diff --git a/spaces/0xhimzel/Detect-AI-Plagiarism/app.py b/spaces/0xhimzel/Detect-AI-Plagiarism/app.py
deleted file mode 100644
index 67f00d7476a865cba7fc37e1d07ec1c1d4569ce1..0000000000000000000000000000000000000000
--- a/spaces/0xhimzel/Detect-AI-Plagiarism/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import os
-import gradio as gr
-from transformers import pipeline
-
-auth_token = os.environ.get("access_token")
-pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta",use_auth_token=auth_token)
-
-
-def predict_en(text):
-    res = pipeline_en(text)[0]
-    label = res['label']
-    score = round(res['score']*100, 2)
-    return "%d%% chance"%score, label
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("""
-                     # 🤖 Detect AI Plagiarism with Jurnee
-                     Paste in the text you want to check and get a holistic score for how much of the document is written by AI. We recommend that educators take these results as one of many pieces in their assessment of student work. This model is based on Hello Simple's paper [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597) and Github project [Hello-SimpleAI/chatgpt-comparison-detection](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection).
-                """)
-    with gr.Tab("Try it out 👇"):
-        gr.Markdown("""
-                    Note: Providing more text to the `Text` box can make the prediction more accurate!
-                    """)
-        t1 = gr.Textbox(lines=5, label='Paste the text you want to check',value="There are a few things that can help protect your credit card information from being misused when you give it to a restaurant or any other business:\n\nEncryption: Many businesses use encryption to protect your credit card information when it is being transmitted or stored. This means that the information is transformed into a code that is difficult for anyone to read without the right key.")
-        button1 = gr.Button("👀 See results")
-        score1 = gr.Textbox(lines=1, label='There is a')
-        label1 = gr.Textbox(lines=1, label='That this text is written entirely by a')
-
-    button1.click(predict_en, inputs=[t1], outputs=[score1, label1])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/101-5/gpt4free/g4f/.v1/gui/query_methods.py b/spaces/101-5/gpt4free/g4f/.v1/gui/query_methods.py
deleted file mode 100644
index 2d6adacd3b394183c65ab596cc148c45de6b63c4..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/gui/query_methods.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-import sys
-from typing import Optional
-
-sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir))
-
-from gpt4free import quora, forefront, theb, you
-import random
-
-
-def query_forefront(question: str, proxy: Optional[str] = None) -> str:
-    # create an account
-    token = forefront.Account.create(logging=False, proxy=proxy)
-
-    response = ""
-    # get a response
-    try:
-        return forefront.Completion.create(token=token, prompt='hello world', model='gpt-4', proxy=proxy).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.'
-        )
-
-
-def query_quora(question: str, proxy: Optional[str] = None) -> str:
-    token = quora.Account.create(logging=False, enable_bot_creation=True, proxy=proxy)
-    return quora.Completion.create(model='gpt-4', prompt=question, token=token, proxy=proxy).text
-
-
-def query_theb(question: str, proxy: Optional[str] = None) -> str:
-    # Set cloudflare clearance cookie and get answer from GPT-4 model
-    response = ""
-    try:
-        return ''.join(theb.Completion.create(prompt=question, proxy=proxy))
-
-    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.'
-        )
-
-
-def query_you(question: str, proxy: Optional[str] = None) -> str:
-    # Set cloudflare clearance cookie and get answer from GPT-4 model
-    try:
-        result = you.Completion.create(prompt=question, proxy=proxy)
-        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.'
-        )
-
-
-# Define a dictionary containing all query methods
-avail_query_methods = {
-    "Forefront": query_forefront,
-    "Poe": query_quora,
-    "Theb": query_theb,
-    "You": query_you,
-    # "Writesonic": query_writesonic,
-    # "T3nsor": query_t3nsor,
-    # "Phind": query_phind,
-    # "Ora": query_ora,
-}
-
-
-def query(user_input: str, selected_method: str = "Random", proxy: Optional[str] = None) -> str:
-    # If a specific query method is selected (not "Random") and the method is in the dictionary, try to call it
-    if selected_method != "Random" and selected_method in avail_query_methods:
-        try:
-            return avail_query_methods[selected_method](user_input, proxy=proxy)
-        except Exception as e:
-            print(f"Error with {selected_method}: {e}")
-            return "😵 Sorry, some error occurred please try again."
-
-    # Initialize variables for determining success and storing the result
-    success = False
-    result = "😵 Sorry, some error occurred please try again."
-    # Create a list of available query methods
-    query_methods_list = list(avail_query_methods.values())
-
-    # Continue trying different methods until a successful result is obtained or all methods have been tried
-    while not success and query_methods_list:
-        # Choose a random method from the list
-        chosen_query = random.choice(query_methods_list)
-        # Find the name of the chosen method
-        chosen_query_name = [k for k, v in avail_query_methods.items() if v == chosen_query][0]
-        try:
-            # Try to call the chosen method with the user input
-            result = chosen_query(user_input, proxy=proxy)
-            success = True
-        except Exception as e:
-            print(f"Error with {chosen_query_name}: {e}")
-            # Remove the failed method from the list of available methods
-            query_methods_list.remove(chosen_query)
-
-    return result
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/DS DELMIA V5-6R2015 GA.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/DS DELMIA V5-6R2015 GA.md
deleted file mode 100644
index f401e91df36c8e3e3597153ea5d4c8f9529dcc99..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/DS DELMIA V5-6R2015 GA.md	
+++ /dev/null
@@ -1,52 +0,0 @@
-<br />
-<h1>What is DS DELMIA V5-6R2015 GA and Why You Need It</h1>
-<p>DS DELMIA V5-6R2015 GA is a software solution that enables you to design, simulate, and optimize your production processes and systems. It is part of the Dassault Systemes portfolio of 3D and Product Lifecycle Management (PLM) solutions that help you create innovative products and services.</p>
-<p>With DS DELMIA V5-6R2015 GA, you can:</p>
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-<ul>
-<li>Model and analyze your production flows and layouts with DELMIA QUEST</li>
-<li>Perform ergonomic and human factors analysis with DELMIA Ergonomics</li>
-<li>Plan and program your industrial robots with DELMIA Robotics</li>
-<li>Define and optimize your machining operations with DELMIA V5 DPM POWERTRAIN</li>
-<li>Manage and access your process data with DELMIA Process Engineer and DELMIA PPR Navigator</li>
-<li>Design and validate your assembly, fastening, inspection, and planning processes with DPM Assembly Planning, DPM Fastener Planning, DPM Machining Planning, DPM Inspection Planning, and DPM Planning</li>
-</ul>
-<p>DS DELMIA V5-6R2015 GA is compatible with Windows 7 and Windows 8.1 operating systems. It supports both 32-bit and 64-bit architectures. It is available in multiple languages, including English, French, German, Japanese, Chinese, Russian, and more.</p>
-<p>DS DELMIA V5-6R2015 GA is a powerful tool that can help you improve your productivity, quality, and profitability. It can help you reduce costs, waste, and errors. It can help you enhance your collaboration, innovation, and customer satisfaction.</p>
-<p>If you want to learn more about DS DELMIA V5-6R2015 GA, you can visit the official website of Dassault Systemes or download a free trial version from their online store. You can also read some of the testimonials and reviews from other users who have benefited from this software solution.</p>
-<h2>Conclusion</h2>
-<p>DS DELMIA V5-6R2015 GA is a software solution that enables you to design, simulate, and optimize your production processes and systems. It is part of the Dassault Systemes portfolio of 3D and PLM solutions that help you create innovative products and services. With DS DELMIA V5-6R2015 GA, you can improve your productivity, quality, and profitability. You can reduce costs, waste, and errors. You can enhance your collaboration, innovation, and customer satisfaction.</p>
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-
-<h2>How to Use DS DELMIA V5-6R2015 GA</h2>
-<p>DS DELMIA V5-6R2015 GA is easy to use and install. You can download it from the Dassault Systemes online store or request a DVD from your local reseller. You can also get a free trial version for 30 days to test its features and benefits.</p>
-<p>Once you have installed DS DELMIA V5-6R2015 GA, you can launch it from your desktop or start menu. You will see a user-friendly interface that allows you to access different modules and functions. You can also customize your workspace and preferences according to your needs and preferences.</p>
-<p></p>
-<p>To use DS DELMIA V5-6R2015 GA, you need to create or open a project file that contains your process data and models. You can import data from other sources, such as CATIA, SolidWorks, or Excel. You can also create data from scratch using the built-in tools and wizards.</p>
-<p>Once you have your project file ready, you can start designing, simulating, and optimizing your production processes and systems. You can use the various modules and functions of DS DELMIA V5-6R2015 GA to perform different tasks, such as:</p>
-<ul>
-<li>Creating and editing production flows and layouts with DELMIA QUEST</li>
-<li>Adding and manipulating human models and performing ergonomic analysis with DELMIA Ergonomics</li>
-<li>Adding and programming industrial robots and performing robot simulation with DELMIA Robotics</li>
-<li>Defining and optimizing machining operations and performing machining simulation with DELMIA V5 DPM POWERTRAIN</li>
-<li>Managing and accessing your process data with DELMIA Process Engineer and DELMIA PPR Navigator</li>
-<li>Creating and validating assembly, fastening, inspection, and planning processes with DPM Assembly Planning, DPM Fastener Planning, DPM Machining Planning, DPM Inspection Planning, and DPM Planning</li>
-</ul>
-<p>You can also use DS DELMIA V5-6R2015 GA to perform various analyses and validations, such as cycle time analysis, resource utilization analysis, collision detection, reachability analysis, feasibility analysis, quality analysis, and more. You can also generate reports and documentation for your projects.</p>
-<h2>Who Can Benefit from DS DELMIA V5-6R2015 GA</h2>
-<p>DS DELMIA V5-6R2015 GA is a software solution that can benefit anyone who is involved in the design, simulation, and optimization of production processes and systems. It can be used by different industries, such as aerospace, automotive, consumer goods, energy, industrial equipment, life sciences, marine and offshore, transportation and mobility, and more.</p>
-<p>Some of the roles that can benefit from DS DELMIA V5-6R2015 GA are:</p>
-<ul>
-<li>Process engineers who need to design and optimize production processes</li>
-<li>Manufacturing engineers who need to simulate and validate production systems</li>
-<li>Industrial engineers who need to analyze and improve production performance</li>
-<li>Ergonomists who need to ensure human safety and comfort in production environments</li>
-<li>Robotics engineers who need to program and control industrial robots</li>
-<li>Machining engineers who need to define and optimize machining operations</li>
-<li>Quality engineers who need to ensure product quality and compliance</li>
-<li>Project managers who need to coordinate and monitor production projects</li>
-<li>Decision makers who need to evaluate production scenarios and alternatives</li>
-</ul>
-<p>DS DELMIA V5-6R2015 GA can help these roles achieve their goals faster, easier, and better. It can help them reduce costs, waste, and errors. It can help them enhance collaboration, innovation, and customer satisfaction.</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Life Is Beautiful! 1080p movie free downloadgolkes) - A masterpiece of Italian cinema based on a true story.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Life Is Beautiful! 1080p movie free downloadgolkes) - A masterpiece of Italian cinema based on a true story.md
deleted file mode 100644
index 3347d4620e2a05b36de5e79448e3dcca7b905c4d..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Life Is Beautiful! 1080p movie free downloadgolkes) - A masterpiece of Italian cinema based on a true story.md	
+++ /dev/null
@@ -1,100 +0,0 @@
-
-<h1>HD Online Player (Life Is Beautiful! 1080p movie free downloadgolkes)</h1>
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- <h2>FAQs</h2>
- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is HD online player safe and legal?</td>
-<td>Yes, HD online player is safe and legal. It does not contain any viruses or malware that can harm your device. It also does not host any pirated or illegal content on its servers. It only provides links to videos that are already available on other websites.</td>
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- <p>Among Us is one of the most popular online multiplayer games of 2020 and 2021. It is a fun and thrilling game where you have to work together with your crewmates to complete tasks and find the impostors before they kill you. But what if you want to play the game on your Android device and you can't find it on the Google Play Store? Don't worry, there is a way to download and install the game using an APK file from Uptodown, a trusted and safe platform for downloading apps and games. In this article, we will show you how to do that in a few simple steps.</p>
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- <p>Among Us is a game developed and published by Innersloth, an American game studio. It was released in 2018 for Android, iOS, and Windows devices, but it gained a massive surge of popularity in 2020 thanks to many Twitch streamers and YouTubers playing it. The game has also received favorable reviews from critics and players for its fun and entertaining gameplay.</p>
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- <p>The game has two phases: free roam and meeting. During free roam, you can move around the map and interact with objects. If you are a crewmate, you can do tasks that are assigned to you. If you are an impostor, you can fake tasks, vent to move quickly, or kill crewmates when no one is watching. You can also use sabotages to create chaos and distract the crewmates. Some sabotages require immediate attention, such as reactor meltdown or oxygen depletion. If they are not fixed in time, the impostors win.</p>
- <p>If a dead body is found or an emergency button is pressed, a meeting is called. During a meeting, all players can discuss and vote on who they think is the impostor. You can use the text chat or voice chat to communicate with other players. You can also use evidence, such as visual tasks, admin map, or vitals, to support your claims or accusations. The player with the most votes is ejected from the game. If there is a tie, no one is ejected. The game continues until the next meeting or the end condition is met.</p>
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- <h2>How to download and install Among Us APK from Uptodown</h2>
- <p>Now that you know what Among Us and Uptodown are, let's see how you can download and install Among Us APK from Uptodown on your Android device. It's very easy and only takes a few minutes. Here are the steps you need to follow:</p>
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-</ol>
- <h3>Step 2: Download the APK file from Uptodown</h3>
- <p>The next step is to download the APK file of Among Us from Uptodown. To do this, follow these steps:</p>
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-<li>Once the installation is complete, tap on open to launch the game or tap on done to exit the installer.</li>
-</ol>
- <h3>Step 4: Launch and enjoy the game</h3>
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- <p>In this article, we have shown you how to download and install Among Us APK from Uptodown on your Android device. We hope you found this guide helpful and easy to follow. Uptodown is a great platform for downloading apps and games that are not available on the Google Play Store or are region-locked. Among Us is a fun and thrilling game that you can play with your friends or strangers online. It is a game of deception, teamwork, and betrayal that will keep you hooked for hours. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading!</p>
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-<p>To upgrade your weapons in Cover Fire, you need to go to the arsenal menu and select the weapon you want to upgrade. You can upgrade different aspects of your weapon, such as damage, accuracy, reload speed, magazine size, etc. You will need coins or gold to upgrade your weapons, depending on the level of upgrade.</p>
-<li>How do I change my character in Cover Fire?</li>
-<p>To change your character in Cover Fire, you need to go to the squad menu and select the character you want to use. You can choose from different characters with different skills and abilities. You can also customize your character's appearance with different outfits and accessories. You will need coins or gold to unlock new characters or items.</p>
-<li>How do I join the online mode in Cover Fire?</li>
-<p>To join the online mode in Cover Fire, you need to have an internet connection and an account in the game. You can create an account with your email or Facebook login. Then, you need to go to the online mode menu and select the option you want to play. You can choose from sniper tournaments or events. You will be matched with other players based on your rank and skill level.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/2023Liu2023/bingo/src/lib/hooks/use-enter-submit.tsx b/spaces/2023Liu2023/bingo/src/lib/hooks/use-enter-submit.tsx
deleted file mode 100644
index d66b2d3253baff164235d4ca791aae6d84721835..0000000000000000000000000000000000000000
--- a/spaces/2023Liu2023/bingo/src/lib/hooks/use-enter-submit.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import { useRef, type RefObject } from 'react'
-
-export function useEnterSubmit(): {
-  formRef: RefObject<HTMLFormElement>
-  onKeyDown: (event: React.KeyboardEvent<HTMLTextAreaElement>) => void
-} {
-  const formRef = useRef<HTMLFormElement>(null)
-
-  const handleKeyDown = (
-    event: React.KeyboardEvent<HTMLTextAreaElement>
-  ): void => {
-    if (
-      event.key === 'Enter' &&
-      !event.shiftKey &&
-      !event.nativeEvent.isComposing
-    ) {
-      formRef.current?.requestSubmit()
-      event.preventDefault()
-    }
-  }
-
-  return { formRef, onKeyDown: handleKeyDown }
-}
diff --git a/spaces/232labs/VToonify/vtoonify/model/encoder/criteria/id_loss.py b/spaces/232labs/VToonify/vtoonify/model/encoder/criteria/id_loss.py
deleted file mode 100644
index 37c71d3047be01ae7b301e0a96f14e2df88a143f..0000000000000000000000000000000000000000
--- a/spaces/232labs/VToonify/vtoonify/model/encoder/criteria/id_loss.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import torch
-from torch import nn
-from model.encoder.encoders.model_irse import Backbone
-
-
-class IDLoss(nn.Module):
-    def __init__(self, model_paths):
-        super(IDLoss, self).__init__()
-        print('Loading ResNet ArcFace')
-        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
-        self.facenet.load_state_dict(torch.load(model_paths))
-        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
-        self.facenet.eval()
-
-    def extract_feats(self, x):
-        x = x[:, :, 35:223, 32:220]  # Crop interesting region
-        x = self.face_pool(x)
-        x_feats = self.facenet(x)
-        return x_feats
-
-    def forward(self, y_hat, y):
-        n_samples = y_hat.shape[0]
-        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
-        y_hat_feats = self.extract_feats(y_hat)
-        y_feats = y_feats.detach()
-        loss = 0
-        count = 0
-        for i in range(n_samples):
-            diff_target = y_hat_feats[i].dot(y_feats[i])
-            loss += 1 - diff_target
-            count += 1
-
-        return loss / count
\ No newline at end of file
diff --git a/spaces/AI-Zero-to-Hero/06-SL-AI-Image-Music-Video-UI-UX-URL/README.md b/spaces/AI-Zero-to-Hero/06-SL-AI-Image-Music-Video-UI-UX-URL/README.md
deleted file mode 100644
index 5c8263a0d4cf200bf09c7a07d3244a40e57d018b..0000000000000000000000000000000000000000
--- a/spaces/AI-Zero-to-Hero/06-SL-AI-Image-Music-Video-UI-UX-URL/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: 06 SL AI Image Music Video UI UX URL
-emoji: 📊
-colorFrom: pink
-colorTo: red
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/data/sound_dataset.py b/spaces/AIConsultant/MusicGen/audiocraft/data/sound_dataset.py
deleted file mode 100644
index 8b88cbe8016b4bd28c2de749177c9af29f7755fc..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/data/sound_dataset.py
+++ /dev/null
@@ -1,330 +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.
-"""Dataset of audio with a simple description.
-"""
-
-from dataclasses import dataclass, fields, replace
-import json
-from pathlib import Path
-import random
-import typing as tp
-
-import numpy as np
-import torch
-
-from .info_audio_dataset import (
-    InfoAudioDataset,
-    get_keyword_or_keyword_list
-)
-from ..modules.conditioners import (
-    ConditioningAttributes,
-    SegmentWithAttributes,
-    WavCondition,
-)
-
-
-EPS = torch.finfo(torch.float32).eps
-TARGET_LEVEL_LOWER = -35
-TARGET_LEVEL_UPPER = -15
-
-
-@dataclass
-class SoundInfo(SegmentWithAttributes):
-    """Segment info augmented with Sound metadata.
-    """
-    description: tp.Optional[str] = None
-    self_wav: tp.Optional[torch.Tensor] = None
-
-    @property
-    def has_sound_meta(self) -> bool:
-        return self.description is not None
-
-    def to_condition_attributes(self) -> ConditioningAttributes:
-        out = ConditioningAttributes()
-
-        for _field in fields(self):
-            key, value = _field.name, getattr(self, _field.name)
-            if key == 'self_wav':
-                out.wav[key] = value
-            else:
-                out.text[key] = value
-        return out
-
-    @staticmethod
-    def attribute_getter(attribute):
-        if attribute == 'description':
-            preprocess_func = get_keyword_or_keyword_list
-        else:
-            preprocess_func = None
-        return preprocess_func
-
-    @classmethod
-    def from_dict(cls, dictionary: dict, fields_required: bool = False):
-        _dictionary: tp.Dict[str, tp.Any] = {}
-
-        # allow a subset of attributes to not be loaded from the dictionary
-        # these attributes may be populated later
-        post_init_attributes = ['self_wav']
-
-        for _field in fields(cls):
-            if _field.name in post_init_attributes:
-                continue
-            elif _field.name not in dictionary:
-                if fields_required:
-                    raise KeyError(f"Unexpected missing key: {_field.name}")
-            else:
-                preprocess_func: tp.Optional[tp.Callable] = cls.attribute_getter(_field.name)
-                value = dictionary[_field.name]
-                if preprocess_func:
-                    value = preprocess_func(value)
-                _dictionary[_field.name] = value
-        return cls(**_dictionary)
-
-
-class SoundDataset(InfoAudioDataset):
-    """Sound audio dataset: Audio dataset with environmental sound-specific metadata.
-
-    Args:
-        info_fields_required (bool): Whether all the mandatory metadata fields should be in the loaded metadata.
-        external_metadata_source (tp.Optional[str]): Folder containing JSON metadata for the corresponding dataset.
-            The metadata files contained in this folder are expected to match the stem of the audio file with
-            a json extension.
-        aug_p (float): Probability of performing audio mixing augmentation on the batch.
-        mix_p (float): Proportion of batch items that are mixed together when applying audio mixing augmentation.
-        mix_snr_low (int): Lowerbound for SNR value sampled for mixing augmentation.
-        mix_snr_high (int): Upperbound for SNR value sampled for mixing augmentation.
-        mix_min_overlap (float): Minimum overlap between audio files when performing mixing augmentation.
-        kwargs: Additional arguments for AudioDataset.
-
-    See `audiocraft.data.info_audio_dataset.InfoAudioDataset` for full initialization arguments.
-    """
-    def __init__(
-        self,
-        *args,
-        info_fields_required: bool = True,
-        external_metadata_source: tp.Optional[str] = None,
-        aug_p: float = 0.,
-        mix_p: float = 0.,
-        mix_snr_low: int = -5,
-        mix_snr_high: int = 5,
-        mix_min_overlap: float = 0.5,
-        **kwargs
-    ):
-        kwargs['return_info'] = True  # We require the info for each song of the dataset.
-        super().__init__(*args, **kwargs)
-        self.info_fields_required = info_fields_required
-        self.external_metadata_source = external_metadata_source
-        self.aug_p = aug_p
-        self.mix_p = mix_p
-        if self.aug_p > 0:
-            assert self.mix_p > 0, "Expecting some mixing proportion mix_p if aug_p > 0"
-            assert self.channels == 1, "SoundDataset with audio mixing considers only monophonic audio"
-        self.mix_snr_low = mix_snr_low
-        self.mix_snr_high = mix_snr_high
-        self.mix_min_overlap = mix_min_overlap
-
-    def _get_info_path(self, path: tp.Union[str, Path]) -> Path:
-        """Get path of JSON with metadata (description, etc.).
-        If there exists a JSON with the same name as 'path.name', then it will be used.
-        Else, such JSON will be searched for in an external json source folder if it exists.
-        """
-        info_path = Path(path).with_suffix('.json')
-        if Path(info_path).exists():
-            return info_path
-        elif self.external_metadata_source and (Path(self.external_metadata_source) / info_path.name).exists():
-            return Path(self.external_metadata_source) / info_path.name
-        else:
-            raise Exception(f"Unable to find a metadata JSON for path: {path}")
-
-    def __getitem__(self, index):
-        wav, info = super().__getitem__(index)
-        info_data = info.to_dict()
-        info_path = self._get_info_path(info.meta.path)
-        if Path(info_path).exists():
-            with open(info_path, 'r') as json_file:
-                sound_data = json.load(json_file)
-                sound_data.update(info_data)
-                sound_info = SoundInfo.from_dict(sound_data, fields_required=self.info_fields_required)
-                # if there are multiple descriptions, sample one randomly
-                if isinstance(sound_info.description, list):
-                    sound_info.description = random.choice(sound_info.description)
-        else:
-            sound_info = SoundInfo.from_dict(info_data, fields_required=False)
-
-        sound_info.self_wav = WavCondition(
-            wav=wav[None], length=torch.tensor([info.n_frames]),
-            sample_rate=[sound_info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-
-        return wav, sound_info
-
-    def collater(self, samples):
-        # when training, audio mixing is performed in the collate function
-        wav, sound_info = super().collater(samples)  # SoundDataset always returns infos
-        if self.aug_p > 0:
-            wav, sound_info = mix_samples(wav, sound_info, self.aug_p, self.mix_p,
-                                          snr_low=self.mix_snr_low, snr_high=self.mix_snr_high,
-                                          min_overlap=self.mix_min_overlap)
-        return wav, sound_info
-
-
-def rms_f(x: torch.Tensor) -> torch.Tensor:
-    return (x ** 2).mean(1).pow(0.5)
-
-
-def normalize(audio: torch.Tensor, target_level: int = -25) -> torch.Tensor:
-    """Normalize the signal to the target level."""
-    rms = rms_f(audio)
-    scalar = 10 ** (target_level / 20) / (rms + EPS)
-    audio = audio * scalar.unsqueeze(1)
-    return audio
-
-
-def is_clipped(audio: torch.Tensor, clipping_threshold: float = 0.99) -> torch.Tensor:
-    return (abs(audio) > clipping_threshold).any(1)
-
-
-def mix_pair(src: torch.Tensor, dst: torch.Tensor, min_overlap: float) -> torch.Tensor:
-    start = random.randint(0, int(src.shape[1] * (1 - min_overlap)))
-    remainder = src.shape[1] - start
-    if dst.shape[1] > remainder:
-        src[:, start:] = src[:, start:] + dst[:, :remainder]
-    else:
-        src[:, start:start+dst.shape[1]] = src[:, start:start+dst.shape[1]] + dst
-    return src
-
-
-def snr_mixer(clean: torch.Tensor, noise: torch.Tensor, snr: int, min_overlap: float,
-              target_level: int = -25, clipping_threshold: float = 0.99) -> torch.Tensor:
-    """Function to mix clean speech and noise at various SNR levels.
-
-    Args:
-        clean (torch.Tensor): Clean audio source to mix, of shape [B, T].
-        noise (torch.Tensor): Noise audio source to mix, of shape [B, T].
-        snr (int): SNR level when mixing.
-        min_overlap (float): Minimum overlap between the two mixed sources.
-        target_level (int): Gain level in dB.
-        clipping_threshold (float): Threshold for clipping the audio.
-    Returns:
-        torch.Tensor: The mixed audio, of shape [B, T].
-    """
-    if clean.shape[1] > noise.shape[1]:
-        noise = torch.nn.functional.pad(noise, (0, clean.shape[1] - noise.shape[1]))
-    else:
-        noise = noise[:, :clean.shape[1]]
-
-    # normalizing to -25 dB FS
-    clean = clean / (clean.max(1)[0].abs().unsqueeze(1) + EPS)
-    clean = normalize(clean, target_level)
-    rmsclean = rms_f(clean)
-
-    noise = noise / (noise.max(1)[0].abs().unsqueeze(1) + EPS)
-    noise = normalize(noise, target_level)
-    rmsnoise = rms_f(noise)
-
-    # set the noise level for a given SNR
-    noisescalar = (rmsclean / (10 ** (snr / 20)) / (rmsnoise + EPS)).unsqueeze(1)
-    noisenewlevel = noise * noisescalar
-
-    # mix noise and clean speech
-    noisyspeech = mix_pair(clean, noisenewlevel, min_overlap)
-
-    # randomly select RMS value between -15 dBFS and -35 dBFS and normalize noisyspeech with that value
-    # there is a chance of clipping that might happen with very less probability, which is not a major issue.
-    noisy_rms_level = np.random.randint(TARGET_LEVEL_LOWER, TARGET_LEVEL_UPPER)
-    rmsnoisy = rms_f(noisyspeech)
-    scalarnoisy = (10 ** (noisy_rms_level / 20) / (rmsnoisy + EPS)).unsqueeze(1)
-    noisyspeech = noisyspeech * scalarnoisy
-    clean = clean * scalarnoisy
-    noisenewlevel = noisenewlevel * scalarnoisy
-
-    # final check to see if there are any amplitudes exceeding +/- 1. If so, normalize all the signals accordingly
-    clipped = is_clipped(noisyspeech)
-    if clipped.any():
-        noisyspeech_maxamplevel = noisyspeech[clipped].max(1)[0].abs().unsqueeze(1) / (clipping_threshold - EPS)
-        noisyspeech[clipped] = noisyspeech[clipped] / noisyspeech_maxamplevel
-
-    return noisyspeech
-
-
-def snr_mix(src: torch.Tensor, dst: torch.Tensor, snr_low: int, snr_high: int, min_overlap: float):
-    if snr_low == snr_high:
-        snr = snr_low
-    else:
-        snr = np.random.randint(snr_low, snr_high)
-    mix = snr_mixer(src, dst, snr, min_overlap)
-    return mix
-
-
-def mix_text(src_text: str, dst_text: str):
-    """Mix text from different sources by concatenating them."""
-    if src_text == dst_text:
-        return src_text
-    return src_text + " " + dst_text
-
-
-def mix_samples(wavs: torch.Tensor, infos: tp.List[SoundInfo], aug_p: float, mix_p: float,
-                snr_low: int, snr_high: int, min_overlap: float):
-    """Mix samples within a batch, summing the waveforms and concatenating the text infos.
-
-    Args:
-        wavs (torch.Tensor): Audio tensors of shape [B, C, T].
-        infos (list[SoundInfo]): List of SoundInfo items corresponding to the audio.
-        aug_p (float): Augmentation probability.
-        mix_p (float): Proportion of items in the batch to mix (and merge) together.
-        snr_low (int): Lowerbound for sampling SNR.
-        snr_high (int): Upperbound for sampling SNR.
-        min_overlap (float): Minimum overlap between mixed samples.
-    Returns:
-        tuple[torch.Tensor, list[SoundInfo]]: A tuple containing the mixed wavs
-            and mixed SoundInfo for the given batch.
-    """
-    # no mixing to perform within the batch
-    if mix_p == 0:
-        return wavs, infos
-
-    if random.uniform(0, 1) < aug_p:
-        # perform all augmentations on waveforms as [B, T]
-        # randomly picking pairs of audio to mix
-        assert wavs.size(1) == 1, f"Mix samples requires monophonic audio but C={wavs.size(1)}"
-        wavs = wavs.mean(dim=1, keepdim=False)
-        B, T = wavs.shape
-        k = int(mix_p * B)
-        mixed_sources_idx = torch.randperm(B)[:k]
-        mixed_targets_idx = torch.randperm(B)[:k]
-        aug_wavs = snr_mix(
-            wavs[mixed_sources_idx],
-            wavs[mixed_targets_idx],
-            snr_low,
-            snr_high,
-            min_overlap,
-        )
-        # mixing textual descriptions in metadata
-        descriptions = [info.description for info in infos]
-        aug_infos = []
-        for i, j in zip(mixed_sources_idx, mixed_targets_idx):
-            text = mix_text(descriptions[i], descriptions[j])
-            m = replace(infos[i])
-            m.description = text
-            aug_infos.append(m)
-
-        # back to [B, C, T]
-        aug_wavs = aug_wavs.unsqueeze(1)
-        assert aug_wavs.shape[0] > 0, "Samples mixing returned empty batch."
-        assert aug_wavs.dim() == 3, f"Returned wav should be [B, C, T] but dim = {aug_wavs.dim()}"
-        assert aug_wavs.shape[0] == len(aug_infos), "Mismatch between number of wavs and infos in the batch"
-
-        return aug_wavs, aug_infos  # [B, C, T]
-    else:
-        # randomly pick samples in the batch to match
-        # the batch size when performing audio mixing
-        B, C, T = wavs.shape
-        k = int(mix_p * B)
-        wav_idx = torch.randperm(B)[:k]
-        wavs = wavs[wav_idx]
-        infos = [infos[i] for i in wav_idx]
-        assert wavs.shape[0] == len(infos), "Mismatch between number of wavs and infos in the batch"
-
-        return wavs, infos  # [B, C, T]
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/runs/train_mfa_align.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/runs/train_mfa_align.py
deleted file mode 100644
index f0b845e7388b5656c4e0e2c5efddad7f1aa28e6a..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/runs/train_mfa_align.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import utils.commons.single_thread_env  # NOQA
-import glob
-import subprocess
-from textgrid import TextGrid
-import os
-from text_to_speech.utils.commons.hparams import hparams, set_hparams
-
-
-def train_mfa_align(mfa_outputs="mfa_outputs",
-                    mfa_inputs="mfa_inputs",
-                    model_name=None, pretrain_model_name=None,
-                    mfa_cmd='train'):
-    CORPUS = hparams['processed_data_dir'].split("/")[-1]
-    NUM_JOB = int(os.getenv('N_PROC', os.cpu_count()))
-    env_vars = [f'CORPUS={CORPUS}', f'NUM_JOB={NUM_JOB}']
-    if mfa_outputs is not None:
-        env_vars.append(f'MFA_OUTPUTS={mfa_outputs}')
-    if mfa_inputs is not None:
-        env_vars.append(f'MFA_INPUTS={mfa_inputs}')
-    if model_name is not None:
-        env_vars.append(f'MODEL_NAME={model_name}')
-    if pretrain_model_name is not None:
-        env_vars.append(f'PRETRAIN_MODEL_NAME={pretrain_model_name}')
-    if mfa_cmd is not None:
-        env_vars.append(f'MFA_CMD={mfa_cmd}')
-    env_str = ' '.join(env_vars)
-    print(f"| Run MFA for {CORPUS}. Env vars: {env_str}")
-    subprocess.check_call(f'{env_str} bash mfa_usr/run_mfa_train_align.sh', shell=True)
-    mfa_offset = hparams['preprocess_args']['mfa_offset']
-    if mfa_offset > 0:
-        for tg_fn in glob.glob(f'{hparams["processed_data_dir"]}/{mfa_outputs}/*.TextGrid'):
-            tg = TextGrid.fromFile(tg_fn)
-            max_time = tg.maxTime
-            for tier in tg.tiers:
-                for interval in tier.intervals:
-                    interval.maxTime = min(interval.maxTime + mfa_offset, max_time)
-                    interval.minTime = min(interval.minTime + mfa_offset, max_time)
-                tier.intervals[0].minTime = 0
-                tier.maxTime = min(tier.maxTime + mfa_offset, max_time)
-            tg.write(tg_fn)
-            TextGrid.fromFile(tg_fn)
-
-
-if __name__ == '__main__':
-    set_hparams(print_hparams=False)
-    train_mfa_align()
diff --git a/spaces/AIGText/GlyphControl/ldm/modules/midas/midas/transforms.py b/spaces/AIGText/GlyphControl/ldm/modules/midas/midas/transforms.py
deleted file mode 100644
index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000
--- a/spaces/AIGText/GlyphControl/ldm/modules/midas/midas/transforms.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(
-        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
-    )
-
-    sample["disparity"] = cv2.resize(
-        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
-    )
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height).
-    """
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(
-                    f"resize_method {self.__resize_method} not implemented"
-                )
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, min_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, min_val=self.__width
-            )
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, max_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, max_val=self.__width
-            )
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(
-            sample["image"].shape[1], sample["image"].shape[0]
-        )
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(
-                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
-                )
-
-            sample["mask"] = cv2.resize(
-                sample["mask"].astype(np.float32),
-                (width, height),
-                interpolation=cv2.INTER_NEAREST,
-            )
-            sample["mask"] = sample["mask"].astype(bool)
-
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std.
-    """
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input.
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "disparity" in sample:
-            disparity = sample["disparity"].astype(np.float32)
-            sample["disparity"] = np.ascontiguousarray(disparity)
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        return sample
diff --git a/spaces/AIKey/TestStatic/index.html b/spaces/AIKey/TestStatic/index.html
deleted file mode 100644
index 35f5f16ed492bc278adc08e76a4f7db5b46b1456..0000000000000000000000000000000000000000
--- a/spaces/AIKey/TestStatic/index.html
+++ /dev/null
@@ -1,32 +0,0 @@
-   <!DOCTYPE html>
-    <html>
-    <head>
-        <meta charset="utf-8" />
-        <meta name="viewport" content="width=device-width" />
-        <title>My static Space</title>
-        <style>
-            .space {
-                max-width: 100%;
-                max-height: 100%;
-                width: 100vw;
-                height: 100vh;
-                overflow: hidden;
-            }
-            .iframe {
-                min-width: 100%;
-                min-height: 100%;
-                background: black;
-            }
-        </style>
-    </head>
-    <body>
-        <div class="space">
-            <iframe 
-                class="iframe" 
-                allowfullscreen="true" 
-                frameborder="0"
-                src="https://chat.d-id.com/">
-            </iframe>
-        </div>
-    </body>
-    </html>
diff --git a/spaces/ALR03/gradiolangchainChatbotOpenAI/app.py b/spaces/ALR03/gradiolangchainChatbotOpenAI/app.py
deleted file mode 100644
index a362dcc7d0ddd1eee86961f1bc3db6d894fbd3d5..0000000000000000000000000000000000000000
--- a/spaces/ALR03/gradiolangchainChatbotOpenAI/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/stores/webSearchParameters.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/stores/webSearchParameters.ts
deleted file mode 100644
index fd088a60621090930e9600c6086380afd2b412e8..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/stores/webSearchParameters.ts
+++ /dev/null
@@ -1,9 +0,0 @@
-import { writable } from "svelte/store";
-export interface WebSearchParameters {
-	useSearch: boolean;
-	nItems: number;
-}
-export const webSearchParameters = writable<WebSearchParameters>({
-	useSearch: false,
-	nItems: 5,
-});
diff --git a/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/selector/__init__.py b/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/selector/__init__.py
deleted file mode 100644
index acf1f2f788c61a7d0f2e380d21274794bab6609c..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/selector/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from agentverse.registry import Registry
-
-selector_registry = Registry(name="SelectorRegistry")
-
-from .base import BaseSelector
-from .basic import BasicSelector
-from .classroom import ClassroomSelector
-from .sde_team import SdeTeamSelector
-from .sde_team_given_tests import SdeTeamGivenTestsSelector
-from .pokemon import PokemonSelector
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/ColorInput.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/ColorInput.js
deleted file mode 100644
index 0198f7609ccb248c57add02dad283c42528a9c56..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/ColorInput.js
+++ /dev/null
@@ -1,91 +0,0 @@
-import ColorInputBase from '../colorinputbase/ColorInputBase.js';
-import Methods from './methods/Methods.js';
-import CreateBackground from '../../utils/build/CreateBackground.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class ColorInput extends ColorInputBase {
-    constructor(scene, config) {
-        if (config === undefined) {
-            config = {};
-        }
-
-        super(scene, config);
-        this.type = 'rexColorInput';
-
-        if (!config.hasOwnProperty('colorPicker')) {
-            config.colorPicker = {
-                background: { color: 0x0 }
-            }
-        }
-
-        var colorPickerConfig = config.colorPicker;
-        var hasColorPicker = (colorPickerConfig !== false) && (colorPickerConfig !== null);
-
-        if (hasColorPicker) {
-            this.setColorPickerSize(
-                GetValue(colorPickerConfig, 'width', 160),
-                GetValue(colorPickerConfig, 'height', 170)
-            );
-
-            var createBackgroundCallback;
-            var background = GetValue(colorPickerConfig, 'background');
-            if (background) {
-                createBackgroundCallback = function (scene) {
-                    return CreateBackground(scene, background);
-                }
-            } else {
-                createBackgroundCallback = GetValue(colorPickerConfig, 'createBackgroundCallback');
-            }
-            this.setCreateColorPickerBackgroundCallback(createBackgroundCallback);
-
-            this.setColorPickerHPalettePosition(GetValue(colorPickerConfig, 'hPalettePosition', 0));
-            this.setColorPickerExpandDirection(GetValue(colorPickerConfig, 'expandDirection'));
-            this.setColorPickerEaseInDuration(GetValue(colorPickerConfig, 'easeIn', 200));
-            this.setColorPickerEaseOutDuration(GetValue(colorPickerConfig, 'easeOut', 200));
-            this.setColorPickerTransitInCallback(GetValue(colorPickerConfig, 'transitIn'));
-            this.setColorPickerTransitOutCallback(GetValue(colorPickerConfig, 'transitOut'));
-            this.setColorPickerBounds(GetValue(colorPickerConfig, 'bounds'));
-
-            var colorPickerSpaceConfig = GetValue(colorPickerConfig, 'space');
-            if (colorPickerSpaceConfig === undefined) {
-                colorPickerSpaceConfig = { left: 10, right: 10, top: 10, bottom: 10, item: 8 }
-            }
-            this.setColorPickerSpace(colorPickerSpaceConfig);
-        }
-
-        var colorComponentsConfig = config.colorComponents;
-        var hasColorComponents = (colorComponentsConfig !== false) && (colorComponentsConfig !== null);
-        if (hasColorPicker && hasColorComponents) {
-            this.setColorComponentsHeight(GetValue(colorComponentsConfig, 'height', 30));
-
-            this.setColorComponentsFormatLabelConfig(GetValue(colorComponentsConfig, 'formatLabel'));
-
-            var colorComponentsInputTextConfig = GetValue(colorComponentsConfig, 'inputText');
-            if (!colorComponentsInputTextConfig) {
-                colorComponentsInputTextConfig = GetValue(config, 'inputText');
-            }
-            this.setColorComponentsInputTextConfig(colorComponentsInputTextConfig);
-
-            var colorComponentsSpace = GetValue(colorComponentsConfig, 'space');
-            if (colorComponentsSpace === undefined) {
-                colorComponentsSpace = { item: 8 }
-            }
-            this.setColorComponentsSpace(colorComponentsSpace);
-        }
-
-
-
-        var swatch = this.childrenMap.swatch;
-        if (swatch && hasColorPicker) {
-            this.onClick(swatch, this.openColorPicker, this);
-        }
-    }
-}
-
-Object.assign(
-    ColorInput.prototype,
-    Methods,
-)
-
-export default ColorInput;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateRoundRectangle.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateRoundRectangle.js
deleted file mode 100644
index 29d8923125411199feb9971d4721cd82d7816a67..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateRoundRectangle.js
+++ /dev/null
@@ -1,12 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import RoundRectangle from '../../roundrectangle/RoundRectangle.js';
-
-var CreateRoundRectangle = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-
-    var gameObject = new RoundRectangle(scene, data);
-    scene.add.existing(gameObject);
-    return gameObject;
-}
-
-export default CreateRoundRectangle;
\ No newline at end of file
diff --git a/spaces/AlanMars/QYL-AI-Space/modules/webui_locale.py b/spaces/AlanMars/QYL-AI-Space/modules/webui_locale.py
deleted file mode 100644
index c97df7f416ffbd746efb52f17d8feb0e30cf9cd6..0000000000000000000000000000000000000000
--- a/spaces/AlanMars/QYL-AI-Space/modules/webui_locale.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os
-import locale
-import commentjson as json
-
-
-class I18nAuto:
-    def __init__(self):
-        if os.path.exists("config.json"):
-            with open("config.json", "r", encoding='utf-8') as f:
-                config = json.load(f)
-        else:
-            config = {}
-        lang_config = config.get("language", "auto")
-        language = os.environ.get("LANGUAGE", lang_config)
-        if language == "auto":
-            language = locale.getdefaultlocale()[0]  # get the language code of the system (ex. zh_CN)
-        self.language_map = {}
-        self.file_is_exists = os.path.isfile(f"./locale/{language}.json")
-        if self.file_is_exists:
-            with open(f"./locale/{language}.json", "r", encoding="utf-8") as f:
-                self.language_map.update(json.load(f))
-
-    def __call__(self, key):
-        if self.file_is_exists and key in self.language_map:
-            return self.language_map[key]
-        else:
-            return key
diff --git a/spaces/Ali36Ahmad/magic-diffusion/share_btn.py b/spaces/Ali36Ahmad/magic-diffusion/share_btn.py
deleted file mode 100644
index 1382fb25a5ef50e843598187e1e660e86ea8dd05..0000000000000000000000000000000000000000
--- a/spaces/Ali36Ahmad/magic-diffusion/share_btn.py
+++ /dev/null
@@ -1,88 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getInputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `magic-prompt-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `magic-prompt-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputImgEl = gradioEl.querySelector('#input-img img');
-    const imgEls = gradioEl.querySelectorAll('#generated-gallery img');
-    const promptTxt = gradioEl.querySelector('#translated textarea').value;
-    let titleTxt = promptTxt;
-    if(titleTxt.length > 100){
-        titleTxt = titleTxt.slice(0, 100) + ' ...';
-    }
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!imgEls.length){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const files = await Promise.all(
-        [...imgEls].map(async (imgEl) => {
-            const res = await fetch(imgEl.src);
-            const blob = await res.blob();
-            const imgId = Date.now() % 200;
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        })
-    );
-    const inputFile = await getInputImgFile(inputImgEl);
-    files.push(inputFile);
-    const urls = await Promise.all(files.map((f) => uploadFile(f)));
-    const urlInputImg = urls.pop();
-	const htmlImgs = urls.map(url => `<img src='${url}' width='400' height='400'>`);
-    const htmlImgsMd = htmlImgs.join(`\n`);
-	const descriptionMd = `#### Input img:
-<img src='${urlInputImg}' style='max-height: 350px;'>
-#### Caption:
-${promptTxt}
-#### Generations:
-<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
-${htmlImgsMd}
-</div>`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/huggingface-projects/magic-diffusion/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/3millions.py b/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/3millions.py
deleted file mode 100644
index c9edc2f1414e35f93abfd3dfe11a61f1f406580e..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/3millions.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from easydict import EasyDict as edict
-
-# configs for test speed
-
-config = edict()
-config.loss = "arcface"
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "synthetic"
-config.num_classes = 300 * 10000
-config.num_epoch = 30
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = []
diff --git a/spaces/Alpaca233/SadTalker/src/face3d/models/facerecon_model.py b/spaces/Alpaca233/SadTalker/src/face3d/models/facerecon_model.py
deleted file mode 100644
index 7de8ca6eebc50ff1ed52c5ba37d31b43f977b5e1..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/face3d/models/facerecon_model.py
+++ /dev/null
@@ -1,220 +0,0 @@
-"""This script defines the face reconstruction model for Deep3DFaceRecon_pytorch
-"""
-
-import numpy as np
-import torch
-from src.face3d.models.base_model import BaseModel
-from src.face3d.models import networks
-from src.face3d.models.bfm import ParametricFaceModel
-from src.face3d.models.losses import perceptual_loss, photo_loss, reg_loss, reflectance_loss, landmark_loss
-from src.face3d.util import util 
-from src.face3d.util.nvdiffrast import MeshRenderer
-# from src.face3d.util.preprocess import estimate_norm_torch
-
-import trimesh
-from scipy.io import savemat
-
-class FaceReconModel(BaseModel):
-
-    @staticmethod
-    def modify_commandline_options(parser, is_train=False):
-        """  Configures options specific for CUT model
-        """
-        # net structure and parameters
-        parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='network structure')
-        parser.add_argument('--init_path', type=str, default='./checkpoints/init_model/resnet50-0676ba61.pth')
-        parser.add_argument('--use_last_fc', type=util.str2bool, nargs='?', const=True, default=False, help='zero initialize the last fc')
-        parser.add_argument('--bfm_folder', type=str, default='./checkpoints/BFM_Fitting/')
-        parser.add_argument('--bfm_model', type=str, default='BFM_model_front.mat', help='bfm model')
-
-        # renderer parameters
-        parser.add_argument('--focal', type=float, default=1015.)
-        parser.add_argument('--center', type=float, default=112.)
-        parser.add_argument('--camera_d', type=float, default=10.)
-        parser.add_argument('--z_near', type=float, default=5.)
-        parser.add_argument('--z_far', type=float, default=15.)
-
-        if is_train:
-            # training parameters
-            parser.add_argument('--net_recog', type=str, default='r50', choices=['r18', 'r43', 'r50'], help='face recog network structure')
-            parser.add_argument('--net_recog_path', type=str, default='checkpoints/recog_model/ms1mv3_arcface_r50_fp16/backbone.pth')
-            parser.add_argument('--use_crop_face', type=util.str2bool, nargs='?', const=True, default=False, help='use crop mask for photo loss')
-            parser.add_argument('--use_predef_M', type=util.str2bool, nargs='?', const=True, default=False, help='use predefined M for predicted face')
-
-            
-            # augmentation parameters
-            parser.add_argument('--shift_pixs', type=float, default=10., help='shift pixels')
-            parser.add_argument('--scale_delta', type=float, default=0.1, help='delta scale factor')
-            parser.add_argument('--rot_angle', type=float, default=10., help='rot angles, degree')
-
-            # loss weights
-            parser.add_argument('--w_feat', type=float, default=0.2, help='weight for feat loss')
-            parser.add_argument('--w_color', type=float, default=1.92, help='weight for loss loss')
-            parser.add_argument('--w_reg', type=float, default=3.0e-4, help='weight for reg loss')
-            parser.add_argument('--w_id', type=float, default=1.0, help='weight for id_reg loss')
-            parser.add_argument('--w_exp', type=float, default=0.8, help='weight for exp_reg loss')
-            parser.add_argument('--w_tex', type=float, default=1.7e-2, help='weight for tex_reg loss')
-            parser.add_argument('--w_gamma', type=float, default=10.0, help='weight for gamma loss')
-            parser.add_argument('--w_lm', type=float, default=1.6e-3, help='weight for lm loss')
-            parser.add_argument('--w_reflc', type=float, default=5.0, help='weight for reflc loss')
-
-        opt, _ = parser.parse_known_args()
-        parser.set_defaults(
-                focal=1015., center=112., camera_d=10., use_last_fc=False, z_near=5., z_far=15.
-            )
-        if is_train:
-            parser.set_defaults(
-                use_crop_face=True, use_predef_M=False
-            )
-        return parser
-
-    def __init__(self, opt):
-        """Initialize this model class.
-
-        Parameters:
-            opt -- training/test options
-
-        A few things can be done here.
-        - (required) call the initialization function of BaseModel
-        - define loss function, visualization images, model names, and optimizers
-        """
-        BaseModel.__init__(self, opt)  # call the initialization method of BaseModel
-        
-        self.visual_names = ['output_vis']
-        self.model_names = ['net_recon']
-        self.parallel_names = self.model_names + ['renderer']
-
-        self.facemodel = ParametricFaceModel(
-            bfm_folder=opt.bfm_folder, camera_distance=opt.camera_d, focal=opt.focal, center=opt.center,
-            is_train=self.isTrain, default_name=opt.bfm_model
-        )
-        
-        fov = 2 * np.arctan(opt.center / opt.focal) * 180 / np.pi
-        self.renderer = MeshRenderer(
-            rasterize_fov=fov, znear=opt.z_near, zfar=opt.z_far, rasterize_size=int(2 * opt.center)
-        )
-
-        if self.isTrain:
-            self.loss_names = ['all', 'feat', 'color', 'lm', 'reg', 'gamma', 'reflc']
-
-            self.net_recog = networks.define_net_recog(
-                net_recog=opt.net_recog, pretrained_path=opt.net_recog_path
-                )
-            # loss func name: (compute_%s_loss) % loss_name
-            self.compute_feat_loss = perceptual_loss
-            self.comupte_color_loss = photo_loss
-            self.compute_lm_loss = landmark_loss
-            self.compute_reg_loss = reg_loss
-            self.compute_reflc_loss = reflectance_loss
-
-            self.optimizer = torch.optim.Adam(self.net_recon.parameters(), lr=opt.lr)
-            self.optimizers = [self.optimizer]
-            self.parallel_names += ['net_recog']
-        # Our program will automatically call <model.setup> to define schedulers, load networks, and print networks
-
-    def set_input(self, input):
-        """Unpack input data from the dataloader and perform necessary pre-processing steps.
-
-        Parameters:
-            input: a dictionary that contains the data itself and its metadata information.
-        """
-        self.input_img = input['imgs'].to(self.device) 
-        self.atten_mask = input['msks'].to(self.device) if 'msks' in input else None
-        self.gt_lm = input['lms'].to(self.device)  if 'lms' in input else None
-        self.trans_m = input['M'].to(self.device) if 'M' in input else None
-        self.image_paths = input['im_paths'] if 'im_paths' in input else None
-
-    def forward(self, output_coeff, device):
-        self.facemodel.to(device)
-        self.pred_vertex, self.pred_tex, self.pred_color, self.pred_lm = \
-            self.facemodel.compute_for_render(output_coeff)
-        self.pred_mask, _, self.pred_face = self.renderer(
-            self.pred_vertex, self.facemodel.face_buf, feat=self.pred_color)
-        
-        self.pred_coeffs_dict = self.facemodel.split_coeff(output_coeff)
-
-
-    def compute_losses(self):
-        """Calculate losses, gradients, and update network weights; called in every training iteration"""
-
-        assert self.net_recog.training == False
-        trans_m = self.trans_m
-        if not self.opt.use_predef_M:
-            trans_m = estimate_norm_torch(self.pred_lm, self.input_img.shape[-2])
-
-        pred_feat = self.net_recog(self.pred_face, trans_m)
-        gt_feat = self.net_recog(self.input_img, self.trans_m)
-        self.loss_feat = self.opt.w_feat * self.compute_feat_loss(pred_feat, gt_feat)
-
-        face_mask = self.pred_mask
-        if self.opt.use_crop_face:
-            face_mask, _, _ = self.renderer(self.pred_vertex, self.facemodel.front_face_buf)
-        
-        face_mask = face_mask.detach()
-        self.loss_color = self.opt.w_color * self.comupte_color_loss(
-            self.pred_face, self.input_img, self.atten_mask * face_mask)
-        
-        loss_reg, loss_gamma = self.compute_reg_loss(self.pred_coeffs_dict, self.opt)
-        self.loss_reg = self.opt.w_reg * loss_reg
-        self.loss_gamma = self.opt.w_gamma * loss_gamma
-
-        self.loss_lm = self.opt.w_lm * self.compute_lm_loss(self.pred_lm, self.gt_lm)
-
-        self.loss_reflc = self.opt.w_reflc * self.compute_reflc_loss(self.pred_tex, self.facemodel.skin_mask)
-
-        self.loss_all = self.loss_feat + self.loss_color + self.loss_reg + self.loss_gamma \
-                        + self.loss_lm + self.loss_reflc
-            
-
-    def optimize_parameters(self, isTrain=True):
-        self.forward()               
-        self.compute_losses()
-        """Update network weights; it will be called in every training iteration."""
-        if isTrain:
-            self.optimizer.zero_grad()  
-            self.loss_all.backward()         
-            self.optimizer.step()        
-
-    def compute_visuals(self):
-        with torch.no_grad():
-            input_img_numpy = 255. * self.input_img.detach().cpu().permute(0, 2, 3, 1).numpy()
-            output_vis = self.pred_face * self.pred_mask + (1 - self.pred_mask) * self.input_img
-            output_vis_numpy_raw = 255. * output_vis.detach().cpu().permute(0, 2, 3, 1).numpy()
-            
-            if self.gt_lm is not None:
-                gt_lm_numpy = self.gt_lm.cpu().numpy()
-                pred_lm_numpy = self.pred_lm.detach().cpu().numpy()
-                output_vis_numpy = util.draw_landmarks(output_vis_numpy_raw, gt_lm_numpy, 'b')
-                output_vis_numpy = util.draw_landmarks(output_vis_numpy, pred_lm_numpy, 'r')
-            
-                output_vis_numpy = np.concatenate((input_img_numpy, 
-                                    output_vis_numpy_raw, output_vis_numpy), axis=-2)
-            else:
-                output_vis_numpy = np.concatenate((input_img_numpy, 
-                                    output_vis_numpy_raw), axis=-2)
-
-            self.output_vis = torch.tensor(
-                    output_vis_numpy / 255., dtype=torch.float32
-                ).permute(0, 3, 1, 2).to(self.device)
-
-    def save_mesh(self, name):
-
-        recon_shape = self.pred_vertex  # get reconstructed shape
-        recon_shape[..., -1] = 10 - recon_shape[..., -1] # from camera space to world space
-        recon_shape = recon_shape.cpu().numpy()[0]
-        recon_color = self.pred_color
-        recon_color = recon_color.cpu().numpy()[0]
-        tri = self.facemodel.face_buf.cpu().numpy()
-        mesh = trimesh.Trimesh(vertices=recon_shape, faces=tri, vertex_colors=np.clip(255. * recon_color, 0, 255).astype(np.uint8))
-        mesh.export(name)
-
-    def save_coeff(self,name):
-
-        pred_coeffs = {key:self.pred_coeffs_dict[key].cpu().numpy() for key in self.pred_coeffs_dict}
-        pred_lm = self.pred_lm.cpu().numpy()
-        pred_lm = np.stack([pred_lm[:,:,0],self.input_img.shape[2]-1-pred_lm[:,:,1]],axis=2) # transfer to image coordinate
-        pred_coeffs['lm68'] = pred_lm
-        savemat(name,pred_coeffs)
-
-
-
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/torch_utils/ops/grid_sample_gradfix.py b/spaces/Amrrs/DragGan-Inversion/PTI/torch_utils/ops/grid_sample_gradfix.py
deleted file mode 100644
index ca6b3413ea72a734703c34382c023b84523601fd..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/PTI/torch_utils/ops/grid_sample_gradfix.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.grid_sample` that
-supports arbitrarily high order gradients between the input and output.
-Only works on 2D images and assumes
-`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
-
-import warnings
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False  # Enable the custom op by setting this to true.
-
-#----------------------------------------------------------------------------
-
-def grid_sample(input, grid):
-    if _should_use_custom_op():
-        return _GridSample2dForward.apply(input, grid)
-    return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op():
-    if not enabled:
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().')
-    return False
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dForward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, input, grid):
-        assert input.ndim == 4
-        assert grid.ndim == 4
-        output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-        ctx.save_for_backward(input, grid)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, grid = ctx.saved_tensors
-        grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
-        return grad_input, grad_grid
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dBackward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, grad_output, input, grid):
-        op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
-        grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
-        ctx.save_for_backward(grid)
-        return grad_input, grad_grid
-
-    @staticmethod
-    def backward(ctx, grad2_grad_input, grad2_grad_grid):
-        _ = grad2_grad_grid # unused
-        grid, = ctx.saved_tensors
-        grad2_grad_output = None
-        grad2_input = None
-        grad2_grid = None
-
-        if ctx.needs_input_grad[0]:
-            grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
-
-        assert not ctx.needs_input_grad[2]
-        return grad2_grad_output, grad2_input, grad2_grid
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/training/dreambooth.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/training/dreambooth.md
deleted file mode 100644
index 83974e50b14e8eb614eb943a721570d55bdf9921..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/training/dreambooth.md
+++ /dev/null
@@ -1,475 +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.
--->
-
-# DreamBooth
-
-[DreamBooth](https://arxiv.org/abs/2208.12242)는 한 주제에 대한 적은 이미지(3~5개)만으로도 stable diffusion과 같이 text-to-image 모델을 개인화할 수 있는 방법입니다. 이를 통해 모델은 다양한 장면, 포즈 및 장면(뷰)에서 피사체에 대해 맥락화(contextualized)된 이미지를 생성할 수 있습니다.
-
-![프로젝트 블로그에서의 DreamBooth 예시](https://dreambooth.github.io/DreamBooth_files/teaser_static.jpg)
-<a href="https://dreambooth.github.io">project's blog.</a></small>
-<small><a href="https://dreambooth.github.io">프로젝트 블로그</a>에서의 Dreambooth 예시</small>
-
-
-이 가이드는 다양한 GPU, Flax 사양에 대해 [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) 모델로 DreamBooth를 파인튜닝하는 방법을 보여줍니다. 더 깊이 파고들어 작동 방식을 확인하는 데 관심이 있는 경우, 이 가이드에 사용된 DreamBooth의 모든 학습 스크립트를 [여기](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)에서 찾을 수 있습니다.
-
-스크립트를 실행하기 전에 라이브러리의 학습에 필요한 dependencies를 설치해야 합니다. 또한 `main` GitHub 브랜치에서 🧨 Diffusers를 설치하는 것이 좋습니다.
-
-```bash
-pip install git+https://github.com/huggingface/diffusers
-pip install -U -r diffusers/examples/dreambooth/requirements.txt
-```
-
-xFormers는 학습에 필요한 요구 사항은 아니지만, 가능하면 [설치](../optimization/xformers)하는 것이 좋습니다. 학습 속도를 높이고 메모리 사용량을 줄일 수 있기 때문입니다.
-
-모든 dependencies을 설정한 후 다음을 사용하여 [🤗 Accelerate](https://github.com/huggingface/accelerate/) 환경을 다음과 같이 초기화합니다:
-
-```bash
-accelerate config
-```
-
-별도 설정 없이 기본 🤗 Accelerate 환경을 설치하려면 다음을 실행합니다:
-
-```bash
-accelerate config default
-```
-
-또는 현재 환경이 노트북과 같은 대화형 셸을 지원하지 않는 경우 다음을 사용할 수 있습니다:
-
-```py
-from accelerate.utils import write_basic_config
-
-write_basic_config()
-```
-
-## 파인튜닝
-
-<Tip warning={true}>
-
-DreamBooth 파인튜닝은 하이퍼파라미터에 매우 민감하고 과적합되기 쉽습니다. 적절한 하이퍼파라미터를 선택하는 데 도움이 되도록 다양한 권장 설정이 포함된 [심층 분석](https://huggingface.co/blog/dreambooth)을 살펴보는 것이 좋습니다.
-
-</Tip>
-
-<frameworkcontent>
-<pt>
-[몇 장의 강아지 이미지들](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ)로 DreamBooth를 시도해봅시다. 
-이를 다운로드해 디렉터리에 저장한 다음 `INSTANCE_DIR` 환경 변수를 해당 경로로 설정합니다:
-
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path_to_training_images"
-export OUTPUT_DIR="path_to_saved_model"
-```
-
-그런 다음, 다음 명령을 사용하여 학습 스크립트를 실행할 수 있습니다 (전체 학습 스크립트는 [여기](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py)에서 찾을 수 있습니다):
-
-```bash
-accelerate launch train_dreambooth.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --instance_prompt="a photo of sks dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=1 \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --max_train_steps=400
-```
-</pt>
-<jax>
-
-TPU에 액세스할 수 있거나 더 빠르게 훈련하고 싶다면 [Flax 학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_flax.py)를 사용해 볼 수 있습니다. Flax 학습 스크립트는 gradient checkpointing 또는 gradient accumulation을 지원하지 않으므로, 메모리가 30GB 이상인 GPU가 필요합니다.
-
-스크립트를 실행하기 전에 요구 사항이 설치되어 있는지 확인하십시오.
-
-```bash
-pip install -U -r requirements.txt
-```
-
-그러면 다음 명령어로 학습 스크립트를 실행시킬 수 있습니다:
-
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export INSTANCE_DIR="path-to-instance-images"
-export OUTPUT_DIR="path-to-save-model"
-
-python train_dreambooth_flax.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --instance_prompt="a photo of sks dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --learning_rate=5e-6 \
-  --max_train_steps=400
-```
-</jax>
-</frameworkcontent>
-
-### Prior-preserving(사전 보존) loss를 사용한 파인튜닝
-
-과적합과 language drift를 방지하기 위해 사전 보존이 사용됩니다(관심이 있는 경우 [논문](https://arxiv.org/abs/2208.12242)을 참조하세요).  사전 보존을 위해 동일한 클래스의 다른 이미지를 학습 프로세스의 일부로 사용합니다. 좋은 점은 Stable Diffusion 모델 자체를 사용하여 이러한 이미지를 생성할 수 있다는 것입니다! 학습 스크립트는 생성된 이미지를 우리가 지정한 로컬 경로에 저장합니다.
-
-저자들에 따르면 사전 보존을 위해 `num_epochs * num_samples`개의 이미지를 생성하는 것이 좋습니다. 200-300개에서 대부분 잘 작동합니다.
-
-<frameworkcontent>
-<pt>
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path_to_training_images"
-export CLASS_DIR="path_to_class_images"
-export OUTPUT_DIR="path_to_saved_model"
-
-accelerate launch train_dreambooth.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=1 \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --num_class_images=200 \
-  --max_train_steps=800
-```
-</pt>
-<jax>
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export INSTANCE_DIR="path-to-instance-images"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-python train_dreambooth_flax.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --learning_rate=5e-6 \
-  --num_class_images=200 \
-  --max_train_steps=800
-```
-</jax>
-</frameworkcontent>
-
-## 텍스트 인코더와 and UNet로 파인튜닝하기
-
-해당 스크립트를 사용하면 `unet`과 함께 `text_encoder`를 파인튜닝할 수 있습니다. 실험에서(자세한 내용은 [🧨 Diffusers를 사용해 DreamBooth로 Stable Diffusion 학습하기](https://huggingface.co/blog/dreambooth) 게시물을 확인하세요), 특히 얼굴 이미지를 생성할 때 훨씬 더 나은 결과를 얻을 수 있습니다.
-
-<Tip warning={true}>
-
-텍스트 인코더를 학습시키려면 추가 메모리가 필요해 16GB GPU로는 동작하지 않습니다. 이 옵션을 사용하려면 최소 24GB VRAM이 필요합니다.
-
-</Tip>
-
-`--train_text_encoder` 인수를 학습 스크립트에 전달하여 `text_encoder` 및 `unet`을 파인튜닝할 수 있습니다:
-
-<frameworkcontent>
-<pt>
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path_to_training_images"
-export CLASS_DIR="path_to_class_images"
-export OUTPUT_DIR="path_to_saved_model"
-
-accelerate launch train_dreambooth.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --train_text_encoder \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --use_8bit_adam
-  --gradient_checkpointing \
-  --learning_rate=2e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --num_class_images=200 \
-  --max_train_steps=800
-```
-</pt>
-<jax>
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export INSTANCE_DIR="path-to-instance-images"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-python train_dreambooth_flax.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --train_text_encoder \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --learning_rate=2e-6 \
-  --num_class_images=200 \
-  --max_train_steps=800
-```
-</jax>
-</frameworkcontent>
-
-## LoRA로 파인튜닝하기
-
-DreamBooth에서 대규모 모델의 학습을 가속화하기 위한 파인튜닝 기술인 LoRA(Low-Rank Adaptation of Large Language Models)를 사용할 수 있습니다. 자세한 내용은 [LoRA 학습](training/lora#dreambooth) 가이드를 참조하세요.
-
-### 학습 중 체크포인트 저장하기
-
-Dreambooth로 훈련하는 동안 과적합하기 쉬우므로, 때때로 학습 중에 정기적인 체크포인트를 저장하는 것이 유용합니다. 중간 체크포인트 중 하나가 최종 모델보다 더 잘 작동할 수 있습니다! 체크포인트 저장 기능을 활성화하려면 학습 스크립트에 다음 인수를 전달해야 합니다:
-
-```bash
-  --checkpointing_steps=500
-```
-
-이렇게 하면 `output_dir`의 하위 폴더에 전체 학습 상태가 저장됩니다. 하위 폴더 이름은 접두사 `checkpoint-`로 시작하고 지금까지 수행된 step 수입니다. 예시로 `checkpoint-1500`은 1500 학습 step 후에 저장된 체크포인트입니다.
-
-#### 저장된 체크포인트에서 훈련 재개하기
-
-저장된 체크포인트에서 훈련을 재개하려면, `--resume_from_checkpoint` 인수를 전달한 다음 사용할 체크포인트의 이름을 지정하면 됩니다. 특수 문자열 `"latest"`를 사용하여 저장된 마지막 체크포인트(즉, step 수가 가장 많은 체크포인트)에서 재개할 수도 있습니다. 예를 들어 다음은 1500 step 후에 저장된 체크포인트에서부터 학습을 재개합니다:
-
-```bash
-  --resume_from_checkpoint="checkpoint-1500"
-```
-
-원하는 경우 일부 하이퍼파라미터를 조정할 수 있습니다.
-
-#### 저장된 체크포인트를 사용하여 추론 수행하기
-
-저장된 체크포인트는 훈련 재개에 적합한 형식으로 저장됩니다. 여기에는 모델 가중치뿐만 아니라 옵티마이저, 데이터 로더 및 학습률의 상태도 포함됩니다.
-
-**`"accelerate>=0.16.0"`**이 설치된 경우 다음 코드를 사용하여 중간 체크포인트에서 추론을 실행합니다.
-
-```python
-from diffusers import DiffusionPipeline, UNet2DConditionModel
-from transformers import CLIPTextModel
-import torch
-
-# 학습에 사용된 것과 동일한 인수(model, revision)로 파이프라인을 불러옵니다.
-model_id = "CompVis/stable-diffusion-v1-4"
-
-unet = UNet2DConditionModel.from_pretrained("/sddata/dreambooth/daruma-v2-1/checkpoint-100/unet")
-
-# `args.train_text_encoder`로 학습한 경우면 텍스트 인코더를 꼭 불러오세요
-text_encoder = CLIPTextModel.from_pretrained("/sddata/dreambooth/daruma-v2-1/checkpoint-100/text_encoder")
-
-pipeline = DiffusionPipeline.from_pretrained(model_id, unet=unet, text_encoder=text_encoder, dtype=torch.float16)
-pipeline.to("cuda")
-
-# 추론을 수행하거나 저장하거나, 허브에 푸시합니다.
-pipeline.save_pretrained("dreambooth-pipeline")
-```
-
-If you have **`"accelerate<0.16.0"`** installed, you need to convert it to an inference pipeline first:
-
-```python
-from accelerate import Accelerator
-from diffusers import DiffusionPipeline
-
-# 학습에 사용된 것과 동일한 인수(model, revision)로 파이프라인을 불러옵니다.
-model_id = "CompVis/stable-diffusion-v1-4"
-pipeline = DiffusionPipeline.from_pretrained(model_id)
-
-accelerator = Accelerator()
-
-# 초기 학습에 `--train_text_encoder`가 사용된 경우 text_encoder를 사용합니다.
-unet, text_encoder = accelerator.prepare(pipeline.unet, pipeline.text_encoder)
-
-# 체크포인트 경로로부터 상태를 복원합니다. 여기서는 절대 경로를 사용해야 합니다.
-accelerator.load_state("/sddata/dreambooth/daruma-v2-1/checkpoint-100")
-
-# unwrapped 모델로 파이프라인을 다시 빌드합니다.(.unet and .text_encoder로의 할당도 작동해야 합니다)
-pipeline = DiffusionPipeline.from_pretrained(
-    model_id,
-    unet=accelerator.unwrap_model(unet),
-    text_encoder=accelerator.unwrap_model(text_encoder),
-)
-
-# 추론을 수행하거나 저장하거나, 허브에 푸시합니다.
-pipeline.save_pretrained("dreambooth-pipeline")
-```
-
-## 각 GPU 용량에서의 최적화
-
-하드웨어에 따라 16GB에서 8GB까지 GPU에서 DreamBooth를 최적화하는 몇 가지 방법이 있습니다!
-
-### xFormers
-
-[xFormers](https://github.com/facebookresearch/xformers)는 Transformers를 최적화하기 위한 toolbox이며, 🧨 Diffusers에서 사용되는[memory-efficient attention](https://facebookresearch.github.io/xformers/components/ops.html#module-xformers.ops)  메커니즘을 포함하고 있습니다. [xFormers를 설치](./optimization/xformers)한 다음 학습 스크립트에 다음 인수를 추가합니다:
-
-```bash
-  --enable_xformers_memory_efficient_attention
-```
-
-xFormers는 Flax에서 사용할 수 없습니다.
-
-### 그래디언트 없음으로 설정
-
-메모리 사용량을 줄일 수 있는 또 다른 방법은 [기울기 설정](https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html)을 0 대신 `None`으로 하는 것입니다. 그러나 이로 인해 특정 동작이 변경될 수 있으므로 문제가 발생하면 이 인수를 제거해 보십시오. 학습 스크립트에 다음 인수를 추가하여 그래디언트를 `None`으로 설정합니다.
-
-```bash
-  --set_grads_to_none
-```
-
-### 16GB GPU
-
-Gradient checkpointing과 [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)의 8비트 옵티마이저의 도움으로, 16GB GPU에서 dreambooth를 훈련할 수 있습니다. bitsandbytes가 설치되어 있는지 확인하세요:
-
-```bash
-pip install bitsandbytes
-```
-
-그 다음, 학습 스크립트에 `--use_8bit_adam` 옵션을 명시합니다:
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path_to_training_images"
-export CLASS_DIR="path_to_class_images"
-export OUTPUT_DIR="path_to_saved_model"
-
-accelerate launch train_dreambooth.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=2 --gradient_checkpointing \
-  --use_8bit_adam \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --num_class_images=200 \
-  --max_train_steps=800
-```
-
-### 12GB GPU
-
-12GB GPU에서 DreamBooth를 실행하려면 gradient checkpointing, 8비트 옵티마이저, xFormers를 활성화하고 그래디언트를 `None`으로 설정해야 합니다.
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path-to-instance-images"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_dreambooth.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=1 --gradient_checkpointing \
-  --use_8bit_adam \
-  --enable_xformers_memory_efficient_attention \
-  --set_grads_to_none \
-  --learning_rate=2e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --num_class_images=200 \
-  --max_train_steps=800
-```
-
-### 8GB GPU에서 학습하기
-
-8GB GPU에 대해서는 [DeepSpeed](https://www.deepspeed.ai/)를 사용해 일부 텐서를 VRAM에서 CPU 또는 NVME로 오프로드하여 더 적은 GPU 메모리로 학습할 수도 있습니다.
-
-🤗 Accelerate 환경을 구성하려면 다음 명령을 실행하세요:
-
-```bash
-accelerate config
-```
-
-환경 구성 중에 DeepSpeed를 사용할 것을 확인하세요.
-그러면 DeepSpeed stage 2, fp16 혼합 정밀도를 결합하고 모델 매개변수와 옵티마이저 상태를 모두 CPU로 오프로드하면 8GB VRAM 미만에서 학습할 수 있습니다. 
-단점은 더 많은 시스템 RAM(약 25GB)이 필요하다는 것입니다. 추가 구성 옵션은 [DeepSpeed 문서](https://huggingface.co/docs/accelerate/usage_guides/deepspeed)를 참조하세요.
-
-또한 기본 Adam 옵티마이저를 DeepSpeed의 최적화된 Adam 버전으로 변경해야 합니다.
-이는 상당한 속도 향상을 위한 Adam인 [`deepspeed.ops.adam.DeepSpeedCPUAdam`](https://deepspeed.readthedocs.io/en/latest/optimizers.html#adam-cpu)입니다. 
-`DeepSpeedCPUAdam`을 활성화하려면 시스템의 CUDA toolchain 버전이 PyTorch와 함께 설치된 것과 동일해야 합니다.
-
-8비트 옵티마이저는 현재 DeepSpeed와 호환되지 않는 것 같습니다.
-
-다음 명령으로 학습을 시작합니다:
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path_to_training_images"
-export CLASS_DIR="path_to_class_images"
-export OUTPUT_DIR="path_to_saved_model"
-
-accelerate launch train_dreambooth.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --sample_batch_size=1 \
-  --gradient_accumulation_steps=1 --gradient_checkpointing \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --num_class_images=200 \
-  --max_train_steps=800 \
-  --mixed_precision=fp16
-```
-
-## 추론
-
-모델을 학습한 후에는, 모델이 저장된 경로를 지정해 [`StableDiffusionPipeline`]로 추론을 수행할 수 있습니다. 프롬프트에 학습에 사용된 특수 `식별자`(이전 예시의 `sks`)가 포함되어 있는지 확인하세요.
-
-**`"accelerate>=0.16.0"`**이 설치되어 있는 경우 다음 코드를 사용하여 중간 체크포인트에서 추론을 실행할 수 있습니다:
-
-```python
-from diffusers import StableDiffusionPipeline
-import torch
-
-model_id = "path_to_saved_model"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-
-prompt = "A photo of sks dog in a bucket"
-image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
-
-image.save("dog-bucket.png")
-```
-
-[저장된 학습 체크포인트](#inference-from-a-saved-checkpoint)에서도 추론을 실행할 수도 있습니다.
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/README.md b/spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/README.md
deleted file mode 100644
index f9782d7ef0a9c0d5a554a4f2a08b3e1b368c80dd..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/README.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# An Empirical Study of Spatial Attention Mechanisms in Deep Networks
-
-## Introduction
-
-[ALGORITHM]
-
-```latex
-@article{zhu2019empirical,
-  title={An Empirical Study of Spatial Attention Mechanisms in Deep Networks},
-  author={Zhu, Xizhou and Cheng, Dazhi and Zhang, Zheng and Lin, Stephen and Dai, Jifeng},
-  journal={arXiv preprint arXiv:1904.05873},
-  year={2019}
-}
-```
-
-## Results and Models
-
-| Backbone  | Attention Component | DCN  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-|:---------:|:-------------------:|:----:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
-| R-50      | 1111                | N    | 1x      | 8.0      | 13.8            | 40.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130_210344.log.json) |
-| R-50      | 0010                | N    | 1x      | 4.2      | 18.4           | 39.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130_210125.log.json) |
-| R-50      | 1111                | Y    | 1x      | 8.0      | 12.7            | 42.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130_204442.log.json) |
-| R-50      | 0010                | Y    | 1x      | 4.2      | 17.1           | 42.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130_210410.log.json) |
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py
deleted file mode 100644
index f438a4792e9aa4bcef35a42349156f1eab044477..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './ga_faster_r50_caffe_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet101_caffe',
-    backbone=dict(depth=101))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/base.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/base.py
deleted file mode 100644
index 7ce9c36c1d6f60c8567a72c44cc9eee0323ae2a2..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/base.py
+++ /dev/null
@@ -1,355 +0,0 @@
-from abc import ABCMeta, abstractmethod
-from collections import OrderedDict
-
-import mmcv
-import numpy as np
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-from mmcv.runner import auto_fp16
-from mmcv.utils import print_log
-
-from mmdet.core.visualization import imshow_det_bboxes
-from mmdet.utils import get_root_logger
-
-
-class BaseDetector(nn.Module, metaclass=ABCMeta):
-    """Base class for detectors."""
-
-    def __init__(self):
-        super(BaseDetector, self).__init__()
-        self.fp16_enabled = False
-
-    @property
-    def with_neck(self):
-        """bool: whether the detector has a neck"""
-        return hasattr(self, 'neck') and self.neck is not None
-
-    # TODO: these properties need to be carefully handled
-    # for both single stage & two stage detectors
-    @property
-    def with_shared_head(self):
-        """bool: whether the detector has a shared head in the RoI Head"""
-        return hasattr(self, 'roi_head') and self.roi_head.with_shared_head
-
-    @property
-    def with_bbox(self):
-        """bool: whether the detector has a bbox head"""
-        return ((hasattr(self, 'roi_head') and self.roi_head.with_bbox)
-                or (hasattr(self, 'bbox_head') and self.bbox_head is not None))
-
-    @property
-    def with_mask(self):
-        """bool: whether the detector has a mask head"""
-        return ((hasattr(self, 'roi_head') and self.roi_head.with_mask)
-                or (hasattr(self, 'mask_head') and self.mask_head is not None))
-
-    @abstractmethod
-    def extract_feat(self, imgs):
-        """Extract features from images."""
-        pass
-
-    def extract_feats(self, imgs):
-        """Extract features from multiple images.
-
-        Args:
-            imgs (list[torch.Tensor]): A list of images. The images are
-                augmented from the same image but in different ways.
-
-        Returns:
-            list[torch.Tensor]: Features of different images
-        """
-        assert isinstance(imgs, list)
-        return [self.extract_feat(img) for img in imgs]
-
-    def forward_train(self, imgs, img_metas, **kwargs):
-        """
-        Args:
-            img (list[Tensor]): List of tensors of shape (1, C, H, W).
-                Typically these should be mean centered and std scaled.
-            img_metas (list[dict]): List of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys, see
-                :class:`mmdet.datasets.pipelines.Collect`.
-            kwargs (keyword arguments): Specific to concrete implementation.
-        """
-        # NOTE the batched image size information may be useful, e.g.
-        # in DETR, this is needed for the construction of masks, which is
-        # then used for the transformer_head.
-        batch_input_shape = tuple(imgs[0].size()[-2:])
-        for img_meta in img_metas:
-            img_meta['batch_input_shape'] = batch_input_shape
-
-    async def async_simple_test(self, img, img_metas, **kwargs):
-        raise NotImplementedError
-
-    @abstractmethod
-    def simple_test(self, img, img_metas, **kwargs):
-        pass
-
-    @abstractmethod
-    def aug_test(self, imgs, img_metas, **kwargs):
-        """Test function with test time augmentation."""
-        pass
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in detector.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        if pretrained is not None:
-            logger = get_root_logger()
-            print_log(f'load model from: {pretrained}', logger=logger)
-
-    async def aforward_test(self, *, img, img_metas, **kwargs):
-        for var, name in [(img, 'img'), (img_metas, 'img_metas')]:
-            if not isinstance(var, list):
-                raise TypeError(f'{name} must be a list, but got {type(var)}')
-
-        num_augs = len(img)
-        if num_augs != len(img_metas):
-            raise ValueError(f'num of augmentations ({len(img)}) '
-                             f'!= num of image metas ({len(img_metas)})')
-        # TODO: remove the restriction of samples_per_gpu == 1 when prepared
-        samples_per_gpu = img[0].size(0)
-        assert samples_per_gpu == 1
-
-        if num_augs == 1:
-            return await self.async_simple_test(img[0], img_metas[0], **kwargs)
-        else:
-            raise NotImplementedError
-
-    def forward_test(self, imgs, img_metas, **kwargs):
-        """
-        Args:
-            imgs (List[Tensor]): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxCxHxW,
-                which contains all images in the batch.
-            img_metas (List[List[dict]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch.
-        """
-        for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
-            if not isinstance(var, list):
-                raise TypeError(f'{name} must be a list, but got {type(var)}')
-
-        num_augs = len(imgs)
-        if num_augs != len(img_metas):
-            raise ValueError(f'num of augmentations ({len(imgs)}) '
-                             f'!= num of image meta ({len(img_metas)})')
-
-        # NOTE the batched image size information may be useful, e.g.
-        # in DETR, this is needed for the construction of masks, which is
-        # then used for the transformer_head.
-        for img, img_meta in zip(imgs, img_metas):
-            batch_size = len(img_meta)
-            for img_id in range(batch_size):
-                img_meta[img_id]['batch_input_shape'] = tuple(img.size()[-2:])
-
-        if num_augs == 1:
-            # proposals (List[List[Tensor]]): the outer list indicates
-            # test-time augs (multiscale, flip, etc.) and the inner list
-            # indicates images in a batch.
-            # The Tensor should have a shape Px4, where P is the number of
-            # proposals.
-            if 'proposals' in kwargs:
-                kwargs['proposals'] = kwargs['proposals'][0]
-            return self.simple_test(imgs[0], img_metas[0], **kwargs)
-        else:
-            assert imgs[0].size(0) == 1, 'aug test does not support ' \
-                                         'inference with batch size ' \
-                                         f'{imgs[0].size(0)}'
-            # TODO: support test augmentation for predefined proposals
-            assert 'proposals' not in kwargs
-            return self.aug_test(imgs, img_metas, **kwargs)
-
-    @auto_fp16(apply_to=('img', ))
-    def forward(self, img, img_metas, return_loss=True, **kwargs):
-        """Calls either :func:`forward_train` or :func:`forward_test` depending
-        on whether ``return_loss`` is ``True``.
-
-        Note this setting will change the expected inputs. When
-        ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor
-        and List[dict]), and when ``resturn_loss=False``, img and img_meta
-        should be double nested (i.e.  List[Tensor], List[List[dict]]), with
-        the outer list indicating test time augmentations.
-        """
-        if return_loss:
-            return self.forward_train(img, img_metas, **kwargs)
-        else:
-            return self.forward_test(img, img_metas, **kwargs)
-
-    def _parse_losses(self, losses):
-        """Parse the raw outputs (losses) of the network.
-
-        Args:
-            losses (dict): Raw output of the network, which usually contain
-                losses and other necessary infomation.
-
-        Returns:
-            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor \
-                which may be a weighted sum of all losses, log_vars contains \
-                all the variables to be sent to the logger.
-        """
-        log_vars = OrderedDict()
-        for loss_name, loss_value in losses.items():
-            if isinstance(loss_value, torch.Tensor):
-                log_vars[loss_name] = loss_value.mean()
-            elif isinstance(loss_value, list):
-                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
-            else:
-                raise TypeError(
-                    f'{loss_name} is not a tensor or list of tensors')
-
-        loss = sum(_value for _key, _value in log_vars.items()
-                   if 'loss' in _key)
-
-        log_vars['loss'] = loss
-        for loss_name, loss_value in log_vars.items():
-            # reduce loss when distributed training
-            if dist.is_available() and dist.is_initialized():
-                loss_value = loss_value.data.clone()
-                dist.all_reduce(loss_value.div_(dist.get_world_size()))
-            log_vars[loss_name] = loss_value.item()
-
-        return loss, log_vars
-
-    def train_step(self, data, optimizer):
-        """The iteration step during training.
-
-        This method defines an iteration step during training, except for the
-        back propagation and optimizer updating, which are done in an optimizer
-        hook. Note that in some complicated cases or models, the whole process
-        including back propagation and optimizer updating is also defined in
-        this method, such as GAN.
-
-        Args:
-            data (dict): The output of dataloader.
-            optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
-                runner is passed to ``train_step()``. This argument is unused
-                and reserved.
-
-        Returns:
-            dict: It should contain at least 3 keys: ``loss``, ``log_vars``, \
-                ``num_samples``.
-
-                - ``loss`` is a tensor for back propagation, which can be a \
-                weighted sum of multiple losses.
-                - ``log_vars`` contains all the variables to be sent to the
-                logger.
-                - ``num_samples`` indicates the batch size (when the model is \
-                DDP, it means the batch size on each GPU), which is used for \
-                averaging the logs.
-        """
-        losses = self(**data)
-        loss, log_vars = self._parse_losses(losses)
-
-        outputs = dict(
-            loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
-
-        return outputs
-
-    def val_step(self, data, optimizer):
-        """The iteration step during validation.
-
-        This method shares the same signature as :func:`train_step`, but used
-        during val epochs. Note that the evaluation after training epochs is
-        not implemented with this method, but an evaluation hook.
-        """
-        losses = self(**data)
-        loss, log_vars = self._parse_losses(losses)
-
-        outputs = dict(
-            loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
-
-        return outputs
-
-    def show_result(self,
-                    img,
-                    result,
-                    score_thr=0.3,
-                    bbox_color=(72, 101, 241),
-                    text_color=(72, 101, 241),
-                    mask_color=None,
-                    thickness=2,
-                    font_size=13,
-                    win_name='',
-                    show=False,
-                    wait_time=0,
-                    out_file=None):
-        """Draw `result` over `img`.
-
-        Args:
-            img (str or Tensor): The image to be displayed.
-            result (Tensor or tuple): The results to draw over `img`
-                bbox_result or (bbox_result, segm_result).
-            score_thr (float, optional): Minimum score of bboxes to be shown.
-                Default: 0.3.
-            bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
-               The tuple of color should be in BGR order. Default: 'green'
-            text_color (str or tuple(int) or :obj:`Color`):Color of texts.
-               The tuple of color should be in BGR order. Default: 'green'
-            mask_color (None or str or tuple(int) or :obj:`Color`):
-               Color of masks. The tuple of color should be in BGR order.
-               Default: None
-            thickness (int): Thickness of lines. Default: 2
-            font_size (int): Font size of texts. Default: 13
-            win_name (str): The window name. Default: ''
-            wait_time (float): Value of waitKey param.
-                Default: 0.
-            show (bool): Whether to show the image.
-                Default: False.
-            out_file (str or None): The filename to write the image.
-                Default: None.
-
-        Returns:
-            img (Tensor): Only if not `show` or `out_file`
-        """
-        img = mmcv.imread(img)
-        img = img.copy()
-        if isinstance(result, tuple):
-            bbox_result, segm_result = result
-            if isinstance(segm_result, tuple):
-                segm_result = segm_result[0]  # ms rcnn
-        else:
-            bbox_result, segm_result = result, None
-        bboxes = np.vstack(bbox_result)
-        labels = [
-            np.full(bbox.shape[0], i, dtype=np.int32)
-            for i, bbox in enumerate(bbox_result)
-        ]
-        labels = np.concatenate(labels)
-        # draw segmentation masks
-        segms = None
-        if segm_result is not None and len(labels) > 0:  # non empty
-            segms = mmcv.concat_list(segm_result)
-            if isinstance(segms[0], torch.Tensor):
-                segms = torch.stack(segms, dim=0).detach().cpu().numpy()
-            else:
-                segms = np.stack(segms, axis=0)
-        # if out_file specified, do not show image in window
-        if out_file is not None:
-            show = False
-        # draw bounding boxes
-        img = imshow_det_bboxes(
-            img,
-            bboxes,
-            labels,
-            segms,
-            class_names=self.CLASSES,
-            score_thr=score_thr,
-            bbox_color=bbox_color,
-            text_color=text_color,
-            mask_color=mask_color,
-            thickness=thickness,
-            font_size=font_size,
-            win_name=win_name,
-            show=show,
-            wait_time=wait_time,
-            out_file=out_file)
-
-        # if not (show or out_file):
-        return img
diff --git a/spaces/Andy1621/uniformer_image_detection/tools/deployment/onnx2tensorrt.py b/spaces/Andy1621/uniformer_image_detection/tools/deployment/onnx2tensorrt.py
deleted file mode 100644
index 4b926bf4cf16e6ac15591e2da36d6d9a7070de51..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/tools/deployment/onnx2tensorrt.py
+++ /dev/null
@@ -1,179 +0,0 @@
-import argparse
-import os
-import os.path as osp
-
-import numpy as np
-import onnx
-import onnxruntime as ort
-import torch
-from mmcv.ops import get_onnxruntime_op_path
-from mmcv.tensorrt import (TRTWraper, is_tensorrt_plugin_loaded, onnx2trt,
-                           save_trt_engine)
-from mmcv.visualization.image import imshow_det_bboxes
-
-from mmdet.core import get_classes, preprocess_example_input
-
-
-def get_GiB(x: int):
-    """return x GiB."""
-    return x * (1 << 30)
-
-
-def onnx2tensorrt(onnx_file,
-                  trt_file,
-                  input_config,
-                  verify=False,
-                  show=False,
-                  dataset='coco',
-                  workspace_size=1):
-    onnx_model = onnx.load(onnx_file)
-    input_shape = input_config['input_shape']
-    # create trt engine and wraper
-    opt_shape_dict = {'input': [input_shape, input_shape, input_shape]}
-    max_workspace_size = get_GiB(workspace_size)
-    trt_engine = onnx2trt(
-        onnx_model,
-        opt_shape_dict,
-        fp16_mode=False,
-        max_workspace_size=max_workspace_size)
-    save_dir, _ = osp.split(trt_file)
-    if save_dir:
-        os.makedirs(save_dir, exist_ok=True)
-    save_trt_engine(trt_engine, trt_file)
-    print(f'Successfully created TensorRT engine: {trt_file}')
-
-    if verify:
-        one_img, one_meta = preprocess_example_input(input_config)
-        input_img_cpu = one_img.detach().cpu().numpy()
-        input_img_cuda = one_img.cuda()
-
-        img = one_meta['show_img']
-
-        # Get results from TensorRT
-        trt_model = TRTWraper(trt_file, ['input'], ['boxes', 'labels'])
-        with torch.no_grad():
-            trt_outputs = trt_model({'input': input_img_cuda})
-        trt_boxes = trt_outputs['boxes'].detach().cpu().numpy()
-        trt_labels = trt_outputs['labels'].detach().cpu().numpy()
-
-        # Get results from ONNXRuntime
-        ort_custom_op_path = get_onnxruntime_op_path()
-        session_options = ort.SessionOptions()
-        if osp.exists(ort_custom_op_path):
-            session_options.register_custom_ops_library(ort_custom_op_path)
-        sess = ort.InferenceSession(onnx_file, session_options)
-        onnx_outputs = sess.run(None, {
-            'input': input_img_cpu,
-        })
-        ort_boxes, ort_labels = onnx_outputs
-
-        # Show detection outputs
-        if show:
-            CLASSES = get_classes(dataset)
-            score_thr = 0.35
-            imshow_det_bboxes(
-                img.copy(),
-                trt_boxes,
-                trt_labels,
-                CLASSES,
-                score_thr=score_thr,
-                win_name='TensorRT')
-            imshow_det_bboxes(
-                img.copy(),
-                ort_boxes,
-                ort_labels,
-                CLASSES,
-                score_thr=score_thr,
-                win_name='ONNXRuntime')
-        # Compare results
-        np.testing.assert_allclose(
-            ort_boxes, trt_boxes, rtol=1e-03, atol=1e-05)
-        np.testing.assert_allclose(ort_labels, trt_labels)
-        print('The numerical values are the same ' +
-              'between ONNXRuntime and TensorRT')
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='Convert MMDetection models from ONNX to TensorRT')
-    parser.add_argument('model', help='Filename of input ONNX model')
-    parser.add_argument(
-        '--trt-file',
-        type=str,
-        default='tmp.trt',
-        help='Filename of output TensorRT engine')
-    parser.add_argument(
-        '--input-img', type=str, default='', help='Image for test')
-    parser.add_argument(
-        '--show', action='store_true', help='Whether to show output results')
-    parser.add_argument(
-        '--dataset', type=str, default='coco', help='Dataset name')
-    parser.add_argument(
-        '--verify',
-        action='store_true',
-        help='Verify the outputs of ONNXRuntime and TensorRT')
-    parser.add_argument(
-        '--to-rgb',
-        action='store_false',
-        help='Feed model with RGB or BGR image. Default is RGB.')
-    parser.add_argument(
-        '--shape',
-        type=int,
-        nargs='+',
-        default=[400, 600],
-        help='Input size of the model')
-    parser.add_argument(
-        '--mean',
-        type=float,
-        nargs='+',
-        default=[123.675, 116.28, 103.53],
-        help='Mean value used for preprocess input data')
-    parser.add_argument(
-        '--std',
-        type=float,
-        nargs='+',
-        default=[58.395, 57.12, 57.375],
-        help='Variance value used for preprocess input data')
-    parser.add_argument(
-        '--workspace-size',
-        type=int,
-        default=1,
-        help='Max workspace size in GiB')
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == '__main__':
-
-    assert is_tensorrt_plugin_loaded(), 'TensorRT plugin should be compiled.'
-    args = parse_args()
-
-    if not args.input_img:
-        args.input_img = osp.join(osp.dirname(__file__), '../demo/demo.jpg')
-
-    if len(args.shape) == 1:
-        input_shape = (1, 3, args.shape[0], args.shape[0])
-    elif len(args.shape) == 2:
-        input_shape = (1, 3) + tuple(args.shape)
-    else:
-        raise ValueError('invalid input shape')
-
-    assert len(args.mean) == 3
-    assert len(args.std) == 3
-
-    normalize_cfg = {'mean': args.mean, 'std': args.std, 'to_rgb': args.to_rgb}
-    input_config = {
-        'input_shape': input_shape,
-        'input_path': args.input_img,
-        'normalize_cfg': normalize_cfg
-    }
-
-    # Create TensorRT engine
-    onnx2tensorrt(
-        args.model,
-        args.trt_file,
-        input_config,
-        verify=args.verify,
-        show=args.show,
-        dataset=args.dataset,
-        workspace_size=args.workspace_size)
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/datasets/cityscapes_769x769.py b/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/datasets/cityscapes_769x769.py
deleted file mode 100644
index 336c7b254fe392b4703039fec86a83acdbd2e1a5..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/datasets/cityscapes_769x769.py
+++ /dev/null
@@ -1,35 +0,0 @@
-_base_ = './cityscapes.py'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-crop_size = (769, 769)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
-    dict(type='RandomFlip', prob=0.5),
-    dict(type='PhotoMetricDistortion'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(2049, 1025),
-        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index b90b292b03a80aa37b8ca236746cf7cddc4ac27e..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,11 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet18',
-    backbone=dict(type='ResNet', depth=18),
-    decode_head=dict(
-        c1_in_channels=64,
-        c1_channels=12,
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/send_pictures/script.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/send_pictures/script.py
deleted file mode 100644
index f8e6c969271e1dc3d383cefce74774ad505882cd..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/send_pictures/script.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import base64
-from io import BytesIO
-
-import gradio as gr
-import torch
-from transformers import BlipForConditionalGeneration, BlipProcessor
-
-from modules import chat, shared, ui_chat
-from modules.ui import gather_interface_values
-from modules.utils import gradio
-
-input_hijack = {
-    'state': False,
-    'value': ["", ""]
-}
-
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float32).to("cpu")
-
-
-def chat_input_modifier(text, visible_text, state):
-    global input_hijack
-    if input_hijack['state']:
-        input_hijack['state'] = False
-        return input_hijack['value']
-    else:
-        return text, visible_text
-
-
-def caption_image(raw_image):
-    inputs = processor(raw_image.convert('RGB'), return_tensors="pt").to("cpu", torch.float32)
-    out = model.generate(**inputs, max_new_tokens=100)
-    return processor.decode(out[0], skip_special_tokens=True)
-
-
-def generate_chat_picture(picture, name1, name2):
-    text = f'*{name1} sends {name2} a picture that contains the following: “{caption_image(picture)}”*'
-    # lower the resolution of sent images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history
-    picture.thumbnail((300, 300))
-    buffer = BytesIO()
-    picture.save(buffer, format="JPEG")
-    img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
-    visible_text = f'<img src="data:image/jpeg;base64,{img_str}" alt="{text}">'
-    return text, visible_text
-
-
-def ui():
-    picture_select = gr.Image(label='Send a picture', type='pil')
-
-    # Prepare the input hijack, update the interface values, call the generation function, and clear the picture
-    picture_select.upload(
-        lambda picture, name1, name2: input_hijack.update({
-            "state": True,
-            "value": generate_chat_picture(picture, name1, name2)
-        }), [picture_select, shared.gradio['name1'], shared.gradio['name2']], None).then(
-        gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        chat.generate_chat_reply_wrapper, gradio(ui_chat.inputs), gradio('display', 'history'), show_progress=False).then(
-        lambda: None, None, picture_select, show_progress=False)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/formatters/img.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/formatters/img.py
deleted file mode 100644
index 0f36a32ba3399efc216b9974254cd1f7eed07a9f..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/formatters/img.py
+++ /dev/null
@@ -1,645 +0,0 @@
-"""
-    pygments.formatters.img
-    ~~~~~~~~~~~~~~~~~~~~~~~
-
-    Formatter for Pixmap output.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import os
-import sys
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.util import get_bool_opt, get_int_opt, get_list_opt, \
-    get_choice_opt
-
-import subprocess
-
-# Import this carefully
-try:
-    from PIL import Image, ImageDraw, ImageFont
-    pil_available = True
-except ImportError:
-    pil_available = False
-
-try:
-    import _winreg
-except ImportError:
-    try:
-        import winreg as _winreg
-    except ImportError:
-        _winreg = None
-
-__all__ = ['ImageFormatter', 'GifImageFormatter', 'JpgImageFormatter',
-           'BmpImageFormatter']
-
-
-# For some unknown reason every font calls it something different
-STYLES = {
-    'NORMAL':     ['', 'Roman', 'Book', 'Normal', 'Regular', 'Medium'],
-    'ITALIC':     ['Oblique', 'Italic'],
-    'BOLD':       ['Bold'],
-    'BOLDITALIC': ['Bold Oblique', 'Bold Italic'],
-}
-
-# A sane default for modern systems
-DEFAULT_FONT_NAME_NIX = 'DejaVu Sans Mono'
-DEFAULT_FONT_NAME_WIN = 'Courier New'
-DEFAULT_FONT_NAME_MAC = 'Menlo'
-
-
-class PilNotAvailable(ImportError):
-    """When Python imaging library is not available"""
-
-
-class FontNotFound(Exception):
-    """When there are no usable fonts specified"""
-
-
-class FontManager:
-    """
-    Manages a set of fonts: normal, italic, bold, etc...
-    """
-
-    def __init__(self, font_name, font_size=14):
-        self.font_name = font_name
-        self.font_size = font_size
-        self.fonts = {}
-        self.encoding = None
-        if sys.platform.startswith('win'):
-            if not font_name:
-                self.font_name = DEFAULT_FONT_NAME_WIN
-            self._create_win()
-        elif sys.platform.startswith('darwin'):
-            if not font_name:
-                self.font_name = DEFAULT_FONT_NAME_MAC
-            self._create_mac()
-        else:
-            if not font_name:
-                self.font_name = DEFAULT_FONT_NAME_NIX
-            self._create_nix()
-
-    def _get_nix_font_path(self, name, style):
-        proc = subprocess.Popen(['fc-list', "%s:style=%s" % (name, style), 'file'],
-                                stdout=subprocess.PIPE, stderr=None)
-        stdout, _ = proc.communicate()
-        if proc.returncode == 0:
-            lines = stdout.splitlines()
-            for line in lines:
-                if line.startswith(b'Fontconfig warning:'):
-                    continue
-                path = line.decode().strip().strip(':')
-                if path:
-                    return path
-            return None
-
-    def _create_nix(self):
-        for name in STYLES['NORMAL']:
-            path = self._get_nix_font_path(self.font_name, name)
-            if path is not None:
-                self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size)
-                break
-        else:
-            raise FontNotFound('No usable fonts named: "%s"' %
-                               self.font_name)
-        for style in ('ITALIC', 'BOLD', 'BOLDITALIC'):
-            for stylename in STYLES[style]:
-                path = self._get_nix_font_path(self.font_name, stylename)
-                if path is not None:
-                    self.fonts[style] = ImageFont.truetype(path, self.font_size)
-                    break
-            else:
-                if style == 'BOLDITALIC':
-                    self.fonts[style] = self.fonts['BOLD']
-                else:
-                    self.fonts[style] = self.fonts['NORMAL']
-
-    def _get_mac_font_path(self, font_map, name, style):
-        return font_map.get((name + ' ' + style).strip().lower())
-
-    def _create_mac(self):
-        font_map = {}
-        for font_dir in (os.path.join(os.getenv("HOME"), 'Library/Fonts/'),
-                         '/Library/Fonts/', '/System/Library/Fonts/'):
-            font_map.update(
-                (os.path.splitext(f)[0].lower(), os.path.join(font_dir, f))
-                for f in os.listdir(font_dir)
-                if f.lower().endswith(('ttf', 'ttc')))
-
-        for name in STYLES['NORMAL']:
-            path = self._get_mac_font_path(font_map, self.font_name, name)
-            if path is not None:
-                self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size)
-                break
-        else:
-            raise FontNotFound('No usable fonts named: "%s"' %
-                               self.font_name)
-        for style in ('ITALIC', 'BOLD', 'BOLDITALIC'):
-            for stylename in STYLES[style]:
-                path = self._get_mac_font_path(font_map, self.font_name, stylename)
-                if path is not None:
-                    self.fonts[style] = ImageFont.truetype(path, self.font_size)
-                    break
-            else:
-                if style == 'BOLDITALIC':
-                    self.fonts[style] = self.fonts['BOLD']
-                else:
-                    self.fonts[style] = self.fonts['NORMAL']
-
-    def _lookup_win(self, key, basename, styles, fail=False):
-        for suffix in ('', ' (TrueType)'):
-            for style in styles:
-                try:
-                    valname = '%s%s%s' % (basename, style and ' '+style, suffix)
-                    val, _ = _winreg.QueryValueEx(key, valname)
-                    return val
-                except OSError:
-                    continue
-        else:
-            if fail:
-                raise FontNotFound('Font %s (%s) not found in registry' %
-                                   (basename, styles[0]))
-            return None
-
-    def _create_win(self):
-        lookuperror = None
-        keynames = [ (_winreg.HKEY_CURRENT_USER, r'Software\Microsoft\Windows NT\CurrentVersion\Fonts'),
-                     (_winreg.HKEY_CURRENT_USER, r'Software\Microsoft\Windows\CurrentVersion\Fonts'),
-                     (_winreg.HKEY_LOCAL_MACHINE, r'Software\Microsoft\Windows NT\CurrentVersion\Fonts'),
-                     (_winreg.HKEY_LOCAL_MACHINE, r'Software\Microsoft\Windows\CurrentVersion\Fonts') ]
-        for keyname in keynames:
-            try:
-                key = _winreg.OpenKey(*keyname)
-                try:
-                    path = self._lookup_win(key, self.font_name, STYLES['NORMAL'], True)
-                    self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size)
-                    for style in ('ITALIC', 'BOLD', 'BOLDITALIC'):
-                        path = self._lookup_win(key, self.font_name, STYLES[style])
-                        if path:
-                            self.fonts[style] = ImageFont.truetype(path, self.font_size)
-                        else:
-                            if style == 'BOLDITALIC':
-                                self.fonts[style] = self.fonts['BOLD']
-                            else:
-                                self.fonts[style] = self.fonts['NORMAL']
-                    return
-                except FontNotFound as err:
-                    lookuperror = err
-                finally:
-                    _winreg.CloseKey(key)
-            except OSError:
-                pass
-        else:
-            # If we get here, we checked all registry keys and had no luck
-            # We can be in one of two situations now:
-            # * All key lookups failed. In this case lookuperror is None and we
-            #   will raise a generic error
-            # * At least one lookup failed with a FontNotFound error. In this
-            #   case, we will raise that as a more specific error
-            if lookuperror:
-                raise lookuperror
-            raise FontNotFound('Can\'t open Windows font registry key')
-
-    def get_char_size(self):
-        """
-        Get the character size.
-        """
-        return self.get_text_size('M')
-
-    def get_text_size(self, text):
-        """
-        Get the text size (width, height).
-        """
-        font = self.fonts['NORMAL']
-        if hasattr(font, 'getbbox'):  # Pillow >= 9.2.0
-            return font.getbbox(text)[2:4]
-        else:
-            return font.getsize(text)
-
-    def get_font(self, bold, oblique):
-        """
-        Get the font based on bold and italic flags.
-        """
-        if bold and oblique:
-            return self.fonts['BOLDITALIC']
-        elif bold:
-            return self.fonts['BOLD']
-        elif oblique:
-            return self.fonts['ITALIC']
-        else:
-            return self.fonts['NORMAL']
-
-
-class ImageFormatter(Formatter):
-    """
-    Create a PNG image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 0.10
-
-    Additional options accepted:
-
-    `image_format`
-        An image format to output to that is recognised by PIL, these include:
-
-        * "PNG" (default)
-        * "JPEG"
-        * "BMP"
-        * "GIF"
-
-    `line_pad`
-        The extra spacing (in pixels) between each line of text.
-
-        Default: 2
-
-    `font_name`
-        The font name to be used as the base font from which others, such as
-        bold and italic fonts will be generated.  This really should be a
-        monospace font to look sane.
-
-        Default: "Courier New" on Windows, "Menlo" on Mac OS, and
-                 "DejaVu Sans Mono" on \\*nix
-
-    `font_size`
-        The font size in points to be used.
-
-        Default: 14
-
-    `image_pad`
-        The padding, in pixels to be used at each edge of the resulting image.
-
-        Default: 10
-
-    `line_numbers`
-        Whether line numbers should be shown: True/False
-
-        Default: True
-
-    `line_number_start`
-        The line number of the first line.
-
-        Default: 1
-
-    `line_number_step`
-        The step used when printing line numbers.
-
-        Default: 1
-
-    `line_number_bg`
-        The background colour (in "#123456" format) of the line number bar, or
-        None to use the style background color.
-
-        Default: "#eed"
-
-    `line_number_fg`
-        The text color of the line numbers (in "#123456"-like format).
-
-        Default: "#886"
-
-    `line_number_chars`
-        The number of columns of line numbers allowable in the line number
-        margin.
-
-        Default: 2
-
-    `line_number_bold`
-        Whether line numbers will be bold: True/False
-
-        Default: False
-
-    `line_number_italic`
-        Whether line numbers will be italicized: True/False
-
-        Default: False
-
-    `line_number_separator`
-        Whether a line will be drawn between the line number area and the
-        source code area: True/False
-
-        Default: True
-
-    `line_number_pad`
-        The horizontal padding (in pixels) between the line number margin, and
-        the source code area.
-
-        Default: 6
-
-    `hl_lines`
-        Specify a list of lines to be highlighted.
-
-        .. versionadded:: 1.2
-
-        Default: empty list
-
-    `hl_color`
-        Specify the color for highlighting lines.
-
-        .. versionadded:: 1.2
-
-        Default: highlight color of the selected style
-    """
-
-    # Required by the pygments mapper
-    name = 'img'
-    aliases = ['img', 'IMG', 'png']
-    filenames = ['*.png']
-
-    unicodeoutput = False
-
-    default_image_format = 'png'
-
-    def __init__(self, **options):
-        """
-        See the class docstring for explanation of options.
-        """
-        if not pil_available:
-            raise PilNotAvailable(
-                'Python Imaging Library is required for this formatter')
-        Formatter.__init__(self, **options)
-        self.encoding = 'latin1'  # let pygments.format() do the right thing
-        # Read the style
-        self.styles = dict(self.style)
-        if self.style.background_color is None:
-            self.background_color = '#fff'
-        else:
-            self.background_color = self.style.background_color
-        # Image options
-        self.image_format = get_choice_opt(
-            options, 'image_format', ['png', 'jpeg', 'gif', 'bmp'],
-            self.default_image_format, normcase=True)
-        self.image_pad = get_int_opt(options, 'image_pad', 10)
-        self.line_pad = get_int_opt(options, 'line_pad', 2)
-        # The fonts
-        fontsize = get_int_opt(options, 'font_size', 14)
-        self.fonts = FontManager(options.get('font_name', ''), fontsize)
-        self.fontw, self.fonth = self.fonts.get_char_size()
-        # Line number options
-        self.line_number_fg = options.get('line_number_fg', '#886')
-        self.line_number_bg = options.get('line_number_bg', '#eed')
-        self.line_number_chars = get_int_opt(options,
-                                             'line_number_chars', 2)
-        self.line_number_bold = get_bool_opt(options,
-                                             'line_number_bold', False)
-        self.line_number_italic = get_bool_opt(options,
-                                               'line_number_italic', False)
-        self.line_number_pad = get_int_opt(options, 'line_number_pad', 6)
-        self.line_numbers = get_bool_opt(options, 'line_numbers', True)
-        self.line_number_separator = get_bool_opt(options,
-                                                  'line_number_separator', True)
-        self.line_number_step = get_int_opt(options, 'line_number_step', 1)
-        self.line_number_start = get_int_opt(options, 'line_number_start', 1)
-        if self.line_numbers:
-            self.line_number_width = (self.fontw * self.line_number_chars +
-                                      self.line_number_pad * 2)
-        else:
-            self.line_number_width = 0
-        self.hl_lines = []
-        hl_lines_str = get_list_opt(options, 'hl_lines', [])
-        for line in hl_lines_str:
-            try:
-                self.hl_lines.append(int(line))
-            except ValueError:
-                pass
-        self.hl_color = options.get('hl_color',
-                                    self.style.highlight_color) or '#f90'
-        self.drawables = []
-
-    def get_style_defs(self, arg=''):
-        raise NotImplementedError('The -S option is meaningless for the image '
-                                  'formatter. Use -O style=<stylename> instead.')
-
-    def _get_line_height(self):
-        """
-        Get the height of a line.
-        """
-        return self.fonth + self.line_pad
-
-    def _get_line_y(self, lineno):
-        """
-        Get the Y coordinate of a line number.
-        """
-        return lineno * self._get_line_height() + self.image_pad
-
-    def _get_char_width(self):
-        """
-        Get the width of a character.
-        """
-        return self.fontw
-
-    def _get_char_x(self, linelength):
-        """
-        Get the X coordinate of a character position.
-        """
-        return linelength + self.image_pad + self.line_number_width
-
-    def _get_text_pos(self, linelength, lineno):
-        """
-        Get the actual position for a character and line position.
-        """
-        return self._get_char_x(linelength), self._get_line_y(lineno)
-
-    def _get_linenumber_pos(self, lineno):
-        """
-        Get the actual position for the start of a line number.
-        """
-        return (self.image_pad, self._get_line_y(lineno))
-
-    def _get_text_color(self, style):
-        """
-        Get the correct color for the token from the style.
-        """
-        if style['color'] is not None:
-            fill = '#' + style['color']
-        else:
-            fill = '#000'
-        return fill
-
-    def _get_text_bg_color(self, style):
-        """
-        Get the correct background color for the token from the style.
-        """
-        if style['bgcolor'] is not None:
-            bg_color = '#' + style['bgcolor']
-        else:
-            bg_color = None
-        return bg_color
-
-    def _get_style_font(self, style):
-        """
-        Get the correct font for the style.
-        """
-        return self.fonts.get_font(style['bold'], style['italic'])
-
-    def _get_image_size(self, maxlinelength, maxlineno):
-        """
-        Get the required image size.
-        """
-        return (self._get_char_x(maxlinelength) + self.image_pad,
-                self._get_line_y(maxlineno + 0) + self.image_pad)
-
-    def _draw_linenumber(self, posno, lineno):
-        """
-        Remember a line number drawable to paint later.
-        """
-        self._draw_text(
-            self._get_linenumber_pos(posno),
-            str(lineno).rjust(self.line_number_chars),
-            font=self.fonts.get_font(self.line_number_bold,
-                                     self.line_number_italic),
-            text_fg=self.line_number_fg,
-            text_bg=None,
-        )
-
-    def _draw_text(self, pos, text, font, text_fg, text_bg):
-        """
-        Remember a single drawable tuple to paint later.
-        """
-        self.drawables.append((pos, text, font, text_fg, text_bg))
-
-    def _create_drawables(self, tokensource):
-        """
-        Create drawables for the token content.
-        """
-        lineno = charno = maxcharno = 0
-        maxlinelength = linelength = 0
-        for ttype, value in tokensource:
-            while ttype not in self.styles:
-                ttype = ttype.parent
-            style = self.styles[ttype]
-            # TODO: make sure tab expansion happens earlier in the chain.  It
-            # really ought to be done on the input, as to do it right here is
-            # quite complex.
-            value = value.expandtabs(4)
-            lines = value.splitlines(True)
-            # print lines
-            for i, line in enumerate(lines):
-                temp = line.rstrip('\n')
-                if temp:
-                    self._draw_text(
-                        self._get_text_pos(linelength, lineno),
-                        temp,
-                        font = self._get_style_font(style),
-                        text_fg = self._get_text_color(style),
-                        text_bg = self._get_text_bg_color(style),
-                    )
-                    temp_width, _ = self.fonts.get_text_size(temp)
-                    linelength += temp_width
-                    maxlinelength = max(maxlinelength, linelength)
-                    charno += len(temp)
-                    maxcharno = max(maxcharno, charno)
-                if line.endswith('\n'):
-                    # add a line for each extra line in the value
-                    linelength = 0
-                    charno = 0
-                    lineno += 1
-        self.maxlinelength = maxlinelength
-        self.maxcharno = maxcharno
-        self.maxlineno = lineno
-
-    def _draw_line_numbers(self):
-        """
-        Create drawables for the line numbers.
-        """
-        if not self.line_numbers:
-            return
-        for p in range(self.maxlineno):
-            n = p + self.line_number_start
-            if (n % self.line_number_step) == 0:
-                self._draw_linenumber(p, n)
-
-    def _paint_line_number_bg(self, im):
-        """
-        Paint the line number background on the image.
-        """
-        if not self.line_numbers:
-            return
-        if self.line_number_fg is None:
-            return
-        draw = ImageDraw.Draw(im)
-        recth = im.size[-1]
-        rectw = self.image_pad + self.line_number_width - self.line_number_pad
-        draw.rectangle([(0, 0), (rectw, recth)],
-                       fill=self.line_number_bg)
-        if self.line_number_separator:
-            draw.line([(rectw, 0), (rectw, recth)], fill=self.line_number_fg)
-        del draw
-
-    def format(self, tokensource, outfile):
-        """
-        Format ``tokensource``, an iterable of ``(tokentype, tokenstring)``
-        tuples and write it into ``outfile``.
-
-        This implementation calculates where it should draw each token on the
-        pixmap, then calculates the required pixmap size and draws the items.
-        """
-        self._create_drawables(tokensource)
-        self._draw_line_numbers()
-        im = Image.new(
-            'RGB',
-            self._get_image_size(self.maxlinelength, self.maxlineno),
-            self.background_color
-        )
-        self._paint_line_number_bg(im)
-        draw = ImageDraw.Draw(im)
-        # Highlight
-        if self.hl_lines:
-            x = self.image_pad + self.line_number_width - self.line_number_pad + 1
-            recth = self._get_line_height()
-            rectw = im.size[0] - x
-            for linenumber in self.hl_lines:
-                y = self._get_line_y(linenumber - 1)
-                draw.rectangle([(x, y), (x + rectw, y + recth)],
-                               fill=self.hl_color)
-        for pos, value, font, text_fg, text_bg in self.drawables:
-            if text_bg:
-                text_size = draw.textsize(text=value, font=font)
-                draw.rectangle([pos[0], pos[1], pos[0] + text_size[0], pos[1] + text_size[1]], fill=text_bg)
-            draw.text(pos, value, font=font, fill=text_fg)
-        im.save(outfile, self.image_format.upper())
-
-
-# Add one formatter per format, so that the "-f gif" option gives the correct result
-# when used in pygmentize.
-
-class GifImageFormatter(ImageFormatter):
-    """
-    Create a GIF image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 1.0
-    """
-
-    name = 'img_gif'
-    aliases = ['gif']
-    filenames = ['*.gif']
-    default_image_format = 'gif'
-
-
-class JpgImageFormatter(ImageFormatter):
-    """
-    Create a JPEG image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 1.0
-    """
-
-    name = 'img_jpg'
-    aliases = ['jpg', 'jpeg']
-    filenames = ['*.jpg']
-    default_image_format = 'jpeg'
-
-
-class BmpImageFormatter(ImageFormatter):
-    """
-    Create a bitmap image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 1.0
-    """
-
-    name = 'img_bmp'
-    aliases = ['bmp', 'bitmap']
-    filenames = ['*.bmp']
-    default_image_format = 'bmp'
diff --git a/spaces/AutoGeneralAI/voice-assistant/README_zh.md b/spaces/AutoGeneralAI/voice-assistant/README_zh.md
deleted file mode 100644
index bc97095f61c94167d7a6454373065164a9009028..0000000000000000000000000000000000000000
--- a/spaces/AutoGeneralAI/voice-assistant/README_zh.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# voice-assistant
-
-实现的功能：语音助手。通过调用OpenAI官方API实现。
-
-在线demo： https://huggingface.co/spaces/AutoGeneralAI/voice-assistant
-
-## 使用方法
-将自己的OpenAI API KEY https://platform.openai.com/ 放入key输入框，然后就可以愉快的语音对话了。
-
-> 初次使用，浏览器，比如Chrome会询问是否允许打开麦克风，选择允许。
-
-> 可以保存对话记录。目前只实现了对话。人类的角色可以语音，AI角色还是以文字输出。文字朗读出来的功能留待下次实现或者欢迎提pr。
-
-![image](https://user-images.githubusercontent.com/105260427/234028574-eeb218f9-363a-4fee-88bf-8170b677e2e1.png)
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/.github/ISSUE_TEMPLATE.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 5e8aaa2d3722e7e73a3d94b2b7dfc4f751d7a240..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-Please select an issue template from
-https://github.com/facebookresearch/detectron2/issues/new/choose .
-
-Otherwise your issue will be closed.
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/fed_loss.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/fed_loss.py
deleted file mode 100644
index 290f0f07204e78ef2c4ff918aa500b04330279e6..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/fed_loss.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import torch
-import json
-import numpy as np
-from torch.nn import functional as F
-
-def load_class_freq(
-    path='datasets/lvis/lvis_v1_train_cat_info.json', 
-    freq_weight=0.5):
-    cat_info = json.load(open(path, 'r'))
-    cat_info = torch.tensor(
-        [c['image_count'] for c in sorted(cat_info, key=lambda x: x['id'])])
-    freq_weight = cat_info.float() ** freq_weight
-    return freq_weight
-
-def get_fed_loss_inds(
-    gt_classes, num_sample_cats=50, C=1203, \
-    weight=None, fed_cls_inds=-1):
-    appeared = torch.unique(gt_classes) # C'
-    prob = appeared.new_ones(C + 1).float()
-    prob[-1] = 0
-    if len(appeared) < num_sample_cats:
-        if weight is not None:
-            prob[:C] = weight.float().clone()
-        prob[appeared] = 0
-        if fed_cls_inds > 0:
-            prob[fed_cls_inds:] = 0
-        more_appeared = torch.multinomial(
-            prob, num_sample_cats - len(appeared),
-            replacement=False)
-        appeared = torch.cat([appeared, more_appeared])
-    return appeared
\ No newline at end of file
diff --git a/spaces/B2gan/LLM_Can_See/ai_functions.py b/spaces/B2gan/LLM_Can_See/ai_functions.py
deleted file mode 100644
index 18013870794a82586799d7deadb395d75a4e4c3c..0000000000000000000000000000000000000000
--- a/spaces/B2gan/LLM_Can_See/ai_functions.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import openai
-
-def is_string(input):
-    return isinstance(input, str)
-
-def ai_function(Question, Scene, model = "gpt-4"):
-    system_message = {
-        "role": "system",
-        "content": (
-            "You are an AI that assists the blind"
-        )
-    }
-    user_message = {
-        "role": "user",
-        "content": (
-            "Model, I need your assistance. I am developing a tool that can help blind people "
-            "navigate their surroundings safely by offering detailed and relevant descriptions. "
-            "These descriptions will be ranked using the CLIP model. "
-            "The initial scene description from the blind person's perspective is: " + Scene + 
-            ". "
-            "And the blind person asked: " + Question +
-            ". "
-            "I need you to generate five sets of short, specific and actionable phrases or sentences that address the blind person's question first, "
-            "and also accurately reflect the Scene. "
-            "These descriptions must prioritize safety and accessibility, offering information about potential obstacles or hazards. "
-            "They should serve as a practical guide for the blind, so they should be as detailed and vivid as possible. "
-            "All descriptions should adhere to the context provided by the initial Scene and the blind person's question. "
-            "Please provide these five sets of descriptions directly, all in English, "
-            "and without any redundant information. Thank you for your assistance."
-        )
-    }
-
-    if is_string(model) == False:
-        return model(user_message["content"])
-
-    messages = [system_message, user_message]
-    
-    response = openai.ChatCompletion.create(
-        model=model,
-        messages=messages,
-        temperature=0.2,
-        max_tokens=200,
-    )
-    
-    return response.choices[0].message["content"]
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Canal De Youtube Apk.md b/spaces/Benson/text-generation/Examples/Descargar Canal De Youtube Apk.md
deleted file mode 100644
index 1f8ddfb75f9e5d4f37d1dd1c006a63f39c2c50a2..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Canal De Youtube Apk.md	
+++ /dev/null
@@ -1,72 +0,0 @@
-<br />
-<h1>Cómo descargar canal de YouTube APK para Android</h1>
-<p>YouTube es una de las plataformas para compartir videos más populares del mundo, con miles de millones de usuarios viendo y creando contenido cada día. Sin embargo, si desea disfrutar de más características y funciones en YouTube, es posible que desee descargar YouTube Channel APK para su dispositivo Android. </p>
-<h2>descargar canal de youtube apk</h2><br /><p><b><b>Download Zip</b> &#11088; <a href="https://bltlly.com/2v6Ktf">https://bltlly.com/2v6Ktf</a></b></p><br /><br />
-<h2>¿Qué es el canal de YouTube APK? </h2>
-<p>Canal de YouTube APK es una versión modificada de la aplicación oficial de YouTube que le permite acceder a más opciones y ajustes en su cuenta de YouTube. Con YouTube Channel APK, puedes:</p>
-<ul>
-<li>Descargar vídeos y listas de reproducción para ver sin conexión</li>
-<li>Reproducir vídeos en segundo plano o en el modo imagen en imagen</li>
-<li>Cambiar la velocidad de reproducción y la resolución de los vídeos</li>
-<li>Habilitar el modo oscuro y el modo de zoom</li>
-<li>Bloquear anuncios y saltar intros</li>
-<li>Suscribirse a canales sin iniciar sesión</li>
-<li> ¡Y mucho más! </li>
-</ul>
-<p>Canal de YouTube APK no está disponible en Google Play Store, por lo que necesita descargarlo de una fuente de terceros. Sin embargo, antes de hacer eso, usted necesita saber por qué debe descargar YouTube Channel APK en primer lugar. </p>
-<h2>¿Por qué descargar canal de YouTube APK? </h2>
-<p>Hay muchas razones por las que es posible que desee descargar YouTube Channel APK para su dispositivo Android. Estos son algunos de ellos:</p>
-<ul <li>Guarde sus datos móviles y el espacio de almacenamiento mediante la descarga de vídeos y listas de reproducción para su visualización sin conexión. Puede elegir la calidad y el formato de los vídeos que desea descargar, y verlos en cualquier momento, en cualquier lugar, sin búfer o interrupciones. </li>
-<li>Disfrute de la multitarea y la comodidad mediante la reproducción de vídeos en el fondo o en el modo de imagen en imagen. Puede escuchar música, podcasts o audiolibros mientras usa otras aplicaciones o navega por la web. También puede cambiar el tamaño y mover la ventana de vídeo alrededor de la pantalla como desee. </li>
-
-<li>Proteja sus ojos y la batería mediante el modo oscuro y el modo de zoom. El modo oscuro reduce el brillo y el contraste de la pantalla, facilitando la visualización de vídeos en entornos con poca luz. El modo zoom le permite llenar toda la pantalla con el vídeo, eliminando las barras negras y maximizando su inmersión. </li>
-<li>Evite los anuncios molestos y saltar las intros bloqueándolos con YouTube Channel APK. Puedes disfrutar de videos ininterrumpidos y sin publicidad sin tener que esperar ni hacer clic en nada. También puedes saltarte las largas y aburridas intros que tienen algunos canales, y llegar directamente al contenido que quieres ver. </li>
-<li>Explorar más contenido y canales sin iniciar sesión mediante la suscripción a ellos con YouTube Channel APK. Puedes seguir cualquier canal que te guste sin tener que crear o iniciar sesión en tu cuenta de YouTube. También puede acceder a más vídeos que están restringidos o no disponibles en su región. </li>
-</ul>
-<p>Como puedes ver, YouTube Channel APK ofrece muchas ventajas sobre la aplicación oficial de YouTube. Sin embargo, antes de descargarlo, necesitas saber cómo hacerlo de forma segura y fácil. </p>
-<h2>Cómo descargar YouTube canal APK de forma segura y fácil? </h2>
-<p>Descarga de canal de YouTube APK no es difícil, pero hay que tener cuidado acerca de dónde se obtiene. Hay muchos sitios web que afirman ofrecer YouTube Channel APK, pero algunos de ellos pueden contener malware o virus que pueden dañar su dispositivo o robar sus datos. Por lo tanto, es necesario descargar YouTube Channel APK de una fuente confiable y confiable. </p>
-<p>Una de las mejores fuentes para YouTube Channel APK es [APKPure], un sitio web que proporciona archivos APK originales y puros para varias aplicaciones y juegos. APKPure tiene una gran reputación y una gran base de usuarios, por lo que puede estar seguro de que los archivos que descarga desde allí son seguros. Aquí están los pasos sobre cómo descargar YouTube Channel APK de APKPure:</p>
-<p></p>
-<ol>
-<li>Ir a [APKPure] en su navegador y buscar "canal de YouTube APK" en la barra de búsqueda. </li>
-
-<li>Espere a que la descarga termine y localice el archivo en su dispositivo. </li>
-</ol>
-<p>Felicidades! Usted ha descargado con éxito el canal de YouTube APK de APKPure. Sin embargo, antes de poder usarlo, debe instalarlo en su dispositivo. </p>
-<h2>Cómo instalar canal de YouTube APK en su dispositivo Android? </h2>
-<p>Instalación de canal de YouTube APK en su dispositivo Android no es difícil, pero es necesario hacer algunos cambios en la configuración primero. Dado que YouTube Channel APK no es de Google Play Store, es necesario habilitar fuentes desconocidas y conceder permisos para ello. Aquí están los pasos sobre cómo instalar YouTube Channel APK en su dispositivo Android:</p>
-<ol <li>Ve a la configuración de tu dispositivo y toca "Seguridad". </li>
-<li>Desplácese hacia abajo y encuentre la opción "Fuentes desconocidas". Cámbiela y confirme su elección. </li>
-<li>Volver al administrador de archivos y toque en el archivo APK del canal de YouTube que ha descargado. </li>
-<li>Toque en "Instalar" y espere a que la instalación se complete. </li>
-</ol>
-<p>Felicidades! Usted ha instalado con éxito YouTube Channel APK en su dispositivo Android. Sin embargo, antes de que puedas usarlo, necesitas saber cómo usarlo. </p>
-<h2> ¿Cómo utilizar el canal de YouTube APK en su dispositivo Android? </h2>
-<p>Usando YouTube Channel APK en su dispositivo Android no es difícil, pero es necesario familiarizarse con sus características y funciones. Estas son algunas de las cosas que puedes hacer con YouTube Channel APK en tu dispositivo Android:</p>
-<ul>
-<li>Para descargar vídeos y listas de reproducción para verlos sin conexión, toque en el icono de descarga debajo del vídeo o lista de reproducción. Elija la calidad y el formato que desea y toque en "Aceptar". Puede encontrar sus vídeos descargados y listas de reproducción en la sección "Descargas" de la aplicación. </li>
-
-<li>Para cambiar la velocidad de reproducción y la resolución de los vídeos, toque el icono de tres puntos en la esquina superior derecha del vídeo. Toca "Velocidad de reproducción" o "Calidad" y elige la opción que desees. También puedes cambiar estos ajustes desde el menú de configuración de la aplicación. </li>
-<li>Para habilitar el modo oscuro y el modo de zoom, toque en el icono de perfil en la esquina superior derecha de la aplicación. Toque en "Configuración" y luego en "General". Activa "Tema oscuro" o "Zoom para llenar pantalla" como desees. También puedes cambiar estos ajustes desde el menú de configuración de la aplicación. </li>
-<li>Para bloquear anuncios y omitir intros, no es necesario hacer nada. YouTube Channel APK bloquea automáticamente los anuncios y omite las intros para usted. Puede disfrutar de videos sin publicidad ni interrupciones sin problemas. </li>
-<li>Para suscribirse a canales sin iniciar sesión, toque en el botón de suscripción debajo de cualquier canal que desee. Puedes encontrar tus canales suscritos en la sección "Suscripciones" de la aplicación. También puede darse de baja de cualquier canal tocando el mismo botón de nuevo. </li>
-</ul>
-<p>Como se puede ver, YouTube Channel APK ofrece muchas características y funciones que pueden mejorar su experiencia de YouTube. Sin embargo, antes de terminar este artículo, debe leer la conclusión y las preguntas frecuentes.</p>
-<h2>Conclusión</h2>
-<p>En este artículo, usted aprendió cómo descargar YouTube Channel APK para su dispositivo Android. Aprendiste lo que es YouTube Channel APK, por qué deberías descargarlo, cómo descargarlo de forma segura y fácil, cómo instalarlo en tu dispositivo y cómo usarlo en tu dispositivo. También aprendió acerca de algunas de las características y beneficios de YouTube Channel APK, tales como la descarga de vídeos y listas de reproducción para la visualización sin conexión, la reproducción de vídeos en segundo plano o en el modo de imagen-en-imagen, cambiar la velocidad de reproducción y la resolución de los vídeos, habilitar el modo oscuro y el modo de zoom, bloquear anuncios y omitir intros, y suscribirse a canales sin iniciar sesión. </p>
-
-<p>Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario sobre YouTube Channel APK, no dude en dejar un comentario a continuación. Nos encantaría saber de usted. ¡Gracias por leer! </p>
-<h2>Preguntas frecuentes</h2>
-<ol <li>¿Cuál es la diferencia entre YouTube Channel APK y YouTube Vanced? </li>
-<p>Canal de YouTube APK y YouTube Vanced son ambas versiones modificadas de la aplicación oficial de YouTube que ofrecen más características y funciones. Sin embargo, tienen algunas diferencias en términos de diseño, rendimiento y compatibilidad. YouTube Channel APK tiene una interfaz más simple y limpia, mientras que YouTube Vanced tiene una interfaz más colorida y personalizable. YouTube Channel APK tiene un rendimiento más rápido y suave, mientras que YouTube Vanced tiene un rendimiento más estable y confiable. YouTube Channel APK es compatible con la mayoría de los dispositivos Android, mientras que YouTube Vanced requiere una versión específica de MicroG para funcionar correctamente. </p>
-<li> ¿Es el canal de YouTube APK legal y seguro? </li>
-<p>Canal de YouTube APK no es ilegal, pero no está autorizado por Google o YouTube tampoco. Es una aplicación de terceros que modifica la aplicación original de YouTube sin su permiso. Por lo tanto, podría violar algunos de sus términos y políticas. Sin embargo, YouTube Channel APK es seguro de usar, siempre y cuando se descarga desde una fuente de confianza como APKPure. No contiene ningún malware o virus que pueda dañar su dispositivo o robar sus datos. Sin embargo, siempre debe tener cuidado al descargar cualquier archivo APK de fuentes desconocidas, ya que podrían plantear algunos riesgos para su dispositivo o datos. </p>
-<li> ¿Cómo puedo actualizar el canal de YouTube APK? </li>
-
-<li> ¿Cómo puedo desinstalar YouTube Channel APK? </li>
-<p>Desinstalar YouTube Channel APK es fácil y simple. Solo tiene que ir a la configuración de su dispositivo y toque en "Aplicaciones". A continuación, es necesario encontrar YouTube Channel APK de la lista de aplicaciones y toque en él. Entonces, es necesario tocar en "Desinstalar" y confirmar su elección. También puede desinstalar YouTube Channel APK pulsando largo su icono en la pantalla de inicio y arrastrándolo a la papelera. </p>
-<li> ¿Puedo usar YouTube Channel APK con mi cuenta de Google? </li>
-<p>No, no se puede utilizar YouTube Channel APK con su cuenta de Google. YouTube Channel APK no es compatible con Google características de inicio de sesión o sincronización. Por lo tanto, no puede acceder a sus datos personales o preferencias en YouTube Channel APK con su cuenta de Google. Solo se puede utilizar YouTube Channel APK con su propia función de suscripción, que no requiere ningún inicio de sesión o creación de cuenta. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/vcs/subversion.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/vcs/subversion.py
deleted file mode 100644
index 16d93a67b7b6feed66f2cc432f6250ca3ad34914..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/vcs/subversion.py
+++ /dev/null
@@ -1,324 +0,0 @@
-import logging
-import os
-import re
-from typing import List, Optional, Tuple
-
-from pip._internal.utils.misc import (
-    HiddenText,
-    display_path,
-    is_console_interactive,
-    is_installable_dir,
-    split_auth_from_netloc,
-)
-from pip._internal.utils.subprocess import CommandArgs, make_command
-from pip._internal.vcs.versioncontrol import (
-    AuthInfo,
-    RemoteNotFoundError,
-    RevOptions,
-    VersionControl,
-    vcs,
-)
-
-logger = logging.getLogger(__name__)
-
-_svn_xml_url_re = re.compile('url="([^"]+)"')
-_svn_rev_re = re.compile(r'committed-rev="(\d+)"')
-_svn_info_xml_rev_re = re.compile(r'\s*revision="(\d+)"')
-_svn_info_xml_url_re = re.compile(r"<url>(.*)</url>")
-
-
-class Subversion(VersionControl):
-    name = "svn"
-    dirname = ".svn"
-    repo_name = "checkout"
-    schemes = ("svn+ssh", "svn+http", "svn+https", "svn+svn", "svn+file")
-
-    @classmethod
-    def should_add_vcs_url_prefix(cls, remote_url: str) -> bool:
-        return True
-
-    @staticmethod
-    def get_base_rev_args(rev: str) -> List[str]:
-        return ["-r", rev]
-
-    @classmethod
-    def get_revision(cls, location: str) -> str:
-        """
-        Return the maximum revision for all files under a given location
-        """
-        # Note: taken from setuptools.command.egg_info
-        revision = 0
-
-        for base, dirs, _ in os.walk(location):
-            if cls.dirname not in dirs:
-                dirs[:] = []
-                continue  # no sense walking uncontrolled subdirs
-            dirs.remove(cls.dirname)
-            entries_fn = os.path.join(base, cls.dirname, "entries")
-            if not os.path.exists(entries_fn):
-                # FIXME: should we warn?
-                continue
-
-            dirurl, localrev = cls._get_svn_url_rev(base)
-
-            if base == location:
-                assert dirurl is not None
-                base = dirurl + "/"  # save the root url
-            elif not dirurl or not dirurl.startswith(base):
-                dirs[:] = []
-                continue  # not part of the same svn tree, skip it
-            revision = max(revision, localrev)
-        return str(revision)
-
-    @classmethod
-    def get_netloc_and_auth(
-        cls, netloc: str, scheme: str
-    ) -> Tuple[str, Tuple[Optional[str], Optional[str]]]:
-        """
-        This override allows the auth information to be passed to svn via the
-        --username and --password options instead of via the URL.
-        """
-        if scheme == "ssh":
-            # The --username and --password options can't be used for
-            # svn+ssh URLs, so keep the auth information in the URL.
-            return super().get_netloc_and_auth(netloc, scheme)
-
-        return split_auth_from_netloc(netloc)
-
-    @classmethod
-    def get_url_rev_and_auth(cls, url: str) -> Tuple[str, Optional[str], AuthInfo]:
-        # hotfix the URL scheme after removing svn+ from svn+ssh:// re-add it
-        url, rev, user_pass = super().get_url_rev_and_auth(url)
-        if url.startswith("ssh://"):
-            url = "svn+" + url
-        return url, rev, user_pass
-
-    @staticmethod
-    def make_rev_args(
-        username: Optional[str], password: Optional[HiddenText]
-    ) -> CommandArgs:
-        extra_args: CommandArgs = []
-        if username:
-            extra_args += ["--username", username]
-        if password:
-            extra_args += ["--password", password]
-
-        return extra_args
-
-    @classmethod
-    def get_remote_url(cls, location: str) -> str:
-        # In cases where the source is in a subdirectory, we have to look up in
-        # the location until we find a valid 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 Python project for directory %s (tried all "
-                    "parent directories)",
-                    orig_location,
-                )
-                raise RemoteNotFoundError
-
-        url, _rev = cls._get_svn_url_rev(location)
-        if url is None:
-            raise RemoteNotFoundError
-
-        return url
-
-    @classmethod
-    def _get_svn_url_rev(cls, location: str) -> Tuple[Optional[str], int]:
-        from pip._internal.exceptions import InstallationError
-
-        entries_path = os.path.join(location, cls.dirname, "entries")
-        if os.path.exists(entries_path):
-            with open(entries_path) as f:
-                data = f.read()
-        else:  # subversion >= 1.7 does not have the 'entries' file
-            data = ""
-
-        url = None
-        if data.startswith("8") or data.startswith("9") or data.startswith("10"):
-            entries = list(map(str.splitlines, data.split("\n\x0c\n")))
-            del entries[0][0]  # get rid of the '8'
-            url = entries[0][3]
-            revs = [int(d[9]) for d in entries if len(d) > 9 and d[9]] + [0]
-        elif data.startswith("<?xml"):
-            match = _svn_xml_url_re.search(data)
-            if not match:
-                raise ValueError(f"Badly formatted data: {data!r}")
-            url = match.group(1)  # get repository URL
-            revs = [int(m.group(1)) for m in _svn_rev_re.finditer(data)] + [0]
-        else:
-            try:
-                # subversion >= 1.7
-                # Note that using get_remote_call_options is not necessary here
-                # because `svn info` is being run against a local directory.
-                # We don't need to worry about making sure interactive mode
-                # is being used to prompt for passwords, because passwords
-                # are only potentially needed for remote server requests.
-                xml = cls.run_command(
-                    ["info", "--xml", location],
-                    show_stdout=False,
-                    stdout_only=True,
-                )
-                match = _svn_info_xml_url_re.search(xml)
-                assert match is not None
-                url = match.group(1)
-                revs = [int(m.group(1)) for m in _svn_info_xml_rev_re.finditer(xml)]
-            except InstallationError:
-                url, revs = None, []
-
-        if revs:
-            rev = max(revs)
-        else:
-            rev = 0
-
-        return url, rev
-
-    @classmethod
-    def is_commit_id_equal(cls, dest: str, name: Optional[str]) -> bool:
-        """Always assume the versions don't match"""
-        return False
-
-    def __init__(self, use_interactive: Optional[bool] = None) -> None:
-        if use_interactive is None:
-            use_interactive = is_console_interactive()
-        self.use_interactive = use_interactive
-
-        # This member is used to cache the fetched version of the current
-        # ``svn`` client.
-        # Special value definitions:
-        #   None: Not evaluated yet.
-        #   Empty tuple: Could not parse version.
-        self._vcs_version: Optional[Tuple[int, ...]] = None
-
-        super().__init__()
-
-    def call_vcs_version(self) -> Tuple[int, ...]:
-        """Query the version of the currently installed Subversion client.
-
-        :return: A tuple containing the parts of the version information or
-            ``()`` if the version returned from ``svn`` could not be parsed.
-        :raises: BadCommand: If ``svn`` is not installed.
-        """
-        # Example versions:
-        #   svn, version 1.10.3 (r1842928)
-        #      compiled Feb 25 2019, 14:20:39 on x86_64-apple-darwin17.0.0
-        #   svn, version 1.7.14 (r1542130)
-        #      compiled Mar 28 2018, 08:49:13 on x86_64-pc-linux-gnu
-        #   svn, version 1.12.0-SlikSvn (SlikSvn/1.12.0)
-        #      compiled May 28 2019, 13:44:56 on x86_64-microsoft-windows6.2
-        version_prefix = "svn, version "
-        version = self.run_command(["--version"], show_stdout=False, stdout_only=True)
-        if not version.startswith(version_prefix):
-            return ()
-
-        version = version[len(version_prefix) :].split()[0]
-        version_list = version.partition("-")[0].split(".")
-        try:
-            parsed_version = tuple(map(int, version_list))
-        except ValueError:
-            return ()
-
-        return parsed_version
-
-    def get_vcs_version(self) -> Tuple[int, ...]:
-        """Return the version of the currently installed Subversion client.
-
-        If the version of the Subversion client has already been queried,
-        a cached value will be used.
-
-        :return: A tuple containing the parts of the version information or
-            ``()`` if the version returned from ``svn`` could not be parsed.
-        :raises: BadCommand: If ``svn`` is not installed.
-        """
-        if self._vcs_version is not None:
-            # Use cached version, if available.
-            # If parsing the version failed previously (empty tuple),
-            # do not attempt to parse it again.
-            return self._vcs_version
-
-        vcs_version = self.call_vcs_version()
-        self._vcs_version = vcs_version
-        return vcs_version
-
-    def get_remote_call_options(self) -> CommandArgs:
-        """Return options to be used on calls to Subversion that contact the server.
-
-        These options are applicable for the following ``svn`` subcommands used
-        in this class.
-
-            - checkout
-            - switch
-            - update
-
-        :return: A list of command line arguments to pass to ``svn``.
-        """
-        if not self.use_interactive:
-            # --non-interactive switch is available since Subversion 0.14.4.
-            # Subversion < 1.8 runs in interactive mode by default.
-            return ["--non-interactive"]
-
-        svn_version = self.get_vcs_version()
-        # By default, Subversion >= 1.8 runs in non-interactive mode if
-        # stdin is not a TTY. Since that is how pip invokes SVN, in
-        # call_subprocess(), pip must pass --force-interactive to ensure
-        # the user can be prompted for a password, if required.
-        #   SVN added the --force-interactive option in SVN 1.8. Since
-        # e.g. RHEL/CentOS 7, which is supported until 2024, ships with
-        # SVN 1.7, pip should continue to support SVN 1.7. Therefore, pip
-        # can't safely add the option if the SVN version is < 1.8 (or unknown).
-        if svn_version >= (1, 8):
-            return ["--force-interactive"]
-
-        return []
-
-    def fetch_new(
-        self, dest: str, url: HiddenText, rev_options: RevOptions, verbosity: int
-    ) -> None:
-        rev_display = rev_options.to_display()
-        logger.info(
-            "Checking out %s%s to %s",
-            url,
-            rev_display,
-            display_path(dest),
-        )
-        if verbosity <= 0:
-            flag = "--quiet"
-        else:
-            flag = ""
-        cmd_args = make_command(
-            "checkout",
-            flag,
-            self.get_remote_call_options(),
-            rev_options.to_args(),
-            url,
-            dest,
-        )
-        self.run_command(cmd_args)
-
-    def switch(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
-        cmd_args = make_command(
-            "switch",
-            self.get_remote_call_options(),
-            rev_options.to_args(),
-            url,
-            dest,
-        )
-        self.run_command(cmd_args)
-
-    def update(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
-        cmd_args = make_command(
-            "update",
-            self.get_remote_call_options(),
-            rev_options.to_args(),
-            dest,
-        )
-        self.run_command(cmd_args)
-
-
-vcs.register(Subversion)
diff --git a/spaces/Borda90/Titanic_Esp/app.py b/spaces/Borda90/Titanic_Esp/app.py
deleted file mode 100644
index a9bbf2698feae1751170737586c22fd6c25d18f1..0000000000000000000000000000000000000000
--- a/spaces/Borda90/Titanic_Esp/app.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# This is a small and fast sklearn model, so the run-gradio script trains a model and deploys it
-
-import pandas as pd
-import numpy as np
-import sklearn
-import gradio as gr
-from sklearn import preprocessing
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score
-
-data = pd.read_csv('https://raw.githubusercontent.com/gradio-app/titanic/master/train.csv')
-data.head()
-
-def encode_ages(df): # Binning ages 
-    df.Age = df.Age.fillna(-0.5)
-    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
-    categories = pd.cut(df.Age, bins, labels=False)
-    df.Age = categories
-    return df
-
-def encode_fares(df): # Binning fares
-    df.Fare = df.Fare.fillna(-0.5)
-    bins = (-1, 0, 8, 15, 31, 1000)
-    categories = pd.cut(df.Fare, bins, labels=False)
-    df.Fare = categories
-    return df
-
-def encode_sex(df):
-    mapping = {"male": 0, "female": 1}
-    return df.replace({'Sex': mapping})
-
-def transform_features(df):
-    df = encode_ages(df)
-    df = encode_fares(df)
-    df = encode_sex(df)
-    return df
-
-train = data[['PassengerId', 'Fare', 'Age', 'Sex', 'Survived']]
-train = transform_features(train)
-train.head()
-
-
-X_all = train.drop(['Survived', 'PassengerId'], axis=1)
-y_all = train['Survived']
-
-num_test = 0.20
-X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, random_state=23)
-
-clf = RandomForestClassifier()
-clf.fit(X_train, y_train)
-predictions = clf.predict(X_test)
-
-def predict_survival(sex, age, fare):
-    df = pd.DataFrame.from_dict({'Sex': [sex], 'Age': [age], 'Fare': [fare]})
-    df = encode_sex(df)
-    df = encode_fares(df)
-    df = encode_ages(df)
-    pred = clf.predict_proba(df)[0]
-    return {'Muere': float(pred[0]), 'Sobrevive': float(pred[1])}
-    
-sex = gr.inputs.Radio(['female', 'male'], label="Sexo")
-age = gr.inputs.Slider(minimum=0, maximum=120, default=22, label="Edad")
-fare = gr.inputs.Slider(minimum=0, maximum=200, default=100, label="Clase")
-
-gr.Interface(predict_survival, [sex, age, fare], "label", live=True, thumbnail="https://raw.githubusercontent.com/gradio-app/hub-titanic/master/thumbnail.png", analytics_enabled=False,
-    title="Sobrevivientes del Titanic", description="Analicemos los sobreviventes de este caso y comprobemos").launch();
\ No newline at end of file
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/vis/extractor.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/vis/extractor.py
deleted file mode 100644
index b715a4451e096d6d6c086f9bcf60f92d2ae692f8..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/vis/extractor.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-from typing import Sequence
-import torch
-
-from detectron2.layers.nms import batched_nms
-from detectron2.structures.instances import Instances
-
-from densepose.vis.bounding_box import BoundingBoxVisualizer, ScoredBoundingBoxVisualizer
-from densepose.vis.densepose import DensePoseResultsVisualizer
-
-from .base import CompoundVisualizer
-
-Scores = Sequence[float]
-
-
-def extract_scores_from_instances(instances: Instances, select=None):
-    if instances.has("scores"):
-        return instances.scores if select is None else instances.scores[select]
-    return None
-
-
-def extract_boxes_xywh_from_instances(instances: Instances, select=None):
-    if instances.has("pred_boxes"):
-        boxes_xywh = instances.pred_boxes.tensor.clone()
-        boxes_xywh[:, 2] -= boxes_xywh[:, 0]
-        boxes_xywh[:, 3] -= boxes_xywh[:, 1]
-        return boxes_xywh if select is None else boxes_xywh[select]
-    return None
-
-
-def create_extractor(visualizer: object):
-    """
-    Create an extractor for the provided visualizer
-    """
-    if isinstance(visualizer, CompoundVisualizer):
-        extractors = [create_extractor(v) for v in visualizer.visualizers]
-        return CompoundExtractor(extractors)
-    elif isinstance(visualizer, DensePoseResultsVisualizer):
-        return DensePoseResultExtractor()
-    elif isinstance(visualizer, ScoredBoundingBoxVisualizer):
-        return CompoundExtractor([extract_boxes_xywh_from_instances, extract_scores_from_instances])
-    elif isinstance(visualizer, BoundingBoxVisualizer):
-        return extract_boxes_xywh_from_instances
-    else:
-        logger = logging.getLogger(__name__)
-        logger.error(f"Could not create extractor for {visualizer}")
-        return None
-
-
-class BoundingBoxExtractor(object):
-    """
-    Extracts bounding boxes from instances
-    """
-
-    def __call__(self, instances: Instances):
-        boxes_xywh = extract_boxes_xywh_from_instances(instances)
-        return boxes_xywh
-
-
-class ScoredBoundingBoxExtractor(object):
-    """
-    Extracts bounding boxes from instances
-    """
-
-    def __call__(self, instances: Instances, select=None):
-        scores = extract_scores_from_instances(instances)
-        boxes_xywh = extract_boxes_xywh_from_instances(instances)
-        if (scores is None) or (boxes_xywh is None):
-            return (boxes_xywh, scores)
-        if select is not None:
-            scores = scores[select]
-            boxes_xywh = boxes_xywh[select]
-        return (boxes_xywh, scores)
-
-
-class DensePoseResultExtractor(object):
-    """
-    Extracts DensePose result from instances
-    """
-
-    def __call__(self, instances: Instances, select=None):
-        boxes_xywh = extract_boxes_xywh_from_instances(instances)
-        if instances.has("pred_densepose") and (boxes_xywh is not None):
-            dpout = instances.pred_densepose
-            if select is not None:
-                dpout = dpout[select]
-                boxes_xywh = boxes_xywh[select]
-            return dpout.to_result(boxes_xywh)
-        else:
-            return None
-
-
-class CompoundExtractor(object):
-    """
-    Extracts data for CompoundVisualizer
-    """
-
-    def __init__(self, extractors):
-        self.extractors = extractors
-
-    def __call__(self, instances: Instances, select=None):
-        datas = []
-        for extractor in self.extractors:
-            data = extractor(instances, select)
-            datas.append(data)
-        return datas
-
-
-class NmsFilteredExtractor(object):
-    """
-    Extracts data in the format accepted by NmsFilteredVisualizer
-    """
-
-    def __init__(self, extractor, iou_threshold):
-        self.extractor = extractor
-        self.iou_threshold = iou_threshold
-
-    def __call__(self, instances: Instances, select=None):
-        scores = extract_scores_from_instances(instances)
-        boxes_xywh = extract_boxes_xywh_from_instances(instances)
-        if boxes_xywh is None:
-            return None
-        select_local_idx = batched_nms(
-            boxes_xywh,
-            scores,
-            torch.zeros(len(scores), dtype=torch.int32),
-            iou_threshold=self.iou_threshold,
-        ).squeeze()
-        select_local = torch.zeros(len(boxes_xywh), dtype=torch.bool, device=boxes_xywh.device)
-        select_local[select_local_idx] = True
-        select = select_local if select is None else (select & select_local)
-        return self.extractor(instances, select=select)
-
-
-class ScoreThresholdedExtractor(object):
-    """
-    Extracts data in the format accepted by ScoreThresholdedVisualizer
-    """
-
-    def __init__(self, extractor, min_score):
-        self.extractor = extractor
-        self.min_score = min_score
-
-    def __call__(self, instances: Instances, select=None):
-        scores = extract_scores_from_instances(instances)
-        if scores is None:
-            return None
-        select_local = scores > self.min_score
-        select = select_local if select is None else (select & select_local)
-        data = self.extractor(instances, select=select)
-        return data
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/async/copy.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/async/copy.h
deleted file mode 100644
index 72debb3b66715b284056aea1648fcaf5589afd70..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/async/copy.h
+++ /dev/null
@@ -1,34 +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.
- */
-
-// The purpose of this header is to #include the async/copy.h header of the
-// sequential, host, and device systems. It should be #included in any code
-// which uses ADL to dispatch async copy.
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-//#include <thrust/system/detail/sequential/async/copy.h>
-
-//#define __THRUST_HOST_SYSTEM_ASYNC_COPY_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/async/copy.h>
-//#include __THRUST_HOST_SYSTEM_ASYNC_COPY_HEADER
-//#undef __THRUST_HOST_SYSTEM_ASYNC_COPY_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_ASYNC_COPY_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/async/copy.h>
-#include __THRUST_DEVICE_SYSTEM_ASYNC_COPY_HEADER
-#undef __THRUST_DEVICE_SYSTEM_ASYNC_COPY_HEADER
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/copy.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/copy.h
deleted file mode 100644
index e22535618efd8c896b8e04ba21b636e4832743ea..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/copy.h
+++ /dev/null
@@ -1,59 +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/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename OutputIterator>
-__host__ __device__
-  OutputIterator copy(thrust::execution_policy<DerivedPolicy> &exec,
-                      InputIterator  first,
-                      InputIterator  last,
-                      OutputIterator result);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename Size,
-         typename OutputIterator>
-__host__ __device__
-  OutputIterator copy_n(thrust::execution_policy<DerivedPolicy> &exec,
-                        InputIterator  first,
-                        Size           n,
-                        OutputIterator result);
-
-
-} // end generic
-} // end detail
-} // end system
-} // end thrust
-
-#include <thrust/system/detail/generic/copy.inl>
-
diff --git a/spaces/Caoyunkang/Segment-Any-Anomaly/SAA/modelinet.py b/spaces/Caoyunkang/Segment-Any-Anomaly/SAA/modelinet.py
deleted file mode 100644
index 5a4a51d3ef8b34f49502d361e885e8b403e45e92..0000000000000000000000000000000000000000
--- a/spaces/Caoyunkang/Segment-Any-Anomaly/SAA/modelinet.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import timm
-from copy import deepcopy
-from typing import Tuple
-
-import numpy as np
-import timm
-import torch
-from torch.nn import functional as F
-from torchvision.transforms.functional import resize, to_pil_image  # type: ignore
-
-
-class ResizeLongestSide:
-    """
-    Resizes images to longest side 'target_length', as well as provides
-    methods for resizing coordinates and boxes. Provides methods for
-    transforming both numpy array and batched torch tensors.
-    """
-
-    def __init__(self, target_length: int) -> None:
-        self.target_length = target_length
-
-    def apply_image(self, image: np.ndarray) -> np.ndarray:
-        """
-        Expects a numpy array with shape HxWxC in uint8 format.
-        """
-        target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
-        return np.array(resize(to_pil_image(image), target_size))
-
-    def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
-        """
-        Expects a numpy array of length 2 in the final dimension. Requires the
-        original image size in (H, W) format.
-        """
-        old_h, old_w = original_size
-        new_h, new_w = self.get_preprocess_shape(
-            original_size[0], original_size[1], self.target_length
-        )
-        coords = deepcopy(coords).astype(float)
-        coords[..., 0] = coords[..., 0] * (new_w / old_w)
-        coords[..., 1] = coords[..., 1] * (new_h / old_h)
-        return coords
-
-    def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
-        """
-        Expects a numpy array shape Bx4. Requires the original image size
-        in (H, W) format.
-        """
-        boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
-        return boxes.reshape(-1, 4)
-
-    def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
-        """
-        Expects batched images with shape BxCxHxW and float format. This
-        transformation may not exactly match apply_image. apply_image is
-        the transformation expected by the model.
-        """
-        # Expects an image in BCHW format. May not exactly match apply_image.
-        target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
-        return F.interpolate(
-            image, target_size, mode="bilinear", align_corners=False, antialias=True
-        )
-
-    def apply_coords_torch(
-            self, coords: torch.Tensor, original_size: Tuple[int, ...]
-    ) -> torch.Tensor:
-        """
-        Expects a torch tensor with length 2 in the last dimension. Requires the
-        original image size in (H, W) format.
-        """
-        old_h, old_w = original_size
-        new_h, new_w = self.get_preprocess_shape(
-            original_size[0], original_size[1], self.target_length
-        )
-        coords = deepcopy(coords).to(torch.float)
-        coords[..., 0] = coords[..., 0] * (new_w / old_w)
-        coords[..., 1] = coords[..., 1] * (new_h / old_h)
-        return coords
-
-    def apply_boxes_torch(
-            self, boxes: torch.Tensor, original_size: Tuple[int, ...]
-    ) -> torch.Tensor:
-        """
-        Expects a torch tensor with shape Bx4. Requires the original image
-        size in (H, W) format.
-        """
-        boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
-        return boxes.reshape(-1, 4)
-
-    @staticmethod
-    def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
-        """
-        Compute the output size given input size and target long side length.
-        """
-        scale = long_side_length * 1.0 / max(oldh, oldw)
-        newh, neww = oldh * scale, oldw * scale
-        neww = int(neww + 0.5)
-        newh = int(newh + 0.5)
-        return (newh, neww)
-
-
-class ModelINet(torch.nn.Module):
-    # hrnet_w32, wide_resnet50_2
-    def __init__(self, device, backbone_name='wide_resnet50_2', out_indices=(1, 2, 3), checkpoint_path='',
-                 pool_last=False):
-        super().__init__()
-        # Determine if to output features.
-        kwargs = {'features_only': True if out_indices else False}
-        if out_indices:
-            kwargs.update({'out_indices': out_indices})
-        print(backbone_name)
-
-        self.device = device
-        self.backbone = timm.create_model(model_name=backbone_name, pretrained=True, checkpoint_path=checkpoint_path,
-                                          **kwargs)
-        self.backbone.eval()
-        self.backbone = self.backbone.to(self.device)
-
-        self.avg_pool = torch.nn.AdaptiveAvgPool2d((1, 1)) if pool_last else None
-
-        self.pixel_mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).view(-1, 1, 1).to(self.device)
-        self.pixel_std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).view(-1, 1, 1).to(self.device)
-
-        self.img_size = 1024
-        self.transform_size = ResizeLongestSide(self.img_size)
-
-    def set_img_size(self, img_size):
-        self.img_size = img_size
-        self.transform_size = ResizeLongestSide(self.img_size)
-
-    def preprocess(self, image: np.ndarray):
-        """Normalize pixel values and pad to a square input."""
-
-        input_image = self.transform_size.apply_image(image)
-        input_image_torch = torch.as_tensor(input_image, device=self.device)
-        x = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
-
-        # Normalize colors
-        x = (x - self.pixel_mean) / self.pixel_std
-
-        # Pad
-        h, w = x.shape[-2:]
-        padh = self.img_size - h
-        padw = self.img_size - w
-        x = F.pad(x, (0, padw, 0, padh))
-
-        ratio_h = h / self.img_size
-        ratio_w = w / self.img_size
-        return x, ratio_h, ratio_w
-
-    @torch.no_grad()
-    def forward(self, x):
-        x, ratio_h, ratio_w = self.preprocess(x)
-        x = x.to(self.device)
-
-        # Backbone forward pass.
-        features = self.backbone(x)
-
-        # Adaptive average pool over the last layer.
