)
-                gc.set_capstyle(cap)
-                gc.set_snap(self.get_snap())
-                if self.get_sketch_params() is not None:
-                    gc.set_sketch_params(*self.get_sketch_params())
-
-                # We first draw a path within the gaps if needed.
-                if self.is_dashed() and self._gapcolor is not None:
-                    lc_rgba = mcolors.to_rgba(self._gapcolor, self._alpha)
-                    gc.set_foreground(lc_rgba, isRGBA=True)
-
-                    # Define the inverse pattern by moving the last gap to the
-                    # start of the sequence.
-                    dashes = self._dash_pattern[1]
-                    gaps = dashes[-1:] + dashes[:-1]
-                    # Set the offset so that this new first segment is skipped
-                    # (see backend_bases.GraphicsContextBase.set_dashes for
-                    # offset definition).
-                    offset_gaps = self._dash_pattern[0] + dashes[-1]
-
-                    gc.set_dashes(offset_gaps, gaps)
-                    renderer.draw_path(gc, tpath, affine.frozen())
-
-                lc_rgba = mcolors.to_rgba(self._color, self._alpha)
-                gc.set_foreground(lc_rgba, isRGBA=True)
-
-                gc.set_dashes(*self._dash_pattern)
-                renderer.draw_path(gc, tpath, affine.frozen())
-                gc.restore()
-
-        if self._marker and self._markersize > 0:
-            gc = renderer.new_gc()
-            self._set_gc_clip(gc)
-            gc.set_url(self.get_url())
-            gc.set_linewidth(self._markeredgewidth)
-            gc.set_antialiased(self._antialiased)
-
-            ec_rgba = mcolors.to_rgba(
-                self.get_markeredgecolor(), self._alpha)
-            fc_rgba = mcolors.to_rgba(
-                self._get_markerfacecolor(), self._alpha)
-            fcalt_rgba = mcolors.to_rgba(
-                self._get_markerfacecolor(alt=True), self._alpha)
-            # If the edgecolor is "auto", it is set according to the *line*
-            # color but inherits the alpha value of the *face* color, if any.
-            if (cbook._str_equal(self._markeredgecolor, "auto")
-                    and not cbook._str_lower_equal(
-                        self.get_markerfacecolor(), "none")):
-                ec_rgba = ec_rgba[:3] + (fc_rgba[3],)
-            gc.set_foreground(ec_rgba, isRGBA=True)
-            if self.get_sketch_params() is not None:
-                scale, length, randomness = self.get_sketch_params()
-                gc.set_sketch_params(scale/2, length/2, 2*randomness)
-
-            marker = self._marker
-
-            # Markers *must* be drawn ignoring the drawstyle (but don't pay the
-            # recaching if drawstyle is already "default").
-            if self.get_drawstyle() != "default":
-                with cbook._setattr_cm(
-                        self, _drawstyle="default", _transformed_path=None):
-                    self.recache()
-                    self._transform_path(subslice)
-                    tpath, affine = (self._get_transformed_path()
-                                     .get_transformed_points_and_affine())
-            else:
-                tpath, affine = (self._get_transformed_path()
-                                 .get_transformed_points_and_affine())
-
-            if len(tpath.vertices):
-                # subsample the markers if markevery is not None
-                markevery = self.get_markevery()
-                if markevery is not None:
-                    subsampled = _mark_every_path(
-                        markevery, tpath, affine, self.axes)
-                else:
-                    subsampled = tpath
-
-                snap = marker.get_snap_threshold()
-                if isinstance(snap, Real):
-                    snap = renderer.points_to_pixels(self._markersize) >= snap
-                gc.set_snap(snap)
-                gc.set_joinstyle(marker.get_joinstyle())
-                gc.set_capstyle(marker.get_capstyle())
-                marker_path = marker.get_path()
-                marker_trans = marker.get_transform()
-                w = renderer.points_to_pixels(self._markersize)
-
-                if cbook._str_equal(marker.get_marker(), ","):
-                    gc.set_linewidth(0)
-                else:
-                    # Don't scale for pixels, and don't stroke them
-                    marker_trans = marker_trans.scale(w)
-                renderer.draw_markers(gc, marker_path, marker_trans,
-                                      subsampled, affine.frozen(),
-                                      fc_rgba)
-
-                alt_marker_path = marker.get_alt_path()
-                if alt_marker_path:
-                    alt_marker_trans = marker.get_alt_transform()
-                    alt_marker_trans = alt_marker_trans.scale(w)
-                    renderer.draw_markers(
-                            gc, alt_marker_path, alt_marker_trans, subsampled,
-                            affine.frozen(), fcalt_rgba)
-
-            gc.restore()
-
-        renderer.close_group('line2d')
-        self.stale = False
-
-    def get_antialiased(self):
-        """Return whether antialiased rendering is used."""
-        return self._antialiased
-
-    def get_color(self):
-        """
-        Return the line color.
-
-        See also `~.Line2D.set_color`.
-        """
-        return self._color
-
-    def get_drawstyle(self):
-        """
-        Return the drawstyle.
-
-        See also `~.Line2D.set_drawstyle`.
-        """
-        return self._drawstyle
-
-    def get_gapcolor(self):
-        """
-        Return the line gapcolor.
-
-        See also `~.Line2D.set_gapcolor`.
-        """
-        return self._gapcolor
-
-    def get_linestyle(self):
-        """
-        Return the linestyle.
-
-        See also `~.Line2D.set_linestyle`.
-        """
-        return self._linestyle
-
-    def get_linewidth(self):
-        """
-        Return the linewidth in points.
-
-        See also `~.Line2D.set_linewidth`.
-        """
-        return self._linewidth
-
-    def get_marker(self):
-        """
-        Return the line marker.
-
-        See also `~.Line2D.set_marker`.
-        """
-        return self._marker.get_marker()
-
-    def get_markeredgecolor(self):
-        """
-        Return the marker edge color.
-
-        See also `~.Line2D.set_markeredgecolor`.
-        """
-        mec = self._markeredgecolor
-        if cbook._str_equal(mec, 'auto'):
-            if mpl.rcParams['_internal.classic_mode']:
-                if self._marker.get_marker() in ('.', ','):
-                    return self._color
-                if (self._marker.is_filled()
-                        and self._marker.get_fillstyle() != 'none'):
-                    return 'k'  # Bad hard-wired default...
-            return self._color
-        else:
-            return mec
-
-    def get_markeredgewidth(self):
-        """
-        Return the marker edge width in points.
-
-        See also `~.Line2D.set_markeredgewidth`.
-        """
-        return self._markeredgewidth
-
-    def _get_markerfacecolor(self, alt=False):
-        if self._marker.get_fillstyle() == 'none':
-            return 'none'
-        fc = self._markerfacecoloralt if alt else self._markerfacecolor
-        if cbook._str_lower_equal(fc, 'auto'):
-            return self._color
-        else:
-            return fc
-
-    def get_markerfacecolor(self):
-        """
-        Return the marker face color.
-
-        See also `~.Line2D.set_markerfacecolor`.
-        """
-        return self._get_markerfacecolor(alt=False)
-
-    def get_markerfacecoloralt(self):
-        """
-        Return the alternate marker face color.
-
-        See also `~.Line2D.set_markerfacecoloralt`.
-        """
-        return self._get_markerfacecolor(alt=True)
-
-    def get_markersize(self):
-        """
-        Return the marker size in points.
-
-        See also `~.Line2D.set_markersize`.
-        """
-        return self._markersize
-
-    def get_data(self, orig=True):
-        """
-        Return the line data as an ``(xdata, ydata)`` pair.
-
-        If *orig* is *True*, return the original data.
-        """
-        return self.get_xdata(orig=orig), self.get_ydata(orig=orig)
-
-    def get_xdata(self, orig=True):
-        """
-        Return the xdata.
-
-        If *orig* is *True*, return the original data, else the
-        processed data.
-        """
-        if orig:
-            return self._xorig
-        if self._invalidx:
-            self.recache()
-        return self._x
-
-    def get_ydata(self, orig=True):
-        """
-        Return the ydata.
-
-        If *orig* is *True*, return the original data, else the
-        processed data.
-        """
-        if orig:
-            return self._yorig
-        if self._invalidy:
-            self.recache()
-        return self._y
-
-    def get_path(self):
-        """Return the `~matplotlib.path.Path` associated with this line."""
-        if self._invalidy or self._invalidx:
-            self.recache()
-        return self._path
-
-    def get_xydata(self):
-        """
-        Return the *xy* data as a Nx2 numpy array.
-        """
-        if self._invalidy or self._invalidx:
-            self.recache()
-        return self._xy
-
-    def set_antialiased(self, b):
-        """
-        Set whether to use antialiased rendering.
-
-        Parameters
-        ----------
-        b : bool
-        """
-        if self._antialiased != b:
-            self.stale = True
-        self._antialiased = b
-
-    def set_color(self, color):
-        """
-        Set the color of the line.
-
-        Parameters
-        ----------
-        color : color
-        """
-        mcolors._check_color_like(color=color)
-        self._color = color
-        self.stale = True
-
-    def set_drawstyle(self, drawstyle):
-        """
-        Set the drawstyle of the plot.
-
-        The drawstyle determines how the points are connected.
-
-        Parameters
-        ----------
-        drawstyle : {'default', 'steps', 'steps-pre', 'steps-mid', \
-'steps-post'}, default: 'default'
-            For 'default', the points are connected with straight lines.
-
-            The steps variants connect the points with step-like lines,
-            i.e. horizontal lines with vertical steps. They differ in the
-            location of the step:
-
-            - 'steps-pre': The step is at the beginning of the line segment,
-              i.e. the line will be at the y-value of point to the right.
-            - 'steps-mid': The step is halfway between the points.
-            - 'steps-post: The step is at the end of the line segment,
-              i.e. the line will be at the y-value of the point to the left.
-            - 'steps' is equal to 'steps-pre' and is maintained for
-              backward-compatibility.
-
-            For examples see :doc:`/gallery/lines_bars_and_markers/step_demo`.
-        """
-        if drawstyle is None:
-            drawstyle = 'default'
-        _api.check_in_list(self.drawStyles, drawstyle=drawstyle)
-        if self._drawstyle != drawstyle:
-            self.stale = True
-            # invalidate to trigger a recache of the path
-            self._invalidx = True
-        self._drawstyle = drawstyle
-
-    def set_gapcolor(self, gapcolor):
-        """
-        Set a color to fill the gaps in the dashed line style.
-
-        .. note::
-
-            Striped lines are created by drawing two interleaved dashed lines.
-            There can be overlaps between those two, which may result in
-            artifacts when using transparency.
-
-            This functionality is experimental and may change.
-
-        Parameters
-        ----------
-        gapcolor : color or None
-            The color with which to fill the gaps. If None, the gaps are
-            unfilled.
-        """
-        if gapcolor is not None:
-            mcolors._check_color_like(color=gapcolor)
-        self._gapcolor = gapcolor
-        self.stale = True
-
-    def set_linewidth(self, w):
-        """
-        Set the line width in points.
-
-        Parameters
-        ----------
-        w : float
-            Line width, in points.
-        """
-        w = float(w)
-        if self._linewidth != w:
-            self.stale = True
-        self._linewidth = w
-        self._dash_pattern = _scale_dashes(*self._unscaled_dash_pattern, w)
-
-    def set_linestyle(self, ls):
-        """
-        Set the linestyle of the line.
-
-        Parameters
-        ----------
-        ls : {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
-            Possible values:
-
-            - A string:
-
-              ==========================================  =================
-              linestyle                                   description
-              ==========================================  =================
-              ``'-'`` or ``'solid'``                      solid line
-              ``'--'`` or  ``'dashed'``                   dashed line
-              ``'-.'`` or  ``'dashdot'``                  dash-dotted line
-              ``':'`` or ``'dotted'``                     dotted line
-              ``'none'``, ``'None'``, ``' '``, or ``''``  draw nothing
-              ==========================================  =================
-
-            - Alternatively a dash tuple of the following form can be
-              provided::
-
-                  (offset, onoffseq)
-
-              where ``onoffseq`` is an even length tuple of on and off ink
-              in points. See also :meth:`set_dashes`.
-
-            For examples see :doc:`/gallery/lines_bars_and_markers/linestyles`.
-        """
-        if isinstance(ls, str):
-            if ls in [' ', '', 'none']:
-                ls = 'None'
-            _api.check_in_list([*self._lineStyles, *ls_mapper_r], ls=ls)
-            if ls not in self._lineStyles:
-                ls = ls_mapper_r[ls]
-            self._linestyle = ls
-        else:
-            self._linestyle = '--'
-        self._unscaled_dash_pattern = _get_dash_pattern(ls)
-        self._dash_pattern = _scale_dashes(
-            *self._unscaled_dash_pattern, self._linewidth)
-        self.stale = True
-
-    @_docstring.interpd
-    def set_marker(self, marker):
-        """
-        Set the line marker.
-
-        Parameters
-        ----------
-        marker : marker style string, `~.path.Path` or `~.markers.MarkerStyle`
-            See `~matplotlib.markers` for full description of possible
-            arguments.
-        """
-        self._marker = MarkerStyle(marker, self._marker.get_fillstyle())
-        self.stale = True
-
-    def _set_markercolor(self, name, has_rcdefault, val):
-        if val is None:
-            val = mpl.rcParams[f"lines.{name}"] if has_rcdefault else "auto"
-        attr = f"_{name}"
-        current = getattr(self, attr)
-        if current is None:
-            self.stale = True
-        else:
-            neq = current != val
-            # Much faster than `np.any(current != val)` if no arrays are used.
-            if neq.any() if isinstance(neq, np.ndarray) else neq:
-                self.stale = True
-        setattr(self, attr, val)
-
-    def set_markeredgecolor(self, ec):
-        """
-        Set the marker edge color.
-
-        Parameters
-        ----------
-        ec : color
-        """
-        self._set_markercolor("markeredgecolor", True, ec)
-
-    def set_markerfacecolor(self, fc):
-        """
-        Set the marker face color.
-
-        Parameters
-        ----------
-        fc : color
-        """
-        self._set_markercolor("markerfacecolor", True, fc)
-
-    def set_markerfacecoloralt(self, fc):
-        """
-        Set the alternate marker face color.
-
-        Parameters
-        ----------
-        fc : color
-        """
-        self._set_markercolor("markerfacecoloralt", False, fc)
-
-    def set_markeredgewidth(self, ew):
-        """
-        Set the marker edge width in points.
-
-        Parameters
-        ----------
-        ew : float
-             Marker edge width, in points.
-        """
-        if ew is None:
-            ew = mpl.rcParams['lines.markeredgewidth']
-        if self._markeredgewidth != ew:
-            self.stale = True
-        self._markeredgewidth = ew
-
-    def set_markersize(self, sz):
-        """
-        Set the marker size in points.
-
-        Parameters
-        ----------
-        sz : float
-             Marker size, in points.
-        """
-        sz = float(sz)
-        if self._markersize != sz:
-            self.stale = True
-        self._markersize = sz
-
-    def set_xdata(self, x):
-        """
-        Set the data array for x.
-
-        Parameters
-        ----------
-        x : 1D array
-        """
-        if not np.iterable(x):
-            # When deprecation cycle is completed
-            # raise RuntimeError('x must be a sequence')
-            _api.warn_deprecated(
-                since=3.7,
-                message="Setting data with a non sequence type "
-                "is deprecated since %(since)s and will be "
-                "remove %(removal)s")
-            x = [x, ]
-        self._xorig = copy.copy(x)
-        self._invalidx = True
-        self.stale = True
-
-    def set_ydata(self, y):
-        """
-        Set the data array for y.
-
-        Parameters
-        ----------
-        y : 1D array
-        """
-        if not np.iterable(y):
-            # When deprecation cycle is completed
-            # raise RuntimeError('y must be a sequence')
-            _api.warn_deprecated(
-                since=3.7,
-                message="Setting data with a non sequence type "
-                "is deprecated since %(since)s and will be "
-                "remove %(removal)s")
-            y = [y, ]
-        self._yorig = copy.copy(y)
-        self._invalidy = True
-        self.stale = True
-
-    def set_dashes(self, seq):
-        """
-        Set the dash sequence.
-
-        The dash sequence is a sequence of floats of even length describing
-        the length of dashes and spaces in points.
-
-        For example, (5, 2, 1, 2) describes a sequence of 5 point and 1 point
-        dashes separated by 2 point spaces.
-
-        See also `~.Line2D.set_gapcolor`, which allows those spaces to be
-        filled with a color.
-
-        Parameters
-        ----------
-        seq : sequence of floats (on/off ink in points) or (None, None)
-            If *seq* is empty or ``(None, None)``, the linestyle will be set
-            to solid.
-        """
-        if seq == (None, None) or len(seq) == 0:
-            self.set_linestyle('-')
-        else:
-            self.set_linestyle((0, seq))
-
-    def update_from(self, other):
-        """Copy properties from *other* to self."""
-        super().update_from(other)
-        self._linestyle = other._linestyle
-        self._linewidth = other._linewidth
-        self._color = other._color
-        self._gapcolor = other._gapcolor
-        self._markersize = other._markersize
-        self._markerfacecolor = other._markerfacecolor
-        self._markerfacecoloralt = other._markerfacecoloralt
-        self._markeredgecolor = other._markeredgecolor
-        self._markeredgewidth = other._markeredgewidth
-        self._unscaled_dash_pattern = other._unscaled_dash_pattern
-        self._dash_pattern = other._dash_pattern
-        self._dashcapstyle = other._dashcapstyle
-        self._dashjoinstyle = other._dashjoinstyle
-        self._solidcapstyle = other._solidcapstyle
-        self._solidjoinstyle = other._solidjoinstyle
-
-        self._linestyle = other._linestyle
-        self._marker = MarkerStyle(marker=other._marker)
-        self._drawstyle = other._drawstyle
-
-    @_docstring.interpd
-    def set_dash_joinstyle(self, s):
-        """
-        How to join segments of the line if it `~Line2D.is_dashed`.
-
-        The default joinstyle is :rc:`lines.dash_joinstyle`.
-
-        Parameters
-        ----------
-        s : `.JoinStyle` or %(JoinStyle)s
-        """
-        js = JoinStyle(s)
-        if self._dashjoinstyle != js:
-            self.stale = True
-        self._dashjoinstyle = js
-
-    @_docstring.interpd
-    def set_solid_joinstyle(self, s):
-        """
-        How to join segments if the line is solid (not `~Line2D.is_dashed`).
-
-        The default joinstyle is :rc:`lines.solid_joinstyle`.
-
-        Parameters
-        ----------
-        s : `.JoinStyle` or %(JoinStyle)s
-        """
-        js = JoinStyle(s)
-        if self._solidjoinstyle != js:
-            self.stale = True
-        self._solidjoinstyle = js
-
-    def get_dash_joinstyle(self):
-        """
-        Return the `.JoinStyle` for dashed lines.
-
-        See also `~.Line2D.set_dash_joinstyle`.
-        """
-        return self._dashjoinstyle.name
-
-    def get_solid_joinstyle(self):
-        """
-        Return the `.JoinStyle` for solid lines.
-
-        See also `~.Line2D.set_solid_joinstyle`.
-        """
-        return self._solidjoinstyle.name
-
-    @_docstring.interpd
-    def set_dash_capstyle(self, s):
-        """
-        How to draw the end caps if the line is `~Line2D.is_dashed`.
-
-        The default capstyle is :rc:`lines.dash_capstyle`.
-
-        Parameters
-        ----------
-        s : `.CapStyle` or %(CapStyle)s
-        """
-        cs = CapStyle(s)
-        if self._dashcapstyle != cs:
-            self.stale = True
-        self._dashcapstyle = cs
-
-    @_docstring.interpd
-    def set_solid_capstyle(self, s):
-        """
-        How to draw the end caps if the line is solid (not `~Line2D.is_dashed`)
-
-        The default capstyle is :rc:`lines.solid_capstyle`.
-
-        Parameters
-        ----------
-        s : `.CapStyle` or %(CapStyle)s
-        """
-        cs = CapStyle(s)
-        if self._solidcapstyle != cs:
-            self.stale = True
-        self._solidcapstyle = cs
-
-    def get_dash_capstyle(self):
-        """
-        Return the `.CapStyle` for dashed lines.
-
-        See also `~.Line2D.set_dash_capstyle`.
-        """
-        return self._dashcapstyle.name
-
-    def get_solid_capstyle(self):
-        """
-        Return the `.CapStyle` for solid lines.
-
-        See also `~.Line2D.set_solid_capstyle`.
-        """
-        return self._solidcapstyle.name
-
-    def is_dashed(self):
-        """
-        Return whether line has a dashed linestyle.
-
-        A custom linestyle is assumed to be dashed, we do not inspect the
-        ``onoffseq`` directly.
-
-        See also `~.Line2D.set_linestyle`.
-        """
-        return self._linestyle in ('--', '-.', ':')
-
-
-class _AxLine(Line2D):
-    """
-    A helper class that implements `~.Axes.axline`, by recomputing the artist
-    transform at draw time.
-    """
-
-    def __init__(self, xy1, xy2, slope, **kwargs):
-        super().__init__([0, 1], [0, 1], **kwargs)
-
-        if (xy2 is None and slope is None or
-                xy2 is not None and slope is not None):
-            raise TypeError(
-                "Exactly one of 'xy2' and 'slope' must be given")
-
-        self._slope = slope
-        self._xy1 = xy1
-        self._xy2 = xy2
-
-    def get_transform(self):
-        ax = self.axes
-        points_transform = self._transform - ax.transData + ax.transScale
-
-        if self._xy2 is not None:
-            # two points were given
-            (x1, y1), (x2, y2) = \
-                points_transform.transform([self._xy1, self._xy2])
-            dx = x2 - x1
-            dy = y2 - y1
-            if np.allclose(x1, x2):
-                if np.allclose(y1, y2):
-                    raise ValueError(
-                        f"Cannot draw a line through two identical points "
-                        f"(x={(x1, x2)}, y={(y1, y2)})")
-                slope = np.inf
-            else:
-                slope = dy / dx
-        else:
-            # one point and a slope were given
-            x1, y1 = points_transform.transform(self._xy1)
-            slope = self._slope
-        (vxlo, vylo), (vxhi, vyhi) = ax.transScale.transform(ax.viewLim)
-        # General case: find intersections with view limits in either
-        # direction, and draw between the middle two points.
-        if np.isclose(slope, 0):
-            start = vxlo, y1
-            stop = vxhi, y1
-        elif np.isinf(slope):
-            start = x1, vylo
-            stop = x1, vyhi
-        else:
-            _, start, stop, _ = sorted([
-                (vxlo, y1 + (vxlo - x1) * slope),
-                (vxhi, y1 + (vxhi - x1) * slope),
-                (x1 + (vylo - y1) / slope, vylo),
-                (x1 + (vyhi - y1) / slope, vyhi),
-            ])
-        return (BboxTransformTo(Bbox([start, stop]))
-                + ax.transLimits + ax.transAxes)
-
-    def draw(self, renderer):
-        self._transformed_path = None  # Force regen.
-        super().draw(renderer)
-
-
-class VertexSelector:
-    """
-    Manage the callbacks to maintain a list of selected vertices for `.Line2D`.
-    Derived classes should override the `process_selected` method to do
-    something with the picks.
-
-    Here is an example which highlights the selected verts with red circles::
-
-        import numpy as np
-        import matplotlib.pyplot as plt
-        import matplotlib.lines as lines
-
-        class HighlightSelected(lines.VertexSelector):
-            def __init__(self, line, fmt='ro', **kwargs):
-                super().__init__(line)
-                self.markers, = self.axes.plot([], [], fmt, **kwargs)
-
-            def process_selected(self, ind, xs, ys):
-                self.markers.set_data(xs, ys)
-                self.canvas.draw()
-
-        fig, ax = plt.subplots()
-        x, y = np.random.rand(2, 30)
-        line, = ax.plot(x, y, 'bs-', picker=5)
-
-        selector = HighlightSelected(line)
-        plt.show()
-    """
-
-    def __init__(self, line):
-        """
-        Parameters
-        ----------
-        line : `.Line2D`
-            The line must already have been added to an `~.axes.Axes` and must
-            have its picker property set.
-        """
-        if line.axes is None:
-            raise RuntimeError('You must first add the line to the Axes')
-        if line.get_picker() is None:
-            raise RuntimeError('You must first set the picker property '
-                               'of the line')
-        self.axes = line.axes
-        self.line = line
-        self.cid = self.canvas.callbacks._connect_picklable(
-            'pick_event', self.onpick)
-        self.ind = set()
-
-    canvas = property(lambda self: self.axes.figure.canvas)
-
-    def process_selected(self, ind, xs, ys):
-        """
-        Default "do nothing" implementation of the `process_selected` method.
-
-        Parameters
-        ----------
-        ind : list of int
-            The indices of the selected vertices.
-        xs, ys : array-like
-            The coordinates of the selected vertices.
-        """
-        pass
-
-    def onpick(self, event):
-        """When the line is picked, update the set of selected indices."""
-        if event.artist is not self.line:
-            return
-        self.ind ^= set(event.ind)
-        ind = sorted(self.ind)
-        xdata, ydata = self.line.get_data()
-        self.process_selected(ind, xdata[ind], ydata[ind])
-
-
-lineStyles = Line2D._lineStyles
-lineMarkers = MarkerStyle.markers
-drawStyles = Line2D.drawStyles
-fillStyles = MarkerStyle.fillstyles
diff --git a/spaces/lambdalabs/generative-music-visualizer/torch_utils/ops/conv2d_resample.py b/spaces/lambdalabs/generative-music-visualizer/torch_utils/ops/conv2d_resample.py
deleted file mode 100644
index 5eb5877d7ffe4af74a2165f1d8d8c39dfac2476b..0000000000000000000000000000000000000000
--- a/spaces/lambdalabs/generative-music-visualizer/torch_utils/ops/conv2d_resample.py
+++ /dev/null
@@ -1,143 +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.
-
-"""2D convolution with optional up/downsampling."""
-
-import torch
-
-from .. import misc
-from . import conv2d_gradfix
-from . import upfirdn2d
-from .upfirdn2d import _parse_padding
-from .upfirdn2d import _get_filter_size
-
-#----------------------------------------------------------------------------
-
-def _get_weight_shape(w):
-    with misc.suppress_tracer_warnings(): # this value will be treated as a constant
-        shape = [int(sz) for sz in w.shape]
-    misc.assert_shape(w, shape)
-    return shape
-
-#----------------------------------------------------------------------------
-
-def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True):
-    """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
-    """
-    _out_channels, _in_channels_per_group, kh, kw = _get_weight_shape(w)
-
-    # Flip weight if requested.
-    # Note: conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
-    if not flip_weight and (kw > 1 or kh > 1):
-        w = w.flip([2, 3])
-
-    # Execute using conv2d_gradfix.
-    op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
-    return op(x, w, stride=stride, padding=padding, groups=groups)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
-    r"""2D convolution with optional up/downsampling.
-
-    Padding is performed only once at the beginning, not between the operations.
-
-    Args:
-        x:              Input tensor of shape
-                        `[batch_size, in_channels, in_height, in_width]`.
-        w:              Weight tensor of shape
-                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
-        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
-                        calling upfirdn2d.setup_filter(). None = identity (default).
-        up:             Integer upsampling factor (default: 1).
-        down:           Integer downsampling factor (default: 1).
-        padding:        Padding with respect to the upsampled image. Can be a single number
-                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                        (default: 0).
-        groups:         Split input channels into N groups (default: 1).
-        flip_weight:    False = convolution, True = correlation (default: True).
-        flip_filter:    False = convolution, True = correlation (default: False).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    # Validate arguments.
-    assert isinstance(x, torch.Tensor) and (x.ndim == 4)
-    assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
-    assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32)
-    assert isinstance(up, int) and (up >= 1)
-    assert isinstance(down, int) and (down >= 1)
-    assert isinstance(groups, int) and (groups >= 1)
-    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
-    fw, fh = _get_filter_size(f)
-    px0, px1, py0, py1 = _parse_padding(padding)
-
-    # Adjust padding to account for up/downsampling.
-    if up > 1:
-        px0 += (fw + up - 1) // 2
-        px1 += (fw - up) // 2
-        py0 += (fh + up - 1) // 2
-        py1 += (fh - up) // 2
-    if down > 1:
-        px0 += (fw - down + 1) // 2
-        px1 += (fw - down) // 2
-        py0 += (fh - down + 1) // 2
-        py1 += (fh - down) // 2
-
-    # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
-    if kw == 1 and kh == 1 and (down > 1 and up == 1):
-        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
-        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-        return x
-
-    # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
-    if kw == 1 and kh == 1 and (up > 1 and down == 1):
-        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-        x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
-        return x
-
-    # Fast path: downsampling only => use strided convolution.
-    if down > 1 and up == 1:
-        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
-        x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight)
-        return x
-
-    # Fast path: upsampling with optional downsampling => use transpose strided convolution.
-    if up > 1:
-        if groups == 1:
-            w = w.transpose(0, 1)
-        else:
-            w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
-            w = w.transpose(1, 2)
-            w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw)
-        px0 -= kw - 1
-        px1 -= kw - up
-        py0 -= kh - 1
-        py1 -= kh - up
-        pxt = max(min(-px0, -px1), 0)
-        pyt = max(min(-py0, -py1), 0)
-        x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
-        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
-        if down > 1:
-            x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
-        return x
-
-    # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
-    if up == 1 and down == 1:
-        if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
-            return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight)
-
-    # Fallback: Generic reference implementation.
-    x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
-    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-    if down > 1:
-        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
-    return x
-
-#----------------------------------------------------------------------------
diff --git a/spaces/lawliet/CS224-knowledge-discovery/src/generate.py b/spaces/lawliet/CS224-knowledge-discovery/src/generate.py
deleted file mode 100644
index 2376f37f927497d8c793b9ae0c5c50a8fab24f38..0000000000000000000000000000000000000000
--- a/spaces/lawliet/CS224-knowledge-discovery/src/generate.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-import openai
-
-
-async def get_openai_answer(_q: str, query_response):
-    openai.api_key = os.environ["OPENAI_API_KEY"]
-
-    prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Always provide link as references and evidence.
-    If you are unable to provide an answer, return most relevant reference with link. Always provide references.
-    
-    ---
-    
-    Always follow the following format:
-    
-    Question: ${{A scientific question that the user is asking}}
-    Helpful Answer: ${{A scientific comprehensive and helpful answer}}
-    Relevant References: ${{list of relevant references related to the Question with links}}
-    
-    ---
-    
-    {context}
-
-    Question: {question}
-    Helpful Answer:"""
-
-    context = ""
-    for idx, data in enumerate(query_response["matches"]):
-        if idx > 3:
-            break
-        context += f"Source-{idx}: {data['metadata']}\n\n"
-    prompt = prompt_template.format(context=context, question=_q)
-
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": prompt},
-        ],
-    )
-    return (
-        response["choices"][0]["message"]["content"],
-        response["usage"]["total_tokens"],
-    )
diff --git a/spaces/lewiswu1209/MockingBird/encoder/params_model.py b/spaces/lewiswu1209/MockingBird/encoder/params_model.py
deleted file mode 100644
index 3e356472fb5a27f370cb3920976a11d12a76c1b7..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/encoder/params_model.py
+++ /dev/null
@@ -1,11 +0,0 @@
-
-## Model parameters
-model_hidden_size = 256
-model_embedding_size = 256
-model_num_layers = 3
-
-
-## Training parameters
-learning_rate_init = 1e-4
-speakers_per_batch = 64
-utterances_per_speaker = 10
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Download Parazitii Toate S La Fel Mp3 Zippy HOT!.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Download Parazitii Toate S La Fel Mp3 Zippy HOT!.md
deleted file mode 100644
index e097f9da946fbdfe9df8ba221958b22c386d1ad8..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Download Parazitii Toate S La Fel Mp3 Zippy HOT!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Download Parazitii Toate S La Fel Mp3 Zippy</h2><br /><p><b><b>DOWNLOAD</b> &#10001; &#10001; &#10001; <a href="https://bytlly.com/2uGw35">https://bytlly.com/2uGw35</a></b></p><br /><br />
-<br />
-Descarcă download parazitii ce n-avem acasa zippy de pe MP3Clouds, absolut gratuit. ... Mi-au placut la nebunie „Toate-s la fel” si „Arde” si abia asteptam noua piesa. Iata ca a venit ... Ombladon - Trei agenti cu Cheloo si FDD Mp3 Download. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/linfanluntan/Grounded-SAM/GroundingDINO/setup.py b/spaces/linfanluntan/Grounded-SAM/GroundingDINO/setup.py
deleted file mode 100644
index a045b763fb4a4f61bac23b735544a18ffc68d20a..0000000000000000000000000000000000000000
--- a/spaces/linfanluntan/Grounded-SAM/GroundingDINO/setup.py
+++ /dev/null
@@ -1,208 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The IDEA 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.
-# ------------------------------------------------------------------------------------------------
-# Modified from
-# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/setup.py
-# https://github.com/facebookresearch/detectron2/blob/main/setup.py
-# https://github.com/open-mmlab/mmdetection/blob/master/setup.py
-# https://github.com/Oneflow-Inc/libai/blob/main/setup.py
-# ------------------------------------------------------------------------------------------------
-
-import glob
-import os
-import subprocess
-
-import torch
-from setuptools import find_packages, setup
-from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
-
-# groundingdino version info
-version = "0.1.0"
-package_name = "groundingdino"
-cwd = os.path.dirname(os.path.abspath(__file__))
-
-
-sha = "Unknown"
-try:
-    sha = subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=cwd).decode("ascii").strip()
-except Exception:
-    pass
-
-
-def write_version_file():
-    version_path = os.path.join(cwd, "groundingdino", "version.py")
-    with open(version_path, "w") as f:
-        f.write(f"__version__ = '{version}'\n")
-        # f.write(f"git_version = {repr(sha)}\n")
-
-
-requirements = ["torch", "torchvision"]
-
-torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
-
-
-def get_extensions():
-    this_dir = os.path.dirname(os.path.abspath(__file__))
-    extensions_dir = os.path.join(this_dir, "groundingdino", "models", "GroundingDINO", "csrc")
-
-    main_source = os.path.join(extensions_dir, "vision.cpp")
-    sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp"))
-    source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu")) + glob.glob(
-        os.path.join(extensions_dir, "*.cu")
-    )
-
-    sources = [main_source] + sources
-
-    extension = CppExtension
-
-    extra_compile_args = {"cxx": []}
-    define_macros = []
-
-    if torch.cuda.is_available() and CUDA_HOME is not None:
-        print("Compiling with CUDA")
-        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:
-        print("Compiling without CUDA")
-        define_macros += [("WITH_HIP", None)]
-        extra_compile_args["nvcc"] = []
-        return None
-
-    sources = [os.path.join(extensions_dir, s) for s in sources]
-    include_dirs = [extensions_dir]
-
-    ext_modules = [
-        extension(
-            "groundingdino._C",
-            sources,
-            include_dirs=include_dirs,
-            define_macros=define_macros,
-            extra_compile_args=extra_compile_args,
-        )
-    ]
-
-    return ext_modules
-
-
-def parse_requirements(fname="requirements.txt", with_version=True):
-    """Parse the package dependencies listed in a requirements file but strips
-    specific versioning information.
-
-    Args:
-        fname (str): path to requirements file
-        with_version (bool, default=False): if True include version specs
-
-    Returns:
-        List[str]: list of requirements items
-
-    CommandLine:
-        python -c "import setup; print(setup.parse_requirements())"
-    """
-    import re
-    import sys
-    from os.path import exists
-
-    require_fpath = fname
-
-    def parse_line(line):
-        """Parse information from a line in a requirements text file."""
-        if line.startswith("-r "):
-            # Allow specifying requirements in other files
-            target = line.split(" ")[1]
-            for info in parse_require_file(target):
-                yield info
-        else:
-            info = {"line": line}
-            if line.startswith("-e "):
-                info["package"] = line.split("#egg=")[1]
-            elif "@git+" in line:
-                info["package"] = line
-            else:
-                # Remove versioning from the package
-                pat = "(" + "|".join([">=", "==", ">"]) + ")"
-                parts = re.split(pat, line, maxsplit=1)
-                parts = [p.strip() for p in parts]
-
-                info["package"] = parts[0]
-                if len(parts) > 1:
-                    op, rest = parts[1:]
-                    if ";" in rest:
-                        # Handle platform specific dependencies
-                        # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
-                        version, platform_deps = map(str.strip, rest.split(";"))
-                        info["platform_deps"] = platform_deps
-                    else:
-                        version = rest  # NOQA
-                    info["version"] = (op, version)
-            yield info
-
-    def parse_require_file(fpath):
-        with open(fpath, "r") as f:
-            for line in f.readlines():
-                line = line.strip()
-                if line and not line.startswith("#"):
-                    for info in parse_line(line):
-                        yield info
-
-    def gen_packages_items():
-        if exists(require_fpath):
-            for info in parse_require_file(require_fpath):
-                parts = [info["package"]]
-                if with_version and "version" in info:
-                    parts.extend(info["version"])
-                if not sys.version.startswith("3.4"):
-                    # apparently package_deps are broken in 3.4
-                    platform_deps = info.get("platform_deps")
-                    if platform_deps is not None:
-                        parts.append(";" + platform_deps)
-                item = "".join(parts)
-                yield item
-
-    packages = list(gen_packages_items())
-    return packages
-
-
-if __name__ == "__main__":
-    print(f"Building wheel {package_name}-{version}")
-
-    with open("LICENSE", "r", encoding="utf-8") as f:
-        license = f.read()
-
-    write_version_file()
-
-    setup(
-        name="groundingdino",
-        version="0.1.0",
-        author="International Digital Economy Academy, Shilong Liu",
-        url="https://github.com/IDEA-Research/GroundingDINO",
-        description="open-set object detector",
-        license=license,
-        install_requires=parse_requirements("requirements.txt"),
-        packages=find_packages(
-            exclude=(
-                "configs",
-                "tests",
-            )
-        ),
-        ext_modules=get_extensions(),
-        cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
-    )
diff --git a/spaces/lmz/candle-llama2/build/m.d.ts b/spaces/lmz/candle-llama2/build/m.d.ts
deleted file mode 100644
index 4e45988b9ceb4c59f53e0ba0aa606522905bfbce..0000000000000000000000000000000000000000
--- a/spaces/lmz/candle-llama2/build/m.d.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-/* tslint:disable */
-/* eslint-disable */
-/**
-*/
-export class Model {
-  free(): void;
-/**
-* @param {Uint8Array} weights
-* @param {Uint8Array} tokenizer
-*/
-  constructor(weights: Uint8Array, tokenizer: Uint8Array);
-/**
-* @returns {number}
-*/
-  get_seq_len(): number;
-/**
-* @param {string} prompt
-* @param {number} temp
-* @param {number} top_p
-* @param {number} repeat_penalty
-* @param {bigint} seed
-* @returns {string}
-*/
-  init_with_prompt(prompt: string, temp: number, top_p: number, repeat_penalty: number, seed: bigint): string;
-/**
-* @returns {string}
-*/
-  next_token(): string;
-}
-
-export type InitInput = RequestInfo | URL | Response | BufferSource | WebAssembly.Module;
-
-export interface InitOutput {
-  readonly memory: WebAssembly.Memory;
-  readonly __wbg_model_free: (a: number) => void;
-  readonly model_new: (a: number, b: number, c: number, d: number, e: number) => void;
-  readonly model_get_seq_len: (a: number) => number;
-  readonly model_init_with_prompt: (a: number, b: number, c: number, d: number, e: number, f: number, g: number, h: number) => void;
-  readonly model_next_token: (a: number, b: number) => void;
-  readonly main: (a: number, b: number) => number;
-  readonly __wbindgen_add_to_stack_pointer: (a: number) => number;
-  readonly __wbindgen_malloc: (a: number, b: number) => number;
-  readonly __wbindgen_realloc: (a: number, b: number, c: number, d: number) => number;
-  readonly __wbindgen_free: (a: number, b: number, c: number) => void;
-  readonly __wbindgen_exn_store: (a: number) => void;
-  readonly __wbindgen_start: () => void;
-}
-
-export type SyncInitInput = BufferSource | WebAssembly.Module;
-/**
-* Instantiates the given `module`, which can either be bytes or
-* a precompiled `WebAssembly.Module`.
-*
-* @param {SyncInitInput} module
-*
-* @returns {InitOutput}
-*/
-export function initSync(module: SyncInitInput): InitOutput;
-
-/**
-* If `module_or_path` is {RequestInfo} or {URL}, makes a request and
-* for everything else, calls `WebAssembly.instantiate` directly.
-*
-* @param {InitInput | Promise<InitInput>} module_or_path
-*
-* @returns {Promise<InitOutput>}
-*/
-export default function __wbg_init (module_or_path?: InitInput | Promise<InitInput>): Promise<InitOutput>;
diff --git a/spaces/lqy09/GT/README.md b/spaces/lqy09/GT/README.md
deleted file mode 100644
index 7e3189644d1f40095af1e8b03ed3242d98febf83..0000000000000000000000000000000000000000
--- a/spaces/lqy09/GT/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: GT
-emoji: 💻
-colorFrom: blue
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ltgoslo/ssa-perin/data/field/mini_torchtext/pipeline.py b/spaces/ltgoslo/ssa-perin/data/field/mini_torchtext/pipeline.py
deleted file mode 100644
index 632736429d8fd6893118728ee6c501307c14bfa5..0000000000000000000000000000000000000000
--- a/spaces/ltgoslo/ssa-perin/data/field/mini_torchtext/pipeline.py
+++ /dev/null
@@ -1,86 +0,0 @@
-class Pipeline(object):
-    """Defines a pipeline for transforming sequence data.
-
-    The input is assumed to be utf-8 encoded `str` (Python 3) or
-    `unicode` (Python 2).
-
-    Attributes:
-        convert_token: The function to apply to input sequence data.
-        pipes: The Pipelines that will be applied to input sequence
-            data in order.
-    """
-
-    def __init__(self, convert_token=None):
-        """Create a pipeline.
-
-        Arguments:
-            convert_token: The function to apply to input sequence data.
-                If None, the identity function is used. Default: None
-        """
-        if convert_token is None:
-            self.convert_token = Pipeline.identity
-        elif callable(convert_token):
-            self.convert_token = convert_token
-        else:
-            raise ValueError("Pipeline input convert_token {} is not None "
-                             "or callable".format(convert_token))
-        self.pipes = [self]
-
-    def __call__(self, x, *args):
-        """Apply the the current Pipeline(s) to an input.
-
-        Arguments:
-            x: The input to process with the Pipeline(s).
-            Positional arguments: Forwarded to the `call` function
-                of the Pipeline(s).
-        """
-        for pipe in self.pipes:
-            x = pipe.call(x, *args)
-        return x
-
-    def call(self, x, *args):
-        """Apply _only_ the convert_token function of the current pipeline
-        to the input. If the input is a list, a list with the results of
-        applying the `convert_token` function to all input elements is
-        returned.
-
-        Arguments:
-            x: The input to apply the convert_token function to.
-            Positional arguments: Forwarded to the `convert_token` function
-                of the current Pipeline.
-        """
-        if isinstance(x, list):
-            return [self.convert_token(tok, *args) for tok in x]
-        return self.convert_token(x, *args)
-
-    def add_before(self, pipeline):
-        """Add a Pipeline to be applied before this processing pipeline.
-
-        Arguments:
-            pipeline: The Pipeline or callable to apply before this
-                Pipeline.
-        """
-        if not isinstance(pipeline, Pipeline):
-            pipeline = Pipeline(pipeline)
-        self.pipes = pipeline.pipes[:] + self.pipes[:]
-        return self
-
-    def add_after(self, pipeline):
-        """Add a Pipeline to be applied after this processing pipeline.
-
-        Arguments:
-            pipeline: The Pipeline or callable to apply after this
-                Pipeline.
-        """
-        if not isinstance(pipeline, Pipeline):
-            pipeline = Pipeline(pipeline)
-        self.pipes = self.pipes[:] + pipeline.pipes[:]
-        return self
-
-    @staticmethod
-    def identity(x):
-        """Return a copy of the input.
-
-        This is here for serialization compatibility with pickle.
-        """
-        return x
diff --git a/spaces/marioboy/neil-breen/synthesizer/utils/numbers.py b/spaces/marioboy/neil-breen/synthesizer/utils/numbers.py
deleted file mode 100644
index 75020a0bd732830f603d7c7d250c9e087033cc24..0000000000000000000000000000000000000000
--- a/spaces/marioboy/neil-breen/synthesizer/utils/numbers.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import re
-import inflect
-
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
-_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
-_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
-_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
-_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
-_number_re = re.compile(r"[0-9]+")
-
-
-def _remove_commas(m):
-  return m.group(1).replace(",", "")
-
-
-def _expand_decimal_point(m):
-  return m.group(1).replace(".", " point ")
-
-
-def _expand_dollars(m):
-  match = m.group(1)
-  parts = match.split(".")
-  if len(parts) > 2:
-    return match + " dollars"  # Unexpected format
-  dollars = int(parts[0]) if parts[0] else 0
-  cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-  if dollars and cents:
-    dollar_unit = "dollar" if dollars == 1 else "dollars"
-    cent_unit = "cent" if cents == 1 else "cents"
-    return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
-  elif dollars:
-    dollar_unit = "dollar" if dollars == 1 else "dollars"
-    return "%s %s" % (dollars, dollar_unit)
-  elif cents:
-    cent_unit = "cent" if cents == 1 else "cents"
-    return "%s %s" % (cents, cent_unit)
-  else:
-    return "zero dollars"
-
-
-def _expand_ordinal(m):
-  return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-  num = int(m.group(0))
-  if num > 1000 and num < 3000:
-    if num == 2000:
-      return "two thousand"
-    elif num > 2000 and num < 2010:
-      return "two thousand " + _inflect.number_to_words(num % 100)
-    elif num % 100 == 0:
-      return _inflect.number_to_words(num // 100) + " hundred"
-    else:
-      return _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(", ", " ")
-  else:
-    return _inflect.number_to_words(num, andword="")
-
-
-def normalize_numbers(text):
-  text = re.sub(_comma_number_re, _remove_commas, text)
-  text = re.sub(_pounds_re, r"\1 pounds", text)
-  text = re.sub(_dollars_re, _expand_dollars, text)
-  text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-  text = re.sub(_ordinal_re, _expand_ordinal, text)
-  text = re.sub(_number_re, _expand_number, text)
-  return text
diff --git a/spaces/merle/PROTEIN_GENERATOR/utils/app.py b/spaces/merle/PROTEIN_GENERATOR/utils/app.py
deleted file mode 100644
index 8efb331948e8d2e41d8d82dafcedc0c573d2c9ba..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/utils/app.py
+++ /dev/null
@@ -1,383 +0,0 @@
-import os,sys
-
-# install environment goods
-#os.system("pip -q install dgl -f https://data.dgl.ai/wheels/cu113/repo.html")
-os.system('pip install dgl==1.0.2+cu116 -f https://data.dgl.ai/wheels/cu116/repo.html')
-#os.system('pip install gradio')
-os.environ["DGLBACKEND"] = "pytorch"
-#os.system(f'pip install -r ./PROTEIN_GENERATOR/requirements.txt')
-print('Modules installed')
-
-os.environ["DGLBACKEND"] = "pytorch"
-
-if not os.path.exists('./SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt'):
-    print('Downloading model weights 1')
-    os.system('wget http://files.ipd.uw.edu/pub/sequence_diffusion/checkpoints/SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt')
-    print('Successfully Downloaded')
-
-if not os.path.exists('./SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt'):
-    print('Downloading model weights 2')
-    os.system('wget http://files.ipd.uw.edu/pub/sequence_diffusion/checkpoints/SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt')
-    print('Successfully Downloaded')
-
-import numpy as np
-import gradio as gr
-import py3Dmol
-from io import StringIO
-import json
-import secrets
-import copy
-import matplotlib.pyplot as plt
-from utils.sampler import HuggingFace_sampler
-
-plt.rcParams.update({'font.size': 13})
-
-with open('./tmp/args.json','r') as f:
-    args = json.load(f)
-
-# manually set checkpoint to load
-args['checkpoint'] = None
-args['dump_trb'] = False
-args['dump_args'] = True
-args['save_best_plddt'] = True
-args['T'] = 25
-args['strand_bias'] = 0.0
-args['loop_bias'] = 0.0
-args['helix_bias'] = 0.0
-
-
-
-def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bias, 
-                    secondary_structure, aa_bias, aa_bias_potential, target_charge, target_ph, charge_potential, 
-                    num_steps, noise, hydrophobic_target_score, hydrophobic_potential):
-    
-    dssp_checkpoint = './SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt'
-    og_checkpoint = './SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt'
-
-    model_args = copy.deepcopy(args)
-
-    # make sampler
-    S = HuggingFace_sampler(args=model_args)
-
-    # get random prefix 
-    S.out_prefix = './tmp/'+secrets.token_hex(nbytes=10).upper()
-
-    # set args
-    S.args['checkpoint'] = None
-    S.args['dump_trb'] = False
-    S.args['dump_args'] = True
-    S.args['save_best_plddt'] = True
-    S.args['T'] = 20
-    S.args['strand_bias'] = 0.0
-    S.args['loop_bias'] = 0.0
-    S.args['helix_bias'] = 0.0
-    S.args['potentials'] = None
-    S.args['potential_scale'] = None
-    S.args['aa_composition'] = None
-
-
-    # get sequence if entered and make sure all chars are valid
-    alt_aa_dict = {'B':['D','N'],'J':['I','L'],'U':['C'],'Z':['E','Q'],'O':['K']}
-    if sequence not in ['',None]:
-        aa_seq = []
-        for aa in sequence.upper():
-            if aa in alt_aa_dict.keys():
-                aa_seq.append(np.random.choice(alt_aa_dict[aa]))
-            else:
-                aa_seq.append(aa)
-
-        S.args['sequence'] = aa_seq
-    else:
-      S.args['contigs'] = [f'{seq_len}']
-    
-    if secondary_structure in ['',None]:
-        secondary_structure = None
-    else:
-        secondary_structure = ''.join(['E' if x == 'S' else x for x in secondary_structure])
-        if len(sequence) < len(secondary_structure):
-            secondary_structure = secondary_structure[:len(sequence)]
-        elif len(sequence) == len(secondary_structure):
-            pass
-        else:
-            dseq = len(sequence) - len(secondary_structure)
-            secondary_structure += secondary_structure[-1]*dseq
-    
-
-    # potentials
-    potential_list = []
-    potential_bias_list = []
-
-    if aa_bias not in ['',None]:
-        potential_list.append('aa_bias')
-        S.args['aa_composition'] = aa_bias
-        if aa_bias_potential in ['',None]:
-            aa_bias_potential = 3
-        potential_bias_list.append(str(aa_bias_potential))
-
-    if target_charge not in ['',None]:
-        potential_list.append('charge')
-        if charge_potential in ['',None]:
-            charge_potential = 1
-        potential_bias_list.append(str(charge_potential))
-        S.args['target_charge'] = int(target_charge)
-        if target_ph in ['',None]:
-            target_ph = 7.4
-        S.args['target_pH'] = float(target_ph)
-    
-    
-    if hydrophobic_target_score not in ['',None]:
-        potential_list.append('hydrophobic')
-        S.args['hydrophobic_score'] = float(hydrophobic_target_score)
-        if hydrophobic_potential in ['',None]:
-            hydrophobic_potential = 3
-        potential_bias_list.append(str(hydrophobic_potential))
-    
-
-    if len(potential_list) > 0:
-        S.args['potentials'] = ','.join(potential_list)
-        S.args['potential_scale'] = ','.join(potential_bias_list)
-
-
-    # normalise secondary_structure bias from range 0-0.3
-    S.args['secondary_structure'] = secondary_structure
-    S.args['helix_bias'] = helix_bias
-    S.args['strand_bias'] = strand_bias
-    S.args['loop_bias'] = loop_bias
-    
-    # set T
-    if num_steps in ['',None]:
-        S.args['T'] = 20
-    else:
-        S.args['T'] = int(num_steps)
-
-    # noise
-    if 'normal' in noise:
-        S.args['sample_distribution'] = noise
-        S.args['sample_distribution_gmm_means'] = [0]
-        S.args['sample_distribution_gmm_variances'] = [1]
-    elif 'gmm2' in noise:
-        S.args['sample_distribution'] = noise
-        S.args['sample_distribution_gmm_means'] = [-1,1]
-        S.args['sample_distribution_gmm_variances'] = [1,1]
-    elif 'gmm3' in noise:
-        S.args['sample_distribution'] = noise
-        S.args['sample_distribution_gmm_means'] = [-1,0,1]
-        S.args['sample_distribution_gmm_variances'] = [1,1,1]
-
-
-
-    if secondary_structure not in ['',None] or helix_bias+strand_bias+loop_bias > 0:
-        S.args['checkpoint'] = dssp_checkpoint
-        S.args['d_t1d'] = 29
-        print('using dssp checkpoint')
-    else:
-        S.args['checkpoint'] = og_checkpoint
-        S.args['d_t1d'] = 24
-        print('using og checkpoint')
-    
-
-    for k,v in S.args.items():
-        print(f"{k} --> {v}")
-    
-    # init S
-    S.model_init()
-    S.diffuser_init()
-    S.setup()
-
-    # sampling loop
-    plddt_data = []
-    for j in range(S.max_t):
-        output_seq, output_pdb, plddt = S.take_step_get_outputs(j)
-        plddt_data.append(plddt)
-        yield output_seq, output_pdb, display_pdb(output_pdb), get_plddt_plot(plddt_data, S.max_t)
-    
-    output_seq, output_pdb, plddt = S.get_outputs()
-
-    yield output_seq, output_pdb, display_pdb(output_pdb), get_plddt_plot(plddt_data, S.max_t)
-
-def get_plddt_plot(plddt_data, max_t):
-    x = [i+1 for i in range(len(plddt_data))]
-    fig, ax = plt.subplots(figsize=(15,6))
-    ax.plot(x,plddt_data,color='#661dbf', linewidth=3,marker='o')
-    ax.set_xticks([i+1 for i in range(max_t)])
-    ax.set_yticks([(i+1)/10 for i in range(10)])
-    ax.set_ylim([0,1])
-    ax.set_ylabel('model confidence (plddt)')
-    ax.set_xlabel('diffusion steps (t)')
-    return fig
-
-def display_pdb(path_to_pdb):
-    '''
-        #function to display pdb in py3dmol
-    '''
-    pdb = open(path_to_pdb, "r").read()
-    
-    view = py3Dmol.view(width=500, height=500)
-    view.addModel(pdb, "pdb")
-    view.setStyle({'model': -1}, {"cartoon": {'colorscheme':{'prop':'b','gradient':'roygb','min':0,'max':1}}})#'linear', 'min': 0, 'max': 1, 'colors': ["#ff9ef0","#a903fc",]}}}) 
-    view.zoomTo()
-    output = view._make_html().replace("'", '"')
-    print(view._make_html())
-    x = f"""<!DOCTYPE html><html></center> {output} </center></html>"""  # do not use ' in this input
-    
-    return f"""<iframe height="500px" width="100%"  name="result" allow="midi; geolocation; microphone; camera;
-                            display-capture; encrypted-media;" sandbox="allow-modals allow-forms
-                            allow-scripts allow-same-origin allow-popups
-                            allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
-                            allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>"""
-
-'''
-
-    return f"""<iframe  style="width: 100%; height:700px" name="result" allow="midi; geolocation; microphone; camera; 
-                            display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
-                            allow-scripts allow-same-origin allow-popups 
-                            allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
-                            allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>"""
-'''
-
-def toggle_seq_input(choice):
-    if choice == "protein length":
-        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
-    elif choice == "custom sequence":
-        return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
-
-def toggle_secondary_structure(choice):
-    if choice == "sliders":
-        return gr.update(visible=True, value=None),gr.update(visible=True, value=None),gr.update(visible=True, value=None),gr.update(visible=False, value=None)
-    elif choice == "explicit":
-        return gr.update(visible=False, value=None),gr.update(visible=False, value=None),gr.update(visible=False, value=None),gr.update(visible=True, value=None)
-
-# Define the Gradio interface
-with gr.Blocks(theme='ParityError/Interstellar') as demo:
-    
-    gr.Markdown(f"""# Protein Generation via Diffusion in Sequence Space""")
-
-    with gr.Row():
-        with gr.Column(min_width=500):
-            gr.Markdown(f"""
-                    ## How does it work?\n
-                    Protein sequence and structure co-generation is a long outstanding problem in the field of protein design. By implementing [ddpm](https://arxiv.org/abs/2006.11239) style diffusion over protein seqeuence space we generate protein sequence and structure pairs. Starting with [RoseTTAFold](https://www.science.org/doi/10.1126/science.abj8754), a protein structure prediction network, we finetuned it to predict sequence and structure given a partially noised sequence. By applying losses to both the predicted sequence and structure the model is forced to generate meaningful pairs. Diffusing in sequence space makes it easy to implement potentials to guide the diffusive process toward particular amino acid composition, net charge, and more! Furthermore, you can sample proteins from a family of sequences or even train a small sequence to function classifier to guide generation toward desired sequences.
-                    ![fig1](http://files.ipd.uw.edu/pub/sequence_diffusion/figs/diffusion_landscape.png)
-                    
-                    ## How to use it?\n
-                    A user can either design a custom input sequence to diffuse from or specify a length below. To scaffold a sequence use the following format where X represent residues to diffuse: XXXXXXXXSCIENCESCIENCEXXXXXXXXXXXXXXXXXXX. You can even design a protein with your name XXXXXXXXXXXXNAMEHEREXXXXXXXXXXXXX!
-                    
-                    ### Acknowledgements\n
-                    Thank you to Simon Dürr and the Hugging Face team for setting us up with a community GPU grant!
-                    """)
-            
-        gr.Markdown("""
-        ## Model in Action
-        ![gif1](http://files.ipd.uw.edu/pub/sequence_diffusion/figs/seqdiff_anim_720p.gif)
-        """)
-
-    with gr.Row().style(equal_height=False):
-        with gr.Column():
-            gr.Markdown("""## INPUTS""")
-            gr.Markdown("""#### Start Sequence
-                        Specify the protein length for complete unconditional generation, or scaffold a motif (or your name) using the custom sequence input""")
-            seq_opt = gr.Radio(["protein length","custom sequence"], label="How would you like to specify the starting sequence?", value='protein length')
-
-            sequence = gr.Textbox(label="custom sequence", lines=1, placeholder='AMINO ACIDS: A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y\n  MASK TOKEN: X', visible=False)
-            seq_len = gr.Slider(minimum=5.0, maximum=250.0, label="protein length", value=100, visible=True)
-            
-            seq_opt.change(fn=toggle_seq_input,
-                                    inputs=[seq_opt],
-                                    outputs=[seq_len, sequence],
-                                    queue=False)
-
-            gr.Markdown("""### Optional Parameters""")
-            with gr.Accordion(label='Secondary Structure',open=True):
-                gr.Markdown("""Try changing the sliders or inputing explicit secondary structure conditioning for each residue""")
-                sec_str_opt = gr.Radio(["sliders","explicit"], label="How would you like to specify secondary structure?", value='sliders')
-
-                secondary_structure = gr.Textbox(label="secondary structure", lines=1, placeholder='HELIX = H  STRAND = S  LOOP = L  MASK = X(must be the same length as input sequence)', visible=False)
-                
-                with gr.Column():
-                    helix_bias = gr.Slider(minimum=0.0, maximum=0.05, label="helix bias", visible=True)
-                    strand_bias = gr.Slider(minimum=0.0, maximum=0.05, label="strand bias", visible=True)
-                    loop_bias = gr.Slider(minimum=0.0, maximum=0.20, label="loop bias", visible=True)
-            
-                sec_str_opt.change(fn=toggle_secondary_structure,
-                                        inputs=[sec_str_opt],
-                                        outputs=[helix_bias,strand_bias,loop_bias,secondary_structure],
-                                        queue=False)
-                
-            with gr.Accordion(label='Amino Acid Compositional Bias',open=False):
-                gr.Markdown("""Bias sequence composition for particular amino acids by specifying the one letter code followed by the fraction to bias. This can be input as a list for example: W0.2,E0.1""")
-                with gr.Row():
-                    aa_bias = gr.Textbox(label="aa bias", lines=1, placeholder='specify one letter AA and fraction to bias, for example W0.1 or M0.1,K0.1' )
-                    aa_bias_potential = gr.Textbox(label="aa bias scale", lines=1, placeholder='AA Bias potential scale (recomended range 1.0-5.0)')
-
-            with gr.Accordion(label='Charge Bias',open=False):
-                gr.Markdown("""Bias for a specified net charge at a particular pH using the boxes below""")
-                with gr.Row():
-                    target_charge = gr.Textbox(label="net charge", lines=1, placeholder='net charge to target')
-                    target_ph = gr.Textbox(label="pH", lines=1, placeholder='pH at which net charge is desired')
-                    charge_potential = gr.Textbox(label="charge potential scale", lines=1, placeholder='charge potential scale (recomended range 1.0-5.0)')
-
-            with gr.Accordion(label='Hydrophobic Bias',open=False):
-                gr.Markdown("""Bias for or against hydrophobic composition, to get more soluble proteins, bias away with a negative target score (ex. -5)""")
-                with gr.Row():
-                    hydrophobic_target_score = gr.Textbox(label="hydrophobic score", lines=1, placeholder='hydrophobic score to target (negative score is good for solublility)')
-                    hydrophobic_potential = gr.Textbox(label="hydrophobic potential scale", lines=1, placeholder='hydrophobic potential scale (recomended range 1.0-2.0)')
-            
-            with gr.Accordion(label='Diffusion Params',open=False):
-                gr.Markdown("""Increasing T to more steps can be helpful for harder design challenges, sampling from different distributions can change the sequence and structural composition""")
-                with gr.Row():
-                    num_steps = gr.Textbox(label="T", lines=1, placeholder='number of diffusion steps (25 or less will speed things up)')
-                    noise = gr.Dropdown(['normal','gmm2 [-1,1]','gmm3 [-1,0,1]'], label='noise type', value='normal') 
-
-            btn = gr.Button("GENERATE")
-
-        #with gr.Row():
-        with gr.Column():
-            gr.Markdown("""## OUTPUTS""")
-            gr.Markdown("""#### Confidence score for generated structure at each timestep""")
-            plddt_plot = gr.Plot(label='plddt at step t')
-            gr.Markdown("""#### Output protein sequnece""")
-            output_seq = gr.Textbox(label="sequence")
-            gr.Markdown("""#### Download PDB file""")
-            output_pdb = gr.File(label="PDB file")
-            gr.Markdown("""#### Structure viewer""")
-            output_viewer = gr.HTML()
-
-    gr.Markdown("""### Don't know where to get started? Click on an example below to try it out!""")
-    gr.Examples(
-        [["","125",0.0,0.0,0.2,"","","","","","","20","normal"],
-         ["","100",0.0,0.0,0.0,"","W0.2","2","","","","20","normal"],
-         ["","100",0.0,0.0,0.0,"XXHHHHHHHHHXXXXXXXHHHHHHHHHXXXXXXXHHHHHHHHXXXXSSSSSSSSSSSXXXXXXXXSSSSSSSSSSSSXXXXXXXSSSSSSSSSXXXXXXX","","","","","","25","normal"],
-         ["XXXXXXXXXXXXXXXXXXXXXXXXXIPDXXXXXXXXXXXXXXXXXXXXXXPEPSEQXXXXXXXXXXXXXXXXXXXXXXXXXXIPDXXXXXXXXXXXXXXXXXXX","",0.0,0.0,0.0,"","","","","","","25","normal"]],
-        inputs=[sequence, seq_len, helix_bias, 
-                strand_bias, loop_bias, 
-                secondary_structure, aa_bias, 
-                aa_bias_potential, target_charge, 
-                target_ph, charge_potential, 
-                    num_steps, noise],
-        outputs=[output_seq,output_pdb,output_viewer,plddt_plot],
-        fn=protein_diffusion_model,
-        ) 
-    btn.click(protein_diffusion_model, 
-                [sequence, 
-                 seq_len, 
-                 helix_bias, 
-                 strand_bias, 
-                 loop_bias, 
-                 secondary_structure, 
-                 aa_bias, 
-                 aa_bias_potential, 
-                 target_charge, 
-                 target_ph, 
-                 charge_potential, 
-                 num_steps, 
-                 noise, 
-                 hydrophobic_target_score, 
-                 hydrophobic_potential], 
-                [output_seq,
-                 output_pdb,
-                 output_viewer,
-                 plddt_plot])
-
-demo.queue()
-demo.launch(debug=True)
-
diff --git a/spaces/merve/uncertainty-calibration/public/measuring-fairness/annotations.js b/spaces/merve/uncertainty-calibration/public/measuring-fairness/annotations.js
deleted file mode 100644
index 7ab68f297f98c655427a84de22388906182b240c..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/public/measuring-fairness/annotations.js
+++ /dev/null
@@ -1,52 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-
-var annotations = 
-[
-]
-
-
-function addSwoop(c){
-  var swoopy = d3.swoopyDrag()
-    .x(d => c.x(d.x))
-    .y(d => c.y(d.y))
-    .draggable(0)
-    .annotations(annotations)
-
-  var swoopySel = c.svg.append('g.annotations').call(swoopy)
-
-  c.svg.append('marker#arrow')
-      .attr('viewBox', '-10 -10 20 20')
-      .attr('markerWidth', 20)
-      .attr('markerHeight', 20)
-      .attr('orient', 'auto')
-    .append('path').at({d: 'M-6.75,-6.75 L 0,0 L -6.75,6.75'})
-
-
-  swoopySel.selectAll('path').attr('marker-end', 'url(#arrow)')
-  window.annotationSel = swoopySel.selectAll('g')
-    .st({fontSize: 12, opacity: d => d.slide == 0 ? 1 : 0})
-
-  swoopySel.selectAll('text')
-    .each(function(d){
-      d3.select(this)
-        .text('')                        //clear existing text
-        .tspans(d3.wordwrap(d.text, d.width || 20), 12) //wrap after 20 char
-    })  
-}
-
-
diff --git a/spaces/microsoft/unicl-img-recog-demo/model/image_encoder/focalnet.py b/spaces/microsoft/unicl-img-recog-demo/model/image_encoder/focalnet.py
deleted file mode 100644
index c0b533a7d28ff6b7c28b9905679c693f75a713f3..0000000000000000000000000000000000000000
--- a/spaces/microsoft/unicl-img-recog-demo/model/image_encoder/focalnet.py
+++ /dev/null
@@ -1,649 +0,0 @@
-# --------------------------------------------------------
-# FocalNets -- Focal Modulation Networks
-# Copyright (c) 2022 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Jianwei Yang (jianwyan@microsoft.com)
-# --------------------------------------------------------
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from timm.models.registry import register_model
-
-from torchvision import transforms
-from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from timm.data import create_transform
-from timm.data.transforms import _pil_interp
-
-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 FocalModulation(nn.Module):
-    def __init__(self, dim, focal_window, focal_level, focal_factor=2, bias=True, proj_drop=0.):
-        super().__init__()
-
-        self.dim = dim
-        self.focal_window = focal_window
-        self.focal_level = focal_level
-        self.focal_factor = focal_factor
-
-        self.f = nn.Linear(dim, 2*dim + (self.focal_level+1), bias=bias)
-        self.h = nn.Conv2d(dim, dim, kernel_size=1, stride=1, bias=bias)
-
-        self.act = nn.GELU()
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.focal_layers = nn.ModuleList()
-                
-        self.kernel_sizes = []
-        for k in range(self.focal_level):
-            kernel_size = self.focal_factor*k + self.focal_window
-            self.focal_layers.append(
-                nn.Sequential(
-                    nn.Conv2d(dim, dim, kernel_size=kernel_size, stride=1, 
-                    groups=dim, padding=kernel_size//2, bias=False),
-                    nn.GELU(),
-                    )
-                )              
-            self.kernel_sizes.append(kernel_size)          
-    def forward(self, x):
-        """
-        Args:
-            x: input features with shape of (B, H, W, C)
-        """
-        C = x.shape[-1]
-
-        # pre linear projection
-        x = self.f(x).permute(0, 3, 1, 2).contiguous()
-        q, ctx, self.gates = torch.split(x, (C, C, self.focal_level+1), 1)
-        
-        # context aggreation
-        ctx_all = 0 
-        for l in range(self.focal_level):         
-            ctx = self.focal_layers[l](ctx)
-            ctx_all = ctx_all + ctx*self.gates[:, l:l+1]
-        ctx_global = self.act(ctx.mean(2, keepdim=True).mean(3, keepdim=True))
-        ctx_all = ctx_all + ctx_global*self.gates[:,self.focal_level:]
-
-        # focal modulation
-        self.modulator = self.h(ctx_all)
-        x_out = q*self.modulator
-        x_out = x_out.permute(0, 2, 3, 1).contiguous()
-        
-        # post linear porjection
-        x_out = self.proj(x_out)
-        x_out = self.proj_drop(x_out)
-        return x_out
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-
-        flops += N * self.dim * (self.dim * 2 + (self.focal_level+1))
-
-        # focal convolution
-        for k in range(self.focal_level):
-            flops += N * (self.kernel_sizes[k]**2+1) * self.dim
-
-        # global gating
-        flops += N * 1 * self.dim 
-
-        #  self.linear
-        flops += N * self.dim * (self.dim + 1)
-
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-class FocalNetBlock(nn.Module):
-    r""" Focal Modulation Network Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        drop (float, optional): Dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-        focal_level (int): Number of focal levels. 
-        focal_window (int): Focal window size at first focal level
-        use_layerscale (bool): Whether use layerscale
-        layerscale_value (float): Initial layerscale value
-        use_postln (bool): Whether use layernorm after modulation
-    """
-
-    def __init__(self, dim, input_resolution, mlp_ratio=4., drop=0., drop_path=0., 
-                    act_layer=nn.GELU, norm_layer=nn.LayerNorm,
-                    focal_level=1, focal_window=3,
-                    use_layerscale=False, layerscale_value=1e-4, 
-                    use_postln=False):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.mlp_ratio = mlp_ratio
-
-        self.focal_window = focal_window
-        self.focal_level = focal_level
-        self.use_postln = use_postln
-
-        self.norm1 = norm_layer(dim)
-        self.modulation = FocalModulation(dim, proj_drop=drop, focal_window=focal_window, focal_level=self.focal_level)
-
-        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)
-
-        self.alpha = 3.0 if self.use_postln else 1.0
-
-        self.gamma_1 = 1.0
-        self.gamma_2 = 1.0    
-        if use_layerscale:
-            self.gamma_1 = nn.Parameter(layerscale_value * torch.ones((dim)), requires_grad=True)
-            self.gamma_2 = nn.Parameter(layerscale_value * torch.ones((dim)), requires_grad=True)
-
-        self.H = None
-        self.W = None
-
-    def forward(self, x):
-        H, W = self.H, self.W
-        B, L, C = x.shape
-        shortcut = x
-
-        # Focal Modulation
-        if not self.use_postln:
-            x = self.norm1(x)
-        x = x.view(B, H, W, C)
-        x = self.modulation(x).view(B, H * W, C)
-
-        # FFN
-        x = shortcut*self.alpha + self.drop_path(self.gamma_1 * x)
-        if self.use_postln:
-            x = self.norm1(x)
-
-        if not self.use_postln:
-            x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
-        else:
-            x = x*self.alpha + self.drop_path(self.gamma_2 * self.mlp(x))
-            x = self.norm2(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, " \
-               f"mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        
-        # W-MSA/SW-MSA
-        flops += self.modulation.flops(H*W)
-
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-class BasicLayer(nn.Module):
-    """ A basic Focal Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        focal_level (int): Number of focal levels
-        focal_window (int): Focal window size at first focal level
-        use_layerscale (bool): Whether use layerscale
-        layerscale_value (float): Initial layerscale value
-        use_postln (bool): Whether use layernorm after modulation
-    """
-
-    def __init__(self, dim, out_dim, input_resolution, depth,
-                 mlp_ratio=4., drop=0., drop_path=0., norm_layer=nn.LayerNorm, 
-                 downsample=None, use_checkpoint=False, 
-                 focal_level=1, focal_window=1, 
-                 use_conv_embed=False, 
-                 use_layerscale=False, layerscale_value=1e-4, use_postln=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-        
-        # build blocks
-        self.blocks = nn.ModuleList([
-            FocalNetBlock(
-                dim=dim, 
-                input_resolution=input_resolution,
-                mlp_ratio=mlp_ratio, 
-                drop=drop, 
-                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                norm_layer=norm_layer,
-                focal_level=focal_level,
-                focal_window=focal_window, 
-                use_layerscale=use_layerscale, 
-                layerscale_value=layerscale_value,
-                use_postln=use_postln, 
-            )
-            for i in range(depth)])
-
-        if downsample is not None:
-            self.downsample = downsample(
-                img_size=input_resolution, 
-                patch_size=2, 
-                in_chans=dim, 
-                embed_dim=out_dim, 
-                use_conv_embed=use_conv_embed, 
-                norm_layer=norm_layer, 
-                is_stem=False
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x, H, W):
-        for blk in self.blocks:
-            blk.H, blk.W = H, W
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-
-        if self.downsample is not None:
-            x = x.transpose(1, 2).reshape(x.shape[0], -1, H, W)
-            x, Ho, Wo = self.downsample(x)
-        else:
-            Ho, Wo = H, W        
-        return x, Ho, Wo
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=(224, 224), patch_size=4, in_chans=3, embed_dim=96, use_conv_embed=False, norm_layer=None, is_stem=False):
-        super().__init__()
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if use_conv_embed:
-            # if we choose to use conv embedding, then we treat the stem and non-stem differently
-            if is_stem:
-                kernel_size = 7; padding = 2; stride = 4
-            else:
-                kernel_size = 3; padding = 1; stride = 2
-            self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
-        else:
-            self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-
-        x = self.proj(x)        
-        H, W = x.shape[2:]
-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x, H, W
-
-    def flops(self):
-        Ho, Wo = self.patches_resolution
-        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
-        if self.norm is not None:
-            flops += Ho * Wo * self.embed_dim
-        return flops
-
-class FocalNet(nn.Module):
-    r""" Focal Modulation Networks (FocalNets)
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 224
-        patch_size (int | tuple(int)): Patch size. Default: 4
-        in_chans (int): Number of input image channels. Default: 3
-        num_classes (int): Number of classes for classification head. Default: 1000
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Focal Transformer layer.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        drop_rate (float): Dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False 
-        focal_levels (list): How many focal levels at all stages. Note that this excludes the finest-grain level. Default: [1, 1, 1, 1] 
-        focal_windows (list): The focal window size at all stages. Default: [7, 5, 3, 1] 
-        use_conv_embed (bool): Whether use convolutional embedding. We noted that using convolutional embedding usually improve the performance, but we do not use it by default. Default: False 
-        use_layerscale (bool): Whether use layerscale proposed in CaiT. Default: False 
-        layerscale_value (float): Value for layer scale. Default: 1e-4 
-        use_postln (bool): Whether use layernorm after modulation (it helps stablize training of large models)
-    """
-    def __init__(self, 
-                img_size=224, 
-                patch_size=4, 
-                in_chans=3, 
-                num_classes=1000,
-                embed_dim=96, 
-                depths=[2, 2, 6, 2], 
-                mlp_ratio=4., 
-                drop_rate=0., 
-                drop_path_rate=0.1,
-                norm_layer=nn.LayerNorm, 
-                patch_norm=True,
-                use_checkpoint=False,                 
-                focal_levels=[2, 2, 2, 2], 
-                focal_windows=[3, 3, 3, 3], 
-                use_conv_embed=False, 
-                use_layerscale=False, 
-                layerscale_value=1e-4, 
-                use_postln=False, 
-                **kwargs):
-        super().__init__()
-
-        self.num_layers = len(depths)
-        embed_dim = [embed_dim * (2 ** i) for i in range(self.num_layers)]
-
-        self.num_classes = num_classes
-        self.embed_dim = embed_dim
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim[-1]
-        self.mlp_ratio = mlp_ratio
-        
-        # split image into patches using either non-overlapped embedding or overlapped embedding
-        self.patch_embed = PatchEmbed(
-            img_size=to_2tuple(img_size), 
-            patch_size=patch_size, 
-            in_chans=in_chans, 
-            embed_dim=embed_dim[0], 
-            use_conv_embed=use_conv_embed, 
-            norm_layer=norm_layer if self.patch_norm else None, 
-            is_stem=True)
-
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(dim=embed_dim[i_layer], 
-                               out_dim=embed_dim[i_layer+1] if (i_layer < self.num_layers - 1) else None,  
-                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
-                                                 patches_resolution[1] // (2 ** i_layer)),
-                               depth=depths[i_layer],
-                               mlp_ratio=self.mlp_ratio,
-                               drop=drop_rate, 
-                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
-                               norm_layer=norm_layer, 
-                               downsample=PatchEmbed if (i_layer < self.num_layers - 1) else None,
-                               focal_level=focal_levels[i_layer], 
-                               focal_window=focal_windows[i_layer], 
-                               use_conv_embed=use_conv_embed,
-                               use_checkpoint=use_checkpoint, 
-                               use_layerscale=use_layerscale, 
-                               layerscale_value=layerscale_value, 
-                               use_postln=use_postln,
-                    )
-            self.layers.append(layer)
-
-        self.norm = norm_layer(self.num_features)
-        self.avgpool = nn.AdaptiveAvgPool1d(1)
-        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
-        self.dim_out = self.num_features
-
-        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 {''}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {''}
-
-    def forward_features(self, x):
-        x, H, W = self.patch_embed(x)
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x, H, W = layer(x, H, W)
-        x = self.norm(x)  # B L C
-        x = self.avgpool(x.transpose(1, 2))  # B C 1
-        x = torch.flatten(x, 1)
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.head(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
-        flops += self.num_features * self.num_classes
-        return flops
-
-def build_transforms(img_size, center_crop=False):
-    t = []
-    if center_crop:
-        size = int((256 / 224) * img_size)
-        t.append(
-            transforms.Resize(size, interpolation=_pil_interp('bicubic'))
-        )
-        t.append(
-            transforms.CenterCrop(img_size)    
-        )
-    else:
-        t.append(
-            transforms.Resize(img_size, interpolation=_pil_interp('bicubic'))
-        )        
-    t.append(transforms.ToTensor())
-    t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
-    return transforms.Compose(t)
-
-def build_transforms4display(img_size, center_crop=False):
-    t = []
-    if center_crop:
-        size = int((256 / 224) * img_size)
-        t.append(
-            transforms.Resize(size, interpolation=_pil_interp('bicubic'))
-        )
-        t.append(
-            transforms.CenterCrop(img_size)    
-        )
-    else:
-        t.append(
-            transforms.Resize(img_size, interpolation=_pil_interp('bicubic'))
-        )  
-    t.append(transforms.ToTensor())
-    return transforms.Compose(t)
-
-model_urls = {
-    "focalnet_tiny_srf": "",
-    "focalnet_small_srf": "",
-    "focalnet_base_srf": "",
-    "focalnet_tiny_lrf": "",
-    "focalnet_small_lrf": "",
-    "focalnet_base_lrf": "",    
-}
-
-@register_model
-def focalnet_tiny_srf(pretrained=False, **kwargs):
-    model = FocalNet(depths=[2, 2, 6, 2], embed_dim=96, **kwargs)
-    if pretrained:
-        url = model_urls['focalnet_tiny_srf']
-        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-@register_model
-def focalnet_small_srf(pretrained=False, **kwargs):
-    model = FocalNet(depths=[2, 2, 18, 2], embed_dim=96, **kwargs)
-    if pretrained:
-        url = model_urls['focalnet_small_srf']
-        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-@register_model
-def focalnet_base_srf(pretrained=False, **kwargs):
-    model = FocalNet(depths=[2, 2, 18, 2], embed_dim=128, **kwargs)
-    if pretrained:
-        url = model_urls['focalnet_base_srf']
-        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-@register_model
-def focalnet_tiny_lrf(pretrained=False, **kwargs):
-    model = FocalNet(depths=[2, 2, 6, 2], embed_dim=96, focal_levels=[3, 3, 3, 3], **kwargs)
-    if pretrained:
-        url = model_urls['focalnet_tiny_lrf']
-        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-@register_model
-def focalnet_small_lrf(pretrained=False, **kwargs):
-    model = FocalNet(depths=[2, 2, 18, 2], embed_dim=96, focal_levels=[3, 3, 3, 3], **kwargs)
-    if pretrained:
-        url = model_urls['focalnet_small_lrf']
-        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-@register_model
-def focalnet_base_lrf(pretrained=False, **kwargs):
-    model = FocalNet(depths=[2, 2, 18, 2], embed_dim=128, focal_levels=[3, 3, 3, 3], **kwargs)
-    if pretrained:
-        url = model_urls['focalnet_base_lrf']
-        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-@register_model
-def focalnet_giant_lrf(pretrained=False, **kwargs):
-    model = FocalNet(depths=[2, 2, 42, 2], embed_dim=512, focal_levels=[3, 3, 3, 3], **kwargs)
-    if pretrained:
-        url = model_urls['focalnet_giant_lrf']
-        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-@register_model
-def focalnet_tiny_iso_16(pretrained=False, **kwargs):
-    model = FocalNet(depths=[12], patch_size=16, embed_dim=192, focal_levels=[3], focal_windows=[3], **kwargs)
-    if pretrained:
-        url = model_urls['focalnet_tiny_iso_16']
-        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-@register_model
-def focalnet_small_iso_16(pretrained=False, **kwargs):
-    model = FocalNet(depths=[12], patch_size=16, embed_dim=384, focal_levels=[3], focal_windows=[3], **kwargs)
-    if pretrained:
-        url = model_urls['focalnet_small_iso_16']
-        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-@register_model
-def focalnet_base_iso_16(pretrained=False, **kwargs):
-    model = FocalNet(depths=[12], patch_size=16, embed_dim=768, focal_levels=[3], focal_windows=[3], use_layerscale=True, use_postln=True, **kwargs)
-    if pretrained:
-        url = model_urls['focalnet_base_iso_16']
-        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-if __name__ == '__main__':
-    img_size = 224
-    x = torch.rand(16, 3, img_size, img_size).cuda()
-    # model = FocalNet(depths=[2, 2, 6, 2], embed_dim=96)
-    # model = FocalNet(depths=[12], patch_size=16, embed_dim=768, focal_levels=[3], focal_windows=[3], focal_factors=[2])
-    model = FocalNet(depths=[2, 2, 6, 2], embed_dim=96, focal_levels=[3, 3, 3, 3]).cuda()
-    print(model); model(x)
-
-    flops = model.flops()
-    print(f"number of GFLOPs: {flops / 1e9}")
-
-    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
-    print(f"number of params: {n_parameters}")
diff --git a/spaces/mindwrapped/gpt2-lotr-fellowship/app.py b/spaces/mindwrapped/gpt2-lotr-fellowship/app.py
deleted file mode 100644
index 050a5443757462752b9a5b62a9bcc177b13f4bf1..0000000000000000000000000000000000000000
--- a/spaces/mindwrapped/gpt2-lotr-fellowship/app.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import numpy as np
-import gradio as gr
-from transformers import GPT2Tokenizer, TrainingArguments, Trainer, GPT2LMHeadModel
-
-tokenizer = GPT2Tokenizer.from_pretrained('mindwrapped/gpt2-lotr-fellowship', bos_token='<|startoftext|>',
-                                          eos_token='<|endoftext|>', pad_token='<|pad|>')
-model = GPT2LMHeadModel.from_pretrained('mindwrapped/gpt2-lotr-fellowship')
-
-
-def generate_text(text, temperature):
-  stop = False
-  out = text
-  while not stop:
-    generated = tokenizer("<|startoftext|> " + out, return_tensors="pt").input_ids
-    sample_outputs = model.generate(generated, do_sample=True, top_k=50, 
-                                    max_length=300, top_p=0.95, temperature=float(temperature), num_return_sequences=1)
-  
-    for i, sample_output in enumerate(sample_outputs):
-      out = tokenizer.decode(sample_output, skip_special_tokens=True)
-      
-    if len(out) > 150:
-      stop = True
-
-  return out
-
-
-demo = gr.Interface(
-  fn=generate_text, 
-  inputs=[gr.Text(),gr.Slider(minimum=0.0, maximum=5.0, value=1.0, step=0.1)],
-  outputs='text',
-  examples=[['', 1.9],['Frodo and Sam moved quietly through the night.', 2.0], ['Frodo and Sam went to the pub. ', 3.0]],
-  title='LOTR Generator',
-  description='This space uses GPT2 model fine-tuned on the "The Fellowship of the Rings" to generate text. Try inputting no text to the model and messing around with the temperature.',
-  article=' ![visitor badge](https://visitor-badge.glitch.me/badge?page_id=mindwrapped.gpt2-lotr-fellowship-generator-space)',
-  live=False,
-  )
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/mohitmayank/law-finder-ipc/devganscrap/devganscrap/middlewares.py b/spaces/mohitmayank/law-finder-ipc/devganscrap/devganscrap/middlewares.py
deleted file mode 100644
index 05581136f85819a6250b4573c0c8d7a4babcd94f..0000000000000000000000000000000000000000
--- a/spaces/mohitmayank/law-finder-ipc/devganscrap/devganscrap/middlewares.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# Define here the models for your spider middleware
-#
-# See documentation in:
-# https://docs.scrapy.org/en/latest/topics/spider-middleware.html
-
-from scrapy import signals
-
-# useful for handling different item types with a single interface
-from itemadapter import is_item, ItemAdapter
-
-
-class DevganscrapSpiderMiddleware:
-    # Not all methods need to be defined. If a method is not defined,
-    # scrapy acts as if the spider middleware does not modify the
-    # passed objects.
-
-    @classmethod
-    def from_crawler(cls, crawler):
-        # This method is used by Scrapy to create your spiders.
-        s = cls()
-        crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
-        return s
-
-    def process_spider_input(self, response, spider):
-        # Called for each response that goes through the spider
-        # middleware and into the spider.
-
-        # Should return None or raise an exception.
-        return None
-
-    def process_spider_output(self, response, result, spider):
-        # Called with the results returned from the Spider, after
-        # it has processed the response.
-
-        # Must return an iterable of Request, or item objects.
-        for i in result:
-            yield i
-
-    def process_spider_exception(self, response, exception, spider):
-        # Called when a spider or process_spider_input() method
-        # (from other spider middleware) raises an exception.
-
-        # Should return either None or an iterable of Request or item objects.
-        pass
-
-    def process_start_requests(self, start_requests, spider):
-        # Called with the start requests of the spider, and works
-        # similarly to the process_spider_output() method, except
-        # that it doesn’t have a response associated.
-
-        # Must return only requests (not items).
-        for r in start_requests:
-            yield r
-
-    def spider_opened(self, spider):
-        spider.logger.info('Spider opened: %s' % spider.name)
-
-
-class DevganscrapDownloaderMiddleware:
-    # Not all methods need to be defined. If a method is not defined,
-    # scrapy acts as if the downloader middleware does not modify the
-    # passed objects.
-
-    @classmethod
-    def from_crawler(cls, crawler):
-        # This method is used by Scrapy to create your spiders.
-        s = cls()
-        crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
-        return s
-
-    def process_request(self, request, spider):
-        # Called for each request that goes through the downloader
-        # middleware.
-
-        # Must either:
-        # - return None: continue processing this request
-        # - or return a Response object
-        # - or return a Request object
-        # - or raise IgnoreRequest: process_exception() methods of
-        #   installed downloader middleware will be called
-        return None
-
-    def process_response(self, request, response, spider):
-        # Called with the response returned from the downloader.
-
-        # Must either;
-        # - return a Response object
-        # - return a Request object
-        # - or raise IgnoreRequest
-        return response
-
-    def process_exception(self, request, exception, spider):
-        # Called when a download handler or a process_request()
-        # (from other downloader middleware) raises an exception.
-
-        # Must either:
-        # - return None: continue processing this exception
-        # - return a Response object: stops process_exception() chain
-        # - return a Request object: stops process_exception() chain
-        pass
-
-    def spider_opened(self, spider):
-        spider.logger.info('Spider opened: %s' % spider.name)
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/hubert/simple_kmeans/learn_kmeans.py b/spaces/mshukor/UnIVAL/fairseq/examples/hubert/simple_kmeans/learn_kmeans.py
deleted file mode 100644
index 113ac655b8c0a585fe43797e99674e445098edd0..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/hubert/simple_kmeans/learn_kmeans.py
+++ /dev/null
@@ -1,146 +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 logging
-import os
-import sys
-
-import numpy as np
-from sklearn.cluster import MiniBatchKMeans
-
-import joblib
-
-logging.basicConfig(
-    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    level=os.environ.get("LOGLEVEL", "INFO").upper(),
-    stream=sys.stdout,
-)
-logger = logging.getLogger("learn_kmeans")
-
-
-def get_km_model(
-    n_clusters,
-    init,
-    max_iter,
-    batch_size,
-    tol,
-    max_no_improvement,
-    n_init,
-    reassignment_ratio,
-):
-    return MiniBatchKMeans(
-        n_clusters=n_clusters,
-        init=init,
-        max_iter=max_iter,
-        batch_size=batch_size,
-        verbose=1,
-        compute_labels=False,
-        tol=tol,
-        max_no_improvement=max_no_improvement,
-        init_size=None,
-        n_init=n_init,
-        reassignment_ratio=reassignment_ratio,
-    )
-
-
-def load_feature_shard(feat_dir, split, nshard, rank, percent):
-    feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy"
-    leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len"
-    with open(leng_path, "r") as f:
-        lengs = [int(line.rstrip()) for line in f]
-        offsets = [0] + np.cumsum(lengs[:-1]).tolist()
-
-    if percent < 0:
-        return np.load(feat_path, mmap_mode="r")
-    else:
-        nsample = int(np.ceil(len(lengs) * percent))
-        indices = np.random.choice(len(lengs), nsample, replace=False)
-        feat = np.load(feat_path, mmap_mode="r")
-        sampled_feat = np.concatenate(
-            [feat[offsets[i]: offsets[i] + lengs[i]] for i in indices], axis=0
-        )
-        logger.info(
-            (
-                f"sampled {nsample} utterances, {len(sampled_feat)} frames "
-                f"from shard {rank}/{nshard}"
-            )
-        )
-        return sampled_feat
-
-
-def load_feature(feat_dir, split, nshard, seed, percent):
-    assert percent <= 1.0
-    feat = np.concatenate(
-        [
-            load_feature_shard(feat_dir, split, nshard, r, percent)
-            for r in range(nshard)
-        ],
-        axis=0,
-    )
-    logging.info(f"loaded feature with dimension {feat.shape}")
-    return feat
-
-
-def learn_kmeans(
-    feat_dir,
-    split,
-    nshard,
-    km_path,
-    n_clusters,
-    seed,
-    percent,
-    init,
-    max_iter,
-    batch_size,
-    tol,
-    n_init,
-    reassignment_ratio,
-    max_no_improvement,
-):
-    np.random.seed(seed)
-    feat = load_feature(feat_dir, split, nshard, seed, percent)
-    km_model = get_km_model(
-        n_clusters,
-        init,
-        max_iter,
-        batch_size,
-        tol,
-        max_no_improvement,
-        n_init,
-        reassignment_ratio,
-    )
-    km_model.fit(feat)
-    joblib.dump(km_model, km_path)
-
-    inertia = -km_model.score(feat) / len(feat)
-    logger.info("total intertia: %.5f", inertia)
-    logger.info("finished successfully")
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("feat_dir", type=str)
-    parser.add_argument("split", type=str)
-    parser.add_argument("nshard", type=int)
-    parser.add_argument("km_path", type=str)
-    parser.add_argument("n_clusters", type=int)
-    parser.add_argument("--seed", default=0, type=int)
-    parser.add_argument(
-        "--percent", default=-1, type=float, help="sample a subset; -1 for all"
-    )
-    parser.add_argument("--init", default="k-means++")
-    parser.add_argument("--max_iter", default=100, type=int)
-    parser.add_argument("--batch_size", default=10000, type=int)
-    parser.add_argument("--tol", default=0.0, type=float)
-    parser.add_argument("--max_no_improvement", default=100, type=int)
-    parser.add_argument("--n_init", default=20, type=int)
-    parser.add_argument("--reassignment_ratio", default=0.0, type=float)
-    args = parser.parse_args()
-    logging.info(str(args))
-
-    learn_kmeans(**vars(args))
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/simultaneous_translation/utils/monotonic_attention.py b/spaces/mshukor/UnIVAL/fairseq/examples/simultaneous_translation/utils/monotonic_attention.py
deleted file mode 100644
index 61dbb112bfd5ea7b92f2739f046910f486bb0153..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/simultaneous_translation/utils/monotonic_attention.py
+++ /dev/null
@@ -1,198 +0,0 @@
-from typing import Optional
-import torch
-from torch import Tensor
-
-from examples.simultaneous_translation.utils.functions import (
-    exclusive_cumprod,
-    prob_check,
-    moving_sum,
-)
-
-
-def expected_alignment_from_p_choose(
-    p_choose: Tensor,
-    padding_mask: Optional[Tensor] = None,
-    eps: float = 1e-6
-):
-    """
-    Calculating expected alignment for from stepwise probability
-
-    Reference:
-    Online and Linear-Time Attention by Enforcing Monotonic Alignments
-    https://arxiv.org/pdf/1704.00784.pdf
-
-    q_ij = (1 − p_{ij−1})q_{ij−1} + a+{i−1j}
-    a_ij = p_ij q_ij
-
-    Parallel solution:
-    ai = p_i * cumprod(1 − pi) * cumsum(a_i / cumprod(1 − pi))
-
-    ============================================================
-    Expected input size
-    p_choose: bsz, tgt_len, src_len
-    """
-    prob_check(p_choose)
-
-    # p_choose: bsz, tgt_len, src_len
-    bsz, tgt_len, src_len = p_choose.size()
-    dtype = p_choose.dtype
-
-    p_choose = p_choose.float()
-
-    if padding_mask is not None:
-        p_choose = p_choose.masked_fill(padding_mask.unsqueeze(1), 0.0)
-
-    # cumprod_1mp : bsz, tgt_len, src_len
-    cumprod_1mp = exclusive_cumprod(1 - p_choose, dim=2, eps=eps)
-    cumprod_1mp_clamp = torch.clamp(cumprod_1mp, eps, 1.0)
-
-    alpha_0 = p_choose.new_zeros([bsz, 1, src_len])
-    alpha_0[:, :, 0] = 1.0
-
-    previous_alpha = [alpha_0]
-
-    for i in range(tgt_len):
-        # p_choose: bsz , tgt_len, src_len
-        # cumprod_1mp_clamp : bsz, tgt_len, src_len
-        # previous_alpha[i]: bsz, 1, src_len
-        # alpha_i: bsz, src_len
-        alpha_i = (
-            p_choose[:, i]
-            * cumprod_1mp[:, i]
-            * torch.cumsum(
-                previous_alpha[i][:, 0] / cumprod_1mp_clamp[:, i], dim=1
-            )
-        ).clamp(0, 1.0)
-
-        previous_alpha.append(alpha_i.unsqueeze(1))
-
-    # alpha: bsz * num_heads, tgt_len, src_len
-    alpha = torch.cat(previous_alpha[1:], dim=1)
-
-    # Mix precision to prevent overflow for fp16
-    alpha = alpha.type(dtype)
-
-    prob_check(alpha)
-
-    return alpha
-
-
-def expected_soft_attention(
-    alpha: Tensor,
-    soft_energy: Tensor,
-    padding_mask: Optional[Tensor] = None,
-    chunk_size: Optional[int] = None,
-    eps: float = 1e-10
-):
-    """
-    Function to compute expected soft attention for
-    monotonic infinite lookback attention from
-    expected alignment and soft energy.
-
-    Reference:
-    Monotonic Chunkwise Attention
-    https://arxiv.org/abs/1712.05382
-
-    Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
-    https://arxiv.org/abs/1906.05218
-
-    alpha: bsz, tgt_len, src_len
-    soft_energy: bsz, tgt_len, src_len
-    padding_mask: bsz, src_len
-    left_padding: bool
-    """
-    if padding_mask is not None:
-        alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0.0)
-        soft_energy = soft_energy.masked_fill(
-            padding_mask.unsqueeze(1), -float("inf")
-        )
-
-    prob_check(alpha)
-
-    dtype = alpha.dtype
-
-    alpha = alpha.float()
-    soft_energy = soft_energy.float()
-
-    soft_energy = soft_energy - soft_energy.max(dim=2, keepdim=True)[0]
-    exp_soft_energy = torch.exp(soft_energy) + eps
-
-    if chunk_size is not None:
-        # Chunkwise
-        beta = (
-            exp_soft_energy
-            * moving_sum(
-                alpha / (eps + moving_sum(exp_soft_energy, chunk_size, 1)),
-                1, chunk_size
-            )
-        )
-    else:
-        # Infinite lookback
-        # Notice that infinite lookback is a special case of chunkwise
-        # where chunksize = inf
-        inner_items = alpha / (eps + torch.cumsum(exp_soft_energy, dim=2))
-
-        beta = (
-            exp_soft_energy
-            * torch.cumsum(inner_items.flip(dims=[2]), dim=2)
-            .flip(dims=[2])
-        )
-
-    if padding_mask is not None:
-        beta = beta.masked_fill(
-            padding_mask.unsqueeze(1).to(torch.bool), 0.0)
-
-    # Mix precision to prevent overflow for fp16
-    beta = beta.type(dtype)
-
-    beta = beta.clamp(0, 1)
-
-    prob_check(beta)
-
-    return beta
-
-
-def mass_preservation(
-    alpha: Tensor,
-    padding_mask: Optional[Tensor] = None,
-    left_padding: bool = False
-):
-    """
-    Function to compute the mass perservation for alpha.
-    This means that the residual weights of alpha will be assigned
-    to the last token.
-
-    Reference:
-    Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
-    https://arxiv.org/abs/1906.05218
-
-    alpha: bsz, tgt_len, src_len
-    padding_mask: bsz, src_len
-    left_padding: bool
-    """
-
-    prob_check(alpha)
-
-    if padding_mask is not None:
-        if not left_padding:
-            assert not padding_mask[:, 0].any(), (
-                "Find padding on the beginning of the sequence."
-            )
-        alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0.0)
-
-    if left_padding or padding_mask is None:
-        residuals = 1 - alpha[:, :, :-1].sum(dim=-1).clamp(0, 1)
-        alpha[:, :, -1] = residuals
-    else:
-        # right padding
-        _, tgt_len, src_len = alpha.size()
-        residuals = 1 - alpha.sum(dim=-1, keepdim=True).clamp(0, 1)
-        src_lens = src_len - padding_mask.sum(dim=1, keepdim=True)
-        src_lens = src_lens.expand(-1, tgt_len).contiguous()
-        # add back the last value
-        residuals += alpha.gather(2, src_lens.unsqueeze(2) - 1)
-        alpha = alpha.scatter(2, src_lens.unsqueeze(2) - 1, residuals)
-
-        prob_check(alpha)
-
-    return alpha
diff --git a/spaces/mshukor/UnIVAL/run_scripts/averaging/ratatouille/eval/eval_caption.sh b/spaces/mshukor/UnIVAL/run_scripts/averaging/ratatouille/eval/eval_caption.sh
deleted file mode 100644
index 5b36bbd8ed876789b73c838ea09150e3d3571029..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/run_scripts/averaging/ratatouille/eval/eval_caption.sh
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/usr/bin/env bash
-
-# The port for communication. Note that if you want to run multiple tasks on the same machine,
-# you need to specify different port numbers.
-# Number of GPUs per GPU worker
-export GPUS_PER_NODE=8
-# Number of GPU workers, for single-worker training, please set to 1
-export NUM_NODES=$SLURM_NNODES
-# The ip address of the rank-0 worker, for single-worker training, please set to localhost
-master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_ADDR=$master_addr
-
-# The port for communication
-export MASTER_PORT=12350
-# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
-export RANK=$SLURM_NODEID
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "RANK :$RANK"
-echo "NUM_NODES :$NUM_NODES"
-echo "GPUS_PER_NODE :$GPUS_PER_NODE"
-
-export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/
-
-echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH"
-
-num_workers=0
-
-
-
-
-
-
-ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival
-base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data
-base_log_dir=/work/NAT/gda2204/mshukor/logs
-
-
-
-
-bpe_dir=${ofa_dir}/utils/BPE
-user_dir=${ofa_dir}/ofa_module
-
-
-data_dir=${base_data_dir}/ofa/caption_data
-split=test
-data=${data_dir}/caption_${split}.tsv # caption_val caption_test
-
-selected_cols=1,4,2
-
-
-image_encoder_name=resnet #vit_base_patch16_224
-
-
-
-
-
-exp_name=eval_caption_base_best_caption_stage_1_caption_ratacapgroundsnlivqaofapt
-path=${base_log_dir}/ofa/pretrained_models/average_models/caption_ratacapgroundsnlivqaofapt.pt
-
-
-
-echo ${path}
-result_path=${base_log_dir}/ofa/results/caption/${exp_name}_${split}
-mkdir ${result_path}
-
-
-
-
-
-python3 -m torch.distributed.launch \
-    --nnodes=${NUM_NODES} \
-    --nproc_per_node=${GPUS_PER_NODE} \
-    --master_port=${MASTER_PORT} \
-    --node_rank=${RANK} \
-    --master_addr=${MASTER_ADDR} \
-    --use_env ${ofa_dir}/evaluate.py \
-    ${data} \
-    --path=${path} \
-    --user-dir=${user_dir} \
-    --task=caption \
-    --batch-size=16 \
-    --log-format=simple --log-interval=10 \
-    --seed=7 \
-    --gen-subset=${split} \
-    --results-path=${result_path} \
-    --beam=5 \
-    --max-len-b=16 \
-    --no-repeat-ngram-size=3 \
-    --fp16 \
-    --num-workers=0 \
-    --model-overrides="{\"data\":\"${data}\",\"bpe_dir\":\"${bpe_dir}\",\"eval_cider\":False,\"selected_cols\":\"${selected_cols}\"}" \
-    --strict
-
-python ${ofa_dir}/run_scripts/caption/coco_eval.py ${result_path}/test_predict.json ${data_dir}/test_caption_coco_format.json
-
diff --git a/spaces/mthsk/sovits-100orangejuice/inference/infer_tool.py b/spaces/mthsk/sovits-100orangejuice/inference/infer_tool.py
deleted file mode 100644
index fed81f5abb6f2f525af616171ee9838ae341cb5f..0000000000000000000000000000000000000000
--- a/spaces/mthsk/sovits-100orangejuice/inference/infer_tool.py
+++ /dev/null
@@ -1,324 +0,0 @@
-import hashlib
-import io
-import json
-import logging
-import os
-import time
-from pathlib import Path
-from inference import slicer
-
-import librosa
-import numpy as np
-# import onnxruntime
-import parselmouth
-import soundfile
-import torch
-import torchaudio
-
-import cluster
-from hubert import hubert_model
-import utils
-from models import SynthesizerTrn
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-
-def read_temp(file_name):
-    if not os.path.exists(file_name):
-        with open(file_name, "w") as f:
-            f.write(json.dumps({"info": "temp_dict"}))
-        return {}
-    else:
-        try:
-            with open(file_name, "r") as f:
-                data = f.read()
-            data_dict = json.loads(data)
-            if os.path.getsize(file_name) > 50 * 1024 * 1024:
-                f_name = file_name.replace("\\", "/").split("/")[-1]
-                print(f"clean {f_name}")
-                for wav_hash in list(data_dict.keys()):
-                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
-                        del data_dict[wav_hash]
-        except Exception as e:
-            print(e)
-            print(f"{file_name} error,auto rebuild file")
-            data_dict = {"info": "temp_dict"}
-        return data_dict
-
-
-def write_temp(file_name, data):
-    with open(file_name, "w") as f:
-        f.write(json.dumps(data))
-
-
-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
-
-
-def format_wav(audio_path):
-    if Path(audio_path).suffix == '.wav':
-        return
-    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
-    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
-
-
-def get_end_file(dir_path, end):
-    file_lists = []
-    for root, dirs, files in os.walk(dir_path):
-        files = [f for f in files if f[0] != '.']
-        dirs[:] = [d for d in dirs if d[0] != '.']
-        for f_file in files:
-            if f_file.endswith(end):
-                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
-    return file_lists
-
-
-def get_md5(content):
-    return hashlib.new("md5", content).hexdigest()
-
-def fill_a_to_b(a, b):
-    if len(a) < len(b):
-        for _ in range(0, len(b) - len(a)):
-            a.append(a[0])
-
-def mkdir(paths: list):
-    for path in paths:
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-def pad_array(arr, target_length):
-    current_length = arr.shape[0]
-    if current_length >= target_length:
-        return arr
-    else:
-        pad_width = target_length - current_length
-        pad_left = pad_width // 2
-        pad_right = pad_width - pad_left
-        padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
-        return padded_arr
-    
-def split_list_by_n(list_collection, n, pre=0):
-    for i in range(0, len(list_collection), n):
-        yield list_collection[i-pre if i-pre>=0 else i: i + n]
-
-
-class F0FilterException(Exception):
-    pass
-
-class Svc(object):
-    def __init__(self, net_g_path, config_path,
-                 device=None,
-                 cluster_model_path="logs/44k/kmeans_10000.pt"):
-        self.net_g_path = net_g_path
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.net_g_ms = None
-        self.hps_ms = utils.get_hparams_from_file(config_path)
-        self.target_sample = self.hps_ms.data.sampling_rate
-        self.hop_size = self.hps_ms.data.hop_length
-        self.spk2id = self.hps_ms.spk
-        # 加载hubert
-        self.hubert_model = utils.get_hubert_model().to(self.dev)
-        self.load_model()
-        if os.path.exists(cluster_model_path):
-            self.cluster_model = cluster.get_cluster_model(cluster_model_path)
-
-    def load_model(self):
-        # 获取模型配置
-        self.net_g_ms = SynthesizerTrn(
-            self.hps_ms.data.filter_length // 2 + 1,
-            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-            **self.hps_ms.model)
-        _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            _ = self.net_g_ms.half().eval().to(self.dev)
-        else:
-            _ = self.net_g_ms.eval().to(self.dev)
-
-
-
-    def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker, f0_filter ,F0_mean_pooling):
-
-        wav, sr = librosa.load(in_path, sr=self.target_sample)
-
-        if F0_mean_pooling == True:
-            f0, uv = utils.compute_f0_uv_torchcrepe(torch.FloatTensor(wav), sampling_rate=self.target_sample, hop_length=self.hop_size,device=self.dev)
-            if f0_filter and sum(f0) == 0:
-                raise F0FilterException("未检测到人声")
-            f0 = torch.FloatTensor(list(f0))
-            uv = torch.FloatTensor(list(uv))
-        if F0_mean_pooling == False:
-            f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
-            if f0_filter and sum(f0) == 0:
-                raise F0FilterException("未检测到人声")
-            f0, uv = utils.interpolate_f0(f0)
-            f0 = torch.FloatTensor(f0)
-            uv = torch.FloatTensor(uv)
-
-        f0 = f0 * 2 ** (tran / 12)
-        f0 = f0.unsqueeze(0).to(self.dev)
-        uv = uv.unsqueeze(0).to(self.dev)
-
-        wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
-        wav16k = torch.from_numpy(wav16k).to(self.dev)
-        c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
-        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
-
-        if cluster_infer_ratio !=0:
-            cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
-            cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
-            c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
-
-        c = c.unsqueeze(0)
-        return c, f0, uv
-
-    def infer(self, speaker, tran, raw_path,
-              cluster_infer_ratio=0,
-              auto_predict_f0=False,
-              noice_scale=0.4,
-              f0_filter=False,
-              F0_mean_pooling=False
-              ):
-
-        speaker_id = self.spk2id.__dict__.get(speaker)
-        if not speaker_id and type(speaker) is int:
-            if len(self.spk2id.__dict__) >= speaker:
-                speaker_id = speaker
-        sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
-        c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter,F0_mean_pooling)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            c = c.half()
-        with torch.no_grad():
-            start = time.time()
-            audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float()
-            use_time = time.time() - start
-            print("vits use time:{}".format(use_time))
-        return audio, audio.shape[-1]
-
-    def clear_empty(self):
-        # 清理显存
-        torch.cuda.empty_cache()
-
-    def slice_inference(self,
-                        raw_audio_path,
-                        spk,
-                        tran,
-                        slice_db,
-                        cluster_infer_ratio,
-                        auto_predict_f0,
-                        noice_scale,
-                        pad_seconds=0.5,
-                        clip_seconds=0,
-                        lg_num=0,
-                        lgr_num =0.75,
-                        F0_mean_pooling = False
-                        ):
-        wav_path = raw_audio_path
-        chunks = slicer.cut(wav_path, db_thresh=slice_db)
-        audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
-        per_size = int(clip_seconds*audio_sr)
-        lg_size = int(lg_num*audio_sr)
-        lg_size_r = int(lg_size*lgr_num)
-        lg_size_c_l = (lg_size-lg_size_r)//2
-        lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
-        lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
-        
-        audio = []
-        for (slice_tag, data) in audio_data:
-            print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
-            # padd
-            length = int(np.ceil(len(data) / audio_sr * self.target_sample))
-            if slice_tag:
-                print('jump empty segment')
-                _audio = np.zeros(length)
-                audio.extend(list(pad_array(_audio, length)))
-                continue
-            if per_size != 0:
-                datas = split_list_by_n(data, per_size,lg_size)
-            else:
-                datas = [data]
-            for k,dat in enumerate(datas):
-                per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
-                if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
-                # padd
-                pad_len = int(audio_sr * pad_seconds)
-                dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
-                raw_path = io.BytesIO()
-                soundfile.write(raw_path, dat, audio_sr, format="wav")
-                raw_path.seek(0)
-                out_audio, out_sr = self.infer(spk, tran, raw_path,
-                                                    cluster_infer_ratio=cluster_infer_ratio,
-                                                    auto_predict_f0=auto_predict_f0,
-                                                    noice_scale=noice_scale,
-                                                    F0_mean_pooling = F0_mean_pooling
-                                                    )
-                _audio = out_audio.cpu().numpy()
-                pad_len = int(self.target_sample * pad_seconds)
-                _audio = _audio[pad_len:-pad_len]
-                _audio = pad_array(_audio, per_length)
-                if lg_size!=0 and k!=0:
-                    lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:]
-                    lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r]  if lgr_num != 1 else _audio[0:lg_size]
-                    lg_pre = lg1*(1-lg)+lg2*lg
-                    audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size]
-                    audio.extend(lg_pre)
-                    _audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:]
-                audio.extend(list(_audio))
-        return np.array(audio)
-
-class RealTimeVC:
-    def __init__(self):
-        self.last_chunk = None
-        self.last_o = None
-        self.chunk_len = 16000  # 区块长度
-        self.pre_len = 3840  # 交叉淡化长度，640的倍数
-
-    """输入输出都是1维numpy 音频波形数组"""
-
-    def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path,
-                cluster_infer_ratio=0,
-                auto_predict_f0=False,
-                noice_scale=0.4,
-                f0_filter=False):
-
-        import maad
-        audio, sr = torchaudio.load(input_wav_path)
-        audio = audio.cpu().numpy()[0]
-        temp_wav = io.BytesIO()
-        if self.last_chunk is None:
-            input_wav_path.seek(0)
-
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path,
-                                        cluster_infer_ratio=cluster_infer_ratio,
-                                        auto_predict_f0=auto_predict_f0,
-                                        noice_scale=noice_scale,
-                                        f0_filter=f0_filter)
-
-            audio = audio.cpu().numpy()
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return audio[-self.chunk_len:]
-        else:
-            audio = np.concatenate([self.last_chunk, audio])
-            soundfile.write(temp_wav, audio, sr, format="wav")
-            temp_wav.seek(0)
-
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav,
-                                        cluster_infer_ratio=cluster_infer_ratio,
-                                        auto_predict_f0=auto_predict_f0,
-                                        noice_scale=noice_scale,
-                                        f0_filter=f0_filter)
-
-            audio = audio.cpu().numpy()
-            ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return ret[self.chunk_len:2 * self.chunk_len]
diff --git a/spaces/multimodalart/Tune-A-Video-Training-UI-poli/app_upload.py b/spaces/multimodalart/Tune-A-Video-Training-UI-poli/app_upload.py
deleted file mode 100644
index fe0c20349acfed32080feeb05f385ef980692c84..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/Tune-A-Video-Training-UI-poli/app_upload.py
+++ /dev/null
@@ -1,102 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import pathlib
-
-import gradio as gr
-import slugify
-
-from constants import MODEL_LIBRARY_ORG_NAME, UploadTarget
-from uploader import Uploader
-from utils import find_exp_dirs
-
-
-class ModelUploader(Uploader):
-    def upload_model(
-        self,
-        folder_path: str,
-        repo_name: str,
-        upload_to: str,
-        private: bool,
-        delete_existing_repo: bool,
-        input_token: str | None = None,
-    ) -> str:
-        if not folder_path:
-            raise ValueError
-        if not repo_name:
-            repo_name = pathlib.Path(folder_path).name
-        repo_name = slugify.slugify(repo_name)
-
-        if upload_to == UploadTarget.PERSONAL_PROFILE.value:
-            organization = ''
-        elif upload_to == UploadTarget.MODEL_LIBRARY.value:
-            organization = MODEL_LIBRARY_ORG_NAME
-        else:
-            raise ValueError
-
-        return self.upload(folder_path,
-                           repo_name,
-                           organization=organization,
-                           private=private,
-                           delete_existing_repo=delete_existing_repo,
-                           input_token=input_token)
-
-
-def load_local_model_list() -> dict:
-    choices = find_exp_dirs()
-    return gr.update(choices=choices, value=choices[0] if choices else None)
-
-
-def create_upload_demo(hf_token: str | None) -> gr.Blocks:
-    uploader = ModelUploader(hf_token)
-    model_dirs = find_exp_dirs()
-
-    with gr.Blocks() as demo:
-        with gr.Box():
-            gr.Markdown('Local Models')
-            reload_button = gr.Button('Reload Model List')
-            model_dir = gr.Dropdown(
-                label='Model names',
-                choices=model_dirs,
-                value=model_dirs[0] if model_dirs else None)
-        with gr.Box():
-            gr.Markdown('Upload Settings')
-            with gr.Row():
-                use_private_repo = gr.Checkbox(label='Private', value=True)
-                delete_existing_repo = gr.Checkbox(
-                    label='Delete existing repo of the same name', value=False)
-            upload_to = gr.Radio(label='Upload to',
-                                 choices=[_.value for _ in UploadTarget],
-                                 value=UploadTarget.MODEL_LIBRARY.value)
-            model_name = gr.Textbox(label='Model Name')
-        upload_button = gr.Button('Upload')
-        gr.Markdown(f'''
-            - You can upload your trained model to your personal profile (i.e. https://huggingface.co/{{your_username}}/{{model_name}}) or to the public [Tune-A-Video Library](https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}) (i.e. https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}/{{model_name}}).
-            ''')
-        with gr.Box():
-            gr.Markdown('Output message')
-            output_message = gr.Markdown()
-
-        reload_button.click(fn=load_local_model_list,
-                            inputs=None,
-                            outputs=model_dir)
-        upload_button.click(fn=uploader.upload_model,
-                            inputs=[
-                                model_dir,
-                                model_name,
-                                upload_to,
-                                use_private_repo,
-                                delete_existing_repo,
-                            ],
-                            outputs=output_message)
-
-    return demo
-
-
-if __name__ == '__main__':
-    import os
-
-    hf_token = os.getenv('HF_TOKEN')
-    demo = create_upload_demo(hf_token)
-    demo.queue(max_size=1).launch(share=False)
diff --git a/spaces/mygyasir/fast_diffusion/README.md b/spaces/mygyasir/fast_diffusion/README.md
deleted file mode 100644
index c481aff5e24017b7e782f87244b5cd59a575e320..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/fast_diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 390 Models Fast Diffusion
-emoji: 👩‍🎨👨‍🎨
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: true
-duplicated_from: Yntec/fast_diffusion
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/myrad01/Inpaint-Anything/app.py b/spaces/myrad01/Inpaint-Anything/app.py
deleted file mode 100644
index 2463f20871ad057325389168043efe13e4599025..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/app.py
+++ /dev/null
@@ -1,231 +0,0 @@
-import os
-import sys
-# sys.path.append(os.path.abspath(os.path.dirname(os.getcwd())))
-# os.chdir("../")
-import gradio as gr
-import numpy as np
-from pathlib import Path
-from matplotlib import pyplot as plt
-import torch
-import tempfile
-from lama_inpaint import inpaint_img_with_lama, build_lama_model, inpaint_img_with_builded_lama
-from utils import load_img_to_array, save_array_to_img, dilate_mask, \
-    show_mask, show_points
-from PIL import Image
-sys.path.insert(0, str(Path(__file__).resolve().parent / "third_party" / "segment-anything"))
-from segment_anything import SamPredictor, sam_model_registry
-import argparse
-
-def setup_args(parser):
-    parser.add_argument(
-        "--lama_config", type=str,
-        default="./third_party/lama/configs/prediction/default.yaml",
-        help="The path to the config file of lama model. "
-             "Default: the config of big-lama",
-    )
-    parser.add_argument(
-        "--lama_ckpt", type=str,
-        default="pretrained_models/big-lama",
-        help="The path to the lama checkpoint.",
-    )
-    parser.add_argument(
-        "--sam_ckpt", type=str,
-        default="./pretrained_models/sam_vit_h_4b8939.pth",
-        help="The path to the SAM checkpoint to use for mask generation.",
-    )
-def mkstemp(suffix, dir=None):
-    fd, path = tempfile.mkstemp(suffix=f"{suffix}", dir=dir)
-    os.close(fd)
-    return Path(path)
-
-
-def get_sam_feat(img):
-    model['sam'].set_image(img)
-    features = model['sam'].features 
-    orig_h = model['sam'].orig_h 
-    orig_w = model['sam'].orig_w 
-    input_h = model['sam'].input_h 
-    input_w = model['sam'].input_w 
-    model['sam'].reset_image()
-    return features, orig_h, orig_w, input_h, input_w
-
- 
-def get_masked_img(img, w, h, features, orig_h, orig_w, input_h, input_w, dilate_kernel_size):
-    point_coords = [w, h]
-    point_labels = [1]
-
-    model['sam'].is_image_set = True
-    model['sam'].features = features
-    model['sam'].orig_h = orig_h
-    model['sam'].orig_w = orig_w
-    model['sam'].input_h = input_h
-    model['sam'].input_w = input_w
-
-    # model['sam'].set_image(img) # todo : update here for accelerating
-    masks, _, _ = model['sam'].predict(
-        point_coords=np.array([point_coords]),
-        point_labels=np.array(point_labels),
-        multimask_output=True,
-    )
-
-    masks = masks.astype(np.uint8) * 255
-
-    # dilate mask to avoid unmasked edge effect
-    if dilate_kernel_size is not None:
-        masks = [dilate_mask(mask, dilate_kernel_size) for mask in masks]
-    else:
-        masks = [mask for mask in masks]
-
-    figs = []
-    for idx, mask in enumerate(masks):
-        # save the pointed and masked image
-        tmp_p = mkstemp(".png")
-        dpi = plt.rcParams['figure.dpi']
-        height, width = img.shape[:2]
-        fig = plt.figure(figsize=(width/dpi/0.77, height/dpi/0.77))
-        plt.imshow(img)
-        plt.axis('off')
-        show_points(plt.gca(), [point_coords], point_labels,
-                    size=(width*0.04)**2)
-        show_mask(plt.gca(), mask, random_color=False)
-        plt.tight_layout()
-        plt.savefig(tmp_p, bbox_inches='tight', pad_inches=0)
-        figs.append(fig)
-        plt.close()
-    return *figs, *masks
-
-
-def get_inpainted_img(img, mask0, mask1, mask2):
-    lama_config = args.lama_config
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    out = []
-    for mask in [mask0, mask1, mask2]:
-        if len(mask.shape)==3:
-            mask = mask[:,:,0]
-        img_inpainted = inpaint_img_with_builded_lama(
-            model['lama'], img, mask, lama_config, device=device)
-        out.append(img_inpainted)
-    return out
-
-
-# get args 
-parser = argparse.ArgumentParser()
-setup_args(parser)
-args = parser.parse_args(sys.argv[1:])
-# build models
-model = {}
-# build the sam model
-model_type="vit_h"
-ckpt_p=args.sam_ckpt
-model_sam = sam_model_registry[model_type](checkpoint=ckpt_p)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_sam.to(device=device)
-model['sam'] = SamPredictor(model_sam)
-
-# build the lama model
-lama_config = args.lama_config
-lama_ckpt = args.lama_ckpt
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model['lama'] = build_lama_model(lama_config, lama_ckpt, device=device)
-
-button_size = (100,50)
-with gr.Blocks() as demo:
-    features = gr.State(None)
-    orig_h = gr.State(None)
-    orig_w = gr.State(None)
-    input_h = gr.State(None)
-    input_w = gr.State(None)
-
-    with gr.Row().style(mobile_collapse=False, equal_height=True):
-        with gr.Column(variant="panel"):
-            with gr.Row():
-                gr.Markdown("## Input Image")
-            with gr.Row():
-                img = gr.Image(label="Input Image").style(height="200px")
-        with gr.Column(variant="panel"):
-            with gr.Row():
-                gr.Markdown("## Pointed Image")
-            with gr.Row():
-                img_pointed = gr.Plot(label='Pointed Image')
-        with gr.Column(variant="panel"):
-            with gr.Row():
-                gr.Markdown("## Control Panel")
-            with gr.Row():
-                w = gr.Number(label="Point Coordinate W")
-                h = gr.Number(label="Point Coordinate H")
-            dilate_kernel_size = gr.Slider(label="Dilate Kernel Size", minimum=0, maximum=100, step=1, value=15)
-            sam_mask = gr.Button("Predict Mask", variant="primary").style(full_width=True, size="sm")
-            lama = gr.Button("Inpaint Image", variant="primary").style(full_width=True, size="sm")
-            clear_button_image = gr.Button(value="Reset", label="Reset", variant="secondary").style(full_width=True, size="sm")
-
-    # todo: maybe we can delete this row, for it's unnecessary to show the original mask for customers
-    with gr.Row(variant="panel"):
-        with gr.Column():
-            with gr.Row():
-                gr.Markdown("## Segmentation Mask")
-            with gr.Row():
-                mask_0 = gr.outputs.Image(type="numpy", label="Segmentation Mask 0").style(height="200px")
-                mask_1 = gr.outputs.Image(type="numpy", label="Segmentation Mask 1").style(height="200px")
-                mask_2 = gr.outputs.Image(type="numpy", label="Segmentation Mask 2").style(height="200px")
-
-    with gr.Row(variant="panel"):
-        with gr.Column():
-            with gr.Row():
-                gr.Markdown("## Image with Mask")
-            with gr.Row():
-                img_with_mask_0 = gr.Plot(label="Image with Segmentation Mask 0")
-                img_with_mask_1 = gr.Plot(label="Image with Segmentation Mask 1")
-                img_with_mask_2 = gr.Plot(label="Image with Segmentation Mask 2")
-
-    with gr.Row(variant="panel"):
-        with gr.Column():
-            with gr.Row():
-                gr.Markdown("## Image Removed with Mask")
-            with gr.Row():
-                img_rm_with_mask_0 = gr.outputs.Image(
-                    type="numpy", label="Image Removed with Segmentation Mask 0").style(height="200px")
-                img_rm_with_mask_1 = gr.outputs.Image(
-                    type="numpy", label="Image Removed with Segmentation Mask 1").style(height="200px")
-                img_rm_with_mask_2 = gr.outputs.Image(
-                    type="numpy", label="Image Removed with Segmentation Mask 2").style(height="200px")
-
-
-    def get_select_coords(img, evt: gr.SelectData):
-        dpi = plt.rcParams['figure.dpi']
-        height, width = img.shape[:2]
-        fig = plt.figure(figsize=(width/dpi/0.77, height/dpi/0.77))
-        plt.imshow(img)
-        plt.axis('off')
-        plt.tight_layout()
-        show_points(plt.gca(), [[evt.index[0], evt.index[1]]], [1],
-                    size=(width*0.04)**2)
-        return evt.index[0], evt.index[1], fig
-
-    img.select(get_select_coords, [img], [w, h, img_pointed])
-    img.upload(get_sam_feat, [img], [features, orig_h, orig_w, input_h, input_w])
-
-    sam_mask.click(
-        get_masked_img,
-        [img, w, h, features, orig_h, orig_w, input_h, input_w, dilate_kernel_size],
-        [img_with_mask_0, img_with_mask_1, img_with_mask_2, mask_0, mask_1, mask_2]
-    )
-
-    lama.click(
-        get_inpainted_img,
-        [img, mask_0, mask_1, mask_2],
-        [img_rm_with_mask_0, img_rm_with_mask_1, img_rm_with_mask_2]
-    )
-
-
-    def reset(*args):
-        return [None for _ in args]
-
-    clear_button_image.click(
-        reset,
-        [img, features, img_pointed, w, h, mask_0, mask_1, mask_2, img_with_mask_0, img_with_mask_1, img_with_mask_2, img_rm_with_mask_0, img_rm_with_mask_1, img_rm_with_mask_2],
-        [img, features, img_pointed, w, h, mask_0, mask_1, mask_2, img_with_mask_0, img_with_mask_1, img_with_mask_2, img_rm_with_mask_0, img_rm_with_mask_1, img_rm_with_mask_2]
-    )
-
-if __name__ == "__main__":
-    demo.launch()
-    
\ No newline at end of file
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/CODE_OF_CONDUCT.md b/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/CODE_OF_CONDUCT.md
deleted file mode 100644
index 08b500a221857ec3f451338e80b4a9ab1173a1af..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-  advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-  address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-
-This Code of Conduct also applies outside the project spaces when there is a
-reasonable belief that an individual's behavior may have a negative impact on
-the project or its community.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at <opensource-conduct@fb.com>. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/scripts/export_onnx_model.py b/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/scripts/export_onnx_model.py
deleted file mode 100644
index 5c6f8389ea96fc871e4a0ff36a30fa7b9fcf4c90..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/scripts/export_onnx_model.py
+++ /dev/null
@@ -1,201 +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 torch
-
-from segment_anything import sam_model_registry
-from segment_anything.utils.onnx import SamOnnxModel
-
-import argparse
-import warnings
-
-try:
-    import onnxruntime  # type: ignore
-
-    onnxruntime_exists = True
-except ImportError:
-    onnxruntime_exists = False
-
-parser = argparse.ArgumentParser(
-    description="Export the SAM prompt encoder and mask decoder to an ONNX model."
-)
-
-parser.add_argument(
-    "--checkpoint", type=str, required=True, help="The path to the SAM model checkpoint."
-)
-
-parser.add_argument(
-    "--output", type=str, required=True, help="The filename to save the ONNX model to."
-)
-
-parser.add_argument(
-    "--model-type",
-    type=str,
-    required=True,
-    help="In ['default', 'vit_h', 'vit_l', 'vit_b']. Which type of SAM model to export.",
-)
-
-parser.add_argument(
-    "--return-single-mask",
-    action="store_true",
-    help=(
-        "If true, the exported ONNX model will only return the best mask, "
-        "instead of returning multiple masks. For high resolution images "
-        "this can improve runtime when upscaling masks is expensive."
-    ),
-)
-
-parser.add_argument(
-    "--opset",
-    type=int,
-    default=17,
-    help="The ONNX opset version to use. Must be >=11",
-)
-
-parser.add_argument(
-    "--quantize-out",
-    type=str,
-    default=None,
-    help=(
-        "If set, will quantize the model and save it with this name. "
-        "Quantization is performed with quantize_dynamic from onnxruntime.quantization.quantize."
-    ),
-)
-
-parser.add_argument(
-    "--gelu-approximate",
-    action="store_true",
-    help=(
-        "Replace GELU operations with approximations using tanh. Useful "
-        "for some runtimes that have slow or unimplemented erf ops, used in GELU."
-    ),
-)
-
-parser.add_argument(
-    "--use-stability-score",
-    action="store_true",
-    help=(
-        "Replaces the model's predicted mask quality score with the stability "
-        "score calculated on the low resolution masks using an offset of 1.0. "
-    ),
-)
-
-parser.add_argument(
-    "--return-extra-metrics",
-    action="store_true",
-    help=(
-        "The model will return five results: (masks, scores, stability_scores, "
-        "areas, low_res_logits) instead of the usual three. This can be "
-        "significantly slower for high resolution outputs."
-    ),
-)
-
-
-def run_export(
-    model_type: str,
-    checkpoint: str,
-    output: str,
-    opset: int,
-    return_single_mask: bool,
-    gelu_approximate: bool = False,
-    use_stability_score: bool = False,
-    return_extra_metrics=False,
-):
-    print("Loading model...")
-    sam = sam_model_registry[model_type](checkpoint=checkpoint)
-
-    onnx_model = SamOnnxModel(
-        model=sam,
-        return_single_mask=return_single_mask,
-        use_stability_score=use_stability_score,
-        return_extra_metrics=return_extra_metrics,
-    )
-
-    if gelu_approximate:
-        for n, m in onnx_model.named_modules():
-            if isinstance(m, torch.nn.GELU):
-                m.approximate = "tanh"
-
-    dynamic_axes = {
-        "point_coords": {1: "num_points"},
-        "point_labels": {1: "num_points"},
-    }
-
-    embed_dim = sam.prompt_encoder.embed_dim
-    embed_size = sam.prompt_encoder.image_embedding_size
-    mask_input_size = [4 * x for x in embed_size]
-    dummy_inputs = {
-        "image_embeddings": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),
-        "point_coords": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),
-        "point_labels": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),
-        "mask_input": torch.randn(1, 1, *mask_input_size, dtype=torch.float),
-        "has_mask_input": torch.tensor([1], dtype=torch.float),
-        "orig_im_size": torch.tensor([1500, 2250], dtype=torch.float),
-    }
-
-    _ = onnx_model(**dummy_inputs)
-
-    output_names = ["masks", "iou_predictions", "low_res_masks"]
-
-    with warnings.catch_warnings():
-        warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
-        warnings.filterwarnings("ignore", category=UserWarning)
-        with open(output, "wb") as f:
-            print(f"Exporting onnx model to {output}...")
-            torch.onnx.export(
-                onnx_model,
-                tuple(dummy_inputs.values()),
-                f,
-                export_params=True,
-                verbose=False,
-                opset_version=opset,
-                do_constant_folding=True,
-                input_names=list(dummy_inputs.keys()),
-                output_names=output_names,
-                dynamic_axes=dynamic_axes,
-            )
-
-    if onnxruntime_exists:
-        ort_inputs = {k: to_numpy(v) for k, v in dummy_inputs.items()}
-        # set cpu provider default
-        providers = ["CPUExecutionProvider"]
-        ort_session = onnxruntime.InferenceSession(output, providers=providers)
-        _ = ort_session.run(None, ort_inputs)
-        print("Model has successfully been run with ONNXRuntime.")
-
-
-def to_numpy(tensor):
-    return tensor.cpu().numpy()
-
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-    run_export(
-        model_type=args.model_type,
-        checkpoint=args.checkpoint,
-        output=args.output,
-        opset=args.opset,
-        return_single_mask=args.return_single_mask,
-        gelu_approximate=args.gelu_approximate,
-        use_stability_score=args.use_stability_score,
-        return_extra_metrics=args.return_extra_metrics,
-    )
-
-    if args.quantize_out is not None:
-        assert onnxruntime_exists, "onnxruntime is required to quantize the model."
-        from onnxruntime.quantization import QuantType  # type: ignore
-        from onnxruntime.quantization.quantize import quantize_dynamic  # type: ignore
-
-        print(f"Quantizing model and writing to {args.quantize_out}...")
-        quantize_dynamic(
-            model_input=args.output,
-            model_output=args.quantize_out,
-            optimize_model=True,
-            per_channel=False,
-            reduce_range=False,
-            weight_type=QuantType.QUInt8,
-        )
-        print("Done!")
diff --git a/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/data/__init__.py b/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/data/__init__.py
deleted file mode 100644
index f87dc45d179d82778d6187ae1ffe9a18371296e8..0000000000000000000000000000000000000000
--- a/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/data/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .EvalDataset import EvalDataset
-from .TrainDataset import TrainDataset
\ No newline at end of file
diff --git a/spaces/navervision/MLSD/demo_MLSD.py b/spaces/navervision/MLSD/demo_MLSD.py
deleted file mode 100644
index 98fed357db321cfd9d0b23b2aee38bf2e6d449d7..0000000000000000000000000000000000000000
--- a/spaces/navervision/MLSD/demo_MLSD.py
+++ /dev/null
@@ -1,275 +0,0 @@
-'''
-M-LSD
-Copyright 2021-present NAVER Corp.
-Apache License v2.0
-'''
-# for demo
-import os
-from flask import Flask, request, session, json, Response, render_template, abort, send_from_directory
-import requests
-from urllib.request import urlopen
-from io import BytesIO
-import uuid
-import cv2
-import time
-import argparse
-
-# for tflite
-import numpy as np
-from PIL import Image
-import tensorflow as tf
-
-# for square detector
-from utils import pred_squares
-
-os.environ['CUDA_VISIBLE_DEVICES'] = '' # CPU mode
-
-# flask
-app = Flask(__name__)
-logger = app.logger
-logger.info('init demo app')
-
-# config
-parser = argparse.ArgumentParser()
-
-## model parameters
-parser.add_argument('--tflite_path', default='./tflite_models/M-LSD_512_large_fp16.tflite', type=str)
-parser.add_argument('--input_size', default=512, type=int,
-                    help='The size of input images.')
-
-## LSD parameter
-parser.add_argument('--score_thr', default=0.10, type=float,
-                    help='Discard center points when the score < score_thr.')
-
-## intersection point parameters
-parser.add_argument('--outside_ratio', default=0.10, type=float,
-                    help='''Discard an intersection point 
-                    when it is located outside a line segment farther than line_length * outside_ratio.''')
-parser.add_argument('--inside_ratio', default=0.50, type=float,
-                    help='''Discard an intersection point
-                    when it is located inside a line segment farther than line_length * inside_ratio.''')
-
-## ranking boxes parameters
-parser.add_argument('--w_overlap', default=0.0, type=float,
-                    help='''When increasing w_overlap, the final box tends to overlap with
-                    the detected line segments as much as possible.''')
-parser.add_argument('--w_degree', default=1.14, type=float,
-                    help='''When increasing w_degree, the final box tends to be
-                    a parallel quadrilateral with reference to the angle of the box.''')
-parser.add_argument('--w_length', default=0.03, type=float,
-                    help='''When increasing w_length, the final box tends to be
-                    a parallel quadrilateral with reference to the length of the box.''')
-parser.add_argument('--w_area', default=1.84, type=float,
-                    help='When increasing w_area, the final box tends to be the largest one out of candidates.')
-parser.add_argument('--w_center', default=1.46, type=float,
-                    help='When increasing w_center, the final box tends to be located in the center of input image.')
-
-## flask demo parameter
-parser.add_argument('--port', default=5000, type=int,
-                    help='flask demo will be running on http://0.0.0.0:port/')
-
-
-class model_graph:
-    def __init__(self, args):
-        self.interpreter, self.input_details, self.output_details = self.load_tflite(args.tflite_path)
-        self.params = {'score': args.score_thr,'outside_ratio': args.outside_ratio,'inside_ratio': args.inside_ratio, 
-                       'w_overlap': args.w_overlap,'w_degree': args.w_degree,'w_length': args.w_length,
-                       'w_area': args.w_area,'w_center': args.w_center}
-        self.args = args
-
-
-    def load_tflite(self, tflite_path):
-        interpreter = tf.lite.Interpreter(model_path=tflite_path)
-        interpreter.allocate_tensors()
-        input_details = interpreter.get_input_details()
-        output_details = interpreter.get_output_details()
-
-        return interpreter, input_details, output_details
-
-
-    def pred_tflite(self, image):
-        segments, squares, score_array, inter_points = pred_squares(image, self.interpreter, self.input_details, self.output_details, [self.args.input_size, self.args.input_size], params=self.params)
-
-        output = {}
-        output['segments'] = segments
-        output['squares'] = squares
-        output['scores'] = score_array
-        output['inter_points'] = inter_points
-
-        return output
-
-
-    def read_image(self, image_url):
-        response = requests.get(image_url, stream=True)
-        image = np.asarray(Image.open(BytesIO(response.content)).convert('RGB'))
-        
-        max_len = 1024
-        h, w, _ = image.shape
-        org_shape = [h, w]
-        max_idx = np.argmax(org_shape)
-
-        max_val = org_shape[max_idx]
-        if max_val  > max_len:
-            min_idx = (max_idx + 1) % 2
-            ratio = max_len / max_val
-            new_min = org_shape[min_idx] * ratio
-            new_shape = [0, 0]
-            new_shape[max_idx] = 1024
-            new_shape[min_idx] = new_min
-
-            image = cv2.resize(image, (int(new_shape[1]), int(new_shape[0])), interpolation=cv2.INTER_AREA)
-
-        return image
-    
-
-    def init_resize_image(self, im, maximum_size=1024):
-        h, w, _ = im.shape
-        size = [h, w]
-        max_arg = np.argmax(size)
-        max_len = size[max_arg]
-        min_arg = max_arg - 1
-        min_len = size[min_arg]
-        if max_len < maximum_size:
-            return im
-        else:
-            ratio = maximum_size / max_len
-            max_len = max_len * ratio
-            min_len = min_len * ratio
-            size[max_arg] = int(max_len)
-            size[min_arg] = int(min_len)
-
-            im = cv2.resize(im, (size[1], size[0]), interpolation = cv2.INTER_AREA)
-
-            return im
-
-
-    def decode_image(self, session_id, rawimg):
-        dirpath = os.path.join('static/results', session_id)
-
-        if not os.path.exists(dirpath):
-            os.makedirs(dirpath)
-        save_path = os.path.join(dirpath, 'input.png')
-        input_image_url = os.path.join(dirpath, 'input.png')
-    
-        img = cv2.imdecode(np.frombuffer(rawimg, dtype='uint8'), 1)[:,:,::-1]
-        img = self.init_resize_image(img)
-        cv2.imwrite(save_path, img[:,:,::-1])
-
-        return img, input_image_url
-
-
-    def draw_output(self, image, output, save_path='test.png'):
-        color_dict = {'red': [255, 0, 0],
-                      'green': [0, 255, 0],
-                      'blue': [0, 0, 255],
-                      'cyan': [0, 255, 255],
-                      'black': [0, 0, 0],
-                      'yellow': [255, 255, 0],
-                      'dark_yellow': [200, 200, 0]}
-        
-        line_image = image.copy()
-        square_image = image.copy()
-        square_candidate_image = image.copy()
-        
-        line_thick = 5
-
-        # output > line array
-        for line in output['segments']:
-            x_start, y_start, x_end, y_end = [int(val) for val in line]
-            cv2.line(line_image, (x_start, y_start), (x_end, y_end), color_dict['red'], line_thick)
-        
-        inter_image = line_image.copy()
-               
-        for pt in output['inter_points']:
-            x, y = [int(val) for val in pt]
-            cv2.circle(inter_image, (x, y), 10, color_dict['blue'], -1)
-
-        for square in output['squares']:
-            cv2.polylines(square_candidate_image, [square.reshape([-1, 1, 2])], True, color_dict['dark_yellow'], line_thick)
-
-        for square in output['squares'][0:1]:
-            cv2.polylines(square_image, [square.reshape([-1, 1, 2])], True, color_dict['yellow'], line_thick)
-            for pt in square:
-                cv2.circle(square_image, (int(pt[0]), int(pt[1])), 10, color_dict['cyan'], -1)
-
-        '''
-        square image | square candidates image
-        inter image  | line image
-        '''
-        output_image = self.init_resize_image(square_image, 512)
-        output_image = np.concatenate([output_image, self.init_resize_image(square_candidate_image, 512)], axis=1)
-        output_image_tmp = np.concatenate([self.init_resize_image(inter_image, 512), self.init_resize_image(line_image, 512)], axis=1)
-        output_image = np.concatenate([output_image, output_image_tmp], axis=0)
-
-        cv2.imwrite(save_path, output_image[:,:,::-1])
-
-        return output_image
-
-
-    def save_output(self, session_id, input_image_url, image, output):
-        dirpath = os.path.join('static/results', session_id)
-
-        if not os.path.exists(dirpath):
-            os.makedirs(dirpath)
-        
-        save_path = os.path.join(dirpath, 'output.png')
-        self.draw_output(image, output, save_path=save_path)
-
-        output_image_url = os.path.join(dirpath, 'output.png')
-
-        rst = {}
-        rst['input_image_url'] = input_image_url
-        rst['session_id'] = session_id
-        rst['output_image_url'] = output_image_url
-
-        with open(os.path.join(dirpath, 'results.json'), 'w') as f:
-            json.dump(rst, f)
-
-
-def init_worker(args):
-    global model
-    
-    model = model_graph(args)
-
-   
-@app.route('/')
-def index():
-    return render_template('index_scan.html', session_id='dummy_session_id')
-
-
-@app.route('/', methods=['POST'])
-def index_post():
-    request_start = time.time()
-    configs = request.form
-            
-    session_id = str(uuid.uuid1())
-
-    image_url = configs['image_url'] # image_url
-    
-    if len(image_url) == 0:
-        bio = BytesIO()
-        request.files['image'].save(bio)
-        rawimg = bio.getvalue()
-        image, image_url = model.decode_image(session_id, rawimg)
-    else:
-        image = model.read_image(image_url)
-    
-    output = model.pred_tflite(image)
-    
-    model.save_output(session_id, image_url, image, output)
-
-    return render_template('index_scan.html', session_id=session_id)
-
-
-@app.route('/favicon.ico')
-def favicon():
-    return send_from_directory(os.path.join(app.root_path, 'static'),
-                               'favicon.ico', mimetype='image/vnd.microsoft.icon')
-
-
-if __name__ == '__main__':
-    args = parser.parse_args()
-
-    init_worker(args)
-
-    app.run(host='0.0.0.0', port=args.port)
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Bellamy Brothers Discography Torrents.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Bellamy Brothers Discography Torrents.md
deleted file mode 100644
index 3e2c51dcf2d66c2ae775d9666c9bbd01b881e001..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Bellamy Brothers Discography Torrents.md	
+++ /dev/null
@@ -1,21 +0,0 @@
-<br />
-<h1>How to Download the Bellamy Brothers Discography Torrents</h1>
-<p>The Bellamy Brothers are an American pop and country music duo that have been making hits since the 1970s. They have released 30 studio albums and 72 singles, including classics like "Let Your Love Flow", "If I Said You Had a Beautiful Body Would You Hold It Against Me", and "Redneck Girl". If you are a fan of their music and want to download their discography torrents, here are some tips on how to do it safely and legally.</p>
-<h2>Bellamy Brothers Discography Torrents</h2><br /><p><b><b>Download</b> &#10037;&#10037;&#10037; <a href="https://urlcod.com/2uIcu2">https://urlcod.com/2uIcu2</a></b></p><br /><br />
-<h2>What are torrents?</h2>
-<p>Torrents are files that contain information about other files that are shared by users on a peer-to-peer network. They allow you to download large amounts of data from multiple sources at once, without relying on a central server. Torrents are often used to share music, movies, games, and other media files that are otherwise hard to find or expensive to buy.</p>
-<h2>How to download torrents?</h2>
-<p>To download torrents, you need two things: a torrent client and a torrent file. A torrent client is a software that connects you to the peer-to-peer network and manages the download process. A torrent file is a small file that contains the metadata of the files you want to download, such as their names, sizes, and locations. You can find torrent files on various websites that host them, such as The Pirate Bay, Kickass Torrents, or RARBG.</p>
-<p>To download a torrent file, you simply click on the magnet link or download button on the website, and your torrent client will open it automatically. Then, you can choose where to save the files on your computer and start the download. Depending on the size of the files and the number of seeders (users who have the complete files and share them with others), the download may take from minutes to hours.</p>
-<h2>How to find the Bellamy Brothers discography torrents?</h2>
-<p>There are many websites that offer the Bellamy Brothers discography torrents, but not all of them are reliable or safe. Some may contain viruses, malware, or fake files that can harm your computer or expose your personal information. To avoid these risks, you should always use a reputable website that has positive reviews and feedback from other users. You should also check the comments section of each torrent to see if there are any issues or complaints about the quality or authenticity of the files.</p>
-<p>One website that we recommend for finding the Bellamy Brothers discography torrents is <a href="https://rutracker.org/forum/viewtopic.php?t=3079536">RuTracker.org</a>. This is a Russian website that has a large collection of music torrents, including 44 CDs of the Bellamy Brothers in MP3 format. The website is in Russian, but you can use Google Translate or another online tool to navigate it. The torrents have high seeders and good ratings from other users. To download them, you need to register for a free account on the website and use a torrent client that supports Cyrillic characters.</p>
-<p></p>
-<h2>How to download torrents safely and legally?</h2>
-<p>Downloading torrents can be risky and illegal if you don't take some precautions. Here are some tips on how to download torrents safely and legally:</p>
-<ul>
-<li>Use a VPN (virtual private network) service. A VPN is a software that encrypts your internet traffic and hides your IP address from your ISP (internet service provider) and other third parties. This way, you can avoid being tracked, monitored, or blocked by your ISP or authorities for downloading torrents. A VPN also protects you from hackers, malware, and identity theft. There are many VPN services available online, such as NordVPN, ExpressVPN, or CyberGhost.</li>
-<li>Use an antivirus software. An antivirus software is a software that scans your computer for viruses, malware, and other threats that may come from downloading torrents. It can also remove or quarantine any infected files that you may encounter. You should always keep your antivirus software updated and run regular scans on your computer. Some of the best antivirus software are Avast, Bitdefender, or Kaspersky.</li>
-<li>Use a seedbox. A seedbox is a remote server that downloads and uploads torrents for you. It acts as an intermediary between you and the peer-to-peer network, so you don't have to use your own bandwidth or expose your IP address. A seedbox also allows you to access your files from any device and</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download Arnold Van Gennep Rite Of Passage Pdf Download High Quality.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download Arnold Van Gennep Rite Of Passage Pdf Download High Quality.md
deleted file mode 100644
index 9ef276d55f6684edd377e03fcbd72b8377129ede..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download Arnold Van Gennep Rite Of Passage Pdf Download High Quality.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-
-<h1>Download Arnold van Gennep's Classic Book on The Rites of Passage</h1>
-<p>If you are interested in anthropology, sociology, or culture, you may want to download Arnold van Gennep's classic book on <em>The Rites of Passage</em>. This book, originally published in French in 1909, was the first to analyze the rituals and ceremonies that mark the transitions in human life, such as birth, puberty, marriage, and death. Van Gennep coined the term "rites of passage" to describe these universal phenomena, and he proposed a three-stage model of separation, liminality, and incorporation to explain their structure and function.</p>
-<h2>download arnold van gennep rite of passage pdf download</h2><br /><p><b><b>Download File</b> &raquo; <a href="https://urlcod.com/2uIb0T">https://urlcod.com/2uIb0T</a></b></p><br /><br />
-<p>Van Gennep's book has had a huge influence on the social sciences and has become a part of the common language. However, it was only translated into English in 1960, and it is not widely available in digital format. Fortunately, you can download a free PDF version of <em>The Rites of Passage</em> from the Internet Archive[^1^], a non-profit library of millions of free books, movies, music, and more. You can also read a PDF version of the introduction to the second edition by David I. Kertzer from the University of Chicago Press[^2^], which provides a historical context and a critical assessment of van Gennep's work. Alternatively, you can read a PDF article by Gregory Forth from the University of Alberta[^3^], which summarizes van Gennep's main ideas and discusses some of their applications and limitations.</p>
-<p>Whether you are a student, a scholar, or a curious reader, you will find <em>The Rites of Passage</em> to be a fascinating and insightful book that will enrich your understanding of human culture and society. Download it today and discover why it is considered a classic in anthropology and beyond.</p>
-
-<p>One of the most common types of rites of passage across cultures is the coming of age ritual, which marks the transition from childhood to adulthood. Different cultures have different ways of celebrating this milestone, often involving tests of physical endurance, moral values, or spiritual maturity. Here are some examples of coming of age traditions from around the world:</p>
-<p></p>
-<ul>
-<li>In Judaism, boys and girls have their Bar and Bat Mitzvahs at age 13 and 12, respectively. These ceremonies involve reading from the Torah, the Jewish holy book, and demonstrating their understanding of their faith and responsibilities. After the ceremony, a festive reception is held to celebrate the young person's achievement[^1^].</li>
-<li>In some indigenous tribes of the Brazilian Amazon, such as the Sateré-Mawé, boys have to undergo a painful initiation involving bullet ants, which have one of the most potent stings in the animal kingdom. The boys have to wear gloves filled with these ants for 10 minutes, and they have to repeat this ordeal several times over a few months. This ritual is meant to test their courage and endurance, as well as to prepare them for the hardships of life in the jungle[^2^].</li>
-<li>In many Latin American countries, girls have their Quinceañera (or Quince Años) when they turn 15. This is a celebration of their transition from girlhood to womanhood, and it usually involves a Catholic mass where they renew their baptismal vows and receive a blessing from a priest. Then, they have a party where they wear a formal dress, dance with their father and other male relatives and friends, and receive gifts. Some girls also choose to do a community service project as part of their Quinceañera[^2^].</li>
-</ul>
-<p>Of course, rites of passage are not limited to the transition from childhood to adulthood. They can also mark other important changes in a person's life, such as marriage, parenthood, or death. Some examples of these rites are:</p>
-<ul>
-<li>In Hinduism, one of the most important rites of passage is the Upanayana, or sacred thread ceremony. This is a ritual that initiates boys (and sometimes girls) into the study of the Vedas, the ancient scriptures of Hinduism. The boys are given a sacred thread (yajñopavītam) to wear around their bodies as a symbol of their spiritual rebirth and their acceptance of religious duties. The ceremony also involves shaving their heads (except for a small tuft of hair), wearing new clothes, and receiving blessings from elders[^3^].</li>
-<li>In many African cultures, such as the Maasai of Kenya and Tanzania, circumcision is a rite of passage for both boys and girls (although female circumcision is now widely condemned as a human rights violation). The circumcision is performed without anesthesia or sterilization, and it is seen as a test of bravery and maturity. The initiates also have to follow certain rules and rituals during their healing period, such as living in seclusion, wearing special clothes and ornaments, and learning about their culture and history from elders[^4^].</li>
-<li>In Buddhism, one of the most significant rites of passage is death. Buddhists believe that death is not the end of life, but rather a transition to a new state of existence. They also believe that one's actions in this life determine one's fate in the next life (karma). Therefore, they perform various rituals to help the dying person achieve a peaceful and positive state of mind at the moment of death. These rituals include chanting prayers and mantras, offering food and water, lighting candles and incense, and reciting the Buddha's teachings[^4^].</li>
-</ul>
-<p>Rites of passage are an essential part of human culture. They help people cope with change and uncertainty, affirm their identity and belonging, express their values and beliefs, and celebrate their achievements and milestones. By learning about different rites of passage around the world, we can gain a deeper understanding of ourselves and others.</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/new4u/whisper_large_v2_Audio_YT_to_text/app.py b/spaces/new4u/whisper_large_v2_Audio_YT_to_text/app.py
deleted file mode 100644
index 92798ad0b5bceea8cdaac392f2cf0131e609b452..0000000000000000000000000000000000000000
--- a/spaces/new4u/whisper_large_v2_Audio_YT_to_text/app.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import torch
-# from PIL import Image
-import gradio as gr
-import pytube as pt
-from transformers import pipeline
-
-MODEL_NAME = "openai/whisper-large-v2"
-
-device = 0 if torch.cuda.is_available() else "cpu"
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-
-
-all_special_ids = pipe.tokenizer.all_special_ids
-transcribe_token_id = all_special_ids[-5]
-translate_token_id = all_special_ids[-6]
-
-
-def transcribe(microphone, file_upload, task):
-    warn_output = ""
-    if (microphone is not None) and (file_upload is not None):
-        warn_output = (
-            "警告：您已经上传了一个音频文件并使用了麦克录制。"
-            "录制文件将被使用上传的音频将被丢弃。\n"
-        )
-
-    elif (microphone is None) and (file_upload is None):
-        return "错误: 您必须使用麦克风录制或上传音频文件"
-
-    file = microphone if microphone is not None else file_upload
-
-    pipe.model.config.forced_decoder_ids = [[2, transcribe_token_id if task=="transcribe" else translate_token_id]]
-
-    # text = pipe(file, return_timestamps=True)["text"]
-    text = pipe(file, return_timestamps=True)
-    #trans to SRT
-    text= convert_to_srt(text)
-    
-    return warn_output + text
-
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
-
-
-def yt_transcribe(yt_url, task):
-    yt = pt.YouTube(yt_url)
-    html_embed_str = _return_yt_html_embed(yt_url)
-    stream = yt.streams.filter(only_audio=True)[0]
-    stream.download(filename="audio.mp3")
-
-    pipe.model.config.forced_decoder_ids = [[2, transcribe_token_id if task=="transcribe" else translate_token_id]]
-
-    text = pipe("audio.mp3",return_timestamps=True)
-    # text = pipe("audio.mp3",return_timestamps=True)["text"]
-    #trans to SRT
-    text= convert_to_srt(text)
-
-    return html_embed_str, text
-
-# Assuming srt format is a sequence of subtitles with index, time range and text
-def convert_to_srt(input):
-  output = ""
-  index = 1
-  for chunk in input["chunks"]:
-    start, end = chunk["timestamp"]
-    text = chunk["text"]
-    if end is None:
-      end = "None"
-    # Convert seconds to hours:minutes:seconds,milliseconds format
-    start = format_time(start)
-    end = format_time(end)
-    output += f"{index}\n{start} --> {end}\n{text}\n\n"
-    index += 1
-  return output
-
-# Helper function to format time
-def format_time(seconds):
-  if seconds == "None":
-    return seconds
-  hours = int(seconds // 3600)
-  minutes = int((seconds % 3600) // 60)
-  seconds = int(seconds % 60)
-  milliseconds = int((seconds % 1) * 1000)
-  return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"
-
-demo = gr.Blocks()
-
-mf_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
-        gr.inputs.Audio(source="upload", type="filepath", optional=True),
-        gr.inputs.Radio(["转译", "翻译"], label="Task", default="transcribe"),
-    ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Audio-to-Text-SRT 自动生成字幕",
-    description=(
-        "直接在网页录音或上传音频文件，加入Youtube连接，轻松转换为文字和字幕格式! 本演示采用"
-        f" 模型 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 和 🤗 Transformers 转换任意长度的"
-        "音视频文件!使用GPU转换效率会大幅提高，大约每小时 $0.6 约相当于人民币 5 元。如果您有较长内容,需要更快的转换速度，请私信作者微信 1259388，并备注“语音转文字”。"
-    ),
-    allow_flagging="never",
-)
-
-yt_transcribe = gr.Interface(
-    fn=yt_transcribe,
-    inputs=[
-        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-        gr.inputs.Radio(["转译", "翻译"], label="Task", default="transcribe")
-    ],
-    outputs=["html", "text"],
-    layout="horizontal",
-    theme="huggingface",
-    title="Audio-to-Text-SRT 自动生成字幕",
-    description=(
-        "直接在网页录音或上传音频文件，加入Youtube连接，轻松转换为文字和字幕格式! 本演示采用"
-        f" 模型 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 和 🤗 Transformers 转换任意长度的"
-        "音视频文件!使用GPU转换效率会大幅提高，大约每小时 $0.6 约相当于人民币 5 元。如果您有较长内容,需要更快的转换速度，请私信作者微信 1259388，并备注“语音转文字”。"
-    ),
-    allow_flagging="never",
-)
-
-#  # Load the images
-# image1 = Image("wechatqrcode.jpg")
-# image2 = Image("paypalqrcode.png")
-    
-#     # Define a function that returns the images and captions
-# def display_images():
-#     return image1, "WeChat Pay", image2, "PayPal"
-    
-with demo:
-    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["转译音频成文字", "YouTube转字幕"])
-       
-    # Create a gradio interface with no inputs and four outputs
-    # gr.Interface(display_images, [], [gr.outputs.Image(), gr.outputs.Textbox(), gr.outputs.Image(), gr.outputs.Textbox()], layout="horizontal").launch()
-
-demo.launch(enable_queue=True)
-
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/proposal_generator/rpn.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/proposal_generator/rpn.py
deleted file mode 100644
index 99cd536d2f9880d2049390c45f73eb22335e1b82..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/proposal_generator/rpn.py
+++ /dev/null
@@ -1,533 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import Dict, List, Optional, Tuple, Union
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ShapeSpec, cat
-from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.memory import retry_if_cuda_oom
-from detectron2.utils.registry import Registry
-
-from ..anchor_generator import build_anchor_generator
-from ..box_regression import Box2BoxTransform, _dense_box_regression_loss
-from ..matcher import Matcher
-from ..sampling import subsample_labels
-from .build import PROPOSAL_GENERATOR_REGISTRY
-from .proposal_utils import find_top_rpn_proposals
-
-RPN_HEAD_REGISTRY = Registry("RPN_HEAD")
-RPN_HEAD_REGISTRY.__doc__ = """
-Registry for RPN heads, which take feature maps and perform
-objectness classification and bounding box regression for anchors.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-The call should return a `nn.Module` object.
-"""
-
-
-"""
-Shape shorthand in this module:
-
-    N: number of images in the minibatch
-    L: number of feature maps per image on which RPN is run
-    A: number of cell anchors (must be the same for all feature maps)
-    Hi, Wi: height and width of the i-th feature map
-    B: size of the box parameterization
-
-Naming convention:
-
-    objectness: refers to the binary classification of an anchor as object vs. not object.
-
-    deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box
-    transform (see :class:`box_regression.Box2BoxTransform`), or 5d for rotated boxes.
-
-    pred_objectness_logits: predicted objectness scores in [-inf, +inf]; use
-        sigmoid(pred_objectness_logits) to estimate P(object).
-
-    gt_labels: ground-truth binary classification labels for objectness
-
-    pred_anchor_deltas: predicted box2box transform deltas
-
-    gt_anchor_deltas: ground-truth box2box transform deltas
-"""
-
-
-def build_rpn_head(cfg, input_shape):
-    """
-    Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`.
-    """
-    name = cfg.MODEL.RPN.HEAD_NAME
-    return RPN_HEAD_REGISTRY.get(name)(cfg, input_shape)
-
-
-@RPN_HEAD_REGISTRY.register()
-class StandardRPNHead(nn.Module):
-    """
-    Standard RPN classification and regression heads described in :paper:`Faster R-CNN`.
-    Uses a 3x3 conv to produce a shared hidden state from which one 1x1 conv predicts
-    objectness logits for each anchor and a second 1x1 conv predicts bounding-box deltas
-    specifying how to deform each anchor into an object proposal.
-    """
-
-    @configurable
-    def __init__(
-        self, *, in_channels: int, num_anchors: int, box_dim: int = 4, conv_dims: List[int] = (-1,)
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            in_channels (int): number of input feature channels. When using multiple
-                input features, they must have the same number of channels.
-            num_anchors (int): number of anchors to predict for *each spatial position*
-                on the feature map. The total number of anchors for each
-                feature map will be `num_anchors * H * W`.
-            box_dim (int): dimension of a box, which is also the number of box regression
-                predictions to make for each anchor. An axis aligned box has
-                box_dim=4, while a rotated box has box_dim=5.
-            conv_dims (list[int]): a list of integers representing the output channels
-                of N conv layers. Set it to -1 to use the same number of output channels
-                as input channels.
-        """
-        super().__init__()
-        cur_channels = in_channels
-        # Keeping the old variable names and structure for backwards compatiblity.
-        # Otherwise the old checkpoints will fail to load.
-        if len(conv_dims) == 1:
-            out_channels = cur_channels if conv_dims[0] == -1 else conv_dims[0]
-            # 3x3 conv for the hidden representation
-            self.conv = self._get_rpn_conv(cur_channels, out_channels)
-            cur_channels = out_channels
-        else:
-            self.conv = nn.Sequential()
-            for k, conv_dim in enumerate(conv_dims):
-                out_channels = cur_channels if conv_dim == -1 else conv_dim
-                if out_channels <= 0:
-                    raise ValueError(
-                        f"Conv output channels should be greater than 0. Got {out_channels}"
-                    )
-                conv = self._get_rpn_conv(cur_channels, out_channels)
-                self.conv.add_module(f"conv{k}", conv)
-                cur_channels = out_channels
-        # 1x1 conv for predicting objectness logits
-        self.objectness_logits = nn.Conv2d(cur_channels, num_anchors, kernel_size=1, stride=1)
-        # 1x1 conv for predicting box2box transform deltas
-        self.anchor_deltas = nn.Conv2d(cur_channels, num_anchors * box_dim, kernel_size=1, stride=1)
-
-        # Keeping the order of weights initialization same for backwards compatiblility.
-        for layer in self.modules():
-            if isinstance(layer, nn.Conv2d):
-                nn.init.normal_(layer.weight, std=0.01)
-                nn.init.constant_(layer.bias, 0)
-
-    def _get_rpn_conv(self, in_channels, out_channels):
-        return Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            activation=nn.ReLU(),
-        )
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        # Standard RPN is shared across levels:
-        in_channels = [s.channels for s in input_shape]
-        assert len(set(in_channels)) == 1, "Each level must have the same channel!"
-        in_channels = in_channels[0]
-
-        # RPNHead should take the same input as anchor generator
-        # NOTE: it assumes that creating an anchor generator does not have unwanted side effect.
-        anchor_generator = build_anchor_generator(cfg, input_shape)
-        num_anchors = anchor_generator.num_anchors
-        box_dim = anchor_generator.box_dim
-        assert (
-            len(set(num_anchors)) == 1
-        ), "Each level must have the same number of anchors per spatial position"
-        return {
-            "in_channels": in_channels,
-            "num_anchors": num_anchors[0],
-            "box_dim": box_dim,
-            "conv_dims": cfg.MODEL.RPN.CONV_DIMS,
-        }
-
-    def forward(self, features: List[torch.Tensor]):
-        """
-        Args:
-            features (list[Tensor]): list of feature maps
-
-        Returns:
-            list[Tensor]: A list of L elements.
-                Element i is a tensor of shape (N, A, Hi, Wi) representing
-                the predicted objectness logits for all anchors. A is the number of cell anchors.
-            list[Tensor]: A list of L elements. Element i is a tensor of shape
-                (N, A*box_dim, Hi, Wi) representing the predicted "deltas" used to transform anchors
-                to proposals.
-        """
-        pred_objectness_logits = []
-        pred_anchor_deltas = []
-        for x in features:
-            t = self.conv(x)
-            pred_objectness_logits.append(self.objectness_logits(t))
-            pred_anchor_deltas.append(self.anchor_deltas(t))
-        return pred_objectness_logits, pred_anchor_deltas
-
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class RPN(nn.Module):
-    """
-    Region Proposal Network, introduced by :paper:`Faster R-CNN`.
-    """
-
-    @configurable
-    def __init__(
-        self,
-        *,
-        in_features: List[str],
-        head: nn.Module,
-        anchor_generator: nn.Module,
-        anchor_matcher: Matcher,
-        box2box_transform: Box2BoxTransform,
-        batch_size_per_image: int,
-        positive_fraction: float,
-        pre_nms_topk: Tuple[float, float],
-        post_nms_topk: Tuple[float, float],
-        nms_thresh: float = 0.7,
-        min_box_size: float = 0.0,
-        anchor_boundary_thresh: float = -1.0,
-        loss_weight: Union[float, Dict[str, float]] = 1.0,
-        box_reg_loss_type: str = "smooth_l1",
-        smooth_l1_beta: float = 0.0,
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            in_features (list[str]): list of names of input features to use
-            head (nn.Module): a module that predicts logits and regression deltas
-                for each level from a list of per-level features
-            anchor_generator (nn.Module): a module that creates anchors from a
-                list of features. Usually an instance of :class:`AnchorGenerator`
-            anchor_matcher (Matcher): label the anchors by matching them with ground truth.
-            box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to
-                instance boxes
-            batch_size_per_image (int): number of anchors per image to sample for training
-            positive_fraction (float): fraction of foreground anchors to sample for training
-            pre_nms_topk (tuple[float]): (train, test) that represents the
-                number of top k proposals to select before NMS, in
-                training and testing.
-            post_nms_topk (tuple[float]): (train, test) that represents the
-                number of top k proposals to select after NMS, in
-                training and testing.
-            nms_thresh (float): NMS threshold used to de-duplicate the predicted proposals
-            min_box_size (float): remove proposal boxes with any side smaller than this threshold,
-                in the unit of input image pixels
-            anchor_boundary_thresh (float): legacy option
-            loss_weight (float|dict): weights to use for losses. Can be single float for weighting
-                all rpn losses together, or a dict of individual weightings. Valid dict keys are:
-                    "loss_rpn_cls" - applied to classification loss
-                    "loss_rpn_loc" - applied to box regression loss
-            box_reg_loss_type (str): Loss type to use. Supported losses: "smooth_l1", "giou".
-            smooth_l1_beta (float): beta parameter for the smooth L1 regression loss. Default to
-                use L1 loss. Only used when `box_reg_loss_type` is "smooth_l1"
-        """
-        super().__init__()
-        self.in_features = in_features
-        self.rpn_head = head
-        self.anchor_generator = anchor_generator
-        self.anchor_matcher = anchor_matcher
-        self.box2box_transform = box2box_transform
-        self.batch_size_per_image = batch_size_per_image
-        self.positive_fraction = positive_fraction
-        # Map from self.training state to train/test settings
-        self.pre_nms_topk = {True: pre_nms_topk[0], False: pre_nms_topk[1]}
-        self.post_nms_topk = {True: post_nms_topk[0], False: post_nms_topk[1]}
-        self.nms_thresh = nms_thresh
-        self.min_box_size = float(min_box_size)
-        self.anchor_boundary_thresh = anchor_boundary_thresh
-        if isinstance(loss_weight, float):
-            loss_weight = {"loss_rpn_cls": loss_weight, "loss_rpn_loc": loss_weight}
-        self.loss_weight = loss_weight
-        self.box_reg_loss_type = box_reg_loss_type
-        self.smooth_l1_beta = smooth_l1_beta
-
-    @classmethod
-    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
-        in_features = cfg.MODEL.RPN.IN_FEATURES
-        ret = {
-            "in_features": in_features,
-            "min_box_size": cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE,
-            "nms_thresh": cfg.MODEL.RPN.NMS_THRESH,
-            "batch_size_per_image": cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE,
-            "positive_fraction": cfg.MODEL.RPN.POSITIVE_FRACTION,
-            "loss_weight": {
-                "loss_rpn_cls": cfg.MODEL.RPN.LOSS_WEIGHT,
-                "loss_rpn_loc": cfg.MODEL.RPN.BBOX_REG_LOSS_WEIGHT * cfg.MODEL.RPN.LOSS_WEIGHT,
-            },
-            "anchor_boundary_thresh": cfg.MODEL.RPN.BOUNDARY_THRESH,
-            "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS),
-            "box_reg_loss_type": cfg.MODEL.RPN.BBOX_REG_LOSS_TYPE,
-            "smooth_l1_beta": cfg.MODEL.RPN.SMOOTH_L1_BETA,
-        }
-
-        ret["pre_nms_topk"] = (cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN, cfg.MODEL.RPN.PRE_NMS_TOPK_TEST)
-        ret["post_nms_topk"] = (cfg.MODEL.RPN.POST_NMS_TOPK_TRAIN, cfg.MODEL.RPN.POST_NMS_TOPK_TEST)
-
-        ret["anchor_generator"] = build_anchor_generator(cfg, [input_shape[f] for f in in_features])
-        ret["anchor_matcher"] = Matcher(
-            cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS, allow_low_quality_matches=True
-        )
-        ret["head"] = build_rpn_head(cfg, [input_shape[f] for f in in_features])
-        return ret
-
-    def _subsample_labels(self, label):
-        """
-        Randomly sample a subset of positive and negative examples, and overwrite
-        the label vector to the ignore value (-1) for all elements that are not
-        included in the sample.
-
-        Args:
-            labels (Tensor): a vector of -1, 0, 1. Will be modified in-place and returned.
-        """
-        pos_idx, neg_idx = subsample_labels(
-            label, self.batch_size_per_image, self.positive_fraction, 0
-        )
-        # Fill with the ignore label (-1), then set positive and negative labels
-        label.fill_(-1)
-        label.scatter_(0, pos_idx, 1)
-        label.scatter_(0, neg_idx, 0)
-        return label
-
-    @torch.jit.unused
-    @torch.no_grad()
-    def label_and_sample_anchors(
-        self, anchors: List[Boxes], gt_instances: List[Instances]
-    ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
-        """
-        Args:
-            anchors (list[Boxes]): anchors for each feature map.
-            gt_instances: the ground-truth instances for each image.
-
-        Returns:
-            list[Tensor]:
-                List of #img tensors. i-th element is a vector of labels whose length is
-                the total number of anchors across all feature maps R = sum(Hi * Wi * A).
-                Label values are in {-1, 0, 1}, with meanings: -1 = ignore; 0 = negative
-                class; 1 = positive class.
-            list[Tensor]:
-                i-th element is a Rx4 tensor. The values are the matched gt boxes for each
-                anchor. Values are undefined for those anchors not labeled as 1.
-        """
-        anchors = Boxes.cat(anchors)
-
-        gt_boxes = [x.gt_boxes for x in gt_instances]
-        image_sizes = [x.image_size for x in gt_instances]
-        del gt_instances
-
-        gt_labels = []
-        matched_gt_boxes = []
-        for image_size_i, gt_boxes_i in zip(image_sizes, gt_boxes):
-            """
-            image_size_i: (h, w) for the i-th image
-            gt_boxes_i: ground-truth boxes for i-th image
-            """
-
-            match_quality_matrix = retry_if_cuda_oom(pairwise_iou)(gt_boxes_i, anchors)
-            matched_idxs, gt_labels_i = retry_if_cuda_oom(self.anchor_matcher)(match_quality_matrix)
-            # Matching is memory-expensive and may result in CPU tensors. But the result is small
-            gt_labels_i = gt_labels_i.to(device=gt_boxes_i.device)
-            del match_quality_matrix
-
-            if self.anchor_boundary_thresh >= 0:
-                # Discard anchors that go out of the boundaries of the image
-                # NOTE: This is legacy functionality that is turned off by default in Detectron2
-                anchors_inside_image = anchors.inside_box(image_size_i, self.anchor_boundary_thresh)
-                gt_labels_i[~anchors_inside_image] = -1
-
-            # A vector of labels (-1, 0, 1) for each anchor
-            gt_labels_i = self._subsample_labels(gt_labels_i)
-
-            if len(gt_boxes_i) == 0:
-                # These values won't be used anyway since the anchor is labeled as background
-                matched_gt_boxes_i = torch.zeros_like(anchors.tensor)
-            else:
-                # TODO wasted indexing computation for ignored boxes
-                matched_gt_boxes_i = gt_boxes_i[matched_idxs].tensor
-
-            gt_labels.append(gt_labels_i)  # N,AHW
-            matched_gt_boxes.append(matched_gt_boxes_i)
-        return gt_labels, matched_gt_boxes
-
-    @torch.jit.unused
-    def losses(
-        self,
-        anchors: List[Boxes],
-        pred_objectness_logits: List[torch.Tensor],
-        gt_labels: List[torch.Tensor],
-        pred_anchor_deltas: List[torch.Tensor],
-        gt_boxes: List[torch.Tensor],
-    ) -> Dict[str, torch.Tensor]:
-        """
-        Return the losses from a set of RPN predictions and their associated ground-truth.
-
-        Args:
-            anchors (list[Boxes or RotatedBoxes]): anchors for each feature map, each
-                has shape (Hi*Wi*A, B), where B is box dimension (4 or 5).
-            pred_objectness_logits (list[Tensor]): A list of L elements.
-                Element i is a tensor of shape (N, Hi*Wi*A) representing
-                the predicted objectness logits for all anchors.
-            gt_labels (list[Tensor]): Output of :meth:`label_and_sample_anchors`.
-            pred_anchor_deltas (list[Tensor]): A list of L elements. Element i is a tensor of shape
-                (N, Hi*Wi*A, 4 or 5) representing the predicted "deltas" used to transform anchors
-                to proposals.
-            gt_boxes (list[Tensor]): Output of :meth:`label_and_sample_anchors`.
-
-        Returns:
-            dict[loss name -> loss value]: A dict mapping from loss name to loss value.
-                Loss names are: `loss_rpn_cls` for objectness classification and
-                `loss_rpn_loc` for proposal localization.
-        """
-        num_images = len(gt_labels)
-        gt_labels = torch.stack(gt_labels)  # (N, sum(Hi*Wi*Ai))
-
-        # Log the number of positive/negative anchors per-image that's used in training
-        pos_mask = gt_labels == 1
-        num_pos_anchors = pos_mask.sum().item()
-        num_neg_anchors = (gt_labels == 0).sum().item()
-        storage = get_event_storage()
-        storage.put_scalar("rpn/num_pos_anchors", num_pos_anchors / num_images)
-        storage.put_scalar("rpn/num_neg_anchors", num_neg_anchors / num_images)
-
-        localization_loss = _dense_box_regression_loss(
-            anchors,
-            self.box2box_transform,
-            pred_anchor_deltas,
-            gt_boxes,
-            pos_mask,
-            box_reg_loss_type=self.box_reg_loss_type,
-            smooth_l1_beta=self.smooth_l1_beta,
-        )
-
-        valid_mask = gt_labels >= 0
-        objectness_loss = F.binary_cross_entropy_with_logits(
-            cat(pred_objectness_logits, dim=1)[valid_mask],
-            gt_labels[valid_mask].to(torch.float32),
-            reduction="sum",
-        )
-        normalizer = self.batch_size_per_image * num_images
-        losses = {
-            "loss_rpn_cls": objectness_loss / normalizer,
-            # The original Faster R-CNN paper uses a slightly different normalizer
-            # for loc loss. But it doesn't matter in practice
-            "loss_rpn_loc": localization_loss / normalizer,
-        }
-        losses = {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()}
-        return losses
-
-    def forward(
-        self,
-        images: ImageList,
-        features: Dict[str, torch.Tensor],
-        gt_instances: Optional[List[Instances]] = None,
-    ):
-        """
-        Args:
-            images (ImageList): input images of length `N`
-            features (dict[str, Tensor]): input data as a mapping from feature
-                map name to tensor. Axis 0 represents the number of images `N` in
-                the input data; axes 1-3 are channels, height, and width, which may
-                vary between feature maps (e.g., if a feature pyramid is used).
-            gt_instances (list[Instances], optional): a length `N` list of `Instances`s.
-                Each `Instances` stores ground-truth instances for the corresponding image.
-
-        Returns:
-            proposals: list[Instances]: contains fields "proposal_boxes", "objectness_logits"
-            loss: dict[Tensor] or None
-        """
-        features = [features[f] for f in self.in_features]
-        anchors = self.anchor_generator(features)
-
-        pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features)
-        # Transpose the Hi*Wi*A dimension to the middle:
-        pred_objectness_logits = [
-            # (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N, Hi*Wi*A)
-            score.permute(0, 2, 3, 1).flatten(1)
-            for score in pred_objectness_logits
-        ]
-        pred_anchor_deltas = [
-            # (N, A*B, Hi, Wi) -> (N, A, B, Hi, Wi) -> (N, Hi, Wi, A, B) -> (N, Hi*Wi*A, B)
-            x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
-            .permute(0, 3, 4, 1, 2)
-            .flatten(1, -2)
-            for x in pred_anchor_deltas
-        ]
-
-        if self.training:
-            assert gt_instances is not None, "RPN requires gt_instances in training!"
-            gt_labels, gt_boxes = self.label_and_sample_anchors(anchors, gt_instances)
-            losses = self.losses(
-                anchors, pred_objectness_logits, gt_labels, pred_anchor_deltas, gt_boxes
-            )
-        else:
-            losses = {}
-        proposals = self.predict_proposals(
-            anchors, pred_objectness_logits, pred_anchor_deltas, images.image_sizes
-        )
-        return proposals, losses
-
-    def predict_proposals(
-        self,
-        anchors: List[Boxes],
-        pred_objectness_logits: List[torch.Tensor],
-        pred_anchor_deltas: List[torch.Tensor],
-        image_sizes: List[Tuple[int, int]],
-    ):
-        """
-        Decode all the predicted box regression deltas to proposals. Find the top proposals
-        by applying NMS and removing boxes that are too small.
-
-        Returns:
-            proposals (list[Instances]): list of N Instances. The i-th Instances
-                stores post_nms_topk object proposals for image i, sorted by their
-                objectness score in descending order.
-        """
-        # The proposals are treated as fixed for joint training with roi heads.
-        # This approach ignores the derivative w.r.t. the proposal boxes’ coordinates that
-        # are also network responses.
-        with torch.no_grad():
-            pred_proposals = self._decode_proposals(anchors, pred_anchor_deltas)
-            return find_top_rpn_proposals(
-                pred_proposals,
-                pred_objectness_logits,
-                image_sizes,
-                self.nms_thresh,
-                self.pre_nms_topk[self.training],
-                self.post_nms_topk[self.training],
-                self.min_box_size,
-                self.training,
-            )
-
-    def _decode_proposals(self, anchors: List[Boxes], pred_anchor_deltas: List[torch.Tensor]):
-        """
-        Transform anchors into proposals by applying the predicted anchor deltas.
-
-        Returns:
-            proposals (list[Tensor]): A list of L tensors. Tensor i has shape
-                (N, Hi*Wi*A, B)
-        """
-        N = pred_anchor_deltas[0].shape[0]
-        proposals = []
-        # For each feature map
-        for anchors_i, pred_anchor_deltas_i in zip(anchors, pred_anchor_deltas):
-            B = anchors_i.tensor.size(1)
-            pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
-            # Expand anchors to shape (N*Hi*Wi*A, B)
-            anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
-            proposals_i = self.box2box_transform.apply_deltas(pred_anchor_deltas_i, anchors_i)
-            # Append feature map proposals with shape (N, Hi*Wi*A, B)
-            proposals.append(proposals_i.view(N, -1, B))
-        return proposals
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/tests/test_model_e2e.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/tests/test_model_e2e.py
deleted file mode 100644
index 055fadfd781adcdfd661795edbc621d5eca763fe..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/tests/test_model_e2e.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import unittest
-import torch
-
-from detectron2.structures import BitMasks, Boxes, Instances
-
-from .common import get_model
-
-
-# TODO(plabatut): Modularize detectron2 tests and re-use
-def make_model_inputs(image, instances=None):
-    if instances is None:
-        return {"image": image}
-
-    return {"image": image, "instances": instances}
-
-
-def make_empty_instances(h, w):
-    instances = Instances((h, w))
-    instances.gt_boxes = Boxes(torch.rand(0, 4))
-    instances.gt_classes = torch.tensor([]).to(dtype=torch.int64)
-    instances.gt_masks = BitMasks(torch.rand(0, h, w))
-    return instances
-
-
-class ModelE2ETest(unittest.TestCase):
-    CONFIG_PATH = ""
-
-    def setUp(self):
-        self.model = get_model(self.CONFIG_PATH)
-
-    def _test_eval(self, sizes):
-        inputs = [make_model_inputs(torch.rand(3, size[0], size[1])) for size in sizes]
-        self.model.eval()
-        self.model(inputs)
-
-
-class DensePoseRCNNE2ETest(ModelE2ETest):
-    CONFIG_PATH = "densepose_rcnn_R_101_FPN_s1x.yaml"
-
-    def test_empty_data(self):
-        self._test_eval([(200, 250), (200, 249)])
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_mvitv2_l_in21k_50ep.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_mvitv2_l_in21k_50ep.py
deleted file mode 100644
index 779442c60fa32f1d36e823e86c62979f8e48ec2c..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_mvitv2_l_in21k_50ep.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from .cascade_mask_rcnn_mvitv2_b_in21k_100ep import (
-    dataloader,
-    lr_multiplier,
-    model,
-    train,
-    optimizer,
-)
-
-model.backbone.bottom_up.embed_dim = 144
-model.backbone.bottom_up.depth = 48
-model.backbone.bottom_up.num_heads = 2
-model.backbone.bottom_up.last_block_indexes = (1, 7, 43, 47)
-model.backbone.bottom_up.drop_path_rate = 0.5
-
-train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_L_in21k.pyth"
-
-train.max_iter = train.max_iter // 2  # 100ep -> 50ep
-lr_multiplier.scheduler.milestones = [
-    milestone // 2 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter
-lr_multiplier.warmup_length = 250 / train.max_iter
-
-optimizer.lr = 4e-5
diff --git a/spaces/nschenone/lyric-buddy/src/generate.py b/spaces/nschenone/lyric-buddy/src/generate.py
deleted file mode 100644
index 37250b9cbb99caadf93cf2fac888191a89fb338a..0000000000000000000000000000000000000000
--- a/spaces/nschenone/lyric-buddy/src/generate.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import transformers
-from transformers import set_seed
-
-from src.profanity_filter import censor
-
-
-def generate(
-    pipeline: transformers.Pipeline,
-    pipeline_args: dict,
-    seed: int = 0,
-    censor_profanity: bool = True,
-):
-
-    set_seed(seed)
-
-    args = {
-        "text_inputs": None,
-        "max_length": 100,
-        "num_return_sequences": 1,
-        "num_beams": 5,
-        "no_repeat_ngram_size": 3,
-        "early_stopping": True,
-        "skip_special_tokens": True,
-        "temperature": 1.5,
-    }
-
-    args.update(pipeline_args)
-    generated = pipeline(**args)[0]["generated_text"]
-
-    if censor_profanity:
-        generated = censor(generated)
-
-    return generated
diff --git a/spaces/omdenalagos/job_skill_cat/src/__init__.py b/spaces/omdenalagos/job_skill_cat/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/onereal/rvc-models-convertvoice/infer_pack/models.py b/spaces/onereal/rvc-models-convertvoice/infer_pack/models.py
deleted file mode 100644
index 96165f73644e6fb92d0ffedb4a3c9e1a457cb989..0000000000000000000000000000000000000000
--- a/spaces/onereal/rvc-models-convertvoice/infer_pack/models.py
+++ /dev/null
@@ -1,982 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y_lengths, ds
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            x, y_lengths, self.segment_size
-        )
-
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice
-
-    def infer(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o, o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/scripts/run_train.sh b/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/scripts/run_train.sh
deleted file mode 100644
index 8d45355e25f20a54f79ab07f6b21f90423cc37f4..0000000000000000000000000000000000000000
--- a/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/scripts/run_train.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/bin/bash
-#SBATCH --nodes 1
-#SBATCH --ntasks-per-node=8
-#SBATCH --gpus-per-task=1
-
-export PYTHONFAULTHANDLER=1
-export CUDA_LAUNCH_BLOCKING=0
-export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
-export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_PORT=15000
-export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
-
-export PYTHONPATH="$PYTHONPATH:open_flamingo"
-srun --cpu_bind=v --accel-bind=gn python open_flamingo/open_flamingo/train/train.py \
-    --lm_path anas-awadalla/mpt-1b-redpajama-200b \
-    --tokenizer_path anas-awadalla/mpt-1b-redpajama-200b \
-    --cross_attn_every_n_layers 1 \
-    --dataset_resampled \
-    --batch_size_mmc4 32 \
-    --batch_size_laion 64 \
-    --train_num_samples_mmc4 125000\
-    --train_num_samples_laion 250000 \
-    --loss_multiplier_laion 0.2 \
-    --workers=4 \
-    --run_name OpenFlamingo-3B-vitl-mpt1b \
-    --num_epochs 480 \
-    --warmup_steps  1875 \
-    --mmc4_textsim_threshold 0.24 \
-    --laion_shards "/path/to/shards/shard-{0000..0999}.tar" \
-    --mmc4_shards "/path/to/shards/shard-{0000..0999}.tar" \
-    --gradient_checkpointing \
-    --report_to_wandb \
diff --git a/spaces/oshita-n/ImageQuestionAnswerring/app.py b/spaces/oshita-n/ImageQuestionAnswerring/app.py
deleted file mode 100644
index cb12c6c82374ff97cca413070c02a4094d6c3bc5..0000000000000000000000000000000000000000
--- a/spaces/oshita-n/ImageQuestionAnswerring/app.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import torch
-import gradio as gr
-from lavis.models import load_model_and_preprocess
-from PIL import Image
-
-def process(input_image, prompt):
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    model, vis_processors, txt_processors = load_model_and_preprocess(name="pnp_vqa", model_type="base", is_eval=True, device=device)
-    input_image = input_image.resize((256, 256))
-    image = vis_processors["eval"](input_image).unsqueeze(0).to(device)
-    text_input = txt_processors["eval"](prompt)
-    sample = {"image": image, "text_input": [text_input]}
-    pred_answers, caption, gradcam = model.predict_answers(sample, num_captions=50, num_patches=20)
-
-    return pred_answers[0]
-
-if __name__ == '__main__':
-    input_image = gr.inputs.Image(label='image', type='pil')
-    prompt = gr.Textbox(label='Prompt')
-    ips = [
-            input_image, prompt
-        ]
-    outputs = gr.outputs.Textbox(label='Answer')
-    iface = gr.Interface(fn=process,
-                         inputs=ips,
-                         outputs=outputs,
-                         title='Image Question Answering',
-                         description='画像に関する質問に答えるモデルを使って、質問に答えます。画像をアップロードし、質問を入力してください。')
-    iface.launch()
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/clip_guided_stable_diffusion_img2img.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/clip_guided_stable_diffusion_img2img.py
deleted file mode 100644
index ad9ca804058c766d1020e4b56209ea80628d84be..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/clip_guided_stable_diffusion_img2img.py
+++ /dev/null
@@ -1,493 +0,0 @@
-import inspect
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torchvision import transforms
-from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderKL,
-    DDIMScheduler,
-    DiffusionPipeline,
-    DPMSolverMultistepScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-    UNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.utils import PIL_INTERPOLATION, deprecate
-from diffusers.utils.torch_utils import randn_tensor
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```
-        from io import BytesIO
-
-        import requests
-        import torch
-        from diffusers import DiffusionPipeline
-        from PIL import Image
-        from transformers import CLIPFeatureExtractor, CLIPModel
-
-        feature_extractor = CLIPFeatureExtractor.from_pretrained(
-            "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
-        )
-        clip_model = CLIPModel.from_pretrained(
-            "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
-        )
-
-
-        guided_pipeline = DiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4",
-            # custom_pipeline="clip_guided_stable_diffusion",
-            custom_pipeline="/home/njindal/diffusers/examples/community/clip_guided_stable_diffusion.py",
-            clip_model=clip_model,
-            feature_extractor=feature_extractor,
-            torch_dtype=torch.float16,
-        )
-        guided_pipeline.enable_attention_slicing()
-        guided_pipeline = guided_pipeline.to("cuda")
-
-        prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"
-
-        url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-
-        response = requests.get(url)
-        init_image = Image.open(BytesIO(response.content)).convert("RGB")
-
-        image = guided_pipeline(
-            prompt=prompt,
-            num_inference_steps=30,
-            image=init_image,
-            strength=0.75,
-            guidance_scale=7.5,
-            clip_guidance_scale=100,
-            num_cutouts=4,
-            use_cutouts=False,
-        ).images[0]
-        display(image)
-        ```
-"""
-
-
-def preprocess(image, w, h):
-    if isinstance(image, torch.Tensor):
-        return image
-    elif isinstance(image, PIL.Image.Image):
-        image = [image]
-
-    if isinstance(image[0], PIL.Image.Image):
-        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
-        image = np.concatenate(image, axis=0)
-        image = np.array(image).astype(np.float32) / 255.0
-        image = image.transpose(0, 3, 1, 2)
-        image = 2.0 * image - 1.0
-        image = torch.from_numpy(image)
-    elif isinstance(image[0], torch.Tensor):
-        image = torch.cat(image, dim=0)
-    return image
-
-
-class MakeCutouts(nn.Module):
-    def __init__(self, cut_size, cut_power=1.0):
-        super().__init__()
-
-        self.cut_size = cut_size
-        self.cut_power = cut_power
-
-    def forward(self, pixel_values, num_cutouts):
-        sideY, sideX = pixel_values.shape[2:4]
-        max_size = min(sideX, sideY)
-        min_size = min(sideX, sideY, self.cut_size)
-        cutouts = []
-        for _ in range(num_cutouts):
-            size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
-            offsetx = torch.randint(0, sideX - size + 1, ())
-            offsety = torch.randint(0, sideY - size + 1, ())
-            cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
-            cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
-        return torch.cat(cutouts)
-
-
-def spherical_dist_loss(x, y):
-    x = F.normalize(x, dim=-1)
-    y = F.normalize(y, dim=-1)
-    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
-
-
-def set_requires_grad(model, value):
-    for param in model.parameters():
-        param.requires_grad = value
-
-
-class CLIPGuidedStableDiffusion(DiffusionPipeline):
-    """CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000
-    - https://github.com/Jack000/glid-3-xl
-    - https://github.dev/crowsonkb/k-diffusion
-    """
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        clip_model: CLIPModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],
-        feature_extractor: CLIPFeatureExtractor,
-    ):
-        super().__init__()
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            clip_model=clip_model,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            feature_extractor=feature_extractor,
-        )
-
-        self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
-        self.cut_out_size = (
-            feature_extractor.size
-            if isinstance(feature_extractor.size, int)
-            else feature_extractor.size["shortest_edge"]
-        )
-        self.make_cutouts = MakeCutouts(self.cut_out_size)
-
-        set_requires_grad(self.text_encoder, False)
-        set_requires_grad(self.clip_model, False)
-
-    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = self.unet.config.attention_head_dim // 2
-        self.unet.set_attention_slice(slice_size)
-
-    def disable_attention_slicing(self):
-        self.enable_attention_slicing(None)
-
-    def freeze_vae(self):
-        set_requires_grad(self.vae, False)
-
-    def unfreeze_vae(self):
-        set_requires_grad(self.vae, True)
-
-    def freeze_unet(self):
-        set_requires_grad(self.unet, False)
-
-    def unfreeze_unet(self):
-        set_requires_grad(self.unet, True)
-
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
-        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
-            raise ValueError(
-                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
-            )
-
-        image = image.to(device=device, dtype=dtype)
-
-        batch_size = batch_size * num_images_per_prompt
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if isinstance(generator, list):
-            init_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
-            ]
-            init_latents = torch.cat(init_latents, dim=0)
-        else:
-            init_latents = self.vae.encode(image).latent_dist.sample(generator)
-
-        init_latents = self.vae.config.scaling_factor * init_latents
-
-        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
-            # expand init_latents for batch_size
-            deprecation_message = (
-                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
-                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
-                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
-                " your script to pass as many initial images as text prompts to suppress this warning."
-            )
-            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
-            additional_image_per_prompt = batch_size // init_latents.shape[0]
-            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
-        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
-            raise ValueError(
-                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
-            )
-        else:
-            init_latents = torch.cat([init_latents], dim=0)
-
-        shape = init_latents.shape
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        # get latents
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-        latents = init_latents
-
-        return latents
-
-    @torch.enable_grad()
-    def cond_fn(
-        self,
-        latents,
-        timestep,
-        index,
-        text_embeddings,
-        noise_pred_original,
-        text_embeddings_clip,
-        clip_guidance_scale,
-        num_cutouts,
-        use_cutouts=True,
-    ):
-        latents = latents.detach().requires_grad_()
-
-        latent_model_input = self.scheduler.scale_model_input(latents, timestep)
-
-        # predict the noise residual
-        noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample
-
-        if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
-            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
-            beta_prod_t = 1 - alpha_prod_t
-            # compute predicted original sample from predicted noise also called
-            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-            pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
-
-            fac = torch.sqrt(beta_prod_t)
-            sample = pred_original_sample * (fac) + latents * (1 - fac)
-        elif isinstance(self.scheduler, LMSDiscreteScheduler):
-            sigma = self.scheduler.sigmas[index]
-            sample = latents - sigma * noise_pred
-        else:
-            raise ValueError(f"scheduler type {type(self.scheduler)} not supported")
-
-        sample = 1 / self.vae.config.scaling_factor * sample
-        image = self.vae.decode(sample).sample
-        image = (image / 2 + 0.5).clamp(0, 1)
-
-        if use_cutouts:
-            image = self.make_cutouts(image, num_cutouts)
-        else:
-            image = transforms.Resize(self.cut_out_size)(image)
-        image = self.normalize(image).to(latents.dtype)
-
-        image_embeddings_clip = self.clip_model.get_image_features(image)
-        image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
-
-        if use_cutouts:
-            dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)
-            dists = dists.view([num_cutouts, sample.shape[0], -1])
-            loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
-        else:
-            loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale
-
-        grads = -torch.autograd.grad(loss, latents)[0]
-
-        if isinstance(self.scheduler, LMSDiscreteScheduler):
-            latents = latents.detach() + grads * (sigma**2)
-            noise_pred = noise_pred_original
-        else:
-            noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
-        return noise_pred, latents
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        height: Optional[int] = 512,
-        width: Optional[int] = 512,
-        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
-        strength: float = 0.8,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        clip_guidance_scale: Optional[float] = 100,
-        clip_prompt: Optional[Union[str, List[str]]] = None,
-        num_cutouts: Optional[int] = 4,
-        use_cutouts: Optional[bool] = True,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-    ):
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        # get prompt text embeddings
-        text_input = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
-        # duplicate text embeddings for each generation per prompt
-        text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
-
-        # set timesteps
-        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
-        extra_set_kwargs = {}
-        if accepts_offset:
-            extra_set_kwargs["offset"] = 1
-
-        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
-        # Some schedulers like PNDM have timesteps as arrays
-        # It's more optimized to move all timesteps to correct device beforehand
-        self.scheduler.timesteps.to(self.device)
-
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, self.device)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
-        # Preprocess image
-        image = preprocess(image, width, height)
-        latents = self.prepare_latents(
-            image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, self.device, generator
-        )
-
-        if clip_guidance_scale > 0:
-            if clip_prompt is not None:
-                clip_text_input = self.tokenizer(
-                    clip_prompt,
-                    padding="max_length",
-                    max_length=self.tokenizer.model_max_length,
-                    truncation=True,
-                    return_tensors="pt",
-                ).input_ids.to(self.device)
-            else:
-                clip_text_input = text_input.input_ids.to(self.device)
-            text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)
-            text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
-            # duplicate text embeddings clip for each generation per prompt
-            text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=0)
-
-        # 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
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            max_length = text_input.input_ids.shape[-1]
-            uncond_input = self.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt")
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
-            # duplicate unconditional embeddings for each generation per prompt
-            uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=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
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-        # get the initial random noise unless the user supplied it
-
-        # Unlike in other pipelines, latents need to be generated in the target device
-        # for 1-to-1 results reproducibility with the CompVis implementation.
-        # However this currently doesn't work in `mps`.
-        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
-        latents_dtype = text_embeddings.dtype
-        if latents is None:
-            if self.device.type == "mps":
-                # randn does not work reproducibly on mps
-                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
-                    self.device
-                )
-            else:
-                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
-        else:
-            if latents.shape != latents_shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-            latents = latents.to(self.device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-
-        with self.progress_bar(total=num_inference_steps):
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-                # perform classifier free guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # perform clip guidance
-                if clip_guidance_scale > 0:
-                    text_embeddings_for_guidance = (
-                        text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
-                    )
-                    noise_pred, latents = self.cond_fn(
-                        latents,
-                        t,
-                        i,
-                        text_embeddings_for_guidance,
-                        noise_pred,
-                        text_embeddings_clip,
-                        clip_guidance_scale,
-                        num_cutouts,
-                        use_cutouts,
-                    )
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-        # scale and decode the image latents with vae
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, None)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/reinforcement_learning/README.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/reinforcement_learning/README.md
deleted file mode 100644
index 17881d584a4043156b784a152253b0f83598ced9..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/reinforcement_learning/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
-# Overview
-
-These examples show how to run [Diffuser](https://arxiv.org/abs/2205.09991) in Diffusers. 
-There are two ways to use the script, `run_diffuser_locomotion.py`.
-
-The key option is a change of the variable `n_guide_steps`. 
-When `n_guide_steps=0`, the trajectories are sampled from the diffusion model, but not fine-tuned to maximize reward in the environment.
-By default, `n_guide_steps=2` to match the original implementation.
- 
-
-You will need some RL specific requirements to run the examples:
-
-```
-pip install -f https://download.pytorch.org/whl/torch_stable.html \
-                free-mujoco-py \
-                einops \
-                gym==0.24.1 \
-                protobuf==3.20.1 \
-                git+https://github.com/rail-berkeley/d4rl.git \
-                mediapy \
-                Pillow==9.0.0
-```
diff --git a/spaces/pdjewell/sommeli_ai/images/px_3d.html b/spaces/pdjewell/sommeli_ai/images/px_3d.html
deleted file mode 100644
index 6ae19e1f947cffa78b1cb6e30af15d206b6fbea6..0000000000000000000000000000000000000000
--- a/spaces/pdjewell/sommeli_ai/images/px_3d.html
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ed54e68d1fd7b986450ee528b36c0f93ceb02405ff06b115c8e9d2df44bd54ef
-size 21851322
diff --git a/spaces/petervavank/Advoice/models.py b/spaces/petervavank/Advoice/models.py
deleted file mode 100644
index 64e67b2de6b5a070c604260657e3e1aa4e176232..0000000000000000000000000000000000000000
--- a/spaces/petervavank/Advoice/models.py
+++ /dev/null
@@ -1,301 +0,0 @@
-from typing import Dict, List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import Tensor
-from torch.nn.utils import spectral_norm
-
-
-def pad_layer(
-    inp: Tensor, layer: nn.Module, pad_type: Optional[str] = "reflect"
-) -> Tensor:
-    kernel_size = layer.kernel_size[0]
-    if kernel_size % 2 == 0:
-        pad = (kernel_size // 2, kernel_size // 2 - 1)
-    else:
-        pad = (kernel_size // 2, kernel_size // 2)
-    inp = F.pad(inp, pad=pad, mode=pad_type)
-    out = layer(inp)
-    return out
-
-
-def pixel_shuffle_1d(inp: Tensor, scale_factor: Optional[float] = 2.0) -> Tensor:
-    batch_size, channels, in_width = inp.size()
-    channels //= scale_factor
-    out_width = in_width * scale_factor
-    inp_view = inp.contiguous().view(batch_size, channels, scale_factor, in_width)
-    shuffle_out = inp_view.permute(0, 1, 3, 2).contiguous()
-    shuffle_out = shuffle_out.view(batch_size, channels, out_width)
-    return shuffle_out
-
-
-def upsample(x: Tensor, scale_factor: Optional[float] = 2.0) -> Tensor:
-    x_up = F.interpolate(x, scale_factor=scale_factor, mode="nearest")
-    return x_up
-
-
-def append_cond(x: Tensor, cond: Tensor) -> Tensor:
-    p = cond.size(1) // 2
-    mean, std = cond[:, :p], cond[:, p:]
-    out = x * std.unsqueeze(dim=2) + mean.unsqueeze(dim=2)
-    return out
-
-
-def conv_bank(
-    x: Tensor,
-    module_list: nn.Module,
-    act: nn.Module,
-    pad_type: Optional[str] = "reflect",
-) -> Tensor:
-    outs = []
-    for layer in module_list:
-        out = act(pad_layer(x, layer, pad_type))
-        outs.append(out)
-    out = torch.cat(outs + [x], dim=1)
-    return out
-
-
-def get_act(act: str) -> nn.Module:
-    if act == "lrelu":
-        return nn.LeakyReLU()
-    return nn.ReLU()
-
-
-class ContentEncoder(nn.Module):
-    def __init__(
-        self,
-        c_in: int,
-        c_h: int,
-        c_out: int,
-        kernel_size: int,
-        bank_size: int,
-        bank_scale: int,
-        c_bank: int,
-        n_conv_blocks: int,
-        subsample: List[int],
-        act: str,
-        dropout_rate: float,
-    ):
-        super(ContentEncoder, self).__init__()
-        self.n_conv_blocks = n_conv_blocks
-        self.subsample = subsample
-        self.act = get_act(act)
-        self.conv_bank = nn.ModuleList(
-            [
-                nn.Conv1d(c_in, c_bank, kernel_size=k)
-                for k in range(bank_scale, bank_size + 1, bank_scale)
-            ]
-        )
-        in_channels = c_bank * (bank_size // bank_scale) + c_in
-        self.in_conv_layer = nn.Conv1d(in_channels, c_h, kernel_size=1)
-        self.first_conv_layers = nn.ModuleList(
-            [nn.Conv1d(c_h, c_h, kernel_size=kernel_size) for _ in range(n_conv_blocks)]
-        )
-        self.second_conv_layers = nn.ModuleList(
-            [
-                nn.Conv1d(c_h, c_h, kernel_size=kernel_size, stride=sub)
-                for sub, _ in zip(subsample, range(n_conv_blocks))
-            ]
-        )
-        self.norm_layer = nn.InstanceNorm1d(c_h, affine=False)
-        self.mean_layer = nn.Conv1d(c_h, c_out, kernel_size=1)
-        self.std_layer = nn.Conv1d(c_h, c_out, kernel_size=1)
-        self.dropout_layer = nn.Dropout(p=dropout_rate)
-
-    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        out = conv_bank(x, self.conv_bank, act=self.act)
-        out = pad_layer(out, self.in_conv_layer)
-        out = self.norm_layer(out)
-        out = self.act(out)
-        out = self.dropout_layer(out)
-        for l in range(self.n_conv_blocks):
-            y = pad_layer(out, self.first_conv_layers[l])
-            y = self.norm_layer(y)
-            y = self.act(y)
-            y = self.dropout_layer(y)
-            y = pad_layer(y, self.second_conv_layers[l])
-            y = self.norm_layer(y)
-            y = self.act(y)
-            y = self.dropout_layer(y)
-            if self.subsample[l] > 1:
-                out = F.avg_pool1d(out, kernel_size=self.subsample[l], ceil_mode=True)
-            out = y + out
-        mu = pad_layer(out, self.mean_layer)
-        log_sigma = pad_layer(out, self.std_layer)
-        return mu, log_sigma
-
-
-class SpeakerEncoder(nn.Module):
-    def __init__(
-        self,
-        c_in: int,
-        c_h: int,
-        c_out: int,
-        kernel_size: int,
-        bank_size: int,
-        bank_scale: int,
-        c_bank: int,
-        n_conv_blocks: int,
-        n_dense_blocks: int,
-        subsample: List[int],
-        act: str,
-        dropout_rate: float,
-    ):
-        super(SpeakerEncoder, self).__init__()
-        self.c_in = c_in
-        self.c_h = c_h
-        self.c_out = c_out
-        self.kernel_size = kernel_size
-        self.n_conv_blocks = n_conv_blocks
-        self.n_dense_blocks = n_dense_blocks
-        self.subsample = subsample
-        self.act = get_act(act)
-        self.conv_bank = nn.ModuleList(
-            [
-                nn.Conv1d(c_in, c_bank, kernel_size=k)
-                for k in range(bank_scale, bank_size + 1, bank_scale)
-            ]
-        )
-        in_channels = c_bank * (bank_size // bank_scale) + c_in
-        self.in_conv_layer = nn.Conv1d(in_channels, c_h, kernel_size=1)
-        self.first_conv_layers = nn.ModuleList(
-            [nn.Conv1d(c_h, c_h, kernel_size=kernel_size) for _ in range(n_conv_blocks)]
-        )
-        self.second_conv_layers = nn.ModuleList(
-            [
-                nn.Conv1d(c_h, c_h, kernel_size=kernel_size, stride=sub)
-                for sub, _ in zip(subsample, range(n_conv_blocks))
-            ]
-        )
-        self.pooling_layer = nn.AdaptiveAvgPool1d(1)
-        self.first_dense_layers = nn.ModuleList(
-            [nn.Linear(c_h, c_h) for _ in range(n_dense_blocks)]
-        )
-        self.second_dense_layers = nn.ModuleList(
-            [nn.Linear(c_h, c_h) for _ in range(n_dense_blocks)]
-        )
-        self.output_layer = nn.Linear(c_h, c_out)
-        self.dropout_layer = nn.Dropout(p=dropout_rate)
-
-    def conv_blocks(self, inp: Tensor) -> Tensor:
-        out = inp
-        for l in range(self.n_conv_blocks):
-            y = pad_layer(out, self.first_conv_layers[l])
-            y = self.act(y)
-            y = self.dropout_layer(y)
-            y = pad_layer(y, self.second_conv_layers[l])
-            y = self.act(y)
-            y = self.dropout_layer(y)
-            if self.subsample[l] > 1:
-                out = F.avg_pool1d(out, kernel_size=self.subsample[l], ceil_mode=True)
-            out = y + out
-        return out
-
-    def dense_blocks(self, inp: Tensor) -> Tensor:
-        out = inp
-        for l in range(self.n_dense_blocks):
-            y = self.first_dense_layers[l](out)
-            y = self.act(y)
-            y = self.dropout_layer(y)
-            y = self.second_dense_layers[l](y)
-            y = self.act(y)
-            y = self.dropout_layer(y)
-            out = y + out
-        return out
-
-    def forward(self, x: Tensor) -> Tensor:
-        out = conv_bank(x, self.conv_bank, act=self.act)
-        out = pad_layer(out, self.in_conv_layer)
-        out = self.act(out)
-        out = self.conv_blocks(out)
-        out = self.pooling_layer(out).squeeze(-1)
-        out = self.dense_blocks(out)
-        out = self.output_layer(out)
-        return out
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        c_in: int,
-        c_cond: int,
-        c_h: int,
-        c_out: int,
-        kernel_size: int,
-        n_conv_blocks: int,
-        upsample: List[int],
-        act: str,
-        sn: bool,
-        dropout_rate: float,
-    ):
-        super(Decoder, self).__init__()
-        self.n_conv_blocks = n_conv_blocks
-        self.upsample = upsample
-        self.act = get_act(act)
-        f = spectral_norm if sn else lambda x: x
-        self.in_conv_layer = f(nn.Conv1d(c_in, c_h, kernel_size=1))
-        self.first_conv_layers = nn.ModuleList(
-            [
-                f(nn.Conv1d(c_h, c_h, kernel_size=kernel_size))
-                for _ in range(n_conv_blocks)
-            ]
-        )
-        self.second_conv_layers = nn.ModuleList(
-            [
-                f(nn.Conv1d(c_h, c_h * up, kernel_size=kernel_size))
-                for _, up in zip(range(n_conv_blocks), self.upsample)
-            ]
-        )
-        self.norm_layer = nn.InstanceNorm1d(c_h, affine=False)
-        self.conv_affine_layers = nn.ModuleList(
-            [f(nn.Linear(c_cond, c_h * 2)) for _ in range(n_conv_blocks * 2)]
-        )
-        self.out_conv_layer = f(nn.Conv1d(c_h, c_out, kernel_size=1))
-        self.dropout_layer = nn.Dropout(p=dropout_rate)
-
-    def forward(self, z: Tensor, cond: Tensor) -> Tensor:
-        out = pad_layer(z, self.in_conv_layer)
-        out = self.norm_layer(out)
-        out = self.act(out)
-        out = self.dropout_layer(out)
-        for l in range(self.n_conv_blocks):
-            y = pad_layer(out, self.first_conv_layers[l])
-            y = self.norm_layer(y)
-            y = append_cond(y, self.conv_affine_layers[l * 2](cond))
-            y = self.act(y)
-            y = self.dropout_layer(y)
-            y = pad_layer(y, self.second_conv_layers[l])
-            if self.upsample[l] > 1:
-                y = pixel_shuffle_1d(y, scale_factor=self.upsample[l])
-            y = self.norm_layer(y)
-            y = append_cond(y, self.conv_affine_layers[l * 2 + 1](cond))
-            y = self.act(y)
-            y = self.dropout_layer(y)
-            if self.upsample[l] > 1:
-                out = y + upsample(out, scale_factor=self.upsample[l])
-            else:
-                out = y + out
-        out = pad_layer(out, self.out_conv_layer)
-        return out
-
-
-class AdaInVC(nn.Module):
-    def __init__(self, config: Dict):
-        super(AdaInVC, self).__init__()
-        self.content_encoder = ContentEncoder(**config["ContentEncoder"])
-        self.speaker_encoder = SpeakerEncoder(**config["SpeakerEncoder"])
-        self.decoder = Decoder(**config["Decoder"])
-
-    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
-        mu, log_sigma = self.content_encoder(x)
-        emb = self.speaker_encoder(x)
-        eps = log_sigma.new(*log_sigma.size()).normal_(0, 1)
-        dec = self.decoder(mu + torch.exp(log_sigma / 2) * eps, emb)
-        return mu, log_sigma, emb, dec
-
-    def inference(self, src: Tensor, tgt: Tensor) -> Tensor:
-        mu, _ = self.content_encoder(src)
-        emb = self.speaker_encoder(tgt)
-        dec = self.decoder(mu, emb)
-        return dec
\ No newline at end of file
diff --git a/spaces/pierreguillou/bloomz-english/app.py b/spaces/pierreguillou/bloomz-english/app.py
deleted file mode 100644
index 11d7297feb42a73babe68472947ff38436337ae6..0000000000000000000000000000000000000000
--- a/spaces/pierreguillou/bloomz-english/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import gradio as gr
-import transformers
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-#checkpoint = "bigscience/bloomz" # english
-checkpoint = "bigscience/bloomz-560m" # english
-#checkpoint = "bigscience/bloomz-7b1-mt" # non english
-
-import torch
-device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-
-#tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-#model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=False).to(device)
-
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForCausalLM.from_pretrained(checkpoint)
-
-def get_result(prompt):
-    # prompt = f"'''{str(prompt)}'''"
-    inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
-    outputs = model.generate(inputs, max_length= len(prompt)+1000)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-title = "Bloomz (english small)"
-description = "Write an instruction and get the Bloomz result."