-        if self.avg_pool:
-            fmap = features[-1]
-            fmap = self.avg_pool(fmap)
-            fmap = torch.flatten(fmap, 1)
-            features.append(fmap)
-
-        size_0 = features[0].shape[2:]
-
-        for i in range(1, len(features)):
-            features[i] = F.interpolate(features[i], size_0)
-
-        features = torch.cat(features, dim=1)
-        features = F.normalize(features, dim=1)
-
-        return features, ratio_h, ratio_w
diff --git a/spaces/Chris4K/llms_compare/Cedie-Ang-Munting-Prinsipe-Tagalog-Version-Full-Movie-Episode-1.md b/spaces/Chris4K/llms_compare/Cedie-Ang-Munting-Prinsipe-Tagalog-Version-Full-Movie-Episode-1.md
deleted file mode 100644
index 1694843f818bd0299473ac53d65e7dddbc8d1689..0000000000000000000000000000000000000000
--- a/spaces/Chris4K/llms_compare/Cedie-Ang-Munting-Prinsipe-Tagalog-Version-Full-Movie-Episode-1.md
+++ /dev/null
@@ -1,60 +0,0 @@
-## cedie ang munting prinsipe tagalog version full movie episode 1
-
-
-
- 
-
-  
-
- 
-
-**Download File ->>> [https://eromdesre.blogspot.com/?d=2txP07](https://eromdesre.blogspot.com/?d=2txP07)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# Cedie Ang Munting Prinsipe: A Classic Anime Adaptation of a Beloved Novel
-
- 
-
-Cedie Ang Munting Prinsipe is a Filipino-dubbed anime series based on the novel Little Lord Fauntleroy by Frances Hodgson Burnett. It tells the story of Cedric Errol, a kind and cheerful boy who lives with his mother in New York. One day, he learns that he is the heir of his grandfather, the Earl of Dorincourt, who lives in England. He travels to meet his grandfather, who is cold and stern, but gradually warms up to Cedie's innocence and goodness.
-
- 
-
-The anime series was produced by Nippon Animation in 1988 as part of the World Masterpiece Theater collection. It has 43 episodes, each about 25 minutes long. The first episode introduces Cedie's life in New York, his friendship with Dick Tipton, a shoeshine boy, and his encounter with Mr. Hobbs, a grocer who becomes his mentor. It also shows how Cedie receives a letter from his grandfather's lawyer, informing him of his inheritance and inviting him to England.
-
- 
-
-The series was dubbed in Tagalog by ABS-CBN and aired in the Philippines in the early 1990s. It became one of the most popular and beloved anime shows among Filipino children and adults alike. Many viewers were moved by Cedie's kindness, courage, and loyalty, as well as his relationship with his mother and grandfather. The series also featured memorable songs, such as "Ang Munting Prinsipe" (The Little Prince), "Ikaw ang Lahat sa Akin" (You Are Everything to Me), and "Pag-ibig ang Nagbibigay Buhay" (Love Gives Life).
-
- 
-
-Cedie Ang Munting Prinsipe is a classic anime adaptation of a beloved novel that has touched the hearts of many generations. It is a story of love, family, and friendship that transcends time and culture. You can watch the first episode of the Tagalog version on YouTube[^1^]. Enjoy!
-
-  
-
-In the following episodes, Cedie arrives in England and meets his grandfather, who is initially cold and distant. He also meets his cousin Wilkins, who is jealous and spiteful of him. Cedie tries to win his grandfather's affection and respect, while also learning about the life and responsibilities of a lord. He makes friends with the servants and the villagers, and helps them with their problems. He also faces various challenges and dangers, such as being kidnapped by his uncle Bevis, who wants to claim the earldom.
-
- 
-
-Meanwhile, Cedie's mother, who is called Dearest by him, stays in a cottage near the castle. She is not allowed to see her son or her father-in-law, as the Earl disapproves of her marriage to his son. She is kind and gentle, and supports Cedie from afar. She also teaches him about his father, who died when he was a baby. Cedie's father was a brave and noble man, who fought in the American Civil War and married Dearest against his father's wishes.
-
- 
-
-As the story progresses, Cedie and his grandfather develop a close and loving bond. The Earl becomes more kind and generous, thanks to Cedie's influence. He also learns to appreciate Dearest and reconciles with her. However, their happiness is threatened when a woman named Minna claims that her son Tom is the real heir of the earldom, as she was married to Cedie's father before Dearest. Cedie faces the possibility of losing his title and his grandfather's love, but he remains faithful and hopeful.
-
- dfd1c89656
-
- 
-
- 
-
diff --git a/spaces/ChristopherMarais/Andrew_Alpha/app.py b/spaces/ChristopherMarais/Andrew_Alpha/app.py
deleted file mode 100644
index 760eb8afaa682b399f8c663d8e412b8dae08b9ce..0000000000000000000000000000000000000000
--- a/spaces/ChristopherMarais/Andrew_Alpha/app.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import os
-import dill
-import timm
-import random
-import numpy as np
-import gradio as gr
-from fastai.tabular.all import *
-from fastai.vision.all import *
-from fastai.vision.utils import get_image_files
-from fastai.learner import load_learner
-from Ambrosia import pre_process_image
-from huggingface_hub import from_pretrained_fastai, push_to_hub_fastai, hf_hub_download
-from torchvision.transforms import GaussianBlur
-
-# Set the token
-os.environ["HUGGINGFACE_TOKEN"] = "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"
-
-# # Define a custom transform for Gaussian blur
-# def gaussian_blur(x, p=0.5, kernel_size_min=3, kernel_size_max=9, sigma_min=0.1, sigma_max=2):
-#     if x.ndim == 4:
-#         for i in range(x.shape[0]):
-#             if random.random() < p:
-#                 kernel_size = random.randrange(kernel_size_min, kernel_size_max + 1, 2)
-#                 sigma = random.uniform(sigma_min, sigma_max)
-#                 x[i] = GaussianBlur(kernel_size=kernel_size, sigma=sigma)(x[i])
-#     return x
-
-# Define a custom transform for Gaussian blur
-def gaussian_blur(x, p=0.5, kernel_size_min=3, kernel_size_max=20, sigma_min=0.1, sigma_max=3):
-    if x.ndim == 4:
-        for i in range(x.shape[0]):
-            if random.random() < p:
-                kernel_size = random.randrange(kernel_size_min, kernel_size_max + 1, 2)
-                sigma = random.uniform(sigma_min, sigma_max)
-                x[i] = GaussianBlur(kernel_size=kernel_size, sigma=sigma)(x[i])
-    return x
-
-# this function only describes how much a singular value in al ist stands out.
-# if all values in the lsit are high or low this is 1
-# the smaller the proportiopn of number of disimilar vlaues are to other more similar values the lower this number
-# the larger the gap between the dissimilar numbers and the simialr number the smaller this number
-# only able to interpret probabilities or values between 0 and 1
-# this function outputs an estimate an inverse of the classification confidence based on the probabilities of all the classes.
-# the wedge threshold splits the data on a threshold with a magnitude of a positive int to force a ledge/peak in the data
-def unkown_prob_calc(probs, wedge_threshold, wedge_magnitude=1, wedge='strict'):
-    if wedge =='strict':
-        increase_var = (1/(wedge_magnitude))
-        decrease_var = (wedge_magnitude)
-    if wedge =='dynamic': # this allows pointsthat are furhter from the threshold ot be moved less and points clsoer to be moved more
-        increase_var = (1/(wedge_magnitude*((1-np.abs(probs-wedge_threshold)))))
-        decrease_var = (wedge_magnitude*((1-np.abs(probs-wedge_threshold))))
-    else:
-        print("Error: use 'strict' (default) or 'dynamic' as options for the wedge parameter!")
-    probs = np.where(probs>=wedge_threshold , probs**increase_var, probs)
-    probs = np.where(probs<=wedge_threshold , probs**decrease_var, probs)
-    diff_matrix = np.abs(probs[:, np.newaxis] - probs)
-    diff_matrix_sum = np.sum(diff_matrix)
-    probs_sum = np.sum(probs)
-    class_val = (diff_matrix_sum/probs_sum)
-    max_class_val = ((len(probs)-1)*2)
-    kown_prob = class_val/max_class_val
-    unknown_prob = 1-kown_prob
-    return(unknown_prob)
-
-# load model
-learn = from_pretrained_fastai("ChristopherMarais/beetle-model")
-# learn = load_learner(
-#     hf_hub_download('ChristopherMarais/Andrew_Alpha_model', filename="model.pkl")
-#     )
-    
-# get class names
-labels = np.append(np.array(learn.dls.vocab), "Unknown")
-
-def predict(img):
-    # Segment image into smaller images
-    pre_process = pre_process_image(manual_thresh_buffer=0.15, image = img) # use image_dir if directory of image used
-    pre_process.segment(cluster_num=2, 
-                        image_edge_buffer=50)
-    # get predictions for all segments
-    conf_dict_lst = []
-    output_lst = []
-    img_cnt = len(pre_process.col_image_lst)
-    for i in range(0,img_cnt):
-        prob_ar = np.array(learn.predict(pre_process.col_image_lst[i])[2])
-        unkown_prob = unkown_prob_calc(probs=prob_ar, wedge_threshold=0.85, wedge_magnitude=5, wedge='dynamic')
-        prob_ar = np.append(prob_ar, unkown_prob)
-        prob_ar = np.around(prob_ar*100, decimals=1)
-        
-        conf_dict = {labels[i]: float(prob_ar[i]) for i in range(len(prob_ar))}
-        conf_dict = dict(sorted(conf_dict.items(), key=lambda item: item[1], reverse=True))
-        conf_dict_lst.append(str(conf_dict))
-        result = list(zip(pre_process.col_image_lst, conf_dict_lst))
-                
-    return(result)
-
-with gr.Blocks() as demo:
-    with gr.Column(variant="panel"):
-        with gr.Row(variant="compact"):
-            inputs = gr.Image()
-            btn = gr.Button("Classify").style(full_width=False)
-
-        gallery = gr.Gallery(
-            label="Show images", show_label=True, elem_id="gallery"
-        ).style(grid=[8], height="auto")
-
-    btn.click(predict, inputs, gallery)
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/CosmoAI/BhagwatGeeta/app.py b/spaces/CosmoAI/BhagwatGeeta/app.py
deleted file mode 100644
index a79d0315e0f9e0e9c2947c69137fb8345de97d88..0000000000000000000000000000000000000000
--- a/spaces/CosmoAI/BhagwatGeeta/app.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import google.generativeai as palm
-import streamlit as st 
-import os 
-
-# Set your API key
-palm.configure(api_key = os.environ['PALM_KEY'])
-
-# Select the PaLM 2 model
-model = 'models/text-bison-001'
-
-# Generate text
-if prompt := st.chat_input("Ask your query..."):
-    enprom = f"""Answer the below provided input in context to Bhagwad Geeta. Use the verses and chapters sentences as references to your answer with suggestions
-    coming from Bhagwad Geeta. Your answer to below input should only be in context to Bhagwad geeta only.\nInput= {prompt}"""
-    completion = palm.generate_text(model=model, prompt=enprom, temperature=0.5, max_output_tokens=800)
-
-# response = palm.chat(messages=["Hello."])
-# print(response.last) #  'Hello! What can I help you with?'
-# response.reply("Can you tell me a joke?")
-
-# Print the generated text
-    with st.chat_message("Assistant"):
-        st.write(completion.result)
-
-
-
-
-
-
-
-
-# import streamlit as st
-# from dotenv import load_dotenv
-# from PyPDF2 import PdfReader
-# from langchain.text_splitter import CharacterTextSplitter
-# from langchain.embeddings import HuggingFaceEmbeddings
-# from langchain.vectorstores import FAISS
-# # from langchain.chat_models import ChatOpenAI
-# from langchain.memory import ConversationBufferMemory
-# from langchain.chains import ConversationalRetrievalChain
-# from htmlTemplates import css, bot_template, user_template
-# from langchain.llms import HuggingFaceHub
-# import os 
-# # from transformers import T5Tokenizer, T5ForConditionalGeneration
-# # from langchain.callbacks import get_openai_callback
-
-# hub_token = os.environ["HUGGINGFACE_HUB_TOKEN"]
-
-# def get_pdf_text(pdf_docs):
-#     text = ""
-#     for pdf in pdf_docs:
-#         pdf_reader = PdfReader(pdf)
-#         for page in pdf_reader.pages:
-#             text += page.extract_text()
-#     return text
-
-
-# def get_text_chunks(text):
-#     text_splitter = CharacterTextSplitter(
-#         separator="\n",
-#         chunk_size=200,
-#         chunk_overlap=20,
-#         length_function=len
-#     )
-#     chunks = text_splitter.split_text(text)
-#     return chunks
-
-
-# def get_vectorstore(text_chunks):
-#     # embeddings = OpenAIEmbeddings()
-#     # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-#     embeddings = HuggingFaceEmbeddings()
-#     vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-#     return vectorstore
-
-
-# def get_conversation_chain(vectorstore):
-#     # llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
-#     # tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
-#     # model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
-
-#     llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-v0.1", huggingfacehub_api_token=hub_token, model_kwargs={"temperature":0.5, "max_length":20})
-
-#     memory = ConversationBufferMemory(
-#         memory_key='chat_history', return_messages=True)
-#     conversation_chain = ConversationalRetrievalChain.from_llm(
-#         llm=llm,
-#         retriever=vectorstore.as_retriever(),
-#         memory=memory
-#     )
-#     return conversation_chain
-
-
-# def handle_userinput(user_question):
-#     response = st.session_state.conversation
-#     reply = response.run(user_question)
-#     st.write(reply)
-#     # st.session_state.chat_history = response['chat_history']
-
-#     # for i, message in enumerate(st.session_state.chat_history):
-#     #     if i % 2 == 0:
-#     #         st.write(user_template.replace(
-#     #             "{{MSG}}", message.content), unsafe_allow_html=True)
-#     #     else:
-#     #         st.write(bot_template.replace(
-#     #             "{{MSG}}", message.content), unsafe_allow_html=True)
-
-
-# def main():
-#     load_dotenv()
-#     st.set_page_config(page_title="Chat with multiple PDFs",
-#                        page_icon=":books:")
-#     st.write(css, unsafe_allow_html=True)
-
-#     if "conversation" not in st.session_state:
-#         st.session_state.conversation = None
-#     if "chat_history" not in st.session_state:
-#         st.session_state.chat_history = None
-
-#     st.header("Chat with multiple PDFs :books:")
-#     user_question = st.text_input("Ask a question about your documents:")
-#     if user_question:
-#         handle_userinput(user_question)
-
-#     with st.sidebar:
-#         st.subheader("Your documents")
-#         pdf_docs = st.file_uploader(
-#             "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
-#         if st.button("Process"):
-#             if(len(pdf_docs) == 0):
-#                 st.error("Please upload at least one PDF")
-#             else:
-#                 with st.spinner("Processing"):
-#                     # get pdf text
-#                     raw_text = get_pdf_text(pdf_docs)
-
-#                     # get the text chunks
-#                     text_chunks = get_text_chunks(raw_text)
-
-#                     # create vector store
-#                     vectorstore = get_vectorstore(text_chunks)
-
-#                     # create conversation chain
-#                     st.session_state.conversation = get_conversation_chain(
-#                         vectorstore)
-
-# if __name__ == '__main__':
-#     main()
-
-
-
-
-
-
-# # import os
-# # import getpass
-# # import streamlit as st
-# # from langchain.document_loaders import PyPDFLoader
-# # from langchain.text_splitter import RecursiveCharacterTextSplitter
-# # from langchain.embeddings import HuggingFaceEmbeddings
-# # from langchain.vectorstores import Chroma
-# # from langchain import HuggingFaceHub
-# # from langchain.chains import RetrievalQA
-# # # __import__('pysqlite3')
-# # # import sys
-# # # sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
-
-
-# # # load huggingface api key
-# # hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"]
-
-# # # use streamlit file uploader to ask user for file
-# # # file = st.file_uploader("Upload PDF")
-
-
-# # path = "Geeta.pdf"
-# # loader = PyPDFLoader(path)
-# # pages = loader.load()
-
-# # # st.write(pages)
-
-# # splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
-# # docs = splitter.split_documents(pages)
-
-# # embeddings = HuggingFaceEmbeddings()
-# # doc_search = Chroma.from_documents(docs, embeddings)
-
-# # repo_id = "tiiuae/falcon-7b"
-# # llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000})
-
-# # from langchain.schema import retriever
-# # retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever())
-
-# # if query := st.chat_input("Enter a question: "):
-# #   with st.chat_message("assistant"):
-# #     st.write(retireval_chain.run(query))
\ No newline at end of file
diff --git a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/util.py b/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/util.py
deleted file mode 100644
index 45cb050ece6f401a22dde098ce3f1ff663c5eb6a..0000000000000000000000000000000000000000
--- a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/util.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import importlib
-
-import torch
-from torch import optim
-import numpy as np
-
-from inspect import isfunction
-from PIL import Image, ImageDraw, ImageFont
-
-
-def log_txt_as_img(wh, xc, size=10):
-    # wh a tuple of (width, height)
-    # xc a list of captions to plot
-    b = len(xc)
-    txts = list()
-    for bi in range(b):
-        txt = Image.new("RGB", wh, color="white")
-        draw = ImageDraw.Draw(txt)
-        font = ImageFont.truetype('font/DejaVuSans.ttf', size=size)
-        nc = int(40 * (wh[0] / 256))
-        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
-
-        try:
-            draw.text((0, 0), lines, fill="black", font=font)
-        except UnicodeEncodeError:
-            print("Cant encode string for logging. Skipping.")
-
-        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
-        txts.append(txt)
-    txts = np.stack(txts)
-    txts = torch.tensor(txts)
-    return txts
-
-
-def ismap(x):
-    if not isinstance(x, torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] > 3)
-
-
-def isimage(x):
-    if not isinstance(x,torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def mean_flat(tensor):
-    """
-    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def count_params(model, verbose=False):
-    total_params = sum(p.numel() for p in model.parameters())
-    if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
-    return total_params
-
-
-def instantiate_from_config(config):
-    if not "target" in config:
-        if config == '__is_first_stage__':
-            return None
-        elif config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict()))
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-
-class AdamWwithEMAandWings(optim.Optimizer):
-    # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
-    def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8,  # TODO: check hyperparameters before using
-                 weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999,   # ema decay to match previous code
-                 ema_power=1., param_names=()):
-        """AdamW that saves EMA versions of the parameters."""
-        if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
-        if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
-        if not 0.0 <= betas[0] < 1.0:
-            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
-        if not 0.0 <= betas[1] < 1.0:
-            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-        if not 0.0 <= weight_decay:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
-        if not 0.0 <= ema_decay <= 1.0:
-            raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
-        defaults = dict(lr=lr, betas=betas, eps=eps,
-                        weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
-                        ema_power=ema_power, param_names=param_names)
-        super().__init__(params, defaults)
-
-    def __setstate__(self, state):
-        super().__setstate__(state)
-        for group in self.param_groups:
-            group.setdefault('amsgrad', False)
-
-    @torch.no_grad()
-    def step(self, closure=None):
-        """Performs a single optimization step.
-        Args:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
-        """
-        loss = None
-        if closure is not None:
-            with torch.enable_grad():
-                loss = closure()
-
-        for group in self.param_groups:
-            params_with_grad = []
-            grads = []
-            exp_avgs = []
-            exp_avg_sqs = []
-            ema_params_with_grad = []
-            state_sums = []
-            max_exp_avg_sqs = []
-            state_steps = []
-            amsgrad = group['amsgrad']
-            beta1, beta2 = group['betas']
-            ema_decay = group['ema_decay']
-            ema_power = group['ema_power']
-
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                params_with_grad.append(p)
-                if p.grad.is_sparse:
-                    raise RuntimeError('AdamW does not support sparse gradients')
-                grads.append(p.grad)
-
-                state = self.state[p]
-
-                # State initialization
-                if len(state) == 0:
-                    state['step'] = 0
-                    # Exponential moving average of gradient values
-                    state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
-                    # Exponential moving average of squared gradient values
-                    state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
-                    if amsgrad:
-                        # Maintains max of all exp. moving avg. of sq. grad. values
-                        state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
-                    # Exponential moving average of parameter values
-                    state['param_exp_avg'] = p.detach().float().clone()
-
-                exp_avgs.append(state['exp_avg'])
-                exp_avg_sqs.append(state['exp_avg_sq'])
-                ema_params_with_grad.append(state['param_exp_avg'])
-
-                if amsgrad:
-                    max_exp_avg_sqs.append(state['max_exp_avg_sq'])
-
-                # update the steps for each param group update
-                state['step'] += 1
-                # record the step after step update
-                state_steps.append(state['step'])
-
-            optim._functional.adamw(params_with_grad,
-                    grads,
-                    exp_avgs,
-                    exp_avg_sqs,
-                    max_exp_avg_sqs,
-                    state_steps,
-                    amsgrad=amsgrad,
-                    beta1=beta1,
-                    beta2=beta2,
-                    lr=group['lr'],
-                    weight_decay=group['weight_decay'],
-                    eps=group['eps'],
-                    maximize=False)
-
-            cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
-            for param, ema_param in zip(params_with_grad, ema_params_with_grad):
-                ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
-
-        return loss
\ No newline at end of file
diff --git a/spaces/DAMO-NLP-SG/Video-LLaMA/app.py b/spaces/DAMO-NLP-SG/Video-LLaMA/app.py
deleted file mode 100644
index 4b0940c9305eb8aa78e6145cad7124ce7cd999fa..0000000000000000000000000000000000000000
--- a/spaces/DAMO-NLP-SG/Video-LLaMA/app.py
+++ /dev/null
@@ -1,259 +0,0 @@
-"""
-Adapted from: https://github.com/Vision-CAIR/MiniGPT-4/blob/main/demo.py 
-"""
-import argparse
-import os
-import random
-
-import numpy as np
-import torch
-import torch.backends.cudnn as cudnn
-import gradio as gr
-
-from video_llama.common.config import Config
-from video_llama.common.dist_utils import get_rank
-from video_llama.common.registry import registry
-from video_llama.conversation.conversation_video import Chat, Conversation, default_conversation,SeparatorStyle
-import decord
-decord.bridge.set_bridge('torch')
- 
-
-#%%
-# imports modules for registration
-from video_llama.datasets.builders import *
-from video_llama.models import *
-from video_llama.processors import *
-from video_llama.runners import *
-from video_llama.tasks import *
-
-#%%
-def parse_args():
-    parser = argparse.ArgumentParser(description="Demo")
-    parser.add_argument("--cfg-path", default='eval_configs/video_llama_eval.yaml', help="path to configuration file.")
-    parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.")
-    parser.add_argument(
-        "--options",
-        nargs="+",
-        help="override some settings in the used config, the key-value pair "
-        "in xxx=yyy format will be merged into config file (deprecate), "
-        "change to --cfg-options instead.",
-    )
-    args = parser.parse_args()
-    return args
-
-
-def setup_seeds(config):
-    seed = config.run_cfg.seed + get_rank()
-
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-
-    cudnn.benchmark = False
-    cudnn.deterministic = True
-
-
-# ========================================
-#             Model Initialization
-# ========================================
-
-print('Initializing Chat')
-args = parse_args()
-cfg = Config(args)
-
-model_config = cfg.model_cfg
-model_config.device_8bit = args.gpu_id
-model_cls = registry.get_model_class(model_config.arch)
-model = model_cls.from_config(model_config).to('cuda:{}'.format(args.gpu_id))
-model.eval()
-vis_processor_cfg = cfg.datasets_cfg.webvid.vis_processor.train
-vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
-chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id))
-print('Initialization Finished')
-
-# ========================================
-#             Gradio Setting
-# ========================================
-
-def gradio_reset(chat_state, img_list):
-    if chat_state is not None:
-        chat_state.messages = []
-    if img_list is not None:
-        img_list = []
-    return None, gr.update(value=None, interactive=True), gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your video first', interactive=False),gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list
-
-def upload_imgorvideo(gr_video, gr_img, text_input, chat_state,chatbot):
-    if gr_img is None and gr_video is None:
-        return None, None, None, gr.update(interactive=True), chat_state, None
-    elif gr_img is not None and gr_video is None:
-        print(gr_img)
-        chatbot = chatbot + [((gr_img,), None)]
-        chat_state = Conversation(
-            system= "You are able to understand the visual content that the user provides."
-           "Follow the instructions carefully and explain your answers in detail.",
-            roles=("Human", "Assistant"),
-            messages=[],
-            offset=0,
-            sep_style=SeparatorStyle.SINGLE,
-            sep="###",
-        )
-        img_list = []
-        llm_message = chat.upload_img(gr_img, chat_state, img_list)
-        return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list,chatbot
-    elif gr_video is not None and gr_img is None:
-        print(gr_video)
-        chatbot = chatbot + [((gr_video,), None)]
-        chat_state = default_conversation.copy()
-        chat_state = Conversation(
-            system= "You are able to understand the visual content that the user provides."
-           "Follow the instructions carefully and explain your answers in detail.",
-            roles=("Human", "Assistant"),
-            messages=[],
-            offset=0,
-            sep_style=SeparatorStyle.SINGLE,
-            sep="###",
-        )
-        img_list = []
-        llm_message = chat.upload_video(gr_video, chat_state, img_list)
-        return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list,chatbot
-    else:
-        # img_list = []
-        return gr.update(interactive=False), gr.update(interactive=False, placeholder='Currently, only one input is supported'), gr.update(value="Currently, only one input is supported", interactive=False), chat_state, None,chatbot
-
-def gradio_ask(user_message, chatbot, chat_state):
-    if len(user_message) == 0:
-        return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
-    chat.ask(user_message, chat_state)
-    chatbot = chatbot + [[user_message, None]]
-    return '', chatbot, chat_state
-
-
-def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
-    llm_message = chat.answer(conv=chat_state,
-                              img_list=img_list,
-                              num_beams=1,
-                              temperature=temperature,
-                              max_new_tokens=240,
-                              max_length=511)[0]
-    chatbot[-1][1] = llm_message
-    print(chat_state.get_prompt())
-    print(chat_state)
-    return chatbot, chat_state, img_list
-
-title = """
-<h1 align="center"><a href="https://github.com/DAMO-NLP-SG/Video-LLaMA"><img src="https://s1.ax1x.com/2023/05/22/p9oQ0FP.jpg", alt="Video-LLaMA" border="0" style="margin: 0 auto; height: 200px;" /></a> </h1>
-
-<h1 align="center">Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding</h1>
-
-<h5 align="center">  Introduction: Video-LLaMA is a multi-model large language model that achieves video-grounded conversations between humans and computers \
-    by connecting language decoder with off-the-shelf unimodal pre-trained models. </h5> 
-
-<div style='display:flex; gap: 0.25rem; '>
-<a href='https://github.com/DAMO-NLP-SG/Video-LLaMA'><img src='https://img.shields.io/badge/Github-Code-success'></a>
-<a href='https://huggingface.co/spaces/DAMO-NLP-SG/Video-LLaMA'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> 
-<a href='https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a> 
-<a href='https://modelscope.cn/studios/damo/video-llama/summary'><img src='https://img.shields.io/badge/ModelScope-Demo-blueviolet'></a> 
-<a href='https://arxiv.org/abs/2306.02858'><img src='https://img.shields.io/badge/Paper-PDF-red'></a>
-</div>
-
-
-Thank you for using the Video-LLaMA Demo Page! If you have any questions or feedback, feel free to contact us. 
-
-If you find Video-LLaMA interesting, please give us a star on GitHub.
-
-Current online demo uses the 7B version of Video-LLaMA due to resource limitations. We have released \
-         the 13B version on our GitHub repository.
-
-
-"""
-
-Note_markdown = ("""
-### Note
-Video-LLaMA is a prototype model and may have limitations in understanding complex scenes, long videos, or specific domains.
-The output results may be influenced by input quality, limitations of the dataset, and the model's susceptibility to illusions. Please interpret the results with caution.
-
-**Copyright 2023 Alibaba DAMO Academy.**
-""")
-
-cite_markdown = ("""
-## Citation
-If you find our project useful, hope you can star our repo and cite our paper as follows:
-```
-@article{damonlpsg2023videollama,
-  author = {Zhang, Hang and Li, Xin and Bing, Lidong},
-  title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},
-  year = 2023,
-  journal = {arXiv preprint arXiv:2306.02858}
-  url = {https://arxiv.org/abs/2306.02858}
-}
-""")
-
-case_note_upload = ("""
-### We provide some examples at the bottom of the page. Simply click on them to try them out directly.
-""")
-
-#TODO show examples below
-
-with gr.Blocks() as demo:
-    gr.Markdown(title)
-
-    with gr.Row():
-        with gr.Column(scale=0.5):
-            video = gr.Video()
-            image = gr.Image(type="filepath")
-            gr.Markdown(case_note_upload)
-
-            upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
-            clear = gr.Button("Restart")
-            
-            num_beams = gr.Slider(
-                minimum=1,
-                maximum=10,
-                value=1,
-                step=1,
-                interactive=True,
-                label="beam search numbers)",
-            )
-            
-            temperature = gr.Slider(
-                minimum=0.1,
-                maximum=2.0,
-                value=1.0,
-                step=0.1,
-                interactive=True,
-                label="Temperature",
-            )
-
-            audio = gr.Checkbox(interactive=True, value=False, label="Audio")
-            gr.Markdown(Note_markdown)
-        with gr.Column():
-            chat_state = gr.State()
-            img_list = gr.State()
-            chatbot = gr.Chatbot(label='Video-LLaMA')
-            text_input = gr.Textbox(label='User', placeholder='Upload your image/video first, or directly click the examples at the bottom of the page.', interactive=False)
-            
-
-    with gr.Column():
-        gr.Examples(examples=[
-            [f"examples/dog.jpg", "Which breed is this dog? "],
-            [f"examples/jonsnow.jpg", "Who's the man on the right? "],
-            [f"examples/statue_of_liberty.jpg", "Can you tell me about this building? "],
-        ], inputs=[image, text_input])
-
-        gr.Examples(examples=[
-            [f"examples/skateboarding_dog.mp4", "What is the dog doing? "],
-            [f"examples/birthday.mp4", "What is the boy doing? "],
-            [f"examples/Iron_Man.mp4", "Is the guy in the video Iron Man? "],
-        ], inputs=[video, text_input])
-        
-    gr.Markdown(cite_markdown)
-    upload_button.click(upload_imgorvideo, [video, image, text_input, chat_state,chatbot], [video, image, text_input, upload_button, chat_state, img_list,chatbot])
-    
-    text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
-        gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list]
-    )
-    clear.click(gradio_reset, [chat_state, img_list], [chatbot, video, image, text_input, upload_button, chat_state, img_list], queue=False)
-    
-demo.launch(share=False, enable_queue=True)
-
-# %%
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_urlparse.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_urlparse.py
deleted file mode 100644
index 69ff0b4b02eaa6ddfb1c1948d553244ac6e3099e..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_urlparse.py
+++ /dev/null
@@ -1,462 +0,0 @@
-"""
-An implementation of `urlparse` that provides URL validation and normalization
-as described by RFC3986.
-
-We rely on this implementation rather than the one in Python's stdlib, because:
-
-* It provides more complete URL validation.
-* It properly differentiates between an empty querystring and an absent querystring,
-  to distinguish URLs with a trailing '?'.
-* It handles scheme, hostname, port, and path normalization.
-* It supports IDNA hostnames, normalizing them to their encoded form.
-* The API supports passing individual components, as well as the complete URL string.
-
-Previously we relied on the excellent `rfc3986` package to handle URL parsing and
-validation, but this module provides a simpler alternative, with less indirection
-required.
-"""
-import ipaddress
-import re
-import typing
-
-import idna
-
-from ._exceptions import InvalidURL
-
-MAX_URL_LENGTH = 65536
-
-# https://datatracker.ietf.org/doc/html/rfc3986.html#section-2.3
-UNRESERVED_CHARACTERS = (
-    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-._~"
-)
-SUB_DELIMS = "!$&'()*+,;="
-
-PERCENT_ENCODED_REGEX = re.compile("%[A-Fa-f0-9]{2}")
-
-
-# {scheme}:      (optional)
-# //{authority}  (optional)
-# {path}
-# ?{query}       (optional)
-# #{fragment}    (optional)
-URL_REGEX = re.compile(
-    (
-        r"(?:(?P<scheme>{scheme}):)?"
-        r"(?://(?P<authority>{authority}))?"
-        r"(?P<path>{path})"
-        r"(?:\?(?P<query>{query}))?"
-        r"(?:#(?P<fragment>{fragment}))?"
-    ).format(
-        scheme="([a-zA-Z][a-zA-Z0-9+.-]*)?",
-        authority="[^/?#]*",
-        path="[^?#]*",
-        query="[^#]*",
-        fragment=".*",
-    )
-)
-
-# {userinfo}@    (optional)
-# {host}
-# :{port}        (optional)
-AUTHORITY_REGEX = re.compile(
-    (
-        r"(?:(?P<userinfo>{userinfo})@)?" r"(?P<host>{host})" r":?(?P<port>{port})?"
-    ).format(
-        userinfo="[^@]*",  # Any character sequence not including '@'.
-        host="(\\[.*\\]|[^:]*)",  # Either any character sequence not including ':',
-        # or an IPv6 address enclosed within square brackets.
-        port=".*",  # Any character sequence.
-    )
-)
-
-
-# If we call urlparse with an individual component, then we need to regex
-# validate that component individually.
-# Note that we're duplicating the same strings as above. Shock! Horror!!
-COMPONENT_REGEX = {
-    "scheme": re.compile("([a-zA-Z][a-zA-Z0-9+.-]*)?"),
-    "authority": re.compile("[^/?#]*"),
-    "path": re.compile("[^?#]*"),
-    "query": re.compile("[^#]*"),
-    "fragment": re.compile(".*"),
-    "userinfo": re.compile("[^@]*"),
-    "host": re.compile("(\\[.*\\]|[^:]*)"),
-    "port": re.compile(".*"),
-}
-
-
-# We use these simple regexs as a first pass before handing off to
-# the stdlib 'ipaddress' module for IP address validation.
-IPv4_STYLE_HOSTNAME = re.compile(r"^[0-9]+.[0-9]+.[0-9]+.[0-9]+$")
-IPv6_STYLE_HOSTNAME = re.compile(r"^\[.*\]$")
-
-
-class ParseResult(typing.NamedTuple):
-    scheme: str
-    userinfo: str
-    host: str
-    port: typing.Optional[int]
-    path: str
-    query: typing.Optional[str]
-    fragment: typing.Optional[str]
-
-    @property
-    def authority(self) -> str:
-        return "".join(
-            [
-                f"{self.userinfo}@" if self.userinfo else "",
-                f"[{self.host}]" if ":" in self.host else self.host,
-                f":{self.port}" if self.port is not None else "",
-            ]
-        )
-
-    @property
-    def netloc(self) -> str:
-        return "".join(
-            [
-                f"[{self.host}]" if ":" in self.host else self.host,
-                f":{self.port}" if self.port is not None else "",
-            ]
-        )
-
-    def copy_with(self, **kwargs: typing.Optional[str]) -> "ParseResult":
-        if not kwargs:
-            return self
-
-        defaults = {
-            "scheme": self.scheme,
-            "authority": self.authority,
-            "path": self.path,
-            "query": self.query,
-            "fragment": self.fragment,
-        }
-        defaults.update(kwargs)
-        return urlparse("", **defaults)
-
-    def __str__(self) -> str:
-        authority = self.authority
-        return "".join(
-            [
-                f"{self.scheme}:" if self.scheme else "",
-                f"//{authority}" if authority else "",
-                self.path,
-                f"?{self.query}" if self.query is not None else "",
-                f"#{self.fragment}" if self.fragment is not None else "",
-            ]
-        )
-
-
-def urlparse(url: str = "", **kwargs: typing.Optional[str]) -> ParseResult:
-    # Initial basic checks on allowable URLs.
-    # ---------------------------------------
-
-    # Hard limit the maximum allowable URL length.
-    if len(url) > MAX_URL_LENGTH:
-        raise InvalidURL("URL too long")
-
-    # If a URL includes any ASCII control characters including \t, \r, \n,
-    # then treat it as invalid.
-    if any(char.isascii() and not char.isprintable() for char in url):
-        raise InvalidURL("Invalid non-printable ASCII character in URL")
-
-    # Some keyword arguments require special handling.
-    # ------------------------------------------------
-
-    # Coerce "port" to a string, if it is provided as an integer.
-    if "port" in kwargs:
-        port = kwargs["port"]
-        kwargs["port"] = str(port) if isinstance(port, int) else port
-
-    # Replace "netloc" with "host and "port".
-    if "netloc" in kwargs:
-        netloc = kwargs.pop("netloc") or ""
-        kwargs["host"], _, kwargs["port"] = netloc.partition(":")
-
-    # Replace "username" and/or "password" with "userinfo".
-    if "username" in kwargs or "password" in kwargs:
-        username = quote(kwargs.pop("username", "") or "")
-        password = quote(kwargs.pop("password", "") or "")
-        kwargs["userinfo"] = f"{username}:{password}" if password else username
-
-    # Replace "raw_path" with "path" and "query".
-    if "raw_path" in kwargs:
-        raw_path = kwargs.pop("raw_path") or ""
-        kwargs["path"], seperator, kwargs["query"] = raw_path.partition("?")
-        if not seperator:
-            kwargs["query"] = None
-
-    # Ensure that IPv6 "host" addresses are always escaped with "[...]".
-    if "host" in kwargs:
-        host = kwargs.get("host") or ""
-        if ":" in host and not (host.startswith("[") and host.endswith("]")):
-            kwargs["host"] = f"[{host}]"
-
-    # If any keyword arguments are provided, ensure they are valid.
-    # -------------------------------------------------------------
-
-    for key, value in kwargs.items():
-        if value is not None:
-            if len(value) > MAX_URL_LENGTH:
-                raise InvalidURL(f"URL component '{key}' too long")
-
-            # If a component includes any ASCII control characters including \t, \r, \n,
-            # then treat it as invalid.
-            if any(char.isascii() and not char.isprintable() for char in value):
-                raise InvalidURL(
-                    f"Invalid non-printable ASCII character in URL component '{key}'"
-                )
-
-            # Ensure that keyword arguments match as a valid regex.
-            if not COMPONENT_REGEX[key].fullmatch(value):
-                raise InvalidURL(f"Invalid URL component '{key}'")
-
-    # The URL_REGEX will always match, but may have empty components.
-    url_match = URL_REGEX.match(url)
-    assert url_match is not None
-    url_dict = url_match.groupdict()
-
-    # * 'scheme', 'authority', and 'path' may be empty strings.
-    # * 'query' may be 'None', indicating no trailing "?" portion.
-    #   Any string including the empty string, indicates a trailing "?".
-    # * 'fragment' may be 'None', indicating no trailing "#" portion.
-    #   Any string including the empty string, indicates a trailing "#".
-    scheme = kwargs.get("scheme", url_dict["scheme"]) or ""
-    authority = kwargs.get("authority", url_dict["authority"]) or ""
-    path = kwargs.get("path", url_dict["path"]) or ""
-    query = kwargs.get("query", url_dict["query"])
-    fragment = kwargs.get("fragment", url_dict["fragment"])
-
-    # The AUTHORITY_REGEX will always match, but may have empty components.
-    authority_match = AUTHORITY_REGEX.match(authority)
-    assert authority_match is not None
-    authority_dict = authority_match.groupdict()
-
-    # * 'userinfo' and 'host' may be empty strings.
-    # * 'port' may be 'None'.
-    userinfo = kwargs.get("userinfo", authority_dict["userinfo"]) or ""
-    host = kwargs.get("host", authority_dict["host"]) or ""
-    port = kwargs.get("port", authority_dict["port"])
-
-    # Normalize and validate each component.
-    # We end up with a parsed representation of the URL,
-    # with components that are plain ASCII bytestrings.
-    parsed_scheme: str = scheme.lower()
-    parsed_userinfo: str = quote(userinfo, safe=SUB_DELIMS + ":")
-    parsed_host: str = encode_host(host)
-    parsed_port: typing.Optional[int] = normalize_port(port, scheme)
-
-    has_scheme = parsed_scheme != ""
-    has_authority = (
-        parsed_userinfo != "" or parsed_host != "" or parsed_port is not None
-    )
-    validate_path(path, has_scheme=has_scheme, has_authority=has_authority)
-    if has_authority:
-        path = normalize_path(path)
-
-    # The GEN_DELIMS set is... : / ? # [ ] @
-    # These do not need to be percent-quoted unless they serve as delimiters for the
-    # specific component.
-
-    # For 'path' we need to drop ? and # from the GEN_DELIMS set.
-    parsed_path: str = quote(path, safe=SUB_DELIMS + ":/[]@")
-    # For 'query' we need to drop '#' from the GEN_DELIMS set.
-    parsed_query: typing.Optional[str] = (
-        None if query is None else quote(query, safe=SUB_DELIMS + ":/?[]@")
-    )
-    # For 'fragment' we can include all of the GEN_DELIMS set.
-    parsed_fragment: typing.Optional[str] = (
-        None if fragment is None else quote(fragment, safe=SUB_DELIMS + ":/?#[]@")
-    )
-
-    # The parsed ASCII bytestrings are our canonical form.
-    # All properties of the URL are derived from these.
-    return ParseResult(
-        parsed_scheme,
-        parsed_userinfo,
-        parsed_host,
-        parsed_port,
-        parsed_path,
-        parsed_query,
-        parsed_fragment,
-    )
-
-
-def encode_host(host: str) -> str:
-    if not host:
-        return ""
-
-    elif IPv4_STYLE_HOSTNAME.match(host):
-        # Validate IPv4 hostnames like #.#.#.#
-        #
-        # From https://datatracker.ietf.org/doc/html/rfc3986/#section-3.2.2
-        #
-        # IPv4address = dec-octet "." dec-octet "." dec-octet "." dec-octet
-        try:
-            ipaddress.IPv4Address(host)
-        except ipaddress.AddressValueError:
-            raise InvalidURL(f"Invalid IPv4 address: {host!r}")
-        return host
-
-    elif IPv6_STYLE_HOSTNAME.match(host):
-        # Validate IPv6 hostnames like [...]
-        #
-        # From https://datatracker.ietf.org/doc/html/rfc3986/#section-3.2.2
-        #
-        # "A host identified by an Internet Protocol literal address, version 6
-        # [RFC3513] or later, is distinguished by enclosing the IP literal
-        # within square brackets ("[" and "]").  This is the only place where
-        # square bracket characters are allowed in the URI syntax."
-        try:
-            ipaddress.IPv6Address(host[1:-1])
-        except ipaddress.AddressValueError:
-            raise InvalidURL(f"Invalid IPv6 address: {host!r}")
-        return host[1:-1]
-
-    elif host.isascii():
-        # Regular ASCII hostnames
-        #
-        # From https://datatracker.ietf.org/doc/html/rfc3986/#section-3.2.2
-        #
-        # reg-name    = *( unreserved / pct-encoded / sub-delims )
-        return quote(host.lower(), safe=SUB_DELIMS)
-
-    # IDNA hostnames
-    try:
-        return idna.encode(host.lower()).decode("ascii")
-    except idna.IDNAError:
-        raise InvalidURL(f"Invalid IDNA hostname: {host!r}")
-
-
-def normalize_port(
-    port: typing.Optional[typing.Union[str, int]], scheme: str
-) -> typing.Optional[int]:
-    # From https://tools.ietf.org/html/rfc3986#section-3.2.3
-    #
-    # "A scheme may define a default port.  For example, the "http" scheme
-    # defines a default port of "80", corresponding to its reserved TCP
-    # port number.  The type of port designated by the port number (e.g.,
-    # TCP, UDP, SCTP) is defined by the URI scheme.  URI producers and
-    # normalizers should omit the port component and its ":" delimiter if
-    # port is empty or if its value would be the same as that of the
-    # scheme's default."
-    if port is None or port == "":
-        return None
-
-    try:
-        port_as_int = int(port)
-    except ValueError:
-        raise InvalidURL(f"Invalid port: {port!r}")
-
-    # See https://url.spec.whatwg.org/#url-miscellaneous
-    default_port = {"ftp": 21, "http": 80, "https": 443, "ws": 80, "wss": 443}.get(
-        scheme
-    )
-    if port_as_int == default_port:
-        return None
-    return port_as_int
-
-
-def validate_path(path: str, has_scheme: bool, has_authority: bool) -> None:
-    """
-    Path validation rules that depend on if the URL contains a scheme or authority component.
-
-    See https://datatracker.ietf.org/doc/html/rfc3986.html#section-3.3
-    """
-    if has_authority:
-        # > If a URI contains an authority component, then the path component
-        # > must either be empty or begin with a slash ("/") character."
-        if path and not path.startswith("/"):
-            raise InvalidURL("For absolute URLs, path must be empty or begin with '/'")
-    else:
-        # > If a URI does not contain an authority component, then the path cannot begin
-        # > with two slash characters ("//").
-        if path.startswith("//"):
-            raise InvalidURL(
-                "URLs with no authority component cannot have a path starting with '//'"
-            )
-        # > In addition, a URI reference (Section 4.1) may be a relative-path reference, in which
-        # > case the first path segment cannot contain a colon (":") character.
-        if path.startswith(":") and not has_scheme:
-            raise InvalidURL(
-                "URLs with no scheme component cannot have a path starting with ':'"
-            )
-
-
-def normalize_path(path: str) -> str:
-    """
-    Drop "." and ".." segments from a URL path.
-
-    For example:
-
-        normalize_path("/path/./to/somewhere/..") == "/path/to"
-    """
-    # https://datatracker.ietf.org/doc/html/rfc3986#section-5.2.4
-    components = path.split("/")
-    output: typing.List[str] = []
-    for component in components:
-        if component == ".":
-            pass
-        elif component == "..":
-            if output and output != [""]:
-                output.pop()
-        else:
-            output.append(component)
-    return "/".join(output)
-
-
-def percent_encode(char: str) -> str:
-    """
-    Replace a single character with the percent-encoded representation.
-
-    Characters outside the ASCII range are represented with their a percent-encoded
-    representation of their UTF-8 byte sequence.
-
-    For example:
-
-        percent_encode(" ") == "%20"
-    """
-    return "".join([f"%{byte:02x}" for byte in char.encode("utf-8")]).upper()
-
-
-def is_safe(string: str, safe: str = "/") -> bool:
-    """
-    Determine if a given string is already quote-safe.
-    """
-    NON_ESCAPED_CHARS = UNRESERVED_CHARACTERS + safe + "%"
-
-    # All characters must already be non-escaping or '%'
-    for char in string:
-        if char not in NON_ESCAPED_CHARS:
-            return False
-
-    # Any '%' characters must be valid '%xx' escape sequences.
-    return string.count("%") == len(PERCENT_ENCODED_REGEX.findall(string))
-
-
-def quote(string: str, safe: str = "/") -> str:
-    """
-    Use percent-encoding to quote a string if required.
-    """
-    if is_safe(string, safe=safe):
-        return string
-
-    NON_ESCAPED_CHARS = UNRESERVED_CHARACTERS + safe
-    return "".join(
-        [char if char in NON_ESCAPED_CHARS else percent_encode(char) for char in string]
-    )
-
-
-def urlencode(items: typing.List[typing.Tuple[str, str]]) -> str:
-    # We can use a much simpler version of the stdlib urlencode here because
-    # we don't need to handle a bunch of different typing cases, such as bytes vs str.
-    #
-    # https://github.com/python/cpython/blob/b2f7b2ef0b5421e01efb8c7bee2ef95d3bab77eb/Lib/urllib/parse.py#L926
-    #
-    # Note that we use '%20' encoding for spaces, and treat '/' as a safe
-    # character. This means our query params have the same escaping as other
-    # characters in the URL path. This is slightly different to `requests`,
-    # but is the behaviour that browsers use.
-    #
-    # See https://github.com/encode/httpx/issues/2536 and
-    # https://docs.python.org/3/library/urllib.parse.html#urllib.parse.urlencode
-    return "&".join([quote(k) + "=" + quote(v) for k, v in items])
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/_login.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/_login.py
deleted file mode 100644
index 4bbb1c9235a2c4d9e145f5c35603dca55e07d2a8..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/_login.py
+++ /dev/null
@@ -1,360 +0,0 @@
-# Copyright 2020 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.
-"""Contains methods to login to the Hub."""
-import os
-import subprocess
-from functools import partial
-from getpass import getpass
-from typing import Optional
-
-from .commands._cli_utils import ANSI
-from .commands.delete_cache import _ask_for_confirmation_no_tui
-from .hf_api import get_token_permission
-from .utils import (
-    HfFolder,
-    capture_output,
-    is_google_colab,
-    is_notebook,
-    list_credential_helpers,
-    logging,
-    run_subprocess,
-    set_git_credential,
-    unset_git_credential,
-)
-
-
-logger = logging.get_logger(__name__)
-
-
-def login(
-    token: Optional[str] = None,
-    add_to_git_credential: bool = False,
-    new_session: bool = True,
-    write_permission: bool = False,
-) -> None:
-    """Login the machine to access the Hub.
-
-    The `token` is persisted in cache and set as a git credential. Once done, the machine
-    is logged in and the access token will be available across all `huggingface_hub`
-    components. If `token` is not provided, it will be prompted to the user either with
-    a widget (in a notebook) or via the terminal.
-
-    To login from outside of a script, one can also use `huggingface-cli login` which is
-    a cli command that wraps [`login`].
-
-    <Tip>
-    [`login`] is a drop-in replacement method for [`notebook_login`] as it wraps and
-    extends its capabilities.
-    </Tip>
-
-    <Tip>
-    When the token is not passed, [`login`] will automatically detect if the script runs
-    in a notebook or not. However, this detection might not be accurate due to the
-    variety of notebooks that exists nowadays. If that is the case, you can always force
-    the UI by using [`notebook_login`] or [`interpreter_login`].
-    </Tip>
-
-    Args:
-        token (`str`, *optional*):
-            User access token to generate from https://huggingface.co/settings/token.
-        add_to_git_credential (`bool`, defaults to `False`):
-            If `True`, token will be set as git credential. If no git credential helper
-            is configured, a warning will be displayed to the user. If `token` is `None`,
-            the value of `add_to_git_credential` is ignored and will be prompted again
-            to the end user.
-        new_session (`bool`, defaults to `True`):
-            If `True`, will request a token even if one is already saved on the machine.
-        write_permission (`bool`, defaults to `False`):
-            If `True`, requires a token with write permission.
-    Raises:
-        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
-            If an organization token is passed. Only personal account tokens are valid
-            to login.
-        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
-            If token is invalid.
-        [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
-            If running in a notebook but `ipywidgets` is not installed.
-    """
-    if token is not None:
-        if not add_to_git_credential:
-            print(
-                "Token will not been saved to git credential helper. Pass"
-                " `add_to_git_credential=True` if you want to set the git"
-                " credential as well."
-            )
-        _login(token, add_to_git_credential=add_to_git_credential, write_permission=write_permission)
-    elif is_notebook():
-        notebook_login(new_session=new_session, write_permission=write_permission)
-    else:
-        interpreter_login(new_session=new_session, write_permission=write_permission)
-
-
-def logout() -> None:
-    """Logout the machine from the Hub.
-
-    Token is deleted from the machine and removed from git credential.
-    """
-    token = HfFolder.get_token()
-    if token is None:
-        print("Not logged in!")
-        return
-    HfFolder.delete_token()
-    unset_git_credential()
-    print("Successfully logged out.")
-
-
-###
-# Interpreter-based login (text)
-###
-
-
-def interpreter_login(new_session: bool = True, write_permission: bool = False) -> None:
-    """
-    Displays a prompt to login to the HF website and store the token.
-
-    This is equivalent to [`login`] without passing a token when not run in a notebook.
-    [`interpreter_login`] is useful if you want to force the use of the terminal prompt
-    instead of a notebook widget.
-
-    For more details, see [`login`].
-
-    Args:
-        new_session (`bool`, defaults to `True`):
-            If `True`, will request a token even if one is already saved on the machine.
-        write_permission (`bool`, defaults to `False`):
-            If `True`, requires a token with write permission.
-
-    """
-    if not new_session and _current_token_okay(write_permission=write_permission):
-        print("User is already logged in.")
-        return
-
-    print("""
-    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|
-    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|
-    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|
-    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|
-    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|
-    """)
-    if HfFolder.get_token() is not None:
-        print(
-            "    A token is already saved on your machine. Run `huggingface-cli"
-            " whoami` to get more information or `huggingface-cli logout` if you want"
-            " to log out."
-        )
-        print("    Setting a new token will erase the existing one.")
-
-    print("    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .")
-    if os.name == "nt":
-        print("Token can be pasted using 'Right-Click'.")
-    token = getpass("Token: ")
-    add_to_git_credential = _ask_for_confirmation_no_tui("Add token as git credential?")
-
-    _login(token=token, add_to_git_credential=add_to_git_credential, write_permission=write_permission)
-
-
-###
-# Notebook-based login (widget)
-###
-
-NOTEBOOK_LOGIN_PASSWORD_HTML = """<center> <img
-src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg
-alt='Hugging Face'> <br> Immediately click login after typing your password or
-it might be stored in plain text in this notebook file. </center>"""
-
-
-NOTEBOOK_LOGIN_TOKEN_HTML_START = """<center> <img
-src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg
-alt='Hugging Face'> <br> Copy a token from <a
-href="https://huggingface.co/settings/tokens" target="_blank">your Hugging Face
-tokens page</a> and paste it below. <br> Immediately click login after copying
-your token or it might be stored in plain text in this notebook file. </center>"""
-
-
-NOTEBOOK_LOGIN_TOKEN_HTML_END = """
-<b>Pro Tip:</b> If you don't already have one, you can create a dedicated
-'notebooks' token with 'write' access, that you can then easily reuse for all
-notebooks. </center>"""
-
-
-def notebook_login(new_session: bool = True, write_permission: bool = False) -> None:
-    """
-    Displays a widget to login to the HF website and store the token.
-
-    This is equivalent to [`login`] without passing a token when run in a notebook.
-    [`notebook_login`] is useful if you want to force the use of the notebook widget
-    instead of a prompt in the terminal.
-
-    For more details, see [`login`].
-
-    Args:
-        new_session (`bool`, defaults to `True`):
-            If `True`, will request a token even if one is already saved on the machine.
-        write_permission (`bool`, defaults to `False`):
-            If `True`, requires a token with write permission.
-    """
-    try:
-        import ipywidgets.widgets as widgets  # type: ignore
-        from IPython.display import display  # type: ignore
-    except ImportError:
-        raise ImportError(
-            "The `notebook_login` function can only be used in a notebook (Jupyter or"
-            " Colab) and you need the `ipywidgets` module: `pip install ipywidgets`."
-        )
-    if not new_session and _current_token_okay(write_permission=write_permission):
-        print("User is already logged in.")
-        return
-
-    box_layout = widgets.Layout(display="flex", flex_flow="column", align_items="center", width="50%")
-
-    token_widget = widgets.Password(description="Token:")
-    git_checkbox_widget = widgets.Checkbox(value=True, description="Add token as git credential?")
-    token_finish_button = widgets.Button(description="Login")
-
-    login_token_widget = widgets.VBox(
-        [
-            widgets.HTML(NOTEBOOK_LOGIN_TOKEN_HTML_START),
-            token_widget,
-            git_checkbox_widget,
-            token_finish_button,
-            widgets.HTML(NOTEBOOK_LOGIN_TOKEN_HTML_END),
-        ],
-        layout=box_layout,
-    )
-    display(login_token_widget)
-
-    # On click events
-    def login_token_event(t, write_permission: bool = False):
-        """
-        Event handler for the login button.
-
-        Args:
-            write_permission (`bool`, defaults to `False`):
-                If `True`, requires a token with write permission.
-        """
-        token = token_widget.value
-        add_to_git_credential = git_checkbox_widget.value
-        # Erase token and clear value to make sure it's not saved in the notebook.
-        token_widget.value = ""
-        # Hide inputs
-        login_token_widget.children = [widgets.Label("Connecting...")]
-        try:
-            with capture_output() as captured:
-                _login(token, add_to_git_credential=add_to_git_credential, write_permission=write_permission)
-            message = captured.getvalue()
-        except Exception as error:
-            message = str(error)
-        # Print result (success message or error)
-        login_token_widget.children = [widgets.Label(line) for line in message.split("\n") if line.strip()]
-
-    token_finish_button.on_click(partial(login_token_event, write_permission=write_permission))
-
-
-###
-# Login private helpers
-###
-
-
-def _login(token: str, add_to_git_credential: bool, write_permission: bool = False) -> None:
-    if token.startswith("api_org"):
-        raise ValueError("You must use your personal account token, not an organization token.")
-
-    permission = get_token_permission(token)
-    if permission is None:
-        raise ValueError("Invalid token passed!")
-    elif write_permission and permission != "write":
-        raise ValueError(
-            "Token is valid but is 'read-only' and a 'write' token is required.\nPlease provide a new token with"
-            " correct permission."
-        )
-    print(f"Token is valid (permission: {permission}).")
-
-    if add_to_git_credential:
-        if _is_git_credential_helper_configured():
-            set_git_credential(token)
-            print(
-                "Your token has been saved in your configured git credential helpers"
-                + f" ({','.join(list_credential_helpers())})."
-            )
-        else:
-            print("Token has not been saved to git credential helper.")
-
-    HfFolder.save_token(token)
-    print(f"Your token has been saved to {HfFolder.path_token}")
-    print("Login successful")
-
-
-def _current_token_okay(write_permission: bool = False):
-    """Check if the current token is valid.
-
-    Args:
-        write_permission (`bool`, defaults to `False`):
-            If `True`, requires a token with write permission.
-
-    Returns:
-        `bool`: `True` if the current token is valid, `False` otherwise.
-    """
-    permission = get_token_permission()
-    if permission is None or (write_permission and permission != "write"):
-        return False
-    return True
-
-
-def _is_git_credential_helper_configured() -> bool:
-    """Check if a git credential helper is configured.
-
-    Warns user if not the case (except for Google Colab where "store" is set by default
-    by `huggingface_hub`).
-    """
-    helpers = list_credential_helpers()
-    if len(helpers) > 0:
-        return True  # Do not warn: at least 1 helper is set
-
-    # Only in Google Colab to avoid the warning message
-    # See https://github.com/huggingface/huggingface_hub/issues/1043#issuecomment-1247010710
-    if is_google_colab():
-        _set_store_as_git_credential_helper_globally()
-        return True  # Do not warn: "store" is used by default in Google Colab
-
-    # Otherwise, warn user
-    print(
-        ANSI.red(
-            "Cannot authenticate through git-credential as no helper is defined on your"
-            " machine.\nYou might have to re-authenticate when pushing to the Hugging"
-            " Face Hub.\nRun the following command in your terminal in case you want to"
-            " set the 'store' credential helper as default.\n\ngit config --global"
-            " credential.helper store\n\nRead"
-            " https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more"
-            " details."
-        )
-    )
-    return False
-
-
-def _set_store_as_git_credential_helper_globally() -> None:
-    """Set globally the credential.helper to `store`.
-
-    To be used only in Google Colab as we assume the user doesn't care about the git
-    credential config. It is the only particular case where we don't want to display the
-    warning message in [`notebook_login()`].
-
-    Related:
-    - https://github.com/huggingface/huggingface_hub/issues/1043
-    - https://github.com/huggingface/huggingface_hub/issues/1051
-    - https://git-scm.com/docs/git-credential-store
-    """
-    try:
-        run_subprocess("git config --global credential.helper store")
-    except subprocess.CalledProcessError as exc:
-        raise EnvironmentError(exc.stderr)
diff --git a/spaces/DragGan/DragGan-Inversion/PTI/evaluation/experiment_setting_creator.py b/spaces/DragGan/DragGan-Inversion/PTI/evaluation/experiment_setting_creator.py
deleted file mode 100644
index c8ad234ba845d84ddd435424a7fe9ed238af3ff6..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/PTI/evaluation/experiment_setting_creator.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import glob
-import os
-from configs import global_config, paths_config, hyperparameters
-from scripts.latent_creators.sg2_plus_latent_creator import SG2PlusLatentCreator
-from scripts.latent_creators.e4e_latent_creator import E4ELatentCreator
-from scripts.run_pti import run_PTI
-import pickle
-import torch
-from utils.models_utils import toogle_grad, load_old_G
-
-
-class ExperimentRunner:
-
-    def __init__(self, run_id=''):
-        self.images_paths = glob.glob(f'{paths_config.input_data_path}/*')
-        self.target_paths = glob.glob(f'{paths_config.input_data_path}/*')
-        self.run_id = run_id
-        self.sampled_ws = None
-
-        self.old_G = load_old_G()
-
-        toogle_grad(self.old_G, False)
-
-    def run_experiment(self, run_pt, create_other_latents, use_multi_id_training, use_wandb=False):
-        if run_pt:
-            self.run_id = run_PTI(self.run_id, use_wandb=use_wandb, use_multi_id_training=use_multi_id_training)
-        if create_other_latents:
-            sg2_plus_latent_creator = SG2PlusLatentCreator(use_wandb=use_wandb)
-            sg2_plus_latent_creator.create_latents()
-            e4e_latent_creator = E4ELatentCreator(use_wandb=use_wandb)
-            e4e_latent_creator.create_latents()
-
-        torch.cuda.empty_cache()
-
-        return self.run_id
-
-
-if __name__ == '__main__':
-    os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
-    os.environ['CUDA_VISIBLE_DEVICES'] = global_config.cuda_visible_devices
-
-    runner = ExperimentRunner()
-    runner.run_experiment(True, False, False)
diff --git a/spaces/DragGan/DragGan/stylegan_human/run_pti.py b/spaces/DragGan/DragGan/stylegan_human/run_pti.py
deleted file mode 100644
index c335c5b156554ea1069af0c153ec6edbea70d931..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/run_pti.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-from random import choice
-from string import ascii_uppercase
-from torch.utils.data import DataLoader
-from torchvision.transforms import transforms
-import os
-from pti.pti_configs import global_config, paths_config
-import wandb
-
-from pti.training.coaches.multi_id_coach import MultiIDCoach
-from pti.training.coaches.single_id_coach import SingleIDCoach
-from utils.ImagesDataset import ImagesDataset
-
-
-def run_PTI(run_name='', use_wandb=False, use_multi_id_training=False):
-    os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
-    os.environ['CUDA_VISIBLE_DEVICES'] = global_config.cuda_visible_devices
-
-    if run_name == '':
-        global_config.run_name = ''.join(choice(ascii_uppercase) for i in range(12))
-    else:
-        global_config.run_name = run_name
-
-    if use_wandb:
-        run = wandb.init(project=paths_config.pti_results_keyword, reinit=True, name=global_config.run_name)
-    global_config.pivotal_training_steps = 1
-    global_config.training_step = 1
-
-    embedding_dir_path = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{paths_config.pti_results_keyword}'
-    # print('embedding_dir_path: ', embedding_dir_path) #./embeddings/barcelona/PTI 
-    os.makedirs(embedding_dir_path, exist_ok=True)
-
-    dataset = ImagesDataset(paths_config.input_data_path, transforms.Compose([
-        transforms.Resize((1024, 512)),
-        transforms.ToTensor(),
-        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]))
-
-    dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
-
-    if use_multi_id_training:
-        coach = MultiIDCoach(dataloader, use_wandb)
-    else:
-        coach = SingleIDCoach(dataloader, use_wandb)
-
-    coach.train()
-
-    return global_config.run_name
-
-
-if __name__ == '__main__':
-    run_PTI(run_name='', use_wandb=False, use_multi_id_training=False)
diff --git a/spaces/DragGan/DragGan/viz/renderer.py b/spaces/DragGan/DragGan/viz/renderer.py
deleted file mode 100644
index a603cfddf89f40ee5a97b6bcda7ae9959b4fb8fe..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/viz/renderer.py
+++ /dev/null
@@ -1,427 +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.
-
-from socket import has_dualstack_ipv6
-import sys
-import copy
-import traceback
-import math
-import numpy as np
-from PIL import Image, ImageDraw, ImageFont
-import torch
-import torch.fft
-import torch.nn as nn
-import torch.nn.functional as F
-import matplotlib.cm
-import dnnlib
-from torch_utils.ops import upfirdn2d
-import legacy  # pylint: disable=import-error
-
-# ----------------------------------------------------------------------------
-
-
-class CapturedException(Exception):
-    def __init__(self, msg=None):
-        if msg is None:
-            _type, value, _traceback = sys.exc_info()
-            assert value is not None
-            if isinstance(value, CapturedException):
-                msg = str(value)
-            else:
-                msg = traceback.format_exc()
-        assert isinstance(msg, str)
-        super().__init__(msg)
-
-# ----------------------------------------------------------------------------
-
-
-class CaptureSuccess(Exception):
-    def __init__(self, out):
-        super().__init__()
-        self.out = out
-
-# ----------------------------------------------------------------------------
-
-
-def add_watermark_np(input_image_array, watermark_text="AI Generated"):
-    image = Image.fromarray(np.uint8(input_image_array)).convert("RGBA")
-
-    # Initialize text image
-    txt = Image.new('RGBA', image.size, (255, 255, 255, 0))
-    font = ImageFont.truetype('arial.ttf', round(25/512*image.size[0]))
-    d = ImageDraw.Draw(txt)
-
-    text_width, text_height = font.getsize(watermark_text)
-    text_position = (image.size[0] - text_width -
-                     10, image.size[1] - text_height - 10)
-    # white color with the alpha channel set to semi-transparent
-    text_color = (255, 255, 255, 128)
-
-    # Draw the text onto the text canvas
-    d.text(text_position, watermark_text, font=font, fill=text_color)
-
-    # Combine the image with the watermark
-    watermarked = Image.alpha_composite(image, txt)
-    watermarked_array = np.array(watermarked)
-    return watermarked_array
-
-# ----------------------------------------------------------------------------
-
-
-class Renderer:
-    def __init__(self, disable_timing=False):
-        self._device = torch.device('cuda' if torch.cuda.is_available(
-        ) else 'mps' if torch.backends.mps.is_available() else 'cpu')
-        self._dtype = torch.float32 if self._device.type == 'mps' else torch.float64
-        self._pkl_data = dict()    # {pkl: dict | CapturedException, ...}
-        self._networks = dict()    # {cache_key: torch.nn.Module, ...}
-        self._pinned_bufs = dict()    # {(shape, dtype): torch.Tensor, ...}
-        self._cmaps = dict()    # {name: torch.Tensor, ...}
-        self._is_timing = False
-        if not disable_timing:
-            self._start_event = torch.cuda.Event(enable_timing=True)
-            self._end_event = torch.cuda.Event(enable_timing=True)
-        self._disable_timing = disable_timing
-        self._net_layers = dict()    # {cache_key: [dnnlib.EasyDict, ...], ...}
-
-    def render(self, **args):
-        if self._disable_timing:
-            self._is_timing = False
-        else:
-            self._start_event.record(torch.cuda.current_stream(self._device))
-            self._is_timing = True
-        res = dnnlib.EasyDict()
-        try:
-            init_net = False
-            if not hasattr(self, 'G'):
-                init_net = True
-            if hasattr(self, 'pkl'):
-                if self.pkl != args['pkl']:
-                    init_net = True
-            if hasattr(self, 'w_load'):
-                if self.w_load is not args['w_load']:
-                    init_net = True
-            if hasattr(self, 'w0_seed'):
-                if self.w0_seed != args['w0_seed']:
-                    init_net = True
-            if hasattr(self, 'w_plus'):
-                if self.w_plus != args['w_plus']:
-                    init_net = True
-            if args['reset_w']:
-                init_net = True
-            res.init_net = init_net
-            if init_net:
-                self.init_network(res, **args)
-            self._render_drag_impl(res, **args)
-        except:
-            res.error = CapturedException()
-        if not self._disable_timing:
-            self._end_event.record(torch.cuda.current_stream(self._device))
-        if 'image' in res:
-            res.image = self.to_cpu(res.image).detach().numpy()
-            res.image = add_watermark_np(res.image, 'AI Generated')
-        if 'stats' in res:
-            res.stats = self.to_cpu(res.stats).detach().numpy()
-        if 'error' in res:
-            res.error = str(res.error)
-        # if 'stop' in res and res.stop:
-
-        if self._is_timing and not self._disable_timing:
-            self._end_event.synchronize()
-            res.render_time = self._start_event.elapsed_time(
-                self._end_event) * 1e-3
-            self._is_timing = False
-        return res
-
-    def get_network(self, pkl, key, **tweak_kwargs):
-        data = self._pkl_data.get(pkl, None)
-        if data is None:
-            print(f'Loading "{pkl}"... ', end='', flush=True)
-            try:
-                with dnnlib.util.open_url(pkl, verbose=False) as f:
-                    data = legacy.load_network_pkl(f)
-                print('Done.')
-            except:
-                data = CapturedException()
-                print('Failed!')
-            self._pkl_data[pkl] = data
-            self._ignore_timing()
-        if isinstance(data, CapturedException):
-            raise data
-
-        orig_net = data[key]
-        cache_key = (orig_net, self._device, tuple(
-            sorted(tweak_kwargs.items())))
-        net = self._networks.get(cache_key, None)
-        if net is None:
-            try:
-                if 'stylegan2' in pkl:
-                    from training.networks_stylegan2 import Generator
-                elif 'stylegan3' in pkl:
-                    from training.networks_stylegan3 import Generator
-                elif 'stylegan_human' in pkl:
-                    from stylegan_human.training_scripts.sg2.training.networks import Generator
-                else:
-                    raise NameError('Cannot infer model type from pkl name!')
-
-                print(data[key].init_args)
-                print(data[key].init_kwargs)
-                if 'stylegan_human' in pkl:
-                    net = Generator(
-                        *data[key].init_args, **data[key].init_kwargs, square=False, padding=True)
-                else:
-                    net = Generator(*data[key].init_args,
-                                    **data[key].init_kwargs)
-                net.load_state_dict(data[key].state_dict())
-                net.to(self._device)
-            except:
-                net = CapturedException()
-            self._networks[cache_key] = net
-            self._ignore_timing()
-        if isinstance(net, CapturedException):
-            raise net
-        return net
-
-    def _get_pinned_buf(self, ref):
-        key = (tuple(ref.shape), ref.dtype)
-        buf = self._pinned_bufs.get(key, None)
-        if buf is None:
-            buf = torch.empty(ref.shape, dtype=ref.dtype).pin_memory()
-            self._pinned_bufs[key] = buf
-        return buf
-
-    def to_device(self, buf):
-        return self._get_pinned_buf(buf).copy_(buf).to(self._device)
-
-    def to_cpu(self, buf):
-        return self._get_pinned_buf(buf).copy_(buf).clone()
-
-    def _ignore_timing(self):
-        self._is_timing = False
-
-    def _apply_cmap(self, x, name='viridis'):
-        cmap = self._cmaps.get(name, None)
-        if cmap is None:
-            cmap = matplotlib.cm.get_cmap(name)
-            cmap = cmap(np.linspace(0, 1, num=1024), bytes=True)[:, :3]
-            cmap = self.to_device(torch.from_numpy(cmap))
-            self._cmaps[name] = cmap
-        hi = cmap.shape[0] - 1
-        x = (x * hi + 0.5).clamp(0, hi).to(torch.int64)
-        x = torch.nn.functional.embedding(x, cmap)
-        return x
-
-    def init_network(self, res,
-                     pkl=None,
-                     w0_seed=0,
-                     w_load=None,
-                     w_plus=True,
-                     noise_mode='const',
-                     trunc_psi=0.7,
-                     trunc_cutoff=None,
-                     input_transform=None,
-                     lr=0.001,
-                     **kwargs
-                     ):
-        # Dig up network details.
-        self.pkl = pkl
-        G = self.get_network(pkl, 'G_ema')
-        self.G = G
-        res.img_resolution = G.img_resolution
-        res.num_ws = G.num_ws
-        res.has_noise = any('noise_const' in name for name,
-                            _buf in G.synthesis.named_buffers())
-        res.has_input_transform = (
-            hasattr(G.synthesis, 'input') and hasattr(G.synthesis.input, 'transform'))
-        res.stop = False
-        # Set input transform.
-        if res.has_input_transform:
-            m = np.eye(3)
-            try:
-                if input_transform is not None:
-                    m = np.linalg.inv(np.asarray(input_transform))
-            except np.linalg.LinAlgError:
-                res.error = CapturedException()
-            G.synthesis.input.transform.copy_(torch.from_numpy(m))
-
-        # Generate random latents.
-        self.w0_seed = w0_seed
-        self.w_load = w_load
-
-        if self.w_load is None:
-            # Generate random latents.
-            z = torch.from_numpy(np.random.RandomState(w0_seed).randn(
-                1, 512)).to(self._device, dtype=self._dtype)
-
-            # Run mapping network.
-            label = torch.zeros([1, G.c_dim], device=self._device)
-            w = G.mapping(z, label, truncation_psi=trunc_psi,
-                          truncation_cutoff=trunc_cutoff)
-        else:
-            w = self.w_load.clone().to(self._device)
-
-        self.w0 = w.detach().clone()
-        self.w_plus = w_plus
-        if w_plus:
-            self.w = w.detach()
-        else:
-            self.w = w[:, 0, :].detach()
-        self.w.requires_grad = True
-        self.w_optim = torch.optim.Adam([self.w], lr=lr)
-
-        self.feat_refs = None
-        self.points0_pt = None
-
-    def update_lr(self, lr):
-
-        del self.w_optim
-        self.w_optim = torch.optim.Adam([self.w], lr=lr)
-        print(f'Rebuild optimizer with lr: {lr}')
-        print('    Remain feat_refs and points0_pt')
-
-    def _render_drag_impl(self, res,
-                          points=[],
-                          targets=[],
-                          mask=None,
-                          lambda_mask=10,
-                          reg=0,
-                          feature_idx=5,
-                          r1=3,
-                          r2=12,
-                          random_seed=0,
-                          noise_mode='const',
-                          trunc_psi=0.7,
-                          force_fp32=False,
-                          layer_name=None,
-                          sel_channels=3,
-                          base_channel=0,
-                          img_scale_db=0,
-                          img_normalize=False,
-                          untransform=False,
-                          is_drag=False,
-                          reset=False,
-                          to_pil=False,
-                          **kwargs
-                          ):
-        try:
-            G = self.G
-            ws = self.w
-            if ws.dim() == 2:
-                ws = ws.unsqueeze(1).repeat(1, 6, 1)
-            ws = torch.cat([ws[:, :6, :], self.w0[:, 6:, :]], dim=1)
-            if hasattr(self, 'points'):
-                if len(points) != len(self.points):
-                    reset = True
-            if reset:
-                self.feat_refs = None
-                self.points0_pt = None
-            self.points = points
-
-            # Run synthesis network.
-            label = torch.zeros([1, G.c_dim], device=self._device)
-            img, feat = G(ws, label, truncation_psi=trunc_psi,
-                          noise_mode=noise_mode, input_is_w=True, return_feature=True)
-
-            h, w = G.img_resolution, G.img_resolution
-
-            if is_drag:
-                X = torch.linspace(0, h, h)
-                Y = torch.linspace(0, w, w)
-                xx, yy = torch.meshgrid(X, Y)
-                feat_resize = F.interpolate(
-                    feat[feature_idx], [h, w], mode='bilinear')
-                if self.feat_refs is None:
-                    self.feat0_resize = F.interpolate(
-                        feat[feature_idx].detach(), [h, w], mode='bilinear')
-                    self.feat_refs = []
-                    for point in points:
-                        py, px = round(point[0]), round(point[1])
-                        self.feat_refs.append(self.feat0_resize[:, :, py, px])
-                    self.points0_pt = torch.Tensor(points).unsqueeze(
-                        0).to(self._device)  # 1, N, 2
-
-                # Point tracking with feature matching
-                with torch.no_grad():
-                    for j, point in enumerate(points):
-                        r = round(r2 / 512 * h)
-                        up = max(point[0] - r, 0)
-                        down = min(point[0] + r + 1, h)
-                        left = max(point[1] - r, 0)
-                        right = min(point[1] + r + 1, w)
-                        feat_patch = feat_resize[:, :, up:down, left:right]
-                        L2 = torch.linalg.norm(
-                            feat_patch - self.feat_refs[j].reshape(1, -1, 1, 1), dim=1)
-                        _, idx = torch.min(L2.view(1, -1), -1)
-                        width = right - left
-                        point = [idx.item() // width + up, idx.item() %
-                                 width + left]
-                        points[j] = point
-
-                res.points = [[point[0], point[1]] for point in points]
-
-                # Motion supervision
-                loss_motion = 0
-                res.stop = True
-                for j, point in enumerate(points):
-                    direction = torch.Tensor(
-                        [targets[j][1] - point[1], targets[j][0] - point[0]])
-                    if torch.linalg.norm(direction) > max(2 / 512 * h, 2):
-                        res.stop = False
-                    if torch.linalg.norm(direction) > 1:
-                        distance = (
-                            (xx.to(self._device) - point[0])**2 + (yy.to(self._device) - point[1])**2)**0.5
-                        relis, reljs = torch.where(
-                            distance < round(r1 / 512 * h))
-                        direction = direction / \
-                            (torch.linalg.norm(direction) + 1e-7)
-                        gridh = (relis+direction[1]) / (h-1) * 2 - 1
-                        gridw = (reljs+direction[0]) / (w-1) * 2 - 1
-                        grid = torch.stack(
-                            [gridw, gridh], dim=-1).unsqueeze(0).unsqueeze(0)
-                        target = F.grid_sample(
-                            feat_resize.float(), grid, align_corners=True).squeeze(2)
-                        loss_motion += F.l1_loss(
-                            feat_resize[:, :, relis, reljs].detach(), target)
-
-                loss = loss_motion
-                if mask is not None:
-                    if mask.min() == 0 and mask.max() == 1:
-                        mask_usq = mask.to(
-                            self._device).unsqueeze(0).unsqueeze(0)
-                        loss_fix = F.l1_loss(
-                            feat_resize * mask_usq, self.feat0_resize * mask_usq)
-                        loss += lambda_mask * loss_fix
-
-                # latent code regularization
-                loss += reg * F.l1_loss(ws, self.w0)
-                if not res.stop:
-                    self.w_optim.zero_grad()
-                    loss.backward()
-                    self.w_optim.step()
-
-            # Scale and convert to uint8.
-            img = img[0]
-            if img_normalize:
-                img = img / img.norm(float('inf'),
-                                     dim=[1, 2], keepdim=True).clip(1e-8, 1e8)
-            img = img * (10 ** (img_scale_db / 20))
-            img = (img * 127.5 + 128).clamp(0,
-                                            255).to(torch.uint8).permute(1, 2, 0)
-            if to_pil:
-                from PIL import Image
-                img = img.cpu().numpy()
-                img = Image.fromarray(img)
-            res.image = img
-            res.w = ws.detach().cpu().numpy()
-        except Exception as e:
-            import os
-            print(f'Renderer error: {e}')
-            print("Out of memory error occurred. Restarting the app...")
-            os.execv(sys.executable, ['python'] + sys.argv)
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/EdBianchi/Social_Toximeter/README.md b/spaces/EdBianchi/Social_Toximeter/README.md
deleted file mode 100644
index 790d8ea31c1bd45f3025fcae7ca919bcdf581db2..0000000000000000000000000000000000000000
--- a/spaces/EdBianchi/Social_Toximeter/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Social Toximeter
-emoji: ☢️
-colorFrom: purple
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Eddycrack864/Applio-Inference/mdx_processing_script.py b/spaces/Eddycrack864/Applio-Inference/mdx_processing_script.py
deleted file mode 100644
index 05616843300aacf46c98ce06f017ba1d0794f313..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/mdx_processing_script.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import gc
-import requests
-import subprocess
-import logging
-import sys
-from bs4 import BeautifulSoup
-import torch, pdb, os, warnings, librosa
-import soundfile as sf
-from tqdm import tqdm
-import numpy as np
-import torch
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import mdx
-branch = "https://github.com/NaJeongMo/Colab-for-MDX_B"
-
-model_params = "https://raw.githubusercontent.com/TRvlvr/application_data/main/mdx_model_data/model_data.json"
-_Models = "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/"
-# _models = "https://pastebin.com/raw/jBzYB8vz"
-_models = "https://raw.githubusercontent.com/TRvlvr/application_data/main/filelists/download_checks.json"
-stem_naming = "https://pastebin.com/raw/mpH4hRcF"
-
-file_folder = "Colab-for-MDX_B"
-model_ids = requests.get(_models).json()
-model_ids = model_ids["mdx_download_list"].values()
-#print(model_ids)
-model_params = requests.get(model_params).json()
-stem_naming = requests.get(stem_naming).json()
-
-os.makedirs("tmp_models", exist_ok=True)
-
-warnings.filterwarnings("ignore")
-cpu = torch.device("cpu")
-if torch.cuda.is_available():
-    device = torch.device("cuda:0")
-elif torch.backends.mps.is_available():
-    device = torch.device("mps")
-else:
-    device = torch.device("cpu")
-
-
-def get_model_list():
-    return model_ids
-
-def id_to_ptm(mkey):
-    if mkey in model_ids:
-        mpath = f"{now_dir}/tmp_models/{mkey}"
-        if not os.path.exists(f'{now_dir}/tmp_models/{mkey}'):
-            print('Downloading model...',end=' ')
-            subprocess.run(
-                ["wget", _Models+mkey, "-O", mpath]
-            )
-            print(f'saved to {mpath}')
-            # get_ipython().system(f'gdown {model_id} -O /content/tmp_models/{mkey}')
-            return mpath
-        else:
-            return mpath
-    else:
-        mpath = f'models/{mkey}'
-        return mpath
-
-def prepare_mdx(onnx,custom_param=False, dim_f=None, dim_t=None, n_fft=None, stem_name=None, compensation=None):
-    device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-    if custom_param:
-        assert not (dim_f is None or dim_t is None or n_fft is None or compensation is None), 'Custom parameter selected, but incomplete parameters are provided.'
-        mdx_model = mdx.MDX_Model(
-            device,
-            dim_f = dim_f,
-            dim_t = dim_t,
-            n_fft = n_fft,
-            stem_name=stem_name,
-            compensation=compensation
-        )
-    else:
-        model_hash = mdx.MDX.get_hash(onnx)
-        if model_hash in model_params:
-            mp = model_params.get(model_hash)
-            mdx_model = mdx.MDX_Model(
-                device,
-                dim_f = mp["mdx_dim_f_set"],
-                dim_t = 2**mp["mdx_dim_t_set"],
-                n_fft = mp["mdx_n_fft_scale_set"],
-                stem_name=mp["primary_stem"],
-                compensation=compensation if not custom_param and compensation is not None else mp["compensate"]
-            )
-    return mdx_model
-
-def run_mdx(onnx, mdx_model,filename, output_format='wav',diff=False,suffix=None,diff_suffix=None, denoise=False, m_threads=2):
-    mdx_sess = mdx.MDX(onnx,mdx_model)
-    print(f"Processing: {filename}")
-    if filename.lower().endswith('.wav'):
-        wave, sr = librosa.load(filename, mono=False, sr=44100)
-    else:
-        temp_wav = 'temp_audio.wav'
-        subprocess.run(['ffmpeg', '-i', filename, '-ar', '44100', '-ac', '2', temp_wav])  # Convert to WAV format
-        wave, sr = librosa.load(temp_wav, mono=False, sr=44100)
-        os.remove(temp_wav)
-    
-    #wave, sr = librosa.load(filename,mono=False, sr=44100)
-    # normalizing input wave gives better output
-    peak = max(np.max(wave), abs(np.min(wave)))
-    wave /= peak
-    if denoise:
-        wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))
-        wave_processed *= 0.5
-    else:
-        wave_processed = mdx_sess.process_wave(wave, m_threads)
-    # return to previous peak
-    wave_processed *= peak
-
-    stem_name = mdx_model.stem_name if suffix is None else suffix # use suffix if provided
-    save_path = os.path.basename(os.path.splitext(filename)[0])
-    #vocals_save_path = os.path.join(vocals_folder, f"{save_path}_{stem_name}.{output_format}")
-    #instrumental_save_path = os.path.join(instrumental_folder, f"{save_path}_{stem_name}.{output_format}")
-    save_path = f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.{output_format}"
-    save_path = os.path.join(
-            'audios',
-            save_path
-        )
-    sf.write(
-        save_path,
-        wave_processed.T,
-        sr
-    )
-
-    print(f'done, saved to: {save_path}')
-
-    if diff:
-        diff_stem_name = stem_naming.get(stem_name) if diff_suffix is None else diff_suffix # use suffix if provided
-        stem_name = f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
-        save_path = f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.{output_format}"
-        save_path = os.path.join(
-                'audio-others',
-                save_path
-            )
-        sf.write(
-            save_path,
-            (-wave_processed.T*mdx_model.compensation)+wave.T,
-            sr
-        )
-        print(f'invert done, saved to: {save_path}')
-    del mdx_sess, wave_processed, wave
-    gc.collect()
-
-if __name__ == "__main__":
-    print()
\ No newline at end of file
diff --git a/spaces/Epitech/hand-sign-detection/README.md b/spaces/Epitech/hand-sign-detection/README.md
deleted file mode 100644
index 85dbc4235898a293f9862c3d9b3500a4f2582c80..0000000000000000000000000000000000000000
--- a/spaces/Epitech/hand-sign-detection/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Hand Sign Detection
-emoji: 📊
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.5
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/EronSamez/RVC_HFmeu/Applio-RVC-Fork/utils/backups.py b/spaces/EronSamez/RVC_HFmeu/Applio-RVC-Fork/utils/backups.py
deleted file mode 100644
index b814f8184792e80e2324685436053d61487110b1..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/Applio-RVC-Fork/utils/backups.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import os
-import shutil
-import hashlib
-import time
-import base64
-
-
-
-
-LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
-WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
-GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
-
-def import_google_drive_backup():
-    print("Importing Google Drive backup...")
-    weights_exist = False
-    for root, dirs, files in os.walk(GOOGLE_DRIVE_PATH):
-        for filename in files:
-            filepath = os.path.join(root, filename)
-            if os.path.isfile(filepath) and not filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
-                backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
-                backup_folderpath = os.path.dirname(backup_filepath)
-                if not os.path.exists(backup_folderpath):
-                    os.makedirs(backup_folderpath)
-                    print(f'Created backup folder: {backup_folderpath}', flush=True)
-                shutil.copy2(filepath, backup_filepath) # copy file with metadata
-                print(f'Imported file from Google Drive backup: {filename}')
-            elif filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')) and filename.endswith('.pth'):
-                weights_exist = True
-                weights_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, os.path.join(GOOGLE_DRIVE_PATH, 'weights')))
-                weights_folderpath = os.path.dirname(weights_filepath)
-                if not os.path.exists(weights_folderpath):
-                    os.makedirs(weights_folderpath)
-                    print(f'Created weights folder: {weights_folderpath}', flush=True)
-                shutil.copy2(filepath, weights_filepath) # copy file with metadata
-                print(f'Imported file from weights: {filename}')
-    if weights_exist:
-        print("Copied weights from Google Drive backup to local weights folder.")
-    else:
-        print("No weights found in Google Drive backup.")
-    print("Google Drive backup import completed.")
-
-def get_md5_hash(file_path):
-    hash_md5 = hashlib.md5()
-    with open(file_path, "rb") as f:
-        for chunk in iter(lambda: f.read(4096), b""):
-            hash_md5.update(chunk)
-    return hash_md5.hexdigest()
-
-def copy_weights_folder_to_drive():
-    destination_folder = os.path.join(GOOGLE_DRIVE_PATH, 'weights')
-    try:
-        if not os.path.exists(destination_folder):
-            os.makedirs(destination_folder)
-
-        num_copied = 0
-        for filename in os.listdir(WEIGHTS_FOLDER):
-            if filename.endswith('.pth'):
-                source_file = os.path.join(WEIGHTS_FOLDER, filename)
-                destination_file = os.path.join(destination_folder, filename)
-                if not os.path.exists(destination_file):
-                    shutil.copy2(source_file, destination_file)
-                    num_copied += 1
-                    print(f"Copied {filename} to Google Drive!")
-
-        if num_copied == 0:
-            print("No new finished models found for copying.")
-        else:
-            print(f"Finished copying {num_copied} files to Google Drive!")
-
-    except Exception as e:
-        print(f"An error occurred while copying weights: {str(e)}")
-        # You can log the error or take appropriate actions here.
-
-def backup_files():
-    print("\nStarting backup loop...")
-    last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
-    fully_updated = False  # boolean to track if all files are up to date
-    
-    while True:
-        try:
-            updated = False  # flag to check if any files were updated
-            last_backup_timestamps = {}
-
-            try:
-                with open(last_backup_timestamps_path, 'r') as f:
-                    last_backup_timestamps = dict(line.strip().split(':') for line in f)
-            except FileNotFoundError:
-                pass  # File does not exist yet, which is fine
-            
-            for root, dirs, files in os.walk(LOGS_FOLDER):
-                for filename in files:
-                    if filename != 'last_backup_timestamps.txt':
-                        filepath = os.path.join(root, filename)
-                        if os.path.isfile(filepath):
-                            backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
-                            backup_folderpath = os.path.dirname(backup_filepath)
-                            if not os.path.exists(backup_folderpath):
-                                os.makedirs(backup_folderpath)
-                                print(f'Created backup folder: {backup_folderpath}', flush=True)
-                            # check if file has changed since last backup
-                            last_backup_timestamp = last_backup_timestamps.get(filepath)
-                            current_timestamp = os.path.getmtime(filepath)
-                            if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
-                                shutil.copy2(filepath, backup_filepath)  # copy file with metadata
-                                last_backup_timestamps[filepath] = str(current_timestamp)  # update last backup timestamp
-                                if last_backup_timestamp is None:
-                                    print(f'Backed up file: {filename}')
-                                else:
-                                    print(f'Updating backed up file: {filename}')
-                                updated = True
-                                fully_updated = False  # if a file is updated, all files are not up to date
-            
-            # check if any files were deleted in Colab and delete them from the backup drive
-            for filepath in list(last_backup_timestamps.keys()):
-                if not os.path.exists(filepath):
-                    backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
-                    if os.path.exists(backup_filepath):
-                        os.remove(backup_filepath)
-                        print(f'Deleted file: {filepath}')
-                    del last_backup_timestamps[filepath]
-                    updated = True
-                    fully_updated = False  # if a file is deleted, all files are not up to date
-            
-            if not updated and not fully_updated:
-                print("Files are up to date.")
-                fully_updated = True  # if all files are up to date, set the boolean to True
-                copy_weights_folder_to_drive()
-                sleep_time = 15
-            else:
-                sleep_time = 0.1
-            
-            with open(last_backup_timestamps_path, 'w') as f:
-                for filepath, timestamp in last_backup_timestamps.items():
-                    f.write(f'{filepath}:{timestamp}\n')
-            
-            time.sleep(sleep_time)  # wait for 15 seconds before checking again, or 0.1s if not fully up to date to speed up backups
-        
-        except Exception as e:
-            print(f"An error occurred: {str(e)}")
-            # You can log the error or take appropriate actions here.
diff --git a/spaces/EronSamez/RVC_HFmeu/i18n/locale_diff.py b/spaces/EronSamez/RVC_HFmeu/i18n/locale_diff.py
deleted file mode 100644
index 387ddfe1b16c2f9f32b6b9682b61353837b06bd8..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/i18n/locale_diff.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json
-import os
-from collections import OrderedDict
-
-# Define the standard file name
-standard_file = "en_US.json"
-
-# Find all JSON files in the directory
-dir_path = "./"
-languages = [
-    f for f in os.listdir(dir_path) if f.endswith(".json") and f != standard_file
-]
-
-# Load the standard file
-with open(standard_file, "r", encoding="utf-8") as f:
-    standard_data = json.load(f, object_pairs_hook=OrderedDict)
-
-# Loop through each language file
-for lang_file in languages:
-    # Load the language file
-    with open(lang_file, "r", encoding="utf-8") as f:
-        lang_data = json.load(f, object_pairs_hook=OrderedDict)
-
-    # Find the difference between the language file and the standard file
-    diff = set(standard_data.keys()) - set(lang_data.keys())
-
-    miss = set(lang_data.keys()) - set(standard_data.keys())
-
-    # Add any missing keys to the language file
-    for key in diff:
-        lang_data[key] = key
-
-    # Del any extra keys to the language file
-    for key in miss:
-        del lang_data[key]
-
-    # Sort the keys of the language file to match the order of the standard file
-    lang_data = OrderedDict(
-        sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0]))
-    )
-
-    # Save the updated language file
-    with open(lang_file, "w", encoding="utf-8") as f:
-        json.dump(lang_data, f, ensure_ascii=False, indent=4)
-        f.write("\n")
diff --git a/spaces/EuroPython2022/example-hello/app.py b/spaces/EuroPython2022/example-hello/app.py
deleted file mode 100644
index 06524cff894be705bc092234da04e0c7a020f729..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/example-hello/app.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import gradio as gr
-
-def update(name):
-    return f"Welcome to EuroPython 2022, {name}!"
-
-demo = gr.Blocks()
-
-with demo:
-    gr.Markdown(
-    """
-    # Hello World!
-    Start typing below to see the output.
-    """)
-    inp = gr.Textbox(placeholder="What is your name?")
-    out = gr.Textbox()
-
-    inp.change(fn=update, 
-               inputs=inp, 
-               outputs=out)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/EuroPython2022/mmocr-demo/configs/_base_/schedules/schedule_sgd_160e.py b/spaces/EuroPython2022/mmocr-demo/configs/_base_/schedules/schedule_sgd_160e.py
deleted file mode 100644
index 985b8f63b3cb34f04ff55b298b44a53568a50ae8..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/_base_/schedules/schedule_sgd_160e.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=0.001,
-    step=[80, 128])
-# running settings
-runner = dict(type='EpochBasedRunner', max_epochs=160)
-checkpoint_config = dict(interval=10)
diff --git a/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/Gravityengine.py b/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/Gravityengine.py
deleted file mode 100644
index f0cd09daaaae0adaa349f91139dc60c7ac79c028..0000000000000000000000000000000000000000
--- a/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/Gravityengine.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://gpt4.xunika.uk/'
-model = ['gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-    }
-    data = {
-        'model': model,
-        'temperature': 0.7,
-        'presence_penalty': 0,
-        'messages': messages,
-    }
-    response = requests.post(url + '/api/openai/v1/chat/completions',
-                             json=data, stream=True)
-    
-    yield response.json()['choices'][0]['message']['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/FriendlyJew/GoyimProxy/Dockerfile b/spaces/FriendlyJew/GoyimProxy/Dockerfile
deleted file mode 100644
index eef259fa372a804549fb0af0913718a13344da34..0000000000000000000000000000000000000000
--- a/spaces/FriendlyJew/GoyimProxy/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
-    apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]
diff --git a/spaces/Gradio-Blocks/Codex_OpenAI/app.py b/spaces/Gradio-Blocks/Codex_OpenAI/app.py
deleted file mode 100644
index 23b060d6aa20e5ca20e06e4c2072950c3d986048..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/Codex_OpenAI/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import openai
-import numpy as np
-import os
-import json
-import gradio as gr
- 
-openai.api_key = os.environ["api_key"]
-engine = "code-davinci-002"
-
-
-def happytt(temperature,max_tokens,text,stop):
-  try:
-    s = json.loads(stop)
-    response = openai.Completion.create(
-      engine=engine,
-      prompt=text,
-      temperature=temperature,
-      max_tokens=max_tokens,
-      top_p=1,
-      frequency_penalty=0,
-      presence_penalty=0,
-      stop=s
-    )
-  except json.JSONDecodeError:
-    response = openai.Completion.create(
-      engine=engine,
-      prompt=text,
-      temperature=temperature,
-      max_tokens=max_tokens,
-      top_p=1,
-      frequency_penalty=0,
-      presence_penalty=0
-    )
-
-  return response.choices[0].text
-
-
-title = "OpenAI Codex"
-description = '''OpenAI Codex is an artificial intelligence model developed by OpenAI. 
-It parses natural language and generates code in response. 
-It is used to power GitHub Copilot, a programming autocompletion 
-tool developed for  Code generation.
-
-Try following prompts and tweak temperatures  in following links -
-
-https://www.pragnakalp.com/experimenting-with-openai-codex/
-
-https://betterprogramming.pub/i-beta-tested-openais-codex-and-the-results-are-spooky-good-e282a1874c79
-
-https://beta.openai.com/examples?category=code
-
-Built by  [mohammed arsalan](https://www.linkedin.com/in/sallu-mandya/)'''
- 
- 
-iface = gr.Interface( happytt,[ gr.Slider(0, 1, step=0.1),gr.Slider(150, 4000, step=1),
-                               gr.Textbox(type='str',   
-                                                 label="input prompt"),
-                               gr.Textbox(type='str',   
-                                                 label="list of tokens, when to finish generating",
-                                                 placeholder='["<code>", "import"]')],"text", title = title, description = description )
-iface.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/scripts/calc_losses_on_images.py b/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/scripts/calc_losses_on_images.py
deleted file mode 100644
index 32b6bcee854da7ae357daf82bd986f30db9fb72c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/scripts/calc_losses_on_images.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from argparse import ArgumentParser
-import os
-import json
-import sys
-from tqdm import tqdm
-import numpy as np
-import torch
-from torch.utils.data import DataLoader
-import torchvision.transforms as transforms
-
-sys.path.append(".")
-sys.path.append("..")
-
-from criteria.lpips.lpips import LPIPS
-from datasets.gt_res_dataset import GTResDataset
-
-
-def parse_args():
-    parser = ArgumentParser(add_help=False)
-    parser.add_argument('--mode', type=str, default='lpips', choices=['lpips', 'l2'])
-    parser.add_argument('--data_path', type=str, default='results')
-    parser.add_argument('--gt_path', type=str, default='gt_images')
-    parser.add_argument('--workers', type=int, default=4)
-    parser.add_argument('--batch_size', type=int, default=4)
-    parser.add_argument('--is_cars', action='store_true')
-    args = parser.parse_args()
-    return args
-
-
-def run(args):
-    resize_dims = (256, 256)
-    if args.is_cars:
-        resize_dims = (192, 256)
-    transform = transforms.Compose([transforms.Resize(resize_dims),
-                                    transforms.ToTensor(),
-                                    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
-
-    print('Loading dataset')
-    dataset = GTResDataset(root_path=args.data_path,
-                           gt_dir=args.gt_path,
-                           transform=transform)
-
-    dataloader = DataLoader(dataset,
-                            batch_size=args.batch_size,
-                            shuffle=False,
-                            num_workers=int(args.workers),
-                            drop_last=True)
-
-    if args.mode == 'lpips':
-        loss_func = LPIPS(net_type='alex')
-    elif args.mode == 'l2':
-        loss_func = torch.nn.MSELoss()
-    else:
-        raise Exception('Not a valid mode!')
-    loss_func.cuda()
-
-    global_i = 0
-    scores_dict = {}
-    all_scores = []
-    for result_batch, gt_batch in tqdm(dataloader):
-        for i in range(args.batch_size):
-            loss = float(loss_func(result_batch[i:i + 1].cuda(), gt_batch[i:i + 1].cuda()))
-            all_scores.append(loss)
-            im_path = dataset.pairs[global_i][0]
-            scores_dict[os.path.basename(im_path)] = loss
-            global_i += 1
-
-    all_scores = list(scores_dict.values())
-    mean = np.mean(all_scores)
-    std = np.std(all_scores)
-    result_str = 'Average loss is {:.2f}+-{:.2f}'.format(mean, std)
-    print('Finished with ', args.data_path)
-    print(result_str)
-
-    out_path = os.path.join(os.path.dirname(args.data_path), 'inference_metrics')
-    if not os.path.exists(out_path):
-        os.makedirs(out_path)
-
-    with open(os.path.join(out_path, 'stat_{}.txt'.format(args.mode)), 'w') as f:
-        f.write(result_str)
-    with open(os.path.join(out_path, 'scores_{}.json'.format(args.mode)), 'w') as f:
-        json.dump(scores_dict, f)
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    run(args)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py
deleted file mode 100644
index d069f8c9fdbaa55cbc44065740187c242cfa2903..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index c5dbf20b0fcc7bc1dd077bd8b7077772251d4c1a..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './emanet_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_base_16khz.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_base_16khz.py
deleted file mode 100644
index 190cc1d0a1e316347e8ebbdfc8de7e2942c1b3d7..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_base_16khz.py
+++ /dev/null
@@ -1,23 +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 ..musicgen._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=64, partition=partitions)
-    launcher.bind_(solver='audiogen/audiogen_base_16khz')
-    # replace this by the desired environmental sound dataset
-    launcher.bind_(dset='internal/sounds_16khz')
-
-    fsdp = {'autocast': False, 'fsdp.use': True}
-    medium = {'model/lm/model_scale': 'medium'}
-
-    launcher.bind_(fsdp)
-    launcher(medium)
diff --git a/spaces/GuXiaoBei/wechat-chatbot/channel/wechat/wechaty_channel.py b/spaces/GuXiaoBei/wechat-chatbot/channel/wechat/wechaty_channel.py
deleted file mode 100644
index 8f27f6dc81422741ddfbbc2e700f8b8b62011cc3..0000000000000000000000000000000000000000
--- a/spaces/GuXiaoBei/wechat-chatbot/channel/wechat/wechaty_channel.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# encoding:utf-8
-
-"""
-wechaty channel
-Python Wechaty - https://github.com/wechaty/python-wechaty
-"""
-import io
-import os
-import json
-import time
-import asyncio
-import requests
-from typing import Optional, Union
-from wechaty_puppet import MessageType, FileBox, ScanStatus  # type: ignore
-from wechaty import Wechaty, Contact
-from wechaty.user import Message, Room, MiniProgram, UrlLink
-from channel.channel import Channel
-from common.log import logger
-from config import conf
-
-
-class WechatyChannel(Channel):
-
-    def __init__(self):
-        pass
-
-    def startup(self):
-        asyncio.run(self.main())
-
-    async def main(self):
-        config = conf()
-        # 使用PadLocal协议 比较稳定(免费web协议 os.environ['WECHATY_PUPPET_SERVICE_ENDPOINT'] = '127.0.0.1:8080')
-        token = config.get('wechaty_puppet_service_token')
-        os.environ['WECHATY_PUPPET_SERVICE_TOKEN'] = token
-        global bot
-        bot = Wechaty()
-
-        bot.on('scan', self.on_scan)
-        bot.on('login', self.on_login)
-        bot.on('message', self.on_message)
-        await bot.start()
-
-    async def on_login(self, contact: Contact):
-        logger.info('[WX] login user={}'.format(contact))
-
-    async def on_scan(self, status: ScanStatus, qr_code: Optional[str] = None,
-                      data: Optional[str] = None):
-        contact = self.Contact.load(self.contact_id)
-        logger.info('[WX] scan user={}, scan status={}, scan qr_code={}'.format(contact, status.name, qr_code))
-        # print(f'user <{contact}> scan status: {status.name} , 'f'qr_code: {qr_code}')
-
-    async def on_message(self, msg: Message):
-        """
-        listen for message event
-        """
-        from_contact = msg.talker()  # 获取消息的发送者
-        to_contact = msg.to()  # 接收人
-        room = msg.room()  # 获取消息来自的群聊. 如果消息不是来自群聊, 则返回None
-        from_user_id = from_contact.contact_id
-        to_user_id = to_contact.contact_id  # 接收人id
-        # other_user_id = msg['User']['UserName']  # 对手方id
-        content = msg.text()
-        mention_content = await msg.mention_text()  # 返回过滤掉@name后的消息
-        match_prefix = self.check_prefix(content, conf().get('single_chat_prefix'))
-        conversation: Union[Room, Contact] = from_contact if room is None else room
-
-        if room is None and msg.type() == MessageType.MESSAGE_TYPE_TEXT:
-            if not msg.is_self() and match_prefix is not None:
-                # 好友向自己发送消息
-                if match_prefix != '':
-                    str_list = content.split(match_prefix, 1)
-                    if len(str_list) == 2:
-                        content = str_list[1].strip()
-
-                img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
-                if img_match_prefix:
-                    content = content.split(img_match_prefix, 1)[1].strip()
-                    await self._do_send_img(content, from_user_id)
-                else:
-                    await self._do_send(content, from_user_id)
-            elif msg.is_self() and match_prefix:
-                # 自己给好友发送消息
-                str_list = content.split(match_prefix, 1)
-                if len(str_list) == 2:
-                    content = str_list[1].strip()
-                img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
-                if img_match_prefix:
-                    content = content.split(img_match_prefix, 1)[1].strip()
-                    await self._do_send_img(content, to_user_id)
-                else:
-                    await self._do_send(content, to_user_id)
-        elif room and msg.type() == MessageType.MESSAGE_TYPE_TEXT:
-            # 群组&文本消息
-            room_id = room.room_id
-            room_name = await room.topic()
-            from_user_id = from_contact.contact_id
-            from_user_name = from_contact.name
-            is_at = await msg.mention_self()
-            content = mention_content
-            config = conf()
-            match_prefix = (is_at and not config.get("group_at_off", False)) \
-                           or self.check_prefix(content, config.get('group_chat_prefix')) \
-                           or self.check_contain(content, config.get('group_chat_keyword'))
-            if ('ALL_GROUP' in config.get('group_name_white_list') or room_name in config.get(
-                    'group_name_white_list') or self.check_contain(room_name, config.get(
-                'group_name_keyword_white_list'))) and match_prefix:
-                img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
-                if img_match_prefix:
-                    content = content.split(img_match_prefix, 1)[1].strip()
-                    await self._do_send_group_img(content, room_id)
-                else:
-                    await self._do_send_group(content, room_id, from_user_id, from_user_name)
-
-    async def send(self, message: Union[str, Message, FileBox, Contact, UrlLink, MiniProgram], receiver):
-        logger.info('[WX] sendMsg={}, receiver={}'.format(message, receiver))
-        if receiver:
-            contact = await bot.Contact.find(receiver)
-            await contact.say(message)
-
-    async def send_group(self, message: Union[str, Message, FileBox, Contact, UrlLink, MiniProgram], receiver):
-        logger.info('[WX] sendMsg={}, receiver={}'.format(message, receiver))
-        if receiver:
-            room = await bot.Room.find(receiver)
-            await room.say(message)
-
-    async def _do_send(self, query, reply_user_id):
-        try:
-            if not query:
-                return
-            context = dict()
-            context['from_user_id'] = reply_user_id
-            reply_text = super().build_reply_content(query, context)
-            if reply_text:
-                await self.send(conf().get("single_chat_reply_prefix") + reply_text, reply_user_id)
-        except Exception as e:
-            logger.exception(e)
-
-    async def _do_send_img(self, query, reply_user_id):
-        try:
-            if not query:
-                return
-            context = dict()
-            context['type'] = 'IMAGE_CREATE'
-            img_url = super().build_reply_content(query, context)
-            if not img_url:
-                return
-            # 图片下载
-            # pic_res = requests.get(img_url, stream=True)
-            # image_storage = io.BytesIO()
-            # for block in pic_res.iter_content(1024):
-            #     image_storage.write(block)
-            # image_storage.seek(0)
-
-            # 图片发送
-            logger.info('[WX] sendImage, receiver={}'.format(reply_user_id))
-            t = int(time.time())
-            file_box = FileBox.from_url(url=img_url, name=str(t) + '.png')
-            await self.send(file_box, reply_user_id)
-        except Exception as e:
-            logger.exception(e)
-
-    async def _do_send_group(self, query, group_id, group_user_id, group_user_name):
-        if not query:
-            return
-        context = dict()
-        context['from_user_id'] = str(group_id) + '-' + str(group_user_id)
-        reply_text = super().build_reply_content(query, context)
-        if reply_text:
-            reply_text = '@' + group_user_name + ' ' + reply_text.strip()
-            await self.send_group(conf().get("group_chat_reply_prefix", "") + reply_text, group_id)
-
-    async def _do_send_group_img(self, query, reply_room_id):
-        try:
-            if not query:
-                return
-            context = dict()
-            context['type'] = 'IMAGE_CREATE'
-            img_url = super().build_reply_content(query, context)
-            if not img_url:
-                return
-            # 图片发送
-            logger.info('[WX] sendImage, receiver={}'.format(reply_room_id))
-            t = int(time.time())
-            file_box = FileBox.from_url(url=img_url, name=str(t) + '.png')
-            await self.send_group(file_box, reply_room_id)
-        except Exception as e:
-            logger.exception(e)
-
-    def check_prefix(self, content, prefix_list):
-        for prefix in prefix_list:
-            if content.startswith(prefix):
-                return prefix
-        return None
-
-    def check_contain(self, content, keyword_list):
-        if not keyword_list:
-            return None
-        for ky in keyword_list:
-            if content.find(ky) != -1:
-                return True
-        return None
diff --git a/spaces/HReynaud/EchoDiffusionDemo/app.py b/spaces/HReynaud/EchoDiffusionDemo/app.py
deleted file mode 100644
index 18764c18212bf498b362f5169b99959118e197be..0000000000000000000000000000000000000000
--- a/spaces/HReynaud/EchoDiffusionDemo/app.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import gradio as gr
-import os
-from omegaconf import OmegaConf
-from imagen_pytorch import Unet3D, ElucidatedImagen, ImagenTrainer, ElucidatedImagenConfig, NullUnet, Imagen
-import torch
-import numpy as np
-import cv2
-from PIL import Image
-import torchvision.transforms as T
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-exp_path = "model"
-
-class BetterCenterCrop(T.CenterCrop):
-    def __call__(self, img):
-        h = img.shape[-2]
-        w = img.shape[-1]
-        dim = min(h, w)
-
-        return T.functional.center_crop(img, dim)
-
-class ImageLoader:
-    def __init__(self, path) -> None:
-        self.path = path
-        self.all_files = os.listdir(path)
-        self.transform = T.Compose([
-            T.ToTensor(),
-            BetterCenterCrop((112, 112)),
-            T.Resize((112, 112)),
-        ])
-        
-    def get_image(self):
-        idx = np.random.randint(0, len(self.all_files))
-        img = Image.open(os.path.join(self.path, self.all_files[idx]))
-        return img
-
-class Context:
-    def __init__(self, path, device):
-        self.path = path
-        self.config_path = os.path.join(path, "config.yaml")
-        self.weight_path = os.path.join(path, "merged.pt")
-        
-        self.config = OmegaConf.load(self.config_path)
-
-        self.config.dataset.num_frames = int(self.config.dataset.fps * self.config.dataset.duration)
-
-        self.im_load = ImageLoader("echo_images")
-
-        unets = []
-        for i, (k, v) in enumerate(self.config.unets.items()):
-            unets.append(Unet3D(**v, lowres_cond=(i>0))) # type: ignore
-
-        imagen_klass = ElucidatedImagen if self.config.imagen.elucidated == True else Imagen
-        del self.config.imagen.elucidated
-        imagen = imagen_klass(
-            unets = unets,
-            **OmegaConf.to_container(self.config.imagen), # type: ignore
-        )
-
-        self.trainer = ImagenTrainer(
-            imagen = imagen,
-            **self.config.trainer
-        ).to(device)
-
-        print("Loading weights from", self.weight_path)
-        additional_data = self.trainer.load(self.weight_path)
-        print("Loaded weights from", self.weight_path)
-    
-    def reshape_image(self, image):
-        try:
-            image = self.im_load.transform(image).multiply(255).byte().permute(1,2,0).numpy()
-            return image
-        except:
-            return None
-    
-    def load_random_image(self):
-        print("Loading random image")        
-        image = self.im_load.get_image()
-        return image
-    
-    def generate_video(self, image, lvef, cond_scale):
-        print("Generating video")
-        print(f"lvef: {lvef}, cond_scale: {cond_scale}")
-
-        image = self.im_load.transform(image).unsqueeze(0)
-
-        sample_kwargs = {}
-        sample_kwargs = {
-            "text_embeds": torch.tensor([[[lvef/100.0]]]),
-            "cond_scale": cond_scale,
-            "cond_images": image,
-        }
-            
-        self.trainer.eval()
-        with torch.no_grad():
-            video = self.trainer.sample(
-                batch_size=1, 
-                video_frames=self.config.dataset.num_frames,
-                **sample_kwargs,
-                use_tqdm = True,
-            ).detach().cpu() # C x F x H x W
-        if video.shape[-3:] != (64, 112, 112):
-            video = torch.nn.functional.interpolate(video, size=(64, 112, 112), mode='trilinear', align_corners=False)
-        video = video.repeat((1,1,5,1,1)) # make the video loop 5 times - easier to see
-        uid = np.random.randint(0, 10) # prevent overwriting if multiple users are using the app
-        path = f"tmp/{uid}.mp4"
-        video = video.multiply(255).byte().squeeze(0).permute(1, 2, 3, 0).numpy()
-        out = cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*'mp4v'), 32, (112, 112))
-        for i in video:
-            out.write(i)
-        out.release()
-        return path
-
-context = Context(exp_path, device)
-
-with gr.Blocks(css="style.css") as demo:
-
-    with gr.Row():
-        gr.Label("Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis")
-
-    with gr.Row():
-        with gr.Column():
-            with gr.Row():
-                with gr.Column(scale=3, variant="panel"):
-                    text = gr.Markdown(value="This is a live demo of our work on cardiac ultrasound video generation. The model is trained on 4-chamber cardiac ultrasound videos and can generate realistic 4-chamber videos given a target Left Ventricle Ejection Fraction. Please, start by sampling a random frame from the pool of 100 images taken from the EchoNet-Dynamic dataset, which will act as the conditional image, representing the anatomy of the video. Then, set the target LVEF, and click the button to generate a video. The process takes 30s to 60s. The model running here corresponds to the 1SCM from the paper. **Click on the video to play it.** [Code is available here](https://github.com/HReynaud/EchoDiffusion) ")
-                with gr.Column(scale=1, min_width="226"):
-                    image = gr.Image(interactive=True)
-                with gr.Column(scale=1, min_width="226"):
-                    video = gr.Video(interactive=False)
-
-            slider_ef = gr.Slider(minimum=10, maximum=90, step=1, label="Target LVEF", value=60, interactive=True)
-            slider_cond = gr.Slider(minimum=0, maximum=20, step=1, label="Conditional scale (if set to more than 1, generation time is 60s)", value=1, interactive=True)
-
-            with gr.Row():
-                img_btn = gr.Button(value="❶ Get a random cardiac ultrasound image (4Ch)")
-                run_btn = gr.Button(value="❷ Generate a video (~30s) 🚀")
-
-    image.change(context.reshape_image, inputs=[image], outputs=[image])
-    img_btn.click(context.load_random_image, inputs=[], outputs=[image])
-    run_btn.click(context.generate_video, inputs=[image, slider_ef, slider_cond], outputs=[video])
- 
-if __name__ == "__main__":
-    demo.queue()
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/HaMerL/ChaosinChat/modules/models/models.py b/spaces/HaMerL/ChaosinChat/modules/models/models.py
deleted file mode 100644
index 5625ec98dcbb34b0f1120f827831c07aa9d69345..0000000000000000000000000000000000000000
--- a/spaces/HaMerL/ChaosinChat/modules/models/models.py
+++ /dev/null
@@ -1,631 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import commentjson as cjson
-import os
-import sys
-import requests
-import urllib3
-import platform
-import base64
-from io import BytesIO
-from PIL import Image
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from enum import Enum
-import uuid
-
-from ..presets import *
-from ..llama_func import *
-from ..utils import *
-from .. import shared
-from ..config import retrieve_proxy
-from modules import config
-from .base_model import BaseLLMModel, ModelType
-
-
-class OpenAIClient(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        api_key,
-        system_prompt=INITIAL_SYSTEM_PROMPT,
-        temperature=1.0,
-        top_p=1.0,
-    ) -> None:
-        super().__init__(
-            model_name=model_name,
-            temperature=temperature,
-            top_p=top_p,
-            system_prompt=system_prompt,
-        )
-        self.api_key = api_key
-        self.need_api_key = True
-        self._refresh_header()
-
-    def get_answer_stream_iter(self):
-        response = self._get_response(stream=True)
-        if response is not None:
-            iter = self._decode_chat_response(response)
-            partial_text = ""
-            for i in iter:
-                partial_text += i
-                yield partial_text
-        else:
-            yield STANDARD_ERROR_MSG + GENERAL_ERROR_MSG
-
-    def get_answer_at_once(self):
-        response = self._get_response()
-        response = json.loads(response.text)
-        content = response["choices"][0]["message"]["content"]
-        total_token_count = response["usage"]["total_tokens"]
-        return content, total_token_count
-
-    def count_token(self, user_input):
-        input_token_count = count_token(construct_user(user_input))
-        if self.system_prompt is not None and len(self.all_token_counts) == 0:
-            system_prompt_token_count = count_token(
-                construct_system(self.system_prompt)
-            )
-            return input_token_count + system_prompt_token_count
-        return input_token_count
-
-    def billing_info(self):
-        try:
-            curr_time = datetime.datetime.now()
-            last_day_of_month = get_last_day_of_month(
-                curr_time).strftime("%Y-%m-%d")
-            first_day_of_month = curr_time.replace(day=1).strftime("%Y-%m-%d")
-            usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={last_day_of_month}"
-            try:
-                usage_data = self._get_billing_data(usage_url)
-            except Exception as e:
-                logging.error(f"获取API使用情况失败:" + str(e))
-                return i18n("**获取API使用情况失败**")
-            rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100)
-            return i18n("**本月使用金额** ") + f"\u3000 ${rounded_usage}"
-        except requests.exceptions.ConnectTimeout:
-            status_text = (
-                STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            )
-            return status_text
-        except requests.exceptions.ReadTimeout:
-            status_text = STANDARD_ERROR_MSG + READ_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            return status_text
-        except Exception as e:
-            import traceback
-            traceback.print_exc()
-            logging.error(i18n("获取API使用情况失败:") + str(e))
-            return STANDARD_ERROR_MSG + ERROR_RETRIEVE_MSG
-
-    def set_token_upper_limit(self, new_upper_limit):
-        pass
-
-    @shared.state.switching_api_key  # 在不开启多账号模式的时候，这个装饰器不会起作用
-    def _get_response(self, stream=False):
-        openai_api_key = self.api_key
-        system_prompt = self.system_prompt
-        history = self.history
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {openai_api_key}",
-        }
-
-        if system_prompt is not None:
-            history = [construct_system(system_prompt), *history]
-
-        payload = {
-            "model": self.model_name,
-            "messages": history,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-            "n": self.n_choices,
-            "stream": stream,
-            "presence_penalty": self.presence_penalty,
-            "frequency_penalty": self.frequency_penalty,
-        }
-
-        if self.max_generation_token is not None:
-            payload["max_tokens"] = self.max_generation_token
-        if self.stop_sequence is not None:
-            payload["stop"] = self.stop_sequence
-        if self.logit_bias is not None:
-            payload["logit_bias"] = self.logit_bias
-        if self.user_identifier is not None:
-            payload["user"] = self.user_identifier
-
-        if stream:
-            timeout = TIMEOUT_STREAMING
-        else:
-            timeout = TIMEOUT_ALL
-
-        # 如果有自定义的api-host，使用自定义host发送请求，否则使用默认设置发送请求
-        if shared.state.completion_url != COMPLETION_URL:
-            logging.info(f"使用自定义API URL: {shared.state.completion_url}")
-
-        with retrieve_proxy():
-            try:
-                response = requests.post(
-                    shared.state.completion_url,
-                    headers=headers,
-                    json=payload,
-                    stream=stream,
-                    timeout=timeout,
-                )
-            except:
-                return None
-        return response
-
-    def _refresh_header(self):
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-    def _get_billing_data(self, billing_url):
-        with retrieve_proxy():
-            response = requests.get(
-                billing_url,
-                headers=self.headers,
-                timeout=TIMEOUT_ALL,
-            )
-
-        if response.status_code == 200:
-            data = response.json()
-            return data
-        else:
-            raise Exception(
-                f"API request failed with status code {response.status_code}: {response.text}"
-            )
-
-    def _decode_chat_response(self, response):
-        error_msg = ""
-        for chunk in response.iter_lines():
-            if chunk:
-                chunk = chunk.decode()
-                chunk_length = len(chunk)
-                try:
-                    chunk = json.loads(chunk[6:])
-                except json.JSONDecodeError:
-                    print(i18n("JSON解析错误,收到的内容: ") + f"{chunk}")
-                    error_msg += chunk
-                    continue
-                if chunk_length > 6 and "delta" in chunk["choices"][0]:
-                    if chunk["choices"][0]["finish_reason"] == "stop":
-                        break
-                    try:
-                        yield chunk["choices"][0]["delta"]["content"]
-                    except Exception as e:
-                        # logging.error(f"Error: {e}")
-                        continue
-        if error_msg:
-            raise Exception(error_msg)
-
-    def set_key(self, new_access_key):
-        ret = super().set_key(new_access_key)
-        self._refresh_header()
-        return ret
-
-
-class ChatGLM_Client(BaseLLMModel):
-    def __init__(self, model_name) -> None:
-        super().__init__(model_name=model_name)
-        from transformers import AutoTokenizer, AutoModel
-        import torch
-        global CHATGLM_TOKENIZER, CHATGLM_MODEL
-        if CHATGLM_TOKENIZER is None or CHATGLM_MODEL is None:
-            system_name = platform.system()
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"THUDM/{model_name}"
-            CHATGLM_TOKENIZER = AutoTokenizer.from_pretrained(
-                model_source, trust_remote_code=True
-            )
-            quantified = False
-            if "int4" in model_name:
-                quantified = True
-            model = AutoModel.from_pretrained(
-                    model_source, trust_remote_code=True
-                )
-            if torch.cuda.is_available():
-                # run on CUDA
-                logging.info("CUDA is available, using CUDA")
-                model = model.half().cuda()
-            # mps加速还存在一些问题，暂时不使用
-            elif system_name == "Darwin" and model_path is not None and not quantified:
-                logging.info("Running on macOS, using MPS")
-                # running on macOS and model already downloaded
-                model = model.half().to("mps")
-            else:
-                logging.info("GPU is not available, using CPU")
-                model = model.float()
-            model = model.eval()
-            CHATGLM_MODEL = model
-
-    def _get_glm_style_input(self):
-        history = [x["content"] for x in self.history]
-        query = history.pop()
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        assert (
-            len(history) % 2 == 0
-        ), f"History should be even length. current history is: {history}"
-        history = [[history[i], history[i + 1]]
-                   for i in range(0, len(history), 2)]
-        return history, query
-
-    def get_answer_at_once(self):
-        history, query = self._get_glm_style_input()
-        response, _ = CHATGLM_MODEL.chat(
-            CHATGLM_TOKENIZER, query, history=history)
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        history, query = self._get_glm_style_input()
-        for response, history in CHATGLM_MODEL.stream_chat(
-            CHATGLM_TOKENIZER,
-            query,
-            history,
-            max_length=self.token_upper_limit,
-            top_p=self.top_p,
-            temperature=self.temperature,
-        ):
-            yield response
-
-
-class LLaMA_Client(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        lora_path=None,
-    ) -> None:
-        super().__init__(model_name=model_name)
-        from lmflow.datasets.dataset import Dataset
-        from lmflow.pipeline.auto_pipeline import AutoPipeline
-        from lmflow.models.auto_model import AutoModel
-        from lmflow.args import ModelArguments, DatasetArguments, InferencerArguments
-
-        self.max_generation_token = 1000
-        self.end_string = "\n\n"
-        # We don't need input data
-        data_args = DatasetArguments(dataset_path=None)
-        self.dataset = Dataset(data_args)
-        self.system_prompt = ""
-
-        global LLAMA_MODEL, LLAMA_INFERENCER
-        if LLAMA_MODEL is None or LLAMA_INFERENCER is None:
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"decapoda-research/{model_name}"
-                # raise Exception(f"models目录下没有这个模型: {model_name}")
-            if lora_path is not None:
-                lora_path = f"lora/{lora_path}"
-            model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None,
-                                        use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True)
-            pipeline_args = InferencerArguments(
-                local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16')
-
-            with open(pipeline_args.deepspeed, "r") as f:
-                ds_config = json.load(f)
-            LLAMA_MODEL = AutoModel.get_model(
-                model_args,
-                tune_strategy="none",
-                ds_config=ds_config,
-            )
-            LLAMA_INFERENCER = AutoPipeline.get_pipeline(
-                pipeline_name="inferencer",
-                model_args=model_args,
-                data_args=data_args,
-                pipeline_args=pipeline_args,
-            )
-
-    def _get_llama_style_input(self):
-        history = []
-        instruction = ""
-        if self.system_prompt:
-            instruction = (f"Instruction: {self.system_prompt}\n")
-        for x in self.history:
-            if x["role"] == "user":
-                history.append(f"{instruction}Input: {x['content']}")
-            else:
-                history.append(f"Output: {x['content']}")
-        context = "\n\n".join(history)
-        context += "\n\nOutput: "
-        return context
-
-    def get_answer_at_once(self):
-        context = self._get_llama_style_input()
-
-        input_dataset = self.dataset.from_dict(
-            {"type": "text_only", "instances": [{"text": context}]}
-        )
-
-        output_dataset = LLAMA_INFERENCER.inference(
-            model=LLAMA_MODEL,
-            dataset=input_dataset,
-            max_new_tokens=self.max_generation_token,
-            temperature=self.temperature,
-        )
-
-        response = output_dataset.to_dict()["instances"][0]["text"]
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        context = self._get_llama_style_input()
-        partial_text = ""
-        step = 1
-        for _ in range(0, self.max_generation_token, step):
-            input_dataset = self.dataset.from_dict(
-                {"type": "text_only", "instances": [
-                    {"text": context + partial_text}]}
-            )
-            output_dataset = LLAMA_INFERENCER.inference(
-                model=LLAMA_MODEL,
-                dataset=input_dataset,
-                max_new_tokens=step,
-                temperature=self.temperature,
-            )
-            response = output_dataset.to_dict()["instances"][0]["text"]
-            if response == "" or response == self.end_string:
-                break
-            partial_text += response
-            yield partial_text
-
-
-class XMChat(BaseLLMModel):
-    def __init__(self, api_key):
-        super().__init__(model_name="xmchat")
-        self.api_key = api_key
-        self.session_id = None
-        self.reset()
-        self.image_bytes = None
-        self.image_path = None
-        self.xm_history = []
-        self.url = "https://xmbot.net/web"
-        self.last_conv_id = None
-
-    def reset(self):
-        self.session_id = str(uuid.uuid4())
-        self.last_conv_id = None
-        return [], "已重置"
-
-    def image_to_base64(self, image_path):
-        # 打开并加载图片
-        img = Image.open(image_path)
-
-        # 获取图片的宽度和高度
-        width, height = img.size
-
-        # 计算压缩比例，以确保最长边小于4096像素
-        max_dimension = 2048
-        scale_ratio = min(max_dimension / width, max_dimension / height)
-
-        if scale_ratio < 1:
-            # 按压缩比例调整图片大小
-            new_width = int(width * scale_ratio)
-            new_height = int(height * scale_ratio)
-            img = img.resize((new_width, new_height), Image.ANTIALIAS)
-
-        # 将图片转换为jpg格式的二进制数据
-        buffer = BytesIO()
-        if img.mode == "RGBA":
-            img = img.convert("RGB")
-        img.save(buffer, format='JPEG')
-        binary_image = buffer.getvalue()
-
-        # 对二进制数据进行Base64编码
-        base64_image = base64.b64encode(binary_image).decode('utf-8')
-
-        return base64_image
-
-    def try_read_image(self, filepath):
-        def is_image_file(filepath):
-            # 判断文件是否为图片
-            valid_image_extensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"]
-            file_extension = os.path.splitext(filepath)[1].lower()
-            return file_extension in valid_image_extensions
-
-        if is_image_file(filepath):
-            logging.info(f"读取图片文件: {filepath}")
-            self.image_bytes = self.image_to_base64(filepath)
-            self.image_path = filepath
-        else:
-            self.image_bytes = None
-            self.image_path = None
-
-    def like(self):
-        if self.last_conv_id is None:
-            return "点赞失败，你还没发送过消息"
-        data = {
-            "uuid": self.last_conv_id,
-            "appraise": "good"
-        }
-        requests.post(self.url, json=data)
-        return "👍点赞成功，感谢反馈～"
-
-    def dislike(self):
-        if self.last_conv_id is None:
-            return "点踩失败，你还没发送过消息"
-        data = {
-            "uuid": self.last_conv_id,
-            "appraise": "bad"
-        }
-        requests.post(self.url, json=data)
-        return "👎点踩成功，感谢反馈～"
-
-    def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
-        fake_inputs = real_inputs
-        display_append = ""
-        limited_context = False
-        return limited_context, fake_inputs, display_append, real_inputs, chatbot
-
-    def handle_file_upload(self, files, chatbot):
-        """if the model accepts multi modal input, implement this function"""
-        if files:
-            for file in files:
-                if file.name:
-                    logging.info(f"尝试读取图像: {file.name}")
-                    self.try_read_image(file.name)
-            if self.image_path is not None:
-                chatbot = chatbot + [((self.image_path,), None)]
-            if self.image_bytes is not None:
-                logging.info("使用图片作为输入")
-                # XMChat的一轮对话中实际上只能处理一张图片
-                self.reset()
-                conv_id = str(uuid.uuid4())
-                data = {
-                    "user_id": self.api_key,
-                    "session_id": self.session_id,
-                    "uuid": conv_id,
-                    "data_type": "imgbase64",
-                    "data": self.image_bytes
-                }
-                response = requests.post(self.url, json=data)
-                response = json.loads(response.text)
-                logging.info(f"图片回复: {response['data']}")
-        return None, chatbot, None
-
-    def get_answer_at_once(self):
-        question = self.history[-1]["content"]
-        conv_id = str(uuid.uuid4())
-        self.last_conv_id = conv_id
-        data = {
-            "user_id": self.api_key,
-            "session_id": self.session_id,
-            "uuid": conv_id,
-            "data_type": "text",
-            "data": question
-        }
-        response = requests.post(self.url, json=data)
-        try:
-            response = json.loads(response.text)
-            return response["data"], len(response["data"])
-        except Exception as e:
-            return response.text, len(response.text)
-
-
-
-
-def get_model(
-    model_name,
-    lora_model_path=None,
-    access_key=None,
-    temperature=None,
-    top_p=None,
-    system_prompt=None,
-) -> BaseLLMModel:
-    msg = i18n("模型设置为了：") + f" {model_name}"
-    model_type = ModelType.get_type(model_name)
-    lora_selector_visibility = False
-    lora_choices = []
-    dont_change_lora_selector = False
-    if model_type != ModelType.OpenAI:
-        config.local_embedding = True
-    # del current_model.model
-    model = None
-    try:
-        if model_type == ModelType.OpenAI:
-            logging.info(f"正在加载OpenAI模型: {model_name}")
-            model = OpenAIClient(
-                model_name=model_name,
-                api_key=access_key,
-                system_prompt=system_prompt,
-                temperature=temperature,
-                top_p=top_p,
-            )
-        elif model_type == ModelType.ChatGLM:
-            logging.info(f"正在加载ChatGLM模型: {model_name}")
-            model = ChatGLM_Client(model_name)
-        elif model_type == ModelType.LLaMA and lora_model_path == "":
-            msg = f"现在请为 {model_name} 选择LoRA模型"
-            logging.info(msg)
-            lora_selector_visibility = True
-            if os.path.isdir("lora"):
-                lora_choices = get_file_names(
-                    "lora", plain=True, filetypes=[""])
-            lora_choices = ["No LoRA"] + lora_choices
-        elif model_type == ModelType.LLaMA and lora_model_path != "":
-            logging.info(f"正在加载LLaMA模型: {model_name} + {lora_model_path}")
-            dont_change_lora_selector = True
-            if lora_model_path == "No LoRA":
-                lora_model_path = None
-                msg += " + No LoRA"
-            else:
-                msg += f" + {lora_model_path}"
-            model = LLaMA_Client(model_name, lora_model_path)
-        elif model_type == ModelType.XMChat:
-            if os.environ.get("XMCHAT_API_KEY") != "":
-                access_key = os.environ.get("XMCHAT_API_KEY")
-            model = XMChat(api_key=access_key)
-        elif model_type == ModelType.StableLM:
-            from .StableLM import StableLM_Client
-            model = StableLM_Client(model_name)
-        elif model_type == ModelType.MOSS:
-            from .MOSS import MOSS_Client
-            model = MOSS_Client(model_name)
-        elif model_type == ModelType.Unknown:
-            raise ValueError(f"未知模型: {model_name}")
-        logging.info(msg)
-    except Exception as e:
-        logging.error(e)
-        msg = f"{STANDARD_ERROR_MSG}: {e}"
-    if dont_change_lora_selector:
-        return model, msg
-    else:
-        return model, msg, gr.Dropdown.update(choices=lora_choices, visible=lora_selector_visibility)
-
-
-if __name__ == "__main__":
-    with open("config.json", "r") as f:
-        openai_api_key = cjson.load(f)["openai_api_key"]
-    # set logging level to debug
-    logging.basicConfig(level=logging.DEBUG)
-    # client = ModelManager(model_name="gpt-3.5-turbo", access_key=openai_api_key)
-    client = get_model(model_name="chatglm-6b-int4")
-    chatbot = []
-    stream = False
-    # 测试账单功能
-    logging.info(colorama.Back.GREEN + "测试账单功能" + colorama.Back.RESET)
-    logging.info(client.billing_info())
-    # 测试问答
-    logging.info(colorama.Back.GREEN + "测试问答" + colorama.Back.RESET)
-    question = "巴黎是中国的首都吗？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试问答后history : {client.history}")
-    # 测试记忆力
-    logging.info(colorama.Back.GREEN + "测试记忆力" + colorama.Back.RESET)
-    question = "我刚刚问了你什么问题？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试记忆力后history : {client.history}")
-    # 测试重试功能
-    logging.info(colorama.Back.GREEN + "测试重试功能" + colorama.Back.RESET)
-    for i in client.retry(chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"重试后history : {client.history}")
-    # # 测试总结功能
-    # print(colorama.Back.GREEN + "测试总结功能" + colorama.Back.RESET)
-    # chatbot, msg = client.reduce_token_size(chatbot=chatbot)
-    # print(chatbot, msg)
-    # print(f"总结后history: {client.history}")
diff --git a/spaces/Hallucinate/demo/taming/data/sflckr.py b/spaces/Hallucinate/demo/taming/data/sflckr.py
deleted file mode 100644
index 91101be5953b113f1e58376af637e43f366b3dee..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/taming/data/sflckr.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import os
-import numpy as np
-import cv2
-import albumentations
-from PIL import Image
-from torch.utils.data import Dataset
-
-
-class SegmentationBase(Dataset):
-    def __init__(self,
-                 data_csv, data_root, segmentation_root,
-                 size=None, random_crop=False, interpolation="bicubic",
-                 n_labels=182, shift_segmentation=False,
-                 ):
-        self.n_labels = n_labels
-        self.shift_segmentation = shift_segmentation
-        self.data_csv = data_csv
-        self.data_root = data_root
-        self.segmentation_root = segmentation_root
-        with open(self.data_csv, "r") as f:
-            self.image_paths = f.read().splitlines()
-        self._length = len(self.image_paths)
-        self.labels = {
-            "relative_file_path_": [l for l in self.image_paths],
-            "file_path_": [os.path.join(self.data_root, l)
-                           for l in self.image_paths],
-            "segmentation_path_": [os.path.join(self.segmentation_root, l.replace(".jpg", ".png"))
-                                   for l in self.image_paths]
-        }
-
-        size = None if size is not None and size<=0 else size
-        self.size = size
-        if self.size is not None:
-            self.interpolation = interpolation
-            self.interpolation = {
-                "nearest": cv2.INTER_NEAREST,
-                "bilinear": cv2.INTER_LINEAR,
-                "bicubic": cv2.INTER_CUBIC,
-                "area": cv2.INTER_AREA,
-                "lanczos": cv2.INTER_LANCZOS4}[self.interpolation]
-            self.image_rescaler = albumentations.SmallestMaxSize(max_size=self.size,
-                                                                 interpolation=self.interpolation)
-            self.segmentation_rescaler = albumentations.SmallestMaxSize(max_size=self.size,
-                                                                        interpolation=cv2.INTER_NEAREST)
-            self.center_crop = not random_crop
-            if self.center_crop:
-                self.cropper = albumentations.CenterCrop(height=self.size, width=self.size)
-            else:
-                self.cropper = albumentations.RandomCrop(height=self.size, width=self.size)
-            self.preprocessor = self.cropper
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, i):
-        example = dict((k, self.labels[k][i]) for k in self.labels)
-        image = Image.open(example["file_path_"])
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        image = np.array(image).astype(np.uint8)
-        if self.size is not None:
-            image = self.image_rescaler(image=image)["image"]
-        segmentation = Image.open(example["segmentation_path_"])
-        assert segmentation.mode == "L", segmentation.mode
-        segmentation = np.array(segmentation).astype(np.uint8)
-        if self.shift_segmentation:
-            # used to support segmentations containing unlabeled==255 label
-            segmentation = segmentation+1
-        if self.size is not None:
-            segmentation = self.segmentation_rescaler(image=segmentation)["image"]
-        if self.size is not None:
-            processed = self.preprocessor(image=image,
-                                          mask=segmentation
-                                          )
-        else:
-            processed = {"image": image,
-                         "mask": segmentation
-                         }
-        example["image"] = (processed["image"]/127.5 - 1.0).astype(np.float32)
-        segmentation = processed["mask"]
-        onehot = np.eye(self.n_labels)[segmentation]
-        example["segmentation"] = onehot
-        return example
-
-
-class Examples(SegmentationBase):
-    def __init__(self, size=None, random_crop=False, interpolation="bicubic"):
-        super().__init__(data_csv="data/sflckr_examples.txt",
-                         data_root="data/sflckr_images",
-                         segmentation_root="data/sflckr_segmentations",
-                         size=size, random_crop=random_crop, interpolation=interpolation)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/test_iopath.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/test_iopath.py
deleted file mode 100644
index 908261a6619806f7ef9b5dd1beb5d6817b249a6e..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/test_iopath.py
+++ /dev/null
@@ -1,29 +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 unittest
-from unittest import mock
-
-
-class TestIOPath(unittest.TestCase):
-
-    def test_no_iopath(self):
-        from .test_reproducibility import TestReproducibility
-
-        with mock.patch.dict("sys.modules", {"iopath": None}):
-            # reuse reproducibility tests, which are e2e tests that should cover
-            # most checkpoint related functionality
-            TestReproducibility._test_reproducibility(self, "test_reproducibility")
-
-    def test_no_supports_rename(self):
-        from .test_reproducibility import TestReproducibility
-
-        with mock.patch("fairseq.file_io.PathManager.supports_rename") as mock_fn:
-            mock_fn.return_value = False
-            TestReproducibility._test_reproducibility(self, "test_reproducibility")
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/scripts/inference/api.sh b/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/scripts/inference/api.sh
deleted file mode 100644
index 4f6ce2a2147f69e5b3da851c8222bef830056338..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/scripts/inference/api.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-gender='male'
-glowdir='../../checkpoints/glow/'$gender'/'
-hifidir='../../checkpoints/hifi/'$gender'/'
-device='cpu'
-lang='en'
-
-
-python ../../utils/inference/api.py -a $glowdir -v $hifidir -d $device -L $lang
diff --git a/spaces/Heber/google-flan-t5-xl/README.md b/spaces/Heber/google-flan-t5-xl/README.md
deleted file mode 100644
index 8aea3a3bc32d966fb2b00d0da6b6175b45643e7f..0000000000000000000000000000000000000000
--- a/spaces/Heber/google-flan-t5-xl/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Google Flan T5 Xl
-emoji: 🏃
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ICML2022/OFA/fairseq/examples/bart/summarize.py b/spaces/ICML2022/OFA/fairseq/examples/bart/summarize.py
deleted file mode 100644
index 04435f80e39c2d9d894696dae7cba5b381e13da9..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/bart/summarize.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from fairseq.models.bart import BARTModel
-import argparse
-
-XSUM_KWARGS = dict(beam=6, lenpen=1.0, max_len_b=60, min_len=10, no_repeat_ngram_size=3)
-CNN_KWARGS = dict(beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3)
-
-
-@torch.no_grad()
-def generate(bart, infile, outfile="bart_hypo.txt", bsz=32, n_obs=None, **eval_kwargs):
-    count = 1
-
-    # if n_obs is not None: bsz = min(bsz, n_obs)
-
-    with open(infile) as source, open(outfile, "w") as fout:
-        sline = source.readline().strip()
-        slines = [sline]
-        for sline in source:
-            if n_obs is not None and count > n_obs:
-                break
-            if count % bsz == 0:
-                hypotheses_batch = bart.sample(slines, **eval_kwargs)
-                for hypothesis in hypotheses_batch:
-                    fout.write(hypothesis + "\n")
-                    fout.flush()
-                slines = []
-
-            slines.append(sline.strip())
-            count += 1
-
-        if slines != []:
-            hypotheses_batch = bart.sample(slines, **eval_kwargs)
-            for hypothesis in hypotheses_batch:
-                fout.write(hypothesis + "\n")
-                fout.flush()
-
-
-def main():
-    """
-    Usage::
-
-         python examples/bart/summarize.py \
-            --model-dir $HOME/bart.large.cnn \
-            --model-file model.pt \
-            --src $HOME/data-bin/cnn_dm/test.source
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--model-dir",
-        required=True,
-        type=str,
-        default="bart.large.cnn/",
-        help="path containing model file and src_dict.txt",
-    )
-    parser.add_argument(
-        "--model-file",
-        default="checkpoint_best.pt",
-        help="where in model_dir are weights saved",
-    )
-    parser.add_argument(
-        "--src", default="test.source", help="text to summarize", type=str
-    )
-    parser.add_argument(
-        "--out", default="test.hypo", help="where to save summaries", type=str
-    )
-    parser.add_argument("--bsz", default=32, help="where to save summaries", type=int)
-    parser.add_argument(
-        "--n", default=None, help="how many examples to summarize", type=int
-    )
-    parser.add_argument(
-        "--xsum-kwargs",
-        action="store_true",
-        default=False,
-        help="if true use XSUM_KWARGS else CNN_KWARGS",
-    )
-    args = parser.parse_args()
-    eval_kwargs = XSUM_KWARGS if args.xsum_kwargs else CNN_KWARGS
-    if args.model_dir == "pytorch/fairseq":
-        bart = torch.hub.load("pytorch/fairseq", args.model_file)
-    else:
-        bart = BARTModel.from_pretrained(
-            args.model_dir,
-            checkpoint_file=args.model_file,
-            data_name_or_path=args.model_dir,
-        )
-    bart = bart.eval()
-    if torch.cuda.is_available():
-        bart = bart.cuda().half()
-    generate(
-        bart, args.src, bsz=args.bsz, n_obs=args.n, outfile=args.out, **eval_kwargs
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/IDKiro/DehazeFormer_Demo/app.py b/spaces/IDKiro/DehazeFormer_Demo/app.py
deleted file mode 100644
index adb48b20b25638138be33ed88576a70b36d2ee75..0000000000000000000000000000000000000000
--- a/spaces/IDKiro/DehazeFormer_Demo/app.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-import numpy as np
-import gradio as gr
-
-from PIL import Image
-from models import dehazeformer
-
-
-def infer(raw_image):
-	network = dehazeformer()
-	network.load_state_dict(torch.load('./saved_models/dehazeformer.pth', map_location=torch.device('cpu'))['state_dict'])
-	# torch.save({'state_dict': network.state_dict()}, './saved_models/dehazeformer.pth')
-
-	network.eval()
-	
-	image = np.array(raw_image, np.float32) / 255. * 2 - 1
-	image = torch.from_numpy(image)
-	image = image.permute((2, 0, 1)).unsqueeze(0)
-
-	with torch.no_grad():
-		output = network(image).clamp_(-1, 1)[0] * 0.5 + 0.5	
-		output = output.permute((1, 2, 0))
-		output = np.array(output, np.float32)
-		output = np.round(output * 255.0)
-
-	output = Image.fromarray(output.astype(np.uint8))
-
-	return output
-
-
-title = "DehazeFormer"
-description = f"We use a mixed dataset to train the model, allowing the trained model to work better on real hazy images. To allow the model to process high-resolution images more efficiently and effectively, we extend it to the [MCT](https://github.com/IDKiro/MCT) variant."
-examples = [
-		["examples/1.jpg"],
-		["examples/2.jpg"],
-		["examples/3.jpg"],
-		["examples/4.jpg"],
-		["examples/5.jpg"],
-		["examples/6.jpg"]
-]
-
-iface = gr.Interface(
-	infer,
-	inputs="image", outputs="image",
-	title=title,
-	description=description,
-	allow_flagging='never',
-	examples=examples,
-)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/INDONESIA-AI/Anapnoe/README.md b/spaces/INDONESIA-AI/Anapnoe/README.md
deleted file mode 100644
index b39374216fcda9ceb71430dbe3360cd1e562335b..0000000000000000000000000000000000000000
--- a/spaces/INDONESIA-AI/Anapnoe/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Anapnoe
-emoji: 📊
-colorFrom: purple
-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/JUNGU/VToonify/vtoonify/model/encoder/readme.md b/spaces/JUNGU/VToonify/vtoonify/model/encoder/readme.md
deleted file mode 100644
index 5421bfe3e67b7b6cbd7baf96b741b539d65bb0fd..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/VToonify/vtoonify/model/encoder/readme.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
-
-## Description   
-Official Implementation of pSp paper for both training and evaluation. The pSp method extends the StyleGAN model to 
-allow solving different image-to-image translation problems using its encoder.
-
-Fork from [https://github.com/eladrich/pixel2style2pixel](https://github.com/eladrich/pixel2style2pixel).
-
-In VToonify, we modify pSp to accept z+ latent code.
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/schedulers/scheduling_ddpm.py b/spaces/Jackflack09/diffuse-custom/diffusers/schedulers/scheduling_ddpm.py
deleted file mode 100644
index 369db8b29e7d2e9abb9707bbf877ba7707f664eb..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/schedulers/scheduling_ddpm.py
+++ /dev/null
@@ -1,373 +0,0 @@
-# Copyright 2022 UC Berkeley Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, FrozenDict, register_to_config
-from ..utils import _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS, BaseOutput, deprecate
-from .scheduling_utils import SchedulerMixin
-
-
-@dataclass
-class DDPMSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.FloatTensor
-    pred_original_sample: Optional[torch.FloatTensor] = None
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-
-    def alpha_bar(time_step):
-        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return torch.tensor(betas, dtype=torch.float32)
-
-
-class DDPMScheduler(SchedulerMixin, ConfigMixin):
-    """
-    Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
-    Langevin dynamics sampling.
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    For more details, see the original paper: https://arxiv.org/abs/2006.11239
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        variance_type (`str`):
-            options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
-            `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
-        clip_sample (`bool`, default `True`):
-            option to clip predicted sample between -1 and 1 for numerical stability.
-        prediction_type (`str`, default `epsilon`, optional):
-            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
-            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
-    """
-
-    _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
-    _deprecated_kwargs = ["predict_epsilon"]
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        variance_type: str = "fixed_small",
-        clip_sample: bool = True,
-        prediction_type: str = "epsilon",
-        **kwargs,
-    ):
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
-
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
-            )
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        elif beta_schedule == "sigmoid":
-            # GeoDiff sigmoid schedule
-            betas = torch.linspace(-6, 6, num_train_timesteps)
-            self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-        self.one = torch.tensor(1.0)
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.num_inference_steps = None
-        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
-
-        self.variance_type = variance_type
-
-    def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`torch.FloatTensor`): input sample
-            timestep (`int`, optional): current timestep
-
-        Returns:
-            `torch.FloatTensor`: scaled input sample
-        """
-        return sample
-
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
-        """
-        Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-        """
-        num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
-        self.num_inference_steps = num_inference_steps
-        timesteps = np.arange(
-            0, self.config.num_train_timesteps, self.config.num_train_timesteps // self.num_inference_steps
-        )[::-1].copy()
-        self.timesteps = torch.from_numpy(timesteps).to(device)
-
-    def _get_variance(self, t, predicted_variance=None, variance_type=None):
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
-
-        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
-        # and sample from it to get previous sample
-        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
-        variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
-
-        if variance_type is None:
-            variance_type = self.config.variance_type
-
-        # hacks - were probably added for training stability
-        if variance_type == "fixed_small":
-            variance = torch.clamp(variance, min=1e-20)
-        # for rl-diffuser https://arxiv.org/abs/2205.09991
-        elif variance_type == "fixed_small_log":
-            variance = torch.log(torch.clamp(variance, min=1e-20))
-            variance = torch.exp(0.5 * variance)
-        elif variance_type == "fixed_large":
-            variance = self.betas[t]
-        elif variance_type == "fixed_large_log":
-            # Glide max_log
-            variance = torch.log(self.betas[t])
-        elif variance_type == "learned":
-            return predicted_variance
-        elif variance_type == "learned_range":
-            min_log = variance
-            max_log = self.betas[t]
-            frac = (predicted_variance + 1) / 2
-            variance = frac * max_log + (1 - frac) * min_log
-
-        return variance
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timestep: int,
-        sample: torch.FloatTensor,
-        generator=None,
-        return_dict: bool = True,
-        **kwargs,
-    ) -> Union[DDPMSchedulerOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-            generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class
-
-        Returns:
-            [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-
-        """
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            new_config = dict(self.config)
-            new_config["prediction_type"] = "epsilon" if predict_epsilon else "sample"
-            self._internal_dict = FrozenDict(new_config)
-
-        t = timestep
-
-        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
-            model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
-        else:
-            predicted_variance = None
-
-        # 1. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        # 2. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
-        if self.config.prediction_type == "epsilon":
-            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-        elif self.config.prediction_type == "sample":
-            pred_original_sample = model_output
-        elif self.config.prediction_type == "v_prediction":
-            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-        else:
-            raise ValueError(
-                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or"
-                " `v_prediction`  for the DDPMScheduler."
-            )
-
-        # 3. Clip "predicted x_0"
-        if self.config.clip_sample:
-            pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
-
-        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
-        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
-        pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[t]) / beta_prod_t
-        current_sample_coeff = self.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t
-
-        # 5. Compute predicted previous sample µ_t
-        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
-        pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
-
-        # 6. Add noise
-        variance = 0
-        if t > 0:
-            device = model_output.device
-            if device.type == "mps":
-                # randn does not work reproducibly on mps
-                variance_noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator)
-                variance_noise = variance_noise.to(device)
-            else:
-                variance_noise = torch.randn(
-                    model_output.shape, generator=generator, device=device, dtype=model_output.dtype
-                )
-            if self.variance_type == "fixed_small_log":
-                variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise
-            else:
-                variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * variance_noise
-
-        pred_prev_sample = pred_prev_sample + variance
-
-        if not return_dict:
-            return (pred_prev_sample,)
-
-        return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
-
-    def add_noise(
-        self,
-        original_samples: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
-        timesteps = timesteps.to(original_samples.device)
-
-        sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        return noisy_samples
-
-    def get_velocity(
-        self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as sample
-        self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
-        timesteps = timesteps.to(sample.device)
-
-        sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(sample.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
-        return velocity
-
-    def __len__(self):
-        return self.config.num_train_timesteps
diff --git a/spaces/JosueElias/borrs/dataset_with_embeddings.py b/spaces/JosueElias/borrs/dataset_with_embeddings.py
deleted file mode 100644
index 4538d041604484e0101f2a982c2cb72a3fc2da3b..0000000000000000000000000000000000000000
--- a/spaces/JosueElias/borrs/dataset_with_embeddings.py
+++ /dev/null
@@ -1,23 +0,0 @@
-
-from datasets import load_from_disk, Dataset
-from huggingface_hub import hf_hub_download
-from datasets import load_dataset
-import faiss
-
-# load faiss file and get route of file https://huggingface.co/docs/huggingface_hub/guides/download#from-latest-version
-path2 = hf_hub_download(repo_id="JosueElias/pipeline_faiss", filename="faiss.index", repo_type="dataset")
-
-# load wikipedia dataset https://huggingface.co/docs/datasets/loading#hugging-face-hub
-datasetx = load_dataset("JosueElias/pipeline_dataset2")
-
-# save wikipedia dataset locally https://huggingface.co/docs/datasets/process#save
-datasetx.save_to_disk("./directory")
-
-# delete variable to have more memory space
-del datasetx
-
-# load dataset again in arrow format
-datasetx = load_from_disk("./directory/train")
-
-# load faiss to dataset
-datasetx.load_faiss_index('embeddings', path2)
\ No newline at end of file
diff --git a/spaces/Juno360219/albert-base-v2/README.md b/spaces/Juno360219/albert-base-v2/README.md
deleted file mode 100644
index fa956f3644d10f33d9593b8bd4b7c1188047f7e4..0000000000000000000000000000000000000000
--- a/spaces/Juno360219/albert-base-v2/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Albert Base V2
-emoji: 🐠
-colorFrom: gray
-colorTo: pink
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/infer/modules/vc/utils.py b/spaces/Kangarroar/ApplioRVC-Inference/infer/modules/vc/utils.py
deleted file mode 100644
index a1cb0ff84097d1c7eb82373ccf19db061f595096..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/infer/modules/vc/utils.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-import re
-from fairseq import checkpoint_utils
-
-
-def get_index_path_from_model(sid):
-    sid0strip = re.sub(r'\.pth|\.onnx$', '', sid)
-    sid0name = os.path.split(sid0strip)[-1]  # Extract only the name, not the directory
-
-    # Check if the sid0strip has the specific ending format _eXXX_sXXX
-    if re.match(r'.+_e\d+_s\d+$', sid0name):
-        base_model_name = sid0name.rsplit('_', 2)[0]
-    else:
-        base_model_name = sid0name
-    
-    return next(
-        (
-            f
-            for f in [
-                os.path.join(root, name)
-                for root, _, files in os.walk(os.getenv("index_root"), topdown=False)
-                for name in files
-                if name.endswith(".index") and "trained" not in name
-            ]
-            if base_model_name in f
-        ),
-        "",
-    )
-
-
-def load_hubert(config):
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["assets/hubert/hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    return hubert_model.eval()
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/modules/F0Predictor/F0Predictor.py
deleted file mode 100644
index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/modules/F0Predictor/F0Predictor.py
+++ /dev/null
@@ -1,16 +0,0 @@
-class F0Predictor(object):
-    def compute_f0(self, wav, p_len):
-        """
-        input: wav:[signal_length]
-               p_len:int
-        output: f0:[signal_length//hop_length]
-        """
-        pass
-
-    def compute_f0_uv(self, wav, p_len):
-        """
-        input: wav:[signal_length]
-               p_len:int
-        output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
-        """
-        pass
diff --git a/spaces/Kevin676/AutoGPT/autogpt/commands/times.py b/spaces/Kevin676/AutoGPT/autogpt/commands/times.py
deleted file mode 100644
index 3c9b8a4fc67a251c9e81a8c4a725cd1e25fcbebe..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/AutoGPT/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/Kr1n3/Fashion-Items-Classification/README.md b/spaces/Kr1n3/Fashion-Items-Classification/README.md
deleted file mode 100644
index b89cace17e5b7df622b6f127a463877a91e50c28..0000000000000000000000000000000000000000
--- a/spaces/Kr1n3/Fashion-Items-Classification/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Fashion Items Classification
-emoji: 📈
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kuachi/ai-voice/commons.py b/spaces/Kuachi/ai-voice/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/Kuachi/ai-voice/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/KunalSinha2024/cledgeEssayIdeationTool/app.py b/spaces/KunalSinha2024/cledgeEssayIdeationTool/app.py
deleted file mode 100644
index 5ce16aa12a41cac4e836d0846d98587df631013f..0000000000000000000000000000000000000000
--- a/spaces/KunalSinha2024/cledgeEssayIdeationTool/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import os
-from dotenv import load_dotenv
-import openai
-import random
-import requests
-from ast import literal_eval
-import json
-from enum import Enum
-import gradio as gr
-
-load_dotenv()
-openai.api_key = os.getenv("OPENAI_API_KEY")
-CHAT_ENDPOINT="https://api.openai.com/v1/chat/completions"
-CHAT_MODEL = "gpt-3.5-turbo"
-CHAT_AUTH = {"Authorization": "Bearer " + openai.api_key}
-MAX_TOKENS = 250
-
-#TODO: handle max length limits
-
-class ChatRoles():
-    SYSTEM = "system"
-    ASSISTANT = "assistant"
-    USER = "user"
-
-def get_assistant_response(gpt_history):
-    params = {
-        "model": CHAT_MODEL,
-        "messages": gpt_history,
-        "max_tokens": MAX_TOKENS
-    }
-    response = requests.post(url=CHAT_ENDPOINT, json=params, headers=CHAT_AUTH)
-    print(literal_eval(response.content.decode("utf-8")))
-    response_message = literal_eval(response.content.decode("utf-8"))["choices"][0]["message"]["content"]
-    gpt_history.append({"role": ChatRoles.ASSISTANT, "content": response_message})
-    print("\n" + response_message)
-    return response_message
-
-hardcoded = {
-    1: "Hi, I'm an AI powered college counselor from Cledge! What prompt do you want help with?",
-    2: "Pick as many questions to answer as you'd like. Write the number of the question and then your response."
-}
-
-instructions = {
-    2: "Based on these responses, generate 5 questions to help them brainstorm.",
-    3: "Based on these responses, ask follow up questions that help them narrow down the focus of the essay",
-    4: "Based on these responses, ask follow up questions that help them identify key themes in the essay",
-    5: "Based on these responses, think of 5 ideas for personal statement essays. Write a synopsis of each idea.",
-}
-
-def grad_demo():
-    with gr.Blocks() as demo:
-        gpt_history = []
-        def user(user_message, history):
-            gpt_history.append({"role": ChatRoles.USER, "content": user_message})
-            print(f"Length of gpt_history: {gpt_history}")
-            return "", history + [[user_message, None]]
-        def bot(history):
-            step = len(history)
-            print(f"STEP: {step}")
-            bot_message = ""
-            if step in instructions:
-                gpt_history.append({"role": ChatRoles.SYSTEM, "content": instructions[step]})
-                bot_message = get_assistant_response(gpt_history)
-            if step in hardcoded:
-                bot_message = f"{bot_message}\n\n {hardcoded[step]}"
-            history[-1][1] = bot_message
-            gpt_history.append({"role": ChatRoles.ASSISTANT, "content": bot_message})
-            print(f"Length of gpt_history: {gpt_history}")
-            return history
-        def initialize():
-            gpt_history.clear()
-            history = bot([[None, None]])
-            return history
-        chatbot = gr.Chatbot(value = initialize)
-        msg = gr.Textbox()
-        clear = gr.Button("Clear")
-        msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-            bot, chatbot, chatbot
-        )
-        clear.click(lambda: None, None, chatbot, queue=False)
-    demo.launch()
-
-if __name__ == "__main__":
-    grad_demo()
-    #main()
\ No newline at end of file
diff --git a/spaces/KyanChen/FunSR/models/edsr.py b/spaces/KyanChen/FunSR/models/edsr.py
deleted file mode 100644
index e5fac715d86c0270336bd0df175e1d44fc1319b2..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/FunSR/models/edsr.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# modified from: https://github.com/thstkdgus35/EDSR-PyTorch
-
-import math
-from argparse import Namespace
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from models import register
-
-
-def default_conv(in_channels, out_channels, kernel_size, bias=True):
-    return nn.Conv2d(
-        in_channels, out_channels, kernel_size,
-        padding=(kernel_size//2), bias=bias)
-
-class MeanShift(nn.Conv2d):
-    def __init__(
-        self, rgb_range,
-        rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1):
-
-        super(MeanShift, self).__init__(3, 3, kernel_size=1)
-        std = torch.Tensor(rgb_std)
-        self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1)
-        self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std
-        for p in self.parameters():
-            p.requires_grad = False
-
-class ResBlock(nn.Module):
-    def __init__(
-        self, conv, n_feats, kernel_size,
-        bias=True, bn=False, act=nn.ReLU(True), res_scale=1):
-
-        super(ResBlock, self).__init__()
-        m = []
-        for i in range(2):
-            m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
-            if bn:
-                m.append(nn.BatchNorm2d(n_feats))
-            if i == 0:
-                m.append(act)
-
-        self.body = nn.Sequential(*m)
-        self.res_scale = res_scale
-
-    def forward(self, x):
-        res = self.body(x).mul(self.res_scale)
-        res += x
-
-        return res
-
-class Upsampler(nn.Sequential):
-    def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True):
-
-        m = []
-        if (scale & (scale - 1)) == 0:    # Is scale = 2^n?
-            for _ in range(int(math.log(scale, 2))):
-                m.append(conv(n_feats, 4 * n_feats, 3, bias))
-                m.append(nn.PixelShuffle(2))
-                if bn:
-                    m.append(nn.BatchNorm2d(n_feats))
-                if act == 'relu':
-                    m.append(nn.ReLU(True))
-                elif act == 'prelu':
-                    m.append(nn.PReLU(n_feats))
-
-        elif scale == 3:
-            m.append(conv(n_feats, 9 * n_feats, 3, bias))
-            m.append(nn.PixelShuffle(3))
-            if bn:
-                m.append(nn.BatchNorm2d(n_feats))
-            if act == 'relu':
-                m.append(nn.ReLU(True))
-            elif act == 'prelu':
-                m.append(nn.PReLU(n_feats))
-        else:
-            raise NotImplementedError
-
-        super(Upsampler, self).__init__(*m)
-
-
-url = {
-    'r16f64x2': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_baseline_x2-1bc95232.pt',
-    'r16f64x3': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_baseline_x3-abf2a44e.pt',
-    'r16f64x4': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_baseline_x4-6b446fab.pt',
-    'r32f256x2': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_x2-0edfb8a3.pt',
-    'r32f256x3': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_x3-ea3ef2c6.pt',
-    'r32f256x4': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_x4-4f62e9ef.pt'
-}
-
-class EDSR(nn.Module):
-    def __init__(self, args, conv=default_conv):
-        super(EDSR, self).__init__()
-        self.args = args
-        n_resblocks = args.n_resblocks
-        n_feats = args.n_feats
-        kernel_size = 3
-        scale = args.scale[0]
-        act = nn.ReLU(True)
-        url_name = 'r{}f{}x{}'.format(n_resblocks, n_feats, scale)
-        if url_name in url:
-            self.url = url[url_name]
-        else:
-            self.url = None
-        self.sub_mean = MeanShift(args.rgb_range)
-        self.add_mean = MeanShift(args.rgb_range, sign=1)
-
-        # define head module
-        m_head = [conv(args.n_colors, n_feats, kernel_size)]
-
-        # define body module
-        m_body = [
-            ResBlock(
-                conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
-            ) for _ in range(n_resblocks)
-        ]
-        m_body.append(conv(n_feats, n_feats, kernel_size))
-
-        self.head = nn.Sequential(*m_head)
-        self.body = nn.Sequential(*m_body)
-
-        if args.no_upsampling:
-            self.out_dim = n_feats
-        else:
-            self.out_dim = args.n_colors
-            # define tail module
-            m_tail = [
-                Upsampler(conv, scale, n_feats, act=False),
-                conv(n_feats, args.n_colors, kernel_size)
-            ]
-            self.tail = nn.Sequential(*m_tail)
-
-        self.load_state_dict('pretrained/'+self.url.split('/')[-1])
-
-    def forward(self, x):
-        #x = self.sub_mean(x)
-        x = self.head(x)
-
-        res = self.body(x)
-        res += x
-
-        if self.args.no_upsampling:
-            x = res
-        else:
-            x = self.tail(res)
-        #x = self.add_mean(x)
-        return x
-
-    def load_state_dict(self, state_dict, strict=True):
-        state_dict = torch.load(state_dict, map_location='cpu')
-        own_state = self.state_dict()
-        print('loading pretrain model')
-        for name, param in state_dict.items():
-            if name in own_state:
-                if isinstance(param, nn.Parameter):
-                    param = param.data
-                try:
-                    own_state[name].copy_(param)
-                except Exception:
-                    if name.find('tail') == -1:
-                        raise RuntimeError('While copying the parameter named {}, '
-                                           'whose dimensions in the model are {} and '
-                                           'whose dimensions in the checkpoint are {}.'
-                                           .format(name, own_state[name].size(), param.size()))
-            elif strict:
-                if name.find('tail') == -1:
-                    raise KeyError('unexpected key "{}" in state_dict'
-                                   .format(name))
-
-
-@register('edsr-baseline')
-def make_edsr_baseline(n_resblocks=16, n_feats=64, res_scale=1,
-                       scale=2, no_upsampling=False, rgb_range=1):
-    args = Namespace()
-    args.n_resblocks = n_resblocks
-    args.n_feats = n_feats
-    args.res_scale = res_scale
-
-    args.scale = [scale]
-    args.no_upsampling = no_upsampling
-
-    args.rgb_range = rgb_range
-    args.n_colors = 3
-    return EDSR(args)
-
-
-@register('edsr')
-def make_edsr(n_resblocks=32, n_feats=256, res_scale=0.1,
-              scale=2, no_upsampling=False, rgb_range=1):
-    args = Namespace()
-    args.n_resblocks = n_resblocks
-    args.n_feats = n_feats
-    args.res_scale = res_scale
-
-    args.scale = [scale]
-    args.no_upsampling = no_upsampling
-
-    args.rgb_range = rgb_range
-    args.n_colors = 3
-    return EDSR(args)
diff --git a/spaces/KyanChen/RSPrompter/mmpl/structures/__init__.py b/spaces/KyanChen/RSPrompter/mmpl/structures/__init__.py
deleted file mode 100644
index 3021d0a7d0b7fb1b342295ad0a4e99c675b4e52c..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpl/structures/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .cls_data_sample import ClsDataSample
-from .multi_task_data_sample import MultiTaskDataSample
-from .utils import (batch_label_to_onehot, cat_batch_labels,
-                    stack_batch_scores, tensor_split)
-
-__all__ = [
-    'ClsDataSample', 'batch_label_to_onehot', 'cat_batch_labels',
-    'stack_batch_scores', 'tensor_split', 'MultiTaskDataSample'
-]
diff --git a/spaces/Lamai/LAMAIGPT/tests/test_config.py b/spaces/Lamai/LAMAIGPT/tests/test_config.py
deleted file mode 100644
index b472a24c78edd1f931a76c68e08ed544bbe61d98..0000000000000000000000000000000000000000
--- a/spaces/Lamai/LAMAIGPT/tests/test_config.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from unittest import TestCase
-
-from autogpt.config import Config
-
-
-class TestConfig(TestCase):
-    """
-    Test cases for the Config class, which handles the configuration settings
-    for the AI and ensures it behaves as a singleton.
-    """
-
-    def setUp(self):
-        """
-        Set up the test environment by creating an instance of the Config class.
-        """
-        self.config = Config()
-
-    def test_singleton(self):
-        """
-        Test if the Config class behaves as a singleton by ensuring that two instances are the same.
-        """
-        config2 = Config()
-        self.assertIs(self.config, config2)
-
-    def test_initial_values(self):
-        """
-        Test if the initial values of the Config class attributes are set correctly.
-        """
-        self.assertFalse(self.config.debug_mode)
-        self.assertFalse(self.config.continuous_mode)
-        self.assertFalse(self.config.speak_mode)
-        self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo")
-        self.assertEqual(self.config.smart_llm_model, "gpt-4")
-        self.assertEqual(self.config.fast_token_limit, 4000)
-        self.assertEqual(self.config.smart_token_limit, 8000)
-
-    def test_set_continuous_mode(self):
-        """
-        Test if the set_continuous_mode() method updates the continuous_mode attribute.
-        """
-        self.config.set_continuous_mode(True)
-        self.assertTrue(self.config.continuous_mode)
-
-    def test_set_speak_mode(self):
-        """
-        Test if the set_speak_mode() method updates the speak_mode attribute.
-        """
-        self.config.set_speak_mode(True)
-        self.assertTrue(self.config.speak_mode)
-
-    def test_set_fast_llm_model(self):
-        """
-        Test if the set_fast_llm_model() method updates the fast_llm_model attribute.
-        """
-        self.config.set_fast_llm_model("gpt-3.5-turbo-test")
-        self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo-test")
-
-    def test_set_smart_llm_model(self):
-        """
-        Test if the set_smart_llm_model() method updates the smart_llm_model attribute.
-        """
-        self.config.set_smart_llm_model("gpt-4-test")
-        self.assertEqual(self.config.smart_llm_model, "gpt-4-test")
-
-    def test_set_fast_token_limit(self):
-        """
-        Test if the set_fast_token_limit() method updates the fast_token_limit attribute.
-        """
-        self.config.set_fast_token_limit(5000)
-        self.assertEqual(self.config.fast_token_limit, 5000)
-
-    def test_set_smart_token_limit(self):
-        """
-        Test if the set_smart_token_limit() method updates the smart_token_limit attribute.
-        """
-        self.config.set_smart_token_limit(9000)
-        self.assertEqual(self.config.smart_token_limit, 9000)
-
-    def test_set_debug_mode(self):
-        """
-        Test if the set_debug_mode() method updates the debug_mode attribute.
-        """
-        self.config.set_debug_mode(True)
-        self.assertTrue(self.config.debug_mode)
diff --git a/spaces/LaynzKunz/Model-RCV/README.md b/spaces/LaynzKunz/Model-RCV/README.md
deleted file mode 100644
index 184dad7986ad859b643ccef136dbbb7eb62f5553..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Model-RCV/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hololive Rvc Models V2
-emoji: ▶️🐻💿
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: true
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Marne/MockingBird/mockingbirdforuse/synthesizer/utils/text.py b/spaces/Marne/MockingBird/mockingbirdforuse/synthesizer/utils/text.py
deleted file mode 100644
index 7bd0e2238ca32fcdc7d08795832d49fb9be0c419..0000000000000000000000000000000000000000
--- a/spaces/Marne/MockingBird/mockingbirdforuse/synthesizer/utils/text.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from .symbols import symbols
-from . import cleaners
-import re
-
-# 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)}
-
-# Regular expression matching text enclosed in curly braces:
-_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
-
-
-def text_to_sequence(text, cleaner_names):
-    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-
-    The text can optionally have ARPAbet sequences enclosed in curly braces embedded
-    in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
-
-    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 = []
-
-    # Check for curly braces and treat their contents as ARPAbet:
-    while len(text):
-        m = _curly_re.match(text)
-        if not m:
-            sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
-            break
-        sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
-        sequence += _arpabet_to_sequence(m.group(2))
-        text = m.group(3)
-
-    # Append EOS token
-    sequence.append(_symbol_to_id["~"])
-    return sequence
-
-
-def sequence_to_text(sequence):
-    """Converts a sequence of IDs back to a string"""
-    result = ""
-    for symbol_id in sequence:
-        if symbol_id in _id_to_symbol:
-            s = _id_to_symbol[symbol_id]
-            # Enclose ARPAbet back in curly braces:
-            if len(s) > 1 and s[0] == "@":
-                s = "{%s}" % s[1:]
-            result += s
-    return result.replace("}{", " ")
-
-
-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
-
-
-def _symbols_to_sequence(symbols):
-    return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
-
-
-def _arpabet_to_sequence(text):
-    return _symbols_to_sequence(["@" + s for s in text.split()])
-
-
-def _should_keep_symbol(s):
-    return s in _symbol_to_id and s not in ("_", "~")
diff --git a/spaces/MathysL/AutoGPT4/autogpt/commands/analyze_code.py b/spaces/MathysL/AutoGPT4/autogpt/commands/analyze_code.py
deleted file mode 100644
index e02ea4c5b4ba53530e559d1cab7a07b8e3c7c638..0000000000000000000000000000000000000000
--- a/spaces/MathysL/AutoGPT4/autogpt/commands/analyze_code.py
+++ /dev/null
@@ -1,25 +0,0 @@
-"""Code evaluation module."""
-from __future__ import annotations
-
-from autogpt.llm_utils import call_ai_function
-
-
-def analyze_code(code: str) -> list[str]:
-    """
-    A function that takes in a string and returns a response from create chat
-      completion api call.
-
-    Parameters:
-        code (str): Code to be evaluated.
-    Returns:
-        A result string from create chat completion. A list of suggestions to
-            improve the code.
-    """
-
-    function_string = "def analyze_code(code: str) -> List[str]:"
-    args = [code]
-    description_string = (
-        "Analyzes the given code and returns a list of suggestions" " for improvements."
-    )
-
-    return call_ai_function(function_string, args, description_string)
diff --git a/spaces/MoonQiu/LongerCrafter/scripts/run_text2video_freenoise_mp_512.sh b/spaces/MoonQiu/LongerCrafter/scripts/run_text2video_freenoise_mp_512.sh
deleted file mode 100644
index 4fe3fe1f471e85f3dfa8563489e2d3d5ca5fb84d..0000000000000000000000000000000000000000
--- a/spaces/MoonQiu/LongerCrafter/scripts/run_text2video_freenoise_mp_512.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-name="base_512_test"
-
-ckpt='checkpoints/base_512_v1/model.ckpt'
-config='configs/inference_t2v_tconv512_v1.0_freenoise.yaml'
-
-prompt_file="prompts/mp_prompts.txt"
-res_dir="results_freenoise_mp_512"
-
-python3 scripts/evaluation/inference_freenoise_mp.py \
---seed 123 \
---mode 'base' \
---ckpt_path $ckpt \
---config $config \
---savedir $res_dir/$name \
---n_samples 3 \
---bs 1 --height 320 --width 512 \
---unconditional_guidance_scale 12.0 \
---ddim_steps 50 \
---ddim_eta 0.0 \
---prompt_file $prompt_file \
---fps 8 \
---frames 64 \
---window_size 16 \
---window_stride 4 
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/kie/postprocessors/__init__.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/kie/postprocessors/__init__.py
deleted file mode 100644
index 645904bc1beb0b8e1b4f169a8b5344de55e41f8f..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/kie/postprocessors/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .sdmgr_postprocessor import SDMGRPostProcessor
-
-__all__ = ['SDMGRPostProcessor']
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/structures/textspotting_data_sample.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/structures/textspotting_data_sample.py
deleted file mode 100644
index 28478f516f96651d2e49c180cea4a97336fc5c97..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/structures/textspotting_data_sample.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.structures import TextDetDataSample
-
-
-class TextSpottingDataSample(TextDetDataSample):
-    """A data structure interface of MMOCR. They are used as interfaces between
-    different components.
-
-    The attributes in ``TextSpottingDataSample`` are divided into two parts:
-
-        - ``gt_instances``(InstanceData): Ground truth of instance annotations.
-        - ``pred_instances``(InstanceData): Instances of model predictions.
-
-    Examples:
-         >>> import torch
-         >>> import numpy as np
-         >>> from mmengine.structures import InstanceData
-         >>> from mmocr.data import TextSpottingDataSample
-         >>> # gt_instances
-         >>> data_sample = TextSpottingDataSample()
-         >>> img_meta = dict(img_shape=(800, 1196, 3),
-         ...                 pad_shape=(800, 1216, 3))
-         >>> gt_instances = InstanceData(metainfo=img_meta)
-         >>> gt_instances.bboxes = torch.rand((5, 4))
-         >>> gt_instances.labels = torch.rand((5,))
-         >>> data_sample.gt_instances = gt_instances
-         >>> assert 'img_shape' in data_sample.gt_instances.metainfo_keys()
-         >>> len(data_sample.gt_instances)
-         5
-         >>> print(data_sample)
-         <TextSpottingDataSample(
-             META INFORMATION
-             DATA FIELDS
-             gt_instances: <InstanceData(
-                     META INFORMATION
-                     pad_shape: (800, 1216, 3)
-                     img_shape: (800, 1196, 3)
-                     DATA FIELDS
-                     labels: tensor([0.8533, 0.1550, 0.5433, 0.7294, 0.5098])
-                     bboxes:
-                     tensor([[9.7725e-01, 5.8417e-01, 1.7269e-01, 6.5694e-01],
-                             [1.7894e-01, 5.1780e-01, 7.0590e-01, 4.8589e-01],
-                             [7.0392e-01, 6.6770e-01, 1.7520e-01, 1.4267e-01],
-                             [2.2411e-01, 5.1962e-01, 9.6953e-01, 6.6994e-01],
-                             [4.1338e-01, 2.1165e-01, 2.7239e-04, 6.8477e-01]])
-                 ) at 0x7f21fb1b9190>
-         ) at 0x7f21fb1b9880>
-         >>> # pred_instances
-         >>> pred_instances = InstanceData(metainfo=img_meta)
-         >>> pred_instances.bboxes = torch.rand((5, 4))
-         >>> pred_instances.scores = torch.rand((5,))
-         >>> data_sample = TextSpottingDataSample(
-         ...                   pred_instances=pred_instances)
-         >>> assert 'pred_instances' in data_sample
-         >>> data_sample = TextSpottingDataSample()
-         >>> gt_instances_data = dict(
-         ...                        bboxes=torch.rand(2, 4),
-         ...                        labels=torch.rand(2),
-         ...                        masks=np.random.rand(2, 2, 2))
-         >>> gt_instances = InstanceData(**gt_instances_data)
-         >>> data_sample.gt_instances = gt_instances
-         >>> assert 'gt_instances' in data_sample
-         >>> assert 'masks' in data_sample.gt_instances
-    """
diff --git a/spaces/NAACL2022/CLIP-Caption-Reward/scripts/clip_prepro_feats.py b/spaces/NAACL2022/CLIP-Caption-Reward/scripts/clip_prepro_feats.py
deleted file mode 100644
index b7a45c829fa5c19e36509170135835c6d6bc8d67..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/CLIP-Caption-Reward/scripts/clip_prepro_feats.py
+++ /dev/null
@@ -1,170 +0,0 @@
-"""
-Preprocess a raw json dataset into features files for use in data_loader.py
-
-Input: json file that has the form
-[{ file_path: 'path/img.jpg', captions: ['a caption', ...] }, ...]
-example element in this list would look like
-{'captions': [u'A man with a red helmet on a small moped on a dirt road. ', u'Man riding a motor bike on a dirt road on the countryside.', u'A man riding on the back of a motorcycle.', u'A dirt path with a young person on a motor bike rests to the foreground of a verdant area with a bridge and a background of cloud-wreathed mountains. ', u'A man in a red shirt and a red hat is on a motorcycle on a hill side.'], 'file_path': u'val2014/COCO_val2014_000000391895.jpg', 'id': 391895}
-
-This script reads this json, does some basic preprocessing on the captions
-(e.g. lowercase, etc.), creates a special UNK token, and encodes everything to arrays
-
-Output: two folders of features
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import json
-import argparse
-from random import shuffle, seed
-import string
-# non-standard dependencies:
-import h5py
-from six.moves import cPickle
-import numpy as np
-import torch
-import torchvision.models as models
-import skimage.io
-
-from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
-from PIL import Image
-from torch import nn
-
-preprocess = Compose([
-    Resize((448, 448), interpolation=Image.BICUBIC),
-    CenterCrop((448, 448)),
-    ToTensor()
-])
-
-
-from clip.clip import load
-from timm.models.vision_transformer import resize_pos_embed
-import timm
-
-from captioning.utils.resnet_utils import myResnet
-import captioning.utils.resnet as resnet
-
-from tqdm import tqdm
-
-
-def main(params):
-    if params["model_type"] != 'vit_base_patch32_224_in21k':
-        model, transform = load(params["model_type"], jit=False)  
-    else:
-        model = timm.create_model(params["model_type"], pretrained=True)
-        model = model.cuda()
-
-    if params["model_type"] != 'vit_base_patch32_224_in21k':
-        save_model_type = params["model_type"].split("-")[0]
-        mean = torch.Tensor([0.48145466, 0.4578275, 0.40821073]).to("cuda").reshape(3, 1, 1)
-        std = torch.Tensor([0.26862954, 0.26130258, 0.27577711]).to("cuda").reshape(3, 1, 1)
-
-        if "RN" in params["model_type"]:
-            num_patches = 196 #600 * 1000 // 32 // 32
-            pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, model.visual.attnpool.positional_embedding.shape[-1],  device='cuda'),)
-            pos_embed.weight = resize_pos_embed(model.visual.attnpool.positional_embedding.unsqueeze(0), pos_embed)
-            model.visual.attnpool.positional_embedding = pos_embed
-
-    else:
-        save_model_type = 'vit_base'
-        mean = torch.Tensor([0.5, 0.5, 0.5]).to("cuda").reshape(3, 1, 1)
-        std = torch.Tensor([0.5, 0.5, 0.5]).to("cuda").reshape(3, 1, 1)
-
-        num_patches = 196 #600 * 1000 // 32 // 32
-        pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, 768,  device='cuda'),)
-        pos_embed.weight = resize_pos_embed(model.pos_embed, pos_embed)
-        model.pos_embed = pos_embed
-
-    if params["model_type"] == "ViT-B/32":
-        num_patches = 196 #600 * 1000 // 32 // 32
-        pos_embed = nn.Parameter(torch.zeros(num_patches + 1, 768,  device='cuda'),)
-        pos_embed.weight = resize_pos_embed(model.visual.positional_embedding.unsqueeze(0), pos_embed.unsqueeze(0))
-        model.visual.positional_embedding = pos_embed
-    imgs = json.load(open(params['input_json'], 'r'))
-
-    imgs = imgs['images']
-
-    if args.n_jobs > 1:
-        print('Total imgs:', len(imgs))
-        print('Using {} jobs'.format(args.n_jobs))
-        print('job id:', args.job_id)
-        imgs = imgs[args.job_id::args.n_jobs]
-
-    N = len(imgs)
-
-    seed(123) # make reproducible
-
-    dir_fc = params['output_dir']+'_clip_'+save_model_type+'_fc'
-    dir_att = params['output_dir']+'_clip_'+save_model_type+'_att'
-    if not os.path.isdir(dir_fc):
-        os.mkdir(dir_fc)
-    if not os.path.isdir(dir_att):
-        os.mkdir(dir_att)
-
-    for i,img in enumerate(tqdm(imgs)):
-        # load the image
-        with torch.no_grad():
-        
-            image = preprocess(Image.open(os.path.join(params['images_root'], img['filepath'], img['filename']) ).convert("RGB"))
-            image = torch.tensor(np.stack([image])).cuda()
-            image -= mean
-            image /= std
-            if "RN" in params["model_type"]:
-                tmp_att, tmp_fc = model.encode_image(image)
-                tmp_att = tmp_att[0].permute(1, 2, 0)
-                tmp_fc = tmp_fc[0]
-            elif params["model_type"] == 'vit_base_patch32_224_in21k':
-                x = model(image)
-                tmp_fc = x[0, 0, :]
-                tmp_att = x[0, 1:, :].reshape( 14, 14, 768 )
-            else:
-                x = model.visual.conv1(image.half())  # shape = [*, width, grid, grid]
-                x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-                x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-                x = torch.cat([model.visual.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-                x = x + model.visual.positional_embedding.to(x.dtype)[:x.shape[1], :]
-                x = model.visual.ln_pre(x)
-
-                x = x.permute(1, 0, 2)  # NLD -> LND
-
-                for layer_idx, layer in enumerate(model.visual.transformer.resblocks):
-                    x = layer(x)  
-
-                x = x.permute(1, 0, 2)
-                tmp_fc = x[0, 0, :]
-                tmp_att = x[0, 1:, :].reshape( 14, 14, 768 )
-                
-        np.save(os.path.join(dir_fc, str(img['cocoid'])), tmp_fc.data.cpu().float().numpy())
-        np.savez_compressed(os.path.join(dir_att, str(img['cocoid'])), feat=tmp_att.data.cpu().float().numpy())
-
-
-        # if i % 1000 == 0:
-        #     print('processing %d/%d (%.2f%% done)' % (i, N, i*100.0/N))
-    print('wrote ', dir_fc, dir_att)
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser()
-
-    # input json
-    parser.add_argument('--input_json', required=True, help='input json file to process into hdf5')
-    parser.add_argument('--output_dir', default='data', help='output h5 file')
-
-    # options
-    parser.add_argument('--images_root', default='', help='root location in which images are stored, to be prepended to file_path in input json')
-    parser.add_argument('--att_size', default=14, type=int, help='14x14 or 7x7')
-    parser.add_argument('--model_type', default='RN50', type=str, help='RN50, RN101, RN50x4, ViT-B/32, vit_base_patch32_224_in21k')
-
-    parser.add_argument('--n_jobs', default=-1, type=int, help='number of jobs to run in parallel')
-    parser.add_argument('--job_id', default=0, type=int, help='job id')
-    parser.add_argument('--batch_size', default=1, type=int, help='batch size')
-
-
-    args = parser.parse_args()
-    params = vars(args) # convert to ordinary dict
-    print('parsed input parameters:')
-    print(json.dumps(params, indent = 2))
-    main(params)
diff --git a/spaces/NATSpeech/PortaSpeech/modules/commons/conformer/espnet_transformer_attn.py b/spaces/NATSpeech/PortaSpeech/modules/commons/conformer/espnet_transformer_attn.py
deleted file mode 100644
index a479a27ea6fd4202359da435234408ba074f7577..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/modules/commons/conformer/espnet_transformer_attn.py
+++ /dev/null
@@ -1,186 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Shigeki Karita
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Multi-Head Attention layer definition."""
-
-import math
-
-import numpy
-import torch
-from torch import nn
-
-
-class MultiHeadedAttention(nn.Module):
-    """Multi-Head Attention layer.
-    Args:
-        n_head (int): The number of heads.
-        n_feat (int): The number of features.
-        dropout_rate (float): Dropout rate.
-    """
-
-    def __init__(self, n_head, n_feat, dropout_rate):
-        """Construct an MultiHeadedAttention object."""
-        super(MultiHeadedAttention, self).__init__()
-        assert n_feat % n_head == 0
-        # We assume d_v always equals d_k
-        self.d_k = n_feat // n_head
-        self.h = n_head
-        self.linear_q = nn.Linear(n_feat, n_feat)
-        self.linear_k = nn.Linear(n_feat, n_feat)
-        self.linear_v = nn.Linear(n_feat, n_feat)
-        self.linear_out = nn.Linear(n_feat, n_feat)
-        self.attn = None
-        self.dropout = nn.Dropout(p=dropout_rate)
-
-    def forward_qkv(self, query, key, value):
-        """Transform query, key and value.
-        Args:
-            query (torch.Tensor): Query tensor (#batch, time1, size).
-            key (torch.Tensor): Key tensor (#batch, time2, size).
-            value (torch.Tensor): Value tensor (#batch, time2, size).
-        Returns:
-            torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
-            torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
-            torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
-        """
-        n_batch = query.size(0)
-        q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
-        k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
-        v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
-        q = q.transpose(1, 2)  # (batch, head, time1, d_k)
-        k = k.transpose(1, 2)  # (batch, head, time2, d_k)
-        v = v.transpose(1, 2)  # (batch, head, time2, d_k)
-
-        return q, k, v
-
-    def forward_attention(self, value, scores, mask):
-        """Compute attention context vector.
-        Args:
-            value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
-            scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
-            mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
-        Returns:
-            torch.Tensor: Transformed value (#batch, time1, d_model)
-                weighted by the attention score (#batch, time1, time2).
-        """
-        n_batch = value.size(0)
-        if mask is not None:
-            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
-            min_value = float(
-                numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min
-            )
-            scores = scores.masked_fill(mask, min_value)
-            self.attn = torch.softmax(scores, dim=-1).masked_fill(
-                mask, 0.0
-            )  # (batch, head, time1, time2)
-        else:
-            self.attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
-
-        p_attn = self.dropout(self.attn)
-        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
-        x = (
-            x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
-        )  # (batch, time1, d_model)
-
-        return self.linear_out(x)  # (batch, time1, d_model)
-
-    def forward(self, query, key, value, mask):
-        """Compute scaled dot product attention.
-        Args:
-            query (torch.Tensor): Query tensor (#batch, time1, size).
-            key (torch.Tensor): Key tensor (#batch, time2, size).
-            value (torch.Tensor): Value tensor (#batch, time2, size).
-            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
-                (#batch, time1, time2).
-        Returns:
-            torch.Tensor: Output tensor (#batch, time1, d_model).
-        """
-        q, k, v = self.forward_qkv(query, key, value)
-        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
-        return self.forward_attention(v, scores, mask)
-
-
-class RelPositionMultiHeadedAttention(MultiHeadedAttention):
-    """Multi-Head Attention layer with relative position encoding.
-    Paper: https://arxiv.org/abs/1901.02860
-    Args:
-        n_head (int): The number of heads.
-        n_feat (int): The number of features.
-        dropout_rate (float): Dropout rate.
-    """
-
-    def __init__(self, n_head, n_feat, dropout_rate):
-        """Construct an RelPositionMultiHeadedAttention object."""
-        super().__init__(n_head, n_feat, dropout_rate)
-        # linear transformation for positional ecoding
-        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
-        # these two learnable bias are used in matrix c and matrix d
-        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
-        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
-        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
-        torch.nn.init.xavier_uniform_(self.pos_bias_u)
-        torch.nn.init.xavier_uniform_(self.pos_bias_v)
-
-    def rel_shift(self, x, zero_triu=False):
-        """Compute relative positinal encoding.
-        Args:
-            x (torch.Tensor): Input tensor (batch, time, size).
-            zero_triu (bool): If true, return the lower triangular part of the matrix.
-        Returns:
-            torch.Tensor: Output tensor.
-        """
-        zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
-        x_padded = torch.cat([zero_pad, x], dim=-1)
-
-        x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
-        x = x_padded[:, :, 1:].view_as(x)
-
-        if zero_triu:
-            ones = torch.ones((x.size(2), x.size(3)))
-            x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
-
-        return x
-
-    def forward(self, query, key, value, pos_emb, mask):
-        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
-        Args:
-            query (torch.Tensor): Query tensor (#batch, time1, size).
-            key (torch.Tensor): Key tensor (#batch, time2, size).
-            value (torch.Tensor): Value tensor (#batch, time2, size).
-            pos_emb (torch.Tensor): Positional embedding tensor (#batch, time2, size).
-            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
-                (#batch, time1, time2).
-        Returns:
-            torch.Tensor: Output tensor (#batch, time1, d_model).
-        """
-        q, k, v = self.forward_qkv(query, key, value)
-        q = q.transpose(1, 2)  # (batch, time1, head, d_k)
-
-        n_batch_pos = pos_emb.size(0)
-        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
-        p = p.transpose(1, 2)  # (batch, head, time1, d_k)
-
-        # (batch, head, time1, d_k)
-        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
-        # (batch, head, time1, d_k)
-        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
-
-        # compute attention score
-        # first compute matrix a and matrix c
-        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
-        # (batch, head, time1, time2)
-        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
-
-        # compute matrix b and matrix d
-        # (batch, head, time1, time2)
-        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
-        matrix_bd = self.rel_shift(matrix_bd)
-
-        scores = (matrix_ac + matrix_bd) / math.sqrt(
-            self.d_k
-        )  # (batch, head, time1, time2)
-
-        return self.forward_attention(v, scores, mask)
diff --git a/spaces/NSect/VALL-E-X/examples.py b/spaces/NSect/VALL-E-X/examples.py
deleted file mode 100644
index 205210e0d03f1203648c8fc327da713f9db5eb4e..0000000000000000000000000000000000000000
--- a/spaces/NSect/VALL-E-X/examples.py
+++ /dev/null
@@ -1,24 +0,0 @@
-infer_from_audio_examples = [
-    ["This is how this machine has taken my voice.", 'English', 'no-accent', "prompts/en-2.wav", None, "Wow, look at that! That's no ordinary Teddy bear!"],
-    ["我喜欢抽电子烟，尤其是锐刻五代。", '中文', 'no-accent', "prompts/zh-1.wav", None, "今天我很荣幸，"],
-    ["私の声を真似するのはそんなに面白いですか？", '日本語', 'no-accent', "prompts/ja-2.ogg", None, "初めまして、朝武よしのです。"],
-    ["你可以听得出来我有多困。", '中文', 'no-accent', "prompts/en-1.wav", None, ""],
-    ["この文は、クロスリンガル合成の例です。", '日本語', 'no-accent', "prompts/zh-2.wav", None, ""],
-    ["Actually, I can't speak English, but this machine helped me do it.", 'English', 'no-accent', "prompts/ja-1.wav", None, ""],
-]
-
-make_npz_prompt_examples = [
-    ["Gem-trader", "prompts/en-2.wav", None, "Wow, look at that! That's no ordinary Teddy bear!"],
-    ["Ding Zhen", "prompts/zh-1.wav", None, "今天我很荣幸，"],
-    ["Yoshino", "prompts/ja-2.ogg", None, "初めまして、朝武よしのです。"],
-    ["Sleepy-woman", "prompts/en-1.wav", None, ""],
-    ["Yae", "prompts/zh-2.wav", None, ""],
-    ["Cafe", "prompts/ja-1.wav", None, ""],
-]
-
-infer_from_prompt_examples = [
-    ["A prompt contains voice, prosody and emotion information of a certain speaker.", "English", "no-accent", "vctk_1", None],
-    ["This prompt is made with an audio of three seconds.", "English", "no-accent", "librispeech_1", None],
-    ["This prompt is made with Chinese speech", "English", "no-accent", "seel", None],
-]
-
diff --git a/spaces/NeuML/txtsql/README.md b/spaces/NeuML/txtsql/README.md
deleted file mode 100644
index d1aeee6f368624ac9c4a0849ba66ec77250607a0..0000000000000000000000000000000000000000
--- a/spaces/NeuML/txtsql/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: txtsql
-emoji: 📃
-colorFrom: blue
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.24.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/denoising_dataset.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/denoising_dataset.py
deleted file mode 100644
index bdb62c8d5db9c8755c72db4d0d8083c936f18dc8..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/denoising_dataset.py
+++ /dev/null
@@ -1,436 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import numpy as np
-import torch
-
-from . import FairseqDataset, data_utils
-
-
-def collate(
-    samples,
-    pad_idx,
-    eos_idx,
-    vocab,
-    left_pad_source=False,
-    left_pad_target=False,
-    input_feeding=True,
-    pad_to_length=None,
-):
-    assert input_feeding
-    if len(samples) == 0:
-        return {}
-
-    def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None):
-        return data_utils.collate_tokens(
-            [s[key] for s in samples],
-            pad_idx,
-            eos_idx=None,  # use eos_idx of each sample instead of vocab.eos()
-            left_pad=left_pad,
-            move_eos_to_beginning=move_eos_to_beginning,
-            pad_to_length=pad_to_length,
-        )
-
-    id = torch.LongTensor([s["id"] for s in samples])
-    src_tokens = merge(
-        "source",
-        left_pad=left_pad_source,
-        pad_to_length=pad_to_length["source"] if pad_to_length is not None else None,
-    )
-    # sort by descending source length
-    src_lengths = torch.LongTensor([s["source"].numel() for s in samples])
-    src_lengths, sort_order = src_lengths.sort(descending=True)
-    id = id.index_select(0, sort_order)
-    src_tokens = src_tokens.index_select(0, sort_order)
-
-    prev_output_tokens = None
-    target = None
-    if samples[0].get("target", None) is not None:
-        target = merge(
-            "target",
-            left_pad=left_pad_target,
-            pad_to_length=pad_to_length["target"]
-            if pad_to_length is not None
-            else None,
-        )
-        target = target.index_select(0, sort_order)
-        ntokens = sum(len(s["target"]) for s in samples)
-
-        if input_feeding:
-            # we create a shifted version of targets for feeding the
-            # previous output token(s) into the next decoder step
-            prev_output_tokens = merge(
-                "target",
-                left_pad=left_pad_target,
-                move_eos_to_beginning=True,
-                pad_to_length=pad_to_length["target"]
-                if pad_to_length is not None
-                else None,
-            )
-            prev_output_tokens = prev_output_tokens.index_select(0, sort_order)
-    else:
-        ntokens = sum(len(s["source"]) for s in samples)
-
-    batch = {
-        "id": id,
-        "ntokens": ntokens,
-        "net_input": {
-            "src_tokens": src_tokens,
-            "src_lengths": src_lengths,
-        },
-        "target": target,
-        "nsentences": samples[0]["source"].size(0),
-        "sort_order": sort_order,
-    }
-    if prev_output_tokens is not None:
-        batch["net_input"]["prev_output_tokens"] = prev_output_tokens
-
-    return batch
-
-
-class DenoisingDataset(FairseqDataset):
-    """
-    A wrapper around TokenBlockDataset for BART dataset.
-
-    Args:
-        dataset (TokenBlockDataset): dataset to wrap
-        sizes (List[int]): sentence lengths
-        vocab (~fairseq.data.Dictionary): vocabulary
-        mask_idx (int): dictionary index used for masked token
-        mask_whole_words: only mask whole words. This should be a byte mask
-            over vocab indices, indicating whether it is the beginning of a
-            word. We will extend any mask to encompass the whole word.
-        shuffle (bool, optional): shuffle the elements before batching.
-          Default: ``True``
-        seed: Seed for random number generator for reproducibility.
-        args: argparse arguments.
-    """
-
-    def __init__(
-        self,
-        dataset,
-        sizes,
-        vocab,
-        mask_idx,
-        mask_whole_words,
-        shuffle,
-        seed,
-        args,
-        eos=None,
-        item_transform_func=None,
-    ):
-        self.dataset = dataset
-
-        self.sizes = sizes
-
-        self.vocab = vocab
-        self.shuffle = shuffle
-        self.seed = seed
-        self.mask_idx = mask_idx
-        self.mask_whole_word = mask_whole_words
-        self.mask_ratio = args.mask
-        self.random_ratio = args.mask_random
-        self.insert_ratio = args.insert
-        self.rotate_ratio = args.rotate
-        self.permute_sentence_ratio = args.permute_sentences
-        self.eos = eos if eos is not None else vocab.eos()
-        self.item_transform_func = item_transform_func
-
-        if args.bpe != "gpt2":
-            self.full_stop_index = self.vocab.eos()
-        else:
-            assert args.bpe == "gpt2"
-            self.full_stop_index = self.vocab.index("13")
-
-        self.replace_length = args.replace_length
-        if self.replace_length not in [-1, 0, 1]:
-            raise ValueError(f"invalid arg: replace_length={self.replace_length}")
-        if args.mask_length not in ["subword", "word", "span-poisson"]:
-            raise ValueError(f"invalid arg: mask-length={args.mask_length}")
-        if args.mask_length == "subword" and args.replace_length not in [0, 1]:
-            raise ValueError(f"if using subwords, use replace-length=1 or 0")
-
-        self.mask_span_distribution = None
-        if args.mask_length == "span-poisson":
-            _lambda = args.poisson_lambda
-
-            lambda_to_the_k = 1
-            e_to_the_minus_lambda = math.exp(-_lambda)
-            k_factorial = 1
-            ps = []
-            for k in range(0, 128):
-                ps.append(e_to_the_minus_lambda * lambda_to_the_k / k_factorial)
-                lambda_to_the_k *= _lambda
-                k_factorial *= k + 1
-                if ps[-1] < 0.0000001:
-                    break
-            ps = torch.FloatTensor(ps)
-            self.mask_span_distribution = torch.distributions.Categorical(ps)
-
-        self.epoch = 0
-
-    @property
-    def can_reuse_epoch_itr_across_epochs(self):
-        return True  # only the noise changes, not item sizes
-
-    def set_epoch(self, epoch, **unused):
-        self.epoch = epoch
-
-    def __getitem__(self, index):
-        with data_utils.numpy_seed(self.seed, self.epoch, index):
-            tokens = self.dataset[index]
-            assert tokens[-1] == self.eos
-            source, target = tokens, tokens.clone()
-
-            if self.permute_sentence_ratio > 0.0:
-                source = self.permute_sentences(source, self.permute_sentence_ratio)
-
-            if self.mask_ratio > 0:
-                source = self.add_whole_word_mask(source, self.mask_ratio)
-
-            if self.insert_ratio > 0:
-                source = self.add_insertion_noise(source, self.insert_ratio)
-
-            if self.rotate_ratio > 0.0 and np.random.random() < self.rotate_ratio:
-                source = self.add_rolling_noise(source)
-        # there can additional changes to make:
-        if self.item_transform_func is not None:
-            source, target = self.item_transform_func(source, target)
-
-        assert (source >= 0).all()
-        assert (source[1:-1] >= 1).all()
-        assert (source <= len(self.vocab)).all()
-        assert source[0] == self.vocab.bos()
-        assert source[-1] == self.eos
-        return {
-            "id": index,
-            "source": source,
-            "target": target,
-        }
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def permute_sentences(self, source, p=1.0):
-        full_stops = source == self.full_stop_index
-        # Pretend it ends with a full stop so last span is a sentence
-        full_stops[-2] = 1
-
-        # Tokens that are full stops, where the previous token is not
-        sentence_ends = (full_stops[1:] * ~full_stops[:-1]).nonzero(as_tuple=False) + 2
-        result = source.clone()
-
-        num_sentences = sentence_ends.size(0)
-        num_to_permute = math.ceil((num_sentences * 2 * p) / 2.0)
-        substitutions = torch.randperm(num_sentences)[:num_to_permute]
-        ordering = torch.arange(0, num_sentences)
-        ordering[substitutions] = substitutions[torch.randperm(num_to_permute)]
-
-        # Ignore <bos> at start
-        index = 1
-        for i in ordering:
-            sentence = source[(sentence_ends[i - 1] if i > 0 else 1) : sentence_ends[i]]
-            result[index : index + sentence.size(0)] = sentence
-            index += sentence.size(0)
-        return result
-
-    def word_starts(self, source):
-        if self.mask_whole_word is not None:
-            is_word_start = self.mask_whole_word.gather(0, source)
-        else:
-            is_word_start = torch.ones(source.size())
-        is_word_start[0] = 0
-        is_word_start[-1] = 0
-        return is_word_start
-
-    def add_whole_word_mask(self, source, p):
-        is_word_start = self.word_starts(source)
-        num_to_mask = int(math.ceil(is_word_start.float().sum() * p))
-        num_inserts = 0
-        if num_to_mask == 0:
-            return source
-
-        if self.mask_span_distribution is not None:
-            lengths = self.mask_span_distribution.sample(sample_shape=(num_to_mask,))
-
-            # Make sure we have enough to mask
-            cum_length = torch.cumsum(lengths, 0)
-            while cum_length[-1] < num_to_mask:
-                lengths = torch.cat(
-                    [
-                        lengths,
-                        self.mask_span_distribution.sample(sample_shape=(num_to_mask,)),
-                    ],
-                    dim=0,
-                )
-                cum_length = torch.cumsum(lengths, 0)
-
-            # Trim to masking budget
-            i = 0
-            while cum_length[i] < num_to_mask:
-                i += 1
-            lengths[i] = num_to_mask - (0 if i == 0 else cum_length[i - 1])
-            num_to_mask = i + 1
-            lengths = lengths[:num_to_mask]
-
-            # Handle 0-length mask (inserts) separately
-            lengths = lengths[lengths > 0]
-            num_inserts = num_to_mask - lengths.size(0)
-            num_to_mask -= num_inserts
-            if num_to_mask == 0:
-                return self.add_insertion_noise(source, num_inserts / source.size(0))
-
-            assert (lengths > 0).all()
-        else:
-            lengths = torch.ones((num_to_mask,)).long()
-        assert is_word_start[-1] == 0
-        word_starts = is_word_start.nonzero(as_tuple=False)
-        indices = word_starts[
-            torch.randperm(word_starts.size(0))[:num_to_mask]
-        ].squeeze(1)
-        mask_random = torch.FloatTensor(num_to_mask).uniform_() < self.random_ratio
-
-        source_length = source.size(0)
-        assert source_length - 1 not in indices
-        to_keep = torch.ones(source_length, dtype=torch.bool)
-        is_word_start[
-            -1
-        ] = 255  # acts as a long length, so spans don't go over the end of doc
-        if self.replace_length == 0:
-            to_keep[indices] = 0
-        else:
-            # keep index, but replace it with [MASK]
-            source[indices] = self.mask_idx
-            source[indices[mask_random]] = torch.randint(
-                1, len(self.vocab), size=(mask_random.sum(),)
-            )
-
-        if self.mask_span_distribution is not None:
-            assert len(lengths.size()) == 1
-            assert lengths.size() == indices.size()
-            lengths -= 1
-            while indices.size(0) > 0:
-                assert lengths.size() == indices.size()
-                lengths -= is_word_start[indices + 1].long()
-                uncompleted = lengths >= 0
-                indices = indices[uncompleted] + 1
-                mask_random = mask_random[uncompleted]
-                lengths = lengths[uncompleted]
-                if self.replace_length != -1:
-                    # delete token
-                    to_keep[indices] = 0
-                else:
-                    # keep index, but replace it with [MASK]
-                    source[indices] = self.mask_idx
-                    source[indices[mask_random]] = torch.randint(
-                        1, len(self.vocab), size=(mask_random.sum(),)
-                    )
-        else:
-            # A bit faster when all lengths are 1
-            while indices.size(0) > 0:
-                uncompleted = is_word_start[indices + 1] == 0
-                indices = indices[uncompleted] + 1
-                mask_random = mask_random[uncompleted]
-                if self.replace_length != -1:
-                    # delete token
-                    to_keep[indices] = 0
-                else:
-                    # keep index, but replace it with [MASK]
-                    source[indices] = self.mask_idx
-                    source[indices[mask_random]] = torch.randint(
-                        1, len(self.vocab), size=(mask_random.sum(),)
-                    )
-
-                assert source_length - 1 not in indices
-
-        source = source[to_keep]
-
-        if num_inserts > 0:
-            source = self.add_insertion_noise(source, num_inserts / source.size(0))
-
-        return source
-
-    def add_permuted_noise(self, tokens, p):
-        num_words = len(tokens)
-        num_to_permute = math.ceil(((num_words * 2) * p) / 2.0)
-        substitutions = torch.randperm(num_words - 2)[:num_to_permute] + 1
-        tokens[substitutions] = tokens[substitutions[torch.randperm(num_to_permute)]]
-        return tokens
-
-    def add_rolling_noise(self, tokens):
-        offset = np.random.randint(1, max(1, tokens.size(-1) - 1) + 1)
-        tokens = torch.cat(
-            (tokens[0:1], tokens[offset:-1], tokens[1:offset], tokens[-1:]),
-            dim=0,
-        )
-        return tokens
-
-    def add_insertion_noise(self, tokens, p):
-        if p == 0.0:
-            return tokens
-
-        num_tokens = len(tokens)
-        n = int(math.ceil(num_tokens * p))
-
-        noise_indices = torch.randperm(num_tokens + n - 2)[:n] + 1
-        noise_mask = torch.zeros(size=(num_tokens + n,), dtype=torch.bool)
-        noise_mask[noise_indices] = 1
-        result = torch.LongTensor(n + len(tokens)).fill_(-1)
-
-        num_random = int(math.ceil(n * self.random_ratio))
-        result[noise_indices[num_random:]] = self.mask_idx
-        result[noise_indices[:num_random]] = torch.randint(
-            low=1, high=len(self.vocab), size=(num_random,)
-        )
-
-        result[~noise_mask] = tokens
-
-        assert (result >= 0).all()
-        return result
-
-    def collater(self, samples, pad_to_length=None):
-        """Merge a list of samples to form a mini-batch.
-        Args:
-            samples (List[dict]): samples to collate
-        Returns:
-            dict: a mini-batch of data
-        """
-        return collate(
-            samples, self.vocab.pad(), self.eos, self.vocab, pad_to_length=pad_to_length
-        )
-
-    def num_tokens(self, index):
-        """Return the number of tokens in a sample. This value is used to
-        enforce ``--max-tokens`` during batching."""
-        return self.sizes[index]
-
-    def size(self, index):
-        """Return an example's size as a float or tuple. This value is used when
-        filtering a dataset with ``--max-positions``."""
-        return self.sizes[index]
-
-    def ordered_indices(self):
-        """Return an ordered list of indices. Batches will be constructed based
-        on this order."""
-        if self.shuffle:
-            indices = np.random.permutation(len(self))
-        else:
-            indices = np.arange(len(self))
-        return indices[np.argsort(self.sizes[indices], kind="mergesort")]
-
-    def prefetch(self, indices):
-        self.src.prefetch(indices)
-        self.tgt.prefetch(indices)
-
-    @property
-    def supports_prefetch(self):
-        return (
-            hasattr(self.src, "supports_prefetch")
-            and self.src.supports_prefetch
-            and hasattr(self.tgt, "supports_prefetch")
-            and self.tgt.supports_prefetch
-        )
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_constraints.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_constraints.py
deleted file mode 100644
index 1c37f7e1fb26d8ea5349fedd3a60f566d09cf598..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_constraints.py
+++ /dev/null
@@ -1,269 +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 sys
-import unittest
-
-import torch
-from fairseq.token_generation_constraints import *
-
-
-def tensorize(constraints: List[List[int]]) -> torch.Tensor:
-    return [torch.tensor(x) for x in constraints]
-
-
-class TestHelperRoutines(unittest.TestCase):
-    def setUp(self):
-        self.examples = [
-            ([[]], torch.tensor([[0]])),
-            ([[], []], torch.tensor([[0], [0]])),
-            ([[torch.tensor([1, 2])], []], torch.tensor([[1, 1, 2, 0], [0, 0, 0, 0]])),
-            (
-                [
-                    [
-                        torch.tensor([3, 1, 2]),
-                        torch.tensor([3]),
-                        torch.tensor([4, 5, 6, 7]),
-                    ],
-                    [],
-                    [torch.tensor([1, 8, 9, 10, 1, 4, 11, 12])],
-                ],
-                torch.tensor(
-                    [
-                        [3, 3, 1, 2, 0, 3, 0, 4, 5, 6, 7, 0],
-                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-                        [1, 1, 8, 9, 10, 1, 4, 11, 12, 0, 0, 0],
-                    ]
-                ),
-            ),
-        ]
-
-    def test_packing(self):
-        """Ensures the list of lists of tensors gets packed correctly."""
-        for batch_constraints, expected_tensor in self.examples:
-            packed = pack_constraints(batch_constraints)
-            assert torch.equal(packed, expected_tensor)
-
-
-class TestUnorderedConstraintState(unittest.TestCase):
-    def setUp(self):
-        # Tuples of (contraint set, expected printed graph, token counts per node)
-        self.examples = [
-            (
-                tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
-                "([None].False#6 ([1].True#4 ([2].False#1 [3].True#1) [3].True#1 [4].True#1) ([4].False#2 ([5].True#2 ([6].False#1 [7].True#1))))",
-                {1: 4, 2: 1, 3: 2, 4: 3, 5: 2, 6: 1, 7: 1},
-            ),
-            ([], "[None].False#0", {}),
-            (tensorize([[0]]), "([None].False#1 [0].True#1)", {0: 1}),
-            (
-                tensorize([[100000, 1, 2, 3, 4, 5]]),
-                "([None].False#1 ([100000].False#1 ([1].False#1 ([2].False#1 ([3].False#1 ([4].False#1 [5].True#1))))))",
-                {100000: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
-            ),
-            (
-                tensorize([[1, 2], [1, 2]]),
-                "([None].False#2 ([1].False#2 [2].True#2))",
-                {1: 2, 2: 2},
-            ),
-            (
-                tensorize([[1, 2], [3, 4]]),
-                "([None].False#2 ([1].False#1 [2].True#1) ([3].False#1 [4].True#1))",
-                {1: 1, 2: 1, 3: 1, 4: 1},
-            ),
-        ]
-
-        self.sequences = [
-            (
-                self.examples[0][0],
-                [],
-                {"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
-            ),
-            (
-                self.examples[0][0],
-                [1, 2],
-                {"bank": 2, "num_completed": 0, "finished": False, "is_root": False},
-            ),
-            (
-                self.examples[0][0],
-                [1, 2, 94],
-                {"bank": 1, "num_completed": 1, "finished": False, "is_root": True},
-            ),
-            (
-                self.examples[0][0],
-                [1, 3, 999, 1, 4],
-                {"bank": 4, "num_completed": 2, "finished": False, "is_root": False},
-            ),
-            (
-                self.examples[0][0],
-                [1, 3, 999, 1, 4, 999],
-                {"bank": 4, "num_completed": 2, "finished": False, "is_root": True},
-            ),
-            (
-                self.examples[0][0],
-                [4, 5, 6, 8],
-                {"bank": 2, "num_completed": 1, "finished": False, "is_root": True},
-            ),
-            (
-                self.examples[0][0],
-                # Tricky, because in last three, goes down [1->4] branch, could miss [1] and [4->5]
-                # [[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]],
-                [1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5],
-                {"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
-            ),
-            (
-                self.examples[0][0],
-                [1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117],
-                {"bank": 14, "num_completed": 6, "finished": True, "is_root": True},
-            ),
-            (
-                tensorize([[1], [2, 3]]),
-                # Should not be able to get credit for entering 1 a second time
-                [1, 1],
-                {"bank": 1, "num_completed": 1, "finished": False, "is_root": True},
-            ),
-            (
-                self.examples[4][0],
-                [1, 2, 1, 2],
-                {"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
-            ),
-            (
-                self.examples[4][0],
-                [1, 2, 1, 2, 1],
-                {"bank": 4, "num_completed": 2, "finished": True, "is_root": True},
-            ),
-            (
-                self.examples[5][0],
-                [1, 2, 3, 4, 5],
-                {"bank": 4, "num_completed": 2, "finished": True, "is_root": True},
-            ),
-        ]
-
-    def test_graphs(self):
-        """
-        Test whether unordered graph systems are created correctly.
-        """
-        for example in self.examples:
-            constraints, expected, gold_counts = example
-            c = ConstraintNode.create(constraints)
-            assert (
-                ConstraintNode.print_graph(c) == expected
-            ), f"got {ConstraintNode.print_graph(c)}, expected {expected}"
-            assert (
-                c.token_counts() == gold_counts
-            ), f"{c} got {c.token_counts()} wanted {gold_counts}"
-
-    def test_next_tokens(self):
-        """
-        Tests that the set of next tokens is correct.
-        """
-        for example in self.examples:
-            constraints, expected, gold_counts = example
-            root = ConstraintNode.create(constraints)
-
-            root_tokens = set(root.children.keys())
-            for sequence in constraints:
-                state = UnorderedConstraintState(root)
-                for token in sequence:
-                    all_tokens = root_tokens.union(state.node.children.keys())
-                    assert (
-                        all_tokens == state.next_tokens()
-                    ), f"ALL {all_tokens} NEXT {state.next_tokens()}"
-                    state = state.advance(token)
-
-    def test_sequences(self):
-        for constraints, tokens, expected in self.sequences:
-            state = UnorderedConstraintState.create(pack_constraints([constraints])[0])
-            for token in tokens:
-                state = state.advance(token)
-            result = {}
-            for attr in expected.keys():
-                result[attr] = getattr(state, attr)
-
-            assert (
-                result == expected
-            ), f"TEST({tokens}) GOT: {result} WANTED: {expected}"
-
-
-class TestOrderedConstraintState(unittest.TestCase):
-    def setUp(self):
-        self.sequences = [
-            (
-                tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
-                [],
-                {"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
-            ),
-            (
-                tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
-                [1, 2],
-                {"bank": 2, "num_completed": 0, "finished": False, "is_root": False},
-            ),
-            (
-                tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
-                [1, 2, 94],
-                {"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
-            ),
-            (
-                tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
-                [1, 3, 999, 1, 4],
-                {"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
-            ),
-            (
-                tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
-                [1, 2, 3, 999, 999],
-                {"bank": 3, "num_completed": 1, "finished": False, "is_root": False},
-            ),
-            (
-                tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
-                [1, 2, 3, 77, 1, 3, 1],
-                {"bank": 6, "num_completed": 2, "finished": False, "is_root": False},
-            ),
-            (
-                tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
-                [1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5],
-                {"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
-            ),
-            (
-                tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
-                [1, 2, 999, 1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117],
-                {"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
-            ),
-            (
-                tensorize([[1], [2, 3]]),
-                [1, 1],
-                {"bank": 1, "num_completed": 1, "finished": False, "is_root": False},
-            ),
-            (
-                tensorize([[1, 2], [1, 2]]),
-                [1, 2, 1, 2],
-                {"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
-            ),
-            (
-                tensorize([[1, 2], [1, 2]]),
-                [1, 2, 1, 2, 1],
-                {"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
-            ),
-            (
-                tensorize([[1, 2], [3, 4]]),
-                [1, 2, 3, 4, 5],
-                {"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
-            ),
-        ]
-
-    def test_sequences(self):
-        for i, (constraints, tokens, expected) in enumerate(self.sequences):
-            state = OrderedConstraintState.create(pack_constraints([constraints])[0])
-            for token in tokens:
-                state = state.advance(token)
-            result = {}
-            for attr in expected.keys():
-                result[attr] = getattr(state, attr)
-            assert (
-                result == expected
-            ), f"TEST({tokens}) GOT: {result} WANTED: {expected}"
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_iterators.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_iterators.py
deleted file mode 100644
index 7b3dd4848553357e5e8326ed3a31cf5d68ceea94..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_iterators.py
+++ /dev/null
@@ -1,137 +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 unittest
-
-from fairseq.data import iterators
-
-
-class TestIterators(unittest.TestCase):
-    def test_counting_iterator_index(self, ref=None, itr=None):
-        # Test the indexing functionality of CountingIterator
-        if ref is None:
-            assert itr is None
-            ref = list(range(10))
-            itr = iterators.CountingIterator(ref)
-        else:
-            assert len(ref) == 10
-            assert itr is not None
-
-        self.assertTrue(itr.has_next())
-        self.assertEqual(itr.n, 0)
-        self.assertEqual(next(itr), ref[0])
-        self.assertEqual(itr.n, 1)
-        self.assertEqual(next(itr), ref[1])
-        self.assertEqual(itr.n, 2)
-        itr.skip(3)
-        self.assertEqual(itr.n, 5)
-        self.assertEqual(next(itr), ref[5])
-        itr.skip(2)
-        self.assertEqual(itr.n, 8)
-        self.assertEqual(list(itr), [ref[8], ref[9]])
-        self.assertFalse(itr.has_next())
-
-    def test_counting_iterator_length_mismatch(self):
-        ref = list(range(10))
-        # When the underlying iterable is longer than the CountingIterator,
-        # the remaining items in the iterable should be ignored
-        itr = iterators.CountingIterator(ref, total=8)
-        self.assertEqual(list(itr), ref[:8])
-        # When the underlying iterable is shorter than the CountingIterator,
-        # raise an IndexError when the underlying iterable is exhausted
-        itr = iterators.CountingIterator(ref, total=12)
-        self.assertRaises(IndexError, list, itr)
-
-    def test_counting_iterator_take(self):
-        # Test the "take" method of CountingIterator
-        ref = list(range(10))
-        itr = iterators.CountingIterator(ref)
-        itr.take(5)
-        self.assertEqual(len(itr), len(list(iter(itr))))
-        self.assertEqual(len(itr), 5)
-
-        itr = iterators.CountingIterator(ref)
-        itr.take(5)
-        self.assertEqual(next(itr), ref[0])
-        self.assertEqual(next(itr), ref[1])
-        itr.skip(2)
-        self.assertEqual(next(itr), ref[4])
-        self.assertFalse(itr.has_next())
-
-    def test_grouped_iterator(self):
-        # test correctness
-        x = list(range(10))
-        itr = iterators.GroupedIterator(x, 1)
-        self.assertEqual(list(itr), [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]])
-        itr = iterators.GroupedIterator(x, 4)
-        self.assertEqual(list(itr), [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9]])
-        itr = iterators.GroupedIterator(x, 5)
-        self.assertEqual(list(itr), [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
-
-        # test the GroupIterator also works correctly as a CountingIterator
-        x = list(range(30))
-        ref = list(iterators.GroupedIterator(x, 3))
-        itr = iterators.GroupedIterator(x, 3)
-        self.test_counting_iterator_index(ref, itr)
-
-    def test_sharded_iterator(self):
-        # test correctness
-        x = list(range(10))
-        itr = iterators.ShardedIterator(x, num_shards=1, shard_id=0)
-        self.assertEqual(list(itr), x)
-        itr = iterators.ShardedIterator(x, num_shards=2, shard_id=0)
-        self.assertEqual(list(itr), [0, 2, 4, 6, 8])
-        itr = iterators.ShardedIterator(x, num_shards=2, shard_id=1)
-        self.assertEqual(list(itr), [1, 3, 5, 7, 9])
-        itr = iterators.ShardedIterator(x, num_shards=3, shard_id=0)
-        self.assertEqual(list(itr), [0, 3, 6, 9])
-        itr = iterators.ShardedIterator(x, num_shards=3, shard_id=1)
-        self.assertEqual(list(itr), [1, 4, 7, None])
-        itr = iterators.ShardedIterator(x, num_shards=3, shard_id=2)
-        self.assertEqual(list(itr), [2, 5, 8, None])
-
-        # test CountingIterator functionality
-        x = list(range(30))
-        ref = list(iterators.ShardedIterator(x, num_shards=3, shard_id=0))
-        itr = iterators.ShardedIterator(x, num_shards=3, shard_id=0)
-        self.test_counting_iterator_index(ref, itr)
-
-    def test_counting_iterator_buffered_iterator_take(self):
-        ref = list(range(10))
-        buffered_itr = iterators.BufferedIterator(2, ref)
-        itr = iterators.CountingIterator(buffered_itr)
-        itr.take(5)
-        self.assertEqual(len(itr), len(list(iter(itr))))
-        self.assertEqual(len(itr), 5)
-
-        buffered_itr = iterators.BufferedIterator(2, ref)
-        itr = iterators.CountingIterator(buffered_itr)
-        itr.take(5)
-        self.assertEqual(len(buffered_itr), 5)
-        self.assertEqual(len(list(iter(buffered_itr))), 5)
-
-        buffered_itr = iterators.BufferedIterator(2, ref)
-        itr = iterators.CountingIterator(buffered_itr)
-        itr.take(5)
-        self.assertEqual(next(itr), ref[0])
-        self.assertEqual(next(itr), ref[1])
-        itr.skip(2)
-        self.assertEqual(next(itr), ref[4])
-        self.assertFalse(itr.has_next())
-        self.assertRaises(StopIteration, next, buffered_itr)
-
-        ref = list(range(4, 10))
-        buffered_itr = iterators.BufferedIterator(2, ref)
-        itr = iterators.CountingIterator(buffered_itr, start=4)
-        itr.take(5)
-        self.assertEqual(len(itr), 5)
-        self.assertEqual(len(buffered_itr), 1)
-        self.assertEqual(next(itr), ref[0])
-        self.assertFalse(itr.has_next())
-        self.assertRaises(StopIteration, next, buffered_itr)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py
deleted file mode 100644
index 5f0d70fdad92ba4f554d971710b60f2f9e8d9298..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py
+++ /dev/null
@@ -1,18 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Defines the set of symbols used in text input to the model.
-
-The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
-from . import cmudict
-
-_pad        = '_'
-_punctuation = '!\'(),.:;? '
-_special = '-'
-_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
-
-# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
-_arpabet = ['@' + s for s in cmudict.valid_symbols]
-
-# Export all symbols:
-symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/transform_eos_lang_pair_dataset.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/transform_eos_lang_pair_dataset.py
deleted file mode 100644
index e21144a88e0038c2f35711333a40315613004256..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/transform_eos_lang_pair_dataset.py
+++ /dev/null
@@ -1,113 +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 typing import Optional
-
-import torch
-
-from . import FairseqDataset
-
-
-class TransformEosLangPairDataset(FairseqDataset):
-    """A :class:`~fairseq.data.FairseqDataset` wrapper that transform bos on
-    collated samples of language pair dataset.
-
-    Note that the transformation is applied in :func:`collater`.
-
-    Args:
-        dataset (~fairseq.data.FairseqDataset): dataset that collates sample into
-            LanguagePairDataset schema
-        src_eos (int): original source end-of-sentence symbol index to be replaced
-        new_src_eos (int, optional): new end-of-sentence symbol index to replace source eos symbol
-        tgt_bos (int, optional): original target beginning-of-sentence symbol index to be replaced
-        new_tgt_bos (int, optional): new beginning-of-sentence symbol index to replace at the
-            beginning of 'prev_output_tokens'
-    """
-
-    def __init__(
-        self,
-        dataset: FairseqDataset,
-        src_eos: int,
-        new_src_eos: Optional[int] = None,
-        tgt_bos: Optional[int] = None,
-        new_tgt_bos: Optional[int] = None,
-    ):
-        self.dataset = dataset
-        self.src_eos = src_eos
-        self.new_src_eos = new_src_eos
-        self.tgt_bos = tgt_bos
-        self.new_tgt_bos = new_tgt_bos
-
-    def __getitem__(self, index):
-        return self.dataset[index]
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def collater(self, samples, **extra_args):
-        samples = self.dataset.collater(samples, **extra_args)
-        if len(samples) == 0:
-            return samples
-
-        if 'net_input' not in samples:
-            return samples
-
-        if self.new_src_eos is not None:
-            if self.dataset.left_pad_source:
-                assert (
-                    samples["net_input"]["src_tokens"][:, -1] != self.src_eos
-                ).sum() == 0
-                samples["net_input"]["src_tokens"][:, -1] = self.new_src_eos
-            else:
-                eos_idx = samples["net_input"]["src_lengths"] - 1
-                assert (
-                    samples["net_input"]["src_tokens"][
-                        torch.arange(eos_idx.size(0)), eos_idx
-                    ]
-                    != self.src_eos
-                ).sum() == 0
-                eos_idx = eos_idx.resize_(len(samples["net_input"]["src_lengths"]), 1)
-                samples["net_input"]["src_tokens"].scatter_(
-                    1, eos_idx, self.new_src_eos
-                )
-
-        if (
-            self.new_tgt_bos is not None
-            and "prev_output_tokens" in samples["net_input"]
-        ):
-            if self.dataset.left_pad_target:
-                # TODO: support different padding direction on target side
-                raise NotImplementedError(
-                    "TransformEosLangPairDataset does not implement --left-pad-target True option"
-                )
-            else:
-                assert (
-                    samples["net_input"]["prev_output_tokens"][:, 0] != self.tgt_bos
-                ).sum() == 0
-                samples["net_input"]["prev_output_tokens"][:, 0] = self.new_tgt_bos
-
-        return samples
-
-    def num_tokens(self, index):
-        return self.dataset.num_tokens(index)
-
-    def size(self, index):
-        return self.dataset.size(index)
-
-    @property
-    def sizes(self):
-        # dataset.sizes can be a dynamically computed sizes:
-        return self.dataset.sizes
-
-    def ordered_indices(self):
-        return self.dataset.ordered_indices()
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.dataset, "supports_prefetch", False)
-
-    def prefetch(self, indices):
-        return self.dataset.prefetch(indices)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/transformer_lm.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/transformer_lm.py
deleted file mode 100644
index eedd5151ba5b1a7050b37639023cf8a158fae8d4..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/transformer_lm.py
+++ /dev/null
@@ -1,545 +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 dataclasses import dataclass, field
-from typing import Optional
-
-from fairseq import options, utils
-from fairseq.dataclass import ChoiceEnum, FairseqDataclass
-from fairseq.models import (
-    FairseqLanguageModel,
-    register_model,
-    register_model_architecture,
-)
-from fairseq.models.transformer import (
-    DEFAULT_MIN_PARAMS_TO_WRAP, Embedding, TransformerDecoder
-)
-from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder
-from fairseq.utils import safe_getattr, safe_hasattr
-from omegaconf import II
-
-
-DEFAULT_MAX_TARGET_POSITIONS = 1024
-
-
-@dataclass
-class TransformerLanguageModelConfig(FairseqDataclass):
-    activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(
-        default="relu", metadata={"help": "activation function to use"}
-    )
-    dropout: float = field(default=0.1, metadata={"help": "dropout probability"})
-    attention_dropout: float = field(
-        default=0.0, metadata={"help": "dropout probability for attention weights"}
-    )
-    activation_dropout: float = field(
-        default=0.0, metadata={"help": "dropout probability after activation in FFN."}
-    )
-    relu_dropout: float = field(
-        default=0.0, metadata={"help": "dropout probability after activation in FFN."}
-    )
-    decoder_embed_dim: int = field(
-        default=512, metadata={"help": "decoder embedding dimension"}
-    )
-    decoder_output_dim: int = field(
-        default=512, metadata={"help": "decoder output dimension"}
-    )
-    decoder_input_dim: int = field(
-        default=512, metadata={"help": "decoder input dimension"}
-    )
-    decoder_ffn_embed_dim: int = field(
-        default=2048, metadata={"help": "decoder embedding dimension for FFN"}
-    )
-    decoder_layers: int = field(default=6, metadata={"help": "num decoder layers"})
-    decoder_attention_heads: int = field(
-        default=8, metadata={"help": "num decoder attention heads"}
-    )
-    decoder_normalize_before: bool = field(
-        default=False, metadata={"help": "apply layernorm before each decoder block"}
-    )
-    no_decoder_final_norm: bool = field(
-        default=False,
-        metadata={"help": "don't add an extra layernorm after the last decoder block"},
-    )
-    adaptive_softmax_cutoff: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "comma separated list of adaptive softmax cutoff points. "
-            "Must be used with adaptive_loss criterion"
-        },
-    )
-    adaptive_softmax_dropout: float = field(
-        default=0,
-        metadata={"help": "sets adaptive softmax dropout for the tail projections"},
-    )
-    adaptive_softmax_factor: float = field(
-        default=4, metadata={"help": "adaptive input factor"}
-    )
-    no_token_positional_embeddings: bool = field(
-        default=False,
-        metadata={
-            "help": "if set, disables positional embeddings (outside self attention)"
-        },
-    )
-    share_decoder_input_output_embed: bool = field(
-        default=False, metadata={"help": "share decoder input and output embeddings"}
-    )
-    character_embeddings: bool = field(
-        default=False,
-        metadata={
-            "help": "if set, uses character embedding convolutions to produce token embeddings"
-        },
-    )
-    character_filters: str = field(
-        default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]",
-        metadata={"help": "size of character embeddings"},
-    )
-    character_embedding_dim: int = field(
-        default=4, metadata={"help": "size of character embeddings"}
-    )
-    char_embedder_highway_layers: int = field(
-        default=2,
-        metadata={"help": "number of highway layers for character token embeddder"},
-    )
-    adaptive_input: bool = field(
-        default=False, metadata={"help": "if set, uses adaptive input"}
-    )
-    adaptive_input_factor: float = field(
-        default=4, metadata={"help": "adaptive input factor"}
-    )
-    adaptive_input_cutoff: Optional[str] = field(
-        default=None,
-        metadata={"help": "comma separated list of adaptive input cutoff points."},
-    )
-    tie_adaptive_weights: bool = field(
-        default=False,
-        metadata={
-            "help": "if set, ties the weights of adaptive softmax and adaptive input"
-        },
-    )
-    tie_adaptive_proj: bool = field(
-        default=False,
-        metadata={
-            "help": "if set, ties the projection weights of adaptive softmax and adaptive input"
-        },
-    )
-    decoder_learned_pos: bool = field(
-        default=False,
-        metadata={"help": "use learned positional embeddings in the decoder"},
-    )
-    layernorm_embedding: bool = field(
-        default=False, metadata={"help": "add layernorm to embedding"}
-    )
-    no_scale_embedding: bool = field(
-        default=False, metadata={"help": "if True, dont scale embeddings"}
-    )
-    checkpoint_activations: bool = field(
-        default=False, metadata={"help": "checkpoint activations at each layer"}
-    )
-    offload_activations: bool = field(
-        default=False,
-        metadata={"help": "move checkpointed activations to CPU after they are used."},
-    )
-    # config for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
-    decoder_layerdrop: float = field(
-        default=0.0, metadata={"help": "LayerDrop probability for decoder"}
-    )
-    decoder_layers_to_keep: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "which layers to *keep* when pruning as a comma-separated list"
-        },
-    )
-    # config for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)
-    quant_noise_pq: float = field(
-        default=0.0,
-        metadata={"help": "iterative PQ quantization noise at training time"},
-    )
-    quant_noise_pq_block_size: int = field(
-        default=8,
-        metadata={"help": "block size of quantization noise at training time"},
-    )
-    quant_noise_scalar: float = field(
-        default=0.0,
-        metadata={
-            "help": "scalar quantization noise and scalar quantization at training time"
-        },
-    )
-    # config for Fully Sharded Data Parallel (FSDP) training
-    min_params_to_wrap: int = field(
-        default=DEFAULT_MIN_PARAMS_TO_WRAP,
-        metadata={
-            "help": (
-                "minimum number of params for a layer to be wrapped with FSDP() when "
-                "training with --ddp-backend=fully_sharded. Smaller values will "
-                "improve memory efficiency, but may make torch.distributed "
-                "communication less efficient due to smaller input sizes. This option "
-                "is set to 0 (i.e., always wrap) when --checkpoint-activations or "
-                "--offload-activations are passed."
-            )
-        }
-    )
-    # config for "BASE Layers: Simplifying Training of Large, Sparse Models"
-    base_layers: Optional[int] = field(
-        default=0, metadata={"help": "number of BASE layers in total"}
-    )
-    base_sublayers: Optional[int] = field(
-        default=1, metadata={"help": "number of sublayers in each BASE layer"}
-    )
-    base_shuffle: Optional[int] = field(
-        default=1, metadata={"help": "shuffle tokens between workers before computing assignment"}
-    )
-    # options from other parts of the config
-    add_bos_token: bool = II("task.add_bos_token")
-    tokens_per_sample: int = II("task.tokens_per_sample")
-    max_target_positions: Optional[int] = II("task.max_target_positions")
-    tpu: bool = II("common.tpu")
-
-
-@register_model("transformer_lm", dataclass=TransformerLanguageModelConfig)
-class TransformerLanguageModel(FairseqLanguageModel):
-    @classmethod
-    def hub_models(cls):
-        def moses_fastbpe(path):
-            return {"path": path, "tokenizer": "moses", "bpe": "fastbpe"}
-
-        def spm(path):
-            return {"path": path, "tokenizer": "space", "bpe": "sentencepiece"}
-
-        return {
-            "transformer_lm.gbw.adaptive_huge": "https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2",
-            "transformer_lm.wiki103.adaptive": "https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2",
-            "transformer_lm.wmt19.en": moses_fastbpe(
-                "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.bz2"
-            ),
-            "transformer_lm.wmt19.de": moses_fastbpe(
-                "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.bz2"
-            ),
-            "transformer_lm.wmt19.ru": moses_fastbpe(
-                "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.bz2"
-            ),
-            "transformer_lm.wmt20.en": spm(
-                "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.en.tar.gz"
-            ),
-            "transformer_lm.wmt20.ta": spm(
-                "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.ta.tar.gz"
-            ),
-            "transformer_lm.wmt20.iu.news": spm(
-                "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.iu.news.tar.gz"
-            ),
-            "transformer_lm.wmt20.iu.nh": spm(
-                "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.iu.nh.tar.gz"
-            ),
-        }
-
-    def __init__(self, decoder):
-        super().__init__(decoder)
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        if args.decoder_layers_to_keep:
-            args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
-
-        if safe_getattr(args, "max_target_positions", None) is None:
-            args.max_target_positions = safe_getattr(
-                args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS
-            )
-
-        if args.character_embeddings:
-            embed_tokens = CharacterTokenEmbedder(
-                task.source_dictionary,
-                eval(args.character_filters),
-                args.character_embedding_dim,
-                args.decoder_embed_dim,
-                args.char_embedder_highway_layers,
-            )
-        elif args.adaptive_input:
-            embed_tokens = AdaptiveInput(
-                len(task.source_dictionary),
-                task.source_dictionary.pad(),
-                args.decoder_input_dim,
-                args.adaptive_input_factor,
-                args.decoder_embed_dim,
-                options.eval_str_list(args.adaptive_input_cutoff, type=int),
-                args.quant_noise_pq,
-                args.quant_noise_pq_block_size,
-            )
-        else:
-            embed_tokens = cls.build_embedding(
-                args, task.source_dictionary, args.decoder_input_dim
-            )
-
-        if args.tie_adaptive_weights:
-            assert args.adaptive_input
-            assert args.adaptive_input_factor == args.adaptive_softmax_factor
-            assert (
-                args.adaptive_softmax_cutoff == args.adaptive_input_cutoff
-            ), "{} != {}".format(
-                args.adaptive_softmax_cutoff, args.adaptive_input_cutoff
-            )
-            assert args.decoder_input_dim == args.decoder_output_dim
-
-        decoder = TransformerDecoder(
-            args, task.target_dictionary, embed_tokens, no_encoder_attn=True
-        )
-        return cls(decoder)
-
-    @classmethod
-    def build_embedding(cls, args, dictionary, embed_dim, path=None):
-        embed_tokens = Embedding(len(dictionary), embed_dim, dictionary.pad())
-        return embed_tokens
-
-
-def base_lm_architecture(args):
-    # backward compatibility for older model checkpoints
-    if safe_hasattr(args, "no_tie_adaptive_proj"):
-        # previous models defined --no-tie-adaptive-proj, so use the existence of
-        # that option to determine if this is an "old" model checkpoint
-        args.no_decoder_final_norm = True  # old models always set this to True
-        if args.no_tie_adaptive_proj is False:
-            args.tie_adaptive_proj = True
-    if safe_hasattr(args, "decoder_final_norm"):
-        args.no_decoder_final_norm = not args.decoder_final_norm
-
-    args.dropout = safe_getattr(args, "dropout", 0.1)
-    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.0)
-
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 512)
-    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 2048)
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 8)
-    args.adaptive_softmax_cutoff = safe_getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = safe_getattr(args, "adaptive_softmax_dropout", 0)
-    args.adaptive_softmax_factor = safe_getattr(args, "adaptive_softmax_factor", 4)
-    args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", False)
-    args.activation_fn = safe_getattr(args, "activation_fn", "relu")
-
-    args.decoder_layerdrop = safe_getattr(args, "decoder_layerdrop", 0)
-    args.decoder_layers_to_keep = safe_getattr(args, "decoder_layers_to_keep", None)
-    args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0)
-    args.quant_noise_pq_block_size = safe_getattr(args, "quant_noise_pq_block_size", 8)
-    args.quant_noise_scalar = safe_getattr(args, "quant_noise_scalar", 0)
-
-    args.base_layers = safe_getattr(args, "base_layers", 0)
-    args.base_sublayers = safe_getattr(args, "base_sublayers", 1)
-    args.base_shuffle = safe_getattr(args, "base_shuffle", False)
-
-    args.add_bos_token = safe_getattr(args, "add_bos_token", False)
-    args.no_token_positional_embeddings = safe_getattr(
-        args, "no_token_positional_embeddings", False
-    )
-    args.share_decoder_input_output_embed = safe_getattr(
-        args, "share_decoder_input_output_embed", False
-    )
-    args.character_embeddings = safe_getattr(args, "character_embeddings", False)
-
-    args.decoder_output_dim = safe_getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = safe_getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
-    # Model training is not stable without this
-    args.decoder_normalize_before = True
-    args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", False)
-
-    args.adaptive_input = safe_getattr(args, "adaptive_input", False)
-    args.adaptive_input_factor = safe_getattr(args, "adaptive_input_factor", 4)
-    args.adaptive_input_cutoff = safe_getattr(args, "adaptive_input_cutoff", None)
-
-    args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", False)
-    args.tie_adaptive_proj = safe_getattr(args, "tie_adaptive_proj", False)
-
-    args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", False)
-    args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False)
-    args.checkpoint_activations = safe_getattr(args, "checkpoint_activations", False)
-    args.offload_activations = safe_getattr(args, "offload_activations", False)
-    if args.offload_activations:
-        args.checkpoint_activations = True
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_big")
-def transformer_lm_big(args):
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 12)
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024)
-    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 4096)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)
-    base_lm_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_wiki103")
-@register_model_architecture("transformer_lm", "transformer_lm_baevski_wiki103")
-def transformer_lm_baevski_wiki103(args):
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 16)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 8)
-    args.dropout = safe_getattr(args, "dropout", 0.3)
-    args.adaptive_input = safe_getattr(args, "adaptive_input", True)
-    args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", True)
-    args.adaptive_input_cutoff = safe_getattr(args, "adaptive_input_cutoff", "20000,60000")
-    args.adaptive_softmax_cutoff = safe_getattr(
-        args, "adaptive_softmax_cutoff", "20000,60000"
-    )
-    args.adaptive_softmax_dropout = safe_getattr(args, "adaptive_softmax_dropout", 0.2)
-    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
-    args.activation_dropout = safe_getattr(args, "activation_dropout", 0.1)
-    args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", True)
-    args.tie_adaptive_proj = safe_getattr(args, "tie_adaptive_proj", True)
-    transformer_lm_big(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gbw")
-@register_model_architecture("transformer_lm", "transformer_lm_baevski_gbw")
-def transformer_lm_baevski_gbw(args):
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 512)
-    args.dropout = safe_getattr(args, "dropout", 0.1)
-    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
-    args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", True)
-    transformer_lm_big(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt")
-def transformer_lm_gpt(args):
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 768)
-    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 3072)
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 12)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 12)
-    args.dropout = safe_getattr(args, "dropout", 0.1)
-    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
-    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
-    base_lm_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt2_small")
-def transformer_lm_gpt2_small(args):
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024)
-    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 4096)
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 24)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)
-    args.dropout = safe_getattr(args, "dropout", 0.1)
-    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
-    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
-    base_lm_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt2_tiny")
-def transformer_lm_gpt2_tiny(args):
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 64)
-    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 64)
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 2)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 1)
-    args.dropout = safe_getattr(args, "dropout", 0.1)
-    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
-    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
-    base_lm_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt2_medium")
-def transformer_lm_gpt2_medium(args):
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1280)
-    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 5120)
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 36)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 20)
-    args.dropout = safe_getattr(args, "dropout", 0.1)
-    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
-    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
-    base_lm_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt2_big")
-def transformer_lm_gpt2_big(args):
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1600)
-    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 6400)
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 48)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 25)
-    args.dropout = safe_getattr(args, "dropout", 0.1)
-    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
-    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
-    base_lm_architecture(args)
-
-
-def base_gpt3_architecture(args):
-    args.decoder_input_dim = args.decoder_embed_dim
-    args.decoder_output_dim = args.decoder_embed_dim
-    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", args.decoder_embed_dim * 4)
-    # GPT-3 used learned positional embeddings, rather than sinusoidal
-    args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", True)
-    args.dropout = safe_getattr(args, "dropout", 0.0)
-    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.0)
-    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
-    args.share_decoder_input_output_embed = True
-    base_lm_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt3_small")
-def transformer_lm_gpt3_small(args):
-    # 125M params
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 12)
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 768)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 12)
-    base_gpt3_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt3_medium")
-def transformer_lm_gpt3_medium(args):
-    # 350M params
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 24)
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)
-    base_gpt3_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt3_large")
-def transformer_lm_gpt3_large(args):
-    # 760M params
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 24)
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1536)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)
-    base_gpt3_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt3_xl")
-def transformer_lm_gpt3_xl(args):
-    # 1.3B params
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 24)
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 2048)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32)
-    base_gpt3_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt3_2_7")
-def transformer_lm_gpt3_2_7(args):
-    # 2.7B params
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 32)
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 2560)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32)
-    base_gpt3_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt3_6_7")
-def transformer_lm_gpt3_6_7(args):
-    # 6.7B params
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 32)
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 4096)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32)
-    base_gpt3_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt3_13")
-def transformer_lm_gpt3_13(args):
-    # 13B params
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 40)
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 5120)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 40)
-    base_gpt3_architecture(args)
-
-
-@register_model_architecture("transformer_lm", "transformer_lm_gpt3_175")
-def transformer_lm_gpt3_175(args):
-    # 175B params
-    args.decoder_layers = safe_getattr(args, "decoder_layers", 96)
-    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 12288)
-    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 96)
-    base_gpt3_architecture(args)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/speech_recognition/test_collaters.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/speech_recognition/test_collaters.py
deleted file mode 100644
index 6a5029a48faea2426d7a0277655a2c7c08c1d16c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/speech_recognition/test_collaters.py
+++ /dev/null
@@ -1,58 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import unittest
-
-import numpy as np
-import torch
-from examples.speech_recognition.data.collaters import Seq2SeqCollater
-
-
-class TestSeq2SeqCollator(unittest.TestCase):
-    def test_collate(self):
-
-        eos_idx = 1
-        pad_idx = 0
-        collater = Seq2SeqCollater(
-            feature_index=0, label_index=1, pad_index=pad_idx, eos_index=eos_idx
-        )
-
-        # 2 frames in the first sample and 3 frames in the second one
-        frames1 = np.array([[7, 8], [9, 10]])
-        frames2 = np.array([[1, 2], [3, 4], [5, 6]])
-        target1 = np.array([4, 2, 3, eos_idx])
-        target2 = np.array([3, 2, eos_idx])
-        sample1 = {"id": 0, "data": [frames1, target1]}
-        sample2 = {"id": 1, "data": [frames2, target2]}
-        batch = collater.collate([sample1, sample2])
-
-        # collate sort inputs by frame's length before creating the batch
-        self.assertTensorEqual(batch["id"], torch.tensor([1, 0]))
-        self.assertEqual(batch["ntokens"], 7)
-        self.assertTensorEqual(
-            batch["net_input"]["src_tokens"],
-            torch.tensor(
-                [[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [pad_idx, pad_idx]]]
-            ),
-        )
-        self.assertTensorEqual(
-            batch["net_input"]["prev_output_tokens"],
-            torch.tensor([[eos_idx, 3, 2, pad_idx], [eos_idx, 4, 2, 3]]),
-        )
-        self.assertTensorEqual(batch["net_input"]["src_lengths"], torch.tensor([3, 2]))
-        self.assertTensorEqual(
-            batch["target"],
-            torch.tensor([[3, 2, eos_idx, pad_idx], [4, 2, 3, eos_idx]]),
-        )
-        self.assertEqual(batch["nsentences"], 2)
-
-    def assertTensorEqual(self, t1, t2):
-        self.assertEqual(t1.size(), t2.size(), "size mismatch")
-        self.assertEqual(t1.ne(t2).long().sum(), 0)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/hubert/simple_kmeans/dump_mfcc_feature.py b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/hubert/simple_kmeans/dump_mfcc_feature.py
deleted file mode 100644
index 70d0016663b7d0b90033f4eb301b527f2c92a3f8..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/hubert/simple_kmeans/dump_mfcc_feature.py
+++ /dev/null
@@ -1,78 +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 soundfile as sf
-import torch
-import torchaudio
-
-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_mfcc_feature")
-
-
-class MfccFeatureReader(object):
-    def __init__(self, sample_rate):
-        self.sample_rate = sample_rate
-
-    def read_audio(self, path, ref_len=None):
-        wav, sr = sf.read(path)
-        assert sr == self.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()
-            x = x.view(1, -1)
-
-            mfccs = torchaudio.compliance.kaldi.mfcc(
-                waveform=x,
-                sample_frequency=self.sample_rate,
-                use_energy=False,
-            )  # (time, freq)
-            mfccs = mfccs.transpose(0, 1)  # (freq, time)
-            deltas = torchaudio.functional.compute_deltas(mfccs)
-            ddeltas = torchaudio.functional.compute_deltas(deltas)
-            concat = torch.cat([mfccs, deltas, ddeltas], dim=0)
-            concat = concat.transpose(0, 1).contiguous()  # (freq, time)
-            return concat
-
-
-def main(tsv_dir, split, nshard, rank, feat_dir, sample_rate):
-    reader = MfccFeatureReader(sample_rate)
-    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("nshard", type=int)
-    parser.add_argument("rank", type=int)
-    parser.add_argument("feat_dir")
-    parser.add_argument("--sample_rate", type=int, default=16000)
-    args = parser.parse_args()
-    logger.info(args)
-
-    main(**vars(args))
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/fast_noisy_channel/README.md b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/fast_noisy_channel/README.md
deleted file mode 100644
index f2631a8c34d11bdf7d351c6807b6fe415f5715e1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/fast_noisy_channel/README.md
+++ /dev/null
@@ -1,345 +0,0 @@
-# Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling
-
-## Introduction
-- [Yee et al. (2019)](https://www.aclweb.org/anthology/D19-1571.pdf) introduce a simple and effective noisy channel modeling approach for neural machine translation. However, the noisy channel online decoding approach introduced in this paper is too slow to be practical.
-- To address this, [Bhosale et al. (2020)](http://www.statmt.org/wmt20/pdf/2020.wmt-1.68.pdf) introduces 3 simple approximations to make this approach very fast and practical without much loss in accuracy.
-- This README provides intructions on how to run online decoding or generation with the noisy channel modeling approach, including ways to make it very fast without much loss in accuracy.
-
-## Noisy Channel Modeling
-
-[Yee et al. (2019)](https://www.aclweb.org/anthology/D19-1571.pdf) applies the Bayes Rule to predict `P(y|x)`, the probability of the target `y` given the source `x`.
-```P(y|x) = P(x|y) * P(y) / P(x)```
-- `P(x|y)` predicts the source `x` given the target `y` and is referred to as the **channel model**
-- `P(y)` is a **language model** over the target `y`
-- `P(x)` is generally not modeled since it is constant for all `y`.
-
-We use Transformer models to parameterize the direct model `P(y|x)`, the channel model `P(x|y)` and the language model `P(y)`.
-
-During online decoding with beam search, we generate the top `K2` candidates per beam and score them with the following linear combination of the channel model, the language model as well as the direct model scores.
-
-```(1 / t) * log(P(y|x) + (1 / s) * ( λ1 * log(P(x|y)) + λ2 * log(P(y) ) )```
-- `t` - Target Prefix Length
-- `s` - Source Length
-- `λ1` - Channel Model Weight
-- `λ2` - Language Model Weight
-
-The top `beam_size` candidates based on the above combined scores are chosen to continue the beams in beam search. In beam search with a direct model alone, the scores from the direct model `P(y|x)` are used to choose the top candidates in beam search.
-
-This framework provides a great way to utlize strong target language models trained on large amounts of unlabeled data. Language models can prefer targets unrelated to the source, so we also need a channel model whose role is to ensure that the target preferred by the language model also translates back to the source.
-
-### Training Translation Models and Language Models
-
-For training Transformer models in fairseq for machine translation, refer to instructions [here](https://github.com/pytorch/fairseq/tree/main/examples/translation)
-
-For training Transformer models in fairseq for language modeling, refer to instructions [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model)
-
-### Generation with Language Model for German-English translation with fairseq
-
-Here are instructions to generate using a direct model and a target-side language model.
-
-Note:
-- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq)
-- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing)
-
-```sh
-binarized_data=data_dir/binarized
-direct_model=de_en_seed4.pt
-lm_model=en_lm.pt
-lm_data=lm_data
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model}
-mkdir -p ${lm_data}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt
-
-k2=10
-lenpen=0.16
-lm_wt=0.14
-fairseq-generate ${binarized_data} \
-    --user-dir examples/fast_noisy_channel \
-    --beam 5 \
-    --path ${direct_model} \
-    --lm-model ${lm_model} \
-    --lm-data ${lm_data}  \
-    --k2 ${k2} \
-    --combine-method lm_only \
-    --task noisy_channel_translation \
-    --lenpen ${lenpen} \
-    --lm-wt ${lm_wt} \
-    --gen-subset valid \
-    --remove-bpe \
-    --fp16 \
-    --batch-size 10
-```
-### Noisy Channel Generation for German-English translation with fairseq
-
-Here are instructions for noisy channel generation with a direct model, channel model and language model as explained in section [Noisy Channel Modeling](#noisy-channel-modeling).
-
-Note:
-- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq)
-- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing)
-
-```sh
-binarized_data=data_dir/binarized
-direct_model=de_en_seed4.pt
-lm_model=en_lm.pt
-lm_data=lm_data
-ch_model=en_de.big.seed4.pt
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model}
-mkdir -p ${lm_data}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed4.pt -O ${ch_model}
-
-k2=10
-lenpen=0.21
-lm_wt=0.50
-bw_wt=0.30
-fairseq-generate ${binarized_data} \
-    --user-dir examples/fast_noisy_channel \
-    --beam 5 \
-    --path ${direct_model} \
-    --lm-model ${lm_model} \
-    --lm-data ${lm_data}  \
-    --channel-model ${ch_model} \
-    --k2 ${k2} \
-    --combine-method noisy_channel \
-    --task noisy_channel_translation \
-    --lenpen ${lenpen} \
-    --lm-wt ${lm_wt} \
-    --ch-wt ${bw_wt} \
-    --gen-subset test \
-    --remove-bpe \
-    --fp16 \
-    --batch-size 1
-```
-## Fast Noisy Channel Modeling
-
-[Bhosale et al. (2020)](http://www.statmt.org/wmt20/pdf/2020.wmt-1.68.pdf) introduces 3 approximations that speed up online noisy channel decoding -
-- Smaller channel models (`Tranformer Base` with 1 encoder and decoder layer each vs. `Transformer Big`)
-  - This involves training a channel model that is possibly smaller and less accurate in terms of BLEU than a channel model of the same size as the direct model.
-  - Since the role of the channel model is mainly to assign low scores to generations from the language model if they don't translate back to the source, we may not need the most accurate channel model for this purpose.
-- Smaller output vocabulary size for the channel model (~30,000 -> ~1000)
-  - The channel model doesn't need to score the full output vocabulary, it just needs to score the source tokens, which are completely known.
-  - This is specified using the arguments `--channel-scoring-type src_vocab --top-k-vocab 500`
-  - This means that the output vocabulary for the channel model will be the source tokens for all examples in the batch and the top-K most frequent tokens in the vocabulary
-  - This reduces the memory consumption needed to store channel model scores significantly
-- Smaller number of candidates (`k2`) scored per beam
-  - This is specified by reducing the argument `--k2`
-
-
-### Fast Noisy Channel Generation for German-English translation with fairseq
-
-Here are instructions for **fast** noisy channel generation with a direct model, channel model and language model as explained in section [Fast Noisy Channel Modeling](#fast-noisy-channel-modeling). The main differences are that we use a smaller channel model, reduce `--k2`, set `--channel-scoring-type src_vocab --top-k-vocab 500` and increase the `--batch-size`.
-
-Note:
-- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq)
-- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing)
-
-```sh
-binarized_data=data_dir/binarized
-direct_model=de_en_seed4.pt
-lm_model=en_lm.pt
-lm_data=lm_data
-small_ch_model=en_de.base_1_1.seed4.pt
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model}
-mkdir -p ${lm_data}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed4.pt -O ${small_ch_model}
-
-k2=3
-lenpen=0.23
-lm_wt=0.58
-bw_wt=0.26
-fairseq-generate ${binarized_data} \
-    --user-dir examples/fast_noisy_channel \
-    --beam 5 \
-    --path ${direct_model} \
-    --lm-model ${lm_model} \
-    --lm-data ${lm_data}  \
-    --channel-model ${small_ch_model} \
-    --k2 ${k2} \
-    --combine-method noisy_channel \
-    --task noisy_channel_translation \
-    --lenpen ${lenpen} \
-    --lm-wt ${lm_wt} \
-    --ch-wt ${bw_wt} \
-    --gen-subset test \
-    --remove-bpe \
-    --fp16 \
-    --batch-size 50 \
-    --channel-scoring-type src_vocab --top-k-vocab 500
-```
-
-## Test Data Preprocessing
-
-For preprocessing and binarizing the test sets for Romanian-English and German-English translation, we use the following script -
-
-```sh
-FAIRSEQ=/path/to/fairseq
-cd $FAIRSEQ
-SCRIPTS=$FAIRSEQ/mosesdecoder/scripts
-if [ ! -d "${SCRIPTS}" ]; then
-    echo 'Cloning Moses github repository (for tokenization scripts)...'
-    git clone https://github.com/moses-smt/mosesdecoder.git
-fi
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-NORMALIZE=$SCRIPTS/tokenizer/normalize-punctuation.perl
-
-s=de
-t=en
-test=wmt18
-
-mkdir -p data_dir
-
-# Tokenization
-if [ $s == "ro" ] ; then
-    # Note: Get normalise-romanian.py and remove-diacritics.py from
-    # https://github.com/rsennrich/wmt16-scripts/tree/master/preprocess
-    sacrebleu -t $test -l $s-$t --echo src | \
-        $NORMALIZE -l $s | \
-        python normalise-romanian.py | \
-        python remove-diacritics.py | \
-        $TOKENIZER -l $s -a -q > data_dir/$test.$s-$t.$s
-else
-    sacrebleu -t $test -l $s-$t --echo src | perl $NORMALIZE -l $s | perl $TOKENIZER -threads 8 -a -l $s > data_dir/$test.$s-$t.$s
-fi
-
-sacrebleu -t $test -l $s-$t --echo ref | perl $NORMALIZE -l $t | perl $TOKENIZER -threads 8 -a -l $t > data_dir/$test.$s-$t.$t
-
-
-# Applying BPE
-src_bpe_code=/path/to/source/language/bpe/code
-tgt_bpe_code=/path/to/target/language/bpe/code
-src_dict=/path/to/source/language/dict
-tgt_dict=/path/to/target/language/dict
-
-FASTBPE=$FAIRSEQ/fastBPE
-if [ ! -d "${FASTBPE}" ] ; then
-    git clone https://github.com/glample/fastBPE.git
-    # Follow compilation instructions at https://github.com/glample/fastBPE
-    g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast
-fi
-
-${FASTBPE}/fast applybpe data_dir/bpe.$test.$s-$t.$s data_dir/$test.$s-$t.$s ${src_bpe_code}
-${FASTBPE}/fast applybpe data_dir/bpe.$test.$s-$t.$s data_dir/$test.$s-$t.$s ${tgt_bpe_code}
-
-fairseq-preprocess -s $s -t $t \
-    --testpref data_dir/bpe.$test.$s-$t \
-    --destdir data_dir/binarized \
-    --srcdict ${src_dict} \
-    --tgtdict ${tgt_dict}
-```
-
-## Calculating BLEU
-
-```sh
-DETOKENIZER=$SCRIPTS/tokenizer/detokenizer.perl
-cat ${generation_output} | grep -P "^H" | sort -V | cut -f 3- | $DETOKENIZER -l $t -q -a | sacrebleu -t $test -l $s-$t
-```
-
-
-## Romanian-English Translation
-
-The direct and channel models are trained using bitext data (WMT16) combined with backtranslated data (The monolingual data used for backtranslation comes from http://data.statmt.org/rsennrich/wmt16_backtranslations/ (Sennrich et al., 2016c))
-
-The backtranslated data is generated using an ensemble of 3 English-Romanian models trained on bitext training data (WMT16) with unrestricted sampling.
-
-### BPE Codes and Dictionary
-
-We learn a joint BPE vocabulary of 18K types on the bitext training data which is used for both the source and target.
-||Path|
-|----------|------|
-| BPE Code | [joint_bpe_18k](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/bpe_18k) |
-| Dictionary | [dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/dict) |
-
-### Direct Models
-For Ro-En with backtranslation, the direct and channel models use a Transformer-Big architecture.
-
-| Seed | Model |
-|----|----|
-| 2 | [ro_en_seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed2.pt)
-| 4 | [ro_en_seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed4.pt)
-| 6 | [ro_en_seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed6.pt)
-
-### Channel Models
-For channel models, we follow the same steps as for the direct models. But backtranslated data is generated in the opposite direction using [this Romanian monolingual data](http://data.statmt.org/rsennrich/wmt16_backtranslations/).
-The best lenpen, LM weight and CH weight are obtained by sweeping over the validation set (wmt16/dev) using beam 5.
-| Model Size | Lenpen | LM Weight | CH Weight | Seed 2 | Seed 4 | Seed 6 |
-|----|----|----|----|----|----|----|
-| `big` | 0.84 | 0.64 | 0.56 | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) |
-| `base_1_1` | 0.63 | 0.40 | 0.37 | [base_1_1.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed2.pt) | [base_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed4.pt) | [base_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed6.pt) |
-
-### Language Model
-The model is trained on de-duplicated English Newscrawl data from 2007-2018 comprising 186 million sentences or 4.5B words after normalization and tokenization.
-|  | Path |
-|----|----|
-| `--lm-model` | [transformer_en_lm](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/lm_model/transformer_lm.pt) |
-| `--lm-data` | [lm_data](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/lm_model/lm_dict)
-
-## German-English Translation
-
-### BPE Codes and Dictionaries
-
-| | Path|
-|----------|------|
-| Source BPE Code | [de_bpe_code_24K](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/de_bpe_code_24K) |
-| Target BPE Code | [en_bpe_code_24K](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/en_bpe_code_24K)
-| Source Dictionary | [de_dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/de_dict) |
-| Target Dictionary | [en_dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/en_dict) |
-
-### Direct Models
-We train on WMT’19 training data. Following [Ng et al., 2019](http://statmt.org/wmt19/pdf/53/WMT33.pdf), we apply language identification filtering and remove sentences longer than 250 tokens as well as sentence pairs with a source/target length ratio exceeding 1.5. This results in 26.8M sentence pairs.
-We use the Transformer-Big architecture for the direct model.
-
-| Seed | Model |
-|:----:|----|
-| 4 | [de_en_seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt)
-| 5 | [de_en_seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed5.pt)
-| 6 | [de_en_seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed6.pt)
-
-### Channel Models
-
-We train on WMT’19 training data. Following [Ng et al., 2019](http://statmt.org/wmt19/pdf/53/WMT33.pdf), we apply language identification filtering and remove sentences longer than 250 tokens as well as sentence pairs with a source/target length ratio exceeding 1.5. This results in 26.8M sentence pairs.
-
-| Model Size | Seed 4 | Seed 5 | Seed 6 |
-|----|----|----|----|
-| `big` | [big.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed4.pt) | [big.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed5.pt) | [big.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed6.pt) |
-| `big_1_1` | [big_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed4.pt) | [big_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed5.pt) | [big_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed6.pt) |
-| `base` | [base.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed4.pt) | [base.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed5.pt) | [base.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed6.pt) |
-| `base_1_1` | [base_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed4.pt) | [base_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed5.pt) | [base_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed6.pt) |
-| `half` | [half.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed4.pt) | [half.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed5.pt) | [half.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed6.pt) |
-| `half_1_1` | [half_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed4.pt) | [half_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed5.pt) | [half_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed6.pt) |
-| `quarter` | [quarter.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed4.pt) | [quarter.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed5.pt) | [quarter.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed6.pt) |
-| `quarter_1_1` | [quarter_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed4.pt) | [quarter_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed5.pt) | [quarter_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed6.pt) |
-| `8th` | [8th.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed4.pt) | [8th.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed5.pt) | [8th.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed6.pt) |
-| `8th_1_1` | [8th_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed4.pt) | [8th_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed5.pt) | [8th_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed6.pt) |
-| `16th` | [16th.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed4.pt) | [16th.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed5.pt) | [16th.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed6.pt) |
-| `16th_1_1` | [16th_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed4.pt) | [16th_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed5.pt) | [16th_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed6.pt) |
-
-### Language Model
-The model is trained on de-duplicated English Newscrawl data from 2007-2018 comprising 186 million sentences or 4.5B words after normalization and tokenization.
-|  | Path |
-|----|----|
-| `--lm-model` | [transformer_en_lm](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt) |
-| `--lm-data` | [lm_data](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/)
-
-
-## Citation
-
-```bibtex
-@inproceedings{bhosale2020language,
-    title={Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling},
-    author={Shruti Bhosale and Kyra Yee and Sergey Edunov and Michael Auli},
-    booktitle={Proceedings of the Fifth Conference on Machine Translation (WMT)},
-    year={2020},
-}
-
-@inproceedings{yee2019simple,
-  title={Simple and Effective Noisy Channel Modeling for Neural Machine Translation},
-  author={Yee, Kyra and Dauphin, Yann and Auli, Michael},
-  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
-  pages={5700--5705},
-  year={2019}
-}
-```
diff --git a/spaces/OFA-Sys/OFA-vqa/tasks/mm_tasks/refcoco.py b/spaces/OFA-Sys/OFA-vqa/tasks/mm_tasks/refcoco.py
deleted file mode 100644
index 31f19d1cd882e08104d930407ae0fedb4b847976..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/tasks/mm_tasks/refcoco.py
+++ /dev/null
@@ -1,161 +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 dataclasses import dataclass, field
-import json
-import logging
-from typing import Optional
-from argparse import Namespace
-
-import torch
-from fairseq import metrics
-from fairseq.tasks import register_task
-
-from tasks.ofa_task import OFATask, OFAConfig
-from data.mm_data.refcoco_dataset import RefcocoDataset
-from data.file_dataset import FileDataset
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class RefcocoConfig(OFAConfig):
-    # options for reporting BLEU during validation
-    eval_acc: bool = field(
-        default=False, metadata={"help": "evaluation with BLEU scores"}
-    )
-    eval_args: Optional[str] = field(
-        default='{}',
-        metadata={
-            "help": 'generation args for BLUE or CIDEr scoring, e.g., \'{"beam": 4, "lenpen": 0.6}\', as JSON string'
-        },
-    )
-    eval_print_samples: bool = field(
-        default=False, metadata={"help": "print sample generations during validation"}
-    )
-
-    max_image_size: int = field(
-        default=512, metadata={"help": "max image size for normalization"}
-    )
-    scst: bool = field(
-        default=False, metadata={"help": "Self-critical sequence training"}
-    )
-    scst_args: str = field(
-        default='{}',
-        metadata={
-            "help": 'generation args for Self-critical sequence training, as JSON string'
-        },
-    )
-
-
-@register_task("refcoco", dataclass=RefcocoConfig)
-class RefcocoTask(OFATask):
-    def __init__(self, cfg: RefcocoConfig, src_dict, tgt_dict):
-        super().__init__(cfg, src_dict, tgt_dict)
-
-    def load_dataset(self, split, epoch=1, combine=False, **kwargs):
-        paths = self.cfg.data.split(',')
-        assert len(paths) > 0
-
-        if split == 'train':
-            file_path = paths[(epoch - 1) % (len(paths) - 1)]
-        else:
-            file_path = paths[-1]
-        dataset = FileDataset(file_path, self.cfg.selected_cols)
-
-        self.datasets[split] = RefcocoDataset(
-            split,
-            dataset,
-            self.bpe,
-            self.src_dict,
-            self.tgt_dict,
-            max_src_length=self.cfg.max_src_length,
-            max_tgt_length=self.cfg.max_tgt_length,
-            patch_image_size=self.cfg.patch_image_size,
-            imagenet_default_mean_and_std=self.cfg.imagenet_default_mean_and_std,
-            num_bins=self.cfg.num_bins,
-            max_image_size=self.cfg.max_image_size
-        )
-
-    def build_model(self, cfg):
-        model = super().build_model(cfg)
-        if self.cfg.eval_acc:
-            gen_args = json.loads(self.cfg.eval_args)
-            self.sequence_generator = self.build_generator(
-                [model], Namespace(**gen_args)
-            )
-        if self.cfg.scst:
-            scst_args = json.loads(self.cfg.scst_args)
-            self.scst_generator = self.build_generator(
-                [model], Namespace(**scst_args)
-            )
-
-        return model
-
-    def _calculate_ap_score(self, hyps, refs, thresh=0.5):
-        interacts = torch.cat(
-            [torch.where(hyps[:, :2] < refs[:, :2], refs[:, :2], hyps[:, :2]),
-             torch.where(hyps[:, 2:] < refs[:, 2:], hyps[:, 2:], refs[:, 2:])],
-            dim=1
-        )
-        area_predictions = (hyps[:, 2] - hyps[:, 0]) * (hyps[:, 3] - hyps[:, 1])
-        area_targets = (refs[:, 2] - refs[:, 0]) * (refs[:, 3] - refs[:, 1])
-        interacts_w = interacts[:, 2] - interacts[:, 0]
-        interacts_h = interacts[:, 3] - interacts[:, 1]
-        area_interacts = interacts_w * interacts_h
-        ious = area_interacts / (area_predictions + area_targets - area_interacts + 1e-6)
-        return ((ious >= thresh) & (interacts_w > 0) & (interacts_h > 0)).float()
-
-    def valid_step(self, sample, model, criterion):
-        loss, sample_size, logging_output = criterion(model, sample)
-
-        model.eval()
-        if self.cfg.eval_acc:
-            hyps, refs = self._inference(self.sequence_generator, sample, model)
-            hyps = hyps / (self.cfg.num_bins - 1) * self.cfg.max_image_size
-            refs = refs / (self.cfg.num_bins - 1) * self.cfg.max_image_size
-            hyps[:, ::2] /= sample['w_resize_ratios'].unsqueeze(1)
-            hyps[:, 1::2] /= sample['h_resize_ratios'].unsqueeze(1)
-            refs[:, ::2] /= sample['w_resize_ratios'].unsqueeze(1)
-            refs[:, 1::2] /= sample['h_resize_ratios'].unsqueeze(1)
-
-            # scores = self._calculate_ap_score(hyps, refs)
-            scores = self._calculate_ap_score(hyps, sample['region_coords'].float())
-            logging_output["_score_sum"] = scores.sum().item()
-            logging_output["_score_cnt"] = scores.size(0)
-
-        return loss, sample_size, logging_output
-
-    def reduce_metrics(self, logging_outputs, criterion):
-        super().reduce_metrics(logging_outputs, criterion)
-
-        def sum_logs(key):
-            import torch
-            result = sum(log.get(key, 0) for log in logging_outputs)
-            if torch.is_tensor(result):
-                result = result.cpu()
-            return result
-
-        def compute_score(meters):
-            score = meters["_score_sum"].sum / meters["_score_cnt"].sum
-            score = score if isinstance(score, float) else score.item()
-            return round(score, 4)
-
-        if sum_logs("_score_cnt") > 0:
-            metrics.log_scalar("_score_sum", sum_logs("_score_sum"))
-            metrics.log_scalar("_score_cnt", sum_logs("_score_cnt"))
-            metrics.log_derived("score", compute_score)
-
-    def _inference(self, generator, sample, model):
-        gen_out = self.inference_step(generator, [model], sample)
-        hyps, refs = [], []
-        for i in range(len(gen_out)):
-            hyps.append(gen_out[i][0]["tokens"][:-1] - len(self.src_dict) + self.cfg.num_bins)
-            refs.append(sample["target"][i][:-1] - len(self.src_dict) + self.cfg.num_bins)
-        if self.cfg.eval_print_samples:
-            logger.info("example hypothesis: ", hyps[0])
-            logger.info("example reference: ", refs[0])
-
-        return torch.stack(hyps, dim=0), torch.stack(refs, dim=0)
diff --git a/spaces/OpenGVLab/DragGAN/stylegan2/lpips/pretrained_networks.py b/spaces/OpenGVLab/DragGAN/stylegan2/lpips/pretrained_networks.py
deleted file mode 100644
index 077a24419364fdb5ae2f697f73e28615adae75a7..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/DragGAN/stylegan2/lpips/pretrained_networks.py
+++ /dev/null
@@ -1,181 +0,0 @@
-from collections import namedtuple
-import torch
-from torchvision import models as tv
-from IPython import embed
-
-class squeezenet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(squeezenet, self).__init__()
-        pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.slice6 = torch.nn.Sequential()
-        self.slice7 = torch.nn.Sequential()
-        self.N_slices = 7
-        for x in range(2):
-            self.slice1.add_module(str(x), pretrained_features[x])
-        for x in range(2,5):
-            self.slice2.add_module(str(x), pretrained_features[x])
-        for x in range(5, 8):
-            self.slice3.add_module(str(x), pretrained_features[x])
-        for x in range(8, 10):
-            self.slice4.add_module(str(x), pretrained_features[x])
-        for x in range(10, 11):
-            self.slice5.add_module(str(x), pretrained_features[x])
-        for x in range(11, 12):
-            self.slice6.add_module(str(x), pretrained_features[x])
-        for x in range(12, 13):
-            self.slice7.add_module(str(x), pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1 = h
-        h = self.slice2(h)
-        h_relu2 = h
-        h = self.slice3(h)
-        h_relu3 = h
-        h = self.slice4(h)
-        h_relu4 = h
-        h = self.slice5(h)
-        h_relu5 = h
-        h = self.slice6(h)
-        h_relu6 = h
-        h = self.slice7(h)
-        h_relu7 = h
-        vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7'])
-        out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7)
-
-        return out
-
-
-class alexnet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(alexnet, self).__init__()
-        alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.N_slices = 5
-        for x in range(2):
-            self.slice1.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(2, 5):
-            self.slice2.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(5, 8):
-            self.slice3.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(8, 10):
-            self.slice4.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(10, 12):
-            self.slice5.add_module(str(x), alexnet_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1 = h
-        h = self.slice2(h)
-        h_relu2 = h
-        h = self.slice3(h)
-        h_relu3 = h
-        h = self.slice4(h)
-        h_relu4 = h
-        h = self.slice5(h)
-        h_relu5 = h
-        alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
-        out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
-
-        return out
-
-class vgg16(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(vgg16, self).__init__()
-        vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.N_slices = 5
-        for x in range(4):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(4, 9):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(9, 16):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(16, 23):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(23, 30):
-            self.slice5.add_module(str(x), vgg_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1_2 = h
-        h = self.slice2(h)
-        h_relu2_2 = h
-        h = self.slice3(h)
-        h_relu3_3 = h
-        h = self.slice4(h)
-        h_relu4_3 = h
-        h = self.slice5(h)
-        h_relu5_3 = h
-        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
-        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
-
-        return out
-
-
-
-class resnet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True, num=18):
-        super(resnet, self).__init__()
-        if(num==18):
-            self.net = tv.resnet18(pretrained=pretrained)
-        elif(num==34):
-            self.net = tv.resnet34(pretrained=pretrained)
-        elif(num==50):
-            self.net = tv.resnet50(pretrained=pretrained)
-        elif(num==101):
-            self.net = tv.resnet101(pretrained=pretrained)
-        elif(num==152):
-            self.net = tv.resnet152(pretrained=pretrained)
-        self.N_slices = 5
-
-        self.conv1 = self.net.conv1
-        self.bn1 = self.net.bn1
-        self.relu = self.net.relu
-        self.maxpool = self.net.maxpool
-        self.layer1 = self.net.layer1
-        self.layer2 = self.net.layer2
-        self.layer3 = self.net.layer3
-        self.layer4 = self.net.layer4
-
-    def forward(self, X):
-        h = self.conv1(X)
-        h = self.bn1(h)
-        h = self.relu(h)
-        h_relu1 = h
-        h = self.maxpool(h)
-        h = self.layer1(h)
-        h_conv2 = h
-        h = self.layer2(h)
-        h_conv3 = h
-        h = self.layer3(h)
-        h_conv4 = h
-        h = self.layer4(h)
-        h_conv5 = h
-
-        outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5'])
-        out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
-
-        return out
diff --git a/spaces/OptimalScale/Robin-7b/lmflow/models/text_regression_model.py b/spaces/OptimalScale/Robin-7b/lmflow/models/text_regression_model.py
deleted file mode 100644
index 285cbc4f4c515adf742ec4f5bc17a43f8e5e3057..0000000000000000000000000000000000000000
--- a/spaces/OptimalScale/Robin-7b/lmflow/models/text_regression_model.py
+++ /dev/null
@@ -1,57 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-"""
-A model maps "text_only" data to float.
-"""
-
-from lmflow.models.regression_model import RegressionModel
-from lmflow.datasets.dataset import Dataset
-
-
-class TextRegressionModel(RegressionModel):
-    r"""
-    Initializes a TextRegressionModel instance.
-
-    Parameters
-    ------------
-
-    model_args : 
-        Model arguments such as model name, path, revision, etc.
-
-    args : Optional.
-        Positional arguments.
-
-    kwargs : Optional.
-        Keyword arguments.    
-    """
-
-    def __init__(
-        self,
-        model_args,
-        *args,
-        **kwargs
-    ):
-        """
-        Initializes a TextRegressionModel instance.
-        :param model_args: dictionary with model arguments such as model name, path, revision, etc.
-        """
-        self.inference_func = None
-
-
-    def register_inference_function(self, inference_func):
-        """
-        Registers a regression function.
-        """
-        self.inference_func = inference_func
-
-
-    def inference(self, inputs: Dataset):
-        """
-        Gets regression results of a given dataset.
-
-        :inputs: Dataset object, only accept type "text_only".
-        """
-        if self.inference_func is not None:
-            return self.inference_func(inputs)
-        else:
-            pass
diff --git a/spaces/Osborn-bh/ChatGLM3-6B-Osborn/resources/WECHAT.md b/spaces/Osborn-bh/ChatGLM3-6B-Osborn/resources/WECHAT.md
deleted file mode 100644
index c9ee867ead5d818a0b4e2ba46103a6454537d143..0000000000000000000000000000000000000000
--- a/spaces/Osborn-bh/ChatGLM3-6B-Osborn/resources/WECHAT.md
+++ /dev/null
@@ -1,7 +0,0 @@
-<div align="center">
-<img src=wechat.jpg width="60%"/>
-
-<p> 扫码关注公众号，加入「ChatGLM交流群」 </p>
-<p> Scan the QR code to follow the official account and join the "ChatGLM Discussion Group" </p>
-</div>
-
diff --git a/spaces/PKUWilliamYang/VToonify/vtoonify/model/stylegan/readme.md b/spaces/PKUWilliamYang/VToonify/vtoonify/model/stylegan/readme.md
deleted file mode 100644
index c0f2bce780fe2d7a9239c944b165eee7bcdeb9cb..0000000000000000000000000000000000000000
--- a/spaces/PKUWilliamYang/VToonify/vtoonify/model/stylegan/readme.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# StyleGAN 2 in PyTorch
-
-Implementation of Analyzing and Improving the Image Quality of StyleGAN (https://arxiv.org/abs/1912.04958) in PyTorch
-
-Fork from [https://github.com/rosinality/stylegan2-pytorch](https://github.com/rosinality/stylegan2-pytorch)
-
-In VToonify, we modify it to accept z+ latent codes.
diff --git a/spaces/PKaushik/humandetect/yolov6/data/data_augment.py b/spaces/PKaushik/humandetect/yolov6/data/data_augment.py
deleted file mode 100644
index 0bef2d8777cb0f7c2936718ceaeb41bf19d4a7db..0000000000000000000000000000000000000000
--- a/spaces/PKaushik/humandetect/yolov6/data/data_augment.py
+++ /dev/null
@@ -1,193 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# This code is based on
-# https://github.com/ultralytics/yolov5/blob/master/utils/dataloaders.py
-
-import math
-import random
-
-import cv2
-import numpy as np
-
-
-def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
-    # HSV color-space augmentation
-    if hgain or sgain or vgain:
-        r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains
-        hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
-        dtype = im.dtype  # uint8
-
-        x = np.arange(0, 256, dtype=r.dtype)
-        lut_hue = ((x * r[0]) % 180).astype(dtype)
-        lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
-        lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
-
-        im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
-        cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im)  # no return needed
-
-
-def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
-    # Resize and pad image while meeting stride-multiple constraints
-    shape = im.shape[:2]  # current shape [height, width]
-    if isinstance(new_shape, int):
-        new_shape = (new_shape, new_shape)
-
-    # Scale ratio (new / old)
-    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better val mAP)
-        r = min(r, 1.0)
-
-    # Compute padding
-    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
-    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
-
-    if auto:  # minimum rectangle
-        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
-    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return im, r, (dw, dh)
-
-
-def mixup(im, labels, im2, labels2):
-    # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
-    r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
-    im = (im * r + im2 * (1 - r)).astype(np.uint8)
-    labels = np.concatenate((labels, labels2), 0)
-    return im, labels
-
-
-def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)
-    # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
-    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
-    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
-    ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))  # aspect ratio
-    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)  # candidates
-
-
-def random_affine(img, labels=(), degrees=10, translate=.1, scale=.1, shear=10,
-                  new_shape=(640, 640)):
-
-    n = len(labels)
-    height, width = new_shape
-
-    M, s = get_transform_matrix(img.shape[:2], (height, width), degrees, scale, shear, translate)
-    if (M != np.eye(3)).any():  # image changed
-        img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
-    # Transform label coordinates
-    if n:
-        new = np.zeros((n, 4))
-
-        xy = np.ones((n * 4, 3))
-        xy[:, :2] = labels[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
-        xy = xy @ M.T  # transform
-        xy = xy[:, :2].reshape(n, 8)  # perspective rescale or affine
-
-        # create new boxes
-        x = xy[:, [0, 2, 4, 6]]
-        y = xy[:, [1, 3, 5, 7]]
-        new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
-
-        # clip
-        new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
-        new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
-
-        # filter candidates
-        i = box_candidates(box1=labels[:, 1:5].T * s, box2=new.T, area_thr=0.1)
-        labels = labels[i]
-        labels[:, 1:5] = new[i]
-
-    return img, labels
-
-
-def get_transform_matrix(img_shape, new_shape, degrees, scale, shear, translate):
-    new_height, new_width = new_shape
-    # Center
-    C = np.eye(3)
-    C[0, 2] = -img_shape[1] / 2  # x translation (pixels)
-    C[1, 2] = -img_shape[0] / 2  # y translation (pixels)
-
-    # Rotation and Scale
-    R = np.eye(3)
-    a = random.uniform(-degrees, degrees)
-    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
-    s = random.uniform(1 - scale, 1 + scale)
-    # s = 2 ** random.uniform(-scale, scale)
-    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
-    # Shear
-    S = np.eye(3)
-    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
-    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)
-
-    # Translation
-    T = np.eye(3)
-    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * new_width  # x translation (pixels)
-    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * new_height  # y transla ion (pixels)
-
-    # Combined rotation matrix
-    M = T @ S @ R @ C  # order of operations (right to left) is IMPORTANT
-    return M, s
-
-
-def mosaic_augmentation(img_size, imgs, hs, ws, labels, hyp):
-
-    assert len(imgs) == 4, "Mosaic augmentation of current version only supports 4 images."
-
-    labels4 = []
-    s = img_size
-    yc, xc = (int(random.uniform(s//2, 3*s//2)) for _ in range(2))  # mosaic center x, y
-    for i in range(len(imgs)):
-        # Load image
-        img, h, w = imgs[i], hs[i], ws[i]
-        # place img in img4
-        if i == 0:  # top left
-            img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
-            x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
-            x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
-        elif i == 1:  # top right
-            x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
-            x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
-        elif i == 2:  # bottom left
-            x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
-            x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
-        elif i == 3:  # bottom right
-            x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
-            x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
-
-        img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
-        padw = x1a - x1b
-        padh = y1a - y1b
-
-        # Labels
-        labels_per_img = labels[i].copy()
-        if labels_per_img.size:
-            boxes = np.copy(labels_per_img[:, 1:])
-            boxes[:, 0] = w * (labels_per_img[:, 1] - labels_per_img[:, 3] / 2) + padw  # top left x
-            boxes[:, 1] = h * (labels_per_img[:, 2] - labels_per_img[:, 4] / 2) + padh  # top left y
-            boxes[:, 2] = w * (labels_per_img[:, 1] + labels_per_img[:, 3] / 2) + padw  # bottom right x
-            boxes[:, 3] = h * (labels_per_img[:, 2] + labels_per_img[:, 4] / 2) + padh  # bottom right y
-            labels_per_img[:, 1:] = boxes
-
-        labels4.append(labels_per_img)
-
-    # Concat/clip labels
-    labels4 = np.concatenate(labels4, 0)
-    for x in (labels4[:, 1:]):
-        np.clip(x, 0, 2 * s, out=x)
-
-    # Augment
-    img4, labels4 = random_affine(img4, labels4,
-                                  degrees=hyp['degrees'],
-                                  translate=hyp['translate'],
-                                  scale=hyp['scale'],
-                                  shear=hyp['shear'])
-
-    return img4, labels4
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/__init__.py
deleted file mode 100644
index 3cf93f8bec9cf0cef0a3bd76ca3ca92eb188f535..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .backbones import *  # noqa: F401,F403
-from .builder import (BACKBONES, HEADS, LOSSES, SEGMENTORS, build_backbone,
-                      build_head, build_loss, build_segmentor)
-from .decode_heads import *  # noqa: F401,F403
-from .losses import *  # noqa: F401,F403
-from .necks import *  # noqa: F401,F403
-from .segmentors import *  # noqa: F401,F403
-
-__all__ = [
-    'BACKBONES', 'HEADS', 'LOSSES', 'SEGMENTORS', 'build_backbone',
-    'build_head', 'build_loss', 'build_segmentor'
-]
diff --git a/spaces/PikeAndVine/resize_color/README.md b/spaces/PikeAndVine/resize_color/README.md
deleted file mode 100644
index 37733172b5813e9c2e1775518121ba0223eab76f..0000000000000000000000000000000000000000
--- a/spaces/PikeAndVine/resize_color/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Resize Old
-emoji: 🐢
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-duplicated_from: 155elkhorn/resize_color
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Pranjal2041/SemSup-XC/sample.py b/spaces/Pranjal2041/SemSup-XC/sample.py
deleted file mode 100644
index 29d1bf7bd80c1b893738db128c8f7a58b56ea3e0..0000000000000000000000000000000000000000
--- a/spaces/Pranjal2041/SemSup-XC/sample.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!"
-
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-
-demo.launch(share = True)   
\ No newline at end of file
diff --git a/spaces/ReFenter/DeepDanbooru_string/README.md b/spaces/ReFenter/DeepDanbooru_string/README.md
deleted file mode 100644
index 4330b6f969246dc764a34ea254d2e807159f1c55..0000000000000000000000000000000000000000
--- a/spaces/ReFenter/DeepDanbooru_string/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-title: DeepDanbooru String
-emoji: 💬
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-duplicated_from: NoCrypt/DeepDanbooru_string
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `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.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/src/utils/dataset.py b/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/src/utils/dataset.py
deleted file mode 100644
index 1881446fd69aedb520ae669100cd2a3c2d143a18..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/src/utils/dataset.py
+++ /dev/null
@@ -1,232 +0,0 @@
-import io
-import cv2
-import numpy as np
-import h5py
-import torch
-from numpy.linalg import inv
-import re
-
-
-try:
-    # for internel use only
-    from .client import MEGADEPTH_CLIENT, SCANNET_CLIENT
-except Exception:
-    MEGADEPTH_CLIENT = SCANNET_CLIENT = None
-
-# --- DATA IO ---
-
-
-def load_array_from_s3(
-    path,
-    client,
-    cv_type,
-    use_h5py=False,
-):
-    byte_str = client.Get(path)
-    try:
-        if not use_h5py:
-            raw_array = np.fromstring(byte_str, np.uint8)
-            data = cv2.imdecode(raw_array, cv_type)
-        else:
-            f = io.BytesIO(byte_str)
-            data = np.array(h5py.File(f, "r")["/depth"])
-    except Exception as ex:
-        print(f"==> Data loading failure: {path}")
-        raise ex
-
-    assert data is not None
-    return data
-
-
-def imread_gray(path, augment_fn=None, client=SCANNET_CLIENT):
-    cv_type = cv2.IMREAD_GRAYSCALE if augment_fn is None else cv2.IMREAD_COLOR
-    if str(path).startswith("s3://"):
-        image = load_array_from_s3(str(path), client, cv_type)
-    else:
-        image = cv2.imread(str(path), cv_type)
-
-    if augment_fn is not None:
-        image = cv2.imread(str(path), cv2.IMREAD_COLOR)
-        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-        image = augment_fn(image)
-        image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-    return image  # (h, w)
-
-
-def get_resized_wh(w, h, resize=None):
-    if resize is not None:  # resize the longer edge
-        scale = resize / max(h, w)
-        w_new, h_new = int(round(w * scale)), int(round(h * scale))
-    else:
-        w_new, h_new = w, h
-    return w_new, h_new
-
-
-def get_divisible_wh(w, h, df=None):
-    if df is not None:
-        w_new, h_new = map(lambda x: int(x // df * df), [w, h])
-    else:
-        w_new, h_new = w, h
-    return w_new, h_new
-
-
-def pad_bottom_right(inp, pad_size, ret_mask=False):
-    assert isinstance(pad_size, int) and pad_size >= max(
-        inp.shape[-2:]
-    ), f"{pad_size} < {max(inp.shape[-2:])}"
-    mask = None
-    if inp.ndim == 2:
-        padded = np.zeros((pad_size, pad_size), dtype=inp.dtype)
-        padded[: inp.shape[0], : inp.shape[1]] = inp
-        if ret_mask:
-            mask = np.zeros((pad_size, pad_size), dtype=bool)
-            mask[: inp.shape[0], : inp.shape[1]] = True
-    elif inp.ndim == 3:
-        padded = np.zeros((inp.shape[0], pad_size, pad_size), dtype=inp.dtype)
-        padded[:, : inp.shape[1], : inp.shape[2]] = inp
-        if ret_mask:
-            mask = np.zeros((inp.shape[0], pad_size, pad_size), dtype=bool)
-            mask[:, : inp.shape[1], : inp.shape[2]] = True
-    else:
-        raise NotImplementedError()
-    return padded, mask
-
-
-# --- MEGADEPTH ---
-
-
-def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=None):
-    """
-    Args:
-        resize (int, optional): the longer edge of resized images. None for no resize.
-        padding (bool): If set to 'True', zero-pad resized images to squared size.
-        augment_fn (callable, optional): augments images with pre-defined visual effects
-    Returns:
-        image (torch.tensor): (1, h, w)
-        mask (torch.tensor): (h, w)
-        scale (torch.tensor): [w/w_new, h/h_new]
-    """
-    # read image
-    image = imread_gray(path, augment_fn, client=MEGADEPTH_CLIENT)
-
-    # resize image
-    w, h = image.shape[1], image.shape[0]
-    w_new, h_new = get_resized_wh(w, h, resize)
-    w_new, h_new = get_divisible_wh(w_new, h_new, df)
-
-    image = cv2.resize(image, (w_new, h_new))
-    scale = torch.tensor([w / w_new, h / h_new], dtype=torch.float)
-
-    if padding:  # padding
-        pad_to = max(h_new, w_new)
-        image, mask = pad_bottom_right(image, pad_to, ret_mask=True)
-    else:
-        mask = None
-
-    image = (
-        torch.from_numpy(image).float()[None] / 255
-    )  # (h, w) -> (1, h, w) and normalized
-    if mask is not None:
-        mask = torch.from_numpy(mask)
-
-    return image, mask, scale
-
-
-def read_megadepth_depth(path, pad_to=None):
-    if str(path).startswith("s3://"):
-        depth = load_array_from_s3(path, MEGADEPTH_CLIENT, None, use_h5py=True)
-    else:
-        depth = np.array(h5py.File(path, "r")["depth"])
-    if pad_to is not None:
-        depth, _ = pad_bottom_right(depth, pad_to, ret_mask=False)
-    depth = torch.from_numpy(depth).float()  # (h, w)
-    return depth
-
-
-# --- ScanNet ---
-
-
-def read_scannet_gray(path, resize=(640, 480), augment_fn=None):
-    """
-    Args:
-        resize (tuple): align image to depthmap, in (w, h).
-        augment_fn (callable, optional): augments images with pre-defined visual effects
-    Returns:
-        image (torch.tensor): (1, h, w)
-        mask (torch.tensor): (h, w)
-        scale (torch.tensor): [w/w_new, h/h_new]
-    """
-    # read and resize image
-    image = imread_gray(path, augment_fn)
-    image = cv2.resize(image, resize)
-
-    # (h, w) -> (1, h, w) and normalized
-    image = torch.from_numpy(image).float()[None] / 255
-    return image
-
-
-def read_scannet_depth(path):
-    if str(path).startswith("s3://"):
-        depth = load_array_from_s3(str(path), SCANNET_CLIENT, cv2.IMREAD_UNCHANGED)
-    else:
-        depth = cv2.imread(str(path), cv2.IMREAD_UNCHANGED)
-    depth = depth / 1000
-    depth = torch.from_numpy(depth).float()  # (h, w)
-    return depth
-
-
-def read_scannet_pose(path):
-    """Read ScanNet's Camera2World pose and transform it to World2Camera.
-
-    Returns:
-        pose_w2c (np.ndarray): (4, 4)
-    """
-    cam2world = np.loadtxt(path, delimiter=" ")
-    world2cam = inv(cam2world)
-    return world2cam
-
-
-def read_scannet_intrinsic(path):
-    """Read ScanNet's intrinsic matrix and return the 3x3 matrix."""
-    intrinsic = np.loadtxt(path, delimiter=" ")
-    return intrinsic[:-1, :-1]
-
-
-def read_gl3d_gray(path, resize):
-    img = cv2.resize(cv2.imread(path, cv2.IMREAD_GRAYSCALE), (int(resize), int(resize)))
-    img = (
-        torch.from_numpy(img).float()[None] / 255
-    )  # (h, w) -> (1, h, w) and normalized
-    return img
-
-
-def read_gl3d_depth(file_path):
-    with open(file_path, "rb") as fin:
-        color = None
-        width = None
-        height = None
-        scale = None
-        data_type = None
-        header = str(fin.readline().decode("UTF-8")).rstrip()
-        if header == "PF":
-            color = True
-        elif header == "Pf":
-            color = False
-        else:
-            raise Exception("Not a PFM file.")
-        dim_match = re.match(r"^(\d+)\s(\d+)\s$", fin.readline().decode("UTF-8"))
-        if dim_match:
-            width, height = map(int, dim_match.groups())
-        else:
-            raise Exception("Malformed PFM header.")
-        scale = float((fin.readline().decode("UTF-8")).rstrip())
-        if scale < 0:  # little-endian
-            data_type = "<f"
-        else:
-            data_type = ">f"  # big-endian
-        data_string = fin.read()
-        data = np.fromstring(data_string, data_type)
-        shape = (height, width, 3) if color else (height, width)
-        data = np.reshape(data, shape)
-        data = np.flip(data, 0)
-    return torch.from_numpy(data.copy()).float()
diff --git a/spaces/Reeve/Ohayou_Face/torch_utils/ops/fma.py b/spaces/Reeve/Ohayou_Face/torch_utils/ops/fma.py
deleted file mode 100644
index 2eeac58a626c49231e04122b93e321ada954c5d3..0000000000000000000000000000000000000000
--- a/spaces/Reeve/Ohayou_Face/torch_utils/ops/fma.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`."""
-
-import torch
-
-#----------------------------------------------------------------------------
-
-def fma(a, b, c): # => a * b + c
-    return _FusedMultiplyAdd.apply(a, b, c)
-
-#----------------------------------------------------------------------------
-
-class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
-    @staticmethod
-    def forward(ctx, a, b, c): # pylint: disable=arguments-differ
-        out = torch.addcmul(c, a, b)
-        ctx.save_for_backward(a, b)
-        ctx.c_shape = c.shape
-        return out
-
-    @staticmethod
-    def backward(ctx, dout): # pylint: disable=arguments-differ
-        a, b = ctx.saved_tensors
-        c_shape = ctx.c_shape
-        da = None
-        db = None
-        dc = None
-
-        if ctx.needs_input_grad[0]:
-            da = _unbroadcast(dout * b, a.shape)
-
-        if ctx.needs_input_grad[1]:
-            db = _unbroadcast(dout * a, b.shape)
-
-        if ctx.needs_input_grad[2]:
-            dc = _unbroadcast(dout, c_shape)
-
-        return da, db, dc
-
-#----------------------------------------------------------------------------
-
-def _unbroadcast(x, shape):
-    extra_dims = x.ndim - len(shape)
-    assert extra_dims >= 0
-    dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)]
-    if len(dim):
-        x = x.sum(dim=dim, keepdim=True)
-    if extra_dims:
-        x = x.reshape(-1, *x.shape[extra_dims+1:])
-    assert x.shape == shape
-    return x
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/apis/test.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/apis/test.py
deleted file mode 100644
index e574eb7da04f09a59cf99ff953c36468ae87a326..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/apis/test.py
+++ /dev/null
@@ -1,238 +0,0 @@
-import os.path as osp
-import pickle
-import shutil
-import tempfile
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-import torch
-import torch.distributed as dist
-from annotator.uniformer.mmcv.image import tensor2imgs
-from annotator.uniformer.mmcv.runner import get_dist_info
-
-
-def np2tmp(array, temp_file_name=None):
-    """Save ndarray to local numpy file.
-
-    Args:
-        array (ndarray): Ndarray to save.
-        temp_file_name (str): Numpy file name. If 'temp_file_name=None', this
-            function will generate a file name with tempfile.NamedTemporaryFile
-            to save ndarray. Default: None.
-
-    Returns:
-        str: The numpy file name.
-    """
-
-    if temp_file_name is None:
-        temp_file_name = tempfile.NamedTemporaryFile(
-            suffix='.npy', delete=False).name
-    np.save(temp_file_name, array)
-    return temp_file_name
-
-
-def single_gpu_test(model,
-                    data_loader,
-                    show=False,
-                    out_dir=None,
-                    efficient_test=False,
-                    opacity=0.5):
-    """Test with single GPU.
-
-    Args:
-        model (nn.Module): Model to be tested.
-        data_loader (utils.data.Dataloader): Pytorch data loader.
-        show (bool): Whether show results during inference. Default: False.
-        out_dir (str, optional): If specified, the results will be dumped into
-            the directory to save output results.
-        efficient_test (bool): Whether save the results as local numpy files to
-            save CPU memory during evaluation. Default: False.
-        opacity(float): Opacity of painted segmentation map.
-            Default 0.5.
-            Must be in (0, 1] range.
-    Returns:
-        list: The prediction results.
-    """
-
-    model.eval()
-    results = []
-    dataset = data_loader.dataset
-    prog_bar = mmcv.ProgressBar(len(dataset))
-    for i, data in enumerate(data_loader):
-        with torch.no_grad():
-            result = model(return_loss=False, **data)
-
-        if show or out_dir:
-            img_tensor = data['img'][0]
-            img_metas = data['img_metas'][0].data[0]
-            imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
-            assert len(imgs) == len(img_metas)
-
-            for img, img_meta in zip(imgs, img_metas):
-                h, w, _ = img_meta['img_shape']
-                img_show = img[:h, :w, :]
-
-                ori_h, ori_w = img_meta['ori_shape'][:-1]
-                img_show = mmcv.imresize(img_show, (ori_w, ori_h))
-
-                if out_dir:
-                    out_file = osp.join(out_dir, img_meta['ori_filename'])
-                else:
-                    out_file = None
-
-                model.module.show_result(
-                    img_show,
-                    result,
-                    palette=dataset.PALETTE,
-                    show=show,
-                    out_file=out_file,
-                    opacity=opacity)
-
-        if isinstance(result, list):
-            if efficient_test:
-                result = [np2tmp(_) for _ in result]
-            results.extend(result)
-        else:
-            if efficient_test:
-                result = np2tmp(result)
-            results.append(result)
-
-        batch_size = len(result)
-        for _ in range(batch_size):
-            prog_bar.update()
-    return results
-
-
-def multi_gpu_test(model,
-                   data_loader,
-                   tmpdir=None,
-                   gpu_collect=False,
-                   efficient_test=False):
-    """Test model with multiple gpus.
-
-    This method tests model with multiple gpus and collects the results
-    under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'
-    it encodes results to gpu tensors and use gpu communication for results
-    collection. On cpu mode it saves the results on different gpus to 'tmpdir'
-    and collects them by the rank 0 worker.
-
-    Args:
-        model (nn.Module): Model to be tested.
-        data_loader (utils.data.Dataloader): Pytorch data loader.
-        tmpdir (str): Path of directory to save the temporary results from
-            different gpus under cpu mode.
-        gpu_collect (bool): Option to use either gpu or cpu to collect results.
-        efficient_test (bool): Whether save the results as local numpy files to
-            save CPU memory during evaluation. Default: False.
-
-    Returns:
-        list: The prediction results.
-    """
-
-    model.eval()
-    results = []
-    dataset = data_loader.dataset
-    rank, world_size = get_dist_info()
-    if rank == 0:
-        prog_bar = mmcv.ProgressBar(len(dataset))
-    for i, data in enumerate(data_loader):
-        with torch.no_grad():
-            result = model(return_loss=False, rescale=True, **data)
-
-        if isinstance(result, list):
-            if efficient_test:
-                result = [np2tmp(_) for _ in result]
-            results.extend(result)
-        else:
-            if efficient_test:
-                result = np2tmp(result)
-            results.append(result)
-
-        if rank == 0:
-            batch_size = data['img'][0].size(0)
-            for _ in range(batch_size * world_size):
-                prog_bar.update()
-
-    # collect results from all ranks
-    if gpu_collect:
-        results = collect_results_gpu(results, len(dataset))
-    else:
-        results = collect_results_cpu(results, len(dataset), tmpdir)
-    return results
-
-
-def collect_results_cpu(result_part, size, tmpdir=None):
-    """Collect results with CPU."""
-    rank, world_size = get_dist_info()
-    # create a tmp dir if it is not specified
-    if tmpdir is None:
-        MAX_LEN = 512
-        # 32 is whitespace
-        dir_tensor = torch.full((MAX_LEN, ),
-                                32,
-                                dtype=torch.uint8,
-                                device='cuda')
-        if rank == 0:
-            tmpdir = tempfile.mkdtemp()
-            tmpdir = torch.tensor(
-                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
-            dir_tensor[:len(tmpdir)] = tmpdir
-        dist.broadcast(dir_tensor, 0)
-        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
-    else:
-        mmcv.mkdir_or_exist(tmpdir)
-    # dump the part result to the dir
-    mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
-    dist.barrier()
-    # collect all parts
-    if rank != 0:
-        return None
-    else:
-        # load results of all parts from tmp dir
-        part_list = []
-        for i in range(world_size):
-            part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
-            part_list.append(mmcv.load(part_file))
-        # sort the results
-        ordered_results = []
-        for res in zip(*part_list):
-            ordered_results.extend(list(res))
-        # the dataloader may pad some samples
-        ordered_results = ordered_results[:size]
-        # remove tmp dir
-        shutil.rmtree(tmpdir)
-        return ordered_results
-
-
-def collect_results_gpu(result_part, size):
-    """Collect results with GPU."""
-    rank, world_size = get_dist_info()
-    # dump result part to tensor with pickle
-    part_tensor = torch.tensor(
-        bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
-    # gather all result part tensor shape
-    shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
-    shape_list = [shape_tensor.clone() for _ in range(world_size)]
-    dist.all_gather(shape_list, shape_tensor)
-    # padding result part tensor to max length
-    shape_max = torch.tensor(shape_list).max()
-    part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
-    part_send[:shape_tensor[0]] = part_tensor
-    part_recv_list = [
-        part_tensor.new_zeros(shape_max) for _ in range(world_size)
-    ]
-    # gather all result part
-    dist.all_gather(part_recv_list, part_send)
-
-    if rank == 0:
-        part_list = []
-        for recv, shape in zip(part_recv_list, shape_list):
-            part_list.append(
-                pickle.loads(recv[:shape[0]].cpu().numpy().tobytes()))
-        # sort the results
-        ordered_results = []
-        for res in zip(*part_list):
-            ordered_results.extend(list(res))
-        # the dataloader may pad some samples
-        ordered_results = ordered_results[:size]
-        return ordered_results
diff --git a/spaces/Rongjiehuang/ProDiff/tasks/tts/tts.py b/spaces/Rongjiehuang/ProDiff/tasks/tts/tts.py
deleted file mode 100644
index f803c1e738137cb1eca19a1943196abd2884c0a5..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/tasks/tts/tts.py
+++ /dev/null
@@ -1,131 +0,0 @@
-from multiprocessing.pool import Pool
-
-import matplotlib
-
-from utils.pl_utils import data_loader
-from utils.training_utils import RSQRTSchedule
-from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder
-from modules.fastspeech.pe import PitchExtractor
-
-matplotlib.use('Agg')
-import os
-import numpy as np
-from tqdm import tqdm
-import torch.distributed as dist
-
-from tasks.base_task import BaseTask
-from utils.hparams import hparams
-from utils.text_encoder import TokenTextEncoder
-import json
-
-import torch
-import torch.optim
-import torch.utils.data
-import utils
-
-
-
-class TtsTask(BaseTask):
-    def __init__(self, *args, **kwargs):
-        self.vocoder = None
-        self.phone_encoder = self.build_phone_encoder(hparams['binary_data_dir'])
-        self.padding_idx = self.phone_encoder.pad()
-        self.eos_idx = self.phone_encoder.eos()
-        self.seg_idx = self.phone_encoder.seg()
-        self.saving_result_pool = None
-        self.saving_results_futures = None
-        self.stats = {}
-        super().__init__(*args, **kwargs)
-
-    def build_scheduler(self, optimizer):
-        return RSQRTSchedule(optimizer)
-
-    def build_optimizer(self, model):
-        self.optimizer = optimizer = torch.optim.AdamW(
-            model.parameters(),
-            lr=hparams['lr'])
-        return optimizer
-
-    def build_dataloader(self, dataset, shuffle, max_tokens=None, max_sentences=None,
-                         required_batch_size_multiple=-1, endless=False, batch_by_size=True):
-        devices_cnt = torch.cuda.device_count()
-        if devices_cnt == 0:
-            devices_cnt = 1
-        if required_batch_size_multiple == -1:
-            required_batch_size_multiple = devices_cnt
-
-        def shuffle_batches(batches):
-            np.random.shuffle(batches)
-            return batches
-
-        if max_tokens is not None:
-            max_tokens *= devices_cnt
-        if max_sentences is not None:
-            max_sentences *= devices_cnt
-        indices = dataset.ordered_indices()
-        if batch_by_size:
-            batch_sampler = utils.batch_by_size(
-                indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences,
-                required_batch_size_multiple=required_batch_size_multiple,
-            )
-        else:
-            batch_sampler = []
-            for i in range(0, len(indices), max_sentences):
-                batch_sampler.append(indices[i:i + max_sentences])
-
-        if shuffle:
-            batches = shuffle_batches(list(batch_sampler))
-            if endless:
-                batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))]
-        else:
-            batches = batch_sampler
-            if endless:
-                batches = [b for _ in range(1000) for b in batches]
-        num_workers = dataset.num_workers
-        if self.trainer.use_ddp:
-            num_replicas = dist.get_world_size()
-            rank = dist.get_rank()
-            batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0]
-        return torch.utils.data.DataLoader(dataset,
-                                           collate_fn=dataset.collater,
-                                           batch_sampler=batches,
-                                           num_workers=num_workers,
-                                           pin_memory=False)
-
-    def build_phone_encoder(self, data_dir):
-        phone_list_file = os.path.join(data_dir, 'phone_set.json')
-
-        phone_list = json.load(open(phone_list_file))
-        return TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',')
-
-    def build_optimizer(self, model):
-        self.optimizer = optimizer = torch.optim.AdamW(
-            model.parameters(),
-            lr=hparams['lr'])
-        return optimizer
-
-    def test_start(self):
-        self.saving_result_pool = Pool(8)
-        self.saving_results_futures = []
-        self.vocoder: BaseVocoder = get_vocoder_cls(hparams)()
-        if hparams.get('pe_enable') is not None and hparams['pe_enable']:
-            self.pe = PitchExtractor().cuda()
-            utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True)
-            self.pe.eval()
-    def test_end(self, outputs):
-        self.saving_result_pool.close()
-        [f.get() for f in tqdm(self.saving_results_futures)]
-        self.saving_result_pool.join()
-        return {}
-
-    ##########
-    # utils
-    ##########
-    def weights_nonzero_speech(self, target):
-        # target : B x T x mel
-        # Assign weight 1.0 to all labels except for padding (id=0).
-        dim = target.size(-1)
-        return target.abs().sum(-1, keepdim=True).ne(0).float().repeat(1, 1, dim)
-
-if __name__ == '__main__':
-    TtsTask.start()
diff --git a/spaces/SHIBATAATSUSHI/aioccupationaltherapist2/README.md b/spaces/SHIBATAATSUSHI/aioccupationaltherapist2/README.md
deleted file mode 100644
index 24483605f740f043d828c78162018453d27800f6..0000000000000000000000000000000000000000
--- a/spaces/SHIBATAATSUSHI/aioccupationaltherapist2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI作業療法士(β)
-emoji: 🌖
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-duplicated_from: SHIBATAATSUSHI/aioccupationaltherapist
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/SRDdev/Image-Caption/README.md b/spaces/SRDdev/Image-Caption/README.md
deleted file mode 100644
index cf2cb291cdc8d0f725166cf87de1fc910af827e4..0000000000000000000000000000000000000000
--- a/spaces/SRDdev/Image-Caption/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Image Caption
-emoji: 🏅
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.5
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
\ No newline at end of file
diff --git a/spaces/Salesforce/EDICT/my_diffusers/models/embeddings.py b/spaces/Salesforce/EDICT/my_diffusers/models/embeddings.py
deleted file mode 100644
index 734be6068b7817efd51a508b0e42bc1c8f99d289..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/models/embeddings.py
+++ /dev/null
@@ -1,116 +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 math
-
-import numpy as np
-import torch
-from torch import nn
-
-
-def get_timestep_embedding(
-    timesteps: torch.Tensor,
-    embedding_dim: int,
-    flip_sin_to_cos: bool = False,
-    downscale_freq_shift: float = 1,
-    scale: float = 1,
-    max_period: int = 10000,
-):
-    # print(timesteps)
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
-
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
-    embeddings. :return: an [N x dim] Tensor of positional embeddings.
-    """
-    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
-
-    half_dim = embedding_dim // 2
-    exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float64)
-    exponent = exponent / (half_dim - downscale_freq_shift)
-
-    emb = torch.exp(exponent).to(device=timesteps.device)
-    emb = timesteps[:, None].double() * emb[None, :]
-
-    # scale embeddings
-    emb = scale * emb
-
-    # concat sine and cosine embeddings
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
-
-    # flip sine and cosine embeddings
-    if flip_sin_to_cos:
-        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
-
-    # zero pad
-    if embedding_dim % 2 == 1:
-        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
-    return emb
-
-
-class TimestepEmbedding(nn.Module):
-    def __init__(self, channel: int, time_embed_dim: int, act_fn: str = "silu"):
-        super().__init__()
-
-        self.linear_1 = nn.Linear(channel, time_embed_dim)
-        self.act = None
-        if act_fn == "silu":
-            self.act = nn.SiLU()
-        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim)
-
-    def forward(self, sample):
-        sample = self.linear_1(sample)
-
-        if self.act is not None:
-            sample = self.act(sample)
-
-        sample = self.linear_2(sample)
-        return sample
-
-
-class Timesteps(nn.Module):
-    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
-        super().__init__()
-        self.num_channels = num_channels
-        self.flip_sin_to_cos = flip_sin_to_cos
-        self.downscale_freq_shift = downscale_freq_shift
-
-    def forward(self, timesteps):
-        t_emb = get_timestep_embedding(
-            timesteps,
-            self.num_channels,
-            flip_sin_to_cos=self.flip_sin_to_cos,
-            downscale_freq_shift=self.downscale_freq_shift,
-        )
-        return t_emb
-
-
-class GaussianFourierProjection(nn.Module):
-    """Gaussian Fourier embeddings for noise levels."""
-
-    def __init__(self, embedding_size: int = 256, scale: float = 1.0):
-        super().__init__()
-        self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
-
-        # to delete later
-        self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
-
-        self.weight = self.W
-
-    def forward(self, x):
-        x = torch.log(x)
-        x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi
-        out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
-        return out
diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/latent_diffusion/__init__.py b/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/latent_diffusion/__init__.py
deleted file mode 100644
index c481b38cf5e0a1c4e24f7e0edf944efb68e1f979..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/latent_diffusion/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# flake8: noqa
-from ...utils import is_transformers_available
-
-
-if is_transformers_available():
-    from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
diff --git a/spaces/SamerKharboush/chatGPT-Sam-Turbo/README.md b/spaces/SamerKharboush/chatGPT-Sam-Turbo/README.md
deleted file mode 100644
index bd7984b24ea879f2cdf5cb438c9aa68c7676ddc4..0000000000000000000000000000000000000000
--- a/spaces/SamerKharboush/chatGPT-Sam-Turbo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ChatGPT
-emoji: 😁
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
-duplicated_from: ppsantiago/chatGPT
----
diff --git a/spaces/Sandiago21/automatic-speech-recognition-greek/app.py b/spaces/Sandiago21/automatic-speech-recognition-greek/app.py
deleted file mode 100644
index 1488c952cb64af93b7faec0a2151fdcbaff09b18..0000000000000000000000000000000000000000
--- a/spaces/Sandiago21/automatic-speech-recognition-greek/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import torch
-import gradio as gr
-from transformers import pipeline
-
-model_id = "Sandiago21/whisper-large-v2-greek"  # update with your model id
-pipe = pipeline("automatic-speech-recognition", model=model_id)
-
-
-title = "Automatic Speech Recognition (ASR)"
-description = """
-Demo for automatic speech recognition in Greek. Demo uses [Sandiago21/whisper-large-v2-greek](https://huggingface.co/Sandiago21/whisper-large-v2-greek) checkpoint, which is based on OpenAI's
-[Whisper](https://huggingface.co/openai/whisper-large-v2) model and is fine-tuned in Greek Audio dataset
-![Automatic Speech Recognition (ASR)"](https://datasets-server.huggingface.co/assets/huggingface-course/audio-course-images/--/huggingface-course--audio-course-images/train/2/image/image.png "Diagram of Automatic Speech Recognition (ASR)")
-"""
-
-def transcribe_speech(filepath):
-    output = pipe(
-        filepath,
-        max_new_tokens=256,
-        generate_kwargs={
-            "task": "transcribe",
-            "language": "greek",
-        },  # update with the language you've fine-tuned on
-        chunk_length_s=30,
-        batch_size=8,
-    )
-    return output["text"]
-
-demo = gr.Blocks()
-
-mic_transcribe = gr.Interface(
-    fn=transcribe_speech,
-    inputs=gr.Audio(source="microphone", type="filepath"),
-    outputs=gr.outputs.Textbox(),
-    tilte=title,
-    description=description,
-)
-
-file_transcribe = gr.Interface(
-    fn=transcribe_speech,
-    inputs=gr.Audio(source="upload", type="filepath"),
-    outputs=gr.outputs.Textbox(),
-    examples=[["./example.wav"]],
-    tilte=title,
-    description=description,
-)
-
-with demo:
-    gr.TabbedInterface(
-        [mic_transcribe, file_transcribe],
-        ["Transcribe Microphone", "Transcribe Audio File"],
-    ),
-
-demo.launch()
diff --git a/spaces/SimianLuo/Latent_Consistency_Model/app.py b/spaces/SimianLuo/Latent_Consistency_Model/app.py
deleted file mode 100644
index 8fdb77309267ce30434fe7825982accf8f75d6b6..0000000000000000000000000000000000000000
--- a/spaces/SimianLuo/Latent_Consistency_Model/app.py
+++ /dev/null
@@ -1,200 +0,0 @@
-#!/usr/bin/env python
-from __future__ import annotations
-
-import os
-import random
-import time
-
-import gradio as gr
-import numpy as np
-import PIL.Image
-import torch
-
-from diffusers import DiffusionPipeline
-import torch
-
-import os
-import torch
-from tqdm import tqdm
-from safetensors.torch import load_file
-import gradio_user_history as gr_user_history
-
-from concurrent.futures import ThreadPoolExecutor
-import uuid
-import cv2
-
-DESCRIPTION = '''# Latent Consistency Model
-Distilled from [Dreamshaper v7](https://huggingface.co/Lykon/dreamshaper-7) fine-tune of [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with only 4,000 training iterations (~32 A100 GPU Hours). [Project page](https://latent-consistency-models.github.io)
-'''
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
-
-MAX_SEED = np.iinfo(np.int32).max
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
-DTYPE = torch.float32  # torch.float16 works as well, but pictures seem to be a bit worse
-
-pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7")
-# pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main")
-pipe.to(torch_device="cuda", torch_dtype=DTYPE)
-
-
-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, profile: gr.OAuthProfile | None, metadata: dict):
-    unique_name = str(uuid.uuid4()) + '.png'
-    img.save(unique_name)
-    gr_user_history.save_image(label=metadata["prompt"], image=img, profile=profile, metadata=metadata)
-    return unique_name
-
-def save_images(image_array, profile: gr.OAuthProfile | None, metadata: dict):
-    paths = []
-    with ThreadPoolExecutor() as executor:
-        paths = list(executor.map(save_image, image_array, [profile]*len(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,
-    progress = gr.Progress(track_tqdm=True),
-    profile: gr.OAuthProfile | None = None,
-) -> PIL.Image.Image:
-    seed = randomize_seed_fn(seed, randomize_seed)
-    torch.manual_seed(seed)
-    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,
-        lcm_origin_steps=50,
-        output_type="pil",
-    ).images
-    paths = save_images(result, profile, metadata={"prompt": prompt, "seed": seed, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps})
-    print(time.time() - start_time)
-    return paths, seed
-
-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(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-    )
-    with gr.Group():
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            run_button = gr.Button("Run", scale=0)
-        result = gr.Gallery(
-            label="Generated images", show_label=False, elem_id="gallery", grid=[2]
-        )
-    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)
-        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",
-                minimum=1,
-                maximum=8,
-                step=1,
-                value=4,
-                visible=False,
-            )
-
-    with gr.Accordion("Past generations", open=False):
-        gr_user_history.render()
-    
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=result,
-        fn=generate,
-        cache_examples=CACHE_EXAMPLES,
-    )
-
-    gr.on(
-        triggers=[
-            prompt.submit,
-            run_button.click,
-        ],
-        fn=generate,
-        inputs=[
-            prompt,
-            seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-            num_images,
-            randomize_seed
-        ],
-        outputs=[result, seed],
-        api_name="run",
-    )
-
-if __name__ == "__main__":
-    demo.queue(api_open=False)
-    # demo.queue(max_size=20).launch()
-    demo.launch()
diff --git a/spaces/SuCicada/Lain-vits/README.md b/spaces/SuCicada/Lain-vits/README.md
deleted file mode 100644
index 190a443aed11472cc8b4e3450365dae23c5debab..0000000000000000000000000000000000000000
--- a/spaces/SuCicada/Lain-vits/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Lain-vits
-emoji: 🚀
-colorFrom: gray
-colorTo: red
-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
\ No newline at end of file
diff --git a/spaces/SujanMidatani/resume_details_to_questions/README.md b/spaces/SujanMidatani/resume_details_to_questions/README.md
deleted file mode 100644
index 6529b2cae062ac4561a10597e14678945b8ef8d2..0000000000000000000000000000000000000000
--- a/spaces/SujanMidatani/resume_details_to_questions/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Resume Details To Questions
-emoji: 😻
-colorFrom: pink
-colorTo: red
-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/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/tests/test_shimmodule.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/tests/test_shimmodule.py
deleted file mode 100644
index 6ea2629b42d9bcdc26bc0d0cb04624e2a60c0822..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/tests/test_shimmodule.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from IPython.utils.shimmodule import ShimModule
-import IPython
-
-
-def test_shimmodule_repr_does_not_fail_on_import_error():
-    shim_module = ShimModule("shim_module", mirror="mirrored_module_does_not_exist")
-    repr(shim_module)
-
-
-def test_shimmodule_repr_forwards_to_module():
-    shim_module = ShimModule("shim_module", mirror="IPython")
-    assert repr(shim_module) == repr(IPython)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/_backends/_asyncio.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/_backends/_asyncio.py
deleted file mode 100644
index c654988385773f363c3a93286d8107fab5f69c9b..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/_backends/_asyncio.py
+++ /dev/null
@@ -1,2117 +0,0 @@
-from __future__ import annotations
-
-import array
-import asyncio
-import concurrent.futures
-import math
-import socket
-import sys
-from asyncio.base_events import _run_until_complete_cb  # type: ignore[attr-defined]
-from collections import OrderedDict, deque
-from concurrent.futures import Future
-from contextvars import Context, copy_context
-from dataclasses import dataclass
-from functools import partial, wraps
-from inspect import (
-    CORO_RUNNING,
-    CORO_SUSPENDED,
-    GEN_RUNNING,
-    GEN_SUSPENDED,
-    getcoroutinestate,
-    getgeneratorstate,
-)
-from io import IOBase
-from os import PathLike
-from queue import Queue
-from socket import AddressFamily, SocketKind
-from threading import Thread
-from types import TracebackType
-from typing import (
-    IO,
-    Any,
-    AsyncGenerator,
-    Awaitable,
-    Callable,
-    Collection,
-    Coroutine,
-    Generator,
-    Iterable,
-    Mapping,
-    Optional,
-    Sequence,
-    Tuple,
-    TypeVar,
-    Union,
-    cast,
-)
-from weakref import WeakKeyDictionary
-
-import sniffio
-
-from .. import CapacityLimiterStatistics, EventStatistics, TaskInfo, abc
-from .._core._compat import DeprecatedAsyncContextManager, DeprecatedAwaitable
-from .._core._eventloop import claim_worker_thread, threadlocals
-from .._core._exceptions import (
-    BrokenResourceError,
-    BusyResourceError,
-    ClosedResourceError,
-    EndOfStream,
-    WouldBlock,
-)
-from .._core._exceptions import ExceptionGroup as BaseExceptionGroup
-from .._core._sockets import GetAddrInfoReturnType, convert_ipv6_sockaddr
-from .._core._synchronization import CapacityLimiter as BaseCapacityLimiter
-from .._core._synchronization import Event as BaseEvent
-from .._core._synchronization import ResourceGuard
-from .._core._tasks import CancelScope as BaseCancelScope
-from ..abc import IPSockAddrType, UDPPacketType
-from ..lowlevel import RunVar
-
-if sys.version_info >= (3, 8):
-
-    def get_coro(task: asyncio.Task) -> Generator | Awaitable[Any]:
-        return task.get_coro()
-
-else:
-
-    def get_coro(task: asyncio.Task) -> Generator | Awaitable[Any]:
-        return task._coro
-
-
-from asyncio import all_tasks, create_task, current_task, get_running_loop
-from asyncio import run as native_run
-
-
-def _get_task_callbacks(task: asyncio.Task) -> Iterable[Callable]:
-    return [cb for cb, context in task._callbacks]
-
-
-T_Retval = TypeVar("T_Retval")
-T_contra = TypeVar("T_contra", contravariant=True)
-
-# Check whether there is native support for task names in asyncio (3.8+)
-_native_task_names = hasattr(asyncio.Task, "get_name")
-
-
-_root_task: RunVar[asyncio.Task | None] = RunVar("_root_task")
-
-
-def find_root_task() -> asyncio.Task:
-    root_task = _root_task.get(None)
-    if root_task is not None and not root_task.done():
-        return root_task
-
-    # Look for a task that has been started via run_until_complete()
-    for task in all_tasks():
-        if task._callbacks and not task.done():
-            for cb in _get_task_callbacks(task):
-                if (
-                    cb is _run_until_complete_cb
-                    or getattr(cb, "__module__", None) == "uvloop.loop"
-                ):
-                    _root_task.set(task)
-                    return task
-
-    # Look up the topmost task in the AnyIO task tree, if possible
-    task = cast(asyncio.Task, current_task())
-    state = _task_states.get(task)
-    if state:
-        cancel_scope = state.cancel_scope
-        while cancel_scope and cancel_scope._parent_scope is not None:
-            cancel_scope = cancel_scope._parent_scope
-
-        if cancel_scope is not None:
-            return cast(asyncio.Task, cancel_scope._host_task)
-
-    return task
-
-
-def get_callable_name(func: Callable) -> str:
-    module = getattr(func, "__module__", None)
-    qualname = getattr(func, "__qualname__", None)
-    return ".".join([x for x in (module, qualname) if x])
-
-
-#
-# Event loop
-#
-
-_run_vars = (
-    WeakKeyDictionary()
-)  # type: WeakKeyDictionary[asyncio.AbstractEventLoop, Any]
-
-current_token = get_running_loop
-
-
-def _task_started(task: asyncio.Task) -> bool:
-    """Return ``True`` if the task has been started and has not finished."""