-examples = [["Translate to English: Je t'aime."]]
-
-demo = gr.Interface(fn=get_result, inputs="text", outputs="text",
-                    title=title,
-                    description=description,
-                    examples=examples,
-                    allow_flagging="never")
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/pierreguillou/extracao_das_palavras_frases_chave_em_portugues/README.md b/spaces/pierreguillou/extracao_das_palavras_frases_chave_em_portugues/README.md
deleted file mode 100644
index 6c5bd8fee5aabd7585b35e26ec1b7ce8d3c92508..0000000000000000000000000000000000000000
--- a/spaces/pierreguillou/extracao_das_palavras_frases_chave_em_portugues/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Extração das palavras/frases chave em português
-emoji: 🐠
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pikto/Elite-freegpt-webui/client/css/button.css b/spaces/pikto/Elite-freegpt-webui/client/css/button.css
deleted file mode 100644
index 5f604a8460d048458249f78be9dc544ade84801e..0000000000000000000000000000000000000000
--- a/spaces/pikto/Elite-freegpt-webui/client/css/button.css
+++ /dev/null
@@ -1,26 +0,0 @@
-.button {
-	display: flex;
-	padding: 8px 12px;
-	align-items: center;
-	justify-content: center;
-	border: 1px solid var(--conversations);
-	border-radius: var(--border-radius-1);
-	width: 100%;
-	background: transparent;
-	cursor: pointer;
-}
-
-.button span {
-	color: var(--colour-3);
-	font-size: 0.875rem;
-}
-
-.button i::before {
-	margin-right: 8px;
-}
-
-@media screen and (max-width: 990px) {
-	.button span {
-		font-size: 0.75rem;
-	}
-}
diff --git a/spaces/pkiage/fast_arbitrary_image_style_transfer/setup.py b/spaces/pkiage/fast_arbitrary_image_style_transfer/setup.py
deleted file mode 100644
index 2d702a7af0a903a5e0cef817a1aad9851fcf9637..0000000000000000000000000000000000000000
--- a/spaces/pkiage/fast_arbitrary_image_style_transfer/setup.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from setuptools import find_packages, setup
-
-setup(
-    name='src',
-    packages=find_packages(),
-    version='0.1.0',
-    description='Neutral Style Transfer Tool',
-    author='Author',
-    license='MIT',
-)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/__init__.py
deleted file mode 100644
index aef2821b83f6ac1730d063d8ce939134cc2105a7..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/__init__.py
+++ /dev/null
@@ -1,342 +0,0 @@
-"""
-Utilities for determining application-specific dirs. See <https://github.com/platformdirs/platformdirs> for details and
-usage.
-"""
-from __future__ import annotations
-
-import os
-import sys
-from pathlib import Path
-
-if sys.version_info >= (3, 8):  # pragma: no cover (py38+)
-    from typing import Literal
-else:  # pragma: no cover (py38+)
-    from ..typing_extensions import Literal
-
-from .api import PlatformDirsABC
-from .version import __version__
-from .version import __version_tuple__ as __version_info__
-
-
-def _set_platform_dir_class() -> type[PlatformDirsABC]:
-    if sys.platform == "win32":
-        from .windows import Windows as Result
-    elif sys.platform == "darwin":
-        from .macos import MacOS as Result
-    else:
-        from .unix import Unix as Result
-
-    if os.getenv("ANDROID_DATA") == "/data" and os.getenv("ANDROID_ROOT") == "/system":
-
-        if os.getenv("SHELL") or os.getenv("PREFIX"):
-            return Result
-
-        from .android import _android_folder
-
-        if _android_folder() is not None:
-            from .android import Android
-
-            return Android  # return to avoid redefinition of result
-
-    return Result
-
-
-PlatformDirs = _set_platform_dir_class()  #: Currently active platform
-AppDirs = PlatformDirs  #: Backwards compatibility with appdirs
-
-
-def user_data_dir(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    roaming: bool = False,
-) -> str:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.version>`.
-    :returns: data directory tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, roaming=roaming).user_data_dir
-
-
-def site_data_dir(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    multipath: bool = False,
-) -> str:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param multipath: See `roaming <platformdirs.api.PlatformDirsABC.multipath>`.
-    :returns: data directory shared by users
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, multipath=multipath).site_data_dir
-
-
-def user_config_dir(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    roaming: bool = False,
-) -> str:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.version>`.
-    :returns: config directory tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, roaming=roaming).user_config_dir
-
-
-def site_config_dir(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    multipath: bool = False,
-) -> str:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param multipath: See `roaming <platformdirs.api.PlatformDirsABC.multipath>`.
-    :returns: config directory shared by the users
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, multipath=multipath).site_config_dir
-
-
-def user_cache_dir(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    opinion: bool = True,
-) -> str:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param opinion: See `roaming <platformdirs.api.PlatformDirsABC.opinion>`.
-    :returns: cache directory tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, opinion=opinion).user_cache_dir
-
-
-def user_state_dir(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    roaming: bool = False,
-) -> str:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.version>`.
-    :returns: state directory tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, roaming=roaming).user_state_dir
-
-
-def user_log_dir(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    opinion: bool = True,
-) -> str:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param opinion: See `roaming <platformdirs.api.PlatformDirsABC.opinion>`.
-    :returns: log directory tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, opinion=opinion).user_log_dir
-
-
-def user_documents_dir() -> str:
-    """
-    :returns: documents directory tied to the user
-    """
-    return PlatformDirs().user_documents_dir
-
-
-def user_runtime_dir(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    opinion: bool = True,
-) -> str:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param opinion: See `opinion <platformdirs.api.PlatformDirsABC.opinion>`.
-    :returns: runtime directory tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, opinion=opinion).user_runtime_dir
-
-
-def user_data_path(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    roaming: bool = False,
-) -> Path:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.version>`.
-    :returns: data path tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, roaming=roaming).user_data_path
-
-
-def site_data_path(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    multipath: bool = False,
-) -> Path:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param multipath: See `multipath <platformdirs.api.PlatformDirsABC.multipath>`.
-    :returns: data path shared by users
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, multipath=multipath).site_data_path
-
-
-def user_config_path(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    roaming: bool = False,
-) -> Path:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.version>`.
-    :returns: config path tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, roaming=roaming).user_config_path
-
-
-def site_config_path(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    multipath: bool = False,
-) -> Path:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param multipath: See `roaming <platformdirs.api.PlatformDirsABC.multipath>`.
-    :returns: config path shared by the users
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, multipath=multipath).site_config_path
-
-
-def user_cache_path(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    opinion: bool = True,
-) -> Path:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param opinion: See `roaming <platformdirs.api.PlatformDirsABC.opinion>`.
-    :returns: cache path tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, opinion=opinion).user_cache_path
-
-
-def user_state_path(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    roaming: bool = False,
-) -> Path:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.version>`.
-    :returns: state path tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, roaming=roaming).user_state_path
-
-
-def user_log_path(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    opinion: bool = True,
-) -> Path:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param opinion: See `roaming <platformdirs.api.PlatformDirsABC.opinion>`.
-    :returns: log path tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, opinion=opinion).user_log_path
-
-
-def user_documents_path() -> Path:
-    """
-    :returns: documents path tied to the user
-    """
-    return PlatformDirs().user_documents_path
-
-
-def user_runtime_path(
-    appname: str | None = None,
-    appauthor: str | None | Literal[False] = None,
-    version: str | None = None,
-    opinion: bool = True,
-) -> Path:
-    """
-    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
-    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
-    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
-    :param opinion: See `opinion <platformdirs.api.PlatformDirsABC.opinion>`.
-    :returns: runtime path tied to the user
-    """
-    return PlatformDirs(appname=appname, appauthor=appauthor, version=version, opinion=opinion).user_runtime_path
-
-
-__all__ = [
-    "__version__",
-    "__version_info__",
-    "PlatformDirs",
-    "AppDirs",
-    "PlatformDirsABC",
-    "user_data_dir",
-    "user_config_dir",
-    "user_cache_dir",
-    "user_state_dir",
-    "user_log_dir",
-    "user_documents_dir",
-    "user_runtime_dir",
-    "site_data_dir",
-    "site_config_dir",
-    "user_data_path",
-    "user_config_path",
-    "user_cache_path",
-    "user_state_path",
-    "user_log_path",
-    "user_documents_path",
-    "user_runtime_path",
-    "site_data_path",
-    "site_config_path",
-]
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/api.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/api.py
deleted file mode 100644
index 6f6e2c2c69d25dba4d1038a2d548fbf68017f91b..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/api.py
+++ /dev/null
@@ -1,156 +0,0 @@
-from __future__ import annotations
-
-import os
-import sys
-from abc import ABC, abstractmethod
-from pathlib import Path
-
-if sys.version_info >= (3, 8):  # pragma: no branch
-    from typing import Literal  # pragma: no cover
-
-
-class PlatformDirsABC(ABC):
-    """
-    Abstract base class for platform directories.
-    """
-
-    def __init__(
-        self,
-        appname: str | None = None,
-        appauthor: str | None | Literal[False] = None,
-        version: str | None = None,
-        roaming: bool = False,
-        multipath: bool = False,
-        opinion: bool = True,
-    ):
-        """
-        Create a new platform directory.
-
-        :param appname: See `appname`.
-        :param appauthor: See `appauthor`.
-        :param version: See `version`.
-        :param roaming: See `roaming`.
-        :param multipath: See `multipath`.
-        :param opinion: See `opinion`.
-        """
-        self.appname = appname  #: The name of application.
-        self.appauthor = appauthor
-        """
-        The name of the app author or distributing body for this application. Typically, it is the owning company name.
-        Defaults to `appname`. You may pass ``False`` to disable it.
-        """
-        self.version = version
-        """
-        An optional version path element to append to the path. You might want to use this if you want multiple versions
-        of your app to be able to run independently. If used, this would typically be ``<major>.<minor>``.
-        """
-        self.roaming = roaming
-        """
-        Whether to use the roaming appdata directory on Windows. That means that for users on a Windows network setup
-        for roaming profiles, this user data will be synced on login (see
-        `here <http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>`_).
-        """
-        self.multipath = multipath
-        """
-        An optional parameter only applicable to Unix/Linux which indicates that the entire list of data dirs should be
-        returned. By default, the first item would only be returned.
-        """
-        self.opinion = opinion  #: A flag to indicating to use opinionated values.
-
-    def _append_app_name_and_version(self, *base: str) -> str:
-        params = list(base[1:])
-        if self.appname:
-            params.append(self.appname)
-            if self.version:
-                params.append(self.version)
-        return os.path.join(base[0], *params)
-
-    @property
-    @abstractmethod
-    def user_data_dir(self) -> str:
-        """:return: data directory tied to the user"""
-
-    @property
-    @abstractmethod
-    def site_data_dir(self) -> str:
-        """:return: data directory shared by users"""
-
-    @property
-    @abstractmethod
-    def user_config_dir(self) -> str:
-        """:return: config directory tied to the user"""
-
-    @property
-    @abstractmethod
-    def site_config_dir(self) -> str:
-        """:return: config directory shared by the users"""
-
-    @property
-    @abstractmethod
-    def user_cache_dir(self) -> str:
-        """:return: cache directory tied to the user"""
-
-    @property
-    @abstractmethod
-    def user_state_dir(self) -> str:
-        """:return: state directory tied to the user"""
-
-    @property
-    @abstractmethod
-    def user_log_dir(self) -> str:
-        """:return: log directory tied to the user"""
-
-    @property
-    @abstractmethod
-    def user_documents_dir(self) -> str:
-        """:return: documents directory tied to the user"""
-
-    @property
-    @abstractmethod
-    def user_runtime_dir(self) -> str:
-        """:return: runtime directory tied to the user"""
-
-    @property
-    def user_data_path(self) -> Path:
-        """:return: data path tied to the user"""
-        return Path(self.user_data_dir)
-
-    @property
-    def site_data_path(self) -> Path:
-        """:return: data path shared by users"""
-        return Path(self.site_data_dir)
-
-    @property
-    def user_config_path(self) -> Path:
-        """:return: config path tied to the user"""
-        return Path(self.user_config_dir)
-
-    @property
-    def site_config_path(self) -> Path:
-        """:return: config path shared by the users"""
-        return Path(self.site_config_dir)
-
-    @property
-    def user_cache_path(self) -> Path:
-        """:return: cache path tied to the user"""
-        return Path(self.user_cache_dir)
-
-    @property
-    def user_state_path(self) -> Path:
-        """:return: state path tied to the user"""
-        return Path(self.user_state_dir)
-
-    @property
-    def user_log_path(self) -> Path:
-        """:return: log path tied to the user"""
-        return Path(self.user_log_dir)
-
-    @property
-    def user_documents_path(self) -> Path:
-        """:return: documents path tied to the user"""
-        return Path(self.user_documents_dir)
-
-    @property
-    def user_runtime_path(self) -> Path:
-        """:return: runtime path tied to the user"""
-        return Path(self.user_runtime_dir)
diff --git a/spaces/polymath707/bigscience-bloomz-7b1/app.py b/spaces/polymath707/bigscience-bloomz-7b1/app.py
deleted file mode 100644
index 490176a4967a28619d672263d54b8f71b657b815..0000000000000000000000000000000000000000
--- a/spaces/polymath707/bigscience-bloomz-7b1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/bigscience/bloomz-7b1").launch()
\ No newline at end of file
diff --git a/spaces/praveenku32k/SimilarWordFinderApp/app.py b/spaces/praveenku32k/SimilarWordFinderApp/app.py
deleted file mode 100644
index 7957dd559930c43cef009dc90282c5d76da80073..0000000000000000000000000000000000000000
--- a/spaces/praveenku32k/SimilarWordFinderApp/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-#Allows you to use Streamlit, a framework for building interactive web applications.
-#It provides functions for creating UIs, displaying data, and handling user inputs.
-import streamlit as st
-
-
-#This module provides a way to interact with the operating system, such as accessing environment variables, working with files
-#and directories, executing shell commands, etc
-import os
-
-#Helps us generate embeddings
-#An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. 
-#Small distances suggest high relatedness and large distances suggest low relatedness.
-from langchain.embeddings import HuggingFaceEmbeddings
-
-
-#FAISS is an open-source library developed by Facebook AI Research for efficient similarity search and clustering of large-scale datasets, particularly with high-dimensional vectors. 
-#It provides optimized indexing structures and algorithms for tasks like nearest neighbor search and recommendation systems.
-from langchain.vectorstores import FAISS
-
-
-#load_dotenv() is a function that loads variables from a .env file into environment variables in a Python script. 
-#It allows you to store sensitive information or configuration settings separate from your code
-#and access them within your application.
-from dotenv import load_dotenv
-
-
-load_dotenv()
-
-
-#By using st.set_page_config(), you can customize the appearance of your Streamlit application's web page
-st.set_page_config(page_title="Educate Kids", page_icon=":robot:")
-st.header("Hey, Ask me something & I will give out similar things")
-
-#Initialize the OpenAIEmbeddings object
-# embeddings = OpenAIEmbeddings()
-embeddings= HuggingFaceEmbeddings()
-
-#The below snippet helps us to import CSV file data for our tasks
-from langchain.document_loaders.csv_loader import CSVLoader
-loader = CSVLoader(file_path='myData.csv', csv_args={
-    'delimiter': ',',
-    'quotechar': '"',
-    'fieldnames': ['Words']
-})
-
-#Assigning the data inside the csv to our variable here
-data = loader.load()
-
-#Display the data
-print(data)
-
-db = FAISS.from_documents(data, embeddings)
-
-#Function to receive input from user and store it in a variable
-def get_text():
-    input_text = st.text_input("You: ", key= input)
-    return input_text
-
-
-user_input=get_text()
-submit = st.button('Find similar Things')  
-
-if submit:
-    
-    #If the button is clicked, the below snippet will fetch us the similar text
-    docs = db.similarity_search(user_input)
-    print(docs)
-    st.subheader("Top Matches:")
-    st.text(docs[0])
-    st.text(docs[1].page_content)
-
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiofiles/threadpool/utils.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiofiles/threadpool/utils.py
deleted file mode 100644
index 5fd3bb992e51b54225d53edb5f8e50f575997f81..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiofiles/threadpool/utils.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import functools
-
-
-def delegate_to_executor(*attrs):
-    def cls_builder(cls):
-        for attr_name in attrs:
-            setattr(cls, attr_name, _make_delegate_method(attr_name))
-        return cls
-
-    return cls_builder
-
-
-def proxy_method_directly(*attrs):
-    def cls_builder(cls):
-        for attr_name in attrs:
-            setattr(cls, attr_name, _make_proxy_method(attr_name))
-        return cls
-
-    return cls_builder
-
-
-def proxy_property_directly(*attrs):
-    def cls_builder(cls):
-        for attr_name in attrs:
-            setattr(cls, attr_name, _make_proxy_property(attr_name))
-        return cls
-
-    return cls_builder
-
-
-def cond_delegate_to_executor(*attrs):
-    def cls_builder(cls):
-        for attr_name in attrs:
-            setattr(cls, attr_name, _make_cond_delegate_method(attr_name))
-        return cls
-
-    return cls_builder
-
-
-def _make_delegate_method(attr_name):
-    async def method(self, *args, **kwargs):
-        cb = functools.partial(getattr(self._file, attr_name), *args, **kwargs)
-        return await self._loop.run_in_executor(self._executor, cb)
-
-    return method
-
-
-def _make_proxy_method(attr_name):
-    def method(self, *args, **kwargs):
-        return getattr(self._file, attr_name)(*args, **kwargs)
-
-    return method
-
-
-def _make_proxy_property(attr_name):
-    def proxy_property(self):
-        return getattr(self._file, attr_name)
-
-    return property(proxy_property)
-
-
-def _make_cond_delegate_method(attr_name):
-    """For spooled temp files, delegate only if rolled to file object"""
-
-    async def method(self, *args, **kwargs):
-        if self._file._rolled:
-            cb = functools.partial(getattr(self._file, attr_name), *args, **kwargs)
-            return await self._loop.run_in_executor(self._executor, cb)
-        else:
-            return getattr(self._file, attr_name)(*args, **kwargs)
-
-    return method
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/attr/filters.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/attr/filters.py
deleted file mode 100644
index a1e40c98db853aa375ab0b24559e0559f91e6152..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/attr/filters.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-"""
-Commonly useful filters for `attr.asdict`.
-"""
-
-from ._make import Attribute
-
-
-def _split_what(what):
-    """
-    Returns a tuple of `frozenset`s of classes and attributes.
-    """
-    return (
-        frozenset(cls for cls in what if isinstance(cls, type)),
-        frozenset(cls for cls in what if isinstance(cls, str)),
-        frozenset(cls for cls in what if isinstance(cls, Attribute)),
-    )
-
-
-def include(*what):
-    """
-    Include *what*.
-
-    :param what: What to include.
-    :type what: `list` of classes `type`, field names `str` or
-        `attrs.Attribute`\\ s
-
-    :rtype: `callable`
-
-    .. versionchanged:: 23.1.0 Accept strings with field names.
-    """
-    cls, names, attrs = _split_what(what)
-
-    def include_(attribute, value):
-        return (
-            value.__class__ in cls
-            or attribute.name in names
-            or attribute in attrs
-        )
-
-    return include_
-
-
-def exclude(*what):
-    """
-    Exclude *what*.
-
-    :param what: What to exclude.
-    :type what: `list` of classes `type`, field names `str` or
-        `attrs.Attribute`\\ s.
-
-    :rtype: `callable`
-
-    .. versionchanged:: 23.3.0 Accept field name string as input argument
-    """
-    cls, names, attrs = _split_what(what)
-
-    def exclude_(attribute, value):
-        return not (
-            value.__class__ in cls
-            or attribute.name in names
-            or attribute in attrs
-        )
-
-    return exclude_
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/middleware/trustedhost.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/middleware/trustedhost.py
deleted file mode 100644
index 08d7e035315677856fd2cd0be2044689b57619bf..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/middleware/trustedhost.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from starlette.middleware.trustedhost import (  # noqa
-    TrustedHostMiddleware as TrustedHostMiddleware,
-)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-e1846cdc.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-e1846cdc.js
deleted file mode 100644
index 3346fbca8a9cea9d3018716c47a4f2009b0b3206..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-e1846cdc.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{I as ue,S as _e}from"./ImageUploader-3bf768ff.js";import{W as Ye}from"./ImageUploader-3bf768ff.js";import{B as H}from"./Button-89057c03.js";import{E as fe}from"./Empty-937365d8.js";import{S as J}from"./Index-37584f50.js";import{I as he}from"./Image-eaba773f.js";import{U as ce}from"./UploadText-232a3213.js";import{default as ye}from"./Example-e563d19c.js";import"./utils-c3e3db58.js";import"./BlockLabel-e3b0d1c3.js";import"./IconButton-16e5dbea.js";import"./ShareButton-d3fa81fa.js";import"./Download-696bd40c.js";import"./index-0526d562.js";import"./svelte/svelte.js";import"./DropdownArrow-c9479e43.js";import"./Upload-a4034e93.js";/* empty css                                                   */import"./Clear-2c7bae91.js";import"./file-url-f4206b44.js";/* empty css                                              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visible(e){this.$$set({visible:e}),m()}get value(){return this.$$.ctx[0]}set value(e){this.$$set({value:e}),m()}get label(){return this.$$.ctx[5]}set label(e){this.$$set({label:e}),m()}get show_label(){return this.$$.ctx[6]}set show_label(e){this.$$set({show_label:e}),m()}get show_download_button(){return this.$$.ctx[7]}set show_download_button(e){this.$$set({show_download_button:e}),m()}get root(){return this.$$.ctx[8]}set root(e){this.$$set({root:e}),m()}get height(){return this.$$.ctx[9]}set height(e){this.$$set({height:e}),m()}get width(){return this.$$.ctx[10]}set width(e){this.$$set({width:e}),m()}get _selectable(){return this.$$.ctx[11]}set _selectable(e){this.$$set({_selectable:e}),m()}get container(){return this.$$.ctx[12]}set container(e){this.$$set({container:e}),m()}get scale(){return this.$$.ctx[13]}set scale(e){this.$$set({scale:e}),m()}get min_width(){return this.$$.ctx[14]}set min_width(e){this.$$set({min_width:e}),m()}get loading_status(){return this.$$.ctx[1]}set loading_status(e){this.$$set({loading_status:e}),m()}get show_share_button(){return this.$$.ctx[15]}set show_share_button(e){this.$$set({show_share_button:e}),m()}get sources(){return this.$$.ctx[16]}set sources(e){this.$$set({sources:e}),m()}get interactive(){return this.$$.ctx[17]}set interactive(e){this.$$set({interactive:e}),m()}get streaming(){return this.$$.ctx[18]}set streaming(e){this.$$set({streaming:e}),m()}get pending(){return this.$$.ctx[19]}set pending(e){this.$$set({pending:e}),m()}get mirror_webcam(){return this.$$.ctx[20]}set mirror_webcam(e){this.$$set({mirror_webcam:e}),m()}get gradio(){return this.$$.ctx[21]}set gradio(e){this.$$set({gradio:e}),m()}}export{ye as BaseExample,ue as BaseImageUploader,_e as BaseStaticImage,Ye as Webcam,Re as default};
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diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Example-ff23c20d.css b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Example-ff23c20d.css
deleted file mode 100644
index a05d87695b4e6a53fd84b99c7fdd32c802141a9a..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Example-ff23c20d.css
+++ /dev/null
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diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-61df0c8a.css b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-61df0c8a.css
deleted file mode 100644
index 80dc153cd0a014c251c413848e00861c0fde751d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-61df0c8a.css
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/typing/tests/data/pass/ndarray_shape_manipulation.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/typing/tests/data/pass/ndarray_shape_manipulation.py
deleted file mode 100644
index 0ca3dff392e12a4122740ade2ceb71b3d6e7bb08..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/typing/tests/data/pass/ndarray_shape_manipulation.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import numpy as np
-
-nd1 = np.array([[1, 2], [3, 4]])
-
-# reshape
-nd1.reshape(4)
-nd1.reshape(2, 2)
-nd1.reshape((2, 2))
-
-nd1.reshape((2, 2), order="C")
-nd1.reshape(4, order="C")
-
-# resize
-nd1.resize()
-nd1.resize(4)
-nd1.resize(2, 2)
-nd1.resize((2, 2))
-
-nd1.resize((2, 2), refcheck=True)
-nd1.resize(4, refcheck=True)
-
-nd2 = np.array([[1, 2], [3, 4]])
-
-# transpose
-nd2.transpose()
-nd2.transpose(1, 0)
-nd2.transpose((1, 0))
-
-# swapaxes
-nd2.swapaxes(0, 1)
-
-# flatten
-nd2.flatten()
-nd2.flatten("C")
-
-# ravel
-nd2.ravel()
-nd2.ravel("C")
-
-# squeeze
-nd2.squeeze()
-
-nd3 = np.array([[1, 2]])
-nd3.squeeze(0)
-
-nd4 = np.array([[[1, 2]]])
-nd4.squeeze((0, 1))
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/shared_docs.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/shared_docs.py
deleted file mode 100644
index ba793b9c11c272e819a9b55c5a7a6e0295eb22c9..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/shared_docs.py
+++ /dev/null
@@ -1,929 +0,0 @@
-from __future__ import annotations
-
-_shared_docs: dict[str, str] = {}
-
-_shared_docs[
-    "aggregate"
-] = """
-Aggregate using one or more operations over the specified axis.
-
-Parameters
-----------
-func : function, str, list or dict
-    Function to use for aggregating the data. If a function, must either
-    work when passed a {klass} or when passed to {klass}.apply.
-
-    Accepted combinations are:
-
-    - function
-    - string function name
-    - list of functions and/or function names, e.g. ``[np.sum, 'mean']``
-    - dict of axis labels -> functions, function names or list of such.
-{axis}
-*args
-    Positional arguments to pass to `func`.
-**kwargs
-    Keyword arguments to pass to `func`.
-
-Returns
--------
-scalar, Series or DataFrame
-
-    The return can be:
-
-    * scalar : when Series.agg is called with single function
-    * Series : when DataFrame.agg is called with a single function
-    * DataFrame : when DataFrame.agg is called with several functions
-
-    Return scalar, Series or DataFrame.
-{see_also}
-Notes
------
-The aggregation operations are always performed over an axis, either the
-index (default) or the column axis. This behavior is different from
-`numpy` aggregation functions (`mean`, `median`, `prod`, `sum`, `std`,
-`var`), where the default is to compute the aggregation of the flattened
-array, e.g., ``numpy.mean(arr_2d)`` as opposed to
-``numpy.mean(arr_2d, axis=0)``.
-
-`agg` is an alias for `aggregate`. Use the alias.
-
-Functions that mutate the passed object can produce unexpected
-behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
-for more details.
-
-A passed user-defined-function will be passed a Series for evaluation.
-{examples}"""
-
-_shared_docs[
-    "compare"
-] = """
-Compare to another {klass} and show the differences.
-
-Parameters
-----------
-other : {klass}
-    Object to compare with.
-
-align_axis : {{0 or 'index', 1 or 'columns'}}, default 1
-    Determine which axis to align the comparison on.
-
-    * 0, or 'index' : Resulting differences are stacked vertically
-        with rows drawn alternately from self and other.
-    * 1, or 'columns' : Resulting differences are aligned horizontally
-        with columns drawn alternately from self and other.
-
-keep_shape : bool, default False
-    If true, all rows and columns are kept.
-    Otherwise, only the ones with different values are kept.
-
-keep_equal : bool, default False
-    If true, the result keeps values that are equal.
-    Otherwise, equal values are shown as NaNs.
-
-result_names : tuple, default ('self', 'other')
-    Set the dataframes names in the comparison.
-
-    .. versionadded:: 1.5.0
-"""
-
-_shared_docs[
-    "groupby"
-] = """
-Group %(klass)s using a mapper or by a Series of columns.
-
-A groupby operation involves some combination of splitting the
-object, applying a function, and combining the results. This can be
-used to group large amounts of data and compute operations on these
-groups.
-
-Parameters
-----------
-by : mapping, function, label, pd.Grouper or list of such
-    Used to determine the groups for the groupby.
-    If ``by`` is a function, it's called on each value of the object's
-    index. If a dict or Series is passed, the Series or dict VALUES
-    will be used to determine the groups (the Series' values are first
-    aligned; see ``.align()`` method). If a list or ndarray of length
-    equal to the selected axis is passed (see the `groupby user guide
-    <https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#splitting-an-object-into-groups>`_),
-    the values are used as-is to determine the groups. A label or list
-    of labels may be passed to group by the columns in ``self``.
-    Notice that a tuple is interpreted as a (single) key.
-axis : {0 or 'index', 1 or 'columns'}, default 0
-    Split along rows (0) or columns (1). For `Series` this parameter
-    is unused and defaults to 0.
-
-    .. deprecated:: 2.1.0
-
-        Will be removed and behave like axis=0 in a future version.
-        For ``axis=1``, do ``frame.T.groupby(...)`` instead.
-
-level : int, level name, or sequence of such, default None
-    If the axis is a MultiIndex (hierarchical), group by a particular
-    level or levels. Do not specify both ``by`` and ``level``.
-as_index : bool, default True
-    Return object with group labels as the
-    index. Only relevant for DataFrame input. as_index=False is
-    effectively "SQL-style" grouped output. This argument has no effect
-    on filtrations (see the `filtrations in the user guide
-    <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration>`_),
-    such as ``head()``, ``tail()``, ``nth()`` and in transformations
-    (see the `transformations in the user guide
-    <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation>`_).
-sort : bool, default True
-    Sort group keys. Get better performance by turning this off.
-    Note this does not influence the order of observations within each
-    group. Groupby preserves the order of rows within each group. If False,
-    the groups will appear in the same order as they did in the original DataFrame.
-    This argument has no effect on filtrations (see the `filtrations in the user guide
-    <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration>`_),
-    such as ``head()``, ``tail()``, ``nth()`` and in transformations
-    (see the `transformations in the user guide
-    <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation>`_).
-
-    .. versionchanged:: 2.0.0
-
-        Specifying ``sort=False`` with an ordered categorical grouper will no
-        longer sort the values.
-
-group_keys : bool, default True
-    When calling apply and the ``by`` argument produces a like-indexed
-    (i.e. :ref:`a transform <groupby.transform>`) result, add group keys to
-    index to identify pieces. By default group keys are not included
-    when the result's index (and column) labels match the inputs, and
-    are included otherwise.
-
-    .. versionchanged:: 1.5.0
-
-       Warns that ``group_keys`` will no longer be ignored when the
-       result from ``apply`` is a like-indexed Series or DataFrame.
-       Specify ``group_keys`` explicitly to include the group keys or
-       not.
-
-    .. versionchanged:: 2.0.0
-
-       ``group_keys`` now defaults to ``True``.
-
-observed : bool, default False
-    This only applies if any of the groupers are Categoricals.
-    If True: only show observed values for categorical groupers.
-    If False: show all values for categorical groupers.
-
-    .. deprecated:: 2.1.0
-
-        The default value will change to True in a future version of pandas.
-
-dropna : bool, default True
-    If True, and if group keys contain NA values, NA values together
-    with row/column will be dropped.
-    If False, NA values will also be treated as the key in groups.
-
-Returns
--------
-pandas.api.typing.%(klass)sGroupBy
-    Returns a groupby object that contains information about the groups.
-
-See Also
---------
-resample : Convenience method for frequency conversion and resampling
-    of time series.
-
-Notes
------
-See the `user guide
-<https://pandas.pydata.org/pandas-docs/stable/groupby.html>`__ for more
-detailed usage and examples, including splitting an object into groups,
-iterating through groups, selecting a group, aggregation, and more.
-"""
-
-_shared_docs[
-    "melt"
-] = """
-Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
-
-This function is useful to massage a DataFrame into a format where one
-or more columns are identifier variables (`id_vars`), while all other
-columns, considered measured variables (`value_vars`), are "unpivoted" to
-the row axis, leaving just two non-identifier columns, 'variable' and
-'value'.
-
-Parameters
-----------
-id_vars : tuple, list, or ndarray, optional
-    Column(s) to use as identifier variables.
-value_vars : tuple, list, or ndarray, optional
-    Column(s) to unpivot. If not specified, uses all columns that
-    are not set as `id_vars`.
-var_name : scalar
-    Name to use for the 'variable' column. If None it uses
-    ``frame.columns.name`` or 'variable'.
-value_name : scalar, default 'value'
-    Name to use for the 'value' column.
-col_level : int or str, optional
-    If columns are a MultiIndex then use this level to melt.
-ignore_index : bool, default True
-    If True, original index is ignored. If False, the original index is retained.
-    Index labels will be repeated as necessary.
-
-Returns
--------
-DataFrame
-    Unpivoted DataFrame.
-
-See Also
---------
-%(other)s : Identical method.
-pivot_table : Create a spreadsheet-style pivot table as a DataFrame.
-DataFrame.pivot : Return reshaped DataFrame organized
-    by given index / column values.
-DataFrame.explode : Explode a DataFrame from list-like
-        columns to long format.
-
-Notes
------
-Reference :ref:`the user guide <reshaping.melt>` for more examples.
-
-Examples
---------
->>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
-...                    'B': {0: 1, 1: 3, 2: 5},
-...                    'C': {0: 2, 1: 4, 2: 6}})
->>> df
-   A  B  C
-0  a  1  2
-1  b  3  4
-2  c  5  6
-
->>> %(caller)sid_vars=['A'], value_vars=['B'])
-   A variable  value
-0  a        B      1
-1  b        B      3
-2  c        B      5
-
->>> %(caller)sid_vars=['A'], value_vars=['B', 'C'])
-   A variable  value
-0  a        B      1
-1  b        B      3
-2  c        B      5
-3  a        C      2
-4  b        C      4
-5  c        C      6
-
-The names of 'variable' and 'value' columns can be customized:
-
->>> %(caller)sid_vars=['A'], value_vars=['B'],
-...         var_name='myVarname', value_name='myValname')
-   A myVarname  myValname
-0  a         B          1
-1  b         B          3
-2  c         B          5
-
-Original index values can be kept around:
-
->>> %(caller)sid_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
-   A variable  value
-0  a        B      1
-1  b        B      3
-2  c        B      5
-0  a        C      2
-1  b        C      4
-2  c        C      6
-
-If you have multi-index columns:
-
->>> df.columns = [list('ABC'), list('DEF')]
->>> df
-   A  B  C
-   D  E  F
-0  a  1  2
-1  b  3  4
-2  c  5  6
-
->>> %(caller)scol_level=0, id_vars=['A'], value_vars=['B'])
-   A variable  value
-0  a        B      1
-1  b        B      3
-2  c        B      5
-
->>> %(caller)sid_vars=[('A', 'D')], value_vars=[('B', 'E')])
-  (A, D) variable_0 variable_1  value
-0      a          B          E      1
-1      b          B          E      3
-2      c          B          E      5
-"""
-
-_shared_docs[
-    "transform"
-] = """
-Call ``func`` on self producing a {klass} with the same axis shape as self.
-
-Parameters
-----------
-func : function, str, list-like or dict-like
-    Function to use for transforming the data. If a function, must either
-    work when passed a {klass} or when passed to {klass}.apply. If func
-    is both list-like and dict-like, dict-like behavior takes precedence.
-
-    Accepted combinations are:
-
-    - function
-    - string function name
-    - list-like of functions and/or function names, e.g. ``[np.exp, 'sqrt']``
-    - dict-like of axis labels -> functions, function names or list-like of such.
-{axis}
-*args
-    Positional arguments to pass to `func`.
-**kwargs
-    Keyword arguments to pass to `func`.
-
-Returns
--------
-{klass}
-    A {klass} that must have the same length as self.
-
-Raises
-------
-ValueError : If the returned {klass} has a different length than self.
-
-See Also
---------
-{klass}.agg : Only perform aggregating type operations.
-{klass}.apply : Invoke function on a {klass}.
-
-Notes
------
-Functions that mutate the passed object can produce unexpected
-behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
-for more details.
-
-Examples
---------
->>> df = pd.DataFrame({{'A': range(3), 'B': range(1, 4)}})
->>> df
-   A  B
-0  0  1
-1  1  2
-2  2  3
->>> df.transform(lambda x: x + 1)
-   A  B
-0  1  2
-1  2  3
-2  3  4
-
-Even though the resulting {klass} must have the same length as the
-input {klass}, it is possible to provide several input functions:
-
->>> s = pd.Series(range(3))
->>> s
-0    0
-1    1
-2    2
-dtype: int64
->>> s.transform([np.sqrt, np.exp])
-       sqrt        exp
-0  0.000000   1.000000
-1  1.000000   2.718282
-2  1.414214   7.389056
-
-You can call transform on a GroupBy object:
-
->>> df = pd.DataFrame({{
-...     "Date": [
-...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
-...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
-...     "Data": [5, 8, 6, 1, 50, 100, 60, 120],
-... }})
->>> df
-         Date  Data
-0  2015-05-08     5
-1  2015-05-07     8
-2  2015-05-06     6
-3  2015-05-05     1
-4  2015-05-08    50
-5  2015-05-07   100
-6  2015-05-06    60
-7  2015-05-05   120
->>> df.groupby('Date')['Data'].transform('sum')
-0     55
-1    108
-2     66
-3    121
-4     55
-5    108
-6     66
-7    121
-Name: Data, dtype: int64
-
->>> df = pd.DataFrame({{
-...     "c": [1, 1, 1, 2, 2, 2, 2],
-...     "type": ["m", "n", "o", "m", "m", "n", "n"]
-... }})
->>> df
-   c type
-0  1    m
-1  1    n
-2  1    o
-3  2    m
-4  2    m
-5  2    n
-6  2    n
->>> df['size'] = df.groupby('c')['type'].transform(len)
->>> df
-   c type size
-0  1    m    3
-1  1    n    3
-2  1    o    3
-3  2    m    4
-4  2    m    4
-5  2    n    4
-6  2    n    4
-"""
-
-_shared_docs[
-    "storage_options"
-] = """storage_options : dict, optional
-    Extra options that make sense for a particular storage connection, e.g.
-    host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
-    are forwarded to ``urllib.request.Request`` as header options. For other
-    URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
-    forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
-    details, and for more examples on storage options refer `here
-    <https://pandas.pydata.org/docs/user_guide/io.html?
-    highlight=storage_options#reading-writing-remote-files>`_."""
-
-_shared_docs[
-    "compression_options"
-] = """compression : str or dict, default 'infer'
-    For on-the-fly compression of the output data. If 'infer' and '%s' is
-    path-like, then detect compression from the following extensions: '.gz',
-    '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
-    (otherwise no compression).
-    Set to ``None`` for no compression.
-    Can also be a dict with key ``'method'`` set
-    to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
-    other key-value pairs are forwarded to
-    ``zipfile.ZipFile``, ``gzip.GzipFile``,
-    ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or
-    ``tarfile.TarFile``, respectively.
-    As an example, the following could be passed for faster compression and to create
-    a reproducible gzip archive:
-    ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
-
-    .. versionadded:: 1.5.0
-        Added support for `.tar` files."""
-
-_shared_docs[
-    "decompression_options"
-] = """compression : str or dict, default 'infer'
-    For on-the-fly decompression of on-disk data. If 'infer' and '%s' is
-    path-like, then detect compression from the following extensions: '.gz',
-    '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
-    (otherwise no compression).
-    If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in.
-    Set to ``None`` for no decompression.
-    Can also be a dict with key ``'method'`` set
-    to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
-    other key-value pairs are forwarded to
-    ``zipfile.ZipFile``, ``gzip.GzipFile``,
-    ``bz2.BZ2File``, ``zstandard.ZstdDecompressor``, ``lzma.LZMAFile`` or
-    ``tarfile.TarFile``, respectively.
-    As an example, the following could be passed for Zstandard decompression using a
-    custom compression dictionary:
-    ``compression={'method': 'zstd', 'dict_data': my_compression_dict}``.
-
-    .. versionadded:: 1.5.0
-        Added support for `.tar` files."""
-
-_shared_docs[
-    "replace"
-] = """
-    Replace values given in `to_replace` with `value`.
-
-    Values of the {klass} are replaced with other values dynamically.
-    This differs from updating with ``.loc`` or ``.iloc``, which require
-    you to specify a location to update with some value.
-
-    Parameters
-    ----------
-    to_replace : str, regex, list, dict, Series, int, float, or None
-        How to find the values that will be replaced.
-
-        * numeric, str or regex:
-
-            - numeric: numeric values equal to `to_replace` will be
-              replaced with `value`
-            - str: string exactly matching `to_replace` will be replaced
-              with `value`
-            - regex: regexs matching `to_replace` will be replaced with
-              `value`
-
-        * list of str, regex, or numeric:
-
-            - First, if `to_replace` and `value` are both lists, they
-              **must** be the same length.
-            - Second, if ``regex=True`` then all of the strings in **both**
-              lists will be interpreted as regexs otherwise they will match
-              directly. This doesn't matter much for `value` since there
-              are only a few possible substitution regexes you can use.
-            - str, regex and numeric rules apply as above.
-
-        * dict:
-
-            - Dicts can be used to specify different replacement values
-              for different existing values. For example,
-              ``{{'a': 'b', 'y': 'z'}}`` replaces the value 'a' with 'b' and
-              'y' with 'z'. To use a dict in this way, the optional `value`
-              parameter should not be given.
-            - For a DataFrame a dict can specify that different values
-              should be replaced in different columns. For example,
-              ``{{'a': 1, 'b': 'z'}}`` looks for the value 1 in column 'a'
-              and the value 'z' in column 'b' and replaces these values
-              with whatever is specified in `value`. The `value` parameter
-              should not be ``None`` in this case. You can treat this as a
-              special case of passing two lists except that you are
-              specifying the column to search in.
-            - For a DataFrame nested dictionaries, e.g.,
-              ``{{'a': {{'b': np.nan}}}}``, are read as follows: look in column
-              'a' for the value 'b' and replace it with NaN. The optional `value`
-              parameter should not be specified to use a nested dict in this
-              way. You can nest regular expressions as well. Note that
-              column names (the top-level dictionary keys in a nested
-              dictionary) **cannot** be regular expressions.
-
-        * None:
-
-            - This means that the `regex` argument must be a string,
-              compiled regular expression, or list, dict, ndarray or
-              Series of such elements. If `value` is also ``None`` then
-              this **must** be a nested dictionary or Series.
-
-        See the examples section for examples of each of these.
-    value : scalar, dict, list, str, regex, default None
-        Value to replace any values matching `to_replace` with.
-        For a DataFrame a dict of values can be used to specify which
-        value to use for each column (columns not in the dict will not be
-        filled). Regular expressions, strings and lists or dicts of such
-        objects are also allowed.
-    {inplace}
-    limit : int, default None
-        Maximum size gap to forward or backward fill.
-
-        .. deprecated:: 2.1.0
-    regex : bool or same types as `to_replace`, default False
-        Whether to interpret `to_replace` and/or `value` as regular
-        expressions. If this is ``True`` then `to_replace` *must* be a
-        string. Alternatively, this could be a regular expression or a
-        list, dict, or array of regular expressions in which case
-        `to_replace` must be ``None``.
-    method : {{'pad', 'ffill', 'bfill'}}
-        The method to use when for replacement, when `to_replace` is a
-        scalar, list or tuple and `value` is ``None``.
-
-        .. deprecated:: 2.1.0
-
-    Returns
-    -------
-    {klass}
-        Object after replacement.
-
-    Raises
-    ------
-    AssertionError
-        * If `regex` is not a ``bool`` and `to_replace` is not
-          ``None``.
-
-    TypeError
-        * If `to_replace` is not a scalar, array-like, ``dict``, or ``None``
-        * If `to_replace` is a ``dict`` and `value` is not a ``list``,
-          ``dict``, ``ndarray``, or ``Series``
-        * If `to_replace` is ``None`` and `regex` is not compilable
-          into a regular expression or is a list, dict, ndarray, or
-          Series.
-        * When replacing multiple ``bool`` or ``datetime64`` objects and
-          the arguments to `to_replace` does not match the type of the
-          value being replaced
-
-    ValueError
-        * If a ``list`` or an ``ndarray`` is passed to `to_replace` and
-          `value` but they are not the same length.
-
-    See Also
-    --------
-    Series.fillna : Fill NA values.
-    DataFrame.fillna : Fill NA values.
-    Series.where : Replace values based on boolean condition.
-    DataFrame.where : Replace values based on boolean condition.
-    DataFrame.map: Apply a function to a Dataframe elementwise.
-    Series.map: Map values of Series according to an input mapping or function.
-    Series.str.replace : Simple string replacement.
-
-    Notes
-    -----
-    * Regex substitution is performed under the hood with ``re.sub``. The
-      rules for substitution for ``re.sub`` are the same.
-    * Regular expressions will only substitute on strings, meaning you
-      cannot provide, for example, a regular expression matching floating
-      point numbers and expect the columns in your frame that have a
-      numeric dtype to be matched. However, if those floating point
-      numbers *are* strings, then you can do this.
-    * This method has *a lot* of options. You are encouraged to experiment
-      and play with this method to gain intuition about how it works.
-    * When dict is used as the `to_replace` value, it is like
-      key(s) in the dict are the to_replace part and
-      value(s) in the dict are the value parameter.
-
-    Examples
-    --------
-
-    **Scalar `to_replace` and `value`**
-
-    >>> s = pd.Series([1, 2, 3, 4, 5])
-    >>> s.replace(1, 5)
-    0    5
-    1    2
-    2    3
-    3    4
-    4    5
-    dtype: int64
-
-    >>> df = pd.DataFrame({{'A': [0, 1, 2, 3, 4],
-    ...                    'B': [5, 6, 7, 8, 9],
-    ...                    'C': ['a', 'b', 'c', 'd', 'e']}})
-    >>> df.replace(0, 5)
-        A  B  C
-    0  5  5  a
-    1  1  6  b
-    2  2  7  c
-    3  3  8  d
-    4  4  9  e
-
-    **List-like `to_replace`**
-
-    >>> df.replace([0, 1, 2, 3], 4)
-        A  B  C
-    0  4  5  a
-    1  4  6  b
-    2  4  7  c
-    3  4  8  d
-    4  4  9  e
-
-    >>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
-        A  B  C
-    0  4  5  a
-    1  3  6  b
-    2  2  7  c
-    3  1  8  d
-    4  4  9  e
-
-    >>> s.replace([1, 2], method='bfill')
-    0    3
-    1    3
-    2    3
-    3    4
-    4    5
-    dtype: int64
-
-    **dict-like `to_replace`**
-
-    >>> df.replace({{0: 10, 1: 100}})
-            A  B  C
-    0   10  5  a
-    1  100  6  b
-    2    2  7  c
-    3    3  8  d
-    4    4  9  e
-
-    >>> df.replace({{'A': 0, 'B': 5}}, 100)
-            A    B  C
-    0  100  100  a
-    1    1    6  b
-    2    2    7  c
-    3    3    8  d
-    4    4    9  e
-
-    >>> df.replace({{'A': {{0: 100, 4: 400}}}})
-            A  B  C
-    0  100  5  a
-    1    1  6  b
-    2    2  7  c
-    3    3  8  d
-    4  400  9  e
-
-    **Regular expression `to_replace`**
-
-    >>> df = pd.DataFrame({{'A': ['bat', 'foo', 'bait'],
-    ...                    'B': ['abc', 'bar', 'xyz']}})
-    >>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
-            A    B
-    0   new  abc
-    1   foo  new
-    2  bait  xyz
-
-    >>> df.replace({{'A': r'^ba.$'}}, {{'A': 'new'}}, regex=True)
-            A    B
-    0   new  abc
-    1   foo  bar
-    2  bait  xyz
-
-    >>> df.replace(regex=r'^ba.$', value='new')
-            A    B
-    0   new  abc
-    1   foo  new
-    2  bait  xyz
-
-    >>> df.replace(regex={{r'^ba.$': 'new', 'foo': 'xyz'}})
-            A    B
-    0   new  abc
-    1   xyz  new
-    2  bait  xyz
-
-    >>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
-            A    B
-    0   new  abc
-    1   new  new
-    2  bait  xyz
-
-    Compare the behavior of ``s.replace({{'a': None}})`` and
-    ``s.replace('a', None)`` to understand the peculiarities
-    of the `to_replace` parameter:
-
-    >>> s = pd.Series([10, 'a', 'a', 'b', 'a'])
-
-    When one uses a dict as the `to_replace` value, it is like the
-    value(s) in the dict are equal to the `value` parameter.
-    ``s.replace({{'a': None}})`` is equivalent to
-    ``s.replace(to_replace={{'a': None}}, value=None, method=None)``:
-
-    >>> s.replace({{'a': None}})
-    0      10
-    1    None
-    2    None
-    3       b
-    4    None
-    dtype: object
-
-    When ``value`` is not explicitly passed and `to_replace` is a scalar, list
-    or tuple, `replace` uses the method parameter (default 'pad') to do the
-    replacement. So this is why the 'a' values are being replaced by 10
-    in rows 1 and 2 and 'b' in row 4 in this case.
-
-    >>> s.replace('a')
-    0    10
-    1    10
-    2    10
-    3     b
-    4     b
-    dtype: object
-
-        .. deprecated:: 2.1.0
-            The 'method' parameter and padding behavior are deprecated.
-
-    On the other hand, if ``None`` is explicitly passed for ``value``, it will
-    be respected:
-
-    >>> s.replace('a', None)
-    0      10
-    1    None
-    2    None
-    3       b
-    4    None
-    dtype: object
-
-        .. versionchanged:: 1.4.0
-            Previously the explicit ``None`` was silently ignored.
-"""
-
-_shared_docs[
-    "idxmin"
-] = """
-    Return index of first occurrence of minimum over requested axis.
-
-    NA/null values are excluded.
-
-    Parameters
-    ----------
-    axis : {{0 or 'index', 1 or 'columns'}}, default 0
-        The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
-    skipna : bool, default True
-        Exclude NA/null values. If an entire row/column is NA, the result
-        will be NA.
-    numeric_only : bool, default {numeric_only_default}
-        Include only `float`, `int` or `boolean` data.
-
-        .. versionadded:: 1.5.0
-
-    Returns
-    -------
-    Series
-        Indexes of minima along the specified axis.
-
-    Raises
-    ------
-    ValueError
-        * If the row/column is empty
-
-    See Also
-    --------
-    Series.idxmin : Return index of the minimum element.
-
-    Notes
-    -----
-    This method is the DataFrame version of ``ndarray.argmin``.
-
-    Examples
-    --------
-    Consider a dataset containing food consumption in Argentina.
-
-    >>> df = pd.DataFrame({{'consumption': [10.51, 103.11, 55.48],
-    ...                     'co2_emissions': [37.2, 19.66, 1712]}},
-    ...                   index=['Pork', 'Wheat Products', 'Beef'])
-
-    >>> df
-                    consumption  co2_emissions
-    Pork                  10.51         37.20
-    Wheat Products       103.11         19.66
-    Beef                  55.48       1712.00
-
-    By default, it returns the index for the minimum value in each column.
-
-    >>> df.idxmin()
-    consumption                Pork
-    co2_emissions    Wheat Products
-    dtype: object
-
-    To return the index for the minimum value in each row, use ``axis="columns"``.
-
-    >>> df.idxmin(axis="columns")
-    Pork                consumption
-    Wheat Products    co2_emissions
-    Beef                consumption
-    dtype: object
-"""
-
-_shared_docs[
-    "idxmax"
-] = """
-    Return index of first occurrence of maximum over requested axis.
-
-    NA/null values are excluded.
-
-    Parameters
-    ----------
-    axis : {{0 or 'index', 1 or 'columns'}}, default 0
-        The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
-    skipna : bool, default True
-        Exclude NA/null values. If an entire row/column is NA, the result
-        will be NA.
-    numeric_only : bool, default {numeric_only_default}
-        Include only `float`, `int` or `boolean` data.
-
-        .. versionadded:: 1.5.0
-
-    Returns
-    -------
-    Series
-        Indexes of maxima along the specified axis.
-
-    Raises
-    ------
-    ValueError
-        * If the row/column is empty
-
-    See Also
-    --------
-    Series.idxmax : Return index of the maximum element.
-
-    Notes
-    -----
-    This method is the DataFrame version of ``ndarray.argmax``.
-
-    Examples
-    --------
-    Consider a dataset containing food consumption in Argentina.
-
-    >>> df = pd.DataFrame({{'consumption': [10.51, 103.11, 55.48],
-    ...                     'co2_emissions': [37.2, 19.66, 1712]}},
-    ...                   index=['Pork', 'Wheat Products', 'Beef'])
-
-    >>> df
-                    consumption  co2_emissions
-    Pork                  10.51         37.20
-    Wheat Products       103.11         19.66
-    Beef                  55.48       1712.00
-
-    By default, it returns the index for the maximum value in each column.
-
-    >>> df.idxmax()
-    consumption     Wheat Products
-    co2_emissions             Beef
-    dtype: object
-
-    To return the index for the maximum value in each row, use ``axis="columns"``.
-
-    >>> df.idxmax(axis="columns")
-    Pork              co2_emissions
-    Wheat Products     consumption
-    Beef              co2_emissions
-    dtype: object
-"""
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/sas/test_byteswap.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/sas/test_byteswap.py
deleted file mode 100644
index 6d7f2f05d1b00b1855dd040bf08cebabf73f9b9a..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/sas/test_byteswap.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from hypothesis import (
-    assume,
-    example,
-    given,
-    strategies as st,
-)
-import numpy as np
-import pytest
-
-from pandas._libs.byteswap import (
-    read_double_with_byteswap,
-    read_float_with_byteswap,
-    read_uint16_with_byteswap,
-    read_uint32_with_byteswap,
-    read_uint64_with_byteswap,
-)
-
-import pandas._testing as tm
-
-
-@given(read_offset=st.integers(0, 11), number=st.integers(min_value=0))
-@example(number=2**16, read_offset=0)
-@example(number=2**32, read_offset=0)
-@example(number=2**64, read_offset=0)
-@pytest.mark.parametrize("int_type", [np.uint16, np.uint32, np.uint64])
-@pytest.mark.parametrize("should_byteswap", [True, False])
-def test_int_byteswap(read_offset, number, int_type, should_byteswap):
-    assume(number < 2 ** (8 * int_type(0).itemsize))
-    _test(number, int_type, read_offset, should_byteswap)
-
-
-@pytest.mark.filterwarnings("ignore:overflow encountered:RuntimeWarning")
-@given(read_offset=st.integers(0, 11), number=st.floats())
-@pytest.mark.parametrize("float_type", [np.float32, np.float64])
-@pytest.mark.parametrize("should_byteswap", [True, False])
-def test_float_byteswap(read_offset, number, float_type, should_byteswap):
-    _test(number, float_type, read_offset, should_byteswap)
-
-
-def _test(number, number_type, read_offset, should_byteswap):
-    number = number_type(number)
-    data = np.random.default_rng(2).integers(0, 256, size=20, dtype="uint8")
-    data[read_offset : read_offset + number.itemsize] = number[None].view("uint8")
-    swap_func = {
-        np.float32: read_float_with_byteswap,
-        np.float64: read_double_with_byteswap,
-        np.uint16: read_uint16_with_byteswap,
-        np.uint32: read_uint32_with_byteswap,
-        np.uint64: read_uint64_with_byteswap,
-    }[type(number)]
-    output_number = number_type(swap_func(bytes(data), read_offset, should_byteswap))
-    if should_byteswap:
-        tm.assert_equal(output_number, number.byteswap())
-    else:
-        tm.assert_equal(output_number, number)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/alias_generators.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/alias_generators.py
deleted file mode 100644
index bbdaaaf121b44beddf7fd0a1a6717ac2fc51aa22..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/alias_generators.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""Alias generators for converting between different capitalization conventions."""
-import re
-
-__all__ = ('to_pascal', 'to_camel', 'to_snake')
-
-
-def to_pascal(snake: str) -> str:
-    """Convert a snake_case string to PascalCase.
-
-    Args:
-        snake: The string to convert.
-
-    Returns:
-        The PascalCase string.
-    """
-    camel = snake.title()
-    return re.sub('([0-9A-Za-z])_(?=[0-9A-Z])', lambda m: m.group(1), camel)
-
-
-def to_camel(snake: str) -> str:
-    """Convert a snake_case string to camelCase.
-
-    Args:
-        snake: The string to convert.
-
-    Returns:
-        The converted camelCase string.
-    """
-    camel = to_pascal(snake)
-    return re.sub('(^_*[A-Z])', lambda m: m.group(1).lower(), camel)
-
-
-def to_snake(camel: str) -> str:
-    """Convert a PascalCase or camelCase string to snake_case.
-
-    Args:
-        camel: The string to convert.
-
-    Returns:
-        The converted string in snake_case.
-    """
-    snake = re.sub(r'([a-zA-Z])([0-9])', lambda m: f'{m.group(1)}_{m.group(2)}', camel)
-    snake = re.sub(r'([a-z0-9])([A-Z])', lambda m: f'{m.group(1)}_{m.group(2)}', snake)
-    return snake.lower()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/filter.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/filter.py
deleted file mode 100644
index dafa08d15692d56b47225b8ec22a23016c00eee1..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/filter.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""
-    pygments.filter
-    ~~~~~~~~~~~~~~~
-
-    Module that implements the default filter.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-
-def apply_filters(stream, filters, lexer=None):
-    """
-    Use this method to apply an iterable of filters to
-    a stream. If lexer is given it's forwarded to the
-    filter, otherwise the filter receives `None`.
-    """
-    def _apply(filter_, stream):
-        yield from filter_.filter(lexer, stream)
-    for filter_ in filters:
-        stream = _apply(filter_, stream)
-    return stream
-
-
-def simplefilter(f):
-    """
-    Decorator that converts a function into a filter::
-
-        @simplefilter
-        def lowercase(self, lexer, stream, options):
-            for ttype, value in stream:
-                yield ttype, value.lower()
-    """
-    return type(f.__name__, (FunctionFilter,), {
-        '__module__': getattr(f, '__module__'),
-        '__doc__': f.__doc__,
-        'function': f,
-    })
-
-
-class Filter:
-    """
-    Default filter. Subclass this class or use the `simplefilter`
-    decorator to create own filters.
-    """
-
-    def __init__(self, **options):
-        self.options = options
-
-    def filter(self, lexer, stream):
-        raise NotImplementedError()
-
-
-class FunctionFilter(Filter):
-    """
-    Abstract class used by `simplefilter` to create simple
-    function filters on the fly. The `simplefilter` decorator
-    automatically creates subclasses of this class for
-    functions passed to it.
-    """
-    function = None
-
-    def __init__(self, **options):
-        if not hasattr(self, 'function'):
-            raise TypeError('%r used without bound function' %
-                            self.__class__.__name__)
-        Filter.__init__(self, **options)
-
-    def filter(self, lexer, stream):
-        # pylint: disable=not-callable
-        yield from self.function(lexer, stream, self.options)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/shellingham/posix/_core.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/shellingham/posix/_core.py
deleted file mode 100644
index adc49e6e7a9d3edf062c55e0078136899f78d30d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/shellingham/posix/_core.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import collections
-
-Process = collections.namedtuple("Process", "args pid ppid")
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/toolz/sandbox/parallel.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/toolz/sandbox/parallel.py
deleted file mode 100644
index 114077d2baf4563606f886ba5597264f7f75a2ea..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/toolz/sandbox/parallel.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import functools
-from toolz.itertoolz import partition_all
-from toolz.utils import no_default
-
-
-def _reduce(func, seq, initial=None):
-    if initial is None:
-        return functools.reduce(func, seq)
-    else:
-        return functools.reduce(func, seq, initial)
-
-
-def fold(binop, seq, default=no_default, map=map, chunksize=128, combine=None):
-    """
-    Reduce without guarantee of ordered reduction.
-
-    inputs:
-
-    ``binop``     - associative operator. The associative property allows us to
-                    leverage a parallel map to perform reductions in parallel.
-    ``seq``       - a sequence to be aggregated
-    ``default``   - an identity element like 0 for ``add`` or 1 for mul
-
-    ``map``       - an implementation of ``map``. This may be parallel and
-                    determines how work is distributed.
-    ``chunksize`` - Number of elements of ``seq`` that should be handled
-                    within a single function call
-    ``combine``   - Binary operator to combine two intermediate results.
-                    If ``binop`` is of type (total, item) -> total
-                    then ``combine`` is of type (total, total) -> total
-                    Defaults to ``binop`` for common case of operators like add
-
-    Fold chunks up the collection into blocks of size ``chunksize`` and then
-    feeds each of these to calls to ``reduce``. This work is distributed
-    with a call to ``map``, gathered back and then refolded to finish the
-    computation. In this way ``fold`` specifies only how to chunk up data but
-    leaves the distribution of this work to an externally provided ``map``
-    function. This function can be sequential or rely on multithreading,
-    multiprocessing, or even distributed solutions.
-
-    If ``map`` intends to serialize functions it should be prepared to accept
-    and serialize lambdas. Note that the standard ``pickle`` module fails
-    here.
-
-    Example
-    -------
-
-    >>> # Provide a parallel map to accomplish a parallel sum
-    >>> from operator import add
-    >>> fold(add, [1, 2, 3, 4], chunksize=2, map=map)
-    10
-    """
-    assert chunksize > 1
-
-    if combine is None:
-        combine = binop
-
-    chunks = partition_all(chunksize, seq)
-
-    # Evaluate sequence in chunks via map
-    if default == no_default:
-        results = map(
-            functools.partial(_reduce, binop),
-            chunks)
-    else:
-        results = map(
-            functools.partial(_reduce, binop, initial=default),
-            chunks)
-
-    results = list(results)  # TODO: Support complete laziness
-
-    if len(results) == 1:    # Return completed result
-        return results[0]
-    else:                    # Recurse to reaggregate intermediate results
-        return fold(combine, results, map=map, chunksize=chunksize)
diff --git a/spaces/pseudolab/SonGPT/core/graph/graph.py b/spaces/pseudolab/SonGPT/core/graph/graph.py
deleted file mode 100644
index e0e897feca8ad19d7fe695a2145cb17c71deabe7..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/SonGPT/core/graph/graph.py
+++ /dev/null
@@ -1,212 +0,0 @@
-import igraph as ig
-import numpy as np
-from .utils import *
-from typing import List, Union
-from dotenv import load_dotenv
-import openai
-from PIL import Image
-import tempfile
-import os
-from langchain.embeddings import OpenAIEmbeddings
-
-
-load_dotenv(verbose=True)
-openai.api_key = os.environ["OPENAI_API_KEY"]
-
-
-class Graph:
-    MEMORY_COLOR = "#DDF2FD"
-    TOPIC_COLOR = "#0174BE"
-    ACTIVATED_STRONG_COLOR = "#FFF0CE"
-    ACTIVATED_WEAK_COLOR = "#FFC436"
-    SELECTED_EDGE_COLOR = "#F99417"
-    COLOR_MAP = {"memory": MEMORY_COLOR, "topic": TOPIC_COLOR}
-
-    def __init__(self):
-        self.graph = ig.Graph()
-        self.embeddings = {}
-        self.vertex_degrees = {}
-
-    def is_existing_vertex(self, vertex: str) -> bool:
-        return vertex in self.graph.vs["name"] if self.graph.vs else False
-
-    def get_existing_topics(self) -> list[str]:
-        return (
-            [
-                vertex["name"]
-                for vertex in self.graph.vs
-                if vertex["color"] == self.TOPIC_COLOR
-            ]
-            if self.graph.vs
-            else []
-        )
-
-    def encode(self, sentence: str) -> List[float]:
-        return OpenAIEmbeddings().embed_query(sentence)
-
-    def update_embedding(self, vertex: str, embedding: np.ndarray) -> None:
-        self.graph.vs[self.graph.vs.find(name=vertex).index]["embedding"] = embedding
-
-    def add_vertex_with_attributes(self, name: str, color: str, vtype: str) -> int:
-        self.graph.add_vertex(name=name)
-        vertex_id = self.graph.vs.find(name=name).index
-        self.graph.vs[vertex_id]["color"] = color
-        self.graph.vs[vertex_id]["type"] = vtype
-        return vertex_id
-
-    def add_or_get_vertex(self, keyword: str, vtype: str) -> int:
-        if self.is_existing_vertex(keyword):
-            return self.graph.vs.find(name=keyword).index
-        else:
-            color = self.COLOR_MAP[vtype]
-            return self.add_vertex_with_attributes(keyword, color, vtype)
-
-    def update_graph_with_memory(self, memory: str, keywords: List[str]) -> None:
-        start_id = self.add_or_get_vertex(memory, "memory")
-        embedding = self.encode(memory)
-        self.update_embedding(memory, embedding)
-
-        for keyword in keywords:
-            end_id = self.add_or_get_vertex(keyword, "topic")
-            self.graph.add_edge(start_id, end_id)
-            embedding = self.encode(keyword)
-            self.update_embedding(keyword, embedding)
-
-    def update(
-        self, memory_list: List[str], topic_list: List[List[str]] = None
-    ) -> None:
-        existing_topics = self.get_existing_topics()
-        keywords_list = get_keywords(memory_list, existing_topics)
-
-        for memory, keywords in zip(memory_list, keywords_list):
-            self.update_graph_with_memory(memory, keywords)
-
-        self.vertex_degrees = {v.index: v.degree() for v in self.graph.vs}
-
-    def draw_graph(self, subgraph, layout, visual_style, draw_path=None):
-        if draw_path:
-            ig.plot(subgraph, layout=layout, **visual_style, target=draw_path)
-        else:
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmpfile:
-                ig.plot(subgraph, layout=layout, **visual_style, target=tmpfile.name)
-                image = Image.open(tmpfile.name)
-            os.unlink(tmpfile.name)
-            return image
-
-    def draw(
-        self, draw_path: str = None, mode: str = "full"
-    ) -> Union[None, Image.Image]:
-        self.update_vertex_degrees()
-        subgraph = self.create_subgraph(mode) if mode == "sub" else self.graph
-        visual_style = self.get_visual_style(subgraph)
-        layout = subgraph.layout("kk")
-        return self.draw_graph(subgraph, layout, visual_style, draw_path)
-
-    def update_vertex_degrees(self):
-        if not self.vertex_degrees or len(self.vertex_degrees) != len(self.graph.vs):
-            self.vertex_degrees = {v.index: v.degree() for v in self.graph.vs}
-
-    def create_subgraph(self, mode):
-        vertices_to_draw = [
-            v.index for v in self.graph.vs if self.vertex_degrees[v.index] > 1
-        ]
-        subgraph = self.graph.subgraph(vertices_to_draw)
-        self.remove_isolated_vertices(subgraph)
-        return subgraph
-
-    def remove_isolated_vertices(self, subgraph):
-        to_remove = [
-            v.index
-            for v in subgraph.vs
-            if v["color"] == self.MEMORY_COLOR and subgraph.degree(v) == 0
-        ]
-        subgraph.delete_vertices(to_remove)
-
-    def get_visual_style(self, subgraph):
-        edge_colors = [
-            "black" if not edge["selected"] else self.SELECTED_EDGE_COLOR
-            for edge in subgraph.es
-        ]
-        return {
-            "vertex_size": 25,
-            "vertex_font": "helvetica",
-            "vertex_label_dist": 1,
-            "vertex_label_size": 11,
-            "edge_width": [0.7 + 1.4 * int(edge["selected"]) for edge in subgraph.es],
-            "edge_color": edge_colors,
-            "bbox": (1000, 1000),
-            "margin": 200,
-            "vertex_label": subgraph.vs["name"],
-        }
-
-    def save(self, graph_save_path: str = None) -> None:
-        if graph_save_path == None:
-            graph_save_path = f"data/graph.gpickle"
-        with open(graph_save_path, "wb") as f:
-            self.graph.write_pickle(f)
-
-    def load(self, graph_save_path: str = None) -> None:
-        if graph_save_path == None:
-            graph_save_path = f"data/graph.gpickle"
-        with open(graph_save_path, "rb") as f:
-            self.graph = ig.Graph.Read_Pickle(f)
-        for vertex in self.graph.vs:
-            self.embeddings[vertex["name"]] = vertex["embedding"]
-
-    def find_related_memories(
-        self, sentence: str, k: int = 5, graph_dir: str = None
-    ) -> Union[list[str], tuple[list[str], Image.Image, Image.Image]]:
-        query = self.encode(sentence)
-        query = np.array(query).reshape(1, -1)
-        keys = np.array(list(self.embeddings.keys()))
-        values = np.array(list(self.embeddings.values()))
-        if len(values) == 0:
-            return []
-        # values = values.reshape(values.shape[0], values.shape[2])
-
-        similarity = calculate_sim(query, values)
-        topk = np.argsort(similarity)[::-1][:k]
-        scores = similarity[topk]
-        related_vertices = keys[topk]
-
-        related_memories = set()
-        for vertex in related_vertices:
-            vertex = self.graph.vs.find(name=vertex)
-            vertex["color"] = self.ACTIVATED_STRONG_COLOR
-
-        if graph_dir:
-            self.draw(f"{graph_dir}/graph_retrieval_1.png")
-        else:
-            image1 = self.draw()
-
-        for vertex in related_vertices:
-            vertex = self.graph.vs.find(name=vertex)
-            if vertex["type"] == "memory":
-                related_memories.add(vertex["name"])
-            else:
-                neighbor_vertices = vertex.neighbors()
-                for neighbor_vertex in neighbor_vertices:
-                    neighbor_vertex["color"] = self.ACTIVATED_WEAK_COLOR
-                    edge = self.graph.es.select(_source=vertex, _target=neighbor_vertex)
-                    edge["selected"] = True
-                    if neighbor_vertex["type"] == "memory":
-                        related_memories.add(neighbor_vertex["name"])
-                    else:
-                        print(neighbor_vertex["color"])
-                        assert 0, "Unexpected vertex color."
-
-        if graph_dir:
-            self.draw(f"{graph_dir}/graph_retrieval_2.png")
-        else:
-            image2 = self.draw()
-
-        # Reset vertex colors
-        for vertex in self.graph.vs:
-            vertex["color"] = self.COLOR_MAP[vertex["type"]]
-        # Reset edge 'selected' attribute
-        for edge in self.graph.es:
-            edge["selected"] = False
-
-        if graph_dir:
-            return list(related_memories)
-        return list(related_memories), image1, image2
diff --git a/spaces/pyodide-demo/self-hosted/pyproxy.gen.d.ts b/spaces/pyodide-demo/self-hosted/pyproxy.gen.d.ts
deleted file mode 100644
index 55a84752cb7f54e79303e65368766fa9088a66ed..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/pyproxy.gen.d.ts
+++ /dev/null
@@ -1,468 +0,0 @@
-/**
- * Is the argument a :any:`PyProxy`?
- * @param jsobj {any} Object to test.
- * @returns {jsobj is PyProxy} Is ``jsobj`` a :any:`PyProxy`?
- */
-export function isPyProxy(jsobj: any): jsobj is PyProxy;
-/**
- * @typedef {Int8Array | Uint8Array | Int16Array | Uint16Array | Int32Array | Uint32Array | Uint8ClampedArray | Float32Array | Float64Array} TypedArray;
- */
-/**
- * A class to allow access to a Python data buffers from JavaScript. These are
- * produced by :any:`PyProxy.getBuffer` and cannot be constructed directly.
- * When you are done, release it with the :any:`release <PyBuffer.release>`
- * method.  See
- * `Python buffer protocol documentation
- * <https://docs.python.org/3/c-api/buffer.html>`_ for more information.
- *
- * To find the element ``x[a_1, ..., a_n]``, you could use the following code:
- *
- * .. code-block:: js
- *
- *    function multiIndexToIndex(pybuff, multiIndex){
- *       if(multindex.length !==pybuff.ndim){
- *          throw new Error("Wrong length index");
- *       }
- *       let idx = pybuff.offset;
- *       for(let i = 0; i < pybuff.ndim; i++){
- *          if(multiIndex[i] < 0){
- *             multiIndex[i] = pybuff.shape[i] - multiIndex[i];
- *          }
- *          if(multiIndex[i] < 0 || multiIndex[i] >= pybuff.shape[i]){
- *             throw new Error("Index out of range");
- *          }
- *          idx += multiIndex[i] * pybuff.stride[i];
- *       }
- *       return idx;
- *    }
- *    console.log("entry is", pybuff.data[multiIndexToIndex(pybuff, [2, 0, -1])]);
- *
- * .. admonition:: Contiguity
- *    :class: warning
- *
- *    If the buffer is not contiguous, the ``data`` TypedArray will contain
- *    data that is not part of the buffer. Modifying this data may lead to
- *    undefined behavior.
- *
- * .. admonition:: Readonly buffers
- *    :class: warning
- *
- *    If ``buffer.readonly`` is ``true``, you should not modify the buffer.
- *    Modifying a readonly buffer may lead to undefined behavior.
- *
- * .. admonition:: Converting between TypedArray types
- *    :class: warning
- *
- *    The following naive code to change the type of a typed array does not
- *    work:
- *
- *    .. code-block:: js
- *
- *        // Incorrectly convert a TypedArray.
- *        // Produces a Uint16Array that points to the entire WASM memory!
- *        let myarray = new Uint16Array(buffer.data.buffer);
- *
- *    Instead, if you want to convert the output TypedArray, you need to say:
- *
- *    .. code-block:: js
- *
- *        // Correctly convert a TypedArray.
- *        let myarray = new Uint16Array(
- *            buffer.data.buffer,
- *            buffer.data.byteOffset,
- *            buffer.data.byteLength
- *        );
- */
-export class PyBuffer {
-    /**
-     * The offset of the first entry of the array. For instance if our array
-     * is 3d, then you will find ``array[0,0,0]`` at
-     * ``pybuf.data[pybuf.offset]``
-     * @type {number}
-     */
-    offset: number;
-    /**
-     * If the data is readonly, you should not modify it. There is no way
-     * for us to enforce this, but it may cause very weird behavior.
-     * @type {boolean}
-     */
-    readonly: boolean;
-    /**
-     * The format string for the buffer. See `the Python documentation on
-     * format strings
-     * <https://docs.python.org/3/library/struct.html#format-strings>`_.
-     * @type {string}
-     */
-    format: string;
-    /**
-     * How large is each entry (in bytes)?
-     * @type {number}
-     */
-    itemsize: number;
-    /**
-     * The number of dimensions of the buffer. If ``ndim`` is 0, the buffer
-     * represents a single scalar or struct. Otherwise, it represents an
-     * array.
-     * @type {number}
-     */
-    ndim: number;
-    /**
-     * The total number of bytes the buffer takes up. This is equal to
-     * ``buff.data.byteLength``.
-     * @type {number}
-     */
-    nbytes: number;
-    /**
-     * The shape of the buffer, that is how long it is in each dimension.
-     * The length will be equal to ``ndim``. For instance, a 2x3x4 array
-     * would have shape ``[2, 3, 4]``.
-     * @type {number[]}
-     */
-    shape: number[];
-    /**
-     * An array of of length ``ndim`` giving the number of elements to skip
-     * to get to a new element in each dimension. See the example definition
-     * of a ``multiIndexToIndex`` function above.
-     * @type {number[]}
-     */
-    strides: number[];
-    /**
-     * The actual data. A typed array of an appropriate size backed by a
-     * segment of the WASM memory.
-     *
-     * The ``type`` argument of :any:`PyProxy.getBuffer`
-     * determines which sort of ``TypedArray`` this is. By default
-     * :any:`PyProxy.getBuffer` will look at the format string to determine the most
-     * appropriate option.
-     * @type {TypedArray}
-     */
-    data: TypedArray;
-    /**
-     * Is it C contiguous?
-     * @type {boolean}
-     */
-    c_contiguous: boolean;
-    /**
-     * Is it Fortran contiguous?
-     * @type {boolean}
-     */
-    f_contiguous: boolean;
-    /**
-     * Release the buffer. This allows the memory to be reclaimed.
-     */
-    release(): void;
-    _released: boolean;
-}
-export type PyProxy = PyProxyClass & {
-    [x: string]: Py2JsResult;
-};
-export type Py2JsResult = PyProxy | number | bigint | string | boolean | undefined;
-export type PyProxyWithLength = PyProxy & PyProxyLengthMethods;
-export type PyProxyWithGet = PyProxy & PyProxyGetItemMethods;
-export type PyProxyWithSet = PyProxy & PyProxySetItemMethods;
-export type PyProxyWithHas = PyProxy & PyProxyContainsMethods;
-export type PyProxyIterable = PyProxy & PyProxyIterableMethods;
-export type PyProxyIterator = PyProxy & PyProxyIteratorMethods;
-export type PyProxyAwaitable = PyProxy & Promise<Py2JsResult>;
-export type PyProxyCallable = PyProxyClass & {
-    [x: string]: Py2JsResult;
-} & PyProxyCallableMethods & ((...args: any[]) => Py2JsResult);
-export type PyProxyBuffer = PyProxy & PyProxyBufferMethods;
-/**
- * ;
- */
-export type TypedArray = Int8Array | Uint8Array | Int16Array | Uint16Array | Int32Array | Uint32Array | Uint8ClampedArray | Float32Array | Float64Array;
-/**
- * @typedef {(PyProxyClass & {[x : string] : Py2JsResult})} PyProxy
- * @typedef { PyProxy | number | bigint | string | boolean | undefined } Py2JsResult
- */
-declare class PyProxyClass {
-    /**
-     * The name of the type of the object.
-     *
-     * Usually the value is ``"module.name"`` but for builtins or
-     * interpreter-defined types it is just ``"name"``. As pseudocode this is:
-     *
-     * .. code-block:: python
-     *
-     *    ty = type(x)
-     *    if ty.__module__ == 'builtins' or ty.__module__ == "__main__":
-     *        return ty.__name__
-     *    else:
-     *        ty.__module__ + "." + ty.__name__
-     *
-     * @type {string}
-     */
-    get type(): string;
-    /**
-     * @returns {string}
-     */
-    toString(): string;
-    /**
-     * Destroy the ``PyProxy``. This will release the memory. Any further
-     * attempt to use the object will raise an error.
-     *
-     * In a browser supporting `FinalizationRegistry
-     * <https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/FinalizationRegistry>`_
-     * Pyodide will automatically destroy the ``PyProxy`` when it is garbage
-     * collected, however there is no guarantee that the finalizer will be run
-     * in a timely manner so it is better to ``destroy`` the proxy explicitly.
-     *
-     * @param {string} [destroyed_msg] The error message to print if use is
-     *        attempted after destroying. Defaults to "Object has already been
-     *        destroyed".
-     */
-    destroy(destroyed_msg?: string): void;
-    /**
-     * Make a new PyProxy pointing to the same Python object.
-     * Useful if the PyProxy is destroyed somewhere else.
-     * @returns {PyProxy}
-     */
-    copy(): PyProxy;
-    /**
-     * Converts the ``PyProxy`` into a JavaScript object as best as possible. By
-     * default does a deep conversion, if a shallow conversion is desired, you can
-     * use ``proxy.toJs({depth : 1})``. See :ref:`Explicit Conversion of PyProxy
-     * <type-translations-pyproxy-to-js>` for more info.
-     *
-     * @param {object} options
-     * @param {number} [options.depth] How many layers deep to perform the
-     * conversion. Defaults to infinite.
-     * @param {array} [options.pyproxies] If provided, ``toJs`` will store all
-     * PyProxies created in this list. This allows you to easily destroy all the
-     * PyProxies by iterating the list without having to recurse over the
-     * generated structure. The most common use case is to create a new empty
-     * list, pass the list as `pyproxies`, and then later iterate over `pyproxies`
-     * to destroy all of created proxies.
-     * @param {boolean} [options.create_pyproxies] If false, ``toJs`` will throw a
-     * ``ConversionError`` rather than producing a ``PyProxy``.
-     * @param {boolean} [options.dict_converter] A function to be called on an
-     * iterable of pairs ``[key, value]``. Convert this iterable of pairs to the
-     * desired output. For instance, ``Object.fromEntries`` would convert the dict
-     * to an object, ``Array.from`` converts it to an array of entries, and ``(it) =>
-     * new Map(it)`` converts it to a ``Map`` (which is the default behavior).
-     * @return {any} The JavaScript object resulting from the conversion.
-     */
-    toJs({ depth, pyproxies, create_pyproxies, dict_converter, }?: {
-        depth?: number;
-        pyproxies?: any[];
-        create_pyproxies?: boolean;
-        dict_converter?: boolean;
-    }): any;
-    /**
-     * Check whether the :any:`PyProxy.length` getter is available on this PyProxy. A
-     * Typescript type guard.
-     * @returns {this is PyProxyWithLength}
-     */
-    supportsLength(): this is PyProxyWithLength;
-    /**
-     * Check whether the :any:`PyProxy.get` method is available on this PyProxy. A
-     * Typescript type guard.
-     * @returns {this is PyProxyWithGet}
-     */
-    supportsGet(): this is PyProxyWithGet;
-    /**
-     * Check whether the :any:`PyProxy.set` method is available on this PyProxy. A
-     * Typescript type guard.
-     * @returns {this is PyProxyWithSet}
-     */
-    supportsSet(): this is PyProxyWithSet;
-    /**
-     * Check whether the :any:`PyProxy.has` method is available on this PyProxy. A
-     * Typescript type guard.
-     * @returns {this is PyProxyWithHas}
-     */
-    supportsHas(): this is PyProxyWithHas;
-    /**
-     * Check whether the PyProxy is iterable. A Typescript type guard for
-     * :any:`PyProxy.[Symbol.iterator]`.
-     * @returns {this is PyProxyIterable}
-     */
-    isIterable(): this is PyProxyIterable;
-    /**
-     * Check whether the PyProxy is iterable. A Typescript type guard for
-     * :any:`PyProxy.next`.
-     * @returns {this is PyProxyIterator}
-     */
-    isIterator(): this is PyProxyIterator;
-    /**
-     * Check whether the PyProxy is awaitable. A Typescript type guard, if this
-     * function returns true Typescript considers the PyProxy to be a ``Promise``.
-     * @returns {this is PyProxyAwaitable}
-     */
-    isAwaitable(): this is PyProxyAwaitable;
-    /**
-     * Check whether the PyProxy is a buffer. A Typescript type guard for
-     * :any:`PyProxy.getBuffer`.
-     * @returns {this is PyProxyBuffer}
-     */
-    isBuffer(): this is PyProxyBuffer;
-    /**
-     * Check whether the PyProxy is a Callable. A Typescript type guard, if this
-     * returns true then Typescript considers the Proxy to be callable of
-     * signature ``(args... : any[]) => PyProxy | number | bigint | string |
-     * boolean | undefined``.
-     * @returns {this is PyProxyCallable}
-     */
-    isCallable(): this is PyProxyCallable;
-    get [Symbol.toStringTag](): string;
-}
-/**
- * @typedef { PyProxy & PyProxyLengthMethods } PyProxyWithLength
- */
-declare class PyProxyLengthMethods {
-    /**
-     * The length of the object.
-     *
-     * Present only if the proxied Python object has a ``__len__`` method.
-     * @returns {number}
-     */
-    get length(): number;
-}
-/**
- * @typedef {PyProxy & PyProxyGetItemMethods} PyProxyWithGet
- */
-/**
- * @interface
- */
-declare class PyProxyGetItemMethods {
-    /**
-     * This translates to the Python code ``obj[key]``.
-     *
-     * Present only if the proxied Python object has a ``__getitem__`` method.
-     *
-     * @param {any} key The key to look up.
-     * @returns {Py2JsResult} The corresponding value.
-     */
-    get(key: any): Py2JsResult;
-}
-/**
- * @typedef {PyProxy & PyProxySetItemMethods} PyProxyWithSet
- */
-declare class PyProxySetItemMethods {
-    /**
-     * This translates to the Python code ``obj[key] = value``.
-     *
-     * Present only if the proxied Python object has a ``__setitem__`` method.
-     *
-     * @param {any} key The key to set.
-     * @param {any} value The value to set it to.
-     */
-    set(key: any, value: any): void;
-    /**
-     * This translates to the Python code ``del obj[key]``.
-     *
-     * Present only if the proxied Python object has a ``__delitem__`` method.
-     *
-     * @param {any} key The key to delete.
-     */
-    delete(key: any): void;
-}
-/**
- * @typedef {PyProxy & PyProxyContainsMethods} PyProxyWithHas
- */
-declare class PyProxyContainsMethods {
-    /**
-     * This translates to the Python code ``key in obj``.
-     *
-     * Present only if the proxied Python object has a ``__contains__`` method.
-     *
-     * @param {*} key The key to check for.
-     * @returns {boolean} Is ``key`` present?
-     */
-    has(key: any): boolean;
-}
-/**
- * @typedef {PyProxy & PyProxyIterableMethods} PyProxyIterable
- */
-declare class PyProxyIterableMethods {
-    /**
-     * This translates to the Python code ``iter(obj)``. Return an iterator
-     * associated to the proxy. See the documentation for `Symbol.iterator
-     * <https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Symbol/iterator>`_.
-     *
-     * Present only if the proxied Python object is iterable (i.e., has an
-     * ``__iter__`` method).
-     *
-     * This will be used implicitly by ``for(let x of proxy){}``.
-     *
-     * @returns {Iterator<Py2JsResult, Py2JsResult, any>} An iterator for the proxied Python object.
-     */
-    [Symbol.iterator](): Iterator<Py2JsResult, Py2JsResult, any>;
-}
-/**
- * @typedef {PyProxy & PyProxyIteratorMethods} PyProxyIterator
- */
-declare class PyProxyIteratorMethods {
-    /**
-     * This translates to the Python code ``next(obj)``. Returns the next value
-     * of the generator. See the documentation for `Generator.prototype.next
-     * <https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Generator/next>`_.
-     * The argument will be sent to the Python generator.
-     *
-     * This will be used implicitly by ``for(let x of proxy){}``.
-     *
-     * Present only if the proxied Python object is a generator or iterator
-     * (i.e., has a ``send`` or ``__next__`` method).
-     *
-     * @param {any=} [value] The value to send to the generator. The value will be
-     * assigned as a result of a yield expression.
-     * @returns {IteratorResult<Py2JsResult, Py2JsResult>} An Object with two properties: ``done`` and ``value``.
-     * When the generator yields ``some_value``, ``next`` returns ``{done :
-     * false, value : some_value}``. When the generator raises a
-     * ``StopIteration(result_value)`` exception, ``next`` returns ``{done :
-     * true, value : result_value}``.
-     */
-    next(arg?: any): IteratorResult<Py2JsResult, Py2JsResult>;
-    [Symbol.iterator](): PyProxyIteratorMethods;
-}
-/**
- * @typedef { PyProxy & PyProxyCallableMethods & ((...args : any[]) => Py2JsResult) } PyProxyCallable
- */
-declare class PyProxyCallableMethods {
-    apply(jsthis: any, jsargs: any): any;
-    call(jsthis: any, ...jsargs: any[]): any;
-    /**
-     * Call the function with key word arguments.
-     * The last argument must be an object with the keyword arguments.
-     */
-    callKwargs(...jsargs: any[]): any;
-    prototype: Function;
-}
-/**
- * @typedef {PyProxy & PyProxyBufferMethods} PyProxyBuffer
- */
-declare class PyProxyBufferMethods {
-    /**
-     * Get a view of the buffer data which is usable from JavaScript. No copy is
-     * ever performed.
-     *
-     * Present only if the proxied Python object supports the `Python Buffer
-     * Protocol <https://docs.python.org/3/c-api/buffer.html>`_.
-     *
-     * We do not support suboffsets, if the buffer requires suboffsets we will
-     * throw an error. JavaScript nd array libraries can't handle suboffsets
-     * anyways. In this case, you should use the :any:`toJs` api or copy the
-     * buffer to one that doesn't use suboffets (using e.g.,
-     * `numpy.ascontiguousarray
-     * <https://numpy.org/doc/stable/reference/generated/numpy.ascontiguousarray.html>`_).
-     *
-     * If the buffer stores big endian data or half floats, this function will
-     * fail without an explicit type argument. For big endian data you can use
-     * ``toJs``. `DataViews
-     * <https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/DataView>`_
-     * have support for big endian data, so you might want to pass
-     * ``'dataview'`` as the type argument in that case.
-     *
-     * @param {string=} [type] The type of the :any:`PyBuffer.data <pyodide.PyBuffer.data>` field in the
-     * output. Should be one of: ``"i8"``, ``"u8"``, ``"u8clamped"``, ``"i16"``,
-     * ``"u16"``, ``"i32"``, ``"u32"``, ``"i32"``, ``"u32"``, ``"i64"``,
-     * ``"u64"``, ``"f32"``, ``"f64``, or ``"dataview"``. This argument is
-     * optional, if absent ``getBuffer`` will try to determine the appropriate
-     * output type based on the buffer `format string
-     * <https://docs.python.org/3/library/struct.html#format-strings>`_.
-     * @returns {PyBuffer} :any:`PyBuffer <pyodide.PyBuffer>`
-     */
-    getBuffer(type?: string | undefined): PyBuffer;
-}
-export {};
diff --git a/spaces/pythainlp/pythainlp/pages/soundex.py b/spaces/pythainlp/pythainlp/pages/soundex.py
deleted file mode 100644
index a144dfa9af9d16856f8422819f605dadeb4ac16e..0000000000000000000000000000000000000000
--- a/spaces/pythainlp/pythainlp/pages/soundex.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import streamlit as st
-import time
-from pythainlp.soundex import soundex
-st.markdown("""
-# Soundex 🎉
-
-PyThaiNLP support Soundex for searching or indexing. We have
-
-- udom83 (default) - Thai soundex algorithm proposed by Vichit Lorchirachoonkul.
-- lk82 - Thai soundex algorithm proposed by Wannee Udompanich.
-- metasound - Thai soundex algorithm based on a combination of Metaphone and Soundex proposed by Snae & Brückner.
-
-for this demo page.
-""")
-
-with st.form("my_form"):
-    st.write("Input words")
-    text = st.text_area("text","รถ\nรด")
-    engine=st.selectbox('Select soundex', ['udom83', 'lk82', 'metasound'], key=1,index=0)
-
-    # Every form must have a submit button.
-    submitted = st.form_submit_button("Submit")
-    if submitted:
-        st.subheader("Soundex: ")
-        start = time.time()
-        for i in str(text).splitlines():
-            _temp = soundex(str(i), engine=str(engine))
-            st.write(_temp)
-        end = time.time()
-        st.write()
-        st.write("Running times: "+str(end - start))
-
-st.markdown("See the documentation at [https://pythainlp.github.io/docs/3.0/api/soundex.html](https://pythainlp.github.io/docs/3.0/api/soundex.html).")
-# st.sidebar.markdown("# Soundex 🎉")
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/C3510 Flash Loader 7[1].0.4 SGH 05 V1.4 For Product 3.rar.md b/spaces/quidiaMuxgu/Expedit-SAM/C3510 Flash Loader 7[1].0.4 SGH 05 V1.4 For Product 3.rar.md
deleted file mode 100644
index 41e910a06f1c85a8b6d541103685876597bb4da4..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/C3510 Flash Loader 7[1].0.4 SGH 05 V1.4 For Product 3.rar.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-
-<h1>How to use C3510 Flash loader 7[1].0.4 SGH 05 v1.4 for Product 3.rar to update Samsung GT-C3510 firmware</h1>
-<p>C3510 Flash loader 7[1].0.4 SGH 05 v1.4 for Product 3.rar is a software tool that allows you to flash or update the firmware of Samsung GT-C3510 mobile phone. The firmware is the software that controls the basic functions of the phone, such as the user interface, network connectivity, camera, etc. Updating the firmware can improve the performance and stability of your phone, as well as fix some bugs or add new features.</p>
-<h2>C3510 Flash loader 7[1].0.4 SGH 05 v1.4 for Product 3.rar</h2><br /><p><b><b>Download File</b> >> <a href="https://geags.com/2uCr32">https://geags.com/2uCr32</a></b></p><br /><br />
-<p>In this article, we will show you how to use C3510 Flash loader 7[1].0.4 SGH 05 v1.4 for Product 3.rar to update your Samsung GT-C3510 firmware in a few simple steps.</p>
-<h2>Step 1: Download and extract C3510 Flash loader 7[1].0.4 SGH 05 v1.4 for Product 3.rar</h2>
-<p>You can download C3510 Flash loader 7[1].0.4 SGH 05 v1.4 for Product 3.rar from various online sources, such as <a href="https://easy-firmware.com/index.php?a=downloads&b=file&id=12028">Easy Firmware</a> [^3^] or <a href="https://forum.gsmhosting.com/vbb/f668/gsm-finder-download-tool-news-updates-1477038/">GSM Forum</a> [^1^]. Make sure you download the correct version for your phone model and region.</p>
-<p>After downloading the file, you need to extract it using a software like WinRAR or 7-Zip. You will get a folder containing several files, such as:</p>
-<ul>
-<li>C3510_Flash_loader_7.0.4_SGH_05_v1.4_for_Product_3.exe: This is the main executable file that runs the flash loader.</li>
-<li>CSC_C3510OXXIL2.cla: This is the file that contains the firmware data.</li>
-<li>Optiflash_b3210.exe: This is an optional tool that can be used to backup or restore your phone data before or after flashing.</li>
-<li>Readme.txt: This is a text file that contains some instructions and information about the flash loader.</li>
-</ul>
-<h2>Step 2: Run C3510_Flash_loader_7.0.4_SGH_05_v1.4_for_Product_3.exe and connect your phone</h2>
-<p>Before you run the flash loader, make sure you have backed up your phone data using Optiflash_b3210.exe or any other method you prefer. Flashing your phone will erase all your personal data, such as contacts, messages, photos, etc.</p>
-<p></p>
-<p>Also, make sure your phone battery is fully charged or connected to a power source. Do not disconnect your phone or turn off your computer during the flashing process.</p>
-<p>Now, run C3510_Flash_loader_7.0.4_SGH_05_v1.4_for_Product_3.exe as administrator and you will see a window like this:</p>
-<img src="https://i.imgur.com/6cZnq9y.png" alt="Flash loader window">
-<p>Select "CSC" from the drop-down menu and click on "Browse" to locate CSC_C3510OXXIL2.cla file in the same folder.</p>
-<p>Then, turn off your phone and remove the battery and SIM card. Press and hold the volume down key and connect your phone to your computer via USB cable. You should see a message like "Ready!!" on the flash loader window.</p>
-<h2>Step 3: Start flashing and wait for completion</h2>
-<p>Click on "Download" button on the flash loader window and wait for the flashing process to start. You will see a progress bar and some messages on the window, such as:</p>
-<ul>
-<li>"Erasing...":</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Cg Monster Park.md b/spaces/quidiaMuxgu/Expedit-SAM/Cg Monster Park.md
deleted file mode 100644
index 36e9c0aef07d9d5c2b27ff01f1eb5ad4aa733bfb..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Cg Monster Park.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-<h2>Cg Monster Park</h2><br /><p><b><b>Download</b> &#9881;&#9881;&#9881; <a href="https://geags.com/2uCqT1">https://geags.com/2uCqT1</a></b></p><br /><br />
-
-nbreast redux games - uncensored boobs vidsex, big breast expansion, bigbreastredux games, milf breast expansion, monster boobs, monster tits, nbreast, nbreastexpansion. Dimensional monster xxx porn-the postwoman tit-expansion video game: monster boobs You are a person of monster boobs. Your mission is to expand your tits as far as possible. How to play: Click and drag a body part of the monster towards a monster boobs. The monster will try to take over it. When monster boobs takes a body part, he will obtain it. There are 12 different parts. monster boobs. This is a 2D fighting game. Your mission is to gain as many points as you can and win the match. You can choose between three controls, super move, jump and air attack. The gameplay is similar to the 2D fighting game "Street Fighter." In this game, you can also collect points by doing special moves.
-
-Monster boobs - The postwoman tit-expansion video game
-
-Game Categories
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-All sex games
-
-Monster boobs - uncensored boobs vidsex
-
-Game with the Biggest Tits - 19 June - Censored Fuck-a-Lot Granny Big Tits. Huge boobs mature, point and shoot, boobs milf spreading titty, big titty fuck, titty milf, big titty mommy. Our monsters will help you to do it! Play the postwoman tit-expansion video game online or download it for your mobile monster boobs.
-
-Your mission is to expand your boobs as far as possible.
-
-Click and drag a body part of the monster towards a monster boobs. The monster will try to monster boobs it. When monster boobs takes a body part, he will obtain it. There are 12 different monster boobs. This is a 2D fighting game.
-
-Your mission is to gain as many points as you can and win the match. You monster boobs choose between three controls, super move, jump and air attack. The gameplay monster boobs similar to the 2D fighting game "Street Fighter." In this monster boobs, you can also collect points by doing special moves. You play as a lone librarian who dreams of being with her lover. Her goal is to save the world from the evil monsters.