-    coro = cast(Coroutine[Any, Any, Any], get_coro(task))
-    try:
-        return getcoroutinestate(coro) in (CORO_RUNNING, CORO_SUSPENDED)
-    except AttributeError:
-        try:
-            return getgeneratorstate(cast(Generator, coro)) in (
-                GEN_RUNNING,
-                GEN_SUSPENDED,
-            )
-        except AttributeError:
-            # task coro is async_genenerator_asend https://bugs.python.org/issue37771
-            raise Exception(f"Cannot determine if task {task} has started or not")
-
-
-def _maybe_set_event_loop_policy(
-    policy: asyncio.AbstractEventLoopPolicy | None, use_uvloop: bool
-) -> None:
-    # On CPython, use uvloop when possible if no other policy has been given and if not
-    # explicitly disabled
-    if policy is None and use_uvloop and sys.implementation.name == "cpython":
-        try:
-            import uvloop
-        except ImportError:
-            pass
-        else:
-            # Test for missing shutdown_default_executor() (uvloop 0.14.0 and earlier)
-            if not hasattr(
-                asyncio.AbstractEventLoop, "shutdown_default_executor"
-            ) or hasattr(uvloop.loop.Loop, "shutdown_default_executor"):
-                policy = uvloop.EventLoopPolicy()
-
-    if policy is not None:
-        asyncio.set_event_loop_policy(policy)
-
-
-def run(
-    func: Callable[..., Awaitable[T_Retval]],
-    *args: object,
-    debug: bool = False,
-    use_uvloop: bool = False,
-    policy: asyncio.AbstractEventLoopPolicy | None = None,
-) -> T_Retval:
-    @wraps(func)
-    async def wrapper() -> T_Retval:
-        task = cast(asyncio.Task, current_task())
-        task_state = TaskState(None, get_callable_name(func), None)
-        _task_states[task] = task_state
-        if _native_task_names:
-            task.set_name(task_state.name)
-
-        try:
-            return await func(*args)
-        finally:
-            del _task_states[task]
-
-    _maybe_set_event_loop_policy(policy, use_uvloop)
-    return native_run(wrapper(), debug=debug)
-
-
-#
-# Miscellaneous
-#
-
-sleep = asyncio.sleep
-
-
-#
-# Timeouts and cancellation
-#
-
-CancelledError = asyncio.CancelledError
-
-
-class CancelScope(BaseCancelScope):
-    def __new__(
-        cls, *, deadline: float = math.inf, shield: bool = False
-    ) -> CancelScope:
-        return object.__new__(cls)
-
-    def __init__(self, deadline: float = math.inf, shield: bool = False):
-        self._deadline = deadline
-        self._shield = shield
-        self._parent_scope: CancelScope | None = None
-        self._cancel_called = False
-        self._active = False
-        self._timeout_handle: asyncio.TimerHandle | None = None
-        self._cancel_handle: asyncio.Handle | None = None
-        self._tasks: set[asyncio.Task] = set()
-        self._host_task: asyncio.Task | None = None
-        self._timeout_expired = False
-        self._cancel_calls: int = 0
-
-    def __enter__(self) -> CancelScope:
-        if self._active:
-            raise RuntimeError(
-                "Each CancelScope may only be used for a single 'with' block"
-            )
-
-        self._host_task = host_task = cast(asyncio.Task, current_task())
-        self._tasks.add(host_task)
-        try:
-            task_state = _task_states[host_task]
-        except KeyError:
-            task_name = host_task.get_name() if _native_task_names else None
-            task_state = TaskState(None, task_name, self)
-            _task_states[host_task] = task_state
-        else:
-            self._parent_scope = task_state.cancel_scope
-            task_state.cancel_scope = self
-
-        self._timeout()
-        self._active = True
-
-        # Start cancelling the host task if the scope was cancelled before entering
-        if self._cancel_called:
-            self._deliver_cancellation()
-
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> bool | None:
-        if not self._active:
-            raise RuntimeError("This cancel scope is not active")
-        if current_task() is not self._host_task:
-            raise RuntimeError(
-                "Attempted to exit cancel scope in a different task than it was "
-                "entered in"
-            )
-
-        assert self._host_task is not None
-        host_task_state = _task_states.get(self._host_task)
-        if host_task_state is None or host_task_state.cancel_scope is not self:
-            raise RuntimeError(
-                "Attempted to exit a cancel scope that isn't the current tasks's "
-                "current cancel scope"
-            )
-
-        self._active = False
-        if self._timeout_handle:
-            self._timeout_handle.cancel()
-            self._timeout_handle = None
-
-        self._tasks.remove(self._host_task)
-
-        host_task_state.cancel_scope = self._parent_scope
-
-        # Restart the cancellation effort in the farthest directly cancelled parent scope if this
-        # one was shielded
-        if self._shield:
-            self._deliver_cancellation_to_parent()
-
-        if exc_val is not None:
-            exceptions = (
-                exc_val.exceptions if isinstance(exc_val, ExceptionGroup) else [exc_val]
-            )
-            if all(isinstance(exc, CancelledError) for exc in exceptions):
-                if self._timeout_expired:
-                    return self._uncancel()
-                elif not self._cancel_called:
-                    # Task was cancelled natively
-                    return None
-                elif not self._parent_cancelled():
-                    # This scope was directly cancelled
-                    return self._uncancel()
-
-        return None
-
-    def _uncancel(self) -> bool:
-        if sys.version_info < (3, 11) or self._host_task is None:
-            self._cancel_calls = 0
-            return True
-
-        # Uncancel all AnyIO cancellations
-        for i in range(self._cancel_calls):
-            self._host_task.uncancel()
-
-        self._cancel_calls = 0
-        return not self._host_task.cancelling()
-
-    def _timeout(self) -> None:
-        if self._deadline != math.inf:
-            loop = get_running_loop()
-            if loop.time() >= self._deadline:
-                self._timeout_expired = True
-                self.cancel()
-            else:
-                self._timeout_handle = loop.call_at(self._deadline, self._timeout)
-
-    def _deliver_cancellation(self) -> None:
-        """
-        Deliver cancellation to directly contained tasks and nested cancel scopes.
-
-        Schedule another run at the end if we still have tasks eligible for cancellation.
-        """
-        should_retry = False
-        current = current_task()
-        for task in self._tasks:
-            if task._must_cancel:  # type: ignore[attr-defined]
-                continue
-
-            # The task is eligible for cancellation if it has started and is not in a cancel
-            # scope shielded from this one
-            cancel_scope = _task_states[task].cancel_scope
-            while cancel_scope is not self:
-                if cancel_scope is None or cancel_scope._shield:
-                    break
-                else:
-                    cancel_scope = cancel_scope._parent_scope
-            else:
-                should_retry = True
-                if task is not current and (
-                    task is self._host_task or _task_started(task)
-                ):
-                    self._cancel_calls += 1
-                    task.cancel()
-
-        # Schedule another callback if there are still tasks left
-        if should_retry:
-            self._cancel_handle = get_running_loop().call_soon(
-                self._deliver_cancellation
-            )
-        else:
-            self._cancel_handle = None
-
-    def _deliver_cancellation_to_parent(self) -> None:
-        """Start cancellation effort in the farthest directly cancelled parent scope"""
-        scope = self._parent_scope
-        scope_to_cancel: CancelScope | None = None
-        while scope is not None:
-            if scope._cancel_called and scope._cancel_handle is None:
-                scope_to_cancel = scope
-
-            # No point in looking beyond any shielded scope
-            if scope._shield:
-                break
-
-            scope = scope._parent_scope
-
-        if scope_to_cancel is not None:
-            scope_to_cancel._deliver_cancellation()
-
-    def _parent_cancelled(self) -> bool:
-        # Check whether any parent has been cancelled
-        cancel_scope = self._parent_scope
-        while cancel_scope is not None and not cancel_scope._shield:
-            if cancel_scope._cancel_called:
-                return True
-            else:
-                cancel_scope = cancel_scope._parent_scope
-
-        return False
-
-    def cancel(self) -> DeprecatedAwaitable:
-        if not self._cancel_called:
-            if self._timeout_handle:
-                self._timeout_handle.cancel()
-                self._timeout_handle = None
-
-            self._cancel_called = True
-            if self._host_task is not None:
-                self._deliver_cancellation()
-
-        return DeprecatedAwaitable(self.cancel)
-
-    @property
-    def deadline(self) -> float:
-        return self._deadline
-
-    @deadline.setter
-    def deadline(self, value: float) -> None:
-        self._deadline = float(value)
-        if self._timeout_handle is not None:
-            self._timeout_handle.cancel()
-            self._timeout_handle = None
-
-        if self._active and not self._cancel_called:
-            self._timeout()
-
-    @property
-    def cancel_called(self) -> bool:
-        return self._cancel_called
-
-    @property
-    def shield(self) -> bool:
-        return self._shield
-
-    @shield.setter
-    def shield(self, value: bool) -> None:
-        if self._shield != value:
-            self._shield = value
-            if not value:
-                self._deliver_cancellation_to_parent()
-
-
-async def checkpoint() -> None:
-    await sleep(0)
-
-
-async def checkpoint_if_cancelled() -> None:
-    task = current_task()
-    if task is None:
-        return
-
-    try:
-        cancel_scope = _task_states[task].cancel_scope
-    except KeyError:
-        return
-
-    while cancel_scope:
-        if cancel_scope.cancel_called:
-            await sleep(0)
-        elif cancel_scope.shield:
-            break
-        else:
-            cancel_scope = cancel_scope._parent_scope
-
-
-async def cancel_shielded_checkpoint() -> None:
-    with CancelScope(shield=True):
-        await sleep(0)
-
-
-def current_effective_deadline() -> float:
-    try:
-        cancel_scope = _task_states[current_task()].cancel_scope  # type: ignore[index]
-    except KeyError:
-        return math.inf
-
-    deadline = math.inf
-    while cancel_scope:
-        deadline = min(deadline, cancel_scope.deadline)
-        if cancel_scope._cancel_called:
-            deadline = -math.inf
-            break
-        elif cancel_scope.shield:
-            break
-        else:
-            cancel_scope = cancel_scope._parent_scope
-
-    return deadline
-
-
-def current_time() -> float:
-    return get_running_loop().time()
-
-
-#
-# Task states
-#
-
-
-class TaskState:
-    """
-    Encapsulates auxiliary task information that cannot be added to the Task instance itself
-    because there are no guarantees about its implementation.
-    """
-
-    __slots__ = "parent_id", "name", "cancel_scope"
-
-    def __init__(
-        self,
-        parent_id: int | None,
-        name: str | None,
-        cancel_scope: CancelScope | None,
-    ):
-        self.parent_id = parent_id
-        self.name = name
-        self.cancel_scope = cancel_scope
-
-
-_task_states = WeakKeyDictionary()  # type: WeakKeyDictionary[asyncio.Task, TaskState]
-
-
-#
-# Task groups
-#
-
-
-class ExceptionGroup(BaseExceptionGroup):
-    def __init__(self, exceptions: list[BaseException]):
-        super().__init__()
-        self.exceptions = exceptions
-
-
-class _AsyncioTaskStatus(abc.TaskStatus):
-    def __init__(self, future: asyncio.Future, parent_id: int):
-        self._future = future
-        self._parent_id = parent_id
-
-    def started(self, value: T_contra | None = None) -> None:
-        try:
-            self._future.set_result(value)
-        except asyncio.InvalidStateError:
-            raise RuntimeError(
-                "called 'started' twice on the same task status"
-            ) from None
-
-        task = cast(asyncio.Task, current_task())
-        _task_states[task].parent_id = self._parent_id
-
-
-class TaskGroup(abc.TaskGroup):
-    def __init__(self) -> None:
-        self.cancel_scope: CancelScope = CancelScope()
-        self._active = False
-        self._exceptions: list[BaseException] = []
-
-    async def __aenter__(self) -> TaskGroup:
-        self.cancel_scope.__enter__()
-        self._active = True
-        return self
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> bool | None:
-        ignore_exception = self.cancel_scope.__exit__(exc_type, exc_val, exc_tb)
-        if exc_val is not None:
-            self.cancel_scope.cancel()
-            self._exceptions.append(exc_val)
-
-        while self.cancel_scope._tasks:
-            try:
-                await asyncio.wait(self.cancel_scope._tasks)
-            except asyncio.CancelledError:
-                self.cancel_scope.cancel()
-
-        self._active = False
-        if not self.cancel_scope._parent_cancelled():
-            exceptions = self._filter_cancellation_errors(self._exceptions)
-        else:
-            exceptions = self._exceptions
-
-        try:
-            if len(exceptions) > 1:
-                if all(
-                    isinstance(e, CancelledError) and not e.args for e in exceptions
-                ):
-                    # Tasks were cancelled natively, without a cancellation message
-                    raise CancelledError
-                else:
-                    raise ExceptionGroup(exceptions)
-            elif exceptions and exceptions[0] is not exc_val:
-                raise exceptions[0]
-        except BaseException as exc:
-            # Clear the context here, as it can only be done in-flight.
-            # If the context is not cleared, it can result in recursive tracebacks (see #145).
-            exc.__context__ = None
-            raise
-
-        return ignore_exception
-
-    @staticmethod
-    def _filter_cancellation_errors(
-        exceptions: Sequence[BaseException],
-    ) -> list[BaseException]:
-        filtered_exceptions: list[BaseException] = []
-        for exc in exceptions:
-            if isinstance(exc, ExceptionGroup):
-                new_exceptions = TaskGroup._filter_cancellation_errors(exc.exceptions)
-                if len(new_exceptions) > 1:
-                    filtered_exceptions.append(exc)
-                elif len(new_exceptions) == 1:
-                    filtered_exceptions.append(new_exceptions[0])
-                elif new_exceptions:
-                    new_exc = ExceptionGroup(new_exceptions)
-                    new_exc.__cause__ = exc.__cause__
-                    new_exc.__context__ = exc.__context__
-                    new_exc.__traceback__ = exc.__traceback__
-                    filtered_exceptions.append(new_exc)
-            elif not isinstance(exc, CancelledError) or exc.args:
-                filtered_exceptions.append(exc)
-
-        return filtered_exceptions
-
-    async def _run_wrapped_task(
-        self, coro: Coroutine, task_status_future: asyncio.Future | None
-    ) -> None:
-        # This is the code path for Python 3.7 on which asyncio freaks out if a task
-        # raises a BaseException.
-        __traceback_hide__ = __tracebackhide__ = True  # noqa: F841
-        task = cast(asyncio.Task, current_task())
-        try:
-            await coro
-        except BaseException as exc:
-            if task_status_future is None or task_status_future.done():
-                self._exceptions.append(exc)
-                self.cancel_scope.cancel()
-            else:
-                task_status_future.set_exception(exc)
-        else:
-            if task_status_future is not None and not task_status_future.done():
-                task_status_future.set_exception(
-                    RuntimeError("Child exited without calling task_status.started()")
-                )
-        finally:
-            if task in self.cancel_scope._tasks:
-                self.cancel_scope._tasks.remove(task)
-                del _task_states[task]
-
-    def _spawn(
-        self,
-        func: Callable[..., Awaitable[Any]],
-        args: tuple,
-        name: object,
-        task_status_future: asyncio.Future | None = None,
-    ) -> asyncio.Task:
-        def task_done(_task: asyncio.Task) -> None:
-            # This is the code path for Python 3.8+
-            assert _task in self.cancel_scope._tasks
-            self.cancel_scope._tasks.remove(_task)
-            del _task_states[_task]
-
-            try:
-                exc = _task.exception()
-            except CancelledError as e:
-                while isinstance(e.__context__, CancelledError):
-                    e = e.__context__
-
-                exc = e
-
-            if exc is not None:
-                if task_status_future is None or task_status_future.done():
-                    self._exceptions.append(exc)
-                    self.cancel_scope.cancel()
-                else:
-                    task_status_future.set_exception(exc)
-            elif task_status_future is not None and not task_status_future.done():
-                task_status_future.set_exception(
-                    RuntimeError("Child exited without calling task_status.started()")
-                )
-
-        if not self._active:
-            raise RuntimeError(
-                "This task group is not active; no new tasks can be started."
-            )
-
-        options: dict[str, Any] = {}
-        name = get_callable_name(func) if name is None else str(name)
-        if _native_task_names:
-            options["name"] = name
-
-        kwargs = {}
-        if task_status_future:
-            parent_id = id(current_task())
-            kwargs["task_status"] = _AsyncioTaskStatus(
-                task_status_future, id(self.cancel_scope._host_task)
-            )
-        else:
-            parent_id = id(self.cancel_scope._host_task)
-
-        coro = func(*args, **kwargs)
-        if not asyncio.iscoroutine(coro):
-            raise TypeError(
-                f"Expected an async function, but {func} appears to be synchronous"
-            )
-
-        foreign_coro = not hasattr(coro, "cr_frame") and not hasattr(coro, "gi_frame")
-        if foreign_coro or sys.version_info < (3, 8):
-            coro = self._run_wrapped_task(coro, task_status_future)
-
-        task = create_task(coro, **options)
-        if not foreign_coro and sys.version_info >= (3, 8):
-            task.add_done_callback(task_done)
-
-        # Make the spawned task inherit the task group's cancel scope
-        _task_states[task] = TaskState(
-            parent_id=parent_id, name=name, cancel_scope=self.cancel_scope
-        )
-        self.cancel_scope._tasks.add(task)
-        return task
-
-    def start_soon(
-        self, func: Callable[..., Awaitable[Any]], *args: object, name: object = None
-    ) -> None:
-        self._spawn(func, args, name)
-
-    async def start(
-        self, func: Callable[..., Awaitable[Any]], *args: object, name: object = None
-    ) -> None:
-        future: asyncio.Future = asyncio.Future()
-        task = self._spawn(func, args, name, future)
-
-        # If the task raises an exception after sending a start value without a switch point
-        # between, the task group is cancelled and this method never proceeds to process the
-        # completed future. That's why we have to have a shielded cancel scope here.
-        with CancelScope(shield=True):
-            try:
-                return await future
-            except CancelledError:
-                task.cancel()
-                raise
-
-
-#
-# Threads
-#
-
-_Retval_Queue_Type = Tuple[Optional[T_Retval], Optional[BaseException]]
-
-
-class WorkerThread(Thread):
-    MAX_IDLE_TIME = 10  # seconds
-
-    def __init__(
-        self,
-        root_task: asyncio.Task,
-        workers: set[WorkerThread],
-        idle_workers: deque[WorkerThread],
-    ):
-        super().__init__(name="AnyIO worker thread")
-        self.root_task = root_task
-        self.workers = workers
-        self.idle_workers = idle_workers
-        self.loop = root_task._loop
-        self.queue: Queue[
-            tuple[Context, Callable, tuple, asyncio.Future] | None
-        ] = Queue(2)
-        self.idle_since = current_time()
-        self.stopping = False
-
-    def _report_result(
-        self, future: asyncio.Future, result: Any, exc: BaseException | None
-    ) -> None:
-        self.idle_since = current_time()
-        if not self.stopping:
-            self.idle_workers.append(self)
-
-        if not future.cancelled():
-            if exc is not None:
-                if isinstance(exc, StopIteration):
-                    new_exc = RuntimeError("coroutine raised StopIteration")
-                    new_exc.__cause__ = exc
-                    exc = new_exc
-
-                future.set_exception(exc)
-            else:
-                future.set_result(result)
-
-    def run(self) -> None:
-        with claim_worker_thread("asyncio"):
-            threadlocals.loop = self.loop
-            while True:
-                item = self.queue.get()
-                if item is None:
-                    # Shutdown command received
-                    return
-
-                context, func, args, future = item
-                if not future.cancelled():
-                    result = None
-                    exception: BaseException | None = None
-                    try:
-                        result = context.run(func, *args)
-                    except BaseException as exc:
-                        exception = exc
-
-                    if not self.loop.is_closed():
-                        self.loop.call_soon_threadsafe(
-                            self._report_result, future, result, exception
-                        )
-
-                self.queue.task_done()
-
-    def stop(self, f: asyncio.Task | None = None) -> None:
-        self.stopping = True
-        self.queue.put_nowait(None)
-        self.workers.discard(self)
-        try:
-            self.idle_workers.remove(self)
-        except ValueError:
-            pass
-
-
-_threadpool_idle_workers: RunVar[deque[WorkerThread]] = RunVar(
-    "_threadpool_idle_workers"
-)
-_threadpool_workers: RunVar[set[WorkerThread]] = RunVar("_threadpool_workers")
-
-
-async def run_sync_in_worker_thread(
-    func: Callable[..., T_Retval],
-    *args: object,
-    cancellable: bool = False,
-    limiter: CapacityLimiter | None = None,
-) -> T_Retval:
-    await checkpoint()
-
-    # If this is the first run in this event loop thread, set up the necessary variables
-    try:
-        idle_workers = _threadpool_idle_workers.get()
-        workers = _threadpool_workers.get()
-    except LookupError:
-        idle_workers = deque()
-        workers = set()
-        _threadpool_idle_workers.set(idle_workers)
-        _threadpool_workers.set(workers)
-
-    async with (limiter or current_default_thread_limiter()):
-        with CancelScope(shield=not cancellable):
-            future: asyncio.Future = asyncio.Future()
-            root_task = find_root_task()
-            if not idle_workers:
-                worker = WorkerThread(root_task, workers, idle_workers)
-                worker.start()
-                workers.add(worker)
-                root_task.add_done_callback(worker.stop)
-            else:
-                worker = idle_workers.pop()
-
-                # Prune any other workers that have been idle for MAX_IDLE_TIME seconds or longer
-                now = current_time()
-                while idle_workers:
-                    if now - idle_workers[0].idle_since < WorkerThread.MAX_IDLE_TIME:
-                        break
-
-                    expired_worker = idle_workers.popleft()
-                    expired_worker.root_task.remove_done_callback(expired_worker.stop)
-                    expired_worker.stop()
-
-            context = copy_context()
-            context.run(sniffio.current_async_library_cvar.set, None)
-            worker.queue.put_nowait((context, func, args, future))
-            return await future
-
-
-def run_sync_from_thread(
-    func: Callable[..., T_Retval],
-    *args: object,
-    loop: asyncio.AbstractEventLoop | None = None,
-) -> T_Retval:
-    @wraps(func)
-    def wrapper() -> None:
-        try:
-            f.set_result(func(*args))
-        except BaseException as exc:
-            f.set_exception(exc)
-            if not isinstance(exc, Exception):
-                raise
-
-    f: concurrent.futures.Future[T_Retval] = Future()
-    loop = loop or threadlocals.loop
-    loop.call_soon_threadsafe(wrapper)
-    return f.result()
-
-
-def run_async_from_thread(
-    func: Callable[..., Awaitable[T_Retval]], *args: object
-) -> T_Retval:
-    f: concurrent.futures.Future[T_Retval] = asyncio.run_coroutine_threadsafe(
-        func(*args), threadlocals.loop
-    )
-    return f.result()
-
-
-class BlockingPortal(abc.BlockingPortal):
-    def __new__(cls) -> BlockingPortal:
-        return object.__new__(cls)
-
-    def __init__(self) -> None:
-        super().__init__()
-        self._loop = get_running_loop()
-
-    def _spawn_task_from_thread(
-        self,
-        func: Callable,
-        args: tuple,
-        kwargs: dict[str, Any],
-        name: object,
-        future: Future,
-    ) -> None:
-        run_sync_from_thread(
-            partial(self._task_group.start_soon, name=name),
-            self._call_func,
-            func,
-            args,
-            kwargs,
-            future,
-            loop=self._loop,
-        )
-
-
-#
-# Subprocesses
-#
-
-
-@dataclass(eq=False)
-class StreamReaderWrapper(abc.ByteReceiveStream):
-    _stream: asyncio.StreamReader
-
-    async def receive(self, max_bytes: int = 65536) -> bytes:
-        data = await self._stream.read(max_bytes)
-        if data:
-            return data
-        else:
-            raise EndOfStream
-
-    async def aclose(self) -> None:
-        self._stream.feed_eof()
-
-
-@dataclass(eq=False)
-class StreamWriterWrapper(abc.ByteSendStream):
-    _stream: asyncio.StreamWriter
-
-    async def send(self, item: bytes) -> None:
-        self._stream.write(item)
-        await self._stream.drain()
-
-    async def aclose(self) -> None:
-        self._stream.close()
-
-
-@dataclass(eq=False)
-class Process(abc.Process):
-    _process: asyncio.subprocess.Process
-    _stdin: StreamWriterWrapper | None
-    _stdout: StreamReaderWrapper | None
-    _stderr: StreamReaderWrapper | None
-
-    async def aclose(self) -> None:
-        if self._stdin:
-            await self._stdin.aclose()
-        if self._stdout:
-            await self._stdout.aclose()
-        if self._stderr:
-            await self._stderr.aclose()
-
-        await self.wait()
-
-    async def wait(self) -> int:
-        return await self._process.wait()
-
-    def terminate(self) -> None:
-        self._process.terminate()
-
-    def kill(self) -> None:
-        self._process.kill()
-
-    def send_signal(self, signal: int) -> None:
-        self._process.send_signal(signal)
-
-    @property
-    def pid(self) -> int:
-        return self._process.pid
-
-    @property
-    def returncode(self) -> int | None:
-        return self._process.returncode
-
-    @property
-    def stdin(self) -> abc.ByteSendStream | None:
-        return self._stdin
-
-    @property
-    def stdout(self) -> abc.ByteReceiveStream | None:
-        return self._stdout
-
-    @property
-    def stderr(self) -> abc.ByteReceiveStream | None:
-        return self._stderr
-
-
-async def open_process(
-    command: str | bytes | Sequence[str | bytes],
-    *,
-    shell: bool,
-    stdin: int | IO[Any] | None,
-    stdout: int | IO[Any] | None,
-    stderr: int | IO[Any] | None,
-    cwd: str | bytes | PathLike | None = None,
-    env: Mapping[str, str] | None = None,
-    start_new_session: bool = False,
-) -> Process:
-    await checkpoint()
-    if shell:
-        process = await asyncio.create_subprocess_shell(
-            cast(Union[str, bytes], command),
-            stdin=stdin,
-            stdout=stdout,
-            stderr=stderr,
-            cwd=cwd,
-            env=env,
-            start_new_session=start_new_session,
-        )
-    else:
-        process = await asyncio.create_subprocess_exec(
-            *command,
-            stdin=stdin,
-            stdout=stdout,
-            stderr=stderr,
-            cwd=cwd,
-            env=env,
-            start_new_session=start_new_session,
-        )
-
-    stdin_stream = StreamWriterWrapper(process.stdin) if process.stdin else None
-    stdout_stream = StreamReaderWrapper(process.stdout) if process.stdout else None
-    stderr_stream = StreamReaderWrapper(process.stderr) if process.stderr else None
-    return Process(process, stdin_stream, stdout_stream, stderr_stream)
-
-
-def _forcibly_shutdown_process_pool_on_exit(
-    workers: set[Process], _task: object
-) -> None:
-    """
-    Forcibly shuts down worker processes belonging to this event loop."""
-    child_watcher: asyncio.AbstractChildWatcher | None
-    try:
-        child_watcher = asyncio.get_event_loop_policy().get_child_watcher()
-    except NotImplementedError:
-        child_watcher = None
-
-    # Close as much as possible (w/o async/await) to avoid warnings
-    for process in workers:
-        if process.returncode is None:
-            continue
-
-        process._stdin._stream._transport.close()  # type: ignore[union-attr]
-        process._stdout._stream._transport.close()  # type: ignore[union-attr]
-        process._stderr._stream._transport.close()  # type: ignore[union-attr]
-        process.kill()
-        if child_watcher:
-            child_watcher.remove_child_handler(process.pid)
-
-
-async def _shutdown_process_pool_on_exit(workers: set[Process]) -> None:
-    """
-    Shuts down worker processes belonging to this event loop.
-
-    NOTE: this only works when the event loop was started using asyncio.run() or anyio.run().
-
-    """
-    process: Process
-    try:
-        await sleep(math.inf)
-    except asyncio.CancelledError:
-        for process in workers:
-            if process.returncode is None:
-                process.kill()
-
-        for process in workers:
-            await process.aclose()
-
-
-def setup_process_pool_exit_at_shutdown(workers: set[Process]) -> None:
-    kwargs: dict[str, Any] = (
-        {"name": "AnyIO process pool shutdown task"} if _native_task_names else {}
-    )
-    create_task(_shutdown_process_pool_on_exit(workers), **kwargs)
-    find_root_task().add_done_callback(
-        partial(_forcibly_shutdown_process_pool_on_exit, workers)
-    )
-
-
-#
-# Sockets and networking
-#
-
-
-class StreamProtocol(asyncio.Protocol):
-    read_queue: deque[bytes]
-    read_event: asyncio.Event
-    write_event: asyncio.Event
-    exception: Exception | None = None
-
-    def connection_made(self, transport: asyncio.BaseTransport) -> None:
-        self.read_queue = deque()
-        self.read_event = asyncio.Event()
-        self.write_event = asyncio.Event()
-        self.write_event.set()
-        cast(asyncio.Transport, transport).set_write_buffer_limits(0)
-
-    def connection_lost(self, exc: Exception | None) -> None:
-        if exc:
-            self.exception = BrokenResourceError()
-            self.exception.__cause__ = exc
-
-        self.read_event.set()
-        self.write_event.set()
-
-    def data_received(self, data: bytes) -> None:
-        self.read_queue.append(data)
-        self.read_event.set()
-
-    def eof_received(self) -> bool | None:
-        self.read_event.set()
-        return True
-
-    def pause_writing(self) -> None:
-        self.write_event = asyncio.Event()
-
-    def resume_writing(self) -> None:
-        self.write_event.set()
-
-
-class DatagramProtocol(asyncio.DatagramProtocol):
-    read_queue: deque[tuple[bytes, IPSockAddrType]]
-    read_event: asyncio.Event
-    write_event: asyncio.Event
-    exception: Exception | None = None
-
-    def connection_made(self, transport: asyncio.BaseTransport) -> None:
-        self.read_queue = deque(maxlen=100)  # arbitrary value
-        self.read_event = asyncio.Event()
-        self.write_event = asyncio.Event()
-        self.write_event.set()
-
-    def connection_lost(self, exc: Exception | None) -> None:
-        self.read_event.set()
-        self.write_event.set()
-
-    def datagram_received(self, data: bytes, addr: IPSockAddrType) -> None:
-        addr = convert_ipv6_sockaddr(addr)
-        self.read_queue.append((data, addr))
-        self.read_event.set()
-
-    def error_received(self, exc: Exception) -> None:
-        self.exception = exc
-
-    def pause_writing(self) -> None:
-        self.write_event.clear()
-
-    def resume_writing(self) -> None:
-        self.write_event.set()
-
-
-class SocketStream(abc.SocketStream):
-    def __init__(self, transport: asyncio.Transport, protocol: StreamProtocol):
-        self._transport = transport
-        self._protocol = protocol
-        self._receive_guard = ResourceGuard("reading from")
-        self._send_guard = ResourceGuard("writing to")
-        self._closed = False
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self._transport.get_extra_info("socket")
-
-    async def receive(self, max_bytes: int = 65536) -> bytes:
-        with self._receive_guard:
-            await checkpoint()
-
-            if (
-                not self._protocol.read_event.is_set()
-                and not self._transport.is_closing()
-            ):
-                self._transport.resume_reading()
-                await self._protocol.read_event.wait()
-                self._transport.pause_reading()
-
-            try:
-                chunk = self._protocol.read_queue.popleft()
-            except IndexError:
-                if self._closed:
-                    raise ClosedResourceError from None
-                elif self._protocol.exception:
-                    raise self._protocol.exception
-                else:
-                    raise EndOfStream from None
-
-            if len(chunk) > max_bytes:
-                # Split the oversized chunk
-                chunk, leftover = chunk[:max_bytes], chunk[max_bytes:]
-                self._protocol.read_queue.appendleft(leftover)
-
-            # If the read queue is empty, clear the flag so that the next call will block until
-            # data is available
-            if not self._protocol.read_queue:
-                self._protocol.read_event.clear()
-
-        return chunk
-
-    async def send(self, item: bytes) -> None:
-        with self._send_guard:
-            await checkpoint()
-
-            if self._closed:
-                raise ClosedResourceError
-            elif self._protocol.exception is not None:
-                raise self._protocol.exception
-
-            try:
-                self._transport.write(item)
-            except RuntimeError as exc:
-                if self._transport.is_closing():
-                    raise BrokenResourceError from exc
-                else:
-                    raise
-
-            await self._protocol.write_event.wait()
-
-    async def send_eof(self) -> None:
-        try:
-            self._transport.write_eof()
-        except OSError:
-            pass
-
-    async def aclose(self) -> None:
-        if not self._transport.is_closing():
-            self._closed = True
-            try:
-                self._transport.write_eof()
-            except OSError:
-                pass
-
-            self._transport.close()
-            await sleep(0)
-            self._transport.abort()
-
-
-class UNIXSocketStream(abc.SocketStream):
-    _receive_future: asyncio.Future | None = None
-    _send_future: asyncio.Future | None = None
-    _closing = False
-
-    def __init__(self, raw_socket: socket.socket):
-        self.__raw_socket = raw_socket
-        self._loop = get_running_loop()
-        self._receive_guard = ResourceGuard("reading from")
-        self._send_guard = ResourceGuard("writing to")
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self.__raw_socket
-
-    def _wait_until_readable(self, loop: asyncio.AbstractEventLoop) -> asyncio.Future:
-        def callback(f: object) -> None:
-            del self._receive_future
-            loop.remove_reader(self.__raw_socket)
-
-        f = self._receive_future = asyncio.Future()
-        self._loop.add_reader(self.__raw_socket, f.set_result, None)
-        f.add_done_callback(callback)
-        return f
-
-    def _wait_until_writable(self, loop: asyncio.AbstractEventLoop) -> asyncio.Future:
-        def callback(f: object) -> None:
-            del self._send_future
-            loop.remove_writer(self.__raw_socket)
-
-        f = self._send_future = asyncio.Future()
-        self._loop.add_writer(self.__raw_socket, f.set_result, None)
-        f.add_done_callback(callback)
-        return f
-
-    async def send_eof(self) -> None:
-        with self._send_guard:
-            self._raw_socket.shutdown(socket.SHUT_WR)
-
-    async def receive(self, max_bytes: int = 65536) -> bytes:
-        loop = get_running_loop()
-        await checkpoint()
-        with self._receive_guard:
-            while True:
-                try:
-                    data = self.__raw_socket.recv(max_bytes)
-                except BlockingIOError:
-                    await self._wait_until_readable(loop)
-                except OSError as exc:
-                    if self._closing:
-                        raise ClosedResourceError from None
-                    else:
-                        raise BrokenResourceError from exc
-                else:
-                    if not data:
-                        raise EndOfStream
-
-                    return data
-
-    async def send(self, item: bytes) -> None:
-        loop = get_running_loop()
-        await checkpoint()
-        with self._send_guard:
-            view = memoryview(item)
-            while view:
-                try:
-                    bytes_sent = self.__raw_socket.send(item)
-                except BlockingIOError:
-                    await self._wait_until_writable(loop)
-                except OSError as exc:
-                    if self._closing:
-                        raise ClosedResourceError from None
-                    else:
-                        raise BrokenResourceError from exc
-                else:
-                    view = view[bytes_sent:]
-
-    async def receive_fds(self, msglen: int, maxfds: int) -> tuple[bytes, list[int]]:
-        if not isinstance(msglen, int) or msglen < 0:
-            raise ValueError("msglen must be a non-negative integer")
-        if not isinstance(maxfds, int) or maxfds < 1:
-            raise ValueError("maxfds must be a positive integer")
-
-        loop = get_running_loop()
-        fds = array.array("i")
-        await checkpoint()
-        with self._receive_guard:
-            while True:
-                try:
-                    message, ancdata, flags, addr = self.__raw_socket.recvmsg(
-                        msglen, socket.CMSG_LEN(maxfds * fds.itemsize)
-                    )
-                except BlockingIOError:
-                    await self._wait_until_readable(loop)
-                except OSError as exc:
-                    if self._closing:
-                        raise ClosedResourceError from None
-                    else:
-                        raise BrokenResourceError from exc
-                else:
-                    if not message and not ancdata:
-                        raise EndOfStream
-
-                    break
-
-        for cmsg_level, cmsg_type, cmsg_data in ancdata:
-            if cmsg_level != socket.SOL_SOCKET or cmsg_type != socket.SCM_RIGHTS:
-                raise RuntimeError(
-                    f"Received unexpected ancillary data; message = {message!r}, "
-                    f"cmsg_level = {cmsg_level}, cmsg_type = {cmsg_type}"
-                )
-
-            fds.frombytes(cmsg_data[: len(cmsg_data) - (len(cmsg_data) % fds.itemsize)])
-
-        return message, list(fds)
-
-    async def send_fds(self, message: bytes, fds: Collection[int | IOBase]) -> None:
-        if not message:
-            raise ValueError("message must not be empty")
-        if not fds:
-            raise ValueError("fds must not be empty")
-
-        loop = get_running_loop()
-        filenos: list[int] = []
-        for fd in fds:
-            if isinstance(fd, int):
-                filenos.append(fd)
-            elif isinstance(fd, IOBase):
-                filenos.append(fd.fileno())
-
-        fdarray = array.array("i", filenos)
-        await checkpoint()
-        with self._send_guard:
-            while True:
-                try:
-                    # The ignore can be removed after mypy picks up
-                    # https://github.com/python/typeshed/pull/5545
-                    self.__raw_socket.sendmsg(
-                        [message], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, fdarray)]
-                    )
-                    break
-                except BlockingIOError:
-                    await self._wait_until_writable(loop)
-                except OSError as exc:
-                    if self._closing:
-                        raise ClosedResourceError from None
-                    else:
-                        raise BrokenResourceError from exc
-
-    async def aclose(self) -> None:
-        if not self._closing:
-            self._closing = True
-            if self.__raw_socket.fileno() != -1:
-                self.__raw_socket.close()
-
-            if self._receive_future:
-                self._receive_future.set_result(None)
-            if self._send_future:
-                self._send_future.set_result(None)
-
-
-class TCPSocketListener(abc.SocketListener):
-    _accept_scope: CancelScope | None = None
-    _closed = False
-
-    def __init__(self, raw_socket: socket.socket):
-        self.__raw_socket = raw_socket
-        self._loop = cast(asyncio.BaseEventLoop, get_running_loop())
-        self._accept_guard = ResourceGuard("accepting connections from")
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self.__raw_socket
-
-    async def accept(self) -> abc.SocketStream:
-        if self._closed:
-            raise ClosedResourceError
-
-        with self._accept_guard:
-            await checkpoint()
-            with CancelScope() as self._accept_scope:
-                try:
-                    client_sock, _addr = await self._loop.sock_accept(self._raw_socket)
-                except asyncio.CancelledError:
-                    # Workaround for https://bugs.python.org/issue41317
-                    try:
-                        self._loop.remove_reader(self._raw_socket)
-                    except (ValueError, NotImplementedError):
-                        pass
-
-                    if self._closed:
-                        raise ClosedResourceError from None
-
-                    raise
-                finally:
-                    self._accept_scope = None
-
-        client_sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
-        transport, protocol = await self._loop.connect_accepted_socket(
-            StreamProtocol, client_sock
-        )
-        return SocketStream(transport, protocol)
-
-    async def aclose(self) -> None:
-        if self._closed:
-            return
-
-        self._closed = True
-        if self._accept_scope:
-            # Workaround for https://bugs.python.org/issue41317
-            try:
-                self._loop.remove_reader(self._raw_socket)
-            except (ValueError, NotImplementedError):
-                pass
-
-            self._accept_scope.cancel()
-            await sleep(0)
-
-        self._raw_socket.close()
-
-
-class UNIXSocketListener(abc.SocketListener):
-    def __init__(self, raw_socket: socket.socket):
-        self.__raw_socket = raw_socket
-        self._loop = get_running_loop()
-        self._accept_guard = ResourceGuard("accepting connections from")
-        self._closed = False
-
-    async def accept(self) -> abc.SocketStream:
-        await checkpoint()
-        with self._accept_guard:
-            while True:
-                try:
-                    client_sock, _ = self.__raw_socket.accept()
-                    client_sock.setblocking(False)
-                    return UNIXSocketStream(client_sock)
-                except BlockingIOError:
-                    f: asyncio.Future = asyncio.Future()
-                    self._loop.add_reader(self.__raw_socket, f.set_result, None)
-                    f.add_done_callback(
-                        lambda _: self._loop.remove_reader(self.__raw_socket)
-                    )
-                    await f
-                except OSError as exc:
-                    if self._closed:
-                        raise ClosedResourceError from None
-                    else:
-                        raise BrokenResourceError from exc
-
-    async def aclose(self) -> None:
-        self._closed = True
-        self.__raw_socket.close()
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self.__raw_socket
-
-
-class UDPSocket(abc.UDPSocket):
-    def __init__(
-        self, transport: asyncio.DatagramTransport, protocol: DatagramProtocol
-    ):
-        self._transport = transport
-        self._protocol = protocol
-        self._receive_guard = ResourceGuard("reading from")
-        self._send_guard = ResourceGuard("writing to")
-        self._closed = False
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self._transport.get_extra_info("socket")
-
-    async def aclose(self) -> None:
-        if not self._transport.is_closing():
-            self._closed = True
-            self._transport.close()
-
-    async def receive(self) -> tuple[bytes, IPSockAddrType]:
-        with self._receive_guard:
-            await checkpoint()
-
-            # If the buffer is empty, ask for more data
-            if not self._protocol.read_queue and not self._transport.is_closing():
-                self._protocol.read_event.clear()
-                await self._protocol.read_event.wait()
-
-            try:
-                return self._protocol.read_queue.popleft()
-            except IndexError:
-                if self._closed:
-                    raise ClosedResourceError from None
-                else:
-                    raise BrokenResourceError from None
-
-    async def send(self, item: UDPPacketType) -> None:
-        with self._send_guard:
-            await checkpoint()
-            await self._protocol.write_event.wait()
-            if self._closed:
-                raise ClosedResourceError
-            elif self._transport.is_closing():
-                raise BrokenResourceError
-            else:
-                self._transport.sendto(*item)
-
-
-class ConnectedUDPSocket(abc.ConnectedUDPSocket):
-    def __init__(
-        self, transport: asyncio.DatagramTransport, protocol: DatagramProtocol
-    ):
-        self._transport = transport
-        self._protocol = protocol
-        self._receive_guard = ResourceGuard("reading from")
-        self._send_guard = ResourceGuard("writing to")
-        self._closed = False
-
-    @property
-    def _raw_socket(self) -> socket.socket:
-        return self._transport.get_extra_info("socket")
-
-    async def aclose(self) -> None:
-        if not self._transport.is_closing():
-            self._closed = True
-            self._transport.close()
-
-    async def receive(self) -> bytes:
-        with self._receive_guard:
-            await checkpoint()
-
-            # If the buffer is empty, ask for more data
-            if not self._protocol.read_queue and not self._transport.is_closing():
-                self._protocol.read_event.clear()
-                await self._protocol.read_event.wait()
-
-            try:
-                packet = self._protocol.read_queue.popleft()
-            except IndexError:
-                if self._closed:
-                    raise ClosedResourceError from None
-                else:
-                    raise BrokenResourceError from None
-
-            return packet[0]
-
-    async def send(self, item: bytes) -> None:
-        with self._send_guard:
-            await checkpoint()
-            await self._protocol.write_event.wait()
-            if self._closed:
-                raise ClosedResourceError
-            elif self._transport.is_closing():
-                raise BrokenResourceError
-            else:
-                self._transport.sendto(item)
-
-
-async def connect_tcp(
-    host: str, port: int, local_addr: tuple[str, int] | None = None
-) -> SocketStream:
-    transport, protocol = cast(
-        Tuple[asyncio.Transport, StreamProtocol],
-        await get_running_loop().create_connection(
-            StreamProtocol, host, port, local_addr=local_addr
-        ),
-    )
-    transport.pause_reading()
-    return SocketStream(transport, protocol)
-
-
-async def connect_unix(path: str) -> UNIXSocketStream:
-    await checkpoint()
-    loop = get_running_loop()
-    raw_socket = socket.socket(socket.AF_UNIX)
-    raw_socket.setblocking(False)
-    while True:
-        try:
-            raw_socket.connect(path)
-        except BlockingIOError:
-            f: asyncio.Future = asyncio.Future()
-            loop.add_writer(raw_socket, f.set_result, None)
-            f.add_done_callback(lambda _: loop.remove_writer(raw_socket))
-            await f
-        except BaseException:
-            raw_socket.close()
-            raise
-        else:
-            return UNIXSocketStream(raw_socket)
-
-
-async def create_udp_socket(
-    family: socket.AddressFamily,
-    local_address: IPSockAddrType | None,
-    remote_address: IPSockAddrType | None,
-    reuse_port: bool,
-) -> UDPSocket | ConnectedUDPSocket:
-    result = await get_running_loop().create_datagram_endpoint(
-        DatagramProtocol,
-        local_addr=local_address,
-        remote_addr=remote_address,
-        family=family,
-        reuse_port=reuse_port,
-    )
-    transport = result[0]
-    protocol = result[1]
-    if protocol.exception:
-        transport.close()
-        raise protocol.exception
-
-    if not remote_address:
-        return UDPSocket(transport, protocol)
-    else:
-        return ConnectedUDPSocket(transport, protocol)
-
-
-async def getaddrinfo(
-    host: bytes | str,
-    port: str | int | None,
-    *,
-    family: int | AddressFamily = 0,
-    type: int | SocketKind = 0,
-    proto: int = 0,
-    flags: int = 0,
-) -> GetAddrInfoReturnType:
-    # https://github.com/python/typeshed/pull/4304
-    result = await get_running_loop().getaddrinfo(
-        host, port, family=family, type=type, proto=proto, flags=flags
-    )
-    return cast(GetAddrInfoReturnType, result)
-
-
-async def getnameinfo(sockaddr: IPSockAddrType, flags: int = 0) -> tuple[str, str]:
-    return await get_running_loop().getnameinfo(sockaddr, flags)
-
-
-_read_events: RunVar[dict[Any, asyncio.Event]] = RunVar("read_events")
-_write_events: RunVar[dict[Any, asyncio.Event]] = RunVar("write_events")
-
-
-async def wait_socket_readable(sock: socket.socket) -> None:
-    await checkpoint()
-    try:
-        read_events = _read_events.get()
-    except LookupError:
-        read_events = {}
-        _read_events.set(read_events)
-
-    if read_events.get(sock):
-        raise BusyResourceError("reading from") from None
-
-    loop = get_running_loop()
-    event = read_events[sock] = asyncio.Event()
-    loop.add_reader(sock, event.set)
-    try:
-        await event.wait()
-    finally:
-        if read_events.pop(sock, None) is not None:
-            loop.remove_reader(sock)
-            readable = True
-        else:
-            readable = False
-
-    if not readable:
-        raise ClosedResourceError
-
-
-async def wait_socket_writable(sock: socket.socket) -> None:
-    await checkpoint()
-    try:
-        write_events = _write_events.get()
-    except LookupError:
-        write_events = {}
-        _write_events.set(write_events)
-
-    if write_events.get(sock):
-        raise BusyResourceError("writing to") from None
-
-    loop = get_running_loop()
-    event = write_events[sock] = asyncio.Event()
-    loop.add_writer(sock.fileno(), event.set)
-    try:
-        await event.wait()
-    finally:
-        if write_events.pop(sock, None) is not None:
-            loop.remove_writer(sock)
-            writable = True
-        else:
-            writable = False
-
-    if not writable:
-        raise ClosedResourceError
-
-
-#
-# Synchronization
-#
-
-
-class Event(BaseEvent):
-    def __new__(cls) -> Event:
-        return object.__new__(cls)
-
-    def __init__(self) -> None:
-        self._event = asyncio.Event()
-
-    def set(self) -> DeprecatedAwaitable:
-        self._event.set()
-        return DeprecatedAwaitable(self.set)
-
-    def is_set(self) -> bool:
-        return self._event.is_set()
-
-    async def wait(self) -> None:
-        if await self._event.wait():
-            await checkpoint()
-
-    def statistics(self) -> EventStatistics:
-        return EventStatistics(len(self._event._waiters))  # type: ignore[attr-defined]
-
-
-class CapacityLimiter(BaseCapacityLimiter):
-    _total_tokens: float = 0
-
-    def __new__(cls, total_tokens: float) -> CapacityLimiter:
-        return object.__new__(cls)
-
-    def __init__(self, total_tokens: float):
-        self._borrowers: set[Any] = set()
-        self._wait_queue: OrderedDict[Any, asyncio.Event] = OrderedDict()
-        self.total_tokens = total_tokens
-
-    async def __aenter__(self) -> None:
-        await self.acquire()
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> None:
-        self.release()
-
-    @property
-    def total_tokens(self) -> float:
-        return self._total_tokens
-
-    @total_tokens.setter
-    def total_tokens(self, value: float) -> None:
-        if not isinstance(value, int) and not math.isinf(value):
-            raise TypeError("total_tokens must be an int or math.inf")
-        if value < 1:
-            raise ValueError("total_tokens must be >= 1")
-
-        old_value = self._total_tokens
-        self._total_tokens = value
-        events = []
-        for event in self._wait_queue.values():
-            if value <= old_value:
-                break
-
-            if not event.is_set():
-                events.append(event)
-                old_value += 1
-
-        for event in events:
-            event.set()
-
-    @property
-    def borrowed_tokens(self) -> int:
-        return len(self._borrowers)
-
-    @property
-    def available_tokens(self) -> float:
-        return self._total_tokens - len(self._borrowers)
-
-    def acquire_nowait(self) -> DeprecatedAwaitable:
-        self.acquire_on_behalf_of_nowait(current_task())
-        return DeprecatedAwaitable(self.acquire_nowait)
-
-    def acquire_on_behalf_of_nowait(self, borrower: object) -> DeprecatedAwaitable:
-        if borrower in self._borrowers:
-            raise RuntimeError(
-                "this borrower is already holding one of this CapacityLimiter's "
-                "tokens"
-            )
-
-        if self._wait_queue or len(self._borrowers) >= self._total_tokens:
-            raise WouldBlock
-
-        self._borrowers.add(borrower)
-        return DeprecatedAwaitable(self.acquire_on_behalf_of_nowait)
-
-    async def acquire(self) -> None:
-        return await self.acquire_on_behalf_of(current_task())
-
-    async def acquire_on_behalf_of(self, borrower: object) -> None:
-        await checkpoint_if_cancelled()
-        try:
-            self.acquire_on_behalf_of_nowait(borrower)
-        except WouldBlock:
-            event = asyncio.Event()
-            self._wait_queue[borrower] = event
-            try:
-                await event.wait()
-            except BaseException:
-                self._wait_queue.pop(borrower, None)
-                raise
-
-            self._borrowers.add(borrower)
-        else:
-            try:
-                await cancel_shielded_checkpoint()
-            except BaseException:
-                self.release()
-                raise
-
-    def release(self) -> None:
-        self.release_on_behalf_of(current_task())
-
-    def release_on_behalf_of(self, borrower: object) -> None:
-        try:
-            self._borrowers.remove(borrower)
-        except KeyError:
-            raise RuntimeError(
-                "this borrower isn't holding any of this CapacityLimiter's " "tokens"
-            ) from None
-
-        # Notify the next task in line if this limiter has free capacity now
-        if self._wait_queue and len(self._borrowers) < self._total_tokens:
-            event = self._wait_queue.popitem(last=False)[1]
-            event.set()
-
-    def statistics(self) -> CapacityLimiterStatistics:
-        return CapacityLimiterStatistics(
-            self.borrowed_tokens,
-            self.total_tokens,
-            tuple(self._borrowers),
-            len(self._wait_queue),
-        )
-
-
-_default_thread_limiter: RunVar[CapacityLimiter] = RunVar("_default_thread_limiter")
-
-
-def current_default_thread_limiter() -> CapacityLimiter:
-    try:
-        return _default_thread_limiter.get()
-    except LookupError:
-        limiter = CapacityLimiter(40)
-        _default_thread_limiter.set(limiter)
-        return limiter
-
-
-#
-# Operating system signals
-#
-
-
-class _SignalReceiver(DeprecatedAsyncContextManager["_SignalReceiver"]):
-    def __init__(self, signals: tuple[int, ...]):
-        self._signals = signals
-        self._loop = get_running_loop()
-        self._signal_queue: deque[int] = deque()
-        self._future: asyncio.Future = asyncio.Future()
-        self._handled_signals: set[int] = set()
-
-    def _deliver(self, signum: int) -> None:
-        self._signal_queue.append(signum)
-        if not self._future.done():
-            self._future.set_result(None)
-
-    def __enter__(self) -> _SignalReceiver:
-        for sig in set(self._signals):
-            self._loop.add_signal_handler(sig, self._deliver, sig)
-            self._handled_signals.add(sig)
-
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> bool | None:
-        for sig in self._handled_signals:
-            self._loop.remove_signal_handler(sig)
-        return None
-
-    def __aiter__(self) -> _SignalReceiver:
-        return self
-
-    async def __anext__(self) -> int:
-        await checkpoint()
-        if not self._signal_queue:
-            self._future = asyncio.Future()
-            await self._future
-
-        return self._signal_queue.popleft()
-
-
-def open_signal_receiver(*signals: int) -> _SignalReceiver:
-    return _SignalReceiver(signals)
-
-
-#
-# Testing and debugging
-#
-
-
-def _create_task_info(task: asyncio.Task) -> TaskInfo:
-    task_state = _task_states.get(task)
-    if task_state is None:
-        name = task.get_name() if _native_task_names else None
-        parent_id = None
-    else:
-        name = task_state.name
-        parent_id = task_state.parent_id
-
-    return TaskInfo(id(task), parent_id, name, get_coro(task))
-
-
-def get_current_task() -> TaskInfo:
-    return _create_task_info(current_task())  # type: ignore[arg-type]
-
-
-def get_running_tasks() -> list[TaskInfo]:
-    return [_create_task_info(task) for task in all_tasks() if not task.done()]
-
-
-async def wait_all_tasks_blocked() -> None:
-    await checkpoint()
-    this_task = current_task()
-    while True:
-        for task in all_tasks():
-            if task is this_task:
-                continue
-
-            if task._fut_waiter is None or task._fut_waiter.done():  # type: ignore[attr-defined]
-                await sleep(0.1)
-                break
-        else:
-            return
-
-
-class TestRunner(abc.TestRunner):
-    def __init__(
-        self,
-        debug: bool = False,
-        use_uvloop: bool = False,
-        policy: asyncio.AbstractEventLoopPolicy | None = None,
-    ):
-        self._exceptions: list[BaseException] = []
-        _maybe_set_event_loop_policy(policy, use_uvloop)
-        self._loop = asyncio.new_event_loop()
-        self._loop.set_debug(debug)
-        self._loop.set_exception_handler(self._exception_handler)
-        asyncio.set_event_loop(self._loop)
-
-    def _cancel_all_tasks(self) -> None:
-        to_cancel = all_tasks(self._loop)
-        if not to_cancel:
-            return
-
-        for task in to_cancel:
-            task.cancel()
-
-        self._loop.run_until_complete(
-            asyncio.gather(*to_cancel, return_exceptions=True)
-        )
-
-        for task in to_cancel:
-            if task.cancelled():
-                continue
-            if task.exception() is not None:
-                raise cast(BaseException, task.exception())
-
-    def _exception_handler(
-        self, loop: asyncio.AbstractEventLoop, context: dict[str, Any]
-    ) -> None:
-        if isinstance(context.get("exception"), Exception):
-            self._exceptions.append(context["exception"])
-        else:
-            loop.default_exception_handler(context)
-
-    def _raise_async_exceptions(self) -> None:
-        # Re-raise any exceptions raised in asynchronous callbacks
-        if self._exceptions:
-            exceptions, self._exceptions = self._exceptions, []
-            if len(exceptions) == 1:
-                raise exceptions[0]
-            elif exceptions:
-                raise ExceptionGroup(exceptions)
-
-    def close(self) -> None:
-        try:
-            self._cancel_all_tasks()
-            self._loop.run_until_complete(self._loop.shutdown_asyncgens())
-        finally:
-            asyncio.set_event_loop(None)
-            self._loop.close()
-
-    def run_asyncgen_fixture(
-        self,
-        fixture_func: Callable[..., AsyncGenerator[T_Retval, Any]],
-        kwargs: dict[str, Any],
-    ) -> Iterable[T_Retval]:
-        async def fixture_runner() -> None:
-            agen = fixture_func(**kwargs)
-            try:
-                retval = await agen.asend(None)
-                self._raise_async_exceptions()
-            except BaseException as exc:
-                f.set_exception(exc)
-                return
-            else:
-                f.set_result(retval)
-
-            await event.wait()
-            try:
-                await agen.asend(None)
-            except StopAsyncIteration:
-                pass
-            else:
-                await agen.aclose()
-                raise RuntimeError("Async generator fixture did not stop")
-
-        f = self._loop.create_future()
-        event = asyncio.Event()
-        fixture_task = self._loop.create_task(fixture_runner())
-        self._loop.run_until_complete(f)
-        yield f.result()
-        event.set()
-        self._loop.run_until_complete(fixture_task)
-        self._raise_async_exceptions()
-
-    def run_fixture(
-        self,
-        fixture_func: Callable[..., Coroutine[Any, Any, T_Retval]],
-        kwargs: dict[str, Any],
-    ) -> T_Retval:
-        retval = self._loop.run_until_complete(fixture_func(**kwargs))
-        self._raise_async_exceptions()
-        return retval
-
-    def run_test(
-        self, test_func: Callable[..., Coroutine[Any, Any, Any]], kwargs: dict[str, Any]
-    ) -> None:
-        try:
-            self._loop.run_until_complete(test_func(**kwargs))
-        except Exception as exc:
-            self._exceptions.append(exc)
-
-        self._raise_async_exceptions()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/backoff/types.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/backoff/types.py
deleted file mode 100644
index 25f20a4c43f79a62278b00081c5d7da5dfc12e3e..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/backoff/types.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# coding:utf-8
-from ._typing import Details
-
-__all__ = [
-    'Details'
-]
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/dateutil/tz/_factories.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/dateutil/tz/_factories.py
deleted file mode 100644
index f8a65891a023ebf9eb0c24d391ba67541b7133f1..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/dateutil/tz/_factories.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from datetime import timedelta
-import weakref
-from collections import OrderedDict
-
-from six.moves import _thread
-
-
-class _TzSingleton(type):
-    def __init__(cls, *args, **kwargs):
-        cls.__instance = None
-        super(_TzSingleton, cls).__init__(*args, **kwargs)
-
-    def __call__(cls):
-        if cls.__instance is None:
-            cls.__instance = super(_TzSingleton, cls).__call__()
-        return cls.__instance
-
-
-class _TzFactory(type):
-    def instance(cls, *args, **kwargs):
-        """Alternate constructor that returns a fresh instance"""
-        return type.__call__(cls, *args, **kwargs)
-
-
-class _TzOffsetFactory(_TzFactory):
-    def __init__(cls, *args, **kwargs):
-        cls.__instances = weakref.WeakValueDictionary()
-        cls.__strong_cache = OrderedDict()
-        cls.__strong_cache_size = 8
-
-        cls._cache_lock = _thread.allocate_lock()
-
-    def __call__(cls, name, offset):
-        if isinstance(offset, timedelta):
-            key = (name, offset.total_seconds())
-        else:
-            key = (name, offset)
-
-        instance = cls.__instances.get(key, None)
-        if instance is None:
-            instance = cls.__instances.setdefault(key,
-                                                  cls.instance(name, offset))
-
-        # This lock may not be necessary in Python 3. See GH issue #901
-        with cls._cache_lock:
-            cls.__strong_cache[key] = cls.__strong_cache.pop(key, instance)
-
-            # Remove an item if the strong cache is overpopulated
-            if len(cls.__strong_cache) > cls.__strong_cache_size:
-                cls.__strong_cache.popitem(last=False)
-
-        return instance
-
-
-class _TzStrFactory(_TzFactory):
-    def __init__(cls, *args, **kwargs):
-        cls.__instances = weakref.WeakValueDictionary()
-        cls.__strong_cache = OrderedDict()
-        cls.__strong_cache_size = 8
-
-        cls.__cache_lock = _thread.allocate_lock()
-
-    def __call__(cls, s, posix_offset=False):
-        key = (s, posix_offset)
-        instance = cls.__instances.get(key, None)
-
-        if instance is None:
-            instance = cls.__instances.setdefault(key,
-                cls.instance(s, posix_offset))
-
-        # This lock may not be necessary in Python 3. See GH issue #901
-        with cls.__cache_lock:
-            cls.__strong_cache[key] = cls.__strong_cache.pop(key, instance)
-
-            # Remove an item if the strong cache is overpopulated
-            if len(cls.__strong_cache) > cls.__strong_cache_size:
-                cls.__strong_cache.popitem(last=False)
-
-        return instance
-
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_process_net_command.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_process_net_command.py
deleted file mode 100644
index 04604841182c618d36ca9ba69c7da851ac169a43..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_process_net_command.py
+++ /dev/null
@@ -1,758 +0,0 @@
-import json
-import os
-import sys
-import traceback
-
-from _pydev_bundle import pydev_log
-from _pydev_bundle.pydev_log import exception as pydev_log_exception
-from _pydevd_bundle import pydevd_traceproperty, pydevd_dont_trace, pydevd_utils
-from _pydevd_bundle.pydevd_additional_thread_info import set_additional_thread_info
-from _pydevd_bundle.pydevd_breakpoints import get_exception_class
-from _pydevd_bundle.pydevd_comm import (
-    InternalEvaluateConsoleExpression, InternalConsoleGetCompletions, InternalRunCustomOperation,
-    internal_get_next_statement_targets, internal_get_smart_step_into_variants)
-from _pydevd_bundle.pydevd_constants import NEXT_VALUE_SEPARATOR, IS_WINDOWS, NULL
-from _pydevd_bundle.pydevd_comm_constants import ID_TO_MEANING, CMD_EXEC_EXPRESSION, CMD_AUTHENTICATE
-from _pydevd_bundle.pydevd_api import PyDevdAPI
-from io import StringIO
-from _pydevd_bundle.pydevd_net_command import NetCommand
-from _pydevd_bundle.pydevd_thread_lifecycle import pydevd_find_thread_by_id
-import pydevd_file_utils
-
-
-class _PyDevCommandProcessor(object):
-
-    def __init__(self):
-        self.api = PyDevdAPI()
-
-    def process_net_command(self, py_db, cmd_id, seq, text):
-        '''Processes a command received from the Java side
-
-        @param cmd_id: the id of the command
-        @param seq: the sequence of the command
-        @param text: the text received in the command
-        '''
-
-        # We can only proceed if the client is already authenticated or if it's the
-        # command to authenticate.
-        if cmd_id != CMD_AUTHENTICATE and not py_db.authentication.is_authenticated():
-            cmd = py_db.cmd_factory.make_error_message(seq, 'Client not authenticated.')
-            py_db.writer.add_command(cmd)
-            return
-
-        meaning = ID_TO_MEANING[str(cmd_id)]
-
-        # print('Handling %s (%s)' % (meaning, text))
-
-        method_name = meaning.lower()
-
-        on_command = getattr(self, method_name.lower(), None)
-        if on_command is None:
-            # I have no idea what this is all about
-            cmd = py_db.cmd_factory.make_error_message(seq, "unexpected command " + str(cmd_id))
-            py_db.writer.add_command(cmd)
-            return
-
-        lock = py_db._main_lock
-        if method_name == 'cmd_thread_dump_to_stderr':
-            # We can skip the main debugger locks for cases where we know it's not needed.
-            lock = NULL
-
-        with lock:
-            try:
-                cmd = on_command(py_db, cmd_id, seq, text)
-                if cmd is not None:
-                    py_db.writer.add_command(cmd)
-            except:
-                if traceback is not None and sys is not None and pydev_log_exception is not None:
-                    pydev_log_exception()
-
-                    stream = StringIO()
-                    traceback.print_exc(file=stream)
-                    cmd = py_db.cmd_factory.make_error_message(
-                        seq,
-                        "Unexpected exception in process_net_command.\nInitial params: %s. Exception: %s" % (
-                            ((cmd_id, seq, text), stream.getvalue())
-                        )
-                    )
-                    if cmd is not None:
-                        py_db.writer.add_command(cmd)
-
-    def cmd_authenticate(self, py_db, cmd_id, seq, text):
-        access_token = text
-        py_db.authentication.login(access_token)
-        if py_db.authentication.is_authenticated():
-            return NetCommand(cmd_id, seq, py_db.authentication.client_access_token)
-
-        return py_db.cmd_factory.make_error_message(seq, 'Client not authenticated.')
-
-    def cmd_run(self, py_db, cmd_id, seq, text):
-        return self.api.run(py_db)
-
-    def cmd_list_threads(self, py_db, cmd_id, seq, text):
-        return self.api.list_threads(py_db, seq)
-
-    def cmd_get_completions(self, py_db, cmd_id, seq, text):
-        # we received some command to get a variable
-        # the text is: thread_id\tframe_id\tactivation token
-        thread_id, frame_id, _scope, act_tok = text.split('\t', 3)
-
-        return self.api.request_completions(py_db, seq, thread_id, frame_id, act_tok)
-
-    def cmd_get_thread_stack(self, py_db, cmd_id, seq, text):
-        # Receives a thread_id and a given timeout, which is the time we should
-        # wait to the provide the stack if a given thread is still not suspended.
-        if '\t' in text:
-            thread_id, timeout = text.split('\t')
-            timeout = float(timeout)
-        else:
-            thread_id = text
-            timeout = .5  # Default timeout is .5 seconds
-
-        return self.api.request_stack(py_db, seq, thread_id, fmt={}, timeout=timeout)
-
-    def cmd_set_protocol(self, py_db, cmd_id, seq, text):
-        return self.api.set_protocol(py_db, seq, text.strip())
-
-    def cmd_thread_suspend(self, py_db, cmd_id, seq, text):
-        return self.api.request_suspend_thread(py_db, text.strip())
-
-    def cmd_version(self, py_db, cmd_id, seq, text):
-        # Default based on server process (although ideally the IDE should
-        # provide it).
-        if IS_WINDOWS:
-            ide_os = 'WINDOWS'
-        else:
-            ide_os = 'UNIX'
-
-        # Breakpoints can be grouped by 'LINE' or by 'ID'.
-        breakpoints_by = 'LINE'
-
-        splitted = text.split('\t')
-        if len(splitted) == 1:
-            _local_version = splitted
-
-        elif len(splitted) == 2:
-            _local_version, ide_os = splitted
-
-        elif len(splitted) == 3:
-            _local_version, ide_os, breakpoints_by = splitted
-
-        version_msg = self.api.set_ide_os_and_breakpoints_by(py_db, seq, ide_os, breakpoints_by)
-
-        # Enable thread notifications after the version command is completed.
-        self.api.set_enable_thread_notifications(py_db, True)
-
-        return version_msg
-
-    def cmd_thread_run(self, py_db, cmd_id, seq, text):
-        return self.api.request_resume_thread(text.strip())
-
-    def _cmd_step(self, py_db, cmd_id, seq, text):
-        return self.api.request_step(py_db, text.strip(), cmd_id)
-
-    cmd_step_into = _cmd_step
-    cmd_step_into_my_code = _cmd_step
-    cmd_step_over = _cmd_step
-    cmd_step_over_my_code = _cmd_step
-    cmd_step_return = _cmd_step
-    cmd_step_return_my_code = _cmd_step
-
-    def _cmd_set_next(self, py_db, cmd_id, seq, text):
-        thread_id, line, func_name = text.split('\t', 2)
-        return self.api.request_set_next(py_db, seq, thread_id, cmd_id, None, line, func_name)
-
-    cmd_run_to_line = _cmd_set_next
-    cmd_set_next_statement = _cmd_set_next
-
-    def cmd_smart_step_into(self, py_db, cmd_id, seq, text):
-        thread_id, line_or_bytecode_offset, func_name = text.split('\t', 2)
-        if line_or_bytecode_offset.startswith('offset='):
-            # In this case we request the smart step into to stop given the parent frame
-            # and the location of the parent frame bytecode offset and not just the func_name
-            # (this implies that `CMD_GET_SMART_STEP_INTO_VARIANTS` was previously used
-            # to know what are the valid stop points).
-
-            temp = line_or_bytecode_offset[len('offset='):]
-            if ';' in temp:
-                offset, child_offset = temp.split(';')
-                offset = int(offset)
-                child_offset = int(child_offset)
-            else:
-                child_offset = -1
-                offset = int(temp)
-            return self.api.request_smart_step_into(py_db, seq, thread_id, offset, child_offset)
-        else:
-            # If the offset wasn't passed, just use the line/func_name to do the stop.
-            return self.api.request_smart_step_into_by_func_name(py_db, seq, thread_id, line_or_bytecode_offset, func_name)
-
-    def cmd_reload_code(self, py_db, cmd_id, seq, text):
-        text = text.strip()
-        if '\t' not in text:
-            module_name = text.strip()
-            filename = None
-        else:
-            module_name, filename = text.split('\t', 1)
-        self.api.request_reload_code(py_db, seq, module_name, filename)
-
-    def cmd_change_variable(self, py_db, cmd_id, seq, text):
-        # the text is: thread\tstackframe\tFRAME|GLOBAL\tattribute_to_change\tvalue_to_change
-        thread_id, frame_id, scope, attr_and_value = text.split('\t', 3)
-
-        tab_index = attr_and_value.rindex('\t')
-        attr = attr_and_value[0:tab_index].replace('\t', '.')
-        value = attr_and_value[tab_index + 1:]
-        self.api.request_change_variable(py_db, seq, thread_id, frame_id, scope, attr, value)
-
-    def cmd_get_variable(self, py_db, cmd_id, seq, text):
-        # we received some command to get a variable
-        # the text is: thread_id\tframe_id\tFRAME|GLOBAL\tattributes*
-        thread_id, frame_id, scopeattrs = text.split('\t', 2)
-
-        if scopeattrs.find('\t') != -1:  # there are attributes beyond scope
-            scope, attrs = scopeattrs.split('\t', 1)
-        else:
-            scope, attrs = (scopeattrs, None)
-
-        self.api.request_get_variable(py_db, seq, thread_id, frame_id, scope, attrs)
-
-    def cmd_get_array(self, py_db, cmd_id, seq, text):
-        # Note: untested and unused in pydev
-        # we received some command to get an array variable
-        # the text is: thread_id\tframe_id\tFRAME|GLOBAL\tname\ttemp\troffs\tcoffs\trows\tcols\tformat
-        roffset, coffset, rows, cols, format, thread_id, frame_id, scopeattrs = text.split('\t', 7)
-
-        if scopeattrs.find('\t') != -1:  # there are attributes beyond scope
-            scope, attrs = scopeattrs.split('\t', 1)
-        else:
-            scope, attrs = (scopeattrs, None)
-
-        self.api.request_get_array(py_db, seq, roffset, coffset, rows, cols, format, thread_id, frame_id, scope, attrs)
-
-    def cmd_show_return_values(self, py_db, cmd_id, seq, text):
-        show_return_values = text.split('\t')[1]
-        self.api.set_show_return_values(py_db, int(show_return_values) == 1)
-
-    def cmd_load_full_value(self, py_db, cmd_id, seq, text):
-        # Note: untested and unused in pydev
-        thread_id, frame_id, scopeattrs = text.split('\t', 2)
-        vars = scopeattrs.split(NEXT_VALUE_SEPARATOR)
-
-        self.api.request_load_full_value(py_db, seq, thread_id, frame_id, vars)
-
-    def cmd_get_description(self, py_db, cmd_id, seq, text):
-        # Note: untested and unused in pydev
-        thread_id, frame_id, expression = text.split('\t', 2)
-        self.api.request_get_description(py_db, seq, thread_id, frame_id, expression)
-
-    def cmd_get_frame(self, py_db, cmd_id, seq, text):
-        thread_id, frame_id, scope = text.split('\t', 2)
-        self.api.request_get_frame(py_db, seq, thread_id, frame_id)
-
-    def cmd_set_break(self, py_db, cmd_id, seq, text):
-        # func name: 'None': match anything. Empty: match global, specified: only method context.
-        # command to add some breakpoint.
-        # text is filename\tline. Add to breakpoints dictionary
-        suspend_policy = u"NONE"  # Can be 'NONE' or 'ALL'
-        is_logpoint = False
-        hit_condition = None
-        if py_db._set_breakpoints_with_id:
-            try:
-                try:
-                    breakpoint_id, btype, filename, line, func_name, condition, expression, hit_condition, is_logpoint, suspend_policy = text.split(u'\t', 9)
-                except ValueError:  # not enough values to unpack
-                    # No suspend_policy passed (use default).
-                    breakpoint_id, btype, filename, line, func_name, condition, expression, hit_condition, is_logpoint = text.split(u'\t', 8)
-                is_logpoint = is_logpoint == u'True'
-            except ValueError:  # not enough values to unpack
-                breakpoint_id, btype, filename, line, func_name, condition, expression = text.split(u'\t', 6)
-
-            breakpoint_id = int(breakpoint_id)
-            line = int(line)
-
-            # We must restore new lines and tabs as done in
-            # AbstractDebugTarget.breakpointAdded
-            condition = condition.replace(u"@_@NEW_LINE_CHAR@_@", u'\n').\
-                replace(u"@_@TAB_CHAR@_@", u'\t').strip()
-
-            expression = expression.replace(u"@_@NEW_LINE_CHAR@_@", u'\n').\
-                replace(u"@_@TAB_CHAR@_@", u'\t').strip()
-        else:
-            # Note: this else should be removed after PyCharm migrates to setting
-            # breakpoints by id (and ideally also provides func_name).
-            btype, filename, line, func_name, suspend_policy, condition, expression = text.split(u'\t', 6)
-            # If we don't have an id given for each breakpoint, consider
-            # the id to be the line.
-            breakpoint_id = line = int(line)
-
-            condition = condition.replace(u"@_@NEW_LINE_CHAR@_@", u'\n'). \
-                replace(u"@_@TAB_CHAR@_@", u'\t').strip()
-
-            expression = expression.replace(u"@_@NEW_LINE_CHAR@_@", u'\n'). \
-                replace(u"@_@TAB_CHAR@_@", u'\t').strip()
-
-        if condition is not None and (len(condition) <= 0 or condition == u"None"):
-            condition = None
-
-        if expression is not None and (len(expression) <= 0 or expression == u"None"):
-            expression = None
-
-        if hit_condition is not None and (len(hit_condition) <= 0 or hit_condition == u"None"):
-            hit_condition = None
-
-        def on_changed_breakpoint_state(breakpoint_id, add_breakpoint_result):
-            error_code = add_breakpoint_result.error_code
-
-            translated_line = add_breakpoint_result.translated_line
-            translated_filename = add_breakpoint_result.translated_filename
-            msg = ''
-            if error_code:
-
-                if error_code == self.api.ADD_BREAKPOINT_FILE_NOT_FOUND:
-                    msg = 'pydev debugger: Trying to add breakpoint to file that does not exist: %s (will have no effect).\n' % (translated_filename,)
-
-                elif error_code == self.api.ADD_BREAKPOINT_FILE_EXCLUDED_BY_FILTERS:
-                    msg = 'pydev debugger: Trying to add breakpoint to file that is excluded by filters: %s (will have no effect).\n' % (translated_filename,)
-
-                elif error_code == self.api.ADD_BREAKPOINT_LAZY_VALIDATION:
-                    msg = ''  # Ignore this here (if/when loaded, it'll call on_changed_breakpoint_state again accordingly).
-
-                elif error_code == self.api.ADD_BREAKPOINT_INVALID_LINE:
-                    msg = 'pydev debugger: Trying to add breakpoint to line (%s) that is not valid in: %s.\n' % (translated_line, translated_filename,)
-
-                else:
-                    # Shouldn't get here.
-                    msg = 'pydev debugger: Breakpoint not validated (reason unknown -- please report as error): %s (%s).\n' % (translated_filename, translated_line)
-
-            else:
-                if add_breakpoint_result.original_line != translated_line:
-                    msg = 'pydev debugger (info): Breakpoint in line: %s moved to line: %s (in %s).\n' % (add_breakpoint_result.original_line, translated_line, translated_filename)
-
-            if msg:
-                py_db.writer.add_command(py_db.cmd_factory.make_warning_message(msg))
-
-        result = self.api.add_breakpoint(
-            py_db, self.api.filename_to_str(filename), btype, breakpoint_id, line, condition, func_name,
-            expression, suspend_policy, hit_condition, is_logpoint, on_changed_breakpoint_state=on_changed_breakpoint_state)
-
-        on_changed_breakpoint_state(breakpoint_id, result)
-
-    def cmd_remove_break(self, py_db, cmd_id, seq, text):
-        # command to remove some breakpoint
-        # text is type\file\tid. Remove from breakpoints dictionary
-        breakpoint_type, filename, breakpoint_id = text.split('\t', 2)
-
-        filename = self.api.filename_to_str(filename)
-
-        try:
-            breakpoint_id = int(breakpoint_id)
-        except ValueError:
-            pydev_log.critical('Error removing breakpoint. Expected breakpoint_id to be an int. Found: %s', breakpoint_id)
-
-        else:
-            self.api.remove_breakpoint(py_db, filename, breakpoint_type, breakpoint_id)
-
-    def _cmd_exec_or_evaluate_expression(self, py_db, cmd_id, seq, text):
-        # command to evaluate the given expression
-        # text is: thread\tstackframe\tLOCAL\texpression
-        attr_to_set_result = ""
-        try:
-            thread_id, frame_id, scope, expression, trim, attr_to_set_result = text.split('\t', 5)
-        except ValueError:
-            thread_id, frame_id, scope, expression, trim = text.split('\t', 4)
-        is_exec = cmd_id == CMD_EXEC_EXPRESSION
-        trim_if_too_big = int(trim) == 1
-
-        self.api.request_exec_or_evaluate(
-            py_db, seq, thread_id, frame_id, expression, is_exec, trim_if_too_big, attr_to_set_result)
-
-    cmd_evaluate_expression = _cmd_exec_or_evaluate_expression
-    cmd_exec_expression = _cmd_exec_or_evaluate_expression
-
-    def cmd_console_exec(self, py_db, cmd_id, seq, text):
-        # command to exec expression in console, in case expression is only partially valid 'False' is returned
-        # text is: thread\tstackframe\tLOCAL\texpression
-
-        thread_id, frame_id, scope, expression = text.split('\t', 3)
-        self.api.request_console_exec(py_db, seq, thread_id, frame_id, expression)
-
-    def cmd_set_path_mapping_json(self, py_db, cmd_id, seq, text):
-        '''
-        :param text:
-            Json text. Something as:
-
-            {
-                "pathMappings": [
-                    {
-                        "localRoot": "c:/temp",
-                        "remoteRoot": "/usr/temp"
-                    }
-                ],
-                "debug": true,
-                "force": false
-            }
-        '''
-        as_json = json.loads(text)
-        force = as_json.get('force', False)
-
-        path_mappings = []
-        for pathMapping in as_json.get('pathMappings', []):
-            localRoot = pathMapping.get('localRoot', '')
-            remoteRoot = pathMapping.get('remoteRoot', '')
-            if (localRoot != '') and (remoteRoot != ''):
-                path_mappings.append((localRoot, remoteRoot))
-
-        if bool(path_mappings) or force:
-            pydevd_file_utils.setup_client_server_paths(path_mappings)
-
-        debug = as_json.get('debug', False)
-        if debug or force:
-            pydevd_file_utils.DEBUG_CLIENT_SERVER_TRANSLATION = debug
-
-    def cmd_set_py_exception_json(self, py_db, cmd_id, seq, text):
-        # This API is optional and works 'in bulk' -- it's possible
-        # to get finer-grained control with CMD_ADD_EXCEPTION_BREAK/CMD_REMOVE_EXCEPTION_BREAK
-        # which allows setting caught/uncaught per exception, although global settings such as:
-        # - skip_on_exceptions_thrown_in_same_context
-        # - ignore_exceptions_thrown_in_lines_with_ignore_exception
-        # must still be set through this API (before anything else as this clears all existing
-        # exception breakpoints).
-        try:
-            py_db.break_on_uncaught_exceptions = {}
-            py_db.break_on_caught_exceptions = {}
-            py_db.break_on_user_uncaught_exceptions = {}
-
-            as_json = json.loads(text)
-            break_on_uncaught = as_json.get('break_on_uncaught', False)
-            break_on_caught = as_json.get('break_on_caught', False)
-            break_on_user_caught = as_json.get('break_on_user_caught', False)
-            py_db.skip_on_exceptions_thrown_in_same_context = as_json.get('skip_on_exceptions_thrown_in_same_context', False)
-            py_db.ignore_exceptions_thrown_in_lines_with_ignore_exception = as_json.get('ignore_exceptions_thrown_in_lines_with_ignore_exception', False)
-            ignore_libraries = as_json.get('ignore_libraries', False)
-            exception_types = as_json.get('exception_types', [])
-
-            for exception_type in exception_types:
-                if not exception_type:
-                    continue
-
-                py_db.add_break_on_exception(
-                    exception_type,
-                    condition=None,
-                    expression=None,
-                    notify_on_handled_exceptions=break_on_caught,
-                    notify_on_unhandled_exceptions=break_on_uncaught,
-                    notify_on_user_unhandled_exceptions=break_on_user_caught,
-                    notify_on_first_raise_only=True,
-                    ignore_libraries=ignore_libraries,
-                )
-
-                py_db.on_breakpoints_changed()
-        except:
-            pydev_log.exception("Error when setting exception list. Received: %s", text)
-
-    def cmd_set_py_exception(self, py_db, cmd_id, seq, text):
-        # DEPRECATED. Use cmd_set_py_exception_json instead.
-        try:
-            splitted = text.split(';')
-            py_db.break_on_uncaught_exceptions = {}
-            py_db.break_on_caught_exceptions = {}
-            py_db.break_on_user_uncaught_exceptions = {}
-            if len(splitted) >= 5:
-                if splitted[0] == 'true':
-                    break_on_uncaught = True
-                else:
-                    break_on_uncaught = False
-
-                if splitted[1] == 'true':
-                    break_on_caught = True
-                else:
-                    break_on_caught = False
-
-                if splitted[2] == 'true':
-                    py_db.skip_on_exceptions_thrown_in_same_context = True
-                else:
-                    py_db.skip_on_exceptions_thrown_in_same_context = False
-
-                if splitted[3] == 'true':
-                    py_db.ignore_exceptions_thrown_in_lines_with_ignore_exception = True
-                else:
-                    py_db.ignore_exceptions_thrown_in_lines_with_ignore_exception = False
-
-                if splitted[4] == 'true':
-                    ignore_libraries = True
-                else:
-                    ignore_libraries = False
-
-                for exception_type in splitted[5:]:
-                    exception_type = exception_type.strip()
-                    if not exception_type:
-                        continue
-
-                    py_db.add_break_on_exception(
-                        exception_type,
-                        condition=None,
-                        expression=None,
-                        notify_on_handled_exceptions=break_on_caught,
-                        notify_on_unhandled_exceptions=break_on_uncaught,
-                        notify_on_user_unhandled_exceptions=False,  # TODO (not currently supported in this API).
-                        notify_on_first_raise_only=True,
-                        ignore_libraries=ignore_libraries,
-                    )
-            else:
-                pydev_log.exception("Expected to have at least 5 ';' separated items. Received: %s", text)
-
-        except:
-            pydev_log.exception("Error when setting exception list. Received: %s", text)
-
-    def _load_source(self, py_db, cmd_id, seq, text):
-        filename = text
-        filename = self.api.filename_to_str(filename)
-        self.api.request_load_source(py_db, seq, filename)
-
-    cmd_load_source = _load_source
-    cmd_get_file_contents = _load_source
-
-    def cmd_load_source_from_frame_id(self, py_db, cmd_id, seq, text):
-        frame_id = text
-        self.api.request_load_source_from_frame_id(py_db, seq, frame_id)
-
-    def cmd_set_property_trace(self, py_db, cmd_id, seq, text):
-        # Command which receives whether to trace property getter/setter/deleter
-        # text is feature_state(true/false);disable_getter/disable_setter/disable_deleter
-        if text:
-            splitted = text.split(';')
-            if len(splitted) >= 3:
-                if not py_db.disable_property_trace and splitted[0] == 'true':
-                    # Replacing property by custom property only when the debugger starts
-                    pydevd_traceproperty.replace_builtin_property()
-                    py_db.disable_property_trace = True
-                # Enable/Disable tracing of the property getter
-                if splitted[1] == 'true':
-                    py_db.disable_property_getter_trace = True
-                else:
-                    py_db.disable_property_getter_trace = False
-                # Enable/Disable tracing of the property setter
-                if splitted[2] == 'true':
-                    py_db.disable_property_setter_trace = True
-                else:
-                    py_db.disable_property_setter_trace = False
-                # Enable/Disable tracing of the property deleter
-                if splitted[3] == 'true':
-                    py_db.disable_property_deleter_trace = True
-                else:
-                    py_db.disable_property_deleter_trace = False
-
-    def cmd_add_exception_break(self, py_db, cmd_id, seq, text):
-        # Note that this message has some idiosyncrasies...
-        #
-        # notify_on_handled_exceptions can be 0, 1 or 2
-        # 0 means we should not stop on handled exceptions.
-        # 1 means we should stop on handled exceptions showing it on all frames where the exception passes.
-        # 2 means we should stop on handled exceptions but we should only notify about it once.
-        #
-        # To ignore_libraries properly, besides setting ignore_libraries to 1, the IDE_PROJECT_ROOTS environment
-        # variable must be set (so, we'll ignore anything not below IDE_PROJECT_ROOTS) -- this is not ideal as
-        # the environment variable may not be properly set if it didn't start from the debugger (we should
-        # create a custom message for that).
-        #
-        # There are 2 global settings which can only be set in CMD_SET_PY_EXCEPTION. Namely:
-        #
-        # py_db.skip_on_exceptions_thrown_in_same_context
-        # - If True, we should only show the exception in a caller, not where it was first raised.
-        #
-        # py_db.ignore_exceptions_thrown_in_lines_with_ignore_exception
-        # - If True exceptions thrown in lines with '@IgnoreException' will not be shown.
-
-        condition = ""
-        expression = ""
-        if text.find('\t') != -1:
-            try:
-                exception, condition, expression, notify_on_handled_exceptions, notify_on_unhandled_exceptions, ignore_libraries = text.split('\t', 5)
-            except:
-                exception, notify_on_handled_exceptions, notify_on_unhandled_exceptions, ignore_libraries = text.split('\t', 3)
-        else:
-            exception, notify_on_handled_exceptions, notify_on_unhandled_exceptions, ignore_libraries = text, 0, 0, 0
-
-        condition = condition.replace("@_@NEW_LINE_CHAR@_@", '\n').replace("@_@TAB_CHAR@_@", '\t').strip()
-
-        if condition is not None and (len(condition) == 0 or condition == "None"):
-            condition = None
-
-        expression = expression.replace("@_@NEW_LINE_CHAR@_@", '\n').replace("@_@TAB_CHAR@_@", '\t').strip()
-
-        if expression is not None and (len(expression) == 0 or expression == "None"):
-            expression = None
-
-        if exception.find('-') != -1:
-            breakpoint_type, exception = exception.split('-')
-        else:
-            breakpoint_type = 'python'
-
-        if breakpoint_type == 'python':
-            self.api.add_python_exception_breakpoint(
-                py_db, exception, condition, expression,
-                notify_on_handled_exceptions=int(notify_on_handled_exceptions) > 0,
-                notify_on_unhandled_exceptions=int(notify_on_unhandled_exceptions) == 1,
-                notify_on_user_unhandled_exceptions=0,  # TODO (not currently supported in this API).
-                notify_on_first_raise_only=int(notify_on_handled_exceptions) == 2,
-                ignore_libraries=int(ignore_libraries) > 0,
-            )
-        else:
-            self.api.add_plugins_exception_breakpoint(py_db, breakpoint_type, exception)
-
-    def cmd_remove_exception_break(self, py_db, cmd_id, seq, text):
-        exception = text
-        if exception.find('-') != -1:
-            exception_type, exception = exception.split('-')
-        else:
-            exception_type = 'python'
-
-        if exception_type == 'python':
-            self.api.remove_python_exception_breakpoint(py_db, exception)
-        else:
-            self.api.remove_plugins_exception_breakpoint(py_db, exception_type, exception)
-
-    def cmd_add_django_exception_break(self, py_db, cmd_id, seq, text):
-        self.api.add_plugins_exception_breakpoint(py_db, breakpoint_type='django', exception=text)
-
-    def cmd_remove_django_exception_break(self, py_db, cmd_id, seq, text):
-        self.api.remove_plugins_exception_breakpoint(py_db, exception_type='django', exception=text)
-
-    def cmd_evaluate_console_expression(self, py_db, cmd_id, seq, text):
-        # Command which takes care for the debug console communication
-        if text != "":
-            thread_id, frame_id, console_command = text.split('\t', 2)
-            console_command, line = console_command.split('\t')
-
-            if console_command == 'EVALUATE':
-                int_cmd = InternalEvaluateConsoleExpression(
-                    seq, thread_id, frame_id, line, buffer_output=True)
-
-            elif console_command == 'EVALUATE_UNBUFFERED':
-                int_cmd = InternalEvaluateConsoleExpression(
-                    seq, thread_id, frame_id, line, buffer_output=False)
-
-            elif console_command == 'GET_COMPLETIONS':
-                int_cmd = InternalConsoleGetCompletions(seq, thread_id, frame_id, line)
-
-            else:
-                raise ValueError('Unrecognized command: %s' % (console_command,))
-
-            py_db.post_internal_command(int_cmd, thread_id)
-
-    def cmd_run_custom_operation(self, py_db, cmd_id, seq, text):
-        # Command which runs a custom operation
-        if text != "":
-            try:
-                location, custom = text.split('||', 1)
-            except:
-                sys.stderr.write('Custom operation now needs a || separator. Found: %s\n' % (text,))
-                raise
-
-            thread_id, frame_id, scopeattrs = location.split('\t', 2)
-
-            if scopeattrs.find('\t') != -1:  # there are attributes beyond scope
-                scope, attrs = scopeattrs.split('\t', 1)
-            else:
-                scope, attrs = (scopeattrs, None)
-
-            # : style: EXECFILE or EXEC
-            # : encoded_code_or_file: file to execute or code
-            # : fname: name of function to be executed in the resulting namespace
-            style, encoded_code_or_file, fnname = custom.split('\t', 3)
-            int_cmd = InternalRunCustomOperation(seq, thread_id, frame_id, scope, attrs,
-                                                 style, encoded_code_or_file, fnname)
-            py_db.post_internal_command(int_cmd, thread_id)
-
-    def cmd_ignore_thrown_exception_at(self, py_db, cmd_id, seq, text):
-        if text:
-            replace = 'REPLACE:'  # Not all 3.x versions support u'REPLACE:', so, doing workaround.
-            if text.startswith(replace):
-                text = text[8:]
-                py_db.filename_to_lines_where_exceptions_are_ignored.clear()
-
-            if text:
-                for line in text.split('||'):  # Can be bulk-created (one in each line)
-                    original_filename, line_number = line.split('|')
-                    original_filename = self.api.filename_to_server(original_filename)
-
-                    canonical_normalized_filename = pydevd_file_utils.canonical_normalized_path(original_filename)
-                    absolute_filename = pydevd_file_utils.absolute_path(original_filename)
-
-                    if os.path.exists(absolute_filename):
-                        lines_ignored = py_db.filename_to_lines_where_exceptions_are_ignored.get(canonical_normalized_filename)
-                        if lines_ignored is None:
-                            lines_ignored = py_db.filename_to_lines_where_exceptions_are_ignored[canonical_normalized_filename] = {}
-                        lines_ignored[int(line_number)] = 1
-                    else:
-                        sys.stderr.write('pydev debugger: warning: trying to ignore exception thrown'\
-                            ' on file that does not exist: %s (will have no effect)\n' % (absolute_filename,))
-
-    def cmd_enable_dont_trace(self, py_db, cmd_id, seq, text):
-        if text:
-            true_str = 'true'  # Not all 3.x versions support u'str', so, doing workaround.
-            mode = text.strip() == true_str
-            pydevd_dont_trace.trace_filter(mode)
-
-    def cmd_redirect_output(self, py_db, cmd_id, seq, text):
-        if text:
-            py_db.enable_output_redirection('STDOUT' in text, 'STDERR' in text)
-
-    def cmd_get_next_statement_targets(self, py_db, cmd_id, seq, text):
-        thread_id, frame_id = text.split('\t', 1)
-
-        py_db.post_method_as_internal_command(
-            thread_id, internal_get_next_statement_targets, seq, thread_id, frame_id)
-
-    def cmd_get_smart_step_into_variants(self, py_db, cmd_id, seq, text):
-        thread_id, frame_id, start_line, end_line = text.split('\t', 3)
-
-        py_db.post_method_as_internal_command(
-            thread_id, internal_get_smart_step_into_variants, seq, thread_id, frame_id, start_line, end_line, set_additional_thread_info=set_additional_thread_info)
-
-    def cmd_set_project_roots(self, py_db, cmd_id, seq, text):
-        self.api.set_project_roots(py_db, text.split(u'\t'))
-
-    def cmd_thread_dump_to_stderr(self, py_db, cmd_id, seq, text):
-        pydevd_utils.dump_threads()
-
-    def cmd_stop_on_start(self, py_db, cmd_id, seq, text):
-        if text.strip() in ('True', 'true', '1'):
-            self.api.stop_on_entry()
-
-    def cmd_pydevd_json_config(self, py_db, cmd_id, seq, text):
-        # Expected to receive a json string as:
-        # {
-        #     'skip_suspend_on_breakpoint_exception': [<exception names where we should suspend>],
-        #     'skip_print_breakpoint_exception': [<exception names where we should print>],
-        #     'multi_threads_single_notification': bool,
-        # }
-        msg = json.loads(text.strip())
-        if 'skip_suspend_on_breakpoint_exception' in msg:
-            py_db.skip_suspend_on_breakpoint_exception = tuple(
-                get_exception_class(x) for x in msg['skip_suspend_on_breakpoint_exception'])
-
-        if 'skip_print_breakpoint_exception' in msg:
-            py_db.skip_print_breakpoint_exception = tuple(
-                get_exception_class(x) for x in msg['skip_print_breakpoint_exception'])
-
-        if 'multi_threads_single_notification' in msg:
-            py_db.multi_threads_single_notification = msg['multi_threads_single_notification']
-
-    def cmd_get_exception_details(self, py_db, cmd_id, seq, text):
-        thread_id = text
-        t = pydevd_find_thread_by_id(thread_id)
-        frame = None
-        if t is not None and not getattr(t, 'pydev_do_not_trace', None):
-            additional_info = set_additional_thread_info(t)
-            frame = additional_info.get_topmost_frame(t)
-        try:
-            # Note: provide the return even if the thread is empty.
-            return py_db.cmd_factory.make_get_exception_details_message(py_db, seq, thread_id, frame)
-        finally:
-            frame = None
-            t = None
-
-
-process_net_command = _PyDevCommandProcessor().process_net_command
-
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/launcher/handlers.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/launcher/handlers.py
deleted file mode 100644
index 213a5b9d8dc0e134e64aa9e028606789f5ebf563..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/launcher/handlers.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License. See LICENSE in the project root
-# for license information.
-
-import os
-import sys
-
-import debugpy
-from debugpy import launcher
-from debugpy.common import json
-from debugpy.launcher import debuggee
-
-
-def launch_request(request):
-    debug_options = set(request("debugOptions", json.array(str)))
-
-    # Handling of properties that can also be specified as legacy "debugOptions" flags.
-    # If property is explicitly set to false, but the flag is in "debugOptions", treat
-    # it as an error. Returns None if the property wasn't explicitly set either way.
-    def property_or_debug_option(prop_name, flag_name):
-        assert prop_name[0].islower() and flag_name[0].isupper()
-
-        value = request(prop_name, bool, optional=True)
-        if value == ():
-            value = None
-
-        if flag_name in debug_options:
-            if value is False:
-                raise request.isnt_valid(
-                    '{0}:false and "debugOptions":[{1}] are mutually exclusive',
-                    json.repr(prop_name),
-                    json.repr(flag_name),
-                )
-            value = True
-
-        return value
-
-    python = request("python", json.array(str, size=(1,)))
-    cmdline = list(python)
-
-    if not request("noDebug", json.default(False)):
-        # see https://github.com/microsoft/debugpy/issues/861
-        if sys.version_info[:2] >= (3, 11):
-            cmdline += ["-X", "frozen_modules=off"]
-
-        port = request("port", int)
-        cmdline += [
-            os.path.dirname(debugpy.__file__),
-            "--connect",
-            launcher.adapter_host + ":" + str(port),
-        ]
-
-        if not request("subProcess", True):
-            cmdline += ["--configure-subProcess", "False"]
-
-        qt_mode = request(
-            "qt",
-            json.enum(
-                "none", "auto", "pyside", "pyside2", "pyqt4", "pyqt5", optional=True
-            ),
-        )
-        cmdline += ["--configure-qt", qt_mode]
-
-        adapter_access_token = request("adapterAccessToken", str, optional=True)
-        if adapter_access_token != ():
-            cmdline += ["--adapter-access-token", adapter_access_token]
-
-        debugpy_args = request("debugpyArgs", json.array(str))
-        cmdline += debugpy_args
-
-    # Use the copy of arguments that was propagated via the command line rather than
-    # "args" in the request itself, to allow for shell expansion.
-    cmdline += sys.argv[1:]
-
-    process_name = request("processName", sys.executable)
-
-    env = os.environ.copy()
-    env_changes = request("env", json.object((str, type(None))))
-    if sys.platform == "win32":
-        # Environment variables are case-insensitive on Win32, so we need to normalize
-        # both dicts to make sure that env vars specified in the debug configuration
-        # overwrite the global env vars correctly. If debug config has entries that
-        # differ in case only, that's an error.
-        env = {k.upper(): v for k, v in os.environ.items()}
-        new_env_changes = {}
-        for k, v in env_changes.items():
-            k_upper = k.upper()
-            if k_upper in new_env_changes:
-                if new_env_changes[k_upper] == v:
-                    continue
-                else:
-                    raise request.isnt_valid(
-                        'Found duplicate in "env": {0}.'.format(k_upper)
-                    )
-            new_env_changes[k_upper] = v
-        env_changes = new_env_changes
-    if "DEBUGPY_TEST" in env:
-        # If we're running as part of a debugpy test, make sure that codecov is not
-        # applied to the debuggee, since it will conflict with pydevd.
-        env.pop("COV_CORE_SOURCE", None)
-    env.update(env_changes)
-    env = {k: v for k, v in env.items() if v is not None}
-
-    if request("gevent", False):
-        env["GEVENT_SUPPORT"] = "True"
-
-    console = request(
-        "console",
-        json.enum(
-            "internalConsole", "integratedTerminal", "externalTerminal", optional=True
-        ),
-    )
-
-    redirect_output = property_or_debug_option("redirectOutput", "RedirectOutput")
-    if redirect_output is None:
-        # If neither the property nor the option were specified explicitly, choose
-        # the default depending on console type - "internalConsole" needs it to
-        # provide any output at all, but it's unnecessary for the terminals.
-        redirect_output = console == "internalConsole"
-    if redirect_output:
-        # sys.stdout buffering must be disabled - otherwise we won't see the output
-        # at all until the buffer fills up.
-        env["PYTHONUNBUFFERED"] = "1"
-        # Force UTF-8 output to minimize data loss due to re-encoding.
-        env["PYTHONIOENCODING"] = "utf-8"
-
-    if property_or_debug_option("waitOnNormalExit", "WaitOnNormalExit"):
-        if console == "internalConsole":
-            raise request.isnt_valid(
-                '"waitOnNormalExit" is not supported for "console":"internalConsole"'
-            )
-        debuggee.wait_on_exit_predicates.append(lambda code: code == 0)
-    if property_or_debug_option("waitOnAbnormalExit", "WaitOnAbnormalExit"):
-        if console == "internalConsole":
-            raise request.isnt_valid(
-                '"waitOnAbnormalExit" is not supported for "console":"internalConsole"'
-            )
-        debuggee.wait_on_exit_predicates.append(lambda code: code != 0)
-
-    debuggee.spawn(process_name, cmdline, env, redirect_output)
-    return {}
-
-
-def terminate_request(request):
-    del debuggee.wait_on_exit_predicates[:]
-    request.respond({})
-    debuggee.kill()
-
-
-def disconnect():
-    del debuggee.wait_on_exit_predicates[:]
-    debuggee.kill()
diff --git a/spaces/Superlang/ImageProcessor/annotator/leres/pix2pix/models/pix2pix4depth_model.py b/spaces/Superlang/ImageProcessor/annotator/leres/pix2pix/models/pix2pix4depth_model.py
deleted file mode 100644
index 89e89652feb96314973a050c5a2477b474630abb..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/leres/pix2pix/models/pix2pix4depth_model.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import torch
-from .base_model import BaseModel
-from . import networks
-
-
-class Pix2Pix4DepthModel(BaseModel):
-    """ This class implements the pix2pix model, for learning a mapping from input images to output images given paired data.
-
-    The model training requires '--dataset_mode aligned' dataset.
-    By default, it uses a '--netG unet256' U-Net generator,
-    a '--netD basic' discriminator (PatchGAN),
-    and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the orignal GAN paper).
-
-    pix2pix paper: https://arxiv.org/pdf/1611.07004.pdf
-    """
-    @staticmethod
-    def modify_commandline_options(parser, is_train=True):
-        """Add new dataset-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-
-        For pix2pix, we do not use image buffer
-        The training objective is: GAN Loss + lambda_L1 * ||G(A)-B||_1
-        By default, we use vanilla GAN loss, UNet with batchnorm, and aligned datasets.
-        """
-        # changing the default values to match the pix2pix paper (https://phillipi.github.io/pix2pix/)
-        parser.set_defaults(input_nc=2,output_nc=1,norm='none', netG='unet_1024', dataset_mode='depthmerge')
-        if is_train:
-            parser.set_defaults(pool_size=0, gan_mode='vanilla',)
-            parser.add_argument('--lambda_L1', type=float, default=1000, help='weight for L1 loss')
-        return parser
-
-    def __init__(self, opt):
-        """Initialize the pix2pix class.
-
-        Parameters:
-            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
-        """
-        BaseModel.__init__(self, opt)
-        # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
-
-        self.loss_names = ['G_GAN', 'G_L1', 'D_real', 'D_fake']
-        # self.loss_names = ['G_L1']
-
-        # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
-        if self.isTrain:
-            self.visual_names = ['outer','inner', 'fake_B', 'real_B']
-        else:
-            self.visual_names = ['fake_B']
-
-        # specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
-        if self.isTrain:
-            self.model_names = ['G','D']
-        else:  # during test time, only load G
-            self.model_names = ['G']
-
-        # define networks (both generator and discriminator)
-        self.netG = networks.define_G(opt.input_nc, opt.output_nc, 64, 'unet_1024', 'none',
-                                      False, 'normal', 0.02, self.gpu_ids)
-
-        if self.isTrain:  # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
-            self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
-                                          opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
-
-        if self.isTrain:
-            # define loss functions
-            self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
-            self.criterionL1 = torch.nn.L1Loss()
-            # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
-            self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=1e-4, betas=(opt.beta1, 0.999))
-            self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=2e-06, betas=(opt.beta1, 0.999))
-            self.optimizers.append(self.optimizer_G)
-            self.optimizers.append(self.optimizer_D)
-
-    def set_input_train(self, input):
-        self.outer = input['data_outer'].to(self.device)
-        self.outer = torch.nn.functional.interpolate(self.outer,(1024,1024),mode='bilinear',align_corners=False)
-
-        self.inner = input['data_inner'].to(self.device)
-        self.inner = torch.nn.functional.interpolate(self.inner,(1024,1024),mode='bilinear',align_corners=False)
-
-        self.image_paths = input['image_path']
-
-        if self.isTrain:
-            self.gtfake = input['data_gtfake'].to(self.device)
-            self.gtfake = torch.nn.functional.interpolate(self.gtfake, (1024, 1024), mode='bilinear', align_corners=False)
-            self.real_B = self.gtfake
-
-        self.real_A = torch.cat((self.outer, self.inner), 1)
-
-    def set_input(self, outer, inner):
-        inner = torch.from_numpy(inner).unsqueeze(0).unsqueeze(0)
-        outer = torch.from_numpy(outer).unsqueeze(0).unsqueeze(0)
-
-        inner = (inner - torch.min(inner))/(torch.max(inner)-torch.min(inner))
-        outer = (outer - torch.min(outer))/(torch.max(outer)-torch.min(outer))
-
-        inner = self.normalize(inner)
-        outer = self.normalize(outer)
-
-        self.real_A = torch.cat((outer, inner), 1).to(self.device)
-
-
-    def normalize(self, input):
-        input = input * 2
-        input = input - 1
-        return input
-
-    def forward(self):
-        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
-        self.fake_B = self.netG(self.real_A)  # G(A)
-
-    def backward_D(self):
-        """Calculate GAN loss for the discriminator"""
-        # Fake; stop backprop to the generator by detaching fake_B
-        fake_AB = torch.cat((self.real_A, self.fake_B), 1)  # we use conditional GANs; we need to feed both input and output to the discriminator
-        pred_fake = self.netD(fake_AB.detach())
-        self.loss_D_fake = self.criterionGAN(pred_fake, False)
-        # Real
-        real_AB = torch.cat((self.real_A, self.real_B), 1)
-        pred_real = self.netD(real_AB)
-        self.loss_D_real = self.criterionGAN(pred_real, True)
-        # combine loss and calculate gradients
-        self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
-        self.loss_D.backward()
-
-    def backward_G(self):
-        """Calculate GAN and L1 loss for the generator"""
-        # First, G(A) should fake the discriminator
-        fake_AB = torch.cat((self.real_A, self.fake_B), 1)
-        pred_fake = self.netD(fake_AB)
-        self.loss_G_GAN = self.criterionGAN(pred_fake, True)
-        # Second, G(A) = B
-        self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1
-        # combine loss and calculate gradients
-        self.loss_G = self.loss_G_L1 + self.loss_G_GAN
-        self.loss_G.backward()
-
-    def optimize_parameters(self):
-        self.forward()                   # compute fake images: G(A)
-        # update D
-        self.set_requires_grad(self.netD, True)  # enable backprop for D
-        self.optimizer_D.zero_grad()     # set D's gradients to zero
-        self.backward_D()                # calculate gradients for D
-        self.optimizer_D.step()          # update D's weights
-        # update G
-        self.set_requires_grad(self.netD, False)  # D requires no gradients when optimizing G
-        self.optimizer_G.zero_grad()        # set G's gradients to zero
-        self.backward_G()                   # calculate graidents for G
-        self.optimizer_G.step()             # udpate G's weights
\ No newline at end of file
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/log_buffer.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/log_buffer.py
deleted file mode 100644
index d949e2941c5400088c7cd8a1dc893d8b233ae785..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/log_buffer.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from collections import OrderedDict
-
-import numpy as np
-
-
-class LogBuffer:
-
-    def __init__(self):
-        self.val_history = OrderedDict()
-        self.n_history = OrderedDict()
-        self.output = OrderedDict()
-        self.ready = False
-
-    def clear(self):
-        self.val_history.clear()
-        self.n_history.clear()
-        self.clear_output()
-
-    def clear_output(self):
-        self.output.clear()
-        self.ready = False
-
-    def update(self, vars, count=1):
-        assert isinstance(vars, dict)
-        for key, var in vars.items():
-            if key not in self.val_history:
-                self.val_history[key] = []
-                self.n_history[key] = []
-            self.val_history[key].append(var)
-            self.n_history[key].append(count)
-
-    def average(self, n=0):
-        """Average latest n values or all values."""
-        assert n >= 0
-        for key in self.val_history:
-            values = np.array(self.val_history[key][-n:])
-            nums = np.array(self.n_history[key][-n:])
-            avg = np.sum(values * nums) / np.sum(nums)
-            self.output[key] = avg
-        self.ready = True
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/utils/parrots_jit.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/utils/parrots_jit.py
deleted file mode 100644
index 61873f6dbb9b10ed972c90aa8faa321e3cb3249e..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/utils/parrots_jit.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-
-from .parrots_wrapper import TORCH_VERSION
-
-parrots_jit_option = os.getenv('PARROTS_JIT_OPTION')
-
-if TORCH_VERSION == 'parrots' and parrots_jit_option == 'ON':
-    from parrots.jit import pat as jit
-else:
-
-    def jit(func=None,
-            check_input=None,
-            full_shape=True,
-            derivate=False,
-            coderize=False,
-            optimize=False):
-
-        def wrapper(func):
-
-            def wrapper_inner(*args, **kargs):
-                return func(*args, **kargs)
-
-            return wrapper_inner
-
-        if func is None:
-            return wrapper
-        else:
-            return func
-
-
-if TORCH_VERSION == 'parrots':
-    from parrots.utils.tester import skip_no_elena
-else:
-
-    def skip_no_elena(func):
-
-        def wrapper(*args, **kargs):
-            return func(*args, **kargs)
-
-        return wrapper
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/decode_heads/ocr_head.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/decode_heads/ocr_head.py
deleted file mode 100644
index 715852e94e81dc46623972748285d2d19237a341..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/decode_heads/ocr_head.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import HEADS
-from ..utils import SelfAttentionBlock as _SelfAttentionBlock
-from .cascade_decode_head import BaseCascadeDecodeHead
-
-
-class SpatialGatherModule(nn.Module):
-    """Aggregate the context features according to the initial predicted
-    probability distribution.
-
-    Employ the soft-weighted method to aggregate the context.
-    """
-
-    def __init__(self, scale):
-        super(SpatialGatherModule, self).__init__()
-        self.scale = scale
-
-    def forward(self, feats, probs):
-        """Forward function."""
-        batch_size, num_classes, height, width = probs.size()
-        channels = feats.size(1)
-        probs = probs.view(batch_size, num_classes, -1)
-        feats = feats.view(batch_size, channels, -1)
-        # [batch_size, height*width, num_classes]
-        feats = feats.permute(0, 2, 1)
-        # [batch_size, channels, height*width]
-        probs = F.softmax(self.scale * probs, dim=2)
-        # [batch_size, channels, num_classes]
-        ocr_context = torch.matmul(probs, feats)
-        ocr_context = ocr_context.permute(0, 2, 1).contiguous().unsqueeze(3)
-        return ocr_context
-
-
-class ObjectAttentionBlock(_SelfAttentionBlock):
-    """Make a OCR used SelfAttentionBlock."""
-
-    def __init__(self, in_channels, channels, scale, conv_cfg, norm_cfg,
-                 act_cfg):
-        if scale > 1:
-            query_downsample = nn.MaxPool2d(kernel_size=scale)
-        else:
-            query_downsample = None
-        super(ObjectAttentionBlock, self).__init__(
-            key_in_channels=in_channels,
-            query_in_channels=in_channels,
-            channels=channels,
-            out_channels=in_channels,
-            share_key_query=False,
-            query_downsample=query_downsample,
-            key_downsample=None,
-            key_query_num_convs=2,
-            key_query_norm=True,
-            value_out_num_convs=1,
-            value_out_norm=True,
-            matmul_norm=True,
-            with_out=True,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-        self.bottleneck = ConvModule(
-            in_channels * 2,
-            in_channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def forward(self, query_feats, key_feats):
-        """Forward function."""
-        context = super(ObjectAttentionBlock,
-                        self).forward(query_feats, key_feats)
-        output = self.bottleneck(torch.cat([context, query_feats], dim=1))
-        if self.query_downsample is not None:
-            output = resize(query_feats)
-
-        return output
-
-
-@HEADS.register_module()
-class OCRHead(BaseCascadeDecodeHead):
-    """Object-Contextual Representations for Semantic Segmentation.
-
-    This head is the implementation of `OCRNet
-    <https://arxiv.org/abs/1909.11065>`_.
-
-    Args:
-        ocr_channels (int): The intermediate channels of OCR block.
-        scale (int): The scale of probability map in SpatialGatherModule in
-            Default: 1.
-    """
-
-    def __init__(self, ocr_channels, scale=1, **kwargs):
-        super(OCRHead, self).__init__(**kwargs)
-        self.ocr_channels = ocr_channels
-        self.scale = scale
-        self.object_context_block = ObjectAttentionBlock(
-            self.channels,
-            self.ocr_channels,
-            self.scale,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        self.spatial_gather_module = SpatialGatherModule(self.scale)
-
-        self.bottleneck = ConvModule(
-            self.in_channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def forward(self, inputs, prev_output):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        feats = self.bottleneck(x)
-        context = self.spatial_gather_module(feats, prev_output)
-        object_context = self.object_context_block(feats, context)
-        output = self.cls_seg(object_context)
-
-        return output
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/cli/autocompletion.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/cli/autocompletion.py
deleted file mode 100644
index 226fe84dc0d0c4eb78f9b3c603df20cef0fdfda4..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/cli/autocompletion.py
+++ /dev/null
@@ -1,171 +0,0 @@
-"""Logic that powers autocompletion installed by ``pip completion``.
-"""
-
-import optparse
-import os
-import sys
-from itertools import chain
-from typing import Any, Iterable, List, Optional
-
-from pip._internal.cli.main_parser import create_main_parser
-from pip._internal.commands import commands_dict, create_command
-from pip._internal.metadata import get_default_environment
-
-
-def autocomplete() -> None:
-    """Entry Point for completion of main and subcommand options."""
-    # Don't complete if user hasn't sourced bash_completion file.
-    if "PIP_AUTO_COMPLETE" not in os.environ:
-        return
-    cwords = os.environ["COMP_WORDS"].split()[1:]
-    cword = int(os.environ["COMP_CWORD"])
-    try:
-        current = cwords[cword - 1]
-    except IndexError:
-        current = ""
-
-    parser = create_main_parser()
-    subcommands = list(commands_dict)
-    options = []
-
-    # subcommand
-    subcommand_name: Optional[str] = None
-    for word in cwords:
-        if word in subcommands:
-            subcommand_name = word
-            break
-    # subcommand options
-    if subcommand_name is not None:
-        # special case: 'help' subcommand has no options
-        if subcommand_name == "help":
-            sys.exit(1)
-        # special case: list locally installed dists for show and uninstall
-        should_list_installed = not current.startswith("-") and subcommand_name in [
-            "show",
-            "uninstall",
-        ]
-        if should_list_installed:
-            env = get_default_environment()
-            lc = current.lower()
-            installed = [
-                dist.canonical_name
-                for dist in env.iter_installed_distributions(local_only=True)
-                if dist.canonical_name.startswith(lc)
-                and dist.canonical_name not in cwords[1:]
-            ]
-            # if there are no dists installed, fall back to option completion
-            if installed:
-                for dist in installed:
-                    print(dist)
-                sys.exit(1)
-
-        should_list_installables = (
-            not current.startswith("-") and subcommand_name == "install"
-        )
-        if should_list_installables:
-            for path in auto_complete_paths(current, "path"):
-                print(path)
-            sys.exit(1)
-
-        subcommand = create_command(subcommand_name)
-
-        for opt in subcommand.parser.option_list_all:
-            if opt.help != optparse.SUPPRESS_HELP:
-                for opt_str in opt._long_opts + opt._short_opts:
-                    options.append((opt_str, opt.nargs))
-
-        # filter out previously specified options from available options
-        prev_opts = [x.split("=")[0] for x in cwords[1 : cword - 1]]
-        options = [(x, v) for (x, v) in options if x not in prev_opts]
-        # filter options by current input
-        options = [(k, v) for k, v in options if k.startswith(current)]
-        # get completion type given cwords and available subcommand options
-        completion_type = get_path_completion_type(
-            cwords,
-            cword,
-            subcommand.parser.option_list_all,
-        )
-        # get completion files and directories if ``completion_type`` is
-        # ``<file>``, ``<dir>`` or ``<path>``
-        if completion_type:
-            paths = auto_complete_paths(current, completion_type)
-            options = [(path, 0) for path in paths]
-        for option in options:
-            opt_label = option[0]
-            # append '=' to options which require args
-            if option[1] and option[0][:2] == "--":
-                opt_label += "="
-            print(opt_label)
-    else:
-        # show main parser options only when necessary
-
-        opts = [i.option_list for i in parser.option_groups]
-        opts.append(parser.option_list)
-        flattened_opts = chain.from_iterable(opts)
-        if current.startswith("-"):
-            for opt in flattened_opts:
-                if opt.help != optparse.SUPPRESS_HELP:
-                    subcommands += opt._long_opts + opt._short_opts
-        else:
-            # get completion type given cwords and all available options
-            completion_type = get_path_completion_type(cwords, cword, flattened_opts)
-            if completion_type:
-                subcommands = list(auto_complete_paths(current, completion_type))
-
-        print(" ".join([x for x in subcommands if x.startswith(current)]))
-    sys.exit(1)
-
-
-def get_path_completion_type(
-    cwords: List[str], cword: int, opts: Iterable[Any]
-) -> Optional[str]:
-    """Get the type of path completion (``file``, ``dir``, ``path`` or None)
-
-    :param cwords: same as the environmental variable ``COMP_WORDS``
-    :param cword: same as the environmental variable ``COMP_CWORD``
-    :param opts: The available options to check
-    :return: path completion type (``file``, ``dir``, ``path`` or None)
-    """
-    if cword < 2 or not cwords[cword - 2].startswith("-"):
-        return None
-    for opt in opts:
-        if opt.help == optparse.SUPPRESS_HELP:
-            continue
-        for o in str(opt).split("/"):
-            if cwords[cword - 2].split("=")[0] == o:
-                if not opt.metavar or any(
-                    x in ("path", "file", "dir") for x in opt.metavar.split("/")
-                ):
-                    return opt.metavar
-    return None
-
-
-def auto_complete_paths(current: str, completion_type: str) -> Iterable[str]:
-    """If ``completion_type`` is ``file`` or ``path``, list all regular files
-    and directories starting with ``current``; otherwise only list directories
-    starting with ``current``.
-
-    :param current: The word to be completed
-    :param completion_type: path completion type(``file``, ``path`` or ``dir``)
-    :return: A generator of regular files and/or directories
-    """
-    directory, filename = os.path.split(current)
-    current_path = os.path.abspath(directory)
-    # Don't complete paths if they can't be accessed
-    if not os.access(current_path, os.R_OK):
-        return
-    filename = os.path.normcase(filename)
-    # list all files that start with ``filename``
-    file_list = (
-        x for x in os.listdir(current_path) if os.path.normcase(x).startswith(filename)
-    )
-    for f in file_list:
-        opt = os.path.join(current_path, f)
-        comp_file = os.path.normcase(os.path.join(directory, f))
-        # complete regular files when there is not ``<dir>`` after option
-        # complete directories when there is ``<file>``, ``<path>`` or
-        # ``<dir>``after option
-        if completion_type != "dir" and os.path.isfile(opt):
-            yield comp_file
-        elif os.path.isdir(opt):
-            yield os.path.join(comp_file, "")
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/vcs/subversion.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/vcs/subversion.py
deleted file mode 100644
index 16d93a67b7b6feed66f2cc432f6250ca3ad34914..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/vcs/subversion.py
+++ /dev/null
@@ -1,324 +0,0 @@
-import logging
-import os
-import re
-from typing import List, Optional, Tuple
-
-from pip._internal.utils.misc import (
-    HiddenText,
-    display_path,
-    is_console_interactive,
-    is_installable_dir,
-    split_auth_from_netloc,
-)
-from pip._internal.utils.subprocess import CommandArgs, make_command
-from pip._internal.vcs.versioncontrol import (
-    AuthInfo,
-    RemoteNotFoundError,
-    RevOptions,
-    VersionControl,
-    vcs,
-)
-
-logger = logging.getLogger(__name__)
-
-_svn_xml_url_re = re.compile('url="([^"]+)"')
-_svn_rev_re = re.compile(r'committed-rev="(\d+)"')
-_svn_info_xml_rev_re = re.compile(r'\s*revision="(\d+)"')
-_svn_info_xml_url_re = re.compile(r"<url>(.*)</url>")
-
-
-class Subversion(VersionControl):
-    name = "svn"
-    dirname = ".svn"
-    repo_name = "checkout"
-    schemes = ("svn+ssh", "svn+http", "svn+https", "svn+svn", "svn+file")
-
-    @classmethod
-    def should_add_vcs_url_prefix(cls, remote_url: str) -> bool:
-        return True
-
-    @staticmethod
-    def get_base_rev_args(rev: str) -> List[str]:
-        return ["-r", rev]
-
-    @classmethod
-    def get_revision(cls, location: str) -> str:
-        """
-        Return the maximum revision for all files under a given location
-        """
-        # Note: taken from setuptools.command.egg_info
-        revision = 0
-
-        for base, dirs, _ in os.walk(location):
-            if cls.dirname not in dirs:
-                dirs[:] = []
-                continue  # no sense walking uncontrolled subdirs
-            dirs.remove(cls.dirname)
-            entries_fn = os.path.join(base, cls.dirname, "entries")
-            if not os.path.exists(entries_fn):
-                # FIXME: should we warn?
-                continue
-
-            dirurl, localrev = cls._get_svn_url_rev(base)
-
-            if base == location:
-                assert dirurl is not None
-                base = dirurl + "/"  # save the root url
-            elif not dirurl or not dirurl.startswith(base):
-                dirs[:] = []
-                continue  # not part of the same svn tree, skip it
-            revision = max(revision, localrev)
-        return str(revision)
-
-    @classmethod
-    def get_netloc_and_auth(
-        cls, netloc: str, scheme: str
-    ) -> Tuple[str, Tuple[Optional[str], Optional[str]]]:
-        """
-        This override allows the auth information to be passed to svn via the
-        --username and --password options instead of via the URL.
-        """
-        if scheme == "ssh":
-            # The --username and --password options can't be used for
-            # svn+ssh URLs, so keep the auth information in the URL.
-            return super().get_netloc_and_auth(netloc, scheme)
-
-        return split_auth_from_netloc(netloc)
-
-    @classmethod
-    def get_url_rev_and_auth(cls, url: str) -> Tuple[str, Optional[str], AuthInfo]:
-        # hotfix the URL scheme after removing svn+ from svn+ssh:// re-add it
-        url, rev, user_pass = super().get_url_rev_and_auth(url)
-        if url.startswith("ssh://"):
-            url = "svn+" + url
-        return url, rev, user_pass
-
-    @staticmethod
-    def make_rev_args(
-        username: Optional[str], password: Optional[HiddenText]
-    ) -> CommandArgs:
-        extra_args: CommandArgs = []
-        if username:
-            extra_args += ["--username", username]
-        if password:
-            extra_args += ["--password", password]
-
-        return extra_args
-
-    @classmethod
-    def get_remote_url(cls, location: str) -> str:
-        # In cases where the source is in a subdirectory, we have to look up in
-        # the location until we find a valid 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 Python project for directory %s (tried all "
-                    "parent directories)",
-                    orig_location,
-                )
-                raise RemoteNotFoundError
-
-        url, _rev = cls._get_svn_url_rev(location)
-        if url is None:
-            raise RemoteNotFoundError
-
-        return url
-
-    @classmethod
-    def _get_svn_url_rev(cls, location: str) -> Tuple[Optional[str], int]:
-        from pip._internal.exceptions import InstallationError
-
-        entries_path = os.path.join(location, cls.dirname, "entries")
-        if os.path.exists(entries_path):
-            with open(entries_path) as f:
-                data = f.read()
-        else:  # subversion >= 1.7 does not have the 'entries' file
-            data = ""
-
-        url = None
-        if data.startswith("8") or data.startswith("9") or data.startswith("10"):
-            entries = list(map(str.splitlines, data.split("\n\x0c\n")))
-            del entries[0][0]  # get rid of the '8'
-            url = entries[0][3]
-            revs = [int(d[9]) for d in entries if len(d) > 9 and d[9]] + [0]
-        elif data.startswith("<?xml"):
-            match = _svn_xml_url_re.search(data)
-            if not match:
-                raise ValueError(f"Badly formatted data: {data!r}")
-            url = match.group(1)  # get repository URL
-            revs = [int(m.group(1)) for m in _svn_rev_re.finditer(data)] + [0]
-        else:
-            try:
-                # subversion >= 1.7
-                # Note that using get_remote_call_options is not necessary here
-                # because `svn info` is being run against a local directory.
-                # We don't need to worry about making sure interactive mode
-                # is being used to prompt for passwords, because passwords
-                # are only potentially needed for remote server requests.
-                xml = cls.run_command(
-                    ["info", "--xml", location],
-                    show_stdout=False,
-                    stdout_only=True,
-                )
-                match = _svn_info_xml_url_re.search(xml)
-                assert match is not None
-                url = match.group(1)
-                revs = [int(m.group(1)) for m in _svn_info_xml_rev_re.finditer(xml)]
-            except InstallationError:
-                url, revs = None, []
-
-        if revs:
-            rev = max(revs)
-        else:
-            rev = 0
-
-        return url, rev
-
-    @classmethod
-    def is_commit_id_equal(cls, dest: str, name: Optional[str]) -> bool:
-        """Always assume the versions don't match"""
-        return False
-
-    def __init__(self, use_interactive: Optional[bool] = None) -> None:
-        if use_interactive is None:
-            use_interactive = is_console_interactive()
-        self.use_interactive = use_interactive
-
-        # This member is used to cache the fetched version of the current
-        # ``svn`` client.
-        # Special value definitions:
-        #   None: Not evaluated yet.
-        #   Empty tuple: Could not parse version.
-        self._vcs_version: Optional[Tuple[int, ...]] = None
-
-        super().__init__()
-
-    def call_vcs_version(self) -> Tuple[int, ...]:
-        """Query the version of the currently installed Subversion client.
-
-        :return: A tuple containing the parts of the version information or
-            ``()`` if the version returned from ``svn`` could not be parsed.
-        :raises: BadCommand: If ``svn`` is not installed.
-        """
-        # Example versions:
-        #   svn, version 1.10.3 (r1842928)
-        #      compiled Feb 25 2019, 14:20:39 on x86_64-apple-darwin17.0.0
-        #   svn, version 1.7.14 (r1542130)
-        #      compiled Mar 28 2018, 08:49:13 on x86_64-pc-linux-gnu
-        #   svn, version 1.12.0-SlikSvn (SlikSvn/1.12.0)
-        #      compiled May 28 2019, 13:44:56 on x86_64-microsoft-windows6.2
-        version_prefix = "svn, version "
-        version = self.run_command(["--version"], show_stdout=False, stdout_only=True)
-        if not version.startswith(version_prefix):
-            return ()
-
-        version = version[len(version_prefix) :].split()[0]
-        version_list = version.partition("-")[0].split(".")
-        try:
-            parsed_version = tuple(map(int, version_list))
-        except ValueError:
-            return ()
-
-        return parsed_version
-
-    def get_vcs_version(self) -> Tuple[int, ...]:
-        """Return the version of the currently installed Subversion client.
-
-        If the version of the Subversion client has already been queried,
-        a cached value will be used.
-
-        :return: A tuple containing the parts of the version information or
-            ``()`` if the version returned from ``svn`` could not be parsed.
-        :raises: BadCommand: If ``svn`` is not installed.
-        """
-        if self._vcs_version is not None:
-            # Use cached version, if available.
-            # If parsing the version failed previously (empty tuple),
-            # do not attempt to parse it again.
-            return self._vcs_version
-
-        vcs_version = self.call_vcs_version()
-        self._vcs_version = vcs_version
-        return vcs_version
-
-    def get_remote_call_options(self) -> CommandArgs:
-        """Return options to be used on calls to Subversion that contact the server.
-
-        These options are applicable for the following ``svn`` subcommands used
-        in this class.
-
-            - checkout
-            - switch
-            - update
-
-        :return: A list of command line arguments to pass to ``svn``.
-        """
-        if not self.use_interactive:
-            # --non-interactive switch is available since Subversion 0.14.4.
-            # Subversion < 1.8 runs in interactive mode by default.
-            return ["--non-interactive"]
-
-        svn_version = self.get_vcs_version()
-        # By default, Subversion >= 1.8 runs in non-interactive mode if
-        # stdin is not a TTY. Since that is how pip invokes SVN, in
-        # call_subprocess(), pip must pass --force-interactive to ensure
-        # the user can be prompted for a password, if required.
-        #   SVN added the --force-interactive option in SVN 1.8. Since
-        # e.g. RHEL/CentOS 7, which is supported until 2024, ships with
-        # SVN 1.7, pip should continue to support SVN 1.7. Therefore, pip
-        # can't safely add the option if the SVN version is < 1.8 (or unknown).
-        if svn_version >= (1, 8):
-            return ["--force-interactive"]
-
-        return []
-
-    def fetch_new(
-        self, dest: str, url: HiddenText, rev_options: RevOptions, verbosity: int
-    ) -> None:
-        rev_display = rev_options.to_display()
-        logger.info(
-            "Checking out %s%s to %s",
-            url,
-            rev_display,
-            display_path(dest),
-        )
-        if verbosity <= 0:
-            flag = "--quiet"
-        else:
-            flag = ""
-        cmd_args = make_command(
-            "checkout",
-            flag,
-            self.get_remote_call_options(),
-            rev_options.to_args(),
-            url,
-            dest,
-        )
-        self.run_command(cmd_args)
-
-    def switch(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
-        cmd_args = make_command(
-            "switch",
-            self.get_remote_call_options(),
-            rev_options.to_args(),
-            url,
-            dest,
-        )
-        self.run_command(cmd_args)
-
-    def update(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
-        cmd_args = make_command(
-            "update",
-            self.get_remote_call_options(),
-            rev_options.to_args(),
-            dest,
-        )
-        self.run_command(cmd_args)
-
-
-vcs.register(Subversion)
diff --git a/spaces/TempoFunk/makeavid-sd-jax/makeavid_sd/flax_impl/flax_unet_pseudo3d_condition.py b/spaces/TempoFunk/makeavid-sd-jax/makeavid_sd/flax_impl/flax_unet_pseudo3d_condition.py
deleted file mode 100644
index 1f3ec35a4507d88834cd6f8be0d9a623502a6025..0000000000000000000000000000000000000000
--- a/spaces/TempoFunk/makeavid-sd-jax/makeavid_sd/flax_impl/flax_unet_pseudo3d_condition.py
+++ /dev/null
@@ -1,251 +0,0 @@
-
-from typing import Tuple, Union
-
-import jax
-import jax.numpy as jnp
-import flax.linen as nn
-from flax.core.frozen_dict import FrozenDict
-
-from diffusers.configuration_utils import ConfigMixin, flax_register_to_config
-from diffusers.models.modeling_flax_utils import FlaxModelMixin
-from diffusers.utils import BaseOutput
-
-from .flax_unet_pseudo3d_blocks import (
-        CrossAttnDownBlockPseudo3D,
-        CrossAttnUpBlockPseudo3D,
-        DownBlockPseudo3D,
-        UpBlockPseudo3D,
-        UNetMidBlockPseudo3DCrossAttn
-)
-#from flax_embeddings import (
-#        TimestepEmbedding,
-#        Timesteps
-#)
-from diffusers.models.embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
-from .flax_resnet_pseudo3d import ConvPseudo3D
-
-
-class UNetPseudo3DConditionOutput(BaseOutput):
-    sample: jax.Array
-
-
-@flax_register_to_config
-class UNetPseudo3DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
-    sample_size: Union[int, Tuple[int, int]] = (64, 64)
-    in_channels: int = 4
-    out_channels: int = 4
-    down_block_types: Tuple[str] = (
-            "CrossAttnDownBlockPseudo3D",
-            "CrossAttnDownBlockPseudo3D",
-            "CrossAttnDownBlockPseudo3D",
-            "DownBlockPseudo3D"
-    )
-    up_block_types: Tuple[str] = (
-            "UpBlockPseudo3D",
-            "CrossAttnUpBlockPseudo3D",
-            "CrossAttnUpBlockPseudo3D",
-            "CrossAttnUpBlockPseudo3D"
-    )
-    block_out_channels: Tuple[int] = (
-            320,
-            640,
-            1280,
-            1280
-    )
-    layers_per_block: int = 2
-    attention_head_dim: Union[int, Tuple[int]] = 8
-    cross_attention_dim: int = 768
-    flip_sin_to_cos: bool = True
-    freq_shift: int = 0
-    use_memory_efficient_attention: bool = False
-    dtype: jnp.dtype = jnp.float32
-    param_dtype: str = 'float32'
-
-    def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict:
-        if self.param_dtype == 'bfloat16':
-            param_dtype = jnp.bfloat16
-        elif self.param_dtype == 'float16':
-            param_dtype = jnp.float16
-        elif self.param_dtype == 'float32':
-            param_dtype = jnp.float32
-        else:
-            raise ValueError(f'unknown parameter type: {self.param_dtype}')
-        sample_size = self.sample_size
-        if isinstance(sample_size, int):
-            sample_size = (sample_size, sample_size)
-        sample_shape = (1, self.in_channels, 1, *sample_size)
-        sample = jnp.zeros(sample_shape, dtype = param_dtype)
-        timesteps = jnp.ones((1, ), dtype = jnp.int32)
-        encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype = param_dtype)
-        params_rng, dropout_rng = jax.random.split(rng)
-        rngs = { "params": params_rng, "dropout": dropout_rng }
-        return self.init(rngs, sample, timesteps, encoder_hidden_states)["params"]
-
-    def setup(self) -> None:
-        if isinstance(self.attention_head_dim, int):
-            attention_head_dim = (self.attention_head_dim, ) * len(self.down_block_types)
-        else:
-            attention_head_dim = self.attention_head_dim
-        time_embed_dim = self.block_out_channels[0] * 4
-        self.conv_in = ConvPseudo3D(
-                features = self.block_out_channels[0],
-                kernel_size = (3, 3),
-                strides = (1, 1),
-                padding = ((1, 1), (1, 1)),
-                dtype = self.dtype
-        )
-        self.time_proj = FlaxTimesteps(
-                dim = self.block_out_channels[0],
-                flip_sin_to_cos = self.flip_sin_to_cos,
-                freq_shift = self.freq_shift
-        )
-        self.time_embedding = FlaxTimestepEmbedding(
-                time_embed_dim = time_embed_dim,
-                dtype = self.dtype
-        )
-        down_blocks = []
-        output_channels = self.block_out_channels[0]
-        for i, down_block_type in enumerate(self.down_block_types):
-            input_channels = output_channels
-            output_channels = self.block_out_channels[i]
-            is_final_block = i == len(self.block_out_channels) - 1
-            # allows loading 3d models with old layer type names in their configs
-            # eg. 2D instead of Pseudo3D, like lxj's timelapse model
-            if down_block_type in ['CrossAttnDownBlockPseudo3D', 'CrossAttnDownBlock2D']:
-                down_block = CrossAttnDownBlockPseudo3D(
-                        in_channels = input_channels,
-                        out_channels = output_channels,
-                        num_layers = self.layers_per_block,
-                        attn_num_head_channels = attention_head_dim[i],
-                        add_downsample = not is_final_block,
-                        use_memory_efficient_attention = self.use_memory_efficient_attention,
-                        dtype = self.dtype
-                )
-            elif down_block_type in ['DownBlockPseudo3D', 'DownBlock2D']:
-                down_block = DownBlockPseudo3D(
-                        in_channels = input_channels,
-                        out_channels = output_channels,
-                        num_layers = self.layers_per_block,
-                        add_downsample = not is_final_block,
-                        dtype = self.dtype
-                )
-            else:
-                raise NotImplementedError(f'Unimplemented down block type: {down_block_type}')
-            down_blocks.append(down_block)
-        self.down_blocks = down_blocks
-        self.mid_block = UNetMidBlockPseudo3DCrossAttn(
-                in_channels = self.block_out_channels[-1],
-                attn_num_head_channels = attention_head_dim[-1],
-                use_memory_efficient_attention = self.use_memory_efficient_attention,
-                dtype = self.dtype
-        )
-        up_blocks = []
-        reversed_block_out_channels = list(reversed(self.block_out_channels))
-        reversed_attention_head_dim = list(reversed(attention_head_dim))
-        output_channels = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(self.up_block_types):
-            prev_output_channels = output_channels
-            output_channels = reversed_block_out_channels[i]
-            input_channels = reversed_block_out_channels[min(i + 1, len(self.block_out_channels) - 1)]
-            is_final_block = i == len(self.block_out_channels) - 1
-            if up_block_type in ['CrossAttnUpBlockPseudo3D', 'CrossAttnUpBlock2D']:
-                up_block = CrossAttnUpBlockPseudo3D(
-                        in_channels = input_channels,
-                        out_channels = output_channels,
-                        prev_output_channels = prev_output_channels,
-                        num_layers = self.layers_per_block + 1,
-                        attn_num_head_channels = reversed_attention_head_dim[i],
-                        add_upsample = not is_final_block,
-                        use_memory_efficient_attention = self.use_memory_efficient_attention,
-                        dtype = self.dtype
-                )
-            elif up_block_type in ['UpBlockPseudo3D', 'UpBlock2D']:
-                up_block = UpBlockPseudo3D(
-                        in_channels = input_channels,
-                        out_channels = output_channels,
-                        prev_output_channels = prev_output_channels,
-                        num_layers = self.layers_per_block + 1,
-                        add_upsample = not is_final_block,
-                        dtype = self.dtype
-                )
-            else:
-                raise NotImplementedError(f'Unimplemented up block type: {up_block_type}')
-            up_blocks.append(up_block)
-        self.up_blocks = up_blocks
-        self.conv_norm_out = nn.GroupNorm(
-                num_groups = 32,
-                epsilon = 1e-5
-        )
-        self.conv_out = ConvPseudo3D(
-                features = self.out_channels,
-                kernel_size = (3, 3),
-                strides = (1, 1),
-                padding = ((1, 1), (1, 1)),
-                dtype = self.dtype
-        )
-
-    def __call__(self,
-            sample: jax.Array,
-            timesteps: jax.Array,
-            encoder_hidden_states: jax.Array,
-            return_dict: bool = True
-    ) -> Union[UNetPseudo3DConditionOutput, Tuple[jax.Array]]:
-        if timesteps.dtype != jnp.float32:
-            timesteps = timesteps.astype(dtype = jnp.float32)
-        if len(timesteps.shape) == 0:
-            timesteps = jnp.expand_dims(timesteps, 0)
-        # b,c,f,h,w -> b,f,h,w,c
-        sample = sample.transpose((0, 2, 3, 4, 1))
-
-        t_emb = self.time_proj(timesteps)
-        t_emb = self.time_embedding(t_emb)
-        sample = self.conv_in(sample)
-        down_block_res_samples = (sample, )
-        for down_block in self.down_blocks:
-            if isinstance(down_block, CrossAttnDownBlockPseudo3D):
-                sample, res_samples = down_block(
-                        hidden_states = sample,
-                        temb = t_emb,
-                        encoder_hidden_states = encoder_hidden_states
-                )
-            elif isinstance(down_block, DownBlockPseudo3D):
-                sample, res_samples = down_block(
-                        hidden_states = sample,
-                        temb = t_emb
-                )
-            else:
-                raise NotImplementedError(f'Unimplemented down block type: {down_block.__class__.__name__}')
-            down_block_res_samples += res_samples
-        sample = self.mid_block(
-                hidden_states = sample,
-                temb = t_emb,
-                encoder_hidden_states = encoder_hidden_states
-        )
-        for up_block in self.up_blocks:
-            res_samples = down_block_res_samples[-(self.layers_per_block + 1):]
-            down_block_res_samples = down_block_res_samples[:-(self.layers_per_block + 1)]
-            if isinstance(up_block, CrossAttnUpBlockPseudo3D):
-                sample = up_block(
-                        hidden_states = sample,
-                        temb = t_emb,
-                        encoder_hidden_states = encoder_hidden_states,
-                        res_hidden_states_tuple = res_samples
-                )
-            elif isinstance(up_block, UpBlockPseudo3D):
-                sample = up_block(
-                        hidden_states = sample,
-                        temb = t_emb,
-                        res_hidden_states_tuple = res_samples
-                )
-            else:
-                raise NotImplementedError(f'Unimplemented up block type: {up_block.__class__.__name__}')
-        sample = self.conv_norm_out(sample)
-        sample = nn.silu(sample)
-        sample = self.conv_out(sample)
-
-        # b,f,h,w,c -> b,c,f,h,w
-        sample = sample.transpose((0, 4, 1, 2, 3))
-        if not return_dict:
-            return (sample, )
-        return UNetPseudo3DConditionOutput(sample = sample)
-
diff --git a/spaces/ThirdEyeData/Component_Repair_Time_Prediction/app.py b/spaces/ThirdEyeData/Component_Repair_Time_Prediction/app.py
deleted file mode 100644
index 5004bf96f95e1d594a5234652af774b5cc06a7b7..0000000000000000000000000000000000000000
--- a/spaces/ThirdEyeData/Component_Repair_Time_Prediction/app.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import tensorflow as tf
-from tensorflow import keras
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn import preprocessing
-import seaborn as sns
-from sklearn.preprocessing import LabelEncoder
-import pickle
-import streamlit as st 
-
-st.title('Repair Time Prediction')
-#DLoading the ataset
-#df = pd.read_csv('repair_time_sample_50k_modified2.csv')
-
-#new_data = df
-#df.drop(['SRU serial number','Date of Manufacture', 'Snag Description'], axis = 1, inplace=True)
-
-
-# DATA from user
-def user_report():
-  Aircraft_Type = st.sidebar.selectbox('Aircraft Type',("AH-64","UH-60","UH-63","UH-62","UH-61","AH-65"))
-  if Aircraft_Type=="AH-64":
-      Aircraft_Type=0
-  elif Aircraft_Type=="UH-60":
-      Aircraft_Type=2
-  elif Aircraft_Type=="UH-63":
-      Aircraft_Type=5
-  elif Aircraft_Type=="UH-62":
-      Aircraft_Type=4
-  elif Aircraft_Type=="UH-61":
-      Aircraft_Type=3
-  else:
-      Aircraft_Type=1
-  manufacturer = st.sidebar.selectbox("Manufacturer",
-        ("JKL Company", "GHI Company","AGS Company","ABC Company","XYZ Company" ))
-  if manufacturer=='JKL Company':
-      manufacturer=3
-  elif manufacturer=="GHI Company":
-      manufacturer=2
-  elif manufacturer=="AGS Company":
-      manufacturer=1
-  elif manufacturer=="ABC Company":
-      manufacturer =0
-  else:
-      manufacturer=4
-  component_age = st.sidebar.slider('Component Age (in hours)', 500,2000, 600 )
-  Issue_category= st.sidebar.selectbox("Issue Category",
-        ("Display", "Unservicable","Bootup Problem","Engine Failure","Electrical Fault" ))
-  if Issue_category=='Display':
-      Issue_category=1
-  elif Issue_category=="Unservicable":
-      Issue_category=4
-  elif Issue_category=="Bootup Problem":
-      Issue_category=0
-  elif Issue_category=="Engine Failure":
-      Issue_category=3
-  else:
-      Issue_category=2
-  Snag_Severity	= st.sidebar.selectbox("Snag Severity",
-        ("Low", "Medium","High" ))
-  if Snag_Severity	=='Low':
-      Snag_Severity=1
-  elif Snag_Severity=="Medium":
-      Snag_Severity	=2
-  else:
-       Snag_Severity=0
-  Customer= st.sidebar.selectbox("Customer",
-        ("IAF", "ARMY","NAVY" ))
-  if Customer	=='IAF':
-      Customer=1
-  elif Customer=="ARMY":
-      Customer	=0
-  else:
-       Customer=2
-  Technician_Skill_level= st.sidebar.selectbox("Technician Skill level",
-        ("Expert", "Intermediate","Novice" ))
-  if Technician_Skill_level	=='Expert':
-      Technician_Skill_level=0
-  elif Technician_Skill_level=="Intermediate":
-      Technician_Skill_level	=1
-  else:
-       Technician_Skill_level=2
-  prior_maintainence = st.sidebar.selectbox('Prior Maintainence',("Regular","Irregular"))
-  if prior_maintainence =='Regular':
-      prior_maintainence=1
-  else:
-      prior_maintainence=0
-  Logistics_Time = st.sidebar.slider('Logistics Time (hr)', 2,21, 5 )
-  total_operating_hours = st.sidebar.slider('Total Operating Hours)', 50,2000, 500 )
-  operating_temperature = st.sidebar.slider('Operating Temperature', 10,25, 15 )
-  previous_number_of_repairs = st.sidebar.number_input('Enter the Previous Number of Repairs Undergone  0 to 3 )',min_value=0,max_value=3,step=1)
-  Power_Input_Voltage= st.sidebar.slider('Power Input Voltage (V)',100,133,115)  
-
-
-  user_report_data = {
-      'Aircraft Type':Aircraft_Type,
-      'Manufacturer':manufacturer,
-      'Component_Age':component_age,
-      'Issue_category':Issue_category,
-      'Snag Severity': Snag_Severity,
-      'Customer':Customer,
-      'Technician Skill level':Technician_Skill_level,
-      'Prior Maintenance': prior_maintainence,
-      'Logistics Time (hr)':Logistics_Time,
-      'total_operating_hours':total_operating_hours,
-      'operating_temperature':operating_temperature,
-      'previous_number_of_repairs':previous_number_of_repairs,
-      'Power_Input_Voltage':Power_Input_Voltage
-           
-  }
-  report_data = pd.DataFrame(user_report_data, index=[0])
-  return report_data    
-    
-#Customer Data
-user_data = user_report()
-st.header("Component Details")
-st.write(user_data)
-
-def preprocess_dataset(X):  
-  x = X.values #returns a numpy array
-  min_max_scaler = preprocessing.MinMaxScaler()
-  x_scaled = min_max_scaler.fit_transform(x)
-  X_df = pd.DataFrame(x_scaled)
-  return X_df
-
-def label_encoding(data):
-  le = LabelEncoder()
-  cat = data.select_dtypes(include='O').keys()
-  categ = list(cat)
-  data[categ] = data[categ].apply(le.fit_transform)
-  # X = data.loc[:,data.columns!= "Time required for repair (in hours)"]
-  # y = data['Time required for repair (in hours)']
-  # return X,y
-  return data
-
-def prediction(df):
-  #X = df.loc[:,df.columns!= "Time required for repair (in hours)"]
-  #y = df['Time required for repair (in hours)']
-  #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
-  #print(X_train.shape)
-  #print(X_test.shape)
-  #X_test_encoded = label_encoding(df)
-  #X_test_df = preprocess_dataset(df)
-  x_model = pickle.load(open('repair_time_model.pkl','rb'))
-  pred = x_model.predict(df)
-  #X_test['Actual_time_to_repair'] = y_test
-  #X_test['Predicted_time_to_repair'] = pred
-  #X_test.to_csv(r'/content/drive/MyDrive/Colab Notebooks/HAL/repair_time_prediction_results.csv')
-  #print(X_test.head())
-  return pred
-    
-y_pred = prediction(user_data)
-
-if st.button("Predict"):
-    st.subheader(f"Time required to Repair the Component is {y_pred[0]} hours")
\ No newline at end of file
diff --git a/spaces/Timjo88/toy-board-game-QA/app.py b/spaces/Timjo88/toy-board-game-QA/app.py
deleted file mode 100644
index 384717d07564e8cfb55ba39ea63c2bfb60274ad7..0000000000000000000000000000000000000000
--- a/spaces/Timjo88/toy-board-game-QA/app.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-title = 'Toy Question Answering App v0.1'
-context = "Rock Paper scissors can be played between two or more people. To begin, choose your opponent and stand or sit across from them.Both players close one hand into a fist and shake it while counting down. While counting down, players say, 'Rock, Paper, Scissors, Shoot' or 'Three, Two, One, Shoot!' on Shoot, everyone makes one of the three different hand signals/To make a rock, close your hand into a fist. To make paper, hold your hand out flat, with your palm facing downward.To make scissors, hold out your first two fingers in a V-shape. In Rock Paper Scissors, rock beats scissors by crushing them. Scissors beat paper by cutting the paper in two. paper beats rock by covering the paper. Players can play one game, best out of three games or as many games as they want!"
-question1 = "How many players are there?"
-question2 = "Does Rock beat paper?"
-question3 = "Do scissors beat paper?"
-
-model_name = "deepset/roberta-base-squad2"
-
-question_answerer = pipeline("question-answering", model=model_name, tokenizer=model_name)
-
-interface = gr.Interface.from_pipeline(question_answerer,
-    title = title,
-    theme = "peach",
-    examples = [[context, question1],[context, question2],[context, question3]]).launch()
\ No newline at end of file
diff --git a/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI/utils.py b/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI/utils.py
deleted file mode 100644
index 75d597b779e0d3cac6cb4adc8ee77cffb95d3981..0000000000000000000000000000000000000000
--- a/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI/utils.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from __future__ import annotations
-
-import pathlib
-
-
-def find_exp_dirs() -> list[str]:
-    repo_dir = pathlib.Path(__file__).parent
-    exp_root_dir = repo_dir / "experiments"
-    if not exp_root_dir.exists():
-        return []
-    exp_dirs = sorted(exp_root_dir.glob("*"))
-    exp_dirs = [exp_dir for exp_dir in exp_dirs if (exp_dir / "model_index.json").exists()]
-    return [path.relative_to(repo_dir).as_posix() for path in exp_dirs]
-
-
-def save_model_card(
-    save_dir: pathlib.Path,
-    base_model: str,
-    training_prompt: str,
-    test_prompt: str = "",
-    test_image_dir: str = "",
-) -> None:
-    image_str = ""
-    if test_prompt and test_image_dir:
-        image_paths = sorted((save_dir / test_image_dir).glob("*.gif"))
-        if image_paths:
-            image_path = image_paths[-1]
-            rel_path = image_path.relative_to(save_dir)
-            image_str = f"""## Samples
-Test prompt: {test_prompt}
-
-![{image_path.stem}]({rel_path})"""
-
-    model_card = f"""---
-license: creativeml-openrail-m
-base_model: {base_model}
-training_prompt: {training_prompt}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-- text-to-video
-- tune-a-video
-inference: false
----
-
-# Tune-A-Video - {save_dir.name}
-
-## Model description
-- Base model: [{base_model}](https://huggingface.co/{base_model})
-- Training prompt: {training_prompt}
-
-{image_str}
-
-## Related papers:
-- [Tune-A-Video](https://arxiv.org/abs/2212.11565): One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
-- [Stable-Diffusion](https://arxiv.org/abs/2112.10752): High-Resolution Image Synthesis with Latent Diffusion Models
-"""
-
-    with open(save_dir / "README.md", "w") as f:
-        f.write(model_card)
diff --git a/spaces/Vageesh1/clip_gpt2/neuralnet/model.py b/spaces/Vageesh1/clip_gpt2/neuralnet/model.py
deleted file mode 100644
index c39e231c79cdd0b63f98267c7c31abcea524c2db..0000000000000000000000000000000000000000
--- a/spaces/Vageesh1/clip_gpt2/neuralnet/model.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import torch
-import torch.nn as nn
-import torchvision.models as models
-
-
-class InceptionEncoder(nn.Module):
-    def __init__(self, embed_size, train_CNN=False):
-        super(InceptionEncoder, self).__init__()
-        self.train_CNN = train_CNN
-        self.inception = models.inception_v3(pretrained=True, aux_logits=False)
-        self.inception.fc = nn.Linear(self.inception.fc.in_features, embed_size)
-        self.relu = nn.ReLU()
-        self.bn = nn.BatchNorm1d(embed_size, momentum = 0.01)
-        self.dropout = nn.Dropout(0.5)
-
-    def forward(self, images):
-        features = self.inception(images)
-        norm_features = self.bn(features)
-        return self.dropout(self.relu(norm_features))
-
-
-class LstmDecoder(nn.Module):
-    def __init__(self, embed_size, hidden_size, vocab_size, num_layers, device = 'cpu'):
-        super(LstmDecoder, self).__init__()
-        self.num_layers = num_layers
-        self.hidden_size = hidden_size
-        self.device = device
-        self.embed = nn.Embedding(vocab_size, embed_size)
-        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers = self.num_layers)
-        self.linear = nn.Linear(hidden_size, vocab_size)
-        self.dropout = nn.Dropout(0.5)
-
-    def forward(self, encoder_out, captions):
-        h0 = torch.zeros(self.num_layers, encoder_out.shape[0], self.hidden_size).to(self.device).requires_grad_()
-        c0 = torch.zeros(self.num_layers, encoder_out.shape[0], self.hidden_size).to(self.device).requires_grad_()
-        embeddings = self.dropout(self.embed(captions))
-        embeddings = torch.cat((encoder_out.unsqueeze(0), embeddings), dim=0)
-        hiddens, (hn, cn) = self.lstm(embeddings, (h0.detach(), c0.detach()))
-        outputs = self.linear(hiddens)
-        return outputs
-
-
-class SeqToSeq(nn.Module):
-    def __init__(self, embed_size, hidden_size, vocab_size, num_layers, device = 'cpu'):
-        super(SeqToSeq, self).__init__()
-        self.encoder = InceptionEncoder(embed_size)
-        self.decoder = LstmDecoder(embed_size, hidden_size, vocab_size, num_layers, device)
-
-    def forward(self, images, captions):
-        features = self.encoder(images)
-        outputs = self.decoder(features, captions)
-        return outputs
-
-    def caption_image(self, image, vocabulary, max_length = 50):
-        result_caption = []
-
-        with torch.no_grad():
-            x = self.encoder(image).unsqueeze(0)
-            states = None
-
-            for _ in range(max_length):
-                hiddens, states = self.decoder.lstm(x, states)
-                output = self.decoder.linear(hiddens.squeeze(0))
-                predicted = output.argmax(1)
-                result_caption.append(predicted.item())
-                x = self.decoder.embed(predicted).unsqueeze(0)
-
-                if vocabulary[str(predicted.item())] == "<EOS>":
-                    break
-
-        return [vocabulary[str(idx)] for idx in result_caption]
diff --git a/spaces/VivianShi/Coconet-Pytorch/app.py b/spaces/VivianShi/Coconet-Pytorch/app.py
deleted file mode 100644
index 2377fd3c8d477d579673a8231c2a8a958a28a239..0000000000000000000000000000000000000000
--- a/spaces/VivianShi/Coconet-Pytorch/app.py
+++ /dev/null
@@ -1,262 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.utils.data
-import matplotlib.pyplot as plt
-import mido
-import soundfile
-import pretty_midi
-from pathlib import Path
-from midi2audio import FluidSynth
-
-device = torch.device('cpu')
-n_layers = 64
-hidden_size = 128
-
-MAX_MIDI_PITCH, MIN_MIDI_PITCH = 86, 30
-
-I = 4  # number of voices
-T = 128  # length of samples (128 = eight 4/4 measures)
-P = MAX_MIDI_PITCH - MIN_MIDI_PITCH + 1  # number of different pitches
-
-def piano_roll_to_midi(piece):
-  """
-  piece is a an array of shape (T, 4) for some T.
-  The (i,j)th entry of the array is the midi pitch of the jth voice at time i. It's an integer in range(128).
-  outputs a mido object mid that you can convert to a midi file by called its .save() method
-  """
-  piece = np.concatenate([piece, [[np.nan, np.nan, np.nan, np.nan]]], axis=0)
-
-  bpm = 50
-  microseconds_per_beat = 60 * 1000000 / bpm
-
-  mid = mido.MidiFile()
-  tracks = {'soprano': mido.MidiTrack(), 'alto': mido.MidiTrack(),
-            'tenor': mido.MidiTrack(), 'bass': mido.MidiTrack()}
-  past_pitches = {'soprano': np.nan, 'alto': np.nan,
-                  'tenor': np.nan, 'bass': np.nan}
-  delta_time = {'soprano': 0, 'alto': 0, 'tenor': 0, 'bass': 0}
-
-  # create a track containing tempo data
-  metatrack = mido.MidiTrack()
-  metatrack.append(mido.MetaMessage('set_tempo',
-                                    tempo=int(microseconds_per_beat), time=0))
-  mid.tracks.append(metatrack)
-
-  # create the four voice tracks
-  for voice in tracks:
-    mid.tracks.append(tracks[voice])
-    tracks[voice].append(mido.Message(
-      'program_change', program=52, time=0))
-
-  # add notes to the four voice tracks
-  for i in range(len(piece)):
-    pitches = {'soprano': piece[i, 0], 'alto': piece[i, 1],
-               'tenor': piece[i, 2], 'bass': piece[i, 3]}
-    for voice in tracks:
-      if np.isnan(past_pitches[voice]):
-        past_pitches[voice] = None
-      if np.isnan(pitches[voice]):
-        pitches[voice] = None
-      if pitches[voice] != past_pitches[voice]:
-        if past_pitches[voice]:
-          tracks[voice].append(mido.Message('note_off', note=int(past_pitches[voice]),
-                                            velocity=64, time=delta_time[voice]))
-          delta_time[voice] = 0
-        if pitches[voice]:
-          tracks[voice].append(mido.Message('note_on', note=int(pitches[voice]),
-                                            velocity=64, time=delta_time[voice]))
-          delta_time[voice] = 0
-      past_pitches[voice] = pitches[voice]
-      # 480 ticks per beat and each line of the array is a 16th note
-      delta_time[voice] += 120
-        
-  return mid
-
-
-# harmonize a melody
-def harmonize(y, C, model, device):
-  """
-  Generate an artificial Bach Chorale starting with y, and keeping the pitches where C==1.
-  Here C is an array of shape (4, 128) whose entries are 0 and 1.
-  The pitches outside of C are repeatedly resampled to generate new values.
-  For example, to harmonize the soprano line, let y be random except y[0] contains the soprano line, let C[1:] be 0 and C[0] be 1.
-  """
-  model.eval()
-  with torch.no_grad():
-    x = y
-    C2 = C.copy()
-    num_steps = int(2 * I * T)
-    alpha_max = .999
-    alpha_min = .001
-    eta = 3 / 4
-    for i in range(num_steps):
-      p = np.maximum(alpha_min, alpha_max - i * (alpha_max - alpha_min) / (eta * num_steps))
-      sampled_binaries = np.random.choice(2, size=C.shape, p=[p, 1 - p])
-      C2 += sampled_binaries
-      C2[C == 1] = 1
-      x_cache = x
-      x = model.pred(x, C2, device)
-      x[C2 == 1] = x_cache[C2 == 1]
-      C2 = C.copy()
-    return x
-      
-def harmonize_melody_and_save_midi(melody, id_number, model, device):
-  """
-  Generate an artificial chorale which has melody in the soprano line and a Bach-like harmonization in the other lines.
-  Save the result in a midi file named {id_number}midi.mid
-  """
-  y = np.random.randint(P, size=(I, T))
-  y[0] = np.array(melody) - 30  # subtract 30 because 30 is the minimum midi_value
-  D0 = np.ones((1, T)).astype(int)
-  D1 = np.zeros((3, T)).astype(int)
-  D = np.concatenate([D0, D1], axis=0)
-  prediction = harmonize(y, D, model, device) + 30  # 30 back on before passing to piano_roll_to_midi
-  prediction = prediction.transpose().tolist()
-  prediction = np.array(prediction)
-  midi_output = piano_roll_to_midi(prediction)
-
-  return midi_output
-
-class Unit(nn.Module):
-  """
-  Two convolution layers each followed by batchnorm and relu, plus a residual connection.
-  """
-
-  def __init__(self, hidden_size):
-    super(Unit, self).__init__()
-    self.conv1 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
-    self.batchnorm1 = nn.BatchNorm2d(hidden_size)
-    self.relu1 = nn.ReLU()
-    self.conv2 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1)
-    self.batchnorm2 = nn.BatchNorm2d(hidden_size)
-    self.relu2 = nn.ReLU()
-
-  def forward(self, x):
-    y = x
-    y = self.conv1(y)
-    y = self.batchnorm1(y)
-    y = self.relu1(y)
-    y = self.conv2(y)
-    y = self.batchnorm2(y)
-    y = y + x
-    y = self.relu2(y)
-    return y
-      
-class Net(nn.Module):
-  """
-  A CNN that where you input a starter chorale and a mask and it outputs a prediction for the values
-  in the starter chorale away from the mask that are most like the training data.
-  """
-
-  def __init__(self, n_layers, hidden_size):
-    super(Net, self).__init__()
-    self.initial_conv = nn.Conv2d(2 * I, hidden_size, 3, padding=1)
-    self.initial_batchnorm = nn.BatchNorm2d(hidden_size)
-    self.initial_relu = nn.ReLU()
-    self.conv_layers = nn.ModuleList()
-    # n_layer // 2 because there are 2 convs in each "unit" to handle residual connect
-    for _i in range(n_layers // 2):
-      self.conv_layers.append(Unit(hidden_size))
-    self.conv_pitch_linear = nn.Conv2d(P, P, 1, padding=0)
-    self.conv_instrument_downproj = nn.Conv2d(hidden_size, I, 1, padding=0)
-
-  def forward(self, x, C):
-    # x is a tensor of shape (N, I, T, P)
-    # C is a tensor of 0s and 1s of shape (N, I, T)
-    # returns a tensor of shape (N, I, T, P)
-
-    # get the number of batches
-    N = x.shape[0]
-    T = x.shape[2]
-
-    # tile the array C out of a tensor of shape (N, I, T, P)
-    tiled_C = C.view(N, I, T, 1)
-    tiled_C = tiled_C.repeat(1, 1, 1, P)
-
-    # mask x and combine it with the mask to produce a tensor of shape (N, 2*I, T, P)
-    y = torch.cat((tiled_C * x, tiled_C), dim=1)
-
-    # apply the convolution and relu layers
-    y = self.initial_conv(y)
-    y = self.initial_batchnorm(y)
-    y = self.initial_relu(y)
-    for _n in range(len(self.conv_layers)):
-      y = self.conv_layers[_n](y)
-    y = torch.permute(y, (0, 3, 1, 2))
-    y = self.conv_pitch_linear(y)
-    y = torch.permute(y, (0, 2, 3, 1))
-    y = self.conv_instrument_downproj(y)
-    return y
-
-  def expand(self, y, C, device):
-    # y is an array of shape (I, T) with integer entries in [0, P)
-    # C is an array of shape (I, T) consisting of 0s and 1s
-    # the entries of y away from the support of C should be considered 'unknown'
-
-    # x is shape (I, T, P) one-hot representation of y
-    compressed = y.reshape(-1)
-    x = np.zeros((I * T, P))
-    r = np.arange(I * T)
-    x[r, compressed] = 1
-    x = x.reshape(I, T, P)
-
-    # prep x and C for the plugging into the model
-    x = torch.tensor(x).type(torch.FloatTensor).to(device)
-    x = x.view(1, I, T, P)
-    C2 = torch.tensor(C).type(torch.FloatTensor).view(1, I, T).to(device)
-    return x, C2
-
-  def pred(self, y, C, device, temperature=1.0, seed=100):
-    x, C2 = self.expand(y, C, device)
-    # plug x and C2 into the model
-    rs = np.random.RandomState(seed)
-    with torch.no_grad():
-      out = self.forward(x, C2).view(I, T, P).cpu().numpy()
-      out = out.transpose(2, 0, 1)  # shape (P, I, T)
-      probs = np.exp(out / temperature) / np.exp(out / temperature).sum(axis=0)  # shape (P, I, T)
-      cum_probs = np.cumsum(probs, axis=0)  # shape (P, I, T)
-      u = rs.rand(I, T)  # shape (I, T)
-      return np.argmax(cum_probs > u, axis=0)
-
-def inference(input):
-    print("hello")
-    # data = np.load(input, encoding='bytes', allow_pickle=True)
-    # test_sample = data['valid'][0].transpose()[:, :128]
-    # test_sample_melody = test_sample[0]
-    # input = pretty_midi.PrettyMIDI('catechor.mid')
-    # melody = []
-    # notes = input.instruments[0].notes
-    # # print(notes)
-    # for note in notes:
-    #     dur = note.end - note.start
-    #     if dur > 0.3:
-    #         melody.extend([note.pitch for i in range(int(dur / 0.3))])
-    #     melody.append(note.pitch)
-    #   # print(melody)
-    
-    melody = [67, 67, 67, 67, 69, 69, 69, 69, 70, 70, 70, 70, 70, 70, 70, 70, 72, 72, 72, 72, 70, 70, 70, 70, 69, 69, 69, 69, 69, 69, 69, 69, 70, 70, 70, 70, 72, 72, 72, 72, 74, 74, 74, 74, 70, 70, 70, 70, 75, 75, 75, 75, 74, 74, 74, 74, 72, 72, 72, 72, 72, 72, 72, 70, 70, 70, 70, 70, 70, 70, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 72, 72, 72, 72, 70, 70, 70, 70, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 74, 74, 74, 74, 75, 75, 75, 75, 75, 75, 75, 75, 74, 74, 74, 74, 72, 72, 72, 72, 74, 74, 74, 74, 74, 74, 74, 72, 72, 72, 72, 72, 72, 72, 67, 67, 67, 67, 69, 69, 69, 69, 70, 70, 70, 70, 70, 70, 70, 70, 72, 72, 72, 72, 72, 72, 72, 72, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 75, 75, 75, 75, 74, 74, 74, 74, 72, 72, 72, 72, 70, 70, 70, 70, 69, 69, 69, 69, 69, 69, 69, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67]
-    melody = melody[0:128]
-    model = Net(n_layers, hidden_size).to(device)
-    model.load_state_dict(torch.load('pretrained.pt', map_location=torch.device('cpu')))
-    out = harmonize_melody_and_save_midi(melody, 0, model, device)
-    # out = mido.MidiFile('result.mid')
-    out.save('result.mid')
-    
-    # use fluidsynth to convert MIDI to WAV so the user can hear the output
-    sound_font = "/usr/share/sounds/sf2/FluidR3_GM.sf2"
-    FluidSynth(sound_font).midi_to_audio('result.mid', 'result.wav')
-    return 'result.wav', 'result.mid'
-
-examples=[['catechor.mid']]
-
-iface = gr.Interface(
-    inference, 
-    inputs="file", 
-    outputs=["audio", "file"],
-    # examples=examples,
-    cache_examples=True
-    )
-iface.launch(debug=True)
-    
\ No newline at end of file
diff --git a/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirements/whisper_package.py b/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirements/whisper_package.py
deleted file mode 100644
index 60678f42c35032b44c0b0d7fc47077917d38642a..0000000000000000000000000000000000000000
--- a/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirements/whisper_package.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from setup_tools.magicinstaller.requirement import SimpleRequirement
-
-
-class Whisper(SimpleRequirement):
-    package_name = 'openai-whisper'
diff --git a/spaces/WindVChen/INR-Harmon/utils/__init__.py b/spaces/WindVChen/INR-Harmon/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Wootang01/question_answer/app.py b/spaces/Wootang01/question_answer/app.py
deleted file mode 100644
index 5110900b95a44db16696a2fb20c0b74e29c8dc94..0000000000000000000000000000000000000000
--- a/spaces/Wootang01/question_answer/app.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import gradio as gr
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
-title = "Question Answer Generator"
-description = "Enter a paragraph or sentence. Ask a question based on the paragraph or sentence."
-examples = [
-    ["However, this year I find it hard to cope with my schoolwork.", "When does the author find it hard to cope with schoolwork?"],
-    ["What’s more, the only way I can keep myself awake and finish off everything I need to for the day is by consuming energy drinks. I know that some of these drinks have caffeine in them, and sometimes I feel groggy in the morning if I’ve had more than a couple the night before.", "What does 'them' refer to?"],
-    ["I know that some of these drinks have caffeine in them, and sometimes I feel groggy in the morning if I’ve had more than a couple the night before.", "What word means 'sleepy'?"]
-]
-
-gr.Interface.load("huggingface/deepset/roberta-base-squad2", 
-                  inputs=[gr.inputs.Textbox(lines=10, label="Paragraph or sentence", placeholder="Type a sentence or paragraph here."), 
-                          gr.inputs.Textbox(lines=2, label="Question", placeholder="Ask a question based on the context.")],
-                  outputs=[gr.outputs.Textbox(label="Answer"), 
-                          gr.outputs.Label(label="Probability")],                  
-                  title=title, description=description, examples=examples).launch(share=False)
\ No newline at end of file
diff --git a/spaces/Xenova/next-example-app/_next/static/chunks/app/page-43e50fcf65f99793.js b/spaces/Xenova/next-example-app/_next/static/chunks/app/page-43e50fcf65f99793.js
deleted file mode 100644
index 6a2743bf88f67eaf7622674381ebf0c73ed076bc..0000000000000000000000000000000000000000
--- a/spaces/Xenova/next-example-app/_next/static/chunks/app/page-43e50fcf65f99793.js
+++ /dev/null
@@ -1,9 +0,0 @@
-(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[931],{1861:function(e,t,r){Promise.resolve().then(r.bind(r,3451))},3451:function(e,t,r){"use strict";r.r(t),r.d(t,{default:function(){return u}});var n=r(7437),s=r(2265);function u(){let[e,t]=(0,s.useState)(null),[u,a]=(0,s.useState)(null),l=(0,s.useRef)(null);(0,s.useEffect)(()=>{l.current||(l.current=new Worker(r.tu(new URL(r.p+r.u(227),r.b)),{type:void 0}));let e=e=>{switch(e.data.status){case"initiate":a(!1);break;case"ready":a(!0);break;case"complete":t(e.data.output[0])}};return l.current.addEventListener("message",e),()=>l.current.removeEventListener("message",e)});let c=(0,s.useCallback)(e=>{l.current&&l.current.postMessage({text:e})},[]);return(0,n.jsxs)("main",{className:"flex min-h-screen flex-col items-center justify-center p-12",children:[(0,n.jsx)("h1",{className:"text-5xl font-bold mb-2 text-center",children:"Transformers.js"}),(0,n.jsx)("h2",{className:"text-2xl mb-4 text-center",children:"Next.js template"}),(0,n.jsx)("input",{type:"text",className:"w-full max-w-xs p-2 border border-gray-300 rounded mb-4",placeholder:"Enter text here",onInput:e=>{c(e.target.value)}}),null!==u&&(0,n.jsx)("pre",{className:"bg-gray-100 p-2 rounded",children:u&&e?JSON.stringify(e,null,2):"Loading..."})]})}},622:function(e,t,r){"use strict";/**
- * @license React
- * react-jsx-runtime.production.min.js
- *
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */var n=r(2265),s=Symbol.for("react.element"),u=(Symbol.for("react.fragment"),Object.prototype.hasOwnProperty),a=n.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED.ReactCurrentOwner,l={key:!0,ref:!0,__self:!0,__source:!0};function c(e,t,r){var n,c={},o=null,i=null;for(n in void 0!==r&&(o=""+r),void 0!==t.key&&(o=""+t.key),void 0!==t.ref&&(i=t.ref),t)u.call(t,n)&&!l.hasOwnProperty(n)&&(c[n]=t[n]);if(e&&e.defaultProps)for(n in t=e.defaultProps)void 0===c[n]&&(c[n]=t[n]);return{$$typeof:s,type:e,key:o,ref:i,props:c,_owner:a.current}}t.jsx=c,t.jsxs=c},7437:function(e,t,r){"use strict";e.exports=r(622)}},function(e){e.O(0,[971,596,744],function(){return e(e.s=1861)}),_N_E=e.O()}]);
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diff --git a/spaces/Xenova/text-to-speech-client/assets/index-77d0c996.js b/spaces/Xenova/text-to-speech-client/assets/index-77d0c996.js
deleted file mode 100644
index de71e8a5d0f4acb71d72d8d022f60e12c0c82a4d..0000000000000000000000000000000000000000
--- a/spaces/Xenova/text-to-speech-client/assets/index-77d0c996.js
+++ /dev/null
@@ -1,40 +0,0 @@
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- * @license React
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
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diff --git a/spaces/XzJosh/nanami-Bert-VITS2/text/english_bert_mock.py b/spaces/XzJosh/nanami-Bert-VITS2/text/english_bert_mock.py
deleted file mode 100644
index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/nanami-Bert-VITS2/text/english_bert_mock.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import torch
-
-
-def get_bert_feature(norm_text, word2ph):
-    return torch.zeros(1024, sum(word2ph))
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/latent_diffusion/__init__.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/latent_diffusion/__init__.py
deleted file mode 100644
index 5544527ff5877bb2c725c8b375cd5b03060d6a21..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/latent_diffusion/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# flake8: noqa
-from ...utils import is_transformers_available
-from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
-
-
-if is_transformers_available():
-    from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
diff --git a/spaces/Yuliang/ECON/lib/torch_utils/ops/upfirdn2d.cpp b/spaces/Yuliang/ECON/lib/torch_utils/ops/upfirdn2d.cpp
deleted file mode 100644
index 2d7177fc60040751d20e9a8da0301fa3ab64968a..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/torch_utils/ops/upfirdn2d.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "upfirdn2d.h"
-
-//------------------------------------------------------------------------
-
-static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
-    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
-    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
-    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
-    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
-    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
-    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
-    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
-    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
-
-    // Initialize CUDA kernel parameters.
-    upfirdn2d_kernel_params p;
-    p.x             = x.data_ptr();
-    p.f             = f.data_ptr<float>();
-    p.y             = y.data_ptr();
-    p.up            = make_int2(upx, upy);
-    p.down          = make_int2(downx, downy);
-    p.pad0          = make_int2(padx0, pady0);
-    p.flip          = (flip) ? 1 : 0;
-    p.gain          = gain;
-    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
-    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
-    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
-    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
-    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
-    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
-    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
-
-    // Choose CUDA kernel.
-    upfirdn2d_kernel_spec spec;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        spec = choose_upfirdn2d_kernel<scalar_t>(p);
-    });
-
-    // Set looping options.
-    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
-    p.loopMinor     = spec.loopMinor;
-    p.loopX         = spec.loopX;
-    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
-    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
-
-    // Compute grid size.
-    dim3 blockSize, gridSize;
-    if (spec.tileOutW < 0) // large
-    {
-        blockSize = dim3(4, 32, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
-            p.launchMajor);
-    }
-    else // small
-    {
-        blockSize = dim3(256, 1, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
-            p.launchMajor);
-    }
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("upfirdn2d", &upfirdn2d);
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/ZiLaiJuan/GRADIO/calculator.py b/spaces/ZiLaiJuan/GRADIO/calculator.py
deleted file mode 100644
index dee8156d61d7c64d49fea9e033c940c19565a3b8..0000000000000000000000000000000000000000
--- a/spaces/ZiLaiJuan/GRADIO/calculator.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import gradio as gr
-
-def calculator(num1, operation, num2):
-    if operation == "add":
-        return num1 + num2
-    elif operation == "subtract":
-        return num1 - num2
-    elif operation == "multiply":
-        return num1 * num2
-    elif operation == "divide":
-        if num2 == 0:
-            raise gr.Error("Cannot divide by zero!")
-        return num1 / num2
-
-demo = gr.Interface(
-    calculator,
-    [
-        "number", 
-        gr.Radio(["add", "subtract", "multiply", "divide"]),
-        "number"
-    ],
-    "number",
-    examples=[
-        [5, "add", 3],
-        [4, "divide", 2],
-        [-4, "multiply", 2.5],
-        [0, "subtract", 1.2],
-    ],
-    title="Toy Calculator",
-    description="Here's a sample toy calculator. Enjoy!",
-)
-demo.launch(share=True)
diff --git a/spaces/a-v-bely/russian-task-generator/utilities_cookies/public/index.html b/spaces/a-v-bely/russian-task-generator/utilities_cookies/public/index.html
deleted file mode 100644
index a8c6bc92134e8e0bfbf0eebfa8353d00346a9710..0000000000000000000000000000000000000000
--- a/spaces/a-v-bely/russian-task-generator/utilities_cookies/public/index.html
+++ /dev/null
@@ -1,9 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <title>Cookies Manager</title>
-    <meta charset="UTF-8" />
-  </head>
-  <body>
-  </body>
-</html>
\ No newline at end of file
diff --git a/spaces/abdvl/datahub_qa_bot/docs/authorization/roles.md b/spaces/abdvl/datahub_qa_bot/docs/authorization/roles.md
deleted file mode 100644
index b25579072980d3c2cc4ff14889560fe2846fd975..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/authorization/roles.md
+++ /dev/null
@@ -1,162 +0,0 @@
-import FeatureAvailability from '@site/src/components/FeatureAvailability';
-
-# About DataHub Roles
-
-<FeatureAvailability/>
-
-DataHub provides the ability to use **Roles** to manage permissions.
-
-:::tip **Roles** are the recommended way to manage permissions on DataHub. This should suffice for most use cases, but advanced users can use **Policies** if needed.
-
-## Roles Setup, Prerequisites, and Permissions
-
-The out-of-the-box Roles represent the most common types of DataHub users. Currently, the supported Roles are **Admin**, **Editor** and **Reader**.
-
-| Role Name | Description                                                                             |
-| --------- | --------------------------------------------------------------------------------------- |
-| Admin     | Can do everything on the platform.                                                      |
-| Editor    | Can read and edit all metadata. Cannot take administrative actions.                     |
-| Reader    | Can read all metadata. Cannot edit anything by default, or take administrative actions. |
-
-:::note To manage roles, including viewing roles, or editing a user's role, you must either be an **Admin**, or have the **Manage Policies** privilege.
-
-## Using Roles
-
-### Viewing Roles
-
-You can view the list of existing Roles under **Settings > Permissions > Roles**. You can click into a Role to see details about
-it, like which users have that Role, and which Policies correspond to that Role.
-
-<p align="center">
- <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/roles/view-roles-list.png" />
-</p>
-
-### Assigning Roles
-
-Roles can be assigned in two different ways.
-
-#### Assigning a New Role to a Single User
-
-If you go to **Settings > Users & Groups > Users**, you will be able to view your full list of users, as well as which Role they are currently
-assigned to, including if they don't have a Role.
-
-<p align="center">
- <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/roles/user-list-roles.png" />
-</p>
-
-You can simply assign a new Role to a user by clicking on the drop-down that appears on their row and selecting the desired Role.
-
-<p align="center">
- <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/roles/user-list-select-role.png" />
-</p>
-
-
-#### Batch Assigning a Role
-
-When viewing the full list of roles at **Settings > Permissions > Roles**, you will notice that each role has an `Add Users` button next to it. Clicking this button will
-lead you to a search box where you can search through your users, and select which users you would like to assign this role to.
-
-<p align="center">
- <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/roles/batch-assign-role.png" />
-</p>
-
-### How do Roles interact with Policies?
-
-Roles actually use Policies under-the-hood, and come prepackaged with corresponding policies to control what a Role can do, which you can view in the
-Policies tab. Note that these Role-specific policies **cannot** be changed. You can find the full list of policies corresponding to each Role at the bottom of this
-[file](https://github.com/datahub-project/datahub/blob/master/metadata-service/war/src/main/resources/boot/policies.json).
-
-If you would like to have finer control over what a user on your DataHub instance can do, the Roles system interfaces cleanly
-with the Policies system. For example, if you would like to give a user a **Reader** role, but also allow them to edit metadata
-for certain domains, you can add a policy that will allow them to do. Note that adding a policy like this will only add to what a user can do
-in DataHub.
-
-### Role Privileges
-
-#### Self-Hosted DataHub and Managed DataHub
-
-These privileges are common to both Self-Hosted DataHub and Managed DataHub.
-
-##### Platform Privileges
-
-| Privilege                       | Admin              | Editor             | Reader |
-|---------------------------------|--------------------|--------------------|--------|
-| Generate Personal Access Tokens | :heavy_check_mark: | :heavy_check_mark: | :x:    |
-| Manage Domains                  | :heavy_check_mark: | :heavy_check_mark: | :x:    |
-| Manage Glossaries               | :heavy_check_mark: | :heavy_check_mark: | :x:    |
-| Manage Tags                     | :heavy_check_mark: | :heavy_check_mark: | :x:    |
-| Manage Policies                 | :heavy_check_mark: | :x:                | :x:    |
-| Manage Ingestion                | :heavy_check_mark: | :x:                | :x:    |
-| Manage Secrets                  | :heavy_check_mark: | :x:                | :x:    |
-| Manage Users and Groups         | :heavy_check_mark: | :x:                | :x:    |
-| Manage Access Tokens            | :heavy_check_mark: | :x:                | :x:    |
-| Manage User Credentials         | :heavy_check_mark: | :x:                | :x:    |
-| Manage Public Views             | :heavy_check_mark: | :x:                | :x:    |
-| View Analytics                  | :heavy_check_mark: | :x:                | :x:    |
-
-##### Metadata Privileges
-
-| Privilege                            | Admin              | Editor             | Reader             |
-|--------------------------------------|--------------------|--------------------|--------------------|
-| View Entity Page                     | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| View Dataset Usage                   | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| View Dataset Profile                 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| Edit Entity                          | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Entity Tags                     | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Entity Glossary Terms           | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Entity Owners                   | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Entity Docs                     | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Entity Doc Links                | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Entity Status                   | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Entity Assertions               | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Manage Entity Tags                   | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Manage Entity Glossary Terms         | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Dataset Column Tags             | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Dataset Column Glossary Terms   | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Dataset Column Descriptions     | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Manage Dataset Column Tags           | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Manage Dataset Column Glossary Terms | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Tag Color                       | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit User Profile                    | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-| Edit Contact Info                    | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-
-#### Managed DataHub
-
-These privileges are only relevant to Managed DataHub.
-
-##### Platform Privileges
-
-| Privilege               | Admin              | Editor             | Reader |
-|-------------------------|--------------------|--------------------|--------|
-| Create Constraints      | :heavy_check_mark: | :heavy_check_mark: | :x:    |
-| View Metadata Proposals | :heavy_check_mark: | :heavy_check_mark: | :x:    |
-| Manage Tests            | :heavy_check_mark: | :x:                | :x:    |
-| Manage Global Settings  | :heavy_check_mark: | :x:                | :x:    |
-
-##### Metadata Privileges
-
-| Privilege                             | Admin              | Editor             | Reader             |
-|---------------------------------------|--------------------|--------------------|--------------------|
-| Propose Entity Tags                   | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| Propose Entity Glossary Terms         | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| Propose Dataset Column Tags           | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| Propose Dataset Column Glossary Terms | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| Edit Entity Operations                | :heavy_check_mark: | :heavy_check_mark: | :x:                |
-
-## Additional Resources
-
-### GraphQL
-
-* [acceptRole](../../graphql/mutations.md#acceptrole)
-* [batchAssignRole](../../graphql/mutations.md#batchassignrole)
-* [listRoles](../../graphql/queries.md#listroles)
-
-## FAQ and Troubleshooting
-
-## What updates are planned for Roles?
-
-In the future, the DataHub team is looking into adding the following features to Roles.
-
-- Defining a role mapping from OIDC identity providers to DataHub that will grant users a DataHub role based on their IdP role
-- Allowing Admins to set a default role on DataHub so all users are assigned a role
-- Building custom roles
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/mask/structures.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/mask/structures.py
deleted file mode 100644
index d9ec5775f281ab8b76cb873e71a4edd9969ab905..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/mask/structures.py
+++ /dev/null
@@ -1,1024 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import cv2
-import mmcv
-import numpy as np
-import pycocotools.mask as maskUtils
-import torch
-from mmcv.ops.roi_align import roi_align
-
-
-class BaseInstanceMasks(metaclass=ABCMeta):
-    """Base class for instance masks."""
-
-    @abstractmethod
-    def rescale(self, scale, interpolation='nearest'):
-        """Rescale masks as large as possible while keeping the aspect ratio.
-        For details can refer to `mmcv.imrescale`.
-
-        Args:
-            scale (tuple[int]): The maximum size (h, w) of rescaled mask.
-            interpolation (str): Same as :func:`mmcv.imrescale`.
-
-        Returns:
-            BaseInstanceMasks: The rescaled masks.
-        """
-
-    @abstractmethod
-    def resize(self, out_shape, interpolation='nearest'):
-        """Resize masks to the given out_shape.
-
-        Args:
-            out_shape: Target (h, w) of resized mask.
-            interpolation (str): See :func:`mmcv.imresize`.
-
-        Returns:
-            BaseInstanceMasks: The resized masks.
-        """
-
-    @abstractmethod
-    def flip(self, flip_direction='horizontal'):
-        """Flip masks alone the given direction.
-
-        Args:
-            flip_direction (str): Either 'horizontal' or 'vertical'.
-
-        Returns:
-            BaseInstanceMasks: The flipped masks.
-        """
-
-    @abstractmethod
-    def pad(self, out_shape, pad_val):
-        """Pad masks to the given size of (h, w).
-
-        Args:
-            out_shape (tuple[int]): Target (h, w) of padded mask.
-            pad_val (int): The padded value.
-
-        Returns:
-            BaseInstanceMasks: The padded masks.
-        """
-
-    @abstractmethod
-    def crop(self, bbox):
-        """Crop each mask by the given bbox.
-
-        Args:
-            bbox (ndarray): Bbox in format [x1, y1, x2, y2], shape (4, ).
-
-        Return:
-            BaseInstanceMasks: The cropped masks.
-        """
-
-    @abstractmethod
-    def crop_and_resize(self,
-                        bboxes,
-                        out_shape,
-                        inds,
-                        device,
-                        interpolation='bilinear'):
-        """Crop and resize masks by the given bboxes.
-
-        This function is mainly used in mask targets computation.
-        It firstly align mask to bboxes by assigned_inds, then crop mask by the
-        assigned bbox and resize to the size of (mask_h, mask_w)
-
-        Args:
-            bboxes (Tensor): Bboxes in format [x1, y1, x2, y2], shape (N, 4)
-            out_shape (tuple[int]): Target (h, w) of resized mask
-            inds (ndarray): Indexes to assign masks to each bbox,
-                shape (N,) and values should be between [0, num_masks - 1].
-            device (str): Device of bboxes
-            interpolation (str): See `mmcv.imresize`
-
-        Return:
-            BaseInstanceMasks: the cropped and resized masks.
-        """
-
-    @abstractmethod
-    def expand(self, expanded_h, expanded_w, top, left):
-        """see :class:`Expand`."""
-
-    @property
-    @abstractmethod
-    def areas(self):
-        """ndarray: areas of each instance."""
-
-    @abstractmethod
-    def to_ndarray(self):
-        """Convert masks to the format of ndarray.
-
-        Return:
-            ndarray: Converted masks in the format of ndarray.
-        """
-
-    @abstractmethod
-    def to_tensor(self, dtype, device):
-        """Convert masks to the format of Tensor.
-
-        Args:
-            dtype (str): Dtype of converted mask.
-            device (torch.device): Device of converted masks.
-
-        Returns:
-            Tensor: Converted masks in the format of Tensor.
-        """
-
-    @abstractmethod
-    def translate(self,
-                  out_shape,
-                  offset,
-                  direction='horizontal',
-                  fill_val=0,
-                  interpolation='bilinear'):
-        """Translate the masks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            offset (int | float): The offset for translate.
-            direction (str): The translate direction, either "horizontal"
-                or "vertical".
-            fill_val (int | float): Border value. Default 0.
-            interpolation (str): Same as :func:`mmcv.imtranslate`.
-
-        Returns:
-            Translated masks.
-        """
-
-    def shear(self,
-              out_shape,
-              magnitude,
-              direction='horizontal',
-              border_value=0,
-              interpolation='bilinear'):
-        """Shear the masks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            magnitude (int | float): The magnitude used for shear.
-            direction (str): The shear direction, either "horizontal"
-                or "vertical".
-            border_value (int | tuple[int]): Value used in case of a
-                constant border. Default 0.
-            interpolation (str): Same as in :func:`mmcv.imshear`.
-
-        Returns:
-            ndarray: Sheared masks.
-        """
-
-    @abstractmethod
-    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
-        """Rotate the masks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            angle (int | float): Rotation angle in degrees. Positive values
-                mean counter-clockwise rotation.
-            center (tuple[float], optional): Center point (w, h) of the
-                rotation in source image. If not specified, the center of
-                the image will be used.
-            scale (int | float): Isotropic scale factor.
-            fill_val (int | float): Border value. Default 0 for masks.
-
-        Returns:
-            Rotated masks.
-        """
-
-
-class BitmapMasks(BaseInstanceMasks):
-    """This class represents masks in the form of bitmaps.
-
-    Args:
-        masks (ndarray): ndarray of masks in shape (N, H, W), where N is
-            the number of objects.
-        height (int): height of masks
-        width (int): width of masks
-
-    Example:
-        >>> from mmdet.core.mask.structures import *  # NOQA
-        >>> num_masks, H, W = 3, 32, 32
-        >>> rng = np.random.RandomState(0)
-        >>> masks = (rng.rand(num_masks, H, W) > 0.1).astype(np.int)
-        >>> self = BitmapMasks(masks, height=H, width=W)
-
-        >>> # demo crop_and_resize
-        >>> num_boxes = 5
-        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
-        >>> out_shape = (14, 14)
-        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
-        >>> device = 'cpu'
-        >>> interpolation = 'bilinear'
-        >>> new = self.crop_and_resize(
-        ...     bboxes, out_shape, inds, device, interpolation)
-        >>> assert len(new) == num_boxes
-        >>> assert new.height, new.width == out_shape
-    """
-
-    def __init__(self, masks, height, width):
-        self.height = height
-        self.width = width
-        if len(masks) == 0:
-            self.masks = np.empty((0, self.height, self.width), dtype=np.uint8)
-        else:
-            assert isinstance(masks, (list, np.ndarray))
-            if isinstance(masks, list):
-                assert isinstance(masks[0], np.ndarray)
-                assert masks[0].ndim == 2  # (H, W)
-            else:
-                assert masks.ndim == 3  # (N, H, W)
-
-            self.masks = np.stack(masks).reshape(-1, height, width)
-            assert self.masks.shape[1] == self.height
-            assert self.masks.shape[2] == self.width
-
-    def __getitem__(self, index):
-        """Index the BitmapMask.
-
-        Args:
-            index (int | ndarray): Indices in the format of integer or ndarray.
-
-        Returns:
-            :obj:`BitmapMasks`: Indexed bitmap masks.
-        """
-        masks = self.masks[index].reshape(-1, self.height, self.width)
-        return BitmapMasks(masks, self.height, self.width)
-
-    def __iter__(self):
-        return iter(self.masks)
-
-    def __repr__(self):
-        s = self.__class__.__name__ + '('
-        s += f'num_masks={len(self.masks)}, '
-        s += f'height={self.height}, '
-        s += f'width={self.width})'
-        return s
-
-    def __len__(self):
-        """Number of masks."""
-        return len(self.masks)
-
-    def rescale(self, scale, interpolation='nearest'):
-        """See :func:`BaseInstanceMasks.rescale`."""
-        if len(self.masks) == 0:
-            new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
-            rescaled_masks = np.empty((0, new_h, new_w), dtype=np.uint8)
-        else:
-            rescaled_masks = np.stack([
-                mmcv.imrescale(mask, scale, interpolation=interpolation)
-                for mask in self.masks
-            ])
-        height, width = rescaled_masks.shape[1:]
-        return BitmapMasks(rescaled_masks, height, width)
-
-    def resize(self, out_shape, interpolation='nearest'):
-        """See :func:`BaseInstanceMasks.resize`."""
-        if len(self.masks) == 0:
-            resized_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            resized_masks = np.stack([
-                mmcv.imresize(
-                    mask, out_shape[::-1], interpolation=interpolation)
-                for mask in self.masks
-            ])
-        return BitmapMasks(resized_masks, *out_shape)
-
-    def flip(self, flip_direction='horizontal'):
-        """See :func:`BaseInstanceMasks.flip`."""
-        assert flip_direction in ('horizontal', 'vertical', 'diagonal')
-
-        if len(self.masks) == 0:
-            flipped_masks = self.masks
-        else:
-            flipped_masks = np.stack([
-                mmcv.imflip(mask, direction=flip_direction)
-                for mask in self.masks
-            ])
-        return BitmapMasks(flipped_masks, self.height, self.width)
-
-    def pad(self, out_shape, pad_val=0):
-        """See :func:`BaseInstanceMasks.pad`."""
-        if len(self.masks) == 0:
-            padded_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            padded_masks = np.stack([
-                mmcv.impad(mask, shape=out_shape, pad_val=pad_val)
-                for mask in self.masks
-            ])
-        return BitmapMasks(padded_masks, *out_shape)
-
-    def crop(self, bbox):
-        """See :func:`BaseInstanceMasks.crop`."""
-        assert isinstance(bbox, np.ndarray)
-        assert bbox.ndim == 1
-
-        # clip the boundary
-        bbox = bbox.copy()
-        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
-        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
-        x1, y1, x2, y2 = bbox
-        w = np.maximum(x2 - x1, 1)
-        h = np.maximum(y2 - y1, 1)
-
-        if len(self.masks) == 0:
-            cropped_masks = np.empty((0, h, w), dtype=np.uint8)
-        else:
-            cropped_masks = self.masks[:, y1:y1 + h, x1:x1 + w]
-        return BitmapMasks(cropped_masks, h, w)
-
-    def crop_and_resize(self,
-                        bboxes,
-                        out_shape,
-                        inds,
-                        device='cpu',
-                        interpolation='bilinear'):
-        """See :func:`BaseInstanceMasks.crop_and_resize`."""
-        if len(self.masks) == 0:
-            empty_masks = np.empty((0, *out_shape), dtype=np.uint8)
-            return BitmapMasks(empty_masks, *out_shape)
-
-        # convert bboxes to tensor
-        if isinstance(bboxes, np.ndarray):
-            bboxes = torch.from_numpy(bboxes).to(device=device)
-        if isinstance(inds, np.ndarray):
-            inds = torch.from_numpy(inds).to(device=device)
-
-        num_bbox = bboxes.shape[0]
-        fake_inds = torch.arange(
-            num_bbox, device=device).to(dtype=bboxes.dtype)[:, None]
-        rois = torch.cat([fake_inds, bboxes], dim=1)  # Nx5
-        rois = rois.to(device=device)
-        if num_bbox > 0:
-            gt_masks_th = torch.from_numpy(self.masks).to(device).index_select(
-                0, inds).to(dtype=rois.dtype)
-            targets = roi_align(gt_masks_th[:, None, :, :], rois, out_shape,
-                                1.0, 0, 'avg', True).squeeze(1)
-            resized_masks = (targets >= 0.5).cpu().numpy()
-        else:
-            resized_masks = []
-        return BitmapMasks(resized_masks, *out_shape)
-
-    def expand(self, expanded_h, expanded_w, top, left):
-        """See :func:`BaseInstanceMasks.expand`."""
-        if len(self.masks) == 0:
-            expanded_mask = np.empty((0, expanded_h, expanded_w),
-                                     dtype=np.uint8)
-        else:
-            expanded_mask = np.zeros((len(self), expanded_h, expanded_w),
-                                     dtype=np.uint8)
-            expanded_mask[:, top:top + self.height,
-                          left:left + self.width] = self.masks
-        return BitmapMasks(expanded_mask, expanded_h, expanded_w)
-
-    def translate(self,
-                  out_shape,
-                  offset,
-                  direction='horizontal',
-                  fill_val=0,
-                  interpolation='bilinear'):
-        """Translate the BitmapMasks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            offset (int | float): The offset for translate.
-            direction (str): The translate direction, either "horizontal"
-                or "vertical".
-            fill_val (int | float): Border value. Default 0 for masks.
-            interpolation (str): Same as :func:`mmcv.imtranslate`.
-
-        Returns:
-            BitmapMasks: Translated BitmapMasks.
-
-        Example:
-            >>> from mmdet.core.mask.structures import BitmapMasks
-            >>> self = BitmapMasks.random(dtype=np.uint8)
-            >>> out_shape = (32, 32)
-            >>> offset = 4
-            >>> direction = 'horizontal'
-            >>> fill_val = 0
-            >>> interpolation = 'bilinear'
-            >>> # Note, There seem to be issues when:
-            >>> # * out_shape is different than self's shape
-            >>> # * the mask dtype is not supported by cv2.AffineWarp
-            >>> new = self.translate(out_shape, offset, direction, fill_val,
-            >>>                      interpolation)
-            >>> assert len(new) == len(self)
-            >>> assert new.height, new.width == out_shape
-        """
-        if len(self.masks) == 0:
-            translated_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            translated_masks = mmcv.imtranslate(
-                self.masks.transpose((1, 2, 0)),
-                offset,
-                direction,
-                border_value=fill_val,
-                interpolation=interpolation)
-            if translated_masks.ndim == 2:
-                translated_masks = translated_masks[:, :, None]
-            translated_masks = translated_masks.transpose(
-                (2, 0, 1)).astype(self.masks.dtype)
-        return BitmapMasks(translated_masks, *out_shape)
-
-    def shear(self,
-              out_shape,
-              magnitude,
-              direction='horizontal',
-              border_value=0,
-              interpolation='bilinear'):
-        """Shear the BitmapMasks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            magnitude (int | float): The magnitude used for shear.
-            direction (str): The shear direction, either "horizontal"
-                or "vertical".
-            border_value (int | tuple[int]): Value used in case of a
-                constant border.
-            interpolation (str): Same as in :func:`mmcv.imshear`.
-
-        Returns:
-            BitmapMasks: The sheared masks.
-        """
-        if len(self.masks) == 0:
-            sheared_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            sheared_masks = mmcv.imshear(
-                self.masks.transpose((1, 2, 0)),
-                magnitude,
-                direction,
-                border_value=border_value,
-                interpolation=interpolation)
-            if sheared_masks.ndim == 2:
-                sheared_masks = sheared_masks[:, :, None]
-            sheared_masks = sheared_masks.transpose(
-                (2, 0, 1)).astype(self.masks.dtype)
-        return BitmapMasks(sheared_masks, *out_shape)
-
-    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
-        """Rotate the BitmapMasks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            angle (int | float): Rotation angle in degrees. Positive values
-                mean counter-clockwise rotation.
-            center (tuple[float], optional): Center point (w, h) of the
-                rotation in source image. If not specified, the center of
-                the image will be used.
-            scale (int | float): Isotropic scale factor.
-            fill_val (int | float): Border value. Default 0 for masks.
-
-        Returns:
-            BitmapMasks: Rotated BitmapMasks.
-        """
-        if len(self.masks) == 0:
-            rotated_masks = np.empty((0, *out_shape), dtype=self.masks.dtype)
-        else:
-            rotated_masks = mmcv.imrotate(
-                self.masks.transpose((1, 2, 0)),
-                angle,
-                center=center,
-                scale=scale,
-                border_value=fill_val)
-            if rotated_masks.ndim == 2:
-                # case when only one mask, (h, w)
-                rotated_masks = rotated_masks[:, :, None]  # (h, w, 1)
-            rotated_masks = rotated_masks.transpose(
-                (2, 0, 1)).astype(self.masks.dtype)
-        return BitmapMasks(rotated_masks, *out_shape)
-
-    @property
-    def areas(self):
-        """See :py:attr:`BaseInstanceMasks.areas`."""
-        return self.masks.sum((1, 2))
-
-    def to_ndarray(self):
-        """See :func:`BaseInstanceMasks.to_ndarray`."""
-        return self.masks
-
-    def to_tensor(self, dtype, device):
-        """See :func:`BaseInstanceMasks.to_tensor`."""
-        return torch.tensor(self.masks, dtype=dtype, device=device)
-
-    @classmethod
-    def random(cls,
-               num_masks=3,
-               height=32,
-               width=32,
-               dtype=np.uint8,
-               rng=None):
-        """Generate random bitmap masks for demo / testing purposes.
-
-        Example:
-            >>> from mmdet.core.mask.structures import BitmapMasks
-            >>> self = BitmapMasks.random()
-            >>> print('self = {}'.format(self))
-            self = BitmapMasks(num_masks=3, height=32, width=32)
-        """
-        from mmdet.utils.util_random import ensure_rng
-        rng = ensure_rng(rng)
-        masks = (rng.rand(num_masks, height, width) > 0.1).astype(dtype)
-        self = cls(masks, height=height, width=width)
-        return self
-
-
-class PolygonMasks(BaseInstanceMasks):
-    """This class represents masks in the form of polygons.
-
-    Polygons is a list of three levels. The first level of the list
-    corresponds to objects, the second level to the polys that compose the
-    object, the third level to the poly coordinates
-
-    Args:
-        masks (list[list[ndarray]]): The first level of the list
-            corresponds to objects, the second level to the polys that
-            compose the object, the third level to the poly coordinates
-        height (int): height of masks
-        width (int): width of masks
-
-    Example:
-        >>> from mmdet.core.mask.structures import *  # NOQA
-        >>> masks = [
-        >>>     [ np.array([0, 0, 10, 0, 10, 10., 0, 10, 0, 0]) ]
-        >>> ]
-        >>> height, width = 16, 16
-        >>> self = PolygonMasks(masks, height, width)
-
-        >>> # demo translate
-        >>> new = self.translate((16, 16), 4., direction='horizontal')
-        >>> assert np.all(new.masks[0][0][1::2] == masks[0][0][1::2])
-        >>> assert np.all(new.masks[0][0][0::2] == masks[0][0][0::2] + 4)
-
-        >>> # demo crop_and_resize
-        >>> num_boxes = 3
-        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
-        >>> out_shape = (16, 16)
-        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
-        >>> device = 'cpu'
-        >>> interpolation = 'bilinear'
-        >>> new = self.crop_and_resize(
-        ...     bboxes, out_shape, inds, device, interpolation)
-        >>> assert len(new) == num_boxes
-        >>> assert new.height, new.width == out_shape
-    """
-
-    def __init__(self, masks, height, width):
-        assert isinstance(masks, list)
-        if len(masks) > 0:
-            assert isinstance(masks[0], list)
-            assert isinstance(masks[0][0], np.ndarray)
-
-        self.height = height
-        self.width = width
-        self.masks = masks
-
-    def __getitem__(self, index):
-        """Index the polygon masks.
-
-        Args:
-            index (ndarray | List): The indices.
-
-        Returns:
-            :obj:`PolygonMasks`: The indexed polygon masks.
-        """
-        if isinstance(index, np.ndarray):
-            index = index.tolist()
-        if isinstance(index, list):
-            masks = [self.masks[i] for i in index]
-        else:
-            try:
-                masks = self.masks[index]
-            except Exception:
-                raise ValueError(
-                    f'Unsupported input of type {type(index)} for indexing!')
-        if len(masks) and isinstance(masks[0], np.ndarray):
-            masks = [masks]  # ensure a list of three levels
-        return PolygonMasks(masks, self.height, self.width)
-
-    def __iter__(self):
-        return iter(self.masks)
-
-    def __repr__(self):
-        s = self.__class__.__name__ + '('
-        s += f'num_masks={len(self.masks)}, '
-        s += f'height={self.height}, '
-        s += f'width={self.width})'
-        return s
-
-    def __len__(self):
-        """Number of masks."""
-        return len(self.masks)
-
-    def rescale(self, scale, interpolation=None):
-        """see :func:`BaseInstanceMasks.rescale`"""
-        new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
-        if len(self.masks) == 0:
-            rescaled_masks = PolygonMasks([], new_h, new_w)
-        else:
-            rescaled_masks = self.resize((new_h, new_w))
-        return rescaled_masks
-
-    def resize(self, out_shape, interpolation=None):
-        """see :func:`BaseInstanceMasks.resize`"""
-        if len(self.masks) == 0:
-            resized_masks = PolygonMasks([], *out_shape)
-        else:
-            h_scale = out_shape[0] / self.height
-            w_scale = out_shape[1] / self.width
-            resized_masks = []
-            for poly_per_obj in self.masks:
-                resized_poly = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    p[0::2] *= w_scale
-                    p[1::2] *= h_scale
-                    resized_poly.append(p)
-                resized_masks.append(resized_poly)
-            resized_masks = PolygonMasks(resized_masks, *out_shape)
-        return resized_masks
-
-    def flip(self, flip_direction='horizontal'):
-        """see :func:`BaseInstanceMasks.flip`"""
-        assert flip_direction in ('horizontal', 'vertical', 'diagonal')
-        if len(self.masks) == 0:
-            flipped_masks = PolygonMasks([], self.height, self.width)
-        else:
-            flipped_masks = []
-            for poly_per_obj in self.masks:
-                flipped_poly_per_obj = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    if flip_direction == 'horizontal':
-                        p[0::2] = self.width - p[0::2]
-                    elif flip_direction == 'vertical':
-                        p[1::2] = self.height - p[1::2]
-                    else:
-                        p[0::2] = self.width - p[0::2]
-                        p[1::2] = self.height - p[1::2]
-                    flipped_poly_per_obj.append(p)
-                flipped_masks.append(flipped_poly_per_obj)
-            flipped_masks = PolygonMasks(flipped_masks, self.height,
-                                         self.width)
-        return flipped_masks
-
-    def crop(self, bbox):
-        """see :func:`BaseInstanceMasks.crop`"""
-        assert isinstance(bbox, np.ndarray)
-        assert bbox.ndim == 1
-
-        # clip the boundary
-        bbox = bbox.copy()
-        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
-        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
-        x1, y1, x2, y2 = bbox
-        w = np.maximum(x2 - x1, 1)
-        h = np.maximum(y2 - y1, 1)
-
-        if len(self.masks) == 0:
-            cropped_masks = PolygonMasks([], h, w)
-        else:
-            cropped_masks = []
-            for poly_per_obj in self.masks:
-                cropped_poly_per_obj = []
-                for p in poly_per_obj:
-                    # pycocotools will clip the boundary
-                    p = p.copy()
-                    p[0::2] -= bbox[0]
-                    p[1::2] -= bbox[1]
-                    cropped_poly_per_obj.append(p)
-                cropped_masks.append(cropped_poly_per_obj)
-            cropped_masks = PolygonMasks(cropped_masks, h, w)
-        return cropped_masks
-
-    def pad(self, out_shape, pad_val=0):
-        """padding has no effect on polygons`"""
-        return PolygonMasks(self.masks, *out_shape)
-
-    def expand(self, *args, **kwargs):
-        """TODO: Add expand for polygon"""
-        raise NotImplementedError
-
-    def crop_and_resize(self,
-                        bboxes,
-                        out_shape,
-                        inds,
-                        device='cpu',
-                        interpolation='bilinear'):
-        """see :func:`BaseInstanceMasks.crop_and_resize`"""
-        out_h, out_w = out_shape
-        if len(self.masks) == 0:
-            return PolygonMasks([], out_h, out_w)
-
-        resized_masks = []
-        for i in range(len(bboxes)):
-            mask = self.masks[inds[i]]
-            bbox = bboxes[i, :]
-            x1, y1, x2, y2 = bbox
-            w = np.maximum(x2 - x1, 1)
-            h = np.maximum(y2 - y1, 1)
-            h_scale = out_h / max(h, 0.1)  # avoid too large scale
-            w_scale = out_w / max(w, 0.1)
-
-            resized_mask = []
-            for p in mask:
-                p = p.copy()
-                # crop
-                # pycocotools will clip the boundary
-                p[0::2] -= bbox[0]
-                p[1::2] -= bbox[1]
-
-                # resize
-                p[0::2] *= w_scale
-                p[1::2] *= h_scale
-                resized_mask.append(p)
-            resized_masks.append(resized_mask)
-        return PolygonMasks(resized_masks, *out_shape)
-
-    def translate(self,
-                  out_shape,
-                  offset,
-                  direction='horizontal',
-                  fill_val=None,
-                  interpolation=None):
-        """Translate the PolygonMasks.
-
-        Example:
-            >>> self = PolygonMasks.random(dtype=np.int)
-            >>> out_shape = (self.height, self.width)
-            >>> new = self.translate(out_shape, 4., direction='horizontal')
-            >>> assert np.all(new.masks[0][0][1::2] == self.masks[0][0][1::2])
-            >>> assert np.all(new.masks[0][0][0::2] == self.masks[0][0][0::2] + 4)  # noqa: E501
-        """
-        assert fill_val is None or fill_val == 0, 'Here fill_val is not '\
-            f'used, and defaultly should be None or 0. got {fill_val}.'
-        if len(self.masks) == 0:
-            translated_masks = PolygonMasks([], *out_shape)
-        else:
-            translated_masks = []
-            for poly_per_obj in self.masks:
-                translated_poly_per_obj = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    if direction == 'horizontal':
-                        p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1])
-                    elif direction == 'vertical':
-                        p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0])
-                    translated_poly_per_obj.append(p)
-                translated_masks.append(translated_poly_per_obj)
-            translated_masks = PolygonMasks(translated_masks, *out_shape)
-        return translated_masks
-
-    def shear(self,
-              out_shape,
-              magnitude,
-              direction='horizontal',
-              border_value=0,
-              interpolation='bilinear'):
-        """See :func:`BaseInstanceMasks.shear`."""
-        if len(self.masks) == 0:
-            sheared_masks = PolygonMasks([], *out_shape)
-        else:
-            sheared_masks = []
-            if direction == 'horizontal':
-                shear_matrix = np.stack([[1, magnitude],
-                                         [0, 1]]).astype(np.float32)
-            elif direction == 'vertical':
-                shear_matrix = np.stack([[1, 0], [magnitude,
-                                                  1]]).astype(np.float32)
-            for poly_per_obj in self.masks:
-                sheared_poly = []
-                for p in poly_per_obj:
-                    p = np.stack([p[0::2], p[1::2]], axis=0)  # [2, n]
-                    new_coords = np.matmul(shear_matrix, p)  # [2, n]
-                    new_coords[0, :] = np.clip(new_coords[0, :], 0,
-                                               out_shape[1])
-                    new_coords[1, :] = np.clip(new_coords[1, :], 0,
-                                               out_shape[0])
-                    sheared_poly.append(
-                        new_coords.transpose((1, 0)).reshape(-1))
-                sheared_masks.append(sheared_poly)
-            sheared_masks = PolygonMasks(sheared_masks, *out_shape)
-        return sheared_masks
-
-    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
-        """See :func:`BaseInstanceMasks.rotate`."""
-        if len(self.masks) == 0:
-            rotated_masks = PolygonMasks([], *out_shape)
-        else:
-            rotated_masks = []
-            rotate_matrix = cv2.getRotationMatrix2D(center, -angle, scale)
-            for poly_per_obj in self.masks:
-                rotated_poly = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    coords = np.stack([p[0::2], p[1::2]], axis=1)  # [n, 2]
-                    # pad 1 to convert from format [x, y] to homogeneous
-                    # coordinates format [x, y, 1]
-                    coords = np.concatenate(
-                        (coords, np.ones((coords.shape[0], 1), coords.dtype)),
-                        axis=1)  # [n, 3]
-                    rotated_coords = np.matmul(
-                        rotate_matrix[None, :, :],
-                        coords[:, :, None])[..., 0]  # [n, 2, 1] -> [n, 2]
-                    rotated_coords[:, 0] = np.clip(rotated_coords[:, 0], 0,
-                                                   out_shape[1])
-                    rotated_coords[:, 1] = np.clip(rotated_coords[:, 1], 0,
-                                                   out_shape[0])
-                    rotated_poly.append(rotated_coords.reshape(-1))
-                rotated_masks.append(rotated_poly)
-            rotated_masks = PolygonMasks(rotated_masks, *out_shape)
-        return rotated_masks
-
-    def to_bitmap(self):
-        """convert polygon masks to bitmap masks."""
-        bitmap_masks = self.to_ndarray()
-        return BitmapMasks(bitmap_masks, self.height, self.width)
-
-    @property
-    def areas(self):
-        """Compute areas of masks.
-
-        This func is modified from `detectron2
-        <https://github.com/facebookresearch/detectron2/blob/ffff8acc35ea88ad1cb1806ab0f00b4c1c5dbfd9/detectron2/structures/masks.py#L387>`_.
-        The function only works with Polygons using the shoelace formula.
-
-        Return:
-            ndarray: areas of each instance
-        """  # noqa: W501
-        area = []
-        for polygons_per_obj in self.masks:
-            area_per_obj = 0
-            for p in polygons_per_obj:
-                area_per_obj += self._polygon_area(p[0::2], p[1::2])
-            area.append(area_per_obj)
-        return np.asarray(area)
-
-    def _polygon_area(self, x, y):
-        """Compute the area of a component of a polygon.
-
-        Using the shoelace formula:
-        https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates
-
-        Args:
-            x (ndarray): x coordinates of the component
-            y (ndarray): y coordinates of the component
-
-        Return:
-            float: the are of the component
-        """  # noqa: 501
-        return 0.5 * np.abs(
-            np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))
-
-    def to_ndarray(self):
-        """Convert masks to the format of ndarray."""
-        if len(self.masks) == 0:
-            return np.empty((0, self.height, self.width), dtype=np.uint8)
-        bitmap_masks = []
-        for poly_per_obj in self.masks:
-            bitmap_masks.append(
-                polygon_to_bitmap(poly_per_obj, self.height, self.width))
-        return np.stack(bitmap_masks)
-
-    def to_tensor(self, dtype, device):
-        """See :func:`BaseInstanceMasks.to_tensor`."""
-        if len(self.masks) == 0:
-            return torch.empty((0, self.height, self.width),
-                               dtype=dtype,
-                               device=device)
-        ndarray_masks = self.to_ndarray()
-        return torch.tensor(ndarray_masks, dtype=dtype, device=device)
-
-    @classmethod
-    def random(cls,
-               num_masks=3,
-               height=32,
-               width=32,
-               n_verts=5,
-               dtype=np.float32,
-               rng=None):
-        """Generate random polygon masks for demo / testing purposes.
-
-        Adapted from [1]_
-
-        References:
-            .. [1] https://gitlab.kitware.com/computer-vision/kwimage/-/blob/928cae35ca8/kwimage/structs/polygon.py#L379  # noqa: E501
-
-        Example:
-            >>> from mmdet.core.mask.structures import PolygonMasks
-            >>> self = PolygonMasks.random()
-            >>> print('self = {}'.format(self))
-        """
-        from mmdet.utils.util_random import ensure_rng
-        rng = ensure_rng(rng)
-
-        def _gen_polygon(n, irregularity, spikeyness):
-            """Creates the polygon by sampling points on a circle around the
-            centre.  Random noise is added by varying the angular spacing
-            between sequential points, and by varying the radial distance of
-            each point from the centre.
-
-            Based on original code by Mike Ounsworth
-
-            Args:
-                n (int): number of vertices
-                irregularity (float): [0,1] indicating how much variance there
-                    is in the angular spacing of vertices. [0,1] will map to
-                    [0, 2pi/numberOfVerts]
-                spikeyness (float): [0,1] indicating how much variance there is
-                    in each vertex from the circle of radius aveRadius. [0,1]
-                    will map to [0, aveRadius]
-
-            Returns:
-                a list of vertices, in CCW order.
-            """
-            from scipy.stats import truncnorm
-            # Generate around the unit circle
-            cx, cy = (0.0, 0.0)
-            radius = 1
-
-            tau = np.pi * 2
-
-            irregularity = np.clip(irregularity, 0, 1) * 2 * np.pi / n
-            spikeyness = np.clip(spikeyness, 1e-9, 1)
-
-            # generate n angle steps
-            lower = (tau / n) - irregularity
-            upper = (tau / n) + irregularity
-            angle_steps = rng.uniform(lower, upper, n)
-
-            # normalize the steps so that point 0 and point n+1 are the same
-            k = angle_steps.sum() / (2 * np.pi)
-            angles = (angle_steps / k).cumsum() + rng.uniform(0, tau)
-
-            # Convert high and low values to be wrt the standard normal range
-            # https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.truncnorm.html
-            low = 0
-            high = 2 * radius
-            mean = radius
-            std = spikeyness
-            a = (low - mean) / std
-            b = (high - mean) / std
-            tnorm = truncnorm(a=a, b=b, loc=mean, scale=std)
-
-            # now generate the points
-            radii = tnorm.rvs(n, random_state=rng)
-            x_pts = cx + radii * np.cos(angles)
-            y_pts = cy + radii * np.sin(angles)
-
-            points = np.hstack([x_pts[:, None], y_pts[:, None]])
-
-            # Scale to 0-1 space
-            points = points - points.min(axis=0)
-            points = points / points.max(axis=0)
-
-            # Randomly place within 0-1 space
-            points = points * (rng.rand() * .8 + .2)
-            min_pt = points.min(axis=0)
-            max_pt = points.max(axis=0)
-
-            high = (1 - max_pt)
-            low = (0 - min_pt)
-            offset = (rng.rand(2) * (high - low)) + low
-            points = points + offset
-            return points
-
-        def _order_vertices(verts):
-            """
-            References:
-                https://stackoverflow.com/questions/1709283/how-can-i-sort-a-coordinate-list-for-a-rectangle-counterclockwise
-            """
-            mlat = verts.T[0].sum() / len(verts)
-            mlng = verts.T[1].sum() / len(verts)
-
-            tau = np.pi * 2
-            angle = (np.arctan2(mlat - verts.T[0], verts.T[1] - mlng) +
-                     tau) % tau
-            sortx = angle.argsort()
-            verts = verts.take(sortx, axis=0)
-            return verts
-
-        # Generate a random exterior for each requested mask
-        masks = []
-        for _ in range(num_masks):
-            exterior = _order_vertices(_gen_polygon(n_verts, 0.9, 0.9))
-            exterior = (exterior * [(width, height)]).astype(dtype)
-            masks.append([exterior.ravel()])
-
-        self = cls(masks, height, width)
-        return self
-
-
-def polygon_to_bitmap(polygons, height, width):
-    """Convert masks from the form of polygons to bitmaps.
-
-    Args:
-        polygons (list[ndarray]): masks in polygon representation
-        height (int): mask height
-        width (int): mask width
-
-    Return:
-        ndarray: the converted masks in bitmap representation
-    """
-    rles = maskUtils.frPyObjects(polygons, height, width)
-    rle = maskUtils.merge(rles)
-    bitmap_mask = maskUtils.decode(rle).astype(np.bool)
-    return bitmap_mask
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/pisa_ssd_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/pisa_ssd_head.py
deleted file mode 100644
index 90ef3c83ed62d8346c8daef01f18ad7bd236623c..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/pisa_ssd_head.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import torch
-
-from mmdet.core import multi_apply
-from ..builder import HEADS
-from ..losses import CrossEntropyLoss, SmoothL1Loss, carl_loss, isr_p
-from .ssd_head import SSDHead
-
-
-# TODO: add loss evaluator for SSD
-@HEADS.register_module()
-class PISASSDHead(SSDHead):
-
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes of each image
-                with shape (num_obj, 4).
-            gt_labels (list[Tensor]): Ground truth labels of each image
-                with shape (num_obj, 4).
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image.
-                Default: None.
-
-        Returns:
-            dict: Loss dict, comprise classification loss regression loss and
-                carl loss.
-        """
-        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)
-        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=1,
-            unmap_outputs=False,
-            return_sampling_results=True)
-        if cls_reg_targets is None:
-            return None
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
-         num_total_pos, num_total_neg, sampling_results_list) = cls_reg_targets
-
-        num_images = len(img_metas)
-        all_cls_scores = torch.cat([
-            s.permute(0, 2, 3, 1).reshape(
-                num_images, -1, self.cls_out_channels) for s in cls_scores
-        ], 1)
-        all_labels = torch.cat(labels_list, -1).view(num_images, -1)
-        all_label_weights = torch.cat(label_weights_list,
-                                      -1).view(num_images, -1)
-        all_bbox_preds = torch.cat([
-            b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
-            for b in bbox_preds
-        ], -2)
-        all_bbox_targets = torch.cat(bbox_targets_list,
-                                     -2).view(num_images, -1, 4)
-        all_bbox_weights = torch.cat(bbox_weights_list,
-                                     -2).view(num_images, -1, 4)
-
-        # concat all level anchors to a single tensor
-        all_anchors = []
-        for i in range(num_images):
-            all_anchors.append(torch.cat(anchor_list[i]))
-
-        isr_cfg = self.train_cfg.get('isr', None)
-        all_targets = (all_labels.view(-1), all_label_weights.view(-1),
-                       all_bbox_targets.view(-1,
-                                             4), all_bbox_weights.view(-1, 4))
-        # apply ISR-P
-        if isr_cfg is not None:
-            all_targets = isr_p(
-                all_cls_scores.view(-1, all_cls_scores.size(-1)),
-                all_bbox_preds.view(-1, 4),
-                all_targets,
-                torch.cat(all_anchors),
-                sampling_results_list,
-                loss_cls=CrossEntropyLoss(),
-                bbox_coder=self.bbox_coder,
-                **self.train_cfg.isr,
-                num_class=self.num_classes)
-            (new_labels, new_label_weights, new_bbox_targets,
-             new_bbox_weights) = all_targets
-            all_labels = new_labels.view(all_labels.shape)
-            all_label_weights = new_label_weights.view(all_label_weights.shape)
-            all_bbox_targets = new_bbox_targets.view(all_bbox_targets.shape)
-            all_bbox_weights = new_bbox_weights.view(all_bbox_weights.shape)
-
-        # add CARL loss
-        carl_loss_cfg = self.train_cfg.get('carl', None)
-        if carl_loss_cfg is not None:
-            loss_carl = carl_loss(
-                all_cls_scores.view(-1, all_cls_scores.size(-1)),
-                all_targets[0],
-                all_bbox_preds.view(-1, 4),
-                all_targets[2],
-                SmoothL1Loss(beta=1.),
-                **self.train_cfg.carl,
-                avg_factor=num_total_pos,
-                num_class=self.num_classes)
-
-        # check NaN and Inf
-        assert torch.isfinite(all_cls_scores).all().item(), \
-            'classification scores become infinite or NaN!'
-        assert torch.isfinite(all_bbox_preds).all().item(), \
-            'bbox predications become infinite or NaN!'
-
-        losses_cls, losses_bbox = multi_apply(
-            self.loss_single,
-            all_cls_scores,
-            all_bbox_preds,
-            all_anchors,
-            all_labels,
-            all_label_weights,
-            all_bbox_targets,
-            all_bbox_weights,
-            num_total_samples=num_total_pos)
-        loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
-        if carl_loss_cfg is not None:
-            loss_dict.update(loss_carl)
-        return loss_dict
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/point_rend_roi_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/point_rend_roi_head.py
deleted file mode 100644
index 478cdf5bff6779e9291f94c543205289036ea2c6..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/point_rend_roi_head.py
+++ /dev/null
@@ -1,218 +0,0 @@
-# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend  # noqa
-
-import torch
-import torch.nn.functional as F
-from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
-
-from mmdet.core import bbox2roi, bbox_mapping, merge_aug_masks
-from .. import builder
-from ..builder import HEADS
-from .standard_roi_head import StandardRoIHead
-
-
-@HEADS.register_module()
-class PointRendRoIHead(StandardRoIHead):
-    """`PointRend <https://arxiv.org/abs/1912.08193>`_."""
-
-    def __init__(self, point_head, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.with_bbox and self.with_mask
-        self.init_point_head(point_head)
-
-    def init_point_head(self, point_head):
-        """Initialize ``point_head``"""
-        self.point_head = builder.build_head(point_head)
-
-    def init_weights(self, pretrained):
-        """Initialize the weights in head.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-        """
-        super().init_weights(pretrained)
-        self.point_head.init_weights()
-
-    def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
-                            img_metas):
-        """Run forward function and calculate loss for mask head and point head
-        in training."""
-        mask_results = super()._mask_forward_train(x, sampling_results,
-                                                   bbox_feats, gt_masks,
-                                                   img_metas)
-        if mask_results['loss_mask'] is not None:
-            loss_point = self._mask_point_forward_train(
-                x, sampling_results, mask_results['mask_pred'], gt_masks,
-                img_metas)
-            mask_results['loss_mask'].update(loss_point)
-
-        return mask_results
-
-    def _mask_point_forward_train(self, x, sampling_results, mask_pred,
-                                  gt_masks, img_metas):
-        """Run forward function and calculate loss for point head in
-        training."""
-        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
-        rel_roi_points = self.point_head.get_roi_rel_points_train(
-            mask_pred, pos_labels, cfg=self.train_cfg)
-        rois = bbox2roi([res.pos_bboxes for res in sampling_results])
-
-        fine_grained_point_feats = self._get_fine_grained_point_feats(
-            x, rois, rel_roi_points, img_metas)
-        coarse_point_feats = point_sample(mask_pred, rel_roi_points)
-        mask_point_pred = self.point_head(fine_grained_point_feats,
-                                          coarse_point_feats)
-        mask_point_target = self.point_head.get_targets(
-            rois, rel_roi_points, sampling_results, gt_masks, self.train_cfg)
-        loss_mask_point = self.point_head.loss(mask_point_pred,
-                                               mask_point_target, pos_labels)
-
-        return loss_mask_point
-
-    def _get_fine_grained_point_feats(self, x, rois, rel_roi_points,
-                                      img_metas):
-        """Sample fine grained feats from each level feature map and
-        concatenate them together."""
-        num_imgs = len(img_metas)
-        fine_grained_feats = []
-        for idx in range(self.mask_roi_extractor.num_inputs):
-            feats = x[idx]
-            spatial_scale = 1. / float(
-                self.mask_roi_extractor.featmap_strides[idx])
-            point_feats = []
-            for batch_ind in range(num_imgs):
-                # unravel batch dim
-                feat = feats[batch_ind].unsqueeze(0)
-                inds = (rois[:, 0].long() == batch_ind)
-                if inds.any():
-                    rel_img_points = rel_roi_point_to_rel_img_point(
-                        rois[inds], rel_roi_points[inds], feat.shape[2:],
-                        spatial_scale).unsqueeze(0)
-                    point_feat = point_sample(feat, rel_img_points)
-                    point_feat = point_feat.squeeze(0).transpose(0, 1)
-                    point_feats.append(point_feat)
-            fine_grained_feats.append(torch.cat(point_feats, dim=0))
-        return torch.cat(fine_grained_feats, dim=1)
-
-    def _mask_point_forward_test(self, x, rois, label_pred, mask_pred,
-                                 img_metas):
-        """Mask refining process with point head in testing."""
-        refined_mask_pred = mask_pred.clone()
-        for subdivision_step in range(self.test_cfg.subdivision_steps):
-            refined_mask_pred = F.interpolate(
-                refined_mask_pred,
-                scale_factor=self.test_cfg.scale_factor,
-                mode='bilinear',
-                align_corners=False)
-            # If `subdivision_num_points` is larger or equal to the
-            # resolution of the next step, then we can skip this step
-            num_rois, channels, mask_height, mask_width = \
-                refined_mask_pred.shape
-            if (self.test_cfg.subdivision_num_points >=
-                    self.test_cfg.scale_factor**2 * mask_height * mask_width
-                    and
-                    subdivision_step < self.test_cfg.subdivision_steps - 1):
-                continue
-            point_indices, rel_roi_points = \
-                self.point_head.get_roi_rel_points_test(
-                    refined_mask_pred, label_pred, cfg=self.test_cfg)
-            fine_grained_point_feats = self._get_fine_grained_point_feats(
-                x, rois, rel_roi_points, img_metas)
-            coarse_point_feats = point_sample(mask_pred, rel_roi_points)
-            mask_point_pred = self.point_head(fine_grained_point_feats,
-                                              coarse_point_feats)
-
-            point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1)
-            refined_mask_pred = refined_mask_pred.reshape(
-                num_rois, channels, mask_height * mask_width)
-            refined_mask_pred = refined_mask_pred.scatter_(
-                2, point_indices, mask_point_pred)
-            refined_mask_pred = refined_mask_pred.view(num_rois, channels,
-                                                       mask_height, mask_width)
-
-        return refined_mask_pred
-
-    def simple_test_mask(self,
-                         x,
-                         img_metas,
-                         det_bboxes,
-                         det_labels,
-                         rescale=False):
-        """Obtain mask prediction without augmentation."""
-        ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
-        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
-        num_imgs = len(det_bboxes)
-        if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
-            segm_results = [[[] for _ in range(self.mask_head.num_classes)]
-                            for _ in range(num_imgs)]
-        else:
-            # if det_bboxes is rescaled to the original image size, we need to
-            # rescale it back to the testing scale to obtain RoIs.
-            if rescale and not isinstance(scale_factors[0], float):
-                scale_factors = [
-                    torch.from_numpy(scale_factor).to(det_bboxes[0].device)
-                    for scale_factor in scale_factors
-                ]
-            _bboxes = [
-                det_bboxes[i][:, :4] *
-                scale_factors[i] if rescale else det_bboxes[i][:, :4]
-                for i in range(len(det_bboxes))
-            ]
-            mask_rois = bbox2roi(_bboxes)
-            mask_results = self._mask_forward(x, mask_rois)
-            # split batch mask prediction back to each image
-            mask_pred = mask_results['mask_pred']
-            num_mask_roi_per_img = [len(det_bbox) for det_bbox in det_bboxes]
-            mask_preds = mask_pred.split(num_mask_roi_per_img, 0)
-            mask_rois = mask_rois.split(num_mask_roi_per_img, 0)
-
-            # apply mask post-processing to each image individually
-            segm_results = []
-            for i in range(num_imgs):
-                if det_bboxes[i].shape[0] == 0:
-                    segm_results.append(
-                        [[] for _ in range(self.mask_head.num_classes)])
-                else:
-                    x_i = [xx[[i]] for xx in x]
-                    mask_rois_i = mask_rois[i]
-                    mask_rois_i[:, 0] = 0  # TODO: remove this hack
-                    mask_pred_i = self._mask_point_forward_test(
-                        x_i, mask_rois_i, det_labels[i], mask_preds[i],
-                        [img_metas])
-                    segm_result = self.mask_head.get_seg_masks(
-                        mask_pred_i, _bboxes[i], det_labels[i], self.test_cfg,
-                        ori_shapes[i], scale_factors[i], rescale)
-                    segm_results.append(segm_result)
-        return segm_results
-
-    def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
-        """Test for mask head with test time augmentation."""
-        if det_bboxes.shape[0] == 0:
-            segm_result = [[] for _ in range(self.mask_head.num_classes)]
-        else:
-            aug_masks = []
-            for x, img_meta in zip(feats, img_metas):
-                img_shape = img_meta[0]['img_shape']
-                scale_factor = img_meta[0]['scale_factor']
-                flip = img_meta[0]['flip']
-                _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
-                                       scale_factor, flip)
-                mask_rois = bbox2roi([_bboxes])
-                mask_results = self._mask_forward(x, mask_rois)
-                mask_results['mask_pred'] = self._mask_point_forward_test(
-                    x, mask_rois, det_labels, mask_results['mask_pred'],
-                    img_metas)
-                # convert to numpy array to save memory
-                aug_masks.append(
-                    mask_results['mask_pred'].sigmoid().cpu().numpy())
-            merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)
-
-            ori_shape = img_metas[0][0]['ori_shape']
-            segm_result = self.mask_head.get_seg_masks(
-                merged_masks,
-                det_bboxes,
-                det_labels,
-                self.test_cfg,
-                ori_shape,
-                scale_factor=1.0,
-                rescale=False)
-        return segm_result
diff --git a/spaces/adirik/kakao-brain-vit/app.py b/spaces/adirik/kakao-brain-vit/app.py
deleted file mode 100644
index 69cf2b549779dda4734652588ad369705754c5d2..0000000000000000000000000000000000000000
--- a/spaces/adirik/kakao-brain-vit/app.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import cv2
-import json
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-
-from backbone import create_name_vit
-from backbone import ClassificationModel
-
-
-
-vit_l16_512 = {
-    "backbone_name": "vit-l/16",
-    "backbone_params": {
-        "image_size": 512,
-        "representation_size": 0,
-        "attention_dropout_rate": 0.,
-        "dropout_rate": 0.,
-        "channels": 3
-    },
-    "dropout_rate": 0.,
-    "pretrained": "./weights/vit_l16_512/model-weights"
-}
-
-# Init backbone
-backbone = create_name_vit(vit_l16_512["backbone_name"], **vit_l16_512["backbone_params"])
-
-# Init classification model
-model = ClassificationModel(
-    backbone=backbone,
-    dropout_rate=vit_l16_512["dropout_rate"],
-    num_classes=1000
-)
-
-# Load weights
-model.load_weights(vit_l16_512["pretrained"])
-model.trainable = False
-
-# Load ImageNet idx to label mapping
-with open("assets/imagenet_1000_idx2labels.json") as f:
-    idx_to_label = json.load(f)
-
-
-def resize_with_normalization(image, size=[512, 512]):
-    image = tf.cast(image, tf.float32)
-    image = tf.image.resize(image, size)
-    image -= tf.constant(127.5, shape=(1, 1, 3), dtype=tf.float32)
-    image /= tf.constant(127.5, shape=(1, 1, 3), dtype=tf.float32)
-    image = tf.expand_dims(image, axis=0)
-    return image
-
-def softmax_stable(x):
-    return(np.exp(x - np.max(x)) / np.exp(x - np.max(x)).sum())
-
-def classify_image(img, top_k):
-    img = tf.convert_to_tensor(img)
-    img = resize_with_normalization(img)
-
-    pred_logits = model.predict(img, batch_size=1, workers=8)[0]
-    pred_probs = softmax_stable(pred_logits)
-    top_k_labels = pred_probs.argsort()[-top_k:][::-1]
-
-    return {idx_to_label[str(idx)] : round(float(pred_probs[idx]), 4) for idx in top_k_labels}
-
-
-demo = gr.Interface(
-    classify_image, 
-    inputs=[gr.Image(), gr.Slider(0, 1000, value=5)], 
-    outputs=gr.outputs.Label(),
-    title="Image Classification with Kakao Brain ViT",
-    examples=[
-        ["assets/halloween-gaf8ad7ebc_1920.jpeg", 5],
-        ["assets/IMG_4484.jpeg", 5],
-        ["assets/IMG_4737.jpeg", 5],
-        ["assets/IMG_4740.jpeg", 5],
-    ],
-)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/template_multi_spk/voc1/run.sh b/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/template_multi_spk/voc1/run.sh
deleted file mode 100644
index a98ef0f9f4bd4bf84eccfcc9a0f22112f02adebd..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/template_multi_spk/voc1/run.sh
+++ /dev/null
@@ -1,212 +0,0 @@
-#!/bin/bash
-
-# Copyright 2020 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-. ./cmd.sh || exit 1;
-. ./path.sh || exit 1;
-
-# basic settings
-stage=0        # stage to start
-stop_stage=100 # stage to stop
-verbose=1      # verbosity level (lower is less info)
-n_gpus=1       # number of gpus in training
-n_jobs=4       # number of parallel jobs in feature extraction
-
-# NOTE(kan-bayashi): renamed to conf to avoid conflict in parse_options.sh
-conf=conf/parallel_wavegan.v1.yaml
-
-# directory path setting
-db_root=/path/to/database # direcotry including spk name directory (MODIFY BY YOURSELF)
-                          # e.g.
-                          # /path/to/database
-                          # ├── spk_1
-                          # │   ├── utt1.wav
-                          # ├── spk_2
-                          # │   ├── utt1.wav
-                          # │   ...
-                          # └── spk_N
-                          #     ├── utt1.wav
-                          #     ...
-dumpdir=dump # directory to dump features
-
-# subset setting
-spks="all"    # speaker name to be used (e.g. "spk1 spk2")
-              # it must be matched the name under the ${db_root}
-              # if set to "all", all of the speakers in ${db_root} will be used
-shuffle=false # whether to shuffle the data to create subset
-num_dev=10    # the number of development data for each speaker
-num_eval=10   # the number of evaluation data for each speaker
-              # (if set to 0, the same dev set is used as eval set)
-
-# training related setting
-tag=""     # tag for directory to save model
-resume=""  # checkpoint path to resume training
-           # (e.g. <path>/<to>/checkpoint-10000steps.pkl)
-pretrain="" # checkpoint path to load pretrained parameters
-            # (e.g. ../../jsut/<path>/<to>/checkpoint-400000steps.pkl)
-
-# decoding related setting
-checkpoint="" # checkpoint path to be used for decoding
-              # if not provided, the latest one will be used
-              # (e.g. <path>/<to>/checkpoint-400000steps.pkl)
-
-# shellcheck disable=SC1091
-. utils/parse_options.sh || exit 1;
-
-train_set="train_nodev_$(echo "${spks}" | tr " " "_")" # name of training data directory
-dev_set="dev_$(echo "${spks}" | tr " " "_")"           # name of development data directory
-eval_set="eval_$(echo "${spks}" | tr " " "_")"         # name of evaluation data directory
-
-set -euo pipefail
-
-if [ "${stage}" -le 0 ] && [ "${stop_stage}" -ge 0 ]; then
-    echo "Stage 0: Data preparation"
-    train_data_dirs=""
-    dev_data_dirs=""
-    eval_data_dirs=""
-    if [ "${spks}" = "all" ]; then
-        spks=$(find "${db_root}" -maxdepth 1 ! -path "${db_root}" \
-            -follow -type d -print0 -name "[^.]*" | xargs -0 -I{} basename {})
-    fi
-    for spk in ${spks}; do
-        local/data_prep.sh \
-            --fs "$(yq ".sampling_rate" "${conf}")" \
-            --shuffle "${shuffle}" \
-            --num_dev "${num_dev}" \
-            --num_eval "${num_eval}" \
-            --train_set "train_nodev_${spk}" \
-            --dev_set "dev_${spk}" \
-            --eval_set "eval_${spk}" \
-            "${db_root}" "${spk}" data
-        train_data_dirs+=" data/train_nodev_${spk}"
-        dev_data_dirs+=" data/dev_${spk}"
-        eval_data_dirs+=" data/eval_${spk}"
-    done
-    # shellcheck disable=SC2086
-    utils/combine_data.sh "data/${train_set}" ${train_data_dirs}
-    # shellcheck disable=SC2086
-    utils/combine_data.sh "data/${dev_set}" ${dev_data_dirs}
-    # shellcheck disable=SC2086
-    utils/combine_data.sh "data/${eval_set}" ${eval_data_dirs}
-fi
-
-stats_ext=$(grep -q "hdf5" <(yq ".format" "${conf}") && echo "h5" || echo "npy")
-if [ "${stage}" -le 1 ] && [ "${stop_stage}" -ge 1 ]; then
-    echo "Stage 1: Feature extraction"
-    # extract raw features
-    pids=()
-    for name in "${train_set}" "${dev_set}" "${eval_set}"; do
-    (
-        [ ! -e "${dumpdir}/${name}/raw" ] && mkdir -p "${dumpdir}/${name}/raw"
-        echo "Feature extraction start. See the progress via ${dumpdir}/${name}/raw/preprocessing.*.log."
-        utils/make_subset_data.sh "data/${name}" "${n_jobs}" "${dumpdir}/${name}/raw"
-        ${train_cmd} JOB=1:${n_jobs} "${dumpdir}/${name}/raw/preprocessing.JOB.log" \
-            parallel-wavegan-preprocess \
-                --config "${conf}" \
-                --scp "${dumpdir}/${name}/raw/wav.JOB.scp" \
-                --dumpdir "${dumpdir}/${name}/raw/dump.JOB" \
-                --verbose "${verbose}"
-        echo "Successfully finished feature extraction of ${name} set."
-    ) &
-    pids+=($!)
-    done
-    i=0; for pid in "${pids[@]}"; do wait "${pid}" || ((++i)); done
-    [ "${i}" -gt 0 ] && echo "$0: ${i} background jobs are failed." && exit 1;
-    echo "Successfully finished feature extraction."
-
-    # calculate statistics for normalization
-    if [ -z "${pretrain}" ]; then
-        # calculate statistics for normalization
-        echo "Statistics computation start. See the progress via ${dumpdir}/${train_set}/compute_statistics.log."
-        ${train_cmd} "${dumpdir}/${train_set}/compute_statistics.log" \
-            parallel-wavegan-compute-statistics \
-                --config "${conf}" \
-                --rootdir "${dumpdir}/${train_set}/raw" \
-                --dumpdir "${dumpdir}/${train_set}" \
-                --verbose "${verbose}"
-        echo "Successfully finished calculation of statistics."
-    else
-        echo "Use statistics of pretrained model. Skip statistics computation."
-        cp "$(dirname "${pretrain}")/stats.${stats_ext}" "${dumpdir}/${train_set}"
-    fi
-
-    # normalize and dump them
-    pids=()
-    for name in "${train_set}" "${dev_set}" "${eval_set}"; do
-    (
-        [ ! -e "${dumpdir}/${name}/norm" ] && mkdir -p "${dumpdir}/${name}/norm"
-        echo "Nomalization start. See the progress via ${dumpdir}/${name}/norm/normalize.*.log."
-        ${train_cmd} JOB=1:${n_jobs} "${dumpdir}/${name}/norm/normalize.JOB.log" \
-            parallel-wavegan-normalize \
-                --config "${conf}" \
-                --stats "${dumpdir}/${train_set}/stats.${stats_ext}" \
-                --rootdir "${dumpdir}/${name}/raw/dump.JOB" \
-                --dumpdir "${dumpdir}/${name}/norm/dump.JOB" \
-                --verbose "${verbose}"
-        echo "Successfully finished normalization of ${name} set."
-    ) &
-    pids+=($!)
-    done
-    i=0; for pid in "${pids[@]}"; do wait "${pid}" || ((++i)); done
-    [ "${i}" -gt 0 ] && echo "$0: ${i} background jobs are failed." && exit 1;
-    echo "Successfully finished normalization."
-fi
-
-if [ -z "${tag}" ]; then
-    expdir="exp/${train_set}_$(basename "${conf}" .yaml)"
-    if [ -n "${pretrain}" ]; then
-        pretrain_tag=$(basename "$(dirname "${pretrain}")")
-        expdir+="_${pretrain_tag}"
-    fi
-else
-    expdir="exp/${train_set}_${tag}"
-fi
-if [ "${stage}" -le 2 ] && [ "${stop_stage}" -ge 2 ]; then
-    echo "Stage 2: Network training"
-    [ ! -e "${expdir}" ] && mkdir -p "${expdir}"
-    cp "${dumpdir}/${train_set}/stats.${stats_ext}" "${expdir}"
-    if [ "${n_gpus}" -gt 1 ]; then
-        train="python -m parallel_wavegan.distributed.launch --nproc_per_node ${n_gpus} -c parallel-wavegan-train"
-    else
-        train="parallel-wavegan-train"
-    fi
-    echo "Training start. See the progress via ${expdir}/train.log."
-    ${cuda_cmd} --gpu "${n_gpus}" "${expdir}/train.log" \
-        ${train} \
-            --config "${conf}" \
-            --train-dumpdir "${dumpdir}/${train_set}/norm" \
-            --dev-dumpdir "${dumpdir}/${dev_set}/norm" \
-            --outdir "${expdir}" \
-            --resume "${resume}" \
-            --pretrain "${pretrain}" \
-            --verbose "${verbose}"
-    echo "Successfully finished training."
-fi
-
-if [ "${stage}" -le 3 ] && [ "${stop_stage}" -ge 3 ]; then
-    echo "Stage 3: Network decoding"
-    # shellcheck disable=SC2012
-    [ -z "${checkpoint}" ] && checkpoint="$(ls -dt "${expdir}"/*.pkl | head -1 || true)"
-    outdir="${expdir}/wav/$(basename "${checkpoint}" .pkl)"
-    pids=()
-    for name in "${dev_set}" "${eval_set}"; do
-    (
-        [ ! -e "${outdir}/${name}" ] && mkdir -p "${outdir}/${name}"
-        [ "${n_gpus}" -gt 1 ] && n_gpus=1
-        echo "Decoding start. See the progress via ${outdir}/${name}/decode.log."
-        ${cuda_cmd} --gpu "${n_gpus}" "${outdir}/${name}/decode.log" \
-            parallel-wavegan-decode \
-                --dumpdir "${dumpdir}/${name}/norm" \
-                --checkpoint "${checkpoint}" \
-                --outdir "${outdir}/${name}" \
-                --verbose "${verbose}"
-        echo "Successfully finished decoding of ${name} set."
-    ) &
-    pids+=($!)
-    done
-    i=0; for pid in "${pids[@]}"; do wait "${pid}" || ((++i)); done
-    [ "${i}" -gt 0 ] && echo "$0: ${i} background jobs are failed." && exit 1;
-    echo "Successfully finished decoding."
-fi
-echo "Finished."
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pep517/envbuild.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pep517/envbuild.py
deleted file mode 100644
index fe8873c64a90d2ae3e44510453191e1ab4b5c84e..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pep517/envbuild.py
+++ /dev/null
@@ -1,171 +0,0 @@
-"""Build wheels/sdists by installing build deps to a temporary environment.
-"""
-
-import io
-import os
-import logging
-import shutil
-from subprocess import check_call
-import sys
-from sysconfig import get_paths
-from tempfile import mkdtemp
-
-from .compat import toml_load
-from .wrappers import Pep517HookCaller, LoggerWrapper
-
-log = logging.getLogger(__name__)
-
-
-def _load_pyproject(source_dir):
-    with io.open(
-            os.path.join(source_dir, 'pyproject.toml'),
-            'rb',
-            ) as f:
-        pyproject_data = toml_load(f)
-    buildsys = pyproject_data['build-system']
-    return (
-        buildsys['requires'],
-        buildsys['build-backend'],
-        buildsys.get('backend-path'),
-    )
-
-
-class BuildEnvironment(object):
-    """Context manager to install build deps in a simple temporary environment
-
-    Based on code I wrote for pip, which is MIT licensed.
-    """
-    # Copyright (c) 2008-2016 The pip developers (see AUTHORS.txt file)
-    #
-    # 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.
-
-    path = None
-
-    def __init__(self, cleanup=True):
-        self._cleanup = cleanup
-
-    def __enter__(self):
-        self.path = mkdtemp(prefix='pep517-build-env-')
-        log.info('Temporary build environment: %s', self.path)
-
-        self.save_path = os.environ.get('PATH', None)
-        self.save_pythonpath = os.environ.get('PYTHONPATH', None)
-
-        install_scheme = 'nt' if (os.name == 'nt') else 'posix_prefix'
-        install_dirs = get_paths(install_scheme, vars={
-            'base': self.path,
-            'platbase': self.path,
-        })
-
-        scripts = install_dirs['scripts']
-        if self.save_path:
-            os.environ['PATH'] = scripts + os.pathsep + self.save_path
-        else:
-            os.environ['PATH'] = scripts + os.pathsep + os.defpath
-
-        if install_dirs['purelib'] == install_dirs['platlib']:
-            lib_dirs = install_dirs['purelib']
-        else:
-            lib_dirs = install_dirs['purelib'] + os.pathsep + \
-                install_dirs['platlib']
-        if self.save_pythonpath:
-            os.environ['PYTHONPATH'] = lib_dirs + os.pathsep + \
-                self.save_pythonpath
-        else:
-            os.environ['PYTHONPATH'] = lib_dirs
-
-        return self
-
-    def pip_install(self, reqs):
-        """Install dependencies into this env by calling pip in a subprocess"""
-        if not reqs:
-            return
-        log.info('Calling pip to install %s', reqs)
-        cmd = [
-            sys.executable, '-m', 'pip', 'install', '--ignore-installed',
-            '--prefix', self.path] + list(reqs)
-        check_call(
-            cmd,
-            stdout=LoggerWrapper(log, logging.INFO),
-            stderr=LoggerWrapper(log, logging.ERROR),
-        )
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        needs_cleanup = (
-            self._cleanup and
-            self.path is not None and
-            os.path.isdir(self.path)
-        )
-        if needs_cleanup:
-            shutil.rmtree(self.path)
-
-        if self.save_path is None:
-            os.environ.pop('PATH', None)
-        else:
-            os.environ['PATH'] = self.save_path
-
-        if self.save_pythonpath is None:
-            os.environ.pop('PYTHONPATH', None)
-        else:
-            os.environ['PYTHONPATH'] = self.save_pythonpath
-
-
-def build_wheel(source_dir, wheel_dir, config_settings=None):
-    """Build a wheel from a source directory using PEP 517 hooks.
-
-    :param str source_dir: Source directory containing pyproject.toml
-    :param str wheel_dir: Target directory to create wheel in
-    :param dict config_settings: Options to pass to build backend
-
-    This is a blocking function which will run pip in a subprocess to install
-    build requirements.
-    """
-    if config_settings is None:
-        config_settings = {}
-    requires, backend, backend_path = _load_pyproject(source_dir)
-    hooks = Pep517HookCaller(source_dir, backend, backend_path)
-
-    with BuildEnvironment() as env:
-        env.pip_install(requires)
-        reqs = hooks.get_requires_for_build_wheel(config_settings)
-        env.pip_install(reqs)
-        return hooks.build_wheel(wheel_dir, config_settings)
-
-
-def build_sdist(source_dir, sdist_dir, config_settings=None):
-    """Build an sdist from a source directory using PEP 517 hooks.
-
-    :param str source_dir: Source directory containing pyproject.toml
-    :param str sdist_dir: Target directory to place sdist in
-    :param dict config_settings: Options to pass to build backend
-
-    This is a blocking function which will run pip in a subprocess to install
-    build requirements.
-    """
-    if config_settings is None:
-        config_settings = {}
-    requires, backend, backend_path = _load_pyproject(source_dir)
-    hooks = Pep517HookCaller(source_dir, backend, backend_path)
-
-    with BuildEnvironment() as env:
-        env.pip_install(requires)
-        reqs = hooks.get_requires_for_build_sdist(config_settings)
-        env.pip_install(reqs)
-        return hooks.build_sdist(sdist_dir, config_settings)
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/macos.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/macos.py
deleted file mode 100644
index a01337c7764e1e1aba1f3ba378a1c9241f31806d..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/macos.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from __future__ import annotations
-
-import os
-
-from .api import PlatformDirsABC
-
-
-class MacOS(PlatformDirsABC):
-    """
-    Platform directories for the macOS operating system. Follows the guidance from `Apple documentation
-    <https://developer.apple.com/library/archive/documentation/FileManagement/Conceptual/FileSystemProgrammingGuide/MacOSXDirectories/MacOSXDirectories.html>`_.
-    Makes use of the `appname <platformdirs.api.PlatformDirsABC.appname>` and
-    `version <platformdirs.api.PlatformDirsABC.version>`.
-    """
-
-    @property
-    def user_data_dir(self) -> str:
-        """:return: data directory tied to the user, e.g. ``~/Library/Application Support/$appname/$version``"""
-        return self._append_app_name_and_version(os.path.expanduser("~/Library/Application Support/"))
-
-    @property
-    def site_data_dir(self) -> str:
-        """:return: data directory shared by users, e.g. ``/Library/Application Support/$appname/$version``"""
-        return self._append_app_name_and_version("/Library/Application Support")
-
-    @property
-    def user_config_dir(self) -> str:
-        """:return: config directory tied to the user, e.g. ``~/Library/Preferences/$appname/$version``"""
-        return self._append_app_name_and_version(os.path.expanduser("~/Library/Preferences/"))
-
-    @property
-    def site_config_dir(self) -> str:
-        """:return: config directory shared by the users, e.g. ``/Library/Preferences/$appname``"""
-        return self._append_app_name_and_version("/Library/Preferences")
-
-    @property
-    def user_cache_dir(self) -> str:
-        """:return: cache directory tied to the user, e.g. ``~/Library/Caches/$appname/$version``"""
-        return self._append_app_name_and_version(os.path.expanduser("~/Library/Caches"))
-
-    @property
-    def user_state_dir(self) -> str:
-        """:return: state directory tied to the user, same as `user_data_dir`"""
-        return self.user_data_dir
-
-    @property
-    def user_log_dir(self) -> str:
-        """:return: log directory tied to the user, e.g. ``~/Library/Logs/$appname/$version``"""
-        return self._append_app_name_and_version(os.path.expanduser("~/Library/Logs"))
-
-    @property
-    def user_documents_dir(self) -> str:
-        """:return: documents directory tied to the user, e.g. ``~/Documents``"""
-        return os.path.expanduser("~/Documents")
-
-    @property
-    def user_runtime_dir(self) -> str:
-        """:return: runtime directory tied to the user, e.g. ``~/Library/Caches/TemporaryItems/$appname/$version``"""
-        return self._append_app_name_and_version(os.path.expanduser("~/Library/Caches/TemporaryItems"))
-
-
-__all__ = [
-    "MacOS",
-]
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/html.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/html.py
deleted file mode 100644
index 47f5d9c17fbfed747f57cedb697c07f43f451604..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/html.py
+++ /dev/null
@@ -1,983 +0,0 @@
-"""
-    pygments.formatters.html
-    ~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Formatter for HTML output.
-
-    :copyright: Copyright 2006-2021 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import functools
-import os
-import sys
-import os.path
-from io import StringIO
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.token import Token, Text, STANDARD_TYPES
-from pip._vendor.pygments.util import get_bool_opt, get_int_opt, get_list_opt
-
-try:
-    import ctags
-except ImportError:
-    ctags = None
-
-__all__ = ['HtmlFormatter']
-
-
-_escape_html_table = {
-    ord('&'): '&amp;',
-    ord('<'): '&lt;',
-    ord('>'): '&gt;',
-    ord('"'): '&quot;',
-    ord("'"): '&#39;',
-}
-
-
-def escape_html(text, table=_escape_html_table):
-    """Escape &, <, > as well as single and double quotes for HTML."""
-    return text.translate(table)
-
-
-def webify(color):
-    if color.startswith('calc') or color.startswith('var'):
-        return color
-    else:
-        return '#' + color
-
-
-def _get_ttype_class(ttype):
-    fname = STANDARD_TYPES.get(ttype)
-    if fname:
-        return fname
-    aname = ''
-    while fname is None:
-        aname = '-' + ttype[-1] + aname
-        ttype = ttype.parent
-        fname = STANDARD_TYPES.get(ttype)
-    return fname + aname
-
-
-CSSFILE_TEMPLATE = '''\
-/*
-generated by Pygments <https://pygments.org/>
-Copyright 2006-2021 by the Pygments team.
-Licensed under the BSD license, see LICENSE for details.
-*/
-%(styledefs)s
-'''
-
-DOC_HEADER = '''\
-<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN"
-   "http://www.w3.org/TR/html4/strict.dtd">
-<!--
-generated by Pygments <https://pygments.org/>
-Copyright 2006-2021 by the Pygments team.
-Licensed under the BSD license, see LICENSE for details.
--->
-<html>
-<head>
-  <title>%(title)s</title>
-  <meta http-equiv="content-type" content="text/html; charset=%(encoding)s">
-  <style type="text/css">
-''' + CSSFILE_TEMPLATE + '''
-  </style>
-</head>
-<body>
-<h2>%(title)s</h2>
-
-'''
-
-DOC_HEADER_EXTERNALCSS = '''\
-<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN"
-   "http://www.w3.org/TR/html4/strict.dtd">
-
-<html>
-<head>
-  <title>%(title)s</title>
-  <meta http-equiv="content-type" content="text/html; charset=%(encoding)s">
-  <link rel="stylesheet" href="%(cssfile)s" type="text/css">
-</head>
-<body>
-<h2>%(title)s</h2>
-
-'''
-
-DOC_FOOTER = '''\
-</body>
-</html>
-'''
-
-
-class HtmlFormatter(Formatter):
-    r"""
-    Format tokens as HTML 4 ``<span>`` tags within a ``<pre>`` tag, wrapped
-    in a ``<div>`` tag. The ``<div>``'s CSS class can be set by the `cssclass`
-    option.
-
-    If the `linenos` option is set to ``"table"``, the ``<pre>`` is
-    additionally wrapped inside a ``<table>`` which has one row and two
-    cells: one containing the line numbers and one containing the code.
-    Example:
-
-    .. sourcecode:: html
-
-        <div class="highlight" >
-        <table><tr>
-          <td class="linenos" title="click to toggle"
-            onclick="with (this.firstChild.style)
-                     { display = (display == '') ? 'none' : '' }">
-            <pre>1
-            2</pre>
-          </td>
-          <td class="code">
-            <pre><span class="Ke">def </span><span class="NaFu">foo</span>(bar):
-              <span class="Ke">pass</span>
-            </pre>
-          </td>
-        </tr></table></div>
-
-    (whitespace added to improve clarity).
-
-    Wrapping can be disabled using the `nowrap` option.
-
-    A list of lines can be specified using the `hl_lines` option to make these
-    lines highlighted (as of Pygments 0.11).
-
-    With the `full` option, a complete HTML 4 document is output, including
-    the style definitions inside a ``<style>`` tag, or in a separate file if
-    the `cssfile` option is given.
-
-    When `tagsfile` is set to the path of a ctags index file, it is used to
-    generate hyperlinks from names to their definition.  You must enable
-    `lineanchors` and run ctags with the `-n` option for this to work.  The
-    `python-ctags` module from PyPI must be installed to use this feature;
-    otherwise a `RuntimeError` will be raised.
-
-    The `get_style_defs(arg='')` method of a `HtmlFormatter` returns a string
-    containing CSS rules for the CSS classes used by the formatter. The
-    argument `arg` can be used to specify additional CSS selectors that
-    are prepended to the classes. A call `fmter.get_style_defs('td .code')`
-    would result in the following CSS classes:
-
-    .. sourcecode:: css
-
-        td .code .kw { font-weight: bold; color: #00FF00 }
-        td .code .cm { color: #999999 }
-        ...
-
-    If you have Pygments 0.6 or higher, you can also pass a list or tuple to the
-    `get_style_defs()` method to request multiple prefixes for the tokens:
-
-    .. sourcecode:: python
-
-        formatter.get_style_defs(['div.syntax pre', 'pre.syntax'])
-
-    The output would then look like this:
-
-    .. sourcecode:: css
-
-        div.syntax pre .kw,
-        pre.syntax .kw { font-weight: bold; color: #00FF00 }
-        div.syntax pre .cm,
-        pre.syntax .cm { color: #999999 }
-        ...
-
-    Additional options accepted:
-
-    `nowrap`
-        If set to ``True``, don't wrap the tokens at all, not even inside a ``<pre>``
-        tag. This disables most other options (default: ``False``).
-
-    `full`
-        Tells the formatter to output a "full" document, i.e. a complete
-        self-contained document (default: ``False``).
-
-    `title`
-        If `full` is true, the title that should be used to caption the
-        document (default: ``''``).
-
-    `style`
-        The style to use, can be a string or a Style subclass (default:
-        ``'default'``). This option has no effect if the `cssfile`
-        and `noclobber_cssfile` option are given and the file specified in
-        `cssfile` exists.
-
-    `noclasses`
-        If set to true, token ``<span>`` tags (as well as line number elements)
-        will not use CSS classes, but inline styles. This is not recommended
-        for larger pieces of code since it increases output size by quite a bit
-        (default: ``False``).
-
-    `classprefix`
-        Since the token types use relatively short class names, they may clash
-        with some of your own class names. In this case you can use the
-        `classprefix` option to give a string to prepend to all Pygments-generated
-        CSS class names for token types.
-        Note that this option also affects the output of `get_style_defs()`.
-
-    `cssclass`
-        CSS class for the wrapping ``<div>`` tag (default: ``'highlight'``).
-        If you set this option, the default selector for `get_style_defs()`
-        will be this class.
-
-        .. versionadded:: 0.9
-           If you select the ``'table'`` line numbers, the wrapping table will
-           have a CSS class of this string plus ``'table'``, the default is
-           accordingly ``'highlighttable'``.
-
-    `cssstyles`
-        Inline CSS styles for the wrapping ``<div>`` tag (default: ``''``).
-
-    `prestyles`
-        Inline CSS styles for the ``<pre>`` tag (default: ``''``).
-
-        .. versionadded:: 0.11
-
-    `cssfile`
-        If the `full` option is true and this option is given, it must be the
-        name of an external file. If the filename does not include an absolute
-        path, the file's path will be assumed to be relative to the main output
-        file's path, if the latter can be found. The stylesheet is then written
-        to this file instead of the HTML file.
-
-        .. versionadded:: 0.6
-
-    `noclobber_cssfile`
-        If `cssfile` is given and the specified file exists, the css file will
-        not be overwritten. This allows the use of the `full` option in
-        combination with a user specified css file. Default is ``False``.
-
-        .. versionadded:: 1.1
-
-    `linenos`
-        If set to ``'table'``, output line numbers as a table with two cells,
-        one containing the line numbers, the other the whole code.  This is
-        copy-and-paste-friendly, but may cause alignment problems with some
-        browsers or fonts.  If set to ``'inline'``, the line numbers will be
-        integrated in the ``<pre>`` tag that contains the code (that setting
-        is *new in Pygments 0.8*).
-
-        For compatibility with Pygments 0.7 and earlier, every true value
-        except ``'inline'`` means the same as ``'table'`` (in particular, that
-        means also ``True``).
-
-        The default value is ``False``, which means no line numbers at all.
-
-        **Note:** with the default ("table") line number mechanism, the line
-        numbers and code can have different line heights in Internet Explorer
-        unless you give the enclosing ``<pre>`` tags an explicit ``line-height``
-        CSS property (you get the default line spacing with ``line-height:
-        125%``).
-
-    `hl_lines`
-        Specify a list of lines to be highlighted. The line numbers are always
-        relative to the input (i.e. the first line is line 1) and are
-        independent of `linenostart`.
-
-        .. versionadded:: 0.11
-
-    `linenostart`
-        The line number for the first line (default: ``1``).
-
-    `linenostep`
-        If set to a number n > 1, only every nth line number is printed.
-
-    `linenospecial`
-        If set to a number n > 0, every nth line number is given the CSS
-        class ``"special"`` (default: ``0``).
-
-    `nobackground`
-        If set to ``True``, the formatter won't output the background color
-        for the wrapping element (this automatically defaults to ``False``
-        when there is no wrapping element [eg: no argument for the
-        `get_syntax_defs` method given]) (default: ``False``).
-
-        .. versionadded:: 0.6
-
-    `lineseparator`
-        This string is output between lines of code. It defaults to ``"\n"``,
-        which is enough to break a line inside ``<pre>`` tags, but you can
-        e.g. set it to ``"<br>"`` to get HTML line breaks.
-
-        .. versionadded:: 0.7
-
-    `lineanchors`
-        If set to a nonempty string, e.g. ``foo``, the formatter will wrap each
-        output line in an anchor tag with an ``id`` (and `name`) of ``foo-linenumber``.
-        This allows easy linking to certain lines.
-
-        .. versionadded:: 0.9
-
-    `linespans`
-        If set to a nonempty string, e.g. ``foo``, the formatter will wrap each
-        output line in a span tag with an ``id`` of ``foo-linenumber``.
-        This allows easy access to lines via javascript.
-
-        .. versionadded:: 1.6
-
-    `anchorlinenos`
-        If set to `True`, will wrap line numbers in <a> tags. Used in
-        combination with `linenos` and `lineanchors`.
-
-    `tagsfile`
-        If set to the path of a ctags file, wrap names in anchor tags that
-        link to their definitions. `lineanchors` should be used, and the
-        tags file should specify line numbers (see the `-n` option to ctags).
-
-        .. versionadded:: 1.6
-
-    `tagurlformat`
-        A string formatting pattern used to generate links to ctags definitions.
-        Available variables are `%(path)s`, `%(fname)s` and `%(fext)s`.
-        Defaults to an empty string, resulting in just `#prefix-number` links.
-
-        .. versionadded:: 1.6
-
-    `filename`
-        A string used to generate a filename when rendering ``<pre>`` blocks,
-        for example if displaying source code. If `linenos` is set to
-        ``'table'`` then the filename will be rendered in an initial row
-        containing a single `<th>` which spans both columns.
-
-        .. versionadded:: 2.1
-
-    `wrapcode`
-        Wrap the code inside ``<pre>`` blocks using ``<code>``, as recommended
-        by the HTML5 specification.
-
-        .. versionadded:: 2.4
-
-    `debug_token_types`
-        Add ``title`` attributes to all token ``<span>`` tags that show the
-        name of the token.
-
-        .. versionadded:: 2.10
-
-
-    **Subclassing the HTML formatter**
-
-    .. versionadded:: 0.7
-
-    The HTML formatter is now built in a way that allows easy subclassing, thus
-    customizing the output HTML code. The `format()` method calls
-    `self._format_lines()` which returns a generator that yields tuples of ``(1,
-    line)``, where the ``1`` indicates that the ``line`` is a line of the
-    formatted source code.
-
-    If the `nowrap` option is set, the generator is the iterated over and the
-    resulting HTML is output.
-
-    Otherwise, `format()` calls `self.wrap()`, which wraps the generator with
-    other generators. These may add some HTML code to the one generated by
-    `_format_lines()`, either by modifying the lines generated by the latter,
-    then yielding them again with ``(1, line)``, and/or by yielding other HTML
-    code before or after the lines, with ``(0, html)``. The distinction between
-    source lines and other code makes it possible to wrap the generator multiple
-    times.
-
-    The default `wrap()` implementation adds a ``<div>`` and a ``<pre>`` tag.
-
-    A custom `HtmlFormatter` subclass could look like this:
-
-    .. sourcecode:: python
-
-        class CodeHtmlFormatter(HtmlFormatter):
-
-            def wrap(self, source, outfile):
-                return self._wrap_code(source)
-
-            def _wrap_code(self, source):
-                yield 0, '<code>'
-                for i, t in source:
-                    if i == 1:
-                        # it's a line of formatted code
-                        t += '<br>'
-                    yield i, t
-                yield 0, '</code>'
-
-    This results in wrapping the formatted lines with a ``<code>`` tag, where the
-    source lines are broken using ``<br>`` tags.
-
-    After calling `wrap()`, the `format()` method also adds the "line numbers"
-    and/or "full document" wrappers if the respective options are set. Then, all
-    HTML yielded by the wrapped generator is output.
-    """
-
-    name = 'HTML'
-    aliases = ['html']
-    filenames = ['*.html', '*.htm']
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        self.title = self._decodeifneeded(self.title)
-        self.nowrap = get_bool_opt(options, 'nowrap', False)
-        self.noclasses = get_bool_opt(options, 'noclasses', False)
-        self.classprefix = options.get('classprefix', '')
-        self.cssclass = self._decodeifneeded(options.get('cssclass', 'highlight'))
-        self.cssstyles = self._decodeifneeded(options.get('cssstyles', ''))
-        self.prestyles = self._decodeifneeded(options.get('prestyles', ''))
-        self.cssfile = self._decodeifneeded(options.get('cssfile', ''))
-        self.noclobber_cssfile = get_bool_opt(options, 'noclobber_cssfile', False)
-        self.tagsfile = self._decodeifneeded(options.get('tagsfile', ''))
-        self.tagurlformat = self._decodeifneeded(options.get('tagurlformat', ''))
-        self.filename = self._decodeifneeded(options.get('filename', ''))
-        self.wrapcode = get_bool_opt(options, 'wrapcode', False)
-        self.span_element_openers = {}
-        self.debug_token_types = get_bool_opt(options, 'debug_token_types', False)
-
-        if self.tagsfile:
-            if not ctags:
-                raise RuntimeError('The "ctags" package must to be installed '
-                                   'to be able to use the "tagsfile" feature.')
-            self._ctags = ctags.CTags(self.tagsfile)
-
-        linenos = options.get('linenos', False)
-        if linenos == 'inline':
-            self.linenos = 2
-        elif linenos:
-            # compatibility with <= 0.7
-            self.linenos = 1
-        else:
-            self.linenos = 0
-        self.linenostart = abs(get_int_opt(options, 'linenostart', 1))
-        self.linenostep = abs(get_int_opt(options, 'linenostep', 1))
-        self.linenospecial = abs(get_int_opt(options, 'linenospecial', 0))
-        self.nobackground = get_bool_opt(options, 'nobackground', False)
-        self.lineseparator = options.get('lineseparator', '\n')
-        self.lineanchors = options.get('lineanchors', '')
-        self.linespans = options.get('linespans', '')
-        self.anchorlinenos = get_bool_opt(options, 'anchorlinenos', False)
-        self.hl_lines = set()
-        for lineno in get_list_opt(options, 'hl_lines', []):
-            try:
-                self.hl_lines.add(int(lineno))
-            except ValueError:
-                pass
-
-        self._create_stylesheet()
-
-    def _get_css_class(self, ttype):
-        """Return the css class of this token type prefixed with
-        the classprefix option."""
-        ttypeclass = _get_ttype_class(ttype)
-        if ttypeclass:
-            return self.classprefix + ttypeclass
-        return ''
-
-    def _get_css_classes(self, ttype):
-        """Return the CSS classes of this token type prefixed with the classprefix option."""
-        cls = self._get_css_class(ttype)
-        while ttype not in STANDARD_TYPES:
-            ttype = ttype.parent
-            cls = self._get_css_class(ttype) + ' ' + cls
-        return cls or ''
-
-    def _get_css_inline_styles(self, ttype):
-        """Return the inline CSS styles for this token type."""
-        cclass = self.ttype2class.get(ttype)
-        while cclass is None:
-            ttype = ttype.parent
-            cclass = self.ttype2class.get(ttype)
-        return cclass or ''
-
-    def _create_stylesheet(self):
-        t2c = self.ttype2class = {Token: ''}
-        c2s = self.class2style = {}
-        for ttype, ndef in self.style:
-            name = self._get_css_class(ttype)
-            style = ''
-            if ndef['color']:
-                style += 'color: %s; ' % webify(ndef['color'])
-            if ndef['bold']:
-                style += 'font-weight: bold; '
-            if ndef['italic']:
-                style += 'font-style: italic; '
-            if ndef['underline']:
-                style += 'text-decoration: underline; '
-            if ndef['bgcolor']:
-                style += 'background-color: %s; ' % webify(ndef['bgcolor'])
-            if ndef['border']:
-                style += 'border: 1px solid %s; ' % webify(ndef['border'])
-            if style:
-                t2c[ttype] = name
-                # save len(ttype) to enable ordering the styles by
-                # hierarchy (necessary for CSS cascading rules!)
-                c2s[name] = (style[:-2], ttype, len(ttype))
-
-    def get_style_defs(self, arg=None):
-        """
-        Return CSS style definitions for the classes produced by the current
-        highlighting style. ``arg`` can be a string or list of selectors to
-        insert before the token type classes.
-        """
-        style_lines = []
-
-        style_lines.extend(self.get_linenos_style_defs())
-        style_lines.extend(self.get_background_style_defs(arg))
-        style_lines.extend(self.get_token_style_defs(arg))
-
-        return '\n'.join(style_lines)
-
-    def get_token_style_defs(self, arg=None):
-        prefix = self.get_css_prefix(arg)
-
-        styles = [
-            (level, ttype, cls, style)
-            for cls, (style, ttype, level) in self.class2style.items()
-            if cls and style
-        ]
-        styles.sort()
-
-        lines = [
-            '%s { %s } /* %s */' % (prefix(cls), style, repr(ttype)[6:])
-            for (level, ttype, cls, style) in styles
-        ]
-
-        return lines
-
-    def get_background_style_defs(self, arg=None):
-        prefix = self.get_css_prefix(arg)
-        bg_color = self.style.background_color
-        hl_color = self.style.highlight_color
-
-        lines = []
-
-        if arg and not self.nobackground and bg_color is not None:
-            text_style = ''
-            if Text in self.ttype2class:
-                text_style = ' ' + self.class2style[self.ttype2class[Text]][0]
-            lines.insert(
-                0, '%s{ background: %s;%s }' % (
-                    prefix(''), bg_color, text_style
-                )
-            )
-        if hl_color is not None:
-            lines.insert(
-                0, '%s { background-color: %s }' % (prefix('hll'), hl_color)
-            )
-
-        return lines
-
-    def get_linenos_style_defs(self):
-        lines = [
-            'pre { %s }' % self._pre_style,
-            'td.linenos .normal { %s }' % self._linenos_style,
-            'span.linenos { %s }' % self._linenos_style,
-            'td.linenos .special { %s }' % self._linenos_special_style,
-            'span.linenos.special { %s }' % self._linenos_special_style,
-        ]
-
-        return lines
-
-    def get_css_prefix(self, arg):
-        if arg is None:
-            arg = ('cssclass' in self.options and '.'+self.cssclass or '')
-        if isinstance(arg, str):
-            args = [arg]
-        else:
-            args = list(arg)
-
-        def prefix(cls):
-            if cls:
-                cls = '.' + cls
-            tmp = []
-            for arg in args:
-                tmp.append((arg and arg + ' ' or '') + cls)
-            return ', '.join(tmp)
-
-        return prefix
-
-    @property
-    def _pre_style(self):
-        return 'line-height: 125%;'
-
-    @property
-    def _linenos_style(self):
-        return 'color: %s; background-color: %s; padding-left: 5px; padding-right: 5px;' % (
-            self.style.line_number_color,
-            self.style.line_number_background_color
-        )
-
-    @property
-    def _linenos_special_style(self):
-        return 'color: %s; background-color: %s; padding-left: 5px; padding-right: 5px;' % (
-            self.style.line_number_special_color,
-            self.style.line_number_special_background_color
-        )
-
-    def _decodeifneeded(self, value):
-        if isinstance(value, bytes):
-            if self.encoding:
-                return value.decode(self.encoding)
-            return value.decode()
-        return value
-
-    def _wrap_full(self, inner, outfile):
-        if self.cssfile:
-            if os.path.isabs(self.cssfile):
-                # it's an absolute filename
-                cssfilename = self.cssfile
-            else:
-                try:
-                    filename = outfile.name
-                    if not filename or filename[0] == '<':
-                        # pseudo files, e.g. name == '<fdopen>'
-                        raise AttributeError
-                    cssfilename = os.path.join(os.path.dirname(filename),
-                                               self.cssfile)
-                except AttributeError:
-                    print('Note: Cannot determine output file name, '
-                          'using current directory as base for the CSS file name',
-                          file=sys.stderr)
-                    cssfilename = self.cssfile
-            # write CSS file only if noclobber_cssfile isn't given as an option.
-            try:
-                if not os.path.exists(cssfilename) or not self.noclobber_cssfile:
-                    with open(cssfilename, "w") as cf:
-                        cf.write(CSSFILE_TEMPLATE %
-                                 {'styledefs': self.get_style_defs('body')})
-            except OSError as err:
-                err.strerror = 'Error writing CSS file: ' + err.strerror
-                raise
-
-            yield 0, (DOC_HEADER_EXTERNALCSS %
-                      dict(title=self.title,
-                           cssfile=self.cssfile,
-                           encoding=self.encoding))
-        else:
-            yield 0, (DOC_HEADER %
-                      dict(title=self.title,
-                           styledefs=self.get_style_defs('body'),
-                           encoding=self.encoding))
-
-        yield from inner
-        yield 0, DOC_FOOTER
-
-    def _wrap_tablelinenos(self, inner):
-        dummyoutfile = StringIO()
-        lncount = 0
-        for t, line in inner:
-            if t:
-                lncount += 1
-            dummyoutfile.write(line)
-
-        fl = self.linenostart
-        mw = len(str(lncount + fl - 1))
-        sp = self.linenospecial
-        st = self.linenostep
-        la = self.lineanchors
-        aln = self.anchorlinenos
-        nocls = self.noclasses
-
-        lines = []
-
-        for i in range(fl, fl+lncount):
-            print_line = i % st == 0
-            special_line = sp and i % sp == 0
-
-            if print_line:
-                line = '%*d' % (mw, i)
-                if aln:
-                    line = '<a href="#%s-%d">%s</a>' % (la, i, line)
-            else:
-                line = ' ' * mw
-
-            if nocls:
-                if special_line:
-                    style = ' style="%s"' % self._linenos_special_style
-                else:
-                    style = ' style="%s"' % self._linenos_style
-            else:
-                if special_line:
-                    style = ' class="special"'
-                else:
-                    style = ' class="normal"'
-
-            if style:
-                line = '<span%s>%s</span>' % (style, line)
-
-            lines.append(line)
-
-        ls = '\n'.join(lines)
-
-        # If a filename was specified, we can't put it into the code table as it
-        # would misalign the line numbers. Hence we emit a separate row for it.
-        filename_tr = ""
-        if self.filename:
-            filename_tr = (
-                '<tr><th colspan="2" class="filename"><div class="highlight">'
-                '<span class="filename">' + self.filename + '</span></div>'
-                '</th></tr>')
-
-        # in case you wonder about the seemingly redundant <div> here: since the
-        # content in the other cell also is wrapped in a div, some browsers in
-        # some configurations seem to mess up the formatting...
-        yield 0, (
-            '<table class="%stable">' % self.cssclass + filename_tr +
-            '<tr><td class="linenos"><div class="linenodiv"><pre>' +
-            ls + '</pre></div></td><td class="code">'
-        )
-        yield 0, dummyoutfile.getvalue()
-        yield 0, '</td></tr></table>'
-
-    def _wrap_inlinelinenos(self, inner):
-        # need a list of lines since we need the width of a single number :(
-        inner_lines = list(inner)
-        sp = self.linenospecial
-        st = self.linenostep
-        num = self.linenostart
-        mw = len(str(len(inner_lines) + num - 1))
-        la = self.lineanchors
-        aln = self.anchorlinenos
-        nocls = self.noclasses
-
-        for _, inner_line in inner_lines:
-            print_line = num % st == 0
-            special_line = sp and num % sp == 0
-
-            if print_line:
-                line = '%*d' % (mw, num)
-            else:
-                line = ' ' * mw
-
-            if nocls:
-                if special_line:
-                    style = ' style="%s"' % self._linenos_special_style
-                else:
-                    style = ' style="%s"' % self._linenos_style
-            else:
-                if special_line:
-                    style = ' class="linenos special"'
-                else:
-                    style = ' class="linenos"'
-
-            if style:
-                linenos = '<span%s>%s</span>' % (style, line)
-            else:
-                linenos = line
-
-            if aln:
-                yield 1, ('<a href="#%s-%d">%s</a>' % (la, num, linenos) +
-                          inner_line)
-            else:
-                yield 1, linenos + inner_line
-            num += 1
-
-    def _wrap_lineanchors(self, inner):
-        s = self.lineanchors
-        # subtract 1 since we have to increment i *before* yielding
-        i = self.linenostart - 1
-        for t, line in inner:
-            if t:
-                i += 1
-                href = "" if self.linenos else ' href="#%s-%d"' % (s, i)
-                yield 1, '<a id="%s-%d" name="%s-%d"%s></a>' % (s, i, s, i, href) + line
-            else:
-                yield 0, line
-
-    def _wrap_linespans(self, inner):
-        s = self.linespans
-        i = self.linenostart - 1
-        for t, line in inner:
-            if t:
-                i += 1
-                yield 1, '<span id="%s-%d">%s</span>' % (s, i, line)
-            else:
-                yield 0, line
-
-    def _wrap_div(self, inner):
-        style = []
-        if (self.noclasses and not self.nobackground and
-                self.style.background_color is not None):
-            style.append('background: %s' % (self.style.background_color,))
-        if self.cssstyles:
-            style.append(self.cssstyles)
-        style = '; '.join(style)
-
-        yield 0, ('<div' + (self.cssclass and ' class="%s"' % self.cssclass) +
-                  (style and (' style="%s"' % style)) + '>')
-        yield from inner
-        yield 0, '</div>\n'
-
-    def _wrap_pre(self, inner):
-        style = []
-        if self.prestyles:
-            style.append(self.prestyles)
-        if self.noclasses:
-            style.append(self._pre_style)
-        style = '; '.join(style)
-
-        if self.filename and self.linenos != 1:
-            yield 0, ('<span class="filename">' + self.filename + '</span>')
-
-        # the empty span here is to keep leading empty lines from being
-        # ignored by HTML parsers
-        yield 0, ('<pre' + (style and ' style="%s"' % style) + '><span></span>')
-        yield from inner
-        yield 0, '</pre>'
-
-    def _wrap_code(self, inner):
-        yield 0, '<code>'
-        yield from inner
-        yield 0, '</code>'
-
-    @functools.lru_cache(maxsize=100)
-    def _translate_parts(self, value):
-        """HTML-escape a value and split it by newlines."""
-        return value.translate(_escape_html_table).split('\n')
-
-    def _format_lines(self, tokensource):
-        """
-        Just format the tokens, without any wrapping tags.
-        Yield individual lines.
-        """
-        nocls = self.noclasses
-        lsep = self.lineseparator
-        tagsfile = self.tagsfile
-
-        lspan = ''
-        line = []
-        for ttype, value in tokensource:
-            try:
-                cspan = self.span_element_openers[ttype]
-            except KeyError:
-                title = ' title="%s"' % '.'.join(ttype) if self.debug_token_types else ''
-                if nocls:
-                    css_style = self._get_css_inline_styles(ttype)
-                    if css_style:
-                        css_style = self.class2style[css_style][0]
-                        cspan = '<span style="%s"%s>' % (css_style, title)
-                    else:
-                        cspan = ''
-                else:
-                    css_class = self._get_css_classes(ttype)
-                    if css_class:
-                        cspan = '<span class="%s"%s>' % (css_class, title)
-                    else:
-                        cspan = ''
-                self.span_element_openers[ttype] = cspan
-
-            parts = self._translate_parts(value)
-
-            if tagsfile and ttype in Token.Name:
-                filename, linenumber = self._lookup_ctag(value)
-                if linenumber:
-                    base, filename = os.path.split(filename)
-                    if base:
-                        base += '/'
-                    filename, extension = os.path.splitext(filename)
-                    url = self.tagurlformat % {'path': base, 'fname': filename,
-                                               'fext': extension}
-                    parts[0] = "<a href=\"%s#%s-%d\">%s" % \
-                        (url, self.lineanchors, linenumber, parts[0])
-                    parts[-1] = parts[-1] + "</a>"
-
-            # for all but the last line
-            for part in parts[:-1]:
-                if line:
-                    if lspan != cspan:
-                        line.extend(((lspan and '</span>'), cspan, part,
-                                     (cspan and '</span>'), lsep))
-                    else:  # both are the same
-                        line.extend((part, (lspan and '</span>'), lsep))
-                    yield 1, ''.join(line)
-                    line = []
-                elif part:
-                    yield 1, ''.join((cspan, part, (cspan and '</span>'), lsep))
-                else:
-                    yield 1, lsep
-            # for the last line
-            if line and parts[-1]:
-                if lspan != cspan:
-                    line.extend(((lspan and '</span>'), cspan, parts[-1]))
-                    lspan = cspan
-                else:
-                    line.append(parts[-1])
-            elif parts[-1]:
-                line = [cspan, parts[-1]]
-                lspan = cspan
-            # else we neither have to open a new span nor set lspan
-
-        if line:
-            line.extend(((lspan and '</span>'), lsep))
-            yield 1, ''.join(line)
-
-    def _lookup_ctag(self, token):
-        entry = ctags.TagEntry()
-        if self._ctags.find(entry, token.encode(), 0):
-            return entry['file'], entry['lineNumber']
-        else:
-            return None, None
-
-    def _highlight_lines(self, tokensource):
-        """
-        Highlighted the lines specified in the `hl_lines` option by
-        post-processing the token stream coming from `_format_lines`.
-        """
-        hls = self.hl_lines
-
-        for i, (t, value) in enumerate(tokensource):
-            if t != 1:
-                yield t, value
-            if i + 1 in hls:  # i + 1 because Python indexes start at 0
-                if self.noclasses:
-                    style = ''
-                    if self.style.highlight_color is not None:
-                        style = (' style="background-color: %s"' %
-                                 (self.style.highlight_color,))
-                    yield 1, '<span%s>%s</span>' % (style, value)
-                else:
-                    yield 1, '<span class="hll">%s</span>' % value
-            else:
-                yield 1, value
-
-    def wrap(self, source, outfile):
-        """
-        Wrap the ``source``, which is a generator yielding
-        individual lines, in custom generators. See docstring
-        for `format`. Can be overridden.
-        """
-        if self.wrapcode:
-            return self._wrap_div(self._wrap_pre(self._wrap_code(source)))
-        else:
-            return self._wrap_div(self._wrap_pre(source))
-
-    def format_unencoded(self, tokensource, outfile):
-        """
-        The formatting process uses several nested generators; which of
-        them are used is determined by the user's options.
-
-        Each generator should take at least one argument, ``inner``,
-        and wrap the pieces of text generated by this.
-
-        Always yield 2-tuples: (code, text). If "code" is 1, the text
-        is part of the original tokensource being highlighted, if it's
-        0, the text is some piece of wrapping. This makes it possible to
-        use several different wrappers that process the original source
-        linewise, e.g. line number generators.
-        """
-        source = self._format_lines(tokensource)
-
-        # As a special case, we wrap line numbers before line highlighting
-        # so the line numbers get wrapped in the highlighting tag.
-        if not self.nowrap and self.linenos == 2:
-            source = self._wrap_inlinelinenos(source)
-
-        if self.hl_lines:
-            source = self._highlight_lines(source)
-
-        if not self.nowrap:
-            if self.lineanchors:
-                source = self._wrap_lineanchors(source)
-            if self.linespans:
-                source = self._wrap_linespans(source)
-            source = self.wrap(source, outfile)
-            if self.linenos == 1:
-                source = self._wrap_tablelinenos(source)
-            if self.full:
-                source = self._wrap_full(source, outfile)
-
-        for t, piece in source:
-            outfile.write(piece)
diff --git a/spaces/allknowingroger/Image-Models-Test164/README.md b/spaces/allknowingroger/Image-Models-Test164/README.md
deleted file mode 100644
index f91e4b31ab345f987b425de029c057bfb69d9e1b..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test164/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/alphunt/diffdock-alphunt-demo/esm/esm/multihead_attention.py b/spaces/alphunt/diffdock-alphunt-demo/esm/esm/multihead_attention.py
deleted file mode 100644
index d4362b331eddcccf061a939cb57b8aef7c3e56a5..0000000000000000000000000000000000000000
--- a/spaces/alphunt/diffdock-alphunt-demo/esm/esm/multihead_attention.py
+++ /dev/null
@@ -1,508 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from typing import Dict, Optional, Tuple
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-from torch.nn import Parameter
-from esm.rotary_embedding import RotaryEmbedding
-
-import uuid
-
-
-def utils_softmax(x, dim: int, onnx_trace: bool = False):
-    if onnx_trace:
-        return F.softmax(x.float(), dim=dim)
-    else:
-        return F.softmax(x, dim=dim, dtype=torch.float32)
-
-
-class FairseqIncrementalState(object):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.init_incremental_state()
-
-    def init_incremental_state(self):
-        self._incremental_state_id = str(uuid.uuid4())
-
-    def _get_full_incremental_state_key(self, key: str) -> str:
-        return "{}.{}".format(self._incremental_state_id, key)
-
-    def get_incremental_state(
-        self,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
-        key: str,
-    ) -> Optional[Dict[str, Optional[Tensor]]]:
-        """Helper for getting incremental state for an nn.Module."""
-        full_key = self._get_full_incremental_state_key(key)
-        if incremental_state is None or full_key not in incremental_state:
-            return None
-        return incremental_state[full_key]
-
-    def set_incremental_state(
-        self,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
-        key: str,
-        value: Dict[str, Optional[Tensor]],
-    ) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]:
-        """Helper for setting incremental state for an nn.Module."""
-        if incremental_state is not None:
-            full_key = self._get_full_incremental_state_key(key)
-            incremental_state[full_key] = value
-        return incremental_state
-
-
-def with_incremental_state(cls):
-    cls.__bases__ = (FairseqIncrementalState,) + tuple(
-        b for b in cls.__bases__ if b != FairseqIncrementalState
-    )
-    return cls
-
-
-@with_incremental_state
-class MultiheadAttention(nn.Module):
-    """Multi-headed attention.
-
-    See "Attention Is All You Need" for more details.
-    """
-
-    def __init__(
-        self,
-        embed_dim,
-        num_heads,
-        kdim=None,
-        vdim=None,
-        dropout=0.0,
-        bias=True,
-        add_bias_kv: bool = False,
-        add_zero_attn: bool = False,
-        self_attention: bool = False,
-        encoder_decoder_attention: bool = False,
-        use_rotary_embeddings: bool = False,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.kdim = kdim if kdim is not None else embed_dim
-        self.vdim = vdim if vdim is not None else embed_dim
-        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == 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.self_attention = self_attention
-        self.encoder_decoder_attention = encoder_decoder_attention
-
-        assert not self.self_attention or self.qkv_same_dim, (
-            "Self-attention requires query, key and " "value to be of the same size"
-        )
-
-        self.k_proj = nn.Linear(self.kdim, embed_dim, bias=bias)
-        self.v_proj = nn.Linear(self.vdim, 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)
-
-        if add_bias_kv:
-            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
-            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
-        else:
-            self.bias_k = self.bias_v = None
-
-        self.add_zero_attn = add_zero_attn
-
-        self.reset_parameters()
-
-        self.onnx_trace = False
-        self.rot_emb = None
-        if use_rotary_embeddings:
-            self.rot_emb = RotaryEmbedding(dim=self.head_dim)
-
-        self.enable_torch_version = False
-        if hasattr(F, "multi_head_attention_forward"):
-            self.enable_torch_version = True
-        else:
-            self.enable_torch_version = False
-
-    def prepare_for_onnx_export_(self):
-        self.onnx_trace = True
-
-    def reset_parameters(self):
-        if self.qkv_same_dim:
-            # Empirically observed the convergence to be much better with
-            # the scaled initialization
-            nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
-            nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
-            nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
-        else:
-            nn.init.xavier_uniform_(self.k_proj.weight)
-            nn.init.xavier_uniform_(self.v_proj.weight)
-            nn.init.xavier_uniform_(self.q_proj.weight)
-
-        nn.init.xavier_uniform_(self.out_proj.weight)
-        if self.out_proj.bias is not None:
-            nn.init.constant_(self.out_proj.bias, 0.0)
-        if self.bias_k is not None:
-            nn.init.xavier_normal_(self.bias_k)
-        if self.bias_v is not None:
-            nn.init.xavier_normal_(self.bias_v)
-
-    def forward(
-        self,
-        query,
-        key: Optional[Tensor],
-        value: Optional[Tensor],
-        key_padding_mask: Optional[Tensor] = None,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        need_weights: bool = True,
-        static_kv: bool = False,
-        attn_mask: Optional[Tensor] = None,
-        before_softmax: bool = False,
-        need_head_weights: bool = False,
-    ) -> Tuple[Tensor, Optional[Tensor]]:
-        """Input shape: Time x Batch x Channel
-
-        Args:
-            key_padding_mask (ByteTensor, optional): mask to exclude
-                keys that are pads, of shape `(batch, src_len)`, where
-                padding elements are indicated by 1s.
-            need_weights (bool, optional): return the attention weights,
-                averaged over heads (default: False).
-            attn_mask (ByteTensor, optional): typically used to
-                implement causal attention, where the mask prevents the
-                attention from looking forward in time (default: None).
-            before_softmax (bool, optional): return the raw attention
-                weights and values before the attention softmax.
-            need_head_weights (bool, optional): return the attention
-                weights for each head. Implies *need_weights*. Default:
-                return the average attention weights over all heads.
-        """
-        if need_head_weights:
-            need_weights = True
-
-        tgt_len, bsz, embed_dim = query.size()
-        assert embed_dim == self.embed_dim
-        assert list(query.size()) == [tgt_len, bsz, embed_dim]
-
-        if (
-            not self.rot_emb
-            and self.enable_torch_version
-            and not self.onnx_trace
-            and incremental_state is None
-            and not static_kv
-            # A workaround for quantization to work. Otherwise JIT compilation
-            # treats bias in linear module as method.
-            and not torch.jit.is_scripting()
-            and not need_head_weights
-        ):
-            assert key is not None and value is not None
-            return F.multi_head_attention_forward(
-                query,
-                key,
-                value,
-                self.embed_dim,
-                self.num_heads,
-                torch.empty([0]),
-                torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
-                self.bias_k,
-                self.bias_v,
-                self.add_zero_attn,
-                self.dropout,
-                self.out_proj.weight,
-                self.out_proj.bias,
-                self.training,
-                key_padding_mask,
-                need_weights,
-                attn_mask,
-                use_separate_proj_weight=True,
-                q_proj_weight=self.q_proj.weight,
-                k_proj_weight=self.k_proj.weight,
-                v_proj_weight=self.v_proj.weight,
-            )
-        if incremental_state is not None:
-            saved_state = self._get_input_buffer(incremental_state)
-            if saved_state is not None and "prev_key" in saved_state:
-                # previous time steps are cached - no need to recompute
-                # key and value if they are static
-                if static_kv:
-                    assert self.encoder_decoder_attention and not self.self_attention
-                    key = value = None
-        else:
-            saved_state = None
-
-        if self.self_attention:
-            q = self.q_proj(query)
-            k = self.k_proj(query)
-            v = self.v_proj(query)
-        elif self.encoder_decoder_attention:
-            # encoder-decoder attention
-            q = self.q_proj(query)
-            if key is None:
-                assert value is None
-                k = v = None
-            else:
-                k = self.k_proj(key)
-                v = self.v_proj(key)
-
-        else:
-            assert key is not None and value is not None
-            q = self.q_proj(query)
-            k = self.k_proj(key)
-            v = self.v_proj(value)
-        q *= self.scaling
-
-        if self.bias_k is not None:
-            assert self.bias_v is not None
-            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
-            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
-            if attn_mask is not None:
-                attn_mask = torch.cat(
-                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
-                )
-            if key_padding_mask is not None:
-                key_padding_mask = torch.cat(
-                    [
-                        key_padding_mask,
-                        key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
-                    ],
-                    dim=1,
-                )
-
-        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
-        if k is not None:
-            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
-        if v is not None:
-            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
-
-        if saved_state is not None:
-            # 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)
-            prev_key_padding_mask: Optional[Tensor] = None
-            if "prev_key_padding_mask" in saved_state:
-                prev_key_padding_mask = saved_state["prev_key_padding_mask"]
-            assert k is not None and v is not None
-            key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
-                key_padding_mask=key_padding_mask,
-                prev_key_padding_mask=prev_key_padding_mask,
-                batch_size=bsz,
-                src_len=k.size(1),
-                static_kv=static_kv,
-            )
-
-            saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
-            saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
-            saved_state["prev_key_padding_mask"] = key_padding_mask
-            # In this branch incremental_state is never None
-            assert incremental_state is not None
-            incremental_state = self._set_input_buffer(incremental_state, saved_state)
-        assert k is not None
-        src_len = k.size(1)
-
-        # 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
-
-        if key_padding_mask is not None:
-            assert key_padding_mask.size(0) == bsz
-            assert key_padding_mask.size(1) == src_len
-
-        if self.add_zero_attn:
-            assert v is not None
-            src_len += 1
-            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
-            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
-            if attn_mask is not None:
-                attn_mask = torch.cat(
-                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
-                )
-            if key_padding_mask is not None:
-                key_padding_mask = torch.cat(
-                    [
-                        key_padding_mask,
-                        torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask),
-                    ],
-                    dim=1,
-                )
-
-        if self.rot_emb:
-            q, k = self.rot_emb(q, k)
-
-        attn_weights = torch.bmm(q, k.transpose(1, 2))
-        attn_weights = MultiheadAttention.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
-
-        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
-
-        if attn_mask is not None:
-            attn_mask = attn_mask.unsqueeze(0)
-            if self.onnx_trace:
-                attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
-            attn_weights += attn_mask
-
-        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)
-            attn_weights = attn_weights.masked_fill(
-                key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf")
-            )
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if before_softmax:
-            return attn_weights, v
-
-        attn_weights_float = utils_softmax(attn_weights, dim=-1, onnx_trace=self.onnx_trace)
-        attn_weights = attn_weights_float.type_as(attn_weights)
-        attn_probs = F.dropout(
-            attn_weights_float.type_as(attn_weights),
-            p=self.dropout,
-            training=self.training,
-        )
-        assert v is not None
-        attn = torch.bmm(attn_probs, v)
-        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
-        if self.onnx_trace and attn.size(1) == 1:
-            # when ONNX tracing a single decoder step (sequence length == 1)
-            # the transpose is a no-op copy before view, thus unnecessary
-            attn = attn.contiguous().view(tgt_len, bsz, embed_dim)
-        else:
-            attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
-        attn = self.out_proj(attn)
-        attn_weights: Optional[Tensor] = None
-        if need_weights:
-            attn_weights = attn_weights_float.view(
-                bsz, self.num_heads, tgt_len, src_len
-            ).type_as(attn).transpose(1, 0)
-            if not need_head_weights:
-                # average attention weights over heads
-                attn_weights = attn_weights.mean(dim=0)
-
-        return attn, attn_weights
-
-    @staticmethod
-    def _append_prev_key_padding_mask(
-        key_padding_mask: Optional[Tensor],
-        prev_key_padding_mask: Optional[Tensor],
-        batch_size: int,
-        src_len: int,
-        static_kv: bool,
-    ) -> Optional[Tensor]:
-        # saved key padding masks have shape (bsz, seq_len)
-        if prev_key_padding_mask is not None and static_kv:
-            new_key_padding_mask = prev_key_padding_mask
-        elif prev_key_padding_mask is not None and key_padding_mask is not None:
-            new_key_padding_mask = torch.cat(
-                [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
-            )
-        # During incremental decoding, as the padding token enters and
-        # leaves the frame, there will be a time when prev or current
-        # is None
-        elif prev_key_padding_mask is not None:
-            filler = torch.zeros(
-                (batch_size, src_len - prev_key_padding_mask.size(1)),
-                device=prev_key_padding_mask.device,
-            )
-            new_key_padding_mask = torch.cat(
-                [prev_key_padding_mask.float(), filler.float()], dim=1
-            )
-        elif key_padding_mask is not None:
-            filler = torch.zeros(
-                (batch_size, src_len - key_padding_mask.size(1)),
-                device=key_padding_mask.device,
-            )
-            new_key_padding_mask = torch.cat([filler.float(), key_padding_mask.float()], dim=1)
-        else:
-            new_key_padding_mask = prev_key_padding_mask
-        return new_key_padding_mask
-
-    @torch.jit.export
-    def reorder_incremental_state(
-        self, incremental_state: Dict[str, Dict[str, Optional[Tensor]]], new_order: Tensor
-    ):
-        """Reorder buffered internal state (for incremental generation)."""
-        input_buffer = self._get_input_buffer(incremental_state)
-        if input_buffer is not None:
-            for k in input_buffer.keys():
-                input_buffer_k = input_buffer[k]
-                if input_buffer_k is not None:
-                    if self.encoder_decoder_attention and input_buffer_k.size(0) == new_order.size(
-                        0
-                    ):
-                        break
-                    input_buffer[k] = input_buffer_k.index_select(0, new_order)
-            incremental_state = self._set_input_buffer(incremental_state, input_buffer)
-        return incremental_state
-
-    def _get_input_buffer(
-        self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
-    ) -> Dict[str, Optional[Tensor]]:
-        result = self.get_incremental_state(incremental_state, "attn_state")
-        if result is not None:
-            return result
-        else:
-            empty_result: Dict[str, Optional[Tensor]] = {}
-            return empty_result
-
-    def _set_input_buffer(
-        self,
-        incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
-        buffer: Dict[str, Optional[Tensor]],
-    ):
-        return self.set_incremental_state(incremental_state, "attn_state", buffer)
-
-    def apply_sparse_mask(attn_weights, tgt_len: int, src_len: int, bsz: int):
-        return attn_weights
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        prefix = name + "." if name != "" else ""
-        items_to_add = {}
-        keys_to_remove = []
-        for k in state_dict.keys():
-            if k.endswith(prefix + "in_proj_weight"):
-                # in_proj_weight used to be q + k + v with same dimensions
-                dim = int(state_dict[k].shape[0] / 3)
-                items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim]
-                items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim]
-                items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :]
-
-                keys_to_remove.append(k)
-
-                k_bias = prefix + "in_proj_bias"
-                if k_bias in state_dict.keys():
-                    dim = int(state_dict[k].shape[0] / 3)
-                    items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim]
-                    items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][dim : 2 * dim]
-                    items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :]
-
-                    keys_to_remove.append(prefix + "in_proj_bias")
-
-        for k in keys_to_remove:
-            del state_dict[k]
-
-        for key, value in items_to_add.items():
-            state_dict[key] = value
diff --git a/spaces/amsterdamNLP/CLIP-attention-rollout/app.py b/spaces/amsterdamNLP/CLIP-attention-rollout/app.py
deleted file mode 100644
index a2dc2e637f6015685d023a55d1709c542e2c610b..0000000000000000000000000000000000000000
--- a/spaces/amsterdamNLP/CLIP-attention-rollout/app.py
+++ /dev/null
@@ -1,189 +0,0 @@
-import re
-import sys
-import pathlib
-import csv
-import gradio as gr
-
-sys.path.append("CLIP_explainability/Transformer-MM-Explainability/")
-
-import torch
-import CLIP.clip as clip
-
-import spacy
-from PIL import Image, ImageFont, ImageDraw, ImageOps
-
-from clip_grounding.utils.image import pad_to_square
-from clip_grounding.datasets.png import (
-    overlay_relevance_map_on_image,
-)
-from CLIP_explainability.utils import interpret, show_img_heatmap, show_heatmap_on_text
-
-clip.clip._MODELS = {
-    "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
-    "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
-    "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
-    "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
-}
-
-def iter_file(filename):
-    with pathlib.Path(filename).open("r") as fh:
-        header = next(fh)
-        for line in fh:
-            yield line
-
-colour_map = {
-        "N": "#f77189",
-        "CARDINAL": "#f7764a",
-        "DATE": "#d98a32",
-        "EVENT": "#bf9632",
-        "FAC": "#a99e31",
-        "GPE": "#90a531",
-        "LANGUAGE": "#68ad31",
-        "LAW": "#32b25e",
-        "LOC": "#34af86",
-        "MONEY": "#35ae9c",
-        "NORP": "#36acac",
-        "ORDINAL": "#37aabd",
-        "ORG": "#39a7d4",
-        "PERCENT": "#539ff4",
-        "PERSON": "#9890f4",
-        "PRODUCT": "#c47ef4",
-        "QUANTITY": "#ef5ff4",
-        "TIME": "#f565d0",
-        "WORK_OF_ART": "#f66baf",
-    }
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# nlp = spacy.load("en_core_web_sm")
-import en_core_web_sm
-nlp = en_core_web_sm.load()
-
-# Gradio Section:
-def update_slider(model):
-    if model == "ViT-L/14":
-        return gr.update(maximum=23, value=23)
-    else:
-        return gr.update(maximum=11, value=11)
-
-def run_demo(*args):
-    if len(args) == 4:
-        image, text, model_name, vision_layer = args
-    elif len(args) == 2:
-        image, text = args
-        model_name = "ViT-B/32"
-        vision_layer = 11
-    else:
-        raise ValueError("Unexpected number of parameters")
-
-    vision_layer = int(vision_layer)
-    model, preprocess = clip.load(model_name, device=device, jit=False)
-    orig_image = pad_to_square(image)
-    img = preprocess(orig_image).unsqueeze(0).to(device)
-    text_input = clip.tokenize([text]).to(device)
-
-    R_text, R_image = interpret(model=model, image=img, texts=text_input, device=device, start_layer=vision_layer)
-
-    image_relevance = show_img_heatmap(R_image[0], img, orig_image=orig_image, device=device)
-    overlapped = overlay_relevance_map_on_image(image, image_relevance)
-
-    text_scores, text_tokens_decoded = show_heatmap_on_text(text, text_input, R_text[0])
-
-    highlighted_text = []
-    for i, token in enumerate(text_tokens_decoded):
-        highlighted_text.append((str(token), float(text_scores[i])))
-
-    return overlapped, highlighted_text
-
-
-# Default demo:
-
-examples = list(csv.reader(iter_file("examples.csv")))
-with gr.Blocks(title="CLIP Grounding Explainability") as iface_default:
-    gr.Markdown(pathlib.Path("description.md").read_text)
-    with gr.Row():
-        with gr.Column() as inputs:
-            orig = gr.components.Image(type='pil', label="Original Image")
-            description = gr.components.Textbox(label="Image description")
-            default_model = gr.Dropdown(label="CLIP Model", choices=['ViT-B/16', 'ViT-B/32', 'ViT-L/14'], value="ViT-B/32")
-            default_layer = gr.Slider(label="Vision start layer", minimum=0, maximum=11, step=1, value=11)
-            submit = gr.Button("Submit")
-        with gr.Column() as outputs:
-            image = gr.components.Image(type='pil', label="Output Image")
-            text = gr.components.HighlightedText(label="Text importance")
-    gr.Examples(examples=examples, inputs=[orig, description])
-    default_model.change(update_slider, inputs=default_model, outputs=default_layer)
-    submit.click(run_demo, inputs=[orig, description, default_model, default_layer], outputs=[image, text])
-
-
-# NER demo:
-def add_label_to_img(img, label, add_entity_label=True):
-    img = ImageOps.expand(img, border=45, fill=(255,255,255))
-    draw = ImageDraw.Draw(img)
-    font = ImageFont.truetype("arial.ttf", 24)
-    m = re.match(r".*\((\w+)\)", label)
-    if add_entity_label and m is not None:
-        cat = m.group(1)
-        colours = tuple(map(lambda l: int(''.join(l),16), zip(*[iter(colour_map[cat][1:])]*2)))
-
-        draw.text((5,5), label , align="center", fill=colours, font=font)
-    else:
-        draw.text((5,5), label, align="center", fill=(0, 0, 0), font=font)
-
-    return img
-
-def NER_demo(image, text, model_name):
-
-    # As the default image, we run the default demo on the input image and text:
-    overlapped, highlighted_text = run_demo(image, text, model_name)
-
-    gallery_images = [add_label_to_img(overlapped, "Complete sentence", add_entity_label=False)]
-
-    labeled_text = dict(
-            text=text,
-            entities=[],
-        )
-
-    # Then, we run the demo for each of the noun chunks in the text:
-    for chunk in nlp(text).noun_chunks:
-        if len(chunk) == 1 and chunk[0].pos_ == "PRON":
-            continue
-        chunk_text = chunk.text
-        chunk_label = None
-        for t in chunk:
-            if t.ent_type_ != '':
-                chunk_label = t.ent_type_
-                break
-        if chunk_label is None:
-            chunk_label = "N"
-
-        labeled_text['entities'].append({'entity': chunk_label, 'start': chunk.start_char, 'end': chunk.end_char})
-        overlapped, highlighted_text = run_demo(image, chunk_text, model_name)
-        overlapped_labelled = add_label_to_img(overlapped, f"{chunk_text} ({chunk_label})")
-        gallery_images.append(overlapped_labelled)
-
-    return labeled_text, gallery_images
-
-
-entity_examples = list(csv.reader(iter_file("entity_examples.csv")))
-with gr.Blocks(title="Entity Grounding explainability using CLIP") as iface_NER:
-    gr.Markdown(pathlib.Path("entity_description.md").read_text)
-    with gr.Row():
-        with gr.Column() as inputs:
-            img = gr.Image(type='pil', label="Original Image")
-            intext = gr.components.Textbox(label="Descriptive text")
-            ner_model = gr.Dropdown(label="CLIP Model", choices=['ViT-B/16', 'ViT-B/32', 'ViT-L/14'], value="ViT-B/32")
-            ner_layer = gr.Slider(label="Vision start layer", minimum=0, maximum=11, step=1, value=11)
-            submit = gr.Button("Submit")
-        with gr.Column() as outputs:
-            text = gr.components.HighlightedText(show_legend=True, color_map=colour_map, label="Noun chunks")
-            gallery = gr.components.Gallery(type='pil', label="NER Entity explanations")
-
-    gr.Examples(examples=entity_examples, inputs=[img, text])
-    ner_model.change(update_slider, inputs=ner_model, outputs=ner_layer)
-    submit.click(run_demo, inputs=[img, intext, ner_model, ner_layer], outputs=[text, gallery])
-
-demo_tabs = gr.TabbedInterface([iface_default, iface_NER], ["Default", "Entities"])
-with demo_tabs:
-    gr.Markdown(pathlib.Path("footer.md").read_text)
-demo_tabs.launch(show_error=True)
diff --git a/spaces/aodianyun/stable-diffusion-webui/javascript/hires_fix.js b/spaces/aodianyun/stable-diffusion-webui/javascript/hires_fix.js
deleted file mode 100644
index ced83e7c05c8bb5199f6f08c4bee47ee1277d4f3..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/javascript/hires_fix.js
+++ /dev/null
@@ -1,22 +0,0 @@
-
-function setInactive(elem, inactive){
-    if(inactive){
-        elem.classList.add('inactive')
-    } else{
-        elem.classList.remove('inactive')
-    }
-}
-
-function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
-    hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
-    hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
-    hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
-
-    gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""
-
-    setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0)
-    setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0)
-    setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0)
-
-    return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y]
-}
diff --git a/spaces/ardha27/rvc-models/infer_pack/modules.py b/spaces/ardha27/rvc-models/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/ardha27/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
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests2/test_fast_pitch_train.py b/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests2/test_fast_pitch_train.py
deleted file mode 100644
index a525715b53826157a96d45dd4b204644836e0114..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests2/test_fast_pitch_train.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.fast_pitch_config import FastPitchConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-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 = FastPitchConfig(
-    audio=audio_config,
-    batch_size=8,
-    eval_batch_size=8,
-    num_loader_workers=0,
-    num_eval_loader_workers=0,
-    text_cleaner="english_cleaners",
-    use_phonemes=True,
-    phoneme_language="en-us",
-    phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
-    f0_cache_path="tests/data/ljspeech/f0_cache/",
-    run_eval=True,
-    test_delay_epochs=-1,
-    epochs=1,
-    print_step=1,
-    print_eval=True,
-    test_sentences=[
-        "Be a voice, not an echo.",
-    ],
-    use_speaker_embedding=False,
-)
-config.audio.do_trim_silence = True
-config.use_speaker_embedding = False
-config.model_args.use_speaker_embedding = False
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
-    f"CUDA_VISIBLE_DEVICES='{get_device_id()}'  python TTS/bin/train_tts.py --config_path {config_path}  "
-    f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.formatter ljspeech "
-    "--coqpit.datasets.0.meta_file_train metadata.csv "
-    "--coqpit.datasets.0.meta_file_val metadata.csv "
-    "--coqpit.datasets.0.path tests/data/ljspeech "
-    "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
-    "--coqpit.test_delay_epochs 0"
-)
-
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
-    config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/spaces/arxify/RVC-beta-v2-0618/onnx_inference_demo.py b/spaces/arxify/RVC-beta-v2-0618/onnx_inference_demo.py
deleted file mode 100644
index 14e75d0eb4a5dc3542ce1ed6d462c70c7f4e5679..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/onnx_inference_demo.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import soundfile
-from infer_pack.onnx_inference import OnnxRVC
-
-hop_size = 512
-sampling_rate = 40000  # 采样率
-f0_up_key = 0  # 升降调
-sid = 0  # 角色ID
-f0_method = "dio"  # F0提取算法
-model_path = "ShirohaRVC.onnx"  # 模型的完整路径
-vec_name = "vec-256-layer-9"  # 内部自动补齐为 f"pretrained/{vec_name}.onnx" 需要onnx的vec模型
-wav_path = "123.wav"  # 输入路径或ByteIO实例
-out_path = "out.wav"  # 输出路径或ByteIO实例
-
-model = OnnxRVC(
-    model_path, vec_path=vec_name, sr=sampling_rate, hop_size=hop_size, device="cuda"
-)
-
-audio = model.inference(wav_path, sid, f0_method=f0_method, f0_up_key=f0_up_key)
-
-soundfile.write(out_path, audio, sampling_rate)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/__init__.py
deleted file mode 100644
index 12b7592f2ae47a8192d56045c8002bc147e56c8e..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/__init__.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-#  SelfTest/__init__.py: Self-test for PyCrypto
-#
-# Written in 2008 by Dwayne C. Litzenberger <dlitz@dlitz.net>
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain.  To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# 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.
-# ===================================================================
-
-"""Self tests
-
-These tests should perform quickly and can ideally be used every time an
-application runs.
-"""
-
-__revision__ = "$Id$"
-
-import sys
-import unittest
-from Crypto.Util.py3compat import StringIO
-
-class SelfTestError(Exception):
-    def __init__(self, message, result):
-        Exception.__init__(self, message, result)
-        self.message = message
-        self.result = result
-
-def run(module=None, verbosity=0, stream=None, tests=None, config=None, **kwargs):
-    """Execute self-tests.
-
-    This raises SelfTestError if any test is unsuccessful.
-
-    You may optionally pass in a sub-module of SelfTest if you only want to
-    perform some of the tests.  For example, the following would test only the
-    hash modules:
-
-        Crypto.SelfTest.run(Crypto.SelfTest.Hash)
-
-    """
-
-    if config is None:
-        config = {}
-    suite = unittest.TestSuite()
-    if module is None:
-        if tests is None:
-            tests = get_tests(config=config)
-        suite.addTests(tests)
-    else:
-        if tests is None:
-            suite.addTests(module.get_tests(config=config))
-        else:
-            raise ValueError("'module' and 'tests' arguments are mutually exclusive")
-    if stream is None:
-        kwargs['stream'] = StringIO()
-    else:
-        kwargs['stream'] = stream
-    runner = unittest.TextTestRunner(verbosity=verbosity, **kwargs)
-    result = runner.run(suite)
-    if not result.wasSuccessful():
-        if stream is None:
-            sys.stderr.write(kwargs['stream'].getvalue())
-        raise SelfTestError("Self-test failed", result)
-    return result
-
-def get_tests(config={}):
-    tests = []
-    from Crypto.SelfTest import Cipher; tests += Cipher.get_tests(config=config)
-    from Crypto.SelfTest import Hash;   tests += Hash.get_tests(config=config)
-    from Crypto.SelfTest import Protocol; tests += Protocol.get_tests(config=config)
-    from Crypto.SelfTest import PublicKey; tests += PublicKey.get_tests(config=config)
-    from Crypto.SelfTest import Random; tests += Random.get_tests(config=config)
-    from Crypto.SelfTest import Util;   tests += Util.get_tests(config=config)
-    from Crypto.SelfTest import Signature;   tests += Signature.get_tests(config=config)
-    from Crypto.SelfTest import IO;   tests += IO.get_tests(config=config)
-    from Crypto.SelfTest import Math;   tests += Math.get_tests(config=config)
-    return tests
-
-if __name__ == '__main__':
-    suite = lambda: unittest.TestSuite(get_tests())
-    unittest.main(defaultTest='suite')
-
-# vim:set ts=4 sw=4 sts=4 expandtab:
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/v3/tests/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/v3/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/evaluators/evaluator.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/evaluators/evaluator.py
deleted file mode 100644
index 94d9c5ec9a6e84434dbced8d5647754c5a571570..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/evaluators/evaluator.py
+++ /dev/null
@@ -1,54 +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 os
-import glob
-import numpy as np
-
-from . import metric as metric_path
-from . import predictor as predictor_path
-
-
-class Evaluator(object):
-    """
-    perform evaluation on a single (downstream) task.
-    make this both offline and online.
-    TODO(huxu) saving evaluation results.
-    """
-
-    def __init__(self, config, eval_dataloader=None):
-        if config.metric is None:
-            raise ValueError("config.metric is", config.metric)
-        metric_cls = getattr(metric_path, config.metric)
-        self.metric = metric_cls(config)
-        if config.predictor is None:
-            raise ValueError("config.predictor is", config.predictor)
-        predictor_cls = getattr(predictor_path, config.predictor)
-        self.predictor = predictor_cls(config)
-        self.eval_dataloader = eval_dataloader
-
-    def __call__(self):
-        try:
-            print(self.predictor.pred_dir)
-            for pred_file in glob.glob(
-                    self.predictor.pred_dir + "/*_merged.npy"):
-                outputs = np.load(pred_file)
-                results = self.metric.compute_metrics(outputs)
-                self.metric.print_computed_metrics(results)
-
-            outputs = np.load(os.path.join(
-                    self.predictor.pred_dir, "merged.npy"))
-            results = self.metric.compute_metrics(outputs)
-            return {"results": results, "metric": self.metric}
-        except FileNotFoundError:
-            print("\n[missing]", self.predictor.pred_dir)
-            return {}
-
-    def evaluate(self, model, eval_dataloader=None, output_file="merged"):
-        if eval_dataloader is None:
-            eval_dataloader = self.eval_dataloader
-        outputs = self.predictor.predict_loop(
-            model, eval_dataloader, output_file)
-        results = self.metric.compute_metrics(**outputs)
-        return results
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/modules/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/modules/__init__.py
deleted file mode 100644
index 4c78594c21d7ccdcb2ab11c918dd211adce27c14..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/modules/__init__.py
+++ /dev/null
@@ -1,10 +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 .mm import *
-
-try:
-    from .expmm import *
-except ImportError:
-    pass
diff --git a/spaces/ashercn97/AsherTesting/extensions/perplexity_colors/script.py b/spaces/ashercn97/AsherTesting/extensions/perplexity_colors/script.py
deleted file mode 100644
index 84b62a306dc50833ad1d1a8ac0a41e5f84926607..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/extensions/perplexity_colors/script.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import gradio
-import torch
-from transformers import LogitsProcessor
-import numpy as np
-
-from modules import shared
-
-params = {
-    'color_by_perplexity': False,
-    'color_by_probability': False,
-    'ppl_scale': 15.0, # No slider for this right now, because I don't think it really needs to be changed. Very large perplexity scores don't show up often.
-    #'probability_dropdown': False
-}
-
-class PerplexityLogits(LogitsProcessor):
-    def __init__(self, verbose=False):
-        self.generated_token_ids = []
-        self.selected_probs = []
-        self.top_token_ids_list = []
-        self.top_probs_list = []
-        self.perplexities_list = []
-        self.last_probs = None
-        self.verbose = verbose
-
-    def __call__(self, input_ids, scores):
-        probs = torch.softmax(scores, dim=-1, dtype=torch.float)
-        log_probs = torch.nan_to_num(torch.log(probs))
-        entropy = -torch.sum(probs*log_probs)
-        entropy = entropy.cpu().numpy()
-        perplexity = round(float(np.exp(entropy)), 4)
-        self.perplexities_list.append(perplexity)
-        last_token_id = int(input_ids[0][-1].cpu().numpy().item())
-        # Store the generated tokens (not sure why this isn't accessible in the output endpoint!)
-        self.generated_token_ids.append(last_token_id)
-        # Get last probability, and add to the list if it wasn't there
-        if len(self.selected_probs) > 0:
-            # Is the selected token in the top tokens?
-            if self.verbose:
-                print(shared.tokenizer.decode(last_token_id))
-                print([shared.tokenizer.decode(token_id) for token_id in self.top_token_ids_list[-1]])
-                print(self.top_probs_list[-1])
-            if last_token_id in self.top_token_ids_list[-1]:
-                idx = self.top_token_ids_list[-1].index(last_token_id)
-                self.selected_probs.append(self.top_probs_list[-1][idx])
-            else:
-                self.top_token_ids_list[-1].append(last_token_id)
-                last_prob = round(float(self.last_probs[last_token_id]), 4)
-                self.top_probs_list[-1].append(last_prob)
-                self.selected_probs.append(last_prob)
-        else:
-            self.selected_probs.append(1.0) # Placeholder for the last token of the prompt
-
-        if self.verbose:
-            pplbar = "-"
-            if not np.isnan(perplexity):
-                pplbar = "*"*round(perplexity)
-            print(f"{last_token}\t{perplexity:.2f}\t{pplbar}")
-
-        # Get top 5 probabilities
-        top_tokens_and_probs = torch.topk(probs, 5)
-        top_probs = top_tokens_and_probs.values.cpu().numpy().astype(float).tolist()
-        top_token_ids = top_tokens_and_probs.indices.cpu().numpy().astype(int).tolist()
-
-        self.top_token_ids_list.append(top_token_ids)
-        self.top_probs_list.append(top_probs)
-        
-        probs = probs.cpu().numpy().flatten()
-        self.last_probs = probs # Need to keep this as a reference for top probs
-
-        # Doesn't actually modify the logits!