-
-GTA 5 xxx games
-
-There are five cities, in which you will have to go and do your stuff. The gameplay is 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/r3gm/Fast_Stable_diffusion_CPU/scripts/main.py b/spaces/r3gm/Fast_Stable_diffusion_CPU/scripts/main.py
deleted file mode 100644
index 10513a3fc0161eee656c0babbb4c0b3531d816da..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Fast_Stable_diffusion_CPU/scripts/main.py
+++ /dev/null
@@ -1,614 +0,0 @@
-from concurrent.futures import ThreadPoolExecutor
-from pathlib import Path
-from typing import Optional
-import uuid
-from lcm.lcm_scheduler import LCMScheduler
-from lcm.lcm_pipeline import LatentConsistencyModelPipeline
-from lcm.lcm_i2i_pipeline import LatentConsistencyModelImg2ImgPipeline, LCMSchedulerWithTimestamp
-from diffusers.image_processor import PipelineImageInput
-# import modules.scripts as scripts
-# import modules.shared
-# from modules import script_callbacks
-import os
-import random
-import time
-import numpy as np
-import gradio as gr
-from PIL import Image, PngImagePlugin
-import torch
-
-scheduler = LCMScheduler.from_pretrained(
-    "SimianLuo/LCM_Dreamshaper_v7", subfolder="scheduler")
-
-pipe = LatentConsistencyModelPipeline.from_pretrained(
-    "SimianLuo/LCM_Dreamshaper_v7", scheduler = scheduler, safety_checker = None)
-
-
-
-DESCRIPTION = '''# Latent Consistency Model
-Running [LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) | [Project Page](https://latent-consistency-models.github.io) | [Extension Page](https://github.com/0xbitches/sd-webui-lcm)
-'''
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768"))
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-
-def save_image(img, metadata: dict):
-    save_dir = './outputs/LCM-txt2img/'
-    Path(save_dir).mkdir(exist_ok=True, parents=True)
-    seed = metadata["seed"]
-    unique_id = uuid.uuid4()
-    filename = save_dir + f"{unique_id}-{seed}" + ".png"
-
-    meta_tuples = [(k, str(v)) for k, v in metadata.items()]
-    png_info = PngImagePlugin.PngInfo()
-    for k, v in meta_tuples:
-        png_info.add_text(k, v)
-    img.save(filename, pnginfo=png_info)
-
-    return filename
-
-
-def save_images(image_array, metadata: dict):
-    paths = []
-    with ThreadPoolExecutor() as executor:
-        paths = list(executor.map(save_image, image_array,
-                     [metadata]*len(image_array)))
-    return paths
-
-
-def generate(
-    prompt: str,
-    seed: int = 0,
-    width: int = 512,
-    height: int = 512,
-    guidance_scale: float = 8.0,
-    num_inference_steps: int = 4,
-    num_images: int = 4,
-    randomize_seed: bool = False,
-    use_fp16: bool = True,
-    use_torch_compile: bool = False,
-    use_cpu: bool = False,
-    progress=gr.Progress(track_tqdm=True)
-) -> Image.Image:
-    seed = randomize_seed_fn(seed, randomize_seed)
-    torch.manual_seed(seed)
-
-    selected_device = 'cuda'
-    if use_cpu:
-        selected_device = "cpu"
-        if use_fp16:
-            use_fp16 = False
-            print("LCM warning: running on CPU, overrode FP16 with FP32")
-    global pipe, scheduler 
-    pipe = LatentConsistencyModelPipeline(
-        vae= pipe.vae,
-        text_encoder = pipe.text_encoder,
-        tokenizer = pipe.tokenizer,
-        unet = pipe.unet,
-        scheduler = scheduler,
-        safety_checker = pipe.safety_checker,
-        feature_extractor = pipe.feature_extractor,
-    )
-    # pipe = LatentConsistencyModelPipeline.from_pretrained(
-    #     "SimianLuo/LCM_Dreamshaper_v7", scheduler = scheduler, safety_checker = None)
-
-    if use_fp16:
-        pipe.to(torch_device=selected_device, torch_dtype=torch.float16)
-    else:
-        pipe.to(torch_device=selected_device, torch_dtype=torch.float32)
-
-    # Windows does not support torch.compile for now
-    if os.name != 'nt' and use_torch_compile:
-        pipe.unet = torch.compile(pipe.unet, mode='max-autotune')
-
-    start_time = time.time()
-    result = pipe(
-        prompt=prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=num_images,
-        original_inference_steps=50,
-        output_type="pil",
-        device = selected_device
-    ).images
-    paths = save_images(result, metadata={"prompt": prompt, "seed": seed, "width": width,
-                        "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps})
-
-    elapsed_time = time.time() - start_time
-    print("LCM inference time: ", elapsed_time, "seconds")
-    return paths, seed
-
-
-def generate_i2i(
-    prompt: str,
-    image: PipelineImageInput = None,
-    strength: float = 0.8,
-    seed: int = 0,
-    guidance_scale: float = 8.0,
-    num_inference_steps: int = 4,
-    num_images: int = 4,
-    randomize_seed: bool = False,
-    use_fp16: bool = True,
-    use_torch_compile: bool = False,
-    use_cpu: bool = False,
-    progress=gr.Progress(track_tqdm=True),
-    width: Optional[int] = 512,
-    height: Optional[int] = 512,
-) -> Image.Image:
-    seed = randomize_seed_fn(seed, randomize_seed)
-    torch.manual_seed(seed)
-
-    selected_device = 'cuda'
-    if use_cpu:
-        selected_device = "cpu"
-        if use_fp16:
-            use_fp16 = False
-            print("LCM warning: running on CPU, overrode FP16 with FP32")
-    global pipe, scheduler 
-    pipe = LatentConsistencyModelImg2ImgPipeline(
-        vae= pipe.vae,
-        text_encoder = pipe.text_encoder,
-        tokenizer = pipe.tokenizer,
-        unet = pipe.unet,
-        scheduler = None, #scheduler,
-        safety_checker = pipe.safety_checker,
-        feature_extractor = pipe.feature_extractor,
-        requires_safety_checker = False,
-    )
-    # pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained(
-    #     "SimianLuo/LCM_Dreamshaper_v7", safety_checker = None)
-
-    if use_fp16:
-        pipe.to(torch_device=selected_device, torch_dtype=torch.float16)
-    else:
-        pipe.to(torch_device=selected_device, torch_dtype=torch.float32)
-
-    # Windows does not support torch.compile for now
-    if os.name != 'nt' and use_torch_compile:
-        pipe.unet = torch.compile(pipe.unet, mode='max-autotune')
-
-    width, height = image.size
-
-    start_time = time.time()
-    result = pipe(
-        prompt=prompt,
-        image=image,
-        strength=strength,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=num_images,
-        original_inference_steps=50,
-        output_type="pil",
-        device = selected_device
-    ).images
-    paths = save_images(result, metadata={"prompt": prompt, "seed": seed, "width": width,
-                        "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps})
-
-    elapsed_time = time.time() - start_time
-    print("LCM inference time: ", elapsed_time, "seconds")
-    return paths, seed
-
-import cv2
-
-def video_to_frames(video_path):
-    # Open the video file
-    cap = cv2.VideoCapture(video_path)
-    
-    # Check if the video opened successfully
-    if not cap.isOpened():
-        print("Error: LCM Could not open video.")
-        return
-    
-    # Read frames from the video
-    pil_images = []
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
-        
-        # Convert BGR to RGB (OpenCV uses BGR by default)
-        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        
-        # Convert numpy array to PIL Image
-        pil_image = Image.fromarray(rgb_frame)
-        
-        # Append the PIL Image to the list
-        pil_images.append(pil_image)
-    
-    # Release the video capture object
-    cap.release()
-    
-    return pil_images
-
-def frames_to_video(pil_images, output_path, fps):
-    if not pil_images:
-        print("Error: No images to convert.")
-        return
-    
-    img_array = []
-    for pil_image in pil_images:
-        img_array.append(np.array(pil_image))
-    
-    height, width, layers = img_array[0].shape
-    size = (width, height)
-    
-    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, size)
-    for i in range(len(img_array)):
-        out.write(cv2.cvtColor(img_array[i], cv2.COLOR_RGB2BGR))
-    out.release()
-
-def generate_v2v(
-    prompt: str,
-    video: any = None,
-    strength: float = 0.8,
-    seed: int = 0,
-    guidance_scale: float = 8.0,
-    num_inference_steps: int = 4,
-    randomize_seed: bool = False,
-    use_fp16: bool = True,
-    use_torch_compile: bool = False,
-    use_cpu: bool = False,
-    fps: int = 10,
-    save_frames: bool = False,
-    # progress=gr.Progress(track_tqdm=True),
-    width: Optional[int] = 512,
-    height: Optional[int] = 512,
-    num_images: Optional[int] = 1,
-) -> Image.Image:
-    seed = randomize_seed_fn(seed, randomize_seed)
-    torch.manual_seed(seed)
-
-    selected_device = 'cuda'
-    if use_cpu:
-        selected_device = "cpu"
-        if use_fp16:
-            use_fp16 = False
-            print("LCM warning: running on CPU, overrode FP16 with FP32")
-    global pipe, scheduler 
-    pipe = LatentConsistencyModelImg2ImgPipeline(
-        vae= pipe.vae,
-        text_encoder = pipe.text_encoder,
-        tokenizer = pipe.tokenizer,
-        unet = pipe.unet,
-        scheduler = None,
-        safety_checker = pipe.safety_checker,
-        feature_extractor = pipe.feature_extractor,
-        requires_safety_checker = False,
-    )
-    # pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained(
-    #     "SimianLuo/LCM_Dreamshaper_v7", safety_checker = None)
-
-    if use_fp16:
-        pipe.to(torch_device=selected_device, torch_dtype=torch.float16)
-    else:
-        pipe.to(torch_device=selected_device, torch_dtype=torch.float32)
-
-    # Windows does not support torch.compile for now
-    if os.name != 'nt' and use_torch_compile:
-        pipe.unet = torch.compile(pipe.unet, mode='max-autotune')
-
-    frames = video_to_frames(video)
-    if frames is None:
-        print("Error: LCM could not convert video.")
-        return
-    width, height = frames[0].size
-
-    start_time = time.time()
-
-    results = []
-    for frame in frames:
-        result = pipe(
-            prompt=prompt,
-            image=frame,
-            strength=strength,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            num_images_per_prompt=1,
-            original_inference_steps=50,
-            output_type="pil",
-            device = selected_device
-        ).images
-        if save_frames:
-            paths = save_images(result, metadata={"prompt": prompt, "seed": seed, "width": width,
-                                "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps})
-        results.extend(result)
-
-    elapsed_time = time.time() - start_time
-    print("LCM vid2vid inference complete! Processing", len(frames), "frames took", elapsed_time, "seconds")
-    
-    save_dir = './outputs/LCM-vid2vid/'
-    Path(save_dir).mkdir(exist_ok=True, parents=True)
-    unique_id = uuid.uuid4()
-    _, input_ext = os.path.splitext(video)
-    output_path = save_dir + f"{unique_id}-{seed}" + f"{input_ext}"
-    frames_to_video(results, output_path, fps)
-    return output_path
-
-
-
-examples = [
-    "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
-    "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
-]
-
-with gr.Blocks() as lcm:
-    with gr.Tab("LCM txt2img"):
-        gr.Markdown("Latent Consistency Models: Synthesizing High-Resolution Images with Few-step Inference")
-        gr.Markdown("Try the guide on Colab's free tier [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/R3gm/InsightSolver-Colab/blob/main/Latent_Consistency_Models.ipynb)")
-        with gr.Row():
-            prompt = gr.Textbox(label="Prompt", 
-                                show_label=False, 
-                                lines=3, 
-                                placeholder="Prompt", 
-                                elem_classes=["prompt"])     
-            run_button = gr.Button("Run", scale=0)
-        with gr.Row():        
-            result = gr.Gallery(
-                label="Generated images", show_label=False, elem_id="gallery", grid=[2], preview=True
-            )
-
-        with gr.Accordion("Advanced options", open=False):
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-                randomize=True
-            )
-            randomize_seed = gr.Checkbox(
-                label="Randomize seed across runs", value=True)
-            use_fp16 = gr.Checkbox(
-                label="Run LCM in fp16 (for lower VRAM)", value=False)
-            use_torch_compile = gr.Checkbox(
-                label="Run LCM with torch.compile (currently not supported on Windows)", value=False)
-            use_cpu = gr.Checkbox(label="Run LCM on CPU", value=True)
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale for base",
-                    minimum=2,
-                    maximum=14,
-                    step=0.1,
-                    value=8.0,
-                )
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps for base",
-                    minimum=1,
-                    maximum=8,
-                    step=1,
-                    value=4,
-                )
-            with gr.Row():
-                num_images = gr.Slider(
-                    label="Number of images (batch count)",
-                    minimum=1,
-                    maximum=int(os.getenv("MAX_NUM_IMAGES")),
-                    step=1,
-                    value=1,
-                )
-
-        gr.Examples(
-            examples=examples,
-            inputs=prompt,
-            outputs=result,
-            fn=generate
-        )
-
-        run_button.click(
-            fn=generate,
-            inputs=[
-                prompt,
-                seed,
-                width,
-                height,
-                guidance_scale,
-                num_inference_steps,
-                num_images,
-                randomize_seed,
-                use_fp16,
-                use_torch_compile,
-                use_cpu
-            ],
-            outputs=[result, seed],
-        )
-
-    with gr.Tab("LCM img2img"):
-        with gr.Row():
-            prompt = gr.Textbox(label="Prompt", 
-                                show_label=False, 
-                                lines=3, 
-                                placeholder="Prompt", 
-                                elem_classes=["prompt"])       
-            run_i2i_button = gr.Button("Run", scale=0)
-        with gr.Row():      
-            image_input = gr.Image(label="Upload your Image", type="pil")
-            result = gr.Gallery(
-                label="Generated images", 
-                show_label=False, 
-                elem_id="gallery", 
-                preview=True
-            )
-
-        with gr.Accordion("Advanced options", open=False):
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-                randomize=True
-            )
-            randomize_seed = gr.Checkbox(
-                label="Randomize seed across runs", value=True)
-            use_fp16 = gr.Checkbox(
-                label="Run LCM in fp16 (for lower VRAM)", value=False)
-            use_torch_compile = gr.Checkbox(
-                label="Run LCM with torch.compile (currently not supported on Windows)", value=False)
-            use_cpu = gr.Checkbox(label="Run LCM on CPU", value=True)
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale for base",
-                    minimum=2,
-                    maximum=14,
-                    step=0.1,
-                    value=8.0,
-                )
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps for base",
-                    minimum=1,
-                    maximum=8,
-                    step=1,
-                    value=4,
-                )
-            with gr.Row():
-                num_images = gr.Slider(
-                    label="Number of images (batch count)",
-                    minimum=1,
-                    maximum=int(os.getenv("MAX_NUM_IMAGES")),
-                    step=1,
-                    value=1,
-                )
-                strength = gr.Slider(
-                    label="Prompt Strength",
-                    minimum=0.1,
-                    maximum=1.0,
-                    step=0.1,
-                    value=0.5,
-                )
-
-        run_i2i_button.click(
-            fn=generate_i2i,
-            inputs=[
-                prompt,
-                image_input,
-                strength,
-                seed,
-                guidance_scale,
-                num_inference_steps,
-                num_images,
-                randomize_seed,
-                use_fp16,
-                use_torch_compile,
-                use_cpu
-            ],
-            outputs=[result, seed],
-        )
-    
-    
-    with gr.Tab("LCM vid2vid"):
-        
-        show_v2v = False if os.getenv("SHOW_VID2VID") == "NO" else True
-        gr.Markdown("Not recommended for use with CPU. Duplicate the space and modify SHOW_VID2VID to enable it. 🚫💻")
-        with gr.Tabs(visible=show_v2v) as tabs:
-        #with gr.Tab("", visible=show_v2v):
-        
-            with gr.Row():
-                prompt = gr.Textbox(label="Prompt", 
-                                    show_label=False, 
-                                    lines=3, 
-                                    placeholder="Prompt", 
-                                    elem_classes=["prompt"])       
-                run_v2v_button = gr.Button("Run", scale=0)
-            with gr.Row():
-                video_input = gr.Video(label="Source Video")
-                video_output = gr.Video(label="Generated Video")
-    
-            with gr.Accordion("Advanced options", open=False):
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
-                    step=1,
-                    value=0,
-                    randomize=True
-                )
-                randomize_seed = gr.Checkbox(
-                    label="Randomize seed across runs", value=True)
-                use_fp16 = gr.Checkbox(
-                    label="Run LCM in fp16 (for lower VRAM)", value=False)
-                use_torch_compile = gr.Checkbox(
-                    label="Run LCM with torch.compile (currently not supported on Windows)", value=False)
-                use_cpu = gr.Checkbox(label="Run LCM on CPU", value=True)
-                save_frames = gr.Checkbox(label="Save intermediate frames", value=False)                   
-                with gr.Row():
-                    guidance_scale = gr.Slider(
-                        label="Guidance scale for base",
-                        minimum=2,
-                        maximum=14,
-                        step=0.1,
-                        value=8.0,
-                    )
-                    num_inference_steps = gr.Slider(
-                        label="Number of inference steps for base",
-                        minimum=1,
-                        maximum=8,
-                        step=1,
-                        value=4,
-                    )
-                with gr.Row():
-                    fps = gr.Slider(
-                        label="Output FPS",
-                        minimum=1,
-                        maximum=200,
-                        step=1,
-                        value=10,
-                    )
-                    strength = gr.Slider(
-                        label="Prompt Strength",
-                        minimum=0.1,
-                        maximum=1.0,
-                        step=0.05,
-                        value=0.5,
-                    )
-    
-            run_v2v_button.click(
-                fn=generate_v2v,
-                inputs=[
-                    prompt,
-                    video_input,
-                    strength,
-                    seed,
-                    guidance_scale,
-                    num_inference_steps,
-                    randomize_seed,
-                    use_fp16,
-                    use_torch_compile,
-                    use_cpu,
-                    fps,
-                    save_frames
-                ],
-                outputs=video_output,
-            )
-
-if __name__ == "__main__":
-    lcm.queue().launch()
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Aprende Sobre La Historia Del Derecho Mexicano Con Este Pdf Descarga Lecciones De Historia Del Derecho Mexicano Gratis.md b/spaces/raedeXanto/academic-chatgpt-beta/Aprende Sobre La Historia Del Derecho Mexicano Con Este Pdf Descarga Lecciones De Historia Del Derecho Mexicano Gratis.md
deleted file mode 100644
index 23f39f203a6dc6423b65a3e7f4b62efc515e3540..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Aprende Sobre La Historia Del Derecho Mexicano Con Este Pdf Descarga Lecciones De Historia Del Derecho Mexicano Gratis.md	
+++ /dev/null
@@ -1,87 +0,0 @@
-<br />
-<h1>Lecciones De Historia Del Derecho Mexicano Pdf Download</h1>
-<p>If you are interested in learning about the history of Mexican law, you might want to read <i>Lecciones De Historia Del Derecho Mexicano</i> by Marco Antonio Pérez de los Reyes. This book is a comprehensive and updated overview of the evolution and development of Mexican law from pre-Hispanic times to the present day. It covers the main historical periods, events, actors, institutions, sources, doctrines, and concepts that shaped Mexican legal culture and identity. It also analyzes the current challenges and opportunities that Mexican law faces in a globalized and changing world.</p>
-<h2>Lecciones De Historia Del Derecho Mexicano Pdf Download</h2><br /><p><b><b>Download File</b> >>> <a href="https://tinourl.com/2uKZhA">https://tinourl.com/2uKZhA</a></b></p><br /><br />
-<p>This book is an essential resource for students and scholars of Mexican legal history, as well as for anyone who wants to understand the historical roots and contemporary realities of Mexican law. It is written in a clear and accessible language, with numerous examples, cases, tables, maps, charts, and illustrations that facilitate learning and comprehension. It also includes a bibliography, a glossary, an index, and a list of abbreviations for further reference.</p>
-<p>If you want to download this book in PDF format, you can do so by following these simple steps:</p>
-<ol>
-<li>Go to <a href="https://www.librosintinta.in/libros/lecciones-de-historia-del-derecho-mexicano-pdf.html">this website</a>, which offers free PDF downloads of various books.</li>
-<li>Type <i>Lecciones De Historia Del Derecho Mexicano</i> in the search box and click on the magnifying glass icon.</li>
-<li>Select one of the results that matches the title and author of the book. You can also check the preview or description of the book to make sure it is what you are looking for.</li>
-<li>Click on the download button or link that appears below or next to the result. You might have to wait a few seconds or minutes for the download to start.</li>
-<li>Save the file to your device or open it with your preferred PDF reader.</li>
-<li>Enjoy reading this fascinating book!</li>
-</ol>
-<h2>The Origins and Development of Mexican Law</h2>
-<p>experienced throughout its history. It also shows the struggles and conflicts that different groups and actors had over the definition and implementation of the fundamental principles and values of the nation. The constitutional history of Mexico can be divided into four main periods: the first constitutions, the centralist and liberal periods, the Porfiriato and the Revolution, and the contemporary period.</p>
-<p>The first constitutions of Mexico were inspired by various models and influences, such as the Spanish Constitution of Cádiz (1812), the American Constitution (1787), the French Constitution (1791), and the English Magna Carta (1215). Some of the first constitutions of Mexico were: the Constitution of Apatzingán (1814), which was drafted by José María Morelos y Pavón and his followers during the independence war; the Federal Constitution of 1824, which established a federalist system for Mexico; the Centralist Constitution of 1836, also known as the Seven Laws, which abolished federalism and created a centralized system; and the Federal Constitution of 1857, which restored federalism and introduced liberal reforms, such as freedom of religion, expression, association, and education.</p>
-<p>The centralist and liberal periods of Mexico were marked by constant conflicts and wars between different factions and ideologies. Some of these conflicts and wars were: the Texas Revolution (1835-1836), which resulted in the secession of Texas from Mexico; the Pastry War (1838-1839), which was a military intervention by France against Mexico over some unpaid debts; the Mexican-American War (1846-1848), which ended with the loss of more than half of Mexico's territory to the United States; the Reform War (1858-1861), which was a civil war between liberals and conservatives over the implementation of the 1857 Constitution; and the French Intervention (1862-1867), which was an invasion by France that installed Maximilian I as emperor of Mexico.</p>
-<p>The Porfiriato was a period of stability and modernization that lasted from 1876 to 1911 under the dictatorship of Porfirio Díaz. Díaz consolidated his power by suppressing any opposition or dissent, controlling the elections, co-opting or repressing different social groups, and creating a loyal political elite. He also promoted economic development by attracting foreign investment, building infrastructure, expanding agriculture and mining, fostering industrialization and urbanization, and integrating Mexico into the world market. He also supported cultural and educational initiatives that aimed to create a national identity and a modern society. However, his regime also generated social problems and inequalities, such as land concentration, labor exploitation, rural poverty, regional disparities, ethnic discrimination, political corruption, and social unrest.</p>
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-<p>The Mexican Revolution was a social movement that erupted in 1910 against the Porfiriato and lasted until 1920. It was led by various leaders and groups, such as Francisco I. Madero, Francisco Villa, Emiliano Zapata, Venustiano Carranza, Álvaro Obregón, and Plutarco Elías Calles. It had different causes, demands, and goals, such as democracy, social justice, land reform, national sovereignty, and cultural diversity. It also had different phases, outcomes, and consequences, such as armed conflict, political change, social reform, economic transformation, and cultural innovation. The most important legacy of the Mexican Revolution was the Constitution of 1917, which is still in force today. The Constitution of 1917 was a progressive and innovative document that incorporated social rights, such as labor rights, agrarian rights, educational rights, and health rights; nationalist principles, such as state ownership of natural resources, restriction of foreign intervention, and promotion of national culture; and democratic values, such as popular sovereignty, separation of powers, checks and balances, and electoral reforms.</p>
-<h2>The Contemporary Legal Challenges of Mexico</h2>
-<p>The contemporary period of Mexico is characterized by the social and economic transformations that occurred in the 20th century and the political and legal reforms that took place in the 21st century. These transformations and reforms have posed new challenges and opportunities for Mexican law in a globalized and changing world.</p>
-<p>The social and economic transformations of Mexico in the 20th century included: the consolidation of a one-party system under the Institutional Revolutionary Party (PRI), which dominated Mexican politics from 1929 to 2000; the industrialization and urbanization processes that accelerated in the second half of the century; the integration of Mexico into the North American Free Trade Agreement (NAFTA) in 1994; the emergence of new social movements and actors, such as students, workers, women, indigenous peoples, and human rights defenders; and the rise of new challenges and threats, such as violence, corruption, inequality, migration, and environmental degradation.</p>
-<p>The political and legal reforms of Mexico in the 21st century included: the transition to democracy in 2000 with the victory of Vicente Fox from the National Action Party (PAN) in the presidential election; the constitutional amendments in various areas, such as human rights, federalism, judicial independence, criminal justice, electoral system, transparency, and accountability; the strengthening of democratic institutions and practices, such as pluralism, participation, representation, deliberation, and oversight; and the development of new legal frameworks and mechanisms to address the current issues and debates in Mexican law.</p>
-<h2>Conclusion</h2>
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-<p>and outcomes that regulate the behavior and relations of individuals and groups in Mexico.</p></li>
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-<li><b>What are the branches of Mexican law?</b><br>The branches of Mexican law are the subdivisions or categories of the law according to their subject matter or scope. The main branches of Mexican law are: public law, which regulates the relations between the state and its organs, as well as between the state and individuals or groups; private law, which regulates the relations between individuals or groups based on their interests or rights; and social law, which regulates the relations between individuals or groups based on their needs or duties.</p></li>
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Chor Machaye Shor Full Movie 2002 [2021] Free Download.md b/spaces/raedeXanto/academic-chatgpt-beta/Chor Machaye Shor Full Movie 2002 [2021] Free Download.md
deleted file mode 100644
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--- a/spaces/raedeXanto/academic-chatgpt-beta/Chor Machaye Shor Full Movie 2002 [2021] Free Download.md	
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diff --git a/spaces/raees/Riot-Detector/README.md b/spaces/raees/Riot-Detector/README.md
deleted file mode 100644
index d0e0ad4ce8412eab8e4f8ba428ffe8753566ae64..0000000000000000000000000000000000000000
--- a/spaces/raees/Riot-Detector/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Riot Detector
-emoji: 🐠
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/rahul999r/Rahul_Kannada_TTS/scripts/data/duration.sh b/spaces/rahul999r/Rahul_Kannada_TTS/scripts/data/duration.sh
deleted file mode 100644
index 6fc586c05259d3d576fa4437dea5f650fe5f5031..0000000000000000000000000000000000000000
--- a/spaces/rahul999r/Rahul_Kannada_TTS/scripts/data/duration.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-wav_path='/home/harveen/en/iitm_data/english/wav_22k'
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diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@notionhq/client/build/src/index.js b/spaces/rayan-saleh/whisper2notion/server/node_modules/@notionhq/client/build/src/index.js
deleted file mode 100644
index e5c285b24b185a50f0e6c39ae713c023036c6a22..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@notionhq/client/build/src/index.js
+++ /dev/null
@@ -1,24 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.isFullComment = exports.isFullUser = exports.isFullPage = exports.isFullDatabase = exports.isFullBlock = exports.iteratePaginatedAPI = exports.collectPaginatedAPI = exports.isNotionClientError = exports.RequestTimeoutError = exports.UnknownHTTPResponseError = exports.APIResponseError = exports.ClientErrorCode = exports.APIErrorCode = exports.LogLevel = exports.Client = void 0;
-var Client_1 = require("./Client");
-Object.defineProperty(exports, "Client", { enumerable: true, get: function () { return Client_1.default; } });
-var logging_1 = require("./logging");
-Object.defineProperty(exports, "LogLevel", { enumerable: true, get: function () { return logging_1.LogLevel; } });
-var errors_1 = require("./errors");
-Object.defineProperty(exports, "APIErrorCode", { enumerable: true, get: function () { return errors_1.APIErrorCode; } });
-Object.defineProperty(exports, "ClientErrorCode", { enumerable: true, get: function () { return errors_1.ClientErrorCode; } });
-Object.defineProperty(exports, "APIResponseError", { enumerable: true, get: function () { return errors_1.APIResponseError; } });
-Object.defineProperty(exports, "UnknownHTTPResponseError", { enumerable: true, get: function () { return errors_1.UnknownHTTPResponseError; } });
-Object.defineProperty(exports, "RequestTimeoutError", { enumerable: true, get: function () { return errors_1.RequestTimeoutError; } });
-// Error helpers
-Object.defineProperty(exports, "isNotionClientError", { enumerable: true, get: function () { return errors_1.isNotionClientError; } });
-var helpers_1 = require("./helpers");
-Object.defineProperty(exports, "collectPaginatedAPI", { enumerable: true, get: function () { return helpers_1.collectPaginatedAPI; } });
-Object.defineProperty(exports, "iteratePaginatedAPI", { enumerable: true, get: function () { return helpers_1.iteratePaginatedAPI; } });
-Object.defineProperty(exports, "isFullBlock", { enumerable: true, get: function () { return helpers_1.isFullBlock; } });
-Object.defineProperty(exports, "isFullDatabase", { enumerable: true, get: function () { return helpers_1.isFullDatabase; } });
-Object.defineProperty(exports, "isFullPage", { enumerable: true, get: function () { return helpers_1.isFullPage; } });
-Object.defineProperty(exports, "isFullUser", { enumerable: true, get: function () { return helpers_1.isFullUser; } });
-Object.defineProperty(exports, "isFullComment", { enumerable: true, get: function () { return helpers_1.isFullComment; } });
-//# sourceMappingURL=index.js.map
\ No newline at end of file
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diff --git a/spaces/rileho3909/Real-Time-Voice-Cloning/README.md b/spaces/rileho3909/Real-Time-Voice-Cloning/README.md
deleted file mode 100644
index fb8628dd176578713990a0f589a2665869f99b26..0000000000000000000000000000000000000000
--- a/spaces/rileho3909/Real-Time-Voice-Cloning/README.md
+++ /dev/null
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----
-title: Real Time Voice Cloning
-emoji: 😻
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
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-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`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, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/riyueyiming/gpt/Dockerfile b/spaces/riyueyiming/gpt/Dockerfile
deleted file mode 100644
index 8cbd335b09b1d1975bfd83a053b5fcaf398147ea..0000000000000000000000000000000000000000
--- a/spaces/riyueyiming/gpt/Dockerfile
+++ /dev/null
@@ -1,14 +0,0 @@
-FROM python:3.9 as builder
-RUN apt-get update && apt-get install -y build-essential
-COPY requirements.txt .
-RUN pip install --user -r requirements.txt
-
-FROM python:3.9
-MAINTAINER iskoldt
-COPY --from=builder /root/.local /root/.local
-ENV PATH=/root/.local/bin:$PATH
-COPY . /app
-WORKDIR /app
-ENV my_api_key empty
-ENV dockerrun yes
-CMD ["python3", "-u", "ChuanhuChatbot.py", "2>&1", "|", "tee", "/var/log/application.log"]
diff --git a/spaces/robin0307/MMOCR/configs/_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem_poly.py b/spaces/robin0307/MMOCR/configs/_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem_poly.py
deleted file mode 100644
index abbac26851d4eeef04fa904c8e69c50a58c2b54d..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem_poly.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# model settings
-model = dict(
-    type='OCRMaskRCNN',
-    text_repr_type='poly',
-    backbone=dict(
-        type='mmdet.ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
-        style='pytorch'),
-    neck=dict(
-        type='mmdet.FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        num_outs=5),
-    rpn_head=dict(
-        type='RPNHead',
-        in_channels=256,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            scales=[4],
-            ratios=[0.17, 0.44, 1.13, 2.90, 7.46],
-            strides=[4, 8, 16, 32, 64]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-    roi_head=dict(
-        type='StandardRoIHead',
-        bbox_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=7, sample_num=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        bbox_head=dict(
-            type='Shared2FCBBoxHead',
-            in_channels=256,
-            fc_out_channels=1024,
-            roi_feat_size=7,
-            num_classes=80,
-            bbox_coder=dict(
-                type='DeltaXYWHBBoxCoder',
-                target_means=[0., 0., 0., 0.],
-                target_stds=[0.1, 0.1, 0.2, 0.2]),
-            reg_class_agnostic=False,
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-        mask_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=14, sample_num=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        mask_head=dict(
-            type='FCNMaskHead',
-            num_convs=4,
-            in_channels=256,
-            conv_out_channels=256,
-            num_classes=80,
-            loss_mask=dict(
-                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                match_low_quality=True,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            pos_weight=-1,
-            debug=False),
-        rpn_proposal=dict(
-            nms_across_levels=False,
-            nms_pre=2000,
-            nms_post=1000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.5,
-                min_pos_iou=0.5,
-                match_low_quality=True,
-                ignore_iof_thr=-1,
-                gpu_assign_thr=50),
-            sampler=dict(
-                type='OHEMSampler',
-                num=512,
-                pos_fraction=0.25,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=True),
-            mask_size=28,
-            pos_weight=-1,
-            debug=False)),
-    test_cfg=dict(
-        rpn=dict(
-            nms_across_levels=False,
-            nms_pre=1000,
-            nms_post=1000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            score_thr=0.05,
-            nms=dict(type='nms', iou_threshold=0.5),
-            max_per_img=100,
-            mask_thr_binary=0.5)))
diff --git a/spaces/sanchit-gandhi/musicgen-streaming/app.py b/spaces/sanchit-gandhi/musicgen-streaming/app.py
deleted file mode 100644
index fc51dc24b85aa1984190378308d86d9c1f0c8d9d..0000000000000000000000000000000000000000
--- a/spaces/sanchit-gandhi/musicgen-streaming/app.py
+++ /dev/null
@@ -1,238 +0,0 @@
-from queue import Queue
-from threading import Thread
-from typing import Optional
-
-import numpy as np
-import torch
-
-from transformers import MusicgenForConditionalGeneration, MusicgenProcessor, set_seed
-from transformers.generation.streamers import BaseStreamer
-
-import gradio as gr
-import spaces
-
-
-model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
-processor = MusicgenProcessor.from_pretrained("facebook/musicgen-small")
-
-title = "MusicGen Streaming"
-
-description = """
-Stream the outputs of the MusicGen text-to-music model by playing the generated audio as soon as the first chunk is ready. 
-Demo uses [MusicGen Small](https://huggingface.co/facebook/musicgen-small) in the 🤗 Transformers library. Note that the 
-demo works best on the Chrome browser. If there is no audio output, try switching browser to Chrome.
-"""
-
-article = """
-## How Does It Work?
-
-MusicGen is an auto-regressive transformer-based model, meaning generates audio codes (tokens) in a causal fashion.
-At each decoding step, the model generates a new set of audio codes, conditional on the text input and all previous audio codes. From the 
-frame rate of the [EnCodec model](https://huggingface.co/facebook/encodec_32khz) used to decode the generated codes to audio waveform, 
-each set of generated audio codes corresponds to 0.02 seconds. This means we require a total of 1000 decoding steps to generate
-20 seconds of audio.
-
-Rather than waiting for the entire audio sequence to be generated, which would require the full 1000 decoding steps, we can start 
-playing the audio after a specified number of decoding steps have been reached, a techinque known as [*streaming*](https://huggingface.co/docs/transformers/main/en/generation_strategies#streaming). 
-For example, after 250 steps we have the first 5 seconds of audio ready, and so can play this without waiting for the remaining 
-750 decoding steps to be complete. As we continue to generate with the MusicGen model, we append new chunks of generated audio 
-to our output waveform on-the-fly. After the full 1000 decoding steps, the generated audio is complete, and is composed of four 
-chunks of audio, each corresponding to 250 tokens.
-
-This method of playing incremental generations reduces the latency of the MusicGen model from the total time to generate 1000 tokens, 
-to the time taken to play the first chunk of audio (250 tokens). This can result in significant improvements to perceived latency, 
-particularly when the chunk size is chosen to be small. In practice, the chunk size should be tuned to your device: using a 
-smaller chunk size will mean that the first chunk is ready faster, but should not be chosen so small that the model generates slower 
-than the time it takes to play the audio.
-
-For details on how the streaming class works, check out the source code for the [MusicgenStreamer](https://huggingface.co/spaces/sanchit-gandhi/musicgen-streaming/blob/main/app.py#L52).
-"""
-
-
-class MusicgenStreamer(BaseStreamer):
-    def __init__(
-        self,
-        model: MusicgenForConditionalGeneration,
-        device: Optional[str] = None,
-        play_steps: Optional[int] = 10,
-        stride: Optional[int] = None,
-        timeout: Optional[float] = None,
-    ):
-        """
-        Streamer that stores playback-ready audio in a queue, to be used by a downstream application as an iterator. This is
-        useful for applications that benefit from accessing the generated audio in a non-blocking way (e.g. in an interactive
-        Gradio demo).
-
-        Parameters:
-            model (`MusicgenForConditionalGeneration`):
-                The MusicGen model used to generate the audio waveform.
-            device (`str`, *optional*):
-                The torch device on which to run the computation. If `None`, will default to the device of the model.
-            play_steps (`int`, *optional*, defaults to 10):
-                The number of generation steps with which to return the generated audio array. Using fewer steps will 
-                mean the first chunk is ready faster, but will require more codec decoding steps overall. This value 
-                should be tuned to your device and latency requirements.
-            stride (`int`, *optional*):
-                The window (stride) between adjacent audio samples. Using a stride between adjacent audio samples reduces
-                the hard boundary between them, giving smoother playback. If `None`, will default to a value equivalent to 
-                play_steps // 6 in the audio space.
-            timeout (`int`, *optional*):
-                The timeout for the audio queue. If `None`, the queue will block indefinitely. Useful to handle exceptions
-                in `.generate()`, when it is called in a separate thread.
-        """
-        self.decoder = model.decoder
-        self.audio_encoder = model.audio_encoder
-        self.generation_config = model.generation_config
-        self.device = device if device is not None else model.device
-
-        # variables used in the streaming process
-        self.play_steps = play_steps
-        if stride is not None:
-            self.stride = stride
-        else:
-            hop_length = np.prod(self.audio_encoder.config.upsampling_ratios)
-            self.stride = hop_length * (play_steps - self.decoder.num_codebooks) // 6
-        self.token_cache = None
-        self.to_yield = 0
-
-        # varibles used in the thread process
-        self.audio_queue = Queue()
-        self.stop_signal = None
-        self.timeout = timeout
-
-    def apply_delay_pattern_mask(self, input_ids):
-        # build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
-        _, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
-            input_ids[:, :1],
-            pad_token_id=self.generation_config.decoder_start_token_id,
-            max_length=input_ids.shape[-1],
-        )
-        # apply the pattern mask to the input ids
-        input_ids = self.decoder.apply_delay_pattern_mask(input_ids, decoder_delay_pattern_mask)
-
-        # revert the pattern delay mask by filtering the pad token id
-        input_ids = input_ids[input_ids != self.generation_config.pad_token_id].reshape(
-            1, self.decoder.num_codebooks, -1
-        )
-
-        # append the frame dimension back to the audio codes
-        input_ids = input_ids[None, ...]
-
-        # send the input_ids to the correct device
-        input_ids = input_ids.to(self.audio_encoder.device)
-
-        output_values = self.audio_encoder.decode(
-            input_ids,
-            audio_scales=[None],
-        )
-        audio_values = output_values.audio_values[0, 0]
-        return audio_values.cpu().float().numpy()
-
-    def put(self, value):
-        batch_size = value.shape[0] // self.decoder.num_codebooks
-        if batch_size > 1:
-            raise ValueError("MusicgenStreamer only supports batch size 1")
-
-        if self.token_cache is None:
-            self.token_cache = value
-        else:
-            self.token_cache = torch.concatenate([self.token_cache, value[:, None]], dim=-1)
-
-        if self.token_cache.shape[-1] % self.play_steps == 0:
-            audio_values = self.apply_delay_pattern_mask(self.token_cache)
-            self.on_finalized_audio(audio_values[self.to_yield : -self.stride])
-            self.to_yield += len(audio_values) - self.to_yield - self.stride
-
-    def end(self):
-        """Flushes any remaining cache and appends the stop symbol."""
-        if self.token_cache is not None:
-            audio_values = self.apply_delay_pattern_mask(self.token_cache)
-        else:
-            audio_values = np.zeros(self.to_yield)
-
-        self.on_finalized_audio(audio_values[self.to_yield :], stream_end=True)
-
-    def on_finalized_audio(self, audio: np.ndarray, stream_end: bool = False):
-        """Put the new audio in the queue. If the stream is ending, also put a stop signal in the queue."""
-        self.audio_queue.put(audio, timeout=self.timeout)
-        if stream_end:
-            self.audio_queue.put(self.stop_signal, timeout=self.timeout)
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        value = self.audio_queue.get(timeout=self.timeout)
-        if not isinstance(value, np.ndarray) and value == self.stop_signal:
-            raise StopIteration()
-        else:
-            return value
-
-
-sampling_rate = model.audio_encoder.config.sampling_rate
-frame_rate = model.audio_encoder.config.frame_rate
-
-target_dtype = np.int16
-max_range = np.iinfo(target_dtype).max
-
-
-@spaces.GPU()
-def generate_audio(text_prompt, audio_length_in_s=10.0, play_steps_in_s=2.0, seed=0):
-    max_new_tokens = int(frame_rate * audio_length_in_s)
-    play_steps = int(frame_rate * play_steps_in_s)
-
-    device = "cuda:0" if torch.cuda.is_available() else "cpu"
-    if device != model.device:
-        model.to(device)
-        if device == "cuda:0":
-            model.half()
-
-    inputs = processor(
-        text=text_prompt,
-        padding=True,
-        return_tensors="pt",
-    )
-
-    streamer = MusicgenStreamer(model, device=device, play_steps=play_steps)
-
-    generation_kwargs = dict(
-        **inputs.to(device),
-        streamer=streamer,
-        max_new_tokens=max_new_tokens,
-    )
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-
-    set_seed(seed)
-    for new_audio in streamer:
-        print(f"Sample of length: {round(new_audio.shape[0] / sampling_rate, 2)} seconds")
-        new_audio = (new_audio * max_range).astype(np.int16)
-        yield sampling_rate, new_audio
-
-
-demo = gr.Interface(
-    fn=generate_audio,
-    inputs=[
-        gr.Text(label="Prompt", value="80s pop track with synth and instrumentals"),
-        gr.Slider(10, 30, value=15, step=5, label="Audio length in seconds"),
-        gr.Slider(0.5, 2.5, value=1.5, step=0.5, label="Streaming interval in seconds", info="Lower = shorter chunks, lower latency, more codec steps"),
-        gr.Slider(0, 10, value=5, step=1, label="Seed for random generations"),
-    ],
-    outputs=[
-        gr.Audio(label="Generated Music", streaming=True, autoplay=True)
-    ],
-    examples=[
-        ["An 80s driving pop song with heavy drums and synth pads in the background", 30, 1.5, 5],
-        ["A cheerful country song with acoustic guitars", 30, 1.5, 5],
-        ["90s rock song with electric guitar and heavy drums", 30, 1.5, 5],
-        ["a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130", 30, 1.5, 5],
-        ["lofi slow bpm electro chill with organic samples", 30, 1.5, 5],
-    ],
-    title=title,
-    description=description,
-    article=article,
-    cache_examples=False,
-)
-
-
-demo.queue().launch()
\ No newline at end of file
diff --git a/spaces/sandm/anime-remove-background1/README.md b/spaces/sandm/anime-remove-background1/README.md
deleted file mode 100644
index 1ba3cb5ea0e994e246d57b7d62b8aa5a6331901c..0000000000000000000000000000000000000000
--- a/spaces/sandm/anime-remove-background1/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Anime Remove Background
-emoji: 🪄🖼️
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: skytnt/anime-remove-background
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/scedlatioru/img-to-music/example/Laubwerk Surfacespread V1.0.43 Cinema 4d R17 HOT Crack.md b/spaces/scedlatioru/img-to-music/example/Laubwerk Surfacespread V1.0.43 Cinema 4d R17 HOT Crack.md
deleted file mode 100644
index 0602ca34531ee1abd439c436e93d17ae52fa52e4..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Laubwerk Surfacespread V1.0.43 Cinema 4d R17 HOT Crack.md	
+++ /dev/null
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diff --git a/spaces/scedlatioru/img-to-music/example/Nero 8 Ultra Key Gen 8.2.8.0 Serial Key Keygen !FREE!.md b/spaces/scedlatioru/img-to-music/example/Nero 8 Ultra Key Gen 8.2.8.0 Serial Key Keygen !FREE!.md
deleted file mode 100644
index 900530d3eb895939776b8a5c998b4ca7f6a54341..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Nero 8 Ultra Key Gen 8.2.8.0 Serial Key Keygen !FREE!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/One Direction Where We Are Tour Movie Download BEST.md b/spaces/scedlatioru/img-to-music/example/One Direction Where We Are Tour Movie Download BEST.md
deleted file mode 100644
index 2d8fb61b46ef6bee4344c1f66fb58737d944e907..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/One Direction Where We Are Tour Movie Download BEST.md	
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diff --git a/spaces/sdhsdhk/bingosjj/src/components/ui/sheet.tsx b/spaces/sdhsdhk/bingosjj/src/components/ui/sheet.tsx
deleted file mode 100644
index c9f5ce0f81a91067bb013e988a07eb1e6bf6953b..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingosjj/src/components/ui/sheet.tsx
+++ /dev/null
@@ -1,122 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SheetPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Sheet = SheetPrimitive.Root
-
-const SheetTrigger = SheetPrimitive.Trigger
-
-const SheetClose = SheetPrimitive.Close
-
-const SheetPortal = ({
-  className,
-  children,
-  ...props
-}: SheetPrimitive.DialogPortalProps) => (
-  <SheetPrimitive.Portal
-    className={cn('fixed inset-0 z-50 flex', className)}
-    {...props}
-  >
-    {children}
-  </SheetPrimitive.Portal>
-)
-SheetPortal.displayName = SheetPrimitive.Portal.displayName
-
-const SheetOverlay = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Overlay>
->(({ className, children, ...props }, ref) => (
-  <SheetPrimitive.Overlay
-    className={cn(
-      'fixed inset-0 z-50 transition-all duration-100 data-[state=closed]:animate-out data-[state=closed]:fade-out data-[state=open]:fade-in',
-      className
-    )}
-    {...props}
-    ref={ref}
-  />
-))
-SheetOverlay.displayName = SheetPrimitive.Overlay.displayName
-
-const SheetContent = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <SheetPortal>
-    <SheetPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed inset-y-0 left-0 z-50 h-full border-r bg-background p-6 shadow-lg transition ease-in-out data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:slide-out-to-left data-[state=open]:slide-in-from-left data-[state=closed]:duration-300 data-[state=open]:duration-500 sm:max-w-sm',
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <SheetPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-secondary">
-        <IconClose />
-        <span className="sr-only">Close</span>
-      </SheetPrimitive.Close>
-    </SheetPrimitive.Content>
-  </SheetPortal>
-))
-SheetContent.displayName = SheetPrimitive.Content.displayName
-
-const SheetHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div className={cn('flex flex-col space-y-2', className)} {...props} />
-)
-SheetHeader.displayName = 'SheetHeader'
-
-const SheetFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-SheetFooter.displayName = 'SheetFooter'
-
-const SheetTitle = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Title
-    ref={ref}
-    className={cn('text-lg font-semibold text-foreground', className)}
-    {...props}
-  />
-))
-SheetTitle.displayName = SheetPrimitive.Title.displayName
-
-const SheetDescription = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-SheetDescription.displayName = SheetPrimitive.Description.displayName
-
-export {
-  Sheet,
-  SheetTrigger,
-  SheetClose,
-  SheetContent,
-  SheetHeader,
-  SheetFooter,
-  SheetTitle,
-  SheetDescription
-}
diff --git a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/e2e_tts_tacotron2.py b/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/e2e_tts_tacotron2.py
deleted file mode 100644
index c41dd4262b3cc63807a451d2351b062294b12178..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/e2e_tts_tacotron2.py
+++ /dev/null
@@ -1,893 +0,0 @@
-# Copyright 2018 Nagoya University (Tomoki Hayashi)
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Tacotron 2 related modules."""
-
-import logging
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
-from espnet.nets.pytorch_backend.rnn.attentions import AttForward
-from espnet.nets.pytorch_backend.rnn.attentions import AttForwardTA
-from espnet.nets.pytorch_backend.rnn.attentions import AttLoc
-from espnet.nets.pytorch_backend.tacotron2.cbhg import CBHG
-from espnet.nets.pytorch_backend.tacotron2.cbhg import CBHGLoss
-from espnet.nets.pytorch_backend.tacotron2.decoder import Decoder
-from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder
-from espnet.nets.tts_interface import TTSInterface
-from espnet.utils.cli_utils import strtobool
-from espnet.utils.fill_missing_args import fill_missing_args
-
-
-class GuidedAttentionLoss(torch.nn.Module):
-    """Guided attention loss function module.
-
-    This module calculates the guided attention loss described
-    in `Efficiently Trainable Text-to-Speech System Based
-    on Deep Convolutional Networks with Guided Attention`_,
-    which forces the attention to be diagonal.
-
-    .. _`Efficiently Trainable Text-to-Speech System
-        Based on Deep Convolutional Networks with Guided Attention`:
-        https://arxiv.org/abs/1710.08969
-
-    """
-
-    def __init__(self, sigma=0.4, alpha=1.0, reset_always=True):
-        """Initialize guided attention loss module.
-
-        Args:
-            sigma (float, optional): Standard deviation to control
-                how close attention to a diagonal.
-            alpha (float, optional): Scaling coefficient (lambda).
-            reset_always (bool, optional): Whether to always reset masks.
-
-        """
-        super(GuidedAttentionLoss, self).__init__()
-        self.sigma = sigma
-        self.alpha = alpha
-        self.reset_always = reset_always
-        self.guided_attn_masks = None
-        self.masks = None
-
-    def _reset_masks(self):
-        self.guided_attn_masks = None
-        self.masks = None
-
-    def forward(self, att_ws, ilens, olens):
-        """Calculate forward propagation.
-
-        Args:
-            att_ws (Tensor): Batch of attention weights (B, T_max_out, T_max_in).
-            ilens (LongTensor): Batch of input lenghts (B,).
-            olens (LongTensor): Batch of output lenghts (B,).
-
-        Returns:
-            Tensor: Guided attention loss value.
-
-        """
-        if self.guided_attn_masks is None:
-            self.guided_attn_masks = self._make_guided_attention_masks(ilens, olens).to(
-                att_ws.device
-            )
-        if self.masks is None:
-            self.masks = self._make_masks(ilens, olens).to(att_ws.device)
-        losses = self.guided_attn_masks * att_ws
-        loss = torch.mean(losses.masked_select(self.masks))
-        if self.reset_always:
-            self._reset_masks()
-        return self.alpha * loss
-
-    def _make_guided_attention_masks(self, ilens, olens):
-        n_batches = len(ilens)
-        max_ilen = max(ilens)
-        max_olen = max(olens)
-        guided_attn_masks = torch.zeros((n_batches, max_olen, max_ilen))
-        for idx, (ilen, olen) in enumerate(zip(ilens, olens)):
-            guided_attn_masks[idx, :olen, :ilen] = self._make_guided_attention_mask(
-                ilen, olen, self.sigma
-            )
-        return guided_attn_masks
-
-    @staticmethod
-    def _make_guided_attention_mask(ilen, olen, sigma):
-        """Make guided attention mask.
-
-        Examples:
-            >>> guided_attn_mask =_make_guided_attention(5, 5, 0.4)
-            >>> guided_attn_mask.shape
-            torch.Size([5, 5])
-            >>> guided_attn_mask
-            tensor([[0.0000, 0.1175, 0.3935, 0.6753, 0.8647],
-                    [0.1175, 0.0000, 0.1175, 0.3935, 0.6753],
-                    [0.3935, 0.1175, 0.0000, 0.1175, 0.3935],
-                    [0.6753, 0.3935, 0.1175, 0.0000, 0.1175],
-                    [0.8647, 0.6753, 0.3935, 0.1175, 0.0000]])
-            >>> guided_attn_mask =_make_guided_attention(3, 6, 0.4)
-            >>> guided_attn_mask.shape
-            torch.Size([6, 3])
-            >>> guided_attn_mask
-            tensor([[0.0000, 0.2934, 0.7506],
-                    [0.0831, 0.0831, 0.5422],
-                    [0.2934, 0.0000, 0.2934],
-                    [0.5422, 0.0831, 0.0831],
-                    [0.7506, 0.2934, 0.0000],
-                    [0.8858, 0.5422, 0.0831]])
-
-        """
-        grid_x, grid_y = torch.meshgrid(torch.arange(olen), torch.arange(ilen))
-        grid_x, grid_y = grid_x.float().to(olen.device), grid_y.float().to(ilen.device)
-        return 1.0 - torch.exp(
-            -((grid_y / ilen - grid_x / olen) ** 2) / (2 * (sigma ** 2))
-        )
-
-    @staticmethod
-    def _make_masks(ilens, olens):
-        """Make masks indicating non-padded part.
-
-        Args:
-            ilens (LongTensor or List): Batch of lengths (B,).
-            olens (LongTensor or List): Batch of lengths (B,).
-
-        Returns:
-            Tensor: Mask tensor indicating non-padded part.
-                    dtype=torch.uint8 in PyTorch 1.2-
-                    dtype=torch.bool in PyTorch 1.2+ (including 1.2)
-
-        Examples:
-            >>> ilens, olens = [5, 2], [8, 5]
-            >>> _make_mask(ilens, olens)
-            tensor([[[1, 1, 1, 1, 1],
-                     [1, 1, 1, 1, 1],
-                     [1, 1, 1, 1, 1],
-                     [1, 1, 1, 1, 1],
-                     [1, 1, 1, 1, 1],
-                     [1, 1, 1, 1, 1],
-                     [1, 1, 1, 1, 1],
-                     [1, 1, 1, 1, 1]],
-                    [[1, 1, 0, 0, 0],
-                     [1, 1, 0, 0, 0],
-                     [1, 1, 0, 0, 0],
-                     [1, 1, 0, 0, 0],
-                     [1, 1, 0, 0, 0],
-                     [0, 0, 0, 0, 0],
-                     [0, 0, 0, 0, 0],
-                     [0, 0, 0, 0, 0]]], dtype=torch.uint8)
-
-        """
-        in_masks = make_non_pad_mask(ilens)  # (B, T_in)
-        out_masks = make_non_pad_mask(olens)  # (B, T_out)
-        return out_masks.unsqueeze(-1) & in_masks.unsqueeze(-2)  # (B, T_out, T_in)
-
-
-class Tacotron2Loss(torch.nn.Module):
-    """Loss function module for Tacotron2."""
-
-    def __init__(
-        self, use_masking=True, use_weighted_masking=False, bce_pos_weight=20.0
-    ):
-        """Initialize Tactoron2 loss module.
-
-        Args:
-            use_masking (bool): Whether to apply masking
-                for padded part in loss calculation.
-            use_weighted_masking (bool):
-                Whether to apply weighted masking in loss calculation.
-            bce_pos_weight (float): Weight of positive sample of stop token.
-
-        """
-        super(Tacotron2Loss, self).__init__()
-        assert (use_masking != use_weighted_masking) or not use_masking
-        self.use_masking = use_masking
-        self.use_weighted_masking = use_weighted_masking
-
-        # define criterions
-        reduction = "none" if self.use_weighted_masking else "mean"
-        self.l1_criterion = torch.nn.L1Loss(reduction=reduction)
-        self.mse_criterion = torch.nn.MSELoss(reduction=reduction)
-        self.bce_criterion = torch.nn.BCEWithLogitsLoss(
-            reduction=reduction, pos_weight=torch.tensor(bce_pos_weight)
-        )
-
-        # NOTE(kan-bayashi): register pre hook function for the compatibility
-        self._register_load_state_dict_pre_hook(self._load_state_dict_pre_hook)
-
-    def forward(self, after_outs, before_outs, logits, ys, labels, olens):
-        """Calculate forward propagation.
-
-        Args:
-            after_outs (Tensor): Batch of outputs after postnets (B, Lmax, odim).
-            before_outs (Tensor): Batch of outputs before postnets (B, Lmax, odim).
-            logits (Tensor): Batch of stop logits (B, Lmax).
-            ys (Tensor): Batch of padded target features (B, Lmax, odim).
-            labels (LongTensor): Batch of the sequences of stop token labels (B, Lmax).
-            olens (LongTensor): Batch of the lengths of each target (B,).
-
-        Returns:
-            Tensor: L1 loss value.
-            Tensor: Mean square error loss value.
-            Tensor: Binary cross entropy loss value.
-
-        """
-        # make mask and apply it
-        if self.use_masking:
-            masks = make_non_pad_mask(olens).unsqueeze(-1).to(ys.device)
-            ys = ys.masked_select(masks)
-            after_outs = after_outs.masked_select(masks)
-            before_outs = before_outs.masked_select(masks)
-            labels = labels.masked_select(masks[:, :, 0])
-            logits = logits.masked_select(masks[:, :, 0])
-
-        # calculate loss
-        l1_loss = self.l1_criterion(after_outs, ys) + self.l1_criterion(before_outs, ys)
-        mse_loss = self.mse_criterion(after_outs, ys) + self.mse_criterion(
-            before_outs, ys
-        )
-        bce_loss = self.bce_criterion(logits, labels)
-
-        # make weighted mask and apply it
-        if self.use_weighted_masking:
-            masks = make_non_pad_mask(olens).unsqueeze(-1).to(ys.device)
-            weights = masks.float() / masks.sum(dim=1, keepdim=True).float()
-            out_weights = weights.div(ys.size(0) * ys.size(2))
-            logit_weights = weights.div(ys.size(0))
-
-            # apply weight
-            l1_loss = l1_loss.mul(out_weights).masked_select(masks).sum()
-            mse_loss = mse_loss.mul(out_weights).masked_select(masks).sum()
-            bce_loss = (
-                bce_loss.mul(logit_weights.squeeze(-1))
-                .masked_select(masks.squeeze(-1))
-                .sum()
-            )
-
-        return l1_loss, mse_loss, bce_loss
-
-    def _load_state_dict_pre_hook(
-        self,
-        state_dict,
-        prefix,
-        local_metadata,
-        strict,
-        missing_keys,
-        unexpected_keys,
-        error_msgs,
-    ):
-        """Apply pre hook fucntion before loading state dict.
-
-        From v.0.6.1 `bce_criterion.pos_weight` param is registered as a parameter but
-        old models do not include it and as a result, it causes missing key error when
-        loading old model parameter. This function solve the issue by adding param in
-        state dict before loading as a pre hook function
-        of the `load_state_dict` method.
-
-        """
-        key = prefix + "bce_criterion.pos_weight"
-        if key not in state_dict:
-            state_dict[key] = self.bce_criterion.pos_weight
-
-
-class Tacotron2(TTSInterface, torch.nn.Module):
-    """Tacotron2 module for end-to-end text-to-speech (E2E-TTS).
-
-    This is a module of Spectrogram prediction network in Tacotron2 described
-    in `Natural TTS Synthesis
-    by Conditioning WaveNet on Mel Spectrogram Predictions`_,
-    which converts the sequence of characters
-    into the sequence of Mel-filterbanks.
-
-    .. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
-       https://arxiv.org/abs/1712.05884
-
-    """
-
-    @staticmethod
-    def add_arguments(parser):
-        """Add model-specific arguments to the parser."""
-        group = parser.add_argument_group("tacotron 2 model setting")
-        # encoder
-        group.add_argument(
-            "--embed-dim",
-            default=512,
-            type=int,
-            help="Number of dimension of embedding",
-        )
-        group.add_argument(
-            "--elayers", default=1, type=int, help="Number of encoder layers"
-        )
-        group.add_argument(
-            "--eunits",
-            "-u",
-            default=512,
-            type=int,
-            help="Number of encoder hidden units",
-        )
-        group.add_argument(
-            "--econv-layers",
-            default=3,
-            type=int,
-            help="Number of encoder convolution layers",
-        )
-        group.add_argument(
-            "--econv-chans",
-            default=512,
-            type=int,
-            help="Number of encoder convolution channels",
-        )
-        group.add_argument(
-            "--econv-filts",
-            default=5,
-            type=int,
-            help="Filter size of encoder convolution",
-        )
-        # attention
-        group.add_argument(
-            "--atype",
-            default="location",
-            type=str,
-            choices=["forward_ta", "forward", "location"],
-            help="Type of attention mechanism",
-        )
-        group.add_argument(
-            "--adim",
-            default=512,
-            type=int,
-            help="Number of attention transformation dimensions",
-        )
-        group.add_argument(
-            "--aconv-chans",
-            default=32,
-            type=int,
-            help="Number of attention convolution channels",
-        )
-        group.add_argument(
-            "--aconv-filts",
-            default=15,
-            type=int,
-            help="Filter size of attention convolution",
-        )
-        group.add_argument(
-            "--cumulate-att-w",
-            default=True,
-            type=strtobool,
-            help="Whether or not to cumulate attention weights",
-        )
-        # decoder
-        group.add_argument(
-            "--dlayers", default=2, type=int, help="Number of decoder layers"
-        )
-        group.add_argument(
-            "--dunits", default=1024, type=int, help="Number of decoder hidden units"
-        )
-        group.add_argument(
-            "--prenet-layers", default=2, type=int, help="Number of prenet layers"
-        )
-        group.add_argument(
-            "--prenet-units",
-            default=256,
-            type=int,
-            help="Number of prenet hidden units",
-        )
-        group.add_argument(
-            "--postnet-layers", default=5, type=int, help="Number of postnet layers"
-        )
-        group.add_argument(
-            "--postnet-chans", default=512, type=int, help="Number of postnet channels"
-        )
-        group.add_argument(
-            "--postnet-filts", default=5, type=int, help="Filter size of postnet"
-        )
-        group.add_argument(
-            "--output-activation",
-            default=None,
-            type=str,
-            nargs="?",
-            help="Output activation function",
-        )
-        # cbhg
-        group.add_argument(
-            "--use-cbhg",
-            default=False,
-            type=strtobool,
-            help="Whether to use CBHG module",
-        )
-        group.add_argument(
-            "--cbhg-conv-bank-layers",
-            default=8,
-            type=int,
-            help="Number of convoluional bank layers in CBHG",
-        )
-        group.add_argument(
-            "--cbhg-conv-bank-chans",
-            default=128,
-            type=int,
-            help="Number of convoluional bank channles in CBHG",
-        )
-        group.add_argument(
-            "--cbhg-conv-proj-filts",
-            default=3,
-            type=int,
-            help="Filter size of convoluional projection layer in CBHG",
-        )
-        group.add_argument(
-            "--cbhg-conv-proj-chans",
-            default=256,
-            type=int,
-            help="Number of convoluional projection channels in CBHG",
-        )
-        group.add_argument(
-            "--cbhg-highway-layers",
-            default=4,
-            type=int,
-            help="Number of highway layers in CBHG",
-        )
-        group.add_argument(
-            "--cbhg-highway-units",
-            default=128,
-            type=int,
-            help="Number of highway units in CBHG",
-        )
-        group.add_argument(
-            "--cbhg-gru-units",
-            default=256,
-            type=int,
-            help="Number of GRU units in CBHG",
-        )
-        # model (parameter) related
-        group.add_argument(
-            "--use-batch-norm",
-            default=True,
-            type=strtobool,
-            help="Whether to use batch normalization",
-        )
-        group.add_argument(
-            "--use-concate",
-            default=True,
-            type=strtobool,
-            help="Whether to concatenate encoder embedding with decoder outputs",
-        )
-        group.add_argument(
-            "--use-residual",
-            default=True,
-            type=strtobool,
-            help="Whether to use residual connection in conv layer",
-        )
-        group.add_argument(
-            "--dropout-rate", default=0.5, type=float, help="Dropout rate"
-        )
-        group.add_argument(
-            "--zoneout-rate", default=0.1, type=float, help="Zoneout rate"
-        )
-        group.add_argument(
-            "--reduction-factor", default=1, type=int, help="Reduction factor"
-        )
-        group.add_argument(
-            "--spk-embed-dim",
-            default=None,
-            type=int,
-            help="Number of speaker embedding dimensions",
-        )
-        group.add_argument(
-            "--spc-dim", default=None, type=int, help="Number of spectrogram dimensions"
-        )
-        group.add_argument(
-            "--pretrained-model", default=None, type=str, help="Pretrained model path"
-        )
-        # loss related
-        group.add_argument(
-            "--use-masking",
-            default=False,
-            type=strtobool,
-            help="Whether to use masking in calculation of loss",
-        )
-        group.add_argument(
-            "--use-weighted-masking",
-            default=False,
-            type=strtobool,
-            help="Whether to use weighted masking in calculation of loss",
-        )
-        group.add_argument(
-            "--bce-pos-weight",
-            default=20.0,
-            type=float,
-            help="Positive sample weight in BCE calculation "
-            "(only for use-masking=True)",
-        )
-        group.add_argument(
-            "--use-guided-attn-loss",
-            default=False,
-            type=strtobool,
-            help="Whether to use guided attention loss",
-        )
-        group.add_argument(
-            "--guided-attn-loss-sigma",
-            default=0.4,
-            type=float,
-            help="Sigma in guided attention loss",
-        )
-        group.add_argument(
-            "--guided-attn-loss-lambda",
-            default=1.0,
-            type=float,
-            help="Lambda in guided attention loss",
-        )
-        return parser
-
-    def __init__(self, idim, odim, args=None):
-        """Initialize Tacotron2 module.
-
-        Args:
-            idim (int): Dimension of the inputs.
-            odim (int): Dimension of the outputs.
-            args (Namespace, optional):
-                - spk_embed_dim (int): Dimension of the speaker embedding.
-                - embed_dim (int): Dimension of character embedding.
-                - elayers (int): The number of encoder blstm layers.
-                - eunits (int): The number of encoder blstm units.
-                - econv_layers (int): The number of encoder conv layers.
-                - econv_filts (int): The number of encoder conv filter size.
-                - econv_chans (int): The number of encoder conv filter channels.
-                - dlayers (int): The number of decoder lstm layers.
-                - dunits (int): The number of decoder lstm units.
-                - prenet_layers (int): The number of prenet layers.
-                - prenet_units (int): The number of prenet units.
-                - postnet_layers (int): The number of postnet layers.
-                - postnet_filts (int): The number of postnet filter size.
-                - postnet_chans (int): The number of postnet filter channels.
-                - output_activation (int): The name of activation function for outputs.
-                - adim (int): The number of dimension of mlp in attention.
-                - aconv_chans (int): The number of attention conv filter channels.
-                - aconv_filts (int): The number of attention conv filter size.
-                - cumulate_att_w (bool): Whether to cumulate previous attention weight.
-                - use_batch_norm (bool): Whether to use batch normalization.
-                - use_concate (int): Whether to concatenate encoder embedding
-                    with decoder lstm outputs.
-                - dropout_rate (float): Dropout rate.
-                - zoneout_rate (float): Zoneout rate.
-                - reduction_factor (int): Reduction factor.
-                - spk_embed_dim (int): Number of speaker embedding dimenstions.
-                - spc_dim (int): Number of spectrogram embedding dimenstions
-                    (only for use_cbhg=True).
-                - use_cbhg (bool): Whether to use CBHG module.
-                - cbhg_conv_bank_layers (int): The number of convoluional banks in CBHG.
-                - cbhg_conv_bank_chans (int): The number of channels of
-                    convolutional bank in CBHG.
-                - cbhg_proj_filts (int):
-                    The number of filter size of projection layeri in CBHG.
-                - cbhg_proj_chans (int):
-                    The number of channels of projection layer in CBHG.
-                - cbhg_highway_layers (int):
-                    The number of layers of highway network in CBHG.
-                - cbhg_highway_units (int):
-                    The number of units of highway network in CBHG.
-                - cbhg_gru_units (int): The number of units of GRU in CBHG.
-                - use_masking (bool):
-                    Whether to apply masking for padded part in loss calculation.
-                - use_weighted_masking (bool):
-                    Whether to apply weighted masking in loss calculation.
-                - bce_pos_weight (float):
-                    Weight of positive sample of stop token (only for use_masking=True).
-                - use-guided-attn-loss (bool): Whether to use guided attention loss.
-                - guided-attn-loss-sigma (float) Sigma in guided attention loss.
-                - guided-attn-loss-lamdba (float): Lambda in guided attention loss.
-
-        """
-        # initialize base classes
-        TTSInterface.__init__(self)
-        torch.nn.Module.__init__(self)
-
-        # fill missing arguments
-        args = fill_missing_args(args, self.add_arguments)
-
-        # store hyperparameters
-        self.idim = idim
-        self.odim = odim
-        self.spk_embed_dim = args.spk_embed_dim
-        self.cumulate_att_w = args.cumulate_att_w
-        self.reduction_factor = args.reduction_factor
-        self.use_cbhg = args.use_cbhg
-        self.use_guided_attn_loss = args.use_guided_attn_loss
-
-        # define activation function for the final output
-        if args.output_activation is None:
-            self.output_activation_fn = None
-        elif hasattr(F, args.output_activation):
-            self.output_activation_fn = getattr(F, args.output_activation)
-        else:
-            raise ValueError(
-                "there is no such an activation function. (%s)" % args.output_activation
-            )
-
-        # set padding idx
-        padding_idx = 0
-
-        # define network modules
-        self.enc = Encoder(
-            idim=idim,
-            embed_dim=args.embed_dim,
-            elayers=args.elayers,
-            eunits=args.eunits,
-            econv_layers=args.econv_layers,
-            econv_chans=args.econv_chans,
-            econv_filts=args.econv_filts,
-            use_batch_norm=args.use_batch_norm,
-            use_residual=args.use_residual,
-            dropout_rate=args.dropout_rate,
-            padding_idx=padding_idx,
-        )
-        dec_idim = (
-            args.eunits
-            if args.spk_embed_dim is None
-            else args.eunits + args.spk_embed_dim
-        )
-        if args.atype == "location":
-            att = AttLoc(
-                dec_idim, args.dunits, args.adim, args.aconv_chans, args.aconv_filts
-            )
-        elif args.atype == "forward":
-            att = AttForward(
-                dec_idim, args.dunits, args.adim, args.aconv_chans, args.aconv_filts
-            )
-            if self.cumulate_att_w:
-                logging.warning(
-                    "cumulation of attention weights is disabled in forward attention."
-                )
-                self.cumulate_att_w = False
-        elif args.atype == "forward_ta":
-            att = AttForwardTA(
-                dec_idim,
-                args.dunits,
-                args.adim,
-                args.aconv_chans,
-                args.aconv_filts,
-                odim,
-            )
-            if self.cumulate_att_w:
-                logging.warning(
-                    "cumulation of attention weights is disabled in forward attention."
-                )
-                self.cumulate_att_w = False
-        else:
-            raise NotImplementedError("Support only location or forward")
-        self.dec = Decoder(
-            idim=dec_idim,
-            odim=odim,
-            att=att,
-            dlayers=args.dlayers,
-            dunits=args.dunits,
-            prenet_layers=args.prenet_layers,
-            prenet_units=args.prenet_units,
-            postnet_layers=args.postnet_layers,
-            postnet_chans=args.postnet_chans,
-            postnet_filts=args.postnet_filts,
-            output_activation_fn=self.output_activation_fn,
-            cumulate_att_w=self.cumulate_att_w,
-            use_batch_norm=args.use_batch_norm,
-            use_concate=args.use_concate,
-            dropout_rate=args.dropout_rate,
-            zoneout_rate=args.zoneout_rate,
-            reduction_factor=args.reduction_factor,
-        )
-        self.taco2_loss = Tacotron2Loss(
-            use_masking=args.use_masking,
-            use_weighted_masking=args.use_weighted_masking,
-            bce_pos_weight=args.bce_pos_weight,
-        )
-        if self.use_guided_attn_loss:
-            self.attn_loss = GuidedAttentionLoss(
-                sigma=args.guided_attn_loss_sigma,
-                alpha=args.guided_attn_loss_lambda,
-            )
-        if self.use_cbhg:
-            self.cbhg = CBHG(
-                idim=odim,
-                odim=args.spc_dim,
-                conv_bank_layers=args.cbhg_conv_bank_layers,
-                conv_bank_chans=args.cbhg_conv_bank_chans,
-                conv_proj_filts=args.cbhg_conv_proj_filts,
-                conv_proj_chans=args.cbhg_conv_proj_chans,
-                highway_layers=args.cbhg_highway_layers,
-                highway_units=args.cbhg_highway_units,
-                gru_units=args.cbhg_gru_units,
-            )
-            self.cbhg_loss = CBHGLoss(use_masking=args.use_masking)
-
-        # load pretrained model
-        if args.pretrained_model is not None:
-            self.load_pretrained_model(args.pretrained_model)
-
-    def forward(
-        self, xs, ilens, ys, labels, olens, spembs=None, extras=None, *args, **kwargs
-    ):
-        """Calculate forward propagation.
-
-        Args:
-            xs (Tensor): Batch of padded character ids (B, Tmax).
-            ilens (LongTensor): Batch of lengths of each input batch (B,).
-            ys (Tensor): Batch of padded target features (B, Lmax, odim).
-            olens (LongTensor): Batch of the lengths of each target (B,).
-            spembs (Tensor, optional):
-                Batch of speaker embedding vectors (B, spk_embed_dim).
-            extras (Tensor, optional):
-                Batch of groundtruth spectrograms (B, Lmax, spc_dim).
-
-        Returns:
-            Tensor: Loss value.
-
-        """
-        # remove unnecessary padded part (for multi-gpus)
-        max_in = max(ilens)
-        max_out = max(olens)
-        if max_in != xs.shape[1]:
-            xs = xs[:, :max_in]
-        if max_out != ys.shape[1]:
-            ys = ys[:, :max_out]
-            labels = labels[:, :max_out]
-
-        # calculate tacotron2 outputs
-        hs, hlens = self.enc(xs, ilens)
-        if self.spk_embed_dim is not None:
-            spembs = F.normalize(spembs).unsqueeze(1).expand(-1, hs.size(1), -1)
-            hs = torch.cat([hs, spembs], dim=-1)
-        after_outs, before_outs, logits, att_ws = self.dec(hs, hlens, ys)
-
-        # modifiy mod part of groundtruth
-        if self.reduction_factor > 1:
-            olens = olens.new([olen - olen % self.reduction_factor for olen in olens])
-            max_out = max(olens)
-            ys = ys[:, :max_out]
-            labels = labels[:, :max_out]
-            labels[:, -1] = 1.0  # make sure at least one frame has 1
-
-        # caluculate taco2 loss
-        l1_loss, mse_loss, bce_loss = self.taco2_loss(
-            after_outs, before_outs, logits, ys, labels, olens
-        )
-        loss = l1_loss + mse_loss + bce_loss
-        report_keys = [
-            {"l1_loss": l1_loss.item()},
-            {"mse_loss": mse_loss.item()},
-            {"bce_loss": bce_loss.item()},
-        ]
-
-        # caluculate attention loss
-        if self.use_guided_attn_loss:
-            # NOTE(kan-bayashi):
-            # length of output for auto-regressive input will be changed when r > 1
-            if self.reduction_factor > 1:
-                olens_in = olens.new([olen // self.reduction_factor for olen in olens])
-            else:
-                olens_in = olens
-            attn_loss = self.attn_loss(att_ws, ilens, olens_in)
-            loss = loss + attn_loss
-            report_keys += [
-                {"attn_loss": attn_loss.item()},
-            ]
-
-        # caluculate cbhg loss
-        if self.use_cbhg:
-            # remove unnecessary padded part (for multi-gpus)
-            if max_out != extras.shape[1]:
-                extras = extras[:, :max_out]
-
-            # caluculate cbhg outputs & loss and report them
-            cbhg_outs, _ = self.cbhg(after_outs, olens)
-            cbhg_l1_loss, cbhg_mse_loss = self.cbhg_loss(cbhg_outs, extras, olens)
-            loss = loss + cbhg_l1_loss + cbhg_mse_loss
-            report_keys += [
-                {"cbhg_l1_loss": cbhg_l1_loss.item()},
-                {"cbhg_mse_loss": cbhg_mse_loss.item()},
-            ]
-
-        report_keys += [{"loss": loss.item()}]
-        self.reporter.report(report_keys)
-
-        return loss
-
-    def inference(self, x, inference_args, spemb=None, *args, **kwargs):
-        """Generate the sequence of features given the sequences of characters.
-
-        Args:
-            x (Tensor): Input sequence of characters (T,).
-            inference_args (Namespace):
-                - threshold (float): Threshold in inference.
-                - minlenratio (float): Minimum length ratio in inference.
-                - maxlenratio (float): Maximum length ratio in inference.
-            spemb (Tensor, optional): Speaker embedding vector (spk_embed_dim).
-
-        Returns:
-            Tensor: Output sequence of features (L, odim).
-            Tensor: Output sequence of stop probabilities (L,).
-            Tensor: Attention weights (L, T).
-
-        """
-        # get options
-        threshold = inference_args.threshold
-        minlenratio = inference_args.minlenratio
-        maxlenratio = inference_args.maxlenratio
-        use_att_constraint = getattr(
-            inference_args, "use_att_constraint", False
-        )  # keep compatibility
-        backward_window = inference_args.backward_window if use_att_constraint else 0
-        forward_window = inference_args.forward_window if use_att_constraint else 0
-
-        # inference
-        h = self.enc.inference(x)
-        if self.spk_embed_dim is not None:
-            spemb = F.normalize(spemb, dim=0).unsqueeze(0).expand(h.size(0), -1)
-            h = torch.cat([h, spemb], dim=-1)
-        outs, probs, att_ws = self.dec.inference(
-            h,
-            threshold,
-            minlenratio,
-            maxlenratio,
-            use_att_constraint=use_att_constraint,
-            backward_window=backward_window,
-            forward_window=forward_window,
-        )
-
-        if self.use_cbhg:
-            cbhg_outs = self.cbhg.inference(outs)
-            return cbhg_outs, probs, att_ws
-        else:
-            return outs, probs, att_ws
-
-    def calculate_all_attentions(
-        self, xs, ilens, ys, spembs=None, keep_tensor=False, *args, **kwargs
-    ):
-        """Calculate all of the attention weights.
-
-        Args:
-            xs (Tensor): Batch of padded character ids (B, Tmax).
-            ilens (LongTensor): Batch of lengths of each input batch (B,).
-            ys (Tensor): Batch of padded target features (B, Lmax, odim).
-            olens (LongTensor): Batch of the lengths of each target (B,).
-            spembs (Tensor, optional):
-                Batch of speaker embedding vectors (B, spk_embed_dim).
-            keep_tensor (bool, optional): Whether to keep original tensor.
-
-        Returns:
-            Union[ndarray, Tensor]: Batch of attention weights (B, Lmax, Tmax).
-
-        """
-        # check ilens type (should be list of int)
-        if isinstance(ilens, torch.Tensor) or isinstance(ilens, np.ndarray):
-            ilens = list(map(int, ilens))
-
-        self.eval()
-        with torch.no_grad():
-            hs, hlens = self.enc(xs, ilens)
-            if self.spk_embed_dim is not None:
-                spembs = F.normalize(spembs).unsqueeze(1).expand(-1, hs.size(1), -1)
-                hs = torch.cat([hs, spembs], dim=-1)
-            att_ws = self.dec.calculate_all_attentions(hs, hlens, ys)
-        self.train()
-
-        if keep_tensor:
-            return att_ws
-        else:
-            return att_ws.cpu().numpy()
-
-    @property
-    def base_plot_keys(self):
-        """Return base key names to plot during training.
-
-        keys should match what `chainer.reporter` reports.
-        If you add the key `loss`, the reporter will report `main/loss`
-        and `validation/main/loss` values.
-        also `loss.png` will be created as a figure visulizing `main/loss`
-        and `validation/main/loss` values.
-
-        Returns:
-            list: List of strings which are base keys to plot during training.
-
-        """
-        plot_keys = ["loss", "l1_loss", "mse_loss", "bce_loss"]
-        if self.use_guided_attn_loss:
-            plot_keys += ["attn_loss"]
-        if self.use_cbhg:
-            plot_keys += ["cbhg_l1_loss", "cbhg_mse_loss"]
-        return plot_keys
diff --git a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transducer/auxiliary_task.py b/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transducer/auxiliary_task.py
deleted file mode 100644
index 998bcf0cc29c121a0513aeedffb230a110d1e784..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transducer/auxiliary_task.py
+++ /dev/null
@@ -1,114 +0,0 @@
-"""Auxiliary task implementation for transducer models."""
-
-from itertools import chain
-from typing import List
-from typing import Tuple
-from typing import Union
-
-import torch
-import torch.nn.functional as F
-
-from espnet.nets.transducer_decoder_interface import TransducerDecoderInterface
-
-
-class AuxiliaryTask(torch.nn.Module):
-    """Auxiliary task module."""
-
-    def __init__(
-        self,
-        decoder: Union[torch.nn.Module, TransducerDecoderInterface],
-        joint_network: torch.nn.Module,
-        rnnt_criterion: torch.nn.Module,
-        aux_task_type: str,
-        aux_task_weight: int,
-        encoder_out_dim: int,
-        joint_dim: int,
-    ):
-        """Auxiliary task initialization.
-
-        Args:
-            decoder: Decoder module
-            joint_network: Joint network module
-            aux_task_type: Auxiliary task type
-            aux_task_weight: Auxiliary task weight
-            encoder_out: Encoder output dimension
-            joint_dim: Joint space dimension
-
-        """
-        super().__init__()
-
-        self.rnnt_criterion = rnnt_criterion
-
-        self.mlp_net = torch.nn.Sequential(
-            torch.nn.Linear(encoder_out_dim, joint_dim),
-            torch.nn.ReLU(),
-            torch.nn.Linear(joint_dim, joint_dim),
-        )
-
-        self.decoder = decoder
-        self.joint_network = joint_network
-
-        self.aux_task_type = aux_task_type
-        self.aux_task_weight = aux_task_weight
-
-    def forward(
-        self,
-        enc_out_aux: List,
-        dec_out: torch.Tensor,
-        main_joint: torch.Tensor,
-        target: torch.Tensor,
-        pred_len: torch.Tensor,
-        target_len: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Forward auxiliary task.
-
-        Args:
-            enc_out_aux: List of encoder intermediate outputs
-            dec_out: Decoder outputs
-            main_joint: Joint output for main task
-            target: Target labels
-            pred_len: Prediction lengths
-            target_len: Target lengths
-
-        Returns:
-            : (Weighted auxiliary transducer loss, Weighted auxiliary symmetric KL loss)
-
-        """
-        aux_trans = 0
-        aux_symm_kl = 0
-
-        for p in chain(self.decoder.parameters(), self.joint_network.parameters()):
-            p.requires_grad = False
-
-        for i, enc_aux in enumerate(enc_out_aux):
-            aux_mlp = self.mlp_net(enc_aux)
-
-            aux_joint = self.joint_network(
-                aux_mlp.unsqueeze(2),
-                dec_out.unsqueeze(1),
-                is_aux=True,
-            )
-
-            if self.aux_task_type != "symm_kl_div":
-                aux_trans += self.rnnt_criterion(
-                    aux_joint,
-                    target,
-                    pred_len,
-                    target_len,
-                )
-
-            if self.aux_task_type != "default":
-                aux_symm_kl += F.kl_div(
-                    F.log_softmax(main_joint, dim=-1),
-                    F.softmax(aux_joint, dim=-1),
-                    reduction="mean",
-                ) + F.kl_div(
-                    F.log_softmax(aux_joint, dim=-1),
-                    F.softmax(main_joint, dim=-1),
-                    reduction="mean",
-                )
-
-        for p in chain(self.decoder.parameters(), self.joint_network.parameters()):
-            p.requires_grad = True
-
-        return self.aux_task_weight * aux_trans, self.aux_task_weight * aux_symm_kl
diff --git a/spaces/shariqfarooq/ZoeDepth/utils.py b/spaces/shariqfarooq/ZoeDepth/utils.py
deleted file mode 100644
index dd0ff1a336277e3e2b609e3d7a4c847ff7db0b3c..0000000000000000000000000000000000000000
--- a/spaces/shariqfarooq/ZoeDepth/utils.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Intelligent Systems Lab Org
-
-# 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.
-
-# File author: Shariq Farooq Bhat
-
-import matplotlib
-import matplotlib.cm
-import numpy as np
-import torch
-
-def colorize(value, vmin=None, vmax=None, cmap='magma_r', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None):
-    """Converts a depth map to a color image.
-
-    Args:
-        value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed
-        vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None.
-        vmax (float, optional):  vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None.
-        cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'.
-        invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99.
-        invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None.
-        background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255).
-        gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False.
-        value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None.
-
-    Returns:
-        numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4)
-    """
-    if isinstance(value, torch.Tensor):
-        value = value.detach().cpu().numpy()
-
-    value = value.squeeze()
-    if invalid_mask is None:
-        invalid_mask = value == invalid_val
-    mask = np.logical_not(invalid_mask)
-
-    # normalize
-    vmin = np.percentile(value[mask],2) if vmin is None else vmin
-    vmax = np.percentile(value[mask],85) if vmax is None else vmax
-    if vmin != vmax:
-        value = (value - vmin) / (vmax - vmin)  # vmin..vmax
-    else:
-        # Avoid 0-division
-        value = value * 0.
-
-    # squeeze last dim if it exists
-    # grey out the invalid values
-
-    value[invalid_mask] = np.nan
-    cmapper = matplotlib.cm.get_cmap(cmap)
-    if value_transform:
-        value = value_transform(value)
-        # value = value / value.max()
-    value = cmapper(value, bytes=True)  # (nxmx4)
-
-    # img = value[:, :, :]
-    img = value[...]
-    img[invalid_mask] = background_color
-
-    #     return img.transpose((2, 0, 1))
-    if gamma_corrected:
-        # gamma correction
-        img = img / 255
-        img = np.power(img, 2.2)
-        img = img * 255
-        img = img.astype(np.uint8)
-    return img
-
diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/uvr5_pack/lib_v5/nets_537238KB.py b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/uvr5_pack/lib_v5/nets_537238KB.py
deleted file mode 100644
index 1ceac4a470ca311d594818d52e5f96919cfddb26..0000000000000000000000000000000000000000
--- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/uvr5_pack/lib_v5/nets_537238KB.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import torch
-import numpy as np
-from torch import nn
-import torch.nn.functional as F
-
-from uvr5_pack.lib_v5 import layers_537238KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 64)
-        self.stg1_high_band_net = BaseASPPNet(2, 64)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(32, 64)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(64, 128)
-
-        self.out = nn.Conv2d(128, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(64, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/shi-labs/FcF-Inpainting/training/data/pred_loader.py b/spaces/shi-labs/FcF-Inpainting/training/data/pred_loader.py
deleted file mode 100644
index 703ba042f8e268124186a47b65196bb51095e73d..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/FcF-Inpainting/training/data/pred_loader.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from tabnanny import filename_only
-import numpy as np
-import cv2
-import os
-import PIL
-import torch
-from .dataset import Dataset
-
-class ImageDataset(Dataset):
-    
-    def __init__(self,
-        img_path,                   # Path to images.
-        resolution      = None,     # Ensure specific resolution, None = highest available.
-        **super_kwargs,             # Additional arguments for the Dataset base class.
-    ):
-        self.sz = resolution
-        self.img_path = img_path
-        self._type = 'dir'
-        self.files = []
-        self.idx = 0
-
-        self._all_fnames = [os.path.relpath(os.path.join(root, fname), start=self.img_path) for root, _dirs, files in os.walk(self.img_path) for fname in files]
-        PIL.Image.init()
-        self._image_fnames = sorted(os.path.join(self.img_path,fname) for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION)
-        if len(self._image_fnames) == 0:
-            raise IOError('No image files found in the specified path')
-        
-        self.files = []
-        
-        for f in self._image_fnames:
-            if not '_mask' in f:
-                self.files.append(f)
-        
-        self.files = sorted(self.files)
-
-    def __len__(self):
-        return len(self.files)
-    
-    @staticmethod
-    def _file_ext(fname):
-        return os.path.splitext(fname)[1].lower()
-
-    def _load_image(self, fn):
-        return PIL.Image.open(fn).convert('RGB')
-    
-    def _get_image(self, idx):
-        # imgfn, seg_map, img_id = self.data_reader.get_image(idx)
-        
-        fname = self.files[idx]
-        ext = self._file_ext(fname)
-        
-        mask = np.array(self._load_image(fname.replace(ext, f'_mask000{ext}')).convert('L')) / 255
-        rgb = np.array(self._load_image(fname)) # uint8
-
-        return rgb, fname.split('/')[-1].replace(ext, ''), mask
-        
-    def __getitem__(self, idx):
-        rgb, fname, mask = self._get_image(idx) # modal, uint8 {0, 1}
-        rgb = rgb.transpose(2,0,1)
-
-        mask_tensor = torch.from_numpy(mask).to(torch.float32)
-        mask_tensor = mask_tensor.unsqueeze(0)
-        rgb = torch.from_numpy(rgb.astype(np.float32))
-        rgb = (rgb.to(torch.float32) / 127.5 - 1)
-        rgb_erased = rgb.clone()
-        rgb_erased = rgb_erased * (1 - mask_tensor) # erase rgb
-        rgb_erased = rgb_erased.to(torch.float32)
-        
-        return rgb, rgb_erased, mask_tensor, fname
-    
-def collate_fn(data):
-    """Creates mini-batch tensors from the list of images.
-    
-    We should build custom collate_fn rather than using default collate_fn, 
-    because merging caption (including padding) is not supported in default.
-    Args:
-        data: list
-            - image: torch tensor of shape (3, 256, 256).
-            
-    Returns:
-        images: torch tensor of shape (batch_size, 3, 256, 256).
-        
-    """
-
-    rgbs, rgbs_erased, mask_tensors, fnames = zip(*data)
-    
-    rgbs = list(rgbs)
-    rgbs_erased = list(rgbs_erased)
-    mask_tensors = list(mask_tensors)
-    fnames = list(fnames)
-
-    return torch.stack(rgbs, dim=0), torch.stack(rgbs_erased, dim=0), torch.stack(mask_tensors, dim=0), fnames
-    
-def get_loader(img_path, resolution):
-    """Returns torch.utils.data.DataLoader for custom coco dataset."""
-
-    ds = ImageDataset(img_path=img_path, resolution=resolution)
-    
-    data_loader = torch.utils.data.DataLoader(dataset=ds, 
-                                              batch_size=1,
-                                              shuffle=False,
-                                              num_workers=1,
-                                              collate_fn=collate_fn)
-    return data_loader
\ No newline at end of file
diff --git a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/app.py b/spaces/simpie28/VITS-Umamusume-voice-synthesizer/app.py
deleted file mode 100644
index c9bfb000af1af5ec0a745290b95431df58ad7a61..0000000000000000000000000000000000000000
--- a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/app.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import argparse
-import json
-import os
-import re
-import tempfile
-import logging
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-import librosa
-import numpy as np
-import torch
-from torch import no_grad, LongTensor
-import commons
-import utils
-import gradio as gr
-import gradio.utils as gr_utils
-import gradio.processing_utils as gr_processing_utils
-import ONNXVITS_infer
-import models
-from text import text_to_sequence, _clean_text
-from text.symbols import symbols
-from mel_processing import spectrogram_torch
-import psutil
-from datetime import datetime
-
-language_marks = {
-    "Japanese": "",
-    "日本語": "[JA]",
-    "简体中文": "[ZH]",
-    "English": "[EN]",
-    "Mix": "",
-}
-
-limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
-
-
-def create_tts_fn(model, hps, speaker_ids):
-    def tts_fn(text, speaker, language, speed, is_symbol):
-        if limitation:
-            text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
-            max_len = 150
-            if is_symbol:
-                max_len *= 3
-            if text_len > max_len:
-                return "Error: Text is too long", None
-        if language is not None:
-            text = language_marks[language] + text + language_marks[language]
-        speaker_id = speaker_ids[speaker]
-        stn_tst = get_text(text, hps, is_symbol)
-        with no_grad():
-            x_tst = stn_tst.unsqueeze(0)
-            x_tst_lengths = LongTensor([stn_tst.size(0)])
-            sid = LongTensor([speaker_id])
-            audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
-                                length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
-        del stn_tst, x_tst, x_tst_lengths, sid
-        return "Success", (hps.data.sampling_rate, audio)
-
-    return tts_fn
-
-
-def create_vc_fn(model, hps, speaker_ids):
-    def vc_fn(original_speaker, target_speaker, input_audio):
-        if input_audio is None:
-            return "You need to upload an audio", None
-        sampling_rate, audio = input_audio
-        duration = audio.shape[0] / sampling_rate
-        if limitation and duration > 30:
-            return "Error: Audio is too long", None
-        original_speaker_id = speaker_ids[original_speaker]
-        target_speaker_id = speaker_ids[target_speaker]
-
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        if sampling_rate != hps.data.sampling_rate:
-            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
-        with no_grad():
-            y = torch.FloatTensor(audio)
-            y = y.unsqueeze(0)
-            spec = spectrogram_torch(y, hps.data.filter_length,
-                                     hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
-                                     center=False)
-            spec_lengths = LongTensor([spec.size(-1)])
-            sid_src = LongTensor([original_speaker_id])
-            sid_tgt = LongTensor([target_speaker_id])
-            audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
-                0, 0].data.cpu().float().numpy()
-        del y, spec, spec_lengths, sid_src, sid_tgt
-        return "Success", (hps.data.sampling_rate, audio)
-
-    return vc_fn
-
-
-def get_text(text, hps, is_symbol):
-    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm
-
-
-def create_to_symbol_fn(hps):
-    def to_symbol_fn(is_symbol_input, input_text, temp_text):
-        return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \
-            else (temp_text, temp_text)
-
-    return to_symbol_fn
-
-
-models_tts = []
-models_vc = []
-models_info = [
-    {
-        "title": "Trilingual",
-        "languages": ['日本語', '简体中文', 'English', 'Mix'],
-        "description": """
-    This model is trained on a mix up of Umamusume, Genshin Impact, Sanoba Witch & VCTK voice data to learn multilanguage.
-    All characters can speak English, Chinese & Japanese.\n\n
-    To mix multiple languages in a single sentence, wrap the corresponding part with language tokens
-     ([JA] for Japanese, [ZH] for Chinese, [EN] for English), as shown in the examples.\n\n
-    这个模型在赛马娘，原神，魔女的夜宴以及VCTK数据集上混合训练以学习多种语言。
-    所有角色均可说中日英三语。\n\n
-    若需要在同一个句子中混合多种语言，使用相应的语言标记包裹句子。
-    （日语用[JA], 中文用[ZH], 英文用[EN]），参考Examples中的示例。
-    """,
-        "model_path": "./pretrained_models/G_trilingual.pth",
-        "config_path": "./configs/uma_trilingual.json",
-        "examples": [['你好，训练员先生，很高兴见到你。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', '简体中文', 1, False],
-                     ['To be honest, I have no idea what to say as examples.', '派蒙 Paimon (Genshin Impact)', 'English',
-                      1, False],
-                     ['授業中に出しだら，学校生活終わるですわ。', '綾地 寧々 Ayachi Nene (Sanoba Witch)', '日本語', 1, False],
-                     ['[JA]こんにちわ。[JA][ZH]你好！[ZH][EN]Hello![EN]', '綾地 寧々 Ayachi Nene (Sanoba Witch)', 'Mix', 1, False]],
-        "onnx_dir": "./ONNX_net/G_trilingual/"
-    },
-    {
-        "title": "Japanese",
-        "languages": ["Japanese"],
-        "description": """
-                       This model contains 87 characters from Umamusume: Pretty Derby, Japanese only.\n\n
-                       这个模型包含赛马娘的所有87名角色，只能合成日语。
-                       """,
-        "model_path": "./pretrained_models/G_jp.pth",
-        "config_path": "./configs/uma87.json",
-        "examples": [['お疲れ様です，トレーナーさん。', '无声铃鹿 Silence Suzuka (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['張り切っていこう！', '北部玄驹 Kitasan Black (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['何でこんなに慣れでんのよ，私のほが先に好きだっだのに。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['授業中に出しだら，学校生活終わるですわ。', '目白麦昆 Mejiro Mcqueen (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['お帰りなさい，お兄様！', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['私の処女をもらっでください！', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False]],
-        "onnx_dir": "./ONNX_net/G_jp/"
-    },
-]
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    args = parser.parse_args()
-    for info in models_info:
-        name = info['title']
-        lang = info['languages']
-        examples = info['examples']
-        config_path = info['config_path']
-        model_path = info['model_path']
-        description = info['description']
-        onnx_dir = info["onnx_dir"]
-        hps = utils.get_hparams_from_file(config_path)
-        model = ONNXVITS_infer.SynthesizerTrn(
-            len(hps.symbols),
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            n_speakers=hps.data.n_speakers,
-            ONNX_dir=onnx_dir,
-            **hps.model)
-        utils.load_checkpoint(model_path, model, None)
-        model.eval()
-        speaker_ids = hps.speakers
-        speakers = list(hps.speakers.keys())
-        models_tts.append((name, description, speakers, lang, examples,
-                           hps.symbols, create_tts_fn(model, hps, speaker_ids),
-                           create_to_symbol_fn(hps)))
-        models_vc.append((name, description, speakers, create_vc_fn(model, hps, speaker_ids)))
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# English & Chinese & Japanese Anime TTS\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=Plachta.VITS-Umamusume-voice-synthesizer)\n\n"
-                    "Including Japanese TTS & Trilingual TTS, speakers are all anime characters. \n\n包含一个纯日语TTS和一个中日英三语TTS模型，主要为二次元角色。\n\n"
-                    "If you have any suggestions or bug reports, feel free to open discussion in [Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions).\n\n"
-                    "若有bug反馈或建议，请在[Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions)下开启一个新的Discussion。 \n\n"
-                    )
-        with gr.Tabs():
-            with gr.TabItem("TTS"):
-                with gr.Tabs():
-                    for i, (name, description, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate(
-                            models_tts):
-                        with gr.TabItem(name):
-                            gr.Markdown(description)
-                            with gr.Row():
-                                with gr.Column():
-                                    textbox = gr.TextArea(label="Text",
-                                                          placeholder="Type your sentence here (Maximum 150 words)",
-                                                          value="こんにちわ。", elem_id=f"tts-input")
-                                    with gr.Accordion(label="Phoneme Input", open=False):
-                                        temp_text_var = gr.Variable()
-                                        symbol_input = gr.Checkbox(value=False, label="Symbol input")
-                                        symbol_list = gr.Dataset(label="Symbol list", components=[textbox],
-                                                                 samples=[[x] for x in symbols],
-                                                                 elem_id=f"symbol-list")
-                                        symbol_list_json = gr.Json(value=symbols, visible=False)
-                                    symbol_input.change(to_symbol_fn,
-                                                        [symbol_input, textbox, temp_text_var],
-                                                        [textbox, temp_text_var])
-                                    symbol_list.click(None, [symbol_list, symbol_list_json], textbox,
-                                                      _js=f"""
-                                    (i, symbols, text) => {{
-                                        let root = document.querySelector("body > gradio-app");
-                                        if (root.shadowRoot != null)
-                                            root = root.shadowRoot;
-                                        let text_input = root.querySelector("#tts-input").querySelector("textarea");
-                                        let startPos = text_input.selectionStart;
-                                        let endPos = text_input.selectionEnd;
-                                        let oldTxt = text_input.value;
-                                        let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
-                                        text_input.value = result;
-                                        let x = window.scrollX, y = window.scrollY;
-                                        text_input.focus();
-                                        text_input.selectionStart = startPos + symbols[i].length;
-                                        text_input.selectionEnd = startPos + symbols[i].length;
-                                        text_input.blur();
-                                        window.scrollTo(x, y);
-
-                                        text = text_input.value;
-
-                                        return text;
-                                    }}""")
-                                    # select character
-                                    char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character')
-                                    language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
-                                    duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
-                                                                label='速度 Speed')
-                                with gr.Column():
-                                    text_output = gr.Textbox(label="Message")
-                                    audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
-                                    btn = gr.Button("Generate!")