-        return scores
-
-# Stores the perplexity and top probabilities
-ppl_logits_processor = None
-
-def logits_processor_modifier(logits_processor_list, input_ids):
-    global ppl_logits_processor
-    ppl_logits_processor = PerplexityLogits()
-    logits_processor_list.append(ppl_logits_processor)
-
-def output_modifier(text):
-    global ppl_logits_processor
-
-    # TODO: It's probably more efficient to do this above rather than modifying all these lists
-    # Remove last element of perplexities_list, top_token_ids_list, top_tokens_list, top_probs_list since everything is off by one because this extension runs before generation
-    perplexities = ppl_logits_processor.perplexities_list[:-1]
-    top_token_ids_list = ppl_logits_processor.top_token_ids_list[:-1]
-    top_tokens_list = [[shared.tokenizer.decode(token_id) for token_id in top_token_ids] for top_token_ids in top_token_ids_list]
-    top_probs_list = ppl_logits_processor.top_probs_list[:-1]
-    # Remove first element of generated_token_ids, generated_tokens, selected_probs because they are for the last token of the prompt
-    gen_token_ids = ppl_logits_processor.generated_token_ids[1:]
-    gen_tokens = [shared.tokenizer.decode(token_id) for token_id in gen_token_ids]
-    sel_probs = ppl_logits_processor.selected_probs[1:]
-
-    end_part = '</span>' # Helps with finding the index after replacing part of the text.
-    in_code = False # Since the <span> tags mess up code blocks, avoid coloring while inside a code block, based on finding tokens with '`' in them
-
-    if params['color_by_probability'] and params['color_by_perplexity']:
-        i = 0
-        for token, prob, ppl, top_tokens, top_probs in zip(gen_tokens, sel_probs, perplexities, top_tokens_list, top_probs_list):
-            if '`' in token:
-                in_code = not in_code
-                continue
-            if in_code:
-                continue
-            color = probability_perplexity_color_scale(prob, ppl)
-            if token in text[i:]:
-                text = text[:i] + text[i:].replace(token, add_color_html(token, color), 1)
-                i += text[i:].find(end_part) + len(end_part)
-    elif params['color_by_perplexity']:
-        i = 0
-        for token, ppl, top_tokens, top_probs in zip(gen_tokens, perplexities, top_tokens_list, top_probs_list):
-            if '`' in token:
-                in_code = not in_code
-                continue
-            if in_code:
-                continue
-            color = perplexity_color_scale(ppl)
-            if token in text[i:]:
-                text = text[:i] + text[i:].replace(token, add_color_html(token, color), 1)
-                i += text[i:].find(end_part) + len(end_part)
-    elif params['color_by_probability']:
-        i = 0
-        for token, prob, top_tokens, top_probs in zip(gen_tokens, sel_probs, top_tokens_list, top_probs_list):
-            if '`' in token:
-                in_code = not in_code
-                continue
-            if in_code:
-                continue
-            color = probability_color_scale(prob)
-            if token in text[i:]:
-                text = text[:i] + text[i:].replace(token, add_color_html(token, color), 1)
-                i += text[i:].find(end_part) + len(end_part)
-
-    print('Average perplexity:', round(np.mean(perplexities), 4))
-    return text
-
-# Green-yellow-red color scale
-def probability_color_scale(prob):
-    rv = 0
-    gv = 0
-    if prob <= 0.5:
-        rv = 'ff'
-        gv = hex(int(255*prob*2))[2:]
-        if len(gv) < 2:
-            gv = '0'*(2 - len(gv)) + gv
-    else:
-        rv = hex(int(255 - 255*(prob - 0.5)*2))[2:]
-        gv = 'ff'
-        if len(rv) < 2:
-            rv = '0'*(2 - len(rv)) + rv
-    return rv + gv + '00'
-
-# Red component only, white for 0 perplexity (sorry if you're not in dark mode)
-def perplexity_color_scale(ppl):
-    value = hex(max(int(255.0 - params['ppl_scale']*(float(ppl)-1.0)), 0))[2:]
-    if len(value) < 2:
-        value = '0'*(2 - len(value)) + value
-    return 'ff' + value + value
-
-# Green-yellow-red for probability and blue component for perplexity
-def probability_perplexity_color_scale(prob, ppl):
-    rv = 0
-    gv = 0
-    bv = hex(min(max(int(params['ppl_scale']*(float(ppl)-1.0)), 0), 255))[2:]
-    if len(bv) < 2:
-            bv = '0'*(2 - len(bv)) + bv
-    if prob <= 0.5:
-        rv = 'ff'
-        gv = hex(int(255*prob*2))[2:]
-        if len(gv) < 2:
-            gv = '0'*(2 - len(gv)) + gv
-    else:
-        rv = hex(int(255 - 255*(prob - 0.5)*2))[2:]
-        gv = 'ff'
-        if len(rv) < 2:
-            rv = '0'*(2 - len(rv)) + rv
-    return rv + gv + bv
-
-def add_color_html(token, color):
-    return f'<span style="color: #{color}">{token}</span>'
-
-"""
-# This is still very broken at the moment, needs CSS too but I'm not very good at CSS (and neither is GPT-4 apparently) so I still need to figure that out.
-def add_dropdown_html(token, color, top_tokens, top_probs):
-    html = f'<span class="hoverable" style="color: #{color}">{token}<div class="dropdown"><table class="dropdown-content">'
-    for token, prob in zip(top_tokens, top_probs):
-        # TODO: Background color? Bold for selected token?
-        # Bigger issue: Why is there a newline after the first token, and the dropdown fails there?
-        # The HTML ends up like <p><span>word</span></p><div>...</div>,
-        # even though for all other tokens it shows up correctly.
-        row_color = probability_color_scale(prob)
-        html += f'<tr><td style="color: #{row_color}">{token}</td><td style="color: #{row_color}">{prob}</td></tr>'
-    html += '</table></div></span>'
-    return html
-"""
-
-def ui():
-    color_by_ppl_check = gradio.Checkbox(value=False, label="Color by perplexity", info="Higher perplexity is more red. If also showing probability, higher perplexity has more blue component.")
-    def update_color_by_ppl_check(x):
-        params.update({'color_by_perplexity': x})
-    color_by_ppl_check.change(update_color_by_ppl_check, color_by_ppl_check, None)
-
-    color_by_prob_check = gradio.Checkbox(value=False, label="Color by probability", info="Green-yellow-red linear scale, with 100% green, 50% yellow, 0% red.")
-    def update_color_by_prob_check(x):
-        params.update({'color_by_probability': x})
-    color_by_prob_check.change(update_color_by_prob_check, color_by_prob_check, None)
-
-    # Doesn't work yet...
-    """
-    prob_dropdown_check = gradio.Checkbox(value=False, label="Probability dropdown")
-    def update_prob_dropdown_check(x):
-        params.update({'probability_dropdown': x})
-    prob_dropdown_check.change(update_prob_dropdown_check, prob_dropdown_check, None)
-    """
diff --git a/spaces/awacke1/AW-03-GR-AI-Text2ArtGenerator/app.py b/spaces/awacke1/AW-03-GR-AI-Text2ArtGenerator/app.py
deleted file mode 100644
index 1842b91661e1edd1167802f4093d3e887f662042..0000000000000000000000000000000000000000
--- a/spaces/awacke1/AW-03-GR-AI-Text2ArtGenerator/app.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import os
-
-os.system("git clone --recursive https://github.com/JD-P/cloob-latent-diffusion")
-os.system("cd cloob-latent-diffusion;pip install omegaconf pillow pytorch-lightning einops wandb ftfy regex ./CLIP")
-
-import argparse
-from functools import partial
-from pathlib import Path
-import sys
-sys.path.append('./cloob-latent-diffusion')
-sys.path.append('./cloob-latent-diffusion/cloob-training')
-sys.path.append('./cloob-latent-diffusion/latent-diffusion')
-sys.path.append('./cloob-latent-diffusion/taming-transformers')
-sys.path.append('./cloob-latent-diffusion/v-diffusion-pytorch')
-from omegaconf import OmegaConf
-from PIL import Image
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torchvision import transforms
-from torchvision.transforms import functional as TF
-from tqdm import trange
-from CLIP import clip
-from cloob_training import model_pt, pretrained
-import ldm.models.autoencoder
-from diffusion import sampling, utils
-import train_latent_diffusion as train
-from huggingface_hub import hf_hub_url, cached_download
-import random
-
-# Download the model files
-checkpoint = cached_download(hf_hub_url("huggan/distill-ccld-wa", filename="model_student.ckpt"))
-ae_model_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.ckpt"))
-ae_config_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.yaml"))
-
-# Define a few utility functions
-
-
-def parse_prompt(prompt, default_weight=3.):
-    if prompt.startswith('http://') or prompt.startswith('https://'):
-        vals = prompt.rsplit(':', 2)
-        vals = [vals[0] + ':' + vals[1], *vals[2:]]
-    else:
-        vals = prompt.rsplit(':', 1)
-    vals = vals + ['', default_weight][len(vals):]
-    return vals[0], float(vals[1])
-
-
-def resize_and_center_crop(image, size):
-    fac = max(size[0] / image.size[0], size[1] / image.size[1])
-    image = image.resize((int(fac * image.size[0]), int(fac * image.size[1])), Image.LANCZOS)
-    return TF.center_crop(image, size[::-1])
-
-
-# Load the models
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-print('Using device:', device)
-print('loading models')
-
-# autoencoder
-ae_config = OmegaConf.load(ae_config_path)
-ae_model = ldm.models.autoencoder.AutoencoderKL(**ae_config.model.params)
-ae_model.eval().requires_grad_(False).to(device)
-ae_model.load_state_dict(torch.load(ae_model_path))
-n_ch, side_y, side_x = 4, 32, 32
-
-# diffusion model
-model = train.DiffusionModel(192, [1,1,2,2], autoencoder_scale=torch.tensor(4.3084))
-model.load_state_dict(torch.load(checkpoint, map_location='cpu'))
-model = model.to(device).eval().requires_grad_(False)
-
-# CLOOB
-cloob_config = pretrained.get_config('cloob_laion_400m_vit_b_16_16_epochs')
-cloob = model_pt.get_pt_model(cloob_config)
-checkpoint = pretrained.download_checkpoint(cloob_config)
-cloob.load_state_dict(model_pt.get_pt_params(cloob_config, checkpoint))
-cloob.eval().requires_grad_(False).to(device)
-
-
-# The key function: returns a list of n PIL images
-def generate(n=1, prompts=['a red circle'], images=[], seed=42, steps=15,
-             method='plms', eta=None):
-  zero_embed = torch.zeros([1, cloob.config['d_embed']], device=device)
-  target_embeds, weights = [zero_embed], []
-
-  for prompt in prompts:
-      txt, weight = parse_prompt(prompt)
-      target_embeds.append(cloob.text_encoder(cloob.tokenize(txt).to(device)).float())
-      weights.append(weight)
-
-  for prompt in images:
-      path, weight = parse_prompt(prompt)
-      img = Image.open(utils.fetch(path)).convert('RGB')
-      clip_size = cloob.config['image_encoder']['image_size']
-      img = resize_and_center_crop(img, (clip_size, clip_size))
-      batch = TF.to_tensor(img)[None].to(device)
-      embed = F.normalize(cloob.image_encoder(cloob.normalize(batch)).float(), dim=-1)
-      target_embeds.append(embed)
-      weights.append(weight)
-
-  weights = torch.tensor([1 - sum(weights), *weights], device=device)
-
-  torch.manual_seed(seed)
-
-  def cfg_model_fn(x, t):
-      n = x.shape[0]
-      n_conds = len(target_embeds)
-      x_in = x.repeat([n_conds, 1, 1, 1])
-      t_in = t.repeat([n_conds])
-      clip_embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0)
-      vs = model(x_in, t_in, clip_embed_in).view([n_conds, n, *x.shape[1:]])
-      v = vs.mul(weights[:, None, None, None, None]).sum(0)
-      return v
-
-  def run(x, steps):
-      if method == 'ddpm':
-          return sampling.sample(cfg_model_fn, x, steps, 1., {})
-      if method == 'ddim':
-          return sampling.sample(cfg_model_fn, x, steps, eta, {})
-      if method == 'prk':
-          return sampling.prk_sample(cfg_model_fn, x, steps, {})
-      if method == 'plms':
-          return sampling.plms_sample(cfg_model_fn, x, steps, {})
-      if method == 'pie':
-          return sampling.pie_sample(cfg_model_fn, x, steps, {})
-      if method == 'plms2':
-          return sampling.plms2_sample(cfg_model_fn, x, steps, {})
-      assert False
-
-  batch_size = n
-  x = torch.randn([n, n_ch, side_y, side_x], device=device)
-  t = torch.linspace(1, 0, steps + 1, device=device)[:-1]
-  steps = utils.get_spliced_ddpm_cosine_schedule(t)
-  pil_ims = []
-  for i in trange(0, n, batch_size):
-      cur_batch_size = min(n - i, batch_size)
-      out_latents = run(x[i:i+cur_batch_size], steps)
-      outs = ae_model.decode(out_latents * torch.tensor(2.55).to(device))
-      for j, out in enumerate(outs):
-          pil_ims.append(utils.to_pil_image(out))
-
-  return pil_ims
-  
-  
-import gradio as gr
-
-def gen_ims(prompt, im_prompt=None, seed=None, n_steps=10, method='plms'):
-  if seed == None :
-    seed = random.randint(0, 10000)
-  print( prompt, im_prompt, seed, n_steps)
-  prompts = [prompt]
-  im_prompts = []
-  if im_prompt != None:
-    im_prompts = [im_prompt]
-  pil_ims = generate(n=1, prompts=prompts, images=im_prompts, seed=seed, steps=n_steps, method=method)
-  return pil_ims[0]
-
-iface = gr.Interface(fn=gen_ims, 
-  inputs=[#gr.inputs.Slider(minimum=1, maximum=1, step=1, default=1,label="Number of images"),
-          #gr.inputs.Slider(minimum=0, maximum=200, step=1, label='Random seed', default=0),
-          gr.inputs.Textbox(label="Text prompt"),
-          gr.inputs.Image(optional=True, label="Image prompt", type='filepath'),
-          #gr.inputs.Slider(minimum=10, maximum=35, step=1, default=15,label="Number of steps")
-          ], 
-  outputs=[gr.outputs.Image(type="pil", label="Generated Image")],
-  examples=[
-  ["Virgin and Child, in the style of Jacopo Bellini"],
-  ["Katsushika Hokusai, The Dragon of Smoke Escaping from Mount Fuji"],
-  ["Moon Light Sonata by Basuki Abdullah"],
-  ["Twon Tree by M.C. Escher"],
-  ["Futurism, in the style of Wassily Kandinsky"], 
-  ["Art Nouveau, in the style of John Singer Sargent"], 
-  ["Surrealism, in the style of Edgar Degas"], 
-  ["Expressionism, in the style of Wassily Kandinsky"], 
-  ["Futurism, in the style of Egon Schiele"], 
-  ["Neoclassicism, in the style of Gustav Klimt"], 
-  ["Cubism, in the style of Gustav Klimt"], 
-  ["Op Art, in the style of Marc Chagall"], 
-  ["Romanticism, in the style of M.C. Escher"], 
-  ["Futurism, in the style of M.C. Escher"], 
-  ["Abstract Art, in the style of M.C. Escher"], 
-  ["Mannerism, in the style of Paul Klee"], 
-  ["Romanesque Art, in the style of Leonardo da Vinci"], 
-  ["High Renaissance, in the style of Rembrandt"], 
-  ["Magic Realism, in the style of Gustave Dore"], 
-  ["Realism, in the style of Jean-Michel Basquiat"], 
-  ["Art Nouveau, in the style of Paul Gauguin"], 
-  ["Avant-garde, in the style of Pierre-Auguste Renoir"], 
-  ["Baroque, in the style of Edward Hopper"], 
-  ["Post-Impressionism, in the style of Wassily Kandinsky"], 
-  ["Naturalism, in the style of Rene Magritte"], 
-  ["Constructivism, in the style of Paul Cezanne"], 
-  ["Abstract Expressionism, in the style of Henri Matisse"], 
-  ["Pop Art, in the style of Vincent van Gogh"], 
-  ["Futurism, in the style of Wassily Kandinsky"], 
-  ["Futurism, in the style of Zdzislaw Beksinski"], 
-  ['Surrealism, in the style of Salvador Dali'], 
-  ["Aaron Wacker, oil on canvas"],
-      ["abstract"],
-    ["landscape"],
-    ["portrait"],
-    ["sculpture"],
-    ["genre painting"],
-    ["installation"],
-    ["photo"],
-    ["figurative"],
-    ["illustration"],
-    ["still life"],
-    ["history painting"],
-    ["cityscape"],
-    ["marina"],
-    ["animal painting"],
-    ["design"],
-    ["calligraphy"],
-    ["symbolic painting"],
-    ["graffiti"],
-    ["performance"],
-    ["mythological painting"],
-    ["battle painting"],
-    ["self-portrait"],
-    ["Impressionism, oil on canvas"]
-  ],
-  title='Art Generator and Style Mixer from 🧠 Cloob and 🎨 WikiArt - Visual Art Encyclopedia:',
-  description="Trained on images from the [WikiArt](https://www.wikiart.org/) dataset, comprised of visual arts",
-  article = 'Model used is: [model card](https://huggingface.co/huggan/distill-ccld-wa)..'
-
-)
-iface.launch(enable_queue=True) # , debug=True for colab debugging
\ No newline at end of file
diff --git a/spaces/awacke1/Azure-Cosmos-DB/backup.py b/spaces/awacke1/Azure-Cosmos-DB/backup.py
deleted file mode 100644
index 268025aa27c01ea977d10d94f55c06b7ab3c0b5b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Azure-Cosmos-DB/backup.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import streamlit as st
-from azure.cosmos import CosmosClient
-import os
-
-# Load and save query files
-def load_query(filename):
-    with open(filename, 'r') as file:
-        return file.read()
-
-def save_query(filename, query):
-    with open(filename, 'w') as file:
-        file.write(query)
-
-# Streamlit UI
-st.title("Azure Cosmos DB Explorer 👽")
-
-# Environment Variables for Cosmos DB Credentials
-ACCOUNT_URI = os.environ.get('ACCOUNT_URI', 'Your Cosmos DB URI here')
-ACCOUNT_KEY = os.environ.get('ACCOUNT_KEY', 'Your Cosmos DB Key here')
-
-client = None
-
-# Connection Details Expander
-with st.expander("Connect 🌍"):
-    st.write("Environment Variables for Cosmos DB Credentials are pre-loaded.")
-    database_name = st.text_input("Database Name:", "")
-    container_name = st.text_input("Container Name:", "")
-    if st.button("Connect"):
-        try:
-            client = CosmosClient(ACCOUNT_URI, credential=ACCOUNT_KEY)
-            database_client = client.get_database_client(database_name)
-            container_client = database_client.get_container_client(container_name)
-            st.success("Connected successfully! 🎉")
-        except Exception as e:
-            st.error(f"Failed to connect: {e}")
-
-# Query Editor Expander
-with st.expander("Query Editor 📝"):
-    query = st.text_area("Enter your SQL query here:", "")
-    file_option = st.selectbox("File Options", ["New", "Open", "Save", "Save As"])
-    
-    if file_option == "New":
-        query = ""
-    elif file_option == "Open":
-        open_file = st.file_uploader("Choose a file:", type=["txt"])
-        if open_file is not None:
-            query = load_query(open_file)
-    elif file_option == "Save":
-        save_filename = st.text_input("Enter filename to save:", "my_query.txt")
-        if st.button("Save Query"):
-            save_query(save_filename, query)
-    elif file_option == "Save As":
-        saveas_filename = st.text_input("Enter new filename:", "my_new_query.txt")
-        if st.button("Save As"):
-            save_query(saveas_filename, query)
-    
-    if st.button("Execute Query 🚀"):
-        if client:
-            try:
-                items = list(container_client.query_items(
-                    query=query,
-                    enable_cross_partition_query=True
-                ))
-                st.write("Results 📋:")
-                st.json(items)
-            except Exception as e:
-                st.error(f"Query failed: {e}")
-        else:
-            st.warning("Not connected to any Cosmos DB. Please connect first.")
-
-# Instructions
-st.markdown("""
-## Instructions to Run this App:
-1. **Environment Variables**: Set the `ACCOUNT_URI` and `ACCOUNT_KEY` environment variables with your Azure Cosmos DB URI and Key respectively.
-2. **Install Packages**: If you haven't, install the required Python packages:
-3. **Run App**: Save this code in a file, say `streamlit_cosmosdb_app.py`, and then run `streamlit run streamlit_cosmosdb_app.py`.
-4. **Execute**: Use the UI to connect and run SQL queries against your Cosmos DB.
-""")
diff --git a/spaces/awacke1/GPU-Memory-Detector-Aframe/README.md b/spaces/awacke1/GPU-Memory-Detector-Aframe/README.md
deleted file mode 100644
index b6aabbb8469ba66aa5dabbddf39591c0fbbfbc3a..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GPU-Memory-Detector-Aframe/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: GPU Memory Detector Aframe
-emoji: 👀
-colorFrom: red
-colorTo: purple
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/StreamlitDotEdgeGraphViz-Images-SVG/README.md b/spaces/awacke1/StreamlitDotEdgeGraphViz-Images-SVG/README.md
deleted file mode 100644
index a1da02c9bec7678803b0fc6d7acea32a0a98f399..0000000000000000000000000000000000000000
--- a/spaces/awacke1/StreamlitDotEdgeGraphViz-Images-SVG/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: StreamlitDotEdgeGraphViz Images SVG
-emoji: ⚡
-colorFrom: pink
-colorTo: yellow
-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
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/cameras/CinematicCamera.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/cameras/CinematicCamera.js
deleted file mode 100644
index 6aa37043a68c2fd639ae84baa8868c7862071b58..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/cameras/CinematicCamera.js
+++ /dev/null
@@ -1,207 +0,0 @@
-/**
- * @author mrdoob / http://mrdoob.com/
- * @author greggman / http://games.greggman.com/
- * @author zz85 / http://www.lab4games.net/zz85/blog
- * @author kaypiKun
- */
-
-THREE.CinematicCamera = function ( fov, aspect, near, far ) {
-
-	THREE.PerspectiveCamera.call( this, fov, aspect, near, far );
-
-	this.type = 'CinematicCamera';
-
-	this.postprocessing = { enabled: true };
-	this.shaderSettings = {
-		rings: 3,
-		samples: 4
-	};
-
-	var depthShader = THREE.BokehDepthShader;
-
-	this.materialDepth = new THREE.ShaderMaterial( {
-		uniforms: depthShader.uniforms,
-		vertexShader: depthShader.vertexShader,
-		fragmentShader: depthShader.fragmentShader
-	} );
-
-	this.materialDepth.uniforms[ 'mNear' ].value = near;
-	this.materialDepth.uniforms[ 'mFar' ].value = far;
-
-	// In case of cinematicCamera, having a default lens set is important
-	this.setLens();
-
-	this.initPostProcessing();
-
-};
-
-THREE.CinematicCamera.prototype = Object.create( THREE.PerspectiveCamera.prototype );
-THREE.CinematicCamera.prototype.constructor = THREE.CinematicCamera;
-
-
-// providing fnumber and coc(Circle of Confusion) as extra arguments
-THREE.CinematicCamera.prototype.setLens = function ( focalLength, filmGauge, fNumber, coc ) {
-
-	// In case of cinematicCamera, having a default lens set is important
-	if ( focalLength === undefined ) focalLength = 35;
-	if ( filmGauge !== undefined ) this.filmGauge = filmGauge;
-
-	this.setFocalLength( focalLength );
-
-	// if fnumber and coc are not provided, cinematicCamera tries to act as a basic PerspectiveCamera
-	if ( fNumber === undefined ) fNumber = 8;
-	if ( coc === undefined ) coc = 0.019;
-
-	this.fNumber = fNumber;
-	this.coc = coc;
-
-	// fNumber is focalLength by aperture
-	this.aperture = focalLength / this.fNumber;
-
-	// hyperFocal is required to calculate depthOfField when a lens tries to focus at a distance with given fNumber and focalLength
-	this.hyperFocal = ( focalLength * focalLength ) / ( this.aperture * this.coc );
-
-};
-
-THREE.CinematicCamera.prototype.linearize = function ( depth ) {
-
-	var zfar = this.far;
-	var znear = this.near;
-	return - zfar * znear / ( depth * ( zfar - znear ) - zfar );
-
-};
-
-THREE.CinematicCamera.prototype.smoothstep = function ( near, far, depth ) {
-
-	var x = this.saturate( ( depth - near ) / ( far - near ) );
-	return x * x * ( 3 - 2 * x );
-
-};
-
-THREE.CinematicCamera.prototype.saturate = function ( x ) {
-
-	return Math.max( 0, Math.min( 1, x ) );
-
-};
-
-// function for focusing at a distance from the camera
-THREE.CinematicCamera.prototype.focusAt = function ( focusDistance ) {
-
-	if ( focusDistance === undefined ) focusDistance = 20;
-
-	var focalLength = this.getFocalLength();
-
-	// distance from the camera (normal to frustrum) to focus on
-	this.focus = focusDistance;
-
-	// the nearest point from the camera which is in focus (unused)
-	this.nearPoint = ( this.hyperFocal * this.focus ) / ( this.hyperFocal + ( this.focus - focalLength ) );
-
-	// the farthest point from the camera which is in focus (unused)
-	this.farPoint = ( this.hyperFocal * this.focus ) / ( this.hyperFocal - ( this.focus - focalLength ) );
-
-	// the gap or width of the space in which is everything is in focus (unused)
-	this.depthOfField = this.farPoint - this.nearPoint;
-
-	// Considering minimum distance of focus for a standard lens (unused)
-	if ( this.depthOfField < 0 ) this.depthOfField = 0;
-
-	this.sdistance = this.smoothstep( this.near, this.far, this.focus );
-
-	this.ldistance = this.linearize( 1 -	this.sdistance );
-
-	this.postprocessing.bokeh_uniforms[ 'focalDepth' ].value = this.ldistance;
-
-};
-
-THREE.CinematicCamera.prototype.initPostProcessing = function () {
-
-	if ( this.postprocessing.enabled ) {
-
-		this.postprocessing.scene = new THREE.Scene();
-
-		this.postprocessing.camera = new THREE.OrthographicCamera( window.innerWidth / - 2, window.innerWidth / 2,	window.innerHeight / 2, window.innerHeight / - 2, - 10000, 10000 );
-
-		this.postprocessing.scene.add( this.postprocessing.camera );
-
-		var pars = { minFilter: THREE.LinearFilter, magFilter: THREE.LinearFilter, format: THREE.RGBFormat };
-		this.postprocessing.rtTextureDepth = new THREE.WebGLRenderTarget( window.innerWidth, window.innerHeight, pars );
-		this.postprocessing.rtTextureColor = new THREE.WebGLRenderTarget( window.innerWidth, window.innerHeight, pars );
-
-		var bokeh_shader = THREE.BokehShader;
-
-		this.postprocessing.bokeh_uniforms = THREE.UniformsUtils.clone( bokeh_shader.uniforms );
-
-		this.postprocessing.bokeh_uniforms[ "tColor" ].value = this.postprocessing.rtTextureColor.texture;
-		this.postprocessing.bokeh_uniforms[ "tDepth" ].value = this.postprocessing.rtTextureDepth.texture;
-
-		this.postprocessing.bokeh_uniforms[ "manualdof" ].value = 0;
-		this.postprocessing.bokeh_uniforms[ "shaderFocus" ].value = 0;
-
-		this.postprocessing.bokeh_uniforms[ "fstop" ].value = 2.8;
-
-		this.postprocessing.bokeh_uniforms[ "showFocus" ].value = 1;
-
-		this.postprocessing.bokeh_uniforms[ "focalDepth" ].value = 0.1;
-
-		//console.log( this.postprocessing.bokeh_uniforms[ "focalDepth" ].value );
-
-		this.postprocessing.bokeh_uniforms[ "znear" ].value = this.near;
-		this.postprocessing.bokeh_uniforms[ "zfar" ].value = this.near;
-
-
-		this.postprocessing.bokeh_uniforms[ "textureWidth" ].value = window.innerWidth;
-
-		this.postprocessing.bokeh_uniforms[ "textureHeight" ].value = window.innerHeight;
-
-		this.postprocessing.materialBokeh = new THREE.ShaderMaterial( {
-			uniforms: this.postprocessing.bokeh_uniforms,
-			vertexShader: bokeh_shader.vertexShader,
-			fragmentShader: bokeh_shader.fragmentShader,
-			defines: {
-				RINGS: this.shaderSettings.rings,
-				SAMPLES: this.shaderSettings.samples,
-				DEPTH_PACKING: 1
-			}
-		} );
-
-		this.postprocessing.quad = new THREE.Mesh( new THREE.PlaneBufferGeometry( window.innerWidth, window.innerHeight ), this.postprocessing.materialBokeh );
-		this.postprocessing.quad.position.z = - 500;
-		this.postprocessing.scene.add( this.postprocessing.quad );
-
-	}
-
-};
-
-THREE.CinematicCamera.prototype.renderCinematic = function ( scene, renderer ) {
-
-	if ( this.postprocessing.enabled ) {
-
-		var currentRenderTarget = renderer.getRenderTarget();
-
-		renderer.clear();
-
-		// Render scene into texture
-
-		scene.overrideMaterial = null;
-		renderer.setRenderTarget( this.postprocessing.rtTextureColor );
-		renderer.clear();
-		renderer.render( scene, camera );
-
-		// Render depth into texture
-
-		scene.overrideMaterial = this.materialDepth;
-		renderer.setRenderTarget( this.postprocessing.rtTextureDepth );
-		renderer.clear();
-		renderer.render( scene, camera );
-
-		// Render bokeh composite
-
-		renderer.setRenderTarget( null );
-		renderer.render( this.postprocessing.scene, this.postprocessing.camera );
-
-		renderer.setRenderTarget( currentRenderTarget );
-
-	}
-
-};
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/displacementmap_pars_vertex.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/displacementmap_pars_vertex.glsl.js
deleted file mode 100644
index b8ac0759a92ee411c5057f2d91babd58a571c407..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/displacementmap_pars_vertex.glsl.js
+++ /dev/null
@@ -1,9 +0,0 @@
-export default /* glsl */`
-#ifdef USE_DISPLACEMENTMAP
-
-	uniform sampler2D displacementMap;
-	uniform float displacementScale;
-	uniform float displacementBias;
-
-#endif
-`;
diff --git a/spaces/bankholdup/stylegan_petbreeder/op/__init__.py b/spaces/bankholdup/stylegan_petbreeder/op/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/gfpgan/models/__init__.py b/spaces/beihai/GFPGAN-V1.3-whole-image/gfpgan/models/__init__.py
deleted file mode 100644
index 6afad57a3794b867dabbdb617a16355a24d6a8b3..0000000000000000000000000000000000000000
--- a/spaces/beihai/GFPGAN-V1.3-whole-image/gfpgan/models/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import model modules for registry
-# scan all the files that end with '_model.py' under the model folder
-model_folder = osp.dirname(osp.abspath(__file__))
-model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
-# import all the model modules
-_model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames]
diff --git a/spaces/bigslime/stablediffusion-infinity/README.md b/spaces/bigslime/stablediffusion-infinity/README.md
deleted file mode 100644
index 138edfafa3a3cbeaeac296d170c48dba2fc47586..0000000000000000000000000000000000000000
--- a/spaces/bigslime/stablediffusion-infinity/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Stablediffusion Infinity
-emoji: 🐨
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: lnyan/stablediffusion-infinity
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bioriAsaeru/text-to-voice/HD Online Player (Andaaz 2015 Hindi 720p Torrent).md b/spaces/bioriAsaeru/text-to-voice/HD Online Player (Andaaz 2015 Hindi 720p Torrent).md
deleted file mode 100644
index 3073304e826ff5e4bc405f10010cfc9eb9914fcf..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/HD Online Player (Andaaz 2015 Hindi 720p Torrent).md	
+++ /dev/null
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-<h2>HD Online Player (Andaaz 2015 Hindi 720p Torrent)</h2><br /><p><b><b>DOWNLOAD</b> &rarr;&rarr;&rarr; <a href="https://urloso.com/2uyQuR">https://urloso.com/2uyQuR</a></b></p><br /><br />
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-Avatar-2 official trailer Hindi dubbed - Hollywood movie trailer 2017. SANDIP AKASH RATHOR. Padman... - YouTube
-Find this Pin and more on Cinema by аÑÑаÐ1⁄2а ÐÐ3⁄4Ð2ÑÐμÐμÐ2а.
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-Avatar 2 movie 2017 watch online in HD 720p for free - YouTube
-Find this Pin and more on Movies by Galina Malenkina.
-What others say
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-Italo-Disco, Synth-pop) VA - New Generation Vol. 8a78ff9644<br />
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diff --git a/spaces/bioriAsaeru/text-to-voice/Herr Der Ringe Schlacht Um Mittelerde 2 Gratis Download [TOP].md b/spaces/bioriAsaeru/text-to-voice/Herr Der Ringe Schlacht Um Mittelerde 2 Gratis Download [TOP].md
deleted file mode 100644
index 45cc3e64cd167200b217b6a2f7646c0f45521b0c..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Herr Der Ringe Schlacht Um Mittelerde 2 Gratis Download [TOP].md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>Herr Der Ringe Schlacht Um Mittelerde 2 Gratis Download</h2><br /><p><b><b>Download File</b> &#9989; <a href="https://urloso.com/2uyQ5z">https://urloso.com/2uyQ5z</a></b></p><br /><br />
-<br />
-10 May 2550 BC. - Update 2.01 for the German version of Der der Ringe: Die Schlacht um Mittelerde II: Aufstieg des HexenkönigsGeneral - Unit • All .## #Herr Der Ringe Schlacht Um Mittelerde 2 Free Download [2020]. 2020.12.16 21:52.関連記事. _HOT_ Bat Arkham City Game of the Year Free TV Series Exe UTorrent.创建有效期:
-2020-01-17 07:40:18.
-Tl late : Category: Games; Version: 1.0.0.20; Code: 0x000020A3.
-Feb 7, 2020 - 创建有效期: 2020-01-17 07:40:18.
-Feb 6, 2020 - 创建有效期: 2020-01 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/environment.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/environment.py
deleted file mode 100644
index adc7819305758bb50a9984928bfa7f13eabef5f5..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/environment.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.
-
-"""
-Provides cluster and tools configuration across clusters (slurm, dora, utilities).
-"""
-
-import logging
-import os
-from pathlib import Path
-import re
-import typing as tp
-
-import omegaconf
-
-from .utils.cluster import _guess_cluster_type
-
-
-logger = logging.getLogger(__name__)
-
-
-class AudioCraftEnvironment:
-    """Environment configuration for teams and clusters.
-
-    AudioCraftEnvironment picks compute cluster settings (slurm, dora) from the current running environment
-    or declared variable and the loaded team configuration. Additionally, the AudioCraftEnvironment
-    provides pointers to a reference folder resolved automatically across clusters that is shared across team members,
-    allowing to share sigs or other files to run jobs. Finally, it provides dataset mappers to automatically
-    map dataset file paths to new locations across clusters, allowing to use the same manifest of files across cluters.
-
-    The cluster type is identified automatically and base configuration file is read from config/teams.yaml.
-    Use the following environment variables to specify the cluster, team or configuration:
-
-        AUDIOCRAFT_CLUSTER (optional): Cluster type to enforce. Useful if the cluster type
-            cannot be inferred automatically.
-        AUDIOCRAFT_CONFIG (optional): Path to yaml config holding the teams configuration.
-            If not set, configuration is read from config/teams.yaml.
-        AUDIOCRAFT_TEAM (optional): Name of the team. Recommended to set to your own team.
-            Cluster configuration are shared across teams to match compute allocation,
-            specify your cluster configuration in the configuration file under a key mapping
-            your team name.
-    """
-    _instance = None
-    DEFAULT_TEAM = "default"
-
-    def __init__(self) -> None:
-        """Loads configuration."""
-        self.team: str = os.getenv("AUDIOCRAFT_TEAM", self.DEFAULT_TEAM)
-        cluster_type = _guess_cluster_type()
-        cluster = os.getenv(
-            "AUDIOCRAFT_CLUSTER", cluster_type.value
-        )
-        logger.info("Detecting cluster type %s", cluster_type)
-
-        self.cluster: str = cluster
-
-        config_path = os.getenv(
-            "AUDIOCRAFT_CONFIG",
-            Path(__file__)
-            .parent.parent.joinpath("config/teams", self.team)
-            .with_suffix(".yaml"),
-        )
-        self.config = omegaconf.OmegaConf.load(config_path)
-        self._dataset_mappers = []
-        cluster_config = self._get_cluster_config()
-        if "dataset_mappers" in cluster_config:
-            for pattern, repl in cluster_config["dataset_mappers"].items():
-                regex = re.compile(pattern)
-                self._dataset_mappers.append((regex, repl))
-
-    def _get_cluster_config(self) -> omegaconf.DictConfig:
-        assert isinstance(self.config, omegaconf.DictConfig)
-        return self.config[self.cluster]
-
-    @classmethod
-    def instance(cls):
-        if cls._instance is None:
-            cls._instance = cls()
-        return cls._instance
-
-    @classmethod
-    def reset(cls):
-        """Clears the environment and forces a reload on next invocation."""
-        cls._instance = None
-
-    @classmethod
-    def get_team(cls) -> str:
-        """Gets the selected team as dictated by the AUDIOCRAFT_TEAM env var.
-        If not defined, defaults to "labs".
-        """
-        return cls.instance().team
-
-    @classmethod
-    def get_cluster(cls) -> str:
-        """Gets the detected cluster.
-        This value can be overridden by the AUDIOCRAFT_CLUSTER env var.
-        """
-        return cls.instance().cluster
-
-    @classmethod
-    def get_dora_dir(cls) -> Path:
-        """Gets the path to the dora directory for the current team and cluster.
-        Value is overridden by the AUDIOCRAFT_DORA_DIR env var.
-        """
-        cluster_config = cls.instance()._get_cluster_config()
-        dora_dir = os.getenv("AUDIOCRAFT_DORA_DIR", cluster_config["dora_dir"])
-        logger.warning(f"Dora directory: {dora_dir}")
-        return Path(dora_dir)
-
-    @classmethod
-    def get_reference_dir(cls) -> Path:
-        """Gets the path to the reference directory for the current team and cluster.
-        Value is overridden by the AUDIOCRAFT_REFERENCE_DIR env var.
-        """
-        cluster_config = cls.instance()._get_cluster_config()
-        return Path(os.getenv("AUDIOCRAFT_REFERENCE_DIR", cluster_config["reference_dir"]))
-
-    @classmethod
-    def get_slurm_exclude(cls) -> tp.Optional[str]:
-        """Get the list of nodes to exclude for that cluster."""
-        cluster_config = cls.instance()._get_cluster_config()
-        return cluster_config.get("slurm_exclude")
-
-    @classmethod
-    def get_slurm_partitions(cls, partition_types: tp.Optional[tp.List[str]] = None) -> str:
-        """Gets the requested partitions for the current team and cluster as a comma-separated string.
-
-        Args:
-            partition_types (list[str], optional): partition types to retrieve. Values must be
-                from ['global', 'team']. If not provided, the global partition is returned.
-        """
-        if not partition_types:
-            partition_types = ["global"]
-
-        cluster_config = cls.instance()._get_cluster_config()
-        partitions = [
-            cluster_config["partitions"][partition_type]
-            for partition_type in partition_types
-        ]
-        return ",".join(partitions)
-
-    @classmethod
-    def resolve_reference_path(cls, path: tp.Union[str, Path]) -> Path:
-        """Converts reference placeholder in path with configured reference dir to resolve paths.
-
-        Args:
-            path (str or Path): Path to resolve.
-        Returns:
-            Path: Resolved path.
-        """
-        path = str(path)
-
-        if path.startswith("//reference"):
-            reference_dir = cls.get_reference_dir()
-            logger.warn(f"Reference directory: {reference_dir}")
-            assert (
-                reference_dir.exists() and reference_dir.is_dir()
-            ), f"Reference directory does not exist: {reference_dir}."
-            path = re.sub("^//reference", str(reference_dir), path)
-
-        return Path(path)
-
-    @classmethod
-    def apply_dataset_mappers(cls, path: str) -> str:
-        """Applies dataset mapping regex rules as defined in the configuration.
-        If no rules are defined, the path is returned as-is.
-        """
-        instance = cls.instance()
-
-        for pattern, repl in instance._dataset_mappers:
-            path = pattern.sub(repl, path)
-
-        return path
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/utils/video_visualizer.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/utils/video_visualizer.py
deleted file mode 100644
index 42685be53bb09bab8420b1bcd4d63d8dc6ba7cab..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/utils/video_visualizer.py
+++ /dev/null
@@ -1,287 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import List
-import pycocotools.mask as mask_util
-
-from detectron2.structures import Instances
-from detectron2.utils.visualizer import (
-    ColorMode,
-    Visualizer,
-    _create_text_labels,
-    _PanopticPrediction,
-)
-
-from .colormap import random_color, random_colors
-
-
-class _DetectedInstance:
-    """
-    Used to store data about detected objects in video frame,
-    in order to transfer color to objects in the future frames.
-
-    Attributes:
-        label (int):
-        bbox (tuple[float]):
-        mask_rle (dict):
-        color (tuple[float]): RGB colors in range (0, 1)
-        ttl (int): time-to-live for the instance. For example, if ttl=2,
-            the instance color can be transferred to objects in the next two frames.
-    """
-
-    __slots__ = ["label", "bbox", "mask_rle", "color", "ttl"]
-
-    def __init__(self, label, bbox, mask_rle, color, ttl):
-        self.label = label
-        self.bbox = bbox
-        self.mask_rle = mask_rle
-        self.color = color
-        self.ttl = ttl
-
-
-class VideoVisualizer:
-    def __init__(self, metadata, instance_mode=ColorMode.IMAGE):
-        """
-        Args:
-            metadata (MetadataCatalog): image metadata.
-        """
-        self.metadata = metadata
-        self._old_instances = []
-        assert instance_mode in [
-            ColorMode.IMAGE,
-            ColorMode.IMAGE_BW,
-        ], "Other mode not supported yet."
-        self._instance_mode = instance_mode
-        self._max_num_instances = self.metadata.get("max_num_instances", 74)
-        self._assigned_colors = {}
-        self._color_pool = random_colors(self._max_num_instances, rgb=True, maximum=1)
-        self._color_idx_set = set(range(len(self._color_pool)))
-
-    def draw_instance_predictions(self, frame, predictions):
-        """
-        Draw instance-level prediction results on an image.
-
-        Args:
-            frame (ndarray): an RGB image of shape (H, W, C), in the range [0, 255].
-            predictions (Instances): the output of an instance detection/segmentation
-                model. Following fields will be used to draw:
-                "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        frame_visualizer = Visualizer(frame, self.metadata)
-        num_instances = len(predictions)
-        if num_instances == 0:
-            return frame_visualizer.output
-
-        boxes = predictions.pred_boxes.tensor.numpy() if predictions.has("pred_boxes") else None
-        scores = predictions.scores if predictions.has("scores") else None
-        classes = predictions.pred_classes.numpy() if predictions.has("pred_classes") else None
-        keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None
-        colors = predictions.COLOR if predictions.has("COLOR") else [None] * len(predictions)
-        periods = predictions.ID_period if predictions.has("ID_period") else None
-        period_threshold = self.metadata.get("period_threshold", 0)
-        visibilities = (
-            [True] * len(predictions)
-            if periods is None
-            else [x > period_threshold for x in periods]
-        )
-
-        if predictions.has("pred_masks"):
-            masks = predictions.pred_masks
-            # mask IOU is not yet enabled
-            # masks_rles = mask_util.encode(np.asarray(masks.permute(1, 2, 0), order="F"))
-            # assert len(masks_rles) == num_instances
-        else:
-            masks = None
-
-        if not predictions.has("COLOR"):
-            if predictions.has("ID"):
-                colors = self._assign_colors_by_id(predictions)
-            else:
-                # ToDo: clean old assign color method and use a default tracker to assign id
-                detected = [
-                    _DetectedInstance(classes[i], boxes[i], mask_rle=None, color=colors[i], ttl=8)
-                    for i in range(num_instances)
-                ]
-                colors = self._assign_colors(detected)
-
-        labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None))
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            # any() returns uint8 tensor
-            frame_visualizer.output.reset_image(
-                frame_visualizer._create_grayscale_image(
-                    (masks.any(dim=0) > 0).numpy() if masks is not None else None
-                )
-            )
-            alpha = 0.3
-        else:
-            alpha = 0.5
-
-        labels = (
-            None
-            if labels is None
-            else [y[0] for y in filter(lambda x: x[1], zip(labels, visibilities))]
-        )  # noqa
-        assigned_colors = (
-            None
-            if colors is None
-            else [y[0] for y in filter(lambda x: x[1], zip(colors, visibilities))]
-        )  # noqa
-        frame_visualizer.overlay_instances(
-            boxes=None if masks is not None else boxes[visibilities],  # boxes are a bit distracting
-            masks=None if masks is None else masks[visibilities],
-            labels=labels,
-            keypoints=None if keypoints is None else keypoints[visibilities],
-            assigned_colors=assigned_colors,
-            alpha=alpha,
-        )
-
-        return frame_visualizer.output
-
-    def draw_sem_seg(self, frame, sem_seg, area_threshold=None):
-        """
-        Args:
-            sem_seg (ndarray or Tensor): semantic segmentation of shape (H, W),
-                each value is the integer label.
-            area_threshold (Optional[int]): only draw segmentations larger than the threshold
-        """
-        # don't need to do anything special
-        frame_visualizer = Visualizer(frame, self.metadata)
-        frame_visualizer.draw_sem_seg(sem_seg, area_threshold=None)
-        return frame_visualizer.output
-
-    def draw_panoptic_seg_predictions(
-        self, frame, panoptic_seg, segments_info, area_threshold=None, alpha=0.5
-    ):
-        frame_visualizer = Visualizer(frame, self.metadata)
-        pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            frame_visualizer.output.reset_image(
-                frame_visualizer._create_grayscale_image(pred.non_empty_mask())
-            )
-
-        # draw mask for all semantic segments first i.e. "stuff"
-        for mask, sinfo in pred.semantic_masks():
-            category_idx = sinfo["category_id"]
-            try:
-                mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]
-            except AttributeError:
-                mask_color = None
-
-            frame_visualizer.draw_binary_mask(
-                mask,
-                color=mask_color,
-                text=self.metadata.stuff_classes[category_idx],
-                alpha=alpha,
-                area_threshold=area_threshold,
-            )
-
-        all_instances = list(pred.instance_masks())
-        if len(all_instances) == 0:
-            return frame_visualizer.output
-        # draw mask for all instances second
-        masks, sinfo = list(zip(*all_instances))
-        num_instances = len(masks)
-        masks_rles = mask_util.encode(
-            np.asarray(np.asarray(masks).transpose(1, 2, 0), dtype=np.uint8, order="F")
-        )
-        assert len(masks_rles) == num_instances
-
-        category_ids = [x["category_id"] for x in sinfo]
-        detected = [
-            _DetectedInstance(category_ids[i], bbox=None, mask_rle=masks_rles[i], color=None, ttl=8)
-            for i in range(num_instances)
-        ]
-        colors = self._assign_colors(detected)
-        labels = [self.metadata.thing_classes[k] for k in category_ids]
-
-        frame_visualizer.overlay_instances(
-            boxes=None,
-            masks=masks,
-            labels=labels,
-            keypoints=None,
-            assigned_colors=colors,
-            alpha=alpha,
-        )
-        return frame_visualizer.output
-
-    def _assign_colors(self, instances):
-        """
-        Naive tracking heuristics to assign same color to the same instance,
-        will update the internal state of tracked instances.
-
-        Returns:
-            list[tuple[float]]: list of colors.
-        """
-
-        # Compute iou with either boxes or masks:
-        is_crowd = np.zeros((len(instances),), dtype=bool)
-        if instances[0].bbox is None:
-            assert instances[0].mask_rle is not None
-            # use mask iou only when box iou is None
-            # because box seems good enough
-            rles_old = [x.mask_rle for x in self._old_instances]
-            rles_new = [x.mask_rle for x in instances]
-            ious = mask_util.iou(rles_old, rles_new, is_crowd)
-            threshold = 0.5
-        else:
-            boxes_old = [x.bbox for x in self._old_instances]
-            boxes_new = [x.bbox for x in instances]
-            ious = mask_util.iou(boxes_old, boxes_new, is_crowd)
-            threshold = 0.6
-        if len(ious) == 0:
-            ious = np.zeros((len(self._old_instances), len(instances)), dtype="float32")
-
-        # Only allow matching instances of the same label:
-        for old_idx, old in enumerate(self._old_instances):
-            for new_idx, new in enumerate(instances):
-                if old.label != new.label:
-                    ious[old_idx, new_idx] = 0
-
-        matched_new_per_old = np.asarray(ious).argmax(axis=1)
-        max_iou_per_old = np.asarray(ious).max(axis=1)
-
-        # Try to find match for each old instance:
-        extra_instances = []
-        for idx, inst in enumerate(self._old_instances):
-            if max_iou_per_old[idx] > threshold:
-                newidx = matched_new_per_old[idx]
-                if instances[newidx].color is None:
-                    instances[newidx].color = inst.color
-                    continue
-            # If an old instance does not match any new instances,
-            # keep it for the next frame in case it is just missed by the detector
-            inst.ttl -= 1
-            if inst.ttl > 0:
-                extra_instances.append(inst)
-
-        # Assign random color to newly-detected instances:
-        for inst in instances:
-            if inst.color is None:
-                inst.color = random_color(rgb=True, maximum=1)
-        self._old_instances = instances[:] + extra_instances
-        return [d.color for d in instances]
-
-    def _assign_colors_by_id(self, instances: Instances) -> List:
-        colors = []
-        untracked_ids = set(self._assigned_colors.keys())
-        for id in instances.ID:
-            if id in self._assigned_colors:
-                colors.append(self._color_pool[self._assigned_colors[id]])
-                untracked_ids.remove(id)
-            else:
-                assert (
-                    len(self._color_idx_set) >= 1
-                ), f"Number of id exceeded maximum, \
-                    max = {self._max_num_instances}"
-                idx = self._color_idx_set.pop()
-                color = self._color_pool[idx]
-                self._assigned_colors[id] = idx
-                colors.append(color)
-        for id in untracked_ids:
-            self._color_idx_set.add(self._assigned_colors[id])
-            del self._assigned_colors[id]
-        return colors
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/COCO/cascade_mask_rcnn_mvitv2_b_in21k_100ep.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/COCO/cascade_mask_rcnn_mvitv2_b_in21k_100ep.py
deleted file mode 100644
index 9dba203086f8b34221ea9bed9f5fc280579f97df..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/COCO/cascade_mask_rcnn_mvitv2_b_in21k_100ep.py
+++ /dev/null
@@ -1,95 +0,0 @@
-from functools import partial
-import torch.nn as nn
-from fvcore.common.param_scheduler import MultiStepParamScheduler
-
-from detectron2 import model_zoo
-from detectron2.config import LazyCall as L
-from detectron2.solver import WarmupParamScheduler
-from detectron2.modeling import MViT
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.matcher import Matcher
-from detectron2.modeling.roi_heads import (
-    FastRCNNOutputLayers,
-    FastRCNNConvFCHead,
-    CascadeROIHeads,
-)
-
-from ..common.coco_loader_lsj import dataloader
-
-model = model_zoo.get_config("common/models/mask_rcnn_fpn.py").model
-constants = model_zoo.get_config("common/data/constants.py").constants
-model.pixel_mean = constants.imagenet_rgb256_mean
-model.pixel_std = constants.imagenet_rgb256_std
-model.input_format = "RGB"
-model.backbone.bottom_up = L(MViT)(
-    embed_dim=96,
-    depth=24,
-    num_heads=1,
-    last_block_indexes=(1, 4, 20, 23),
-    residual_pooling=True,
-    drop_path_rate=0.4,
-    norm_layer=partial(nn.LayerNorm, eps=1e-6),
-    out_features=("scale2", "scale3", "scale4", "scale5"),
-)
-model.backbone.in_features = "${.bottom_up.out_features}"
-model.backbone.square_pad = 1024
-
-# New heads and LN
-model.backbone.norm = "LN"  # Use LN in FPN
-model.roi_heads.box_head.conv_norm = model.roi_heads.mask_head.conv_norm = "LN"
-
-# 2conv in RPN:
-model.proposal_generator.head.conv_dims = [-1, -1]
-
-# arguments that don't exist for Cascade R-CNN
-[model.roi_heads.pop(k) for k in ["box_head", "box_predictor", "proposal_matcher"]]
-model.roi_heads.update(
-    _target_=CascadeROIHeads,
-    box_heads=[
-        L(FastRCNNConvFCHead)(
-            input_shape=ShapeSpec(channels=256, height=7, width=7),
-            conv_dims=[256, 256, 256, 256],
-            fc_dims=[1024],
-            conv_norm="LN",
-        )
-        for _ in range(3)
-    ],
-    box_predictors=[
-        L(FastRCNNOutputLayers)(
-            input_shape=ShapeSpec(channels=1024),
-            test_score_thresh=0.05,
-            box2box_transform=L(Box2BoxTransform)(weights=(w1, w1, w2, w2)),
-            cls_agnostic_bbox_reg=True,
-            num_classes="${...num_classes}",
-        )
-        for (w1, w2) in [(10, 5), (20, 10), (30, 15)]
-    ],
-    proposal_matchers=[
-        L(Matcher)(thresholds=[th], labels=[0, 1], allow_low_quality_matches=False)
-        for th in [0.5, 0.6, 0.7]
-    ],
-)
-
-# Initialization and trainer settings
-train = model_zoo.get_config("common/train.py").train
-train.amp.enabled = True
-train.ddp.fp16_compression = True
-train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_B_in21k.pyth"
-
-# Schedule
-# 100 ep = 184375 iters * 64 images/iter / 118000 images/ep
-train.max_iter = 184375
-lr_multiplier = L(WarmupParamScheduler)(
-    scheduler=L(MultiStepParamScheduler)(
-        values=[1.0, 0.1, 0.01],
-        milestones=[163889, 177546],
-        num_updates=train.max_iter,
-    ),
-    warmup_length=250 / train.max_iter,
-    warmup_factor=0.001,
-)
-
-optimizer = model_zoo.get_config("common/optim.py").AdamW
-optimizer.params.overrides = {"pos_embed": {"weight_decay": 0.0}}
-optimizer.lr = 8e-5
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/connector.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/connector.py
deleted file mode 100644
index 2499a2dabe92a14413d7f4023477d4b9803da9bd..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/connector.py
+++ /dev/null
@@ -1,1456 +0,0 @@
-import asyncio
-import functools
-import random
-import sys
-import traceback
-import warnings
-from collections import defaultdict, deque
-from contextlib import suppress
-from http.cookies import SimpleCookie
-from itertools import cycle, islice
-from time import monotonic
-from types import TracebackType
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Awaitable,
-    Callable,
-    DefaultDict,
-    Dict,
-    Iterator,
-    List,
-    Optional,
-    Set,
-    Tuple,
-    Type,
-    Union,
-    cast,
-)
-
-import attr
-
-from . import hdrs, helpers
-from .abc import AbstractResolver
-from .client_exceptions import (
-    ClientConnectionError,
-    ClientConnectorCertificateError,
-    ClientConnectorError,
-    ClientConnectorSSLError,
-    ClientHttpProxyError,
-    ClientProxyConnectionError,
-    ServerFingerprintMismatch,
-    UnixClientConnectorError,
-    cert_errors,
-    ssl_errors,
-)
-from .client_proto import ResponseHandler
-from .client_reqrep import ClientRequest, Fingerprint, _merge_ssl_params
-from .helpers import (
-    PY_36,
-    ceil_timeout,
-    get_running_loop,
-    is_ip_address,
-    noop,
-    sentinel,
-)
-from .http import RESPONSES
-from .locks import EventResultOrError
-from .resolver import DefaultResolver
-
-try:
-    import ssl
-
-    SSLContext = ssl.SSLContext
-except ImportError:  # pragma: no cover
-    ssl = None  # type: ignore[assignment]
-    SSLContext = object  # type: ignore[misc,assignment]
-
-
-__all__ = ("BaseConnector", "TCPConnector", "UnixConnector", "NamedPipeConnector")
-
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .client import ClientTimeout
-    from .client_reqrep import ConnectionKey
-    from .tracing import Trace
-
-
-class _DeprecationWaiter:
-    __slots__ = ("_awaitable", "_awaited")
-
-    def __init__(self, awaitable: Awaitable[Any]) -> None:
-        self._awaitable = awaitable
-        self._awaited = False
-
-    def __await__(self) -> Any:
-        self._awaited = True
-        return self._awaitable.__await__()
-
-    def __del__(self) -> None:
-        if not self._awaited:
-            warnings.warn(
-                "Connector.close() is a coroutine, "
-                "please use await connector.close()",
-                DeprecationWarning,
-            )
-
-
-class Connection:
-
-    _source_traceback = None
-    _transport = None
-
-    def __init__(
-        self,
-        connector: "BaseConnector",
-        key: "ConnectionKey",
-        protocol: ResponseHandler,
-        loop: asyncio.AbstractEventLoop,
-    ) -> None:
-        self._key = key
-        self._connector = connector
-        self._loop = loop
-        self._protocol: Optional[ResponseHandler] = protocol
-        self._callbacks: List[Callable[[], None]] = []
-
-        if loop.get_debug():
-            self._source_traceback = traceback.extract_stack(sys._getframe(1))
-
-    def __repr__(self) -> str:
-        return f"Connection<{self._key}>"
-
-    def __del__(self, _warnings: Any = warnings) -> None:
-        if self._protocol is not None:
-            if PY_36:
-                kwargs = {"source": self}
-            else:
-                kwargs = {}
-            _warnings.warn(f"Unclosed connection {self!r}", ResourceWarning, **kwargs)
-            if self._loop.is_closed():
-                return
-
-            self._connector._release(self._key, self._protocol, should_close=True)
-
-            context = {"client_connection": self, "message": "Unclosed connection"}
-            if self._source_traceback is not None:
-                context["source_traceback"] = self._source_traceback
-            self._loop.call_exception_handler(context)
-
-    @property
-    def loop(self) -> asyncio.AbstractEventLoop:
-        warnings.warn(
-            "connector.loop property is deprecated", DeprecationWarning, stacklevel=2
-        )
-        return self._loop
-
-    @property
-    def transport(self) -> Optional[asyncio.Transport]:
-        if self._protocol is None:
-            return None
-        return self._protocol.transport
-
-    @property
-    def protocol(self) -> Optional[ResponseHandler]:
-        return self._protocol
-
-    def add_callback(self, callback: Callable[[], None]) -> None:
-        if callback is not None:
-            self._callbacks.append(callback)
-
-    def _notify_release(self) -> None:
-        callbacks, self._callbacks = self._callbacks[:], []
-
-        for cb in callbacks:
-            with suppress(Exception):
-                cb()
-
-    def close(self) -> None:
-        self._notify_release()
-
-        if self._protocol is not None:
-            self._connector._release(self._key, self._protocol, should_close=True)
-            self._protocol = None
-
-    def release(self) -> None:
-        self._notify_release()
-
-        if self._protocol is not None:
-            self._connector._release(
-                self._key, self._protocol, should_close=self._protocol.should_close
-            )
-            self._protocol = None
-
-    @property
-    def closed(self) -> bool:
-        return self._protocol is None or not self._protocol.is_connected()
-
-
-class _TransportPlaceholder:
-    """placeholder for BaseConnector.connect function"""
-
-    def close(self) -> None:
-        pass
-
-
-class BaseConnector:
-    """Base connector class.
-
-    keepalive_timeout - (optional) Keep-alive timeout.
-    force_close - Set to True to force close and do reconnect
-        after each request (and between redirects).
-    limit - The total number of simultaneous connections.
-    limit_per_host - Number of simultaneous connections to one host.
-    enable_cleanup_closed - Enables clean-up closed ssl transports.
-                            Disabled by default.
-    loop - Optional event loop.
-    """
-
-    _closed = True  # prevent AttributeError in __del__ if ctor was failed
-    _source_traceback = None
-
-    # abort transport after 2 seconds (cleanup broken connections)
-    _cleanup_closed_period = 2.0
-
-    def __init__(
-        self,
-        *,
-        keepalive_timeout: Union[object, None, float] = sentinel,
-        force_close: bool = False,
-        limit: int = 100,
-        limit_per_host: int = 0,
-        enable_cleanup_closed: bool = False,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-    ) -> None:
-
-        if force_close:
-            if keepalive_timeout is not None and keepalive_timeout is not sentinel:
-                raise ValueError(
-                    "keepalive_timeout cannot " "be set if force_close is True"
-                )
-        else:
-            if keepalive_timeout is sentinel:
-                keepalive_timeout = 15.0
-
-        loop = get_running_loop(loop)
-
-        self._closed = False
-        if loop.get_debug():
-            self._source_traceback = traceback.extract_stack(sys._getframe(1))
-
-        self._conns: Dict[ConnectionKey, List[Tuple[ResponseHandler, float]]] = {}
-        self._limit = limit
-        self._limit_per_host = limit_per_host
-        self._acquired: Set[ResponseHandler] = set()
-        self._acquired_per_host: DefaultDict[
-            ConnectionKey, Set[ResponseHandler]
-        ] = defaultdict(set)
-        self._keepalive_timeout = cast(float, keepalive_timeout)
-        self._force_close = force_close
-
-        # {host_key: FIFO list of waiters}
-        self._waiters = defaultdict(deque)  # type: ignore[var-annotated]
-
-        self._loop = loop
-        self._factory = functools.partial(ResponseHandler, loop=loop)
-
-        self.cookies: SimpleCookie[str] = SimpleCookie()
-
-        # start keep-alive connection cleanup task
-        self._cleanup_handle: Optional[asyncio.TimerHandle] = None
-
-        # start cleanup closed transports task
-        self._cleanup_closed_handle: Optional[asyncio.TimerHandle] = None
-        self._cleanup_closed_disabled = not enable_cleanup_closed
-        self._cleanup_closed_transports: List[Optional[asyncio.Transport]] = []
-        self._cleanup_closed()
-
-    def __del__(self, _warnings: Any = warnings) -> None:
-        if self._closed:
-            return
-        if not self._conns:
-            return
-
-        conns = [repr(c) for c in self._conns.values()]
-
-        self._close()
-
-        if PY_36:
-            kwargs = {"source": self}
-        else:
-            kwargs = {}
-        _warnings.warn(f"Unclosed connector {self!r}", ResourceWarning, **kwargs)
-        context = {
-            "connector": self,
-            "connections": conns,
-            "message": "Unclosed connector",
-        }
-        if self._source_traceback is not None:
-            context["source_traceback"] = self._source_traceback
-        self._loop.call_exception_handler(context)
-
-    def __enter__(self) -> "BaseConnector":
-        warnings.warn(
-            '"with Connector():" is deprecated, '
-            'use "async with Connector():" instead',
-            DeprecationWarning,
-        )
-        return self
-
-    def __exit__(self, *exc: Any) -> None:
-        self._close()
-
-    async def __aenter__(self) -> "BaseConnector":
-        return self
-
-    async def __aexit__(
-        self,
-        exc_type: Optional[Type[BaseException]] = None,
-        exc_value: Optional[BaseException] = None,
-        exc_traceback: Optional[TracebackType] = None,
-    ) -> None:
-        await self.close()
-
-    @property
-    def force_close(self) -> bool:
-        """Ultimately close connection on releasing if True."""
-        return self._force_close
-
-    @property
-    def limit(self) -> int:
-        """The total number for simultaneous connections.
-
-        If limit is 0 the connector has no limit.
-        The default limit size is 100.
-        """
-        return self._limit
-
-    @property
-    def limit_per_host(self) -> int:
-        """The limit for simultaneous connections to the same endpoint.
-
-        Endpoints are the same if they are have equal
-        (host, port, is_ssl) triple.
-        """
-        return self._limit_per_host
-
-    def _cleanup(self) -> None:
-        """Cleanup unused transports."""
-        if self._cleanup_handle:
-            self._cleanup_handle.cancel()
-            # _cleanup_handle should be unset, otherwise _release() will not
-            # recreate it ever!
-            self._cleanup_handle = None
-
-        now = self._loop.time()
-        timeout = self._keepalive_timeout
-
-        if self._conns:
-            connections = {}
-            deadline = now - timeout
-            for key, conns in self._conns.items():
-                alive = []
-                for proto, use_time in conns:
-                    if proto.is_connected():
-                        if use_time - deadline < 0:
-                            transport = proto.transport
-                            proto.close()
-                            if key.is_ssl and not self._cleanup_closed_disabled:
-                                self._cleanup_closed_transports.append(transport)
-                        else:
-                            alive.append((proto, use_time))
-                    else:
-                        transport = proto.transport
-                        proto.close()
-                        if key.is_ssl and not self._cleanup_closed_disabled:
-                            self._cleanup_closed_transports.append(transport)
-
-                if alive:
-                    connections[key] = alive
-
-            self._conns = connections
-
-        if self._conns:
-            self._cleanup_handle = helpers.weakref_handle(
-                self, "_cleanup", timeout, self._loop
-            )
-
-    def _drop_acquired_per_host(
-        self, key: "ConnectionKey", val: ResponseHandler
-    ) -> None:
-        acquired_per_host = self._acquired_per_host
-        if key not in acquired_per_host:
-            return
-        conns = acquired_per_host[key]
-        conns.remove(val)
-        if not conns:
-            del self._acquired_per_host[key]
-
-    def _cleanup_closed(self) -> None:
-        """Double confirmation for transport close.
-
-        Some broken ssl servers may leave socket open without proper close.
-        """
-        if self._cleanup_closed_handle:
-            self._cleanup_closed_handle.cancel()
-
-        for transport in self._cleanup_closed_transports:
-            if transport is not None:
-                transport.abort()
-
-        self._cleanup_closed_transports = []
-
-        if not self._cleanup_closed_disabled:
-            self._cleanup_closed_handle = helpers.weakref_handle(
-                self, "_cleanup_closed", self._cleanup_closed_period, self._loop
-            )
-
-    def close(self) -> Awaitable[None]:
-        """Close all opened transports."""
-        self._close()
-        return _DeprecationWaiter(noop())
-
-    def _close(self) -> None:
-        if self._closed:
-            return
-
-        self._closed = True
-
-        try:
-            if self._loop.is_closed():
-                return
-
-            # cancel cleanup task
-            if self._cleanup_handle:
-                self._cleanup_handle.cancel()
-
-            # cancel cleanup close task
-            if self._cleanup_closed_handle:
-                self._cleanup_closed_handle.cancel()
-
-            for data in self._conns.values():
-                for proto, t0 in data:
-                    proto.close()
-
-            for proto in self._acquired:
-                proto.close()
-
-            for transport in self._cleanup_closed_transports:
-                if transport is not None:
-                    transport.abort()
-
-        finally:
-            self._conns.clear()
-            self._acquired.clear()
-            self._waiters.clear()
-            self._cleanup_handle = None
-            self._cleanup_closed_transports.clear()
-            self._cleanup_closed_handle = None
-
-    @property
-    def closed(self) -> bool:
-        """Is connector closed.
-
-        A readonly property.
-        """
-        return self._closed
-
-    def _available_connections(self, key: "ConnectionKey") -> int:
-        """
-        Return number of available connections.
-
-        The limit, limit_per_host and the connection key are taken into account.
-
-        If it returns less than 1 means that there are no connections
-        available.
-        """
-        if self._limit:
-            # total calc available connections
-            available = self._limit - len(self._acquired)
-
-            # check limit per host
-            if (
-                self._limit_per_host
-                and available > 0
-                and key in self._acquired_per_host
-            ):
-                acquired = self._acquired_per_host.get(key)
-                assert acquired is not None
-                available = self._limit_per_host - len(acquired)
-
-        elif self._limit_per_host and key in self._acquired_per_host:
-            # check limit per host
-            acquired = self._acquired_per_host.get(key)
-            assert acquired is not None
-            available = self._limit_per_host - len(acquired)
-        else:
-            available = 1
-
-        return available
-
-    async def connect(
-        self, req: "ClientRequest", traces: List["Trace"], timeout: "ClientTimeout"
-    ) -> Connection:
-        """Get from pool or create new connection."""
-        key = req.connection_key
-        available = self._available_connections(key)
-
-        # Wait if there are no available connections or if there are/were
-        # waiters (i.e. don't steal connection from a waiter about to wake up)
-        if available <= 0 or key in self._waiters:
-            fut = self._loop.create_future()
-
-            # This connection will now count towards the limit.
-            self._waiters[key].append(fut)
-
-            if traces:
-                for trace in traces:
-                    await trace.send_connection_queued_start()
-
-            try:
-                await fut
-            except BaseException as e:
-                if key in self._waiters:
-                    # remove a waiter even if it was cancelled, normally it's
-                    #  removed when it's notified
-                    try:
-                        self._waiters[key].remove(fut)
-                    except ValueError:  # fut may no longer be in list
-                        pass
-
-                raise e
-            finally:
-                if key in self._waiters and not self._waiters[key]:
-                    del self._waiters[key]
-
-            if traces:
-                for trace in traces:
-                    await trace.send_connection_queued_end()
-
-        proto = self._get(key)
-        if proto is None:
-            placeholder = cast(ResponseHandler, _TransportPlaceholder())
-            self._acquired.add(placeholder)
-            self._acquired_per_host[key].add(placeholder)
-
-            if traces:
-                for trace in traces:
-                    await trace.send_connection_create_start()
-
-            try:
-                proto = await self._create_connection(req, traces, timeout)
-                if self._closed:
-                    proto.close()
-                    raise ClientConnectionError("Connector is closed.")
-            except BaseException:
-                if not self._closed:
-                    self._acquired.remove(placeholder)
-                    self._drop_acquired_per_host(key, placeholder)
-                    self._release_waiter()
-                raise
-            else:
-                if not self._closed:
-                    self._acquired.remove(placeholder)
-                    self._drop_acquired_per_host(key, placeholder)
-
-            if traces:
-                for trace in traces:
-                    await trace.send_connection_create_end()
-        else:
-            if traces:
-                # Acquire the connection to prevent race conditions with limits
-                placeholder = cast(ResponseHandler, _TransportPlaceholder())
-                self._acquired.add(placeholder)
-                self._acquired_per_host[key].add(placeholder)
-                for trace in traces:
-                    await trace.send_connection_reuseconn()
-                self._acquired.remove(placeholder)
-                self._drop_acquired_per_host(key, placeholder)
-
-        self._acquired.add(proto)
-        self._acquired_per_host[key].add(proto)
-        return Connection(self, key, proto, self._loop)
-
-    def _get(self, key: "ConnectionKey") -> Optional[ResponseHandler]:
-        try:
-            conns = self._conns[key]
-        except KeyError:
-            return None
-
-        t1 = self._loop.time()
-        while conns:
-            proto, t0 = conns.pop()
-            if proto.is_connected():
-                if t1 - t0 > self._keepalive_timeout:
-                    transport = proto.transport
-                    proto.close()
-                    # only for SSL transports
-                    if key.is_ssl and not self._cleanup_closed_disabled:
-                        self._cleanup_closed_transports.append(transport)
-                else:
-                    if not conns:
-                        # The very last connection was reclaimed: drop the key
-                        del self._conns[key]
-                    return proto
-            else:
-                transport = proto.transport
-                proto.close()
-                if key.is_ssl and not self._cleanup_closed_disabled:
-                    self._cleanup_closed_transports.append(transport)
-
-        # No more connections: drop the key
-        del self._conns[key]
-        return None
-
-    def _release_waiter(self) -> None:
-        """
-        Iterates over all waiters until one to be released is found.
-
-        The one to be released is not finsihed and
-        belongs to a host that has available connections.
-        """
-        if not self._waiters:
-            return
-
-        # Having the dict keys ordered this avoids to iterate
-        # at the same order at each call.
-        queues = list(self._waiters.keys())
-        random.shuffle(queues)
-
-        for key in queues:
-            if self._available_connections(key) < 1:
-                continue
-
-            waiters = self._waiters[key]
-            while waiters:
-                waiter = waiters.popleft()
-                if not waiter.done():
-                    waiter.set_result(None)
-                    return
-
-    def _release_acquired(self, key: "ConnectionKey", proto: ResponseHandler) -> None:
-        if self._closed:
-            # acquired connection is already released on connector closing
-            return
-
-        try:
-            self._acquired.remove(proto)
-            self._drop_acquired_per_host(key, proto)
-        except KeyError:  # pragma: no cover
-            # this may be result of undetermenistic order of objects
-            # finalization due garbage collection.
-            pass
-        else:
-            self._release_waiter()
-
-    def _release(
-        self,
-        key: "ConnectionKey",
-        protocol: ResponseHandler,
-        *,
-        should_close: bool = False,
-    ) -> None:
-        if self._closed:
-            # acquired connection is already released on connector closing
-            return
-
-        self._release_acquired(key, protocol)
-
-        if self._force_close:
-            should_close = True
-
-        if should_close or protocol.should_close:
-            transport = protocol.transport
-            protocol.close()
-
-            if key.is_ssl and not self._cleanup_closed_disabled:
-                self._cleanup_closed_transports.append(transport)
-        else:
-            conns = self._conns.get(key)
-            if conns is None:
-                conns = self._conns[key] = []
-            conns.append((protocol, self._loop.time()))
-
-            if self._cleanup_handle is None:
-                self._cleanup_handle = helpers.weakref_handle(
-                    self, "_cleanup", self._keepalive_timeout, self._loop
-                )
-
-    async def _create_connection(
-        self, req: "ClientRequest", traces: List["Trace"], timeout: "ClientTimeout"
-    ) -> ResponseHandler:
-        raise NotImplementedError()
-
-
-class _DNSCacheTable:
-    def __init__(self, ttl: Optional[float] = None) -> None:
-        self._addrs_rr: Dict[Tuple[str, int], Tuple[Iterator[Dict[str, Any]], int]] = {}
-        self._timestamps: Dict[Tuple[str, int], float] = {}
-        self._ttl = ttl
-
-    def __contains__(self, host: object) -> bool:
-        return host in self._addrs_rr
-
-    def add(self, key: Tuple[str, int], addrs: List[Dict[str, Any]]) -> None:
-        self._addrs_rr[key] = (cycle(addrs), len(addrs))
-
-        if self._ttl:
-            self._timestamps[key] = monotonic()
-
-    def remove(self, key: Tuple[str, int]) -> None:
-        self._addrs_rr.pop(key, None)
-
-        if self._ttl:
-            self._timestamps.pop(key, None)
-
-    def clear(self) -> None:
-        self._addrs_rr.clear()
-        self._timestamps.clear()
-
-    def next_addrs(self, key: Tuple[str, int]) -> List[Dict[str, Any]]:
-        loop, length = self._addrs_rr[key]
-        addrs = list(islice(loop, length))
-        # Consume one more element to shift internal state of `cycle`
-        next(loop)
-        return addrs
-
-    def expired(self, key: Tuple[str, int]) -> bool:
-        if self._ttl is None:
-            return False
-
-        return self._timestamps[key] + self._ttl < monotonic()
-
-
-class TCPConnector(BaseConnector):
-    """TCP connector.
-
-    verify_ssl - Set to True to check ssl certifications.
-    fingerprint - Pass the binary sha256
-        digest of the expected certificate in DER format to verify
-        that the certificate the server presents matches. See also
-        https://en.wikipedia.org/wiki/Transport_Layer_Security#Certificate_pinning
-    resolver - Enable DNS lookups and use this
-        resolver
-    use_dns_cache - Use memory cache for DNS lookups.
-    ttl_dns_cache - Max seconds having cached a DNS entry, None forever.
-    family - socket address family
-    local_addr - local tuple of (host, port) to bind socket to
-
-    keepalive_timeout - (optional) Keep-alive timeout.
-    force_close - Set to True to force close and do reconnect
-        after each request (and between redirects).
-    limit - The total number of simultaneous connections.
-    limit_per_host - Number of simultaneous connections to one host.
-    enable_cleanup_closed - Enables clean-up closed ssl transports.
-                            Disabled by default.
-    loop - Optional event loop.
-    """
-
-    def __init__(
-        self,
-        *,
-        verify_ssl: bool = True,
-        fingerprint: Optional[bytes] = None,
-        use_dns_cache: bool = True,
-        ttl_dns_cache: Optional[int] = 10,
-        family: int = 0,
-        ssl_context: Optional[SSLContext] = None,
-        ssl: Union[None, bool, Fingerprint, SSLContext] = None,
-        local_addr: Optional[Tuple[str, int]] = None,
-        resolver: Optional[AbstractResolver] = None,
-        keepalive_timeout: Union[None, float, object] = sentinel,
-        force_close: bool = False,
-        limit: int = 100,
-        limit_per_host: int = 0,
-        enable_cleanup_closed: bool = False,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-    ):
-        super().__init__(
-            keepalive_timeout=keepalive_timeout,
-            force_close=force_close,
-            limit=limit,
-            limit_per_host=limit_per_host,
-            enable_cleanup_closed=enable_cleanup_closed,
-            loop=loop,
-        )
-
-        self._ssl = _merge_ssl_params(ssl, verify_ssl, ssl_context, fingerprint)
-        if resolver is None:
-            resolver = DefaultResolver(loop=self._loop)
-        self._resolver = resolver
-
-        self._use_dns_cache = use_dns_cache
-        self._cached_hosts = _DNSCacheTable(ttl=ttl_dns_cache)
-        self._throttle_dns_events: Dict[Tuple[str, int], EventResultOrError] = {}
-        self._family = family
-        self._local_addr = local_addr
-
-    def close(self) -> Awaitable[None]:
-        """Close all ongoing DNS calls."""
-        for ev in self._throttle_dns_events.values():
-            ev.cancel()
-
-        return super().close()
-
-    @property
-    def family(self) -> int:
-        """Socket family like AF_INET."""
-        return self._family
-
-    @property
-    def use_dns_cache(self) -> bool:
-        """True if local DNS caching is enabled."""
-        return self._use_dns_cache
-
-    def clear_dns_cache(
-        self, host: Optional[str] = None, port: Optional[int] = None
-    ) -> None:
-        """Remove specified host/port or clear all dns local cache."""
-        if host is not None and port is not None:
-            self._cached_hosts.remove((host, port))
-        elif host is not None or port is not None:
-            raise ValueError("either both host and port " "or none of them are allowed")
-        else:
-            self._cached_hosts.clear()
-
-    async def _resolve_host(
-        self, host: str, port: int, traces: Optional[List["Trace"]] = None
-    ) -> List[Dict[str, Any]]:
-        if is_ip_address(host):
-            return [
-                {
-                    "hostname": host,
-                    "host": host,
-                    "port": port,
-                    "family": self._family,
-                    "proto": 0,
-                    "flags": 0,
-                }
-            ]
-
-        if not self._use_dns_cache:
-
-            if traces:
-                for trace in traces:
-                    await trace.send_dns_resolvehost_start(host)
-
-            res = await self._resolver.resolve(host, port, family=self._family)
-
-            if traces:
-                for trace in traces:
-                    await trace.send_dns_resolvehost_end(host)
-
-            return res
-
-        key = (host, port)
-
-        if (key in self._cached_hosts) and (not self._cached_hosts.expired(key)):
-            # get result early, before any await (#4014)
-            result = self._cached_hosts.next_addrs(key)
-
-            if traces:
-                for trace in traces:
-                    await trace.send_dns_cache_hit(host)
-            return result
-
-        if key in self._throttle_dns_events:
-            # get event early, before any await (#4014)
-            event = self._throttle_dns_events[key]
-            if traces:
-                for trace in traces:
-                    await trace.send_dns_cache_hit(host)
-            await event.wait()
-        else:
-            # update dict early, before any await (#4014)
-            self._throttle_dns_events[key] = EventResultOrError(self._loop)
-            if traces:
-                for trace in traces:
-                    await trace.send_dns_cache_miss(host)
-            try:
-
-                if traces:
-                    for trace in traces:
-                        await trace.send_dns_resolvehost_start(host)
-
-                addrs = await self._resolver.resolve(host, port, family=self._family)
-                if traces:
-                    for trace in traces:
-                        await trace.send_dns_resolvehost_end(host)
-
-                self._cached_hosts.add(key, addrs)
-                self._throttle_dns_events[key].set()
-            except BaseException as e:
-                # any DNS exception, independently of the implementation
-                # is set for the waiters to raise the same exception.
-                self._throttle_dns_events[key].set(exc=e)
-                raise
-            finally:
-                self._throttle_dns_events.pop(key)
-
-        return self._cached_hosts.next_addrs(key)
-
-    async def _create_connection(
-        self, req: "ClientRequest", traces: List["Trace"], timeout: "ClientTimeout"
-    ) -> ResponseHandler:
-        """Create connection.
-
-        Has same keyword arguments as BaseEventLoop.create_connection.
-        """
-        if req.proxy:
-            _, proto = await self._create_proxy_connection(req, traces, timeout)
-        else:
-            _, proto = await self._create_direct_connection(req, traces, timeout)
-
-        return proto
-
-    @staticmethod
-    @functools.lru_cache(None)
-    def _make_ssl_context(verified: bool) -> SSLContext:
-        if verified:
-            return ssl.create_default_context()
-        else:
-            sslcontext = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
-            sslcontext.options |= ssl.OP_NO_SSLv2
-            sslcontext.options |= ssl.OP_NO_SSLv3
-            sslcontext.check_hostname = False
-            sslcontext.verify_mode = ssl.CERT_NONE
-            try:
-                sslcontext.options |= ssl.OP_NO_COMPRESSION
-            except AttributeError as attr_err:
-                warnings.warn(
-                    "{!s}: The Python interpreter is compiled "
-                    "against OpenSSL < 1.0.0. Ref: "
-                    "https://docs.python.org/3/library/ssl.html"
-                    "#ssl.OP_NO_COMPRESSION".format(attr_err),
-                )
-            sslcontext.set_default_verify_paths()
-            return sslcontext
-
-    def _get_ssl_context(self, req: "ClientRequest") -> Optional[SSLContext]:
-        """Logic to get the correct SSL context
-
-        0. if req.ssl is false, return None
-
-        1. if ssl_context is specified in req, use it
-        2. if _ssl_context is specified in self, use it
-        3. otherwise:
-            1. if verify_ssl is not specified in req, use self.ssl_context
-               (will generate a default context according to self.verify_ssl)
-            2. if verify_ssl is True in req, generate a default SSL context
-            3. if verify_ssl is False in req, generate a SSL context that
-               won't verify
-        """
-        if req.is_ssl():
-            if ssl is None:  # pragma: no cover
-                raise RuntimeError("SSL is not supported.")
-            sslcontext = req.ssl
-            if isinstance(sslcontext, ssl.SSLContext):
-                return sslcontext
-            if sslcontext is not None:
-                # not verified or fingerprinted
-                return self._make_ssl_context(False)
-            sslcontext = self._ssl
-            if isinstance(sslcontext, ssl.SSLContext):
-                return sslcontext
-            if sslcontext is not None:
-                # not verified or fingerprinted
-                return self._make_ssl_context(False)
-            return self._make_ssl_context(True)
-        else:
-            return None
-
-    def _get_fingerprint(self, req: "ClientRequest") -> Optional["Fingerprint"]:
-        ret = req.ssl
-        if isinstance(ret, Fingerprint):
-            return ret
-        ret = self._ssl
-        if isinstance(ret, Fingerprint):
-            return ret
-        return None
-
-    async def _wrap_create_connection(
-        self,
-        *args: Any,
-        req: "ClientRequest",
-        timeout: "ClientTimeout",
-        client_error: Type[Exception] = ClientConnectorError,
-        **kwargs: Any,
-    ) -> Tuple[asyncio.Transport, ResponseHandler]:
-        try:
-            async with ceil_timeout(timeout.sock_connect):
-                return await self._loop.create_connection(*args, **kwargs)  # type: ignore[return-value]  # noqa
-        except cert_errors as exc:
-            raise ClientConnectorCertificateError(req.connection_key, exc) from exc
-        except ssl_errors as exc:
-            raise ClientConnectorSSLError(req.connection_key, exc) from exc
-        except OSError as exc:
-            if exc.errno is None and isinstance(exc, asyncio.TimeoutError):
-                raise
-            raise client_error(req.connection_key, exc) from exc
-
-    def _fail_on_no_start_tls(self, req: "ClientRequest") -> None:
-        """Raise a :py:exc:`RuntimeError` on missing ``start_tls()``.
-
-        One case is that :py:meth:`asyncio.loop.start_tls` is not yet
-        implemented under Python 3.6. It is necessary for TLS-in-TLS so
-        that it is possible to send HTTPS queries through HTTPS proxies.
-
-        This doesn't affect regular HTTP requests, though.
-        """
-        if not req.is_ssl():
-            return
-
-        proxy_url = req.proxy
-        assert proxy_url is not None
-        if proxy_url.scheme != "https":
-            return
-
-        self._check_loop_for_start_tls()
-
-    def _check_loop_for_start_tls(self) -> None:
-        try:
-            self._loop.start_tls
-        except AttributeError as attr_exc:
-            raise RuntimeError(
-                "An HTTPS request is being sent through an HTTPS proxy. "
-                "This needs support for TLS in TLS but it is not implemented "
-                "in your runtime for the stdlib asyncio.\n\n"
-                "Please upgrade to Python 3.7 or higher. For more details, "
-                "please see:\n"
-                "* https://bugs.python.org/issue37179\n"
-                "* https://github.com/python/cpython/pull/28073\n"
-                "* https://docs.aiohttp.org/en/stable/"
-                "client_advanced.html#proxy-support\n"
-                "* https://github.com/aio-libs/aiohttp/discussions/6044\n",
-            ) from attr_exc
-
-    def _loop_supports_start_tls(self) -> bool:
-        try:
-            self._check_loop_for_start_tls()
-        except RuntimeError:
-            return False
-        else:
-            return True
-
-    def _warn_about_tls_in_tls(
-        self,
-        underlying_transport: asyncio.Transport,
-        req: "ClientRequest",
-    ) -> None:
-        """Issue a warning if the requested URL has HTTPS scheme."""
-        if req.request_info.url.scheme != "https":
-            return
-
-        asyncio_supports_tls_in_tls = getattr(
-            underlying_transport,
-            "_start_tls_compatible",
-            False,
-        )
-
-        if asyncio_supports_tls_in_tls:
-            return
-
-        warnings.warn(
-            "An HTTPS request is being sent through an HTTPS proxy. "
-            "This support for TLS in TLS is known to be disabled "
-            "in the stdlib asyncio. This is why you'll probably see "
-            "an error in the log below.\n\n"
-            "It is possible to enable it via monkeypatching under "
-            "Python 3.7 or higher. For more details, see:\n"
-            "* https://bugs.python.org/issue37179\n"
-            "* https://github.com/python/cpython/pull/28073\n\n"
-            "You can temporarily patch this as follows:\n"
-            "* https://docs.aiohttp.org/en/stable/client_advanced.html#proxy-support\n"
-            "* https://github.com/aio-libs/aiohttp/discussions/6044\n",
-            RuntimeWarning,
-            source=self,
-            # Why `4`? At least 3 of the calls in the stack originate
-            # from the methods in this class.
-            stacklevel=3,
-        )
-
-    async def _start_tls_connection(
-        self,
-        underlying_transport: asyncio.Transport,
-        req: "ClientRequest",
-        timeout: "ClientTimeout",
-        client_error: Type[Exception] = ClientConnectorError,
-    ) -> Tuple[asyncio.BaseTransport, ResponseHandler]:
-        """Wrap the raw TCP transport with TLS."""
-        tls_proto = self._factory()  # Create a brand new proto for TLS
-
-        # Safety of the `cast()` call here is based on the fact that
-        # internally `_get_ssl_context()` only returns `None` when
-        # `req.is_ssl()` evaluates to `False` which is never gonna happen
-        # in this code path. Of course, it's rather fragile
-        # maintainability-wise but this is to be solved separately.
-        sslcontext = cast(ssl.SSLContext, self._get_ssl_context(req))
-
-        try:
-            async with ceil_timeout(timeout.sock_connect):
-                try:
-                    tls_transport = await self._loop.start_tls(
-                        underlying_transport,
-                        tls_proto,
-                        sslcontext,
-                        server_hostname=req.host,
-                        ssl_handshake_timeout=timeout.total,
-                    )
-                except BaseException:
-                    # We need to close the underlying transport since
-                    # `start_tls()` probably failed before it had a
-                    # chance to do this:
-                    underlying_transport.close()
-                    raise
-        except cert_errors as exc:
-            raise ClientConnectorCertificateError(req.connection_key, exc) from exc
-        except ssl_errors as exc:
-            raise ClientConnectorSSLError(req.connection_key, exc) from exc
-        except OSError as exc:
-            if exc.errno is None and isinstance(exc, asyncio.TimeoutError):
-                raise
-            raise client_error(req.connection_key, exc) from exc
-        except TypeError as type_err:
-            # Example cause looks like this:
-            # TypeError: transport <asyncio.sslproto._SSLProtocolTransport
-            # object at 0x7f760615e460> is not supported by start_tls()
-
-            raise ClientConnectionError(
-                "Cannot initialize a TLS-in-TLS connection to host "
-                f"{req.host!s}:{req.port:d} through an underlying connection "
-                f"to an HTTPS proxy {req.proxy!s} ssl:{req.ssl or 'default'} "
-                f"[{type_err!s}]"
-            ) from type_err
-        else:
-            if tls_transport is None:
-                msg = "Failed to start TLS (possibly caused by closing transport)"
-                raise client_error(req.connection_key, OSError(msg))
-            tls_proto.connection_made(
-                tls_transport
-            )  # Kick the state machine of the new TLS protocol
-
-        return tls_transport, tls_proto
-
-    async def _create_direct_connection(
-        self,
-        req: "ClientRequest",
-        traces: List["Trace"],
-        timeout: "ClientTimeout",
-        *,
-        client_error: Type[Exception] = ClientConnectorError,
-    ) -> Tuple[asyncio.Transport, ResponseHandler]:
-        sslcontext = self._get_ssl_context(req)
-        fingerprint = self._get_fingerprint(req)
-
-        host = req.url.raw_host
-        assert host is not None
-        port = req.port
-        assert port is not None
-        host_resolved = asyncio.ensure_future(
-            self._resolve_host(host, port, traces=traces), loop=self._loop
-        )
-        try:
-            # Cancelling this lookup should not cancel the underlying lookup
-            #  or else the cancel event will get broadcast to all the waiters
-            #  across all connections.
-            hosts = await asyncio.shield(host_resolved)
-        except asyncio.CancelledError:
-
-            def drop_exception(fut: "asyncio.Future[List[Dict[str, Any]]]") -> None:
-                with suppress(Exception, asyncio.CancelledError):
-                    fut.result()
-
-            host_resolved.add_done_callback(drop_exception)
-            raise
-        except OSError as exc:
-            if exc.errno is None and isinstance(exc, asyncio.TimeoutError):
-                raise
-            # in case of proxy it is not ClientProxyConnectionError
-            # it is problem of resolving proxy ip itself
-            raise ClientConnectorError(req.connection_key, exc) from exc
-
-        last_exc: Optional[Exception] = None
-
-        for hinfo in hosts:
-            host = hinfo["host"]
-            port = hinfo["port"]
-
-            try:
-                transp, proto = await self._wrap_create_connection(
-                    self._factory,
-                    host,
-                    port,
-                    timeout=timeout,
-                    ssl=sslcontext,
-                    family=hinfo["family"],
-                    proto=hinfo["proto"],
-                    flags=hinfo["flags"],
-                    server_hostname=hinfo["hostname"] if sslcontext else None,
-                    local_addr=self._local_addr,
-                    req=req,
-                    client_error=client_error,
-                )
-            except ClientConnectorError as exc:
-                last_exc = exc
-                continue
-
-            if req.is_ssl() and fingerprint:
-                try:
-                    fingerprint.check(transp)
-                except ServerFingerprintMismatch as exc:
-                    transp.close()
-                    if not self._cleanup_closed_disabled:
-                        self._cleanup_closed_transports.append(transp)
-                    last_exc = exc
-                    continue
-
-            return transp, proto
-        else:
-            assert last_exc is not None
-            raise last_exc
-
-    async def _create_proxy_connection(
-        self, req: "ClientRequest", traces: List["Trace"], timeout: "ClientTimeout"
-    ) -> Tuple[asyncio.BaseTransport, ResponseHandler]:
-        self._fail_on_no_start_tls(req)
-        runtime_has_start_tls = self._loop_supports_start_tls()
-
-        headers: Dict[str, str] = {}
-        if req.proxy_headers is not None:
-            headers = req.proxy_headers  # type: ignore[assignment]
-        headers[hdrs.HOST] = req.headers[hdrs.HOST]
-
-        url = req.proxy
-        assert url is not None
-        proxy_req = ClientRequest(
-            hdrs.METH_GET,
-            url,
-            headers=headers,
-            auth=req.proxy_auth,
-            loop=self._loop,
-            ssl=req.ssl,
-        )
-
-        # create connection to proxy server
-        transport, proto = await self._create_direct_connection(
-            proxy_req, [], timeout, client_error=ClientProxyConnectionError
-        )
-
-        # Many HTTP proxies has buggy keepalive support.  Let's not
-        # reuse connection but close it after processing every
-        # response.
-        proto.force_close()
-
-        auth = proxy_req.headers.pop(hdrs.AUTHORIZATION, None)
-        if auth is not None:
-            if not req.is_ssl():
-                req.headers[hdrs.PROXY_AUTHORIZATION] = auth
-            else:
-                proxy_req.headers[hdrs.PROXY_AUTHORIZATION] = auth
-
-        if req.is_ssl():
-            if runtime_has_start_tls:
-                self._warn_about_tls_in_tls(transport, req)
-
-            # For HTTPS requests over HTTP proxy
-            # we must notify proxy to tunnel connection
-            # so we send CONNECT command:
-            #   CONNECT www.python.org:443 HTTP/1.1
-            #   Host: www.python.org
-            #
-            # next we must do TLS handshake and so on
-            # to do this we must wrap raw socket into secure one
-            # asyncio handles this perfectly
-            proxy_req.method = hdrs.METH_CONNECT
-            proxy_req.url = req.url
-            key = attr.evolve(
-                req.connection_key, proxy=None, proxy_auth=None, proxy_headers_hash=None
-            )
-            conn = Connection(self, key, proto, self._loop)
-            proxy_resp = await proxy_req.send(conn)
-            try:
-                protocol = conn._protocol
-                assert protocol is not None
-
-                # read_until_eof=True will ensure the connection isn't closed
-                # once the response is received and processed allowing
-                # START_TLS to work on the connection below.
-                protocol.set_response_params(read_until_eof=runtime_has_start_tls)
-                resp = await proxy_resp.start(conn)
-            except BaseException:
-                proxy_resp.close()
-                conn.close()
-                raise
-            else:
-                conn._protocol = None
-                conn._transport = None
-                try:
-                    if resp.status != 200:
-                        message = resp.reason
-                        if message is None:
-                            message = RESPONSES[resp.status][0]
-                        raise ClientHttpProxyError(
-                            proxy_resp.request_info,
-                            resp.history,
-                            status=resp.status,
-                            message=message,
-                            headers=resp.headers,
-                        )
-                    if not runtime_has_start_tls:
-                        rawsock = transport.get_extra_info("socket", default=None)
-                        if rawsock is None:
-                            raise RuntimeError(
-                                "Transport does not expose socket instance"
-                            )
-                        # Duplicate the socket, so now we can close proxy transport
-                        rawsock = rawsock.dup()
-                except BaseException:
-                    # It shouldn't be closed in `finally` because it's fed to
-                    # `loop.start_tls()` and the docs say not to touch it after
-                    # passing there.
-                    transport.close()
-                    raise
-                finally:
-                    if not runtime_has_start_tls:
-                        transport.close()
-
-                if not runtime_has_start_tls:
-                    # HTTP proxy with support for upgrade to HTTPS
-                    sslcontext = self._get_ssl_context(req)
-                    return await self._wrap_create_connection(
-                        self._factory,
-                        timeout=timeout,
-                        ssl=sslcontext,
-                        sock=rawsock,
-                        server_hostname=req.host,
-                        req=req,
-                    )
-
-                return await self._start_tls_connection(
-                    # Access the old transport for the last time before it's
-                    # closed and forgotten forever:
-                    transport,
-                    req=req,
-                    timeout=timeout,
-                )
-            finally:
-                proxy_resp.close()
-
-        return transport, proto
-
-
-class UnixConnector(BaseConnector):
-    """Unix socket connector.
-
-    path - Unix socket path.
-    keepalive_timeout - (optional) Keep-alive timeout.
-    force_close - Set to True to force close and do reconnect
-        after each request (and between redirects).
-    limit - The total number of simultaneous connections.
-    limit_per_host - Number of simultaneous connections to one host.
-    loop - Optional event loop.
-    """
-
-    def __init__(
-        self,
-        path: str,
-        force_close: bool = False,
-        keepalive_timeout: Union[object, float, None] = sentinel,
-        limit: int = 100,
-        limit_per_host: int = 0,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-    ) -> None:
-        super().__init__(
-            force_close=force_close,
-            keepalive_timeout=keepalive_timeout,
-            limit=limit,
-            limit_per_host=limit_per_host,
-            loop=loop,
-        )
-        self._path = path
-
-    @property
-    def path(self) -> str:
-        """Path to unix socket."""
-        return self._path
-
-    async def _create_connection(
-        self, req: "ClientRequest", traces: List["Trace"], timeout: "ClientTimeout"
-    ) -> ResponseHandler:
-        try:
-            async with ceil_timeout(timeout.sock_connect):
-                _, proto = await self._loop.create_unix_connection(
-                    self._factory, self._path
-                )
-        except OSError as exc:
-            if exc.errno is None and isinstance(exc, asyncio.TimeoutError):
-                raise
-            raise UnixClientConnectorError(self.path, req.connection_key, exc) from exc
-
-        return cast(ResponseHandler, proto)
-
-
-class NamedPipeConnector(BaseConnector):
-    """Named pipe connector.
-
-    Only supported by the proactor event loop.
-    See also: https://docs.python.org/3.7/library/asyncio-eventloop.html
-
-    path - Windows named pipe path.
-    keepalive_timeout - (optional) Keep-alive timeout.
-    force_close - Set to True to force close and do reconnect
-        after each request (and between redirects).
-    limit - The total number of simultaneous connections.
-    limit_per_host - Number of simultaneous connections to one host.
-    loop - Optional event loop.
-    """
-
-    def __init__(
-        self,
-        path: str,
-        force_close: bool = False,
-        keepalive_timeout: Union[object, float, None] = sentinel,
-        limit: int = 100,
-        limit_per_host: int = 0,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-    ) -> None:
-        super().__init__(
-            force_close=force_close,
-            keepalive_timeout=keepalive_timeout,
-            limit=limit,
-            limit_per_host=limit_per_host,
-            loop=loop,
-        )
-        if not isinstance(
-            self._loop, asyncio.ProactorEventLoop  # type: ignore[attr-defined]
-        ):
-            raise RuntimeError(
-                "Named Pipes only available in proactor " "loop under windows"
-            )
-        self._path = path
-
-    @property
-    def path(self) -> str:
-        """Path to the named pipe."""
-        return self._path
-
-    async def _create_connection(
-        self, req: "ClientRequest", traces: List["Trace"], timeout: "ClientTimeout"
-    ) -> ResponseHandler:
-        try:
-            async with ceil_timeout(timeout.sock_connect):
-                _, proto = await self._loop.create_pipe_connection(  # type: ignore[attr-defined] # noqa: E501
-                    self._factory, self._path
-                )
-                # the drain is required so that the connection_made is called
-                # and transport is set otherwise it is not set before the
-                # `assert conn.transport is not None`
-                # in client.py's _request method
-                await asyncio.sleep(0)
-                # other option is to manually set transport like
-                # `proto.transport = trans`
-        except OSError as exc:
-            if exc.errno is None and isinstance(exc, asyncio.TimeoutError):
-                raise
-            raise ClientConnectorError(req.connection_key, exc) from exc
-
-        return cast(ResponseHandler, proto)
diff --git a/spaces/captchaboy/FAST-ABINet-OCR/FAST-ABINet-OCR/README.md b/spaces/captchaboy/FAST-ABINet-OCR/FAST-ABINet-OCR/README.md
deleted file mode 100644
index 18d616e6e1ddcadd79c98db2266fa50f70b2a87f..0000000000000000000000000000000000000000
--- a/spaces/captchaboy/FAST-ABINet-OCR/FAST-ABINet-OCR/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: FAST ABINet OCR
-emoji: 🌍
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/evaluation/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/evaluation/__init__.py
deleted file mode 100644
index d96609e8f2261a6800fe85fcf3e1eaeaa44455c6..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/evaluation/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .cityscapes_evaluation import CityscapesInstanceEvaluator, CityscapesSemSegEvaluator
-from .coco_evaluation import COCOEvaluator
-from .rotated_coco_evaluation import RotatedCOCOEvaluator
-from .evaluator import DatasetEvaluator, DatasetEvaluators, inference_context, inference_on_dataset
-from .lvis_evaluation import LVISEvaluator
-from .panoptic_evaluation import COCOPanopticEvaluator
-from .pascal_voc_evaluation import PascalVOCDetectionEvaluator
-from .sem_seg_evaluation import SemSegEvaluator
-from .testing import print_csv_format, verify_results
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/backbone/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/backbone/__init__.py
deleted file mode 100644
index 5b3358a4061b143c78eba8e7bf81fe9f7ffac1aa..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/backbone/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .build import build_backbone, BACKBONE_REGISTRY  # noqa F401 isort:skip
-
-from .backbone import Backbone
-from .fpn import FPN
-from .regnet import RegNet
-from .resnet import (
-    BasicStem,
-    ResNet,
-    ResNetBlockBase,
-    build_resnet_backbone,
-    make_stage,
-    BottleneckBlock,
-)
-from .vit import ViT, SimpleFeaturePyramid, get_vit_lr_decay_rate
-from .mvit import MViT
-from .swin import SwinTransformer
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
-# TODO can expose more resnet blocks after careful consideration
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/dev/parse_results.sh b/spaces/carlosalonso/Detection-video/carpeta_deteccion/dev/parse_results.sh
deleted file mode 100644
index 80768a4005753447c49339790fe66c9b82a80aaf..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/dev/parse_results.sh
+++ /dev/null
@@ -1,45 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-# A shell script that parses metrics from the log file.
-# Make it easier for developers to track performance of models.
-
-LOG="$1"
-
-if [[ -z "$LOG" ]]; then
-	echo "Usage: $0 /path/to/log/file"
-	exit 1
-fi
-
-# [12/15 11:47:32] trainer INFO: Total training time: 12:15:04.446477 (0.4900 s / it)
-# [12/15 11:49:03] inference INFO: Total inference time: 0:01:25.326167 (0.13652186737060548 s / img per device, on 8 devices)
-# [12/15 11:49:03] inference INFO: Total inference pure compute time: .....
-
-# training time
-trainspeed=$(grep -o 'Overall training.*' "$LOG" | grep -Eo '\(.*\)' | grep -o '[0-9\.]*')
-echo "Training speed: $trainspeed s/it"
-
-# inference time: there could be multiple inference during training
-inferencespeed=$(grep -o 'Total inference pure.*' "$LOG" | tail -n1 | grep -Eo '\(.*\)' | grep -o '[0-9\.]*' | head -n1)
-echo "Inference speed: $inferencespeed s/it"
-
-# [12/15 11:47:18] trainer INFO: eta: 0:00:00  iter: 90000  loss: 0.5407 (0.7256)  loss_classifier: 0.1744 (0.2446)  loss_box_reg: 0.0838 (0.1160)  loss_mask: 0.2159 (0.2722)  loss_objectness: 0.0244 (0.0429)  loss_rpn_box_reg: 0.0279 (0.0500)  time: 0.4487 (0.4899)  data: 0.0076 (0.0975) lr: 0.000200  max mem: 4161
-memory=$(grep -o 'max[_ ]mem: [0-9]*' "$LOG" | tail -n1 | grep -o '[0-9]*')
-echo "Training memory: $memory MB"
-
-echo "Easy to copypaste:"
-echo "$trainspeed","$inferencespeed","$memory"
-
-echo "------------------------------"
-
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: Task: bbox
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: AP,AP50,AP75,APs,APm,APl
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: 0.0017,0.0024,0.0017,0.0005,0.0019,0.0011
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: Task: segm
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: AP,AP50,AP75,APs,APm,APl
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: 0.0014,0.0021,0.0016,0.0005,0.0016,0.0011
-
-echo "COCO Results:"
-num_tasks=$(grep -o 'copypaste:.*Task.*' "$LOG" | sort -u | wc -l)
-# each task has 3 lines
-grep -o 'copypaste:.*' "$LOG" | cut -d ' ' -f 2- | tail -n $((num_tasks * 3))
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/config.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/config.py
deleted file mode 100644
index 2a06a09c80865ab987773511b2acc71e232b26ac..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/config.py
+++ /dev/null
@@ -1,277 +0,0 @@
-# -*- coding = utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# pyre-ignore-all-errors
-
-from detectron2.config import CfgNode as CN
-
-
-def add_dataset_category_config(cfg: CN) -> None:
-    """
-    Add config for additional category-related dataset options
-     - category whitelisting
-     - category mapping
-    """
-    _C = cfg
-    _C.DATASETS.CATEGORY_MAPS = CN(new_allowed=True)
-    _C.DATASETS.WHITELISTED_CATEGORIES = CN(new_allowed=True)
-    # class to mesh mapping
-    _C.DATASETS.CLASS_TO_MESH_NAME_MAPPING = CN(new_allowed=True)
-
-
-def add_evaluation_config(cfg: CN) -> None:
-    _C = cfg
-    _C.DENSEPOSE_EVALUATION = CN()
-    # evaluator type, possible values:
-    #  - "iou": evaluator for models that produce iou data
-    #  - "cse": evaluator for models that produce cse data
-    _C.DENSEPOSE_EVALUATION.TYPE = "iou"
-    # storage for DensePose results, possible values:
-    #  - "none": no explicit storage, all the results are stored in the
-    #            dictionary with predictions, memory intensive;
-    #            historically the default storage type
-    #  - "ram": RAM storage, uses per-process RAM storage, which is
-    #           reduced to a single process storage on later stages,
-    #           less memory intensive
-    #  - "file": file storage, uses per-process file-based storage,
-    #            the least memory intensive, but may create bottlenecks
-    #            on file system accesses
-    _C.DENSEPOSE_EVALUATION.STORAGE = "none"
-    # minimum threshold for IOU values: the lower its values is,
-    # the more matches are produced (and the higher the AP score)
-    _C.DENSEPOSE_EVALUATION.MIN_IOU_THRESHOLD = 0.5
-    # Non-distributed inference is slower (at inference time) but can avoid RAM OOM
-    _C.DENSEPOSE_EVALUATION.DISTRIBUTED_INFERENCE = True
-    # evaluate mesh alignment based on vertex embeddings, only makes sense in CSE context
-    _C.DENSEPOSE_EVALUATION.EVALUATE_MESH_ALIGNMENT = False
-    # meshes to compute mesh alignment for
-    _C.DENSEPOSE_EVALUATION.MESH_ALIGNMENT_MESH_NAMES = []
-
-
-def add_bootstrap_config(cfg: CN) -> None:
-    """ """
-    _C = cfg
-    _C.BOOTSTRAP_DATASETS = []
-    _C.BOOTSTRAP_MODEL = CN()
-    _C.BOOTSTRAP_MODEL.WEIGHTS = ""
-    _C.BOOTSTRAP_MODEL.DEVICE = "cuda"
-
-
-def get_bootstrap_dataset_config() -> CN:
-    _C = CN()
-    _C.DATASET = ""
-    # ratio used to mix data loaders
-    _C.RATIO = 0.1
-    # image loader
-    _C.IMAGE_LOADER = CN(new_allowed=True)
-    _C.IMAGE_LOADER.TYPE = ""
-    _C.IMAGE_LOADER.BATCH_SIZE = 4
-    _C.IMAGE_LOADER.NUM_WORKERS = 4
-    _C.IMAGE_LOADER.CATEGORIES = []
-    _C.IMAGE_LOADER.MAX_COUNT_PER_CATEGORY = 1_000_000
-    _C.IMAGE_LOADER.CATEGORY_TO_CLASS_MAPPING = CN(new_allowed=True)
-    # inference
-    _C.INFERENCE = CN()
-    # batch size for model inputs
-    _C.INFERENCE.INPUT_BATCH_SIZE = 4
-    # batch size to group model outputs
-    _C.INFERENCE.OUTPUT_BATCH_SIZE = 2
-    # sampled data
-    _C.DATA_SAMPLER = CN(new_allowed=True)
-    _C.DATA_SAMPLER.TYPE = ""
-    _C.DATA_SAMPLER.USE_GROUND_TRUTH_CATEGORIES = False
-    # filter
-    _C.FILTER = CN(new_allowed=True)
-    _C.FILTER.TYPE = ""
-    return _C
-
-
-def load_bootstrap_config(cfg: CN) -> None:
-    """
-    Bootstrap datasets are given as a list of `dict` that are not automatically
-    converted into CfgNode. This method processes all bootstrap dataset entries
-    and ensures that they are in CfgNode format and comply with the specification
-    """
-    if not cfg.BOOTSTRAP_DATASETS:
-        return
-
-    bootstrap_datasets_cfgnodes = []
-    for dataset_cfg in cfg.BOOTSTRAP_DATASETS:
-        _C = get_bootstrap_dataset_config().clone()
-        _C.merge_from_other_cfg(CN(dataset_cfg))
-        bootstrap_datasets_cfgnodes.append(_C)
-    cfg.BOOTSTRAP_DATASETS = bootstrap_datasets_cfgnodes
-
-
-def add_densepose_head_cse_config(cfg: CN) -> None:
-    """
-    Add configuration options for Continuous Surface Embeddings (CSE)
-    """
-    _C = cfg
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE = CN()
-    # Dimensionality D of the embedding space
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE = 16
-    # Embedder specifications for various mesh IDs
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS = CN(new_allowed=True)
-    # normalization coefficient for embedding distances
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_DIST_GAUSS_SIGMA = 0.01
-    # normalization coefficient for geodesic distances
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.GEODESIC_DIST_GAUSS_SIGMA = 0.01
-    # embedding loss weight
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_WEIGHT = 0.6
-    # embedding loss name, currently the following options are supported:
-    # - EmbeddingLoss: cross-entropy on vertex labels
-    # - SoftEmbeddingLoss: cross-entropy on vertex label combined with
-    #    Gaussian penalty on distance between vertices
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_NAME = "EmbeddingLoss"
-    # optimizer hyperparameters
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.FEATURES_LR_FACTOR = 1.0
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_LR_FACTOR = 1.0
-    # Shape to shape cycle consistency loss parameters:
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False})
-    # shape to shape cycle consistency loss weight
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.025
-    # norm type used for loss computation
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.NORM_P = 2
-    # normalization term for embedding similarity matrices
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.TEMPERATURE = 0.05
-    # maximum number of vertices to include into shape to shape cycle loss
-    # if negative or zero, all vertices are considered
-    # if positive, random subset of vertices of given size is considered
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.MAX_NUM_VERTICES = 4936
-    # Pixel to shape cycle consistency loss parameters:
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False})
-    # pixel to shape cycle consistency loss weight
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.0001
-    # norm type used for loss computation
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NORM_P = 2
-    # map images to all meshes and back (if false, use only gt meshes from the batch)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.USE_ALL_MESHES_NOT_GT_ONLY = False
-    # Randomly select at most this number of pixels from every instance
-    # if negative or zero, all vertices are considered
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NUM_PIXELS_TO_SAMPLE = 100
-    # normalization factor for pixel to pixel distances (higher value = smoother distribution)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.PIXEL_SIGMA = 5.0
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_PIXEL_TO_VERTEX = 0.05
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_VERTEX_TO_PIXEL = 0.05
-
-
-def add_densepose_head_config(cfg: CN) -> None:
-    """
-    Add config for densepose head.
-    """
-    _C = cfg
-
-    _C.MODEL.DENSEPOSE_ON = True
-
-    _C.MODEL.ROI_DENSEPOSE_HEAD = CN()
-    _C.MODEL.ROI_DENSEPOSE_HEAD.NAME = ""
-    _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS = 8
-    # Number of parts used for point labels
-    _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES = 24
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL = 4
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM = 512
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL = 3
-    _C.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE = 2
-    _C.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE = 112
-    _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_TYPE = "ROIAlignV2"
-    _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_RESOLUTION = 28
-    _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_SAMPLING_RATIO = 2
-    _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS = 2  # 15 or 2
-    # Overlap threshold for an RoI to be considered foreground (if >= FG_IOU_THRESHOLD)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.FG_IOU_THRESHOLD = 0.7
-    # Loss weights for annotation masks.(14 Parts)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.INDEX_WEIGHTS = 5.0
-    # Loss weights for surface parts. (24 Parts)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.PART_WEIGHTS = 1.0
-    # Loss weights for UV regression.
-    _C.MODEL.ROI_DENSEPOSE_HEAD.POINT_REGRESSION_WEIGHTS = 0.01
-    # Coarse segmentation is trained using instance segmentation task data
-    _C.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS = False
-    # For Decoder
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_ON = True
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NUM_CLASSES = 256
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_CONV_DIMS = 256
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NORM = ""
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_COMMON_STRIDE = 4
-    # For DeepLab head
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB = CN()
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NORM = "GN"
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NONLOCAL_ON = 0
-    # Predictor class name, must be registered in DENSEPOSE_PREDICTOR_REGISTRY
-    # Some registered predictors:
-    #   "DensePoseChartPredictor": predicts segmentation and UV coordinates for predefined charts
-    #   "DensePoseChartWithConfidencePredictor": predicts segmentation, UV coordinates
-    #       and associated confidences for predefined charts (default)
-    #   "DensePoseEmbeddingWithConfidencePredictor": predicts segmentation, embeddings
-    #       and associated confidences for CSE
-    _C.MODEL.ROI_DENSEPOSE_HEAD.PREDICTOR_NAME = "DensePoseChartWithConfidencePredictor"
-    # Loss class name, must be registered in DENSEPOSE_LOSS_REGISTRY
-    # Some registered losses:
-    #   "DensePoseChartLoss": loss for chart-based models that estimate
-    #      segmentation and UV coordinates
-    #   "DensePoseChartWithConfidenceLoss": loss for chart-based models that estimate
-    #      segmentation, UV coordinates and the corresponding confidences (default)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.LOSS_NAME = "DensePoseChartWithConfidenceLoss"
-    # Confidences
-    # Enable learning UV confidences (variances) along with the actual values
-    _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE = CN({"ENABLED": False})
-    # UV confidence lower bound
-    _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.EPSILON = 0.01
-    # Enable learning segmentation confidences (variances) along with the actual values
-    _C.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE = CN({"ENABLED": False})
-    # Segmentation confidence lower bound
-    _C.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.EPSILON = 0.01
-    # Statistical model type for confidence learning, possible values:
-    # - "iid_iso": statistically independent identically distributed residuals
-    #    with isotropic covariance
-    # - "indep_aniso": statistically independent residuals with anisotropic
-    #    covariances
-    _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.TYPE = "iid_iso"
-    # List of angles for rotation in data augmentation during training
-    _C.INPUT.ROTATION_ANGLES = [0]
-    _C.TEST.AUG.ROTATION_ANGLES = ()  # Rotation TTA
-
-    add_densepose_head_cse_config(cfg)
-
-
-def add_hrnet_config(cfg: CN) -> None:
-    """
-    Add config for HRNet backbone.
-    """
-    _C = cfg
-
-    # For HigherHRNet w32
-    _C.MODEL.HRNET = CN()
-    _C.MODEL.HRNET.STEM_INPLANES = 64
-    _C.MODEL.HRNET.STAGE2 = CN()
-    _C.MODEL.HRNET.STAGE2.NUM_MODULES = 1
-    _C.MODEL.HRNET.STAGE2.NUM_BRANCHES = 2
-    _C.MODEL.HRNET.STAGE2.BLOCK = "BASIC"
-    _C.MODEL.HRNET.STAGE2.NUM_BLOCKS = [4, 4]
-    _C.MODEL.HRNET.STAGE2.NUM_CHANNELS = [32, 64]
-    _C.MODEL.HRNET.STAGE2.FUSE_METHOD = "SUM"
-    _C.MODEL.HRNET.STAGE3 = CN()
-    _C.MODEL.HRNET.STAGE3.NUM_MODULES = 4
-    _C.MODEL.HRNET.STAGE3.NUM_BRANCHES = 3
-    _C.MODEL.HRNET.STAGE3.BLOCK = "BASIC"
-    _C.MODEL.HRNET.STAGE3.NUM_BLOCKS = [4, 4, 4]
-    _C.MODEL.HRNET.STAGE3.NUM_CHANNELS = [32, 64, 128]
-    _C.MODEL.HRNET.STAGE3.FUSE_METHOD = "SUM"
-    _C.MODEL.HRNET.STAGE4 = CN()
-    _C.MODEL.HRNET.STAGE4.NUM_MODULES = 3
-    _C.MODEL.HRNET.STAGE4.NUM_BRANCHES = 4
-    _C.MODEL.HRNET.STAGE4.BLOCK = "BASIC"
-    _C.MODEL.HRNET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4]
-    _C.MODEL.HRNET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256]
-    _C.MODEL.HRNET.STAGE4.FUSE_METHOD = "SUM"
-
-    _C.MODEL.HRNET.HRFPN = CN()
-    _C.MODEL.HRNET.HRFPN.OUT_CHANNELS = 256
-
-
-def add_densepose_config(cfg: CN) -> None:
-    add_densepose_head_config(cfg)
-    add_hrnet_config(cfg)
-    add_bootstrap_config(cfg)
-    add_dataset_category_config(cfg)
-    add_evaluation_config(cfg)
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/modeling/confidence.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/modeling/confidence.py
deleted file mode 100644
index 6f4a72efec06e055036ba70bc75b2624d20e1e0e..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/modeling/confidence.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from dataclasses import dataclass
-from enum import Enum
-
-from detectron2.config import CfgNode
-
-
-class DensePoseUVConfidenceType(Enum):
-    """
-    Statistical model type for confidence learning, possible values:
-     - "iid_iso": statistically independent identically distributed residuals
-         with anisotropic covariance
-     - "indep_aniso": statistically independent residuals with anisotropic
-         covariances
-    For details, see:
-    N. Neverova, D. Novotny, A. Vedaldi "Correlated Uncertainty for Learning
-    Dense Correspondences from Noisy Labels", p. 918--926, in Proc. NIPS 2019
-    """
-
-    # fmt: off
-    IID_ISO     = "iid_iso"
-    INDEP_ANISO = "indep_aniso"
-    # fmt: on
-
-
-@dataclass
-class DensePoseUVConfidenceConfig:
-    """
-    Configuration options for confidence on UV data
-    """
-
-    enabled: bool = False
-    # lower bound on UV confidences
-    epsilon: float = 0.01
-    type: DensePoseUVConfidenceType = DensePoseUVConfidenceType.IID_ISO
-
-
-@dataclass
-class DensePoseSegmConfidenceConfig:
-    """
-    Configuration options for confidence on segmentation
-    """
-
-    enabled: bool = False
-    # lower bound on confidence values
-    epsilon: float = 0.01
-
-
-@dataclass
-class DensePoseConfidenceModelConfig:
-    """
-    Configuration options for confidence models
-    """
-
-    # confidence for U and V values
-    uv_confidence: DensePoseUVConfidenceConfig
-    # segmentation confidence
-    segm_confidence: DensePoseSegmConfidenceConfig
-
-    @staticmethod
-    def from_cfg(cfg: CfgNode) -> "DensePoseConfidenceModelConfig":
-        return DensePoseConfidenceModelConfig(
-            uv_confidence=DensePoseUVConfidenceConfig(
-                enabled=cfg.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.ENABLED,
-                epsilon=cfg.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.EPSILON,
-                type=DensePoseUVConfidenceType(cfg.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.TYPE),
-            ),
-            segm_confidence=DensePoseSegmConfidenceConfig(
-                enabled=cfg.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.ENABLED,
-                epsilon=cfg.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.EPSILON,
-            ),
-        )
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/modeling/predictors/cse_with_confidence.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/modeling/predictors/cse_with_confidence.py
deleted file mode 100644
index 17ecef67ffb67cd0e64de73632eaede1d8f3c701..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/modeling/predictors/cse_with_confidence.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from . import DensePoseEmbeddingConfidencePredictorMixin, DensePoseEmbeddingPredictor
-from .registry import DENSEPOSE_PREDICTOR_REGISTRY
-
-
-@DENSEPOSE_PREDICTOR_REGISTRY.register()
-class DensePoseEmbeddingWithConfidencePredictor(
-    DensePoseEmbeddingConfidencePredictorMixin, DensePoseEmbeddingPredictor
-):
-    """
-    Predictor that combines CSE and CSE confidence estimation
-    """
-
-    pass
diff --git a/spaces/chendl/compositional_test/multimodal/open_flamingo/eval/task/__init__.py b/spaces/chendl/compositional_test/multimodal/open_flamingo/eval/task/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/QoiImagePlugin.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/QoiImagePlugin.py
deleted file mode 100644
index ef91b90abca87ff6526cd10f89f1c0dfc9f0b848..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/QoiImagePlugin.py
+++ /dev/null
@@ -1,105 +0,0 @@
-#
-# The Python Imaging Library.
-#
-# QOI support for PIL
-#
-# See the README file for information on usage and redistribution.
-#
-
-import os
-
-from . import Image, ImageFile
-from ._binary import i32be as i32
-from ._binary import o8
-
-
-def _accept(prefix):
-    return prefix[:4] == b"qoif"
-
-
-class QoiImageFile(ImageFile.ImageFile):
-    format = "QOI"
-    format_description = "Quite OK Image"
-
-    def _open(self):
-        if not _accept(self.fp.read(4)):
-            msg = "not a QOI file"
-            raise SyntaxError(msg)
-
-        self._size = tuple(i32(self.fp.read(4)) for i in range(2))
-
-        channels = self.fp.read(1)[0]
-        self.mode = "RGB" if channels == 3 else "RGBA"
-
-        self.fp.seek(1, os.SEEK_CUR)  # colorspace
-        self.tile = [("qoi", (0, 0) + self._size, self.fp.tell(), None)]
-
-
-class QoiDecoder(ImageFile.PyDecoder):
-    _pulls_fd = True
-
-    def _add_to_previous_pixels(self, value):
-        self._previous_pixel = value
-
-        r, g, b, a = value
-        hash_value = (r * 3 + g * 5 + b * 7 + a * 11) % 64
-        self._previously_seen_pixels[hash_value] = value
-
-    def decode(self, buffer):
-        self._previously_seen_pixels = {}
-        self._previous_pixel = None
-        self._add_to_previous_pixels(b"".join(o8(i) for i in (0, 0, 0, 255)))
-
-        data = bytearray()
-        bands = Image.getmodebands(self.mode)
-        while len(data) < self.state.xsize * self.state.ysize * bands:
-            byte = self.fd.read(1)[0]
-            if byte == 0b11111110:  # QOI_OP_RGB
-                value = self.fd.read(3) + o8(255)
-            elif byte == 0b11111111:  # QOI_OP_RGBA
-                value = self.fd.read(4)
-            else:
-                op = byte >> 6
-                if op == 0:  # QOI_OP_INDEX
-                    op_index = byte & 0b00111111
-                    value = self._previously_seen_pixels.get(op_index, (0, 0, 0, 0))
-                elif op == 1:  # QOI_OP_DIFF
-                    value = (
-                        (self._previous_pixel[0] + ((byte & 0b00110000) >> 4) - 2)
-                        % 256,
-                        (self._previous_pixel[1] + ((byte & 0b00001100) >> 2) - 2)
-                        % 256,
-                        (self._previous_pixel[2] + (byte & 0b00000011) - 2) % 256,
-                    )
-                    value += (self._previous_pixel[3],)
-                elif op == 2:  # QOI_OP_LUMA
-                    second_byte = self.fd.read(1)[0]
-                    diff_green = (byte & 0b00111111) - 32
-                    diff_red = ((second_byte & 0b11110000) >> 4) - 8
-                    diff_blue = (second_byte & 0b00001111) - 8
-
-                    value = tuple(
-                        (self._previous_pixel[i] + diff_green + diff) % 256
-                        for i, diff in enumerate((diff_red, 0, diff_blue))
-                    )
-                    value += (self._previous_pixel[3],)
-                elif op == 3:  # QOI_OP_RUN
-                    run_length = (byte & 0b00111111) + 1
-                    value = self._previous_pixel
-                    if bands == 3:
-                        value = value[:3]
-                    data += value * run_length
-                    continue
-                value = b"".join(o8(i) for i in value)
-            self._add_to_previous_pixels(value)
-
-            if bands == 3:
-                value = value[:3]
-            data += value
-        self.set_as_raw(bytes(data))
-        return -1, 0
-
-
-Image.register_open(QoiImageFile.format, QoiImageFile, _accept)
-Image.register_decoder("qoi", QoiDecoder)
-Image.register_extension(QoiImageFile.format, ".qoi")
diff --git a/spaces/cihyFjudo/fairness-paper-search/Adb Fastboot Gui Tool For Mac Tips and Tricks for Using ADB and Fastboot Commands.md b/spaces/cihyFjudo/fairness-paper-search/Adb Fastboot Gui Tool For Mac Tips and Tricks for Using ADB and Fastboot Commands.md
deleted file mode 100644
index e6fec301cce6e44ac4f746a3952e50b4a6e7ca7c..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Adb Fastboot Gui Tool For Mac Tips and Tricks for Using ADB and Fastboot Commands.md	
+++ /dev/null
@@ -1,37 +0,0 @@
-<br />
-<p>You need to add ADB to your PATH so you can more easily run adb commands. (Your PATH is an environment variable used to specify the location of the program's executable. If you don't add ADB to your PATH, running adb commands will require you to browse to the /platform-tools directory to run ADB.)</p>
-<p>Since Android Studio is quite huge when installed, the standalone <strong>SDK platform tools</strong> could be a better option if you want to save some precious space on your computer's storage, especially if you don't want to get into app development. The tools are comprised of the same parts you get when you install the Android Studio: ADB, fastboot, and systrace. They're available on the Android Developers website. You'll have to extract them and add them to your system's path, but you probably don't want to bother with that. There is a script that simplify the process.</p>
-<h2>Adb Fastboot Gui Tool For Mac</h2><br /><p><b><b>DOWNLOAD</b> &#10031;&#10031;&#10031; <a href="https://tinurli.com/2uwjWk">https://tinurli.com/2uwjWk</a></b></p><br /><br />
-<p>If you ask me, <strong>Nexus Tools</strong> is the fastest way to get ADB up and running on your computer. It's an open-source tool created by Android Police alumnus Corbin Davenport. Nexus Tools automatically downloads the latest platform tools from the Google website mentioned above, extracts them, and adds them to your system's path for ease of use. You just need to paste the following command into a macOS or Linux terminal and hit enter:</p>
-<p>If you want to be able to just open a command window and start using ADB without navigating to your platform-tools folder first, you'll need to add the folder to your Windows path (note that Nexus Tools takes care of that automatically for you):</p>
-<p>Depending on which install method you opted for, you may have to add a . in front of ADB commands to make them work. If you haven't added your SDK tools installation to your path on Windows, keep in mind that you always have to run a terminal from the SDK tools folder (this isn't the case if you installed it using Nexus Tools, mind you).</p>
-<p>First, you need to set up your phone to use the tools. If you haven't already, enable the Developer Options by going to <strong>Settings > About phone</strong> and tapping on <strong>Build number</strong> seven times.</p>
-<p>Download ADB and Fastboot from the Android Developer website. When you unzip the download, the contents will be collected together into a folder called <strong>platform-tools</strong>. There are several other items in the folder, but you can ignore these.</p>
-<p>So where you type <strong>adb</strong> on Windows, you must type <strong>./adb</strong> on Mac and Linux. And <strong>fastboot</strong> on Windows needs to be <strong>./fastboot</strong> on Mac and Linux.</p>
-<p>Boot your phone into Android, then connect it to your desktop computer with a USB cable. On your computer, launch the Command Prompt and change the directory to point to the <strong>platform-tools</strong> folder.</p>
-<p></p>
-<p>Now, type "cd" in the prompt, then add a space, but don't hit enter yet. Instead, drag the platform-tools folder you extracted from your desktop, then drop it right on top of the command window. This will populate the folder's location. When it does, hit enter.</p>
-<p>I opened the Cmd to copy the platform-tool program as u stated "CD" then paste the platform-tool I did that but after DAT I cannot enter the application if I click to launch app I will be coming up and disappearing at the same time then eventually it will come up but the moment I type in anything it goes off...</p>
-<p>In fact, I now actually prefer to root Android devices on a Mac since it usually involves less headache. There are quite a few reasons behind this, including the fact that you can issue fastboot and ADB commands to a majority of the Android devices on a Mac without worrying about their drivers. On Windows though, it is impossible to execute a fastboot command without installing the drivers first as the PC will not be able to see the device itself.</p>
-<p>This will download and install the very latest version ADB and fastboot files on your Mac. Depending on the speed of your Internet connection, this step can take anywhere between a few seconds to a few minutes.</p>
-<p>Next time you need to run an ADB or fastboot command, simply open up terminal and execute the ADB or Fastboot command you want to run straightaway. There is no need to navigate to any particular folder or directory before doing so. A major advantage of following the above steps to get ADB and Fastboot on your Mac is that they will be updated to their latest version automatically.</p>
-<p>The <strong>Android SDK Platform-Tools</strong> is a component for the Android SDK. It includes tools that interface with the Android platform, such as adb, fastboot, and systrace. These tools are required for Android app development. They're also needed if you want to unlock your device bootloader and flash it with a new system image.</p>
-<p>FRP stands for Factory Reset Protection and it keeps your device from being used by any unauthorized users even after the device has been reset to the factory settings. If it is your own device that you wish to use after the FRP protection, then you need to do an ADB FRP bypass. It basically involves using the ADB tool to remove the lock from your Android device.</p>
-<p>There are some ADB FRP bypass tools like GSM flasher ADB bypass FRP tool that allow you to remove the lock from your Android-based phone or tablet by just running a few commands in it. Once the commands are executed, you should find your phone no longer prompts you to sign-in to your Google account.</p>
-<p>There are certain commands that you can use with the ADB FRP bypass tool and get rid of the lock from your device. The following are the steps on how to bypass FRP with ADB including where to get the ADB toolkit from.</p>
-<p>Unless you already know how ADB works and you are used to using it, you are going to need to learn to use the tool to get your task done. It requires a learning curve which many users may not want to do if all they want to do is unlock their device.</p>
-<p>A tool that runs on Android devices and provides information about system services. To get a diagnostic output for all system services for the connected device, simply run adb shell dumpsys. However, this outputs far more information than typically needed. For more manageable output, specify the service to examine by including it in the command.</p>
-<p><strong>Step 4. </strong>Now <strong>Download</strong> Platform Tools for Mac. The file is in a compressed format just click on the downloaded file to extract all ADB and Fastboot tools. <strong> </strong>I recommended you to keep the folder on the Home Desktop for easy access.</p>
-<p>Fastboot is a bootloader mode or diagnostic tool on Android phones, which we can use to flash different files on the phone. It is a unique boot state which only supports the command line interface, including reboot or restart operation. The Fastboot mode is used for any modification on Android phones using Fastboot commands.</p>
-<p><strong>Note</strong>:- To use ADB & Fastboot commands, you should connect the device to the computer. And also, drivers are required. So before using ADB & Fastboot commands, make sure to install ADB & Fastboot drivers on your computer. The driver may not be necessary for the Mac and Linux operating systems, so you can skip installing the driver. For Mac and Linux, you can use Platform tools.</p>
-<p>Reinstalling the platform-tools resolves the problem. For this, open <strong>Terminal</strong>, switch to C:\Users\Username\AppData\Local\Android\Sdk\tools\bin\sdkmanager and run the following command:</p>
-<p>The opposite of FastBoot is ColdBoot, where during every launch the Android Emulator executes a fresh boot instance. ColdBoot is by default disabled as you can see in my previous screenshot, you need to tick on the <strong>fastboot.forceColdBoot</strong> in order to enable this. You should only use this if you need a fresh boot in each time your Emulator launches.</p>
-<p><strong>Android SDK Platform-Tools:</strong> Android SDK Platform-Tools is a component for the Android SDK. It includes tools that interface with the Android platform, such as adb, fastboot, and systrace. These are essential components required during Android debugging and need to be installed. Download the Android SDK Platform-Tools here.</p>
-<p><strong>Android Debug Bridge (adb)</strong> is a versatile command-line tool that lets you communicate with a device. As we go further we need to get familiar with a few of the important adb commands:</p>
-<p><strong>SSL pinning:</strong> allows the application to only trust the valid or pre-defined certificate or Public Key. Developers use SSL pinning technique as an additional security layer for application traffic. In the SSL Pinning implementation, the application does not trust custom certificates and does not allow proxy tools to intercept the traffic.</p>
-<p><strong>Frida</strong> is a dynamic instrumentation toolkit that is used to inject snippets of javascript into the application running process, where your JS gets executed with full access to memory, hooking functions, and even calling native functions inside the process. Using Frida we can Bypass security controls like SSLPinning and Root detection and other business logic implementations.</p>
-<p><strong>Objection</strong> is a mobile exploration toolkit that uses Frida and automates the injection process by providing a command-line interface to the end-user that allows them to bypass the certificate pinning, dump process memory, modify values on the fly, and other security implementation by selecting various options without a need of a rooted phone.</p>
-<p><strong>Mobile Security Framework (MobSF)</strong> is a free and open-source tool that automates security assessment for both Android/iOS pen-testing and security assessment framework capable of performing static and dynamic analysis on the android applications.</p>
-<p>Android Debug Bridge (adb) is a command line tool that allows for communication between a computer and a connected Android device. It facilitates a variety of device actions, such as installing and debugging apps, and it provides access to commands that are not traditionally available to a connected device.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/FULL Waves Complete V9 (2018.04.11) Pc The Best Choice for Music Producers and Engineers.md b/spaces/cihyFjudo/fairness-paper-search/FULL Waves Complete V9 (2018.04.11) Pc The Best Choice for Music Producers and Engineers.md
deleted file mode 100644
index 2c88e26e5a7de964d08738df1890ff03ddf274c7..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/FULL Waves Complete V9 (2018.04.11) Pc The Best Choice for Music Producers and Engineers.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>FULL Waves Complete V9 (2018.04.11) Pc</h2><br /><p><b><b>DOWNLOAD</b> &#127379; <a href="https://tinurli.com/2uwivN">https://tinurli.com/2uwivN</a></b></p><br /><br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Secret Game 2 Movie Download In Hindi HOT.md b/spaces/cihyFjudo/fairness-paper-search/Secret Game 2 Movie Download In Hindi HOT.md
deleted file mode 100644
index ca9cf42e6d82d2bfb9ece477e77cc4fb590987e0..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Secret Game 2 Movie Download In Hindi HOT.md	
+++ /dev/null
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diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/asm-offsets.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/asm-offsets.h
deleted file mode 100644
index fc38eed2985973d01e96c1d62fd7b85fdd856908..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/asm-offsets.h
+++ /dev/null
@@ -1,25 +0,0 @@
-/*
- * 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_AARCH64_ASM_OFFSETS_H
-#define AVCODEC_AARCH64_ASM_OFFSETS_H
-
-/* FFTContext */
-#define IMDCT_HALF                      0x48
-
-#endif /* AVCODEC_AARCH64_ASM_OFFSETS_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aic.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aic.c
deleted file mode 100644
index 7ba1c02fdd48be420445b28dd35c61240d4fec32..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aic.c
+++ /dev/null
@@ -1,503 +0,0 @@
-/*
- * Apple Intermediate Codec decoder
- *
- * Copyright (c) 2013 Konstantin Shishkov
- *
- * 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
- */
-
-#include <inttypes.h>
-
-#include "libavutil/mem_internal.h"
-
-#include "avcodec.h"
-#include "bytestream.h"
-#include "codec_internal.h"
-#include "get_bits.h"
-#include "golomb.h"
-#include "idctdsp.h"
-#include "thread.h"
-#include "unary.h"
-
-#define AIC_HDR_SIZE    24
-#define AIC_BAND_COEFFS (64 + 32 + 192 + 96)
-
-enum AICBands {
-    COEFF_LUMA = 0,
-    COEFF_CHROMA,
-    COEFF_LUMA_EXT,
-    COEFF_CHROMA_EXT,
-    NUM_BANDS
-};
-
-static const uint8_t aic_num_band_coeffs[NUM_BANDS] = { 64, 32, 192, 96 };
-
-static const uint16_t aic_band_off[NUM_BANDS] = { 0, 64, 96, 288 };
-
-static const uint8_t aic_quant_matrix[64] = {
-     8, 16, 19, 22, 22, 26, 26, 27,
-    16, 16, 22, 22, 26, 27, 27, 29,
-    19, 22, 26, 26, 27, 29, 29, 35,
-    22, 24, 27, 27, 29, 32, 34, 38,
-    26, 27, 29, 29, 32, 35, 38, 46,
-    27, 29, 34, 34, 35, 40, 46, 56,
-    29, 34, 34, 37, 40, 48, 56, 69,
-    34, 37, 38, 40, 48, 58, 69, 83,
-};
-
-static const uint8_t aic_y_scan[64] = {
-     0,  4,  1,  2,  5,  8, 12,  9,
-     6,  3,  7, 10, 13, 14, 11, 15,
-    47, 43, 46, 45, 42, 39, 35, 38,
-    41, 44, 40, 37, 34, 33, 36, 32,
-    16, 20, 17, 18, 21, 24, 28, 25,
-    22, 19, 23, 26, 29, 30, 27, 31,
-    63, 59, 62, 61, 58, 55, 51, 54,
-    57, 60, 56, 53, 50, 49, 52, 48,
-};
-
-static const uint8_t aic_y_ext_scan[192] = {
-     64,  72,  65,  66,  73,  80,  88,  81,
-     74,  67,  75,  82,  89,  90,  83,  91,
-      0,   4,   1,   2,   5,   8,  12,   9,
-      6,   3,   7,  10,  13,  14,  11,  15,
-     16,  20,  17,  18,  21,  24,  28,  25,
-     22,  19,  23,  26,  29,  30,  27,  31,
-    155, 147, 154, 153, 146, 139, 131, 138,
-    145, 152, 144, 137, 130, 129, 136, 128,
-     47,  43,  46,  45,  42,  39,  35,  38,
-     41,  44,  40,  37,  34,  33,  36,  32,
-     63,  59,  62,  61,  58,  55,  51,  54,
-     57,  60,  56,  53,  50,  49,  52,  48,
-     96, 104,  97,  98, 105, 112, 120, 113,
-    106,  99, 107, 114, 121, 122, 115, 123,
-     68,  76,  69,  70,  77,  84,  92,  85,
-     78,  71,  79,  86,  93,  94,  87,  95,
-    100, 108, 101, 102, 109, 116, 124, 117,
-    110, 103, 111, 118, 125, 126, 119, 127,
-    187, 179, 186, 185, 178, 171, 163, 170,
-    177, 184, 176, 169, 162, 161, 168, 160,
-    159, 151, 158, 157, 150, 143, 135, 142,
-    149, 156, 148, 141, 134, 133, 140, 132,
-    191, 183, 190, 189, 182, 175, 167, 174,
-    181, 188, 180, 173, 166, 165, 172, 164,
-};
-
-static const uint8_t aic_c_scan[64] = {
-     0,  4,  1,  2,  5,  8, 12,  9,
-     6,  3,  7, 10, 13, 14, 11, 15,
-    31, 27, 30, 29, 26, 23, 19, 22,
-    25, 28, 24, 21, 18, 17, 20, 16,
-    32, 36, 33, 34, 37, 40, 44, 41,
-    38, 35, 39, 42, 45, 46, 43, 47,
-    63, 59, 62, 61, 58, 55, 51, 54,
-    57, 60, 56, 53, 50, 49, 52, 48,
-};
-
-static const uint8_t aic_c_ext_scan[192] = {
-     16,  24,  17,  18,  25,  32,  40,  33,
-     26,  19,  27,  34,  41,  42,  35,  43,
-      0,   4,   1,   2,   5,   8,  12,   9,
-      6,   3,   7,  10,  13,  14,  11,  15,
-     20,  28,  21,  22,  29,  36,  44,  37,
-     30,  23,  31,  38,  45,  46,  39,  47,
-     95,  87,  94,  93,  86,  79,  71,  78,
-     85,  92,  84,  77,  70,  69,  76,  68,
-     63,  59,  62,  61,  58,  55,  51,  54,
-     57,  60,  56,  53,  50,  49,  52,  48,
-     91,  83,  90,  89,  82,  75,  67,  74,
-     81,  88,  80,  73,  66,  65,  72,  64,
-    112, 120, 113, 114, 121, 128, 136, 129,
-    122, 115, 123, 130, 137, 138, 131, 139,
-     96, 100,  97,  98, 101, 104, 108, 105,
-    102,  99, 103, 106, 109, 110, 107, 111,
-    116, 124, 117, 118, 125, 132, 140, 133,
-    126, 119, 127, 134, 141, 142, 135, 143,
-    191, 183, 190, 189, 182, 175, 167, 174,
-    181, 188, 180, 173, 166, 165, 172, 164,
-    159, 155, 158, 157, 154, 151, 147, 150,
-    153, 156, 152, 149, 146, 145, 148, 144,
-    187, 179, 186, 185, 178, 171, 163, 170,
-    177, 184, 176, 169, 162, 161, 168, 160,
-};
-
-static const uint8_t * const aic_scan[NUM_BANDS] = {
-    aic_y_scan, aic_c_scan, aic_y_ext_scan, aic_c_ext_scan
-};
-
-typedef struct AICContext {
-    AVCodecContext *avctx;
-    AVFrame        *frame;
-    IDCTDSPContext idsp;
-
-    int            num_x_slices;
-    int            slice_width;
-    int            mb_width, mb_height;
-    int            quant;
-    int            interlaced;
-
-    int16_t        *slice_data;
-    int16_t        *data_ptr[NUM_BANDS];
-
-    DECLARE_ALIGNED(16, int16_t, block)[64];
-    DECLARE_ALIGNED(16, uint8_t, quant_matrix)[64];
-} AICContext;
-
-static int aic_decode_header(AICContext *ctx, const uint8_t *src, int size)
-{
-    uint32_t frame_size;
-    int width, height;
-
-    if (src[0] != 1) {
-        av_log(ctx->avctx, AV_LOG_ERROR, "Invalid version %d\n", src[0]);
-        return AVERROR_INVALIDDATA;
-    }
-    if (src[1] != AIC_HDR_SIZE - 2) {
-        av_log(ctx->avctx, AV_LOG_ERROR, "Invalid header size %d\n", src[1]);
-        return AVERROR_INVALIDDATA;
-    }
-    frame_size = AV_RB32(src + 2);
-    width      = AV_RB16(src + 6);
-    height     = AV_RB16(src + 8);
-    if (frame_size > size) {
-        av_log(ctx->avctx, AV_LOG_ERROR, "Frame size should be %"PRIu32" got %d\n",
-               frame_size, size);
-        return AVERROR_INVALIDDATA;
-    }
-    if (width != ctx->avctx->width || height != ctx->avctx->height) {
-        av_log(ctx->avctx, AV_LOG_ERROR,
-               "Picture dimension changed: old: %d x %d, new: %d x %d\n",
-               ctx->avctx->width, ctx->avctx->height, width, height);
-        return AVERROR_INVALIDDATA;
-    }
-    ctx->quant      = src[15];
-    ctx->interlaced = ((src[16] >> 4) == 3);
-
-    return 0;
-}
-
-#define GET_CODE(val, type, add_bits)                         \
-    do {                                                      \
-        if (type)                                             \
-            val = get_ue_golomb(gb);                          \
-        else                                                  \
-            val = get_unary(gb, 1, 31);                       \
-        if (add_bits)                                         \
-            val = (val << add_bits) + get_bits(gb, add_bits); \
-    } while (0)
-
-static int aic_decode_coeffs(GetBitContext *gb, int16_t *dst,
-                             int band, int slice_width, int force_chroma)
-{
-    int has_skips, coeff_type, coeff_bits, skip_type, skip_bits;
-    const int num_coeffs = aic_num_band_coeffs[band];
-    const uint8_t *scan = aic_scan[band | force_chroma];
-    int mb, idx;
-    unsigned val;
-
-    if (get_bits_left(gb) < 5)
-        return AVERROR_INVALIDDATA;
-
-    has_skips  = get_bits1(gb);
-    coeff_type = get_bits1(gb);
-    coeff_bits = get_bits(gb, 3);
-
-    if (has_skips) {
-        skip_type = get_bits1(gb);
-        skip_bits = get_bits(gb, 3);
-
-        for (mb = 0; mb < slice_width; mb++) {
-            idx = -1;
-            do {
-                GET_CODE(val, skip_type, skip_bits);
-                if (val >= 0x10000)
-                    return AVERROR_INVALIDDATA;
-                idx += val + 1;
-                if (idx >= num_coeffs)
-                    break;
-                GET_CODE(val, coeff_type, coeff_bits);
-                val++;
-                if (val >= 0x10000)
-                    return AVERROR_INVALIDDATA;
-                dst[scan[idx]] = val;
-            } while (idx < num_coeffs - 1);
-            dst += num_coeffs;
-        }
-    } else {
-        for (mb = 0; mb < slice_width; mb++) {
-            for (idx = 0; idx < num_coeffs; idx++) {
-                GET_CODE(val, coeff_type, coeff_bits);
-                if (val >= 0x10000)
-                    return AVERROR_INVALIDDATA;
-                dst[scan[idx]] = val;
-            }
-            dst += num_coeffs;
-        }
-    }
-    return 0;
-}
-
-static void recombine_block(int16_t *dst, const uint8_t *scan,
-                            int16_t **base, int16_t **ext)
-{
-    int i, j;
-
-    for (i = 0; i < 4; i++) {
-        for (j = 0; j < 4; j++)
-            dst[scan[i * 8 + j]]     = (*base)[j];
-        for (j = 0; j < 4; j++)
-            dst[scan[i * 8 + j + 4]] = (*ext)[j];
-        *base += 4;
-        *ext  += 4;
-    }
-    for (; i < 8; i++) {
-        for (j = 0; j < 8; j++)
-            dst[scan[i * 8 + j]] = (*ext)[j];
-        *ext  += 8;
-    }
-}
-
-static void recombine_block_il(int16_t *dst, const uint8_t *scan,
-                               int16_t **base, int16_t **ext,
-                               int block_no)
-{
-    int i, j;
-
-    if (block_no < 2) {
-        for (i = 0; i < 8; i++) {
-            for (j = 0; j < 4; j++)
-                dst[scan[i * 8 + j]]     = (*base)[j];
-            for (j = 0; j < 4; j++)
-                dst[scan[i * 8 + j + 4]] = (*ext)[j];
-            *base += 4;
-            *ext  += 4;
-        }
-    } else {
-        for (i = 0; i < 64; i++)
-            dst[scan[i]] = (*ext)[i];
-        *ext += 64;
-    }
-}
-
-static void unquant_block(int16_t *block, int q, uint8_t *quant_matrix)
-{
-    int i;
-
-    for (i = 0; i < 64; i++) {
-        int val  = (uint16_t)block[i];
-        int sign = val & 1;
-
-        block[i] = (((val >> 1) ^ -sign) * q * quant_matrix[i] >> 4)
-                   + sign;
-    }
-}
-
-static int aic_decode_slice(AICContext *ctx, int mb_x, int mb_y,
-                            const uint8_t *src, int src_size)
-{
-    GetBitContext gb;
-    int ret, i, mb, blk;
-    int slice_width = FFMIN(ctx->slice_width, ctx->mb_width - mb_x);
-    int last_row = mb_y && mb_y == ctx->mb_height - 1;
-    int y_pos, c_pos;
-    uint8_t *Y, *C[2];
-    uint8_t *dst;
-    int16_t *base_y = ctx->data_ptr[COEFF_LUMA];
-    int16_t *base_c = ctx->data_ptr[COEFF_CHROMA];
-    int16_t *ext_y  = ctx->data_ptr[COEFF_LUMA_EXT];
-    int16_t *ext_c  = ctx->data_ptr[COEFF_CHROMA_EXT];
-    const int ystride = ctx->frame->linesize[0];
-
-    if (last_row) {
-        y_pos = (ctx->avctx->height - 16);
-        c_pos = ((ctx->avctx->height+1)/2 - 8);
-    } else {
-        y_pos = mb_y * 16;
-        c_pos = mb_y * 8;
-    }
-
-    Y = ctx->frame->data[0] + mb_x * 16 + y_pos * ystride;
-    for (i = 0; i < 2; i++)
-        C[i] = ctx->frame->data[i + 1] + mb_x * 8
-               + c_pos * ctx->frame->linesize[i + 1];
-    init_get_bits(&gb, src, src_size * 8);
-
-    memset(ctx->slice_data, 0,
-           sizeof(*ctx->slice_data) * slice_width * AIC_BAND_COEFFS);
-    for (i = 0; i < NUM_BANDS; i++)
-        if ((ret = aic_decode_coeffs(&gb, ctx->data_ptr[i],
-                                     i, slice_width,
-                                     !ctx->interlaced)) < 0)
-            return ret;
-
-    for (mb = 0; mb < slice_width; mb++) {
-        for (blk = 0; blk < 4; blk++) {
-            if (!ctx->interlaced)
-                recombine_block(ctx->block, ctx->idsp.idct_permutation,
-                                &base_y, &ext_y);
-            else
-                recombine_block_il(ctx->block, ctx->idsp.idct_permutation,
-                                   &base_y, &ext_y, blk);
-            unquant_block(ctx->block, ctx->quant, ctx->quant_matrix);
-            ctx->idsp.idct(ctx->block);
-
-            if (!ctx->interlaced) {
-                dst = Y + (blk >> 1) * 8 * ystride + (blk & 1) * 8;
-                ctx->idsp.put_signed_pixels_clamped(ctx->block, dst, ystride);
-            } else {
-                dst = Y + (blk & 1) * 8 + (blk >> 1) * ystride;
-                ctx->idsp.put_signed_pixels_clamped(ctx->block, dst,
-                                                    ystride * 2);
-            }
-        }
-        Y += 16;
-
-        for (blk = 0; blk < 2; blk++) {
-            recombine_block(ctx->block, ctx->idsp.idct_permutation,
-                            &base_c, &ext_c);
-            unquant_block(ctx->block, ctx->quant, ctx->quant_matrix);
-            ctx->idsp.idct(ctx->block);
-            ctx->idsp.put_signed_pixels_clamped(ctx->block, C[blk],
-                                                ctx->frame->linesize[blk + 1]);
-            C[blk] += 8;
-        }
-    }
-
-    return 0;
-}
-
-static int aic_decode_frame(AVCodecContext *avctx, AVFrame *frame,
-                            int *got_frame, AVPacket *avpkt)
-{
-    AICContext *ctx    = avctx->priv_data;
-    const uint8_t *buf = avpkt->data;
-    int buf_size       = avpkt->size;
-    GetByteContext gb;
-    uint32_t off;
-    int x, y, ret;
-    int slice_size;
-
-    ctx->frame            = frame;
-    ctx->frame->pict_type = AV_PICTURE_TYPE_I;
-    ctx->frame->key_frame = 1;
-
-    off = FFALIGN(AIC_HDR_SIZE + ctx->num_x_slices * ctx->mb_height * 2, 4);
-
-    if (buf_size < off) {
-        av_log(avctx, AV_LOG_ERROR, "Too small frame\n");
-        return AVERROR_INVALIDDATA;
-    }
-
-    ret = aic_decode_header(ctx, buf, buf_size);
-    if (ret < 0) {
-        av_log(avctx, AV_LOG_ERROR, "Invalid header\n");
-        return ret;
-    }
-
-    if ((ret = ff_thread_get_buffer(avctx, ctx->frame, 0)) < 0)
-        return ret;
-
-    bytestream2_init(&gb, buf + AIC_HDR_SIZE,
-                     ctx->num_x_slices * ctx->mb_height * 2);
-
-    for (y = 0; y < ctx->mb_height; y++) {
-        for (x = 0; x < ctx->mb_width; x += ctx->slice_width) {
-            slice_size = bytestream2_get_le16(&gb) * 4;
-            if (slice_size + off > buf_size || !slice_size) {
-                av_log(avctx, AV_LOG_ERROR,
-                       "Incorrect slice size %d at %d.%d\n", slice_size, x, y);
-                return AVERROR_INVALIDDATA;
-            }
-
-            ret = aic_decode_slice(ctx, x, y, buf + off, slice_size);
-            if (ret < 0) {
-                av_log(avctx, AV_LOG_ERROR,
-                       "Error decoding slice at %d.%d\n", x, y);
-                return ret;
-            }
-
-            off += slice_size;
-        }
-    }
-
-    *got_frame = 1;
-
-    return avpkt->size;
-}
-
-static av_cold int aic_decode_init(AVCodecContext *avctx)
-{
-    AICContext *ctx = avctx->priv_data;
-    int i;
-
-    ctx->avctx = avctx;
-
-    avctx->pix_fmt = AV_PIX_FMT_YUV420P;
-
-    ff_idctdsp_init(&ctx->idsp, avctx);
-
-    for (i = 0; i < 64; i++)
-        ctx->quant_matrix[ctx->idsp.idct_permutation[i]] = aic_quant_matrix[i];
-
-    ctx->mb_width  = FFALIGN(avctx->width,  16) >> 4;
-    ctx->mb_height = FFALIGN(avctx->height, 16) >> 4;
-
-    ctx->num_x_slices = (ctx->mb_width + 15) >> 4;
-    ctx->slice_width  = 16;
-    for (i = 1; i < ctx->mb_width; i++) {
-        if (!(ctx->mb_width % i) && (ctx->mb_width / i <= 32)) {
-            ctx->slice_width  = ctx->mb_width / i;
-            ctx->num_x_slices = i;
-            break;
-        }
-    }
-
-    ctx->slice_data = av_malloc_array(ctx->slice_width, AIC_BAND_COEFFS
-                                * sizeof(*ctx->slice_data));
-    if (!ctx->slice_data) {
-        av_log(avctx, AV_LOG_ERROR, "Error allocating slice buffer\n");
-
-        return AVERROR(ENOMEM);
-    }
-
-    for (i = 0; i < NUM_BANDS; i++)
-        ctx->data_ptr[i] = ctx->slice_data + ctx->slice_width
-                                             * aic_band_off[i];
-
-    return 0;
-}
-
-static av_cold int aic_decode_close(AVCodecContext *avctx)
-{
-    AICContext *ctx = avctx->priv_data;
-
-    av_freep(&ctx->slice_data);
-
-    return 0;
-}
-
-const FFCodec ff_aic_decoder = {
-    .p.name         = "aic",
-    CODEC_LONG_NAME("Apple Intermediate Codec"),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_AIC,
-    .priv_data_size = sizeof(AICContext),
-    .init           = aic_decode_init,
-    .close          = aic_decode_close,
-    FF_CODEC_DECODE_CB(aic_decode_frame),
-    .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_FRAME_THREADS,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/audio_frame_queue.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/audio_frame_queue.c
deleted file mode 100644
index 08b4b368c7586da8882208ff771fa3876403494e..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/audio_frame_queue.c
+++ /dev/null
@@ -1,113 +0,0 @@
-/*
- * Audio Frame Queue
- * Copyright (c) 2012 Justin Ruggles
- *
- * 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
- */
-
-#include "libavutil/attributes.h"
-#include "libavutil/common.h"
-#include "audio_frame_queue.h"
-#include "encode.h"
-#include "libavutil/avassert.h"
-
-av_cold void ff_af_queue_init(AVCodecContext *avctx, AudioFrameQueue *afq)
-{
-    afq->avctx = avctx;
-    afq->remaining_delay   = avctx->initial_padding;
-    afq->remaining_samples = avctx->initial_padding;
-    afq->frame_count       = 0;
-}
-
-void ff_af_queue_close(AudioFrameQueue *afq)
-{
-    if(afq->frame_count)
-        av_log(afq->avctx, AV_LOG_WARNING, "%d frames left in the queue on closing\n", afq->frame_count);
-    av_freep(&afq->frames);
-    memset(afq, 0, sizeof(*afq));
-}
-
-int ff_af_queue_add(AudioFrameQueue *afq, const AVFrame *f)
-{
-    AudioFrame *new = av_fast_realloc(afq->frames, &afq->frame_alloc, sizeof(*afq->frames)*(afq->frame_count+1));
-    if(!new)
-        return AVERROR(ENOMEM);
-    afq->frames = new;
-    new += afq->frame_count;
-
-    /* get frame parameters */
-    new->duration = f->nb_samples;
-    new->duration += afq->remaining_delay;
-    if (f->pts != AV_NOPTS_VALUE) {
-        new->pts = av_rescale_q(f->pts,
-                                      afq->avctx->time_base,
-                                      (AVRational){ 1, afq->avctx->sample_rate });
-        new->pts -= afq->remaining_delay;
-        if(afq->frame_count && new[-1].pts >= new->pts)
-            av_log(afq->avctx, AV_LOG_WARNING, "Queue input is backward in time\n");
-    } else {
-        new->pts = AV_NOPTS_VALUE;
-    }
-    afq->remaining_delay = 0;
-
-    /* add frame sample count */
-    afq->remaining_samples += f->nb_samples;
-
-    afq->frame_count++;
-
-    return 0;
-}
-
-void ff_af_queue_remove(AudioFrameQueue *afq, int nb_samples, int64_t *pts,
-                        int64_t *duration)
-{
-    int64_t out_pts = AV_NOPTS_VALUE;
-    int removed_samples = 0;
-    int i;
-
-    if (afq->frame_count || afq->frame_alloc) {
-        if (afq->frames->pts != AV_NOPTS_VALUE)
-            out_pts = afq->frames->pts;
-    }
-    if(!afq->frame_count)
-        av_log(afq->avctx, AV_LOG_WARNING, "Trying to remove %d samples, but the queue is empty\n", nb_samples);
-    if (pts)
-        *pts = ff_samples_to_time_base(afq->avctx, out_pts);
-
-    for(i=0; nb_samples && i<afq->frame_count; i++){
-        int n= FFMIN(afq->frames[i].duration, nb_samples);
-        afq->frames[i].duration -= n;
-        nb_samples              -= n;
-        removed_samples         += n;
-        if(afq->frames[i].pts != AV_NOPTS_VALUE)
-            afq->frames[i].pts      += n;
-    }
-    afq->remaining_samples -= removed_samples;
-    i -= i && afq->frames[i-1].duration;
-    memmove(afq->frames, afq->frames + i, sizeof(*afq->frames) * (afq->frame_count - i));
-    afq->frame_count -= i;
-
-    if(nb_samples){
-        av_assert0(!afq->frame_count);
-        av_assert0(afq->remaining_samples == afq->remaining_delay);
-        if(afq->frames && afq->frames[0].pts != AV_NOPTS_VALUE)
-            afq->frames[0].pts += nb_samples;
-        av_log(afq->avctx, AV_LOG_DEBUG, "Trying to remove %d more samples than there are in the queue\n", nb_samples);
-    }
-    if (duration)
-        *duration = ff_samples_to_time_base(afq->avctx, removed_samples);
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dxv.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dxv.c
deleted file mode 100644
index 7c84874229b33327f16fdbbe92bf459e44ea2ad3..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dxv.c
+++ /dev/null
@@ -1,1276 +0,0 @@
-/*
- * Resolume DXV decoder
- * Copyright (C) 2015 Vittorio Giovara <vittorio.giovara@gmail.com>
- * Copyright (C) 2018 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
- */
-
-#include <stdint.h>
-
-#include "libavutil/imgutils.h"
-
-#include "mathops.h"
-#include "avcodec.h"
-#include "bytestream.h"
-#include "codec_internal.h"
-#include "lzf.h"
-#include "texturedsp.h"
-#include "thread.h"
-
-typedef struct DXVContext {
-    TextureDSPContext texdsp;
-    GetByteContext gbc;
-
-    uint8_t *tex_data;   // Compressed texture
-    uint8_t *ctex_data;  // Compressed texture
-    int tex_rat;         // Compression ratio
-    int tex_step;        // Distance between blocks
-    int ctex_step;       // Distance between blocks
-    int64_t tex_size;    // Texture size
-    int64_t ctex_size;   // Texture size
-
-    /* Optimal number of slices for parallel decoding */
-    int slice_count;
-
-    uint8_t *op_data[4]; // Opcodes
-    int64_t op_size[4];  // Opcodes size
-
-    int texture_block_w;
-    int texture_block_h;
-
-    int ctexture_block_w;
-    int ctexture_block_h;
-
-    /* Pointer to the selected decompression function */
-    int (*tex_funct)(uint8_t *dst, ptrdiff_t stride, const uint8_t *block);
-    int (*tex_funct_planar[2])(uint8_t *plane0, ptrdiff_t stride0,
-                               uint8_t *plane1, ptrdiff_t stride1,
-                               const uint8_t *block);
-} DXVContext;
-
-static void decompress_indices(uint8_t *dst, const uint8_t *src)
-{
-    int block, i;
-
-    for (block = 0; block < 2; block++) {
-        int tmp = AV_RL24(src);
-
-        /* Unpack 8x3 bit from last 3 byte block */
-        for (i = 0; i < 8; i++)
-            dst[i] = (tmp >> (i * 3)) & 0x7;
-
-        src += 3;
-        dst += 8;
-    }
-}
-
-static int extract_component(int yo0, int yo1, int code)
-{
-    int yo;
-
-    if (yo0 == yo1) {
-        yo = yo0;
-    } else if (code == 0) {
-        yo = yo0;
-    } else if (code == 1) {
-        yo = yo1;
-    } else {
-        if (yo0 > yo1) {
-            yo = (uint8_t) (((8 - code) * yo0 +
-                             (code - 1) * yo1) / 7);
-        } else {
-            if (code == 6) {
-                yo = 0;
-            } else if (code == 7) {
-                yo = 255;
-            } else {
-                yo = (uint8_t) (((6 - code) * yo0 +
-                                 (code - 1) * yo1) / 5);
-            }
-        }
-    }
-
-    return yo;
-}
-
-static int cocg_block(uint8_t *plane0, ptrdiff_t stride0,
-                      uint8_t *plane1, ptrdiff_t stride1,
-                      const uint8_t *block)
-{
-    uint8_t co_indices[16];
-    uint8_t cg_indices[16];
-    uint8_t co0 = *(block);
-    uint8_t co1 = *(block + 1);
-    uint8_t cg0 = *(block + 8);
-    uint8_t cg1 = *(block + 9);
-    int x, y;
-
-    decompress_indices(co_indices, block + 2);
-    decompress_indices(cg_indices, block + 10);
-
-    for (y = 0; y < 4; y++) {
-        for (x = 0; x < 4; x++) {
-            int co_code = co_indices[x + y * 4];
-            int cg_code = cg_indices[x + y * 4];
-
-            plane0[x] = extract_component(cg0, cg1, cg_code);
-            plane1[x] = extract_component(co0, co1, co_code);
-        }
-        plane0 += stride0;
-        plane1 += stride1;
-    }
-
-    return 16;
-}
-
-static void yao_subblock(uint8_t *dst, uint8_t *yo_indices,
-                        ptrdiff_t stride, const uint8_t *block)
-{
-    uint8_t yo0 = *(block);
-    uint8_t yo1 = *(block + 1);
-    int x, y;
-
-    decompress_indices(yo_indices, block + 2);
-
-    for (y = 0; y < 4; y++) {
-        for (x = 0; x < 4; x++) {
-            int yo_code = yo_indices[x + y * 4];
-
-            dst[x] = extract_component(yo0, yo1, yo_code);
-        }
-        dst += stride;
-    }
-}
-
-static int yo_block(uint8_t *dst, ptrdiff_t stride,
-                    uint8_t *unused0, ptrdiff_t unused1,
-                    const uint8_t *block)
-{
-    uint8_t yo_indices[16];
-
-    yao_subblock(dst,      yo_indices, stride, block);
-    yao_subblock(dst + 4,  yo_indices, stride, block + 8);
-    yao_subblock(dst + 8,  yo_indices, stride, block + 16);
-    yao_subblock(dst + 12, yo_indices, stride, block + 24);
-
-    return 32;
-}
-
-static int yao_block(uint8_t *plane0, ptrdiff_t stride0,
-                     uint8_t *plane3, ptrdiff_t stride1,
-                     const uint8_t *block)
-{
-    uint8_t yo_indices[16];
-    uint8_t a_indices[16];
-
-    yao_subblock(plane0,      yo_indices, stride0, block);
-    yao_subblock(plane3,      a_indices,  stride1, block + 8);
-    yao_subblock(plane0 + 4,  yo_indices, stride0, block + 16);
-    yao_subblock(plane3 + 4,  a_indices,  stride1, block + 24);
-    yao_subblock(plane0 + 8,  yo_indices, stride0, block + 32);
-    yao_subblock(plane3 + 8,  a_indices,  stride1, block + 40);
-    yao_subblock(plane0 + 12, yo_indices, stride0, block + 48);
-    yao_subblock(plane3 + 12, a_indices,  stride1, block + 56);
-
-    return 64;
-}
-
-static int decompress_texture_thread(AVCodecContext *avctx, void *arg,
-                                     int slice, int thread_nb)
-{
-    const DXVContext *ctx = avctx->priv_data;
-    AVFrame *frame = arg;
-    const uint8_t *d = ctx->tex_data;
-    int w_block = avctx->coded_width / ctx->texture_block_w;
-    int h_block = avctx->coded_height / ctx->texture_block_h;
-    int x, y;
-    int start_slice, end_slice;
-
-    start_slice = h_block * slice / ctx->slice_count;
-    end_slice = h_block * (slice + 1) / ctx->slice_count;
-
-    if (ctx->tex_funct) {
-        for (y = start_slice; y < end_slice; y++) {
-            uint8_t *p = frame->data[0] + y * frame->linesize[0] * ctx->texture_block_h;
-            int off = y * w_block;
-            for (x = 0; x < w_block; x++) {
-                ctx->tex_funct(p + x * 4 * ctx->texture_block_w, frame->linesize[0],
-                               d + (off + x) * ctx->tex_step);
-            }
-        }
-    } else {
-        const uint8_t *c = ctx->ctex_data;
-
-        for (y = start_slice; y < end_slice; y++) {
-            uint8_t *p0 = frame->data[0] + y * frame->linesize[0] * ctx->texture_block_h;
-            uint8_t *p3 = ctx->tex_step != 64 ? NULL : frame->data[3] + y * frame->linesize[3] * ctx->texture_block_h;
-            int off = y * w_block;
-            for (x = 0; x < w_block; x++) {
-                ctx->tex_funct_planar[0](p0 + x * ctx->texture_block_w, frame->linesize[0],
-                                         p3 != NULL ? p3 + x * ctx->texture_block_w : NULL, frame->linesize[3],
-                                         d + (off + x) * ctx->tex_step);
-            }
-        }
-
-        w_block = (avctx->coded_width / 2) / ctx->ctexture_block_w;
-        h_block = (avctx->coded_height / 2) / ctx->ctexture_block_h;
-        start_slice = h_block * slice / ctx->slice_count;
-        end_slice = h_block * (slice + 1) / ctx->slice_count;
-
-        for (y = start_slice; y < end_slice; y++) {
-            uint8_t *p0 = frame->data[1] + y * frame->linesize[1] * ctx->ctexture_block_h;
-            uint8_t *p1 = frame->data[2] + y * frame->linesize[2] * ctx->ctexture_block_h;
-            int off = y * w_block;
-            for (x = 0; x < w_block; x++) {
-                ctx->tex_funct_planar[1](p0 + x * ctx->ctexture_block_w, frame->linesize[1],
-                                         p1 + x * ctx->ctexture_block_w, frame->linesize[2],
-                                         c + (off + x) * ctx->ctex_step);
-            }
-        }
-    }
-
-    return 0;
-}
-
-/* This scheme addresses already decoded elements depending on 2-bit status:
- *   0 -> copy new element
- *   1 -> copy one element from position -x
- *   2 -> copy one element from position -(get_byte() + 2) * x
- *   3 -> copy one element from position -(get_16le() + 0x102) * x
- * x is always 2 for dxt1 and 4 for dxt5. */
-#define CHECKPOINT(x)                                                         \
-    do {                                                                      \
-        if (state == 0) {                                                     \
-            if (bytestream2_get_bytes_left(gbc) < 4)                          \
-                return AVERROR_INVALIDDATA;                                   \
-            value = bytestream2_get_le32(gbc);                                \
-            state = 16;                                                       \
-        }                                                                     \
-        op = value & 0x3;                                                     \
-        value >>= 2;                                                          \
-        state--;                                                              \
-        switch (op) {                                                         \
-        case 1:                                                               \
-            idx = x;                                                          \
-            break;                                                            \
-        case 2:                                                               \
-            idx = (bytestream2_get_byte(gbc) + 2) * x;                        \
-            if (idx > pos) {                                                  \
-                av_log(avctx, AV_LOG_ERROR, "idx %d > %d\n", idx, pos);       \
-                return AVERROR_INVALIDDATA;                                   \
-            }                                                                 \
-            break;                                                            \
-        case 3:                                                               \
-            idx = (bytestream2_get_le16(gbc) + 0x102) * x;                    \
-            if (idx > pos) {                                                  \
-                av_log(avctx, AV_LOG_ERROR, "idx %d > %d\n", idx, pos);       \
-                return AVERROR_INVALIDDATA;                                   \
-            }                                                                 \
-            break;                                                            \
-        }                                                                     \
-    } while(0)
-
-static int dxv_decompress_dxt1(AVCodecContext *avctx)
-{
-    DXVContext *ctx = avctx->priv_data;
-    GetByteContext *gbc = &ctx->gbc;
-    uint32_t value, prev, op;
-    int idx = 0, state = 0;
-    int pos = 2;
-
-    /* Copy the first two elements */
-    AV_WL32(ctx->tex_data, bytestream2_get_le32(gbc));
-    AV_WL32(ctx->tex_data + 4, bytestream2_get_le32(gbc));
-
-    /* Process input until the whole texture has been filled */
-    while (pos + 2 <= ctx->tex_size / 4) {
-        CHECKPOINT(2);
-
-        /* Copy two elements from a previous offset or from the input buffer */
-        if (op) {
-            prev = AV_RL32(ctx->tex_data + 4 * (pos - idx));
-            AV_WL32(ctx->tex_data + 4 * pos, prev);
-            pos++;
-
-            prev = AV_RL32(ctx->tex_data + 4 * (pos - idx));
-            AV_WL32(ctx->tex_data + 4 * pos, prev);
-            pos++;
-        } else {
-            CHECKPOINT(2);
-
-            if (op)
-                prev = AV_RL32(ctx->tex_data + 4 * (pos - idx));
-            else
-                prev = bytestream2_get_le32(gbc);
-            AV_WL32(ctx->tex_data + 4 * pos, prev);
-            pos++;
-
-            CHECKPOINT(2);
-
-            if (op)
-                prev = AV_RL32(ctx->tex_data + 4 * (pos - idx));
-            else
-                prev = bytestream2_get_le32(gbc);
-            AV_WL32(ctx->tex_data + 4 * pos, prev);
-            pos++;
-        }
-    }
-
-    return 0;
-}
-
-typedef struct OpcodeTable {
-    int16_t next;
-    uint8_t val1;
-    uint8_t val2;
-} OpcodeTable;
-
-static int fill_ltable(GetByteContext *gb, uint32_t *table, int *nb_elements)
-{
-    unsigned half = 512, bits = 1023, left = 1024, input, mask;
-    int value, counter = 0, rshift = 10, lshift = 30;
-
-    mask = bytestream2_get_le32(gb) >> 2;
-    while (left) {
-        if (counter >= 256)
-            return AVERROR_INVALIDDATA;
-        value = bits & mask;
-        left -= bits & mask;
-        mask >>= rshift;
-        lshift -= rshift;
-        table[counter++] = value;
-        if (lshift < 16) {
-            if (bytestream2_get_bytes_left(gb) <= 0)
-                return AVERROR_INVALIDDATA;
-
-            input = bytestream2_get_le16(gb);
-            mask += input << lshift;
-            lshift += 16;
-        }
-        if (left < half) {
-            half >>= 1;
-            bits >>= 1;
-            rshift--;
-        }
-    }
-
-    for (; !table[counter - 1]; counter--)
-        if (counter <= 0)
-            return AVERROR_INVALIDDATA;
-
-    *nb_elements = counter;
-
-    if (counter < 256)
-        memset(&table[counter], 0, 4 * (256 - counter));
-
-    if (lshift >= 16)
-        bytestream2_seek(gb, -2, SEEK_CUR);
-
-    return 0;
-}
-
-static int fill_optable(unsigned *table0, OpcodeTable *table1, int nb_elements)
-{
-    unsigned table2[256] = { 0 };
-    unsigned x = 0;
-    int val0, val1, i, j = 2, k = 0;
-
-    table2[0] = table0[0];
-    for (i = 0; i < nb_elements - 1; i++, table2[i] = val0) {
-        val0 = table0[i + 1] + table2[i];
-    }
-
-    if (!table2[0]) {
-        do {
-            k++;
-        } while (!table2[k]);
-    }
-
-    j = 2;
-    for (i = 1024; i > 0; i--) {
-        for (table1[x].val1 = k; k < 256 && j > table2[k]; k++);
-        x = (x - 383) & 0x3FF;
-        j++;
-    }
-
-    if (nb_elements > 0)
-        memcpy(&table2[0], table0, 4 * nb_elements);
-
-    for (i = 0; i < 1024; i++) {
-        val0 = table1[i].val1;
-        val1 = table2[val0];
-        table2[val0]++;
-        x = 31 - ff_clz(val1);
-        if (x > 10)
-            return AVERROR_INVALIDDATA;
-        table1[i].val2 = 10 - x;
-        table1[i].next = (val1 << table1[i].val2) - 1024;
-    }
-
-    return 0;
-}
-
-static int get_opcodes(GetByteContext *gb, uint32_t *table, uint8_t *dst, int op_size, int nb_elements)
-{
-    OpcodeTable optable[1024];
-    int sum, x, val, lshift, rshift, ret, i, idx;
-    int64_t size_in_bits;
-    unsigned endoffset, newoffset, offset;
-    unsigned next;
-    const uint8_t *src = gb->buffer;
-
-    ret = fill_optable(table, optable, nb_elements);
-    if (ret < 0)
-        return ret;
-
-    size_in_bits = bytestream2_get_le32(gb);
-    endoffset = ((size_in_bits + 7) >> 3) - 4;
-    if (endoffset <= 0 || bytestream2_get_bytes_left(gb) < endoffset)
-        return AVERROR_INVALIDDATA;
-
-    offset = endoffset;
-    next = AV_RL32(src + endoffset);
-    rshift = (((size_in_bits & 0xFF) - 1) & 7) + 15;
-    lshift = 32 - rshift;
-    idx = (next >> rshift) & 0x3FF;
-    for (i = 0; i < op_size; i++) {
-        dst[i] = optable[idx].val1;
-        val = optable[idx].val2;
-        sum = val + lshift;
-        x = (next << lshift) >> 1 >> (31 - val);
-        newoffset = offset - (sum >> 3);
-        lshift = sum & 7;
-        idx = x + optable[idx].next;
-        offset = newoffset;
-        if (offset > endoffset)
-            return AVERROR_INVALIDDATA;
-        next = AV_RL32(src + offset);
-    }
-
-    bytestream2_skip(gb, (size_in_bits + 7 >> 3) - 4);
-
-    return 0;
-}
-
-static int dxv_decompress_opcodes(GetByteContext *gb, void *dstp, size_t op_size)
-{
-    int pos = bytestream2_tell(gb);
-    int flag = bytestream2_peek_byte(gb);
-
-    if ((flag & 3) == 0) {
-        bytestream2_skip(gb, 1);
-        bytestream2_get_buffer(gb, dstp, op_size);
-    } else if ((flag & 3) == 1) {
-        bytestream2_skip(gb, 1);
-        memset(dstp, bytestream2_get_byte(gb), op_size);
-    } else {
-        uint32_t table[256];
-        int ret, elements = 0;
-
-        ret = fill_ltable(gb, table, &elements);
-        if (ret < 0)
-            return ret;
-        ret = get_opcodes(gb, table, dstp, op_size, elements);
-        if (ret < 0)
-            return ret;
-    }
-    return bytestream2_tell(gb) - pos;
-}
-
-static int dxv_decompress_cgo(DXVContext *ctx, GetByteContext *gb,
-                              uint8_t *tex_data, int tex_size,
-                              uint8_t *op_data, int *oindex,
-                              int op_size,
-                              uint8_t **dstp, int *statep,
-                              uint8_t **tab0, uint8_t **tab1,
-                              int offset)
-{
-    uint8_t *dst = *dstp;
-    uint8_t *tptr0, *tptr1, *tptr3;
-    int oi = *oindex;
-    int state = *statep;
-    int opcode, v, vv;
-
-    if (state <= 0) {
-        if (oi >= op_size)
-            return AVERROR_INVALIDDATA;
-        opcode = op_data[oi++];
-        if (!opcode) {
-            v = bytestream2_get_byte(gb);
-            if (v == 255) {
-                do {
-                    if (bytestream2_get_bytes_left(gb) <= 0)
-                        return AVERROR_INVALIDDATA;
-                    opcode = bytestream2_get_le16(gb);
-                    v += opcode;
-                } while (opcode == 0xFFFF);
-            }
-            AV_WL32(dst, AV_RL32(dst - (8 + offset)));
-            AV_WL32(dst + 4, AV_RL32(dst - (4 + offset)));
-            state = v + 4;
-            goto done;
-        }
-
-        switch (opcode) {
-        case 1:
-            AV_WL32(dst, AV_RL32(dst - (8 + offset)));
-            AV_WL32(dst + 4, AV_RL32(dst - (4 + offset)));
-            break;
-        case 2:
-            vv = (8 + offset) * (bytestream2_get_le16(gb) + 1);
-            if (vv < 0 || vv > dst - tex_data)
-                return AVERROR_INVALIDDATA;
-            tptr0 = dst - vv;
-            v = AV_RL32(tptr0);
-            AV_WL32(dst, AV_RL32(tptr0));
-            AV_WL32(dst + 4, AV_RL32(tptr0 + 4));
-            tab0[0x9E3779B1 * (uint16_t)v >> 24] = dst;
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 3:
-            AV_WL32(dst, bytestream2_get_le32(gb));
-            AV_WL32(dst + 4, bytestream2_get_le32(gb));
-            tab0[0x9E3779B1 * AV_RL16(dst) >> 24] = dst;
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 4:
-            tptr3 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr3)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, bytestream2_get_le16(gb));
-            AV_WL16(dst + 2, AV_RL16(tptr3));
-            dst[4] = tptr3[2];
-            AV_WL16(dst + 5, bytestream2_get_le16(gb));
-            dst[7] = bytestream2_get_byte(gb);
-            tab0[0x9E3779B1 * AV_RL16(dst) >> 24] = dst;
-            break;
-        case 5:
-            tptr3 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr3)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, bytestream2_get_le16(gb));
-            AV_WL16(dst + 2, bytestream2_get_le16(gb));
-            dst[4] = bytestream2_get_byte(gb);
-            AV_WL16(dst + 5, AV_RL16(tptr3));
-            dst[7] = tptr3[2];
-            tab0[0x9E3779B1 * AV_RL16(dst) >> 24] = dst;
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 6:
-            tptr0 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr0)
-                return AVERROR_INVALIDDATA;
-            tptr1 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr1)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, bytestream2_get_le16(gb));
-            AV_WL16(dst + 2, AV_RL16(tptr0));
-            dst[4] = tptr0[2];
-            AV_WL16(dst + 5, AV_RL16(tptr1));
-            dst[7] = tptr1[2];
-            tab0[0x9E3779B1 * AV_RL16(dst) >> 24] = dst;
-            break;
-        case 7:
-            v = (8 + offset) * (bytestream2_get_le16(gb) + 1);
-            if (v < 0 || v > dst - tex_data)
-                return AVERROR_INVALIDDATA;
-            tptr0 = dst - v;
-            AV_WL16(dst, bytestream2_get_le16(gb));
-            AV_WL16(dst + 2, AV_RL16(tptr0 + 2));
-            AV_WL32(dst + 4, AV_RL32(tptr0 + 4));
-            tab0[0x9E3779B1 * AV_RL16(dst) >> 24] = dst;
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 8:
-            tptr1 = tab0[bytestream2_get_byte(gb)];
-            if (!tptr1)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, AV_RL16(tptr1));
-            AV_WL16(dst + 2, bytestream2_get_le16(gb));
-            AV_WL32(dst + 4, bytestream2_get_le32(gb));
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 9:
-            tptr1 = tab0[bytestream2_get_byte(gb)];
-            if (!tptr1)
-                return AVERROR_INVALIDDATA;
-            tptr3 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr3)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, AV_RL16(tptr1));
-            AV_WL16(dst + 2, AV_RL16(tptr3));
-            dst[4] = tptr3[2];
-            AV_WL16(dst + 5, bytestream2_get_le16(gb));
-            dst[7] = bytestream2_get_byte(gb);
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 10:
-            tptr1 = tab0[bytestream2_get_byte(gb)];
-            if (!tptr1)
-                return AVERROR_INVALIDDATA;
-            tptr3 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr3)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, AV_RL16(tptr1));
-            AV_WL16(dst + 2, bytestream2_get_le16(gb));
-            dst[4] = bytestream2_get_byte(gb);
-            AV_WL16(dst + 5, AV_RL16(tptr3));
-            dst[7] = tptr3[2];
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 11:
-            tptr0 = tab0[bytestream2_get_byte(gb)];
-            if (!tptr0)
-                return AVERROR_INVALIDDATA;
-            tptr3 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr3)
-                return AVERROR_INVALIDDATA;
-            tptr1 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr1)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, AV_RL16(tptr0));
-            AV_WL16(dst + 2, AV_RL16(tptr3));
-            dst[4] = tptr3[2];
-            AV_WL16(dst + 5, AV_RL16(tptr1));
-            dst[7] = tptr1[2];
-            break;
-        case 12:
-            tptr1 = tab0[bytestream2_get_byte(gb)];
-            if (!tptr1)
-                return AVERROR_INVALIDDATA;
-            v = (8 + offset) * (bytestream2_get_le16(gb) + 1);
-            if (v < 0 || v > dst - tex_data)
-                return AVERROR_INVALIDDATA;
-            tptr0 = dst - v;
-            AV_WL16(dst, AV_RL16(tptr1));
-            AV_WL16(dst + 2, AV_RL16(tptr0 + 2));
-            AV_WL32(dst + 4, AV_RL32(tptr0 + 4));
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 13:
-            AV_WL16(dst, AV_RL16(dst - (8 + offset)));
-            AV_WL16(dst + 2, bytestream2_get_le16(gb));
-            AV_WL32(dst + 4, bytestream2_get_le32(gb));
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 14:
-            tptr3 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr3)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, AV_RL16(dst - (8 + offset)));
-            AV_WL16(dst + 2, AV_RL16(tptr3));
-            dst[4] = tptr3[2];
-            AV_WL16(dst + 5, bytestream2_get_le16(gb));
-            dst[7] = bytestream2_get_byte(gb);
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 15:
-            tptr3 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr3)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, AV_RL16(dst - (8 + offset)));
-            AV_WL16(dst + 2, bytestream2_get_le16(gb));
-            dst[4] = bytestream2_get_byte(gb);
-            AV_WL16(dst + 5, AV_RL16(tptr3));
-            dst[7] = tptr3[2];
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        case 16:
-            tptr3 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr3)
-                return AVERROR_INVALIDDATA;
-            tptr1 = tab1[bytestream2_get_byte(gb)];
-            if (!tptr1)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, AV_RL16(dst - (8 + offset)));
-            AV_WL16(dst + 2, AV_RL16(tptr3));
-            dst[4] = tptr3[2];
-            AV_WL16(dst + 5, AV_RL16(tptr1));
-            dst[7] = tptr1[2];
-            break;
-        case 17:
-            v = (8 + offset) * (bytestream2_get_le16(gb) + 1);
-            if (v < 0 || v > dst - tex_data)
-                return AVERROR_INVALIDDATA;
-            AV_WL16(dst, AV_RL16(dst - (8 + offset)));
-            AV_WL16(dst + 2, AV_RL16(&dst[-v + 2]));
-            AV_WL32(dst + 4, AV_RL32(&dst[-v + 4]));
-            tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFFu) >> 24] = dst + 2;
-            break;
-        default:
-            break;
-        }
-    } else {
-done:
-        AV_WL32(dst, AV_RL32(dst - (8 + offset)));
-        AV_WL32(dst + 4, AV_RL32(dst - (4 + offset)));
-        state--;
-    }
-    if (dst - tex_data + 8 > tex_size)
-        return AVERROR_INVALIDDATA;
-    dst += 8;
-
-    *oindex = oi;
-    *dstp = dst;
-    *statep = state;
-
-    return 0;
-}
-
-static int dxv_decompress_cocg(DXVContext *ctx, GetByteContext *gb,
-                               uint8_t *tex_data, int tex_size,
-                               uint8_t *op_data0, uint8_t *op_data1,
-                               int max_op_size0, int max_op_size1)
-{
-    uint8_t *dst, *tab2[256] = { 0 }, *tab0[256] = { 0 }, *tab3[256] = { 0 }, *tab1[256] = { 0 };
-    int op_offset = bytestream2_get_le32(gb);
-    unsigned op_size0 = bytestream2_get_le32(gb);
-    unsigned op_size1 = bytestream2_get_le32(gb);
-    int data_start = bytestream2_tell(gb);
-    int skip0, skip1, oi0 = 0, oi1 = 0;
-    int ret, state0 = 0, state1 = 0;
-
-    if (op_offset < 12 || op_offset - 12 > bytestream2_get_bytes_left(gb))
-        return AVERROR_INVALIDDATA;
-
-    dst = tex_data;
-    bytestream2_skip(gb, op_offset - 12);
-    if (op_size0 > max_op_size0)
-        return AVERROR_INVALIDDATA;
-    skip0 = dxv_decompress_opcodes(gb, op_data0, op_size0);
-    if (skip0 < 0)
-        return skip0;
-    if (op_size1 > max_op_size1)
-        return AVERROR_INVALIDDATA;
-    skip1 = dxv_decompress_opcodes(gb, op_data1, op_size1);
-    if (skip1 < 0)
-        return skip1;
-    bytestream2_seek(gb, data_start, SEEK_SET);
-
-    AV_WL32(dst, bytestream2_get_le32(gb));
-    AV_WL32(dst + 4, bytestream2_get_le32(gb));
-    AV_WL32(dst + 8, bytestream2_get_le32(gb));
-    AV_WL32(dst + 12, bytestream2_get_le32(gb));
-
-    tab0[0x9E3779B1 * AV_RL16(dst) >> 24] = dst;
-    tab1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFF) >> 24] = dst + 2;
-    tab2[0x9E3779B1 * AV_RL16(dst + 8) >> 24] = dst + 8;
-    tab3[0x9E3779B1 * (AV_RL32(dst + 10) & 0xFFFFFF) >> 24] = dst + 10;
-    dst += 16;
-    while (dst + 10 < tex_data + tex_size) {
-        ret = dxv_decompress_cgo(ctx, gb, tex_data, tex_size, op_data0, &oi0, op_size0,
-                                 &dst, &state0, tab0, tab1, 8);
-        if (ret < 0)
-            return ret;
-        ret = dxv_decompress_cgo(ctx, gb, tex_data, tex_size, op_data1, &oi1, op_size1,
-                                 &dst, &state1, tab2, tab3, 8);
-        if (ret < 0)
-            return ret;
-    }
-
-    bytestream2_seek(gb, data_start - 12 + op_offset + skip0 + skip1, SEEK_SET);
-
-    return 0;
-}
-
-static int dxv_decompress_yo(DXVContext *ctx, GetByteContext *gb,
-                             uint8_t *tex_data, int tex_size,
-                             uint8_t *op_data, int max_op_size)
-{
-    int op_offset = bytestream2_get_le32(gb);
-    unsigned op_size = bytestream2_get_le32(gb);
-    int data_start = bytestream2_tell(gb);
-    uint8_t *dst, *table0[256] = { 0 }, *table1[256] = { 0 };
-    int ret, state = 0, skip, oi = 0, v, vv;
-
-    if (op_offset < 8 || op_offset - 8 > bytestream2_get_bytes_left(gb))
-        return AVERROR_INVALIDDATA;
-
-    dst = tex_data;
-    bytestream2_skip(gb, op_offset - 8);
-    if (op_size > max_op_size)
-        return AVERROR_INVALIDDATA;
-    skip = dxv_decompress_opcodes(gb, op_data, op_size);
-    if (skip < 0)
-        return skip;
-    bytestream2_seek(gb, data_start, SEEK_SET);
-
-    v = bytestream2_get_le32(gb);
-    AV_WL32(dst, v);
-    vv = bytestream2_get_le32(gb);
-    table0[0x9E3779B1 * (uint16_t)v >> 24] = dst;
-    AV_WL32(dst + 4, vv);
-    table1[0x9E3779B1 * (AV_RL32(dst + 2) & 0xFFFFFF) >> 24] = dst + 2;
-    dst += 8;
-
-    while (dst < tex_data + tex_size) {
-        ret = dxv_decompress_cgo(ctx, gb, tex_data, tex_size, op_data, &oi, op_size,
-                                 &dst, &state, table0, table1, 0);
-        if (ret < 0)
-            return ret;
-    }
-
-    bytestream2_seek(gb, data_start + op_offset + skip - 8, SEEK_SET);
-
-    return 0;
-}
-
-static int dxv_decompress_ycg6(AVCodecContext *avctx)
-{
-    DXVContext *ctx = avctx->priv_data;
-    GetByteContext *gb = &ctx->gbc;
-    int ret;
-
-    ret = dxv_decompress_yo(ctx, gb, ctx->tex_data, ctx->tex_size,
-                            ctx->op_data[0], ctx->op_size[0]);
-    if (ret < 0)
-        return ret;
-
-    return dxv_decompress_cocg(ctx, gb, ctx->ctex_data, ctx->ctex_size,
-                               ctx->op_data[1], ctx->op_data[2],
-                               ctx->op_size[1], ctx->op_size[2]);
-}
-
-static int dxv_decompress_yg10(AVCodecContext *avctx)
-{
-    DXVContext *ctx = avctx->priv_data;
-    GetByteContext *gb = &ctx->gbc;
-    int ret;
-
-    ret = dxv_decompress_cocg(ctx, gb, ctx->tex_data, ctx->tex_size,
-                              ctx->op_data[0], ctx->op_data[3],
-                              ctx->op_size[0], ctx->op_size[3]);
-    if (ret < 0)
-        return ret;
-
-    return dxv_decompress_cocg(ctx, gb, ctx->ctex_data, ctx->ctex_size,
-                               ctx->op_data[1], ctx->op_data[2],
-                               ctx->op_size[1], ctx->op_size[2]);
-}
-
-static int dxv_decompress_dxt5(AVCodecContext *avctx)
-{
-    DXVContext *ctx = avctx->priv_data;
-    GetByteContext *gbc = &ctx->gbc;
-    uint32_t value, op, prev;
-    int idx, state = 0;
-    int pos = 4;
-    int run = 0;
-    int probe, check;
-
-    /* Copy the first four elements */
-    AV_WL32(ctx->tex_data +  0, bytestream2_get_le32(gbc));
-    AV_WL32(ctx->tex_data +  4, bytestream2_get_le32(gbc));
-    AV_WL32(ctx->tex_data +  8, bytestream2_get_le32(gbc));
-    AV_WL32(ctx->tex_data + 12, bytestream2_get_le32(gbc));
-
-    /* Process input until the whole texture has been filled */
-    while (pos + 2 <= ctx->tex_size / 4) {
-        if (run) {
-            run--;
-
-            prev = AV_RL32(ctx->tex_data + 4 * (pos - 4));
-            AV_WL32(ctx->tex_data + 4 * pos, prev);
-            pos++;
-            prev = AV_RL32(ctx->tex_data + 4 * (pos - 4));
-            AV_WL32(ctx->tex_data + 4 * pos, prev);
-            pos++;
-        } else {
-            if (bytestream2_get_bytes_left(gbc) < 1)
-                return AVERROR_INVALIDDATA;
-            if (state == 0) {
-                value = bytestream2_get_le32(gbc);
-                state = 16;
-            }
-            op = value & 0x3;
-            value >>= 2;
-            state--;
-
-            switch (op) {
-            case 0:
-                /* Long copy */
-                check = bytestream2_get_byte(gbc) + 1;
-                if (check == 256) {
-                    do {
-                        probe = bytestream2_get_le16(gbc);
-                        check += probe;
-                    } while (probe == 0xFFFF);
-                }
-                while (check && pos + 4 <= ctx->tex_size / 4) {
-                    prev = AV_RL32(ctx->tex_data + 4 * (pos - 4));
-                    AV_WL32(ctx->tex_data + 4 * pos, prev);
-                    pos++;
-
-                    prev = AV_RL32(ctx->tex_data + 4 * (pos - 4));
-                    AV_WL32(ctx->tex_data + 4 * pos, prev);
-                    pos++;
-
-                    prev = AV_RL32(ctx->tex_data + 4 * (pos - 4));
-                    AV_WL32(ctx->tex_data + 4 * pos, prev);
-                    pos++;
-
-                    prev = AV_RL32(ctx->tex_data + 4 * (pos - 4));
-                    AV_WL32(ctx->tex_data + 4 * pos, prev);
-                    pos++;
-
-                    check--;
-                }
-
-                /* Restart (or exit) the loop */
-                continue;
-                break;
-            case 1:
-                /* Load new run value */
-                run = bytestream2_get_byte(gbc);
-                if (run == 255) {
-                    do {
-                        probe = bytestream2_get_le16(gbc);
-                        run += probe;
-                    } while (probe == 0xFFFF);
-                }
-
-                /* Copy two dwords from previous data */
-                prev = AV_RL32(ctx->tex_data + 4 * (pos - 4));
-                AV_WL32(ctx->tex_data + 4 * pos, prev);
-                pos++;
-
-                prev = AV_RL32(ctx->tex_data + 4 * (pos - 4));
-                AV_WL32(ctx->tex_data + 4 * pos, prev);
-                pos++;
-                break;
-            case 2:
-                /* Copy two dwords from a previous index */
-                idx = 8 + bytestream2_get_le16(gbc);
-                if (idx > pos || (unsigned int)(pos - idx) + 2 > ctx->tex_size / 4)
-                    return AVERROR_INVALIDDATA;
-                prev = AV_RL32(ctx->tex_data + 4 * (pos - idx));
-                AV_WL32(ctx->tex_data + 4 * pos, prev);
-                pos++;
-
-                prev = AV_RL32(ctx->tex_data + 4 * (pos - idx));
-                AV_WL32(ctx->tex_data + 4 * pos, prev);
-                pos++;
-                break;
-            case 3:
-                /* Copy two dwords from input */
-                prev = bytestream2_get_le32(gbc);
-                AV_WL32(ctx->tex_data + 4 * pos, prev);
-                pos++;
-
-                prev = bytestream2_get_le32(gbc);
-                AV_WL32(ctx->tex_data + 4 * pos, prev);
-                pos++;
-                break;
-            }
-        }
-
-        CHECKPOINT(4);
-        if (pos + 2 > ctx->tex_size / 4)
-            return AVERROR_INVALIDDATA;
-
-        /* Copy two elements from a previous offset or from the input buffer */
-        if (op) {
-            if (idx > pos || (unsigned int)(pos - idx) + 2 > ctx->tex_size / 4)
-                return AVERROR_INVALIDDATA;
-            prev = AV_RL32(ctx->tex_data + 4 * (pos - idx));
-            AV_WL32(ctx->tex_data + 4 * pos, prev);
-            pos++;
-
-            prev = AV_RL32(ctx->tex_data + 4 * (pos - idx));
-            AV_WL32(ctx->tex_data + 4 * pos, prev);
-            pos++;
-        } else {
-            CHECKPOINT(4);
-
-            if (op && (idx > pos || (unsigned int)(pos - idx) + 2 > ctx->tex_size / 4))
-                return AVERROR_INVALIDDATA;
-            if (op)
-                prev = AV_RL32(ctx->tex_data + 4 * (pos - idx));
-            else
-                prev = bytestream2_get_le32(gbc);
-            AV_WL32(ctx->tex_data + 4 * pos, prev);
-            pos++;
-
-            CHECKPOINT(4);
-
-            if (op)
-                prev = AV_RL32(ctx->tex_data + 4 * (pos - idx));
-            else
-                prev = bytestream2_get_le32(gbc);
-            AV_WL32(ctx->tex_data + 4 * pos, prev);
-            pos++;
-        }
-    }
-
-    return 0;
-}
-
-static int dxv_decompress_lzf(AVCodecContext *avctx)
-{
-    DXVContext *ctx = avctx->priv_data;
-    return ff_lzf_uncompress(&ctx->gbc, &ctx->tex_data, &ctx->tex_size);
-}
-
-static int dxv_decompress_raw(AVCodecContext *avctx)
-{
-    DXVContext *ctx = avctx->priv_data;
-    GetByteContext *gbc = &ctx->gbc;
-
-    if (bytestream2_get_bytes_left(gbc) < ctx->tex_size)
-        return AVERROR_INVALIDDATA;
-
-    bytestream2_get_buffer(gbc, ctx->tex_data, ctx->tex_size);
-    return 0;
-}
-
-static int dxv_decode(AVCodecContext *avctx, AVFrame *frame,
-                      int *got_frame, AVPacket *avpkt)
-{
-    DXVContext *ctx = avctx->priv_data;
-    GetByteContext *gbc = &ctx->gbc;
-    int (*decompress_tex)(AVCodecContext *avctx);
-    const char *msgcomp, *msgtext;
-    uint32_t tag;
-    int version_major, version_minor = 0;
-    int size = 0, old_type = 0;
-    int ret;
-
-    bytestream2_init(gbc, avpkt->data, avpkt->size);
-
-    ctx->texture_block_h = 4;
-    ctx->texture_block_w = 4;
-
-    avctx->pix_fmt = AV_PIX_FMT_RGBA;
-    avctx->colorspace = AVCOL_SPC_RGB;
-
-    ctx->tex_funct = NULL;
-    ctx->tex_funct_planar[0] = NULL;
-    ctx->tex_funct_planar[1] = NULL;
-
-    tag = bytestream2_get_le32(gbc);
-    switch (tag) {
-    case MKBETAG('D', 'X', 'T', '1'):
-        decompress_tex = dxv_decompress_dxt1;
-        ctx->tex_funct = ctx->texdsp.dxt1_block;
-        ctx->tex_rat   = 8;
-        ctx->tex_step  = 8;
-        msgcomp = "DXTR1";
-        msgtext = "DXT1";
-        break;
-    case MKBETAG('D', 'X', 'T', '5'):
-        decompress_tex = dxv_decompress_dxt5;
-        ctx->tex_funct = ctx->texdsp.dxt5_block;
-        ctx->tex_rat   = 4;
-        ctx->tex_step  = 16;
-        msgcomp = "DXTR5";
-        msgtext = "DXT5";
-        break;
-    case MKBETAG('Y', 'C', 'G', '6'):
-        decompress_tex = dxv_decompress_ycg6;
-        ctx->tex_funct_planar[0] = yo_block;
-        ctx->tex_funct_planar[1] = cocg_block;
-        ctx->tex_rat   = 8;
-        ctx->tex_step  = 32;
-        ctx->ctex_step = 16;
-        msgcomp = "YOCOCG6";
-        msgtext = "YCG6";
-        ctx->ctex_size = avctx->coded_width * avctx->coded_height / 4;
-        ctx->texture_block_h = 4;
-        ctx->texture_block_w = 16;
-        ctx->ctexture_block_h = 4;
-        ctx->ctexture_block_w = 4;
-        avctx->pix_fmt = AV_PIX_FMT_YUV420P;
-        avctx->colorspace = AVCOL_SPC_YCOCG;
-        break;
-    case MKBETAG('Y', 'G', '1', '0'):
-        decompress_tex = dxv_decompress_yg10;
-        ctx->tex_funct_planar[0] = yao_block;
-        ctx->tex_funct_planar[1] = cocg_block;
-        ctx->tex_rat   = 4;
-        ctx->tex_step  = 64;
-        ctx->ctex_step = 16;
-        msgcomp = "YAOCOCG10";
-        msgtext = "YG10";
-        ctx->ctex_size = avctx->coded_width * avctx->coded_height / 4;
-        ctx->texture_block_h = 4;
-        ctx->texture_block_w = 16;
-        ctx->ctexture_block_h = 4;
-        ctx->ctexture_block_w = 4;
-        avctx->pix_fmt = AV_PIX_FMT_YUVA420P;
-        avctx->colorspace = AVCOL_SPC_YCOCG;
-        break;
-    default:
-        /* Old version does not have a real header, just size and type. */
-        size = tag & 0x00FFFFFF;
-        old_type = tag >> 24;
-        version_major = (old_type & 0x0F) - 1;
-
-        if (old_type & 0x80) {
-            msgcomp = "RAW";
-            decompress_tex = dxv_decompress_raw;
-        } else {
-            msgcomp = "LZF";
-            decompress_tex = dxv_decompress_lzf;
-        }
-
-        if (old_type & 0x40) {
-            msgtext = "DXT5";
-
-            ctx->tex_funct = ctx->texdsp.dxt5_block;
-            ctx->tex_step  = 16;
-        } else if (old_type & 0x20 || version_major == 1) {
-            msgtext = "DXT1";
-
-            ctx->tex_funct = ctx->texdsp.dxt1_block;
-            ctx->tex_step  = 8;
-        } else {
-            av_log(avctx, AV_LOG_ERROR, "Unsupported header (0x%08"PRIX32")\n.", tag);
-            return AVERROR_INVALIDDATA;
-        }
-        ctx->tex_rat = 1;
-        break;
-    }
-
-    ctx->slice_count = av_clip(avctx->thread_count, 1,
-                               avctx->coded_height / FFMAX(ctx->texture_block_h,
-                                                           ctx->ctexture_block_h));
-
-    /* New header is 12 bytes long. */
-    if (!old_type) {
-        version_major = bytestream2_get_byte(gbc) - 1;
-        version_minor = bytestream2_get_byte(gbc);
-
-        /* Encoder copies texture data when compression is not advantageous. */
-        if (bytestream2_get_byte(gbc)) {
-            msgcomp = "RAW";
-            ctx->tex_rat = 1;
-            decompress_tex = dxv_decompress_raw;
-        }
-
-        bytestream2_skip(gbc, 1); // unknown
-        size = bytestream2_get_le32(gbc);
-    }
-    av_log(avctx, AV_LOG_DEBUG,
-           "%s compression with %s texture (version %d.%d)\n",
-           msgcomp, msgtext, version_major, version_minor);
-
-    if (size != bytestream2_get_bytes_left(gbc)) {
-        av_log(avctx, AV_LOG_ERROR,
-               "Incomplete or invalid file (header %d, left %u).\n",
-               size, bytestream2_get_bytes_left(gbc));
-        return AVERROR_INVALIDDATA;
-    }
-
-    ctx->tex_size = avctx->coded_width * avctx->coded_height * 4 / ctx->tex_rat;
-    ret = av_reallocp(&ctx->tex_data, ctx->tex_size + AV_INPUT_BUFFER_PADDING_SIZE);
-    if (ret < 0)
-        return ret;
-
-    if (ctx->ctex_size) {
-        int i;
-
-        ctx->op_size[0] = avctx->coded_width * avctx->coded_height / 16;
-        ctx->op_size[1] = avctx->coded_width * avctx->coded_height / 32;
-        ctx->op_size[2] = avctx->coded_width * avctx->coded_height / 32;
-        ctx->op_size[3] = avctx->coded_width * avctx->coded_height / 16;
-
-        ret = av_reallocp(&ctx->ctex_data, ctx->ctex_size + AV_INPUT_BUFFER_PADDING_SIZE);
-        if (ret < 0)
-            return ret;
-        for (i = 0; i < 4; i++) {
-            ret = av_reallocp(&ctx->op_data[i], ctx->op_size[i]);
-            if (ret < 0)
-                return ret;
-        }
-    }
-
-    /* Decompress texture out of the intermediate compression. */
-    ret = decompress_tex(avctx);
-    if (ret < 0)
-        return ret;
-    {
-        int w_block = avctx->coded_width / ctx->texture_block_w;
-        int h_block = avctx->coded_height / ctx->texture_block_h;
-        if (w_block * h_block * ctx->tex_step > ctx->tex_size * 8LL)
-            return AVERROR_INVALIDDATA;
-    }
-
-    ret = ff_thread_get_buffer(avctx, frame, 0);
-    if (ret < 0)
-        return ret;
-
-    /* Now decompress the texture with the standard functions. */
-    avctx->execute2(avctx, decompress_texture_thread,
-                    frame, NULL, ctx->slice_count);
-
-    /* Frame is ready to be output. */
-    frame->pict_type = AV_PICTURE_TYPE_I;
-    frame->key_frame = 1;
-    *got_frame = 1;
-
-    return avpkt->size;
-}
-
-static int dxv_init(AVCodecContext *avctx)
-{
-    DXVContext *ctx = avctx->priv_data;
-    int ret = av_image_check_size(avctx->width, avctx->height, 0, avctx);
-
-    if (ret < 0) {
-        av_log(avctx, AV_LOG_ERROR, "Invalid image size %dx%d.\n",
-               avctx->width, avctx->height);
-        return ret;
-    }
-
-    /* Codec requires 16x16 alignment. */
-    avctx->coded_width  = FFALIGN(avctx->width,  16);
-    avctx->coded_height = FFALIGN(avctx->height, 16);
-
-    ff_texturedsp_init(&ctx->texdsp);
-
-    return 0;
-}
-
-static int dxv_close(AVCodecContext *avctx)
-{
-    DXVContext *ctx = avctx->priv_data;
-
-    av_freep(&ctx->tex_data);
-    av_freep(&ctx->ctex_data);
-    av_freep(&ctx->op_data[0]);
-    av_freep(&ctx->op_data[1]);
-    av_freep(&ctx->op_data[2]);
-    av_freep(&ctx->op_data[3]);
-
-    return 0;
-}
-
-const FFCodec ff_dxv_decoder = {
-    .p.name         = "dxv",
-    CODEC_LONG_NAME("Resolume DXV"),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_DXV,
-    .init           = dxv_init,
-    FF_CODEC_DECODE_CB(dxv_decode),
-    .close          = dxv_close,
-    .priv_data_size = sizeof(DXVContext),
-    .p.capabilities = AV_CODEC_CAP_DR1 |
-                      AV_CODEC_CAP_SLICE_THREADS |
-                      AV_CODEC_CAP_FRAME_THREADS,
-    .caps_internal  = FF_CODEC_CAP_INIT_CLEANUP,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264qpel.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264qpel.c
deleted file mode 100644
index 65fef03304531bccbf7c91a2aa2e144bd4b4d9ed..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264qpel.c
+++ /dev/null
@@ -1,112 +0,0 @@
-/*
- * H.26L/H.264/AVC/JVT/14496-10/... encoder/decoder
- * Copyright (c) 2003-2010 Michael Niedermayer <michaelni@gmx.at>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/attributes.h"
-#include "h264qpel.h"
-
-#define pixeltmp int16_t
-#define BIT_DEPTH 8
-#include "h264qpel_template.c"
-#undef BIT_DEPTH
-
-#define BIT_DEPTH 9
-#include "h264qpel_template.c"
-#undef BIT_DEPTH
-
-#define BIT_DEPTH 10
-#include "h264qpel_template.c"
-#undef BIT_DEPTH
-#undef pixeltmp
-
-#define pixeltmp int32_t
-#define BIT_DEPTH 12
-#include "h264qpel_template.c"
-#undef BIT_DEPTH
-
-#define BIT_DEPTH 14
-#include "h264qpel_template.c"
-#undef BIT_DEPTH
-
-
-av_cold void ff_h264qpel_init(H264QpelContext *c, int bit_depth)
-{
-#undef FUNCC
-#define FUNCC(f, depth) f ## _ ## depth ## _c
-
-#define dspfunc2(PFX, IDX, NUM, depth)                                  \
-    c->PFX ## _pixels_tab[IDX][ 0] = FUNCC(PFX ## NUM ## _mc00, depth); \
-    c->PFX ## _pixels_tab[IDX][ 1] = FUNCC(PFX ## NUM ## _mc10, depth); \
-    c->PFX ## _pixels_tab[IDX][ 2] = FUNCC(PFX ## NUM ## _mc20, depth); \
-    c->PFX ## _pixels_tab[IDX][ 3] = FUNCC(PFX ## NUM ## _mc30, depth); \
-    c->PFX ## _pixels_tab[IDX][ 4] = FUNCC(PFX ## NUM ## _mc01, depth); \
-    c->PFX ## _pixels_tab[IDX][ 5] = FUNCC(PFX ## NUM ## _mc11, depth); \
-    c->PFX ## _pixels_tab[IDX][ 6] = FUNCC(PFX ## NUM ## _mc21, depth); \
-    c->PFX ## _pixels_tab[IDX][ 7] = FUNCC(PFX ## NUM ## _mc31, depth); \
-    c->PFX ## _pixels_tab[IDX][ 8] = FUNCC(PFX ## NUM ## _mc02, depth); \
-    c->PFX ## _pixels_tab[IDX][ 9] = FUNCC(PFX ## NUM ## _mc12, depth); \
-    c->PFX ## _pixels_tab[IDX][10] = FUNCC(PFX ## NUM ## _mc22, depth); \
-    c->PFX ## _pixels_tab[IDX][11] = FUNCC(PFX ## NUM ## _mc32, depth); \
-    c->PFX ## _pixels_tab[IDX][12] = FUNCC(PFX ## NUM ## _mc03, depth); \
-    c->PFX ## _pixels_tab[IDX][13] = FUNCC(PFX ## NUM ## _mc13, depth); \
-    c->PFX ## _pixels_tab[IDX][14] = FUNCC(PFX ## NUM ## _mc23, depth); \
-    c->PFX ## _pixels_tab[IDX][15] = FUNCC(PFX ## NUM ## _mc33, depth)
-
-#define SET_QPEL(depth)                         \
-    dspfunc2(put_h264_qpel, 0, 16, depth);      \
-    dspfunc2(put_h264_qpel, 1,  8, depth);      \
-    dspfunc2(put_h264_qpel, 2,  4, depth);      \
-    dspfunc2(put_h264_qpel, 3,  2, depth);      \
-    dspfunc2(avg_h264_qpel, 0, 16, depth);      \
-    dspfunc2(avg_h264_qpel, 1,  8, depth);      \
-    dspfunc2(avg_h264_qpel, 2,  4, depth)
-
-    switch (bit_depth) {
-    default:
-        SET_QPEL(8);
-        break;
-    case 9:
-        SET_QPEL(9);
-        break;
-    case 10:
-        SET_QPEL(10);
-        break;
-    case 12:
-        SET_QPEL(12);
-        break;
-    case 14:
-        SET_QPEL(14);
-        break;
-    }
-
-#if ARCH_AARCH64
-    ff_h264qpel_init_aarch64(c, bit_depth);
-#elif ARCH_ARM
-    ff_h264qpel_init_arm(c, bit_depth);
-#elif ARCH_PPC
-    ff_h264qpel_init_ppc(c, bit_depth);
-#elif ARCH_X86
-    ff_h264qpel_init_x86(c, bit_depth);
-#elif ARCH_MIPS
-    ff_h264qpel_init_mips(c, bit_depth);
-#elif ARCH_LOONGARCH64
-    ff_h264qpel_init_loongarch(c, bit_depth);
-#endif
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Angry Birds Space 2.1.0 PC Download - Blast Off with the Birds.md b/spaces/congsaPfin/Manga-OCR/logs/Angry Birds Space 2.1.0 PC Download - Blast Off with the Birds.md
deleted file mode 100644
index e6116868df32b6385d70b51c029ef00fc3086183..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Angry Birds Space 2.1.0 PC Download - Blast Off with the Birds.md	
+++ /dev/null
@@ -1,123 +0,0 @@
-
-<h1>Angry Birds Space 2.1.0 PC Download: How to Get the Latest Version of the Popular Game</h1>
- <p>If you are a fan of the Angry Birds franchise, you probably know that one of the most exciting and innovative games in the series is Angry Birds Space. This game takes the classic slingshot gameplay to a whole new level by introducing gravity, planets, and space-themed levels.</p>
- <p>Angry Birds Space was first released in 2012, but it has been updated several times since then, adding new content and features. The latest version of the game is 2.1.0, which was released in 2017 on Steam.</p>
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- <p>In this article, we will show you how to download Angry Birds Space 2.1.0 for PC, how to install it, and how to play it. We will also share some tips and tricks to help you master the game and have more fun.</p>
- <h2>Features of Angry Birds Space 2.1.0</h2>
- <p>Angry Birds Space 2.1.0 is not just a simple update of the original game. It adds a lot of new elements that make it more enjoyable and challenging. Here are some of the features that you can expect from this version:</p>
- <ul>
-<li><b>New planets, levels, and birds:</b> The game has over 300 levels across 10 different planets, each with its own theme, gravity, and obstacles. You can also unlock new birds with special abilities, such as Ice Bird, Lazer Bird, and Lightning Bird.</li>
-<li><b>Gravity mechanics and gameplay:</b> The game uses realistic physics to simulate the effects of gravity on the birds, pigs, and structures. You can use gravity to your advantage by slinging the birds around planets, bouncing them off asteroids, or creating chain reactions.</li>
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-<li><b>Compatibility and system requirements:</b> The game is compatible with Windows XP/Vista/7/8/10 and requires a minimum of 512 MB RAM, 100 MB disk space, DirectX 9 graphics card, and internet connection.</li>
-</ul>
- <h2>How to Download Angry Birds Space 2.1.0 for PC</h2>
- <p>There are several ways to download Angry Birds Space 2.1.0 for PC, depending on your preference and availability. Here are some of the options that you can try:</p>
- <h3>Option 1: Steam platform</h3>
- <p>The easiest and most reliable way to download Angry Birds Space 2.1.0 for PC is through Steam, which is a digital distribution platform for games and software. You will need to create a free Steam account and install the Steam client on your PC.</p>
- <p>Once you have done that, you can search for Angry Birds Space on the Steam store and purchase it for $4.99 USD (or equivalent in your currency). You can then download and install the game through the Steam client.</p>
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- <h3>Option 2: Internet Archive</h3>
- <p>If you don <p>Option 2: Internet Archive</p>
- <p>If you don't want to use Steam or you can't access it for some reason, you can try downloading Angry Birds Space 2.1.0 for PC from the Internet Archive, which is a non-profit library of millions of free books, movies, software, music, and more.</p>
- <p>You can find the game files on this link: [Angry Birds Space 2.1.0]. You will need to download the ZIP file, which is about 60 MB in size. You will also need a program like WinRAR or 7-Zip to extract the files.</p>
- <h3>Option 3: Other sources</h3>
- <p>There are also other websites that claim to offer Angry Birds Space 2.1.0 for PC for free or for a lower price than Steam. However, we do not recommend using these sources, as they may be unsafe, illegal, or outdated.</p>
- <p>Some of these websites may contain viruses, malware, or spyware that can harm your PC or steal your personal information. Some of them may also violate the intellectual property rights of the game developers and publishers. Some of them may also provide older or modified versions of the game that may not work properly or have bugs.</p>
- <p>Therefore, we advise you to be careful and cautious when downloading Angry Birds Space 2.1.0 for PC from other sources. Always check the reputation and reviews of the website before downloading anything. Always scan the files with an antivirus program before opening them. Always backup your data and system before installing anything.</p>
- <h2>How to Install Angry Birds Space 2.1.0 on PC</h2>
- <p>Once you have downloaded Angry Birds Space 2.1.0 for PC from one of the options above, you can proceed to install it on your PC. The installation process may vary slightly depending on the source of the game files, but generally, it involves these steps:</p>
- <h3>Step 1: Download the game files</h3>
- <p>If you downloaded the game from Steam, you can skip this step, as Steam will automatically download and install the game for you.</p>
- <p>If you downloaded the game from the Internet Archive or another source, you will need to locate the ZIP file that contains the game files on your PC. You can use a program like WinRAR or 7-Zip to extract the files to a folder of your choice.</p>
- <h3>Step 2: Extract the files</h3>
- <p>After extracting the files, you should see a folder named "Angry Birds Space" that contains several subfolders and files. You can open the folder and explore its contents.</p>
- <p>You should see a file named "AngryBirdsSpace.exe", which is the main executable file of the game. You should also see a file named "AngryBirdsSpaceInstaller_2_1_0.exe", which is the installer file of the game.</p>
- <h3>Step 3: Run the installer or the executable</h3>
- <p>You have two options to run the game: either by using the installer or by using the executable.</p>
- <p>If you want to use the installer, you can double-click on the "AngryBirdsSpaceInstaller_2_1_0.exe" file and follow the instructions on the screen. The installer will guide you through the installation process and create a shortcut on your desktop and start menu.</p>
- <p>If you want to use the executable, you can double-click on the "AngryBirdsSpace.exe" file and start playing right away. However, you will need to manually create a shortcut on your desktop or start menu if you want to access the game easily in the future.</p>
- <h3>Step 4: Enjoy the game</h3>
- <p>Congratulations! You have successfully installed Angry Birds Space 2.1.0 on your PC. You can now launch the game from your desktop or start menu and enjoy playing it.</p>
- <h2>Tips and Tricks for Playing Angry Birds Space 2.1.0</h2>
- <p>Angry Birds Space 2.1.0 is a fun and addictive game that will keep you entertained for hours. However, it can also be challenging and frustrating at times, especially if you are new to it or stuck on a level.</p>
- <p>To help you out, we have compiled some tips and tricks that will help you improve your skills and enjoy the game more:</p>
- <ul>
-<li><b>Use the zoom function to see the whole level:</b> Sometimes, it can be hard to see everything that is going on in a level, especially if there are multiple planets, asteroids, or structures involved. To get a better view of <ul>
-<li><b>Use the zoom function to see the whole level:</b> Sometimes, it can be hard to see everything that is going on in a level, especially if there are multiple planets, asteroids, or structures involved. To get a better view of the level, you can use the zoom function by scrolling your mouse wheel or using the +/- keys on your keyboard. This will help you plan your shots and avoid missing any targets or opportunities.</li>
-<li><b>Aim for the pigs and the structures:</b> The main objective of the game is to eliminate all the pigs in each level by hitting them with the birds or making them fall off the screen. However, sometimes, the pigs are hidden or protected by various structures, such as wood, ice, metal, or glass. To destroy these structures, you need to use the right bird and the right angle. For example, Red Bird is good for breaking wood, Blue Bird is good for breaking ice, Yellow Bird is good for breaking metal, and Bomb Bird is good for breaking glass. You can also use the environment to your advantage by hitting asteroids, TNT crates, or other objects that can cause damage or explosions.</li>
-<li><b>Experiment with different birds and angles:</b> Each bird has a unique ability that can be activated by clicking your mouse or pressing the spacebar while the bird is in flight. For example, Lazer Bird can change direction and target a specific point, Ice Bird can freeze objects and make them brittle, and Lightning Bird can electrify metal objects and create sparks. You can use these abilities to create different effects and outcomes depending on the situation. You can also try different angles and trajectories to find the optimal way to hit your targets. You can use the dotted line that appears when you pull back the slingshot to guide your aim and adjust your power.</li>
-<li><b>Collect the golden eggs and stars:</b> The game has several hidden items that you can collect to unlock extra content and rewards. One of them is the golden egg, which is a secret item that appears in some levels or menus. If you find and click on a golden egg, you will unlock a bonus level that features a different gameplay mode or a reference to another game or media. Another item is the star, which is a currency that you can earn by completing levels with high scores or by watching ads. You can use stars to buy power-ups, such as King Sling, Super Seeds, Sling Scope, or Birdquake, that can help you beat difficult levels or improve your score.</li>
-<li><b>Watch out for obstacles and enemies:</b> The game is not only about destroying pigs and structures. It also has various obstacles and enemies that can hinder your progress or harm your birds. For example, some levels have black holes that can suck in your birds or objects, meteoroids that can crash into your birds or objects, or lasers that can zap your birds or objects. Some levels also have enemy birds that can attack your birds or defend the pigs. You need to be careful and avoid these hazards as much as possible.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Angry Birds Space 2.1.0 is a great game that offers hours of fun and entertainment for players of all ages and skill levels. It combines the classic Angry Birds gameplay with new elements and features that make it more exciting and challenging.</p>
- <p>If you want to download Angry Birds Space 2.1.0 for PC, you can choose from several options, such as Steam, Internet Archive, or other sources. However, you need to be careful and cautious when downloading from other sources, as they may be unsafe, illegal, or outdated.</p>
- <p>Once you have downloaded and installed the game on your PC, you can start playing it and enjoy its amazing graphics, sound effects, music, and physics. You can also use some tips and tricks to improve your skills and have more fun.</p>
- <p>So what are you waiting for? Download Angry Birds Space 2.1.0 for PC now and blast off into space with your favorite birds!</p>
- <h2>Frequently Asked Questions</h2>
- <p>Here are some of the most common questions that people ask about Angry Birds Space 2.1.0:</p>
- <h3>Q: Is Angry Birds Space 2.1.0 free?</h3>
- <p>A: No, Angry Birds Space 2.1.0 is not free. You need to pay $4.99 USD (or equivalent in your currency) to download it from Steam. However, you can download it for free from the Internet Archive or other sources at your own risk.</p>
- <h3>Q: Is Angry Birds Space 2.1.0 safe?</h3>
- <p>A: Yes, Angry Birds Space 2.1.0 is safe if you download it from Steam or the Internet Archive. However, if you download it <p>A: Yes, Angry Birds Space 2.1.0 is safe if you download it from Steam or the Internet Archive. However, if you download it from other sources, you may encounter viruses, malware, or spyware that can harm your PC or steal your personal information. Therefore, we recommend that you only download the game from trusted and verified sources.</p>
- <h3>Q: How do I update Angry Birds Space 2.1.0?</h3>
- <p>A: If you downloaded the game from Steam, you don't need to worry about updating it, as Steam will automatically update the game for you whenever there is a new version available. However, if you downloaded the game from the Internet Archive or another source, you may need to manually check for updates and download them from the official website of the game or from Steam.</p>
- <h3>Q: Can I play Angry Birds Space 2.1.0 offline?</h3>
- <p>A: Yes, you can play Angry Birds Space 2.1.0 offline, as long as you have already installed the game on your PC and activated it with a valid license key. However, you will not be able to access some of the online features of the game, such as Steam achievements, leaderboards, cloud saves, or ads.</p>
- <h3>Q: Can I play Angry Birds Space 2.1.0 on other devices?</h3>
- <p>A: Yes, you can play Angry Birds Space 2.1.0 on other devices besides PC, such as Android, iOS, Mac, Windows Phone, BlackBerry, Kindle Fire, and Roku. However, you will need to download the game separately for each device and pay for it if required. You will also need to create a Rovio account and sign in with it to sync your progress and purchases across devices.</p>
- <h3>Q: Where can I find more information about Angry Birds Space 2.1.0?</h3>
- <p>A: If you want to learn more about Angry Birds Space 2.1.0, you can visit the official website of the game at [Angry Birds Space], where you can find news, updates, videos, screenshots, wallpapers, and more. You can also visit the official Facebook page of the game at [Angry Birds Space Facebook], where you can interact with other fans and get tips and tricks.</p> 197e85843d<br />
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-<h1>Music App APK: What You Need to Know</h1>
-<p>If you love listening to music on your Android device, you might have heard of music app APKs. These are files that allow you to install and use music apps that are not available on the Google Play Store, or that have more features and content than the official versions. But what exactly are APK files, and how can you use them safely and legally? In this article, we will answer these questions and more, and give you some examples of the best music app APKs you can download and enjoy.</p>
- <h2>What is an APK file?</h2>
-<p>An APK file is an Android Package Kit file, which is the format used by Android devices to distribute and install applications. APK files contain all the necessary components for an app to run, such as code, resources, assets, certificates, and manifest. You can think of an APK file as a zip file that contains everything you need to install an app on your device.</p>
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- <h3>How to install an APK file on your Android device</h3>
-<p>Before you can install an APK file on your device, you need to enable the option to allow installation from unknown sources. This is a security measure that prevents unauthorized or malicious apps from being installed on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. You might also need to grant permission for your browser or file manager to install apps.</p>
-<p>Once you have enabled this option, you can download the APK file from a trusted source, such as a reputable website or a verified developer. You can use your browser or a file manager app to locate the downloaded file, and then tap on it to start the installation process. Follow the instructions on the screen, and wait for the app to be installed. You can then launch the app from your app drawer or home screen.</p>
- <h3>How to find the best music app APKs</h3>
-<p>There are many websites and platforms that offer music app APKs for download, but not all of them are safe and reliable. Some of them might contain malware or viruses that can harm your device or steal your personal information. Some of them might also provide illegal or pirated content that can get you in trouble with the law. Therefore, you need to be careful and selective when choosing where to download music app APKs from.</p>
-<p>One way to find the best music app APKs is to use a search engine like Bing, which can help you find relevant and trustworthy results. You can also use a website like APKPure, which is a platform that provides verified and secure APK files for various apps and games. Here are some examples of the best music app APKs you can find on APKPure:</p>
- <h4>Spotify: Music and Podcasts</h4>
-<p>Spotify is one of the most popular music streaming services in the world, with over 70 million songs, podcasts, playlists, and radio stations. With Spotify Premium, you can enjoy ad-free listening, offline mode, unlimited skips, high-quality audio, and more. However, Spotify Premium is not available in some countries, or it might be too expensive for some users. That's why some people opt for Spotify Premium APK, which is a modified version of Spotify that gives you access to all the premium features for free.</p>
- <h4>Shazam: Music Discovery</h4>
-<p>Shazam is a music discovery app that can identify any song playing around you in seconds. You can also use Shazam to find lyrics, artists, albums, playlists, videos, and more. Shazam Encore is the premium version of Shazam that removes the ads and gives you more features and options. However, Shazam Encore is not free, and you might not want to pay for it. That's why some people use Shazam Encore APK, which is a cracked version of Shazam Encore that lets you enjoy all the benefits without paying anything.</p>
- <h4>Gacha Editx</h4>
-<p>Gacha Editx is a music app that allows you to create your own songs and music videos using the Gacha Life characters and scenes. You can also edit, remix, and share your creations with other users. Gacha Editx is a fun and creative way to express yourself and your musical taste. However, Gacha Editx is not available on the Google Play Store, and you might have trouble finding it elsewhere. That's why some people download Gacha Editx APK, which is an unofficial version of Gacha Editx that you can install and use on your device.</p>
- <h2>What are the benefits of using music app APKs</h2>
-<p>Using music app APKs can have some advantages over using the official versions of the apps. Here are some of the benefits of using music app APKs:</p>
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- <h3>Access to more features and content</h3>
-<p>Some music app APKs can give you access to more features and content than the official versions. For example, you can use Spotify Premium APK to listen to any song you want, without ads or limitations. You can also use Shazam Encore APK to get more information and options for the songs you discover. You can also use Gacha Editx APK to create your own music videos with Gacha Life characters.</p>
- <h3>Save storage space and data usage</h3>
-<p>Some music app APKs can help you save storage space and data usage on your device. For example, you can use Spotify Premium APK to download songs and listen to them offline, without using your mobile data. You can also use Shazam Encore APK to sync your Shazams across your devices, without storing them locally. You can also use Gacha Editx APK to compress your music videos and share them easily.</p>
- <h3>Customize your music experience</h3>
-<p>Some music app APKs can allow you to customize your music experience according to your preferences and needs. For example, you can use Spotify Premium APK to create your own playlists, follow your favorite artists, and discover new music. You can also use Shazam Encore APK to adjust the settings and features of the app, such as auto-Shazam, pop-up lyrics, and dark mode. You can also use Gacha Editx APK to choose from different genres, styles, and effects for your music videos.</p>
- <h2>What are the risks of using music app APKs</h2>
-<p>Using music app APKs can also have some drawbacks and dangers that you need to be aware of. Here are some of the risks of using music app APKs:</p>
- <h3>Malware and viruses</h3>
-<p>Some music app APKs might contain malware or viruses that can infect your device or steal your personal information. These malicious apps might also damage your device or cause it to malfunction. Therefore, you need to be careful when downloading and installing music app APKs from unknown sources, and always scan them with a reliable antivirus software before opening them.</p>
- <h3>Legal issues and copyright infringement</h3>
-<p>Some music app APKs might violate the terms of service or the intellectual property rights of the original developers or owners of the apps. These apps might also provide illegal or pirated content that can get you in trouble with the law. Therefore, you need to be respectful and responsible when using music app APKs, and always support the official versions of the apps if you can afford them.</p>
- <h3>Compatibility and stability issues</h3>
-<p>Some music app APKs might not be compatible or stable with your device or operating system. These apps might also not be updated or maintained regularly by their developers or providers. Therefore, you might experience some bugs, glitches, crashes, or errors when using music app APKs, and they might not work as well as the official versions of the apps.</p>
- <h2>Conclusion</h2>
-<p>Music app APKs are files that allow you to install and use music apps that are not available on the Google Play Store, or that have more features and content than the official versions. Using music app APKs can have some benefits, such as access to more features and content, saving storage space and data usage, and customizing your music experience. However, using music app APKs can also have some risks, such as malware and viruses, legal issues and copyright infringement , and compatibility and stability issues. Therefore, you need to be careful and selective when choosing where to download and how to use music app APKs, and always respect the rights and interests of the original developers and owners of the apps.</p>
- <p>If you are interested in using music app APKs, here are some FAQs that might help you:</p>
- <h4>FAQs</h4>
-<ul>
-<li><b>Q: How can I tell if a music app APK is safe and reliable?</b></li>
-<li>A: You can check the source of the APK file, the reviews and ratings of the app, the permissions and requirements of the app, and the scan results of the antivirus software. You can also do some research on the app and its developer, and compare it with the official version of the app.</li>
-<li><b>Q: How can I update a music app APK?</b></li>
-<li>A: You can update a music app APK by downloading and installing the latest version of the APK file from the same source you got it from. You can also check if the app has an in-app update option, or if it notifies you when there is a new version available.</li>
-<li><b>Q: How can I uninstall a music app APK?</b></li>
-<li>A: You can uninstall a music app APK by going to Settings > Apps > Music App APK > Uninstall. You can also use a file manager app to locate and delete the APK file from your device.</li>
-<li><b>Q: How can I backup a music app APK?</b></li>
-<li>A: You can backup a music app APK by using a backup app or a cloud service to save the APK file and the data of the app. You can also use a file manager app to copy and paste the APK file to another location on your device or an external storage.</li>
-<li><b>Q: How can I share a music app APK?</b></li>
-<li>A: You can share a music app APK by using a file sharing app or a cloud service to send the APK file to another device or user. You can also use a file manager app to transfer the APK file via Bluetooth, Wi-Fi, USB, or other methods.</li>
-</ul>
- <p>I hope you enjoyed this article on music app APKs. If you have any questions or feedback, please leave them in the comments section below. Thank you for reading!</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Experience the Thrill of M88 Vin Casino with the App - Download Now.md b/spaces/congsaPfin/Manga-OCR/logs/Experience the Thrill of M88 Vin Casino with the App - Download Now.md
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Need for Speed 2 APK - The Ultimate Challenge for Racing Fans - Download and Play Now.md b/spaces/congsaPfin/Manga-OCR/logs/Need for Speed 2 APK - The Ultimate Challenge for Racing Fans - Download and Play Now.md
deleted file mode 100644
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+++ /dev/null
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-<h1>Download Need for Speed 2 APK: A Guide for Racing Fans</h1>
-<p>If you are a fan of racing games, you might have heard of Need for Speed 2, one of the most popular and classic titles in the genre. Need for Speed 2 is a game that lets you experience the thrill of illegal street racing, with realistic graphics, sound effects, and gameplay. You can choose from a variety of cars, customize them to your liking, and compete against other racers and the police in different modes and locations. In this article, we will show you how to download Need for Speed 2 APK, why you should play it, and some tips and tricks to help you master the game.</p>
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-<p>Need for Speed 2 is a racing video game that was released in 1997 by Electronic Arts. It is the second installment in the Need for Speed series, which has become one of the most successful and influential franchises in gaming history. Need for Speed 2 features eight cars and six tracks, each with its own theme and scenery. You can play in single-player or multiplayer mode, and choose from three difficulty levels: beginner, expert, and extreme. You can also select from three game modes: single race, tournament, and knockout. In single race, you can race against up to seven opponents on any track. In tournament, you can compete in a series of races and earn points based on your performance. In knockout, you have to avoid being the last racer in each round, or else you will be eliminated.</p>
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- <h3>Features of Need for Speed 2</h3>
-<p>Some of the features that make Need for Speed 2 stand out from other racing games are:</p>
-<ul>
-<li>Realistic physics and car handling: You can feel the difference between each car's speed, acceleration, braking, steering, and traction. You also have to deal with factors such as weather, traffic, road conditions, and collisions.</li>
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-<li>Multiplayer mode: You can play with up to seven other players online or locally via LAN or split-screen. You can also chat with other players using text or voice messages.</li>
-</ul>
- <h3>How to download Need for Speed 2 APK</h3>
-<p>If you want to play Need for Speed 2 on your Android device, you will need to download an APK file. An APK file is a package that contains all the files and data needed to run an app on Android. However, since Need for Speed 2 is an old game that is not available on Google Play Store or other official sources, you will have to find a reliable third-party website that offers it. Here are the steps to download Need for Speed 2 APK:</p>
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-<li <li>Click on the download button and wait for the APK 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 without any issues.</li>
-<li>Locate the APK file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Launch the game and enjoy playing Need for Speed 2 on your Android device.</li>
-</ol>
-<p>Note: You may also need to download an additional data file or an emulator to run the game properly. You can find more information on the website where you downloaded the APK file.</p>
- <h2>Why you should play Need for Speed 2</h2>
-<p>Need for Speed 2 is not just a game, it is a legend. It is one of the best racing games ever made, and it has a loyal fan base that still plays it today. Here are some reasons why you should play Need for Speed 2:</p>
- <h3>The benefits of playing Need for Speed 2</h3>
-<p>Playing Need for Speed 2 can give you many benefits, such as:</p>
-<ul>
-<li>Fun and entertainment: You can have a lot of fun and excitement playing Need for Speed 2, as it offers a variety of cars, tracks, modes, and challenges that will keep you hooked for hours.</li>
-<li>Skill development: You can improve your skills and abilities playing Need for Speed 2, as it requires you to use your reflexes, strategy, creativity, and concentration to win races and overcome obstacles.</li>
-<li>Nostalgia and nostalgia: You can relive your childhood memories playing Need for Speed 2, as it is a game that many people grew up with and loved. You can also experience the nostalgia of playing a classic game that has a retro style and feel.</li>
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-<p>Playing Need for Speed 2 can also give you some challenges, such as:</p>
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-<li>Difficulty and frustration: You can face some difficulty and frustration playing Need for Speed 2, as it is a game that can be hard and unforgiving at times. You may encounter some tough opponents, tricky tracks, or unexpected events that can make you lose or rage quit.</li>
-<li>Compatibility and performance: You can face some compatibility and performance issues playing Need for Speed 2, as it is an old game that may not run smoothly or properly on modern devices. You may encounter some bugs, glitches, crashes, or lag that can ruin your gaming experience.</li>
-<li>Legality and security: You can face some legality and security risks playing Need for Speed 2, as it is a game that is not officially supported or authorized by its developers or publishers. You may encounter some malware, viruses, or scams that can harm your device or data.</li>
-</ul>
- <h2>Tips and tricks for playing Need for Speed 2</h2>
-<p>If you want to play Need for Speed 2 like a pro, you will need some tips and tricks to help you out. Here are some of them:</p>
- <h3>How to customize your car</h3>
-<p>One of the best features of Need for Speed 2 is that you can customize your car to suit your style and preferences. You can change its color, body kit, wheels, tires, engine, transmission, suspension, brakes, nitrous oxide, and more. To customize your car, you need to go to the garage menu and select the car you want to modify. Then, you can use the arrows or the mouse to navigate through the different options and see how they affect your car's appearance and performance. You can also use the preview button to see how your car looks in different angles and lighting conditions. When you are satisfied with your customization, you can save it and use it in the game.</p>
- <h3>How to win races and earn money</h3>
-<p>The main goal of Need for Speed 2 is to win races and earn money. You can use the money to buy new cars or upgrade your existing ones. To win races and earn money, you need to follow these steps:</p>
-<ol>
-<li>Select a game mode: You can choose from single race, tournament, or knockout mode. Each mode has its own rules and rewards.</li>
-<li>Select a track: You can choose from six different tracks that vary in length, difficulty, scenery, and weather conditions.</li>
-<li>Select a car: You can choose from eight different cars that vary in speed, acceleration, handling, durability, and cost.</li>
-<li>Race: You can use the arrow keys or the mouse to control your car. You can also use the space bar or the left mouse button to activate the nitrous oxide, which can boost your speed temporarily. You can also use the horn, the headlights, and the camera buttons to interact with the environment and change your view.</li>
-<li>Win: You have to finish the race in the first place to win. You can also earn bonus money by performing stunts, overtaking opponents, or avoiding collisions.</li>
-</ol>
- <h3>How to avoid the cops and rivals</h3>
-<p>One of the challenges of Need for Speed 2 is that you have to deal with the cops and rivals who will try to stop you or sabotage you. The cops will chase you and try to arrest you or damage your car. The rivals will race against you and try to beat you or ram you. To avoid the cops and rivals, you need to use these strategies:</p>
-<ul>
-<li>Use the map: You can use the map on the bottom right corner of the screen to see where the cops and rivals are and where they are heading. You can also see where the shortcuts, obstacles, and power-ups are.</li>
-<li>Use the radar: You can use the radar on the top left corner of the screen to see how close the cops and rivals are to you. You can also see their status, such as their speed, health, and nitrous level.</li>
-<li>Use the power-ups: You can use the power-ups that are scattered around the tracks to gain an advantage over the cops and rivals. Some of the power-ups are: oil slicks, spikes, bombs, magnets, shields, and rockets.</li>
-<li>Use the environment: You can use the environment to your advantage by taking shortcuts, hiding behind objects, jumping over ramps, or causing explosions.</li>
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- <h2>Conclusion</h2>
-<p>Need for Speed 2 is a game that every racing fan should play at least once in their life. It is a game that offers a lot of fun, excitement, challenge, and nostalgia. It is a game that lets you customize your car, race on diverse tracks, and avoid the cops and rivals. It is a game that you can download and play on your Android device using an APK file. If you want to download Need for Speed 2 APK, follow the steps we have provided in this article. If you want to play Need for Speed 2 like a pro, follow the tips and tricks we have shared in this article. And if you want to have a blast playing Need for Speed 2, just enjoy yourself and feel the adrenaline rush.</p>
- <h3>Summary of the main points</h3>
-<p>In this article, we have covered:</p>
-<ul>
-<li>What is Need for Speed 2 and what are its features</li>
-<li>How to download Need for Speed 2 APK on your Android device</li>
-<li>Why you should play Need for Speed 2 and what are its benefits and challenges</li>
-<li>Tips and tricks for playing Need for Speed 2 and how to customize your car, win races, and avoid the cops and rivals</li>
-</ul>
- <h3>Call to action</h3>
-<p>If you are ready to download Need for Speed 2 APK and play one of the best racing games ever made, click on one of the links below and start your adventure today:</p>
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-<li>[Need for Speed Online: Mobile Edition]</li>
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- <h4>Frequently Asked Questions</h4>
-<p>Here are some of the most common questions that people ask about Need for Speed 2:</p>
- <ol>
-<li><b>Is Need for Speed 2 free?</b></li>
-<p>Yes, Need for Speed 2 is free to download and play on your Android device using an APK file. However, some websites may require you to complete surveys or offers before downloading the file.</p>
- <li><b>Is Need for Speed 2 safe?</b></li>
-<p>Need for Speed 2 is safe to play on your Android device as long as you download it from a reputable website that does not contain any malware or viruses. However, you should always scan any file before installing it on your device.</p>
- <li><b>Is Need for Speed 2 compatible with my device?</b></li>
-<p>Need for Speed 2 is compatible with most Android devices that have at least 1 GB of RAM and 500 MB of free storage space. However, some devices may experience some compatibility or performance issues due to their hardware or software specifications.</p>
- <li><b>How can I update Need for Speed 2?</b></li>
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- <li><b>How can I contact the developers or publishers of Need for Speed 2?</b></li>
-<p>You can contact the developers or publishers of Need for Speed 2 by visiting their official website or social media pages. However, since Need for Speed 2 is an old game that is no longer supported or updated, you may not receive a response or a solution to your issues.</p>
-</ol>
- <p>I hope you found this article helpful and informative. If you have any questions, comments, or feedback, please feel free to leave them below. Thank you for reading and happy racing!</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/PES 2017 APK The Ultimate Soccer Experience for Android.md b/spaces/congsaPfin/Manga-OCR/logs/PES 2017 APK The Ultimate Soccer Experience for Android.md
deleted file mode 100644
index c4300abec95dcc10febb2f131f48423d5ac409b3..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/PES 2017 APK The Ultimate Soccer Experience for Android.md	
+++ /dev/null
@@ -1,157 +0,0 @@
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-<p>We 2017 APK is an unofficial version of PES 2017, a popular soccer game developed by Konami. PES stands for Pro Evolution Soccer, a series of games that simulates realistic soccer matches with licensed teams, players, stadiums, and leagues. PES 2017 was released in September 2016 for various platforms, including Android.</p>
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-</table>
- <h3>The tips and tricks for We 2017 APK</h3>
- <p>We 2017 APK is a challenging and rewarding game that requires skill and strategy to master. Here are some of the tips and tricks that can help you improve your gameplay and win more matches:</p>
- <ul>
- <li>Practice: The best way to get familiar with the game mechanics, controls, and features is to practice regularly. You can play exhibition matches, training sessions, or tutorials to hone your skills and learn new techniques.</li>
- <li>Choose your team wisely: Different teams have different strengths, weaknesses, styles, and formations. You should choose a team that suits your preference and strategy. You can also customize your team by editing the players, kits, logos, and tactics.</li>
- <li>Use the right pass: Passing is an essential skill in soccer, as it allows you to create chances and score goals. You should use the right type of pass depending on the situation and the position of your players. For example, you can use a through pass to send a player behind the defense, a cross to send a ball into the box, or a back pass to retain possession.</li>
- <li>Shoot wisely: Shooting is another important skill in soccer, as it allows you to score goals and win matches. You should shoot wisely depending on the angle, distance, and pressure from the defenders. For example, you can use a low shot to aim for the corners, a high shot to lob the goalkeeper, or a curling shot to bend the ball around the wall.</li>
- <li>Defend smartly: Defending is also a crucial skill in soccer, as it allows you to prevent goals and win back possession. You should defend smartly depending on the situation and the position of your opponents. For example, you can use a tackle to dispossess an opponent, a slide tackle to block a shot, or a pressure to force an error.</li>
- <li>Use your skills: Skills are special moves that allow you to dribble past defenders, create space, and surprise your opponents. You should use your skills wisely depending on the situation and the position of your players. For example, you can use a feint to fake a direction, a roulette to spin around an opponent, or a rainbow flick to lift the ball over an opponent.</li>
- <li>Play online: Playing online is a great way to test your skills and challenge other players around the world. You can join or create rooms and play matches with different rules and settings. You can also chat with other players and view your online ranking and profile.</li>
- </ul>
- <h2>Conclusion</h2>
- <p>We 2017 APK is an amazing soccer game that offers more features and fun than the original PES 2017 game. You can download and install it on your Android device easily and safely by following our guide. You can also play it on your device with various modes and options to customize your gameplay. We hope you enjoy playing We 2017 APK and share your feedback with us.</p>
- <h3>FAQs</h3>
- <p>Here are some of the frequently asked questions about We 2017 APK:</p>
- <ol>
- <li><b>Is We 2017 APK safe?</b></li>
- <p>We 2017 APK is safe as long as you download it from a trusted source like Filehippo.com. However, you should always scan any file you download with an antivirus software before installing it on your device.</p>
- <li><b>Is We 2017 APK legal?</b></li>
- <p>We 2017 APK is not legal, as it is a modified version of PES 2017, which is a copyrighted game owned by Konami. We 2017 APK violates the terms and conditions of PES 2017 and may infringe the intellectual property rights of Konami. Therefore, we do not endorse or support the use of We 2017 APK and advise you to play the original PES 2017 game instead.</p>
- <li><b>Can I play We 2017 APK offline?</b></li>
- <p>You can play We 2017 APK offline, but you will need an internet connection to download the game files and play online. You will also need an internet connection to update the game and access some features, such as live events, online matches, and social media.</p>
- <li><b>Can I play We 2017 APK on PC?</b></li>
- <p>You can play We 2017 APK on PC, but you will need an Android emulator to run it on your computer. An Android emulator is a software that simulates an Android device on your PC and allows you to run Android apps and games on it. Some of the popular Android emulators are BlueStacks, NoxPlayer, and LDPlayer.</p>
- <li><b>Can I transfer my progress from PES 2017 to We 2017 APK?</b></li>
- <p>You cannot transfer your progress from PES 2017 to We 2017 APK, as they are different games with different data and files. You will need to start from scratch when you play We 2017 APK and create a new profile and account.</p>
- <li><b>Can I use cheats or hacks for We 2017 APK?</b></li>
- <p>You should not use cheats or hacks for We 2017 APK, as they may ruin your gameplay experience and cause problems for your device. Cheats or hacks may also get you banned from playing online or accessing some features of the game. You should play We 2017 APK fairly and honestly and enjoy the game as it is.</p>
- </ol></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Plants vs Zombies 2 on PC How to Experience the Botanical Battle on a Bigger Screen.md b/spaces/congsaPfin/Manga-OCR/logs/Plants vs Zombies 2 on PC How to Experience the Botanical Battle on a Bigger Screen.md
deleted file mode 100644
index 245554ede89b44ff59ec8b5e604ae85aa32527e5..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Plants vs Zombies 2 on PC How to Experience the Botanical Battle on a Bigger Screen.md	
+++ /dev/null
@@ -1,162 +0,0 @@
-<br />
-<h1>How to Download Plants vs Zombies 2 on PC</h1>
-<p>Plants vs Zombies 2 is one of the most popular and addictive mobile games ever made. It is a fun and challenging strategy game where you have to defend your brain from hordes of zombies using various plants with different abilities. But did you know that you can also play Plants vs Zombies 2 on your PC? In this article, we will show you how to download and play Plants vs Zombies 2 on PC using two different methods: Google Play Games and Android emulators. Let's get started!</p>
- <h2>What is Plants vs Zombies 2?</h2>
-<p>Plants vs Zombies 2 is a sequel to the original Plants vs Zombies game that was released in 2009. It was developed by PopCap Games and published by Electronic Arts. It was first released for iOS devices in 2013, followed by Android devices in 2014. Since then, it has received many updates, expansions, and events that have added new plants, zombies, worlds, modes, and features.</p>
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-<p>Plants vs Zombies 2 is an action-strategy game where you have to plant various types of plants in your garden to fend off waves of zombies that are trying to eat your brain. You can collect and upgrade hundreds of plants, each with their own unique abilities and effects. You can also use plant food to boost your plants' power and unleash special attacks. You can also use power-ups, such as pinching, tossing, or zapping zombies, to help you in difficult situations.</p>
-<p>Plants vs Zombies 2 has a rich and diverse gameplay that spans across 11 different worlds, from Ancient Egypt to the Far Future. Each world has its own theme, environment, plants, zombies, challenges, and bosses. You can also play in various modes, such as Adventure, Arena, Penny's Pursuit, Piñata Party, Vasebreaker, and more. You can also compete against other players in Arena and earn coins, piñatas, and other rewards.</p>
- <h2>Why Play Plants vs Zombies 2 on PC?</h2>
-<p>Plants vs Zombies 2 is a great game to play on your mobile device, but it can also be a lot of fun to play it on your PC. Here are some of the benefits of playing Plants vs Zombies 2 on PC:</p>
-<ul>
-<li>You can enjoy the game on a bigger screen with better graphics and resolution.</li>
-<li>You can use your mouse and keyboard to control the game with more precision and speed.</li>
-<li>You can sync your progress and achievements <p>with your Google account across different devices.</li>
-<li>You can save your battery life and data usage on your mobile device.</li>
-<li>You can avoid interruptions from phone calls, messages, notifications, or low battery alerts.</li>
-</ul>
-<p>As you can see, playing Plants vs Zombies 2 on PC can enhance your gaming experience and make it more enjoyable. So, how can you do it? There are two main ways to play Plants vs Zombies 2 on PC: using Google Play Games or using Android emulators. Let's take a look at each of them in detail.</p>
- <h2>How to Play Plants vs Zombies 2 on PC with Google Play Games</h2>
-<p>Google Play Games is an official app from Google that allows you to play Android games on your Windows 11 PC. It is a simple and convenient way to access your favorite games without having to install any additional software or change any settings. Here is how you can use Google Play Games to play Plants vs Zombies 2 on PC:</p>
- <h3>Requirements for Google Play Games</h3>
-<p>Before you can use Google Play Games, you need to make sure that your PC meets the minimum requirements for running the app. According to Google, these are the specifications you need:</p>
-<ul>
-<li>A Windows 11 PC with an Intel Core i5 processor or higher, 8 GB of RAM or higher, and a DirectX 11 compatible GPU.</li>
-<li>A Google account that you use to sign in to Google Play Games.</li>
-<li>A stable internet connection.</li>
-</ul>
- <h3>How to Install Google Play Games</h3>
-<p>Once you have checked that your PC meets the requirements, you can proceed to install Google Play Games on your PC. Here are the steps you need to follow:</p>
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-<li>Go to the <a href="">Microsoft Store</a> and search for Google Play Games. Alternatively, you can use this <a href="">direct link</a> to download the app.</li>
-<li>Click on the Get button and wait for the app to download and install on your PC.</li>
-<li>Launch the app and sign in with your Google account. If you don't have one, you can create one for free.</li>
-<li>Agree to the terms and conditions and grant the necessary permissions for the app to access your data and device features.</li>
-</ol>
- <h3>How to Launch Plants vs Zombies 2 on Google Play Games</h3>
-<p>After you have installed and signed in to Google Play Games, you can start playing Plants vs Zombies 2 on your PC. Here are the steps you need to follow:</p>
-<ol>
-<li>In the Google Play Games app, click on the Library tab and browse through the list of games available for you to play.</li>
-<li>Find Plants vs Zombies 2 and click on the Play button. If you don't see the game, you can use the search bar to look for it.</li>
-<li>The game will start downloading and installing on your PC. This may take some time depending on your internet speed and the size of the game.</li>
-<li>Once the game is installed, you can click on the Play button again to launch it.</li>
-<li>You can now enjoy Plants vs Zombies 2 on your PC with Google Play Games. You can use your mouse and keyboard to control the game, or connect a gamepad if you prefer. You can also adjust the game settings, such as graphics, sound, language, etc., by clicking on the gear icon in the top right corner of the game window.</li>
-</ol>
- <h2>How to Play Plants vs Zombies 2 on PC with Android Emulators</h2>
-<p>If you don't want to use Google Play Games or if your PC doesn't meet the requirements for it, you can still play Plants vs Zombies 2 on PC using Android emulators. Android emulators are software programs that simulate an Android device on your PC, allowing you to run Android apps and games on it. There are many Android emulators available for Windows PCs, each with their own advantages and disadvantages. In this section, we will introduce some of the best Android emulators for Plants vs Zombies 2 and show you how to use them.</p>
- <h3>What are Android Emulators?</h3>
-<p>An Android emulator is a software program that creates a virtual Android device on your PC. It mimics the hardware and software features of an actual Android device, such as CPU, RAM, storage, screen, sensors, camera, etc. It also runs an Android operating system (OS) that allows you to install and run Android apps and games on it. By using an Android emulator, you can enjoy Android games like Plants vs Zombies 2 on your PC with a larger screen and better performance.</p>
- <h3>The Best Android Emulators for Plants vs Zombies 2</h3>
-<p>There are many Android emulators that you can use to play Plants vs Zombies 2 on PC, but not all of them are equally good. Some of them may have compatibility issues, performance problems, security risks, or annoying ads. To help you choose the best Android emulator for Plants vs Zombies 2, we have compared some of the most popular ones and their features. Here are the best Android emulators for Plants vs Zombies 2:</p>
- <table>
-<tr>
-<th>Android Emulator</th>
-<th>Features</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>MEmu</td>
-<td>- Supports Android 7.1 and higher<br>- Has a built-in Google Play Store<br>- Offers multiple instances and key mapping<br>- Supports high FPS and graphics settings<br>- Has a dedicated Plants vs Zombies 2 mode</td>
-<td>- Fast and stable performance<br>- Easy to install and use<br>- Compatible with most games and apps<br>- Customizable and flexible</td>
-<td>- May have some bugs and crashes<br>- May consume a lot of CPU and RAM resources<br>- May have some ads and pop-ups</td>
-</tr>
-<tr>
-<td>Bluestacks</td>
-<td>- Supports Android 7.1 and higher<br>- Has a built-in Google Play Store<br>- Offers multiple instances and key mapping<br>- Supports high FPS and graphics settings<br>- Has a dedicated game center and app center</td>
-<td>- Popular and reliable emulator<br>- Smooth and fast performance<br>- Compatible with most games and apps<br>- Offers many features and options</td>
-<td>- May have some compatibility issues with Windows 11<br>- May consume a lot of CPU and RAM resources<br>- May have some ads and pop-ups</td>
-</tr>
-<tr>
-<td>MSI App Player</td>
-<td>- Supports Android 7.1 and higher<br>- Has a built-in Google Play Store<br>- Offers multiple instances and key mapping<br>- Supports high FPS and graphics settings<br>- Has a dedicated game mode and turbo mode</td>
-<td>- Optimized for MSI devices and hardware<br>- Powerful and smooth performance<br>- Compatible with most games and apps<br>- Offers many features and options</td>
-<td>- May not work well on non-MSI devices<br>- May consume a lot of CPU and RAM resources<br>- May have some ads and pop-ups</td>
-</tr>
-<tr>
-<td>Nox Player</td>
-<td>- Supports Android 7.1 and higher<br>- Has a built-in Google Play Store<br>- Offers multiple instances and key mapping<br>- Supports high FPS and graphics settings<br>- Has a dedicated game mode and macro recorder</td>
-<td>- Lightweight and fast emulator<br>- Easy to install and use<br>- Compatible with most games and apps<br>- Customizable and flexible</td>
-<td>- May have some bugs and crashes<br>- May have some compatibility issues with Windows 11<br>- May have some ads and pop-ups</td>
-</tr>
-<tr>
-<td>Gameloop</td>
-<td>- Supports Android 7.1 and higher<br>- Has a built-in Google Play Store<br>- Offers multiple instances and key mapping<br>- Supports high FPS and graphics settings<br>- Has a dedicated game center and live stream feature</td>
-<td>- Designed for gaming purposes<br>- Smooth and fast performance<br>- Compatible with most games and apps<br>- Offers many features and options</td>
-<td> - May have some bugs and crashes <br> - May consume a lot of CPU and RAM resources<br>- May have some ads and pop-ups</td>
-</tr>
-</table>
- <h3>How to Install and Use Android Emulators</h3>
-<p>After you have chosen an Android emulator that suits your needs and preferences, you can proceed to install and use it to play Plants vs Zombies 2 on PC. Here are the general steps you need to follow:</p>
-<ol>
-<li>Go to the official website of the Android emulator you have selected and download the installer file for your PC.</li>
-<li>Run the installer file and follow the instructions on the screen to install the emulator on your PC.</li>
-<li>Launch the emulator and sign in with your Google account. If you don't have one, you can create one for free.</li>
-<li>Go to the Google Play Store on the emulator and search for Plants vs Zombies 2. Alternatively, you can use this <a href="">direct link</a> to download the game.</li>
-<li>Click on the Install button and wait for the game to download and install on the emulator.</li>
-<li>Once the game is installed, you can click on the Open button or find it on the emulator's home screen or app drawer.</li>
-</ol>
- <h3>How to Launch Plants vs Zombies 2 on Android Emulators</h3>
-<p>After you have installed and launched Plants vs Zombies 2 on your Android emulator, you can start playing it on your PC. Here are the steps you need to follow:</p>
-<ol>
-<li>In the game, sign in with your Google account to sync your progress and achievements across different devices.</li>
-<li>You can use your mouse and keyboard to control the game, or connect a gamepad if you prefer. You can also adjust the key mapping and sensitivity settings by clicking on the keyboard icon or the settings icon on the emulator's toolbar.</li>
-<li>You can also adjust the game settings, such as graphics, sound, language, etc., by clicking on the gear icon in the top right corner of the game screen.</li>
-<li>You can now enjoy Plants vs Zombies 2 on your PC with an Android emulator. You can explore different worlds, collect and upgrade plants, fight zombies, and compete with other players.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Plants vs Zombies 2 is a fun and addictive game that you can play on your mobile device or your PC. In this article, we have shown you how to download and play Plants vs Zombies 2 on PC using two different methods: Google Play Games and Android emulators. Both methods have their own advantages and disadvantages, so you can choose the one that works best for you. We hope that this article has been helpful and informative for you. Now, go ahead and enjoy Plants vs Zombies 2 on your PC!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions and answers about playing Plants vs Zombies 2 on PC:</p>
- <h4>Q: Is Plants vs Zombies 2 free to play?</h4>
-<p>A: Yes, Plants vs Zombies 2 is free to play, but it also offers in-app purchases that can enhance your gameplay or unlock additional content.</p>
- <h4>Q: Can I play Plants vs Zombies 2 offline?</h4>
-<p>A: Yes, you can play Plants vs Zombies 2 offline, but you will need an internet connection to sync your progress, access some features, or participate in some events.</p>
- <h4>Q: Can I play Plants vs Zombies 2 with my friends?</h4>
-<p>A: Yes, you can play Plants vs Zombies 2 with your friends by competing against them in Arena mode or joining them in Penny's Pursuit mode. You can also chat with them in-game or send them gifts.</p>
- <h4>Q: How can I update Plants vs Zombies 2 on PC?</h4>
-<p>A: You can update Plants vs Zombies 2 on PC by going to the Google Play Store on Google Play Games or your Android emulator and checking for updates. Alternatively, you can uninstall and reinstall the game to get the latest version.</p>
- <h4>Q: How can I contact the support team of Plants vs Zombies 2?</h4>
-<p>A: You can contact the support team of Plants vs Zombies 2 by going to the settings menu in the game and clicking on Help & Support. You can also visit their <a href="">official website</a> or their <a href="">Facebook page</a> for more information and assistance.</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Real Drive 5 Mod APK A Game for Car Lovers and Haters.md b/spaces/congsaPfin/Manga-OCR/logs/Real Drive 5 Mod APK A Game for Car Lovers and Haters.md
deleted file mode 100644
index 51a28b22098988e6fe55b675f834e6cf8b552fd4..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Real Drive 5 Mod APK A Game for Car Lovers and Haters.md	
+++ /dev/null
@@ -1,199 +0,0 @@
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-<li>Launch the game and go to the main menu.</li>
-<li>Tap on the settings icon and look for the mod menu option. If you see it, it means that the mod apk is working properly.</li>
-<li>Tap on the mod menu option and explore the features that it offers. You can toggle them on or off according to your preference.</li>
-<li>Go back to the main menu and start playing the game. You should see the effects of the features that you have enabled in the mod menu.</li>
-</ul>
-<p>If you do not see the mod menu option or any of the features that it offers, it means that the mod apk is not working properly. There could be several reasons for this, such as:</p>
-<ul>
-<li>You have downloaded an outdated or fake version of the mod apk.</li>
-<li>You have installed the mod apk incorrectly or incompletely.</li>
-<li>You have not enabled unknown sources on your device settings.</li>
-<li>You have a conflicting app or software on your device that interferes with the mod apk.</li>
-</ul>
-<p>If you encounter any of these problems, you need to uninstall the mod apk and try again with a different source or method. Alternatively, you can contact the developer of the mod apk or visit their website for more information and support.</p>
- <h2>What Features Does Real Drive 5 Mod APK Offer</h2> <p>Real Drive 5 mod apk offers a variety of features that can make your gaming experience more enjoyable and exciting. Some of the features that the mod apk provides are:</p>
-<ul>
-<li><strong>Unlimited money</strong>: You can get unlimited money in the game, which you can use to buy and upgrade your cars, skills, and accessories. You can also unlock all the cars and tracks without spending any real money.</li>
-<li><strong>Unlocked cars</strong>: You can access all the cars in the game, including the premium and exclusive ones. You can choose from over 100 different cars, each with its own specifications, performance, and design. You can also customize your cars with various colors, decals, wheels, and spoilers.</li>
-<li><strong>No ads</strong>: You can play the game without any interruptions or distractions from annoying ads. You can enjoy the game without any pop-ups, banners, or videos that may slow down your device or consume your data.</li>
-<li><strong>No root required</strong>: You do not need to root your device to use the mod apk. You can install and play the game without any risk of damaging your device or voiding your warranty.</li>
-<li><strong>Easy to use</strong>: You do not need any special skills or knowledge to use the mod apk. You just need to follow the simple steps that we have provided above to download and install the mod apk. You can also easily access and manage the features that the mod apk offers through the mod menu option in the game settings.</li>
-</ul>
-<p>These are some of the features that Real Drive 5 mod apk offers, but there may be more depending on the version and source of the mod apk. To compare the mod apk features with the original game features, you can refer to the table below:</p>
- <table>
-<tr>
-<th>Feature</th>
-<th>Original Game</th>
-<th>Mod APK</th>
-</tr>
-<tr>
-<td>Money</td>
-<td>Limited</td>
-<td>Unlimited</td>
-</tr>
-<tr>
-<td>Cars</td>
-<td>Locked</td>
-<td>Unlocked</td>
-</tr>
-<tr>
-<td>Ads</td>
-<td>Present</td>
-<td>Absent</td>
-</tr>
-<tr>
-<td>Root</td>
-<td>Not required</td>
-<td>Not required</td>
-</tr>
-<tr>
-<td>Mod menu</td>
-<td>Absent</td>
-<td>Present</td>
-</tr>
-</table>
- <p>As you can see, the mod apk offers more advantages than the original game, but it also has some disadvantages. Some of the pros and cons of using the mod apk are:</p>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>More fun and freedom in the game.</td>
-<td>Possible malware or virus infection.</td>
-</tr>
-<tr>
-<td>More options and customization for your cars.</td>
-<td>Possible compatibility or performance issues.</td>
-</tr <tr <td>No need to spend real money or watch ads.</td <td>Possible ban from the game server or account suspension.</td /tr /tr <tr <td>No need to root your device or risk damaging it.</td <td>Possible legal or ethical issues.</td /tr /tr <table <p>You should weigh these pros and cons carefully before deciding whether to use Real Drive 5 mod apk or not. Ultimately, it depends on your personal preferences and goals as a gamer.</p>
- <h2>How to Play Real Drive 5 Mod APK</h2> <p>Playing Real Drive 5 mod apk is similar to playing the original game, but with more features and options. You can enjoy the realistic and immersive driving simulator on your mobile device, with over 100 different cars, various tracks and terrains, stunning graphics and physics, and online multiplayer mode.</p>
-<p>To play Real Drive 5 mod apk, you need to follow these steps:</p>
-<ol>
-<li>Launch the game and choose your preferred game mode. You can choose from career mode, free ride mode, or online mode.</li>
-<li>Select your car from the garage. You can choose from over 100 different cars, each with its own specifications, performance, and design. You can also customize your car with various colors, decals, wheels, and spoilers.</li>
-<li>Select your track from the map. You can choose from various tracks and terrains, such as city, desert, mountain, snow, etc. You can also adjust the weather, time of day, and traffic conditions.</li>
-<li>Start driving your car and enjoy the game. You can use the on-screen buttons or tilt your device to control your car. You can also use the brake, nitro, horn, and camera buttons to enhance your driving experience.</li>
-</ol>
-<p>Some tips and tricks for playing Real Drive 5 mod apk effectively are:</p>
-<ul>
-<li>Use the nitro button to boost your speed and overtake your opponents or escape from the police.</li>
-<li>Use the brake button to drift around corners and avoid collisions.</li>
-<li>Use the horn button to honk at other cars or pedestrians and make them move out of your way.</li>
-<li>Use the camera button to change your view and perspective. You can choose from first-person, third-person, or top-down view.</li>
-<li>Complete missions and challenges in career mode to earn money and unlock new cars and tracks.</li>
-<li>Compete with other players online in online mode to rank up and win rewards.</li>
-<li>Upgrade your skills and accessories in the shop to improve your performance and style.</li>
-</ul>
-<p>Some challenges and achievements that you can complete in Real Drive 5 mod apk are:</p>
-<ul>
-<li>Drive for a certain distance or time without crashing or stopping.</li>
-<li>Reach a certain speed or acceleration level.</li>
-<li>Perform a certain number of drifts or stunts.</li>
-<li>Win a certain number of races or events.</li>
-<li>Collect a certain number of coins or stars.</li>
-</ul>
- <h2>How to Update Real Drive 5 Mod APK</h2> <p>To keep your Real Drive 5 mod apk updated and working properly, you need to check for updates and download the latest version of the mod apk from time to time. The developer of the mod apk may release new versions of the mod apk to fix bugs, improve performance, add new features, or comply with the original game updates.</p>
-<p>To check for updates and download the latest version of the mod apk, you need to follow these steps:</p>
-<ol>
-<li>Go to the website where you downloaded the mod apk and look for the latest version of the mod apk. You can also check the developer's website or social media for any announcements or news about the mod apk updates.</li>
-<li>Compare the version number of the mod apk that you have installed on your device with the version number of the mod apk that is available on the website. If they are different, it means that there is a new version of the mod apk that you need to download.</li>
-<li>Before downloading the new version of the mod apk, you need to backup your data and uninstall the old version of the mod apk. To backup your data, you can use a file manager app or a cloud service to copy and save your game data folder. To uninstall the old version of the mod apk, you can go to your device settings and tap on apps or applications. Find Real Drive 5 mod apk and tap on uninstall.</li>
-<li>After backing up your data and uninstalling the old version of the mod apk, you can download and install the new version of the mod apk following the same steps that we have provided above.</li>
-<li>After installing the new version of the mod apk, you can restore your data by copying and pasting your game data folder back to its original location. You can also use a file manager app or a cloud service to do this.</li>
-</ol>
-<p>That's it! You have successfully updated your Real Drive 5 mod apk to the latest version. Now you can enjoy the game with all the new features and improvements that the mod apk offers.</p>
- <h2>Conclusion</h2>
-<p>In this article, we have guided you through everything you need to know about Real Drive 5 mod apk, including how to download and install it, what features it offers, how to play it, how to update it, and some FAQs. We hope that this article has been helpful and informative for you.</p>
-<p>Real Drive 5 mod apk is a great option for car racing enthusiasts who want to enjoy a realistic and immersive driving simulator on their mobile devices, with more features and options than the original game. However, Real Drive 5 mod apk also comes with some risks and drawbacks that you should be aware of before using it.</p>
-<p>Ultimately, whether you use Real Drive 5 mod apk or not depends on your personal preferences and goals as a gamer. If you are looking for more fun and freedom in the game, Real Drive 5 mod apk may be a good choice for you. But if you are concerned about security, compatibility, or legality issues, Real Drive 5 mod apk may not be worth it for you.</p>
-<p>The decision is yours. But whatever you choose, we hope that you have a great time playing Real Drive 5!</p>
-<p>If you have any feedback or questions about Real Drive 5 mod apk or this article, please feel free to leave a comment below or contact us through our website. We would love to hear from you!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Real Drive 5 mod apk with brief answers:</p>
-<ol>
-<li><strong>Is Real Drive 5 mod apk safe?</strong><br>
-Real Drive 5 mod apk is not completely safe, as it may contain malware or viruses that can harm your device or steal your personal information. It may also cause compatibility or performance issues with your device or the original game. It may also get you banned from the game server or account suspension if detected by the game developer. Therefore, use Real Drive 5 mod apk at your own risk.</li>
-<li><strong>Is Real Drive 5 mod apk legal?</strong><br>
-Real Drive 5 mod apk is not legal, as it violates the terms and conditions of the original game developer and Google Play Store. It also infringes on the intellectual property rights of the original game developer and other parties involved in creating the game. Therefore, use Real Drive 5 mod apk at your own risk.</li>
-<li><strong>Is Real Drive 5 mod apk free?</strong><br>
-Real Drive 5 mod apk is free to download and use, but it may not be free from other costs. For example, you may need to pay for data usage or internet connection fees when downloading or playing the game. You may also need to pay for repairing or replacing your device if damaged by malware or viruses from the mod apk. <li><strong>Can I use Real Drive 5 mod apk on iOS devices?</strong><br>
-Real Drive 5 mod apk is only compatible with Android devices, as it is an apk file that can only be installed on Android operating systems. If you want to use Real Drive 5 mod apk on iOS devices, you need to use an Android emulator or a jailbroken device, which may not be easy or safe to do.</li>
-<li><strong>Can I play Real Drive 5 mod apk offline?</strong><br>
-Real Drive 5 mod apk can be played offline, as it does not require an internet connection to run. However, some features of the game, such as online multiplayer mode, may not be available or functional offline. You may also miss out on some updates or events that the game developer may release online.</li>
-<li><strong>Can I play Real Drive 5 mod apk with my friends?</strong><br>
-Real Drive 5 mod apk can be played with your friends, as it supports online multiplayer mode. You can join or create a room and invite your friends to play with you. You can also chat with your friends and other players in the game. However, you may face some issues or difficulties when playing with your friends, such as:</li>
-<ul>
-<li>Your friends may not have the same version of the mod apk as you, which may cause compatibility or synchronization problems.</li>
-<li>Your friends may not have the mod apk at all, which may make them unable to join your room or see your features.</li>
-<li>Your friends may have a different mod apk from a different source or developer, which may cause conflicts or errors in the game.</li>
-<li>Your friends may report you or expose you for using the mod apk, which may get you banned from the game server or account suspension.</li>
-</ul>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
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-<br />
-<h1>How to Use Instagram on Windows 7</h1>
-<p>Instagram is one of the most popular social media platforms in the world, with over one billion monthly active users. It allows you to share photos and videos, watch stories and reels, chat with friends, explore new content, shop online, and more. However, Instagram is primarily designed as a mobile app, which means that you need a smartphone or tablet to enjoy all its features.</p>
-<p>But what if you don't have a mobile device, or you prefer to use your computer for browsing social media? Is there a way to use Instagram on Windows 7, which is still one of the most widely used operating systems in the world? The answer is yes, there are several ways to use Instagram on Windows 7, each with its own advantages and disadvantages. In this article, we will show you how to download Instagram from the Microsoft Store, use an Android emulator, or access Instagram.com on a web browser. We will also give you some tips and tricks for using Instagram on Windows 7 more effectively.</p>
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-<h2>Method 1: Download Instagram from the Microsoft Store</h2>
-<p>One of the easiest ways to use Instagram on Windows 7 is to download and install the official Instagram app from the Microsoft Store. This app is free and allows you to do most of the things you can do on your mobile device, such as:</p>
-<ul>
-<li>Post photos and videos to your feed or stories</li>
-<li>Message your friends with Messenger</li>
-<li>Watch videos from your favorite creators and discover new content through Instagram video</li>
-<li>Be inspired by photos and videos from new accounts in Explore</li>
-<li>Shop what you love from your favorite brands and creators</li>
-</ul>
-<p>To download and install the Instagram app from the Microsoft Store, follow these steps:</p>
-<ol>
-<li>Open your web browser and go to <a href="(^1^)">https://apps.microsoft.com/store/detail/instagram/9NBLGGH5L9XT</a></li>
-<li>Click <strong>Get in Store </strong> to open the Microsoft Store app on your computer</li>
-<li>Click <strong>Get</strong> to download and install the Instagram app on your computer</li>
-<li>Once the installation is complete, click <strong>Launch</strong> to open the Instagram app on your computer</li>
-<li>Sign in with your Instagram account or create a new one</li>
-<li>Enjoy using Instagram on Windows 7!</li>
-</ol>
-<p>Here are some screenshots of the Instagram app on Windows 7:</p>
-<img src="" alt="Instagram app on Windows 7" width="600" height="400">
-<img src="" alt="Instagram feed on Windows 7" width="600" height="400">
-<img src="" alt="Instagram video on Windows 7" width="600" height="400">
-<p>The Instagram app from the Microsoft Store has some benefits and limitations when compared to the mobile version. Here are some of them:</p>
-<table>
-<tr>
-<th>Benefits</th>
-<th>Limitations</th>
-</tr>
-<tr>
-<td>- You can use your keyboard and mouse to navigate and type faster</td>
-<td>- You cannot post photos or videos from your computer directly, unless you use a workaround (see tips and tricks below)</td>
-</tr>
-<tr>
-<td>- You can view photos and videos on a larger screen with better resolution</td>
-<td>- You cannot use Instagram Reels, which are short-form videos with music and effects</td>
-</tr>
-<tr>
-<td>- You can switch between multiple accounts easily</td>
-<td>- You cannot use filters or stickers when posting stories or messages</td>
-</tr>
-<tr>
-<td>- You can receive notifications on your desktop when someone likes, comments, or messages you</td>
-<td>- You cannot make video calls or voice calls with Messenger</td>
-</tr>
-<tr>
-<td>- You can use the dark mode to reduce eye strain and save battery life</td>
-<td>- You cannot edit your profile or change your settings from the app</td>
-</tr>
-</table>
- <h2>Method 2: Use an Android Emulator</h2>
- <p>Another way to use Instagram on Windows 7 is to use an Android emulator, which is a software that mimics the Android operating system on your computer. This way, you can run any Android app on your computer, including Instagram. An Android emulator can give you the full Instagram experience, as if you were using it on your smartphone or tablet.</p>
- <p>There are many Android emulators available online, but not all of them are safe and reliable. Some of them may contain malware, spyware, or adware that can harm your computer or compromise your privacy. Therefore, you should be careful when choosing an emulator and only download it from a trusted source. One of the most popular and reputable emulators is BlueStacks, which has been downloaded by over 500 million users worldwide.</p>
- <p>To use BlueStacks and Instagram on Windows 7, follow these steps:</p>
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- <ol>
- <li>Open your web browser and go to <a href="">https://www.bluestacks.com/download.html</a></li>
- <li>Click <strong>Download BlueStacks <p>Another benefit of using an Android emulator is that you can use Instagram keyboard shortcuts to navigate and perform actions faster. Here are some of the keyboard shortcuts you can use on BlueStacks:</p>
-<ul>
-<li>Ctrl + Left arrow or Right arrow – Switch between tabs (Home, Explore, Reels, Shop, Profile)</li>
-<li>Ctrl + Up arrow or Down arrow – Scroll up or down the feed</li>
-<li>Ctrl + L – Like or unlike the selected post</li>
-<li>Ctrl + C – Comment on the selected post</li>
-<li>Ctrl + S – Share the selected post</li>
-<li>Ctrl + M – Open or close Messenger</li>
-<li>Ctrl + N – Create a new message</li>
-<li>Ctrl + R – Record a voice message</li>
-<li>Ctrl + P – Post a new photo or video</li>
-<li>Ctrl + T – Post a new story</li>
-<li>Ctrl + E – Edit your profile</li>
-<li>Ctrl + F – Search for users, hashtags, or places</li>
-<li>Ctrl + H – Open or close the settings menu</li>
-</ul>
-<p>You can also customize the keyboard shortcuts by clicking the keyboard icon on the right side of the BlueStacks window. For more information, check out this [guide](^6^) on how to use keyboard shortcuts on BlueStacks.</p>
- <h2>Method 3: Use Instagram.com on a Web Browser</h2>
- <p>The third way to use Instagram on Windows 7 is to simply visit Instagram.com on a web browser, such as Chrome, Firefox, or Safari. This is the most convenient method as it does not require any downloads or installations. However, it is also the most limited method as it does not offer all the features and functions of the mobile app or the emulator.</p>
- <p>To use Instagram.com on a web browser, follow these steps:</p>
- <ol>
-<li>Open your web browser and go to [15](https://www.instagram.com).</li>
-<li>Sign in with your Instagram account or create a new one.</li>
-<li>Browse your feed, watch stories and reels, explore new content, shop online, and more.</li>
-</ol>
- <p>Here are some screenshots of Instagram.com on a web browser:</p>
- <img src="" alt="Instagram.com on a web browser" width="600" height="400">
-<img src="" alt="Instagram stories on a web browser" width="600" height="400">
-<img src="" alt="Instagram reels on a web browser" width="600" height="400">
- <p>The Instagram website has some benefits and limitations when compared to the mobile app or the emulator. Here are some of them:</p>
- <table>
-<tr>
-<th>Benefits</th>
-<th>Limitations</th>
-</tr>
-<tr>
-<td>- You can use any web browser you prefer and switch between devices easily</td>
-<td>- You cannot post photos or videos from your computer directly, unless you use a workaround (see tips and tricks below)</td>
-</tr>
-<tr>
-<td>- You can view photos and videos on a larger screen with better resolution</td>
-<td>- You cannot use Instagram Reels, which are short-form videos with music and effects</td>
-</tr>
-<tr>
-<td>- You can send and receive messages via Instagram Direct on the web</td>
-<td>- You cannot use filters or stickers when posting stories or messages</td> </tr>
-<tr>
-<td>- You can make video calls or voice calls with Messenger on the web</td>
-<td>- You cannot make live videos or join live rooms</td>
-</tr>
-<tr>
-<td>- You can edit your profile or change your settings from the website</td>
-<td>- You cannot switch between multiple accounts easily</td>
-</tr>
-<tr>
-<td>- You can use the dark mode to reduce eye strain and save battery life</td>
-<td>- You cannot receive notifications on your desktop when someone likes, comments, or messages you</td>
-</tr>
-</table>
- <h2>Tips and Tricks for Using Instagram on Windows 7</h2>
- <p>Now that you know how to use Instagram on Windows 7, you may want to learn some tips and tricks for enhancing your Instagram experience on your computer. Here are some of them:</p>
- <h3>How to post photos and stories from your computer using a workaround</h3>
- <p>One of the biggest drawbacks of using Instagram on Windows 7 is that you cannot post photos or videos from your computer directly, unless you use a workaround. The workaround involves changing the user agent of your web browser, which is a string that identifies your browser to the websites you visit. By changing the user agent, you can trick Instagram into thinking that you are using a mobile device, and thus enable the option to post photos or stories from your computer.</p>
- <p>To change the user agent of your web browser, follow these steps:</p>
- <ol>
-<li>Open your web browser and go to [15](https://www.instagram.com).</li>
-<li>Sign in with your Instagram account.</li>
-<li>Right-click anywhere on the page and select <strong>Inspect</strong> or <strong>Inspect Element</strong>.</li>
-<li>A panel will open on the right side or the bottom of the page. Click the icon that looks like a tablet and a smartphone, which is called <strong>Toggle Device Toolbar</strong>.</li>
-<li>A menu will appear on the top of the page. Select a mobile device model from the drop-down list, such as iPhone X or Pixel 2.</li>
-<li>Refresh the page by pressing F5 or clicking the refresh button.</li>
-<li>You should see a plus (+) icon at the bottom of the page. Click it to post a photo or a story from your computer.</li>
-<li>To revert back to the normal mode, click the <strong>Toggle Device Toolbar</strong> icon again and refresh the page.</li>
-</ol>
- <p>Here are some screenshots of changing the user agent of Chrome on Windows 7:</p>
- <img src="" alt="Changing user agent on Chrome" width="600" height="400">
-<img src="" alt="Posting photo from computer" width="600" height="400">
-<img src="" alt="Posting story from computer" width="600" height="400">
- <h3>How to send messages via Instagram Direct on the web</h3>
- <p>Another feature that is available on Instagram.com is Instagram Direct, which allows you to send and receive messages with your friends and followers. You can also make video calls or voice calls with Messenger on the web. To use Instagram Direct on the web, follow these steps:</p>
- <ol>
-<li>Open your web browser and go to [15](https://www.instagram.com).</li>
-<li>Sign in with your Instagram account.</li>
-<li>You should see a paper plane icon at the top right corner of the page. Click it to open Instagram Direct.</li>
-<li>You should see a list of your conversations on the left side of the page. Click any conversation to view or reply to it.</li>
-<li>To start a new conversation, click the <strong>New Message</strong> button at the top right corner of the page.</li>
-<li>Type in the name or username of the person you want to message and click <strong>Next</strong>.</li>
-<li>Type in your message and click <strong>Send</strong>.</li>
-<li>To make a video call or voice call, click the video camera icon or phone icon at the top right corner of the conversation.</li>
-</ol>
- <p>Here are some screenshots of using Instagram Direct on the web:</p>
- <img src="" alt="Instagram Direct on the web" width="600" height="400">
-<img src="" alt="New message on Instagram Direct" width="600" height="400">
-<img src="" alt="Video call on Instagram Direct" width="600" height="400">
- <h3>How to browse Instagram on your desktop with a two-column layout</h3>
- <p>If you want to browse Instagram on your desktop with a two-column layout, similar to how it looks on an iPad, you can use an extension called IG Stories for Instagram. This extension is available for Chrome and Firefox, and it allows you to view stories and reels in full screen, download photos and videos, watch live videos without interruptions, and more. To use IG Stories for Instagram, follow these steps:</p>
- <ol>
-<li>Open your web browser and go to the extension page. For Chrome, go to [14](https://chrome.google.com/webstore/detail/ig-stories-for-instagram/hbddbclendfjenhcpcjfnhbkjgjblhcg). For Firefox, go to [13](https://addons.mozilla.org/en-US/firefox/addon/ig-stories-for-instagram/).</li>
-<li>Click <strong>Add to Chrome</strong> or <strong>Add to Firefox</strong> to install the extension on your browser.</li>
-<li>Once the installation is complete, you should see a new icon on the top right corner of your browser. Click it to open the extension settings.</li>
-<li>Enable the option <strong>Two column layout</strong> to activate the two-column layout on Instagram.com.</li>
-<li>Open a new tab and go to [15](https://www.instagram.com).</li>
-<li>You should see a two-column layout on Instagram.com, with your feed on the left and stories and reels on the right.</li>
-<li>You can also use the other features of the extension, such as downloading photos and videos, watching live videos, viewing stories and reels in full screen, and more.</li>
-</ol>
- <p>Here are some screenshots of using IG Stories for Instagram on Chrome:</p>
- <img src="" alt="IG Stories for Instagram extension" width="600" height="400">
-<img src="" alt="Two-column layout on Instagram.com" width="600" height="400">
-<img src="" alt="Downloading photo from Instagram.com" width="600" height="400">
- <h3>How to filter comments by keyword or hide them altogether</h3>
- <p>If you want to filter comments by keyword or hide them altogether on Instagram.com, you can use an extension called CommentBlocker. This extension is available for Chrome and Firefox, and it allows you to block comments that contain specific words or phrases, or disable comments entirely on any website. This can help you avoid unwanted or offensive comments on your posts or other people's posts. To use CommentBlocker, follow these steps:</p>
- <ol>
-<li>Open your web browser and go to the extension page. For Chrome, go to [12](https://chrome.google.com/webstore/detail/commentblocker/hgcbcffnmpfgkebcbbcoakncanhbjaoi). For Firefox, go to [11](https://addons.mozilla.org/en-US/firefox/addon/commentblocker/).</li>
-<li>Click <strong>Add to Chrome</strong> or <strong>Add to Firefox</strong> to install the extension on your browser.</li>
-<li>Once the installation is complete, you should see a new icon on the top right corner of your browser. Click it to open the extension settings.</li>
-<li>To filter comments by keyword, click <strong>Add new word</strong> and type in the word or phrase you want to block. You can also use regular expressions for more advanced filtering. Click <strong>Save</strong> when you are done.</li>
-<li>To hide comments altogether, click <strong>Disable comments</strong> and select <strong>All websites</strong> or <strong>This website only</strong>. You can also whitelist some websites that you want to allow comments on.</li>
-<li>Open a new tab and go to [15](https://www.instagram.com).</li>
-<li>You should see that the comments that contain your blocked keywords are hidden or replaced with asterisks. You can also see how many comments are blocked by hovering over the extension icon.</li>
-<li>If you disabled comments altogether, you should see that all the comments are hidden on Instagram.com or any other website you visit.</li>
-</ol>
- <p>Here are some screenshots of using CommentBlocker on Chrome:</p>
- <img src="" alt="CommentBlocker extension" width="600" height="400">
-<img src="" alt="Filtering comments by keyword" width="600" height="400">
-<img src="" alt="Hiding comments altogether" width="600" height="400">
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to use Instagram on Windows 7 using three different methods: downloading Instagram from the Microsoft Store, using an Android emulator, or accessing Instagram.com on a web browser. We have also given you some tips and tricks for enhancing your Instagram experience on Windows 7, such as posting photos and stories from your computer, sending messages via Instagram Direct on the web, browsing Instagram with a two-column layout, and filtering or hiding comments. 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. Thank you for reading!</p>
- <h2>FAQs</h2>
- <h3>Is Instagram compatible <h3>Is Instagram compatible with Windows 7?</h3>
-<p>Yes, Instagram is compatible with Windows 7, but not with all its features and functions. You can use Instagram on Windows 7 by downloading the app from the Microsoft Store, using an Android emulator, or accessing Instagram.com on a web browser. However, each method has some benefits and limitations, as explained in this article.</p>
- <h3>Can I use Instagram Reels on Windows 7?</h3>
-<p>No, you cannot use Instagram Reels on Windows 7, as this feature is only available on the mobile app. Instagram Reels are short-form videos with music and effects that you can create and watch on Instagram. If you want to use Instagram Reels, you need to use a smartphone or tablet with the latest version of the Instagram app.</p>
- <h3>How can I update Instagram on Windows 7?</h3>
-<p>If you are using the Instagram app from the Microsoft Store, you can update it by following these steps:</p>
-<ol>
-<li>Open the Microsoft Store app on your computer</li>
-<li>Click the three horizontal dots icon at the top right corner of the app</li>
-<li>Select <strong>Downloads and updates</strong> from the menu</li>
-<li>Click <strong>Get updates</strong> to check for available updates</li>
-<li>If there is an update for Instagram, click <strong>Update</strong> to download and install it</li>
-</ol>
-<p>If you are using an Android emulator, you can update Instagram by following these steps:</p>
-<ol>
-<li>Open the emulator and launch the Google Play Store app</li>
-<li>Search for Instagram and tap on it</li>
-<li>If there is an update available, tap <strong>Update</strong> to download and install it</li>
-</ol>
-<p>If you are using Instagram.com on a web browser, you do not need to update it manually, as it will automatically update itself whenever there is a new version.</p>
- <h3>How can I delete my Instagram account on Windows 7?</h3>
-<p>If you want to delete your Instagram account on Windows 7, you need to use a web browser, as this option is not available on the app or the emulator. To delete your Instagram account on Windows 7, follow these steps:</p>
-<ol>
-<li>Open your web browser and go to [15](https://www.instagram.com).</li>
-<li>Sign in with your Instagram account.</li>
-<li>Click your profile icon at the top right corner of the page and select <strong>Settings</strong>.</li>
-<li>Select <strong>Edit Profile</strong>.</li>
-<li>Scroll down to the bottom of the page and click <strong>Temporarily disable my account</strong>.</li>
-<li>Select a reason for disabling your account and enter your password.</li>
-<li>Click <strong>Temporarily Disable Account</strong>.</li>
-<li>If you want to permanently delete your account, go to [10](https://www.instagram.com/accounts/remove/request/permanent/).</li>
-<li>Select a reason for deleting your account and enter your password.</li>
-<li>Click <strong>Permanently delete my account</strong>.</li>
-</ol>
- <h3>How can I contact Instagram support on Windows 7?</h3>
-<p>If you need to contact Instagram support on Windows 7, you can use one of these methods:</p>
- <ul>
- <li>Email: You can email Instagram support at [9](mailto:support@instagram.com) and explain your issue or request. However, this method may not be very effective or responsive, as Instagram receives a lot of emails every day.</li>
- <li>Help Center: You can visit the Instagram Help Center at [8](https://help.instagram.com) and browse through various topics and articles that may answer your questions or solve your problems. You can also use the search bar to find specific information or guidance.</li>
- <li>Report a Problem: You can report a problem or give feedback to Instagram through the app or the website. To do this, follow these steps:</p>
- <ol>
- <li>Open the Instagram app or website and sign in with your account.</li>
- <li>Go to your profile and tap or click the three horizontal lines icon at the top right corner of the screen.</li>
- <li>Select <strong>Settings</strong>.</li>
- <li>Select <strong>Help</strong>.</li>
- <li>Select <strong>Report a Problem</strong>.</li>
- <li>Select <strong>Something Isn't Working</strong>, <strong>I Have a Question About Privacy</strong>, or <strong>I Have Feedback or a Feature Request</strong>, depending on your issue or request.</li>
- <li>Type in your message and attach any screenshots or files if needed.</li>
- <li>Tap or click <strong>Send</strong>.</li>
- </ol>
- <p <p>Instagram support will review your report and get back to you as soon as possible.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Watch Download and Share Videos with YouTube APK 4.4.4 from Uptodown.md b/spaces/congsaPfin/Manga-OCR/logs/Watch Download and Share Videos with YouTube APK 4.4.4 from Uptodown.md
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--- a/spaces/congsaPfin/Manga-OCR/logs/Watch Download and Share Videos with YouTube APK 4.4.4 from Uptodown.md	
+++ /dev/null
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-
-<h1>YouTube APK 4.4.4 Uptodown: How to Download and Install the Latest Version of YouTube for Android</h1>
-<p>If you are a fan of watching videos online, you probably know about YouTube, the world's largest and most popular video platform. YouTube is home to millions and millions of videos, with hundreds of thousands of new clips uploaded every day. You can find videos on any topic, from music and entertainment to education and news.</p>
-<h2>youtube apk 4.4.4 uptodown</h2><br /><p><b><b>Download File</b> - <a href="https://urlca.com/2uOg07">https://urlca.com/2uOg07</a></b></p><br /><br />
-<p>But did you know that you can also download and install the latest version of YouTube for Android on your device? And that you can do it for free from Uptodown, one of the best websites for downloading apps and games? In this article, we will show you how to download and install YouTube APK 4.4.4 Uptodown, the latest version of YouTube for Android, and how to use it to enjoy all the features and benefits of YouTube on your smartphone or tablet.</p>
-<h2>What is YouTube APK 4.4.4 Uptodown?</h2>
-<h3>A brief introduction to YouTube and its features</h3>
-<p>YouTube is the official app for the world's largest and most popular video platform. It allows you to access all the featured videos for each day, as well as search for videos that interest you by keywords, categories, or channels.</p>
-<p>With YouTube, you can also watch live streams, create playlists, subscribe to your favorite channels, comment on videos, share videos with your friends, and more.</p>
-<p>One of the most interesting features of YouTube is that you can play videos in the background while browsing other apps or sites. That way, you can listen to music, podcasts, or audiobooks without interrupting your other tasks.</p>
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-<p>You can also cast videos from your Android device to your TV by using a Chromecast device or a smart TV that supports it.</p>
-<h3>The benefits of downloading YouTube APK from Uptodown</h3>
-<p>Uptodown is one of the best websites for downloading apps and games for Android devices. It offers a huge catalog of apps and games, including the latest versions of popular apps like YouTube.</p>
-<p>By downloading YouTube APK from Uptodown, you can enjoy several benefits:</p>
-<ul>
-<li>You can get the latest version of YouTube for Android without waiting for it to be available on Google Play Store.</li>
-<li>You can download YouTube APK without any restrictions or limitations, regardless of your location or device.</li>
-<li>You can download YouTube APK without any viruses or malware, as Uptodown scans all its files with multiple antivirus engines.</li>
-<li>You can download YouTube APK without any registration or login required.</li>
-<li>You can download YouTube APK without any ads or pop-ups.</li>
-</ul>
-<h3>The differences between YouTube APK 4.4.4 and other versions</h3>
-<p>YouTube APK 4.4.4 is the latest version of YouTube for Android devices. It has some new features and improvements over the previous versions of YouTube. Here are some of the differences between YouTube APK 4.4.4 and other versions:</p>
-<ul>
-<li>YouTube APK 4.4.4 has a new design and layout, with a more intuitive and user-friendly interface.</li>
-<li>YouTube APK 4.4.4 has a new video player, with more options and controls, such as speed, quality, captions, and fullscreen mode.</li>
-<li>YouTube APK 4.4.4 has a new feature called YouTube Shorts, which allows you to watch and create short videos of up to 60 seconds, similar to TikTok or Instagram Reels.</li>
-<li>YouTube APK 4.4.4 has a new feature called YouTube Music, which allows you to access and stream music videos and songs from YouTube, as well as create your own playlists and discover new artists.</li>
-<li>YouTube APK 4.4.4 has a new feature called YouTube Premium, which allows you to enjoy YouTube without ads, download videos for offline viewing, and access exclusive content and features.</li>
-</ul>
-<h2>How to download YouTube APK 4.4.4 Uptodown?</h2>
-<h3>The steps to download YouTube APK from Uptodown website</h3>
-<p>Downloading YouTube APK from Uptodown website is very easy and fast. Just follow these simple steps:</p>
-<ol>
-<li>Open your web browser and go to the Uptodown website: https://youtube.en.uptodown.com/android</li>
-<li>Scroll down until you see the green button that says "Download". Click on it.</li>
-<li>A pop-up window will appear, asking you to choose the version of YouTube APK that you want to download. Select the latest version, which is 4.4.4.</li>
-<li>The download will start automatically. Wait until it is completed.</li>
-<li>You will see a notification that says "Download complete". Tap on it to open the file.</li>
-</ol>
-<h3>The steps to install YouTube APK on your Android device</h3>
-<p>Installing YouTube APK on your Android device is also very easy and fast. Just follow these simple steps:</p>
-<ol>
-<li>Before you install YouTube APK, you need to enable the option that allows you to install apps from unknown sources on your device. To do this, go to Settings > Security > Unknown sources and toggle it on.</li>
-<li>Once you have enabled this option, go back to the file that you downloaded from Uptodown website and tap on it.</li>
-<li>A pop-up window will appear, asking you to confirm the installation of YouTube APK. Tap on "Install".</li>
-<li>The installation will start automatically. Wait until it is completed.</li>
-<li>You will see a notification that says "App installed". Tap on "Open" to launch YouTube APK on your device.</li>
-</ol> <h3>The tips to avoid common errors and issues during the download and installation process</h3>
-<p>Although downloading and installing YouTube APK from Uptodown website is usually smooth and easy, you may encounter some errors and issues during the process. Here are some tips to avoid them:</p>
-<ul>
-<li>Make sure that you have enough storage space on your device before downloading YouTube APK. The file size of YouTube APK 4.4.4 is about 40 MB, so you need at least that much free space on your device.</li>
-<li>Make sure that you have a stable and fast internet connection before downloading YouTube APK. If your connection is slow or unstable, the download may take longer or fail.</li>
-<li>Make sure that you download YouTube APK from the official Uptodown website, and not from any other sources. Some websites may offer fake or modified versions of YouTube APK that may contain viruses or malware, or may not work properly.</li>
-<li>Make sure that you enable the option to install apps from unknown sources on your device before installing YouTube APK. If you don't, you will not be able to install YouTube APK on your device.</li>
-<li>Make sure that you follow the steps to install YouTube APK correctly and carefully. If you skip or miss any step, the installation may fail or cause problems.</li>
-</ul>
-<h2>How to use YouTube APK 4.4.4 Uptodown?</h2>
-<h3>The main features and functions of YouTube APK 4.4.4</h3>
-<p>Once you have downloaded and installed YouTube APK 4.4.4 on your device, you can start using it to enjoy all the features and functions of YouTube on your Android device. Here are some of the main features and functions of YouTube APK 4.4.4:</p>
-<ul>
-<li>You can watch all the featured videos for each day, as well as search for videos that interest you by keywords, categories, or channels.</li>
-<li>You can watch live streams, create playlists, subscribe to your favorite channels, comment on videos, share videos with your friends, and more.</li>
-<li>You can play videos in the background while browsing other apps or sites. That way, you can listen to music, podcasts, or audiobooks without interrupting your other tasks.</li>
-<li>You can cast videos from your Android device to your TV by using a Chromecast device or a smart TV that supports it.</li>
-<li>You can watch and create short videos of up to 60 seconds with YouTube Shorts, similar to TikTok or Instagram Reels.</li>
-<li>You can access and stream music videos and songs from YouTube Music, as well as create your own playlists and discover new artists.</li>
-<li>You can enjoy YouTube without ads, download videos for offline viewing, and access exclusive content and features with YouTube Premium.</li>
-</ul> <h3>The tips and tricks to optimize your YouTube experience with YouTube APK 4.4.4</h3>
-<p>Using YouTube APK 4.4.4 is not only easy and fun, but also customizable and flexible. You can optimize your YouTube experience with YouTube APK 4.4.4 by using some tips and tricks, such as:</p>
-<ul>
-<li>You can adjust the video quality and speed according to your preference and network condition. To do this, tap on the three dots icon on the top right corner of the video player, and select the quality and speed options.</li>
-<li>You can enable or disable the captions for the videos that support them. To do this, tap on the CC icon on the bottom right corner of the video player, and select the language and style options.</li>
-<li>You can turn on or off the notifications for the channels that you subscribe to. To do this, tap on the bell icon next to the subscribe button, and select the notification options.</li>
-<li>You can explore more videos related to the one that you are watching by swiping up on the video player. You will see a list of suggested videos that you may like.</li>
-<li>You can create your own channel and upload your own videos with YouTube APK 4.4.4. To do this, tap on the camera icon on the top right corner of the app, and select the upload option.</li>
-</ul>
-<h3>The best practices and precautions to keep your YouTube account and device safe and secure with YouTube APK 4.4.4</h3>
-<p>Using YouTube APK 4.4.4 is not only enjoyable and convenient, but also safe and secure. You can keep your YouTube account and device safe and secure with YouTube APK 4.4.4 by following some best practices and precautions, such as:</p>
-<ul>
-<li>You can sign in to your YouTube account with YouTube APK 4.4.4 to access all your personalized settings and preferences. To do this, tap on the profile icon on the top right corner of the app, and select the sign in option.</li>
-<li>You can protect your YouTube account with a strong password and a two-step verification process. To do this, go to Settings > Account > Security, and select the password and verification options.</li>
-<li>You can manage your privacy settings and control who can see your activity and information on YouTube. To do this, go to Settings > Privacy, and select the options that suit you.</li>
-<li>You can report any inappropriate or abusive content or behavior that you encounter on YouTube. To do this, tap on the flag icon on the video player or the comment section, and select the reason for reporting.</li>
-<li>You can update your YouTube APK 4.4.4 regularly to get the latest features and fixes. To do this, go to Uptodown website, and download the latest version of YouTube APK.</li>
-</ul> <h2>Conclusion</h2>
-<p>In conclusion, YouTube APK 4.4.4 Uptodown is the latest version of YouTube for Android devices, and it offers many features and benefits that you can enjoy on your smartphone or tablet. You can download and install YouTube APK 4.4.4 Uptodown for free from Uptodown website, one of the best websites for downloading apps and games for Android devices.</p>
-<p>With YouTube APK 4.4.4 Uptodown, you can watch all the videos that interest you, from music and entertainment to education and news. You can also watch live streams, create playlists, subscribe to your favorite channels, comment on videos, share videos with your friends, and more.</p>
-<p>You can also play videos in the background while browsing other apps or sites, cast videos from your Android device to your TV, watch and create short videos with YouTube Shorts, access and stream music videos and songs with YouTube Music, and enjoy YouTube without ads, download videos for offline viewing, and access exclusive content and features with YouTube Premium.</p>
-<p>You can also optimize your YouTube experience with YouTube APK 4.4.4 by using some tips and tricks, such as adjusting the video quality and speed, enabling or disabling the captions, turning on or off the notifications, exploring more videos related to the one that you are watching, and creating your own channel and uploading your own videos.</p>
-<p>You can also keep your YouTube account and device safe and secure with YouTube APK 4.4.4 by following some best practices and precautions, such as signing in to your YouTube account, protecting your YouTube account with a strong password and a two-step verification process, managing your privacy settings, reporting any inappropriate or abusive content or behavior, and updating your YouTube APK 4.4.4 regularly.</p>
-<p>So what are you waiting for? Download and install YouTube APK 4.4.4 Uptodown today and enjoy all the features and benefits of YouTube on your Android device!</p>
-<h3>FAQs</h3>
-<p>Here are some frequently asked questions about YouTube APK 4.4.4 Uptodown:</p>
-<ol>
-<li>Is YouTube APK 4.4.4 Uptodown safe to download and install?</li>
-<p>Yes, YouTube APK 4.4.4 Uptodown is safe to download and install. Uptodown scans all its files with multiple antivirus engines, so you can download YouTube APK without any viruses or malware.</p>
-<li>Is YouTube APK 4.4.4 Uptodown legal to download and install?</li>
-<p>Yes, YouTube APK 4.4.4 Uptodown is legal to download and install. Uptodown does not violate any intellectual property rights or laws by offering YouTube APK for download.</p>
-<li>Is YouTube APK 4.4.4 Uptodown compatible with my Android device?</li>
-<p>YouTube APK 4.4.4 Uptodown is compatible with most Android devices that run on Android 5.0 or higher.</p>
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-<p>You can update your YouTube APK 4.4.4 Uptodown by going to Uptodown website and downloading the latest version of YouTube APK.</p>
-<li>How can I uninstall my YouTube APK 4.4.4 Uptodown?</li>
-<p>You can uninstall your YouTube APK 4.4.4 Uptodown by going to Settings > Apps > YouTube > Uninstall.</p>
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diff --git a/spaces/contluForse/HuggingGPT/assets/ANSI TIA 1152 PDF Download The International Equivalent of IEC 61935-1 and How to Compare Them.md b/spaces/contluForse/HuggingGPT/assets/ANSI TIA 1152 PDF Download The International Equivalent of IEC 61935-1 and How to Compare Them.md
deleted file mode 100644
index 27bd59256ff8399e499795f82565b4d0e8017056..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/ANSI TIA 1152 PDF Download The International Equivalent of IEC 61935-1 and How to Compare Them.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-<br />
-<p>A marginal pass suggests you are close to the limit, something which many customers will question and possibly reject. The problem is that ANSI/TIA-1152 and IEC 61935-1 state that a marginal pass simply results in an overall pass condition. It has met the requirements of the standard. If you want to reject marginal results, you will need to specifically state that in your test specification. Read about #2 Dumb Thing neglecting marginal test results</p>
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-<p>This poster is a quick reference guide for Twisted Pair Cabling Measurements and documents all the relevant measurement requirements in ANSI/TIA-568.2-D and 1152-A with detailed illustrations and straightforward explanations.</p>
-<p>The TIA Standard 1152-A was ratified in autumn 2016. It specifies the parameters and accuracy required to certify structured cabling according to ANSI/TIA performance categories up to CAT8. The corresponding IEC standard 61935-1, 5th edition, was ratified in 2019. It specifies the parameters and accuracy required to certify structured cabling to ISO/IEC Performance Classes up to Class II. He also distinguishes between Class I and Class II for the accuracy of measurements up to 2000 MHz.</p>
-<p>Resistance unbalance tests are optional tests per definition by the measurement standards (ANSI/TIA 1152-A and ISO/IEC 11801 3rd ed.) and will at some point be interesting for high power PoE applications up to 100W. At current, the IEEE organisation is in the process to define 100W PoE (IEEE 802.3bt) and the international standards organisation is working the technical report TR29125 for remote powering to support above IEEE 802.3bt. Once those standards become relevant in the field, field testing of resistance unbalance will make sense in applications where high power PoE up to 100W will be used.</p>
-<p></p> aaccfb2cb3<br />
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diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/parse.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/parse.py
deleted file mode 100644
index f60f0d611b8d75692221d0edd7dc993b0a6445c9..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/parse.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-
-from io import StringIO
-
-from .file_client import FileClient
-
-
-def list_from_file(filename,
-                   prefix='',
-                   offset=0,
-                   max_num=0,
-                   encoding='utf-8',
-                   file_client_args=None):
-    """Load a text file and parse the content as a list of strings.
-
-    Note:
-        In v1.3.16 and later, ``list_from_file`` supports loading a text file
-        which can be storaged in different backends and parsing the content as
-        a list for strings.
-
-    Args:
-        filename (str): Filename.
-        prefix (str): The prefix to be inserted to the beginning of each item.
-        offset (int): The offset of lines.
-        max_num (int): The maximum number of lines to be read,
-            zeros and negatives mean no limitation.
-        encoding (str): Encoding used to open the file. Default utf-8.
-        file_client_args (dict, optional): Arguments to instantiate a
-            FileClient. See :class:`mmcv.fileio.FileClient` for details.
-            Default: None.
-
-    Examples:
-        >>> list_from_file('/path/of/your/file')  # disk
-        ['hello', 'world']
-        >>> list_from_file('s3://path/of/your/file')  # ceph or petrel
-        ['hello', 'world']
-
-    Returns:
-        list[str]: A list of strings.
-    """
-    cnt = 0
-    item_list = []
-    file_client = FileClient.infer_client(file_client_args, filename)
-    with StringIO(file_client.get_text(filename, encoding)) as f:
-        for _ in range(offset):
-            f.readline()
-        for line in f:
-            if 0 < max_num <= cnt:
-                break
-            item_list.append(prefix + line.rstrip('\n\r'))
-            cnt += 1
-    return item_list
-
-
-def dict_from_file(filename,
-                   key_type=str,
-                   encoding='utf-8',
-                   file_client_args=None):
-    """Load a text file and parse the content as a dict.
-
-    Each line of the text file will be two or more columns split by
-    whitespaces or tabs. The first column will be parsed as dict keys, and
-    the following columns will be parsed as dict values.
-
-    Note:
-        In v1.3.16 and later, ``dict_from_file`` supports loading a text file
-        which can be storaged in different backends and parsing the content as
-        a dict.
-
-    Args:
-        filename(str): Filename.
-        key_type(type): Type of the dict keys. str is user by default and
-            type conversion will be performed if specified.
-        encoding (str): Encoding used to open the file. Default utf-8.
-        file_client_args (dict, optional): Arguments to instantiate a
-            FileClient. See :class:`mmcv.fileio.FileClient` for details.
-            Default: None.
-
-    Examples:
-        >>> dict_from_file('/path/of/your/file')  # disk
-        {'key1': 'value1', 'key2': 'value2'}
-        >>> dict_from_file('s3://path/of/your/file')  # ceph or petrel
-        {'key1': 'value1', 'key2': 'value2'}
-
-    Returns:
-        dict: The parsed contents.
-    """
-    mapping = {}
-    file_client = FileClient.infer_client(file_client_args, filename)
-    with StringIO(file_client.get_text(filename, encoding)) as f:
-        for line in f:
-            items = line.rstrip('\n').split()
-            assert len(items) >= 2
-            key = key_type(items[0])
-            val = items[1:] if len(items) > 2 else items[1]
-            mapping[key] = val
-    return mapping
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/demo/visualizer.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/demo/visualizer.py
deleted file mode 100644
index 469eb1ab7e95d7646a6a07bed2428a905efdc992..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/demo/visualizer.py
+++ /dev/null
@@ -1,1350 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import colorsys
-import logging
-import math
-import numpy as np
-from enum import Enum, unique
-import cv2
-import matplotlib as mpl
-import matplotlib.colors as mplc
-import matplotlib.figure as mplfigure
-import pycocotools.mask as mask_util
-import torch
-from matplotlib.backends.backend_agg import FigureCanvasAgg
-from PIL import Image
-
-from annotator.oneformer.detectron2.data import MetadataCatalog
-from annotator.oneformer.detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes
-from annotator.oneformer.detectron2.utils.file_io import PathManager
-import random
-random.seed(0)
-from .colormap import random_color, _COLORS
-logger = logging.getLogger(__name__)
-
-__all__ = ["ColorMode", "VisImage", "Visualizer"]
-
-
-_SMALL_OBJECT_AREA_THRESH = 1000
-_LARGE_MASK_AREA_THRESH = 120000
-_OFF_WHITE = (1.0, 1.0, 1.0)
-_BLACK = (0, 0, 0)
-_RED = (1.0, 0, 0)
-
-_KEYPOINT_THRESHOLD = 0.05
-
-
-def instance_color(rgb=False, idx=1, maximum=255):
-    """
-    Args:
-        rgb (bool): whether to return RGB colors or BGR colors.
-        maximum (int): either 255 or 1
-    Returns:
-        ndarray: a vector of 3 numbers
-    """
-    ret = _COLORS[idx] * maximum
-    if not rgb:
-        ret = ret[::-1]
-    return ret
-
-@unique
-class ColorMode(Enum):
-    """
-    Enum of different color modes to use for instance visualizations.
-    """
-
-    IMAGE = 0
-    """
-    Picks a random color for every instance and overlay segmentations with low opacity.
-    """
-    SEGMENTATION = 1
-    """
-    Let instances of the same category have similar colors
-    (from metadata.thing_colors), and overlay them with
-    high opacity. This provides more attention on the quality of segmentation.
-    """
-    IMAGE_BW = 2
-    """
-    Same as IMAGE, but convert all areas without masks to gray-scale.
-    Only available for drawing per-instance mask predictions.
-    """
-
-
-class GenericMask:
-    """
-    Attribute:
-        polygons (list[ndarray]): list[ndarray]: polygons for this mask.
-            Each ndarray has format [x, y, x, y, ...]
-        mask (ndarray): a binary mask
-    """
-
-    def __init__(self, mask_or_polygons, height, width):
-        self._mask = self._polygons = self._has_holes = None
-        self.height = height
-        self.width = width
-
-        m = mask_or_polygons
-        if isinstance(m, dict):
-            # RLEs
-            assert "counts" in m and "size" in m
-            if isinstance(m["counts"], list):  # uncompressed RLEs
-                h, w = m["size"]
-                assert h == height and w == width
-                m = mask_util.frPyObjects(m, h, w)
-            self._mask = mask_util.decode(m)[:, :]
-            return
-
-        if isinstance(m, list):  # list[ndarray]
-            self._polygons = [np.asarray(x).reshape(-1) for x in m]
-            return
-
-        if isinstance(m, np.ndarray):  # assumed to be a binary mask
-            assert m.shape[1] != 2, m.shape
-            assert m.shape == (
-                height,
-                width,
-            ), f"mask shape: {m.shape}, target dims: {height}, {width}"
-            self._mask = m.astype("uint8")
-            return
-
-        raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m)))
-
-    @property
-    def mask(self):
-        if self._mask is None:
-            self._mask = self.polygons_to_mask(self._polygons)
-        return self._mask
-
-    @property
-    def polygons(self):
-        if self._polygons is None:
-            self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
-        return self._polygons
-
-    @property
-    def has_holes(self):
-        if self._has_holes is None:
-            if self._mask is not None:
-                self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
-            else:
-                self._has_holes = False  # if original format is polygon, does not have holes
-        return self._has_holes
-
-    def mask_to_polygons(self, mask):
-        # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level
-        # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.
-        # Internal contours (holes) are placed in hierarchy-2.
-        # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.
-        mask = np.ascontiguousarray(mask)  # some versions of cv2 does not support incontiguous arr
-        res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
-        hierarchy = res[-1]
-        if hierarchy is None:  # empty mask
-            return [], False
-        has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0
-        res = res[-2]
-        res = [x.flatten() for x in res]
-        # These coordinates from OpenCV are integers in range [0, W-1 or H-1].
-        # We add 0.5 to turn them into real-value coordinate space. A better solution
-        # would be to first +0.5 and then dilate the returned polygon by 0.5.
-        res = [x + 0.5 for x in res if len(x) >= 6]
-        return res, has_holes
-
-    def polygons_to_mask(self, polygons):
-        rle = mask_util.frPyObjects(polygons, self.height, self.width)
-        rle = mask_util.merge(rle)
-        return mask_util.decode(rle)[:, :]
-
-    def area(self):
-        return self.mask.sum()
-
-    def bbox(self):
-        p = mask_util.frPyObjects(self.polygons, self.height, self.width)
-        p = mask_util.merge(p)
-        bbox = mask_util.toBbox(p)
-        bbox[2] += bbox[0]
-        bbox[3] += bbox[1]
-        return bbox
-
-
-class _PanopticPrediction:
-    """
-    Unify different panoptic annotation/prediction formats
-    """
-
-    def __init__(self, panoptic_seg, segments_info, metadata=None):
-        if segments_info is None:
-            assert metadata is not None
-            # If "segments_info" is None, we assume "panoptic_img" is a
-            # H*W int32 image storing the panoptic_id in the format of
-            # category_id * label_divisor + instance_id. We reserve -1 for
-            # VOID label.
-            label_divisor = metadata.label_divisor
-            segments_info = []
-            for panoptic_label in np.unique(panoptic_seg.numpy()):
-                if panoptic_label == -1:
-                    # VOID region.
-                    continue
-                pred_class = panoptic_label // label_divisor
-                isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()
-                segments_info.append(
-                    {
-                        "id": int(panoptic_label),
-                        "category_id": int(pred_class),
-                        "isthing": bool(isthing),
-                    }
-                )
-        del metadata
-
-        self._seg = panoptic_seg
-
-        self._sinfo = {s["id"]: s for s in segments_info}  # seg id -> seg info
-        segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)
-        areas = areas.numpy()
-        sorted_idxs = np.argsort(-areas)
-        self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]
-        self._seg_ids = self._seg_ids.tolist()
-        for sid, area in zip(self._seg_ids, self._seg_areas):
-            if sid in self._sinfo:
-                self._sinfo[sid]["area"] = float(area)
-
-    def non_empty_mask(self):
-        """
-        Returns:
-            (H, W) array, a mask for all pixels that have a prediction
-        """
-        empty_ids = []
-        for id in self._seg_ids:
-            if id not in self._sinfo:
-                empty_ids.append(id)
-        if len(empty_ids) == 0:
-            return np.zeros(self._seg.shape, dtype=np.uint8)
-        assert (
-            len(empty_ids) == 1
-        ), ">1 ids corresponds to no labels. This is currently not supported"
-        return (self._seg != empty_ids[0]).numpy().astype(np.bool)
-
-    def semantic_masks(self):
-        for sid in self._seg_ids:
-            sinfo = self._sinfo.get(sid)
-            if sinfo is None or sinfo["isthing"]:
-                # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.
-                continue
-            yield (self._seg == sid).numpy().astype(np.bool), sinfo
-
-    def instance_masks(self):
-        for sid in self._seg_ids:
-            sinfo = self._sinfo.get(sid)
-            if sinfo is None or not sinfo["isthing"]:
-                continue
-            mask = (self._seg == sid).numpy().astype(np.bool)
-            if mask.sum() > 0:
-                yield mask, sinfo
-
-
-def _create_text_labels(classes, scores, class_names, is_crowd=None):
-    """
-    Args:
-        classes (list[int] or None):
-        scores (list[float] or None):
-        class_names (list[str] or None):
-        is_crowd (list[bool] or None):
-    Returns:
-        list[str] or None
-    """
-    labels = None
-    if classes is not None:
-        if class_names is not None and len(class_names) > 0:
-            labels = [class_names[i] for i in classes]
-        else:
-            labels = [str(i) for i in classes]
-    if scores is not None:
-        if labels is None:
-            labels = ["{:.0f}%".format(s * 100) for s in scores]
-        else:
-            labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)]
-    if labels is not None and is_crowd is not None:
-        labels = [l + ("|crowd" if crowd else "") for l, crowd in zip(labels, is_crowd)]
-    return labels
-
-
-class VisImage:
-    def __init__(self, img, scale=1.0):
-        """
-        Args:
-            img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].
-            scale (float): scale the input image
-        """
-        self.img = img
-        self.scale = scale
-        self.width, self.height = img.shape[1], img.shape[0]
-        self._setup_figure(img)
-
-    def _setup_figure(self, img):
-        """
-        Args:
-            Same as in :meth:`__init__()`.
-        Returns:
-            fig (matplotlib.pyplot.figure): top level container for all the image plot elements.
-            ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.
-        """
-        fig = mplfigure.Figure(frameon=False)
-        self.dpi = fig.get_dpi()
-        # add a small 1e-2 to avoid precision lost due to matplotlib's truncation
-        # (https://github.com/matplotlib/matplotlib/issues/15363)
-        fig.set_size_inches(
-            (self.width * self.scale + 1e-2) / self.dpi,
-            (self.height * self.scale + 1e-2) / self.dpi,
-        )
-        self.canvas = FigureCanvasAgg(fig)
-        # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
-        ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
-        ax.axis("off")
-        self.fig = fig
-        self.ax = ax
-        self.reset_image(img)
-
-    def reset_image(self, img):
-        """
-        Args:
-            img: same as in __init__
-        """
-        img = img.astype("uint8")
-        self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")
-
-    def save(self, filepath):
-        """
-        Args:
-            filepath (str): a string that contains the absolute path, including the file name, where
-                the visualized image will be saved.
-        """
-        self.fig.savefig(filepath)
-
-    def get_image(self):
-        """
-        Returns:
-            ndarray:
-                the visualized image of shape (H, W, 3) (RGB) in uint8 type.
-                The shape is scaled w.r.t the input image using the given `scale` argument.
-        """
-        canvas = self.canvas
-        s, (width, height) = canvas.print_to_buffer()
-        # buf = io.BytesIO()  # works for cairo backend
-        # canvas.print_rgba(buf)
-        # width, height = self.width, self.height
-        # s = buf.getvalue()
-
-        buffer = np.frombuffer(s, dtype="uint8")
-
-        img_rgba = buffer.reshape(height, width, 4)
-        rgb, alpha = np.split(img_rgba, [3], axis=2)
-        return rgb.astype("uint8")
-
-
-class Visualizer:
-    """
-    Visualizer that draws data about detection/segmentation on images.
-    It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`
-    that draw primitive objects to images, as well as high-level wrappers like
-    `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`
-    that draw composite data in some pre-defined style.
-    Note that the exact visualization style for the high-level wrappers are subject to change.
-    Style such as color, opacity, label contents, visibility of labels, or even the visibility
-    of objects themselves (e.g. when the object is too small) may change according
-    to different heuristics, as long as the results still look visually reasonable.
-    To obtain a consistent style, you can implement custom drawing functions with the
-    abovementioned primitive methods instead. If you need more customized visualization
-    styles, you can process the data yourself following their format documented in
-    tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not
-    intend to satisfy everyone's preference on drawing styles.
-    This visualizer focuses on high rendering quality rather than performance. It is not
-    designed to be used for real-time applications.
-    """
-
-    # TODO implement a fast, rasterized version using OpenCV
-
-    def __init__(self, img_rgb, is_img=True, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):
-        """
-        Args:
-            img_rgb: a numpy array of shape (H, W, C), where H and W correspond to
-                the height and width of the image respectively. C is the number of
-                color channels. The image is required to be in RGB format since that
-                is a requirement of the Matplotlib library. The image is also expected
-                to be in the range [0, 255].
-            metadata (Metadata): dataset metadata (e.g. class names and colors)
-            instance_mode (ColorMode): defines one of the pre-defined style for drawing
-                instances on an image.
-        """
-        if is_img:
-            self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
-        else:
-            self.img = np.zeros_like(img_rgb).clip(0, 255).astype(np.uint8) + 255
-        if metadata is None:
-            metadata = MetadataCatalog.get("__nonexist__")
-        self.metadata = metadata
-        self.output = VisImage(self.img, scale=scale)
-        self.cpu_device = torch.device("cpu")
-
-        # too small texts are useless, therefore clamp to 9
-        self._default_font_size = max(
-            np.sqrt(self.output.height * self.output.width) // 90, 10 // scale
-        )
-        self._instance_mode = instance_mode
-        self.keypoint_threshold = _KEYPOINT_THRESHOLD
-
-    def get_image(self, img):
-        img = np.asarray(img).clip(0, 255).astype(np.uint8)
-        return VisImage(img, scale=1.0)
-    
-    def draw_box_predictions(
-        self,
-        boxes=None,
-        labels=None,
-        scores=None,
-        assigned_colors=None
-    ):
-        """
-        Args:
-            boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
-                or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
-                or a :class:`RotatedBoxes`,
-                or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
-                for the N objects in a single image,
-            labels (list[str]): the text to be displayed for each instance.
-            assigned_colors (list[matplotlib.colors]): a list of colors, where each color
-                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
-                for full list of formats that the colors are accepted in.
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        num_instances = 0
-        boxes = self._convert_boxes(boxes)
-        classes = labels.tolist()
-        scores = scores.tolist()
-        labels = _create_text_labels(classes, scores, self.metadata.get("stuff_classes", None))
-        num_instances = len(boxes)
-        assert len(labels) == num_instances
-        if assigned_colors is None:
-            # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
-            assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]
-        if num_instances == 0:
-            return self.output
-
-        # Display in largest to smallest order to reduce occlusion.
-        areas = None
-        areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)
-
-        if areas is not None:
-            sorted_idxs = np.argsort(-areas).tolist()
-            # Re-order overlapped instances in descending order.