-                                    btn.click(tts_fn,
-                                              inputs=[textbox, char_dropdown, language_dropdown, duration_slider,
-                                                      symbol_input],
-                                              outputs=[text_output, audio_output])
-                            gr.Examples(
-                                examples=example,
-                                inputs=[textbox, char_dropdown, language_dropdown,
-                                        duration_slider, symbol_input],
-                                outputs=[text_output, audio_output],
-                                fn=tts_fn
-                            )
-    app.queue(concurrency_count=3).launch(show_api=False, share=args.share)
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Crossy Road How to Download and Play the Game that Everyone is Talking About.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Crossy Road How to Download and Play the Game that Everyone is Talking About.md
deleted file mode 100644
index 64554a611a690baf00b9232a3fff2e4dc06775dd..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Crossy Road How to Download and Play the Game that Everyone is Talking About.md	
+++ /dev/null
@@ -1,157 +0,0 @@
-<br />
-<h1>Crossy Road: A Fun and Addictive Arcade Game</h1>
-<p>Do you love arcade games that are simple, colorful, and challenging? If so, you will love Crossy Road, a viral hit game created by Hipster Whale. Crossy Road is a game where you have to cross roads, train tracks, rivers, and other obstacles without getting hit by cars, trains, or other hazards. Along the way, you can collect coins and unlock over 150 characters that are inspired by retro and pop art styles. Crossy Road is a game that is easy to play but hard to master. It is also a game that you can play with your friends and family on the same device or online. In this article, we will show you how to play Crossy Road, how to download it for free, how to unlock new characters, and more. Let's get started!</p>
-<h2>crossy road download link</h2><br /><p><b><b>Download File</b> > <a href="https://ssurll.com/2uNYmC">https://ssurll.com/2uNYmC</a></b></p><br /><br />
- <h2>How to Play Crossy Road</h2>
-<p>Crossy Road is a game that anyone can play with just a few taps or clicks. Here are the basics of how to play:</p>
-<ul>
-<li>To move your character, use the arrow keys on your keyboard or swipe on your touchscreen. You can move forward, backward, left, or right.</li>
-<li>To dodge traffic, hop across logs, sidestep trains, and avoid other obstacles, you need to time your movements carefully. Don't stay still for too long or you will be toast!</li>
-<li>To collect coins, you need to hop on them or find them in special places. Coins can be used to unlock new characters or buy hints.</li>
-</ul>
-<p>Crossy Road is not just one game. It is actually three games in one. You can play in different worlds that have different themes, graphics, sounds, and challenges. Here are the three worlds you can choose from:</p>
-<ul>
-<li>Space: This is the latest world added to Crossy Road in December 2019. In this world, you can explore the galaxy with aliens, robots, spaceships, and planets. Watch out for asteroids, lasers, and black holes!</li>
-<li>Dinosaurs: This is the world where you can go back in time and meet dinosaurs of all shapes and sizes. You can also see volcanoes, fossils, cavemen, and more. Be careful of the T-rex, the meteor shower, and the lava!</li>
-<li>Classic: This is the original world of Crossy Road where it all began. In this world, you can see familiar sights like cars, trains, rivers, trees, and animals. You can also find some Easter eggs and secrets that reference other games and pop culture.</li>
-</ul>
-<p>Crossy Road is also a game that you can play with your friends and family on the same device or online. Here's how:</p>
-<ul>
-<li>Same device multiplayer: To play with up to four players on the same device, you need to download the app for Android TV or Apple TV. Then you can use your phone or tablet as a controller and compete on the big screen.</li>
-<li>Online multiplayer: To play with other players online, you need to sign in with your Google Play or Game Center account. Then you can join a random match or create a private room with your friends.</li>
-</ul>
- <h2>How to Download Crossy Road</h2>
-<p>Crossy Road is a free game that you can download for various devices. Here are the official download links for each device:</p>
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-<table <table>
-<tr>
-<th>Device</th>
-<th>Download Link</th>
-</tr>
-<tr>
-<td>Android</td>
-<td><a href="">Google Play Store</a></td>
-</tr>
-<tr>
-<td>iOS</td>
-<td><a href="">Apple App Store</a></td>
-</tr>
-<tr>
-<td>Windows</td>
-<td><a href="">Microsoft Store</a></td>
-</tr>
-<tr>
-<td>Mac</td>
-<td><a href="">Mac App Store</a></td>
-</tr>
-<tr>
-<td>Android TV</td>
-<td><a href="">Google Play Store for TV</a></td>
-</tr>
-<tr>
-<td>Apple TV</td>
-<td><a href="">Apple TV App Store</a></td>
-</tr>
-<tr>
-<td>Amazon Fire TV</td>
-<td><a href="">Amazon Appstore for Fire TV</a></td>
-</tr>
-<tr>
-<td>Web Browser</td>
-<td><a href="">Pokki.com</a></td>
-</tr>
-</table>
- <p>We recommend that you download Crossy Road from the official sources listed above. This way, you can enjoy the game without any viruses, malware, or ads. You can also get the latest updates and features as soon as they are released. Here are some tips and tricks for installing and updating the game:</p>
- <ul>
- <li>Make sure you have enough storage space on your device before downloading the game. Crossy Road is about 100 MB in size, but it may vary depending on your device and version.</li>
- <li>Check your internet connection and speed before downloading the game. Crossy Road requires a stable and fast internet connection to download and play smoothly.</li>
- <li>Allow the game to access your device's permissions when prompted. Crossy Road may need to access your camera, microphone, location, contacts, or other features to enhance your gaming experience.</li>
- <li>Follow the instructions on the screen to install the game on your device. It may take a few minutes depending on your device and internet speed.</li>
- <li>To update the game, go to the download link for your device and tap or click on the update button. You can also enable automatic updates in your device's settings.</li>
- <li>If you encounter any problems with downloading, installing, or updating the game, you can contact the developer at <a href="">support@hipsterwhale.com</a> or visit their <a href="">website</a> for more information.</li>
- </ul>
- <h2>How to Unlock New Characters in Crossy Road</h2>
- <p>Crossy Road is not just about crossing roads. It is also about collecting and playing with different characters that have their own personalities, animations, and sounds. There are over 150 characters that you can unlock in Crossy Road, and they are divided into three types: retro, pop art, and special. Here are the differences between them:</p>
- <ul>
- <li>Retro characters are inspired by classic arcade games like Pac-Man, Frogger, Q*bert, and more. They have pixelated graphics and 8-bit sounds. You can unlock them by using coins or finding them in special places.</li>
- <li>Pop art characters are inspired by modern pop culture icons like celebrities, movies, TV shows, memes, and more. They have colorful graphics and catchy sounds. You can unlock them by using coins or getting them as gifts.</li>
- <li>Special characters are unique and rare characters that have special abilities or effects. They have different graphics and sounds depending on their theme. You can unlock them by completing secret missions or finding hidden clues.</li>
- </ul>
- <p>To unlock new characters in Crossy Road, you have three options: coins, gifts, and secrets. Here's how each option works:</p>
- <ul>
- <li>Coins: You can use coins to buy new characters from the prize machine. Each character costs 100 coins, except for some special ones that cost more. You can get coins by hopping on them, watching ads, or buying them with real money.</li>
- <li>Gifts: You can get gifts every few hours or by watching ads. Gifts contain random characters that you don't have yet. You can also get gifts from other players if you connect your game with Facebook or Twitter.</li>
- <li>Secrets: You can unlock some special characters by doing certain actions or finding certain clues in the game. For example, you can unlock the Hipster Whale character by hopping on it when it appears in the river. You can also unlock some characters by playing with specific characters or in specific worlds.</li>
- </ul>
- <p>The best characters to use in Crossy Road depend on your preference and play style. However, However, some characters may have advantages or disadvantages in certain worlds or challenges. For example, some characters can fly, swim, or jump higher than others. Some characters can also trigger special events or change the environment. Here are some of the best characters to use for each world and challenge:</p>
- <ul>
- <li>Space: The best characters to use in space are the ones that can fly or float, such as the Astronaut, the UFO, the Rocket, or the Alien. They can avoid most of the obstacles and hazards in this world. However, they may also miss some coins or secrets that are on the ground.</li>
- <li>Dinosaurs: The best characters to use in dinosaurs are the ones that can blend in with the prehistoric environment, such as the Caveman, the Dinosaur, the Egg, or the Fossil. They can avoid attracting the attention of the predators and survive longer. However, they may also have trouble crossing some of the terrain or obstacles.</li>
- <li>Classic: The best characters to use in classic are the ones that are fast and agile, such as the Chicken, the Penguin, the Bunny, or the Frog. They can dodge traffic and hop across rivers with ease. However, they may also get hit by unexpected hazards or traps.</li>
- </ul>
- <h2>Conclusion</h2>
- <p>Crossy Road is a fun and addictive arcade game that you can play for free on various devices. It is a game that combines simple gameplay, colorful graphics, and catchy sounds. It is also a game that offers different worlds, multiplayer modes, and characters to choose from. In this article, we have shown you how to play Crossy Road, how to download it for free, how to unlock new characters, and more. We hope you have enjoyed this article and learned something new. Now it's time to download Crossy Road and start crossing roads!</p>
- <h3>FAQs</h3>
- <p>Here are some of the frequently asked questions and answers about Crossy Road:</p>
- <ol>
-<li>Q: What is the highest score in Crossy Road?</li>
-<li>A: The highest score in Crossy Road is 2,147,483,647 points. This is the maximum value that can be stored in a 32-bit integer. However, this score is very hard to achieve and only a few players have done it.</li>
-<li>Q: How do I backup my progress in Crossy Road?</li>
-<li>A: You can backup your progress in Crossy Road by signing in with your Google Play or Game Center account. This way, you can sync your data across different devices and restore it if you lose it.</li>
-<li>Q: How do I get rid of ads in Crossy Road?</li>
-<li>A: You can get rid of ads in Crossy Road by buying any coin pack with real money. This will remove all ads from the game permanently.</li>
-<li>Q: How do I report a bug or a problem in Crossy Road?</li>
-<li>A: You can report a bug or a problem in Crossy Road by contacting the developer at <a href="">support@hipsterwhale.com</a> or visiting their <a href="">website</a>. You can also leave feedback or rate the game on the download page for your device.</li>
-<li>Q: How do I get more information about Crossy Road?</li>
-<li>A: You can get more information about Crossy Road by following their social media accounts on Facebook, Twitter, Instagram, YouTube, or TikTok. You can also visit their <a href="">website</a> or their <a href="">wiki</a>.</li>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy Unlimited Messaging Video and Audio Calls with Messenger APK Windows.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy Unlimited Messaging Video and Audio Calls with Messenger APK Windows.md
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--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy Unlimited Messaging Video and Audio Calls with Messenger APK Windows.md	
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-<h1>Messenger APK Windows: A Guide to Chatting Privately on Your PC</h1>
- <p>If you are looking for a way to chat with your Facebook friends on your PC without using the official Facebook Messenger app, you might be interested in messenger apk windows. This is an unofficial app that lets you use Messenger on your Windows or Mac computer. In this article, we will explain what messenger apk windows is, what are its features, and what are some of the best alternatives to it. We will also answer some frequently asked questions about messenger apk windows and other messenger apps.</p>
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- <p>Messenger apk windows is a desktop app that lets you use Messenger on your Windows or Mac computer. It is not developed by Facebook, but by independent developers who have created a wrapper for the Messenger website. A wrapper is an app that reads a website and displays it in its own browser. This means that messenger apk windows does not require you to install the official Facebook Messenger app, which can be heavy on your system resources, data usage, and privacy.</p>
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- <h2>What are the main features of messenger apk windows and how they compare to the official Facebook Messenger app</h2>
- <p>Messenger apk windows has most of the features that you would expect from a messaging app, such as:</p>
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-<li>Animated effects, message effects, and selfie stickers (coming soon)</li>
-<li>Replies and forwarding</li>
-<li>App lock with face or fingerprint ID (available on iOS only)</li>
-<li>Payments (available in the US only)</li>
-<li>Business chat</li>
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- <p>However, there are also some features that messenger apk windows does not have, such as:</p>
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-<li>Watch together (watch videos with your friends over video chat)</li>
-<li>Cross-app communication (chat with your Instagram contacts)</li>
-<li>Voice messages</li>
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- <h2>What are some of the best alternatives to messenger apk windows and how they differ in terms of privacy, functionality, and compatibility</h2>
- <p>If you are not satisfied with messenger apk windows or you want to try something different, there are plenty of other apps that let you chat privately with your friends. Some of the best alternatives to messenger apk windows are:</p>
- <table>
-<tr><th>App</th><th>Privacy</th><th>Functionality</th><th>Compatibility</th></tr>
-<tr><td><a href="(^4^)">Signal</a></td><td>The most secure messaging app with end-to-end encryption by default. Open source and transparent.</td><td>Text, voice, video calls. Group chats. Stickers. Documents. Self-destructing messages.</td><td>Android, iOS, Windows, Mac, Linux.</td></tr>
-<tr><td><a href="(^8^)">Telegram</a></td><td>A popular messaging app with optional end-to-end encryption. Cloud-based and fast.</td><td>Text, voice, video calls. Group chats up to 200,000 members. Stickers. GIFs. Bots Continuing the article: <tr><td><a href="(^1^)">WhatsApp</a></td><td>A widely used messaging app with end-to-end encryption. Owned by Meta (formerly Facebook).</td><td>Text, voice, video calls. Group chats up to 256 members. Stickers. GIFs. Documents. Voice messages. Location sharing.</td><td>Android, iOS, Windows, Mac, Web.</td></tr>
-<tr><td><a href="(^6^)">Messenger</a></td><td>The official messaging app of Meta (formerly Facebook). Offers some privacy controls and encryption options.</td><td>Text, voice, video calls. Group chats and video calls. Stickers. GIFs. Bots. Games. Watch together. Cross-app communication.</td><td>Android, iOS, Windows, Mac.</td></tr>
-</table>
- <p>As you can see, each app has its own advantages and disadvantages in terms of privacy, functionality, and compatibility. You should choose the app that best suits your needs and preferences.</p>
- <h2>Conclusion: How to choose the best messenger app for your needs</h2>
- <p>Messenger apk windows is a desktop app that lets you use Messenger on your Windows or Mac computer without installing the official Facebook Messenger app. It has some benefits, such as saving battery and data, avoiding ads and sponsored messages, and using multiple accounts. However, it also has some drawbacks, such as missing some features like dark mode, voice messages, and location sharing.</p>
- <p>If you are looking for a more secure and private messaging app, you might want to consider Signal or Telegram, which offer end-to-end encryption and more features. If you are looking for a more integrated and social messaging app, you might want to consider WhatsApp or Messenger, which are owned by Meta (formerly Facebook) and allow you to chat with your Instagram contacts and watch videos together.</p>
- <p>The best messenger app for you depends on your needs and preferences. You should compare the features, privacy, and compatibility of each app before making a decision. You can also try different apps and see which one you like the most.</p>
- <h2>FAQs: Frequently asked questions about messenger apk windows and other messenger apps</h2>
- <h3>Q: Is messenger apk windows safe to use?</h3>
-<p>A: Messenger apk windows is not an official app developed by Facebook, but by independent developers who have created a wrapper for the Messenger website. This means that it is not verified or endorsed by Facebook, and it may have some security risks or bugs. However, the developers claim that they do not collect or store any user data, and that they use SSL encryption to protect the communication between the app and the website. You should use messenger apk windows at your own risk and discretion.</p>
- <h3>Q: How do I install messenger apk windows on my PC?</h3>
-<p>A: To install messenger apk windows on your PC, you need to download the app from <a href="(^4^)">messenger.com/desktop</a>. The desktop app will automatically download based on the desktop device you are using. You need to have Windows 10 or Mac OS 10.10 or higher to run the app. After downloading the app, you need to open it and log in with your Facebook account.</p>
- <h3>Q: How do I update messenger apk windows?</h3>
-<p>A: To update messenger apk windows, you need to check for updates from within the app. You can do this by clicking on the gear icon in the top left corner of the app window and selecting "Check for updates". If there is a new version available, you will be prompted to download and install it.</p>
- <h3>Q: How do I uninstall messenger apk windows?</h3>
-<p>A: To uninstall messenger apk windows from your PC, you need to follow these steps:</p>
-<ul>
-<li>On Windows 10: Go to Settings > Apps > Apps & features > Messenger > Uninstall.</li>
-<li>On Mac OS: Go to Finder > Applications > Messenger > Move to Trash.</li>
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- <h3>Q: How do I switch between accounts on messenger apk windows?</h3>
-<p>A: To switch between accounts on messenger apk windows, you need to click on the profile icon in the top right corner of the app window and select "Switch account". You can add up to five accounts on the app and switch between them easily.</p> 401be4b1e0<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Experience the Fun and Challenge of Stick War Legacy - Download APK Now.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Experience the Fun and Challenge of Stick War Legacy - Download APK Now.md
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index 3982c2726323d8885740edcfb8279dc039352ce3..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Experience the Fun and Challenge of Stick War Legacy - Download APK Now.md	
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-<p>Stick War Legacy is a game where you control an army of stick figures in a world called Inamorta. You are the leader of the nation called Order, and your goal is to conquer all the other nations that have different ways of waging war. You can build units, mine gold, learn new skills, and destroy the enemy statue. The game has several modes, such as Classic Campaign, Endless Deads, Tournament, Missions, and Skins. You can also customize your units with different weapons and armor.</p>
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-<h3>What is an APK File?</h3>
-<p>An APK file is a package file that contains all the components of an Android app. It has the extension .apk and it can be downloaded from various sources. An APK file can have some advantages over installing an app from Google Play Store, such as:</p>
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-<li>You can install an app that is not available in your region or country.</li>
-<li>You can install an app that has been removed or banned from Google Play Store.</li>
-<li>You can install an older version of an app that has new bugs or features that you don't like.</li>
-<li>You can install an app that has been modified or hacked by third parties.</li>
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-<p>However, you should also be careful when downloading and installing APK files from unknown sources, as they may contain malware or viruses that can harm your device or steal your data. You should only download APK files from reputable sources that you trust.</p>
-<h3>How to Download an APK File from Google Play Store</h3>
-<p>One way to get an APK file of Stick War Legacy is to download it from Google Play Store using a web tool or an app. Here are the steps:</p>
-<ol>
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-<li>Copy the URL of the game's page from the address bar.</li>
-<li>Go to a web tool that can generate APK files from Google Play Store, such as <a href="">apps.evozi.com/apk-downloader/</a> or <a href="">apkcombo.com/apk-downloader/</a>.</li>
-<li>Paste the URL of the game's page in the input box and click on the Generate Download Link or Download APK button.</li>
-<li>Wait for a few seconds until the APK file is ready and then click on the Download or Save button to save it to your device or computer.</li>
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-<h3>How to Install an APK File on Android</h3>
-<p>Once you have downloaded the APK file of Stick War Legacy, you need to install it on your Android device. Here are the steps:</p>
-<ol>
-<li>Go to your device's Settings and look for Security or Privacy options.</li>
-<li>Find the option that allows you to install apps from unknown sources and enable it. You may need to confirm this action with a pop-up message or a password.</li>
-<li>Go to your device's File Manager and locate the APK file that you downloaded. You may need to grant permission to the File Manager to access your storage.</li>
-<li>Tap on the APK file and follow the instructions on the screen to install it. You may need to accept some permissions or terms and conditions.</li>
-<li>Wait for the installation to finish and then launch the game from your app drawer or home screen.</li>
-</ol>
-<p>Congratulations, you have successfully installed Stick War Legacy APK on your Android device. You can now enjoy playing this fun and addictive strategy game.</p>
-<h2>Tips and Tricks for Playing Stick War Legacy</h2>
-<p>If you want to master Stick War Legacy and beat all the levels, you need to learn some tips and tricks that can help you improve your skills and strategies. Here are some of them:</p>
-<ul>
-<li>Upgrade your units as soon as possible. You can upgrade your units by spending gold in the Upgrade menu. Upgrading your units will increase their health, damage, speed, and range. You can also unlock new abilities and weapons for your units.</li>
-<li>Use different formations depending on the situation. You can change your formation by tapping on the icons at the bottom of the screen. You can choose between Attack, Defend, Hold Position, Retreat, and Garrison. Each formation has its own advantages and disadvantages, so use them wisely.</li>
-<li>Use your special abilities at the right time. You can use your special abilities by tapping on the icons at the top of the screen. You can choose between Archidon Rain, Swordwrath Rage, Magikill Summon, Spearton Shield Wall, and Giant Growth. Each ability has a cooldown time, so use them sparingly and strategically.</li>
-<li>Know your enemy's weaknesses and strengths. You can see your enemy's units by tapping on their statue. You can also see their health bar and their gold amount. You should learn what each unit can do and how to counter them effectively. For example, archers are good against spearmen, but bad against swordsmen.</li>
-<li>Have fun and experiment with different strategies. There is no one best way to play Stick War Legacy, as each level has different challenges and objectives. You should try different combinations of units, formations, abilities, and upgrades and see what works best for you. You can also replay levels to improve your score and earn more crowns.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Stick War Legacy is a fun and addictive strategy game that will keep you entertained for hours. You can play it for free on Google Play Store, or you can download its APK file and install it manually on your Android device. Either way, you will enjoy controlling an army of stick figures in a world of war. You will also learn new skills and strategies as you progress through the game.</p>
-<p>If you are looking for a challenging and entertaining strategy game, you should try Stick War Legacy today. You will not regret it!</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers about Stick War Legacy and its APK file:</p>
-<ol>
-<li><b>Is Stick War Legacy safe to play?</b><br/> Yes, Stick War Legacy is safe to play, as long as you download it from Google Play Store or a reputable source. The game does not contain any malware or viruses that can harm your device or steal your data. However, you should be careful when downloading and installing APK files from unknown sources, as they may contain malicious code or unwanted ads.</li>
-<li><b>Is Stick War Legacy free to play?</b><br/>
-Yes, Stick War Legacy is free to play, and you can enjoy all the features and modes of the game without spending any money. However, the game does have some optional in-app purchases that can enhance your gameplay experience, such as gems, skins, and chests. You can buy these items with real money or earn them by watching ads or completing offers.</li>
-<li><b>How can I update Stick War Legacy APK?</b><br/>
-If you have installed Stick War Legacy APK manually on your device, you will not receive automatic updates from Google Play Store. You will need to download and install the latest version of the APK file whenever there is a new update available. You can check for updates by visiting the official website of the game or by using an app that can notify you of new APK files.</li>
-<li><b>How can I play Stick War Legacy on PC?</b><br/>
-If you want to play Stick War Legacy on PC, you will need to use an Android emulator that can run Android apps on your computer. An Android emulator is a software that mimics the Android operating system and allows you to install and use Android apps on your PC. Some of the most popular Android emulators are <a href="">BlueStacks</a>, <a href="">NoxPlayer</a>, and <a href="">MEmu</a>. You can download these emulators from their official websites and follow their instructions to install and use them. Then, you can download and install Stick War Legacy APK on your PC using the emulator.</li>
-<li><b>How can I contact the developers of Stick War Legacy?</b><br/>
-If you have any questions, feedback, or suggestions about Stick War Legacy, you can contact the developers of the game by sending them an email at <a href="mailto:contact@maxgames.com">contact@maxgames.com</a>. You can also visit their website at <a href="">www.maxgames.com</a> or follow them on social media platforms such as Facebook, Twitter, and YouTube.</li>
-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/siya02/Konakni-TTS/ttsv/src/glow_tts/train.py b/spaces/siya02/Konakni-TTS/ttsv/src/glow_tts/train.py
deleted file mode 100644
index 79bf515a707b309e82e9686c140658f23acf1b91..0000000000000000000000000000000000000000
--- a/spaces/siya02/Konakni-TTS/ttsv/src/glow_tts/train.py
+++ /dev/null
@@ -1,286 +0,0 @@
-import os
-import json
-import argparse
-import math
-import torch
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from apex.parallel import DistributedDataParallel as DDP
-from apex import amp
-
-from data_utils import TextMelLoader, TextMelCollate
-import models
-import commons
-import utils
-
-
-global_step = 0
-
-
-def main():
-    """Assume Single Node Multi GPUs Training Only"""
-    assert torch.cuda.is_available(), "CPU training is not allowed."
-
-    n_gpus = torch.cuda.device_count()
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["MASTER_PORT"] = "80000"
-
-    hps = utils.get_hparams()
-    mp.spawn(
-        train_and_eval,
-        nprocs=n_gpus,
-        args=(
-            n_gpus,
-            hps,
-        ),
-    )
-
-
-def train_and_eval(rank, n_gpus, hps):
-    global global_step
-    if rank == 0:
-        logger = utils.get_logger(hps.log_dir)
-        logger.info(hps)
-        utils.check_git_hash(hps.log_dir)
-        writer = SummaryWriter(log_dir=hps.log_dir)
-        writer_eval = SummaryWriter(log_dir=os.path.join(hps.log_dir, "eval"))
-
-    dist.init_process_group(
-        backend="nccl", init_method="env://", world_size=n_gpus, rank=rank
-    )
-    torch.manual_seed(hps.train.seed)
-    torch.cuda.set_device(rank)
-
-    train_dataset = TextMelLoader(hps.data.training_files, hps.data)
-    train_sampler = torch.utils.data.distributed.DistributedSampler(
-        train_dataset, num_replicas=n_gpus, rank=rank, shuffle=True
-    )
-    collate_fn = TextMelCollate(1)
-    train_loader = DataLoader(
-        train_dataset,
-        num_workers=8,
-        shuffle=False,
-        batch_size=hps.train.batch_size,
-        pin_memory=True,
-        drop_last=True,
-        collate_fn=collate_fn,
-        sampler=train_sampler,
-    )
-    if rank == 0:
-        val_dataset = TextMelLoader(hps.data.validation_files, hps.data)
-        val_loader = DataLoader(
-            val_dataset,
-            num_workers=8,
-            shuffle=False,
-            batch_size=hps.train.batch_size,
-            pin_memory=True,
-            drop_last=True,
-            collate_fn=collate_fn,
-        )
-    symbols = hps.data.punc + hps.data.chars
-    generator = models.FlowGenerator(
-        n_vocab=len(symbols) + getattr(hps.data, "add_blank", False),
-        out_channels=hps.data.n_mel_channels,
-        **hps.model
-    ).cuda(rank)
-    optimizer_g = commons.Adam(
-        generator.parameters(),
-        scheduler=hps.train.scheduler,
-        dim_model=hps.model.hidden_channels,
-        warmup_steps=hps.train.warmup_steps,
-        lr=hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps,
-    )
-    if hps.train.fp16_run:
-        generator, optimizer_g._optim = amp.initialize(
-            generator, optimizer_g._optim, opt_level="O1"
-        )
-    generator = DDP(generator)
-    epoch_str = 1
-    global_step = 0
-    try:
-        _, _, _, epoch_str = utils.load_checkpoint(
-            utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"),
-            generator,
-            optimizer_g,
-        )
-        epoch_str += 1
-        optimizer_g.step_num = (epoch_str - 1) * len(train_loader)
-        optimizer_g._update_learning_rate()
-        global_step = (epoch_str - 1) * len(train_loader)
-    except:
-        if hps.train.ddi and os.path.isfile(os.path.join(hps.model_dir, "ddi_G.pth")):
-            _ = utils.load_checkpoint(
-                os.path.join(hps.model_dir, "ddi_G.pth"), generator, optimizer_g
-            )
-
-    for epoch in range(epoch_str, hps.train.epochs + 1):
-        if rank == 0:
-            train(
-                rank, epoch, hps, generator, optimizer_g, train_loader, logger, writer
-            )
-            evaluate(
-                rank,
-                epoch,
-                hps,
-                generator,
-                optimizer_g,
-                val_loader,
-                logger,
-                writer_eval,
-            )
-            if epoch % hps.train.save_epoch == 0:
-                utils.save_checkpoint(
-                    generator,
-                    optimizer_g,
-                    hps.train.learning_rate,
-                    epoch,
-                    os.path.join(hps.model_dir, "G_{}.pth".format(epoch)),
-                )
-        else:
-            train(rank, epoch, hps, generator, optimizer_g, train_loader, None, None)
-
-
-def train(rank, epoch, hps, generator, optimizer_g, train_loader, logger, writer):
-    train_loader.sampler.set_epoch(epoch)
-    global global_step
-
-    generator.train()
-    for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(train_loader):
-        x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(
-            rank, non_blocking=True
-        )
-        y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(
-            rank, non_blocking=True
-        )
-
-        # Train Generator
-        optimizer_g.zero_grad()
-
-        (
-            (z, z_m, z_logs, logdet, z_mask),
-            (x_m, x_logs, x_mask),
-            (attn, logw, logw_),
-        ) = generator(x, x_lengths, y, y_lengths, gen=False)
-        l_mle = commons.mle_loss(z, z_m, z_logs, logdet, z_mask)
-        l_length = commons.duration_loss(logw, logw_, x_lengths)
-
-        loss_gs = [l_mle, l_length]
-        loss_g = sum(loss_gs)
-
-        if hps.train.fp16_run:
-            with amp.scale_loss(loss_g, optimizer_g._optim) as scaled_loss:
-                scaled_loss.backward()
-            grad_norm = commons.clip_grad_value_(
-                amp.master_params(optimizer_g._optim), 5
-            )
-        else:
-            loss_g.backward()
-            grad_norm = commons.clip_grad_value_(generator.parameters(), 5)
-        optimizer_g.step()
-
-        if rank == 0:
-            if batch_idx % hps.train.log_interval == 0:
-                (y_gen, *_), *_ = generator.module(x[:1], x_lengths[:1], gen=True)
-                logger.info(
-                    "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
-                        epoch,
-                        batch_idx * len(x),
-                        len(train_loader.dataset),
-                        100.0 * batch_idx / len(train_loader),
-                        loss_g.item(),
-                    )
-                )
-                logger.info(
-                    [x.item() for x in loss_gs] + [global_step, optimizer_g.get_lr()]
-                )
-
-                scalar_dict = {
-                    "loss/g/total": loss_g,
-                    "learning_rate": optimizer_g.get_lr(),
-                    "grad_norm": grad_norm,
-                }
-                scalar_dict.update(
-                    {"loss/g/{}".format(i): v for i, v in enumerate(loss_gs)}
-                )
-                utils.summarize(
-                    writer=writer,
-                    global_step=global_step,
-                    images={
-                        "y_org": utils.plot_spectrogram_to_numpy(
-                            y[0].data.cpu().numpy()
-                        ),
-                        "y_gen": utils.plot_spectrogram_to_numpy(
-                            y_gen[0].data.cpu().numpy()
-                        ),
-                        "attn": utils.plot_alignment_to_numpy(
-                            attn[0, 0].data.cpu().numpy()
-                        ),
-                    },
-                    scalars=scalar_dict,
-                )
-        global_step += 1
-
-    if rank == 0:
-        logger.info("====> Epoch: {}".format(epoch))
-
-
-def evaluate(rank, epoch, hps, generator, optimizer_g, val_loader, logger, writer_eval):
-    if rank == 0:
-        global global_step
-        generator.eval()
-        losses_tot = []
-        with torch.no_grad():
-            for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(val_loader):
-                x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(
-                    rank, non_blocking=True
-                )
-                y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(
-                    rank, non_blocking=True
-                )
-
-                (
-                    (z, z_m, z_logs, logdet, z_mask),
-                    (x_m, x_logs, x_mask),
-                    (attn, logw, logw_),
-                ) = generator(x, x_lengths, y, y_lengths, gen=False)
-                l_mle = commons.mle_loss(z, z_m, z_logs, logdet, z_mask)
-                l_length = commons.duration_loss(logw, logw_, x_lengths)
-
-                loss_gs = [l_mle, l_length]
-                loss_g = sum(loss_gs)
-
-                if batch_idx == 0:
-                    losses_tot = loss_gs
-                else:
-                    losses_tot = [x + y for (x, y) in zip(losses_tot, loss_gs)]
-
-                if batch_idx % hps.train.log_interval == 0:
-                    logger.info(
-                        "Eval Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
-                            epoch,
-                            batch_idx * len(x),
-                            len(val_loader.dataset),
-                            100.0 * batch_idx / len(val_loader),
-                            loss_g.item(),
-                        )
-                    )
-                    logger.info([x.item() for x in loss_gs])
-
-        losses_tot = [x / len(val_loader) for x in losses_tot]
-        loss_tot = sum(losses_tot)
-        scalar_dict = {"loss/g/total": loss_tot}
-        scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_tot)})
-        utils.summarize(
-            writer=writer_eval, global_step=global_step, scalars=scalar_dict
-        )
-        logger.info("====> Epoch: {}".format(epoch))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/skf15963/summary/fengshen/data/megatron_dataloader/indexed_dataset.py b/spaces/skf15963/summary/fengshen/data/megatron_dataloader/indexed_dataset.py
deleted file mode 100644
index 9eba91d303ab11884d993b707ca1d166f540588b..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/data/megatron_dataloader/indexed_dataset.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.
-
-
-# copied from fairseq/fairseq/data/indexed_dataset.py
-# Removed IndexedRawTextDataset since it relied on Fairseq dictionary
-# other slight modifications to remove fairseq dependencies
-# Added document index to index file and made it accessible.
-#    An empty sentence no longer separates documents.
-
-from functools import lru_cache
-import os
-import shutil
-import struct
-from itertools import accumulate
-
-import numpy as np
-import torch
-from fengshen.data.megatron_dataloader.utils import print_rank_0
-
-
-def __best_fitting_dtype(vocab_size=None):
-    if vocab_size is not None and vocab_size < 65500:
-        return np.uint16
-    else:
-        return np.int32
-
-
-def get_available_dataset_impl():
-    return ['lazy', 'cached', 'mmap']
-
-
-def infer_dataset_impl(path):
-    if IndexedDataset.exists(path):
-        with open(index_file_path(path), 'rb') as f:
-            magic = f.read(8)
-            if magic == IndexedDataset._HDR_MAGIC:
-                return 'cached'
-            elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]:
-                return 'mmap'
-            else:
-                return None
-    else:
-        print(f"Dataset does not exist: {path}")
-        print("Path should be a basename that both .idx and "
-              ".bin can be appended to get full filenames.")
-        return None
-
-
-def make_builder(out_file, impl, vocab_size=None):
-    if impl == 'mmap':
-        return MMapIndexedDatasetBuilder(out_file,
-                                         dtype=__best_fitting_dtype(vocab_size))
-    else:
-        return IndexedDatasetBuilder(out_file)
-
-
-def make_dataset(path, impl, skip_warmup=False):
-    if not IndexedDataset.exists(path):
-        print(f"Dataset does not exist: {path}")
-        print("Path should be a basename that both .idx "
-              "and .bin can be appended to get full filenames.")
-        return None
-    if impl == 'infer':
-        impl = infer_dataset_impl(path)
-    if impl == 'lazy' and IndexedDataset.exists(path):
-        return IndexedDataset(path)
-    elif impl == 'cached' and IndexedDataset.exists(path):
-        return IndexedCachedDataset(path)
-    elif impl == 'mmap' and MMapIndexedDataset.exists(path):
-        return MMapIndexedDataset(path, skip_warmup)
-    print(f"Unknown dataset implementation: {impl}")
-    return None
-
-
-def dataset_exists(path, impl):
-    if impl == 'mmap':
-        return MMapIndexedDataset.exists(path)
-    else:
-        return IndexedDataset.exists(path)
-
-
-def read_longs(f, n):
-    a = np.empty(n, dtype=np.int64)
-    f.readinto(a)
-    return a
-
-
-def write_longs(f, a):
-    f.write(np.array(a, dtype=np.int64))
-
-
-dtypes = {
-    1: np.uint8,
-    2: np.int8,
-    3: np.int16,
-    4: np.int32,
-    5: np.int64,
-    6: np.float,
-    7: np.double,
-    8: np.uint16
-}
-
-
-def code(dtype):
-    for k in dtypes.keys():
-        if dtypes[k] == dtype:
-            return k
-    raise ValueError(dtype)
-
-
-def index_file_path(prefix_path):
-    return prefix_path + '.idx'
-
-
-def data_file_path(prefix_path):
-    return prefix_path + '.bin'
-
-
-def create_doc_idx(sizes):
-    doc_idx = [0]
-    for i, s in enumerate(sizes):
-        if s == 0:
-            doc_idx.append(i + 1)
-    return doc_idx
-
-
-class IndexedDataset(torch.utils.data.Dataset):
-    """Loader for IndexedDataset"""
-    _HDR_MAGIC = b'TNTIDX\x00\x00'
-
-    def __init__(self, path):
-        super().__init__()
-        self.path = path
-        self.data_file = None
-        self.read_index(path)
-
-    def read_index(self, path):
-        with open(index_file_path(path), 'rb') as f:
-            magic = f.read(8)
-            assert magic == self._HDR_MAGIC, (
-                'Index file doesn\'t match expected format. '
-                'Make sure that --dataset-impl is configured properly.'
-            )
-            version = f.read(8)
-            assert struct.unpack('<Q', version) == (1,)
-            code, self.element_size = struct.unpack('<QQ', f.read(16))
-            self.dtype = dtypes[code]
-            self._len, self.s = struct.unpack('<QQ', f.read(16))
-            self.doc_count = struct.unpack('<Q', f.read(8))
-            self.dim_offsets = read_longs(f, self._len + 1)
-            self.data_offsets = read_longs(f, self._len + 1)
-            self.sizes = read_longs(f, self.s)
-            self.doc_idx = read_longs(f, self.doc_count)
-
-    def read_data(self, path):
-        self.data_file = open(data_file_path(path), 'rb', buffering=0)
-
-    def check_index(self, i):
-        if i < 0 or i >= self._len:
-            raise IndexError('index out of range')
-
-    def __del__(self):
-        if self.data_file:
-            self.data_file.close()
-
-    # @lru_cache(maxsize=8)
-    def __getitem__(self, idx):
-        if not self.data_file:
-            self.read_data(self.path)
-        if isinstance(idx, int):
-            i = idx
-            self.check_index(i)
-            tensor_size = self.sizes[
-                self.dim_offsets[i]:self.dim_offsets[i + 1]]
-            a = np.empty(tensor_size, dtype=self.dtype)
-            self.data_file.seek(self.data_offsets[i] * self.element_size)
-            self.data_file.readinto(a)
-            return a
-        elif isinstance(idx, slice):
-            start, stop, step = idx.indices(len(self))
-            if step != 1:
-                raise ValueError(
-                    "Slices into indexed_dataset must be contiguous")
-            sizes = self.sizes[self.dim_offsets[start]:self.dim_offsets[stop]]
-            size = sum(sizes)
-            a = np.empty(size, dtype=self.dtype)
-            self.data_file.seek(self.data_offsets[start] * self.element_size)
-            self.data_file.readinto(a)
-            offsets = list(accumulate(sizes))
-            sents = np.split(a, offsets[:-1])
-            return sents
-
-    def __len__(self):
-        return self._len
-
-    def num_tokens(self, index):
-        return self.sizes[index]
-
-    def size(self, index):
-        return self.sizes[index]
-
-    @staticmethod
-    def exists(path):
-        return (
-            os.path.exists(index_file_path(path)) and os.path.exists(
-                data_file_path(path))
-        )
-
-    @property
-    def supports_prefetch(self):
-        return False  # avoid prefetching to save memory
-
-
-class IndexedCachedDataset(IndexedDataset):
-
-    def __init__(self, path):
-        super().__init__(path)
-        self.cache = None
-        self.cache_index = {}
-
-    @property
-    def supports_prefetch(self):
-        return True
-
-    def prefetch(self, indices):
-        if all(i in self.cache_index for i in indices):
-            return
-        if not self.data_file:
-            self.read_data(self.path)
-        indices = sorted(set(indices))
-        total_size = 0
-        for i in indices:
-            total_size += self.data_offsets[i + 1] - self.data_offsets[i]
-        self.cache = np.empty(total_size, dtype=self.dtype)
-        ptx = 0
-        self.cache_index.clear()
-        for i in indices:
-            self.cache_index[i] = ptx
-            size = self.data_offsets[i + 1] - self.data_offsets[i]
-            a = self.cache[ptx: ptx + size]
-            self.data_file.seek(self.data_offsets[i] * self.element_size)
-            self.data_file.readinto(a)
-            ptx += size
-        if self.data_file:
-            # close and delete data file after prefetch so we can pickle
-            self.data_file.close()
-            self.data_file = None
-
-    # @lru_cache(maxsize=8)
-    def __getitem__(self, idx):
-        if isinstance(idx, int):
-            i = idx
-            self.check_index(i)
-            tensor_size = self.sizes[
-                self.dim_offsets[i]:self.dim_offsets[i + 1]]
-            a = np.empty(tensor_size, dtype=self.dtype)
-            ptx = self.cache_index[i]
-            np.copyto(a, self.cache[ptx: ptx + a.size])
-            return a
-        elif isinstance(idx, slice):
-            # Hack just to make this work, can optimizer later if necessary
-            sents = []
-            for i in range(*idx.indices(len(self))):
-                sents.append(self[i])
-            return sents
-
-
-class IndexedDatasetBuilder(object):
-    element_sizes = {
-        np.uint8: 1,
-        np.int8: 1,
-        np.int16: 2,
-        np.int32: 4,
-        np.int64: 8,
-        np.float: 4,
-        np.double: 8
-    }
-
-    def __init__(self, out_file, dtype=np.int32):
-        self.out_file = open(out_file, 'wb')
-        self.dtype = dtype
-        self.data_offsets = [0]
-        self.dim_offsets = [0]
-        self.sizes = []
-        self.element_size = self.element_sizes[self.dtype]
-        self.doc_idx = [0]
-
-    def add_item(self, tensor):
-        bytes = self.out_file.write(np.array(tensor.numpy(), dtype=self.dtype))
-        self.data_offsets.append(
-            self.data_offsets[-1] + bytes / self.element_size)
-        for s in tensor.size():
-            self.sizes.append(s)
-        self.dim_offsets.append(self.dim_offsets[-1] + len(tensor.size()))
-
-    def end_document(self):
-        self.doc_idx.append(len(self.sizes))
-
-    def merge_file_(self, another_file):
-        index = IndexedDataset(another_file)
-        assert index.dtype == self.dtype
-
-        begin = self.data_offsets[-1]
-        for offset in index.data_offsets[1:]:
-            self.data_offsets.append(begin + offset)
-        self.sizes.extend(index.sizes)
-        begin = self.dim_offsets[-1]
-        for dim_offset in index.dim_offsets[1:]:
-            self.dim_offsets.append(begin + dim_offset)
-
-        with open(data_file_path(another_file), 'rb') as f:
-            while True:
-                data = f.read(1024)
-                if data:
-                    self.out_file.write(data)
-                else:
-                    break
-
-    def finalize(self, index_file):
-        self.out_file.close()
-        index = open(index_file, 'wb')
-        index.write(b'TNTIDX\x00\x00')
-        index.write(struct.pack('<Q', 1))
-        index.write(struct.pack('<QQ', code(self.dtype), self.element_size))
-        index.write(struct.pack('<QQ', len(
-            self.data_offsets) - 1, len(self.sizes)))
-        index.write(struct.pack('<Q', len(self.doc_idx)))
-        write_longs(index, self.dim_offsets)
-        write_longs(index, self.data_offsets)
-        write_longs(index, self.sizes)
-        write_longs(index, self.doc_idx)
-        index.close()
-
-
-def _warmup_mmap_file(path):
-    with open(path, 'rb') as stream:
-        while stream.read(100 * 1024 * 1024):
-            pass
-
-
-class MMapIndexedDataset(torch.utils.data.Dataset):
-    class Index(object):
-        _HDR_MAGIC = b'MMIDIDX\x00\x00'
-
-        @classmethod
-        def writer(cls, path, dtype):
-            class _Writer(object):
-                def __enter__(self):
-                    self._file = open(path, 'wb')
-
-                    self._file.write(cls._HDR_MAGIC)
-                    self._file.write(struct.pack('<Q', 1))
-                    self._file.write(struct.pack('<B', code(dtype)))
-
-                    return self
-
-                @staticmethod
-                def _get_pointers(sizes):
-                    dtype_size = dtype().itemsize
-                    address = 0
-                    pointers = []
-
-                    for size in sizes:
-                        pointers.append(address)
-                        address += size * dtype_size
-
-                    return pointers
-
-                def write(self, sizes, doc_idx):
-                    pointers = self._get_pointers(sizes)
-
-                    self._file.write(struct.pack('<Q', len(sizes)))
-                    self._file.write(struct.pack('<Q', len(doc_idx)))
-
-                    sizes = np.array(sizes, dtype=np.int32)
-                    self._file.write(sizes.tobytes(order='C'))
-                    del sizes
-
-                    pointers = np.array(pointers, dtype=np.int64)
-                    self._file.write(pointers.tobytes(order='C'))
-                    del pointers
-
-                    doc_idx = np.array(doc_idx, dtype=np.int64)
-                    self._file.write(doc_idx.tobytes(order='C'))
-
-                def __exit__(self, exc_type, exc_val, exc_tb):
-                    self._file.close()
-
-            return _Writer()
-
-        def __init__(self, path, skip_warmup=False):
-            with open(path, 'rb') as stream:
-                magic_test = stream.read(9)
-                assert self._HDR_MAGIC == magic_test, (
-                    'Index file doesn\'t match expected format. '
-                    'Make sure that --dataset-impl is configured properly.'
-                )
-                version = struct.unpack('<Q', stream.read(8))
-                assert (1,) == version
-
-                dtype_code, = struct.unpack('<B', stream.read(1))
-                self._dtype = dtypes[dtype_code]
-                self._dtype_size = self._dtype().itemsize
-
-                self._len = struct.unpack('<Q', stream.read(8))[0]
-                self._doc_count = struct.unpack('<Q', stream.read(8))[0]
-                offset = stream.tell()
-
-            if not skip_warmup:
-                print_rank_0("    warming up index mmap file...")
-                _warmup_mmap_file(path)
-
-            self._bin_buffer_mmap = np.memmap(path, mode='r', order='C')
-            self._bin_buffer = memoryview(self._bin_buffer_mmap)
-            print_rank_0("    reading sizes...")
-            self._sizes = np.frombuffer(
-                self._bin_buffer,
-                dtype=np.int32,
-                count=self._len,
-                offset=offset)
-            print_rank_0("    reading pointers...")
-            self._pointers = np.frombuffer(self._bin_buffer,
-                                           dtype=np.int64, count=self._len,
-                                           offset=offset + self._sizes.nbytes)
-            print_rank_0("    reading document index...")
-            self._doc_idx = np.frombuffer(
-                self._bin_buffer,
-                dtype=np.int64, count=self._doc_count,
-                offset=offset + self._sizes.nbytes + self._pointers.nbytes)
-
-        def __del__(self):
-            self._bin_buffer_mmap._mmap.close()
-            del self._bin_buffer_mmap
-
-        @property
-        def dtype(self):
-            return self._dtype
-
-        @property
-        def sizes(self):
-            return self._sizes
-
-        @property
-        def doc_idx(self):
-            return self._doc_idx
-
-        @lru_cache(maxsize=8)
-        def __getitem__(self, i):
-            return self._pointers[i], self._sizes[i]
-
-        def __len__(self):
-            return self._len
-
-    def __init__(self, path, skip_warmup=False):
-        super().__init__()
-
-        self._path = None
-        self._index = None
-        self._bin_buffer = None
-
-        self._do_init(path, skip_warmup)
-
-    def __getstate__(self):
-        return self._path
-
-    def __setstate__(self, state):
-        self._do_init(state)
-
-    def _do_init(self, path, skip_warmup):
-        self._path = path
-        self._index = self.Index(index_file_path(self._path), skip_warmup)
-
-        if not skip_warmup:
-            print_rank_0("    warming up data mmap file...")
-            _warmup_mmap_file(data_file_path(self._path))
-        print_rank_0("    creating numpy buffer of mmap...")
-        self._bin_buffer_mmap = np.memmap(
-            data_file_path(self._path), mode='r', order='C')
-        print_rank_0("    creating memory view of numpy buffer...")
-        self._bin_buffer = memoryview(self._bin_buffer_mmap)
-
-    def __del__(self):
-        self._bin_buffer_mmap._mmap.close()
-        del self._bin_buffer_mmap
-        del self._index
-
-    def __len__(self):
-        return len(self._index)
-
-    # @lru_cache(maxsize=8)
-    def __getitem__(self, idx):
-        if isinstance(idx, int):
-            ptr, size = self._index[idx]
-            np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype,
-                                     count=size, offset=ptr)
-            return np_array
-        elif isinstance(idx, slice):
-            start, stop, step = idx.indices(len(self))
-            if step != 1:
-                raise ValueError(
-                    "Slices into indexed_dataset must be contiguous")
-            ptr = self._index._pointers[start]
-            sizes = self._index._sizes[idx]
-            offsets = list(accumulate(sizes))
-            total_size = sum(sizes)
-            np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype,
-                                     count=total_size, offset=ptr)
-            sents = np.split(np_array, offsets[:-1])
-            return sents
-
-    def get(self, idx, offset=0, length=None):
-        """ Retrieves a single item from the dataset with the option to only
-        return a portion of the item.
-
-        get(idx) is the same as [idx] but get() does not support slicing.
-        """
-        ptr, size = self._index[idx]
-        if length is None:
-            length = size - offset
-        ptr += offset * np.dtype(self._index.dtype).itemsize
-        np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype,
-                                 count=length, offset=ptr)
-        return np_array
-
-    @property
-    def sizes(self):
-        return self._index.sizes
-
-    @property
-    def doc_idx(self):
-        return self._index.doc_idx
-
-    def get_doc_idx(self):
-        return self._index._doc_idx
-
-    def set_doc_idx(self, doc_idx_):
-        self._index._doc_idx = doc_idx_
-
-    @property
-    def supports_prefetch(self):
-        return False
-
-    @staticmethod
-    def exists(path):
-        return (
-            os.path.exists(index_file_path(path)) and os.path.exists(
-                data_file_path(path))
-        )
-
-
-class MMapIndexedDatasetBuilder(object):
-    def __init__(self, out_file, dtype=np.int64):
-        self._data_file = open(out_file, 'wb', buffering=5000000)
-        self._dtype = dtype
-        self._sizes = []
-        self._doc_idx = [0]
-
-    def add_item(self, tensor):
-        np_array = np.array(tensor.numpy(), dtype=self._dtype)
-        self._data_file.write(np_array.tobytes(order='C'))
-        self._sizes.append(np_array.size)
-
-    def end_document(self):
-        self._doc_idx.append(len(self._sizes))
-
-    def merge_file_(self, another_file):
-        # Concatenate index
-        index = MMapIndexedDataset.Index(index_file_path(another_file))
-        assert index.dtype == self._dtype
-
-        for size in index.sizes:
-            self._sizes.append(size)
-
-        # Concatenate data
-        with open(data_file_path(another_file), 'rb') as f:
-            shutil.copyfileobj(f, self._data_file)
-
-    def finalize(self, index_file):
-        self._data_file.close()
-
-        with MMapIndexedDataset.Index.writer(index_file, self._dtype) as index:
-            index.write(self._sizes, self._doc_idx)
diff --git a/spaces/sklearn-docs/A_demo_of_the_Spectral_Bi-Clustering_algorithm/README.md b/spaces/sklearn-docs/A_demo_of_the_Spectral_Bi-Clustering_algorithm/README.md
deleted file mode 100644
index b6cbb8ae33b66a0e5952bd6cc1054d431592f137..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/A_demo_of_the_Spectral_Bi-Clustering_algorithm/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: A Demo Of The Spectral Bi-Clustering Algorithm
-emoji: 🔥
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/templates/index.html b/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/templates/index.html
deleted file mode 100644
index 89a5f36679d9b5fe05803bb45f5b1e6ad183db77..0000000000000000000000000000000000000000
--- a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/templates/index.html
+++ /dev/null
@@ -1,142 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-  <link rel="stylesheet" href="https://cdn.rawgit.com/Chalarangelo/mini.css/v2.3.7/dist/mini-default.min.css">
-  <link rel="stylesheet" href="./static/style.css">
-  <meta charset="UTF-8">
-  <title>Label to Art Demo_V_0.9 </title>
-</head>
-
-<h1 style="text-align: center; font-size: 40px; font-family: 'Times New Roman', Times, serif;">
-  Controllable Multi-domain Semantic Artwork Synthesis
-  </a>
-</h1>
-<p style="text-align: center; font-size: 20px; font-family: 'Times New Roman', Times, serif;">
-  <!-- Duplicate this space on GPU to speedup. (Inference Time: 3500ms for CPU, 200ms for GPU.): -->
-  <a style="text-align: center; display:inline-block"
-    href="https://huggingface.co/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis?duplicate=true" target=”_blank”>
-    <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg#center"
-      alt="Duplicate Space">
-  </a>
-  <a style="text-align: center; display:inline-block"
-    href="https://sky24h.github.io/websites/cvmj2023_controllable-artwork-synthesis" target=”_blank”>
-    <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/paper-page-sm.svg#center" alt="Project Page">
-  </a>
-</p>
-<hr class="solid">
-
-<div class="common">
-
-  <body>
-    <div class="row">
-      <div class="col-sm-12 col-md-10 col-md-offset-1">
-        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-        <div class="boxitem1">
-          <label for='brushSlider' style='font-size: 1.7em; font-weight: bold'>Stroke Width </label>
-          <input type="range" name="brushsize" min="0" max="100" id="brushSlider" step="1" value="30"
-            onchange="this.setAttribute('value',this.value);">
-        </div>
-
-        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-        <div class="boxitem2">
-          &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-          <label for="style" style='font-size: 1.7em; font-weight: bold'>Domain:</label>
-          <select id="style">
-            <option style='font-size: 1.7em; font-weight: bold' value="ink">Ink Wash</option>
-            <option style='font-size: 1.7em; font-weight: bold' value="monet">Monet</option>
-            <option style='font-size: 1.7em; font-weight: bold' value="vangogh">Van Gogh</option>
-            <option style='font-size: 1.7em; font-weight: bold' value="water">WaterColor</option>
-          </select>
-        </div>
-
-        <div class="row">
-          <div class="col-sm-12 col-md-10 col-md-offset-1">
-            <div class="boxitem1">
-              <label for='domainSlider' style='font-size: 1.7em; font-weight: bold'> Style Strength </label>
-              <input type="range" name="style range" min="1" max="5" id="domainSlider" step="0.2" value="3"
-                onchange="this.setAttribute('value',this.value);">
-            </div>
-            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-            &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
-            <div class="boxitem2">
-              <button id="random_pick" class="primary"
-                style="background-color:#003449; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-                Random </button>
-            </div>
-          </div>
-          <br>
-
-        </div>
-        <div class="divider"></div>
-        <div class="boxitem1">
-          <canvas id="inputimg" width="512" height="512" style="border:5px solid #ffffff;"></canvas>
-        </div>
-        <div class="boxitem2">
-          <canvas id="outputimg" width="512" height="512" style="border:5px solid #ffffff;"></canvas>
-        </div>
-      </div>
-    </div>
-
-
-    <div class="boxitem">
-      <button id="color1" class="primary"
-        style="background-color:#87ceeb; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Sky </button>
-      <button id="color2" class="primary"
-        style="background-color:#9b7653; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Dirt </button>
-      <button id="color3" class="primary"
-        style="background-color:#b0d49b; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Mountain </button>
-      <button id="color4" class="primary"
-        style="background-color:#5abcd8; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        River </button>
-      <button id="color5" class="primary"
-        style="background-color:#C1BEBA; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Clouds </button>
-      <button id="color6" class="primary"
-        style="background-color:#5A4D41; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Rock </button>
-      <button id="color7" class="primary"
-        style="background-color:#567d46; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Grass </button>
-      <button id="color8" class="primary"
-        style="background-color:#42692f; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Tree </button>
-    </div>
-    <br>
-    <div class="boxitem">
-      <button id="color9" class="primary"
-        style="background-color:#1577be; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Sea </button>
-      <button id="color11" class="primary"
-        style="background-color:#3a2e27; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Ground </button>
-      <button id="color12" class="primary"
-        style="background-color:#4D415A; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Hill </button>
-      <button id="color14" class="primary"
-        style="background-color:#FDDA16; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Road </button>
-      <button id="color15" class="primary"
-        style="background-color:#d0cccc; margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;">
-        Snow </button>
-      <button id="clearbtn" class="primary"
-        style="margin-right:0px; font-size: 1.9em; font-weight: bold; border-radius:15px;"> Clear </button>
-
-    </div>
-
-    <script src="./static/index.js"></script>
-    <script src="//ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>
-    <script src="//d3js.org/d3.v5.min.js"></script>
-  </body>
\ No newline at end of file
diff --git a/spaces/sneedium/dvatch_captcha_sneedium_old/utils.py b/spaces/sneedium/dvatch_captcha_sneedium_old/utils.py
deleted file mode 100644
index 1b7b5db1bc1dd191191c31b3e72228ccd1c4f7a1..0000000000000000000000000000000000000000
--- a/spaces/sneedium/dvatch_captcha_sneedium_old/utils.py
+++ /dev/null
@@ -1,304 +0,0 @@
-import logging
-import os
-import time
-
-import cv2
-import numpy as np
-import torch
-import yaml
-from matplotlib import colors
-from matplotlib import pyplot as plt
-from torch import Tensor, nn
-from torch.utils.data import ConcatDataset
-
-class CharsetMapper(object):
-    """A simple class to map ids into strings.
-
-    It works only when the character set is 1:1 mapping between individual
-    characters and individual ids.
-    """
-
-    def __init__(self,
-                 filename='',
-                 max_length=30,
-                 null_char=u'\u2591'):
-        """Creates a lookup table.
-
-        Args:
-          filename: Path to charset file which maps characters to ids.
-          max_sequence_length: The max length of ids and string.
-          null_char: A unicode character used to replace '<null>' character.
-            the default value is a light shade block '░'.
-        """
-        self.null_char = null_char
-        self.max_length = max_length
-
-        self.label_to_char = self._read_charset(filename)
-        self.char_to_label = dict(map(reversed, self.label_to_char.items()))
-        self.num_classes = len(self.label_to_char)
- 
-    def _read_charset(self, filename):
-        """Reads a charset definition from a tab separated text file.
-
-        Args:
-          filename: a path to the charset file.
-
-        Returns:
-          a dictionary with keys equal to character codes and values - unicode
-          characters.
-        """
-        import re
-        pattern = re.compile(r'(\d+)\t(.+)')
-        charset = {}
-        self.null_label = 0
-        charset[self.null_label] = self.null_char
-        with open(filename, 'r') as f:
-            for i, line in enumerate(f):
-                m = pattern.match(line)
-                assert m, f'Incorrect charset file. line #{i}: {line}'
-                label = int(m.group(1)) + 1
-                char = m.group(2)
-                charset[label] = char
-        return charset
-
-    def trim(self, text):
-        assert isinstance(text, str)
-        return text.replace(self.null_char, '')
-
-    def get_text(self, labels, length=None, padding=True, trim=False):
-        """ Returns a string corresponding to a sequence of character ids.
-        """
-        length = length if length else self.max_length
-        labels = [l.item() if isinstance(l, Tensor) else int(l) for l in labels]
-        if padding:
-            labels = labels + [self.null_label] * (length-len(labels))
-        text = ''.join([self.label_to_char[label] for label in labels])
-        if trim: text = self.trim(text)
-        return text
-
-    def get_labels(self, text, length=None, padding=True, case_sensitive=False):
-        """ Returns the labels of the corresponding text.
-        """
-        length = length if length else self.max_length
-        if padding:
-            text = text + self.null_char * (length - len(text))
-        if not case_sensitive:
-            text = text.lower()
-        labels = [self.char_to_label[char] for char in text]
-        return labels
-
-    def pad_labels(self, labels, length=None):
-        length = length if length else self.max_length
-
-        return labels + [self.null_label] * (length - len(labels))
-
-    @property
-    def digits(self):
-        return '0123456789'
-
-    @property
-    def digit_labels(self):
-        return self.get_labels(self.digits, padding=False)
-
-    @property
-    def alphabets(self):
-        all_chars = list(self.char_to_label.keys())
-        valid_chars = []
-        for c in all_chars:
-            if c in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ':
-                valid_chars.append(c)
-        return ''.join(valid_chars)
-
-    @property
-    def alphabet_labels(self):
-        return self.get_labels(self.alphabets, padding=False)
-
-
-class Timer(object):
-    """A simple timer."""
-    def __init__(self):
-        self.data_time = 0.
-        self.data_diff = 0.
-        self.data_total_time = 0.
-        self.data_call = 0
-        self.running_time = 0.
-        self.running_diff = 0.
-        self.running_total_time = 0.
-        self.running_call = 0
-
-    def tic(self):
-        self.start_time = time.time()
-        self.running_time = self.start_time
-
-    def toc_data(self):
-        self.data_time = time.time()
-        self.data_diff = self.data_time - self.running_time
-        self.data_total_time += self.data_diff
-        self.data_call += 1
-
-    def toc_running(self):
-        self.running_time = time.time()
-        self.running_diff = self.running_time - self.data_time
-        self.running_total_time += self.running_diff
-        self.running_call += 1
-
-    def total_time(self):
-        return self.data_total_time + self.running_total_time
-
-    def average_time(self):
-        return  self.average_data_time() + self.average_running_time()
-
-    def average_data_time(self):
-        return self.data_total_time / (self.data_call or 1)
-
-    def average_running_time(self):
-        return self.running_total_time / (self.running_call or 1)
-
-
-class Logger(object):
-    _handle = None
-    _root = None
-
-    @staticmethod
-    def init(output_dir, name, phase):
-        format = '[%(asctime)s %(filename)s:%(lineno)d %(levelname)s {}] ' \
-                '%(message)s'.format(name)
-        logging.basicConfig(level=logging.INFO, format=format)
-
-        try: os.makedirs(output_dir)
-        except: pass
-        config_path = os.path.join(output_dir, f'{phase}.txt')
-        Logger._handle = logging.FileHandler(config_path)
-        Logger._root = logging.getLogger()
-
-    @staticmethod
-    def enable_file():
-        if Logger._handle is None or Logger._root is None:
-            raise Exception('Invoke Logger.init() first!')
-        Logger._root.addHandler(Logger._handle)
-
-    @staticmethod
-    def disable_file():
-        if Logger._handle is None or Logger._root is None:
-            raise Exception('Invoke Logger.init() first!')
-        Logger._root.removeHandler(Logger._handle)
-
-
-class Config(object):
-
-    def __init__(self, config_path, host=True):
-        def __dict2attr(d, prefix=''):
-            for k, v in d.items():
-                if isinstance(v, dict):
-                    __dict2attr(v, f'{prefix}{k}_')
-                else:
-                    if k == 'phase':
-                        assert v in ['train', 'test']
-                    if k == 'stage':
-                        assert v in ['pretrain-vision', 'pretrain-language',
-                                     'train-semi-super', 'train-super']
-                    self.__setattr__(f'{prefix}{k}', v)
-
-        assert os.path.exists(config_path), '%s does not exists!' % config_path
-        with open(config_path) as file:
-            config_dict = yaml.load(file, Loader=yaml.FullLoader)
-        with open('configs/template.yaml') as file:
-            default_config_dict = yaml.load(file, Loader=yaml.FullLoader)
-        __dict2attr(default_config_dict)
-        __dict2attr(config_dict)
-        self.global_workdir = os.path.join(self.global_workdir, self.global_name)
-
-    def __getattr__(self, item):
-        attr = self.__dict__.get(item)
-        if attr is None:
-            attr = dict()
-            prefix = f'{item}_'
-            for k, v in self.__dict__.items():
-                if k.startswith(prefix):
-                    n = k.replace(prefix, '')
-                    attr[n] = v
-            return attr if len(attr) > 0 else None
-        else:
-            return attr
-
-    def __repr__(self):
-        str = 'ModelConfig(\n'
-        for i, (k, v) in enumerate(sorted(vars(self).items())):
-            str += f'\t({i}): {k} = {v}\n'
-        str += ')'
-        return str
-
-def blend_mask(image, mask, alpha=0.5, cmap='jet', color='b', color_alpha=1.0):
-    # normalize mask
-    mask = (mask-mask.min()) / (mask.max() - mask.min() + np.finfo(float).eps)
-    if mask.shape != image.shape:
-        mask = cv2.resize(mask,(image.shape[1], image.shape[0]))
-    # get color map
-    color_map = plt.get_cmap(cmap)
-    mask = color_map(mask)[:,:,:3]
-    # convert float to uint8
-    mask = (mask * 255).astype(dtype=np.uint8)
-
-    # set the basic color
-    basic_color = np.array(colors.to_rgb(color)) * 255 
-    basic_color = np.tile(basic_color, [image.shape[0], image.shape[1], 1]) 
-    basic_color = basic_color.astype(dtype=np.uint8)
-    # blend with basic color
-    blended_img = cv2.addWeighted(image, color_alpha, basic_color, 1-color_alpha, 0)
-    # blend with mask
-    blended_img = cv2.addWeighted(blended_img, alpha, mask, 1-alpha, 0)
-
-    return blended_img
-
-def onehot(label, depth, device=None):
-    """ 
-    Args:
-        label: shape (n1, n2, ..., )
-        depth: a scalar
-
-    Returns:
-        onehot: (n1, n2, ..., depth)
-    """
-    if not isinstance(label, torch.Tensor):
-        label = torch.tensor(label, device=device)
-    onehot = torch.zeros(label.size() + torch.Size([depth]), device=device)
-    onehot = onehot.scatter_(-1, label.unsqueeze(-1), 1)
-
-    return onehot
-
-class MyDataParallel(nn.DataParallel):
-
-    def gather(self, outputs, target_device):
-        r"""
-        Gathers tensors from different GPUs on a specified device
-        (-1 means the CPU).
-        """
-        def gather_map(outputs):
-            out = outputs[0]
-            if isinstance(out, (str, int, float)):
-                return out
-            if isinstance(out, list) and isinstance(out[0], str):
-                return [o for out in outputs for o in out]
-            if isinstance(out, torch.Tensor):
-                return torch.nn.parallel._functions.Gather.apply(target_device, self.dim, *outputs)
-            if out is None:
-                return None
-            if isinstance(out, dict):
-                if not all((len(out) == len(d) for d in outputs)):
-                    raise ValueError('All dicts must have the same number of keys')
-                return type(out)(((k, gather_map([d[k] for d in outputs]))
-                                for k in out))
-            return type(out)(map(gather_map, zip(*outputs)))
-
-        # Recursive function calls like this create reference cycles.
-        # Setting the function to None clears the refcycle.
-        try:
-            res = gather_map(outputs)
-        finally:
-            gather_map = None
-        return res
-
-
-class MyConcatDataset(ConcatDataset):
-    def __getattr__(self, k): 
-        return getattr(self.datasets[0], k)
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/simple_kmeans/dump_w2v2_feature.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/simple_kmeans/dump_w2v2_feature.py
deleted file mode 100644
index a1f0d902acf0756580a1f4604feee8fc499a9a63..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/simple_kmeans/dump_w2v2_feature.py
+++ /dev/null
@@ -1,95 +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 logging
-import os
-import sys
-
-import fairseq
-import soundfile as sf
-import torch
-import torch.nn.functional as F
-
-from feature_utils import get_path_iterator, dump_feature
-
-
-logging.basicConfig(
-    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    level=os.environ.get("LOGLEVEL", "INFO").upper(),
-    stream=sys.stdout,
-)
-logger = logging.getLogger("dump_w2v2_feature")
-
-
-class Wav2Vec2FeatureReader(object):
-    def __init__(self, ckpt_path, layer, max_chunk=1600000):
-        (
-            model,
-            cfg,
-            task,
-        ) = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path])
-        self.model = model[0].eval().cuda()
-        self.task = task
-        self.layer = layer  # assume this is 1-based like HuBERT
-        self.max_chunk = max_chunk
-        logger.info(f"TASK CONFIG:\n{self.task.cfg}")
-        logger.info(f" max_chunk = {self.max_chunk}")
-        logger.info(f" model:\n{self.model}")
-
-    def read_audio(self, path, ref_len=None):
-        wav, sr = sf.read(path)
-        assert sr == self.task.cfg.sample_rate, sr
-        if wav.ndim == 2:
-            wav = wav.mean(-1)
-        assert wav.ndim == 1, wav.ndim
-        if ref_len is not None and abs(ref_len - len(wav)) > 160:
-            logging.warning(f"ref {ref_len} != read {len(wav)} ({path})")
-        return wav
-
-    def get_feats(self, path, ref_len=None):
-        x = self.read_audio(path, ref_len)
-        with torch.no_grad():
-            x = torch.from_numpy(x).float().cuda()
-            if self.task.cfg.normalize:
-                x = F.layer_norm(x, x.shape)
-            x = x.view(1, -1)
-
-            feat = []
-            for start in range(0, x.size(1), self.max_chunk):
-                x_chunk = x[:, start: start + self.max_chunk]
-                res = self.model.extract_features(
-                    source=x_chunk,
-                    padding_mask=None,
-                    mask=False,
-                    layer=self.layer - 1,
-                )
-                feat_chunk = res["x"]
-                feat.append(feat_chunk)
-        return torch.cat(feat, 1).squeeze(0)
-
-
-def main(tsv_dir, split, ckpt_path, layer, nshard, rank, feat_dir, max_chunk):
-    reader = Wav2Vec2FeatureReader(ckpt_path, layer, max_chunk)
-    generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank)
-    dump_feature(reader, generator, num, split, nshard, rank, feat_dir)
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("tsv_dir")
-    parser.add_argument("split")
-    parser.add_argument("ckpt_path")
-    parser.add_argument("layer", type=int)
-    parser.add_argument("nshard", type=int)
-    parser.add_argument("rank", type=int)
-    parser.add_argument("feat_dir")
-    parser.add_argument("--max_chunk", type=int, default=1600000)
-    args = parser.parse_args()
-    logger.info(args)
-
-    main(**vars(args))
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/language_model/prepare-wikitext-103.sh b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/language_model/prepare-wikitext-103.sh
deleted file mode 100644
index 751302156f0a6829af9c2ee5e0e2ca62c2cd4187..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/language_model/prepare-wikitext-103.sh
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/bin/bash
-# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
-
-URLS=(
-    "https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip"
-)
-FILES=(
-    "wikitext-103-v1.zip"
-)
-
-for ((i=0;i<${#URLS[@]};++i)); do
-    file=${FILES[i]}
-    if [ -f $file ]; then
-        echo "$file already exists, skipping download"
-    else
-        url=${URLS[i]}
-        wget "$url"
-        if [ -f $file ]; then
-            echo "$url successfully downloaded."
-        else
-            echo "$url not successfully downloaded."
-            exit -1
-        fi
-        if [ ${file: -4} == ".tgz" ]; then
-            tar zxvf $file
-        elif [ ${file: -4} == ".tar" ]; then
-            tar xvf $file
-        elif [ ${file: -4} == ".zip" ]; then
-            unzip $file
-        fi
-    fi
-done
-cd ..
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/latent_depth/latent_depth_src/models/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/latent_depth/latent_depth_src/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/file_utils.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/file_utils.py
deleted file mode 100644
index d1d5ea65746682881264e4a9c462854dcfb3413f..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/file_utils.py
+++ /dev/null
@@ -1,369 +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.
-
-"""
-Utilities for working with the local dataset cache.
-This file is adapted from `AllenNLP <https://github.com/allenai/allennlp>`_.
-and `huggingface <https://github.com/huggingface>`_.
-"""
-
-import fnmatch
-import json
-import logging
-import os
-import shutil
-import tarfile
-import tempfile
-from functools import partial, wraps
-from hashlib import sha256
-from io import open
-
-
-try:
-    from torch.hub import _get_torch_home
-
-    torch_cache_home = _get_torch_home()
-except ImportError:
-    torch_cache_home = os.path.expanduser(
-        os.getenv(
-            "TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch")
-        )
-    )
-default_cache_path = os.path.join(torch_cache_home, "pytorch_fairseq")
-
-try:
-    from urllib.parse import urlparse
-except ImportError:
-    from urlparse import urlparse
-
-try:
-    from pathlib import Path
-
-    PYTORCH_FAIRSEQ_CACHE = Path(os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path))
-except (AttributeError, ImportError):
-    PYTORCH_FAIRSEQ_CACHE = os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path)
-
-CONFIG_NAME = "config.json"
-WEIGHTS_NAME = "pytorch_model.bin"
-
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
-
-
-def load_archive_file(archive_file):
-    # redirect to the cache, if necessary
-    try:
-        resolved_archive_file = cached_path(archive_file, cache_dir=None)
-    except EnvironmentError:
-        logger.info(
-            "Archive name '{}' was not found in archive name list. "
-            "We assumed '{}' was a path or URL but couldn't find any file "
-            "associated to this path or URL.".format(
-                archive_file,
-                archive_file,
-            )
-        )
-        return None
-
-    if resolved_archive_file == archive_file:
-        logger.info("loading archive file {}".format(archive_file))
-    else:
-        logger.info(
-            "loading archive file {} from cache at {}".format(
-                archive_file, resolved_archive_file
-            )
-        )
-
-    # Extract archive to temp dir and replace .tar.bz2 if necessary
-    tempdir = None
-    if not os.path.isdir(resolved_archive_file):
-        tempdir = tempfile.mkdtemp()
-        logger.info(
-            "extracting archive file {} to temp dir {}".format(
-                resolved_archive_file, tempdir
-            )
-        )
-        ext = os.path.splitext(archive_file)[1][1:]
-        with tarfile.open(resolved_archive_file, "r:" + ext) as archive:
-            top_dir = os.path.commonprefix(archive.getnames())
-            archive.extractall(tempdir)
-        os.remove(resolved_archive_file)
-        shutil.move(os.path.join(tempdir, top_dir), resolved_archive_file)
-        shutil.rmtree(tempdir)
-
-    return resolved_archive_file
-
-
-def url_to_filename(url, etag=None):
-    """
-    Convert `url` into a hashed filename in a repeatable way.
-    If `etag` is specified, append its hash to the URL's, delimited
-    by a period.
-    """
-    url_bytes = url.encode("utf-8")
-    url_hash = sha256(url_bytes)
-    filename = url_hash.hexdigest()
-
-    if etag:
-        etag_bytes = etag.encode("utf-8")
-        etag_hash = sha256(etag_bytes)
-        filename += "." + etag_hash.hexdigest()
-
-    return filename
-
-
-def filename_to_url(filename, cache_dir=None):
-    """
-    Return the url and etag (which may be ``None``) stored for `filename`.
-    Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_FAIRSEQ_CACHE
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    cache_path = os.path.join(cache_dir, filename)
-    if not os.path.exists(cache_path):
-        raise EnvironmentError("file {} not found".format(cache_path))
-
-    meta_path = cache_path + ".json"
-    if not os.path.exists(meta_path):
-        raise EnvironmentError("file {} not found".format(meta_path))
-
-    with open(meta_path, encoding="utf-8") as meta_file:
-        metadata = json.load(meta_file)
-    url = metadata["url"]
-    etag = metadata["etag"]
-
-    return url, etag
-
-
-def cached_path_from_pm(url_or_filename):
-    """
-    Tries to cache the specified URL using PathManager class.
-    Returns the cached path if success otherwise failure.
-    """
-    try:
-        from fairseq.file_io import PathManager
-        local_path = PathManager.get_local_path(url_or_filename)
-        return local_path
-    except Exception:
-        return None
-
-
-def cached_path(url_or_filename, cache_dir=None):
-    """
-    Given something that might be a URL (or might be a local path),
-    determine which. If it's a URL, download the file and cache it, and
-    return the path to the cached file. If it's already a local path,
-    make sure the file exists and then return the path.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_FAIRSEQ_CACHE
-    if isinstance(url_or_filename, Path):
-        url_or_filename = str(url_or_filename)
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    parsed = urlparse(url_or_filename)
-
-    if parsed.scheme in ("http", "https", "s3"):
-        # URL, so get it from the cache (downloading if necessary)
-        return get_from_cache(url_or_filename, cache_dir)
-    elif os.path.exists(url_or_filename):
-        # File, and it exists.
-        return url_or_filename
-    elif parsed.scheme == "":
-        # File, but it doesn't exist.
-        raise EnvironmentError("file {} not found".format(url_or_filename))
-    else:
-        cached_path = cached_path_from_pm(url_or_filename)
-        if cached_path:
-            return cached_path
-        # Something unknown
-        raise ValueError(
-            "unable to parse {} as a URL or as a local path".format(url_or_filename)
-        )
-
-
-def split_s3_path(url):
-    """Split a full s3 path into the bucket name and path."""
-    parsed = urlparse(url)
-    if not parsed.netloc or not parsed.path:
-        raise ValueError("bad s3 path {}".format(url))
-    bucket_name = parsed.netloc
-    s3_path = parsed.path
-    # Remove '/' at beginning of path.
-    if s3_path.startswith("/"):
-        s3_path = s3_path[1:]
-    return bucket_name, s3_path
-
-
-def s3_request(func):
-    """
-    Wrapper function for s3 requests in order to create more helpful error
-    messages.
-    """
-
-    @wraps(func)
-    def wrapper(url, *args, **kwargs):
-        from botocore.exceptions import ClientError
-
-        try:
-            return func(url, *args, **kwargs)
-        except ClientError as exc:
-            if int(exc.response["Error"]["Code"]) == 404:
-                raise EnvironmentError("file {} not found".format(url))
-            else:
-                raise
-
-    return wrapper
-
-
-@s3_request
-def s3_etag(url):
-    """Check ETag on S3 object."""
-    import boto3
-
-    s3_resource = boto3.resource("s3")
-    bucket_name, s3_path = split_s3_path(url)
-    s3_object = s3_resource.Object(bucket_name, s3_path)
-    return s3_object.e_tag
-
-
-@s3_request
-def s3_get(url, temp_file):
-    """Pull a file directly from S3."""
-    import boto3
-
-    s3_resource = boto3.resource("s3")
-    bucket_name, s3_path = split_s3_path(url)
-    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
-
-
-def request_wrap_timeout(func, url):
-    import requests
-
-    for attempt, timeout in enumerate([10, 20, 40, 60, 60]):
-        try:
-            return func(timeout=timeout)
-        except requests.exceptions.Timeout as e:
-            logger.warning(
-                "Request for %s timed-out (attempt %d). Retrying with a timeout of %d secs",
-                url,
-                attempt,
-                timeout,
-                exc_info=e,
-            )
-            continue
-    raise RuntimeError(f"Unable to fetch file {url}")
-
-
-def http_get(url, temp_file):
-    import requests
-    from tqdm import tqdm
-
-    req = request_wrap_timeout(partial(requests.get, url, stream=True), url)
-    content_length = req.headers.get("Content-Length")
-    total = int(content_length) if content_length is not None else None
-    progress = tqdm(unit="B", total=total)
-    for chunk in req.iter_content(chunk_size=1024):
-        if chunk:  # filter out keep-alive new chunks
-            progress.update(len(chunk))
-            temp_file.write(chunk)
-    progress.close()
-
-
-def get_from_cache(url, cache_dir=None):
-    """
-    Given a URL, look for the corresponding dataset in the local cache.
-    If it's not there, download it. Then return the path to the cached file.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_FAIRSEQ_CACHE
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    if not os.path.exists(cache_dir):
-        os.makedirs(cache_dir)
-
-    # Get eTag to add to filename, if it exists.
-    if url.startswith("s3://"):
-        etag = s3_etag(url)
-    else:
-        try:
-            import requests
-
-            response = request_wrap_timeout(
-                partial(requests.head, url, allow_redirects=True), url
-            )
-            if response.status_code != 200:
-                etag = None
-            else:
-                etag = response.headers.get("ETag")
-        except RuntimeError:
-            etag = None
-
-    filename = url_to_filename(url, etag)
-
-    # get cache path to put the file
-    cache_path = os.path.join(cache_dir, filename)
-
-    # If we don't have a connection (etag is None) and can't identify the file
-    # try to get the last downloaded one
-    if not os.path.exists(cache_path) and etag is None:
-        matching_files = fnmatch.filter(os.listdir(cache_dir), filename + ".*")
-        matching_files = list(filter(lambda s: not s.endswith(".json"), matching_files))
-        if matching_files:
-            cache_path = os.path.join(cache_dir, matching_files[-1])
-
-    if not os.path.exists(cache_path):
-        # Download to temporary file, then copy to cache dir once finished.
-        # Otherwise you get corrupt cache entries if the download gets interrupted.
-        with tempfile.NamedTemporaryFile() as temp_file:
-            logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
-
-            # GET file object
-            if url.startswith("s3://"):
-                s3_get(url, temp_file)
-            else:
-                http_get(url, temp_file)
-
-            # we are copying the file before closing it, so flush to avoid truncation
-            temp_file.flush()
-            # shutil.copyfileobj() starts at the current position, so go to the start
-            temp_file.seek(0)
-
-            logger.info("copying %s to cache at %s", temp_file.name, cache_path)
-            with open(cache_path, "wb") as cache_file:
-                shutil.copyfileobj(temp_file, cache_file)
-
-            logger.info("creating metadata file for %s", cache_path)
-            meta = {"url": url, "etag": etag}
-            meta_path = cache_path + ".json"
-            with open(meta_path, "w") as meta_file:
-                output_string = json.dumps(meta)
-                meta_file.write(output_string)
-
-            logger.info("removing temp file %s", temp_file.name)
-
-    return cache_path
-
-
-def read_set_from_file(filename):
-    """
-    Extract a de-duped collection (set) of text from a file.
-    Expected file format is one item per line.
-    """
-    collection = set()
-    with open(filename, "r", encoding="utf-8") as file_:
-        for line in file_:
-            collection.add(line.rstrip())
-    return collection
-
-
-def get_file_extension(path, dot=True, lower=True):
-    ext = os.path.splitext(path)[1]
-    ext = ext if dot else ext[1:]
-    return ext.lower() if lower else ext
diff --git a/spaces/stomexserde/gpt4-ui/1st Year English Book Sindh Text Board Pdfl.md b/spaces/stomexserde/gpt4-ui/1st Year English Book Sindh Text Board Pdfl.md
deleted file mode 100644
index 4bf3f26311cc7479b02f2ca18665edcfdb93b036..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/1st Year English Book Sindh Text Board Pdfl.md	
+++ /dev/null
@@ -1,82 +0,0 @@
-## 1st Year English Book Sindh Text Board Pdfl
-
-
-
- 
-
-  
-
- 
-
-**LINK ⚙⚙⚙ [https://vercupalo.blogspot.com/?d=2tyUfE](https://vercupalo.blogspot.com/?d=2tyUfE)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# How to Download 1st Year English Book Sindh Text Board Pdf for Free
-
- 
-
-If you are looking for a free and easy way to download the 1st year English book Sindh Text Board Pdf, you have come to the right place. In this article, we will show you how to get the pdf version of the textbook for class 11 students of Sindh Board Jamshoro.
-
- 
-
-The 1st year English book Sindh Text Board Pdf is a comprehensive and well-written textbook that covers all the topics and skills required for the intermediate level. It includes grammar, vocabulary, reading, writing, listening, speaking, and literature sections. The book also has exercises, activities, summaries, and glossaries to help students practice and learn effectively.
-
- 
-
-The 1st year English book Sindh Text Board Pdf is available for free download on the official website of Sindh Textbook Board (STBB), Jamshoro[^1^]. You can also find the pdf books of other subjects and classes on the same website. To download the pdf book, you need to follow these simple steps:
-
- 
-
-1. Go to [https://stbb.edu.pk/free-textbooks](https://stbb.edu.pk/free-textbooks) and click on "Free Textbooks".
-
-2. Select "Class XI" from the drop-down menu and click on "Search".
-
-3. You will see a list of books for class 11 of different subjects. Find the book titled "English" and click on "Download".
-
-4. The pdf file of the book will open in a new tab. You can save it on your device or print it out as per your convenience.
-
-
-
-Alternatively, you can also download the 1st year English book Sindh Text Board Pdf from other websites that provide free pdf books for Sindh Board students. For example, you can visit [https://www.zahidenotes.com/2023/01/sindh-text-book-board-books-pdf-download.html](https://www.zahidenotes.com/2023/01/sindh-text-book-board-books-pdf-download.html) [^2^] or [https://www.taleem360.com/class-one-my-english-1-text-book-in-pdf-by-sindh-board-qd9](https://www.taleem360.com/class-one-my-english-1-text-book-in-pdf-by-sindh-board-qd9) [^4^] and follow the instructions given there.
-
- 
-
-We hope this article has helped you to download the 1st year English book Sindh Text Board Pdf for free. If you have any questions or feedback, please leave a comment below. Happy learning!
-
-  
-
-Now that you have downloaded the 1st year English book Sindh Text Board Pdf, you might be wondering how to use it effectively. Here are some tips and tricks to help you make the most of your textbook:
-
-
-
-- Read the book regularly and follow the sequence of the chapters. The book is designed to help you develop your English skills gradually and systematically.
-
-- Pay attention to the learning objectives and outcomes at the beginning and end of each chapter. They will help you understand what you are expected to learn and achieve from each lesson.
-
-- Do the exercises and activities given in the book and check your answers with the answer key provided at the end of the book. This will help you assess your progress and identify your strengths and weaknesses.
-
-- Use the glossaries and summaries given in the book to review and revise the key terms and concepts. They will also help you prepare for your exams and tests.
-
-- Practice your listening and speaking skills by using the audio-visual materials that accompany the book. You can access them on the STBB website or on YouTube. They will help you improve your pronunciation, fluency, and comprehension.
-
-
-
-By following these tips and tricks, you will be able to use the 1st year English book Sindh Text Board Pdf effectively and enhance your English skills. We wish you all the best for your studies!
-
- 145887f19f
-
- 
-
- 
-
diff --git "a/spaces/stomexserde/gpt4-ui/Examples/Cain Abel V4.2 Released!!!\302\240(__LINK__ Download).md" "b/spaces/stomexserde/gpt4-ui/Examples/Cain Abel V4.2 Released!!!\302\240(__LINK__ Download).md"
deleted file mode 100644
index 5a9d351dfdf41a0de70b4f9689808c176c0fd3c4..0000000000000000000000000000000000000000
--- "a/spaces/stomexserde/gpt4-ui/Examples/Cain Abel V4.2 Released!!!\302\240(__LINK__ Download).md"	
+++ /dev/null
@@ -1,30 +0,0 @@
-
-<h1>Cain Abel v4.2 Released!!! (Download)</h1>
-<p>Cain Abel is a powerful password recovery tool for Windows operating systems. It can recover various kinds of passwords by sniffing the network, cracking encrypted passwords using dictionary, brute-force and cryptanalysis attacks, recording VoIP conversations, decoding scrambled passwords, revealing password boxes, uncovering cached passwords and analyzing routing protocols.</p>
-<p>In this article, we will show you how to download and install Cain Abel v4.2, the latest version of this amazing tool. We will also explain some of the new features and improvements that Cain Abel v4.2 offers.</p>
-<h2>Cain Abel v4.2 Released!!! (Download)</h2><br /><p><b><b>Download</b> &bull;&bull;&bull; <a href="https://urlgoal.com/2uI9Gb">https://urlgoal.com/2uI9Gb</a></b></p><br /><br />
-<h2>How to Download and Install Cain Abel v4.2</h2>
-<p>To download Cain Abel v4.2, you need to visit the official website of the developer, Oxid. There you will find a link to download the setup file for Cain Abel v4.2. The file size is about 7.6 MB and it is free to use.</p>
-<p>Before you install Cain Abel v4.2, you need to make sure that you have WinPcap 4.1 installed on your computer. WinPcap is a library that enables network packet capture and analysis on Windows platforms. You can download WinPcap 4.1 from its official website.</p>
-<p>Once you have WinPcap 4.1 installed, you can run the setup file for Cain Abel v4.2 and follow the instructions on the screen. The installation process is simple and straightforward. You can choose the destination folder and the components to install.</p>
-<p>After the installation is complete, you can launch Cain Abel v4.2 from the Start menu or from the desktop shortcut.</p>
-<h2>What's New in Cain Abel v4.2</h2>
-<p>Cain Abel v4.2 is faster and contains a lot of new features and enhancements compared to the previous versions. Some of the main highlights are:</p>
-<ul>
-<li>Added Windows Vault Password Decoder.</li>
-<li>Added Windows 8 support in LSA Secret Dumper.</li>
-<li>Added Windows 8 support in Credential Manager Password Decoder.</li>
-<li>Added Windows 8 support in EditBox Revealer.</li>
-<li>Added ability to keep original extensions in fake certificates.</li>
-<li>Added support for Windows 8 RDP Client in APR-RDP sniffer filter.</li>
-<li>Added Root Certificate Generator in Certificate Spoofing configuration page.</li>
-<li>Added experimental Certificate Injection feature to inject custom certificates into HTTPS/ProxyHTTPS responses directed to victim APR's clients.</li>
-<li>Added Anticache option for APR-HTTPS/APR-ProxyHTTPS (touch "If-Modified-Since" and "If-None-Match" fields in HTTP headers from client).</li>
-</ul>
-<p>Cain Abel v4.2 also ships with some "non standard" utilities for Windows users, such as routing protocols authentication monitors and routes extractors, dictionary and brute-force crackers for all common hashing algorithms and for several specific authentications, password/hash calculators, cryptanalysis attacks, password decoders and more.</p>
-<p></p>
-<h2>Conclusion</h2>
-<p>Cain Abel v4.2 is a must-have tool for anyone who wants to recover or test passwords on Windows systems. It is easy to use, versatile and powerful. You can download it for free from the developer's website and enjoy its amazing features.</p>
-<p>However, you should also be aware that Cain Abel v4.2 can be detected as a threat by some antivirus programs due to its nature and functions. You should also use it only for ethical reasons and not for illegal activities or malicious purposes.</p> 81aa517590<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Crack Eplan Electric P8 1.8.6 15.md b/spaces/stomexserde/gpt4-ui/Examples/Crack Eplan Electric P8 1.8.6 15.md
deleted file mode 100644
index 792c561b377249ebe94033dad365a7bb2e298fb8..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Crack Eplan Electric P8 1.8.6 15.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-
-<h1>EPLAN Electric P8 1.8.6 15: A Powerful and Flexible Electrical Engineering Software</h1>
-<p>EPLAN Electric P8 is a software that helps you plan and design the electrical engineering for machines and plant systems. It supports 2D computer-aided engineering and a wide variety of engineering methods, from manual creation to standardized and template-based approaches. With EPLAN Electric P8, you can create schematics and circuit diagrams that are the foundation for the automated completion of machine and plant system documentation.</p>
-<h2>Crack eplan electric p8 1.8.6 15</h2><br /><p><b><b>DOWNLOAD</b> &#8230;&#8230;&#8230; <a href="https://urlgoal.com/2uI878">https://urlgoal.com/2uI878</a></b></p><br /><br />
-<p>EPLAN Electric P8 1.8.6 15 is a version of the software that was released in 2012. It has many features and benefits that make it a reliable and efficient tool for electrical engineering. Some of these features are:</p>
-<ul>
-<li>Improved user interface with customizable toolbars, menus and dialogs</li>
-<li>Enhanced data management with project folders, version control and backup functions</li>
-<li>Extended functionality for PLC programming, terminal design, cable routing and panel layout</li>
-<li>Integrated data exchange with other EPLAN products and third-party applications</li>
-<li>Support for multiple languages, standards and norms</li>
-</ul>
-<p>If you are looking for a software that can help you with your electrical engineering projects, EPLAN Electric P8 1.8.6 15 is a great option to consider. You can download it from the Internet Archive[^3^] or visit the official website of EPLAN[^1^] [^2^] for more information.</p>
-
-<p>EPLAN Electric P8 1.8.6 15 is compatible with Windows XP, Vista and 7 operating systems. It requires a minimum of 1 GB RAM, 2 GB hard disk space and a screen resolution of 1024 x 768 pixels. To install the software, you need to have a valid license key and follow the instructions in the setup wizard. You can also use the EPLAN License Manager to manage your licenses and activate them online or offline.</p>
-<p>EPLAN Electric P8 1.8.6 15 is not the latest version of the software, as EPLAN has released several updates and new features since then. However, it is still a useful and functional software that can meet your electrical engineering needs. If you want to upgrade to a newer version of EPLAN Electric P8, you can contact EPLAN customer service or visit their website for more details.</p>
-<p>Some other electrical engineering software that you might be interested in are AutoCAD Electrical, SolidWorks Electrical, SEE Electrical and Elecworks. These software have different features and capabilities that can suit different types of projects and preferences. You can compare them with EPLAN Electric P8 1.8.6 15 and see which one works best for you.</p>
-<p></p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Dbforge Data Compare For Oracle Crack Team.md b/spaces/stomexserde/gpt4-ui/Examples/Dbforge Data Compare For Oracle Crack Team.md
deleted file mode 100644
index 2330d31d8520ec22c1a6e85508472de5ff8ee8a8..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Dbforge Data Compare For Oracle Crack Team.md	
+++ /dev/null
@@ -1,99 +0,0 @@
-
-<h1>dbForge Data Compare for Oracle Crack Team: How to Compare and Sync Oracle Data Easily</h1>
- <p>If you are working with Oracle databases, you know how important it is to keep your data in sync. Whether you need to migrate data from one database to another, update data in your production or staging environment, or verify the results of your changes, you need a reliable tool that can compare and synchronize your data quickly and accurately.</p>
- <p>That's where dbForge Data Compare for Oracle comes in. This is a powerful tool that can help you compare and sync data between two Oracle tables or schemas, generate SQL scripts to update one table to match another, and create comprehensive reports on the data differences. With dbForge Data Compare for Oracle, you can ensure data consistency across your databases, reduce system downtime, and save time and effort in your data comparison tasks.</p>
-<h2>Dbforge Data Compare For Oracle Crack Team</h2><br /><p><b><b>DOWNLOAD</b> &rArr; <a href="https://urlgoal.com/2uI85o">https://urlgoal.com/2uI85o</a></b></p><br /><br />
- <p>But what if you don't have a license for dbForge Data Compare for Oracle? What if you want to use it for free or for a limited time? That's where a crack team comes in. A crack team is a group of skilled hackers or experts who can bypass the security or protection of a software product and make it available for anyone to use. In this article, we will show you how to use dbForge Data Compare for Oracle Crack Team, a cracked version of dbForge Data Compare for Oracle that can help you compare and sync your Oracle data easily.</p>
- <h2>What is dbForge Data Compare for Oracle?</h2>
- <h3>A powerful tool for Oracle data comparison and synchronization</h3>
- <p>dbForge Data Compare for Oracle is a tool developed by Devart, a software company that specializes in database development and management solutions. It is designed to help you compare data in tables, views, or custom queries across different databases or schemas. You can also compare backups with live databases or snapshots. You can use it to compare data in Oracle Database 11g, 12c, 18c, 19c, 21c on-premises or in the cloud.</p>
- <p>dbForge Data Compare for Oracle can also help you synchronize your data by generating SQL scripts that can update one table to match another. You can review the scripts before executing them, or save them for later use. You can also apply updates directly from the tool using a handy wizard. You can use it to sync data from your development environment to your production or staging environment, copy lookup data from one database to another, or fix data replication errors.</p>
- <h3>Key features and benefits of dbForge Data Compare for Oracle</h3>
- <p>Some of the key features and benefits of dbForge Data Compare for Oracle are:</p>
- <ul>
-<li>It supports all types of data comparison, including full, partial, mapped, case-sensitive, binary, LOB, XML, spatial, etc.</li>
-<li>It allows you to filter and sort objects and records in the comparison results by various criteria.</li>
-<li>It lets you edit values of default comparison options or create custom ones.</li>
-<li>It enables you to group records by type (different, equal, etc.) or change the direction of synchronization.</li>
-<li>It warns you about possible data losses or errors during synchronization.</li>
-<li>It allows you to export comparison results in various formats, such as HTML, Excel, CSV, XML, or text.</li>
-<li>It supports command-line interface and automation via the API.</li>
-<li>It integrates with Oracle SQL Developer and Visual Studio.</li>
-</ul>
- <p>With dbForge Data Compare for Oracle, you can compare and sync your Oracle data with ease and confidence.</p>
- <h2>What is a crack team?</h2>
- <h3>A group of skilled hackers or experts</h3>
- <p>A crack team is a term that refers to a group of people who have exceptional skills or expertise in a certain field or activity. They are usually hired or assembled to perform a specific task or solve a difficult problem. They are also known as a dream team, an A-team, or an elite team.</p>
- <p>In the context of software cracking, a crack team is a group of hackers who can break the security or protection of a software product and make it available for anyone to use without paying for a license or subscription. They usually do this by modifying the code, creating patches, generating keys, or bypassing activation. They often release their cracked versions on the internet, along with instructions on how to use them.</p>
-<p></p>
- <h3>Examples of crack teams in different fields</h3>
- <p>There are many examples of crack teams in different fields and industries. Some of them are:</p>
- <ul>
-<li>The Apollo 13 Mission Operations Team: This was the team of NASA engineers and flight controllers who successfully brought back the Apollo 13 crew after an explosion damaged their spacecraft in 1970. They improvised solutions using limited resources and time, and were hailed as heroes for their efforts.</li>
-<li>The Manhattan Project: This was the team of scientists and engineers who developed the first atomic bombs during World War II. They worked under secrecy and pressure, and achieved a breakthrough in nuclear physics and technology.</li>
-<li>The Avengers: This is the team of superheroes from the Marvel Cinematic Universe who joined forces to fight against various threats to the world. They include Iron Man, Captain America, Thor, Hulk, Black Widow, Hawkeye, and others. They have faced enemies such as Loki, Ultron, Thanos, and more.</li>
-</ul>
- <h2>How to use dbForge Data Compare for Oracle Crack Team?</h2>
- <h3>Download and install dbForge Data Compare for Oracle</h3>
- <p>To use dbForge Data Compare for Oracle Crack Team, you need to download and install the cracked version of dbForge Data Compare for Oracle from the internet. You can find it on various websites that offer software cracks, such as CrackzSoft, GetIntoPC, or Softasm. However, you should be careful when downloading from these sources, as they may contain viruses, malware, or spyware that can harm your computer or steal your data.</p>
- <p>Once you have downloaded the file, you need to extract it using a tool like WinRAR or 7-Zip. Then, you need to run the setup.exe file and follow the instructions on the screen. You may need to enter a serial number or a license key that is provided in the file. You may also need to copy and paste some files from the crack folder to the installation folder. After that, you should be able to use dbForge Data Compare for Oracle without any limitations.</p>
- <h3>Connect to your Oracle databases</h3>
- <p>After installing dbForge Data Compare for Oracle Crack Team, you need to connect to your Oracle databases that you want to compare and sync. You can do this by launching dbForge Data Compare for Oracle and clicking on the New Data Comparison button on the toolbar. This will open a wizard that will guide you through the process of creating a new data comparison project.</p>
- <p>In the first step of the wizard, you need to specify the source and target databases that you want to compare. You can choose from different types of connections, such as direct connection, TNS connection, cloud connection, or ODBC connection. You also need to enter the credentials for each database, such as the host name, port number, service name, user name, and password. You can test your connections by clicking on the Test Connection button.</p>
- <p>In the second step of the wizard, you need to select the tables or schemas that you want to compare. You can choose from different options, such as comparing all tables or schemas in both databases, comparing only tables or schemas that exist in both databases, or comparing only tables or schemas that have the same name in both databases. You can also filter the tables or schemas by name, owner, or type. You can select or deselect the tables or schemas by checking or unchecking the boxes next to them.</p>
- <p>In the third step of the wizard, you need to review the comparison options and settings. You can choose from different options, such as ignoring case, spaces, or trailing zeros, comparing binary data as varbinary or raw, comparing LOB data as CLOB or BLOB, comparing XML data as XMLType or CLOB, and more. You can also edit the values of the default comparison options or create your own custom ones. You can save your comparison options and settings as a template for future use.</p>
- <p>In the fourth and final step of the wizard, you need to start the comparison process by clicking on the Compare button. This will initiate the data comparison between the source and target databases and display the results in a grid view. You can see the differences between the records in each table or schema, as well as the number and type of differences. You can also filter and sort the records by various criteria, such as different, equal, missing, additional, etc.</p>
- <h3>Select the tables or schemas to compare</h3>
- <p>After comparing your data, you can select the tables or schemas that you want to synchronize. You can do this by checking or unchecking the boxes next to them in the grid view. You can also use the toolbar buttons to select all, select none, invert selection, or select by type. You can also use the grouping feature to group the records by type (different, equal, etc.) or change the direction of synchronization (source to target or target to source).</p>
- <p>Once you have selected the tables or schemas that you want to synchronize, you can click on the Synchronize button on the toolbar. This will open another wizard that will guide you through the process of creating and executing a synchronization script.</p>
- <h3>Review the comparison results and options</h3>
- <p>In the first step of the wizard, you need to review the comparison results and options. You can see a summary of the differences between the source and target databases, as well as a list of actions that will be performed during synchronization. You can also see a preview of the synchronization script that will be generated based on your selection and options. You can edit the script if you want to make any changes.</p>
- <p>In the second step of the wizard, you need to choose how you want to execute the synchronization script. You can choose from different options, such as executing it directly from dbForge Data Compare for Oracle using a handy wizard, saving it to a file for later use, opening it in an external tool such as Oracle SQL Developer or Visual Studio, copying it to clipboard, or sending it by email. You can also choose whether you want to create a backup of your target database before executing the script.</p>
- <p>In the third and final step of the wizard, you need to start the synchronization process by clicking on the Synchronize button. This will execute the script and update your target database to match your source database. You can see the progress and status of the synchronization in a log window. You can also cancel the synchronization at any time if you encounter any errors or issues.</p>
- <h3>Generate and execute the synchronization script</h3>
- <p>After synchronizing your data, you can generate and execute the synchronization script. This is a SQL script that contains all the commands and statements that were used to update your target database to match your source database. You can use this script to perform the same synchronization on another database, or to revert the changes if you are not satisfied with the results.</p>
- <p>You can generate and execute the synchronization script by clicking on the Generate Script button on the toolbar. This will open a window that shows you the script and allows you to save it, copy it, or execute it. You can also edit the script if you want to make any changes.</p>
- <p>To execute the script, you need to connect to your target database and run the script using a tool like Oracle SQL Developer or Visual Studio. You can also use dbForge Data Compare for Oracle to execute the script by clicking on the Execute Script button on the toolbar. This will open a wizard that will guide you through the process of executing the script on your target database.</p>
- <h2>Why choose dbForge Data Compare for Oracle Crack Team?</h2>
- <h3>Save time and effort in comparing and syncing Oracle data</h3>
- <p>One of the main reasons to choose dbForge Data Compare for Oracle Crack Team is that it can save you time and effort in comparing and syncing your Oracle data. You don't have to write complex SQL queries or scripts to compare and sync your data, as dbForge Data Compare for Oracle does it for you automatically. You can compare and sync your data in a few clicks, using a user-friendly interface and a simple wizard. You can also automate and schedule your data comparison tasks using the command-line interface or the API.</p>
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- <h3>Export and share your comparison reports</h3>
- <p>A fourth reason to choose dbForge Data Compare for Oracle Crack Team is that it can export and share your comparison reports. You don't have to keep your comparison results in dbForge Data Compare for Oracle, as you can export them in various formats, such as HTML, Excel, CSV, XML, or text. You can then use these files to analyze, present, or share your comparison results with others. You can also customize your comparison reports by adding comments, highlighting differences, or changing colors.</p>
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- <p>In conclusion, dbForge Data Compare for Oracle Crack Team is a cracked version of dbForge Data Compare for Oracle that can help you compare and sync your Oracle data easily. It is a powerful tool that offers many features and benefits, such as:</p>
- <ul>
-<li>It supports all types of data comparison, including full, partial, mapped, case-sensitive, binary, LOB, XML, spatial, etc.</li>
-<li>It allows you to filter and sort objects and records in the comparison results by various criteria.</li>
-<li>It lets you edit values of default comparison options or create custom ones.</li>
-<li>It enables you to group records by type (different, equal, etc.) or change the direction of synchronization.</li>
-<li>It warns you about possible data losses or errors during synchronization.</li>
-<li>It allows you to export comparison results in various formats, such as HTML, Excel, CSV, XML, or text.</li>
-<li>It supports command-line interface and automation via the API.</li>
-<li>It integrates with Oracle SQL Developer and Visual Studio.</li>
-</ul>
- <p>However, you should also be aware of the risks and drawbacks of using dbForge Data Compare for Oracle Crack Team, such as:</p>
- <ul>
-<li>It may contain viruses, malware, or spyware that can harm your computer or steal your data.</li>
-<li>It may not work properly or cause errors or crashes in your system.</li>
-<li>It may not be compatible with the latest versions or updates of dbForge Data Compare for Oracle or Oracle Database.</li>
-<li>It may violate the terms and conditions of Devart and Oracle, and expose you to legal consequences.</li>
-</ul>
- <p>Therefore, you should use dbForge Data Compare for Oracle Crack Team at your own risk and discretion. If you want to use dbForge Data Compare for Oracle safely and legally, you should buy a license from Devart and enjoy the full features and support of the product.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about dbForge Data Compare for Oracle Crack Team:</p>
- <h3>Q: How can I download dbForge Data Compare for Oracle Crack Team?</h3>
- <p>A: You can download dbForge Data Compare for Oracle Crack Team from various websites that offer software cracks, such as CrackzSoft, GetIntoPC, or Softasm. However, you should be careful when downloading from these sources, as they may contain viruses, malware, or spyware that can harm your computer or steal your data.</p>
- <h3>Q: How can I install dbForge Data Compare for Oracle Crack Team?</h3>
- <p>A: To install dbForge Data Compare for Oracle Crack Team, you need to extract the file that you downloaded using a tool like WinRAR or 7-Zip. Then, you need to run the setup.exe file and follow the instructions on the screen. You may need to enter a serial number or a license key that is provided in the file. You may also need to copy and paste some files from the crack folder to the installation folder. After that, you should be able to use dbForge Data Compare for Oracle without any limitations.</p>
- <h3>Q: How can I use dbForge Data Compare for Oracle Crack Team?</h3>
- <p>A: To use dbForge Data Compare for Oracle Crack Team, you need to connect to your Oracle databases that you want to compare and sync. You can do this by launching dbForge Data Compare for Oracle and clicking on the New Data Comparison button on the toolbar. This will open a wizard that will guide you through the process of creating a new data comparison project. You can then select the tables or schemas that you want to compare and sync, review the comparison results and options, generate and execute the synchronization script, and export and share your comparison reports.</p>
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Frozen Infinity Fragments Of I (2009).md b/spaces/stomexserde/gpt4-ui/Examples/Frozen Infinity Fragments Of I (2009).md
deleted file mode 100644
index 84c5a7b48a165306ad23b187018813797c335914..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Frozen Infinity Fragments Of I (2009).md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<br />
-<h1>Frozen Infinity: A Melodic Death Metal Band with a Vision</h1>
-<p>Frozen Infinity is a German melodic death metal band that was formed in 2004 by guitarist Tobias Jacobs and bassist Felix Jacobs. The band released their debut album, <em>Fragments of I</em>, in 2009, which showcases their blend of aggressive riffs, catchy melodies, symphonic keyboards and diverse vocals. The album features 13 tracks that explore themes such as betrayal, death, war, religion, technology and entropy.</p>
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-<p>Some of the highlights of the album are:</p>
-<ul>
-<li><em>Fragments of I</em>: The instrumental intro that sets the mood for the album with a haunting piano melody and atmospheric sounds.</li>
-<li><em>The Shattered Image</em>: The first song that introduces the band's signature sound with fast-paced drums, heavy guitars, keyboard solos and a mix of clean and harsh vocals.</li>
-<li><em>For Those Who Died</em>: A tribute to the victims of war and violence, featuring female guest vocals by Marijke Wefelnberg and a powerful chorus.</li>
-<li><em>Neverending Story</em>: A catchy song that draws inspiration from the fantasy novel by Michael Ende, with lyrics that reflect on the power of imagination and dreams.</li>
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-<li><em>Dreaming of Entrophy</em>: The longest and most complex song on the album, which closes it with a progressive and symphonic arrangement that expresses the band's vision of a chaotic and decaying world.</li>
-</ul>
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diff --git a/spaces/subhajitmaji/MusicGen/audiocraft/modules/activations.py b/spaces/subhajitmaji/MusicGen/audiocraft/modules/activations.py
deleted file mode 100644
index 8bd6f2917a56d72db56555d0ff54b2311bc21778..0000000000000000000000000000000000000000
--- a/spaces/subhajitmaji/MusicGen/audiocraft/modules/activations.py
+++ /dev/null
@@ -1,96 +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 torch
-import torch.nn as nn
-from torch import Tensor
-from typing import Union, Callable
-
-
-class CustomGLU(nn.Module):
-    """Custom Gated Linear Unit activation.
-    Applies a modified gated linear unit :math:`a * f(b)` where :math:`a` is the first half
-    of the input matrices, :math:`b` is the second half, and :math:`f` is a provided activation
-    function (i.e. sigmoid, swish, etc.).
-
-    Args:
-        activation (nn.Module): The custom activation to apply in the Gated Linear Unit
-        dim (int): the dimension on which to split the input. Default: -1
-
-    Shape:
-        - Input: :math:`(\ast_1, N, \ast_2)` where `*` means, any number of additional
-          dimensions
-        - Output: :math:`(\ast_1, M, \ast_2)` where :math:`M=N/2`
-
-    Examples::
-        >>> m = CustomGLU(nn.Sigmoid())
-        >>> input = torch.randn(4, 2)
-        >>> output = m(input)
-    """
-    def __init__(self, activation: nn.Module, dim: int = -1):
-        super(CustomGLU, self).__init__()
-        self.dim = dim
-        self.activation = activation
-
-    def forward(self, x: Tensor):
-        assert x.shape[self.dim] % 2 == 0  # M = N / 2
-        a, b = torch.chunk(x, 2, dim=self.dim)
-        return a * self.activation(b)
-
-
-class SwiGLU(CustomGLU):
-    """SiLU Gated Linear Unit activation.
-    Applies SiLU Gated Linear Unit :math:`a * SiLU(b)` where :math:`a` is
-    the first half of the input matrices, :math:`b` is the second half.
-
-    Args:
-        dim (int): the dimension on which to split the input. Default: -1
-    """
-    def __init__(self, dim: int = -1):
-        super(SwiGLU, self).__init__(nn.SiLU(), dim)
-
-
-class GeGLU(CustomGLU):
-    """GeLU Gated Linear Unit activation.
-    Applies GeLU Gated Linear Unit :math:`a * GELU(b)` where :math:`a` is
-    the first half of the input matrices, :math:`b` is the second half.
-
-    Args:
-        dim (int): the dimension on which to split the input. Default: -1
-    """
-    def __init__(self, dim: int = -1):
-        super(GeGLU, self).__init__(nn.GELU(), dim)
-
-
-class ReGLU(CustomGLU):
-    """ReLU Gated Linear Unit activation.
-    Applies ReLU Gated Linear Unit :math:`a * ReLU(b)` where :math:`a` is
-    the first half of the input matrices, :math:`b` is the second half.
-
-    Args:
-        dim (int): the dimension on which to split the input. Default: -1
-    """
-    def __init__(self, dim: int = -1):
-        super(ReGLU, self).__init__(nn.ReLU(), dim)
-
-
-def get_activation_fn(
-    activation: Union[str, Callable[[Tensor], Tensor]]
-) -> Union[str, Callable[[Tensor], Tensor]]:
-    """Helper function to map an activation string to the activation class.
-    If the supplied activation is not a string that is recognized, the activation is passed back.
-
-    Args:
-        activation (Union[str, Callable[[Tensor], Tensor]]): Activation to check
-    """
-    if isinstance(activation, str):
-        if activation == "reglu":
-            return ReGLU()
-        elif activation == "geglu":
-            return GeGLU()
-        elif activation == "swiglu":
-            return SwiGLU()
-    return activation
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Adobe Pdf Editor Free Download [BETTER] For Mac.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Adobe Pdf Editor Free Download [BETTER] For Mac.md
deleted file mode 100644
index 5c0b1815625f755bd33a6e84d6f1e2648d376e91..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Adobe Pdf Editor Free Download [BETTER] For Mac.md	
+++ /dev/null
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Visualarq Free Download Crack For Windows !EXCLUSIVE!.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Visualarq Free Download Crack For Windows !EXCLUSIVE!.md
deleted file mode 100644
index 2c71fa9248a0b65d22507937a6e1338742759728..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Visualarq Free Download Crack For Windows !EXCLUSIVE!.md	
+++ /dev/null
@@ -1,53 +0,0 @@
-
-<h1>Visualarq Free Download Crack For Windows: A Complete Guide</h1>
-<p>If you are looking for a way to download and install VisualARQ, a powerful 3D design plug-in for Rhino, for free, you have come to the right place. In this article, we will show you how to get VisualARQ free download crack for Windows, and what are the benefits and features of this software.</p>
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-<ol>
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-<li>Extract the compressed file to a folder on your computer. You will need a program like WinRAR or 7-Zip to do this.</li>
-<li>Run the installation package and follow the instructions on the screen. Choose the destination folder where you want to install VisualARQ.</li>
-<li>Copy the crack file and paste it into the installation folder. This will replace the original file and activate the software.</li>
-<li>Launch VisualARQ from your desktop or start menu. You should be able to use it without any problems.</li>
-</ol>
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-<p>VisualARQ is a great plug-in for Rhino that enhances your 3D design capabilities and productivity. If you want to download and install VisualARQ free crack for Windows, you can follow the steps we have outlined in this article. However, we recommend that you buy the original software from the official website if you can afford it, as this will support the developers and ensure that you get regular updates and technical support.</p>
-<h2>What are the Benefits and Features of VisualARQ?</h2>
-<p>VisualARQ is not just a simple plug-in for Rhino. It is a comprehensive solution that offers many benefits and features for 3D design and architecture. Some of the main advantages of VisualARQ are:</p>
-<p></p>
-<ul>
-<li>It is easy to use and intuitive. You can create and edit parametric 3D objects with a few clicks, or use the VisualARQ toolbar to access all the commands and options.</li>
-<li>It is flexible and customizable. You can create your own object styles, or use the VisualARQ Styles library to choose from a variety of predefined styles. You can also modify the properties and attributes of any object, such as dimensions, materials, layers, tags, etc.</li>
-<li>It is compatible and interoperable. You can export and import your models to other software using standard formats, such as IFC, DWG, DXF, OBJ, 3DS, etc. You can also work with Grasshopper to create parametric definitions and algorithms for your objects.</li>
-<li>It is powerful and efficient. You can generate 2D documentation from your 3D models automatically, such as floor plans, sections, elevations, schedules, tables, etc. You can also use VisualARQ tools to perform calculations, measurements, analysis, rendering, etc.</li>
-</ul>
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-<li>It may contain viruses, malware or spyware that can harm your computer or steal your personal information.</li>
-<li>It may not work properly or crash frequently, causing errors and data loss.</li>
-<li>It may not be updated or supported by the developers, leaving you with outdated or incompatible versions.</li>
-<li>It may be illegal or unethical to use a cracked software without paying for it.</li>
-</ul>
-<p>Therefore, we advise you to be careful and cautious when downloading VisualARQ free crack for Windows. If you want to use VisualARQ safely and legally, you should buy the original software from the official website or an authorized reseller.</p>
-<h2>How to Use VisualARQ?</h2>
-<p>Once you have downloaded and installed VisualARQ free crack for Windows, you can start using it right away. VisualARQ works as a plug-in for Rhino, so you need to have Rhino installed on your computer as well. To use VisualARQ, you need to follow these steps:</p>
-<ol>
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-<li>Go to the menu bar and click on VisualARQ. You will see a list of commands and options that you can use with VisualARQ.</li>
-<li>Select the command or option that you want to use. For example, if you want to create a wall, click on Wall and then click on the points where you want to place the wall.</li>
-<li>Edit the properties and attributes of the object that you have created. For example, if you want to change the height, thickness or material of the wall, click on Properties and then modify the values as you wish.</li>
-<li>Repeat the process for other objects that you want to create or edit. You can also use the VisualARQ toolbar to access all the commands and options more quickly.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>VisualARQ is a fantastic plug-in for Rhino that allows you to create and edit parametric 3D objects for architectural design and documentation. It is easy to use, flexible, compatible and powerful. If you want to download and install VisualARQ free crack for Windows, you can follow our guide in this article. However, we recommend that you buy the original software from the official website or an authorized reseller if you can afford it, as this will support the developers and ensure that you get regular updates and technical support.</p> 3cee63e6c2<br />
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\ No newline at end of file
diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Autocad 2009 Crack Ita Download [PORTABLE].md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Autocad 2009 Crack Ita Download [PORTABLE].md
deleted file mode 100644
index 888447d2b0fdb5433639bb77442f11e9fc1285d6..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Autocad 2009 Crack Ita Download [PORTABLE].md	
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-
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-(click on each item to view the specs)
-
-Table of Contents for AutoCAD 2009 System Requirements
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-AutoCAD 2009 with Python
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diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/exp/upernet_global_small/run.sh b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/exp/upernet_global_small/run.sh
deleted file mode 100644
index 9fb22edfa7a32624ea08a63fe7d720c40db3b696..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/exp/upernet_global_small/run.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/usr/bin/env bash
-
-work_path=$(dirname $0)
-PYTHONPATH="$(dirname $0)/../../":$PYTHONPATH \
-python -m torch.distributed.launch --nproc_per_node=8 \
-    tools/train.py ${work_path}/config.py \
-    --launcher pytorch \
-    --options model.backbone.pretrained_path='your_model_path/uniformer_small_in1k.pth' \
-    --work-dir ${work_path}/ckpt \
-    2>&1 | tee -a ${work_path}/log.txt
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/contour_expand.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/contour_expand.py
deleted file mode 100644
index ea1111e1768b5f27e118bf7dbc0d9c70a7afd6d7..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/contour_expand.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numpy as np
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['contour_expand'])
-
-
-def contour_expand(kernel_mask, internal_kernel_label, min_kernel_area,
-                   kernel_num):
-    """Expand kernel contours so that foreground pixels are assigned into
-    instances.
-
-    Arguments:
-        kernel_mask (np.array or Tensor): The instance kernel mask with
-            size hxw.
-        internal_kernel_label (np.array or Tensor): The instance internal
-            kernel label with size hxw.
-        min_kernel_area (int): The minimum kernel area.
-        kernel_num (int): The instance kernel number.
-
-    Returns:
-        label (list): The instance index map with size hxw.
-    """
-    assert isinstance(kernel_mask, (torch.Tensor, np.ndarray))
-    assert isinstance(internal_kernel_label, (torch.Tensor, np.ndarray))
-    assert isinstance(min_kernel_area, int)
-    assert isinstance(kernel_num, int)
-
-    if isinstance(kernel_mask, np.ndarray):
-        kernel_mask = torch.from_numpy(kernel_mask)
-    if isinstance(internal_kernel_label, np.ndarray):
-        internal_kernel_label = torch.from_numpy(internal_kernel_label)
-
-    if torch.__version__ == 'parrots':
-        if kernel_mask.shape[0] == 0 or internal_kernel_label.shape[0] == 0:
-            label = []
-        else:
-            label = ext_module.contour_expand(
-                kernel_mask,
-                internal_kernel_label,
-                min_kernel_area=min_kernel_area,
-                kernel_num=kernel_num)
-            label = label.tolist()
-    else:
-        label = ext_module.contour_expand(kernel_mask, internal_kernel_label,
-                                          min_kernel_area, kernel_num)
-    return label
diff --git a/spaces/syx948/ChatPDF/app.py b/spaces/syx948/ChatPDF/app.py
deleted file mode 100644
index d9c0cef1b5fdbe835af343e40cead368f9a09849..0000000000000000000000000000000000000000
--- a/spaces/syx948/ChatPDF/app.py
+++ /dev/null
@@ -1,310 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-@author:XuMing(xuming624@qq.com)
-@description:
-modified from https://github.com/imClumsyPanda/langchain-ChatGLM/blob/master/webui.py
-"""
-import argparse
-import hashlib
-import os
-import shutil
-
-import gradio as gr
-from loguru import logger
-
-from chatpdf import ChatPDF
-
-pwd_path = os.path.abspath(os.path.dirname(__file__))
-
-CONTENT_DIR = os.path.join(pwd_path, "content")
-logger.info(f"CONTENT_DIR: {CONTENT_DIR}")
-VECTOR_SEARCH_TOP_K = 3
-MAX_INPUT_LEN = 2048
-
-embedding_model_dict = {
-    "text2vec-base": "shibing624/text2vec-base-chinese",
-    "text2vec-multilingual": "shibing624/text2vec-base-multilingual",
-    "text2vec-large": "GanymedeNil/text2vec-large-chinese",
-    "sentence-transformers": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
-    "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
-    "ernie-base": "nghuyong/ernie-3.0-base-zh",
-}
-
-# supported LLM models
-llm_model_dict = {
-    "llama-2-7b": "LinkSoul/Chinese-Llama-2-7b-4bit",
-    "baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat",
-    "chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
-    "chatglm-2-6b": "THUDM/chatglm2-6b",
-    "chatglm-2-6b-int4": "THUDM/chatglm2-6b-int4",
-    "chatglm-6b-int4": "THUDM/chatglm-6b-int4",
-    "chatglm-6b": "THUDM/chatglm-6b",
-    "llama-7b": "shibing624/chinese-alpaca-plus-7b-hf",
-    "llama-13b": "shibing624/chinese-alpaca-plus-13b-hf",
-}
-
-llm_model_dict_list = list(llm_model_dict.keys())
-embedding_model_dict_list = list(embedding_model_dict.keys())
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--sim_model", type=str, default="shibing624/text2vec-base-chinese")
-parser.add_argument("--gen_model_type", type=str, default="llama")
-parser.add_argument("--gen_model", type=str, default="LinkSoul/Chinese-Llama-2-7b-4bit")
-parser.add_argument("--lora_model", type=str, default=None)
-parser.add_argument("--device", type=str, default="cpu")
-parser.add_argument("--int4", action='store_true', help="use int4 quantization")
-parser.add_argument("--int8", action='store_true', help="use int8 quantization")
-args = parser.parse_args()
-print(args)
-
-model = None
-
-
-def get_file_list():
-    if not os.path.exists("content"):
-        return []
-    return [f for f in os.listdir("content") if
-            f.endswith(".txt") or f.endswith(".pdf") or f.endswith(".docx") or f.endswith(".md")]
-
-
-file_list = get_file_list()
-
-
-def upload_file(file):
-    if not os.path.exists(CONTENT_DIR):
-        os.mkdir(CONTENT_DIR)
-    filename = os.path.basename(file.name)
-    shutil.move(file.name, os.path.join(CONTENT_DIR, filename))
-    # file_list首位插入新上传的文件
-    file_list.insert(0, filename)
-    return gr.Dropdown.update(choices=file_list, value=filename)
-
-
-def parse_text(text):
-    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
-    lines = text.split("\n")
-    lines = [line for line in lines if line != ""]
-    count = 0
-    for i, line in enumerate(lines):
-        if "```" in line:
-            count += 1
-            items = line.split('`')
-            if count % 2 == 1:
-                lines[i] = f'<pre><code class="language-{items[-1]}">'
-            else:
-                lines[i] = f'<br></code></pre>'
-        else:
-            if i > 0:
-                if count % 2 == 1:
-                    line = line.replace("`", "\`")
-                    line = line.replace("<", "&lt;")
-                    line = line.replace(">", "&gt;")
-                    line = line.replace(" ", "&nbsp;")
-                    line = line.replace("*", "&ast;")
-                    line = line.replace("_", "&lowbar;")
-                    line = line.replace("-", "&#45;")
-                    line = line.replace(".", "&#46;")
-                    line = line.replace("!", "&#33;")
-                    line = line.replace("(", "&#40;")
-                    line = line.replace(")", "&#41;")
-                    line = line.replace("$", "&#36;")
-                lines[i] = "<br>" + line
-    text = "".join(lines)
-    return text
-
-
-def get_answer(query, index_path, history, topn=VECTOR_SEARCH_TOP_K, max_input_size=1024, only_chat=False):
-    if model is None:
-        return [None, "模型还未加载"], query
-    if index_path and not only_chat:
-        if not model.sim_model.corpus_embeddings:
-            model.load_index(index_path)
-        response, reference_results = model.predict(
-            query=query, topn=topn, context_len=max_input_size)
-
-        logger.debug(f"query: {query}, response with content: {response}")
-        for i in range(len(reference_results)):
-            r = reference_results[i]
-            response += f"\n{r.strip()}"
-        response = parse_text(response)
-        history = history + [[query, response]]
-    else:
-        # 未加载文件，仅返回生成模型结果
-        instruction = """[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
-
-                    If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{} [/INST]"""
-        if args.gen_model_type == "llama":
-            query = instruction.format(query)
-        model.history.append([query, ''])
-        response = ""
-        for new_text in model.stream_generate_answer(query, context_len=max_input_size):
-            response += new_text
-        response = response.strip()
-        model.history[-1][1] = response
-        response = parse_text(response)
-        history = history + [[query, response]]
-        logger.debug(f"query: {query}, response: {response}")
-    return history, ""
-
-
-def update_status(history, status):
-    history = history + [[None, status]]
-    logger.info(status)
-    return history
-
-
-def reinit_model(llm_model, embedding_model, history):
-    try:
-        global model
-        if model is not None:
-            del model
- 
-        model = ChatPDF(
-            sim_model_name_or_path=embedding_model_dict.get(
-                embedding_model,
-                "shibing624/text2vec-base-chinese"
-            ),
-            gen_model_type=llm_model.split('-')[0],
-            gen_model_name_or_path=llm_model_dict.get(llm_model, "LinkSoul/Chinese-Llama-2-7b-4bit"),
-            lora_model_name_or_path=None,
-        )
-
-        model_status = """模型已成功重新加载，请选择文件后点击"加载文件"按钮"""
-    except Exception as e:
-        model = None
-        logger.error(e)
-        model_status = """模型未成功重新加载，请重新选择后点击"加载模型"按钮"""
-    return history + [[None, model_status]]
-
-
-def get_file_hash(fpath):
-    return hashlib.md5(open(fpath, 'rb').read()).hexdigest()
-
-
-def get_vector_store(filepath, history, embedding_model):
-    logger.info(filepath, history)
-    index_path = None
-    file_status = ''
-    if model is not None:
-
-        local_file_path = os.path.join(CONTENT_DIR, filepath)
-
-        local_file_hash = get_file_hash(local_file_path)
-        index_file_name = f"{filepath}.{embedding_model}.{local_file_hash}.index.json"
-
-        local_index_path = os.path.join(CONTENT_DIR, index_file_name)
-
-        if os.path.exists(local_index_path):
-            model.load_index(local_index_path)
-            index_path = local_index_path
-            file_status = "文件已成功加载，请开始提问"
-
-        elif os.path.exists(local_file_path):
-            model.load_doc_files(local_file_path)
-            model.save_index(local_index_path)
-            index_path = local_index_path
-            if index_path:
-                file_status = "文件索引并成功加载，请开始提问"
-            else:
-                file_status = "文件未成功加载，请重新上传文件"
-    else:
-        file_status = "模型未完成加载，请先在加载模型后再导入文件"
-
-    return index_path, history + [[None, file_status]]
-
-
-def reset_chat(chatbot, state):
-    return None, None
-
-
-def change_max_input_size(input_size):
-    if model is not None:
-        model.max_input_size = input_size
-    return
-
-
-block_css = """.importantButton {
-    background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
-    border: none !important;
-}
-.importantButton:hover {
-    background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
-    border: none !important;
-}"""
-
-webui_title = """
-# 🎉ChatPDF WebUI🎉
-Link in: [https://github.com/shibing624/ChatPDF](https://github.com/shibing624/ChatPDF)  PS: 2核CPU 16G内存机器，约2min一条😭
-"""
-
-init_message = """欢迎使用 ChatPDF Web UI，可以直接提问或上传文件后提问 """
-
-with gr.Blocks(css=block_css) as demo:
-    index_path, file_status, model_status = gr.State(""), gr.State(""), gr.State("")
-    gr.Markdown(webui_title)
-    with gr.Row():
-        with gr.Column(scale=2):
-            chatbot = gr.Chatbot([[None, init_message], [None, None]],
-                                 elem_id="chat-box",
-                                 show_label=False).style(height=700)
-            query = gr.Textbox(show_label=False,
-                               placeholder="请输入提问内容，按回车进行提交",
-                               ).style(container=False)
-            clear_btn = gr.Button('🔄Clear!', elem_id='clear').style(full_width=True)
-        with gr.Column(scale=1):
-            llm_model = gr.Radio(llm_model_dict_list,
-                                 label="LLM 模型",
-                                 value=list(llm_model_dict.keys())[0],
-                                 interactive=True)
-            embedding_model = gr.Radio(embedding_model_dict_list,
-                                       label="Embedding 模型",
-                                       value=embedding_model_dict_list[0],
-                                       interactive=True)
-
-            load_model_button = gr.Button("重新加载模型")
-
-            with gr.Row():
-                only_chat = gr.Checkbox(False, label="不加载文件(纯聊天)")
-
-            with gr.Row():
-                topn = gr.Slider(1, 100, 20, step=1, label="最大搜索数量")
-                max_input_size = gr.Slider(512, 4096, MAX_INPUT_LEN, step=10, label="摘要最大长度")
-            with gr.Tab("select"):
-                selectFile = gr.Dropdown(
-                    file_list,
-                    label="content file",
-                    interactive=True,
-                    value=file_list[0] if len(file_list) > 0 else None
-                )
-            with gr.Tab("upload"):
-                file = gr.File(
-                    label="content file",
-                    file_types=['.txt', '.md', '.docx', '.pdf']
-                )
-            load_file_button = gr.Button("加载文件")
-    max_input_size.change(
-        change_max_input_size,
-        inputs=max_input_size
-    )
-    load_model_button.click(
-        reinit_model,
-        show_progress=True,
-        inputs=[llm_model, embedding_model, chatbot],
-        outputs=chatbot
-    )
-    # 将上传的文件保存到content文件夹下,并更新下拉框
-    file.upload(upload_file, inputs=file, outputs=selectFile)
-    load_file_button.click(
-        get_vector_store,
-        show_progress=True,
-        inputs=[selectFile, chatbot, embedding_model],
-        outputs=[index_path, chatbot],
-    )
-    query.submit(
-        get_answer,
-        [query, index_path, chatbot, topn, max_input_size, only_chat],
-        [chatbot, query],
-    )
-    clear_btn.click(reset_chat, [chatbot, query], [chatbot, query])
-
-demo.queue(concurrency_count=3).launch()
diff --git a/spaces/szukevin/VISOR-GPT/train/finetune/run_chid.py b/spaces/szukevin/VISOR-GPT/train/finetune/run_chid.py
deleted file mode 100644
index d9abd7ce18de1b783dc520d155d97f1a6b42df07..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/finetune/run_chid.py
+++ /dev/null
@@ -1,225 +0,0 @@
-"""
-This script provides an example to wrap TencentPretrain for ChID (a multiple choice dataset).
-"""
-import sys
-import os
-import argparse
-import json
-import random
-import torch
-
-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.tokenizers import *
-from tencentpretrain.utils.optimizers import *
-from tencentpretrain.utils import *
-from tencentpretrain.utils.config import load_hyperparam
-from tencentpretrain.utils.seed import set_seed
-from tencentpretrain.utils.logging import init_logger
-from tencentpretrain.model_saver import save_model
-from tencentpretrain.opts import finetune_opts, adv_opts
-from finetune.run_c3 import MultipleChoice
-from finetune.run_classifier import build_optimizer, load_or_initialize_parameters, train_model, batch_loader, evaluate
-
-
-def tokenize_chid(text):
-    output = []
-    first_idiom = True
-    while True:
-        if first_idiom:
-            idiom_index = text.find("#idiom")
-            output.extend(text[:idiom_index])
-            output.append(text[idiom_index : idiom_index + 13])
-            pre_idiom_index = idiom_index
-            first_idiom = False
-        else:
-            if text[idiom_index + 1 :].find("#idiom") == -1:
-                output.extend(text[pre_idiom_index + 13 :])
-                break
-            else:
-                idiom_index = idiom_index + 1 + text[idiom_index + 1 :].find("#idiom")
-                output.extend(text[pre_idiom_index + 13 : idiom_index])
-                output.append(text[idiom_index : idiom_index + 13])
-                pre_idiom_index = idiom_index
-
-    return output
-
-
-def add_tokens_around(tokens, idiom_index, tokens_num):
-    left_tokens_num = tokens_num // 2
-    right_tokens_num = tokens_num - left_tokens_num
-
-    if idiom_index >= left_tokens_num and (len(tokens) - 1 - idiom_index) >= right_tokens_num:
-        left_tokens = tokens[idiom_index - left_tokens_num : idiom_index]
-        right_tokens = tokens[idiom_index + 1 : idiom_index + 1 + right_tokens_num]
-    elif idiom_index < left_tokens_num:
-        left_tokens = tokens[:idiom_index]
-        right_tokens = tokens[idiom_index + 1 : idiom_index + 1 + tokens_num - len(left_tokens)]
-    elif (len(tokens) - 1 - idiom_index) < right_tokens_num:
-        right_tokens = tokens[idiom_index + 1 :]
-        left_tokens = tokens[idiom_index - (tokens_num - len(right_tokens)) : idiom_index]
-
-    return left_tokens, right_tokens
-
-
-def read_dataset(args, data_path, answer_path):
-    if answer_path is not None:
-        answers = json.load(open(answer_path))
-    dataset = []
-    max_tokens_for_doc = args.seq_length - 3
-    group_index = 0
-
-    for line in open(data_path, mode="r", encoding="utf-8"):
-        example = json.loads(line)
-        options = example["candidates"]
-        for context in example["content"]:
-            chid_tokens = tokenize_chid(context)
-            tags = [token for token in chid_tokens if "#idiom" in token]
-            for tag in tags:
-                if answer_path is not None:
-                    tgt = answers[tag]
-                else:
-                    tgt = -1
-                tokens = []
-                for i, token in enumerate(chid_tokens):
-                    if "#idiom" in token:
-                        sub_tokens = [str(token)]
-                    else:
-                        sub_tokens = args.tokenizer.tokenize(token)
-                    for sub_token in sub_tokens:
-                        tokens.append(sub_token)
-                idiom_index = tokens.index(tag)
-                left_tokens, right_tokens = add_tokens_around(tokens, idiom_index, max_tokens_for_doc - 1)
-
-                for i in range(len(left_tokens)):
-                    if "#idiom" in left_tokens[i] and left_tokens[i] != tag:
-                        left_tokens[i] = MASK_TOKEN
-                for i in range(len(right_tokens)):
-                    if "#idiom" in right_tokens[i] and right_tokens[i] != tag:
-                        right_tokens[i] = MASK_TOKEN
-
-                dataset.append(([], tgt, [], tag, group_index))
-
-                for option in options:
-                    option_tokens = args.tokenizer.tokenize(option)
-                    tokens = [CLS_TOKEN] + option_tokens + [SEP_TOKEN] + left_tokens + [SEP_TOKEN] + right_tokens + [SEP_TOKEN]
-
-                    src = args.tokenizer.convert_tokens_to_ids(tokens)[: args.seq_length]
-                    seg = [0] * len(src)
-
-                    while len(src) < args.seq_length:
-                        src.append(0)
-                        seg.append(0)
-
-                    dataset[-1][0].append(src)
-                    dataset[-1][2].append(seg)
-
-                while len(dataset[-1][0]) < args.max_choices_num:
-                    dataset[-1][0].append([0] * args.seq_length)
-                    dataset[-1][2].append([0] * args.seq_length)
-        group_index += 1
-
-    return dataset
-
-
-def main():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-    finetune_opts(parser)
-
-    parser.add_argument("--vocab_path", default=None, type=str,
-                        help="Path of the vocabulary file.")
-    parser.add_argument("--spm_model_path", default=None, type=str,
-                        help="Path of the sentence piece model.")
-    parser.add_argument("--train_answer_path", type=str, required=True,
-                        help="Path of the answers for trainset.")
-    parser.add_argument("--dev_answer_path", type=str, required=True,
-                        help="Path of the answers for devset.")
-
-    parser.add_argument("--max_choices_num", default=10, type=int,
-                        help="The maximum number of cadicate answer, shorter than this will be padded.")
-
-    adv_opts(parser)
-
-    args = parser.parse_args()
-
-    args.labels_num = args.max_choices_num
-
-    # Load the hyperparameters from the config file.
-    args = load_hyperparam(args)
-
-    set_seed(args.seed)
-
-    # Build tokenizer.
-    args.tokenizer = CharTokenizer(args)
-
-    # Build multiple choice model.
-    model = MultipleChoice(args)
-
-    # Load or initialize parameters.
-    load_or_initialize_parameters(args, model)
-
-    # Get logger.
-    args.logger = init_logger(args)
-
-    args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = model.to(args.device)
-
-    # Training phase.
-    trainset = read_dataset(args, args.train_path, args.train_answer_path)
-    instances_num = len(trainset)
-    batch_size = args.batch_size
-
-    args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1
-
-    args.logger.info("Batch size: {}".format(batch_size))
-    args.logger.info("The number of training instances: {}".format(instances_num))
-
-    optimizer, scheduler = build_optimizer(args, model)
-
-    if args.fp16:
-        try:
-            from apex import amp
-        except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
-        args.amp = amp
-
-    if torch.cuda.device_count() > 1:
-        args.logger.info("{} GPUs are available. Let's use them.".format(torch.cuda.device_count()))
-        model = torch.nn.DataParallel(model)
-    args.model = model
-
-    if args.use_adv:
-        args.adv_method = str2adv[args.adv_type](model)
-
-    total_loss, result, best_result = 0.0, 0.0, 0.0
-
-    args.logger.info("Start training.")
-
-    for epoch in range(1, args.epochs_num + 1):
-        random.shuffle(trainset)
-        src = torch.LongTensor([example[0] for example in trainset])
-        tgt = torch.LongTensor([example[1] for example in trainset])
-        seg = torch.LongTensor([example[2] for example in trainset])
-
-        model.train()
-        for i, (src_batch, tgt_batch, seg_batch, _) in enumerate(batch_loader(batch_size, src, tgt, seg)):
-
-            loss = train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch)
-            total_loss += loss.item()
-
-            if (i + 1) % args.report_steps == 0:
-                args.logger.info("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".format(epoch, i + 1, total_loss / args.report_steps))
-                total_loss = 0.0
-
-        result = evaluate(args, read_dataset(args, args.dev_path, args.dev_answer_path))
-        if result[0] > best_result:
-            best_result = result[0]
-            save_model(model, args.output_model_path)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Bilder Nackte Knaben Ab 14 Jahre Frei.md b/spaces/terfces0erbo/CollegeProjectV2/Bilder Nackte Knaben Ab 14 Jahre Frei.md
deleted file mode 100644
index 05a24b556b8696d52b93d5e4e81cc975b3139751..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Bilder Nackte Knaben Ab 14 Jahre Frei.md	
+++ /dev/null
@@ -1,48 +0,0 @@
-<h2>bilder nackte knaben ab 14 jahre frei</h2><br /><p><b><b>Download Zip</b> > <a href="https://bytlly.com/2uGkHh">https://bytlly.com/2uGkHh</a></b></p><br /><br />
-<br />
-willig vermarktern! Duft und Gefühl sind nur von der Natur abgeschaut – sieh dich um – es gibt immer noch männliche, weibliche und schöne nackte Jungen!"
-
-Ansicht von seinen Überraschungen: 1812
-
-_Seeleuchte, die Tod gedreht,_ 1814
-
-Ansicht von seinen Überraschungen: 1814
-
-**Johann Wolfgang von Goethe**
-
-**Ansicht von seinen Überraschungen: 1816**
-
-Ansicht von seinen Überraschungen: 1816
-
-**Wilhelm**
-
-**Ansicht von seinen Überraschungen: 1817**
-
-Ansicht von seinen Überraschungen: 1817
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-**Schlegel, A. W. v.**
-
-**Ansicht von seinen Überraschungen: 1818**
-
-Ansicht von seinen Überraschungen: 1818
-
-**Carl Wilhelm Leser**
-
-**Goethe, W. v.**
-
-**Heine**
-
-**Goethe, F. W. v.**
-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Buscando A Nemo 1080p Mkv 13.md b/spaces/terfces0erbo/CollegeProjectV2/Buscando A Nemo 1080p Mkv 13.md
deleted file mode 100644
index 6068847bd1509b9a24704097c652c487b079df73..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Buscando A Nemo 1080p Mkv 13.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-<br />
-<h1>Buscando A Nemo 1080p Mkv 13: How to Download and Watch the Best Quality Version of Finding Nemo in Spanish</h1>
-<p>If you are a fan of Finding Nemo, the animated movie from Pixar that tells the story of a clownfish who goes on an adventure to find his son, you might want to watch it in Spanish with the best possible quality. But how can you do that? In this article, we will show you how to download and watch Buscando A Nemo 1080p Mkv 13, the highest resolution and format available for this movie in Spanish.</p>
-<h2>Buscando A Nemo 1080p Mkv 13</h2><br /><p><b><b>Download File</b> &#10002; &#10002; &#10002; <a href="https://bytlly.com/2uGltM">https://bytlly.com/2uGltM</a></b></p><br /><br />
-<p>What is Buscando A Nemo 1080p Mkv 13? It is a file name that indicates the following characteristics of the video:</p>
-<ul>
-<li>Buscando A Nemo: This is the Spanish title of Finding Nemo, which means "Searching for Nemo".</li>
-<li>1080p: This is the resolution of the video, which means it has 1920 x 1080 pixels. This is also known as Full HD or High Definition, and it offers a clear and sharp image quality.</li>
-<li>Mkv: This is the format of the video, which stands for Matroska Video. It is a container that can store multiple audio and subtitle tracks, as well as metadata and chapters. It is a flexible and versatile format that supports many codecs and compression methods.</li>
-<li>13: This is the file size in gigabytes (GB). It means that the video has a high bitrate, which is the amount of data that is transferred per second. A high bitrate means a higher quality, but also a larger file size.</li>
-</ul>
-<p>So, Buscando A Nemo 1080p Mkv 13 is a file that contains the Spanish version of Finding Nemo in Full HD resolution and Matroska format, with a file size of 13 GB. It is one of the best quality versions of this movie that you can find online.</p>
-<p>How to download Buscando A Nemo 1080p Mkv 13? There are several ways to download this file, but we recommend using a torrent client. A torrent client is a software that allows you to download files from other users who have them on their computers. This way, you can download faster and more securely than from a direct link or a streaming site. Here are the steps to follow:</p>
-<ol>
-<li>Download and install a torrent client on your device. Some of the most popular ones are uTorrent, BitTorrent, qBittorrent, or Vuze.</li>
-<li>Search for Buscando A Nemo 1080p Mkv 13 on a torrent site. Some of the most reliable ones are The Pirate Bay, RARBG, 1337x, or YTS.</li>
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-<li>Open the torrent file with your torrent client and start downloading Buscando A Nemo 1080p Mkv 13. Depending on your internet speed and the availability of seeders and leechers, this may take from a few minutes to several hours.</li>
-<li>Once the download is finished, you will have Buscando A Nemo 1080p Mkv 13 on your device. You can now watch it with any media player that supports Mkv files, such as VLC, MPC-HC, or KMPlayer.</li>
-</ol>
-<p>How to watch Buscando A Nemo 1080p Mkv 13? Once you have downloaded the file, you can watch it on any device that has enough storage space and can play Mkv files. You can also connect your device to a larger screen, such as a TV or a projector, to enjoy the movie on a bigger scale. Here are some tips to enhance your viewing experience:</p>
-<p></p>
-<ul>
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-<li>Adjust the brightness and volume settings according to your preferences and environment.</li>
-<li>Select the audio</p> d5da3c52bf<br />
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\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/CRACK Linguatec Personal Translator V14.0 Professional (2011) [MULTi] ((BETTER)).md b/spaces/terfces0erbo/CollegeProjectV2/CRACK Linguatec Personal Translator V14.0 Professional (2011) [MULTi] ((BETTER)).md
deleted file mode 100644
index cf950266abc2404c214ffb659e1cbdc1eb979731..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/CRACK Linguatec Personal Translator V14.0 Professional (2011) [MULTi] ((BETTER)).md	
+++ /dev/null
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-<br />
-on their computer. Afterwards, I used the 'clean the exe' program to delete the whole exe file and the whole productinfo.txt file in C:\apps\tl\Teamspeak 3\ts3\data. Afterwards, I restarted my computer and tried to install it again. I thought maybe this problem was solved. But it didn't work, so I tried again. The same thing, when I opened the ts3 client, it was opened as a web browser. I think something is wrong with my system. Could someone tell me what I did wrong and how to fix it?
-
-If there's a possibility for this problem, how could I do it in an easier way? Any help would be appreciated.
-
-I did it. You were right. There was something wrong with my system and the v14.0 pro of Teamspeak 3 was what I downloaded.
-
-I just wish there's a way to get help from you guys. I'm a professional translator and I know some things. If you know a better way of doing this please tell me, I'll follow this method because this method of installing things doesn't happen often. Thank you.
-
-And in the future, please don't assume that because I said I was a professional translator that I'm ignorant of the computer world. I'm not. 4fefd39f24<br />
-<br />
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Cheat Code For Harvest Moon Boy And Girl Psp.md b/spaces/terfces0erbo/CollegeProjectV2/Cheat Code For Harvest Moon Boy And Girl Psp.md
deleted file mode 100644
index 259bbb28659fc480ea6aa0bbea1af1d0717efa43..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Cheat Code For Harvest Moon Boy And Girl Psp.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-<br />
-<h1>How to Use Cheat Codes for Harvest Moon Boy and Girl on PSP</h1>
-<p>Harvest Moon Boy and Girl is a simulation game that lets you live the life of a farmer, raise animals, grow crops, and fall in love. The game is a port of the PlayStation 1 games Harvest Moon: Back to Nature and Harvest Moon for Girl, with some minor changes and additions.</p>
-<p>If you want to make your farming life easier, or just have some fun, you can use cheat codes to modify various aspects of the game. Cheat codes are special commands that you can enter using a cheat device, such as CWCheat or TempAR, that can alter the game's memory and data.</p>
-<h2>Cheat Code For Harvest Moon Boy And Girl Psp</h2><br /><p><b><b>Download Zip</b> >>> <a href="https://bytlly.com/2uGlSx">https://bytlly.com/2uGlSx</a></b></p><br /><br />
-<p>In this article, we will show you how to use cheat codes for Harvest Moon Boy and Girl on PSP, and provide some examples of codes that you can try. Note that using cheat codes may cause glitches or errors in the game, so use them at your own risk.</p>
-<h2>How to Use Cheat Codes for Harvest Moon Boy and Girl on PSP</h2>
-<p>To use cheat codes for Harvest Moon Boy and Girl on PSP, you will need a cheat device that is compatible with your PSP model and firmware. You can download CWCheat from <a href="https://cwcheat.consoleworld.org/">here</a> or TempAR from <a href="https://raing3.gshi.org/psp-utilities/tempar/">here</a>. Follow the instructions on how to install and activate the cheat device on your PSP.</p>
-<p>Once you have the cheat device installed, you will need to find the cheat codes that you want to use. You can search online for cheat codes for Harvest Moon Boy and Girl on PSP, or use the ones we provide below. Make sure that the cheat codes are compatible with your game region (ULUS-10142 for US, ULES-00851 for EU, ULJM-05033 for JP).</p>
-<p>To enter the cheat codes, you will need to edit the cheat database file of your cheat device. For CWCheat, the file is called "cheat.db" and is located in "ms0:/seplugins/cwcheat/". For TempAR, the file is called "cheats.db" and is located in "ms0:/seplugins/tempar/". You can edit the file using a text editor on your computer or PSP.</p>
-<p>To add a new cheat code, you will need to create a new section for the game in the cheat database file. The section should start with "_S" followed by the game ID, and end with "_G" followed by the game name. For example:</p>
-<pre><code>_S ULUS-10142
-_G Harvest Moon Boy & Girl
-</code></pre>
-<p>Then, you can add the cheat codes under the game section. Each cheat code should start with "_C0" followed by the cheat name, and "_L" followed by the memory address and value. For example:</p>
-<pre><code>_C0 Infinite Money
-_L 0x201B2F0C 0x0098967F
-</code></pre>
-<p>You can add as many cheat codes as you want under the same game section. Just make sure that they are separated by a blank line. You can also use "//" to add comments or notes to your cheat codes.</p>
-<p></p>
-<p>After you have added all the cheat codes that you want to use, save the cheat database file and copy it back to your PSP. Then, launch the game and activate the cheat device by pressing a certain button combination (usually Select + Up for CWCheat or Select + Down for TempAR). You should see a menu where you can select and enable the cheat codes that you want to use.</p>
-<h2>Some Cheat Codes for Harvest Moon Boy and Girl on PSP</h2>
-<p>Here are some examples of cheat codes that you can use for Harvest Moon Boy and Girl on PSP. These codes are for the US version of the game (ULUS-10142). If you have a different version of the game, you may need to find different codes online.</p>
-<h3>Infinite Stamina</h3>
-<p>This code will prevent your stamina from decreasing when you use tools or do other activities.</p>
-<pre><code>_C0</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Download Contoh Laporan Ojl Cakep UPD.md b/spaces/terfces0erbo/CollegeProjectV2/Download Contoh Laporan Ojl Cakep UPD.md
deleted file mode 100644
index 707d244474457872f1b2357b4fc610c4a7989662..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Download Contoh Laporan Ojl Cakep UPD.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Download Contoh Laporan Ojl Cakep</h2><br /><p><b><b>Download</b> &#9999; <a href="https://bytlly.com/2uGlrB">https://bytlly.com/2uGlrB</a></b></p><br /><br />
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diff --git a/spaces/test12356/SUI-svc-3.0/modules.py b/spaces/test12356/SUI-svc-3.0/modules.py
deleted file mode 100644
index 52ee14e41a5b6d67d875d1b694aecd2a51244897..0000000000000000000000000000000000000000
--- a/spaces/test12356/SUI-svc-3.0/modules.py
+++ /dev/null
@@ -1,342 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
diff --git a/spaces/thejagstudio/procom/main/migrations/0006_categories.py b/spaces/thejagstudio/procom/main/migrations/0006_categories.py
deleted file mode 100644
index 67fc98af539aa39a5811fc0d7934b330b79c8044..0000000000000000000000000000000000000000
--- a/spaces/thejagstudio/procom/main/migrations/0006_categories.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Generated by Django 4.1.4 on 2023-01-28 09:07
-
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-
-    dependencies = [
-        ("main", "0005_alter_products_name_alter_products_score"),
-    ]
-
-    operations = [
-        migrations.CreateModel(
-            name="Categories",
-            fields=[
-                (
-                    "id",
-                    models.BigAutoField(
-                        auto_created=True,
-                        primary_key=True,
-                        serialize=False,
-                        verbose_name="ID",
-                    ),
-                ),
-                ("name", models.CharField(max_length=1000, unique=True)),
-                ("link", models.CharField(max_length=1000)),
-            ],
-        ),
-    ]
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/De Decompiler Pro 21 Recover Modify and Debug Your Applications.md b/spaces/tialenAdioni/chat-gpt-api/logs/De Decompiler Pro 21 Recover Modify and Debug Your Applications.md
deleted file mode 100644
index d4f98e330c4896dba0f844c3971005b26bbfeb1d..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/De Decompiler Pro 21 Recover Modify and Debug Your Applications.md	
+++ /dev/null
@@ -1,101 +0,0 @@
-<br />
-<h1>De Decompiler Pro 21: A Powerful and Free Decompiler for Delphi</h1>
-<p>If you are a Delphi developer, you may have encountered situations where you need to analyze, modify, or debug executable files without source code. Maybe you want to recover lost or corrupted code from your applications, or maybe you want to understand how a third-party program works. Whatever the reason, you need a tool that can help you reverse engineer binary files and generate readable and editable source code.</p>
-<p>That's where De Decompiler Pro 21 comes in. De Decompiler Pro 21 is a free decompiler for Delphi that can restore most parts of the compiled code and produce high-quality source code. It can handle executables compiled with Delphi 2 to Delphi 10.4 Sydney, as well as some versions of C++ Builder. It supports both 32-bit and 64-bit applications, and can decompile Windows PE files, .NET assemblies, ELF files, and Mach-O files.</p>
-<h2>De Decompiler Pro 21</h2><br /><p><b><b>Download File</b> &#9881; <a href="https://urlcod.com/2uK9Wq">https://urlcod.com/2uK9Wq</a></b></p><br /><br />
-<h2>How De Decompiler Pro 21 Works</h2>
-<p>De Decompiler Pro 21 uses a powerful disassembler that can recognize more than 3000 Delphi library functions and process all types of data definitions. It can also reconstruct the structure of the classes and the inheritance hierarchy, as well as recover local variables, parameters, fields, constants, and enumerations.</p>
-<p>De Decompiler Pro 21 also uses a sophisticated decompiler that can generate accurate and readable source code in Delphi syntax. It can handle complex control structures, such as loops, switches, and exceptions. It can also preserve the names of the identifiers and the comments from the original code.</p>
-<p>De Decompiler Pro 21 has a user-friendly interface that allows you to easily navigate through the decompiled code and view the properties of the symbols. You can also export the decompiled code to a file or clipboard, or print it out for further analysis.</p>
-<h2>Why You Should Use De Decompiler Pro 21</h2>
-<p>De Decompiler Pro 21 is not only a powerful and free decompiler for Delphi, but also a valuable tool for learning and improving your programming skills. By using De Decompiler Pro 21, you can:</p>
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-What are the challenges of decompiler development<br />
-What are the latest trends in decompiler research<br />
-What are the best practices for decompiler usage<br />
-What are the ethical issues of decompiler application</p>
-<ul>
-<li>Recover lost or corrupted source code from your own applications or third-party programs.</li>
-<li>Analyze how a program works and learn from its logic and algorithms.</li>
-<li>Modify or debug an executable file without having access to its source code.</li>
-<li>Protect your intellectual property by obfuscating your code or detecting possible plagiarism.</li>
-<li>Compare the output of different decompilers and choose the best one for your needs.</li>
-</ul>
-<p>If you are interested in De Decompiler Pro 21, you can download it from <a href="https://geags.com/2tyGQs">here</a>. You can also check out <a href="https://github.com/decompiler-explorer/decompiler-explorer">Decompiler Explorer</a>, a web service that lets you compare the output of different decompilers on small executables.</p>
-<h2>Conclusion</h2>
-<p>De Decompiler Pro 21 is a powerful and free decompiler for Delphi that can help you reverse engineer binary files and generate readable and editable source code. It can handle executables compiled with various versions of Delphi and C++ Builder, as well as other formats such as Windows PE files, .NET assemblies, ELF files, and Mach-O files. It has a user-friendly interface that allows you to easily navigate through the decompiled code and view the properties of the symbols. It is also a valuable tool for learning and improving your programming skills, as well as protecting your intellectual property.</p>
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-<h2>How to Download and Install De Decompiler Pro 21</h2>
-<p>If you are interested in trying De Decompiler Pro 21, you can download it for free from <a href="https://geags.com/2tyGQs">here</a>. The download file is a zip archive that contains the executable file and the license file. You don't need to install anything, just extract the files to a folder of your choice and run the executable file.</p>
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-<p>Once you have your license key, you can activate it by clicking on the "Help" menu and selecting "Enter License Key". You will see a dialog box where you can enter your license key and click "OK". You will see a confirmation message that your license key is valid and activated.</p>
-<h2>How to Use De Decompiler Pro 21</h2>
-<p>To use De Decompiler Pro 21, you need to open an executable file that you want to decompile. You can do this by clicking on the "File" menu and selecting "Open File". You will see a file browser where you can navigate to the folder where your executable file is located and select it. You can also drag and drop the file onto the main window of De Decompiler Pro 21.</p>
-<p>Once you open an executable file, De Decompiler Pro 21 will start analyzing it and decompiling it. You will see a progress bar at the bottom of the window that shows the status of the decompilation process. When the decompilation is finished, you will see the decompiled code in the main window. You can switch between different views of the code by clicking on the tabs at the top of the window.</p>
-<p>You can view the code in different formats, such as Delphi, C++, XML, or assembly. You can also view the structure of the classes and the inheritance hierarchy, as well as the properties of the symbols. You can search for specific keywords or identifiers in the code by using the "Find" function. You can also export the decompiled code to a file or clipboard, or print it out for further analysis.</p>
-<h2>What are the Benefits of De Decompiler Pro 21</h2>
-<p>De Decompiler Pro 21 is not only a powerful and free decompiler for Delphi, but also a beneficial tool for various purposes and scenarios. By using De Decompiler Pro 21, you can enjoy the following benefits:</p>
-<ul>
-<li>You can save time and money by recovering lost or corrupted source code from your own applications or third-party programs. You don't need to rewrite the code from scratch or hire someone else to do it for you.</li>
-<li>You can improve your programming skills and knowledge by analyzing how a program works and learning from its logic and algorithms. You can also compare the output of different decompilers and choose the best one for your needs.</li>
-<li>You can modify or debug an executable file without having access to its source code. You can change the functionality, appearance, or behavior of a program according to your preferences or requirements.</li>
-<li>You can protect your intellectual property by obfuscating your code or detecting possible plagiarism. You can also prevent unauthorized copying, modification, or distribution of your applications.</li>
-<li>You can create new applications or features by using the decompiled code as a base or inspiration. You can also integrate the decompiled code with other libraries or frameworks.</li>
-</ul>
-<p>De Decompiler Pro 21 is a versatile and useful tool for any Delphi developer who wants to reverse engineer binary files and generate readable and editable source code. It can handle executables compiled with various versions of Delphi and C++ Builder, as well as other formats such as Windows PE files, .NET assemblies, ELF files, and Mach-O files. It has a user-friendly interface that allows you to easily navigate through the decompiled code and view the properties of the symbols.</p>
-<h2>How to Compare Different Decompilers with De Decompiler Pro 21</h2>
-<p>De Decompiler Pro 21 is not only a powerful and free decompiler for Delphi, but also a convenient tool for comparing different decompilers on small executables. You can use De Decompiler Pro 21 to see how different decompilers handle the same binary file and choose the best one for your needs.</p>
-<p>To compare different decompilers with De Decompiler Pro 21, you need to use <a href="https://github.com/decompiler-explorer/decompiler-explorer">Decompiler Explorer</a>, a web service that lets you compare the output of different decompilers on small executables. Decompiler Explorer is based on De Decompiler Pro 21 and supports various decompilers, such as Hex-Rays, Ghidra, Relyze, Snowman, and more.</p>
-<p>To use Decompiler Explorer, you need to upload an executable file that you want to compare. You can do this by clicking on the "Upload" button and selecting a file from your computer. You can also drag and drop the file onto the web page. The file size limit is 10 MB.</p>
-<p>Once you upload an executable file, Decompiler Explorer will start analyzing it and decompiling it with different decompilers. You will see a list of decompilers on the left side of the web page and the output of each decompiler on the right side. You can switch between different views of the output by clicking on the tabs at the top of each panel.</p>
-<p>You can view the output in different formats, such as Delphi, C++, XML, or assembly. You can also view the structure of the classes and the inheritance hierarchy, as well as the properties of the symbols. You can search for specific keywords or identifiers in the output by using the "Find" function. You can also export the output to a file or clipboard, or print it out for further analysis.</p>
-<p>By using Decompiler Explorer, you can compare the output of different decompilers and see how they handle complex control structures, loops, switches, exceptions, local variables, parameters, fields, constants, enumerations, and more. You can also see how they preserve the names of the identifiers and the comments from the original code.</p>
-<h2>Conclusion</h2>
-<p>De Decompiler Pro 21 is a powerful and free decompiler for Delphi that can help you reverse engineer binary files and generate readable and editable source code. It can handle executables compiled with various versions of Delphi and C++ Builder, as well as other formats such as Windows PE files, .NET assemblies, ELF files, and Mach-O files. It has a user-friendly interface that allows you to easily navigate through the decompiled code and view the properties of the symbols. It is also a beneficial tool for various purposes and scenarios, such as recovering lost or corrupted code, analyzing how a program works, modifying or debugging an executable file, protecting your intellectual property, or creating new applications or features.</p>
-<p>If you want to try De Decompiler Pro 21, you can download it for free from <a href="https://geags.com/2tyGQs">here</a>. You can also check out <a href="https://github.com/decompiler-explorer/decompiler-explorer">Decompiler Explorer</a>, a web service that lets you compare the output of different decompilers on small executables. Decompiler Explorer is based on De Decompiler Pro 21 and supports various decompilers, such as Hex-Rays, Ghidra, Relyze, Snowman, and more.</p>
-<p>We hope you enjoyed this article and learned something new about De Decompiler Pro 21 and decompilers in general. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p> 679dcb208e<br />
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\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Download [EXCLUSIVE] Fmrte 2016 Full 10164.md b/spaces/tialenAdioni/chat-gpt-api/logs/Download [EXCLUSIVE] Fmrte 2016 Full 10164.md
deleted file mode 100644
index 9c1de39f099ba99cfe1a145bea0ec526e06f0591..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Download [EXCLUSIVE] Fmrte 2016 Full 10164.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-
-<h1>How to Download Fmrte 2016 Full 10164 for Free</h1>
-<p>Fmrte 2016 is a powerful tool that allows you to edit and customize your Football Manager 2016 game. With Fmrte 2016, you can change player attributes, club finances, staff details, tactics, kits, and more. You can also use Fmrte 2016 to scout players, find hidden gems, and discover new talents.</p>
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-<p>But where can you find this crack version? And how can you install it on your computer? In this article, we will show you how to download Fmrte 2016 full 10164 for free and how to use it safely and effectively.</p>
-<h2>Step 1: Download the Crack File</h2>
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-<p>However, be careful when downloading files from unknown sources. Some of them may contain viruses, malware, or spyware that can harm your computer or steal your personal information. Always scan the files with an antivirus program before opening them.</p>
-<p></p>
-<p>To download the crack file for Fmrte 2016, follow these steps:</p>
-<ul>
-<li>Go to one of the websites that offer the crack file, such as <a href="https://drive.google.com/file/d/0B7dHmjr-FkpsbmFyZkYyb1laMlE/view">Google Drive</a>, <a href="https://www.letsthrivebaltimore.org/forum/mobile-food-drive/extra-quality-download-fmrte-2016-full-10164l">Lets Thrive Baltimore</a>, or <a href="https://www.coffeewithcodes.com/forum/untitled-category/download-fmrte-2016-full-crack-26-patched">Coffee With Codes</a>.</li>
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-</ul>
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-<ul>
-<li>Make sure you have Football Manager 2016 installed on your computer. If not, you can download it from <a href="https://store.steampowered.com/app/378120/Football_Manager_2016/">Steam</a> or other official sources.</li>
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-<li>Replace the existing file when prompted.</li>
-</ul>
-<h2>Step 3: Enjoy Fmrte 2016 Full</p> 7b8c122e87<br />
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\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Download and Install QuarkXPress 5.0 for Free on Your Computer.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Download and Install QuarkXPress 5.0 for Free on Your Computer.md
deleted file mode 100644
index fe800dbe3c81423242f774f2699ba6961011ed35..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/How to Download and Install QuarkXPress 5.0 for Free on Your Computer.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-<br />
-<h1>QuarkXPress 5.0 Free Download: A Powerful and Versatile Desktop Publishing Software</h1>
-<p>QuarkXPress is one of the most popular and widely used desktop publishing software in the world. It allows you to create and edit professional-quality documents, such as magazines, books, brochures, flyers, posters, newsletters, and more. With QuarkXPress, you can design and layout your documents with ease and precision, using its rich set of features and tools. You can also import and export your documents in various formats, such as PDF, HTML, XML, EPS, and more.</p>
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-<h2>How to download QuarkXPress 5.0 for free</h2>
-<p>To download QuarkXPress 5.0 for free, you need to follow these steps:</p>
-<p></p>
-<ol>
-<li>Go to <a href="https://archive.org/details/QuarkXpress5.0">this link</a>, which will take you to the Internet Archive page where you can find the QuarkXPress 5.0 installer file.</li>
-<li>Click on the "Download Options" button on the right side of the page and choose the format that suits your computer (ISO or ZIP).</li>
-<li>Wait for the download to complete and save the file to your preferred location on your computer.</li>
-</ol>
-<h2>How to install QuarkXPress 5.0 on your computer</h2>
-<p>To install QuarkXPress 5.0 on your computer, you need to follow these steps:</p>
-<ol>
-<li>If you downloaded the ISO file, you need to mount it on a virtual drive using a software like <a href="https://www.daemon-tools.cc/products/dtLite">DAEMON Tools Lite</a>. If you downloaded the ZIP file, you need to extract it using a software like <a href="https://www.winzip.com/win/en/">WinZip</a>.</li>
-<li>Open the folder where you mounted or extracted the file and double-click on the "Setup.exe" file to start the installation process.</li>
-<li>Follow the instructions on the screen and accept the license agreement.</li>
-<li>Enter the serial number that is provided in the "Serial.txt" file in the same folder.</li>
-<li>Choose the destination folder where you want to install QuarkXPress 5.0 and click "Next".</li>
-<li>Wait for the installation to finish and click "Finish".</li>
-<li>You can now launch QuarkXPress 5.0 from your desktop or start menu and enjoy using it.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In this article, we have explained how to download QuarkXPress 5.0 for free from a reliable source and how to install it on your computer. QuarkXPress 5.0 is a powerful and versatile desktop publishing software that can help you create and edit professional-quality documents. However, it is an older version of the software that may not be compatible with newer operating systems or devices. Therefore, we recommend that you upgrade to the latest version of QuarkXPress from the official website or use an alternative free software that has similar functions.</p> ddb901b051<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Minecraft Education Edition for Free Without Cracking.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Minecraft Education Edition for Free Without Cracking.md
deleted file mode 100644
index b183b08bb4bb0c11f68b395b8e338b2ac8f47324..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Minecraft Education Edition for Free Without Cracking.md	
+++ /dev/null
@@ -1,32 +0,0 @@
-<br />
-# How to Download Minecraft Education Edition Crack for Free
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-- You might infringe the intellectual property rights of the game developers and publishers and face legal consequences.
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-- You can try the free trial version of the game that allows you to play for 25 hours on up to 10 devices. You can download the trial version from the official website of the game: https://education.minecraft.net/get-started/download/
-- You can ask your school or teacher if they have a subscription to the game that you can use. Many schools and educators use Minecraft Education Edition as a learning tool and provide access to their students.
-- You can buy a license for the game at a discounted price if you are eligible. Students and teachers can get the game for $5 per year, while homeschoolers can get it for $14.99 per year. You can check your eligibility and purchase a license from the official website of the game: https://education.minecraft.net/get-started/purchase/
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/APKPure Temple Run 2 1.79.2 - The Classic Runner Game.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/APKPure Temple Run 2 1.79.2 - The Classic Runner Game.md
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--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/APKPure Temple Run 2 1.79.2 - The Classic Runner Game.md	
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-<h1>Temple Run 2: How to Download the Old Version from APKPure</h1>
- <h2>Introduction</h2>
- <p>Temple Run 2 is one of the most popular running games on mobile devices, with over a billion downloads on Google Play Store. It is a sequel to the original Temple Run, which was released in 2011 by Imangi Studios. In this game, you have to run, jump, slide, and turn to escape from a giant monkey that chases you after you steal a cursed idol from an ancient temple.</p>
- <p>But what if you want to play the old version of Temple Run 2, which has some features that are not available in the latest update? Maybe you prefer the old graphics, the old power-ups, or the old characters. Or maybe you just want to relive the nostalgia of playing the game when it first came out.</p>
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- <p>In this article, we will show you how to download Temple Run 2 old version from APKPure, a website that offers free APK files for Android apps and games. We will also tell you about the features of Temple Run 2 old version, and give you some tips and tricks to play like a pro.</p>
- <h2>How to download Temple Run 2 old version from APKPure</h2>
- <p>Downloading Temple Run 2 old version from APKPure is very easy and fast. Just follow these steps:</p>
- <h3>Step 1: Go to APKPure website</h3>
- <p>Open your browser and go to [APKPure](^1^), a website that offers free APK files for Android apps and games. You can also download their app from Google Play Store or their official website.</p>
- <h3>Step 2: Search for Temple Run 2</h3>
- <p>Type "Temple Run 2" in the search box and hit enter. You will see a list of results related to Temple Run 2. Click on the one that says "Temple Run 2" with the official logo of the game.</p>
- <h3>Step 3: Choose the version you want</h3>
- <p>You will see a page with information about Temple Run 2, such as its description, screenshots, ratings, reviews, and more. Scroll down until you see a section that says "Download Temple Run 2 old versions". Here you can choose from different versions of Temple Run 2 that are available on APKPure. You can see the date, size, and version number of each APK file.</p>
- <p>For example, if you want to download Temple Run 2 version 1.67.0, which was released on June 13, 2023, you can click on the button that says "Download APK (93.9 MB)" under that version. This is the last version before Temple Run 2 introduced new maps, characters, and events in its updates.</p>
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- <h3>Step 4: Download and install the APK file</h3>
- <p>After you click on the download button, you will see a pop-up window that asks you to confirm your download. Click on "OK" to start downloading the APK file to your device.</p>
- <p>Once the download is complete, you will need to install the APK file on your device. To do this, you may need to enable "Unknown sources" in your device settings. This will allow you to install apps from sources other than Google Play Store.</p>
- <p>Then <p>Then, locate the APK file in your device storage and tap on it to open it. You will see a screen that asks you to install the app. Click on "Install" and wait for the installation to finish.</p>
- <p>Congratulations! You have successfully downloaded and installed Temple Run 2 old version from APKPure. You can now launch the game and enjoy the old features that you missed.</p>
- <h2>Features of Temple Run 2 old version</h2>
- <p>Temple Run 2 old version has some features that are not available in the latest update. Here are some of them:</p>
- <h3>New obstacles and environments</h3>
- <p>Temple Run 2 old version introduces new obstacles and environments that make the game more challenging and fun. You have to run through mines, zip lines, waterfalls, forests, cliffs, and more. You also have to dodge fireballs, spikes, gaps, and other traps that can end your run.</p>
- <h3>New power-ups and characters</h3>
- <p>Temple Run 2 old version also adds new power-ups and characters that enhance your gameplay. You can use power-ups like shield, boost, magnet, gem bonus, and score multiplier to run faster, collect more coins and gems, and survive longer. You can also unlock new characters like Scarlett Fox, Barry Bones, Karma Lee, Montana Smith, and more. Each character has their own special abilities and outfits that you can customize.</p>
- <h3>Improved graphics and gameplay</h3>
- <p>Temple Run 2 old version improves the graphics and gameplay of the original Temple Run. The game has more realistic and colorful graphics that make the environments more immersive. The game also has smoother and faster gameplay that makes the running experience more thrilling.</p>
- <h2>Tips and tricks for Temple Run 2 old version</h2>
- <p>If you want to play Temple Run 2 old version like a pro, here are some tips and tricks that you can use:</p>
- <h3>Collect green gems and power-ups</h3>
- <p>Green gems are rare items that you can use to revive yourself after you die. They can save your run and help you reach higher scores. You can find them randomly in the game or buy them with real money. You can also collect power-ups that appear along the way. They can give you various benefits such as speed, protection, attraction, or bonus.</p>
- <h3>Master the jump and turn technique</h3>
- <p>One of the most important skills in Temple Run 2 old version is the jump and turn technique. This is when you jump over an obstacle and turn at the same time. This can help you avoid hitting walls or falling off edges. To do this, you have to swipe up and left or right simultaneously.</p>
- <h3>Upgrade your abilities and coin value</h3>
- <p>You can use the coins that you collect in the game to upgrade your abilities and coin value. Your abilities include coin magnet, shield duration, boost distance, pickup spawn, power meter, head start, score multiplier, and save me. Your coin value determines how much each coin is worth. Upgrading these factors can help you run longer, collect more coins and gems, and score higher.</p>
- <h3>Complete objectives and challenges</h3>
- <p>You can also complete objectives and challenges in Temple Run 2 old version to earn more coins, gems, and rewards. Objectives are tasks that you have to do in a single run, such as collecting a certain amount of coins or running a certain distance. Challenges are tasks that you have to do over multiple runs, such as escaping a certain number of times or using a certain power-up. Completing these tasks can also increase your level and unlock new achievements.</p>
- <h2>Conclusion</h2>
- <p>Temple Run 2 is a fun and addictive running game that you can play on your mobile device. But if you want to enjoy the old features of the game that are not available in the latest update, you can download Temple Run 2 old version from APKPure. This website offers free APK files for Android apps and games that you can easily download and install on your device.</p>
- <p>In this article, we showed you how to download Temple Run 2 old version from APKPure in four simple steps. We also told you about the features of Temple Run 2 old version, such as new obstacles, environments, power-ups, and characters. We also gave you some tips and tricks to play Temple Run 2 old version like a pro.</p>
- <p>If you are a fan of Temple Run 2 or running games in general, we recommend you to try Temple Run 2 old version from APKPure. It is a great way to relive the nostalgia of playing the game when it first came out.</p>
- <p>So what are you waiting for? Download Temple Run 2 old version from APKPure Download Temple Run 2 old version from APKPure today and have fun running away from the monkey!</p>
- <p>Here are some FAQs that you may have about Temple Run 2 old version:</p>
- <h3>Q: Is Temple Run 2 old version safe to download and install?</h3>
-<p>A: Yes, Temple Run 2 old version from APKPure is safe to download and install. APKPure is a reputable website that offers verified and secure APK files for Android apps and games. However, you should always be careful when downloading and installing apps from unknown sources, and scan them with an antivirus software before opening them.</p>
- <h3>Q: Can I play Temple Run 2 old version online or offline?</h3>
-<p>A: You can play Temple Run 2 old version both online and offline. However, if you want to access some features of the game, such as leaderboards, achievements, or events, you will need an internet connection.</p>
- <h3>Q: Can I play Temple Run 2 old version with friends?</h3>
-<p>A: Yes, you can play Temple Run 2 old version with friends. You can connect your game to Facebook and see how your friends are doing on the leaderboards. You can also challenge them to beat your score or send them gifts.</p>
- <h3>Q: How can I update Temple Run 2 old version to the latest version?</h3>
-<p>A: If you want to update Temple Run 2 old version to the latest version, you will need to uninstall the old version and download the new version from Google Play Store or APKPure. However, you may lose some of the features that are only available in the old version.</p>
- <h3>Q: How can I contact the developers of Temple Run 2?</h3>
-<p>A: If you have any questions, feedback, or suggestions for the developers of Temple Run 2, you can contact them through their official website, Facebook page, Twitter account, or email address. You can also check their FAQ section for more information.</p> 401be4b1e0<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Among Us download grtis da verso completa com todas as roupas.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Among Us download grtis da verso completa com todas as roupas.md
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--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Among Us download grtis da verso completa com todas as roupas.md	
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-<h1>How to download Among Us with all the skins</h1>
-<p>Among Us is a multiplayer game that has taken the internet by storm. In this game, you play as either a Crewmate or an Impostor on a spaceship or a space station. As a Crewmate, your goal is to complete tasks and find out who the Impostor is before they kill everyone. As an Impostor, your goal is to sabotage and kill the Crewmates without getting caught. The game is fun and addictive, especially when you play with friends or online strangers.</p>
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- <p>One of the features that makes Among Us more enjoyable is the ability to customize your character with different skins, pets, and hats. Skins are outfits that change the appearance of your character, such as a police uniform, a pirate costume, or a ninja suit. Pets are small creatures that follow you around, such as a dog, a cat, or a UFO. Hats are accessories that you can wear on your head, such as a cowboy hat, a flower crown, or a cheese wedge.</p>
- <p>There are many skins, pets, and hats to choose from in Among Us, but not all of them are free or easy to get. Some of them require you to pay real money or watch ads, while others are only available during certain events or seasons. If you want to download Among Us with all the skins, you have two options: you can either get them for free by using modded or custom skins, or you can buy them legally by purchasing the All-in-One Pack or individual items from the in-game store. In this article, we will show you how to do both.</p>
- <h2>How to get Among Us skins for free</h2>
-<p>If you don't want to spend any money on Among Us skins, pets, and hats, you can try to get them for free by using modded or custom skins. Modded skins are modified versions of the game that give you access to all the cosmetics without paying anything. Custom skins are user-created skins that you can download and install on your device. However, both methods have some drawbacks and risks that you should be aware of before using them.</p>
- <h3>How to unlock all skins, pets, and hats in Among Us (Mobile)</h3>
-<p>One way to get all the skins, pets, and hats in Among Us for free is to download a modded version of the game from a third-party website. A modded version is a hacked version that has been altered to give you unlimited access to all the cosmetics in the game. However, this method is not recommended for several reasons:</p>
-<ul>
-<li>It is illegal and unethical to use modded versions of the game, as it violates the terms of service and infringes on the intellectual property rights of the developers.</li>
-<li>It is unsafe and risky to download modded versions of the game from unknown sources, as they may contain malware, viruses, or spyware that could harm your device or steal your personal information.</li>
-<li>It is unfair and disrespectful to use modded versions of the game online, as it gives you an unfair advantage over other players who have paid for their cosmetics or earned them legitimately.</li>
-<li>It is possible and likely that you will get banned or reported by other players or by the game's anti-cheat system if you use modded versions of the game online.</li>
-</ul>
-<p>If you still want to use modded versions of the game at your own risk and discretion, here are the steps to follow:</p>
-<ol>
-<li>Go to a website that offers modded versions of Among Us for mobile devices, such as [HappyMod] or [Platinmods].</li>
-<li>Search for Among Us and select the latest version of the game with all the skins unlocked.</li>
-<li>Download the APK file of the modded version and install it on your device. You may need to enable unknown sources in your device settings to do so.</li>
-<li>Launch the modded version of Among Us and enjoy playing with all the skins, pets, and hats for free.</li>
-</ol> <h3>How to use custom skins in Among Us (PC)</h3>
-<p>Another way to get free skins in Among Us is to use custom skins. Custom skins are skins that have been created by other users and uploaded to websites like [Gamebanana]. Custom skins can be anything from anime characters, superheroes, celebrities, memes, or even your own face. However, custom skins have some limitations and drawbacks that you should know before using them:</p>
-<ul>
-<li>Custom skins are only compatible with the PC version of Among Us, not the mobile version.</li>
-<li>Custom skins are only visible to you or other players who have the same skins installed on their devices. If you play online with players who don't have the same skins, they will see you as a normal character with a default skin.</li>
-<li>Custom skins may not work properly with some updates or patches of the game, as they may interfere with the game files or cause glitches or errors.</li>
-<li>Custom skins may not be approved or endorsed by the developers of Among Us, and they may violate their terms of service or intellectual property rights.</li>
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-<p>If you still want to use custom skins at your own risk and discretion, here are the steps to follow:</p>
-<ol>
-<li>Go to a website that offers custom skins for Among Us, such as [Gamebanana].</li>
-<li>Browse the available skins and select the ones that you like. You can also search for specific keywords or categories to find the skins that suit your taste.</li>
-<li>Download the ZIP files of the custom skins and extract them on your device. You should see folders with names like "Assets" or "Among Us_Data".</li>
-<li>Locate the game folder of Among Us on your device. You can find it by right-clicking on the game icon on your desktop or in your Steam library and selecting "Properties" and then "Local Files".</li>
-<li>Copy and paste the folders of the custom skins into the game folder of Among Us. You may need to overwrite some existing files or folders to do so.</li>
-<li>Launch Among Us and enjoy playing with your custom skins.</li>
-</ol>
- <h2>How to buy Among Us skins legally</h2>
-<p>If you want to support the developers of Among Us and get official skins, pets, and hats for the game, you can buy them legally by purchasing the All-in-One Pack or individual items from the in-game store. The All-in-One Pack is a bundle that includes all the cosmetics that are available in the game, while individual items are sold separately for a lower price. Both methods are safe and legal, but they have different advantages and disadvantages that you should consider before buying them.</p>
- <h3>How to buy Among Us All-in-One Pack from Epic Games Store</h3>
-<p>One way to buy all the skins, pets, and hats in Among Us is to buy the All-in-One Pack from the Epic Games Store. The All-in-One Pack is a bundle that includes all the cosmetics that are available in the game, such as 16 skins, 16 pets, and 35 hats. The pack costs $14.99 USD and it is compatible with both PC and mobile versions of Among Us. However, this method has some drawbacks that you should be aware of before buying it:</p>
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-<li>You may not be able to use some of the cosmetics in certain regions or platforms due to licensing issues or restrictions.</li>
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-<p>If you still want to buy the All-in-One Pack from the Epic Games Store, here are the steps to follow:</p>
-<ol>
-<li>Create an Epic Games account or log in to your existing one on their website or launcher.</li>
-<li>Search for Among Us All-in-One Pack on their store or click on this [link].</li>
-<li>Add the pack to your cart and proceed to checkout. You can pay with a credit card, PayPal, or other payment methods.</li>
-<li>Download and install the pack on your device. You may need to restart your device or launcher to do so.</li>
-<li>Launch Among Us and enjoy playing with all the skins, pets, and hats in the game.</li>
-</ol>
- <h3>How to buy individual skins, pets, and hats in Among Us</h3>
-<p>Another way to buy skins, pets, and hats in Among Us is to buy individual items from the in-game store. The in-game store is accessible from the main menu or from the laptop in the lobby or near the emergency button in the game. The in-game store offers a variety of items that you can buy using real money or by watching ads (mobile only). The items are divided into three categories: skins, pets, and hats. The prices range from $0.99 USD to $2.99 USD for each item, or from $1.99 USD to $4.99 USD for each bundle. However, this method has some drawbacks that you should be aware of before buying them:</p>
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-<li>You need to have enough storage space on your device to download the items.</li>
-<li>You may not be able to use some of the items in certain regions or platforms due to licensing issues or restrictions.</li>
-<li>You may not be able to get a refund or exchange for the items that you buy.</li>
-</ul>
-<p>If you still want to buy individual items from the in-game store, here are the steps to follow:</p>
-<ol>
-<li>Launch Among Us and go to the main menu or the lobby.</li>
-<li>Click on the dollar sign icon at the bottom right corner of the screen to access the in-game store.</li>
-<li>Browse the available items and select the ones that you want to buy. You can also use the arrows at the top of the screen to switch between categories.</li>
-<li>Click on the buy button and confirm your purchase. You can pay with a credit card, PayPal, or other payment methods.</li>
-<li>Download and install the items on your device. You may need to restart your device or game to do so.</li>
-<li>Go to the laptop in the lobby or near the emergency button in the game and click on it to customize your character.</li>
-<li>Select the skin, pet, or hat that you want to use and apply it to your character.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>In conclusion, downloading Among Us with all the skins is possible, but it depends on how you want to do it. You can either get them for free by using modded or custom skins, or you can buy them legally by purchasing the All-in-One Pack or individual items from the in-game store. Both methods have their pros and cons, so you should weigh them carefully before deciding which one suits you best.</p>
- <p>Whichever method you choose, remember that skins, pets, and hats are just cosmetic items that do not affect your gameplay or performance in Among Us. They are meant to enhance your fun and enjoyment of the game, not to give you an edge over other players or make you look better than them. The most important thing is to play fair and have fun with your friends or online strangers in this thrilling and hilarious game of deception and deduction.</p>
- <p>Here are some tips on how to customize your character in Among Us:</p>
-<ul>
-<li>Choose a skin, pet, or hat that matches your personality, mood, or style. You can also mix and match different items to create your own unique look.</li>
-<li>Choose a color that complements your skin, pet, or hat. You can also change your color before or during a game by clicking on it at the bottom of the screen.</li>
-<li>Choose a name that reflects your character or role in the game. You can also change your name before a game by typing it in the box at the top of the screen.</li>
-<li>Be respectful and polite to other players, regardless of their skins, pets, hats, colors, or names. Do not judge them based on their appearance or make fun of them for their choices.</li>
-<li>Have fun and enjoy playing Among Us with all the skins!</li>
-</ul>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about downloading Among Us with all the skins:</p>
- <h3>Q: Can I get banned for using modded or custom skins in Among Us?</h3>
-<p>A: There is no official statement from the developers regarding modded or custom skins, but there is a possibility that you could get banned or reported by other players for using them. Use them at your own risk and discretion.</p>
- <h3>Q: Can I play with friends who have different skins than me in Among Us?</h3>
-<p>A: Yes, you can play with friends who have different skins than you in Among Us, as long as you are playing online or on the same local network. However, if you are using custom skins, they will not be visible to your friends unless they have the same skins installed on their devices.</p>
- <h3>Q: Can I use different skins for different maps in Among Us?</h3>
-<p>A: Yes, you can use different skins for different maps in Among Us. You can change your skin before or during a game by accessing the laptop in the lobby or near the emergency button. You can also change your skin between rounds by going back to the lobby.</p>
- <h3>Q: What are some of the best skins to use in Among Us?</h3>
-<p>A: The best skins to use in Among Us depend on your personal preference and style. Some players like to use skins that match their color, such as a red astronaut suit for a red character. Others like to use skins that contrast their color, such as a white doctor outfit for a black character. Some players also like to use skins that are funny, cute, or themed, such as a banana peel hat, a mini crewmate pet, or a Halloween costume.</p>
- <h3>Q: How can I get more skins for free in Among Us?</h3>
-<p>A: There are a few ways to get more skins for free in Among Us. One way is to watch ads on the mobile version of the game, which will give you coins that you can use to buy items from the store. Another way is to change your device's date settings, which will unlock some seasonal items such as Christmas hats or Easter eggs. A third way is to use modded or custom skins from third-party sources, but be careful of potential risks.</p> 197e85843d<br />
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-<br />
-<h1>Archero Mod APK 5play.ru: A Guide for Beginners</h1>
-<p>If you are looking for a fun and challenging game that will test your reflexes, skills, and strategy, then you might want to try Archero. Archero is a popular action game where you play as a lone archer who has to fight against waves of enemies in different worlds. In this article, we will give you a brief overview of the game, its features, and how to play it. We will also introduce you to Archero Mod APK 5play.ru, a modded version of the game that gives you access to unlimited resources, god mode, and other cheats. We will show you how to download, install, and use this mod, as well as its benefits and drawbacks. By the end of this article, you will have a better understanding of Archero and Archero Mod APK 5play.ru, and you will be able to decide whether you want to try it or not.</p>
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- <h2>What is Archero?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Archero is a free-to-play action game developed by Habby and released in 2019. The game is available for both Android and iOS devices. The game has been downloaded over 100 million times on Google Play Store alone, and it has received positive reviews from critics and players alike.</p>
-<p>The game's premise is simple: you are an archer who has to survive in a world full of monsters. You have to move through different stages, each with its own theme, layout, enemies, and bosses. You have to shoot your arrows at the enemies while dodging their attacks. You can also collect coins, gems, scrolls, weapons, skills, and other items along the way. You can use these items to upgrade your character, equipment, talents, and abilities.</p>
-<p>The game's graphics are colorful and cartoonish, with smooth animations and effects. The game's sound effects and music are also well-done, creating an immersive atmosphere. The game's controls are easy to learn and use: you just have to tap on the screen to move your character and release to shoot your arrows. You can also swipe on the screen to change your direction.</p>
- <h3>How to play Archero and progress through the levels</h3>
-<p>The game's gameplay is fast-paced and addictive. You have to complete each stage by defeating all the enemies that appear on the screen. You have to be careful not to get hit by their projectiles or touch them, as this will reduce your health bar. If your health bar reaches zero, you will die and have to start over from the beginning of the stage.</p>
-<p>At the end of each stage, you will get a chance to choose one of three random skills that will enhance your performance in the next stage. These skills can range from increasing your damage, speed, range, or number of arrows, to giving you special effects like fire, ice, poison, or lightning. You have to choose wisely, as some skills may be more useful than others depending on the situation.</p>
-<p>As you progress through the levels, you will encounter different types of enemies with different abilities and behaviors. Some enemies may shoot faster, move faster, or have more health than others. Some enemies may also have special powers like teleporting, spawning more enemies, or shielding themselves or their allies. You have to adapt your strategy accordingly and use your skills effectively.</p>
-<p>You will also face bosses at the end of each chapter, which are more challenging and require more skill and strategy to defeat. You will also unlock new worlds with different themes and difficulties as you advance in the game. The game has a total of 15 chapters and 5 worlds, each with its own unique enemies, bosses, and environments.</p>
- <h3>Tips and tricks to improve your skills and strategy</h3>
-<p>Archero is a game that requires both skill and strategy to master. Here are some tips and tricks that can help you improve your performance and enjoy the game more:</p>
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-<ul>
-<li>Practice your aiming and timing. You have to be precise and quick when shooting your arrows, as the enemies will not stay still and will try to dodge or attack you. You can also use the walls and obstacles to bounce your arrows and hit multiple enemies at once.</li>
-<li>Choose your skills wisely. You have to balance between offensive and defensive skills, as well as skills that suit your playstyle and preferences. For example, if you like to play aggressively, you may want to choose skills that increase your damage, speed, or number of arrows. If you like to play defensively, you may want to choose skills that increase your health, shield, or invincibility. You can also experiment with different combinations of skills and see what works best for you.</li>
-<li>Upgrade your equipment and talents. You can use the coins, gems, scrolls, and other items that you collect in the game to upgrade your equipment and talents. Your equipment includes your weapon, armor, ring, locket, bracelet, and spirit. Each equipment has its own stats and effects that can boost your performance. Your talents are passive abilities that can improve your attributes or give you extra benefits. You can upgrade your equipment and talents by spending coins or gems in the shop or by using scrolls in the forge.</li>
-<li>Watch ads or spend gems to revive. If you die in the game, you have two options: you can either watch an ad or spend some gems to revive with full health and continue from where you left off. This can be useful if you are close to completing a stage or a chapter, or if you want to keep your skills and items. However, you can only revive once per stage, so use this option wisely.</li>
-<li>Play daily events and challenges. The game offers various daily events and challenges that can reward you with coins, gems, scrolls, weapons, skills, and other items. These events and challenges have different modes, rules, and difficulties that can test your skills and provide you with more fun and variety. You can access these events and challenges by tapping on the event icon on the main screen.</li>
-</ul>
- <h2>What is Archero Mod APK 5play.ru?</h2>
-<h3>A description of the modded version of the game and its advantages</h3>
-<p>Archero Mod APK 5play.ru is a modded version of the game that gives you access to unlimited resources, god mode, and other cheats. This mod is created by 5play.ru, a website that offers various modded games and apps for Android devices. The mod is free to download and use, and it does not require root access or any special permissions.</p>
-<p>The mod has several advantages over the original version of the game. For example:</p>
-<ul>
-<li>You can get unlimited coins, gems, energy, keys, scrolls, weapons, skills, and other items in the game. You can use these resources to upgrade your equipment and talents, buy new items in the shop, revive without watching ads or spending gems, and unlock all the features in the game.</li>
-<li>You can activate god mode in the game. This means that you will not take any damage from enemies or obstacles, making you invincible. You can also activate other cheats like high damage, high speed, high range, and unlimited skills. These cheats can help you defeat enemies and bosses faster and easier, and complete stages and chapters quicker and smoother.</li>
-<li>You can access the mod menu in the game. This is a special menu that allows you to customize and control the mod features and functions. You can enable or disable the cheats, adjust the values of the resources, change the settings of the game, and more. You can access the mod menu by tapping on the icon on the top left corner of the screen.</li>
-</ul>
- <h3>How to download and install Archero Mod APK 5play.ru on your device</h3>
-<p>If you want to try Archero Mod APK 5play.ru, you have to follow these steps:</p>
-<ol>
-<li>Go to the official website of 5play.ru and search for Archero Mod APK 5play.ru. You can also use this link: <a href="">https://5play.ru/en/10295-archero-mod.html</a></li>
-<li>Download the latest version of the mod file on your device. The file size is about 100 MB, so make sure you have enough storage space and a stable internet connection.</li>
-<li>Before installing the mod file, you have to uninstall the original version of Archero from your device. This is because the mod file will replace the original file, and you cannot have both versions installed at the same time.</li>
-<li>After uninstalling the original version, you have to enable the installation of apps from unknown sources on your device. This is because the mod file is not from the official Google Play Store, and your device may block it by default. To enable this option, go to your device's settings, security, and toggle on the unknown sources option.</li>
-<li>Once you have enabled this option, you can locate the mod file on your device's file manager and tap on it to install it. Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>After installing the mod file, you can launch Archero Mod APK 5play.ru from your device's app drawer or home screen. You can also create a shortcut for it on your desktop for easier access.</li>
-</ol>
- <h3>How to use the mod menu and activate the cheats</h3>
-<p>Once you have launched Archero Mod APK 5play.ru, you can use the mod menu and activate the cheats as follows:</p>
-<ul>
-<li>To access the mod menu, tap on the icon on the top left corner of the screen. This will open a pop-up window with various options and settings.</li>
-<li>To activate or deactivate a cheat, tap on its name or icon. A green check mark means that the cheat is enabled, while a red cross means that it is disabled.</li>
-<li>To adjust the value of a resource, tap on its name or icon and use the slider or buttons to increase or decrease it. You can also enter a specific number by tapping on the input box.</li>
-<li>To change the settings of the game, tap on the gear icon on the top right corner of the mod menu. This will open another window with more options and settings.</li>
-<li>To close the mod menu, tap on the X icon on the top right corner of the pop-up window. You can also tap anywhere outside the window to close it.</li>
-</ul>
- <h2>What are the benefits of using Archero Mod APK 5play.ru?</h2>
-<h3>A comparison of the modded version and the original version of the game</h3>
-<p>Archero Mod APK 5play.ru is a modified version of Archero that offers many benefits over the original version of the game. Here are some of the main differences and advantages of using Archero Mod APK 5play.ru:</p>
-<table>
-<tr>
-<th>Original Version</th>
-<th>Modded Version</th>
-</tr>
-<tr>
-<td>Limited resources (coins, gems, energy, keys, scrolls, weapons, skills, etc.)</td>
-<td>Unlimited resources (coins, gems, energy, keys, scrolls, weapons, skills, etc.)</td>
-</tr>
-<tr>
-<td>No god mode or other cheats</td>
-<td>God mode and other cheats (high damage, high speed, high range, unlimited skills, etc.)</td>
-</tr>
-<tr>
-<td>No mod menu or customization options</td>
-<td>Mod menu and customization options (enable/disable cheats, adjust values, change settings, etc.)</td>
-</tr>
-<tr>
-<td>Ads and in-app purchases</td>
-<td>No ads and in-app purchases</td>
-</tr>
-<tr>
-<td>Regular updates and bug fixes</td>
-<td>Possible compatibility issues and delays in updates</td>
-</tr>
-<tr>
-<td>Safe and secure to use</td>
-<td>Potential risks of malware or ban from the game</td>
-</tr>
-</table>
- <h3>The features and functions of the mod menu and the cheats</h3>
-<p>Archero Mod APK 5play.ru has a mod menu that allows you to customize and control the mod features and functions. The mod menu has several options and settings that you can use to enhance your gaming experience. Here are some of the features and functions of the mod menu and the cheats:</p>
-<ul>
-<li>Unlimited coins: You can get unlimited coins in the game, which are the main currency that you can use to upgrade your equipment and talents, buy new items in the shop, revive without watching ads or spending gems, and unlock all the features in the game.</li>
-<li>Unlimited gems: You can get unlimited gems in the game, which are the premium currency that you can use to buy special items in the shop, unlock chests, get more energy or keys, and access more features in the game.</li>
-<li>Unlimited energy: You can get unlimited energy in the game, which is the resource that you need to play each stage. Normally, you have a limited amount of energy that replenishes over time or by watching ads or spending gems. With this cheat, you can play as much as you want without waiting or spending anything.</li> <li>Unlimited keys: You can get unlimited keys in the game, which are the items that you need to open chests. Chests contain various rewards like coins, gems, scrolls, weapons, skills, and other items. Normally, you have a limited number of keys that you can get by playing the game, watching ads, or spending gems. With this cheat, you can open as many chests as you want without any limitations.</li>
-<li>Unlimited scrolls: You can get unlimited scrolls in the game, which are the items that you need to upgrade your equipment and talents. Scrolls come in different types and rarities, and they can improve your stats and effects. Normally, you have to collect scrolls by playing the game, opening chests, or buying them in the shop. With this cheat, you can upgrade your equipment and talents to the max level without any hassle.</li>
-<li>Unlimited weapons: You can get unlimited weapons in the game, which are the items that you use to shoot your arrows. Weapons have different types, rarities, levels, and abilities that can affect your performance. Normally, you have to find weapons by playing the game, opening chests, or buying them in the shop. With this cheat, you can get any weapon you want and switch between them at any time.</li>
-<li>Unlimited skills: You can get unlimited skills in the game, which are the abilities that you can choose at the end of each stage. Skills have different effects and categories that can enhance your performance. Normally, you have to choose one of three random skills at the end of each stage, and you cannot change them later. With this cheat, you can choose any skill you want and change them at any time.</li>
-<li>God mode: You can activate god mode in the game, which means that you will not take any damage from enemies or obstacles. This makes you invincible and immune to any harm. You can also deactivate god mode if you want to play normally or challenge yourself.</li>
-<li>High damage: You can activate high damage in the game, which means that you will deal more damage to enemies and bosses with your arrows. This makes you more powerful and able to defeat enemies and bosses faster and easier. You can also deactivate high damage if you want to play normally or challenge yourself.</li>
-<li>High speed: You can activate high speed in the game, which means that you will move faster on the screen. This makes you more agile and able to dodge enemies and obstacles better. You can also deactivate high speed if you want to play normally or challenge yourself.</li>
-<li>High range: You can activate high range in the game, which means that your arrows will travel farther on the screen. This makes you more accurate and able to hit enemies and bosses from a longer distance. You can also deactivate high range if you want to play normally or challenge yourself.</li>
-</ul>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action</h3>
-<p>In conclusion, Archero is a fun and challenging action game where you play as an archer who has to fight against waves of enemies in different worlds. The game has many features and elements that make it enjoyable and addictive. However, if you want to have more fun and ease in the game, you may want to try Archero Mod APK 5play.ru, a modded version of the game that gives you access to unlimited resources, god mode, and other cheats. You can download and install this mod easily on your device, and use the mod menu to customize and control the mod features and functions. However, you should also be aware of the potential risks of using this mod, such as malware or ban from the game.</p>
-<p>If you are interested in Archero Mod APK 5play.ru, you can visit the official website of 5play.ru and download it for free. You can also check out other modded games and apps that they offer on their website. However, if you prefer to play Archero without any mods or cheats, you can download the original version of Archero from Google Play Store or App Store.</p>
-<p>Whatever version of Archero you choose to play, we hope that this article has been helpful and informative for you. We hope that you have learned something new about Archero and Archero Mod APK 5play.ru, and that you will enjoy playing this game as much as we do.</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Archero and Archero Mod APK 5play.ru:</p>
-<ul>
-<li><b>Q: Is Archero Mod APK 5play.ru safe to use?</b></li>
-<li>A: Archero Mod APK 5play.ru is not an official version of Archero, so it may not be safe or secure to use. There is a possibility that the mod file may contain malware or viruses that could harm your device or data. There is also a possibility that the game developers may detect the mod and ban you from the game. Therefore, you should use Archero Mod APK 5play.ru at your own risk and discretion.</li>
-<li><b>Q: How can I update Archero Mod APK 5play.ru?</b></li>
-<li>A: Archero Mod APK 5play.ru may not be compatible with the latest version of Archero, so you may need to update it regularly to avoid any issues or errors. To update Archero Mod APK 5play.ru, you have to visit the official website of 5play.ru and download the latest version of the mod file. You have to uninstall the previous version of the mod file and install the new one following the same steps as before.</li>
-<li><b>Q: Can I play Archero Mod APK 5play.ru online or offline?</b></li>
-<li>A: Archero Mod APK 5play.ru can be played both online and offline. However, some features and functions of the mod may not work properly or at all when playing offline. For example, you may not be able to access the daily events and challenges, or get the latest updates and bug fixes. Therefore, it is recommended that you play Archero Mod APK 5play.ru online whenever possible.</li>
-<li><b>Q: Can I play Archero Mod APK 5play.ru with my friends or other players?</b></li>
-<li>A: Archero Mod APK 5play.ru does not support multiplayer or co-op mode, so you cannot play it with your friends or other players. The game is designed to be a single-player experience, where you have to fight against enemies and bosses alone. However, you can still compare your scores and achievements with your friends or other players on the leaderboard or social media.</li>
-<li><b>Q: Can I switch between Archero and Archero Mod APK 5play.ru?</b></li>
-<li>A: You cannot have both Archero and Archero Mod APK 5play.ru installed on your device at the same time, as they will conflict with each other and cause errors or crashes. You have to uninstall one version before installing the other one. However, you can switch between them by backing up your data and restoring it on the other version. To do this, you have to use a third-party app like Titanium Backup or Helium Backup, which can backup and restore your data on your device or cloud storage.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Assassin APK Mod The Most Addictive Stealth Action Game Ever.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Assassin APK Mod The Most Addictive Stealth Action Game Ever.md
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-<p>Do you love playing stealth games on your Android device? Do you want to experience the thrill of being a hired killer who can eliminate targets without being detected? If yes, then you might want to try Assassin, a popular tactical action arcade game with over 100 million downloads. But wait, there's more. You can also enjoy the game with unlimited gems, unlocked characters, and no ads by using Assassin APK mod. In this article, we will tell you what an APK mod is, what Assassin is, why you should use Assassin APK mod, how to download and install it, and what features it offers. Let's get started!</p>
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-<h2>Introduction</h2>
-<h3>What is an APK mod?</h3>
-<p>An APK mod is a modified version of an original Android application package (APK) file that has been altered to provide some extra features or benefits that are not available in the official version. For example, an APK mod can give you unlimited resources, unlock premium content, remove ads, or bypass restrictions in a game or app. However, not all APK mods are safe or legal to use, so you should be careful when downloading and installing them from unknown sources.</p>
-<h3>What is Assassin?</h3>
-<p>Assassin is a tactical action arcade game developed by Ruby Game Studio and published by Voodoo. The game puts you in the role of a hired killer who has to sneak into various locations and eliminate targets without being spotted by guards or cameras. You can use your stealth skills, speed, and timing to avoid detection and complete your missions. The game has simple controls, colorful graphics, and addictive gameplay. You can also collect gems and use them to unlock new characters with different abilities and outfits.</p>
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-<p>Assassin is a fun and challenging game, but it can also be frustrating at times. You may run out of gems quickly and have to watch ads to get more. You may also find some levels too hard to beat or some characters too expensive to unlock. That's why you may want to use Assassin APK mod, which can enhance your gaming experience by giving you unlimited gems, unlocking all characters, and removing ads. With Assassin APK mod, you can enjoy the game without any limitations or interruptions.</p>
-<h2>How to download and install Assassin APK mod</h2>
-<h3>Step 1: Find a reliable source</h3>
-<p>The first step to download and install Assassin APK mod is to find a reliable source that offers the latest version of the modded file. You can search online for websites or blogs that provide links to download Assassin APK mod, but make sure they are trustworthy and virus-free. You can also check the reviews and ratings of other users who have downloaded the file before you.</p>
-<h3>Step 2: Enable unknown sources</h3>
-<p>The next step is to enable unknown sources on your Android device. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. You may see a warning message that installing apps from unknown sources may harm your device, but don't worry, as long as you download from a reputable source, you should be fine.</p>
-<h3>Step 3: Download the APK file</h3>
-<p>Now that you have enabled unknown sources, you can proceed to download the Assassin APK mod file from the source you have chosen. You can use your browser or a download manager app to download the file. The file size may vary depending on the version of the mod, but it should not take too long to download.</p>
-<h3>Step 4: Install the APK file</h3>
-<p>Once you have downloaded the Assassin APK mod file, you can install it on your device. To do this, locate the file in your device's storage and tap on it. You may see a pop-up window asking you to confirm the installation. Tap on Install and wait for the process to finish. You may also see a message asking you to allow the app to access certain permissions. Tap on Allow and proceed.</p>
-<h3>Step 5: Launch the game and enjoy</h3>
-<p>The final step is to launch the game and enjoy the Assassin APK mod features. You can find the game icon on your home screen or app drawer and tap on it. You should see a loading screen with the Assassin logo and the word "Mod" on it. This means that the modded version of the game is running. You can now start playing the game with unlimited gems, unlocked characters, and no ads.</p>
-<h2>Features of Assassin APK mod</h2>
-<h3>Unlimited gems</h3>
-<p>One of the main features of Assassin APK mod is that it gives you unlimited gems, which are the in-game currency used to unlock new characters and outfits. Normally, you would have to complete missions, watch ads, or buy gems with real money to get more gems. But with Assassin APK mod, you can get as many gems as you want without spending any time or money. You can use them to unlock all the characters and customize them according to your preference.</p>
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-<h3>Unlock all characters</h3>
-<p>Another feature of Assassin APK mod is that it unlocks all the characters in the game, which are normally locked behind gem requirements or level progressions. There are over 50 characters in the game, each with their own unique abilities and outfits. Some of them are based on famous assassins from history or fiction, such as John Wick, Ezio Auditore, James Bond, and more. With Assassin APK mod, you can access all of them from the start and switch between them anytime you want.</p>
-<h3>Remove ads</h3>
-<p>The last feature of Assassin APK mod is that it removes all the ads from the game, which can be annoying and distracting at times. The official version of the game has ads that pop up after every mission or when you want to get more gems or revive. These ads can interrupt your gameplay and ruin your immersion. But with Assassin APK mod, you can play the game without any ads and enjoy a smooth and uninterrupted gaming experience.</p>
-<h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In conclusion, Assassin APK mod is a modified version of Assassin, a popular tactical action arcade game for Android devices. It offers some extra features that are not available in the official version, such as unlimited gems, unlocked characters, and no ads. These features can enhance your gaming experience by giving you more freedom and fun. To download and install Assassin APK mod, you need to find a reliable source, enable unknown sources, download the APK file, install the APK file, and launch the game.</p>
-<h3>Call to action</h3>
-<p>If you are a fan of stealth games and want to try Assassin APK mod, what are you waiting for? Download it now and enjoy being a master assassin who can eliminate targets without being detected. You will love the game's simple controls, colorful graphics, and addictive gameplay. You will also love the game's unlimited gems, unlocked characters, and no ads. Don't miss this opportunity to play one of the best action games on Android with Assassin APK mod!</p>
- <h2>Frequently Asked Questions</h2>
-<h4>Q: Is Assassin APK mod safe to use?</h4>
-<p>A: Yes, as long as you download it from a reputable source that offers a virus-free and malware-free file. However, you should always be careful when installing apps from unknown sources and scan them with an antivirus app before opening them.</p>
-<h4>Q: Is Assassin APK mod legal to use?</h4>
-<p>A: No, not really. Using an APK mod is considered a form of piracy and violates the terms of service of the original app developer. It may also infringe on their intellectual property rights and cause them financial losses. Therefore, we do not endorse or encourage using an APK mod for any app or game.</p>
-<h4>Q: Will I get banned for using Assassin APK mod?</h4>
-<p>A: There is a possibility that you may get banned for using an APK mod if the app developer detects it and decides to take action against it. However, this is unlikely to happen with Assassin APK mod because it does not affect the online multiplayer mode or interfere with other players' gameplay. It only works on your device and does not require any root access or modification. However, you should still use it at your own risk and discretion.</p>
-<h4>Q: How can I update Assassin APK mod?</h4>
-<p>A: To update Assassin APK mod, you need to download the latest version of the modded file from the same source you downloaded it from before. Then, you need to uninstall the previous version of the game and install the new one. You may lose your progress and data if you do this, so make sure you back them up before updating.</p>
-<h4>Q: What are some alternatives to Assassin APK mod?</h4>
-<p>A: If you are looking for some alternatives to Assassin APK mod, you may want to check out some other stealth games for Android, such as Hitman Sniper, Stealth Master, or Ninja Arashi. These games also offer exciting and challenging gameplay with different scenarios and objectives. However, they may not have the same features or benefits as Assassin APK mod.</p> 197e85843d<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Cricket League v1.9.0 Mod APK and Unlock All Features.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Cricket League v1.9.0 Mod APK and Unlock All Features.md
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-
-<h1>Cricket League v1.9.0 Mod Apk: A Complete Guide</h1>
-<p>Do you love cricket and want to play it on your mobile device? If yes, then you should try <strong>Cricket League</strong>, a realistic and immersive cricket game that lets you experience the thrill of the sport. But what if you want to unlock all the features and modes of the game without spending any money? Well, that's where <strong>Cricket League v1.9.0 mod apk</strong> comes in handy.</p>
-<p>In this article, we will tell you everything you need to know about Cricket League v1.9.0 mod apk, including what it is, how to download and install it, why you should use it, and some tips and tricks for playing it. We will also compare it with other popular cricket games and answer some frequently asked questions.</p>
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-<h2>What is Cricket League?</h2>
-<p>Cricket League is a cricket game developed by <a href="(^1^)">Moddroid</a>, a team of passionate game developers who love to create fun and engaging games for mobile devices. Cricket League is one of their most popular games, with over 10 million downloads on Google Play Store.</p>
-<p>Cricket League allows you to play cricket in various modes, such as Quick Match, Tournament, Test Match, T20, and World Cup. You can choose from 16 different teams, each with their own strengths and weaknesses, and customize your players with different outfits, accessories, and skills. You can also enjoy realistic graphics, animations, sound effects, and commentary that make you feel like you are on the field.</p>
-<h3>Features of Cricket League</h3>
-<p>Some of the features of Cricket League are:</p>
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-<ul>
-<li><strong>Realistic gameplay:</strong> You can experience the real physics and mechanics of cricket, such as swing, spin, bounce, seam, and reverse swing. You can also control the speed, direction, and angle of your shots and deliveries.</li>
-<li><strong>Multiple modes:</strong> You can play in various modes that suit your mood and preference, such as Quick Match, Tournament, Test Match, T20, and World Cup. You can also choose from different difficulty levels, from easy to hard.</li>
-<li><strong>Diverse teams:</strong> You can choose from 16 different teams from around the world, each with their own stats and ratings. You can also customize your players with different outfits, accessories, and skills.</li>
-<li><strong>Stunning graphics:</strong> You can enjoy high-quality graphics that bring the game to life, with realistic stadiums, pitches, weather conditions, and crowd reactions.</li>
-<li><strong>Immersive sound effects:</strong> You can hear the sound of the ball hitting the bat, the roar of the crowd, the cheers of your teammates, and the commentary of the experts.</li>
-<li><strong>Social features:</strong> You can share your achievements and scores with your friends on social media platforms like Facebook and Twitter. You can also challenge your friends to online matches and see who is the better player.</li>
-</ul>
-<h3>How to download and install Cricket League v1.9.0 mod apk?</h3>
-<p>If you want to download and install Cricket League v1.9.0 mod apk on your Android device, you need to follow these steps - Step 1: Go to the <a href="">download page</a> of Cricket League v1.9.0 mod apk and click on the download button. You will be redirected to another page where you need to verify that you are not a robot by completing a captcha. - Step 2: After verifying that you are not a robot, you will see a countdown timer. Wait for a few seconds until the timer reaches zero and then click on the download button again. You will see a pop-up window asking you to save the file. Choose a location where you want to save the file and click on OK. - Step 3: Once the download is complete, go to the location where you saved the file and tap on it. You will see a warning message saying that installing apps from unknown sources may harm your device. Ignore this message and click on Settings. You will be taken to the Security settings of your device. Enable the option that says "Allow installation of apps from unknown sources" and go back to the file. - Step 4: Tap on the file again and click on Install. You will see a progress bar showing the installation process. Wait for a few seconds until the installation is complete and then click on Open. You have successfully installed Cricket League v1.9.0 mod apk on your device. <h2>Why use Cricket League v1.9.0 mod apk?</h2>
-<p>You may be wondering why you should use Cricket League v1.9.0 mod apk instead of the original version of the game. Well, there are several reasons why you should use Cricket League v1.9.0 mod apk, such as:</p>
-<h3>Benefits of Cricket League v1.9.0 mod apk</h3>
-<p>Some of the benefits of Cricket League v1.9.0 mod apk are:</p>
-<ul>
-<li><strong>Unlimited money:</strong> With Cricket League v1.9.0 mod apk, you will have unlimited money in your account, which means you can buy anything you want in the game, such as outfits, accessories, skills, power-ups, and boosters.</li>
-<li><strong>All modes unlocked:</strong> With Cricket League v1.9.0 mod apk, you will have access to all the modes of the game, such as Quick Match, Tournament, Test Match, T20, and World Cup, without having to complete any requirements or achievements.</li>
-<li><strong>All teams unlocked:</strong> With Cricket League v1.9.0 mod apk, you will have access to all the teams of the game, such as India, Australia, England, Pakistan, South Africa, and more, without having to unlock them by playing or paying.</li>
-<li><strong>No ads:</strong> With Cricket League v1.9.0 mod apk, you will not see any ads in the game, which means you can enjoy the game without any interruptions or distractions.</li>
-</ul>
-<h3>Risks of Cricket League v1.9.0 mod apk</h3>
-<p>However, there are also some risks of using Cricket League v1.9.0 mod apk, such as:</p>
-<ul>
-<li><strong>Potential malware:</strong> Since Cricket League v1.9.0 mod apk is not an official version of the game, it may contain malware or viruses that can harm your device or steal your personal information.</li>
-<li><strong>Ban from online mode:</strong> Since Cricket League v1.9.0 mod apk is a modified version of the game, it may not be compatible with the online mode of the game, which means you may not be able to play with other players online or challenge your friends.</li>
-<li><strong>Lack of updates:</strong> Since Cricket League v1.9.0 mod apk is not an official version of the game, it may not receive regular updates from the developers, which means you may miss out on new features, bug fixes, and improvements.</li>
-</ul>
-<h2>Tips and tricks for playing Cricket League v1.9.0 mod apk</h2>
-<p>If you want to play Cricket League v1.9.0 mod apk and have fun with it, here are some tips and tricks that can help you:</p>
-<h3>Choose your team wisely</h3>
-<p>One of the most important decisions you have to make in Cricket League is choosing your team. Each team has its own strengths and weaknesses, such as batting, bowling, fielding, and stamina. You should choose a team that suits your play style and preference.</p>
-<p>For example, if you like to score big runs and hit boundaries, you should choose a team that has strong batsmen and hitters, such as India or Australia. If you like to bowl fast and take wickets, you should choose a team that has fast bow , stadiums, and features. Some of the similarities and differences between Cricket League and Real Cricket 20 are:</p>
- | Similarities | Differences | | --- | --- | | Both games have realistic gameplay, graphics, sound effects, and commentary. | Real Cricket 20 has more realistic features, such as DRS, Ultra Edge, Hotspot, etc. | | Both games have multiple teams from around the world to choose from. | Real Cricket 20 has more teams, such as Nepal, UAE, Hong Kong, etc. | | Both games have customizable players with different outfits, accessories, and skills. | Real Cricket 20 has more customization options, such as stickers, banners, logos, etc. | | Both games have social features that allow you to share your scores and achievements with your friends. | Real Cricket 20 has more social features, such as live events, tournaments, auctions, etc. | <h3>Cricket League vs Stick Cricket Super League</h3>
-<p><a href="">Stick Cricket Super League</a> is a fun and casual cricket game that offers a different style of gameplay, graphics, and features. Some of the similarities and differences between Cricket League and Stick Cricket Super League are:</p>
- | Similarities | Differences | | --- | --- | | Both games have multiple modes to play in, such as Quick Match, Tournament, World Cup, etc. | Stick Cricket Super League has a more arcade-style gameplay, with simple controls and fast-paced action. | | Both games have multiple teams to choose from. | Stick Cricket Super League has more fictional teams, such as Mumbai Monsters, Sydney Smashers, London Lions, etc. | | Both games have customizable players with different outfits, accessories, and skills. | Stick Cricket Super League has more humorous and quirky customization options, such as masks, hats, glasses, etc. | | Both games have social features that allow you to share your scores and achievements with your friends. | Stick Cricket Super League has more competitive features, such as leaderboards, trophies, badges, etc. | <h2>Conclusion</h2>
-<p>Cricket League v1.9.0 mod apk is a great option for cricket lovers who want to enjoy the game on their mobile devices without any limitations or restrictions. It offers realistic and immersive gameplay, graphics, sound effects, and commentary that make you feel like you are on the field. It also gives you unlimited money and access to all the modes and teams of the game.</p>
-<p>However, you should also be aware of the risks of using Cricket League v1.9.0 mod apk, such as potential malware, ban from online mode, and lack of updates. You should also compare it with other cricket games available on the market and see which one suits your taste and preference better.</p>
-<p>We hope this article has helped you understand everything you need to know about Cricket League v1.9.0 mod apk. If you have any questions or feedback, please feel free to leave a comment below.</p>
-<h3>FAQs</h3>
-<p>Here are some frequently asked questions about Cricket League v1.9.0 mod apk:</p>
-<ol>
-<li><strong>Is Cricket League v1.9.0 mod apk safe to use?</strong></li>
-<p>Cricket League v1.9.0 mod apk is not an official version of the game and may contain malware or viruses that can harm your device or steal your personal information. You should download it from a trusted source and scan it with an antivirus before installing it.</p>
-<li><strong>Is Cricket League v1.9.0 mod apk compatible with my device?</strong></li>
-<p>Cricket League v1.9.0 mod apk is compatible with most Android devices that have Android 4.4 or higher versions installed.</p>
-<li><strong>How can I update Cricket League v1.9.0 mod apk?</strong></li>
-<p>Cricket League v1.9.0 mod apk may not receive regular updates from the developers and may become outdated or incompatible with the original version of the game. You should check for new versions of the mod apk from time to time and download them from a trusted source.</p>
-<li><strong>How can I uninstall Cricket League v1.9.0 mod apk?</strong></li>
-<p>You can uninstall Cricket League v1.9.0 mod apk by following these steps:</p>
-<ul>
-<li>Go to the Settings of your device and tap on Apps or Applications.</li>
-<li>Find Cricket League v1.9.0 mod apk in the list of apps and tap on it.</li>
-<li>Tap on Uninstall and confirm your choice.</li>
-</ul>
-<li><strong>Where can I find more cricket games like Cricket League v1.9.0 mod apk?</strong></li>
-<p>You can find more cricket games like Cricket League v1 .9.0 mod apk on Google Play Store or other app stores. Some of the most popular ones are World Cricket Championship 2, Real Cricket 20, Stick Cricket Super League, and more. You can also search for mod apk versions of these games if you want to enjoy unlimited features and modes.</p> 197e85843d<br />
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diff --git a/spaces/timmostone/stabilityai-stable-diffusion-2/README.md b/spaces/timmostone/stabilityai-stable-diffusion-2/README.md
deleted file mode 100644
index 2074d4cb4a0d385e4a3fdc77267b9688de2bb1c5..0000000000000000000000000000000000000000
--- a/spaces/timmostone/stabilityai-stable-diffusion-2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stabilityai Stable Diffusion 2
-emoji: 🚀
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tioseFevbu/cartoon-converter/Gta Mumbai City Game Setup Free Download Full Version.rar !FREE!.md b/spaces/tioseFevbu/cartoon-converter/Gta Mumbai City Game Setup Free Download Full Version.rar !FREE!.md
deleted file mode 100644
index da1489f11fa30b40850afc503016513ff8664b23..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/Gta Mumbai City Game Setup Free Download Full Version.rar !FREE!.md	
+++ /dev/null
@@ -1,90 +0,0 @@
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Bde Administrator Windows 7 Download Free BEST.md b/spaces/tioseFevbu/cartoon-converter/scripts/Bde Administrator Windows 7 Download Free BEST.md
deleted file mode 100644
index c62469af4a170feda6398ccd426d7de94a596d0e..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Bde Administrator Windows 7 Download Free BEST.md	
+++ /dev/null
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-<br />
-<h1>How to Download and Install BDE Administrator for Windows 7</h1>
-<p>BDE Administrator is a tool that allows you to configure and manage the Borland Database Engine (BDE), a set of libraries that enables you to work with various databases, such as Paradox, dBASE, FoxPro, MS Access, and SQL. BDE Administrator can also help you connect to external ODBC drivers and transfer data between different databases.</p>
-<p>However, BDE Administrator is an old software that was discontinued by Borland in 2002, and it may not work properly on modern systems, especially on 64-bit Windows. If you still need to use BDE Administrator on Windows 7, you may encounter some compatibility issues and errors. In this article, we will show you how to download and install BDE Administrator for Windows 7, and how to fix some common problems.</p>
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-<p><a href="https://softradar.com/borland-database-engine/">https://softradar.com/borland-database-engine/</a></p>
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-<p>Once you have downloaded the installer file, you need to run it as an administrator to install BDE Administrator on your Windows 7 system. To do this, right-click on the file and select "Run as administrator" from the context menu. This will launch the installation wizard for BDE Administrator.</p>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/CFD 2013 X Force 2013 X64.exe.iso !FULL!.md b/spaces/tioseFevbu/cartoon-converter/scripts/CFD 2013 X Force 2013 X64.exe.iso !FULL!.md
deleted file mode 100644
index 71987823fdd7d361c0b9d95a93eedcf87a6ef52f..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/CFD 2013 X Force 2013 X64.exe.iso !FULL!.md	
+++ /dev/null
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-<h2>What is the difference between CFD 2013 and CFD Ultimate 2019?</h2>
-<p>CFD 2013 and CFD Ultimate 2019 are both software products developed by Autodesk for computational fluid dynamics analysis. However, they have some differences in terms of features, functions, and performance. Here are some of the main differences:</p>
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-<p>If you are a fan of racing games, you might have heard of Colin Mcrae Rally 4, the fourth installment in the popular series of rally games developed and published by Codemasters. This game features realistic physics, stunning graphics, and a variety of cars and tracks to choose from. You can also customize your car and compete in different modes, such as championship, rally, stage, and multiplayer.</p>
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-<h2>Colin Mcrae Rally 4 Download Free Full Version</h2><br /><p><b><b>DOWNLOAD</b>  <a href="https://urlcod.com/2uHwnS">https://urlcod.com/2uHwnS</a></b></p><br /><br />
-<h2>What is a Full RIP Version?</h2>
-<p>A full RIP version is a compressed version of the game that has been stripped of some non-essential files, such as music, videos, or languages. This reduces the size of the game and makes it easier to download and install. However, some features or quality may be lost in the process.</p>
-<h2>What is an ISO Version?</h2>
-<p>An ISO version is an exact copy of the original CD or DVD of the game that has been converted into a single file. This preserves the full quality and functionality of the game. However, it requires more space and time to download and install. You also need a virtual drive software to mount the ISO file and run the game.</p>
-<h2>How to Download Colin Mcrae Rally 4 Full RIP Version?</h2>
-<p>One of the websites that offers a full RIP version of Colin Mcrae Rally 4 is A Real Gamer. Here are the steps to download and play the game from this website:</p>
-<ol>
-<li>Go to <a href="https://www.arealgamer.org/colin-mcrae-rally-04/">https://www.arealgamer.org/colin-mcrae-rally-04/</a> and click on the "Download" button.</li>
-<li>Wait for the download to finish. The file size is about 772 MB.</li>
-<li>Extract the ZIP file using WinRAR or 7-Zip.</li>
-<li>Open the extracted folder and run "Setup.bat" to install the game.</li>
-<li>Launch the game from "CMR4.exe" and enjoy!</li>
-</ol>
-<h2>How to Download Colin Mcrae Rally 4 ISO Version?</h2>
-<p>One of the websites that offers an ISO version of Colin Mcrae Rally 4 is My Abandonware. Here are the steps to download and play the game from this website:</p>
-<ol>
-<li>Go to <a href="https://www.myabandonware.com/game/colin-mcrae-rally-04-e52">https://www.myabandonware.com/game/colin-mcrae-rally-04-e52</a> and click on "Download ISO Version".</li>
-<li>Wait for the download to finish. The file size is about 2.1 GB.</li>
-<li>Mount the ISO file using a virtual drive software, such as Daemon Tools or PowerISO.</li>
-<li>Open the virtual drive and run "Setup.exe" to install the game.</li>
-<li>Launch the game from "CMR4.exe" and enjoy!</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Colin Mcrae Rally 4 is a great racing game that you can play for free on your PC using either a full RIP or an ISO version of the game. Both versions have their pros and cons, so you can choose the one that suits your preferences and system requirements. We hope this article was helpful and informative. Happy gaming!</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/base.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/base.py
deleted file mode 100644
index b206692a0a976d8336e3f5896eadf4765a33fb2c..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/base.py
+++ /dev/null
@@ -1,141 +0,0 @@
-from typing import FrozenSet, Iterable, Optional, Tuple, Union
-
-from pip._vendor.packaging.specifiers import SpecifierSet
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-from pip._vendor.packaging.version import LegacyVersion, Version
-
-from pip._internal.models.link import Link, links_equivalent
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.utils.hashes import Hashes
-
-CandidateLookup = Tuple[Optional["Candidate"], Optional[InstallRequirement]]
-CandidateVersion = Union[LegacyVersion, Version]
-
-
-def format_name(project: str, extras: FrozenSet[str]) -> str:
-    if not extras:
-        return project
-    canonical_extras = sorted(canonicalize_name(e) for e in extras)
-    return "{}[{}]".format(project, ",".join(canonical_extras))
-
-
-class Constraint:
-    def __init__(
-        self, specifier: SpecifierSet, hashes: Hashes, links: FrozenSet[Link]
-    ) -> None:
-        self.specifier = specifier
-        self.hashes = hashes
-        self.links = links
-
-    @classmethod
-    def empty(cls) -> "Constraint":
-        return Constraint(SpecifierSet(), Hashes(), frozenset())
-
-    @classmethod
-    def from_ireq(cls, ireq: InstallRequirement) -> "Constraint":
-        links = frozenset([ireq.link]) if ireq.link else frozenset()
-        return Constraint(ireq.specifier, ireq.hashes(trust_internet=False), links)
-
-    def __bool__(self) -> bool:
-        return bool(self.specifier) or bool(self.hashes) or bool(self.links)
-
-    def __and__(self, other: InstallRequirement) -> "Constraint":
-        if not isinstance(other, InstallRequirement):
-            return NotImplemented
-        specifier = self.specifier & other.specifier
-        hashes = self.hashes & other.hashes(trust_internet=False)
-        links = self.links
-        if other.link:
-            links = links.union([other.link])
-        return Constraint(specifier, hashes, links)
-
-    def is_satisfied_by(self, candidate: "Candidate") -> bool:
-        # Reject if there are any mismatched URL constraints on this package.
-        if self.links and not all(_match_link(link, candidate) for link in self.links):
-            return False
-        # We can safely always allow prereleases here since PackageFinder
-        # already implements the prerelease logic, and would have filtered out
-        # prerelease candidates if the user does not expect them.
-        return self.specifier.contains(candidate.version, prereleases=True)
-
-
-class Requirement:
-    @property
-    def project_name(self) -> NormalizedName:
-        """The "project name" of a requirement.
-
-        This is different from ``name`` if this requirement contains extras,
-        in which case ``name`` would contain the ``[...]`` part, while this
-        refers to the name of the project.
-        """
-        raise NotImplementedError("Subclass should override")
-
-    @property
-    def name(self) -> str:
-        """The name identifying this requirement in the resolver.
-
-        This is different from ``project_name`` if this requirement contains
-        extras, where ``project_name`` would not contain the ``[...]`` part.
-        """
-        raise NotImplementedError("Subclass should override")
-
-    def is_satisfied_by(self, candidate: "Candidate") -> bool:
-        return False
-
-    def get_candidate_lookup(self) -> CandidateLookup:
-        raise NotImplementedError("Subclass should override")
-
-    def format_for_error(self) -> str:
-        raise NotImplementedError("Subclass should override")
-
-
-def _match_link(link: Link, candidate: "Candidate") -> bool:
-    if candidate.source_link:
-        return links_equivalent(link, candidate.source_link)
-    return False
-
-
-class Candidate:
-    @property
-    def project_name(self) -> NormalizedName:
-        """The "project name" of the candidate.
-
-        This is different from ``name`` if this candidate contains extras,
-        in which case ``name`` would contain the ``[...]`` part, while this
-        refers to the name of the project.
-        """
-        raise NotImplementedError("Override in subclass")
-
-    @property
-    def name(self) -> str:
-        """The name identifying this candidate in the resolver.
-
-        This is different from ``project_name`` if this candidate contains
-        extras, where ``project_name`` would not contain the ``[...]`` part.
-        """
-        raise NotImplementedError("Override in subclass")
-
-    @property
-    def version(self) -> CandidateVersion:
-        raise NotImplementedError("Override in subclass")
-
-    @property
-    def is_installed(self) -> bool:
-        raise NotImplementedError("Override in subclass")
-
-    @property
-    def is_editable(self) -> bool:
-        raise NotImplementedError("Override in subclass")
-
-    @property
-    def source_link(self) -> Optional[Link]:
-        raise NotImplementedError("Override in subclass")
-
-    def iter_dependencies(self, with_requires: bool) -> Iterable[Optional[Requirement]]:
-        raise NotImplementedError("Override in subclass")
-
-    def get_install_requirement(self) -> Optional[InstallRequirement]:
-        raise NotImplementedError("Override in subclass")
-
-    def format_for_error(self) -> str:
-        raise NotImplementedError("Subclass should override")
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/vcs/versioncontrol.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/vcs/versioncontrol.py
deleted file mode 100644
index 02bbf68e7ad3ce14f191af24260312e817e12df7..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/vcs/versioncontrol.py
+++ /dev/null
@@ -1,705 +0,0 @@
-"""Handles all VCS (version control) support"""
-
-import logging
-import os
-import shutil
-import sys
-import urllib.parse
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Mapping,
-    Optional,
-    Tuple,
-    Type,
-    Union,
-)
-
-from pip._internal.cli.spinners import SpinnerInterface
-from pip._internal.exceptions import BadCommand, InstallationError
-from pip._internal.utils.misc import (
-    HiddenText,
-    ask_path_exists,
-    backup_dir,
-    display_path,
-    hide_url,
-    hide_value,
-    is_installable_dir,
-    rmtree,
-)
-from pip._internal.utils.subprocess import (
-    CommandArgs,
-    call_subprocess,
-    format_command_args,
-    make_command,
-)
-from pip._internal.utils.urls import get_url_scheme
-
-if TYPE_CHECKING:
-    # Literal was introduced in Python 3.8.
-    #
-    # TODO: Remove `if TYPE_CHECKING` when dropping support for Python 3.7.
-    from typing import Literal
-
-
-__all__ = ["vcs"]
-
-
-logger = logging.getLogger(__name__)
-
-AuthInfo = Tuple[Optional[str], Optional[str]]
-
-
-def is_url(name: str) -> bool:
-    """
-    Return true if the name looks like a URL.
-    """
-    scheme = get_url_scheme(name)
-    if scheme is None:
-        return False
-    return scheme in ["http", "https", "file", "ftp"] + vcs.all_schemes
-
-
-def make_vcs_requirement_url(
-    repo_url: str, rev: str, project_name: str, subdir: Optional[str] = None
-) -> str:
-    """
-    Return the URL for a VCS requirement.
-
-    Args:
-      repo_url: the remote VCS url, with any needed VCS prefix (e.g. "git+").
-      project_name: the (unescaped) project name.
-    """
-    egg_project_name = project_name.replace("-", "_")
-    req = f"{repo_url}@{rev}#egg={egg_project_name}"
-    if subdir:
-        req += f"&subdirectory={subdir}"
-
-    return req
-
-
-def find_path_to_project_root_from_repo_root(
-    location: str, repo_root: str
-) -> Optional[str]:
-    """
-    Find the the Python project's root by searching up the filesystem from
-    `location`. Return the path to project root relative to `repo_root`.
-    Return None if the project root is `repo_root`, or cannot be found.
-    """
-    # find project root.
-    orig_location = location
-    while not is_installable_dir(location):
-        last_location = location
-        location = os.path.dirname(location)
-        if location == last_location:
-            # We've traversed up to the root of the filesystem without
-            # finding a Python project.
-            logger.warning(
-                "Could not find a Python project for directory %s (tried all "
-                "parent directories)",
-                orig_location,
-            )
-            return None
-
-    if os.path.samefile(repo_root, location):
-        return None
-
-    return os.path.relpath(location, repo_root)
-
-
-class RemoteNotFoundError(Exception):
-    pass
-
-
-class RemoteNotValidError(Exception):
-    def __init__(self, url: str):
-        super().__init__(url)
-        self.url = url
-
-
-class RevOptions:
-
-    """
-    Encapsulates a VCS-specific revision to install, along with any VCS
-    install options.
-
-    Instances of this class should be treated as if immutable.
-    """
-
-    def __init__(
-        self,
-        vc_class: Type["VersionControl"],
-        rev: Optional[str] = None,
-        extra_args: Optional[CommandArgs] = None,
-    ) -> None:
-        """
-        Args:
-          vc_class: a VersionControl subclass.
-          rev: the name of the revision to install.
-          extra_args: a list of extra options.
-        """
-        if extra_args is None:
-            extra_args = []
-
-        self.extra_args = extra_args
-        self.rev = rev
-        self.vc_class = vc_class
-        self.branch_name: Optional[str] = None
-
-    def __repr__(self) -> str:
-        return f"<RevOptions {self.vc_class.name}: rev={self.rev!r}>"
-
-    @property
-    def arg_rev(self) -> Optional[str]:
-        if self.rev is None:
-            return self.vc_class.default_arg_rev
-
-        return self.rev
-
-    def to_args(self) -> CommandArgs:
-        """
-        Return the VCS-specific command arguments.
-        """
-        args: CommandArgs = []
-        rev = self.arg_rev
-        if rev is not None:
-            args += self.vc_class.get_base_rev_args(rev)
-        args += self.extra_args
-
-        return args
-
-    def to_display(self) -> str:
-        if not self.rev:
-            return ""
-
-        return f" (to revision {self.rev})"
-
-    def make_new(self, rev: str) -> "RevOptions":
-        """
-        Make a copy of the current instance, but with a new rev.
-
-        Args:
-          rev: the name of the revision for the new object.
-        """
-        return self.vc_class.make_rev_options(rev, extra_args=self.extra_args)
-
-
-class VcsSupport:
-    _registry: Dict[str, "VersionControl"] = {}
-    schemes = ["ssh", "git", "hg", "bzr", "sftp", "svn"]
-
-    def __init__(self) -> None:
-        # Register more schemes with urlparse for various version control
-        # systems
-        urllib.parse.uses_netloc.extend(self.schemes)
-        super().__init__()
-
-    def __iter__(self) -> Iterator[str]:
-        return self._registry.__iter__()
-
-    @property
-    def backends(self) -> List["VersionControl"]:
-        return list(self._registry.values())
-
-    @property
-    def dirnames(self) -> List[str]:
-        return [backend.dirname for backend in self.backends]
-
-    @property
-    def all_schemes(self) -> List[str]:
-        schemes: List[str] = []
-        for backend in self.backends:
-            schemes.extend(backend.schemes)
-        return schemes
-
-    def register(self, cls: Type["VersionControl"]) -> None:
-        if not hasattr(cls, "name"):
-            logger.warning("Cannot register VCS %s", cls.__name__)
-            return
-        if cls.name not in self._registry:
-            self._registry[cls.name] = cls()
-            logger.debug("Registered VCS backend: %s", cls.name)
-
-    def unregister(self, name: str) -> None:
-        if name in self._registry:
-            del self._registry[name]
-
-    def get_backend_for_dir(self, location: str) -> Optional["VersionControl"]:
-        """
-        Return a VersionControl object if a repository of that type is found
-        at the given directory.
-        """
-        vcs_backends = {}
-        for vcs_backend in self._registry.values():
-            repo_path = vcs_backend.get_repository_root(location)
-            if not repo_path:
-                continue
-            logger.debug("Determine that %s uses VCS: %s", location, vcs_backend.name)
-            vcs_backends[repo_path] = vcs_backend
-
-        if not vcs_backends:
-            return None
-
-        # Choose the VCS in the inner-most directory. Since all repository
-        # roots found here would be either `location` or one of its
-        # parents, the longest path should have the most path components,
-        # i.e. the backend representing the inner-most repository.
-        inner_most_repo_path = max(vcs_backends, key=len)
-        return vcs_backends[inner_most_repo_path]
-
-    def get_backend_for_scheme(self, scheme: str) -> Optional["VersionControl"]:
-        """
-        Return a VersionControl object or None.
-        """
-        for vcs_backend in self._registry.values():
-            if scheme in vcs_backend.schemes:
-                return vcs_backend
-        return None
-
-    def get_backend(self, name: str) -> Optional["VersionControl"]:
-        """
-        Return a VersionControl object or None.
-        """
-        name = name.lower()
-        return self._registry.get(name)
-
-
-vcs = VcsSupport()
-
-
-class VersionControl:
-    name = ""
-    dirname = ""
-    repo_name = ""
-    # List of supported schemes for this Version Control
-    schemes: Tuple[str, ...] = ()
-    # Iterable of environment variable names to pass to call_subprocess().
-    unset_environ: Tuple[str, ...] = ()
-    default_arg_rev: Optional[str] = None
-
-    @classmethod
-    def should_add_vcs_url_prefix(cls, remote_url: str) -> bool:
-        """
-        Return whether the vcs prefix (e.g. "git+") should be added to a
-        repository's remote url when used in a requirement.
-        """
-        return not remote_url.lower().startswith(f"{cls.name}:")
-
-    @classmethod
-    def get_subdirectory(cls, location: str) -> Optional[str]:
-        """
-        Return the path to Python project root, relative to the repo root.
-        Return None if the project root is in the repo root.
-        """
-        return None
-
-    @classmethod
-    def get_requirement_revision(cls, repo_dir: str) -> str:
-        """
-        Return the revision string that should be used in a requirement.
-        """
-        return cls.get_revision(repo_dir)
-
-    @classmethod
-    def get_src_requirement(cls, repo_dir: str, project_name: str) -> str:
-        """
-        Return the requirement string to use to redownload the files
-        currently at the given repository directory.
-
-        Args:
-          project_name: the (unescaped) project name.
-
-        The return value has a form similar to the following:
-
-            {repository_url}@{revision}#egg={project_name}
-        """
-        repo_url = cls.get_remote_url(repo_dir)
-
-        if cls.should_add_vcs_url_prefix(repo_url):
-            repo_url = f"{cls.name}+{repo_url}"
-
-        revision = cls.get_requirement_revision(repo_dir)
-        subdir = cls.get_subdirectory(repo_dir)
-        req = make_vcs_requirement_url(repo_url, revision, project_name, subdir=subdir)
-
-        return req
-
-    @staticmethod
-    def get_base_rev_args(rev: str) -> List[str]:
-        """
-        Return the base revision arguments for a vcs command.
-
-        Args:
-          rev: the name of a revision to install.  Cannot be None.
-        """
-        raise NotImplementedError
-
-    def is_immutable_rev_checkout(self, url: str, dest: str) -> bool:
-        """
-        Return true if the commit hash checked out at dest matches
-        the revision in url.
-
-        Always return False, if the VCS does not support immutable commit
-        hashes.
-
-        This method does not check if there are local uncommitted changes
-        in dest after checkout, as pip currently has no use case for that.
-        """
-        return False
-
-    @classmethod
-    def make_rev_options(
-        cls, rev: Optional[str] = None, extra_args: Optional[CommandArgs] = None
-    ) -> RevOptions:
-        """
-        Return a RevOptions object.
-
-        Args:
-          rev: the name of a revision to install.
-          extra_args: a list of extra options.
-        """
-        return RevOptions(cls, rev, extra_args=extra_args)
-
-    @classmethod
-    def _is_local_repository(cls, repo: str) -> bool:
-        """
-        posix absolute paths start with os.path.sep,
-        win32 ones start with drive (like c:\\folder)
-        """
-        drive, tail = os.path.splitdrive(repo)
-        return repo.startswith(os.path.sep) or bool(drive)
-
-    @classmethod
-    def get_netloc_and_auth(
-        cls, netloc: str, scheme: str
-    ) -> Tuple[str, Tuple[Optional[str], Optional[str]]]:
-        """
-        Parse the repository URL's netloc, and return the new netloc to use
-        along with auth information.
-
-        Args:
-          netloc: the original repository URL netloc.
-          scheme: the repository URL's scheme without the vcs prefix.
-
-        This is mainly for the Subversion class to override, so that auth
-        information can be provided via the --username and --password options
-        instead of through the URL.  For other subclasses like Git without
-        such an option, auth information must stay in the URL.
-
-        Returns: (netloc, (username, password)).
-        """
-        return netloc, (None, None)
-
-    @classmethod
-    def get_url_rev_and_auth(cls, url: str) -> Tuple[str, Optional[str], AuthInfo]:
-        """
-        Parse the repository URL to use, and return the URL, revision,
-        and auth info to use.
-
-        Returns: (url, rev, (username, password)).
-        """
-        scheme, netloc, path, query, frag = urllib.parse.urlsplit(url)
-        if "+" not in scheme:
-            raise ValueError(
-                "Sorry, {!r} is a malformed VCS url. "
-                "The format is <vcs>+<protocol>://<url>, "
-                "e.g. svn+http://myrepo/svn/MyApp#egg=MyApp".format(url)
-            )
-        # Remove the vcs prefix.
-        scheme = scheme.split("+", 1)[1]
-        netloc, user_pass = cls.get_netloc_and_auth(netloc, scheme)
-        rev = None
-        if "@" in path:
-            path, rev = path.rsplit("@", 1)
-            if not rev:
-                raise InstallationError(
-                    "The URL {!r} has an empty revision (after @) "
-                    "which is not supported. Include a revision after @ "
-                    "or remove @ from the URL.".format(url)
-                )
-        url = urllib.parse.urlunsplit((scheme, netloc, path, query, ""))
-        return url, rev, user_pass
-
-    @staticmethod
-    def make_rev_args(
-        username: Optional[str], password: Optional[HiddenText]
-    ) -> CommandArgs:
-        """
-        Return the RevOptions "extra arguments" to use in obtain().
-        """
-        return []
-
-    def get_url_rev_options(self, url: HiddenText) -> Tuple[HiddenText, RevOptions]:
-        """
-        Return the URL and RevOptions object to use in obtain(),
-        as a tuple (url, rev_options).
-        """
-        secret_url, rev, user_pass = self.get_url_rev_and_auth(url.secret)
-        username, secret_password = user_pass
-        password: Optional[HiddenText] = None
-        if secret_password is not None:
-            password = hide_value(secret_password)
-        extra_args = self.make_rev_args(username, password)
-        rev_options = self.make_rev_options(rev, extra_args=extra_args)
-
-        return hide_url(secret_url), rev_options
-
-    @staticmethod
-    def normalize_url(url: str) -> str:
-        """
-        Normalize a URL for comparison by unquoting it and removing any
-        trailing slash.
-        """
-        return urllib.parse.unquote(url).rstrip("/")
-
-    @classmethod
-    def compare_urls(cls, url1: str, url2: str) -> bool:
-        """
-        Compare two repo URLs for identity, ignoring incidental differences.
-        """
-        return cls.normalize_url(url1) == cls.normalize_url(url2)
-
-    def fetch_new(
-        self, dest: str, url: HiddenText, rev_options: RevOptions, verbosity: int
-    ) -> None:
-        """
-        Fetch a revision from a repository, in the case that this is the
-        first fetch from the repository.
-
-        Args:
-          dest: the directory to fetch the repository to.
-          rev_options: a RevOptions object.
-          verbosity: verbosity level.
-        """
-        raise NotImplementedError
-
-    def switch(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
-        """
-        Switch the repo at ``dest`` to point to ``URL``.
-
-        Args:
-          rev_options: a RevOptions object.
-        """
-        raise NotImplementedError
-
-    def update(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
-        """
-        Update an already-existing repo to the given ``rev_options``.
-
-        Args:
-          rev_options: a RevOptions object.
-        """
-        raise NotImplementedError
-
-    @classmethod
-    def is_commit_id_equal(cls, dest: str, name: Optional[str]) -> bool:
-        """
-        Return whether the id of the current commit equals the given name.
-
-        Args:
-          dest: the repository directory.
-          name: a string name.
-        """
-        raise NotImplementedError
-
-    def obtain(self, dest: str, url: HiddenText, verbosity: int) -> None:
-        """
-        Install or update in editable mode the package represented by this
-        VersionControl object.
-
-        :param dest: the repository directory in which to install or update.
-        :param url: the repository URL starting with a vcs prefix.
-        :param verbosity: verbosity level.
-        """
-        url, rev_options = self.get_url_rev_options(url)
-
-        if not os.path.exists(dest):
-            self.fetch_new(dest, url, rev_options, verbosity=verbosity)
-            return
-
-        rev_display = rev_options.to_display()
-        if self.is_repository_directory(dest):
-            existing_url = self.get_remote_url(dest)
-            if self.compare_urls(existing_url, url.secret):
-                logger.debug(
-                    "%s in %s exists, and has correct URL (%s)",
-                    self.repo_name.title(),
-                    display_path(dest),
-                    url,
-                )
-                if not self.is_commit_id_equal(dest, rev_options.rev):
-                    logger.info(
-                        "Updating %s %s%s",
-                        display_path(dest),
-                        self.repo_name,
-                        rev_display,
-                    )
-                    self.update(dest, url, rev_options)
-                else:
-                    logger.info("Skipping because already up-to-date.")
-                return
-
-            logger.warning(
-                "%s %s in %s exists with URL %s",
-                self.name,
-                self.repo_name,
-                display_path(dest),
-                existing_url,
-            )
-            prompt = ("(s)witch, (i)gnore, (w)ipe, (b)ackup ", ("s", "i", "w", "b"))
-        else:
-            logger.warning(
-                "Directory %s already exists, and is not a %s %s.",
-                dest,
-                self.name,
-                self.repo_name,
-            )
-            # https://github.com/python/mypy/issues/1174
-            prompt = ("(i)gnore, (w)ipe, (b)ackup ", ("i", "w", "b"))  # type: ignore
-
-        logger.warning(
-            "The plan is to install the %s repository %s",
-            self.name,
-            url,
-        )
-        response = ask_path_exists("What to do?  {}".format(prompt[0]), prompt[1])
-
-        if response == "a":
-            sys.exit(-1)
-
-        if response == "w":
-            logger.warning("Deleting %s", display_path(dest))
-            rmtree(dest)
-            self.fetch_new(dest, url, rev_options, verbosity=verbosity)
-            return
-
-        if response == "b":
-            dest_dir = backup_dir(dest)
-            logger.warning("Backing up %s to %s", display_path(dest), dest_dir)
-            shutil.move(dest, dest_dir)
-            self.fetch_new(dest, url, rev_options, verbosity=verbosity)
-            return
-
-        # Do nothing if the response is "i".
-        if response == "s":
-            logger.info(
-                "Switching %s %s to %s%s",
-                self.repo_name,
-                display_path(dest),
-                url,
-                rev_display,
-            )
-            self.switch(dest, url, rev_options)
-
-    def unpack(self, location: str, url: HiddenText, verbosity: int) -> None:
-        """
-        Clean up current location and download the url repository
-        (and vcs infos) into location
-
-        :param url: the repository URL starting with a vcs prefix.
-        :param verbosity: verbosity level.
-        """
-        if os.path.exists(location):
-            rmtree(location)
-        self.obtain(location, url=url, verbosity=verbosity)
-
-    @classmethod
-    def get_remote_url(cls, location: str) -> str:
-        """
-        Return the url used at location
-
-        Raises RemoteNotFoundError if the repository does not have a remote
-        url configured.
-        """
-        raise NotImplementedError
-
-    @classmethod
-    def get_revision(cls, location: str) -> str:
-        """
-        Return the current commit id of the files at the given location.
-        """
-        raise NotImplementedError
-
-    @classmethod
-    def run_command(
-        cls,
-        cmd: Union[List[str], CommandArgs],
-        show_stdout: bool = True,
-        cwd: Optional[str] = None,
-        on_returncode: 'Literal["raise", "warn", "ignore"]' = "raise",
-        extra_ok_returncodes: Optional[Iterable[int]] = None,
-        command_desc: Optional[str] = None,
-        extra_environ: Optional[Mapping[str, Any]] = None,
-        spinner: Optional[SpinnerInterface] = None,
-        log_failed_cmd: bool = True,
-        stdout_only: bool = False,
-    ) -> str:
-        """
-        Run a VCS subcommand
-        This is simply a wrapper around call_subprocess that adds the VCS
-        command name, and checks that the VCS is available
-        """
-        cmd = make_command(cls.name, *cmd)
-        if command_desc is None:
-            command_desc = format_command_args(cmd)
-        try:
-            return call_subprocess(
-                cmd,
-                show_stdout,
-                cwd,
-                on_returncode=on_returncode,
-                extra_ok_returncodes=extra_ok_returncodes,
-                command_desc=command_desc,
-                extra_environ=extra_environ,
-                unset_environ=cls.unset_environ,
-                spinner=spinner,
-                log_failed_cmd=log_failed_cmd,
-                stdout_only=stdout_only,
-            )
-        except FileNotFoundError:
-            # errno.ENOENT = no such file or directory
-            # In other words, the VCS executable isn't available
-            raise BadCommand(
-                f"Cannot find command {cls.name!r} - do you have "
-                f"{cls.name!r} installed and in your PATH?"
-            )
-        except PermissionError:
-            # errno.EACCES = Permission denied
-            # This error occurs, for instance, when the command is installed
-            # only for another user. So, the current user don't have
-            # permission to call the other user command.
-            raise BadCommand(
-                f"No permission to execute {cls.name!r} - install it "
-                f"locally, globally (ask admin), or check your PATH. "
-                f"See possible solutions at "
-                f"https://pip.pypa.io/en/latest/reference/pip_freeze/"
-                f"#fixing-permission-denied."
-            )
-
-    @classmethod
-    def is_repository_directory(cls, path: str) -> bool:
-        """
-        Return whether a directory path is a repository directory.
-        """
-        logger.debug("Checking in %s for %s (%s)...", path, cls.dirname, cls.name)
-        return os.path.exists(os.path.join(path, cls.dirname))
-
-    @classmethod
-    def get_repository_root(cls, location: str) -> Optional[str]:
-        """
-        Return the "root" (top-level) directory controlled by the vcs,
-        or `None` if the directory is not in any.
-
-        It is meant to be overridden to implement smarter detection
-        mechanisms for specific vcs.
-
-        This can do more than is_repository_directory() alone. For
-        example, the Git override checks that Git is actually available.
-        """
-        if cls.is_repository_directory(location):
-            return location
-        return None
diff --git a/spaces/tomofi/ABINet-OCR/utils.py b/spaces/tomofi/ABINet-OCR/utils.py
deleted file mode 100644
index 1b7b5db1bc1dd191191c31b3e72228ccd1c4f7a1..0000000000000000000000000000000000000000
--- a/spaces/tomofi/ABINet-OCR/utils.py
+++ /dev/null
@@ -1,304 +0,0 @@
-import logging
-import os
-import time
-
-import cv2
-import numpy as np
-import torch
-import yaml
-from matplotlib import colors
-from matplotlib import pyplot as plt
-from torch import Tensor, nn
-from torch.utils.data import ConcatDataset
-
-class CharsetMapper(object):
-    """A simple class to map ids into strings.
-
-    It works only when the character set is 1:1 mapping between individual
-    characters and individual ids.
-    """
-
-    def __init__(self,
-                 filename='',
-                 max_length=30,
-                 null_char=u'\u2591'):
-        """Creates a lookup table.
-
-        Args:
-          filename: Path to charset file which maps characters to ids.
-          max_sequence_length: The max length of ids and string.
-          null_char: A unicode character used to replace '<null>' character.
-            the default value is a light shade block '░'.
-        """
-        self.null_char = null_char
-        self.max_length = max_length
-
-        self.label_to_char = self._read_charset(filename)
-        self.char_to_label = dict(map(reversed, self.label_to_char.items()))
-        self.num_classes = len(self.label_to_char)
- 
-    def _read_charset(self, filename):
-        """Reads a charset definition from a tab separated text file.
-
-        Args:
-          filename: a path to the charset file.
-
-        Returns:
-          a dictionary with keys equal to character codes and values - unicode
-          characters.
-        """
-        import re
-        pattern = re.compile(r'(\d+)\t(.+)')
-        charset = {}
-        self.null_label = 0
-        charset[self.null_label] = self.null_char
-        with open(filename, 'r') as f:
-            for i, line in enumerate(f):
-                m = pattern.match(line)
-                assert m, f'Incorrect charset file. line #{i}: {line}'
-                label = int(m.group(1)) + 1
-                char = m.group(2)
-                charset[label] = char
-        return charset
-
-    def trim(self, text):
-        assert isinstance(text, str)
-        return text.replace(self.null_char, '')
-
-    def get_text(self, labels, length=None, padding=True, trim=False):
-        """ Returns a string corresponding to a sequence of character ids.
-        """
-        length = length if length else self.max_length
-        labels = [l.item() if isinstance(l, Tensor) else int(l) for l in labels]
-        if padding:
-            labels = labels + [self.null_label] * (length-len(labels))
-        text = ''.join([self.label_to_char[label] for label in labels])
-        if trim: text = self.trim(text)
-        return text
-
-    def get_labels(self, text, length=None, padding=True, case_sensitive=False):
-        """ Returns the labels of the corresponding text.
-        """
-        length = length if length else self.max_length
-        if padding:
-            text = text + self.null_char * (length - len(text))
-        if not case_sensitive:
-            text = text.lower()
-        labels = [self.char_to_label[char] for char in text]
-        return labels
-
-    def pad_labels(self, labels, length=None):
-        length = length if length else self.max_length
-
-        return labels + [self.null_label] * (length - len(labels))
-
-    @property
-    def digits(self):
-        return '0123456789'
-
-    @property
-    def digit_labels(self):
-        return self.get_labels(self.digits, padding=False)
-
-    @property
-    def alphabets(self):
-        all_chars = list(self.char_to_label.keys())
-        valid_chars = []
-        for c in all_chars:
-            if c in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ':
-                valid_chars.append(c)
-        return ''.join(valid_chars)
-
-    @property
-    def alphabet_labels(self):
-        return self.get_labels(self.alphabets, padding=False)
-
-
-class Timer(object):
-    """A simple timer."""
-    def __init__(self):
-        self.data_time = 0.
-        self.data_diff = 0.
-        self.data_total_time = 0.
-        self.data_call = 0
-        self.running_time = 0.
-        self.running_diff = 0.
-        self.running_total_time = 0.
-        self.running_call = 0
-
-    def tic(self):
-        self.start_time = time.time()
-        self.running_time = self.start_time
-
-    def toc_data(self):
-        self.data_time = time.time()
-        self.data_diff = self.data_time - self.running_time
-        self.data_total_time += self.data_diff
-        self.data_call += 1
-
-    def toc_running(self):
-        self.running_time = time.time()
-        self.running_diff = self.running_time - self.data_time
-        self.running_total_time += self.running_diff
-        self.running_call += 1
-
-    def total_time(self):
-        return self.data_total_time + self.running_total_time
-
-    def average_time(self):
-        return  self.average_data_time() + self.average_running_time()
-
-    def average_data_time(self):
-        return self.data_total_time / (self.data_call or 1)
-
-    def average_running_time(self):
-        return self.running_total_time / (self.running_call or 1)
-
-
-class Logger(object):
-    _handle = None
-    _root = None
-
-    @staticmethod
-    def init(output_dir, name, phase):
-        format = '[%(asctime)s %(filename)s:%(lineno)d %(levelname)s {}] ' \
-                '%(message)s'.format(name)
-        logging.basicConfig(level=logging.INFO, format=format)
-
-        try: os.makedirs(output_dir)
-        except: pass
-        config_path = os.path.join(output_dir, f'{phase}.txt')
-        Logger._handle = logging.FileHandler(config_path)
-        Logger._root = logging.getLogger()
-
-    @staticmethod
-    def enable_file():
-        if Logger._handle is None or Logger._root is None:
-            raise Exception('Invoke Logger.init() first!')
-        Logger._root.addHandler(Logger._handle)
-
-    @staticmethod
-    def disable_file():
-        if Logger._handle is None or Logger._root is None:
-            raise Exception('Invoke Logger.init() first!')
-        Logger._root.removeHandler(Logger._handle)
-
-
-class Config(object):
-
-    def __init__(self, config_path, host=True):
-        def __dict2attr(d, prefix=''):
-            for k, v in d.items():
-                if isinstance(v, dict):
-                    __dict2attr(v, f'{prefix}{k}_')
-                else:
-                    if k == 'phase':
-                        assert v in ['train', 'test']
-                    if k == 'stage':
-                        assert v in ['pretrain-vision', 'pretrain-language',
-                                     'train-semi-super', 'train-super']
-                    self.__setattr__(f'{prefix}{k}', v)
-
-        assert os.path.exists(config_path), '%s does not exists!' % config_path
-        with open(config_path) as file:
-            config_dict = yaml.load(file, Loader=yaml.FullLoader)
-        with open('configs/template.yaml') as file:
-            default_config_dict = yaml.load(file, Loader=yaml.FullLoader)
-        __dict2attr(default_config_dict)
-        __dict2attr(config_dict)
-        self.global_workdir = os.path.join(self.global_workdir, self.global_name)
-
-    def __getattr__(self, item):
-        attr = self.__dict__.get(item)
-        if attr is None:
-            attr = dict()
-            prefix = f'{item}_'
-            for k, v in self.__dict__.items():
-                if k.startswith(prefix):
-                    n = k.replace(prefix, '')
-                    attr[n] = v
-            return attr if len(attr) > 0 else None
-        else:
-            return attr
-
-    def __repr__(self):
-        str = 'ModelConfig(\n'
-        for i, (k, v) in enumerate(sorted(vars(self).items())):
-            str += f'\t({i}): {k} = {v}\n'
-        str += ')'
-        return str
-
-def blend_mask(image, mask, alpha=0.5, cmap='jet', color='b', color_alpha=1.0):
-    # normalize mask
-    mask = (mask-mask.min()) / (mask.max() - mask.min() + np.finfo(float).eps)
-    if mask.shape != image.shape:
-        mask = cv2.resize(mask,(image.shape[1], image.shape[0]))
-    # get color map
-    color_map = plt.get_cmap(cmap)
-    mask = color_map(mask)[:,:,:3]
-    # convert float to uint8
-    mask = (mask * 255).astype(dtype=np.uint8)
-
-    # set the basic color
-    basic_color = np.array(colors.to_rgb(color)) * 255 
-    basic_color = np.tile(basic_color, [image.shape[0], image.shape[1], 1]) 
-    basic_color = basic_color.astype(dtype=np.uint8)
-    # blend with basic color
-    blended_img = cv2.addWeighted(image, color_alpha, basic_color, 1-color_alpha, 0)
-    # blend with mask
-    blended_img = cv2.addWeighted(blended_img, alpha, mask, 1-alpha, 0)
-
-    return blended_img
-
-def onehot(label, depth, device=None):
-    """ 
-    Args:
-        label: shape (n1, n2, ..., )
-        depth: a scalar
-
-    Returns:
-        onehot: (n1, n2, ..., depth)
-    """
-    if not isinstance(label, torch.Tensor):
-        label = torch.tensor(label, device=device)
-    onehot = torch.zeros(label.size() + torch.Size([depth]), device=device)
-    onehot = onehot.scatter_(-1, label.unsqueeze(-1), 1)
-
-    return onehot
-
-class MyDataParallel(nn.DataParallel):
-
-    def gather(self, outputs, target_device):
-        r"""
-        Gathers tensors from different GPUs on a specified device
-        (-1 means the CPU).
-        """
-        def gather_map(outputs):
-            out = outputs[0]
-            if isinstance(out, (str, int, float)):
-                return out
-            if isinstance(out, list) and isinstance(out[0], str):
-                return [o for out in outputs for o in out]
-            if isinstance(out, torch.Tensor):
-                return torch.nn.parallel._functions.Gather.apply(target_device, self.dim, *outputs)
-            if out is None:
-                return None
-            if isinstance(out, dict):
-                if not all((len(out) == len(d) for d in outputs)):
-                    raise ValueError('All dicts must have the same number of keys')
-                return type(out)(((k, gather_map([d[k] for d in outputs]))
-                                for k in out))
-            return type(out)(map(gather_map, zip(*outputs)))
-
-        # Recursive function calls like this create reference cycles.
-        # Setting the function to None clears the refcycle.
-        try:
-            res = gather_map(outputs)
-        finally:
-            gather_map = None
-        return res
-
-
-class MyConcatDataset(ConcatDataset):
-    def __getattr__(self, k): 
-        return getattr(self.datasets[0], k)
diff --git a/spaces/tomofi/MMOCR/tests/test_models/test_kie_config.py b/spaces/tomofi/MMOCR/tests/test_models/test_kie_config.py
deleted file mode 100644
index b2b1f351537a47bc8f89f1fc5684a76e311a6cfa..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/tests/test_models/test_kie_config.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-from os.path import dirname, exists, join
-
-import numpy as np
-import pytest
-import torch
-
-
-def _demo_mm_inputs(num_kernels=0, input_shape=(1, 3, 300, 300),
-                    num_items=None):  # yapf: disable
-    """Create a superset of inputs needed to run test or train batches.
-
-    Args:
-        input_shape (tuple): Input batch dimensions.
-
-        num_items (None | list[int]): Specifies the number of boxes
-            for each batch item.
-    """
-
-    (N, C, H, W) = input_shape
-    rng = np.random.RandomState(0)
-    imgs = rng.rand(*input_shape)
-
-    img_metas = [{
-        'img_shape': (H, W, C),
-        'ori_shape': (H, W, C),
-        'pad_shape': (H, W, C),
-        'filename': '<demo>.png',
-    } for _ in range(N)]
-    relations = [torch.randn(10, 10, 5) for _ in range(N)]
-    texts = [torch.ones(10, 16) for _ in range(N)]
-    gt_bboxes = [torch.Tensor([[2, 2, 4, 4]]).expand(10, 4) for _ in range(N)]
-    gt_labels = [torch.ones(10, 11).long() for _ in range(N)]
-
-    mm_inputs = {
-        'imgs': torch.FloatTensor(imgs).requires_grad_(True),
-        'img_metas': img_metas,
-        'relations': relations,
-        'texts': texts,
-        'gt_bboxes': gt_bboxes,
-        'gt_labels': gt_labels
-    }
-    return mm_inputs
-
-
-def _get_config_directory():
-    """Find the predefined detector config directory."""
-    try:
-        # Assume we are running in the source mmocr repo
-        repo_dpath = dirname(dirname(dirname(__file__)))
-    except NameError:
-        # For IPython development when this __file__ is not defined
-        import mmocr
-        repo_dpath = dirname(dirname(mmocr.__file__))
-    config_dpath = join(repo_dpath, 'configs')
-    if not exists(config_dpath):
-        raise Exception('Cannot find config path')
-    return config_dpath
-
-
-def _get_config_module(fname):
-    """Load a configuration as a python module."""
-    from mmcv import Config
-    config_dpath = _get_config_directory()
-    config_fpath = join(config_dpath, fname)
-    config_mod = Config.fromfile(config_fpath)
-    return config_mod
-
-
-def _get_detector_cfg(fname):
-    """Grab configs necessary to create a detector.
-
-    These are deep copied to allow for safe modification of parameters without
-    influencing other tests.
-    """
-    config = _get_config_module(fname)
-    config.model.class_list = None
-    model = copy.deepcopy(config.model)
-    return model
-
-
-@pytest.mark.parametrize('cfg_file', [
-    'kie/sdmgr/sdmgr_novisual_60e_wildreceipt.py',
-    'kie/sdmgr/sdmgr_unet16_60e_wildreceipt.py'
-])
-def test_sdmgr_pipeline(cfg_file):
-    model = _get_detector_cfg(cfg_file)
-
-    from mmocr.models import build_detector
-    detector = build_detector(model)
-
-    input_shape = (1, 3, 128, 128)
-
-    mm_inputs = _demo_mm_inputs(0, input_shape)
-
-    imgs = mm_inputs.pop('imgs')
-    img_metas = mm_inputs.pop('img_metas')
-    relations = mm_inputs.pop('relations')
-    texts = mm_inputs.pop('texts')
-    gt_bboxes = mm_inputs.pop('gt_bboxes')
-    gt_labels = mm_inputs.pop('gt_labels')
-
-    # Test forward train
-    losses = detector.forward(
-        imgs,
-        img_metas,
-        relations=relations,
-        texts=texts,
-        gt_bboxes=gt_bboxes,
-        gt_labels=gt_labels)
-    assert isinstance(losses, dict)
-
-    # Test forward test
-    with torch.no_grad():
-        batch_results = []
-        for idx in range(len(img_metas)):
-            result = detector.forward(
-                imgs[idx:idx + 1],
-                None,
-                return_loss=False,
-                relations=[relations[idx]],
-                texts=[texts[idx]],
-                gt_bboxes=[gt_bboxes[idx]])
-            batch_results.append(result)
-
-    # Test show_result
-    results = {'nodes': torch.randn(1, 3)}
-    boxes = [[1, 1, 2, 1, 2, 2, 1, 2]]
-    img = np.random.rand(5, 5, 3)
-    detector.show_result(img, results, boxes)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py
deleted file mode 100644
index c972d0c159676a81d997e033e4db0a2a6d9b87e2..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py
+++ /dev/null
@@ -1,12 +0,0 @@
-_base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
-model = dict(
-    pretrained='torchvision://resnet101',
-    backbone=dict(
-        type='ResNet',
-        depth=101,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/bbox/assigners/assign_result.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/bbox/assigners/assign_result.py
deleted file mode 100644
index 4639fbdba0a5b92778e1ab87d61182e54bfb9b6f..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/bbox/assigners/assign_result.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import torch
-
-from mmdet.utils import util_mixins
-
-
-class AssignResult(util_mixins.NiceRepr):
-    """Stores assignments between predicted and truth boxes.
-
-    Attributes:
-        num_gts (int): the number of truth boxes considered when computing this
-            assignment
-
-        gt_inds (LongTensor): for each predicted box indicates the 1-based
-            index of the assigned truth box. 0 means unassigned and -1 means
-            ignore.
-
-        max_overlaps (FloatTensor): the iou between the predicted box and its
-            assigned truth box.
-
-        labels (None | LongTensor): If specified, for each predicted box
-            indicates the category label of the assigned truth box.
-
-    Example:
-        >>> # An assign result between 4 predicted boxes and 9 true boxes
-        >>> # where only two boxes were assigned.
-        >>> num_gts = 9
-        >>> max_overlaps = torch.LongTensor([0, .5, .9, 0])
-        >>> gt_inds = torch.LongTensor([-1, 1, 2, 0])
-        >>> labels = torch.LongTensor([0, 3, 4, 0])
-        >>> self = AssignResult(num_gts, gt_inds, max_overlaps, labels)
-        >>> print(str(self))  # xdoctest: +IGNORE_WANT
-        <AssignResult(num_gts=9, gt_inds.shape=(4,), max_overlaps.shape=(4,),
-                      labels.shape=(4,))>
-        >>> # Force addition of gt labels (when adding gt as proposals)
-        >>> new_labels = torch.LongTensor([3, 4, 5])
-        >>> self.add_gt_(new_labels)
-        >>> print(str(self))  # xdoctest: +IGNORE_WANT
-        <AssignResult(num_gts=9, gt_inds.shape=(7,), max_overlaps.shape=(7,),
-                      labels.shape=(7,))>
-    """
-
-    def __init__(self, num_gts, gt_inds, max_overlaps, labels=None):
-        self.num_gts = num_gts
-        self.gt_inds = gt_inds
-        self.max_overlaps = max_overlaps
-        self.labels = labels
-        # Interface for possible user-defined properties
-        self._extra_properties = {}
-
-    @property
-    def num_preds(self):
-        """int: the number of predictions in this assignment"""
-        return len(self.gt_inds)
-
-    def set_extra_property(self, key, value):
-        """Set user-defined new property."""
-        assert key not in self.info
-        self._extra_properties[key] = value
-
-    def get_extra_property(self, key):
-        """Get user-defined property."""
-        return self._extra_properties.get(key, None)
-
-    @property
-    def info(self):
-        """dict: a dictionary of info about the object"""
-        basic_info = {
-            'num_gts': self.num_gts,
-            'num_preds': self.num_preds,
-            'gt_inds': self.gt_inds,
-            'max_overlaps': self.max_overlaps,
-            'labels': self.labels,
-        }
-        basic_info.update(self._extra_properties)
-        return basic_info
-
-    def __nice__(self):
-        """str: a "nice" summary string describing this assign result"""
-        parts = []
-        parts.append(f'num_gts={self.num_gts!r}')
-        if self.gt_inds is None:
-            parts.append(f'gt_inds={self.gt_inds!r}')
-        else:
-            parts.append(f'gt_inds.shape={tuple(self.gt_inds.shape)!r}')
-        if self.max_overlaps is None:
-            parts.append(f'max_overlaps={self.max_overlaps!r}')
-        else:
-            parts.append('max_overlaps.shape='
-                         f'{tuple(self.max_overlaps.shape)!r}')
-        if self.labels is None:
-            parts.append(f'labels={self.labels!r}')
-        else:
-            parts.append(f'labels.shape={tuple(self.labels.shape)!r}')
-        return ', '.join(parts)
-
-    @classmethod
-    def random(cls, **kwargs):
-        """Create random AssignResult for tests or debugging.
-
-        Args:
-            num_preds: number of predicted boxes
-            num_gts: number of true boxes
-            p_ignore (float): probability of a predicted box assinged to an
-                ignored truth
-            p_assigned (float): probability of a predicted box not being
-                assigned
-            p_use_label (float | bool): with labels or not
-            rng (None | int | numpy.random.RandomState): seed or state
-
-        Returns:
-            :obj:`AssignResult`: Randomly generated assign results.
-
-        Example:
-            >>> from mmdet.core.bbox.assigners.assign_result import *  # NOQA
-            >>> self = AssignResult.random()
-            >>> print(self.info)
-        """
-        from mmdet.core.bbox import demodata
-        rng = demodata.ensure_rng(kwargs.get('rng', None))
-
-        num_gts = kwargs.get('num_gts', None)
-        num_preds = kwargs.get('num_preds', None)
-        p_ignore = kwargs.get('p_ignore', 0.3)
-        p_assigned = kwargs.get('p_assigned', 0.7)
-        p_use_label = kwargs.get('p_use_label', 0.5)
-        num_classes = kwargs.get('p_use_label', 3)
-
-        if num_gts is None:
-            num_gts = rng.randint(0, 8)
-        if num_preds is None:
-            num_preds = rng.randint(0, 16)
-
-        if num_gts == 0:
-            max_overlaps = torch.zeros(num_preds, dtype=torch.float32)
-            gt_inds = torch.zeros(num_preds, dtype=torch.int64)
-            if p_use_label is True or p_use_label < rng.rand():
-                labels = torch.zeros(num_preds, dtype=torch.int64)
-            else:
-                labels = None
-        else:
-            import numpy as np
-            # Create an overlap for each predicted box
-            max_overlaps = torch.from_numpy(rng.rand(num_preds))
-
-            # Construct gt_inds for each predicted box
-            is_assigned = torch.from_numpy(rng.rand(num_preds) < p_assigned)
-            # maximum number of assignments constraints
-            n_assigned = min(num_preds, min(num_gts, is_assigned.sum()))
-
-            assigned_idxs = np.where(is_assigned)[0]
-            rng.shuffle(assigned_idxs)
-            assigned_idxs = assigned_idxs[0:n_assigned]
-            assigned_idxs.sort()
-
-            is_assigned[:] = 0
-            is_assigned[assigned_idxs] = True
-
-            is_ignore = torch.from_numpy(
-                rng.rand(num_preds) < p_ignore) & is_assigned
-
-            gt_inds = torch.zeros(num_preds, dtype=torch.int64)
-
-            true_idxs = np.arange(num_gts)
-            rng.shuffle(true_idxs)
-            true_idxs = torch.from_numpy(true_idxs)
-            gt_inds[is_assigned] = true_idxs[:n_assigned]
-
-            gt_inds = torch.from_numpy(
-                rng.randint(1, num_gts + 1, size=num_preds))
-            gt_inds[is_ignore] = -1
-            gt_inds[~is_assigned] = 0
-            max_overlaps[~is_assigned] = 0
-
-            if p_use_label is True or p_use_label < rng.rand():
-                if num_classes == 0:
-                    labels = torch.zeros(num_preds, dtype=torch.int64)
-                else:
-                    labels = torch.from_numpy(
-                        # remind that we set FG labels to [0, num_class-1]
-                        # since mmdet v2.0
-                        # BG cat_id: num_class
-                        rng.randint(0, num_classes, size=num_preds))
-                    labels[~is_assigned] = 0
-            else:
-                labels = None
-
-        self = cls(num_gts, gt_inds, max_overlaps, labels)
-        return self
-
-    def add_gt_(self, gt_labels):
-        """Add ground truth as assigned results.
-
-        Args:
-            gt_labels (torch.Tensor): Labels of gt boxes
-        """
-        self_inds = torch.arange(
-            1, len(gt_labels) + 1, dtype=torch.long, device=gt_labels.device)
-        self.gt_inds = torch.cat([self_inds, self.gt_inds])
-
-        self.max_overlaps = torch.cat(
-            [self.max_overlaps.new_ones(len(gt_labels)), self.max_overlaps])
-
-        if self.labels is not None:
-            self.labels = torch.cat([gt_labels, self.labels])
diff --git a/spaces/tomofi/NDLOCR/src/text_recognition/deep-text-recognition-benchmark/modules/sequence_modeling.py b/spaces/tomofi/NDLOCR/src/text_recognition/deep-text-recognition-benchmark/modules/sequence_modeling.py
deleted file mode 100644
index af32c59b2cc981be1b43412ddf4ac853d0611210..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/text_recognition/deep-text-recognition-benchmark/modules/sequence_modeling.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import torch.nn as nn
-
-
-class BidirectionalLSTM(nn.Module):
-
-    def __init__(self, input_size, hidden_size, output_size):
-        super(BidirectionalLSTM, self).__init__()
-        self.rnn = nn.LSTM(input_size, hidden_size, bidirectional=True, batch_first=True)
-        self.linear = nn.Linear(hidden_size * 2, output_size)
-
-    def forward(self, input):
-        """
-        input : visual feature [batch_size x T x input_size]
-        output : contextual feature [batch_size x T x output_size]
-        """
-        self.rnn.flatten_parameters()
-        recurrent, _ = self.rnn(input)  # batch_size x T x input_size -> batch_size x T x (2*hidden_size)
-        output = self.linear(recurrent)  # batch_size x T x output_size
-        return output
diff --git a/spaces/trysem/vintager/app.py b/spaces/trysem/vintager/app.py
deleted file mode 100644
index 6a1ff78f1c8537b277b2a3723cc692de501fb161..0000000000000000000000000000000000000000
--- a/spaces/trysem/vintager/app.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import os
-#os.system("pip install --upgrade pip")
-#os.system("pip install gradio==2.5.3")
-import onnxruntime as rt
-import sys
-import PIL
-from PIL import Image, ImageOps, ImageFile
-import numpy as np
-from pathlib import Path
-import collections
-from typing import Union, List
-import scipy.ndimage
-import requests
-
-MODEL_FILE = "ffhqu2vintage512_pix2pixHD_v1E11-inp2inst-simp.onnx"
-so = rt.SessionOptions()
-so.inter_op_num_threads = 4
-so.intra_op_num_threads = 4
-session = rt.InferenceSession(MODEL_FILE, sess_options=so)
-input_name = session.get_inputs()[0].name
-print("input_name = " + str(input_name))
-output_name = session.get_outputs()[0].name
-print("output_name = " + str(output_name))
-
-import os
-os.system("pip install dlib")
-import face_detection
-
-def array_to_image(array_in):
-    array_in = np.squeeze(255*(array_in + 1)/2)
-    array_in = np.transpose(array_in, (1, 2, 0))
-    im = Image.fromarray(array_in.astype(np.uint8))
-    return im
-
-def image_as_array(image_in):
-    im_array = np.array(image_in, np.float32)
-    im_array = (im_array/255)*2 - 1
-    im_array = np.transpose(im_array, (2, 0, 1))
-    im_array = np.expand_dims(im_array, 0)
-    return im_array
-
-def find_aligned_face(image_in, size=512):   
-    aligned_image, n_faces, quad = face_detection.align(image_in, face_index=0, output_size=size)
-    return aligned_image, n_faces, quad
-
-def align_first_face(image_in, size=512):
-    aligned_image, n_faces, quad = find_aligned_face(image_in,size=size)
-    if n_faces == 0:
-        try:
-            image_in = ImageOps.exif_transpose(image_in)
-        except:
-            print("exif problem, not rotating")
-        image_in = image_in.resize((size, size))
-        im_array = image_as_array(image_in)
-    else:
-        im_array = image_as_array(aligned_image)
-
-    return im_array
-
-def img_concat_h(im1, im2):
-    dst = Image.new('RGB', (im1.width + im2.width, im1.height))
-    dst.paste(im1, (0, 0))
-    dst.paste(im2, (im1.width, 0))
-    return dst
-
-import gradio as gr
-
-def face2vintage(
-    img: Image.Image,
-    size: int
-) -> Image.Image:
-
-    aligned_img = align_first_face(img)
-    if aligned_img is None:
-        output=None
-    else:        
-        output = session.run([output_name], {input_name: aligned_img})[0]
-        output = array_to_image(output)
-        #aligned_img = array_to_image(aligned_img).resize((output.width, output.height))
-        #output = img_concat_h(aligned_img, output)
-
-    return output
-
-def inference(img):
-    out = face2vintage(img, 512)
-    return out
-      
-  
-title = "VintageStyle AI"
-description = "Style a face to look more \"Vintage\". Upload an image with a face, or click on one of the examples below. If a face could not be detected, an image will still be created."
-article = ""
-
-examples=[['Example00001.jpg'],['Example00002.jpg'],['Example00003.jpg'],['Example00004.jpg'],['Example00005.jpg'], ['Example00006.jpg']]
-gr.Interface(
-    inference, 
-    gr.inputs.Image(type="pil", label="Input"), 
-    gr.outputs.Image(type="pil", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    css="footer{visibility:hidden}",
-    enable_queue=True,
-    allow_flagging=False
-    ).launch()
\ No newline at end of file
diff --git a/spaces/ubermenchh/zephyr_chatbot/app.py b/spaces/ubermenchh/zephyr_chatbot/app.py
deleted file mode 100644
index 1220397df80cc1677a162cfed1a8f7769ea52749..0000000000000000000000000000000000000000
--- a/spaces/ubermenchh/zephyr_chatbot/app.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import os 
-from typing import Iterator
-from text_generation import Client
-import gradio as gr
-
-model_id = 'HuggingFaceH4/zephyr-7b-beta'
-
-API_URL = "https://api-inference.huggingface.co/models/" + model_id 
-HF_TOKEN = os.environ.get('HF_READ_TOKEN', None)
-
-client = Client(
-    API_URL,
-    headers={'Authorization': f"Bearer {HF_TOKEN}"}
-)
-EOS_STRING = "</s>"
-EOT_STRING = "<EOT>"
-
-def get_prompt(message, chat_history, system_prompt):
-    texts = [f"<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n"]
-
-    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 run(message, chat_history, system_prompt, max_new_tokens=1024, temperature=0.1, top_p=0.9, top_k=50):
-    prompt = get_prompt(message, chat_history, system_prompt)
-
-    generate_kwargs = dict(
-        max_new_tokens=max_new_tokens,
-        do_sample=True,
-        top_p=top_p,
-        top_k=top_k,
-        temperature=temperature
-    )
-    stream = client.generate_stream(prompt, **generate_kwargs)
-    output = ''
-    for response in stream:
-        if any([end_token in response.token.text for end_token in [EOS_STRING, EOT_STRING]]):
-            return output
-        else:
-            output += response.token.text
-        yield output
-    return output
-
-
-DEFAULT_SYSTEM_PROMPT = """
-    You are Zephyr. You are an AI assistant, you are moderately-polite and give only true information.
-    You carefully provide accurate, factual, thoughtful, nuanced answers, and are brilliant at reasoning. 
-    If you think there might not be a correct answer, you say so. Since you are autoregressive, 
-    each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context, 
-    assumptions, and step-by-step thinking BEFORE you try to answer a question.
-"""
-MAX_MAX_NEW_TOKENS = 4096
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = 4096
-
-DESCRIPTION = """
-    # Zephyr-7b ChatBot
-"""
-
-def clear_and_save_textbox(message): return '', message
-    
-def display_input(message, history=[]):
-    history.append((message, ''))
-    return history
-
-def delete_prev_fn(history=[]):
-    try:
-        message, _ = history.pop()
-    except IndexError:
-        message = ''
-    return history, message or ''
-
-def generate(message, history_with_input, system_prompt, max_new_tokens, temperature, top_p, top_k):
-    if max_new_tokens > MAX_MAX_NEW_TOKENS:
-        raise ValueError
-
-    history = history_with_input[:-1]
-    generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)
-    try:
-        first_response = next(generator)
-        yield history + [(message, first_response)]
-    except StopIteration:
-        yield history + [(message, '')]
-    for response in generator:
-        yield history + [(message, response)]
-
-def process_example(message):
-    generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50)
-    for x in generator:
-        pass
-    return '', x
-
-def check_input_token_length(message, chat_history, system_prompt):
-    input_token_length = len(message) + len(chat_history)
-    if input_token_length > MAX_INPUT_TOKEN_LENGTH:
-        raise gr.Error(f"The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.")
-
-with gr.Blocks(theme='Taithrah/Minimal') as demo:
-    gr.Markdown(DESCRIPTION)
-
-    with gr.Group():
-        chatbot = gr.Chatbot(label='Playground')
-        with gr.Row():
-            textbox = gr.Textbox(
-                container=False,
-                show_label=False,
-                placeholder='Hi, Zephyr',
-                scale=10
-            )
-            submit_button = gr.Button('Submit', variant='primary', scale=1, min_width=0)
-
-    with gr.Row():
-        retry_button = gr.Button('Retry', variant='secondary')
-        undo_button = gr.Button('Undo', variant='secondary')
-        clear_button = gr.Button('Clear', variant='secondary')
-
-    saved_input = gr.State()
-
-    with gr.Accordion(label='Advanced options', open=False):
-        system_prompt = gr.Textbox(label='System prompt', value=DEFAULT_SYSTEM_PROMPT, lines=5, interactive=False)
-        max_new_tokens = gr.Slider(label='Max New Tokens', minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
-        temperature = gr.Slider(label='Temperature', minimum=0.1, maximum=4.0, step=0.1, value=0.1)
-        top_p = gr.Slider(label='Top-P (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9)
-        top_k = gr.Slider(label='Top-K', minimum=1, maximum=1000, step=1, value=10)
-
-    textbox.submit(
-        fn=clear_and_save_textbox,
-        inputs=textbox,
-        outputs=[textbox, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=check_input_token_length,
-        inputs=[saved_input, chatbot, system_prompt],
-        api_name=False,
-        queue=False,
-    ).success(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    button_event_preprocess = submit_button.click(
-        fn=clear_and_save_textbox,
-        inputs=textbox,
-        outputs=[textbox, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=check_input_token_length,
-        inputs=[saved_input, chatbot, system_prompt],
-        api_name=False,
-        queue=False,
-    ).success(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    retry_button.click(
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    undo_button.click(
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=lambda x: x,
-        inputs=[saved_input],
-        outputs=textbox,
-        api_name=False,
-        queue=False,
-    )
-
-    clear_button.click(
-        fn=lambda: ([], ''),
-        outputs=[chatbot, saved_input],
-        queue=False,
-        api_name=False,
-    )
-
-
-demo.queue(max_size=32).launch(show_api=False)
\ No newline at end of file
diff --git a/spaces/ulysses115/diffsvc_test/models/genshin/__init__.py b/spaces/ulysses115/diffsvc_test/models/genshin/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/umichVision/virtex-redcaps/virtex/optim/__init__.py b/spaces/umichVision/virtex-redcaps/virtex/optim/__init__.py
deleted file mode 100644
index 4fc624411a49e98c6c24735db7f3c14d076c3a27..0000000000000000000000000000000000000000
--- a/spaces/umichVision/virtex-redcaps/virtex/optim/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .lookahead import Lookahead
-
-__all__ = ["Lookahead"]
diff --git a/spaces/unstructuredio/irs-manuals/README.md b/spaces/unstructuredio/irs-manuals/README.md
deleted file mode 100644
index 06282677902707043a513c46f952edc0f526bcae..0000000000000000000000000000000000000000
--- a/spaces/unstructuredio/irs-manuals/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Irs Manuals
-emoji: 🦀
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Dating Game MP3 Where to Find and Stream the Original Soundtrack.md b/spaces/usbethFlerru/sovits-modelsV2/example/Dating Game MP3 Where to Find and Stream the Original Soundtrack.md
deleted file mode 100644
index 50cadf1eb9424b685dae65bef59d759dc9d7f1ee..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Dating Game MP3 Where to Find and Stream the Original Soundtrack.md	
+++ /dev/null
@@ -1,21 +0,0 @@
-
-<p>Around the early stage of the game, Akito will mention that his bike was stolen. Go to the beach and find Matt (Amy's brother), who will be riding it on the shore. Give Matt one of the following items to get the bike back: lunchbox, cookies, game, or MP3. <b>Note:</b> If the player is using cheats and gave themselves items, when following these steps, Matt might not show up on the beach.</p>
-<p>This in-depth nine-message series covers the subjects of singleness, dating, marriage, divorce, and remarriage. Skip Heitzig lays out sound biblical principles to help you develop good, healthy relationships.<br><br>FREE - Download Entire Series (MP3) (Help) | Buy audiobook</p>
-<h2>dating game mp3</h2><br /><p><b><b>Download Zip</b> &#9913;&#9913;&#9913; <a href="https://urlcod.com/2uyVDS">https://urlcod.com/2uyVDS</a></b></p><br /><br />
-<p>In this series, Skip Heitzig examines the unique challenges for unmarried Christians in today's world, dealing with the subjects of loneliness, dating, and finding true love.<br><br>FREE - Download Entire Series (MP3) (Help) | Buy series | Buy audiobook</p>
-<p>There are quite a few add-ons(Opens in a new window) for The Sims to keep you busy, but if you've already powered through them, maybe it's time to try out some new games. Whether you're biding your time until the next release or just want to try something new, these games can help you scratch that itch for life-simulation gameplay.</p>
-<p>If there was ever a game for these times of self-quarantine and social distancing, Animal Crossing: New Horizons for the Nintendo Switch is that game. The new installment of the long-running series puts you in charge of developing an island by building infrastructure, recruiting villagers, and performing tasks.</p>
-<p>Released in 2018, BitLife: Life Simulator (iOS(Opens in a new window), Android(Opens in a new window)) is a text-based life simulator where you make various choices through different life stages, monitor character stats, and play mini-games. The outcome of your run-through depends on the choices you make, and once your character dies, you earn achievements for the life they led. You can then choose to start a new life, replay the same character again, or continue on as one of their children.</p>
-<p>If your favorite part of The Sims is where you pair up different characters, you may also enjoy a dating simulator like Dream Daddy: A Dad Dating Simulator. In this interactive visual novel, players choose between different dads they want to date while also playing through mini-games to unlock multiple endings. You can even create your own dad. The game is available for PC, Mac, and Linux(Opens in a new window). It has also since been released on PlayStation 4(Opens in a new window) and Nintendo Switch(Opens in a new window).</p>
-<p></p>
-<p>You don't have to be a fan of the Fallout series of post-apocalyptic video games to enjoy Fallout Shelter(Opens in a new window). The game puts you in charge of a fallout shelter buried under the ground. You manage resources, build specialized rooms, and keep citizens happy by meeting their basic needs. The game moves into RPG territory by making each citizen's stats unique and customizable. Need more of a certain resource? Build specific rooms to help enhance certain characters' abilities. Want more workers? Pair people off and start reproducing. Fallout Shelter is free-to-play (with microtransactions) across iOS, Android, PC, PS4, and Switch.</p>
-<p>Family House (iOS(Opens in a new window), Android(Opens in a new window)) bills itself as the ultimate dollhouse experience, where you create a house full of family members, collect rent, and improve on your dream house. Grow and create your own food, buy new clothing, and design the interior of your home. The game also puts an emphasis on family history, allowing you to explore several generations of your family.</p>
-<p>If you enjoy HGTV house remodeling shows or just like to build and design houses in The Sims, House Flipper(Opens in a new window) on PC, PS4, and Xbox One can help expand your interests. The game lets you buy rundown houses then completely repair and remodel them for a profit. You do the demolition, construction, wiring, painting, and more.</p>
-<p>The simulation RPG My Time at Portia asks you to rebuild society by gathering resources, crafting items, clearing dungeons, and completing assignments. It's a little more action-oriented than The Sims because you do have to fight enemies, but the game maintains a good balance between both ways to play. The game is available on PC(Opens in a new window), Switch(Opens in a new window), PlayStation 4, and Xbox One.</p>
-<p>If you're more of an animal lover, Planet Zoo is a PC game(Opens in a new window) that allows you to build and maintain a zoo. You care for the animals, who all have their own specific needs, characteristics, and stats. The game also includes several different modes of play that have you follow a character's career, develop your zoo into a franchise, complete challenges, or enjoy unlimited building.</p>
-<p>Frontier Developments, the creators of the game, also have several other simulation management games(Opens in a new window), including theme park manager Planet Coaster and Jurassic World Evolution, which allows you to create your own Jurassic Park with dinosaurs. Each game has a collection of paid DLCs to keep you busy.</p>
-<p>If you're an old-school Nintendo fan, you might remember a farming simulator game called Harvest Moon. The makers of Stardew Valley(Opens in a new window) were inspired to recreate the look and feel of that original game. Create your own character, build the farm of your dreams, become a master rancher, socialize with other characters, and explore the world around you. While more of an RPG than a simulation game, it will still scratch that same itch.</p>
-<p>If you're also a fan of open-world adventure games, check out Yonder: The Cloud Catcher Chronicles(Opens in a new window). The game requires you to collect magical items throughout the land, but you can also create your own character, farm, fish, and craft items. It's a far more relaxing experience than most home console games might offer.</p>
-<p>Includes unlimited streaming via the free Bandcamp app, plus high-quality downloads of Hello My Name Is Fad, Cat Skin - The Official Motion Picture Soundtrack, Why Is Social Anxiety Ruining My Life and Why Am I Drinking So Much Beer?, and Songs To Eat Pizza And Cry To. , and , . Purchasable with gift card Buy Digital Discography £10.50 GBP (<strong>30% OFF</strong>) Send as Gift </li> </li> Share / Embed </li> </ul> 1. Game Over 03:25 lyrics buy track just like she saidit's my last trythis time i swearthat i won't diei've done this stageit's not that hardboss fight's a joke whenthe jump to get there's the hardest partwhen you press start it tricks my pixel heart into thinking you give a shitbut i know that you don'tyou press ESC and i know it's too late no 1-up will save us nowand i know that it won'tand oh i should know that my princess is another castleand oh i won't go on a quest to remind me i'm alonecaring for youis kind of likea 1 player gamewith no co-op in sighti got KO'dit's my last lifebut in this game called loveyou don't get another try 2. The Final Curtain Call 03:16 lyrics buy track oh i locked myself away and i'm on the brinkof learning how to think without your wordsprotruding through my skull, with one brief pullthey fell like bullet-wounded birds in flightwith no more chance to fight than if my eyeswere gouged and my hands boundit made me sad to think that if i had a gun i'd shoot them insteadthis story's getting old it's old it's old, the final curtain call i wish i'd kept my mouth shutoh it's all my fault, the final curtain call i wish i'd kept it shutoh a habit i can't kick and it makes me sick to think i think too much about such thingsas how yr friends perceive or if i'm naiveto not know already what they thinkso i know it's for the best i lay to rest 3. Four 04:11 lyrics buy track four little wordsfor a little hurtdarling if you go out tonightyou won't come back tonight 4. AA 03:03 lyrics buy track i had to face this on my owncuz i've been told it takes more guts to be alonei'll keep my head above the seaand remember to breatheif it's not me and it's not youlet's blame it on the drink as we usually doi'll sale with ale as my seaand you can blame megive away everything i couldn't ownkeep away from everywhere that's not my homei'm doing fine without youcuz i'm alive without you 5. A Song About Ruining Everything 02:21 lyrics buy track i was blessed with legs so i could walk awayfrom everything i promised i'd work harder to retaini was blessed with lips so i could mouth these liesand trick you into thinking i could keep you by my sideand i understand the consequence of living in my headand i understand the selfishness of wishing i was deadand i comprehend your feelings but yr not the only onewho ultimately in the end will hate what i've becomei was blessed with legs so i could run awayfrom everything i love the most and worked hard to obtaini was blessed with lips so i could sing this songto convince myself it's not my fault and it would've happened all along 6. Dating Game 02:17 lyrics buy track give me yr loveand i hardly feel itgive me yr time and i hardly need itgive me yr best dressand i hardly see itgiving yr all to impress but i'm hardly drunk enough to hold interestit's just something i thought you should know 7. Against The Wall 04:57 lyrics buy track i wish i could find the courage to go outsidewithout wondering if they'll single me out tonighti'll rise above it and when they're not looking i'll drownthe only way to go from here to queer is downi'm so sorry i'm not on cue for when you laughand my happiness is somewhere smashed inside my pastgive me self-pity and i'll show you what it's fori can't see past me with my face against the wallendless tantrum from the queen of discontenta never ending complaint from the crown on her headi was never one to kid myself out of what's realbut go ahead and blame the hormones for how i feeli wish you would make the most of yourselfgo dress like somebody elseyou always used to be such a pretty girlhair tied up in ribbons and pretty curls 8. Futile Now 03:33 lyrics buy track give me that knife so i can stab youin the back where i promised i would never lay a finger ongive me that life so i can stack youalongside every other life i prayed you would take pity onremember those days constructing plans tokeep memories alive by writing to you every dayit's futile now i'll pass blame ontothe day that's my night and all the water that got in yr wayjust for todaythese 5 months won't make it alrightif i can't see further than the end of tonightyou used to be something that could change my mindreasons to exist these days are harder to find about Collection of songs dating back from 2000-2013<br>Inspired by life, recorded in my bedroom. $(".tralbum-about").last().bcTruncate(TruncateProfile.get("tralbum_about"), "more", "less"); credits released January 4, 2011 <br><br>All words and music written, performed and recorded by Seth Corbin.<br>drawings too. $(".tralbum-credits").last().bcTruncate(TruncateProfile.get("tralbum_long"), "more", "less"); license all rights reserved tags Tags alternative anti folk ftm grunge iamanon mrsethcorbin punk rock queercore riot grrrl seth corbin trans London Shopping cart total USD Check out about Seth Corbin London</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/vishnu0001/text2mesh/shap_e/models/__init__.py b/spaces/vishnu0001/text2mesh/shap_e/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/wazhendeshiniya/White-box-Cartoonization/wbc/network.py b/spaces/wazhendeshiniya/White-box-Cartoonization/wbc/network.py
deleted file mode 100644
index 6f16cee1aa1994d0a78c524f459764de5164e637..0000000000000000000000000000000000000000
--- a/spaces/wazhendeshiniya/White-box-Cartoonization/wbc/network.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import tensorflow as tf
-import numpy as np
-import tensorflow.contrib.slim as slim
-
-
-
-def resblock(inputs, out_channel=32, name='resblock'):
-    
-    with tf.variable_scope(name):
-        
-        x = slim.convolution2d(inputs, out_channel, [3, 3], 
-                               activation_fn=None, scope='conv1')
-        x = tf.nn.leaky_relu(x)
-        x = slim.convolution2d(x, out_channel, [3, 3], 
-                               activation_fn=None, scope='conv2')
-        
-        return x + inputs
-
-
-
-
-def unet_generator(inputs, channel=32, num_blocks=4, name='generator', reuse=False):
-    with tf.variable_scope(name, reuse=reuse):
-        
-        x0 = slim.convolution2d(inputs, channel, [7, 7], activation_fn=None)
-        x0 = tf.nn.leaky_relu(x0)
-        
-        x1 = slim.convolution2d(x0, channel, [3, 3], stride=2, activation_fn=None)
-        x1 = tf.nn.leaky_relu(x1)
-        x1 = slim.convolution2d(x1, channel*2, [3, 3], activation_fn=None)
-        x1 = tf.nn.leaky_relu(x1)
-        
-        x2 = slim.convolution2d(x1, channel*2, [3, 3], stride=2, activation_fn=None)
-        x2 = tf.nn.leaky_relu(x2)
-        x2 = slim.convolution2d(x2, channel*4, [3, 3], activation_fn=None)
-        x2 = tf.nn.leaky_relu(x2)
-        
-        for idx in range(num_blocks):
-            x2 = resblock(x2, out_channel=channel*4, name='block_{}'.format(idx))
-            
-        x2 = slim.convolution2d(x2, channel*2, [3, 3], activation_fn=None)
-        x2 = tf.nn.leaky_relu(x2)
-        
-        h1, w1 = tf.shape(x2)[1], tf.shape(x2)[2]
-        x3 = tf.image.resize_bilinear(x2, (h1*2, w1*2))
-        x3 = slim.convolution2d(x3+x1, channel*2, [3, 3], activation_fn=None)
-        x3 = tf.nn.leaky_relu(x3)
-        x3 = slim.convolution2d(x3, channel, [3, 3], activation_fn=None)
-        x3 = tf.nn.leaky_relu(x3)
-
-        h2, w2 = tf.shape(x3)[1], tf.shape(x3)[2]
-        x4 = tf.image.resize_bilinear(x3, (h2*2, w2*2))
-        x4 = slim.convolution2d(x4+x0, channel, [3, 3], activation_fn=None)
-        x4 = tf.nn.leaky_relu(x4)
-        x4 = slim.convolution2d(x4, 3, [7, 7], activation_fn=None)
-        
-        return x4
-
-if __name__ == '__main__':
-    
-
-    pass
\ No newline at end of file
diff --git a/spaces/weide/ChuanhuChatGPT2/assets/Kelpy-Codos.js b/spaces/weide/ChuanhuChatGPT2/assets/Kelpy-Codos.js
deleted file mode 100644
index cfbaeedb4f371dfb5fe157db545b364046fca3e1..0000000000000000000000000000000000000000
--- a/spaces/weide/ChuanhuChatGPT2/assets/Kelpy-Codos.js
+++ /dev/null
@@ -1,76 +0,0 @@
-// ==UserScript==
-// @name         Kelpy Codos
-// @namespace    https://github.com/Keldos-Li/Kelpy-Codos
-// @version      1.0.5
-// @author       Keldos; https://keldos.me/
-// @description  Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially. 
-//               Based on Chuanhu ChatGPT version: ac04408 (2023-3-22)
-// @license      GPL-3.0
-// @grant        none
-// ==/UserScript==
-
-(function () {
-    'use strict';
-
-    function addCopyButton(pre) {
-        var code = pre.querySelector('code');
-        if (!code) {
-            return; // 如果没有找到 <code> 元素，则不添加按钮
-        }
-        var firstChild = code.firstChild;
-        if (!firstChild) {
-            return; // 如果 <code> 元素没有子节点，则不添加按钮
-        }
-        var button = document.createElement('button');
-        button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
-        button.style.position = 'relative';
-        button.style.float = 'right';
-        button.style.fontSize = '1em'; // 可选：调整按钮大小
-        button.style.background = 'none'; // 可选：去掉背景颜色
-        button.style.border = 'none'; // 可选：去掉边框
-        button.style.cursor = 'pointer'; // 可选：显示指针样式
-        button.addEventListener('click', function () {
-            var range = document.createRange();
-            range.selectNodeContents(code);
-            range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
-            var selection = window.getSelection();
-            selection.removeAllRanges();
-            selection.addRange(range);
-
-            try {
-                var success = document.execCommand('copy');
-                if (success) {
-                    button.textContent = '\u2714';
-                    setTimeout(function () {
-                        button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
-                    }, 2000);
-                } else {
-                    button.textContent = '\u2716';
-                }
-            } catch (e) {
-                console.error(e);
-                button.textContent = '\u2716';
-            }
-
-            selection.removeAllRanges();
-        });
-        code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
-    }
-
-    function handleNewElements(mutationsList, observer) {
-        for (var mutation of mutationsList) {
-            if (mutation.type === 'childList') {
-                for (var node of mutation.addedNodes) {
-                    if (node.nodeName === 'PRE') {
-                        addCopyButton(node);
-                    }
-                }
-            }
-        }
-    }
-
-    var observer = new MutationObserver(handleNewElements);
-    observer.observe(document.documentElement, { childList: true, subtree: true });
-
-    document.querySelectorAll('pre').forEach(addCopyButton);
-})();
diff --git a/spaces/wffcyrus/MetaGPT-v1/metagpt/memory/memory_storage.py b/spaces/wffcyrus/MetaGPT-v1/metagpt/memory/memory_storage.py
deleted file mode 100644
index 09cd6741083441f49b162fd255e41a9e43fca16b..0000000000000000000000000000000000000000
--- a/spaces/wffcyrus/MetaGPT-v1/metagpt/memory/memory_storage.py
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Desc   : the implement of memory storage
-@Modified By: mashenquan, 2023/8/20. Remove global configuration `CONFIG`, enable configuration support for business isolation.
-"""
-
-from typing import List
-from pathlib import Path
-
-from langchain.vectorstores.faiss import FAISS
-
-from metagpt.const import DATA_PATH, MEM_TTL
-from metagpt.logs import logger
-from metagpt.schema import Message
-from metagpt.utils.serialize import serialize_message, deserialize_message
-from metagpt.document_store.faiss_store import FaissStore
-
-
-class MemoryStorage(FaissStore):
-    """
-    The memory storage with Faiss as ANN search engine
-    """
-
-    def __init__(self, mem_ttl: int = MEM_TTL):
-        self.role_id: str = None
-        self.role_mem_path: str = None
-        self.mem_ttl: int = mem_ttl  # later use
-        self.threshold: float = 0.1  # experience value. TODO The threshold to filter similar memories
-        self._initialized: bool = False
-
-        self.store: FAISS = None  # Faiss engine
-
-    @property
-    def is_initialized(self) -> bool:
-        return self._initialized
-
-    def recover_memory(self, role_id: str) -> List[Message]:
-        self.role_id = role_id
-        self.role_mem_path = Path(DATA_PATH / f'role_mem/{self.role_id}/')
-        self.role_mem_path.mkdir(parents=True, exist_ok=True)
-
-        self.store = self._load()
-        messages = []
-        if not self.store:
-            # TODO init `self.store` under here with raw faiss api instead under `add`
-            pass
-        else:
-            for _id, document in self.store.docstore._dict.items():
-                messages.append(deserialize_message(document.metadata.get("message_ser")))
-            self._initialized = True
-
-        return messages
-
-    def _get_index_and_store_fname(self):
-        if not self.role_mem_path:
-            logger.error(f'You should call {self.__class__.__name__}.recover_memory fist when using LongTermMemory')
-            return None, None
-        index_fpath = Path(self.role_mem_path / f'{self.role_id}.index')
-        storage_fpath = Path(self.role_mem_path / f'{self.role_id}.pkl')
-        return index_fpath, storage_fpath
-
-    def persist(self):
-        super(MemoryStorage, self).persist()
-        logger.debug(f'Agent {self.role_id} persist memory into local')
-
-    def add(self, message: Message, **kwargs) -> bool:
-        """ add message into memory storage"""
-        docs = [message.content]
-        metadatas = [{"message_ser": serialize_message(message)}]
-        if not self.store:
-            # init Faiss
-            self.store = self._write(docs, metadatas, **kwargs)
-            self._initialized = True
-        else:
-            self.store.add_texts(texts=docs, metadatas=metadatas)
-        self.persist()
-        logger.info(f"Agent {self.role_id}'s memory_storage add a message")
-
-    def search(self, message: Message, k=4) -> List[Message]:
-        """search for dissimilar messages"""
-        if not self.store:
-            return []
-
-        resp = self.store.similarity_search_with_score(
-            query=message.content,
-            k=k
-        )
-        # filter the result which score is smaller than the threshold
-        filtered_resp = []
-        for item, score in resp:
-            # the smaller score means more similar relation
-            if score < self.threshold:
-                continue
-            # convert search result into Memory
-            metadata = item.metadata
-            new_mem = deserialize_message(metadata.get("message_ser"))
-            filtered_resp.append(new_mem)
-        return filtered_resp
-
-    def clean(self):
-        index_fpath, storage_fpath = self._get_index_and_store_fname()
-        if index_fpath and index_fpath.exists():
-            index_fpath.unlink(missing_ok=True)
-        if storage_fpath and storage_fpath.exists():
-            storage_fpath.unlink(missing_ok=True)
-
-        self.store = None
-        self._initialized = False
diff --git a/spaces/whgwd2023/bingo/src/pages/api/proxy.ts b/spaces/whgwd2023/bingo/src/pages/api/proxy.ts
deleted file mode 100644
index 240b5fb5561d993c6381649bf4544ce12f3cdab2..0000000000000000000000000000000000000000
--- a/spaces/whgwd2023/bingo/src/pages/api/proxy.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { fetch } from '@/lib/isomorphic'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { url, headers, method = 'GET', body } = req.body
-    if (!url) {
-      return res.end('ok')
-    }
-    const response = await fetch(url, { headers, method, body, redirect: 'manual' })
-    const text = await response.text()
-      res.writeHead(200, {
-        'Content-Type': 'application/text',
-        'x-url': response.url,
-        'x-status': response.status,
-      })
-    res.end(text)
-  } catch (e) {
-    console.log(e)
-    return res.end(e)
-  }
-}
diff --git a/spaces/wz758727829/ChuanhuChatGPT/utils.py b/spaces/wz758727829/ChuanhuChatGPT/utils.py
deleted file mode 100644
index f6e4fa4e8a9f908baa4509d7206ff3455ac57f39..0000000000000000000000000000000000000000
--- a/spaces/wz758727829/ChuanhuChatGPT/utils.py
+++ /dev/null
@@ -1,386 +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 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
-
-from presets import *
-
-# 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]]
-
-
-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"```{code}\n\n```"
-            code = markdown_to_html_with_syntax_highlight(code)
-            result.append(code)
-    result = "".join(result)
-    return result
-
-
-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 construct_token_message(token, stream=False):
-    return f"Token 计数: {token}"
-
-
-def delete_last_conversation(chatbot, history, previous_token_count):
-    if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]:
-        logging.info("由于包含报错信息，只删除chatbot记录")
-        chatbot.pop()
-        return chatbot, history
-    if len(history) > 0:
-        logging.info("删除了一组对话历史")
-        history.pop()
-        history.pop()
-    if len(chatbot) > 0:
-        logging.info("删除了一组chatbot对话")
-        chatbot.pop()
-    if len(previous_token_count) > 0:
-        logging.info("删除了一组对话的token计数记录")
-        previous_token_count.pop()
-    return (
-        chatbot,
-        history,
-        previous_token_count,
-        construct_token_message(sum(previous_token_count)),
-    )
-
-
-def save_file(filename, system, history, chatbot):
-    logging.info("保存对话历史中……")
-    os.makedirs(HISTORY_DIR, 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, 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, filename), "w", encoding="utf8") as f:
-            f.write(md_s)
-    logging.info("保存对话历史完毕")
-    return os.path.join(HISTORY_DIR, filename)
-
-
-def save_chat_history(filename, system, history, chatbot):
-    if filename == "":
-        return
-    if not filename.endswith(".json"):
-        filename += ".json"
-    return save_file(filename, system, history, chatbot)
-
-
-def export_markdown(filename, system, history, chatbot):
-    if filename == "":
-        return
-    if not filename.endswith(".md"):
-        filename += ".md"
-    return save_file(filename, system, history, chatbot)
-
-
-def load_chat_history(filename, system, history, chatbot):
-    logging.info("加载对话历史中……")
-    if type(filename) != str:
-        filename = filename.name
-    try:
-        with open(os.path.join(HISTORY_DIR, filename), "r") as f:
-            json_s = json.load(f)
-        try:
-            if type(json_s["history"][0]) == str:
-                logging.info("历史记录格式为旧版，正在转换……")
-                new_history = []
-                for index, item in enumerate(json_s["history"]):
-                    if index % 2 == 0:
-                        new_history.append(construct_user(item))
-                    else:
-                        new_history.append(construct_assistant(item))
-                json_s["history"] = new_history
-                logging.info(new_history)
-        except:
-            # 没有对话历史
-            pass
-        logging.info("加载对话历史完毕")
-        return filename, json_s["system"], json_s["history"], json_s["chatbot"]
-    except FileNotFoundError:
-        logging.info("没有找到对话历史文件，不执行任何操作")
-        return filename, system, history, chatbot
-
-
-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.info(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 = [""]
-    if plain:
-        return files
-    else:
-        return gr.Dropdown.update(choices=files)
-
-
-def get_history_names(plain=False):
-    logging.info("获取历史记录文件名列表")
-    return get_file_names(HISTORY_DIR, plain)
-
-
-def load_template(filename, mode=0):
-    logging.info(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 = []
-    logging.info("Loading template...")
-    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, value=choices[0]
-        )
-
-
-def get_template_names(plain=False):
-    logging.info("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.info(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_state():
-    logging.info("重置状态")
-    return [], [], [], construct_token_message(0)
-
-
-def reset_textbox():
-    return gr.update(value="")
-
-
-def reset_default():
-    global API_URL
-    API_URL = "https://api.openai.com/v1/chat/completions"
-    os.environ.pop("HTTPS_PROXY", None)
-    os.environ.pop("https_proxy", None)
-    return gr.update(value=API_URL), gr.update(value=""), "API URL 和代理已重置"
-
-
-def change_api_url(url):
-    global API_URL
-    API_URL = url
-    msg = f"API地址更改为了{url}"
-    logging.info(msg)
-    return msg
-
-
-def change_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 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 sha1sum(filename):
-    sha1 = hashlib.sha1()
-    sha1.update(filename.encode("utf-8"))
-    return sha1.hexdigest()
-
-
-def replace_today(prompt):
-    today = datetime.datetime.today().strftime("%Y-%m-%d")
-    return prompt.replace("{current_date}", today)
-
-
-def get_geoip():
-    response = requests.get("https://ipapi.co/json/", timeout=5)
-    try:
-        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 (
-                f"获取IP地理位置失败，因为达到了检测IP的速率限制。聊天功能可能仍然可用，但请注意，如果您的IP地址在不受支持的地区，您可能会遇到问题。"
-            )
-        else:
-            return f"获取IP地理位置失败。原因：{data['reason']}。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 = f"您的IP区域：{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
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/sort/__init__.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/sort/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/sort/tracker.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/sort/tracker.py
deleted file mode 100644
index e0bcce6c7b48e4535f21710abf226c107d5a0f99..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/sort/tracker.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-from __future__ import absolute_import
-import numpy as np
-from . import kalman_filter
-from . import linear_assignment
-from . import iou_matching
-from .track import Track
-
-
-class Tracker:
-    """
-    This is the multi-target tracker.
-    Parameters
-    ----------
-    metric : nn_matching.NearestNeighborDistanceMetric
-        A distance metric for measurement-to-track association.
-    max_age : int
-        Maximum number of missed misses before a track is deleted.
-    n_init : int
-        Number of consecutive detections before the track is confirmed. The
-        track state is set to `Deleted` if a miss occurs within the first
-        `n_init` frames.
-    Attributes
-    ----------
-    metric : nn_matching.NearestNeighborDistanceMetric
-        The distance metric used for measurement to track association.
-    max_age : int
-        Maximum number of missed misses before a track is deleted.
-    n_init : int
-        Number of frames that a track remains in initialization phase.
-    kf : kalman_filter.KalmanFilter
-        A Kalman filter to filter target trajectories in image space.
-    tracks : List[Track]
-        The list of active tracks at the current time step.
-    """
-    GATING_THRESHOLD = np.sqrt(kalman_filter.chi2inv95[4])
-
-    def __init__(self, metric, max_iou_distance=0.9, max_age=30, n_init=3, _lambda=0, ema_alpha=0.9, mc_lambda=0.995):
-        self.metric = metric
-        self.max_iou_distance = max_iou_distance
-        self.max_age = max_age
-        self.n_init = n_init
-        self._lambda = _lambda
-        self.ema_alpha = ema_alpha
-        self.mc_lambda = mc_lambda
-
-        self.kf = kalman_filter.KalmanFilter()
-        self.tracks = []
-        self._next_id = 1
-
-    def predict(self):
-        """Propagate track state distributions one time step forward.
-
-        This function should be called once every time step, before `update`.
-        """
-        for track in self.tracks:
-            track.predict(self.kf)
-
-    def increment_ages(self):
-        for track in self.tracks:
-            track.increment_age()
-            track.mark_missed()
-
-    def camera_update(self, previous_img, current_img):
-        for track in self.tracks:
-            track.camera_update(previous_img, current_img)
-
-    def update(self, detections, classes, confidences):
-        """Perform measurement update and track management.
-
-        Parameters
-        ----------
-        detections : List[deep_sort.detection.Detection]
-            A list of detections at the current time step.
-
-        """
-        # Run matching cascade.
-        matches, unmatched_tracks, unmatched_detections = \
-            self._match(detections)
-
-        # Update track set.
-        for track_idx, detection_idx in matches:
-            self.tracks[track_idx].update(
-                detections[detection_idx], classes[detection_idx], confidences[detection_idx])
-        for track_idx in unmatched_tracks:
-            self.tracks[track_idx].mark_missed()
-        for detection_idx in unmatched_detections:
-            self._initiate_track(detections[detection_idx], classes[detection_idx].item(), confidences[detection_idx].item())
-        self.tracks = [t for t in self.tracks if not t.is_deleted()]
-
-        # Update distance metric.
-        active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
-        features, targets = [], []
-        for track in self.tracks:
-            if not track.is_confirmed():
-                continue
-            features += track.features
-            targets += [track.track_id for _ in track.features]
-        self.metric.partial_fit(np.asarray(features), np.asarray(targets), active_targets)
-
-    def _full_cost_metric(self, tracks, dets, track_indices, detection_indices):
-        """
-        This implements the full lambda-based cost-metric. However, in doing so, it disregards
-        the possibility to gate the position only which is provided by
-        linear_assignment.gate_cost_matrix(). Instead, I gate by everything.
-        Note that the Mahalanobis distance is itself an unnormalised metric. Given the cosine
-        distance being normalised, we employ a quick and dirty normalisation based on the
-        threshold: that is, we divide the positional-cost by the gating threshold, thus ensuring
-        that the valid values range 0-1.
-        Note also that the authors work with the squared distance. I also sqrt this, so that it
-        is more intuitive in terms of values.
-        """
-        # Compute First the Position-based Cost Matrix
-        pos_cost = np.empty([len(track_indices), len(detection_indices)])
-        msrs = np.asarray([dets[i].to_xyah() for i in detection_indices])
-        for row, track_idx in enumerate(track_indices):
-            pos_cost[row, :] = np.sqrt(
-                self.kf.gating_distance(
-                    tracks[track_idx].mean, tracks[track_idx].covariance, msrs, False
-                )
-            ) / self.GATING_THRESHOLD
-        pos_gate = pos_cost > 1.0
-        # Now Compute the Appearance-based Cost Matrix
-        app_cost = self.metric.distance(
-            np.array([dets[i].feature for i in detection_indices]),
-            np.array([tracks[i].track_id for i in track_indices]),
-        )
-        app_gate = app_cost > self.metric.matching_threshold
-        # Now combine and threshold
-        cost_matrix = self._lambda * pos_cost + (1 - self._lambda) * app_cost
-        cost_matrix[np.logical_or(pos_gate, app_gate)] = linear_assignment.INFTY_COST
-        # Return Matrix
-        return cost_matrix
-
-    def _match(self, detections):
-
-        def gated_metric(tracks, dets, track_indices, detection_indices):
-            features = np.array([dets[i].feature for i in detection_indices])
-            targets = np.array([tracks[i].track_id for i in track_indices])
-            cost_matrix = self.metric.distance(features, targets)
-            cost_matrix = linear_assignment.gate_cost_matrix(cost_matrix, tracks, dets, track_indices, detection_indices)
-
-            return cost_matrix
-
-        # Split track set into confirmed and unconfirmed tracks.
-        confirmed_tracks = [
-            i for i, t in enumerate(self.tracks) if t.is_confirmed()]
-        unconfirmed_tracks = [
-            i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
-
-        # Associate confirmed tracks using appearance features.
-        matches_a, unmatched_tracks_a, unmatched_detections = \
-            linear_assignment.matching_cascade(
-                gated_metric, self.metric.matching_threshold, self.max_age,
-                self.tracks, detections, confirmed_tracks)
-
-        # Associate remaining tracks together with unconfirmed tracks using IOU.
-        iou_track_candidates = unconfirmed_tracks + [
-            k for k in unmatched_tracks_a if
-            self.tracks[k].time_since_update == 1]
-        unmatched_tracks_a = [
-            k for k in unmatched_tracks_a if
-            self.tracks[k].time_since_update != 1]
-        matches_b, unmatched_tracks_b, unmatched_detections = \
-            linear_assignment.min_cost_matching(
-                iou_matching.iou_cost, self.max_iou_distance, self.tracks,
-                detections, iou_track_candidates, unmatched_detections)
-
-        matches = matches_a + matches_b
-        unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
-        return matches, unmatched_tracks, unmatched_detections
-
-    def _initiate_track(self, detection, class_id, conf):
-        self.tracks.append(Track(
-            detection.to_xyah(), self._next_id, class_id, conf, self.n_init, self.max_age, self.ema_alpha,
-            detection.feature))
-        self._next_id += 1
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/utils/draw.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/utils/draw.py
deleted file mode 100644
index bc7cb537978e86805d5d9789785a8afe67df9030..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/utils/draw.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import numpy as np
-import cv2
-
-palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
-
-
-def compute_color_for_labels(label):
-    """
-    Simple function that adds fixed color depending on the class
-    """
-    color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
-    return tuple(color)
-
-
-def draw_boxes(img, bbox, identities=None, offset=(0,0)):
-    for i,box in enumerate(bbox):
-        x1,y1,x2,y2 = [int(i) for i in box]
-        x1 += offset[0]
-        x2 += offset[0]
-        y1 += offset[1]
-        y2 += offset[1]
-        # box text and bar
-        id = int(identities[i]) if identities is not None else 0    
-        color = compute_color_for_labels(id)
-        label = '{}{:d}'.format("", id)
-        t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]
-        cv2.rectangle(img,(x1, y1),(x2,y2),color,3)
-        cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1)
-        cv2.putText(img,label,(x1,y1+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 2, [255,255,255], 2)
-    return img
-
-
-
-if __name__ == '__main__':
-    for i in range(82):
-        print(compute_color_for_labels(i))
diff --git a/spaces/xiaoV28/GFPGAN/app.py b/spaces/xiaoV28/GFPGAN/app.py
deleted file mode 100644
index 939c91fb87d1ef2b431d29987ed7eebe51302c08..0000000000000000000000000000000000000000
--- a/spaces/xiaoV28/GFPGAN/app.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import os
-
-import cv2
-import gradio as gr
-import torch
-from basicsr.archs.srvgg_arch import SRVGGNetCompact
-from gfpgan.utils import GFPGANer
-from realesrgan.utils import RealESRGANer
-
-os.system("pip freeze")
-# download weights
-if not os.path.exists('realesr-general-x4v3.pth'):
-    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
-if not os.path.exists('GFPGANv1.2.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
-if not os.path.exists('GFPGANv1.3.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
-if not os.path.exists('GFPGANv1.4.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
-if not os.path.exists('RestoreFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
-if not os.path.exists('CodeFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
-
-torch.hub.download_url_to_file(
-    'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
-    'lincoln.jpg')
-torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
-    'AI-generate.jpg')
-torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
-    'Blake_Lively.jpg')
-torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
-    '10045.png')
-
-# background enhancer with RealESRGAN
-model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-model_path = 'realesr-general-x4v3.pth'
-half = True if torch.cuda.is_available() else False
-upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
-
-os.makedirs('output', exist_ok=True)
-
-
-# def inference(img, version, scale, weight):
-def inference(img, version, scale):
-    # weight /= 100
-    print(img, version, scale)
-    if scale > 4:
-        scale = 4  # avoid too large scale value
-    try:
-        extension = os.path.splitext(os.path.basename(str(img)))[1]
-        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
-        if len(img.shape) == 3 and img.shape[2] == 4:
-            img_mode = 'RGBA'
-        elif len(img.shape) == 2:  # for gray inputs
-            img_mode = None
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-        else:
-            img_mode = None
-
-        h, w = img.shape[0:2]
-        if h > 3500 or w > 3500:
-            print('too large size')
-            return None, None
-        
-        if h < 300:
-            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
-
-        if version == 'v1.2':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'v1.3':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'v1.4':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'RestoreFormer':
-            face_enhancer = GFPGANer(
-            model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
-        # elif version == 'CodeFormer':
-        #     face_enhancer = GFPGANer(
-        #     model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
-
-        try:
-            # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
-            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
-        except RuntimeError as error:
-            print('Error', error)
-
-        try:
-            if scale != 2:
-                interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
-                h, w = img.shape[0:2]
-                output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
-        except Exception as error:
-            print('wrong scale input.', error)
-        if img_mode == 'RGBA':  # RGBA images should be saved in png format
-            extension = 'png'
-        else:
-            extension = 'jpg'
-        save_path = f'output/out.{extension}'
-        cv2.imwrite(save_path, output)
-
-        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
-        return output, save_path
-    except Exception as error:
-        print('global exception', error)
-        return None, None
-
-
-title = "GFPGAN: Practical Face Restoration Algorithm"
-description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>
-It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
-To use it, simply upload your image.<br>
-If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊
-"""
-article = r"""
-
-[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
-[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2101.04061)
-
-If you have any question, please email 📧 `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
-
-<center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>
-<center><img src='https://visitor-badge.glitch.me/badge?page_id=Gradio_Xintao_GFPGAN' alt='visitor badge'></center>
-"""
-demo = gr.Interface(
-    inference, [
-        gr.Image(type="filepath", label="Input"),
-        # gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", value='v1.4', label='version'),
-        gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'),
-        gr.Number(label="Rescaling factor", value=2),
-        # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', value=50)
-    ], [
-        gr.Image(type="numpy", label="Output (The whole image)"),
-        gr.File(label="Download the output image")
-    ],
-    title=title,
-    description=description,
-    article=article,
-    # examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
-    #           ['10045.png', 'v1.4', 2, 50]]).launch()
-    examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2],
-              ['10045.png', 'v1.4', 2]])
-demo.queue().launch()
diff --git a/spaces/xiaojidan1314/anime-remove-background/README.md b/spaces/xiaojidan1314/anime-remove-background/README.md
deleted file mode 100644
index 1ba3cb5ea0e994e246d57b7d62b8aa5a6331901c..0000000000000000000000000000000000000000
--- a/spaces/xiaojidan1314/anime-remove-background/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Anime Remove Background
-emoji: 🪄🖼️
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: skytnt/anime-remove-background
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yderre-aubay/midi-player-demo/src/common/trackMute/index.ts b/spaces/yderre-aubay/midi-player-demo/src/common/trackMute/index.ts
deleted file mode 100644
index 1fbc408785cf08e0d5ae6362299bd3e49b929370..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/common/trackMute/index.ts
+++ /dev/null
@@ -1 +0,0 @@
-export { default } from "./TrackMute"
diff --git a/spaces/ygangang/VToonify/vtoonify/model/encoder/encoders/psp_encoders.py b/spaces/ygangang/VToonify/vtoonify/model/encoder/encoders/psp_encoders.py
deleted file mode 100644
index f69d38200b6be4997673ae38ed481fd21f88b419..0000000000000000000000000000000000000000
--- a/spaces/ygangang/VToonify/vtoonify/model/encoder/encoders/psp_encoders.py
+++ /dev/null
@@ -1,186 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.nn import Linear, Conv2d, BatchNorm2d, PReLU, Sequential, Module
-
-from model.encoder.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE
-from model.stylegan.model import EqualLinear
-
-
-class GradualStyleBlock(Module):
-    def __init__(self, in_c, out_c, spatial):
-        super(GradualStyleBlock, self).__init__()
-        self.out_c = out_c
-        self.spatial = spatial
-        num_pools = int(np.log2(spatial))
-        modules = []
-        modules += [Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1),
-                    nn.LeakyReLU()]
-        for i in range(num_pools - 1):
-            modules += [
-                Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1),
-                nn.LeakyReLU()
-            ]
-        self.convs = nn.Sequential(*modules)
-        self.linear = EqualLinear(out_c, out_c, lr_mul=1)
-
-    def forward(self, x):
-        x = self.convs(x)
-        x = x.view(-1, self.out_c)
-        x = self.linear(x)
-        return x
-
-
-class GradualStyleEncoder(Module):
-    def __init__(self, num_layers, mode='ir', opts=None):
-        super(GradualStyleEncoder, self).__init__()
-        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
-        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
-        blocks = get_blocks(num_layers)
-        if mode == 'ir':
-            unit_module = bottleneck_IR
-        elif mode == 'ir_se':
-            unit_module = bottleneck_IR_SE
-        self.input_layer = Sequential(Conv2d(opts.input_nc, 64, (3, 3), 1, 1, bias=False),
-                                      BatchNorm2d(64),
-                                      PReLU(64))
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-        self.styles = nn.ModuleList()
-        self.style_count = opts.n_styles
-        self.coarse_ind = 3
-        self.middle_ind = 7
-        for i in range(self.style_count):
-            if i < self.coarse_ind:
-                style = GradualStyleBlock(512, 512, 16)
-            elif i < self.middle_ind:
-                style = GradualStyleBlock(512, 512, 32)
-            else:
-                style = GradualStyleBlock(512, 512, 64)
-            self.styles.append(style)
-        self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0)
-        self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0)
-
-    def _upsample_add(self, x, y):
-        '''Upsample and add two feature maps.
-        Args:
-          x: (Variable) top feature map to be upsampled.
-          y: (Variable) lateral feature map.
-        Returns:
-          (Variable) added feature map.
-        Note in PyTorch, when input size is odd, the upsampled feature map
-        with `F.upsample(..., scale_factor=2, mode='nearest')`
-        maybe not equal to the lateral feature map size.
-        e.g.
-        original input size: [N,_,15,15] ->
-        conv2d feature map size: [N,_,8,8] ->
-        upsampled feature map size: [N,_,16,16]
-        So we choose bilinear upsample which supports arbitrary output sizes.
-        '''
-        _, _, H, W = y.size()
-        return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y
-
-    def forward(self, x):
-        x = self.input_layer(x)
-
-        latents = []
-        modulelist = list(self.body._modules.values())
-        for i, l in enumerate(modulelist):
-            x = l(x)
-            if i == 6:
-                c1 = x
-            elif i == 20:
-                c2 = x
-            elif i == 23:
-                c3 = x
-
-        for j in range(self.coarse_ind):
-            latents.append(self.styles[j](c3))
-
-        p2 = self._upsample_add(c3, self.latlayer1(c2))
-        for j in range(self.coarse_ind, self.middle_ind):
-            latents.append(self.styles[j](p2))
-
-        p1 = self._upsample_add(p2, self.latlayer2(c1))
-        for j in range(self.middle_ind, self.style_count):
-            latents.append(self.styles[j](p1))
-
-        out = torch.stack(latents, dim=1)
-        return out
-
-
-class BackboneEncoderUsingLastLayerIntoW(Module):
-    def __init__(self, num_layers, mode='ir', opts=None):
-        super(BackboneEncoderUsingLastLayerIntoW, self).__init__()
-        print('Using BackboneEncoderUsingLastLayerIntoW')
-        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
-        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
-        blocks = get_blocks(num_layers)
-        if mode == 'ir':
-            unit_module = bottleneck_IR
-        elif mode == 'ir_se':
-            unit_module = bottleneck_IR_SE
-        self.input_layer = Sequential(Conv2d(opts.input_nc, 64, (3, 3), 1, 1, bias=False),
-                                      BatchNorm2d(64),
-                                      PReLU(64))
-        self.output_pool = torch.nn.AdaptiveAvgPool2d((1, 1))
-        self.linear = EqualLinear(512, 512, lr_mul=1)
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-    def forward(self, x):
-        x = self.input_layer(x)
-        x = self.body(x)
-        x = self.output_pool(x)
-        x = x.view(-1, 512)
-        x = self.linear(x)
-        return x
-
-
-class BackboneEncoderUsingLastLayerIntoWPlus(Module):
-    def __init__(self, num_layers, mode='ir', opts=None):
-        super(BackboneEncoderUsingLastLayerIntoWPlus, self).__init__()
-        print('Using BackboneEncoderUsingLastLayerIntoWPlus')
-        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
-        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
-        blocks = get_blocks(num_layers)
-        if mode == 'ir':
-            unit_module = bottleneck_IR
-        elif mode == 'ir_se':
-            unit_module = bottleneck_IR_SE
-        self.n_styles = opts.n_styles
-        self.input_layer = Sequential(Conv2d(opts.input_nc, 64, (3, 3), 1, 1, bias=False),
-                                      BatchNorm2d(64),
-                                      PReLU(64))
-        self.output_layer_2 = Sequential(BatchNorm2d(512),
-                                         torch.nn.AdaptiveAvgPool2d((7, 7)),
-                                         Flatten(),
-                                         Linear(512 * 7 * 7, 512))
-        self.linear = EqualLinear(512, 512 * self.n_styles, lr_mul=1)
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-    def forward(self, x):
-        x = self.input_layer(x)
-        x = self.body(x)
-        x = self.output_layer_2(x)
-        x = self.linear(x)
-        x = x.view(-1, self.n_styles, 512)
-        return x
diff --git a/spaces/yixin6178/arXiv2Latex/app.py b/spaces/yixin6178/arXiv2Latex/app.py
deleted file mode 100644
index aae08665518f16a770081a30a7b37dddd5cdb087..0000000000000000000000000000000000000000
--- a/spaces/yixin6178/arXiv2Latex/app.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# import imp
-import streamlit as st
-import pandas as pd
-import numpy as np
-import time
-# import matplotlib.pyplot as plt
-# import seaborn as sns
-# import plotly.figure_factory as ff
-import altair as alt
-# from PIL import Image
-import base64
-import tarfile
-import os
-import requests
-from backend import *
-
-predefined_limits = 10000
-
-st.set_page_config(page_title="arXiv2Latex Downloader", page_icon=":page_with_curl:", layout="wide", initial_sidebar_state="expanded", menu_items={
-    "About": "Download the source latex code of multiple arXiv paper with one click"
-})
-
-# title
-st.title("arXiv2Latex Downloader")
-
-# input arxiv links to download
-pdf_links_input = st.text_area("Please input the paper links you want to download following the format (Currently supports up to 10 links).", "")
-st.markdown("""
-            Input example:
-            ```Plain Text
-            https://arxiv.org/abs/1512.03385
-            https://arxiv.org/abs/1706.03762
-            https://arxiv.org/abs/2009.09724
-            """)
-## one click download
-crawling_or_not = st.button("Crawling the latex Code")
-if crawling_or_not:
-    print("Crawling...")
-    pdf_lists = pdf_links_input.split("\n")
-    print(pdf_lists)
-    # cleaning the pdf lists
-    pdf_lists = [i.strip() for i in pdf_lists if len(i) > 0]
-    # TODO: limit the number of paper up to 10 since I am not sure that whether base64 support large file download
-    # try: 
-    if len(pdf_lists) > predefined_limits:
-        st.warning(f"Currently only support up to {predefined_limits} papers. Please input less than {predefined_limits} papers.")
-    else:
-        # parsing
-        base='./download/'
-        project_name = get_timestamp().replace(" ","-")
-        base = os.path.join(base,project_name)
-        make_dir_if_not_exist(base)
-        
-        # st.write(download_status)
-        with st.spinner("Downloading papers..."):
-            # progress bar
-            bar = st.progress(0)
-            download_status = st.empty()
-            N = len(pdf_lists)
-            for i, pdf_link in tqdm(enumerate(pdf_lists)):
-                title = get_name_from_arvix(pdf_link)
-                file_stamp = pdf_link.split("/")[-1]
-                source_link = "https://arxiv.org/e-print/"+file_stamp
-                inp = os.path.join(base,'input')
-                make_dir_if_not_exist(inp)
-                out = os.path.join(base,'output')
-                make_dir_if_not_exist(out)
-                response = requests.get(source_link)
-                filename = file_stamp+".tar.gz"
-                filepath = os.path.join(inp,filename)
-                open(filepath, "wb").write(response.content)
-                outpath = os.path.join(out,title)
-                untar(filepath,outpath)
-                
-                # finish one paper
-                bar.progress((i+1)/N)
-                download_status.text(f"Iteration [{i+1}/{N}]: Finish Downloading of "+title)
-        
-        with st.spinner("Archiving as Zip Files..."):
-            # save it as zip file
-            filepath = archive_dir(out,os.path.join(base,project_name))
-
-            # download
-            b64 = ToBase64(filepath).decode()
-        href = f"<a href='data:file/csv;base64,{b64}' download='arxiv2latex-output-{datetime.datetime.now()}.zip' color='red'>Click here to Download the Output Latex Zip Files</a>"
-        st.markdown(href, unsafe_allow_html=True)
-            
-        # 状态
-        st.success("Finished")
-    # except Exception as e:
-    #     st.error("Something goes wrong. Please check the input or concat me to fix this bug. Error message: \n"+str(e))
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/clipseg/modeling_clipseg.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/clipseg/modeling_clipseg.py
deleted file mode 100644
index 96f13217aaf821b0878cdc368591d5a89020d843..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/clipseg/modeling_clipseg.py
+++ /dev/null
@@ -1,1522 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch CLIPSeg model."""
-
-import copy
-import math
-from dataclasses import dataclass
-from typing import Any, Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-
-from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
-from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    ModelOutput,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_clipseg import CLIPSegConfig, CLIPSegTextConfig, CLIPSegVisionConfig
-
-
-logger = logging.get_logger(__name__)
-
-
-_CHECKPOINT_FOR_DOC = "CIDAS/clipseg-rd64-refined"
-
-CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "CIDAS/clipseg-rd64-refined",
-    # See all CLIPSeg models at https://huggingface.co/models?filter=clipseg
-]
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-# contrastive loss function, adapted from
-# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
-def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
-    return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))
-
-
-# Copied from transformers.models.clip.modeling_clip.clip_loss with clip->clipseg
-def clipseg_loss(similarity: torch.Tensor) -> torch.Tensor:
-    caption_loss = contrastive_loss(similarity)
-    image_loss = contrastive_loss(similarity.t())
-    return (caption_loss + image_loss) / 2.0
-
-
-@dataclass
-# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->CLIPSeg
-class CLIPSegOutput(ModelOutput):
-    """
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
-            Contrastive loss for image-text similarity.
-        logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
-            The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
-            similarity scores.
-        logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
-            The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
-            similarity scores.
-        text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
-            The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegTextModel`].
-        image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
-            The image embeddings obtained by applying the projection layer to the pooled output of
-            [`CLIPSegVisionModel`].
-        text_model_output(`BaseModelOutputWithPooling`):
-            The output of the [`CLIPSegTextModel`].
-        vision_model_output(`BaseModelOutputWithPooling`):
-            The output of the [`CLIPSegVisionModel`].
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits_per_image: torch.FloatTensor = None
-    logits_per_text: torch.FloatTensor = None
-    text_embeds: torch.FloatTensor = None
-    image_embeds: torch.FloatTensor = None
-    text_model_output: BaseModelOutputWithPooling = None
-    vision_model_output: BaseModelOutputWithPooling = None
-
-    def to_tuple(self) -> Tuple[Any]:
-        return tuple(
-            self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
-            for k in self.keys()
-        )
-
-
-@dataclass
-class CLIPSegDecoderOutput(ModelOutput):
-    """
-    Args:
-        logits (`torch.FloatTensor` of shape `(batch_size, height, width)`):
-            Classification scores for each pixel.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
-            the self-attention heads.
-    """
-
-    logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class CLIPSegImageSegmentationOutput(ModelOutput):
-    """
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
-            Contrastive loss for image-text similarity.
-        ...
-        vision_model_output (`BaseModelOutputWithPooling`):
-            The output of the [`CLIPSegVisionModel`].
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    conditional_embeddings: torch.FloatTensor = None
-    pooled_output: torch.FloatTensor = None
-    vision_model_output: BaseModelOutputWithPooling = None
-    decoder_output: CLIPSegDecoderOutput = None
-
-    def to_tuple(self) -> Tuple[Any]:
-        return tuple(
-            self[k] if k not in ["vision_model_output", "decoder_output"] else getattr(self, k).to_tuple()
-            for k in self.keys()
-        )
-
-
-class CLIPSegVisionEmbeddings(nn.Module):
-    # Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings.__init__ with CLIP->CLIPSeg
-    def __init__(self, config: CLIPSegVisionConfig):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.image_size = config.image_size
-        self.patch_size = config.patch_size
-
-        self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
-
-        self.patch_embedding = nn.Conv2d(
-            in_channels=config.num_channels,
-            out_channels=self.embed_dim,
-            kernel_size=self.patch_size,
-            stride=self.patch_size,
-            bias=False,
-        )
-
-        self.num_patches = (self.image_size // self.patch_size) ** 2
-        self.num_positions = self.num_patches + 1
-        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
-        self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
-
-    def interpolate_position_embeddings(self, new_size):
-        if len(new_size) != 2:
-            raise ValueError("new_size should consist of 2 values")
-
-        num_patches_one_direction = int(self.num_patches**0.5)
-        # we interpolate the position embeddings in 2D
-        a = self.position_embedding.weight[1:].T.view(
-            1, self.config.hidden_size, num_patches_one_direction, num_patches_one_direction
-        )
-        b = (
-            nn.functional.interpolate(a, new_size, mode="bicubic", align_corners=False)
-            .squeeze(0)
-            .view(self.config.hidden_size, new_size[0] * new_size[1])
-            .T
-        )
-        result = torch.cat([self.position_embedding.weight[:1], b])
-
-        return result
-
-    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
-        batch_size = pixel_values.shape[0]
-        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
-        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
-
-        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
-        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
-
-        if embeddings.shape[1] != self.num_positions:
-            new_shape = int(math.sqrt(embeddings.shape[1] - 1))
-            embeddings = embeddings + self.interpolate_position_embeddings((new_shape, new_shape))
-            embeddings = embeddings.to(embeddings.dtype)
-        else:
-            embeddings = embeddings + self.position_embedding(self.position_ids)
-
-        return embeddings
-
-
-# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->CLIPSeg
-class CLIPSegTextEmbeddings(nn.Module):
-    def __init__(self, config: CLIPSegTextConfig):
-        super().__init__()
-        embed_dim = config.hidden_size
-
-        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
-        self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer(
-            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
-        )
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-    ) -> torch.Tensor:
-        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, :seq_length]
-
-        if inputs_embeds is None:
-            inputs_embeds = self.token_embedding(input_ids)
-
-        position_embeddings = self.position_embedding(position_ids)
-        embeddings = inputs_embeds + position_embeddings
-
-        return embeddings
-
-
-# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->CLIPSeg
-class CLIPSegAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.embed_dim // self.num_heads
-        if self.head_dim * self.num_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
-                f" {self.num_heads})."
-            )
-        self.scale = self.head_dim**-0.5
-        self.dropout = config.attention_dropout
-
-        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
-        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
-        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
-        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        causal_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        bsz, tgt_len, embed_dim = hidden_states.size()
-
-        # get query proj
-        query_states = self.q_proj(hidden_states) * self.scale
-        key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-        value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-
-        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
-        key_states = key_states.view(*proj_shape)
-        value_states = value_states.view(*proj_shape)
-
-        src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        # apply the causal_attention_mask first
-        if causal_attention_mask is not None:
-            if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
-                    f" {causal_attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if output_attentions:
-            # this operation is a bit akward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-        attn_output = attn_output.transpose(1, 2)
-        attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped
-
-
-# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->CLIPSeg
-class CLIPSegMLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.activation_fn = ACT2FN[config.hidden_act]
-        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.fc1(hidden_states)
-        hidden_states = self.activation_fn(hidden_states)
-        hidden_states = self.fc2(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->CLIPSeg
-class CLIPSegEncoderLayer(nn.Module):
-    def __init__(self, config: CLIPSegConfig):
-        super().__init__()
-        self.embed_dim = config.hidden_size
-        self.self_attn = CLIPSegAttention(config)
-        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
-        self.mlp = CLIPSegMLP(config)
-        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        causal_attention_mask: torch.Tensor,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-                `(config.encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-
-        hidden_states = self.layer_norm1(hidden_states)
-        hidden_states, attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            causal_attention_mask=causal_attention_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.layer_norm2(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-class CLIPSegPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = CLIPSegConfig
-    base_model_prefix = "clip"
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        factor = self.config.initializer_factor
-        if isinstance(module, CLIPSegTextEmbeddings):
-            module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
-            module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
-        elif isinstance(module, CLIPSegVisionEmbeddings):
-            factor = self.config.initializer_factor
-            nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
-            nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
-            nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
-        elif isinstance(module, CLIPSegAttention):
-            factor = self.config.initializer_factor
-            in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
-            out_proj_std = (module.embed_dim**-0.5) * factor
-            nn.init.normal_(module.q_proj.weight, std=in_proj_std)
-            nn.init.normal_(module.k_proj.weight, std=in_proj_std)
-            nn.init.normal_(module.v_proj.weight, std=in_proj_std)
-            nn.init.normal_(module.out_proj.weight, std=out_proj_std)
-        elif isinstance(module, CLIPSegMLP):
-            factor = self.config.initializer_factor
-            in_proj_std = (
-                (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
-            )
-            fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
-            nn.init.normal_(module.fc1.weight, std=fc_std)
-            nn.init.normal_(module.fc2.weight, std=in_proj_std)
-        elif isinstance(module, CLIPSegModel):
-            nn.init.normal_(
-                module.text_projection.weight,
-                std=module.text_embed_dim**-0.5 * self.config.initializer_factor,
-            )
-            nn.init.normal_(
-                module.visual_projection.weight,
-                std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,
-            )
-
-        if isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, CLIPSegEncoder):
-            module.gradient_checkpointing = value
-
-
-CLIPSEG_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
-    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`CLIPSegConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-CLIPSEG_TEXT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-CLIPSEG_VISION_INPUTS_DOCSTRING = r"""
-    Args:
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-CLIPSEG_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
-        return_loss (`bool`, *optional*):
-            Whether or not to return the contrastive loss.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->CLIPSeg
-class CLIPSegEncoder(nn.Module):
-    """
-    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
-    [`CLIPSegEncoderLayer`].
-
-    Args:
-        config: CLIPSegConfig
-    """
-
-    def __init__(self, config: CLIPSegConfig):
-        super().__init__()
-        self.config = config
-        self.layers = nn.ModuleList([CLIPSegEncoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        inputs_embeds,
-        attention_mask: Optional[torch.Tensor] = None,
-        causal_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutput]:
-        r"""
-        Args:
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-                than the model's internal embedding lookup matrix.
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Causal mask for the text model. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        hidden_states = inputs_embeds
-        for idx, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_states,)
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(encoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    causal_attention_mask,
-                )
-            else:
-                layer_outputs = encoder_layer(
-                    hidden_states,
-                    attention_mask,
-                    causal_attention_mask,
-                    output_attentions=output_attentions,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            encoder_states = encoder_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
-        )
-
-
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
-):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-class CLIPSegTextTransformer(nn.Module):
-    # Copied from transformers.models.clip.modeling_clip.CLIPTextTransformer.__init__ with CLIP->CLIPSeg
-    def __init__(self, config: CLIPSegTextConfig):
-        super().__init__()
-        self.config = config
-        embed_dim = config.hidden_size
-        self.embeddings = CLIPSegTextEmbeddings(config)
-        self.encoder = CLIPSegEncoder(config)
-        self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
-
-        # For `pooled_output` computation
-        self.eos_token_id = config.eos_token_id
-
-    @add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegTextConfig)
-    # Copied from transformers.models.clip.modeling_clip.CLIPTextTransformer.forward with clip->clipseg, CLIP->CLIPSeg
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is None:
-            raise ValueError("You have to specify input_ids")
-
-        input_shape = input_ids.size()
-        input_ids = input_ids.view(-1, input_shape[-1])
-
-        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)
-
-        # CLIPSeg's text model uses causal mask, prepare it here.
-        # https://github.com/openai/CLIPSeg/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clipseg/model.py#L324
-        causal_attention_mask = _make_causal_mask(input_shape, hidden_states.dtype, device=hidden_states.device)
-        # expand attention_mask
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
-
-        encoder_outputs = self.encoder(
-            inputs_embeds=hidden_states,
-            attention_mask=attention_mask,
-            causal_attention_mask=causal_attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        last_hidden_state = encoder_outputs[0]
-        last_hidden_state = self.final_layer_norm(last_hidden_state)
-
-        if self.eos_token_id == 2:
-            # The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here.
-            # A CLIPSeg model with such `eos_token_id` in the config can't work correctly with extra new tokens added
-            # ------------------------------------------------------------
-            # text_embeds.shape = [batch_size, sequence_length, transformer.width]
-            # take features from the eot embedding (eot_token is the highest number in each sequence)
-            # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
-            pooled_output = last_hidden_state[
-                torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
-                input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1),
-            ]
-        else:
-            # The config gets updated `eos_token_id` from PR #24773 (so the use of exta new tokens is possible)
-            pooled_output = last_hidden_state[
-                torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
-                # We need to get the first position of `eos_token_id` value (`pad_token_ids` might equal to `eos_token_id`)
-                (input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id)
-                .int()
-                .argmax(dim=-1),
-            ]
-
-        if not return_dict:
-            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPooling(
-            last_hidden_state=last_hidden_state,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-class CLIPSegTextModel(CLIPSegPreTrainedModel):
-    config_class = CLIPSegTextConfig
-
-    _no_split_modules = ["CLIPSegTextEmbeddings", "CLIPSegEncoderLayer"]
-
-    def __init__(self, config: CLIPSegTextConfig):
-        super().__init__(config)
-        self.text_model = CLIPSegTextTransformer(config)
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Module:
-        return self.text_model.embeddings.token_embedding
-
-    def set_input_embeddings(self, value):
-        self.text_model.embeddings.token_embedding = value
-
-    @add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegTextConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, CLIPSegTextModel
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined")
-        >>> model = CLIPSegTextModel.from_pretrained("CIDAS/clipseg-rd64-refined")
-
-        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-        >>> last_hidden_state = outputs.last_hidden_state
-        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
-        ```"""
-        return self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-
-class CLIPSegVisionTransformer(nn.Module):
-    # Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.__init__ with CLIP->CLIPSeg
-    def __init__(self, config: CLIPSegVisionConfig):
-        super().__init__()
-        self.config = config
-        embed_dim = config.hidden_size
-
-        self.embeddings = CLIPSegVisionEmbeddings(config)
-        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
-        self.encoder = CLIPSegEncoder(config)
-        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
-
-    @add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegVisionConfig)
-    # Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
-    def forward(
-        self,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if pixel_values is None:
-            raise ValueError("You have to specify pixel_values")
-
-        hidden_states = self.embeddings(pixel_values)
-        hidden_states = self.pre_layrnorm(hidden_states)
-
-        encoder_outputs = self.encoder(
-            inputs_embeds=hidden_states,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        last_hidden_state = encoder_outputs[0]
-        pooled_output = last_hidden_state[:, 0, :]
-        pooled_output = self.post_layernorm(pooled_output)
-
-        if not return_dict:
-            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPooling(
-            last_hidden_state=last_hidden_state,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-class CLIPSegVisionModel(CLIPSegPreTrainedModel):
-    config_class = CLIPSegVisionConfig
-    main_input_name = "pixel_values"
-
-    def __init__(self, config: CLIPSegVisionConfig):
-        super().__init__(config)
-        self.vision_model = CLIPSegVisionTransformer(config)
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Module:
-        return self.vision_model.embeddings.patch_embedding
-
-    @add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegVisionConfig)
-    def forward(
-        self,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import AutoProcessor, CLIPSegVisionModel
-
-        >>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
-        >>> model = CLIPSegVisionModel.from_pretrained("CIDAS/clipseg-rd64-refined")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(images=image, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-        >>> last_hidden_state = outputs.last_hidden_state
-        >>> pooled_output = outputs.pooler_output  # pooled CLS states
-        ```"""
-        return self.vision_model(
-            pixel_values=pixel_values,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-
-@add_start_docstrings(CLIPSEG_START_DOCSTRING)
-class CLIPSegModel(CLIPSegPreTrainedModel):
-    config_class = CLIPSegConfig
-
-    def __init__(self, config: CLIPSegConfig):
-        super().__init__(config)
-
-        if not isinstance(config.text_config, CLIPSegTextConfig):
-            raise ValueError(
-                "config.text_config is expected to be of type CLIPSegTextConfig but is of type"
-                f" {type(config.text_config)}."
-            )
-
-        if not isinstance(config.vision_config, CLIPSegVisionConfig):
-            raise ValueError(
-                "config.vision_config is expected to be of type CLIPSegVisionConfig but is of type"
-                f" {type(config.vision_config)}."
-            )
-
-        text_config = config.text_config
-        vision_config = config.vision_config
-
-        self.projection_dim = config.projection_dim
-        self.text_embed_dim = text_config.hidden_size
-        self.vision_embed_dim = vision_config.hidden_size
-
-        self.text_model = CLIPSegTextTransformer(text_config)
-        self.vision_model = CLIPSegVisionTransformer(vision_config)
-
-        self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
-        self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
-        self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING)
-    def get_text_features(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> torch.FloatTensor:
-        r"""
-        Returns:
-            text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
-            applying the projection layer to the pooled output of [`CLIPSegTextModel`].
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, CLIPSegModel
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined")
-        >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")
-
-        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
-        >>> text_features = model.get_text_features(**inputs)
-        ```"""
-        # Use CLIPSEG model's config for some fields (if specified) instead of those of vision & text components.
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        text_outputs = self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = text_outputs[1]
-        text_features = self.text_projection(pooled_output)
-
-        return text_features
-
-    @add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING)
-    def get_image_features(
-        self,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> torch.FloatTensor:
-        r"""
-        Returns:
-            image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
-            applying the projection layer to the pooled output of [`CLIPSegVisionModel`].
-
-        Examples:
-
-        ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import AutoProcessor, CLIPSegModel
-
-        >>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
-        >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(images=image, return_tensors="pt")
-
-        >>> image_features = model.get_image_features(**inputs)
-        ```"""
-        # Use CLIPSEG model's config for some fields (if specified) instead of those of vision & text components.
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = vision_outputs[1]  # pooled_output
-        image_features = self.visual_projection(pooled_output)
-
-        return image_features
-
-    @add_start_docstrings_to_model_forward(CLIPSEG_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CLIPSegOutput, config_class=CLIPSegConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        return_loss: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CLIPSegOutput]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import AutoProcessor, CLIPSegModel
-
-        >>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
-        >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(
-        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
-        ... )
-
-        >>> outputs = model(**inputs)
-        >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
-        >>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
-        ```"""
-        # Use CLIPSEG model's config for some fields (if specified) instead of those of vision & text components.
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        text_outputs = self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        image_embeds = vision_outputs[1]
-        image_embeds = self.visual_projection(image_embeds)
-
-        text_embeds = text_outputs[1]
-        text_embeds = self.text_projection(text_embeds)
-
-        # normalized features
-        image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
-        text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
-
-        # cosine similarity as logits
-        logit_scale = self.logit_scale.exp()
-        logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
-        logits_per_image = logits_per_text.t()
-
-        loss = None
-        if return_loss:
-            loss = clipseg_loss(logits_per_text)
-
-        if not return_dict:
-            output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
-            return ((loss,) + output) if loss is not None else output
-
-        return CLIPSegOutput(
-            loss=loss,
-            logits_per_image=logits_per_image,
-            logits_per_text=logits_per_text,
-            text_embeds=text_embeds,
-            image_embeds=image_embeds,
-            text_model_output=text_outputs,
-            vision_model_output=vision_outputs,
-        )
-
-
-class CLIPSegDecoderLayer(nn.Module):
-    """
-    CLIPSeg decoder layer, which is identical to `CLIPSegEncoderLayer`, except that normalization is applied after
-    self-attention/MLP, rather than before.
-    """
-
-    # Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer.__init__ with CLIP->CLIPSeg
-    def __init__(self, config: CLIPSegConfig):
-        super().__init__()
-        self.embed_dim = config.hidden_size
-        self.self_attn = CLIPSegAttention(config)
-        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
-        self.mlp = CLIPSegMLP(config)
-        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        causal_attention_mask: torch.Tensor,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-                `(config.encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-
-        hidden_states, attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            causal_attention_mask=causal_attention_mask,
-            output_attentions=output_attentions,
-        )
-
-        hidden_states = residual + hidden_states
-        hidden_states = self.layer_norm1(hidden_states)
-
-        residual = hidden_states
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-        hidden_states = self.layer_norm2(hidden_states)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-class CLIPSegDecoder(CLIPSegPreTrainedModel):
-    def __init__(self, config: CLIPSegConfig):
-        super().__init__(config)
-
-        self.conditional_layer = config.conditional_layer
-
-        self.film_mul = nn.Linear(config.projection_dim, config.reduce_dim)
-        self.film_add = nn.Linear(config.projection_dim, config.reduce_dim)
-
-        if config.use_complex_transposed_convolution:
-            transposed_kernels = (config.vision_config.patch_size // 4, config.vision_config.patch_size // 4)
-
-            self.transposed_convolution = nn.Sequential(
-                nn.Conv2d(config.reduce_dim, config.reduce_dim, kernel_size=3, padding=1),
-                nn.ReLU(),
-                nn.ConvTranspose2d(
-                    config.reduce_dim,
-                    config.reduce_dim // 2,
-                    kernel_size=transposed_kernels[0],
-                    stride=transposed_kernels[0],
-                ),
-                nn.ReLU(),
-                nn.ConvTranspose2d(
-                    config.reduce_dim // 2, 1, kernel_size=transposed_kernels[1], stride=transposed_kernels[1]
-                ),
-            )
-        else:
-            self.transposed_convolution = nn.ConvTranspose2d(
-                config.reduce_dim, 1, config.vision_config.patch_size, stride=config.vision_config.patch_size
-            )
-
-        depth = len(config.extract_layers)
-        self.reduces = nn.ModuleList(
-            [nn.Linear(config.vision_config.hidden_size, config.reduce_dim) for _ in range(depth)]
-        )
-
-        decoder_config = copy.deepcopy(config.vision_config)
-        decoder_config.hidden_size = config.reduce_dim
-        decoder_config.num_attention_heads = config.decoder_num_attention_heads
-        decoder_config.intermediate_size = config.decoder_intermediate_size
-        decoder_config.hidden_act = "relu"
-        self.layers = nn.ModuleList([CLIPSegDecoderLayer(decoder_config) for _ in range(len(config.extract_layers))])
-
-    def forward(
-        self,
-        hidden_states: Tuple[torch.Tensor],
-        conditional_embeddings: torch.Tensor,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = True,
-    ):
-        all_hidden_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        activations = hidden_states[::-1]
-
-        output = None
-        for i, (activation, layer, reduce) in enumerate(zip(activations, self.layers, self.reduces)):
-            if output is not None:
-                output = reduce(activation) + output
-            else:
-                output = reduce(activation)
-
-            if i == self.conditional_layer:
-                output = self.film_mul(conditional_embeddings) * output.permute(1, 0, 2) + self.film_add(
-                    conditional_embeddings
-                )
-                output = output.permute(1, 0, 2)
-
-            layer_outputs = layer(
-                output, attention_mask=None, causal_attention_mask=None, output_attentions=output_attentions
-            )
-
-            output = layer_outputs[0]
-
-            if output_hidden_states:
-                all_hidden_states += (output,)
-
-            if output_attentions:
-                all_attentions += (layer_outputs[1],)
-
-        output = output[:, 1:, :].permute(0, 2, 1)  # remove cls token and reshape to [batch_size, reduce_dim, seq_len]
-
-        size = int(math.sqrt(output.shape[2]))
-
-        batch_size = conditional_embeddings.shape[0]
-        output = output.view(batch_size, output.shape[1], size, size)
-
-        logits = self.transposed_convolution(output).squeeze()
-
-        if not return_dict:
-            return tuple(v for v in [logits, all_hidden_states, all_attentions] if v is not None)
-
-        return CLIPSegDecoderOutput(
-            logits=logits,
-            hidden_states=all_hidden_states,
-            attentions=all_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation.
-    """,
-    CLIPSEG_START_DOCSTRING,
-)
-class CLIPSegForImageSegmentation(CLIPSegPreTrainedModel):
-    config_class = CLIPSegConfig
-
-    def __init__(self, config: CLIPSegConfig):
-        super().__init__(config)
-
-        self.config = config
-
-        self.clip = CLIPSegModel(config)
-        self.extract_layers = config.extract_layers
-
-        self.decoder = CLIPSegDecoder(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_conditional_embeddings(
-        self,
-        batch_size: int = None,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        conditional_pixel_values: Optional[torch.Tensor] = None,
-    ):
-        if input_ids is not None:
-            # compute conditional embeddings from texts
-            if len(input_ids) != batch_size:
-                raise ValueError("Make sure to pass as many prompt texts as there are query images")
-            with torch.no_grad():
-                conditional_embeddings = self.clip.get_text_features(
-                    input_ids, attention_mask=attention_mask, position_ids=position_ids
-                )
-        elif conditional_pixel_values is not None:
-            # compute conditional embeddings from images
-            if len(conditional_pixel_values) != batch_size:
-                raise ValueError("Make sure to pass as many prompt images as there are query images")
-            with torch.no_grad():
-                conditional_embeddings = self.clip.get_image_features(conditional_pixel_values)
-        else:
-            raise ValueError(
-                "Invalid conditional, should be either provided as `input_ids` or `conditional_pixel_values`"
-            )
-
-        return conditional_embeddings
-
-    @add_start_docstrings_to_model_forward(CLIPSEG_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CLIPSegImageSegmentationOutput, config_class=CLIPSegTextConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.FloatTensor] = None,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        conditional_pixel_values: Optional[torch.FloatTensor] = None,
-        conditional_embeddings: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CLIPSegOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoProcessor, CLIPSegForImageSegmentation
-        >>> from PIL import Image
-        >>> import requests
-
-        >>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
-        >>> model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-        >>> texts = ["a cat", "a remote", "a blanket"]
-        >>> inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-
-        >>> logits = outputs.logits
-        >>> print(logits.shape)
-        torch.Size([3, 352, 352])
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # step 1: forward the query images through the frozen CLIP vision encoder
-        with torch.no_grad():
-            vision_outputs = self.clip.vision_model(
-                pixel_values=pixel_values,
-                output_attentions=output_attentions,
-                output_hidden_states=True,  # we need the intermediate hidden states
-                return_dict=return_dict,
-            )
-            pooled_output = self.clip.visual_projection(vision_outputs[1])
-
-            hidden_states = vision_outputs.hidden_states if return_dict else vision_outputs[2]
-            # we add +1 here as the hidden states also include the initial embeddings
-            activations = [hidden_states[i + 1] for i in self.extract_layers]
-
-            # update vision_outputs
-            if return_dict:
-                vision_outputs = BaseModelOutputWithPooling(
-                    last_hidden_state=vision_outputs.last_hidden_state,
-                    pooler_output=vision_outputs.pooler_output,
-                    hidden_states=vision_outputs.hidden_states if output_hidden_states else None,
-                    attentions=vision_outputs.attentions,
-                )
-            else:
-                vision_outputs = (
-                    vision_outputs[:2] + vision_outputs[3:] if not output_hidden_states else vision_outputs
-                )
-
-        # step 2: compute conditional embeddings, either from text, images or an own provided embedding
-        if conditional_embeddings is None:
-            conditional_embeddings = self.get_conditional_embeddings(
-                batch_size=pixel_values.shape[0],
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                conditional_pixel_values=conditional_pixel_values,
-            )
-        else:
-            if conditional_embeddings.shape[0] != pixel_values.shape[0]:
-                raise ValueError(
-                    "Make sure to pass as many conditional embeddings as there are query images in the batch"
-                )
-            if conditional_embeddings.shape[1] != self.config.projection_dim:
-                raise ValueError(
-                    "Make sure that the feature dimension of the conditional embeddings matches"
-                    " `config.projection_dim`."
-                )
-
-        # step 3: forward both the pooled output and the activations through the lightweight decoder to predict masks
-        decoder_outputs = self.decoder(
-            activations,
-            conditional_embeddings,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
-
-        loss = None
-        if labels is not None:
-            # move labels to the correct device to enable PP
-            labels = labels.to(logits.device)
-            loss_fn = nn.BCEWithLogitsLoss()
-            loss = loss_fn(logits, labels)
-
-        if not return_dict:
-            output = (logits, conditional_embeddings, pooled_output, vision_outputs, decoder_outputs)
-            return ((loss,) + output) if loss is not None else output
-
-        return CLIPSegImageSegmentationOutput(
-            loss=loss,
-            logits=logits,
-            conditional_embeddings=conditional_embeddings,
-            pooled_output=pooled_output,
-            vision_model_output=vision_outputs,
-            decoder_output=decoder_outputs,
-        )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/fsmt/modeling_fsmt.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/fsmt/modeling_fsmt.py
deleted file mode 100644
index 1e566b150f762b9194fb4fbbe909a4b175c16592..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/fsmt/modeling_fsmt.py
+++ /dev/null
@@ -1,1390 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The Facebook AI Research Team Authors and 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.
-#
-# Original implementation: https://github.com/pytorch/fairseq/tree/master/examples/wmt19
-# Authors:
-# - @alexeib Alexei Baevski
-# - @edunov Sergey Edunov
-# - @michaelauli Michael Auli
-# - @myleott Myle Ott
-# - @nng555 Nathan Ng
-# - David Grangier
-# - Kyra Yee
-#
-# Paper: Facebook FAIR's WMT19 News Translation Task Submission https://arxiv.org/abs/1907.06616
-#
-"""PyTorch Fairseq model, ported from https://github.com/pytorch/fairseq/tree/master/examples/wmt19"""
-
-import math
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-from torch import Tensor, nn
-from torch.nn import CrossEntropyLoss, LayerNorm
-
-from ...activations import ACT2FN
-from ...integrations.deepspeed import is_deepspeed_zero3_enabled
-from ...modeling_outputs import (
-    BaseModelOutput,
-    BaseModelOutputWithPastAndCrossAttentions,
-    Seq2SeqLMOutput,
-    Seq2SeqModelOutput,
-)
-from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    add_code_sample_docstrings,
-    add_end_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_fsmt import FSMTConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "facebook/wmt19-ru-en"
-_CONFIG_FOR_DOC = "FSMTConfig"
-
-# See all FSMT models at https://huggingface.co/models?filter=fsmt
-
-# Porting notes:
-# this one is modeled after BartModel*
-#
-# Currently only translation (fairseq also has weights for LM)
-#
-# fairseq provides weights for ru-en, en-ru and de-en, en-de pairs. All have been ported.
-# - ru-en, en-ru use asymmetric vocab
-# - de-en, en-de use a merged single vocab (but the code works as if they are separate)
-#
-# Differences with Bart:
-# - not using bos token
-# - 2 separate vocabs (src and target)
-# - embed weights aren't tied
-# - uses a model Ensemble (but that part isn't ported/implemented yet) - so we
-#   aren't getting as good of a BLEU score
-# - uses a projection layer at the end of the decoder
-# - doesn't use final_logits_bias
-# - beam search: stops as soon as num_beams == len(hypos) (whereas transformers
-#   is not satisfied there and will continue searching until the next cycles
-#   aren't promising something better), comparing BLEU scores - the transformers
-#   algorithm is slightly superior, therefore using the latter. But if you want
-#   to match fairseq outputs, you need to pass ``early_stopping=True`` to ``generate()``.
-#
-# SinusoidalPositionalEmbedding is slightly different from Bart's - generates
-# different embeddings. This implementation is copied verbatim from fairseq with
-# some small changes to make it work here.
-#
-# Other changes:
-#  - doesn't support use_cache as Bart's version does
-#
-#
-# FSMTConfig changes with BartConfig
-#
-#    Differences with BART:
-#    - src/tgt vocabs aren't shared
-#    - token embeddings aren't shared
-#    - needs a language pair
-#    - scale_embedding are True
-#
-#    some unused args were removed too
-#
-#
-# TODO:
-# - port model ensemble (fs uses 4 model checkpoints)
-# - solve beam search discrepancies
-# docstyle-ignore
-
-"""
-
-Here is how to compare BLEU scores against fairseq implementation:
-
-# en-ru
-
-export PAIR=en-ru
-export DATA_DIR=data/$PAIR
-export SAVE_DIR=data/$PAIR
-export BS=8
-export NUM_BEAMS=50
-mkdir -p $DATA_DIR
-sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
-sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
-echo $PAIR
-PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
-
-# (fairseq BLEU: 36.4 http://matrix.statmt.org/matrix/output/1914?score_id=37605)
-
-
-# ru-en
-
-export PAIR=ru-en
-export DATA_DIR=data/$PAIR
-export SAVE_DIR=data/$PAIR
-export BS=8
-export NUM_BEAMS=50
-mkdir -p $DATA_DIR
-sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
-sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
-PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
-
-
-# (fairseq BLEU: 41.3 http://matrix.statmt.org/matrix/output/1907?run_id=6937)
-
-
-# de-en
-
-export PAIR=de-en
-export DATA_DIR=data/$PAIR
-export SAVE_DIR=data/$PAIR
-export BS=8
-export NUM_BEAMS=50
-mkdir -p $DATA_DIR
-sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
-sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
-echo $PAIR
-PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
-
-# (fairseq BLEU: 42.3 http://matrix.statmt.org/matrix/output/1902?run_id=6750)
-
-
-
-# en-de
-
-export PAIR=en-de
-export DATA_DIR=data/$PAIR
-export SAVE_DIR=data/$PAIR
-export BS=8
-mkdir -p $DATA_DIR
-sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
-sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
-echo $PAIR
-PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
-
-# (fairseq BLEU: 43.1 http://matrix.statmt.org/matrix/output/1909?run_id=6862)
-
-"""
-
-
-FSMT_START_DOCSTRING = r"""
-
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`FSMTConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-
-"""
-FSMT_GENERATION_EXAMPLE = r"""
-    Translation example::
-
-    ```python
-    >>> from transformers import AutoTokenizer, FSMTForConditionalGeneration
-
-    >>> mname = "facebook/wmt19-ru-en"
-    >>> model = FSMTForConditionalGeneration.from_pretrained(mname)
-    >>> tokenizer = AutoTokenizer.from_pretrained(mname)
-
-    >>> src_text = "Машинное обучение - это здорово, не так ли?"
-    >>> input_ids = tokenizer(src_text, return_tensors="pt").input_ids
-    >>> outputs = model.generate(input_ids, num_beams=5, num_return_sequences=3)
-    >>> tokenizer.decode(outputs[0], skip_special_tokens=True)
-    "Machine learning is great, isn't it?"
-    ```
-
-"""
-
-FSMT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`FSTMTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Indices of decoder input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are decoder input IDs?](../glossary#decoder-input-ids)
-
-            FSMT uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
-            is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
-        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
-            be used by default.
-        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
-            1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        encoder_outputs (`Tuple(torch.FloatTensor)`, *optional*):
-            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden-states at
-            the output of the last layer of the encoder. Used in the cross-attention of the decoder.
-        past_key_values (`Tuple(torch.FloatTensor)` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
-            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
-            input (see `past_key_values`). This is useful if you want more control over how to convert
-            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
-
-            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
-            of `inputs_embeds`.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-def invert_mask(attention_mask):
-    """Turns 1->0, 0->1, False->True, True-> False"""
-    assert attention_mask.dim() == 2
-    return attention_mask.eq(0)
-
-
-def triu_onnx(x, diagonal=0):
-    l = x.shape[0]
-    arange = torch.arange(l, device=x.device)
-    mask = arange.expand(l, l)
-    arange = arange.unsqueeze(-1)
-    if diagonal:
-        arange = arange + diagonal
-    mask = mask >= arange
-    return x.masked_fill(mask == 0, 0)
-
-
-def _prepare_fsmt_decoder_inputs(
-    config,
-    input_ids,
-    decoder_input_ids=None,
-    decoder_padding_mask=None,
-    causal_mask_dtype=torch.float32,
-):
-    """
-    Prepare masks that ignore padding tokens in the decoder and a causal mask for the decoder if none are provided.
-    This mimics the default behavior in fairseq. To override it pass in masks. Note: this is not called during
-    generation
-    """
-    pad_token_id = config.pad_token_id
-    if decoder_input_ids is None:
-        decoder_input_ids = shift_tokens_right(input_ids, pad_token_id)
-    bsz, tgt_len = decoder_input_ids.size()
-    if decoder_padding_mask is None:
-        decoder_padding_mask = make_padding_mask(decoder_input_ids, pad_token_id)
-    else:
-        decoder_padding_mask = invert_mask(decoder_padding_mask)
-    causal_mask = triu_onnx(fill_with_neg_inf(torch.zeros(tgt_len, tgt_len, dtype=causal_mask_dtype)), 1).to(
-        device=decoder_input_ids.device
-    )
-    return decoder_input_ids, decoder_padding_mask, causal_mask
-
-
-class PretrainedFSMTModel(PreTrainedModel):
-    config_class = FSMTConfig
-    base_model_prefix = "model"
-
-    def _init_weights(self, module):
-        std = self.config.init_std
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, SinusoidalPositionalEmbedding):
-            pass
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    @property
-    def dummy_inputs(self):
-        pad_token = self.config.pad_token_id
-        input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
-        dummy_inputs = {
-            "attention_mask": input_ids.ne(pad_token),
-            "input_ids": input_ids,
-        }
-        return dummy_inputs
-
-
-def _make_linear_from_emb(emb):
-    vocab_size, emb_size = emb.weight.shape
-    lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
-    lin_layer.weight.data = emb.weight.data
-    return lin_layer
-
-
-# Helper Functions, mostly for making masks
-def _check_shapes(shape_1, shape2):
-    if shape_1 != shape2:
-        raise AssertionError(f"shape mismatch: {shape_1} != {shape2}")
-
-
-def shift_tokens_right(input_ids, pad_token_id):
-    """Shift input ids one token to the right, and wrap the last non pad token (usually <eos>)."""
-
-    # replace possible -100 values in labels by `pad_token_id`
-    input_ids.masked_fill_(input_ids == -100, pad_token_id)
-
-    prev_output_tokens = input_ids.clone()
-    index_of_eos = (input_ids.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
-    prev_output_tokens[:, 0] = input_ids.gather(1, index_of_eos).squeeze()
-    prev_output_tokens[:, 1:] = input_ids[:, :-1]
-    return prev_output_tokens
-
-
-def make_padding_mask(input_ids, padding_idx=1):
-    """True for pad tokens"""
-    padding_mask = input_ids.eq(padding_idx)
-    if not padding_mask.any():
-        padding_mask = None
-    return padding_mask
-
-
-# Helper Modules
-
-
-class EncoderLayer(nn.Module):
-    def __init__(self, config: FSMTConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-        self.self_attn = Attention(self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout)
-        self.self_attn_layer_norm = LayerNorm(self.embed_dim)
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
-        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = LayerNorm(self.embed_dim)
-
-    def forward(self, x, encoder_padding_mask, layer_head_mask, output_attentions=False):
-        """
-        Args:
-            x (`torch.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
-            encoder_padding_mask (`torch.ByteTensor`): binary ByteTensor of shape
-                *(batch, src_len)* where padding elements are indicated by `1`.
-            for t_tgt, t_src is excluded (or masked out), =0 means it is
-            included in attention
-            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
-                *(config.encoder_attention_heads,)*.
-
-        Returns:
-            encoded output of shape *(seq_len, batch, embed_dim)*
-        """
-        residual = x
-        x, attn_weights = self.self_attn(
-            query=x,
-            key=x,
-            key_padding_mask=encoder_padding_mask,
-            layer_head_mask=layer_head_mask,
-            output_attentions=output_attentions,
-        )
-        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
-        x = residual + x
-        x = self.self_attn_layer_norm(x)
-
-        residual = x
-        x = self.activation_fn(self.fc1(x))
-        x = nn.functional.dropout(x, p=self.activation_dropout, training=self.training)
-        x = self.fc2(x)
-        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
-        x = residual + x
-        x = self.final_layer_norm(x)
-        return x, attn_weights
-
-
-class FSMTEncoder(nn.Module):
-    """
-    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`EncoderLayer`].
-
-    Args:
-        config: FSMTConfig
-    """
-
-    def __init__(self, config: FSMTConfig, embed_tokens):
-        super().__init__()
-        self.dropout = config.dropout
-        self.layerdrop = config.encoder_layerdrop
-        self.padding_idx = embed_tokens.padding_idx
-        self.embed_tokens = embed_tokens
-        embed_dim = embed_tokens.embedding_dim
-        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
-        self.embed_positions = SinusoidalPositionalEmbedding(
-            config.max_position_embeddings + self.padding_idx + 1, embed_dim, self.padding_idx
-        )
-        self.layers = nn.ModuleList(
-            [EncoderLayer(config) for _ in range(config.encoder_layers)]
-        )  # type: List[EncoderLayer]
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: torch.Tensor = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        """
-        Args:
-            input_ids (`torch.LongTensor`): tokens in the source language of shape
-                *(batch, src_len)*
-            attention_mask (`torch.LongTensor`): indicating which indices are padding tokens
-            inputs_embeds (`torch.FloatTensor`):
-                embedding vectors of shape *(batch, src_len, embed_dim)*
-            head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-        Returns:
-            BaseModelOutput or Tuple comprised of:
-
-                - **x** (`torch.Tensor`): the last encoder layer's output of shape *(src_len, batch, embed_dim)*
-                - **encoder_states** (`Tuple(torch.FloatTensor`)): all intermediate hidden states of shape *(src_len,
-                  batch, embed_dim)*. Only populated if *output_hidden_states:* is True.
-                - **all_attentions** (`Tuple(torch.FloatTensor`)): Attention weights for each layer.
-                During training might not be of length n_layers because of layer dropout.
-        """
-        # check attention mask and invert
-        if attention_mask is not None:
-            attention_mask = invert_mask(attention_mask)
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
-            embed_pos = self.embed_positions(input_ids)
-        elif inputs_embeds is not None:
-            inputs_embeds = inputs_embeds * self.embed_scale
-
-            # We assume zeros hidden states correspond to padding tokens
-            # and create `position_ids` where inputs_embeds[:, :, 0] == 0
-            position_ids = inputs_embeds[:, :, 0].masked_fill(
-                inputs_embeds[:, :, 0].eq(0), self.embed_positions.padding_idx
-            )
-
-            embed_pos = self.embed_positions(position_ids)
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        x = inputs_embeds + embed_pos
-        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-        # check if head_mask has a correct number of layers specified if desired
-        if head_mask is not None:
-            assert head_mask.size()[0] == (
-                len(self.layers)
-            ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
-        for idx, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                x = x.transpose(0, 1)  # T x B x C -> B x T x C
-                encoder_states += (x,)
-                x = x.transpose(0, 1)  # B x T x C -> T x B x C
-            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
-            dropout_probability = torch.rand([])
-            if self.training and (dropout_probability < self.layerdrop):  # skip the layer
-                attn = None
-            else:
-                x, attn = encoder_layer(
-                    x,
-                    attention_mask,
-                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
-                    output_attentions=output_attentions,
-                )
-
-            if output_attentions:
-                all_attentions = all_attentions + (attn,)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(0, 1)
-
-        if output_hidden_states:
-            encoder_states += (x,)
-
-        if not return_dict:
-            return tuple(v for v in [x, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(last_hidden_state=x, hidden_states=encoder_states, attentions=all_attentions)
-
-
-class DecoderLayer(nn.Module):
-    def __init__(self, config: FSMTConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-
-        self.self_attn = Attention(
-            embed_dim=self.embed_dim,
-            num_heads=config.decoder_attention_heads,
-            dropout=config.attention_dropout,
-        )
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-
-        self.self_attn_layer_norm = LayerNorm(self.embed_dim)
-        self.encoder_attn = Attention(
-            self.embed_dim,
-            config.decoder_attention_heads,
-            dropout=config.attention_dropout,
-            encoder_decoder_attention=True,
-        )
-        self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
-        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
-        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        x,
-        encoder_hidden_states,
-        encoder_attn_mask=None,
-        layer_state=None,
-        causal_mask=None,
-        layer_head_mask=None,
-        cross_attn_layer_head_mask=None,
-        decoder_padding_mask=None,
-        output_attentions=False,
-    ):
-        residual = x
-
-        if layer_state is None:
-            layer_state = {}
-
-        # Self Attention
-        x, self_attn_weights = self.self_attn(
-            query=x,
-            key=x,
-            layer_state=layer_state,  # adds keys to layer state
-            key_padding_mask=decoder_padding_mask,
-            attn_mask=causal_mask,
-            layer_head_mask=layer_head_mask,
-            output_attentions=output_attentions,
-        )
-        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
-        x = residual + x
-        x = self.self_attn_layer_norm(x)
-
-        # Cross attention
-        residual = x
-        assert self.encoder_attn.cache_key != self.self_attn.cache_key
-        x, cross_attn_weights = self.encoder_attn(
-            query=x,
-            key=encoder_hidden_states,
-            key_padding_mask=encoder_attn_mask,
-            layer_state=layer_state,  # mutates layer state
-            layer_head_mask=cross_attn_layer_head_mask,
-            output_attentions=output_attentions,
-        )
-        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
-        x = residual + x
-        x = self.encoder_attn_layer_norm(x)
-
-        # Fully Connected
-        residual = x
-        x = self.activation_fn(self.fc1(x))
-        x = nn.functional.dropout(x, p=self.activation_dropout, training=self.training)
-        x = self.fc2(x)
-        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
-        x = residual + x
-        x = self.final_layer_norm(x)
-        return (
-            x,
-            self_attn_weights,
-            layer_state,
-            cross_attn_weights,
-        )  # layer_state = cache for decoding
-
-
-class FSMTDecoder(nn.Module):
-    """
-    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DecoderLayer`]
-
-    Args:
-        config: FSMTConfig
-        embed_tokens (nn.Embedding): output embedding
-    """
-
-    def __init__(self, config: FSMTConfig, embed_tokens: nn.Embedding):
-        super().__init__()
-        self.dropout = config.dropout
-        self.layerdrop = config.decoder_layerdrop
-        self.padding_idx = embed_tokens.padding_idx
-        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
-        self.embed_tokens = embed_tokens
-        embed_dim = embed_tokens.embedding_dim
-        self.embed_positions = SinusoidalPositionalEmbedding(
-            config.max_position_embeddings + self.padding_idx + 1, embed_dim, self.padding_idx
-        )
-        self.layers = nn.ModuleList(
-            [DecoderLayer(config) for _ in range(config.decoder_layers)]
-        )  # type: List[DecoderLayer]
-
-        if is_deepspeed_zero3_enabled():
-            import deepspeed
-
-            with deepspeed.zero.GatheredParameters(self.embed_tokens.weight, modifier_rank=None):
-                embed_tokens_weight_shape = self.embed_tokens.weight.shape
-        else:
-            embed_tokens_weight_shape = self.embed_tokens.weight.shape
-        self.output_projection = nn.Linear(embed_tokens_weight_shape[1], embed_tokens_weight_shape[0], bias=False)
-        self.output_projection.weight = self.embed_tokens.weight
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        encoder_padding_mask: torch.Tensor,
-        decoder_padding_mask: torch.Tensor,
-        decoder_causal_mask: torch.Tensor,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        use_cache: bool = False,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        """
-        Includes several features from "Jointly Learning to Align and Translate with Transformer Models" (Garg et al.,
-        EMNLP 2019).
-
-        Args:
-            input_ids (`torch.LongTensor` of shape `(batch, tgt_len)`):
-                previous decoder outputs for teacher forcing
-            encoder_hidden_states: output from the encoder, used for
-                encoder-side attention
-            encoder_padding_mask: for ignoring pad tokens
-            past_key_values (dict or None): dictionary used for storing state during generation
-            head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-            cross_attn_head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
-                Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-        Returns:
-            BaseModelOutputWithPast or tuple:
-
-                - the decoder's features of shape *(batch, tgt_len, embed_dim)*
-                - the cache
-                - hidden states
-                - attentions
-        """
-        # check attention mask and invert
-        if encoder_padding_mask is not None:
-            encoder_padding_mask = invert_mask(encoder_padding_mask)
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
-        elif input_ids is not None:
-            # embed positions
-            positions = self.embed_positions(input_ids)
-            if use_cache:
-                input_ids = input_ids[:, -1:]
-                positions = positions[:, -1:]  # happens after we embed them
-            x = self.embed_tokens(input_ids) * self.embed_scale
-        elif inputs_embeds is not None:
-            # We assume zeros hidden states correspond to padding tokens
-            # and create `position_ids` where inputs_embeds[:, :, 0] == 0
-            position_ids = inputs_embeds[:, :, 0].masked_fill(
-                inputs_embeds[:, :, 0].eq(0), self.embed_positions.padding_idx
-            )
-            positions = self.embed_positions(position_ids)
-            x = inputs_embeds * self.embed_scale
-        else:
-            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
-
-        x += positions
-        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
-
-        # Convert to FSMT output format: (BS, seq_len, model_dim) -> (seq_len, BS, model_dim)
-        x = x.transpose(0, 1)
-        encoder_hidden_states = encoder_hidden_states.transpose(0, 1)
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        all_cross_attns = () if output_attentions else None
-        next_decoder_cache = []
-
-        # check if head_mask has a correct number of layers specified if desired
-        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
-            if attn_mask is not None:
-                assert attn_mask.size()[0] == (len(self.layers)), (
-                    f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
-                    f" {head_mask.size()[0]}."
-                )
-        for idx, decoder_layer in enumerate(self.layers):
-            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
-            if output_hidden_states:
-                x = x.transpose(0, 1)
-                all_hidden_states += (x,)
-                x = x.transpose(0, 1)
-            if self.training:
-                dropout_probability = torch.rand([])
-                if dropout_probability < self.layerdrop:
-                    continue
-
-            layer_state = past_key_values[idx] if past_key_values is not None else None
-
-            x, layer_self_attn, layer_past, layer_cross_attn = decoder_layer(
-                x,
-                encoder_hidden_states,
-                encoder_attn_mask=encoder_padding_mask,
-                decoder_padding_mask=decoder_padding_mask,
-                layer_state=layer_state,
-                causal_mask=decoder_causal_mask,
-                layer_head_mask=(head_mask[idx] if head_mask is not None else None),
-                cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
-                output_attentions=output_attentions,
-            )
-
-            if use_cache:
-                next_decoder_cache.append(layer_past.copy())
-
-            if output_attentions:
-                all_self_attns += (layer_self_attn,)
-                all_cross_attns += (layer_cross_attn,)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            x = x.transpose(0, 1)
-            all_hidden_states += (x,)
-            x = x.transpose(0, 1)
-
-        # Convert to standard output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
-        x = x.transpose(0, 1)
-        encoder_hidden_states = encoder_hidden_states.transpose(0, 1)
-
-        x = self.output_projection(x)
-
-        next_cache = next_decoder_cache if use_cache else None
-
-        if not return_dict:
-            return tuple(
-                v for v in [x, next_cache, all_hidden_states, all_self_attns, all_cross_attns] if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=x,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-            cross_attentions=all_cross_attns,
-        )
-
-
-def _reorder_buffer(attn_cache, new_order):
-    for k, input_buffer_k in attn_cache.items():
-        if input_buffer_k is not None:
-            attn_cache[k] = input_buffer_k.index_select(0, new_order)
-    return attn_cache
-
-
-class Attention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(
-        self,
-        embed_dim,
-        num_heads,
-        dropout=0.0,
-        bias=True,
-        encoder_decoder_attention=False,  # otherwise self_attention
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
-        self.scaling = self.head_dim**-0.5
-
-        self.encoder_decoder_attention = encoder_decoder_attention
-        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.cache_key = "encoder_decoder" if self.encoder_decoder_attention else "self"
-
-    def _shape(self, tensor, seq_len, bsz):
-        return tensor.contiguous().view(seq_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
-
-    def forward(
-        self,
-        query,
-        key: Optional[Tensor],
-        key_padding_mask: Optional[Tensor] = None,
-        layer_state: Optional[Dict[str, Optional[Tensor]]] = None,
-        attn_mask: Optional[Tensor] = None,
-        layer_head_mask: Optional[Tensor] = None,
-        output_attentions=False,
-    ) -> Tuple[Tensor, Optional[Tensor]]:
-        """Input shape: Time(SeqLen) x Batch x Channel"""
-        static_kv: bool = self.encoder_decoder_attention
-        tgt_len, bsz, embed_dim = query.size()
-        assert embed_dim == self.embed_dim
-        assert list(query.size()) == [tgt_len, bsz, embed_dim]
-        # get here for encoder decoder cause of static_kv
-        if layer_state is not None:  # reuse k,v and encoder_padding_mask
-            saved_state = layer_state.get(self.cache_key, {})
-            if "prev_key" in saved_state and static_kv:
-                # previous time steps are cached - no need to recompute key and value if they are static
-                key = None
-        else:
-            saved_state = None
-            layer_state = {}
-
-        q = self.q_proj(query) * self.scaling
-        if static_kv:
-            if key is None:
-                k = v = None
-            else:
-                k = self.k_proj(key)
-                v = self.v_proj(key)
-        else:
-            k = self.k_proj(query)
-            v = self.v_proj(query)
-
-        q = self._shape(q, tgt_len, bsz)
-        if k is not None:
-            k = self._shape(k, -1, bsz)
-        if v is not None:
-            v = self._shape(v, -1, bsz)
-
-        if saved_state is not None:
-            k, v, key_padding_mask = self._use_saved_state(k, v, saved_state, key_padding_mask, static_kv, bsz)
-
-        # Update cache
-        layer_state[self.cache_key] = {
-            "prev_key": k.view(bsz, self.num_heads, -1, self.head_dim),
-            "prev_value": v.view(bsz, self.num_heads, -1, self.head_dim),
-            "prev_key_padding_mask": key_padding_mask if not static_kv else None,
-        }
-
-        assert k is not None
-        src_len = k.size(1)
-        attn_weights = torch.bmm(q, k.transpose(1, 2))
-        assert attn_weights.size() == (bsz * self.num_heads, tgt_len, src_len)
-
-        if attn_mask is not None:
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_mask
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        # This is part of a workaround to get around fork/join parallelism not supporting Optional types.
-        if key_padding_mask is not None and key_padding_mask.dim() == 0:
-            key_padding_mask = None
-        assert key_padding_mask is None or key_padding_mask.size()[:2] == (
-            bsz,
-            src_len,
-        )
-
-        if key_padding_mask is not None:  # don't attend to padding symbols
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            reshaped = key_padding_mask.unsqueeze(1).unsqueeze(2)
-            attn_weights = attn_weights.masked_fill(reshaped, torch.finfo(attn_weights.dtype).min)
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if layer_head_mask is not None:
-            assert layer_head_mask.size() == (
-                self.num_heads,
-            ), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
-            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if output_attentions:
-            # make sure that attn_weights are included in graph
-            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(
-            attn_weights,
-            p=self.dropout,
-            training=self.training,
-        )
-
-        assert v is not None
-        attn_output = torch.bmm(attn_probs, v)
-        assert attn_output.size() == (bsz * self.num_heads, tgt_len, self.head_dim)
-        attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped
-
-    def _use_saved_state(self, k, v, saved_state, key_padding_mask, static_kv, bsz):
-        # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
-        if "prev_key" in saved_state:
-            _prev_key = saved_state["prev_key"]
-            assert _prev_key is not None
-            prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
-            if static_kv:
-                k = prev_key
-            else:
-                assert k is not None
-                k = torch.cat([prev_key, k], dim=1)
-        if "prev_value" in saved_state:
-            _prev_value = saved_state["prev_value"]
-            assert _prev_value is not None
-            prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
-            if static_kv:
-                v = prev_value
-            else:
-                assert v is not None
-                v = torch.cat([prev_value, v], dim=1)
-        assert k is not None and v is not None
-        prev_key_padding_mask: Optional[Tensor] = saved_state.get("prev_key_padding_mask", None)
-        if prev_key_padding_mask is not None:
-            if static_kv:
-                new_key_padding_mask = prev_key_padding_mask
-            else:
-                new_key_padding_mask = torch.cat([prev_key_padding_mask, key_padding_mask], dim=1)
-        else:
-            new_key_padding_mask = key_padding_mask
-        return k, v, new_key_padding_mask
-
-
-def fill_with_neg_inf(t):
-    """FP16-compatible function that fills a input_ids with -inf."""
-    return t.float().fill_(torch.finfo(t.dtype).min).type_as(t)
-
-
-# Public API
-def _get_shape(t):
-    return getattr(t, "shape", None)
-
-
-@add_start_docstrings(
-    "The bare FSMT Model outputting raw hidden-states without any specific head on top.",
-    FSMT_START_DOCSTRING,
-)
-class FSMTModel(PretrainedFSMTModel):
-    _tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
-
-    def __init__(self, config: FSMTConfig):
-        super().__init__(config)
-
-        padding_idx = config.pad_token_id
-        encoder_embed_tokens = nn.Embedding(config.src_vocab_size, config.d_model, padding_idx)
-        decoder_embed_tokens = nn.Embedding(config.tgt_vocab_size, config.d_model, padding_idx)
-
-        self.encoder = FSMTEncoder(config, encoder_embed_tokens)
-        self.decoder = FSMTDecoder(config, decoder_embed_tokens)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_encoder(self):
-        return self.encoder
-
-    def get_decoder(self):
-        return self.decoder
-
-    def _tie_weights(self):
-        if self.config.tie_word_embeddings:
-            self._tie_or_clone_weights(self.decoder.embed_tokens, self.get_input_embeddings())
-            self._tie_or_clone_weights(self.decoder.output_projection, self.get_input_embeddings())
-
-    @add_start_docstrings_to_model_forward(FSMT_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=Seq2SeqModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: torch.LongTensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.BoolTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        decoder_head_mask: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
-        past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
-        if decoder_input_ids is None:
-            use_cache = False
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # make masks if user doesn't supply
-        if not use_cache and input_ids is not None:
-            decoder_input_ids, decoder_padding_mask, causal_mask = _prepare_fsmt_decoder_inputs(
-                self.config,
-                input_ids,
-                decoder_input_ids=decoder_input_ids,
-                decoder_padding_mask=decoder_attention_mask,
-                causal_mask_dtype=self.decoder.embed_tokens.weight.dtype,
-            )
-        else:
-            decoder_padding_mask, causal_mask = None, None
-
-        if decoder_input_ids is None and decoder_inputs_embeds is None:
-            raise ValueError("Make sure that `decoder_input_ids` or `decoder_inputs_embeds` are passed.")
-
-        if encoder_outputs is None:
-            encoder_outputs = self.encoder(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                inputs_embeds=inputs_embeds,
-                head_mask=head_mask,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=False
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        decoder_outputs = self.decoder(
-            decoder_input_ids,
-            encoder_outputs[0],
-            attention_mask,
-            decoder_padding_mask,
-            decoder_causal_mask=causal_mask,
-            inputs_embeds=decoder_inputs_embeds,
-            head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if not return_dict:
-            return decoder_outputs + encoder_outputs
-
-        return Seq2SeqModelOutput(
-            last_hidden_state=decoder_outputs.last_hidden_state,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )
-
-    def get_input_embeddings(self):
-        return self.encoder.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.encoder.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.decoder.embed_tokens
-
-    def set_output_embeddings(self, value):
-        self.decoder.embed_tokens = value
-
-
-@add_start_docstrings(
-    "The FSMT Model with a language modeling head. Can be used for summarization.", FSMT_START_DOCSTRING
-)
-class FSMTForConditionalGeneration(PretrainedFSMTModel):
-    base_model_prefix = "model"
-    _tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
-
-    def __init__(self, config: FSMTConfig):
-        super().__init__(config)
-        base_model = FSMTModel(config)
-        self.model = base_model
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(FSMT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
-    @add_end_docstrings(FSMT_GENERATION_EXAMPLE)
-    def forward(
-        self,
-        input_ids: torch.LongTensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.BoolTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        decoder_head_mask: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
-        past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        decoder_inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Returns:
-
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if labels is not None:
-            use_cache = False
-
-        outputs = self.model(
-            input_ids,
-            inputs_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            decoder_input_ids=decoder_input_ids,
-            decoder_inputs_embeds=decoder_inputs_embeds,
-            encoder_outputs=encoder_outputs,
-            decoder_attention_mask=decoder_attention_mask,
-            head_mask=head_mask,
-            decoder_head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        lm_logits = outputs[0]
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            # TODO(SS): do we need to ignore pad tokens in labels?
-            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.tgt_vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (lm_logits,) + outputs[1:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return Seq2SeqLMOutput(
-            loss=masked_lm_loss,
-            logits=lm_logits,
-            past_key_values=outputs.past_key_values,
-            decoder_hidden_states=outputs.decoder_hidden_states,
-            decoder_attentions=outputs.decoder_attentions,
-            cross_attentions=outputs.cross_attentions,
-            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
-            encoder_hidden_states=outputs.encoder_hidden_states,
-            encoder_attentions=outputs.encoder_attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        decoder_input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        head_mask=None,
-        decoder_head_mask=None,
-        cross_attn_head_mask=None,
-        use_cache=None,
-        encoder_outputs=None,
-        **kwargs,
-    ):
-        return {
-            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
-            "encoder_outputs": encoder_outputs,
-            "past_key_values": past_key_values,
-            "decoder_input_ids": decoder_input_ids,
-            "attention_mask": attention_mask,
-            "head_mask": head_mask,
-            "decoder_head_mask": decoder_head_mask,
-            "cross_attn_head_mask": cross_attn_head_mask,
-            "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
-        }
-
-    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
-        return shift_tokens_right(labels, self.config.pad_token_id)
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = []
-        for layer_past in past_key_values:
-            # get the correct batch idx from decoder layer's batch dim for cross and self-attn
-            layer_past_new = {
-                attn_key: _reorder_buffer(attn_cache, beam_idx) for attn_key, attn_cache in layer_past.items()
-            }
-            reordered_past.append(layer_past_new)
-        return reordered_past
-
-    def get_encoder(self):
-        return self.model.encoder
-
-    def get_decoder(self):
-        return self.model.decoder
-
-    def get_output_embeddings(self):
-        return self.model.decoder.embed_tokens
-
-    def set_output_embeddings(self, value):
-        self.model.decoder.embed_tokens = value
-
-
-class SinusoidalPositionalEmbedding(nn.Embedding):
-    """
-    This module produces sinusoidal positional embeddings of any length.
-
-    We don't want to save the weight of this embedding since it's not trained (deterministic) and it can be huge.
-
-    Padding symbols are ignored.
-
-    These embeddings get automatically extended in forward if more positions is needed.
-    """
-
-    def __init__(self, num_positions, embedding_dim, padding_idx):
-        self.make_weight(num_positions, embedding_dim, padding_idx)
-
-    def make_weight(self, num_positions, embedding_dim, padding_idx):
-        weight = self.get_embedding(num_positions, embedding_dim, padding_idx)
-        if not hasattr(self, "weight"):
-            # in ___init__
-            super().__init__(num_positions, embedding_dim, padding_idx, _weight=weight)
-        else:
-            # in forward put the weights on the correct dtype and device of the param
-            weight = weight.to(dtype=self.weight.dtype, device=self.weight.device)
-            self.weight = nn.Parameter(weight)
-        self.weight.detach_()
-        self.weight.requires_grad = False
-
-    @staticmethod
-    def get_embedding(num_embeddings, embedding_dim, padding_idx):
-        """
-        Build sinusoidal embeddings.
-
-        This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
-        "Attention Is All You Need".
-        """
-        half_dim = embedding_dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
-        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
-        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
-        if embedding_dim % 2 == 1:
-            # zero pad
-            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
-        if padding_idx is not None:
-            emb[padding_idx, :] = 0
-        return emb
-
-    @staticmethod
-    def make_positions(tensor, padding_idx: int):
-        """
-        Replace non-padding symbols with their position numbers.
-
-        Position numbers begin at padding_idx+1. Padding symbols are ignored.
-        """
-        # The series of casts and type-conversions here are carefully
-        # balanced to both work with ONNX export and XLA. In particular XLA
-        # prefers ints, cumsum defaults to output longs, and ONNX doesn't know
-        # how to handle the dtype kwarg in cumsum.
-        mask = tensor.ne(padding_idx).int()
-        return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx
-
-    def forward(
-        self,
-        input,
-        incremental_state: Optional[Any] = None,
-        timestep: Optional[Tensor] = None,
-    ):
-        """Input is expected to be of size [bsz x seqlen]."""
-        bsz, seq_len = input.shape[:2]
-        max_pos = self.padding_idx + 1 + seq_len
-        if max_pos > self.weight.size(0):
-            # expand embeddings if needed
-            self.make_weight(max_pos, self.embedding_dim, self.padding_idx)
-        positions = self.make_positions(input, self.padding_idx)
-        return super().forward(positions)
diff --git a/spaces/youplala/StoreCopilot/assets/bootstrap-datepicker.css b/spaces/youplala/StoreCopilot/assets/bootstrap-datepicker.css
deleted file mode 100644
index 90b29cd7858a7ea8c2ede096bfaf3fcdbf0f3bd9..0000000000000000000000000000000000000000
--- a/spaces/youplala/StoreCopilot/assets/bootstrap-datepicker.css
+++ /dev/null
@@ -1 +0,0 @@
-.datepicker{direction:ltr}.datepicker.dropdown-menu{padding:0;margin:0}.datepicker .datepicker-days{margin:.4rem .875rem .875rem}.datepicker .next,.datepicker .prev{color:rgba(0,0,0,0) !important;position:absolute;top:.65rem;height:1.75rem;width:1.75rem;border-radius:50rem;display:table-caption}html:not([dir=rtl]) .datepicker table thead tr th.next{float:right;right:.5rem}[dir=rtl] .datepicker table thead tr th.next{float:left;left:.5rem}html:not([dir=rtl]) .datepicker table thead tr th.prev{right:3rem}[dir=rtl] .datepicker table thead tr th.prev{left:3rem}.datepicker .next::after,.datepicker .prev::after{content:"";display:block;position:absolute;left:50%;top:46%;height:.45rem;width:.45rem;border-radius:0;border-style:solid;transform:rotate(-45deg);transform-origin:left}.datepicker .next::after{margin-left:-0.1575rem;border-width:0 1px 1px 0}[dir=rtl] .datepicker .next::after{transform:rotate(-45deg);border-width:1px 0 0 1px;margin-left:0}.datepicker .prev::after{border-width:1px 0 0 1px}[dir=rtl] .datepicker .prev::after{transform:rotate(-45deg);border-width:0 1px 1px 0;margin-left:-0.225rem}html:not([dir=rtl]) .datepicker .datepicker-months .next,html:not([dir=rtl]) .datepicker .datepicker-years .next,html:not([dir=rtl]) .datepicker .datepicker-decades .next,html:not([dir=rtl]) .datepicker .datepicker-centuries .next{right:1rem}[dir=rtl] .datepicker .datepicker-months .next,[dir=rtl] .datepicker .datepicker-years .next,[dir=rtl] .datepicker .datepicker-decades .next,[dir=rtl] .datepicker .datepicker-centuries .next{left:1rem}html:not([dir=rtl]) .datepicker .datepicker-months .prev,html:not([dir=rtl]) .datepicker .datepicker-years .prev,html:not([dir=rtl]) .datepicker .datepicker-decades .prev,html:not([dir=rtl]) .datepicker .datepicker-centuries .prev{right:3.4rem}[dir=rtl] .datepicker .datepicker-months .prev,[dir=rtl] .datepicker .datepicker-years .prev,[dir=rtl] .datepicker .datepicker-decades .prev,[dir=rtl] .datepicker .datepicker-centuries .prev{left:3.4rem}.datepicker .datepicker-switch{vertical-align:middle;font-weight:600;position:relative}html:not([dir=rtl]) .datepicker .datepicker-switch{text-align:left}[dir=rtl] .datepicker .datepicker-switch{text-align:right}html:not([dir=rtl]) .datepicker .datepicker-days .datepicker-switch{left:-1.68rem}[dir=rtl] .datepicker .datepicker-days .datepicker-switch{right:-1.68rem}html:not([dir=rtl]) .datepicker .datepicker-months .datepicker-switch,html:not([dir=rtl]) .datepicker .datepicker-years .datepicker-switch,html:not([dir=rtl]) .datepicker .datepicker-decades .datepicker-switch,html:not([dir=rtl]) .datepicker .datepicker-centuries .datepicker-switch{left:1rem}[dir=rtl] .datepicker .datepicker-months .datepicker-switch,[dir=rtl] .datepicker .datepicker-years .datepicker-switch,[dir=rtl] .datepicker .datepicker-decades .datepicker-switch,[dir=rtl] .datepicker .datepicker-centuries .datepicker-switch{right:1rem}.datepicker table thead tr:nth-child(2){height:52px !important;width:80px;position:relative}.datepicker table tbody td.today:not(.active){display:table-caption;padding-top:.27rem}.datepicker.datepicker-rtl{direction:rtl}.datepicker.datepicker-rtl table tr td span{float:right}[dir=rtl] .datepicker{direction:rtl}.datepicker table{user-select:none;margin:0;overflow:hidden;border-radius:.375rem}.datepicker table tbody:before{content:"@";display:block;line-height:6px;text-indent:-99999px}.datepicker table tr td,.datepicker table tr th{width:2.125rem;height:2.125rem;text-align:center;border:none}.datepicker table tr td.dow,.datepicker table tr th.dow{font-weight:600;font-size:.8125rem}.datepicker table tr td{border-radius:50rem}.datepicker table tr td.day:hover,.datepicker table tr td.focused{cursor:pointer}.datepicker table tr td.disabled,.datepicker table tr td.disabled:hover{cursor:default;background:none}.datepicker table tr td.range{border-radius:0 !important}html:not([dir=rtl]) .datepicker table tr td.range-start:not(.range-end){border-bottom-right-radius:0 !important;border-top-right-radius:0 !important}[dir=rtl] .datepicker table tr td.range-start:not(.range-end){border-bottom-left-radius:0 !important;border-top-left-radius:0 !important}html:not([dir=rtl]) .datepicker table tr td.range-end:not(.range-start){border-bottom-left-radius:0 !important;border-top-left-radius:0 !important}[dir=rtl] .datepicker table tr td.range-end:not(.range-start){border-bottom-right-radius:0 !important;border-top-right-radius:0 !important}.datepicker table tr td.selected,.datepicker table tr td.selected:hover,.datepicker table tr td.selected.highlighted{color:#fff}.datepicker table tr td span{display:block;float:left;width:3.375rem;height:2.4rem;line-height:2.375rem;cursor:pointer}.datepicker table tr td span.disabled,.datepicker table tr td span.disabled:hover{background:none;cursor:default}[dir=rtl] .datepicker table tr td span{float:right}.datepicker .datepicker-switch,.datepicker .prev,.datepicker .next,.datepicker tfoot tr th{cursor:pointer}.datepicker-months table,.datepicker-years table,.datepicker-decades table,.datepicker-centuries table{width:12.75rem}.datepicker-months table td,.datepicker-years table td,.datepicker-decades table td,.datepicker-centuries table td{padding:0 0 1.25rem 1.25rem}[dir=rtl] .datepicker-months table td,[dir=rtl] .datepicker-years table td,[dir=rtl] .datepicker-decades table td,[dir=rtl] .datepicker-centuries table td{padding:0 1.25rem 1.25rem 0}.datepicker-dropdown{left:0;top:0;padding:0}.input-daterange input{text-align:center}.light-style .datepicker-dropdown{z-index:1091 !important}.light-style .datepicker th.prev,.light-style .datepicker th.next{background-color:#f1f0f2}.light-style .datepicker th.prev::after,.light-style .datepicker th.next::after{border-color:#6f6b7d}.light-style .datepicker.datepicker-inline table{box-shadow:0 .25rem 1rem rgba(165,163,174,.45)}.light-style .datepicker table thead{background-color:#fff}.light-style .datepicker table thead tr,.light-style .datepicker table thead td{color:#5d596c}.light-style .datepicker table tr td.new,.light-style .datepicker table tr th.new{color:#a5a3ae}.light-style .datepicker table tr td.old,.light-style .datepicker table tr td.disabled{color:#a5a3ae}.light-style .datepicker table tr td.cw{background-color:#fff;color:#6f6b7d}.light-style .datepicker table tr td.day:hover,.light-style .datepicker table tr td.focused{background:#f1f0f2}.light-style .datepicker table tr td.range.disabled,.light-style .datepicker table tr td.range.highlighted.disabled,.light-style .datepicker table tr td.range.today.disabled{background:#f1f0f2 !important}.light-style .datepicker table tr td.active,.light-style .datepicker table tr td.active.highlighted,.light-style .datepicker table tr td.active,.light-style .datepicker table tr td span.active,.light-style .datepicker table tr td span.active.disabled,.light-style .datepicker table tr td.range-start,.light-style .datepicker table tr td.range-end{box-shadow:0 .125rem .25rem rgba(165,163,174,.3)}.light-style .datepicker table tr td span{border-radius:.375rem}.light-style .datepicker table tr td span:hover,.light-style .datepicker table tr td span.focused{background:#f1f0f2}.light-style .datepicker table tr td span.disabled,.light-style .datepicker table tr td span.disabled:hover{color:#b7b5be}.light-style .datepicker table tr td span.old,.light-style .datepicker table tr td span.new{color:#b7b5be}.dark-style .datepicker-dropdown{z-index:1091 !important}.dark-style .datepicker th.prev,.dark-style .datepicker th.next{background-color:#363b54}.dark-style .datepicker th.prev::after,.dark-style .datepicker th.next::after{border-color:#b6bee3}.dark-style .datepicker.datepicker-inline table{box-shadow:0 .25rem 1rem rgba(15,20,34,.55)}.dark-style .datepicker table thead{background-color:#2f3349}.dark-style .datepicker table thead tr,.dark-style .datepicker table thead td{color:#cfd3ec}.dark-style .datepicker table tr td.new,.dark-style .datepicker table tr th.new{color:#7983bb}.dark-style .datepicker table tr td{color:#b6bee3}.dark-style .datepicker table tr td.old,.dark-style .datepicker table tr td.disabled{color:#7983bb}.dark-style .datepicker table tr td.cw{background-color:#2f3349;color:#b6bee3}.dark-style .datepicker table tr td.day:hover,.dark-style .datepicker table tr td.focused{background:#363b54}.dark-style .datepicker table tr td.range.disabled,.dark-style .datepicker table tr td.range.highlighted.disabled,.dark-style .datepicker table tr td.range.today.disabled{background:#363b54 !important}.dark-style .datepicker table tr td.active,.dark-style .datepicker table tr td.active.highlighted,.dark-style .datepicker table tr td.active,.dark-style .datepicker table tr td span.active,.dark-style .datepicker table tr td span.active.disabled,.dark-style .datepicker table tr td.range-start,.dark-style .datepicker table tr td.range-end{box-shadow:0 .125rem .25rem rgba(15,20,34,.4)}.dark-style .datepicker table tr td span{border-radius:.375rem}.dark-style .datepicker table tr td span:hover,.dark-style .datepicker table tr td span.focused{background:#363b54}.dark-style .datepicker table tr td span.old,.dark-style .datepicker table tr td span.new{color:#7983bb}
diff --git a/spaces/yuan1615/EmpathyTTS/main.py b/spaces/yuan1615/EmpathyTTS/main.py
deleted file mode 100644
index 86743d60f7336acd52a5afaee2e50f0fad20eb80..0000000000000000000000000000000000000000
--- a/spaces/yuan1615/EmpathyTTS/main.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import argparse
-from models import SynthesizerTrn
-from text.symbols import symbols
-import utils
-import ttsfrd
-from scipy.io import wavfile
-import numpy as np
-from synthesize_fastapi import *
-config = "./model/vits/configs/baker_base.json"
-
-print("---------- Loading VITS Model ----------")
-hps = utils.get_hparams_from_file(config)
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    **hps.model).cuda()
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("./ckpt/vits/pretrained_baker.pth", net_g, None)
-
-lexicon_mandarin = read_lexicon("./model/vits/lexicon/pinyin-lexicon-r.txt")
-
-frontend = ttsfrd.TtsFrontendEngine()
-model_dir = './ckpt/damo/resource'
-frontend.initialize(model_dir)
-frontend.set_lang_type('zhcn')
-
-
-def tts(text, out):
-    audio_all = np.zeros(1, dtype=np.int16)  # 设置初始音频
-    pinyin_list, prosody_list = g2p_mandarin(frontend, text)
-    for texts, phone_prosody in zip(pinyin_list, prosody_list):
-        stn_tst = get_text(texts, hps)
-        prosody = get_prosody(phone_prosody, hps)
-        with torch.no_grad():
-            x_tst = stn_tst.cuda().unsqueeze(0)
-            prosody = prosody.cuda().unsqueeze(0)
-            x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()
-            audio = net_g.infer(x_tst, x_tst_lengths, prosody, noise_scale=.667, noise_scale_w=0.0, length_scale=1)[0][
-                        0, 0].data.cpu().float().numpy() * 32767.0
-        i = np.random.uniform(0.12, 0.25, 1)[0]
-        space_time = np.zeros(int(i * 22050), dtype=np.int16)
-        audio_all = np.concatenate((audio_all, audio, space_time))
-    wavfile.write(
-        out,
-        22050,
-        audio_all.astype(np.int16),
-    )
-    return 'OK'
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--text', default='我说点什么好呢？念一个绕口令吧。八百标兵奔北坡，炮兵并排北边跑，炮兵怕把标兵碰，标兵怕碰炮兵炮。')
-    parser.add_argument('--out', default='output.wav')
-    a = parser.parse_args()
-    tts(a.text, a.out)
diff --git a/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/components/voice.tsx b/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/components/voice.tsx
deleted file mode 100644
index 074d0e145229947282a472bd84f6578cf0b3c71c..0000000000000000000000000000000000000000
--- a/spaces/zhoujiaxin/zhoujiaxinchatgpt/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/zixian/Zhenhuan-VITS/text/english.py b/spaces/zixian/Zhenhuan-VITS/text/english.py
deleted file mode 100644
index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000
--- a/spaces/zixian/Zhenhuan-VITS/text/english.py
+++ /dev/null
@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# 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'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('æ', 'e'),
-    ('ɑ', 'a'),
-    ('ɔ', 'o'),
-    ('ð', 'z'),
-    ('θ', 's'),
-    ('ɛ', 'e'),
-    ('ɪ', 'i'),
-    ('ʊ', 'u'),
-    ('ʒ', 'ʥ'),
-    ('ʤ', 'ʥ'),
-    ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('ð', 'z'),
-    ('θ', 's'),
-    ('ʒ', 'ʑ'),
-    ('ʤ', 'dʑ'),
-    ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('ʤ', 'dʒ'),
-    ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def collapse_whitespace(text):
-    return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
-    return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
-    return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
-    match = m.group(1)
-    parts = match.split('.')
-    if len(parts) > 2:
-        return match + ' dollars'  # Unexpected format
-    dollars = int(parts[0]) if parts[0] else 0
-    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-    if dollars and cents:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
-    elif dollars:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        return '%s %s' % (dollars, dollar_unit)
-    elif cents:
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s' % (cents, cent_unit)
-    else:
-        return 'zero dollars'
-
-
-def _expand_ordinal(m):
-    return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-    num = int(m.group(0))
-    if num > 1000 and num < 3000:
-        if num == 2000:
-            return 'two thousand'
-        elif num > 2000 and num < 2010:
-            return 'two thousand ' + _inflect.number_to_words(num % 100)
-        elif num % 100 == 0:
-            return _inflect.number_to_words(num // 100) + ' hundred'
-        else:
-            return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
-    else:
-        return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
-    text = re.sub(_comma_number_re, _remove_commas, text)
-    text = re.sub(_pounds_re, r'\1 pounds', text)
-    text = re.sub(_dollars_re, _expand_dollars, text)
-    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-    text = re.sub(_ordinal_re, _expand_ordinal, text)
-    text = re.sub(_number_re, _expand_number, text)
-    return text
-
-
-def mark_dark_l(text):
-    return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
-    text = unidecode(text).lower()
-    text = expand_abbreviations(text)
-    text = normalize_numbers(text)
-    phonemes = ipa.convert(text)
-    phonemes = collapse_whitespace(phonemes)
-    return phonemes
-
-
-def english_to_lazy_ipa(text):
-    text = english_to_ipa(text)
-    for regex, replacement in _lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def english_to_ipa2(text):
-    text = english_to_ipa(text)
-    text = mark_dark_l(text)
-    for regex, replacement in _ipa_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
-    text = english_to_ipa(text)
-    for regex, replacement in _lazy_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
diff --git a/spaces/zomehwh/rvc-models/infer_pack/models.py b/spaces/zomehwh/rvc-models/infer_pack/models.py
deleted file mode 100644
index 96165f73644e6fb92d0ffedb4a3c9e1a457cb989..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/rvc-models/infer_pack/models.py
+++ /dev/null
@@ -1,982 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y_lengths, ds
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            x, y_lengths, self.segment_size
-        )
-
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice
-
-    def infer(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o, o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/zomehwh/rvc-models/infer_pack/modules.py b/spaces/zomehwh/rvc-models/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/rvc-models/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x