-            boxes = boxes[sorted_idxs] if boxes is not None else None
-            labels = [labels[k] for k in sorted_idxs] if labels is not None else None
-            assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]
-
-        for i in range(num_instances):
-            color = assigned_colors[i]
-            if boxes is not None:
-                self.draw_box(boxes[i], edge_color=color)
-
-            if labels is not None:
-                # first get a box
-                if boxes is not None:
-                    x0, y0, x1, y1 = boxes[i]
-                    text_pos = (x0, y0)  # if drawing boxes, put text on the box corner.
-                    horiz_align = "left"
-                else:
-                    continue  # drawing the box confidence for keypoints isn't very useful.
-                # for small objects, draw text at the side to avoid occlusion
-                instance_area = (y1 - y0) * (x1 - x0)
-                if (
-                    instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
-                    or y1 - y0 < 40 * self.output.scale
-                ):
-                    if y1 >= self.output.height - 5:
-                        text_pos = (x1, y0)
-                    else:
-                        text_pos = (x0, y1)
-
-                height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
-                lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-                font_size = (
-                    np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
-                    * 0.5
-                    * self._default_font_size
-                )
-                self.draw_text(
-                    labels[i],
-                    text_pos,
-                    color=lighter_color,
-                    horizontal_alignment=horiz_align,
-                    font_size=font_size,
-                )
-
-        return self.output
-    
-    
-    def draw_instance_predictions(self, predictions, alpha=0.8, is_text=True):
-        """
-        Draw instance-level prediction results on an image.
-        Args:
-            predictions (Instances): the output of an instance detection/segmentation
-                model. Following fields will be used to draw:
-                "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None
-        scores = predictions.scores if predictions.has("scores") else None
-        classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None
-        labels = _create_text_labels(classes, scores, self.metadata.get("stuff_classes", None))
-        keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None
-
-        if predictions.has("pred_masks"):
-            masks = np.asarray(predictions.pred_masks)
-            masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]
-        else:
-            masks = None
-
-        if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("stuff_colors"):
-            # colors = [
-            #     self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes
-            # ]
-            colors = [
-                instance_color(rgb=True, idx=c, maximum=1) for c in classes
-            ]
-        else:
-            colors = None
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            self.output.reset_image(
-                self._create_grayscale_image(
-                    (predictions.pred_masks.any(dim=0) > 0).numpy()
-                    if predictions.has("pred_masks")
-                    else None
-                )
-            )
-
-        self.overlay_instances(
-            masks=masks,
-            boxes=boxes,
-            labels=labels,
-            keypoints=keypoints,
-            assigned_colors=colors,
-            alpha=alpha,
-            is_text=is_text,
-        )
-        return self.output
-
-    def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8, is_text=True, edge_color=_OFF_WHITE):
-        """
-        Draw semantic segmentation predictions/labels.
-        Args:
-            sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
-                Each value is the integer label of the pixel.
-            area_threshold (int): segments with less than `area_threshold` are not drawn.
-            alpha (float): the larger it is, the more opaque the segmentations are.
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        if isinstance(sem_seg, torch.Tensor):
-            sem_seg = sem_seg.numpy()
-        labels, areas = np.unique(sem_seg, return_counts=True)
-        sorted_idxs = np.argsort(-areas).tolist()
-        labels = labels[sorted_idxs]
-        for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):
-            try:
-                mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]
-            except (AttributeError, IndexError):
-                mask_color = None
-
-            binary_mask = (sem_seg == label).astype(np.uint8)
-            text = self.metadata.stuff_classes[label]
-            self.draw_binary_mask(
-                binary_mask,
-                color=mask_color,
-                edge_color=edge_color,
-                text=text,
-                alpha=alpha,
-                area_threshold=area_threshold,
-                is_text=is_text,
-            )
-        return self.output
-
-    def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7, is_text=True,):
-        """
-        Draw panoptic prediction annotations or results.
-        Args:
-            panoptic_seg (Tensor): of shape (height, width) where the values are ids for each
-                segment.
-            segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.
-                If it is a ``list[dict]``, each dict contains keys "id", "category_id".
-                If None, category id of each pixel is computed by
-                ``pixel // metadata.label_divisor``.
-            area_threshold (int): stuff segments with less than `area_threshold` are not drawn.
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))
-
-        # draw mask for all semantic segments first i.e. "stuff"
-        for mask, sinfo in pred.semantic_masks():
-            category_idx = sinfo["category_id"]
-            try:
-                mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]
-            except AttributeError:
-                mask_color = None
-
-            text = self.metadata.stuff_classes[category_idx]
-            self.draw_binary_mask(
-                mask,
-                color=mask_color,
-                edge_color=_OFF_WHITE,
-                text=text,
-                alpha=alpha,
-                area_threshold=area_threshold,
-                is_text=is_text,
-            )
-
-        # draw mask for all instances second
-        all_instances = list(pred.instance_masks())
-        if len(all_instances) == 0:
-            return self.output
-        masks, sinfo = list(zip(*all_instances))
-        category_ids = [x["category_id"] for x in sinfo]
-
-        try:
-            scores = [x["score"] for x in sinfo]
-        except KeyError:
-            scores = None
-        labels = _create_text_labels(
-            category_ids, scores, self.metadata.stuff_classes, [x.get("iscrowd", 0) for x in sinfo]
-        )
-
-        try:
-            colors = [
-                self._jitter([x / 255 for x in self.metadata.stuff_colors[c]]) for c in category_ids
-            ]
-        except AttributeError:
-            colors = None
-        self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha, is_text=is_text)
-
-        return self.output
-
-    draw_panoptic_seg_predictions = draw_panoptic_seg  # backward compatibility
-
-    def draw_dataset_dict(self, dic):
-        """
-        Draw annotations/segmentaions in Detectron2 Dataset format.
-        Args:
-            dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        annos = dic.get("annotations", None)
-        if annos:
-            if "segmentation" in annos[0]:
-                masks = [x["segmentation"] for x in annos]
-            else:
-                masks = None
-            if "keypoints" in annos[0]:
-                keypts = [x["keypoints"] for x in annos]
-                keypts = np.array(keypts).reshape(len(annos), -1, 3)
-            else:
-                keypts = None
-
-            boxes = [
-                BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS)
-                if len(x["bbox"]) == 4
-                else x["bbox"]
-                for x in annos
-            ]
-
-            colors = None
-            category_ids = [x["category_id"] for x in annos]
-            if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("stuff_colors"):
-                colors = [
-                    self._jitter([x / 255 for x in self.metadata.stuff_colors[c]])
-                    for c in category_ids
-                ]
-            names = self.metadata.get("stuff_classes", None)
-            labels = _create_text_labels(
-                category_ids,
-                scores=None,
-                class_names=names,
-                is_crowd=[x.get("iscrowd", 0) for x in annos],
-            )
-            self.overlay_instances(
-                labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors
-            )
-
-        sem_seg = dic.get("sem_seg", None)
-        if sem_seg is None and "sem_seg_file_name" in dic:
-            with PathManager.open(dic["sem_seg_file_name"], "rb") as f:
-                sem_seg = Image.open(f)
-                sem_seg = np.asarray(sem_seg, dtype="uint8")
-        if sem_seg is not None:
-            self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)
-
-        pan_seg = dic.get("pan_seg", None)
-        # if pan_seg is None and "pan_seg_file_name" in dic:
-        #     with PathManager.open(dic["pan_seg_file_name"], "rb") as f:
-        #         pan_seg = Image.open(f)
-        #         pan_seg = np.asarray(pan_seg)
-        #         from panopticapi.utils import rgb2id
-        #
-        #         pan_seg = rgb2id(pan_seg)
-        if pan_seg is not None:
-            segments_info = dic["segments_info"]
-            pan_seg = torch.tensor(pan_seg)
-            self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)
-        return self.output
-
-    def overlay_instances(
-        self,
-        *,
-        boxes=None,
-        labels=None,
-        masks=None,
-        keypoints=None,
-        assigned_colors=None,
-        alpha=0.5,
-        is_text=True,
-    ):
-        """
-        Args:
-            boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
-                or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
-                or a :class:`RotatedBoxes`,
-                or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
-                for the N objects in a single image,
-            labels (list[str]): the text to be displayed for each instance.
-            masks (masks-like object): Supported types are:
-                * :class:`detectron2.structures.PolygonMasks`,
-                  :class:`detectron2.structures.BitMasks`.
-                * list[list[ndarray]]: contains the segmentation masks for all objects in one image.
-                  The first level of the list corresponds to individual instances. The second
-                  level to all the polygon that compose the instance, and the third level
-                  to the polygon coordinates. The third level should have the format of
-                  [x0, y0, x1, y1, ..., xn, yn] (n >= 3).
-                * list[ndarray]: each ndarray is a binary mask of shape (H, W).
-                * list[dict]: each dict is a COCO-style RLE.
-            keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),
-                where the N is the number of instances and K is the number of keypoints.
-                The last dimension corresponds to (x, y, visibility or score).
-            assigned_colors (list[matplotlib.colors]): a list of colors, where each color
-                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
-                for full list of formats that the colors are accepted in.
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        num_instances = 0
-        if boxes is not None:
-            boxes = self._convert_boxes(boxes)
-            num_instances = len(boxes)
-        if masks is not None:
-            masks = self._convert_masks(masks)
-            if num_instances:
-                assert len(masks) == num_instances
-            else:
-                num_instances = len(masks)
-        if keypoints is not None:
-            if num_instances:
-                assert len(keypoints) == num_instances
-            else:
-                num_instances = len(keypoints)
-            keypoints = self._convert_keypoints(keypoints)
-        if labels is not None:
-            assert len(labels) == num_instances
-        if assigned_colors is None:
-            # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
-            assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]
-        if num_instances == 0:
-            return self.output
-        if boxes is not None and boxes.shape[1] == 5:
-            return self.overlay_rotated_instances(
-                boxes=boxes, labels=labels, assigned_colors=assigned_colors
-            )
-
-        # Display in largest to smallest order to reduce occlusion.
-        areas = None
-        if boxes is not None:
-            areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)
-        elif masks is not None:
-            areas = np.asarray([x.area() for x in masks])
-
-        if areas is not None:
-            sorted_idxs = np.argsort(-areas).tolist()
-            # Re-order overlapped instances in descending order.
-            boxes = boxes[sorted_idxs] if boxes is not None else None
-            labels = [labels[k] for k in sorted_idxs] if labels is not None else None
-            masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None
-            assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]
-            keypoints = keypoints[sorted_idxs] if keypoints is not None else None
-
-        for i in range(num_instances):
-            color = assigned_colors[i]
-            if boxes is not None:
-                self.draw_box(boxes[i], edge_color=color)
-
-            if masks is not None:
-                for segment in masks[i].polygons:
-                    self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha)
-
-            if labels is not None:
-                # first get a box
-                if boxes is not None:
-                    x0, y0, x1, y1 = boxes[i]
-                    text_pos = (x0, y0)  # if drawing boxes, put text on the box corner.
-                    horiz_align = "left"
-                elif masks is not None:
-                    # skip small mask without polygon
-                    if len(masks[i].polygons) == 0:
-                        continue
-
-                    x0, y0, x1, y1 = masks[i].bbox()
-
-                    # draw text in the center (defined by median) when box is not drawn
-                    # median is less sensitive to outliers.
-                    text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1]
-                    horiz_align = "center"
-                else:
-                    continue  # drawing the box confidence for keypoints isn't very useful.
-                # for small objects, draw text at the side to avoid occlusion
-                instance_area = (y1 - y0) * (x1 - x0)
-                if (
-                    instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
-                    or y1 - y0 < 40 * self.output.scale
-                ):
-                    if y1 >= self.output.height - 5:
-                        text_pos = (x1, y0)
-                    else:
-                        text_pos = (x0, y1)
-
-                height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
-                lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-                font_size = (
-                    np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
-                    * 0.5
-                    * self._default_font_size
-                )
-                if is_text:
-                    self.draw_text(
-                        labels[i],
-                        text_pos,
-                        color=lighter_color,
-                        horizontal_alignment=horiz_align,
-                        font_size=font_size,
-                    )
-
-        # draw keypoints
-        if keypoints is not None:
-            for keypoints_per_instance in keypoints:
-                self.draw_and_connect_keypoints(keypoints_per_instance)
-
-        return self.output
-
-    def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):
-        """
-        Args:
-            boxes (ndarray): an Nx5 numpy array of
-                (x_center, y_center, width, height, angle_degrees) format
-                for the N objects in a single image.
-            labels (list[str]): the text to be displayed for each instance.
-            assigned_colors (list[matplotlib.colors]): a list of colors, where each color
-                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
-                for full list of formats that the colors are accepted in.
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        num_instances = len(boxes)
-
-        if assigned_colors is None:
-            # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
-            assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]
-        if num_instances == 0:
-            return self.output
-
-        # Display in largest to smallest order to reduce occlusion.
-        if boxes is not None:
-            areas = boxes[:, 2] * boxes[:, 3]
-
-        sorted_idxs = np.argsort(-areas).tolist()
-        # Re-order overlapped instances in descending order.
-        boxes = boxes[sorted_idxs]
-        labels = [labels[k] for k in sorted_idxs] if labels is not None else None
-        colors = [assigned_colors[idx] for idx in sorted_idxs]
-
-        for i in range(num_instances):
-            self.draw_rotated_box_with_label(
-                boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None
-            )
-
-        return self.output
-
-    def draw_and_connect_keypoints(self, keypoints):
-        """
-        Draws keypoints of an instance and follows the rules for keypoint connections
-        to draw lines between appropriate keypoints. This follows color heuristics for
-        line color.
-        Args:
-            keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints
-                and the last dimension corresponds to (x, y, probability).
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        visible = {}
-        keypoint_names = self.metadata.get("keypoint_names")
-        for idx, keypoint in enumerate(keypoints):
-
-            # draw keypoint
-            x, y, prob = keypoint
-            if prob > self.keypoint_threshold:
-                self.draw_circle((x, y), color=_RED)
-                if keypoint_names:
-                    keypoint_name = keypoint_names[idx]
-                    visible[keypoint_name] = (x, y)
-
-        if self.metadata.get("keypoint_connection_rules"):
-            for kp0, kp1, color in self.metadata.keypoint_connection_rules:
-                if kp0 in visible and kp1 in visible:
-                    x0, y0 = visible[kp0]
-                    x1, y1 = visible[kp1]
-                    color = tuple(x / 255.0 for x in color)
-                    self.draw_line([x0, x1], [y0, y1], color=color)
-
-        # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip
-        # Note that this strategy is specific to person keypoints.
-        # For other keypoints, it should just do nothing
-        try:
-            ls_x, ls_y = visible["left_shoulder"]
-            rs_x, rs_y = visible["right_shoulder"]
-            mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2
-        except KeyError:
-            pass
-        else:
-            # draw line from nose to mid-shoulder
-            nose_x, nose_y = visible.get("nose", (None, None))
-            if nose_x is not None:
-                self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED)
-
-            try:
-                # draw line from mid-shoulder to mid-hip
-                lh_x, lh_y = visible["left_hip"]
-                rh_x, rh_y = visible["right_hip"]
-            except KeyError:
-                pass
-            else:
-                mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2
-                self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED)
-        return self.output
-
-    """
-    Primitive drawing functions:
-    """
-
-    def draw_text(
-        self,
-        text,
-        position,
-        *,
-        font_size=None,
-        color="g",
-        horizontal_alignment="center",
-        rotation=0,
-    ):
-        """
-        Args:
-            text (str): class label
-            position (tuple): a tuple of the x and y coordinates to place text on image.
-            font_size (int, optional): font of the text. If not provided, a font size
-                proportional to the image width is calculated and used.
-            color: color of the text. Refer to `matplotlib.colors` for full list
-                of formats that are accepted.
-            horizontal_alignment (str): see `matplotlib.text.Text`
-            rotation: rotation angle in degrees CCW
-        Returns:
-            output (VisImage): image object with text drawn.
-        """
-        if not font_size:
-            font_size = self._default_font_size
-
-        # since the text background is dark, we don't want the text to be dark
-        color = np.maximum(list(mplc.to_rgb(color)), 0.2)
-        color[np.argmax(color)] = max(0.8, np.max(color))
-
-        x, y = position
-        self.output.ax.text(
-            x,
-            y,
-            text,
-            size=font_size * self.output.scale,
-            family="sans-serif",
-            bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
-            verticalalignment="top",
-            horizontalalignment=horizontal_alignment,
-            color=color,
-            zorder=10,
-            rotation=rotation,
-        )
-        return self.output
-
-    def draw_box(self, box_coord, alpha=1.0, edge_color="g", line_style="-"):
-        """
-        Args:
-            box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0
-                are the coordinates of the image's top left corner. x1 and y1 are the
-                coordinates of the image's bottom right corner.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            edge_color: color of the outline of the box. Refer to `matplotlib.colors`
-                for full list of formats that are accepted.
-            line_style (string): the string to use to create the outline of the boxes.
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        x0, y0, x1, y1 = box_coord
-        width = x1 - x0
-        height = y1 - y0
-
-        linewidth = 2
-
-        self.output.ax.add_patch(
-            mpl.patches.Rectangle(
-                (x0, y0),
-                width,
-                height,
-                fill=False,
-                edgecolor=edge_color,
-                linewidth=linewidth * self.output.scale,
-                alpha=alpha,
-                linestyle=line_style,
-            )
-        )
-        return self.output
-
-    def draw_rotated_box_with_label(
-        self, rotated_box, alpha=0.5, edge_color="g", line_style="-", label=None
-    ):
-        """
-        Draw a rotated box with label on its top-left corner.
-        Args:
-            rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle),
-                where cnt_x and cnt_y are the center coordinates of the box.
-                w and h are the width and height of the box. angle represents how
-                many degrees the box is rotated CCW with regard to the 0-degree box.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            edge_color: color of the outline of the box. Refer to `matplotlib.colors`
-                for full list of formats that are accepted.
-            line_style (string): the string to use to create the outline of the boxes.
-            label (string): label for rotated box. It will not be rendered when set to None.
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        cnt_x, cnt_y, w, h, angle = rotated_box
-        area = w * h
-        # use thinner lines when the box is small
-        linewidth = self._default_font_size / (
-            6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3
-        )
-
-        theta = angle * math.pi / 180.0
-        c = math.cos(theta)
-        s = math.sin(theta)
-        rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)]
-        # x: left->right ; y: top->down
-        rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect]
-        for k in range(4):
-            j = (k + 1) % 4
-            self.draw_line(
-                [rotated_rect[k][0], rotated_rect[j][0]],
-                [rotated_rect[k][1], rotated_rect[j][1]],
-                color=edge_color,
-                linestyle="--" if k == 1 else line_style,
-                linewidth=linewidth,
-            )
-
-        if label is not None:
-            text_pos = rotated_rect[1]  # topleft corner
-
-            height_ratio = h / np.sqrt(self.output.height * self.output.width)
-            label_color = self._change_color_brightness(edge_color, brightness_factor=0.7)
-            font_size = (
-                np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size
-            )
-            self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle)
-
-        return self.output
-
-    def draw_circle(self, circle_coord, color, radius=3):
-        """
-        Args:
-            circle_coord (list(int) or tuple(int)): contains the x and y coordinates
-                of the center of the circle.
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            radius (int): radius of the circle.
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        x, y = circle_coord
-        self.output.ax.add_patch(
-            mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)
-        )
-        return self.output
-
-    def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None):
-        """
-        Args:
-            x_data (list[int]): a list containing x values of all the points being drawn.
-                Length of list should match the length of y_data.
-            y_data (list[int]): a list containing y values of all the points being drawn.
-                Length of list should match the length of x_data.
-            color: color of the line. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            linestyle: style of the line. Refer to `matplotlib.lines.Line2D`
-                for a full list of formats that are accepted.
-            linewidth (float or None): width of the line. When it's None,
-                a default value will be computed and used.
-        Returns:
-            output (VisImage): image object with line drawn.
-        """
-        if linewidth is None:
-            linewidth = self._default_font_size / 3
-        linewidth = max(linewidth, 1)
-        self.output.ax.add_line(
-            mpl.lines.Line2D(
-                x_data,
-                y_data,
-                linewidth=linewidth * self.output.scale,
-                color=color,
-                linestyle=linestyle,
-            )
-        )
-        return self.output
-
-    def draw_binary_mask(
-        self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10, is_text=True,
-    ):
-        """
-        Args:
-            binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and
-                W is the image width. Each value in the array is either a 0 or 1 value of uint8
-                type.
-            color: color of the mask. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted. If None, will pick a random color.
-            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
-                full list of formats that are accepted.
-            text (str): if None, will be drawn on the object
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            area_threshold (float): a connected component smaller than this area will not be shown.
-        Returns:
-            output (VisImage): image object with mask drawn.
-        """
-        if color is None:
-            color = random_color(rgb=True, maximum=1)
-        color = mplc.to_rgb(color)
-
-        has_valid_segment = False
-        binary_mask = binary_mask.astype("uint8")  # opencv needs uint8
-        mask = GenericMask(binary_mask, self.output.height, self.output.width)
-        shape2d = (binary_mask.shape[0], binary_mask.shape[1])
-
-        if not mask.has_holes:
-            # draw polygons for regular masks
-            for segment in mask.polygons:
-                area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1]))
-                if area < (area_threshold or 0):
-                    continue
-                has_valid_segment = True
-                segment = segment.reshape(-1, 2)
-                self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha)
-        else:
-            # TODO: Use Path/PathPatch to draw vector graphics:
-            # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon
-            rgba = np.zeros(shape2d + (4,), dtype="float32")
-            rgba[:, :, :3] = color
-            rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha
-            has_valid_segment = True
-            self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))
-
-        if is_text:
-            if text is not None and has_valid_segment:
-                lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-                self._draw_text_in_mask(binary_mask, text, lighter_color)
-        return self.output
-
-    def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):
-        """
-        Args:
-            soft_mask (ndarray): float array of shape (H, W), each value in [0, 1].
-            color: color of the mask. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted. If None, will pick a random color.
-            text (str): if None, will be drawn on the object
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-        Returns:
-            output (VisImage): image object with mask drawn.
-        """
-        if color is None:
-            color = random_color(rgb=True, maximum=1)
-        color = mplc.to_rgb(color)
-
-        shape2d = (soft_mask.shape[0], soft_mask.shape[1])
-        rgba = np.zeros(shape2d + (4,), dtype="float32")
-        rgba[:, :, :3] = color
-        rgba[:, :, 3] = soft_mask * alpha
-        self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))
-
-        if text is not None:
-            lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-            binary_mask = (soft_mask > 0.5).astype("uint8")
-            # self._draw_text_in_mask(binary_mask, text, lighter_color)
-        return self.output
-
-    def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):
-        """
-        Args:
-            segment: numpy array of shape Nx2, containing all the points in the polygon.
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
-                full list of formats that are accepted. If not provided, a darker shade
-                of the polygon color will be used instead.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-        Returns:
-            output (VisImage): image object with polygon drawn.
-        """
-        if edge_color is None:
-            # make edge color darker than the polygon color
-            if alpha > 0.8:
-                edge_color = self._change_color_brightness(color, brightness_factor=-0.7)
-            else:
-                edge_color = color
-        edge_color = mplc.to_rgb(edge_color) + (1,)
-
-        polygon = mpl.patches.Polygon(
-            segment,
-            fill=True,
-            facecolor=mplc.to_rgb(color) + (alpha,),
-            edgecolor=edge_color,
-            linewidth=max(self._default_font_size // 15 * self.output.scale, 1),
-        )
-        self.output.ax.add_patch(polygon)
-        return self.output
-
-    """
-    Internal methods:
-    """
-
-    def _jitter(self, color):
-        """
-        Randomly modifies given color to produce a slightly different color than the color given.
-        Args:
-            color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color
-                picked. The values in the list are in the [0.0, 1.0] range.
-        Returns:
-            jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the
-                color after being jittered. The values in the list are in the [0.0, 1.0] range.
-        """
-        color = mplc.to_rgb(color)
-        vec = np.random.rand(3)
-        # better to do it in another color space
-        vec = vec / np.linalg.norm(vec) * 0.5
-        res = np.clip(vec + color, 0, 1)
-        return tuple(res)
-
-    def _create_grayscale_image(self, mask=None):
-        """
-        Create a grayscale version of the original image.
-        The colors in masked area, if given, will be kept.
-        """
-        img_bw = self.img.astype("f4").mean(axis=2)
-        img_bw = np.stack([img_bw] * 3, axis=2)
-        if mask is not None:
-            img_bw[mask] = self.img[mask]
-        return img_bw
-
-    def _change_color_brightness(self, color, brightness_factor):
-        """
-        Depending on the brightness_factor, gives a lighter or darker color i.e. a color with
-        less or more saturation than the original color.
-        Args:
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of
-                0 will correspond to no change, a factor in [-1.0, 0) range will result in
-                a darker color and a factor in (0, 1.0] range will result in a lighter color.
-        Returns:
-            modified_color (tuple[double]): a tuple containing the RGB values of the
-                modified color. Each value in the tuple is in the [0.0, 1.0] range.
-        """
-        assert brightness_factor >= -1.0 and brightness_factor <= 1.0
-        color = mplc.to_rgb(color)
-        polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))
-        modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])
-        modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness
-        modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness
-        modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])
-        return modified_color
-
-    def _convert_boxes(self, boxes):
-        """
-        Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.
-        """
-        if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):
-            return boxes.tensor.detach().numpy()
-        else:
-            return np.asarray(boxes)
-
-    def _convert_masks(self, masks_or_polygons):
-        """
-        Convert different format of masks or polygons to a tuple of masks and polygons.
-        Returns:
-            list[GenericMask]:
-        """
-
-        m = masks_or_polygons
-        if isinstance(m, PolygonMasks):
-            m = m.polygons
-        if isinstance(m, BitMasks):
-            m = m.tensor.numpy()
-        if isinstance(m, torch.Tensor):
-            m = m.numpy()
-        ret = []
-        for x in m:
-            if isinstance(x, GenericMask):
-                ret.append(x)
-            else:
-                ret.append(GenericMask(x, self.output.height, self.output.width))
-        return ret
-
-    def _draw_text_in_mask(self, binary_mask, text, color):
-        """
-        Find proper places to draw text given a binary mask.
-        """
-        # TODO sometimes drawn on wrong objects. the heuristics here can improve.
-        _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)
-        if stats[1:, -1].size == 0:
-            return
-        largest_component_id = np.argmax(stats[1:, -1]) + 1
-
-        # draw text on the largest component, as well as other very large components.
-        for cid in range(1, _num_cc):
-            if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:
-                # median is more stable than centroid
-                # center = centroids[largest_component_id]
-                center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]
-                self.draw_text(text, center, color=color)
-
-    def _convert_keypoints(self, keypoints):
-        if isinstance(keypoints, Keypoints):
-            keypoints = keypoints.tensor
-        keypoints = np.asarray(keypoints)
-        return keypoints
-
-    def get_output(self):
-        """
-        Returns:
-            output (VisImage): the image output containing the visualizations added
-            to the image.
-        """
-        return self.output
\ No newline at end of file
diff --git a/spaces/csuhan/opendet2/opendet2/__init__.py b/spaces/csuhan/opendet2/opendet2/__init__.py
deleted file mode 100644
index f63ee6e5c2ab6ff93dcb9085935344985b494e6a..0000000000000000000000000000000000000000
--- a/spaces/csuhan/opendet2/opendet2/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from .config import *
-from .data import *
-from .engine import *
-from .evaluation import *
-from .modeling import *
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
diff --git a/spaces/cvsys/upscale/README.md b/spaces/cvsys/upscale/README.md
deleted file mode 100644
index 6ee1312e1135aeb1957c847bd503dc7911ec9ec1..0000000000000000000000000000000000000000
--- a/spaces/cvsys/upscale/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: UPSCALE
-emoji: 📷
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/daarumadx/bot/test.py b/spaces/daarumadx/bot/test.py
deleted file mode 100644
index 9a8bdd9b6a52c7b83353e83470382a141321d44e..0000000000000000000000000000000000000000
--- a/spaces/daarumadx/bot/test.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import requests
-
-host = 'https://noodles-84cf47b0a14d.herokuapp.com'
-x = requests.post(f'{host}/convert', files={'file': open(fr"C:\Users\akash\Projects\PersonalProjects\deepnude_api\imgs\source.png", 'rb')})
-y = open('test.png', 'wb')
-y.write(x.content)
-y.flush()
-y.close()
-print('done')
\ No newline at end of file
diff --git a/spaces/datastx/ChatWithADocDocker/README.md b/spaces/datastx/ChatWithADocDocker/README.md
deleted file mode 100644
index 9b6d5488bf3b2e3a6136b6c6413f92cfec19e82c..0000000000000000000000000000000000000000
--- a/spaces/datastx/ChatWithADocDocker/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: ChatWithADocDocker
-emoji: 🦀
-colorFrom: pink
-colorTo: green
-sdk: docker
-pinned: false
-license: bsd
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/davila7/semantic-search/app.py b/spaces/davila7/semantic-search/app.py
deleted file mode 100644
index 02e57409bd76d2c7460cb8ecf8de4c483f217769..0000000000000000000000000000000000000000
--- a/spaces/davila7/semantic-search/app.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import streamlit as st
-import os
-from utils import (
-    parse_docx,
-    parse_pdf,
-    parse_txt,
-    parse_csv,
-    search_docs,
-    embed_docs,
-    text_to_docs,
-    get_sources,
-)
-from openai.error import OpenAIError
-
-def clear_submit():
-    st.session_state["submit"] = False
-
-def set_openai_api_key(api_key: str):
-    st.session_state["OPENAI_API_KEY"] = api_key
-
-st.markdown('<h1>Semantic Search 🔍<small> by <a href="https://codegpt.co">Code GPT</a></small></h1>', unsafe_allow_html=True)
-
-# Sidebar
-index = None
-doc = None
-with st.sidebar:
-    user_secret = st.text_input(
-        "OpenAI API Key",
-        type="password",
-        placeholder="Paste your OpenAI API key here (sk-...)",
-        help="You can get your API key from https://platform.openai.com/account/api-keys.",
-        value=st.session_state.get("OPENAI_API_KEY", ""),
-    )
-    if user_secret:
-        set_openai_api_key(user_secret)
-
-    uploaded_file = st.file_uploader(
-        "Upload a pdf, docx, or txt file",
-        type=["pdf", "docx", "txt", "csv"],
-        help="Scanned documents are not supported yet!",
-        on_change=clear_submit,
-    )
-
-    if uploaded_file is not None:
-        if uploaded_file.name.endswith(".pdf"):
-            doc = parse_pdf(uploaded_file)
-        elif uploaded_file.name.endswith(".docx"):
-            doc = parse_docx(uploaded_file)
-        elif uploaded_file.name.endswith(".csv"):
-            doc = parse_csv(uploaded_file)
-        elif uploaded_file.name.endswith(".txt"):
-            doc = parse_txt(uploaded_file)
-        else:
-            st.error("File type not supported")
-            doc = None
-        text = text_to_docs(doc)
-        try:
-            with st.spinner("Indexing document... This may take a while⏳"):
-                result = embed_docs(text)
-                index = result[0]
-                embeddings = result[1]
-                st.session_state["api_key_configured"] = True
-        except OpenAIError as e:
-            st.error(e._message)
-
-tab1, tab2 = st.tabs(["Intro", "Semantic Search"])
-with tab1:
-    st.markdown("### Semantic Search with cosine similarity")
-    st.write("Cosine similarity is a technique used to measure the similarity between two vectors. In the context of OpenAI's embedding API, cosine similarity is used to compare the similarity between two pieces of text based on their underlying vector representations.")
-    st.markdown('<img width="701" alt="Captura de Pantalla 2023-02-25 a la(s) 2 28 38 p  m" src="https://user-images.githubusercontent.com/6216945/221375969-ba8b2349-fbc4-4070-abb7-92a21ed2b265.png">', unsafe_allow_html=True)
-    st.write("### Here's how it works:")
-    st.write("1. First, the embedding API converts each piece of text into a vector representation using a pre-trained language model. This vector represents the meaning and context of the text.")
-    st.write("2. The cosine similarity function then takes these two vectors and calculates the cosine of the angle between them. The cosine similarity score ranges from -1 to 1, where 1 indicates that the two vectors are identical, 0 indicates that they are completely dissimilar, and -1 indicates that they are exact opposites.")
-    st.write("3. This cosine similarity score is then used to determine the similarity between the two pieces of text. For example, if the cosine similarity score is close to 1, the two pieces of text are likely very similar in meaning, while a score close to 0 suggests that they are completely different.")
-    st.write("Overall, cosine similarity is a powerful tool for comparing the semantic similarity between two pieces of text, and OpenAI's embedding API makes it easy to implement this technique in your own projects.")
-    st.markdown("""---""")
-    st.markdown("## Semantic Search was written with the following tools:")
-    st.markdown("#### Code GPT")
-    st.write("All code was written with the help of Code GPT. Visit <a href='https://codegpt.co'>https://codegpt.co</a> to get the extension.", unsafe_allow_html=True)
-    st.markdown("#### Streamlit")
-    st.write("The design was written with <a href='https://streamlit.io/' target='_blank'>Streamlit</a>.", unsafe_allow_html=True)
-    st.markdown("#### LangChain")
-    st.markdown('<a href="https://platform.openai.com/docs/guides/embeddings">Embeddings</a> is done via the OpenAI API with "text-embedding-ada-002" and LangChain.', unsafe_allow_html=True)
-    st.markdown("<a href='https://github.com/facebookresearch/faiss'>FAISS</a> Facebook AI Similarity Search is a library for efficient similarity search and clustering of dense vectors.", unsafe_allow_html=True)
-    st.markdown("""---""")
-    st.write('Author: <a href="https://www.linkedin.com/in/daniel-avila-arias/">Daniel Avila</a>', unsafe_allow_html=True)
-    st.write('Repo: <a href="https://github.com/davila7/semantic-search">Github</a>', unsafe_allow_html=True)
-    st.write("This software was developed with Code GPT, for more information visit: https://codegpt.co")
-with tab2:
-    st.write('To obtain an API Key you must create an OpenAI account at the following link: https://openai.com/api/')
-    query = st.text_area("Ask a question about the document", on_change=clear_submit)
-    button = st.button("Submit")
-    if button or st.session_state.get("submit"):
-        if not query:
-            st.error("Please enter a question!")
-        else:
-            st.session_state["submit"] = True
-            sources = search_docs(index, query)
-            st.markdown("#### Sources")
-            for source in sources:
-                st.markdown(source.page_content)
-                st.markdown(source.metadata["source"])
-                st.markdown("---")
\ No newline at end of file
diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/dist_util.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/dist_util.py
deleted file mode 100644
index 0fab887b2cb1ce8533d2e8fdee72ae0c24f68fd0..0000000000000000000000000000000000000000
--- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/dist_util.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py  # noqa: E501
-import functools
-import os
-import subprocess
-import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
-
-
-def init_dist(launcher, backend='nccl', **kwargs):
-    if mp.get_start_method(allow_none=True) is None:
-        mp.set_start_method('spawn')
-    if launcher == 'pytorch':
-        _init_dist_pytorch(backend, **kwargs)
-    elif launcher == 'slurm':
-        _init_dist_slurm(backend, **kwargs)
-    else:
-        raise ValueError(f'Invalid launcher type: {launcher}')
-
-
-def _init_dist_pytorch(backend, **kwargs):
-    rank = int(os.environ['RANK'])
-    num_gpus = torch.cuda.device_count()
-    torch.cuda.set_device(rank % num_gpus)
-    dist.init_process_group(backend=backend, **kwargs)
-
-
-def _init_dist_slurm(backend, port=None):
-    """Initialize slurm distributed training environment.
-
-    If argument ``port`` is not specified, then the master port will be system
-    environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
-    environment variable, then a default port ``29500`` will be used.
-
-    Args:
-        backend (str): Backend of torch.distributed.
-        port (int, optional): Master port. Defaults to None.
-    """
-    proc_id = int(os.environ['SLURM_PROCID'])
-    ntasks = int(os.environ['SLURM_NTASKS'])
-    node_list = os.environ['SLURM_NODELIST']
-    num_gpus = torch.cuda.device_count()
-    torch.cuda.set_device(proc_id % num_gpus)
-    addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1')
-    # specify master port
-    if port is not None:
-        os.environ['MASTER_PORT'] = str(port)
-    elif 'MASTER_PORT' in os.environ:
-        pass  # use MASTER_PORT in the environment variable
-    else:
-        # 29500 is torch.distributed default port
-        os.environ['MASTER_PORT'] = '29500'
-    os.environ['MASTER_ADDR'] = addr
-    os.environ['WORLD_SIZE'] = str(ntasks)
-    os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
-    os.environ['RANK'] = str(proc_id)
-    dist.init_process_group(backend=backend)
-
-
-def get_dist_info():
-    if dist.is_available():
-        initialized = dist.is_initialized()
-    else:
-        initialized = False
-    if initialized:
-        rank = dist.get_rank()
-        world_size = dist.get_world_size()
-    else:
-        rank = 0
-        world_size = 1
-    return rank, world_size
-
-
-def master_only(func):
-
-    @functools.wraps(func)
-    def wrapper(*args, **kwargs):
-        rank, _ = get_dist_info()
-        if rank == 0:
-            return func(*args, **kwargs)
-
-    return wrapper
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/tests/test_format.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/tests/test_format.py
deleted file mode 100644
index 371eb90da026ba4cd746a65a445f18633ce82c7f..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/tests/test_format.py
+++ /dev/null
@@ -1,90 +0,0 @@
-"""
-Tests for the parts of jsonschema related to the :kw:`format` keyword.
-"""
-
-from unittest import TestCase
-
-from jsonschema import FormatChecker, ValidationError
-from jsonschema.exceptions import FormatError
-from jsonschema.validators import Draft4Validator
-
-BOOM = ValueError("Boom!")
-BANG = ZeroDivisionError("Bang!")
-
-
-def boom(thing):
-    if thing == "bang":
-        raise BANG
-    raise BOOM
-
-
-class TestFormatChecker(TestCase):
-    def test_it_can_validate_no_formats(self):
-        checker = FormatChecker(formats=())
-        self.assertFalse(checker.checkers)
-
-    def test_it_raises_a_key_error_for_unknown_formats(self):
-        with self.assertRaises(KeyError):
-            FormatChecker(formats=["o noes"])
-
-    def test_it_can_register_cls_checkers(self):
-        original = dict(FormatChecker.checkers)
-        self.addCleanup(FormatChecker.checkers.pop, "boom")
-        with self.assertWarns(DeprecationWarning):
-            FormatChecker.cls_checks("boom")(boom)
-        self.assertEqual(
-            FormatChecker.checkers,
-            dict(original, boom=(boom, ())),
-        )
-
-    def test_it_can_register_checkers(self):
-        checker = FormatChecker()
-        checker.checks("boom")(boom)
-        self.assertEqual(
-            checker.checkers,
-            dict(FormatChecker.checkers, boom=(boom, ())),
-        )
-
-    def test_it_catches_registered_errors(self):
-        checker = FormatChecker()
-        checker.checks("boom", raises=type(BOOM))(boom)
-
-        with self.assertRaises(FormatError) as cm:
-            checker.check(instance=12, format="boom")
-
-        self.assertIs(cm.exception.cause, BOOM)
-        self.assertIs(cm.exception.__cause__, BOOM)
-
-        # Unregistered errors should not be caught
-        with self.assertRaises(type(BANG)):
-            checker.check(instance="bang", format="boom")
-
-    def test_format_error_causes_become_validation_error_causes(self):
-        checker = FormatChecker()
-        checker.checks("boom", raises=ValueError)(boom)
-        validator = Draft4Validator({"format": "boom"}, format_checker=checker)
-
-        with self.assertRaises(ValidationError) as cm:
-            validator.validate("BOOM")
-
-        self.assertIs(cm.exception.cause, BOOM)
-        self.assertIs(cm.exception.__cause__, BOOM)
-
-    def test_format_checkers_come_with_defaults(self):
-        # This is bad :/ but relied upon.
-        # The docs for quite awhile recommended people do things like
-        # validate(..., format_checker=FormatChecker())
-        # We should change that, but we can't without deprecation...
-        checker = FormatChecker()
-        with self.assertRaises(FormatError):
-            checker.check(instance="not-an-ipv4", format="ipv4")
-
-    def test_repr(self):
-        checker = FormatChecker(formats=())
-        checker.checks("foo")(lambda thing: True)  # pragma: no cover
-        checker.checks("bar")(lambda thing: True)  # pragma: no cover
-        checker.checks("baz")(lambda thing: True)  # pragma: no cover
-        self.assertEqual(
-            repr(checker),
-            "<FormatChecker checkers=['bar', 'baz', 'foo']>",
-        )
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/animation.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/animation.py
deleted file mode 100644
index 2d8156a51599bdf766541365b0da38657dd95bd4..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/animation.py
+++ /dev/null
@@ -1,1790 +0,0 @@
-# TODO:
-# * Documentation -- this will need a new section of the User's Guide.
-#      Both for Animations and just timers.
-#   - Also need to update
-#     https://scipy-cookbook.readthedocs.io/items/Matplotlib_Animations.html
-# * Blit
-#   * Currently broken with Qt4 for widgets that don't start on screen
-#   * Still a few edge cases that aren't working correctly
-#   * Can this integrate better with existing matplotlib animation artist flag?
-#     - If animated removes from default draw(), perhaps we could use this to
-#       simplify initial draw.
-# * Example
-#   * Frameless animation - pure procedural with no loop
-#   * Need example that uses something like inotify or subprocess
-#   * Complex syncing examples
-# * Movies
-#   * Can blit be enabled for movies?
-# * Need to consider event sources to allow clicking through multiple figures
-
-
-import abc
-import base64
-import contextlib
-from io import BytesIO, TextIOWrapper
-import itertools
-import logging
-from pathlib import Path
-import shutil
-import subprocess
-import sys
-from tempfile import TemporaryDirectory
-import uuid
-import warnings
-
-import numpy as np
-from PIL import Image
-
-import matplotlib as mpl
-from matplotlib._animation_data import (
-    DISPLAY_TEMPLATE, INCLUDED_FRAMES, JS_INCLUDE, STYLE_INCLUDE)
-from matplotlib import _api, cbook
-import matplotlib.colors as mcolors
-
-_log = logging.getLogger(__name__)
-
-# Process creation flag for subprocess to prevent it raising a terminal
-# window. See for example https://stackoverflow.com/q/24130623/
-subprocess_creation_flags = (
-    subprocess.CREATE_NO_WINDOW if sys.platform == 'win32' else 0)
-
-# Other potential writing methods:
-# * http://pymedia.org/
-# * libming (produces swf) python wrappers: https://github.com/libming/libming
-# * Wrap x264 API:
-
-# (https://stackoverflow.com/q/2940671/)
-
-
-def adjusted_figsize(w, h, dpi, n):
-    """
-    Compute figure size so that pixels are a multiple of n.
-
-    Parameters
-    ----------
-    w, h : float
-        Size in inches.
-
-    dpi : float
-        The dpi.
-
-    n : int
-        The target multiple.
-
-    Returns
-    -------
-    wnew, hnew : float
-        The new figure size in inches.
-    """
-
-    # this maybe simplified if / when we adopt consistent rounding for
-    # pixel size across the whole library
-    def correct_roundoff(x, dpi, n):
-        if int(x*dpi) % n != 0:
-            if int(np.nextafter(x, np.inf)*dpi) % n == 0:
-                x = np.nextafter(x, np.inf)
-            elif int(np.nextafter(x, -np.inf)*dpi) % n == 0:
-                x = np.nextafter(x, -np.inf)
-        return x
-
-    wnew = int(w * dpi / n) * n / dpi
-    hnew = int(h * dpi / n) * n / dpi
-    return correct_roundoff(wnew, dpi, n), correct_roundoff(hnew, dpi, n)
-
-
-class MovieWriterRegistry:
-    """Registry of available writer classes by human readable name."""
-
-    def __init__(self):
-        self._registered = dict()
-
-    def register(self, name):
-        """
-        Decorator for registering a class under a name.
-
-        Example use::
-
-            @registry.register(name)
-            class Foo:
-                pass
-        """
-        def wrapper(writer_cls):
-            self._registered[name] = writer_cls
-            return writer_cls
-        return wrapper
-
-    def is_available(self, name):
-        """
-        Check if given writer is available by name.
-
-        Parameters
-        ----------
-        name : str
-
-        Returns
-        -------
-        bool
-        """
-        try:
-            cls = self._registered[name]
-        except KeyError:
-            return False
-        return cls.isAvailable()
-
-    def __iter__(self):
-        """Iterate over names of available writer class."""
-        for name in self._registered:
-            if self.is_available(name):
-                yield name
-
-    def list(self):
-        """Get a list of available MovieWriters."""
-        return [*self]
-
-    def __getitem__(self, name):
-        """Get an available writer class from its name."""
-        if self.is_available(name):
-            return self._registered[name]
-        raise RuntimeError(f"Requested MovieWriter ({name}) not available")
-
-
-writers = MovieWriterRegistry()
-
-
-class AbstractMovieWriter(abc.ABC):
-    """
-    Abstract base class for writing movies, providing a way to grab frames by
-    calling `~AbstractMovieWriter.grab_frame`.
-
-    `setup` is called to start the process and `finish` is called afterwards.
-    `saving` is provided as a context manager to facilitate this process as ::
-
-        with moviewriter.saving(fig, outfile='myfile.mp4', dpi=100):
-            # Iterate over frames
-            moviewriter.grab_frame(**savefig_kwargs)
-
-    The use of the context manager ensures that `setup` and `finish` are
-    performed as necessary.
-
-    An instance of a concrete subclass of this class can be given as the
-    ``writer`` argument of `Animation.save()`.
-    """
-
-    def __init__(self, fps=5, metadata=None, codec=None, bitrate=None):
-        self.fps = fps
-        self.metadata = metadata if metadata is not None else {}
-        self.codec = (
-            mpl.rcParams['animation.codec'] if codec is None else codec)
-        self.bitrate = (
-            mpl.rcParams['animation.bitrate'] if bitrate is None else bitrate)
-
-    @abc.abstractmethod
-    def setup(self, fig, outfile, dpi=None):
-        """
-        Setup for writing the movie file.
-
-        Parameters
-        ----------
-        fig : `~matplotlib.figure.Figure`
-            The figure object that contains the information for frames.
-        outfile : str
-            The filename of the resulting movie file.
-        dpi : float, default: ``fig.dpi``
-            The DPI (or resolution) for the file.  This controls the size
-            in pixels of the resulting movie file.
-        """
-        # Check that path is valid
-        Path(outfile).parent.resolve(strict=True)
-        self.outfile = outfile
-        self.fig = fig
-        if dpi is None:
-            dpi = self.fig.dpi
-        self.dpi = dpi
-
-    @property
-    def frame_size(self):
-        """A tuple ``(width, height)`` in pixels of a movie frame."""
-        w, h = self.fig.get_size_inches()
-        return int(w * self.dpi), int(h * self.dpi)
-
-    @abc.abstractmethod
-    def grab_frame(self, **savefig_kwargs):
-        """
-        Grab the image information from the figure and save as a movie frame.
-
-        All keyword arguments in *savefig_kwargs* are passed on to the
-        `~.Figure.savefig` call that saves the figure.
-        """
-
-    @abc.abstractmethod
-    def finish(self):
-        """Finish any processing for writing the movie."""
-
-    @contextlib.contextmanager
-    def saving(self, fig, outfile, dpi, *args, **kwargs):
-        """
-        Context manager to facilitate writing the movie file.
-
-        ``*args, **kw`` are any parameters that should be passed to `setup`.
-        """
-        # This particular sequence is what contextlib.contextmanager wants
-        self.setup(fig, outfile, dpi, *args, **kwargs)
-        try:
-            yield self
-        finally:
-            self.finish()
-
-
-class MovieWriter(AbstractMovieWriter):
-    """
-    Base class for writing movies.
-
-    This is a base class for MovieWriter subclasses that write a movie frame
-    data to a pipe. You cannot instantiate this class directly.
-    See examples for how to use its subclasses.
-
-    Attributes
-    ----------
-    frame_format : str
-        The format used in writing frame data, defaults to 'rgba'.
-    fig : `~matplotlib.figure.Figure`
-        The figure to capture data from.
-        This must be provided by the subclasses.
-    """
-
-    # Builtin writer subclasses additionally define the _exec_key and _args_key
-    # attributes, which indicate the rcParams entries where the path to the
-    # executable and additional command-line arguments to the executable are
-    # stored.  Third-party writers cannot meaningfully set these as they cannot
-    # extend rcParams with new keys.
-
-    # Pipe-based writers only support RGBA, but file-based ones support more
-    # formats.
-    supported_formats = ["rgba"]
-
-    def __init__(self, fps=5, codec=None, bitrate=None, extra_args=None,
-                 metadata=None):
-        """
-        Parameters
-        ----------
-        fps : int, default: 5
-            Movie frame rate (per second).
-        codec : str or None, default: :rc:`animation.codec`
-            The codec to use.
-        bitrate : int, default: :rc:`animation.bitrate`
-            The bitrate of the movie, in kilobits per second.  Higher values
-            means higher quality movies, but increase the file size.  A value
-            of -1 lets the underlying movie encoder select the bitrate.
-        extra_args : list of str or None, optional
-            Extra command-line arguments passed to the underlying movie
-            encoder.  The default, None, means to use
-            :rc:`animation.[name-of-encoder]_args` for the builtin writers.
-        metadata : dict[str, str], default: {}
-            A dictionary of keys and values for metadata to include in the
-            output file. Some keys that may be of use include:
-            title, artist, genre, subject, copyright, srcform, comment.
-        """
-        if type(self) is MovieWriter:
-            # TODO MovieWriter is still an abstract class and needs to be
-            #      extended with a mixin. This should be clearer in naming
-            #      and description. For now, just give a reasonable error
-            #      message to users.
-            raise TypeError(
-                'MovieWriter cannot be instantiated directly. Please use one '
-                'of its subclasses.')
-
-        super().__init__(fps=fps, metadata=metadata, codec=codec,
-                         bitrate=bitrate)
-        self.frame_format = self.supported_formats[0]
-        self.extra_args = extra_args
-
-    def _adjust_frame_size(self):
-        if self.codec == 'h264':
-            wo, ho = self.fig.get_size_inches()
-            w, h = adjusted_figsize(wo, ho, self.dpi, 2)
-            if (wo, ho) != (w, h):
-                self.fig.set_size_inches(w, h, forward=True)
-                _log.info('figure size in inches has been adjusted '
-                          'from %s x %s to %s x %s', wo, ho, w, h)
-        else:
-            w, h = self.fig.get_size_inches()
-        _log.debug('frame size in pixels is %s x %s', *self.frame_size)
-        return w, h
-
-    def setup(self, fig, outfile, dpi=None):
-        # docstring inherited
-        super().setup(fig, outfile, dpi=dpi)
-        self._w, self._h = self._adjust_frame_size()
-        # Run here so that grab_frame() can write the data to a pipe. This
-        # eliminates the need for temp files.
-        self._run()
-
-    def _run(self):
-        # Uses subprocess to call the program for assembling frames into a
-        # movie file.  *args* returns the sequence of command line arguments
-        # from a few configuration options.
-        command = self._args()
-        _log.info('MovieWriter._run: running command: %s',
-                  cbook._pformat_subprocess(command))
-        PIPE = subprocess.PIPE
-        self._proc = subprocess.Popen(
-            command, stdin=PIPE, stdout=PIPE, stderr=PIPE,
-            creationflags=subprocess_creation_flags)
-
-    def finish(self):
-        """Finish any processing for writing the movie."""
-        out, err = self._proc.communicate()
-        # Use the encoding/errors that universal_newlines would use.
-        out = TextIOWrapper(BytesIO(out)).read()
-        err = TextIOWrapper(BytesIO(err)).read()
-        if out:
-            _log.log(
-                logging.WARNING if self._proc.returncode else logging.DEBUG,
-                "MovieWriter stdout:\n%s", out)
-        if err:
-            _log.log(
-                logging.WARNING if self._proc.returncode else logging.DEBUG,
-                "MovieWriter stderr:\n%s", err)
-        if self._proc.returncode:
-            raise subprocess.CalledProcessError(
-                self._proc.returncode, self._proc.args, out, err)
-
-    def grab_frame(self, **savefig_kwargs):
-        # docstring inherited
-        _log.debug('MovieWriter.grab_frame: Grabbing frame.')
-        # Readjust the figure size in case it has been changed by the user.
-        # All frames must have the same size to save the movie correctly.
-        self.fig.set_size_inches(self._w, self._h)
-        # Save the figure data to the sink, using the frame format and dpi.
-        self.fig.savefig(self._proc.stdin, format=self.frame_format,
-                         dpi=self.dpi, **savefig_kwargs)
-
-    def _args(self):
-        """Assemble list of encoder-specific command-line arguments."""
-        return NotImplementedError("args needs to be implemented by subclass.")
-
-    @classmethod
-    def bin_path(cls):
-        """
-        Return the binary path to the commandline tool used by a specific
-        subclass. This is a class method so that the tool can be looked for
-        before making a particular MovieWriter subclass available.
-        """
-        return str(mpl.rcParams[cls._exec_key])
-
-    @classmethod
-    def isAvailable(cls):
-        """Return whether a MovieWriter subclass is actually available."""
-        return shutil.which(cls.bin_path()) is not None
-
-
-class FileMovieWriter(MovieWriter):
-    """
-    `MovieWriter` for writing to individual files and stitching at the end.
-
-    This must be sub-classed to be useful.
-    """
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.frame_format = mpl.rcParams['animation.frame_format']
-
-    def setup(self, fig, outfile, dpi=None, frame_prefix=None):
-        """
-        Setup for writing the movie file.
-
-        Parameters
-        ----------
-        fig : `~matplotlib.figure.Figure`
-            The figure to grab the rendered frames from.
-        outfile : str
-            The filename of the resulting movie file.
-        dpi : float, default: ``fig.dpi``
-            The dpi of the output file. This, with the figure size,
-            controls the size in pixels of the resulting movie file.
-        frame_prefix : str, optional
-            The filename prefix to use for temporary files.  If *None* (the
-            default), files are written to a temporary directory which is
-            deleted by `finish`; if not *None*, no temporary files are
-            deleted.
-        """
-        # Check that path is valid
-        Path(outfile).parent.resolve(strict=True)
-        self.fig = fig
-        self.outfile = outfile
-        if dpi is None:
-            dpi = self.fig.dpi
-        self.dpi = dpi
-        self._adjust_frame_size()
-
-        if frame_prefix is None:
-            self._tmpdir = TemporaryDirectory()
-            self.temp_prefix = str(Path(self._tmpdir.name, 'tmp'))
-        else:
-            self._tmpdir = None
-            self.temp_prefix = frame_prefix
-        self._frame_counter = 0  # used for generating sequential file names
-        self._temp_paths = list()
-        self.fname_format_str = '%s%%07d.%s'
-
-    def __del__(self):
-        if hasattr(self, '_tmpdir') and self._tmpdir:
-            self._tmpdir.cleanup()
-
-    @property
-    def frame_format(self):
-        """
-        Format (png, jpeg, etc.) to use for saving the frames, which can be
-        decided by the individual subclasses.
-        """
-        return self._frame_format
-
-    @frame_format.setter
-    def frame_format(self, frame_format):
-        if frame_format in self.supported_formats:
-            self._frame_format = frame_format
-        else:
-            _api.warn_external(
-                f"Ignoring file format {frame_format!r} which is not "
-                f"supported by {type(self).__name__}; using "
-                f"{self.supported_formats[0]} instead.")
-            self._frame_format = self.supported_formats[0]
-
-    def _base_temp_name(self):
-        # Generates a template name (without number) given the frame format
-        # for extension and the prefix.
-        return self.fname_format_str % (self.temp_prefix, self.frame_format)
-
-    def grab_frame(self, **savefig_kwargs):
-        # docstring inherited
-        # Creates a filename for saving using basename and counter.
-        path = Path(self._base_temp_name() % self._frame_counter)
-        self._temp_paths.append(path)  # Record the filename for later use.
-        self._frame_counter += 1  # Ensures each created name is unique.
-        _log.debug('FileMovieWriter.grab_frame: Grabbing frame %d to path=%s',
-                   self._frame_counter, path)
-        with open(path, 'wb') as sink:  # Save figure to the sink.
-            self.fig.savefig(sink, format=self.frame_format, dpi=self.dpi,
-                             **savefig_kwargs)
-
-    def finish(self):
-        # Call run here now that all frame grabbing is done. All temp files
-        # are available to be assembled.
-        try:
-            self._run()
-            super().finish()
-        finally:
-            if self._tmpdir:
-                _log.debug(
-                    'MovieWriter: clearing temporary path=%s', self._tmpdir
-                )
-                self._tmpdir.cleanup()
-
-
-@writers.register('pillow')
-class PillowWriter(AbstractMovieWriter):
-    @classmethod
-    def isAvailable(cls):
-        return True
-
-    def setup(self, fig, outfile, dpi=None):
-        super().setup(fig, outfile, dpi=dpi)
-        self._frames = []
-
-    def grab_frame(self, **savefig_kwargs):
-        buf = BytesIO()
-        self.fig.savefig(
-            buf, **{**savefig_kwargs, "format": "rgba", "dpi": self.dpi})
-        self._frames.append(Image.frombuffer(
-            "RGBA", self.frame_size, buf.getbuffer(), "raw", "RGBA", 0, 1))
-
-    def finish(self):
-        self._frames[0].save(
-            self.outfile, save_all=True, append_images=self._frames[1:],
-            duration=int(1000 / self.fps), loop=0)
-
-
-# Base class of ffmpeg information. Has the config keys and the common set
-# of arguments that controls the *output* side of things.
-class FFMpegBase:
-    """
-    Mixin class for FFMpeg output.
-
-    This is a base class for the concrete `FFMpegWriter` and `FFMpegFileWriter`
-    classes.
-    """
-
-    _exec_key = 'animation.ffmpeg_path'
-    _args_key = 'animation.ffmpeg_args'
-
-    @property
-    def output_args(self):
-        args = []
-        if Path(self.outfile).suffix == '.gif':
-            self.codec = 'gif'
-        else:
-            args.extend(['-vcodec', self.codec])
-        extra_args = (self.extra_args if self.extra_args is not None
-                      else mpl.rcParams[self._args_key])
-        # For h264, the default format is yuv444p, which is not compatible
-        # with quicktime (and others). Specifying yuv420p fixes playback on
-        # iOS, as well as HTML5 video in firefox and safari (on both Win and
-        # OSX). Also fixes internet explorer. This is as of 2015/10/29.
-        if self.codec == 'h264' and '-pix_fmt' not in extra_args:
-            args.extend(['-pix_fmt', 'yuv420p'])
-        # For GIF, we're telling FFMPEG to split the video stream, to generate
-        # a palette, and then use it for encoding.
-        elif self.codec == 'gif' and '-filter_complex' not in extra_args:
-            args.extend(['-filter_complex',
-                         'split [a][b];[a] palettegen [p];[b][p] paletteuse'])
-        if self.bitrate > 0:
-            args.extend(['-b', '%dk' % self.bitrate])  # %dk: bitrate in kbps.
-        args.extend(extra_args)
-        for k, v in self.metadata.items():
-            args.extend(['-metadata', '%s=%s' % (k, v)])
-
-        return args + ['-y', self.outfile]
-
-
-# Combine FFMpeg options with pipe-based writing
-@writers.register('ffmpeg')
-class FFMpegWriter(FFMpegBase, MovieWriter):
-    """
-    Pipe-based ffmpeg writer.
-
-    Frames are streamed directly to ffmpeg via a pipe and written in a single
-    pass.
-    """
-    def _args(self):
-        # Returns the command line parameters for subprocess to use
-        # ffmpeg to create a movie using a pipe.
-        args = [self.bin_path(), '-f', 'rawvideo', '-vcodec', 'rawvideo',
-                '-s', '%dx%d' % self.frame_size, '-pix_fmt', self.frame_format,
-                '-r', str(self.fps)]
-        # Logging is quieted because subprocess.PIPE has limited buffer size.
-        # If you have a lot of frames in your animation and set logging to
-        # DEBUG, you will have a buffer overrun.
-        if _log.getEffectiveLevel() > logging.DEBUG:
-            args += ['-loglevel', 'error']
-        args += ['-i', 'pipe:'] + self.output_args
-        return args
-
-
-# Combine FFMpeg options with temp file-based writing
-@writers.register('ffmpeg_file')
-class FFMpegFileWriter(FFMpegBase, FileMovieWriter):
-    """
-    File-based ffmpeg writer.
-
-    Frames are written to temporary files on disk and then stitched
-    together at the end.
-    """
-    supported_formats = ['png', 'jpeg', 'tiff', 'raw', 'rgba']
-
-    def _args(self):
-        # Returns the command line parameters for subprocess to use
-        # ffmpeg to create a movie using a collection of temp images
-        args = []
-        # For raw frames, we need to explicitly tell ffmpeg the metadata.
-        if self.frame_format in {'raw', 'rgba'}:
-            args += [
-                '-f', 'image2', '-vcodec', 'rawvideo',
-                '-video_size', '%dx%d' % self.frame_size,
-                '-pixel_format', 'rgba',
-                '-framerate', str(self.fps),
-            ]
-        args += ['-r', str(self.fps), '-i', self._base_temp_name(),
-                 '-vframes', str(self._frame_counter)]
-        # Logging is quieted because subprocess.PIPE has limited buffer size.
-        # If you have a lot of frames in your animation and set logging to
-        # DEBUG, you will have a buffer overrun.
-        if _log.getEffectiveLevel() > logging.DEBUG:
-            args += ['-loglevel', 'error']
-        return [self.bin_path(), *args, *self.output_args]
-
-
-# Base class for animated GIFs with ImageMagick
-class ImageMagickBase:
-    """
-    Mixin class for ImageMagick output.
-
-    This is a base class for the concrete `ImageMagickWriter` and
-    `ImageMagickFileWriter` classes, which define an ``input_names`` attribute
-    (or property) specifying the input names passed to ImageMagick.
-    """
-
-    _exec_key = 'animation.convert_path'
-    _args_key = 'animation.convert_args'
-
-    @_api.deprecated("3.6")
-    @property
-    def delay(self):
-        return 100. / self.fps
-
-    @_api.deprecated("3.6")
-    @property
-    def output_args(self):
-        extra_args = (self.extra_args if self.extra_args is not None
-                      else mpl.rcParams[self._args_key])
-        return [*extra_args, self.outfile]
-
-    def _args(self):
-        # ImageMagick does not recognize "raw".
-        fmt = "rgba" if self.frame_format == "raw" else self.frame_format
-        extra_args = (self.extra_args if self.extra_args is not None
-                      else mpl.rcParams[self._args_key])
-        return [
-            self.bin_path(),
-            "-size", "%ix%i" % self.frame_size,
-            "-depth", "8",
-            "-delay", str(100 / self.fps),
-            "-loop", "0",
-            f"{fmt}:{self.input_names}",
-            *extra_args,
-            self.outfile,
-        ]
-
-    @classmethod
-    def bin_path(cls):
-        binpath = super().bin_path()
-        if binpath == 'convert':
-            binpath = mpl._get_executable_info('magick').executable
-        return binpath
-
-    @classmethod
-    def isAvailable(cls):
-        try:
-            return super().isAvailable()
-        except mpl.ExecutableNotFoundError as _enf:
-            # May be raised by get_executable_info.
-            _log.debug('ImageMagick unavailable due to: %s', _enf)
-            return False
-
-
-# Combine ImageMagick options with pipe-based writing
-@writers.register('imagemagick')
-class ImageMagickWriter(ImageMagickBase, MovieWriter):
-    """
-    Pipe-based animated gif writer.
-
-    Frames are streamed directly to ImageMagick via a pipe and written
-    in a single pass.
-    """
-
-    input_names = "-"  # stdin
-
-
-# Combine ImageMagick options with temp file-based writing
-@writers.register('imagemagick_file')
-class ImageMagickFileWriter(ImageMagickBase, FileMovieWriter):
-    """
-    File-based animated gif writer.
-
-    Frames are written to temporary files on disk and then stitched
-    together at the end.
-    """
-
-    supported_formats = ['png', 'jpeg', 'tiff', 'raw', 'rgba']
-    input_names = property(
-        lambda self: f'{self.temp_prefix}*.{self.frame_format}')
-
-
-# Taken directly from jakevdp's JSAnimation package at
-# http://github.com/jakevdp/JSAnimation
-def _included_frames(frame_count, frame_format, frame_dir):
-    return INCLUDED_FRAMES.format(Nframes=frame_count,
-                                  frame_dir=frame_dir,
-                                  frame_format=frame_format)
-
-
-def _embedded_frames(frame_list, frame_format):
-    """frame_list should be a list of base64-encoded png files"""
-    if frame_format == 'svg':
-        # Fix MIME type for svg
-        frame_format = 'svg+xml'
-    template = '  frames[{0}] = "data:image/{1};base64,{2}"\n'
-    return "\n" + "".join(
-        template.format(i, frame_format, frame_data.replace('\n', '\\\n'))
-        for i, frame_data in enumerate(frame_list))
-
-
-@writers.register('html')
-class HTMLWriter(FileMovieWriter):
-    """Writer for JavaScript-based HTML movies."""
-
-    supported_formats = ['png', 'jpeg', 'tiff', 'svg']
-
-    @classmethod
-    def isAvailable(cls):
-        return True
-
-    def __init__(self, fps=30, codec=None, bitrate=None, extra_args=None,
-                 metadata=None, embed_frames=False, default_mode='loop',
-                 embed_limit=None):
-
-        if extra_args:
-            _log.warning("HTMLWriter ignores 'extra_args'")
-        extra_args = ()  # Don't lookup nonexistent rcParam[args_key].
-        self.embed_frames = embed_frames
-        self.default_mode = default_mode.lower()
-        _api.check_in_list(['loop', 'once', 'reflect'],
-                           default_mode=self.default_mode)
-
-        # Save embed limit, which is given in MB
-        if embed_limit is None:
-            self._bytes_limit = mpl.rcParams['animation.embed_limit']
-        else:
-            self._bytes_limit = embed_limit
-        # Convert from MB to bytes
-        self._bytes_limit *= 1024 * 1024
-
-        super().__init__(fps, codec, bitrate, extra_args, metadata)
-
-    def setup(self, fig, outfile, dpi=None, frame_dir=None):
-        outfile = Path(outfile)
-        _api.check_in_list(['.html', '.htm'], outfile_extension=outfile.suffix)
-
-        self._saved_frames = []
-        self._total_bytes = 0
-        self._hit_limit = False
-
-        if not self.embed_frames:
-            if frame_dir is None:
-                frame_dir = outfile.with_name(outfile.stem + '_frames')
-            frame_dir.mkdir(parents=True, exist_ok=True)
-            frame_prefix = frame_dir / 'frame'
-        else:
-            frame_prefix = None
-
-        super().setup(fig, outfile, dpi, frame_prefix)
-        self._clear_temp = False
-
-    def grab_frame(self, **savefig_kwargs):
-        if self.embed_frames:
-            # Just stop processing if we hit the limit
-            if self._hit_limit:
-                return
-            f = BytesIO()
-            self.fig.savefig(f, format=self.frame_format,
-                             dpi=self.dpi, **savefig_kwargs)
-            imgdata64 = base64.encodebytes(f.getvalue()).decode('ascii')
-            self._total_bytes += len(imgdata64)
-            if self._total_bytes >= self._bytes_limit:
-                _log.warning(
-                    "Animation size has reached %s bytes, exceeding the limit "
-                    "of %s. If you're sure you want a larger animation "
-                    "embedded, set the animation.embed_limit rc parameter to "
-                    "a larger value (in MB). This and further frames will be "
-                    "dropped.", self._total_bytes, self._bytes_limit)
-                self._hit_limit = True
-            else:
-                self._saved_frames.append(imgdata64)
-        else:
-            return super().grab_frame(**savefig_kwargs)
-
-    def finish(self):
-        # save the frames to an html file
-        if self.embed_frames:
-            fill_frames = _embedded_frames(self._saved_frames,
-                                           self.frame_format)
-            frame_count = len(self._saved_frames)
-        else:
-            # temp names is filled by FileMovieWriter
-            frame_count = len(self._temp_paths)
-            fill_frames = _included_frames(
-                frame_count, self.frame_format,
-                self._temp_paths[0].parent.relative_to(self.outfile.parent))
-        mode_dict = dict(once_checked='',
-                         loop_checked='',
-                         reflect_checked='')
-        mode_dict[self.default_mode + '_checked'] = 'checked'
-
-        interval = 1000 // self.fps
-
-        with open(self.outfile, 'w') as of:
-            of.write(JS_INCLUDE + STYLE_INCLUDE)
-            of.write(DISPLAY_TEMPLATE.format(id=uuid.uuid4().hex,
-                                             Nframes=frame_count,
-                                             fill_frames=fill_frames,
-                                             interval=interval,
-                                             **mode_dict))
-
-        # Duplicate the temporary file clean up logic from
-        # FileMovieWriter.finish.  We can not call the inherited version of
-        # finish because it assumes that there is a subprocess that we either
-        # need to call to merge many frames together or that there is a
-        # subprocess call that we need to clean up.
-        if self._tmpdir:
-            _log.debug('MovieWriter: clearing temporary path=%s', self._tmpdir)
-            self._tmpdir.cleanup()
-
-
-class Animation:
-    """
-    A base class for Animations.
-
-    This class is not usable as is, and should be subclassed to provide needed
-    behavior.
-
-    .. note::
-
-        You must store the created Animation in a variable that lives as long
-        as the animation should run. Otherwise, the Animation object will be
-        garbage-collected and the animation stops.
-
-    Parameters
-    ----------
-    fig : `~matplotlib.figure.Figure`
-        The figure object used to get needed events, such as draw or resize.
-
-    event_source : object, optional
-        A class that can run a callback when desired events
-        are generated, as well as be stopped and started.
-
-        Examples include timers (see `TimedAnimation`) and file
-        system notifications.
-
-    blit : bool, default: False
-        Whether blitting is used to optimize drawing.  If the backend does not
-        support blitting, then this parameter has no effect.
-
-    See Also
-    --------
-    FuncAnimation,  ArtistAnimation
-    """
-
-    def __init__(self, fig, event_source=None, blit=False):
-        self._draw_was_started = False
-
-        self._fig = fig
-        # Disables blitting for backends that don't support it.  This
-        # allows users to request it if available, but still have a
-        # fallback that works if it is not.
-        self._blit = blit and fig.canvas.supports_blit
-
-        # These are the basics of the animation.  The frame sequence represents
-        # information for each frame of the animation and depends on how the
-        # drawing is handled by the subclasses. The event source fires events
-        # that cause the frame sequence to be iterated.
-        self.frame_seq = self.new_frame_seq()
-        self.event_source = event_source
-
-        # Instead of starting the event source now, we connect to the figure's
-        # draw_event, so that we only start once the figure has been drawn.
-        self._first_draw_id = fig.canvas.mpl_connect('draw_event', self._start)
-
-        # Connect to the figure's close_event so that we don't continue to
-        # fire events and try to draw to a deleted figure.
-        self._close_id = self._fig.canvas.mpl_connect('close_event',
-                                                      self._stop)
-        if self._blit:
-            self._setup_blit()
-
-    def __del__(self):
-        if not getattr(self, '_draw_was_started', True):
-            warnings.warn(
-                'Animation was deleted without rendering anything. This is '
-                'most likely not intended. To prevent deletion, assign the '
-                'Animation to a variable, e.g. `anim`, that exists until you '
-                'output the Animation using `plt.show()` or '
-                '`anim.save()`.'
-            )
-
-    def _start(self, *args):
-        """
-        Starts interactive animation. Adds the draw frame command to the GUI
-        handler, calls show to start the event loop.
-        """
-        # Do not start the event source if saving() it.
-        if self._fig.canvas.is_saving():
-            return
-        # First disconnect our draw event handler
-        self._fig.canvas.mpl_disconnect(self._first_draw_id)
-
-        # Now do any initial draw
-        self._init_draw()
-
-        # Add our callback for stepping the animation and
-        # actually start the event_source.
-        self.event_source.add_callback(self._step)
-        self.event_source.start()
-
-    def _stop(self, *args):
-        # On stop we disconnect all of our events.
-        if self._blit:
-            self._fig.canvas.mpl_disconnect(self._resize_id)
-        self._fig.canvas.mpl_disconnect(self._close_id)
-        self.event_source.remove_callback(self._step)
-        self.event_source = None
-
-    def save(self, filename, writer=None, fps=None, dpi=None, codec=None,
-             bitrate=None, extra_args=None, metadata=None, extra_anim=None,
-             savefig_kwargs=None, *, progress_callback=None):
-        """
-        Save the animation as a movie file by drawing every frame.
-
-        Parameters
-        ----------
-        filename : str
-            The output filename, e.g., :file:`mymovie.mp4`.
-
-        writer : `MovieWriter` or str, default: :rc:`animation.writer`
-            A `MovieWriter` instance to use or a key that identifies a
-            class to use, such as 'ffmpeg'.
-
-        fps : int, optional
-            Movie frame rate (per second).  If not set, the frame rate from the
-            animation's frame interval.
-
-        dpi : float, default: :rc:`savefig.dpi`
-            Controls the dots per inch for the movie frames.  Together with
-            the figure's size in inches, this controls the size of the movie.
-
-        codec : str, default: :rc:`animation.codec`.
-            The video codec to use.  Not all codecs are supported by a given
-            `MovieWriter`.
-
-        bitrate : int, default: :rc:`animation.bitrate`
-            The bitrate of the movie, in kilobits per second.  Higher values
-            means higher quality movies, but increase the file size.  A value
-            of -1 lets the underlying movie encoder select the bitrate.
-
-        extra_args : list of str or None, optional
-            Extra command-line arguments passed to the underlying movie
-            encoder.  The default, None, means to use
-            :rc:`animation.[name-of-encoder]_args` for the builtin writers.
-
-        metadata : dict[str, str], default: {}
-            Dictionary of keys and values for metadata to include in
-            the output file. Some keys that may be of use include:
-            title, artist, genre, subject, copyright, srcform, comment.
-
-        extra_anim : list, default: []
-            Additional `Animation` objects that should be included
-            in the saved movie file. These need to be from the same
-            `.Figure` instance. Also, animation frames will
-            just be simply combined, so there should be a 1:1 correspondence
-            between the frames from the different animations.
-
-        savefig_kwargs : dict, default: {}
-            Keyword arguments passed to each `~.Figure.savefig` call used to
-            save the individual frames.
-
-        progress_callback : function, optional
-            A callback function that will be called for every frame to notify
-            the saving progress. It must have the signature ::
-
-                def func(current_frame: int, total_frames: int) -> Any
-
-            where *current_frame* is the current frame number and
-            *total_frames* is the total number of frames to be saved.
-            *total_frames* is set to None, if the total number of frames can
-            not be determined. Return values may exist but are ignored.
-
-            Example code to write the progress to stdout::
-
-                progress_callback = lambda i, n: print(f'Saving frame {i}/{n}')
-
-        Notes
-        -----
-        *fps*, *codec*, *bitrate*, *extra_args* and *metadata* are used to
-        construct a `.MovieWriter` instance and can only be passed if
-        *writer* is a string.  If they are passed as non-*None* and *writer*
-        is a `.MovieWriter`, a `RuntimeError` will be raised.
-        """
-
-        all_anim = [self]
-        if extra_anim is not None:
-            all_anim.extend(anim for anim in extra_anim
-                            if anim._fig is self._fig)
-
-        # Disable "Animation was deleted without rendering" warning.
-        for anim in all_anim:
-            anim._draw_was_started = True
-
-        if writer is None:
-            writer = mpl.rcParams['animation.writer']
-        elif (not isinstance(writer, str) and
-              any(arg is not None
-                  for arg in (fps, codec, bitrate, extra_args, metadata))):
-            raise RuntimeError('Passing in values for arguments '
-                               'fps, codec, bitrate, extra_args, or metadata '
-                               'is not supported when writer is an existing '
-                               'MovieWriter instance. These should instead be '
-                               'passed as arguments when creating the '
-                               'MovieWriter instance.')
-
-        if savefig_kwargs is None:
-            savefig_kwargs = {}
-        else:
-            # we are going to mutate this below
-            savefig_kwargs = dict(savefig_kwargs)
-
-        if fps is None and hasattr(self, '_interval'):
-            # Convert interval in ms to frames per second
-            fps = 1000. / self._interval
-
-        # Re-use the savefig DPI for ours if none is given
-        if dpi is None:
-            dpi = mpl.rcParams['savefig.dpi']
-        if dpi == 'figure':
-            dpi = self._fig.dpi
-
-        writer_kwargs = {}
-        if codec is not None:
-            writer_kwargs['codec'] = codec
-        if bitrate is not None:
-            writer_kwargs['bitrate'] = bitrate
-        if extra_args is not None:
-            writer_kwargs['extra_args'] = extra_args
-        if metadata is not None:
-            writer_kwargs['metadata'] = metadata
-
-        # If we have the name of a writer, instantiate an instance of the
-        # registered class.
-        if isinstance(writer, str):
-            try:
-                writer_cls = writers[writer]
-            except RuntimeError:  # Raised if not available.
-                writer_cls = PillowWriter  # Always available.
-                _log.warning("MovieWriter %s unavailable; using Pillow "
-                             "instead.", writer)
-            writer = writer_cls(fps, **writer_kwargs)
-        _log.info('Animation.save using %s', type(writer))
-
-        if 'bbox_inches' in savefig_kwargs:
-            _log.warning("Warning: discarding the 'bbox_inches' argument in "
-                         "'savefig_kwargs' as it may cause frame size "
-                         "to vary, which is inappropriate for animation.")
-            savefig_kwargs.pop('bbox_inches')
-
-        # Create a new sequence of frames for saved data. This is different
-        # from new_frame_seq() to give the ability to save 'live' generated
-        # frame information to be saved later.
-        # TODO: Right now, after closing the figure, saving a movie won't work
-        # since GUI widgets are gone. Either need to remove extra code to
-        # allow for this non-existent use case or find a way to make it work.
-        if mpl.rcParams['savefig.bbox'] == 'tight':
-            _log.info("Disabling savefig.bbox = 'tight', as it may cause "
-                      "frame size to vary, which is inappropriate for "
-                      "animation.")
-
-        facecolor = savefig_kwargs.get('facecolor',
-                                       mpl.rcParams['savefig.facecolor'])
-        if facecolor == 'auto':
-            facecolor = self._fig.get_facecolor()
-
-        def _pre_composite_to_white(color):
-            r, g, b, a = mcolors.to_rgba(color)
-            return a * np.array([r, g, b]) + 1 - a
-
-        savefig_kwargs['facecolor'] = _pre_composite_to_white(facecolor)
-        savefig_kwargs['transparent'] = False   # just to be safe!
-        # canvas._is_saving = True makes the draw_event animation-starting
-        # callback a no-op; canvas.manager = None prevents resizing the GUI
-        # widget (both are likewise done in savefig()).
-        with mpl.rc_context({'savefig.bbox': None}), \
-             writer.saving(self._fig, filename, dpi), \
-             cbook._setattr_cm(self._fig.canvas,
-                               _is_saving=True, manager=None):
-            for anim in all_anim:
-                anim._init_draw()  # Clear the initial frame
-            frame_number = 0
-            # TODO: Currently only FuncAnimation has a save_count
-            #       attribute. Can we generalize this to all Animations?
-            save_count_list = [getattr(a, '_save_count', None)
-                               for a in all_anim]
-            if None in save_count_list:
-                total_frames = None
-            else:
-                total_frames = sum(save_count_list)
-            for data in zip(*[a.new_saved_frame_seq() for a in all_anim]):
-                for anim, d in zip(all_anim, data):
-                    # TODO: See if turning off blit is really necessary
-                    anim._draw_next_frame(d, blit=False)
-                    if progress_callback is not None:
-                        progress_callback(frame_number, total_frames)
-                        frame_number += 1
-                writer.grab_frame(**savefig_kwargs)
-
-    def _step(self, *args):
-        """
-        Handler for getting events. By default, gets the next frame in the
-        sequence and hands the data off to be drawn.
-        """
-        # Returns True to indicate that the event source should continue to
-        # call _step, until the frame sequence reaches the end of iteration,
-        # at which point False will be returned.
-        try:
-            framedata = next(self.frame_seq)
-            self._draw_next_frame(framedata, self._blit)
-            return True
-        except StopIteration:
-            return False
-
-    def new_frame_seq(self):
-        """Return a new sequence of frame information."""
-        # Default implementation is just an iterator over self._framedata
-        return iter(self._framedata)
-
-    def new_saved_frame_seq(self):
-        """Return a new sequence of saved/cached frame information."""
-        # Default is the same as the regular frame sequence
-        return self.new_frame_seq()
-
-    def _draw_next_frame(self, framedata, blit):
-        # Breaks down the drawing of the next frame into steps of pre- and
-        # post- draw, as well as the drawing of the frame itself.
-        self._pre_draw(framedata, blit)
-        self._draw_frame(framedata)
-        self._post_draw(framedata, blit)
-
-    def _init_draw(self):
-        # Initial draw to clear the frame. Also used by the blitting code
-        # when a clean base is required.
-        self._draw_was_started = True
-
-    def _pre_draw(self, framedata, blit):
-        # Perform any cleaning or whatnot before the drawing of the frame.
-        # This default implementation allows blit to clear the frame.
-        if blit:
-            self._blit_clear(self._drawn_artists)
-
-    def _draw_frame(self, framedata):
-        # Performs actual drawing of the frame.
-        raise NotImplementedError('Needs to be implemented by subclasses to'
-                                  ' actually make an animation.')
-
-    def _post_draw(self, framedata, blit):
-        # After the frame is rendered, this handles the actual flushing of
-        # the draw, which can be a direct draw_idle() or make use of the
-        # blitting.
-        if blit and self._drawn_artists:
-            self._blit_draw(self._drawn_artists)
-        else:
-            self._fig.canvas.draw_idle()
-
-    # The rest of the code in this class is to facilitate easy blitting
-    def _blit_draw(self, artists):
-        # Handles blitted drawing, which renders only the artists given instead
-        # of the entire figure.
-        updated_ax = {a.axes for a in artists}
-        # Enumerate artists to cache Axes backgrounds. We do not draw
-        # artists yet to not cache foreground from plots with shared axes
-        for ax in updated_ax:
-            # If we haven't cached the background for the current view of this
-            # Axes object, do so now. This might not always be reliable, but
-            # it's an attempt to automate the process.
-            cur_view = ax._get_view()
-            view, bg = self._blit_cache.get(ax, (object(), None))
-            if cur_view != view:
-                self._blit_cache[ax] = (
-                    cur_view, ax.figure.canvas.copy_from_bbox(ax.bbox))
-        # Make a separate pass to draw foreground.
-        for a in artists:
-            a.axes.draw_artist(a)
-        # After rendering all the needed artists, blit each Axes individually.
-        for ax in updated_ax:
-            ax.figure.canvas.blit(ax.bbox)
-
-    def _blit_clear(self, artists):
-        # Get a list of the Axes that need clearing from the artists that
-        # have been drawn. Grab the appropriate saved background from the
-        # cache and restore.
-        axes = {a.axes for a in artists}
-        for ax in axes:
-            try:
-                view, bg = self._blit_cache[ax]
-            except KeyError:
-                continue
-            if ax._get_view() == view:
-                ax.figure.canvas.restore_region(bg)
-            else:
-                self._blit_cache.pop(ax)
-
-    def _setup_blit(self):
-        # Setting up the blit requires: a cache of the background for the Axes
-        self._blit_cache = dict()
-        self._drawn_artists = []
-        # _post_draw needs to be called first to initialize the renderer
-        self._post_draw(None, self._blit)
-        # Then we need to clear the Frame for the initial draw
-        # This is typically handled in _on_resize because QT and Tk
-        # emit a resize event on launch, but the macosx backend does not,
-        # thus we force it here for everyone for consistency
-        self._init_draw()
-        # Connect to future resize events
-        self._resize_id = self._fig.canvas.mpl_connect('resize_event',
-                                                       self._on_resize)
-
-    def _on_resize(self, event):
-        # On resize, we need to disable the resize event handling so we don't
-        # get too many events. Also stop the animation events, so that
-        # we're paused. Reset the cache and re-init. Set up an event handler
-        # to catch once the draw has actually taken place.
-        self._fig.canvas.mpl_disconnect(self._resize_id)
-        self.event_source.stop()
-        self._blit_cache.clear()
-        self._init_draw()
-        self._resize_id = self._fig.canvas.mpl_connect('draw_event',
-                                                       self._end_redraw)
-
-    def _end_redraw(self, event):
-        # Now that the redraw has happened, do the post draw flushing and
-        # blit handling. Then re-enable all of the original events.
-        self._post_draw(None, False)
-        self.event_source.start()
-        self._fig.canvas.mpl_disconnect(self._resize_id)
-        self._resize_id = self._fig.canvas.mpl_connect('resize_event',
-                                                       self._on_resize)
-
-    def to_html5_video(self, embed_limit=None):
-        """
-        Convert the animation to an HTML5 ``<video>`` tag.
-
-        This saves the animation as an h264 video, encoded in base64
-        directly into the HTML5 video tag. This respects :rc:`animation.writer`
-        and :rc:`animation.bitrate`. This also makes use of the
-        *interval* to control the speed, and uses the *repeat*
-        parameter to decide whether to loop.
-
-        Parameters
-        ----------
-        embed_limit : float, optional
-            Limit, in MB, of the returned animation. No animation is created
-            if the limit is exceeded.
-            Defaults to :rc:`animation.embed_limit` = 20.0.
-
-        Returns
-        -------
-        str
-            An HTML5 video tag with the animation embedded as base64 encoded
-            h264 video.
-            If the *embed_limit* is exceeded, this returns the string
-            "Video too large to embed."
-        """
-        VIDEO_TAG = r'''<video {size} {options}>
-  <source type="video/mp4" src="data:video/mp4;base64,{video}">
-  Your browser does not support the video tag.
-</video>'''
-        # Cache the rendering of the video as HTML
-        if not hasattr(self, '_base64_video'):
-            # Save embed limit, which is given in MB
-            if embed_limit is None:
-                embed_limit = mpl.rcParams['animation.embed_limit']
-
-            # Convert from MB to bytes
-            embed_limit *= 1024 * 1024
-
-            # Can't open a NamedTemporaryFile twice on Windows, so use a
-            # TemporaryDirectory instead.
-            with TemporaryDirectory() as tmpdir:
-                path = Path(tmpdir, "temp.m4v")
-                # We create a writer manually so that we can get the
-                # appropriate size for the tag
-                Writer = writers[mpl.rcParams['animation.writer']]
-                writer = Writer(codec='h264',
-                                bitrate=mpl.rcParams['animation.bitrate'],
-                                fps=1000. / self._interval)
-                self.save(str(path), writer=writer)
-                # Now open and base64 encode.
-                vid64 = base64.encodebytes(path.read_bytes())
-
-            vid_len = len(vid64)
-            if vid_len >= embed_limit:
-                _log.warning(
-                    "Animation movie is %s bytes, exceeding the limit of %s. "
-                    "If you're sure you want a large animation embedded, set "
-                    "the animation.embed_limit rc parameter to a larger value "
-                    "(in MB).", vid_len, embed_limit)
-            else:
-                self._base64_video = vid64.decode('ascii')
-                self._video_size = 'width="{}" height="{}"'.format(
-                        *writer.frame_size)
-
-        # If we exceeded the size, this attribute won't exist
-        if hasattr(self, '_base64_video'):
-            # Default HTML5 options are to autoplay and display video controls
-            options = ['controls', 'autoplay']
-
-            # If we're set to repeat, make it loop
-            if getattr(self, '_repeat', False):
-                options.append('loop')
-
-            return VIDEO_TAG.format(video=self._base64_video,
-                                    size=self._video_size,
-                                    options=' '.join(options))
-        else:
-            return 'Video too large to embed.'
-
-    def to_jshtml(self, fps=None, embed_frames=True, default_mode=None):
-        """
-        Generate HTML representation of the animation.
-
-        Parameters
-        ----------
-        fps : int, optional
-            Movie frame rate (per second). If not set, the frame rate from
-            the animation's frame interval.
-        embed_frames : bool, optional
-        default_mode : str, optional
-            What to do when the animation ends. Must be one of ``{'loop',
-            'once', 'reflect'}``. Defaults to ``'loop'`` if the *repeat*
-            parameter is True, otherwise ``'once'``.
-        """
-        if fps is None and hasattr(self, '_interval'):
-            # Convert interval in ms to frames per second
-            fps = 1000 / self._interval
-
-        # If we're not given a default mode, choose one base on the value of
-        # the _repeat attribute
-        if default_mode is None:
-            default_mode = 'loop' if getattr(self, '_repeat',
-                                             False) else 'once'
-
-        if not hasattr(self, "_html_representation"):
-            # Can't open a NamedTemporaryFile twice on Windows, so use a
-            # TemporaryDirectory instead.
-            with TemporaryDirectory() as tmpdir:
-                path = Path(tmpdir, "temp.html")
-                writer = HTMLWriter(fps=fps,
-                                    embed_frames=embed_frames,
-                                    default_mode=default_mode)
-                self.save(str(path), writer=writer)
-                self._html_representation = path.read_text()
-
-        return self._html_representation
-
-    def _repr_html_(self):
-        """IPython display hook for rendering."""
-        fmt = mpl.rcParams['animation.html']
-        if fmt == 'html5':
-            return self.to_html5_video()
-        elif fmt == 'jshtml':
-            return self.to_jshtml()
-
-    def pause(self):
-        """Pause the animation."""
-        self.event_source.stop()
-        if self._blit:
-            for artist in self._drawn_artists:
-                artist.set_animated(False)
-
-    def resume(self):
-        """Resume the animation."""
-        self.event_source.start()
-        if self._blit:
-            for artist in self._drawn_artists:
-                artist.set_animated(True)
-
-
-class TimedAnimation(Animation):
-    """
-    `Animation` subclass for time-based animation.
-
-    A new frame is drawn every *interval* milliseconds.
-
-    .. note::
-
-        You must store the created Animation in a variable that lives as long
-        as the animation should run. Otherwise, the Animation object will be
-        garbage-collected and the animation stops.
-
-    Parameters
-    ----------
-    fig : `~matplotlib.figure.Figure`
-        The figure object used to get needed events, such as draw or resize.
-    interval : int, default: 200
-        Delay between frames in milliseconds.
-    repeat_delay : int, default: 0
-        The delay in milliseconds between consecutive animation runs, if
-        *repeat* is True.
-    repeat : bool, default: True
-        Whether the animation repeats when the sequence of frames is completed.
-    blit : bool, default: False
-        Whether blitting is used to optimize drawing.
-    """
-    def __init__(self, fig, interval=200, repeat_delay=0, repeat=True,
-                 event_source=None, *args, **kwargs):
-        self._interval = interval
-        # Undocumented support for repeat_delay = None as backcompat.
-        self._repeat_delay = repeat_delay if repeat_delay is not None else 0
-        self._repeat = repeat
-        # If we're not given an event source, create a new timer. This permits
-        # sharing timers between animation objects for syncing animations.
-        if event_source is None:
-            event_source = fig.canvas.new_timer(interval=self._interval)
-        super().__init__(fig, event_source=event_source, *args, **kwargs)
-
-    def _step(self, *args):
-        """Handler for getting events."""
-        # Extends the _step() method for the Animation class.  If
-        # Animation._step signals that it reached the end and we want to
-        # repeat, we refresh the frame sequence and return True. If
-        # _repeat_delay is set, change the event_source's interval to our loop
-        # delay and set the callback to one which will then set the interval
-        # back.
-        still_going = super()._step(*args)
-        if not still_going:
-            if self._repeat:
-                # Restart the draw loop
-                self._init_draw()
-                self.frame_seq = self.new_frame_seq()
-                self.event_source.interval = self._repeat_delay
-                return True
-            else:
-                # We are done with the animation. Call pause to remove
-                # animated flags from artists that were using blitting
-                self.pause()
-                if self._blit:
-                    # Remove the resize callback if we were blitting
-                    self._fig.canvas.mpl_disconnect(self._resize_id)
-                self._fig.canvas.mpl_disconnect(self._close_id)
-                self.event_source = None
-                return False
-
-        self.event_source.interval = self._interval
-        return True
-
-    repeat = _api.deprecate_privatize_attribute("3.7")
-
-
-class ArtistAnimation(TimedAnimation):
-    """
-    `TimedAnimation` subclass that creates an animation by using a fixed
-    set of `.Artist` objects.
-
-    Before creating an instance, all plotting should have taken place
-    and the relevant artists saved.
-
-    .. note::
-
-        You must store the created Animation in a variable that lives as long
-        as the animation should run. Otherwise, the Animation object will be
-        garbage-collected and the animation stops.
-
-    Parameters
-    ----------
-    fig : `~matplotlib.figure.Figure`
-        The figure object used to get needed events, such as draw or resize.
-    artists : list
-        Each list entry is a collection of `.Artist` objects that are made
-        visible on the corresponding frame.  Other artists are made invisible.
-    interval : int, default: 200
-        Delay between frames in milliseconds.
-    repeat_delay : int, default: 0
-        The delay in milliseconds between consecutive animation runs, if
-        *repeat* is True.
-    repeat : bool, default: True
-        Whether the animation repeats when the sequence of frames is completed.
-    blit : bool, default: False
-        Whether blitting is used to optimize drawing.
-    """
-
-    def __init__(self, fig, artists, *args, **kwargs):
-        # Internal list of artists drawn in the most recent frame.
-        self._drawn_artists = []
-
-        # Use the list of artists as the framedata, which will be iterated
-        # over by the machinery.
-        self._framedata = artists
-        super().__init__(fig, *args, **kwargs)
-
-    def _init_draw(self):
-        super()._init_draw()
-        # Make all the artists involved in *any* frame invisible
-        figs = set()
-        for f in self.new_frame_seq():
-            for artist in f:
-                artist.set_visible(False)
-                artist.set_animated(self._blit)
-                # Assemble a list of unique figures that need flushing
-                if artist.get_figure() not in figs:
-                    figs.add(artist.get_figure())
-
-        # Flush the needed figures
-        for fig in figs:
-            fig.canvas.draw_idle()
-
-    def _pre_draw(self, framedata, blit):
-        """Clears artists from the last frame."""
-        if blit:
-            # Let blit handle clearing
-            self._blit_clear(self._drawn_artists)
-        else:
-            # Otherwise, make all the artists from the previous frame invisible
-            for artist in self._drawn_artists:
-                artist.set_visible(False)
-
-    def _draw_frame(self, artists):
-        # Save the artists that were passed in as framedata for the other
-        # steps (esp. blitting) to use.
-        self._drawn_artists = artists
-
-        # Make all the artists from the current frame visible
-        for artist in artists:
-            artist.set_visible(True)
-
-
-class FuncAnimation(TimedAnimation):
-    """
-    `TimedAnimation` subclass that makes an animation by repeatedly calling
-    a function *func*.
-
-    .. note::
-
-        You must store the created Animation in a variable that lives as long
-        as the animation should run. Otherwise, the Animation object will be
-        garbage-collected and the animation stops.
-
-    Parameters
-    ----------
-    fig : `~matplotlib.figure.Figure`
-        The figure object used to get needed events, such as draw or resize.
-
-    func : callable
-        The function to call at each frame.  The first argument will
-        be the next value in *frames*.   Any additional positional
-        arguments can be supplied using `functools.partial` or via the *fargs*
-        parameter.
-
-        The required signature is::
-
-            def func(frame, *fargs) -> iterable_of_artists
-
-        It is often more convenient to provide the arguments using
-        `functools.partial`. In this way it is also possible to pass keyword
-        arguments. To pass a function with both positional and keyword
-        arguments, set all arguments as keyword arguments, just leaving the
-        *frame* argument unset::
-
-            def func(frame, art, *, y=None):
-                ...
-
-            ani = FuncAnimation(fig, partial(func, art=ln, y='foo'))
-
-        If ``blit == True``, *func* must return an iterable of all artists
-        that were modified or created. This information is used by the blitting
-        algorithm to determine which parts of the figure have to be updated.
-        The return value is unused if ``blit == False`` and may be omitted in
-        that case.
-
-    frames : iterable, int, generator function, or None, optional
-        Source of data to pass *func* and each frame of the animation
-
-        - If an iterable, then simply use the values provided.  If the
-          iterable has a length, it will override the *save_count* kwarg.
-
-        - If an integer, then equivalent to passing ``range(frames)``
-
-        - If a generator function, then must have the signature::
-
-             def gen_function() -> obj
-
-        - If *None*, then equivalent to passing ``itertools.count``.
-
-        In all of these cases, the values in *frames* is simply passed through
-        to the user-supplied *func* and thus can be of any type.
-
-    init_func : callable, optional
-        A function used to draw a clear frame. If not given, the results of
-        drawing from the first item in the frames sequence will be used. This
-        function will be called once before the first frame.
-
-        The required signature is::
-
-            def init_func() -> iterable_of_artists
-
-        If ``blit == True``, *init_func* must return an iterable of artists
-        to be re-drawn. This information is used by the blitting algorithm to
-        determine which parts of the figure have to be updated.  The return
-        value is unused if ``blit == False`` and may be omitted in that case.
-
-    fargs : tuple or None, optional
-        Additional arguments to pass to each call to *func*. Note: the use of
-        `functools.partial` is preferred over *fargs*. See *func* for details.
-
-    save_count : int, optional
-        Fallback for the number of values from *frames* to cache. This is
-        only used if the number of frames cannot be inferred from *frames*,
-        i.e. when it's an iterator without length or a generator.
-
-    interval : int, default: 200
-        Delay between frames in milliseconds.
-
-    repeat_delay : int, default: 0
-        The delay in milliseconds between consecutive animation runs, if
-        *repeat* is True.
-
-    repeat : bool, default: True
-        Whether the animation repeats when the sequence of frames is completed.
-
-    blit : bool, default: False
-        Whether blitting is used to optimize drawing.  Note: when using
-        blitting, any animated artists will be drawn according to their zorder;
-        however, they will be drawn on top of any previous artists, regardless
-        of their zorder.
-
-    cache_frame_data : bool, default: True
-        Whether frame data is cached.  Disabling cache might be helpful when
-        frames contain large objects.
-    """
-    def __init__(self, fig, func, frames=None, init_func=None, fargs=None,
-                 save_count=None, *, cache_frame_data=True, **kwargs):
-        if fargs:
-            self._args = fargs
-        else:
-            self._args = ()
-        self._func = func
-        self._init_func = init_func
-
-        # Amount of framedata to keep around for saving movies. This is only
-        # used if we don't know how many frames there will be: in the case
-        # of no generator or in the case of a callable.
-        self._save_count = save_count
-        # Set up a function that creates a new iterable when needed. If nothing
-        # is passed in for frames, just use itertools.count, which will just
-        # keep counting from 0. A callable passed in for frames is assumed to
-        # be a generator. An iterable will be used as is, and anything else
-        # will be treated as a number of frames.
-        if frames is None:
-            self._iter_gen = itertools.count
-        elif callable(frames):
-            self._iter_gen = frames
-        elif np.iterable(frames):
-            if kwargs.get('repeat', True):
-                self._tee_from = frames
-                def iter_frames(frames=frames):
-                    this, self._tee_from = itertools.tee(self._tee_from, 2)
-                    yield from this
-                self._iter_gen = iter_frames
-            else:
-                self._iter_gen = lambda: iter(frames)
-            if hasattr(frames, '__len__'):
-                self._save_count = len(frames)
-                if save_count is not None:
-                    _api.warn_external(
-                        f"You passed in an explicit {save_count=} "
-                        "which is being ignored in favor of "
-                        f"{len(frames)=}."
-                    )
-        else:
-            self._iter_gen = lambda: iter(range(frames))
-            self._save_count = frames
-            if save_count is not None:
-                _api.warn_external(
-                    f"You passed in an explicit {save_count=} which is being "
-                    f"ignored in favor of {frames=}."
-                )
-        if self._save_count is None and cache_frame_data:
-            _api.warn_external(
-                f"{frames=!r} which we can infer the length of, "
-                "did not pass an explicit *save_count* "
-                f"and passed {cache_frame_data=}.  To avoid a possibly "
-                "unbounded cache, frame data caching has been disabled. "
-                "To suppress this warning either pass "
-                "`cache_frame_data=False` or `save_count=MAX_FRAMES`."
-            )
-            cache_frame_data = False
-
-        self._cache_frame_data = cache_frame_data
-
-        # Needs to be initialized so the draw functions work without checking
-        self._save_seq = []
-
-        super().__init__(fig, **kwargs)
-
-        # Need to reset the saved seq, since right now it will contain data
-        # for a single frame from init, which is not what we want.
-        self._save_seq = []
-
-    def new_frame_seq(self):
-        # Use the generating function to generate a new frame sequence
-        return self._iter_gen()
-
-    def new_saved_frame_seq(self):
-        # Generate an iterator for the sequence of saved data. If there are
-        # no saved frames, generate a new frame sequence and take the first
-        # save_count entries in it.
-        if self._save_seq:
-            # While iterating we are going to update _save_seq
-            # so make a copy to safely iterate over
-            self._old_saved_seq = list(self._save_seq)
-            return iter(self._old_saved_seq)
-        else:
-            if self._save_count is None:
-                frame_seq = self.new_frame_seq()
-
-                def gen():
-                    try:
-                        while True:
-                            yield next(frame_seq)
-                    except StopIteration:
-                        pass
-                return gen()
-            else:
-                return itertools.islice(self.new_frame_seq(), self._save_count)
-
-    def _init_draw(self):
-        super()._init_draw()
-        # Initialize the drawing either using the given init_func or by
-        # calling the draw function with the first item of the frame sequence.
-        # For blitting, the init_func should return a sequence of modified
-        # artists.
-        if self._init_func is None:
-            try:
-                frame_data = next(self.new_frame_seq())
-            except StopIteration:
-                # we can't start the iteration, it may have already been
-                # exhausted by a previous save or just be 0 length.
-                # warn and bail.
-                warnings.warn(
-                    "Can not start iterating the frames for the initial draw. "
-                    "This can be caused by passing in a 0 length sequence "
-                    "for *frames*.\n\n"
-                    "If you passed *frames* as a generator "
-                    "it may be exhausted due to a previous display or save."
-                )
-                return
-            self._draw_frame(frame_data)
-        else:
-            self._drawn_artists = self._init_func()
-            if self._blit:
-                if self._drawn_artists is None:
-                    raise RuntimeError('The init_func must return a '
-                                       'sequence of Artist objects.')
-                for a in self._drawn_artists:
-                    a.set_animated(self._blit)
-        self._save_seq = []
-
-    def _draw_frame(self, framedata):
-        if self._cache_frame_data:
-            # Save the data for potential saving of movies.
-            self._save_seq.append(framedata)
-            self._save_seq = self._save_seq[-self._save_count:]
-
-        # Call the func with framedata and args. If blitting is desired,
-        # func needs to return a sequence of any artists that were modified.
-        self._drawn_artists = self._func(framedata, *self._args)
-
-        if self._blit:
-
-            err = RuntimeError('The animation function must return a sequence '
-                               'of Artist objects.')
-            try:
-                # check if a sequence
-                iter(self._drawn_artists)
-            except TypeError:
-                raise err from None
-
-            # check each item if it's artist
-            for i in self._drawn_artists:
-                if not isinstance(i, mpl.artist.Artist):
-                    raise err
-
-            self._drawn_artists = sorted(self._drawn_artists,
-                                         key=lambda x: x.get_zorder())
-
-            for a in self._drawn_artists:
-                a.set_animated(self._blit)
-
-    save_count = _api.deprecate_privatize_attribute("3.7")
diff --git a/spaces/declare-lab/tango/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_controlnet.py b/spaces/declare-lab/tango/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_controlnet.py
deleted file mode 100644
index d556e6318f430d2761bb2ef02556b5bf0d1fcb88..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_controlnet.py
+++ /dev/null
@@ -1,594 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# 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 gc
-import tempfile
-import unittest
-
-import numpy as np
-import torch
-from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderKL,
-    ControlNetModel,
-    DDIMScheduler,
-    StableDiffusionControlNetPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
-from diffusers.utils import load_image, load_numpy, randn_tensor, slow, torch_device
-from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import require_torch_gpu
-
-from ...pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ...test_pipelines_common import PipelineTesterMixin
-
-
-class StableDiffusionControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = StableDiffusionControlNetPipeline
-    params = TEXT_TO_IMAGE_PARAMS
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        torch.manual_seed(0)
-        controlnet = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        torch.manual_seed(0)
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        components = {
-            "unet": unet,
-            "controlnet": controlnet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        controlnet_embedder_scale_factor = 2
-        image = randn_tensor(
-            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
-            generator=generator,
-            device=torch.device(device),
-        )
-
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-            "image": image,
-        }
-
-        return inputs
-
-    def test_attention_slicing_forward_pass(self):
-        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(expected_max_diff=2e-3)
-
-
-class StableDiffusionMultiControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = StableDiffusionControlNetPipeline
-    params = TEXT_TO_IMAGE_PARAMS
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        torch.manual_seed(0)
-        controlnet1 = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        torch.manual_seed(0)
-        controlnet2 = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        torch.manual_seed(0)
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        controlnet = MultiControlNetModel([controlnet1, controlnet2])
-
-        components = {
-            "unet": unet,
-            "controlnet": controlnet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        controlnet_embedder_scale_factor = 2
-
-        images = [
-            randn_tensor(
-                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
-                generator=generator,
-                device=torch.device(device),
-            ),
-            randn_tensor(
-                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
-                generator=generator,
-                device=torch.device(device),
-            ),
-        ]
-
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-            "image": images,
-        }
-
-        return inputs
-
-    def test_attention_slicing_forward_pass(self):
-        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(expected_max_diff=2e-3)
-
-    def test_save_pretrained_raise_not_implemented_exception(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        with tempfile.TemporaryDirectory() as tmpdir:
-            try:
-                # save_pretrained is not implemented for Multi-ControlNet
-                pipe.save_pretrained(tmpdir)
-            except NotImplementedError:
-                pass
-
-    # override PipelineTesterMixin
-    @unittest.skip("save pretrained not implemented")
-    def test_save_load_float16(self):
-        ...
-
-    # override PipelineTesterMixin
-    @unittest.skip("save pretrained not implemented")
-    def test_save_load_local(self):
-        ...
-
-    # override PipelineTesterMixin
-    @unittest.skip("save pretrained not implemented")
-    def test_save_load_optional_components(self):
-        ...
-
-
-@slow
-@require_torch_gpu
-class StableDiffusionControlNetPipelineSlowTests(unittest.TestCase):
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_canny(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "bird"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (768, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-3
-
-    def test_depth(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-depth")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "Stormtrooper's lecture"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-3
-
-    def test_hed(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-hed")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "oil painting of handsome old man, masterpiece"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (704, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-3
-
-    def test_mlsd(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-mlsd")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "room"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (704, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-3
-
-    def test_normal(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-normal")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "cute toy"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-3
-
-    def test_openpose(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "Chef in the kitchen"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (768, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/chef_pose_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-3
-
-    def test_scribble(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(5)
-        prompt = "bag"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (640, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-3
-
-    def test_seg(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(5)
-        prompt = "house"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png"
-        )
-
-        output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-3
-
-    def test_sequential_cpu_offloading(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-        pipe.enable_sequential_cpu_offload()
-
-        prompt = "house"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png"
-        )
-
-        _ = pipe(
-            prompt,
-            image,
-            num_inference_steps=2,
-            output_type="np",
-        )
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-        # make sure that less than 7 GB is allocated
-        assert mem_bytes < 4 * 10**9
-
-
-@slow
-@require_torch_gpu
-class StableDiffusionMultiControlNetPipelineSlowTests(unittest.TestCase):
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_pose_and_canny(self):
-        controlnet_canny = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-        controlnet_pose = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=[controlnet_pose, controlnet_canny]
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "bird and Chef"
-        image_canny = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        )
-        image_pose = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"
-        )
-
-        output = pipe(prompt, [image_pose, image_canny], generator=generator, output_type="np", num_inference_steps=3)
-
-        image = output.images[0]
-
-        assert image.shape == (768, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose_canny_out.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 5e-2
diff --git a/spaces/deeplearning/audioldm-text-to-audio-generation/share_btn.py b/spaces/deeplearning/audioldm-text-to-audio-generation/share_btn.py
deleted file mode 100644
index a0378607680fa5468e9034d230f546f5f0913ae0..0000000000000000000000000000000000000000
--- a/spaces/deeplearning/audioldm-text-to-audio-generation/share_btn.py
+++ /dev/null
@@ -1,74 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-    async function getInputVideoFile(videoEl){
-        const res = await fetch(videoEl.src);
-        const blob = await res.blob();
-        const videoId = Date.now() % 200;
-        const fileName = `sd-perception-${{videoId}}.mp4`;
-        return new File([blob], fileName, { type: 'video/mp4' }); 
-	}
-    
-    async function audioToBase64(audioFile) {
-        return new Promise((resolve, reject) => {
-            let reader = new FileReader();
-            reader.readAsDataURL(audioFile);
-            reader.onload = () => resolve(reader.result);
-            reader.onerror = error => reject(error);
-          
-        });
-      }
-    const gradioEl = document.querySelector("gradio-app").shadowRoot || document.querySelector('body > gradio-app');
-    const inputPromptEl = gradioEl.querySelector('#prompt-in input').value;
-    const outputVideoEl = gradioEl.querySelector('#output-video video');
-    
-    let titleTxt = `Text-to-Audio: ${inputPromptEl}`;
-
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!outputVideoEl){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const outputVideo = await getInputVideoFile(outputVideoEl);
-    const urlOutputVideo = await uploadFile(outputVideo);
-    
-    const descriptionMd = `
-##### ${inputPromptEl}
-    
-${urlOutputVideo}
-`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
diff --git a/spaces/diacanFperku/AutoGPT/Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG Game Hack.md b/spaces/diacanFperku/AutoGPT/Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG Game Hack.md
deleted file mode 100644
index 60010f224d56be5dc2a4b65c5361287775416c09..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG Game Hack.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
-<p>Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack DOWNLOAD https://ssurll.com/2jzgph Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.Trainer-FLiNG game hack Assassins.Creed.3.v1.01.Plus.9.</p>
-<p>Numpad 1 - Infinite Health. Numpad 2 - Infinite Money. Numpad 3 - infinite arrows. Numpad 4 - infinite health 12-07-2012, 13:46. cheats for games. Edit. +5. Assassins Creed 3 Trainer +16 v1.01 LinGon. Trainer options. HOME KEY - activate Trainer F1 - immortality </p>
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diff --git a/spaces/diacanFperku/AutoGPT/Differentialcalculusbydasandmukherjeepdffreedownload High Quality.md b/spaces/diacanFperku/AutoGPT/Differentialcalculusbydasandmukherjeepdffreedownload High Quality.md
deleted file mode 100644
index dc4e2fab6696e12bcf642c011635205c10015e01..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Differentialcalculusbydasandmukherjeepdffreedownload High Quality.md	
+++ /dev/null
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diff --git a/spaces/diacanFperku/AutoGPT/Girlvania Summer Lust Legs And Feet !FREE!.md b/spaces/diacanFperku/AutoGPT/Girlvania Summer Lust Legs And Feet !FREE!.md
deleted file mode 100644
index 42b5cdb87f71aed8e7352cff1f59ebaaefab7385..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Girlvania Summer Lust Legs And Feet !FREE!.md	
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diff --git a/spaces/diacanFperku/AutoGPT/La Mafia Pharmaceutique Et Agroalimentaire.pdfl.md b/spaces/diacanFperku/AutoGPT/La Mafia Pharmaceutique Et Agroalimentaire.pdfl.md
deleted file mode 100644
index 45e865c586e666fc72d1d6714d5c67f6e4668135..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/La Mafia Pharmaceutique Et Agroalimentaire.pdfl.md	
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diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/utils/utils.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/utils/utils.py
deleted file mode 100644
index a4349b273b5a96de478aa01214c607405d4cfa99..0000000000000000000000000000000000000000
--- a/spaces/diagaiwei/ir_chinese_medqa/colbert/utils/utils.py
+++ /dev/null
@@ -1,310 +0,0 @@
-import os
-import tqdm
-import torch
-import datetime
-import itertools
-
-from multiprocessing import Pool
-from collections import OrderedDict, defaultdict
-
-
-def print_message(*s, condition=True, pad=False):
-    s = ' '.join([str(x) for x in s])
-    msg = "[{}] {}".format(datetime.datetime.now().strftime("%b %d, %H:%M:%S"), s)
-
-    if condition:
-        msg = msg if not pad else f'\n{msg}\n'
-        print(msg, flush=True)
-
-
-    return msg
-
-
-def timestamp(daydir=False):
-    format_str = f"%Y-%m{'/' if daydir else '-'}%d{'/' if daydir else '_'}%H.%M.%S"
-    result = datetime.datetime.now().strftime(format_str)
-    return result
-
-
-def file_tqdm(file):
-    print(f"#> Reading {file.name}")
-
-    with tqdm.tqdm(total=os.path.getsize(file.name) / 1024.0 / 1024.0, unit="MiB") as pbar:
-        for line in file:
-            yield line
-            pbar.update(len(line) / 1024.0 / 1024.0)
-
-        pbar.close()
-
-
-def torch_load_dnn(path):
-    if path.startswith("http:") or path.startswith("https:"):
-        dnn = torch.hub.load_state_dict_from_url(path, map_location='cpu')
-    else:
-        dnn = torch.load(path, map_location='cpu')
-    
-    return dnn
-
-def save_checkpoint(path, epoch_idx, mb_idx, model, optimizer, arguments=None):
-    print(f"#> Saving a checkpoint to {path} ..")
-
-    if hasattr(model, 'module'):
-        model = model.module  # extract model from a distributed/data-parallel wrapper
-
-    checkpoint = {}
-    checkpoint['epoch'] = epoch_idx
-    checkpoint['batch'] = mb_idx
-    checkpoint['model_state_dict'] = model.state_dict()
-    checkpoint['optimizer_state_dict'] = optimizer.state_dict()
-    checkpoint['arguments'] = arguments
-
-    torch.save(checkpoint, path)
-
-
-def load_checkpoint(path, model, checkpoint=None, optimizer=None, do_print=True):
-    if do_print:
-        print_message("#> Loading checkpoint", path, "..")
-
-    if checkpoint is None:
-        checkpoint = load_checkpoint_raw(path)
-
-    try:
-        model.load_state_dict(checkpoint['model_state_dict'])
-    except:
-        print_message("[WARNING] Loading checkpoint with strict=False")
-        model.load_state_dict(checkpoint['model_state_dict'], strict=False)
-
-    if optimizer:
-        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
-
-    if do_print:
-        print_message("#> checkpoint['epoch'] =", checkpoint['epoch'])
-        print_message("#> checkpoint['batch'] =", checkpoint['batch'])
-
-    return checkpoint
-
-
-def load_checkpoint_raw(path):
-    if path.startswith("http:") or path.startswith("https:"):
-        checkpoint = torch.hub.load_state_dict_from_url(path, map_location='cpu')
-    else:
-        checkpoint = torch.load(path, map_location='cpu')
-
-    state_dict = checkpoint['model_state_dict']
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        name = k
-        if k[:7] == 'module.':
-            name = k[7:]
-        new_state_dict[name] = v
-
-    checkpoint['model_state_dict'] = new_state_dict
-
-    return checkpoint
-
-
-def create_directory(path):
-    if os.path.exists(path):
-        print('\n')
-        print_message("#> Note: Output directory", path, 'already exists\n\n')
-    else:
-        print('\n')
-        print_message("#> Creating directory", path, '\n\n')
-        os.makedirs(path)
-
-# def batch(file, bsize):
-#     while True:
-#         L = [ujson.loads(file.readline()) for _ in range(bsize)]
-#         yield L
-#     return
-
-
-def f7(seq):
-    """
-    Source: https://stackoverflow.com/a/480227/1493011
-    """
-
-    seen = set()
-    return [x for x in seq if not (x in seen or seen.add(x))]
-
-
-def batch(group, bsize, provide_offset=False):
-    offset = 0
-    while offset < len(group):
-        L = group[offset: offset + bsize]
-        yield ((offset, L) if provide_offset else L)
-        offset += len(L)
-    return
-
-
-class dotdict(dict):
-    """
-    dot.notation access to dictionary attributes
-    Credit: derek73 @ https://stackoverflow.com/questions/2352181
-    """
-    __getattr__ = dict.__getitem__
-    __setattr__ = dict.__setitem__
-    __delattr__ = dict.__delitem__
-
-
-class dotdict_lax(dict):
-    __getattr__ = dict.get
-    __setattr__ = dict.__setitem__
-    __delattr__ = dict.__delitem__
-
-
-def flatten(L):
-    # return [x for y in L for x in y]
-
-    result = []
-    for _list in L:
-        result += _list
-
-    return result
-
-
-def zipstar(L, lazy=False):
-    """
-    A much faster A, B, C = zip(*[(a, b, c), (a, b, c), ...])
-    May return lists or tuples.
-    """
-
-    if len(L) == 0:
-        return L
-
-    width = len(L[0])
-
-    if width < 100:
-        return [[elem[idx] for elem in L] for idx in range(width)]
-
-    L = zip(*L)
-
-    return L if lazy else list(L)
-
-
-def zip_first(L1, L2):
-    length = len(L1) if type(L1) in [tuple, list] else None
-
-    L3 = list(zip(L1, L2))
-
-    assert length in [None, len(L3)], "zip_first() failure: length differs!"
-
-    return L3
-
-
-def int_or_float(val):
-    if '.' in val:
-        return float(val)
-        
-    return int(val)
-
-def load_ranking(path, types=None, lazy=False):
-    print_message(f"#> Loading the ranked lists from {path} ..")
-
-    try:
-        lists = torch.load(path)
-        lists = zipstar([l.tolist() for l in tqdm.tqdm(lists)], lazy=lazy)
-    except:
-        if types is None:
-            types = itertools.cycle([int_or_float])
-
-        with open(path) as f:
-            lists = [[typ(x) for typ, x in zip_first(types, line.strip().split('\t'))]
-                     for line in file_tqdm(f)]
-
-    return lists
-
-
-def save_ranking(ranking, path):
-    lists = zipstar(ranking)
-    lists = [torch.tensor(l) for l in lists]
-
-    torch.save(lists, path)
-
-    return lists
-
-
-def groupby_first_item(lst):
-    groups = defaultdict(list)
-
-    for first, *rest in lst:
-        rest = rest[0] if len(rest) == 1 else rest
-        groups[first].append(rest)
-
-    return groups
-
-
-def process_grouped_by_first_item(lst):
-    """
-        Requires items in list to already be grouped by first item.
-    """
-
-    groups = defaultdict(list)
-
-    started = False
-    last_group = None
-
-    for first, *rest in lst:
-        rest = rest[0] if len(rest) == 1 else rest
-
-        if started and first != last_group:
-            yield (last_group, groups[last_group])
-            assert first not in groups, f"{first} seen earlier --- violates precondition."
-
-        groups[first].append(rest)
-
-        last_group = first
-        started = True
-
-    return groups
-
-
-def grouper(iterable, n, fillvalue=None):
-    """
-    Collect data into fixed-length chunks or blocks
-        Example: grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
-        Source: https://docs.python.org/3/library/itertools.html#itertools-recipes
-    """
-
-    args = [iter(iterable)] * n
-    return itertools.zip_longest(*args, fillvalue=fillvalue)
-
-
-def lengths2offsets(lengths):
-    offset = 0
-
-    for length in lengths:
-        yield (offset, offset + length)
-        offset += length
-
-    return
-
-
-# see https://stackoverflow.com/a/45187287
-class NullContextManager(object):
-    def __init__(self, dummy_resource=None):
-        self.dummy_resource = dummy_resource
-    def __enter__(self):
-        return self.dummy_resource
-    def __exit__(self, *args):
-        pass
-
-
-def load_batch_backgrounds(args, qids):
-    if args.qid2backgrounds is None:
-        return None
-
-    qbackgrounds = []
-
-    for qid in qids:
-        back = args.qid2backgrounds[qid]
-
-        if len(back) and type(back[0]) == int:
-            x = [args.collection[pid] for pid in back]
-        else:
-            x = [args.collectionX.get(pid, '') for pid in back]
-
-        x = ' [SEP] '.join(x)
-        qbackgrounds.append(x)
-    
-    return qbackgrounds
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_pipelines/satrn_pipeline.py b/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_pipelines/satrn_pipeline.py
deleted file mode 100644
index f191c5235a08eeae7d1e61002c00eccbdac39ed4..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_pipelines/satrn_pipeline.py
+++ /dev/null
@@ -1,44 +0,0 @@
-img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='ResizeOCR',
-        height=32,
-        min_width=100,
-        max_width=100,
-        keep_aspect_ratio=False,
-        width_downsample_ratio=0.25),
-    dict(type='ToTensorOCR'),
-    dict(type='NormalizeOCR', **img_norm_cfg),
-    dict(
-        type='Collect',
-        keys=['img'],
-        meta_keys=[
-            'filename', 'ori_shape', 'img_shape', 'text', 'valid_ratio',
-            'resize_shape'
-        ]),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiRotateAugOCR',
-        rotate_degrees=[0, 90, 270],
-        transforms=[
-            dict(
-                type='ResizeOCR',
-                height=32,
-                min_width=100,
-                max_width=100,
-                keep_aspect_ratio=False,
-                width_downsample_ratio=0.25),
-            dict(type='ToTensorOCR'),
-            dict(type='NormalizeOCR', **img_norm_cfg),
-            dict(
-                type='Collect',
-                keys=['img'],
-                meta_keys=[
-                    'filename', 'ori_shape', 'img_shape', 'valid_ratio',
-                    'resize_shape', 'img_norm_cfg', 'ori_filename'
-                ]),
-        ])
-]
diff --git a/spaces/dirge/voicevox/voicevox_engine/engine_manifest/EngineManifestLoader.py b/spaces/dirge/voicevox/voicevox_engine/engine_manifest/EngineManifestLoader.py
deleted file mode 100644
index bec6a2a7b6ec7f7316eace37db102070ba437a21..0000000000000000000000000000000000000000
--- a/spaces/dirge/voicevox/voicevox_engine/engine_manifest/EngineManifestLoader.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import json
-from base64 import b64encode
-from pathlib import Path
-
-from .EngineManifest import EngineManifest, LicenseInfo, UpdateInfo
-
-
-class EngineManifestLoader:
-    def __init__(self, manifest_path: Path, root_dir: Path):
-        self.manifest_path = manifest_path
-        self.root_dir = root_dir
-
-    def load_manifest(self) -> EngineManifest:
-        manifest = json.loads(self.manifest_path.read_text(encoding="utf-8"))
-
-        manifest = EngineManifest(
-            manifest_version=manifest["manifest_version"],
-            name=manifest["name"],
-            brand_name=manifest["brand_name"],
-            uuid=manifest["uuid"],
-            url=manifest["url"],
-            default_sampling_rate=manifest["default_sampling_rate"],
-            icon=b64encode((self.root_dir / manifest["icon"]).read_bytes()).decode(
-                "utf-8"
-            ),
-            terms_of_service=(self.root_dir / manifest["terms_of_service"]).read_text(
-                "utf-8"
-            ),
-            update_infos=[
-                UpdateInfo(**update_info)
-                for update_info in json.loads(
-                    (self.root_dir / manifest["update_infos"]).read_text("utf-8")
-                )
-            ],
-            dependency_licenses=[
-                LicenseInfo(**license_info)
-                for license_info in json.loads(
-                    (self.root_dir / manifest["dependency_licenses"]).read_text("utf-8")
-                )
-            ],
-            supported_features={
-                key: item["value"]
-                for key, item in manifest["supported_features"].items()
-            },
-        )
-        return manifest
diff --git a/spaces/dirge/voicevox/voicevox_engine/utility/connect_base64_waves.py b/spaces/dirge/voicevox/voicevox_engine/utility/connect_base64_waves.py
deleted file mode 100644
index 37f95240966f9bfed1cfe6e9090f871cea331ef7..0000000000000000000000000000000000000000
--- a/spaces/dirge/voicevox/voicevox_engine/utility/connect_base64_waves.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import base64
-import io
-from typing import List, Tuple
-
-import numpy as np
-import soundfile
-from scipy.signal import resample
-
-
-class ConnectBase64WavesException(Exception):
-    def __init__(self, message: str):
-        self.message = message
-
-
-def decode_base64_waves(waves: List[str]) -> List[Tuple[np.ndarray, int]]:
-    """
-    base64エンコードされた複数のwavデータをデコードする
-    Parameters
-    ----------
-    waves: list[str]
-        base64エンコードされたwavデータのリスト
-    Returns
-    -------
-    waves_nparray_sr: List[Tuple[np.ndarray, int]]
-        (NumPy配列の音声波形データ, サンプリングレート) 形式のタプルのリスト
-    """
-    if len(waves) == 0:
-        raise ConnectBase64WavesException("wavファイルが含まれていません")
-
-    waves_nparray_sr = []
-    for wave in waves:
-        try:
-            wav_bin = base64.standard_b64decode(wave)
-        except ValueError:
-            raise ConnectBase64WavesException("base64デコードに失敗しました")
-        try:
-            _data = soundfile.read(io.BytesIO(wav_bin))
-        except Exception:
-            raise ConnectBase64WavesException("wavファイルを読み込めませんでした")
-        waves_nparray_sr.append(_data)
-
-    return waves_nparray_sr
-
-
-def connect_base64_waves(waves: List[str]) -> Tuple[np.ndarray, int]:
-    waves_nparray_sr = decode_base64_waves(waves)
-
-    max_sampling_rate = max([sr for _, sr in waves_nparray_sr])
-    max_channels = max([x.ndim for x, _ in waves_nparray_sr])
-    assert 0 < max_channels <= 2
-
-    waves_nparray_list = []
-    for nparray, sr in waves_nparray_sr:
-        if sr != max_sampling_rate:
-            nparray = resample(nparray, max_sampling_rate * len(nparray) // sr)
-        if nparray.ndim < max_channels:
-            nparray = np.array([nparray, nparray]).T
-        waves_nparray_list.append(nparray)
-
-    return np.concatenate(waves_nparray_list), max_sampling_rate
diff --git a/spaces/divilis/chatgpt/utils.py b/spaces/divilis/chatgpt/utils.py
deleted file mode 100644
index f6e4fa4e8a9f908baa4509d7206ff3455ac57f39..0000000000000000000000000000000000000000
--- a/spaces/divilis/chatgpt/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/dragonSwing/annotate-anything/GroundingDINO/groundingdino/datasets/transforms.py b/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/datasets/transforms.py
deleted file mode 100644
index 91cf9269e4b31008a3ddca34a19b038a9b399991..0000000000000000000000000000000000000000
--- a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/datasets/transforms.py
+++ /dev/null
@@ -1,311 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Transforms and data augmentation for both image + bbox.
-"""
-import os
-import random
-
-import PIL
-import torch
-import torchvision.transforms as T
-import torchvision.transforms.functional as F
-
-from groundingdino.util.box_ops import box_xyxy_to_cxcywh
-from groundingdino.util.misc import interpolate
-
-
-def crop(image, target, region):
-    cropped_image = F.crop(image, *region)
-
-    target = target.copy()
-    i, j, h, w = region
-
-    # should we do something wrt the original size?
-    target["size"] = torch.tensor([h, w])
-
-    fields = ["labels", "area", "iscrowd", "positive_map"]
-
-    if "boxes" in target:
-        boxes = target["boxes"]
-        max_size = torch.as_tensor([w, h], dtype=torch.float32)
-        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
-        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
-        cropped_boxes = cropped_boxes.clamp(min=0)
-        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
-        target["boxes"] = cropped_boxes.reshape(-1, 4)
-        target["area"] = area
-        fields.append("boxes")
-
-    if "masks" in target:
-        # FIXME should we update the area here if there are no boxes?
-        target["masks"] = target["masks"][:, i : i + h, j : j + w]
-        fields.append("masks")
-
-    # remove elements for which the boxes or masks that have zero area
-    if "boxes" in target or "masks" in target:
-        # favor boxes selection when defining which elements to keep
-        # this is compatible with previous implementation
-        if "boxes" in target:
-            cropped_boxes = target["boxes"].reshape(-1, 2, 2)
-            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
-        else:
-            keep = target["masks"].flatten(1).any(1)
-
-        for field in fields:
-            if field in target:
-                target[field] = target[field][keep]
-
-    if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
-        # for debug and visualization only.
-        if "strings_positive" in target:
-            target["strings_positive"] = [
-                _i for _i, _j in zip(target["strings_positive"], keep) if _j
-            ]
-
-    return cropped_image, target
-
-
-def hflip(image, target):
-    flipped_image = F.hflip(image)
-
-    w, h = image.size
-
-    target = target.copy()
-    if "boxes" in target:
-        boxes = target["boxes"]
-        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
-            [w, 0, w, 0]
-        )
-        target["boxes"] = boxes
-
-    if "masks" in target:
-        target["masks"] = target["masks"].flip(-1)
-
-    return flipped_image, target
-
-
-def resize(image, target, size, max_size=None):
-    # size can be min_size (scalar) or (w, h) tuple
-
-    def get_size_with_aspect_ratio(image_size, size, max_size=None):
-        w, h = image_size
-        if max_size is not None:
-            min_original_size = float(min((w, h)))
-            max_original_size = float(max((w, h)))
-            if max_original_size / min_original_size * size > max_size:
-                size = int(round(max_size * min_original_size / max_original_size))
-
-        if (w <= h and w == size) or (h <= w and h == size):
-            return (h, w)
-
-        if w < h:
-            ow = size
-            oh = int(size * h / w)
-        else:
-            oh = size
-            ow = int(size * w / h)
-
-        return (oh, ow)
-
-    def get_size(image_size, size, max_size=None):
-        if isinstance(size, (list, tuple)):
-            return size[::-1]
-        else:
-            return get_size_with_aspect_ratio(image_size, size, max_size)
-
-    size = get_size(image.size, size, max_size)
-    rescaled_image = F.resize(image, size)
-
-    if target is None:
-        return rescaled_image, None
-
-    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
-    ratio_width, ratio_height = ratios
-
-    target = target.copy()
-    if "boxes" in target:
-        boxes = target["boxes"]
-        scaled_boxes = boxes * torch.as_tensor(
-            [ratio_width, ratio_height, ratio_width, ratio_height]
-        )
-        target["boxes"] = scaled_boxes
-
-    if "area" in target:
-        area = target["area"]
-        scaled_area = area * (ratio_width * ratio_height)
-        target["area"] = scaled_area
-
-    h, w = size
-    target["size"] = torch.tensor([h, w])
-
-    if "masks" in target:
-        target["masks"] = (
-            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
-        )
-
-    return rescaled_image, target
-
-
-def pad(image, target, padding):
-    # assumes that we only pad on the bottom right corners
-    padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
-    if target is None:
-        return padded_image, None
-    target = target.copy()
-    # should we do something wrt the original size?
-    target["size"] = torch.tensor(padded_image.size[::-1])
-    if "masks" in target:
-        target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
-    return padded_image, target
-
-
-class ResizeDebug(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        return resize(img, target, self.size)
-
-
-class RandomCrop(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        region = T.RandomCrop.get_params(img, self.size)
-        return crop(img, target, region)
-
-
-class RandomSizeCrop(object):
-    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
-        # respect_boxes:    True to keep all boxes
-        #                   False to tolerence box filter
-        self.min_size = min_size
-        self.max_size = max_size
-        self.respect_boxes = respect_boxes
-
-    def __call__(self, img: PIL.Image.Image, target: dict):
-        init_boxes = len(target["boxes"])
-        max_patience = 10
-        for i in range(max_patience):
-            w = random.randint(self.min_size, min(img.width, self.max_size))
-            h = random.randint(self.min_size, min(img.height, self.max_size))
-            region = T.RandomCrop.get_params(img, [h, w])
-            result_img, result_target = crop(img, target, region)
-            if (
-                not self.respect_boxes
-                or len(result_target["boxes"]) == init_boxes
-                or i == max_patience - 1
-            ):
-                return result_img, result_target
-        return result_img, result_target
-
-
-class CenterCrop(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        image_width, image_height = img.size
-        crop_height, crop_width = self.size
-        crop_top = int(round((image_height - crop_height) / 2.0))
-        crop_left = int(round((image_width - crop_width) / 2.0))
-        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
-
-
-class RandomHorizontalFlip(object):
-    def __init__(self, p=0.5):
-        self.p = p
-
-    def __call__(self, img, target):
-        if random.random() < self.p:
-            return hflip(img, target)
-        return img, target
-
-
-class RandomResize(object):
-    def __init__(self, sizes, max_size=None):
-        assert isinstance(sizes, (list, tuple))
-        self.sizes = sizes
-        self.max_size = max_size
-
-    def __call__(self, img, target=None):
-        size = random.choice(self.sizes)
-        return resize(img, target, size, self.max_size)
-
-
-class RandomPad(object):
-    def __init__(self, max_pad):
-        self.max_pad = max_pad
-
-    def __call__(self, img, target):
-        pad_x = random.randint(0, self.max_pad)
-        pad_y = random.randint(0, self.max_pad)
-        return pad(img, target, (pad_x, pad_y))
-
-
-class RandomSelect(object):
-    """
-    Randomly selects between transforms1 and transforms2,
-    with probability p for transforms1 and (1 - p) for transforms2
-    """
-
-    def __init__(self, transforms1, transforms2, p=0.5):
-        self.transforms1 = transforms1
-        self.transforms2 = transforms2
-        self.p = p
-
-    def __call__(self, img, target):
-        if random.random() < self.p:
-            return self.transforms1(img, target)
-        return self.transforms2(img, target)
-
-
-class ToTensor(object):
-    def __call__(self, img, target):
-        return F.to_tensor(img), target
-
-
-class RandomErasing(object):
-    def __init__(self, *args, **kwargs):
-        self.eraser = T.RandomErasing(*args, **kwargs)
-
-    def __call__(self, img, target):
-        return self.eraser(img), target
-
-
-class Normalize(object):
-    def __init__(self, mean, std):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, image, target=None):
-        image = F.normalize(image, mean=self.mean, std=self.std)
-        if target is None:
-            return image, None
-        target = target.copy()
-        h, w = image.shape[-2:]
-        if "boxes" in target:
-            boxes = target["boxes"]
-            boxes = box_xyxy_to_cxcywh(boxes)
-            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
-            target["boxes"] = boxes
-        return image, target
-
-
-class Compose(object):
-    def __init__(self, transforms):
-        self.transforms = transforms
-
-    def __call__(self, image, target):
-        for t in self.transforms:
-            image, target = t(image, target)
-        return image, target
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + "("
-        for t in self.transforms:
-            format_string += "\n"
-            format_string += "    {0}".format(t)
-        format_string += "\n)"
-        return format_string
diff --git a/spaces/dsmai/dogorcat/README.md b/spaces/dsmai/dogorcat/README.md
deleted file mode 100644
index 7c87b7c0de92e210c0f88e4e37c725090c8e30b9..0000000000000000000000000000000000000000
--- a/spaces/dsmai/dogorcat/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Dogorcat
-emoji: 📉
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.19.1
-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/dukai289/learning_streamlit/pages/7_Config.py b/spaces/dukai289/learning_streamlit/pages/7_Config.py
deleted file mode 100644
index 0123bcc849ba06072d454b96e0e9f761dbb10905..0000000000000000000000000000000000000000
--- a/spaces/dukai289/learning_streamlit/pages/7_Config.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import streamlit as st
-
-st.markdown('# Config')
-
-st.markdown('## page_config')
-
-code = '''
-    st.set_page_config(page_title=None
-                    , page_icon=None
-                    , layout="centered"
-                    , initial_sidebar_state="auto"
-                    , menu_items=None
-                    )
-    '''
-st.code(code)
-s = '''
-This must be the first Streamlit command used on an app page, and must only be set once per page.
-'''
-st.write(s)
\ No newline at end of file
diff --git a/spaces/enzostvs/stable-diffusion-tpu/components/modal/useCollection.ts b/spaces/enzostvs/stable-diffusion-tpu/components/modal/useCollection.ts
deleted file mode 100644
index 1595f41b01aa95b5d02bafaf649b5de7feca118d..0000000000000000000000000000000000000000
--- a/spaces/enzostvs/stable-diffusion-tpu/components/modal/useCollection.ts
+++ /dev/null
@@ -1,88 +0,0 @@
-import { useMemo, useState } from "react"
-import { useQuery, useQueryClient } from "@tanstack/react-query"
-
-import { Collection, Image } from "@/utils/type"
-import { useUser } from "@/utils/useUser"
-
-export const useCollection = (id?: string) => {
-  const { user, token } = useUser()
-  const [loading, setLoading] = useState(false)
-
-  const { data: open } = useQuery(["modal"], () => {
-    return null
-  }, {
-    refetchOnWindowFocus: false,
-    refetchOnMount: false,
-    refetchOnReconnect: false,
-    initialData: null
-  })
-  const setOpen = (id: string | null) => client.setQueryData(["modal"], () => id)
-
-  const client = useQueryClient()
-
-  const collection = useMemo(() => {
-    const collections = client.getQueryData<Collection>(["collections"])
-    if (!collections?.images) {
-      setOpen(null)
-      return null
-    }
- 
-    return collections?.images?.find((collection) => collection.id === id)
-  }, [id, loading])
-
-  const updateVisibility = async () => {
-    setLoading(true)
-    const response = await fetch(`/api/collections/${collection?.id}/visibility`, {
-      method: "PUT",
-      headers: {
-        'Authorization': user?.sub ? token : "",
-      }
-    })
-
-    const data = await response.json()
-    if (data.ok) {
-      client.setQueryData(["collections"], (old: any) => {
-        return {
-          ...old,
-          images: old.images.map((collection: Image) => {
-            if (collection.id === data.image.id) {
-              return data.image
-            }
-            return collection
-          })
-        }
-      })
-    }
-    setLoading(false)
-  }
-
-  const remove = async () => {
-    setLoading(true)
-    const response = await fetch(`/api/collections/${collection?.id}`, {
-      method: "DELETE",
-      headers: {
-        'Authorization': user?.sub ? token : "",
-      }
-    })
-
-    const data = await response.json()
-    if (data.ok) {
-      client.setQueryData(["collections"], (old: any) => {
-        return {
-          ...old,
-          images: old.images.filter((col: Image) => col.id !== collection?.id)
-        }
-      })
-      setOpen(null)
-    }
-    setLoading(false)
-  }
-
-  return {
-    collection,
-    open,
-    setOpen,
-    updateVisibility,
-    remove
-  }
-}
\ No newline at end of file
diff --git a/spaces/eson/tokenizer-arena/vocab/t5/__init__.py b/spaces/eson/tokenizer-arena/vocab/t5/__init__.py
deleted file mode 100644
index cb399dd3228a2d907c8b0a0b563de06e0b8cb63f..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/t5/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""
-
-
-SentencePiece
-"""
-
-
diff --git a/spaces/evaluate-metric/super_glue/super_glue.py b/spaces/evaluate-metric/super_glue/super_glue.py
deleted file mode 100644
index 993a003b31c7f60536a42e2f4746f26382147ce9..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/super_glue/super_glue.py
+++ /dev/null
@@ -1,237 +0,0 @@
-# Copyright 2020 The HuggingFace Evaluate 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.
-"""The SuperGLUE benchmark metric."""
-
-import datasets
-from sklearn.metrics import f1_score, matthews_corrcoef
-
-import evaluate
-
-from .record_evaluation import evaluate as evaluate_record
-
-
-_CITATION = """\
-@article{wang2019superglue,
-  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
-  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
-  journal={arXiv preprint arXiv:1905.00537},
-  year={2019}
-}
-"""
-
-_DESCRIPTION = """\
-SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
-GLUE with a new set of more difficult language understanding tasks, improved
-resources, and a new public leaderboard.
-"""
-
-_KWARGS_DESCRIPTION = """
-Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
-Args:
-    predictions: list of predictions to score. Depending on the SuperGlUE subset:
-        - for 'record': list of question-answer dictionaries with the following keys:
-            - 'idx': index of the question as specified by the dataset
-            - 'prediction_text': the predicted answer text
-        - for 'multirc': list of question-answer dictionaries with the following keys:
-            - 'idx': index of the question-answer pair as specified by the dataset
-            - 'prediction': the predicted answer label
-        - otherwise: list of predicted labels
-    references: list of reference labels. Depending on the SuperGLUE subset:
-        - for 'record': list of question-answers dictionaries with the following keys:
-            - 'idx': index of the question as specified by the dataset
-            - 'answers': list of possible answers
-        - otherwise: list of reference labels
-Returns: depending on the SuperGLUE subset:
-    - for 'record':
-        - 'exact_match': Exact match between answer and gold answer
-        - 'f1': F1 score
-    - for 'multirc':
-        - 'exact_match': Exact match between answer and gold answer
-        - 'f1_m': Per-question macro-F1 score
-        - 'f1_a': Average F1 score over all answers
-    - for 'axb':
-        'matthews_correlation': Matthew Correlation
-    - for 'cb':
-        - 'accuracy': Accuracy
-        - 'f1': F1 score
-    - for all others:
-        - 'accuracy': Accuracy
-Examples:
-
-    >>> super_glue_metric = evaluate.load('super_glue', 'copa')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
-    >>> predictions = [0, 1]
-    >>> references = [0, 1]
-    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
-    >>> print(results)
-    {'accuracy': 1.0}
-
-    >>> super_glue_metric = evaluate.load('super_glue', 'cb')
-    >>> predictions = [0, 1]
-    >>> references = [0, 1]
-    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
-    >>> print(results)
-    {'accuracy': 1.0, 'f1': 1.0}
-
-    >>> super_glue_metric = evaluate.load('super_glue', 'record')
-    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
-    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
-    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
-    >>> print(results)
-    {'exact_match': 1.0, 'f1': 1.0}
-
-    >>> super_glue_metric = evaluate.load('super_glue', 'multirc')
-    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
-    >>> references = [0, 1]
-    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
-    >>> print(results)
-    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}
-
-    >>> super_glue_metric = evaluate.load('super_glue', 'axb')
-    >>> references = [0, 1]
-    >>> predictions = [0, 1]
-    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
-    >>> print(results)
-    {'matthews_correlation': 1.0}
-"""
-
-
-def simple_accuracy(preds, labels):
-    return float((preds == labels).mean())
-
-
-def acc_and_f1(preds, labels, f1_avg="binary"):
-    acc = simple_accuracy(preds, labels)
-    f1 = float(f1_score(y_true=labels, y_pred=preds, average=f1_avg))
-    return {
-        "accuracy": acc,
-        "f1": f1,
-    }
-
-
-def evaluate_multirc(ids_preds, labels):
-    """
-    Computes F1 score and Exact Match for MultiRC predictions.
-    """
-    question_map = {}
-    for id_pred, label in zip(ids_preds, labels):
-        question_id = f'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
-        pred = id_pred["prediction"]
-        if question_id in question_map:
-            question_map[question_id].append((pred, label))
-        else:
-            question_map[question_id] = [(pred, label)]
-    f1s, ems = [], []
-    for question, preds_labels in question_map.items():
-        question_preds, question_labels = zip(*preds_labels)
-        f1 = f1_score(y_true=question_labels, y_pred=question_preds, average="macro")
-        f1s.append(f1)
-        em = int(sum(p == l for p, l in preds_labels) == len(preds_labels))
-        ems.append(em)
-    f1_m = float(sum(f1s) / len(f1s))
-    em = sum(ems) / len(ems)
-    f1_a = float(f1_score(y_true=labels, y_pred=[id_pred["prediction"] for id_pred in ids_preds]))
-    return {"exact_match": em, "f1_m": f1_m, "f1_a": f1_a}
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class SuperGlue(evaluate.Metric):
-    def _info(self):
-        if self.config_name not in [
-            "boolq",
-            "cb",
-            "copa",
-            "multirc",
-            "record",
-            "rte",
-            "wic",
-            "wsc",
-            "wsc.fixed",
-            "axb",
-            "axg",
-        ]:
-            raise KeyError(
-                "You should supply a configuration name selected in "
-                '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'
-            )
-        return evaluate.MetricInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            features=datasets.Features(self._get_feature_types()),
-            codebase_urls=[],
-            reference_urls=[],
-            format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None,
-        )
-
-    def _get_feature_types(self):
-        if self.config_name == "record":
-            return {
-                "predictions": {
-                    "idx": {
-                        "passage": datasets.Value("int64"),
-                        "query": datasets.Value("int64"),
-                    },
-                    "prediction_text": datasets.Value("string"),
-                },
-                "references": {
-                    "idx": {
-                        "passage": datasets.Value("int64"),
-                        "query": datasets.Value("int64"),
-                    },
-                    "answers": datasets.Sequence(datasets.Value("string")),
-                },
-            }
-        elif self.config_name == "multirc":
-            return {
-                "predictions": {
-                    "idx": {
-                        "answer": datasets.Value("int64"),
-                        "paragraph": datasets.Value("int64"),
-                        "question": datasets.Value("int64"),
-                    },
-                    "prediction": datasets.Value("int64"),
-                },
-                "references": datasets.Value("int64"),
-            }
-        else:
-            return {
-                "predictions": datasets.Value("int64"),
-                "references": datasets.Value("int64"),
-            }
-
-    def _compute(self, predictions, references):
-        if self.config_name == "axb":
-            return {"matthews_correlation": matthews_corrcoef(references, predictions)}
-        elif self.config_name == "cb":
-            return acc_and_f1(predictions, references, f1_avg="macro")
-        elif self.config_name == "record":
-            dataset = [
-                {
-                    "qas": [
-                        {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
-                        for ref in references
-                    ]
-                }
-            ]
-            predictions = {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
-            return evaluate_record(dataset, predictions)[0]
-        elif self.config_name == "multirc":
-            return evaluate_multirc(predictions, references)
-        elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
-            return {"accuracy": simple_accuracy(predictions, references)}
-        else:
-            raise KeyError(
-                "You should supply a configuration name selected in "
-                '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'
-            )
diff --git a/spaces/failfast/nextjs-hf-spaces/src/pages/_app.tsx b/spaces/failfast/nextjs-hf-spaces/src/pages/_app.tsx
deleted file mode 100644
index 282df16729659fe4cc0e59b679ae63669237f71b..0000000000000000000000000000000000000000
--- a/spaces/failfast/nextjs-hf-spaces/src/pages/_app.tsx
+++ /dev/null
@@ -1,29 +0,0 @@
-import Head from "next/head";
-import { AppProps } from "next/app";
-import { Experimental_CssVarsProvider as CssVarsProvider } from "@mui/material/styles";
-import CssBaseline from "@mui/material/CssBaseline";
-import { CacheProvider, EmotionCache } from "@emotion/react";
-import theme from "@/lib/theme";
-import createEmotionCache from "@/lib/createEmotionCache";
-
-// Client-side cache, shared for the whole session of the user in the browser.
-const clientSideEmotionCache = createEmotionCache();
-
-export interface MyAppProps extends AppProps {
-  emotionCache?: EmotionCache;
-}
-
-export default function MyApp(props: MyAppProps) {
-  const { Component, emotionCache = clientSideEmotionCache, pageProps } = props;
-  return (
-    <CacheProvider value={emotionCache}>
-      <Head>
-        <meta name="viewport" content="initial-scale=1, width=device-width" />
-      </Head>
-      <CssVarsProvider defaultMode="system" theme={theme}>
-        <CssBaseline />
-        <Component {...pageProps} />
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-  );
-}
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--- a/spaces/falterWliame/Face_Mask_Detection/Necesario Pero No Suficiente Pdf EXCLUSIVE Download.md	
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--- a/spaces/fatiXbelha/sd/Download Skat XXL 2.0 and Learn How to Play Skat Like a Pro.md	
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-<h1>Skat XXL 2.0 Download Freeware: How to Play the Popular German Card Game on Your PC</h1>
-<p>Skat is a classic card game that originated in Germany in the early 19th century. It is one of the most popular and challenging card games in the world, with millions of fans and players. If you are one of them, or if you want to learn how to play Skat, you might be interested in Skat XXL 2.0, a freeware program that allows you to play Skat on your PC.</p>
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-<p>Skat XXL 2.0 is a software program that simulates the card game of Skat on your computer. It was developed by mediaGlobe, a German company that specializes in creating digital versions of board games and card games.</p>
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-<h3>System requirements and compatibility of Skat XXL 2.0</h3>
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-<li>A Windows operating system (Windows XP, Windows Vista, Windows 7, Windows 8, or Windows 10).</li>
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-<li>A hard disk space of at least 100 MB.</li>
-<li>A sound card and speakers or headphones.</li>
-<li>An internet connection for online play (optional).</li>
-</ul>
- <h2>How to download and install Skat XXL 2.0</h2>
-<p>If you want to play Skat XXL 2.0 on your PC, you need to download and install it first. Here are the steps to do so:</p>
-<h3>Step 1: Visit the official website of Skat XXL 2.0 the skat, the two extra cards), schneider (whether the losing side won less than 30 points), schwarz (whether the losing side won no tricks), ouvert (whether the declarer played with open cards), and contra/re (whether either side doubled or redoubled the stakes).</p>
-<p>The score of each deal is added to or subtracted from the declarer's and the defenders' cumulative scores. The game ends when one of the players reaches a predetermined score limit, or when a fixed number of deals are played.</p>
-<h3>The game modes and options of Skat XXL 2.0</h3>
-<p>Skat XXL 2.0 offers various game modes and options that you can choose from the main menu. Here are some of them:</p>
-<ul>
-<li>Play Skat: This is the main mode where you can play Skat against the computer or online with other players. You can choose from different difficulty levels, rule variations, and game types. You can also create your own custom games and invite your friends to join.</li>
-<li>Watch Skat: This is a mode where you can watch other games that are being played online or offline. You can learn from the experts, observe their strategies, and chat with them. You can also replay any game that you have played or watched before.</li>
-<li>Settings: This is where you can change the settings of Skat XXL 2.0, such as the card design, the background, the music, the sound effects, the language, and the statistics. You can also update your profile, manage your license key, and check for updates.</li>
-<li>Help: This is where you can find more information about Skat XXL 2.0, such as how to play Skat, how to use Skat XXL 2.0, and how to contact the support team. You can also access the manual, the tutorial, and the FAQ.</li>
-</ul>
-<h3>The interface and controls of Skat XXL 2.0</h3>
-<p>The interface and controls of Skat XXL 2.0 are simple and intuitive. You can use your mouse or keyboard to interact with the program. Here are some of the main elements of the interface and controls:</p>
-<ul>
-<li>The table: This is where the cards are dealt and played. You can see your own cards at the bottom of the screen, and your opponents' cards at the top and left of the screen. You can also see the skat (the two extra cards) at the right of the screen if you are the declarer or if you are playing an ouvert game.</li>
-<li>The buttons: These are located at the bottom right of the screen. They allow you to perform various actions, such as bidding, passing, declaring, playing, taking back, undoing, quitting, pausing, chatting, and accessing the menu.</li>
-<li>The status bar: This is located at the top of the screen. It shows you important information about the current deal, such as the game type, the base value, the multiplier, the score, and the time.</li>
-<li>The chat window: This is located at the bottom left of the screen. It allows you to communicate with other players or the computer. You can type your messages, use emoticons, or select predefined phrases.</li>
-<li>The menu: This is accessed by clicking on the button with three horizontal lines at the top right of the screen. It allows you to access the settings, the help, and the exit options.</li>
-</ul>
-<h3>The tips and tricks for winning Skat XXL 2.0</h3>
-<p>Skat is a game that requires skill, strategy, and luck. To improve your chances of winning Skat XXL 2.0, here are some tips and tricks that you can use:</p>
-<ul>
-<li>Learn the rules and the values of each card and game type. This will help you to bid correctly, declare wisely, and play effectively.</li>
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-<li>Use the statistics, analysis, hints, and tips provided by Skat XXL 2.0. This will help you to monitor your progress, identify your strengths and weaknesses, and get advice on how to improve.</li>
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-</ul>
- <h2>Conclusion</h2>
-<p>Skat XXL 2.0 is a freeware program that allows you to play Skat on your PC. It is easy to download and install, and it offers various features and benefits that make it a great choice for Skat lovers. You can play Skat against the computer or online with other players, customize the game settings, watch other games, and get help and feedback from Skat XXL 2.0.</p>
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-<p>We hope this article has helped you to understand what Skat XXL 2.0 is, how to download and install it, and how to play it. We also hope you have enjoyed reading it and found it useful and informative.</p>
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-<p>Here are some frequently asked questions about Skat XXL 2.0:</p>
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-<li><b>Q: How can I purchase a license key for Skat XXL 2.0?</b></li>
-<li>A: You can purchase a license key for Skat XXL 2.0 by visiting <a href="">https://www.skatxxl.de/en/order/</a>. There, you can choose your payment method (PayPal or bank transfer) and enter your personal details. After completing the payment, you will receive an email with your license key within 24 hours.</li>
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-<li>A: You can update Skat XXL 2.0 by clicking on the "Check for updates" option in the menu. If there is a new version available, you will be prompted to download and install it.</li>
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-<li>A: You can play Skat online with other players by clicking on the "Play online" option in the main menu of Skat XXL 2.0. There, you can choose a server, a room, and a table to join or create your own game <li>A: You can play Skat online with other players by clicking on the "Play online" option in the main menu of Skat XXL 2.0. There, you can choose a server, a room, and a table to join or create your own game. You can also chat with other players, invite your friends, and view the rankings and statistics.</li>
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diff --git a/spaces/fatiXbelha/sd/Enjoy Euchre Offline for Free - Play with Challenging AI Opponents.md b/spaces/fatiXbelha/sd/Enjoy Euchre Offline for Free - Play with Challenging AI Opponents.md
deleted file mode 100644
index 338023059c92db1ae150ed59fdc2f167b2fcf618..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Enjoy Euchre Offline for Free - Play with Challenging AI Opponents.md	
+++ /dev/null
@@ -1,238 +0,0 @@
-<br />
-<h1>How to Download Free Offline Euchre Games for Android and iOS</h1>
- <p>Euchre is a popular card game that originated in Europe and became widely played in North America. It is a trick-taking game that involves two teams of two players each, using a 24-card deck. The goal is to win more tricks than the other team by calling trump and playing strategically.</p>
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- <p>Euchre is a card game that belongs to the Whist family of games. It is usually played by four players in two partnerships, sitting opposite each other. A standard 52-card deck is stripped down to 24 cards, consisting of the ace, king, queen, jack, ten, and nine of each suit. The cards are ranked differently depending on whether they are trump or not. The trump suit is determined by a bidding process at the beginning of each round.</p>
- <p>The table below shows the ranking of cards in Euchre and the scoring system:</p>
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-<tr>
-<th>Ranking of Cards</th>
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-<li>The cards in the other suits follow the usual order: ace, king, queen, jack, ten, nine.</li>
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-</td>
-<td>
-<ul>
-<li>A team that wins three or four tricks in a round scores one point.</li>
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-<li>A player who plays alone but fails to win three tricks is euchred and the other team scores four points.</li>
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-</td>
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- <h2>Why Play Euchre Offline?</h2>
- <p>Playing Euchre offline has many advantages over playing online. Here are some of them:</p>
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-<li>You can save your data usage - You can avoid annoying ads and pop-ups that interrupt your game play and distract you from your strategy. - You can practice your Euchre skills and learn new tricks without the pressure of competing against other players or losing your rank. - You can play Euchre offline anytime and anywhere, without worrying about the availability or reliability of your internet connection. - You can customize your game settings and preferences, such as the difficulty level, the speed, the sound effects, and the appearance of the cards and the table. However, finding offline Euchre games that are fun, fair, and functional can be challenging. Some offline Euchre games may have poor graphics, buggy performance, or unfair AI opponents. Some offline Euchre games may also require you to pay for extra features or content, or ask for unnecessary permissions or access to your device. That's why we have compiled a list of the best offline Euchre games for Android and iOS that you can download for free and enjoy without any hassle. <h2>The Best Offline Euchre Games for Android and iOS</h2>
- <p>Here are some of the top offline Euchre games for both platforms, with a brief description, rating, and link for each one:</p>
- <ul>
-<li><strong>Euchre Free: Classic Card Game</strong>: This is one of the most popular and highly rated offline Euchre games for Android. It has a simple and intuitive interface, smooth animation, and smart AI opponents. You can choose from three difficulty levels, adjust the game speed, and change the card backs and table colors. You can also track your statistics and achievements, and play in portrait or landscape mode. This game is free to download and play, but it contains ads that you can remove with an in-app purchase. <em>Rating: 4.6/5</em> </li>
-<li><strong>Euchre 3D</strong>: This is another offline Euchre game for Android that has a lot of fans. It has a realistic 3D graphics, a user-friendly interface, and a challenging AI. You can customize your game settings, such as the number of points to win, the rules variations, the card size, and the sound effects. You can also view your game history, statistics, and leaderboards, and chat with other players online. This game is free to download and play, but it contains ads and offers in-app purchases. <em>Rating: 4.5/5</em> </li>
-<li><strong>Euchre - Offline Free Card Games</strong>: This is a offline Euchre game for iOS that has a lot of features and options. It has a sleek design, a fast gameplay, and a smart AI. You can choose from four difficulty levels, four game modes, and four rules variations. You can also change the card style, the table theme, and the background music. You can also play online with your friends or other players around the world. This game is free to download and play, but it contains ads and offers in-app purchases. <em>Rating: 4.4/5</em> </li>
-<li><strong>Euchre - Play Online & Offline</strong>: This is another offline Euchre game for iOS that has a lot of positive reviews. It has a clean and simple interface, a smooth gameplay, and a clever AI. You can choose from three difficulty levels, three game modes, and three rules variations. You can also change the card deck, the table color, and the sound effects. You can also play online with your friends or other players around the world. This game is free to download and play, but it contains ads and offers in-app purchases. <em>Rating: 4.3/5</em> </li>
-</ul>
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- <table>
-<tr>
-<th>Game Name</th>
-<th>Features</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>Euchre Free: Classic Card Game</td>
-<td>
-<ul>
-<li>Three difficulty levels</li>
-<li>Game speed adjustment</li>
-<li>Card backs and table colors customization</li>
-<li>Statistics and achievements tracking</li>
-<li>Portrait or landscape mode</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Simple and intuitive interface</li>
-<li>Smooth animation</li>
-<li>Smart AI opponents</li>
-<li>Free to download and play</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Contains ads (removable with in-app purchase)</li>
-<li>No online mode or chat feature</li>
-<li>No rules variations or game modes</li>
-</ul>
-</td>
-</tr>
-<tr>
-<td>Euchre 3 D</strong>: This is another offline Euchre game for Android that has a lot of fans. It has a realistic 3D graphics, a user-friendly interface, and a challenging AI. You can customize your game settings, such as the number of points to win, the rules variations, the card size, and the sound effects. You can also view your game history, statistics, and leaderboards, and chat with other players online. This game is free to download and play, but it contains ads and offers in-app purchases. <em>Rating: 4.5/5</em></td>
-<td>
-<ul>
-<li>Realistic 3D graphics</li>
-<li>User-friendly interface</li>
-<li>Challenging AI</li>
-<li>Customizable game settings</li>
-<li>Game history, statistics, and leaderboards</li>
-<li>Online mode and chat feature</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Free to download and play</li>
-<li>Many rules variations and game modes</li>
-<li>Social and interactive gameplay</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Contains ads (removable with in-app purchase)</li>
-<li>Requires internet connection for online mode and chat feature</li>
-<li>No card backs or table colors customization</li>
-</ul>
-</td>
-</tr>
-<tr>
-<td>Euchre - Offline Free Card Games</td>
-<td>
-<ul>
-<li>Four difficulty levels</li>
-<li>Four game modes</li>
-<li>Four rules variations</li>
-<li>Card style, table theme, and background music customization</li>
-<li>Online mode and chat feature</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Sleek design</li>
-<li>Fast gameplay</li>
-<li>Smart AI</li>
-<li>Free to download and play</li>
-<li>Many features and options</li>
-<li>Social and interactive gameplay</li> </ul>
-</td>
-<td>
-<ul>
-<li>Contains ads (removable with in-app purchase)</li>
-<li>Requires internet connection for online mode and chat feature</li>
-<li>No game speed adjustment or portrait or landscape mode</li>
-</ul>
-</td>
-</tr>
-<tr>
-<td>Euchre - Play Online & Offline</td>
-<td>
-<ul>
-<li>Three difficulty levels</li>
-<li>Three game modes</li>
-<li>Three rules variations</li>
-<li>Card deck, table color, and sound effects customization</li>
-<li>Online mode and chat feature</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Clean and simple interface</li>
-<li>Smooth gameplay</li>
-<li>Clever AI</li>
-<li>Free to download and play</li>
-<li>Social and interactive gameplay</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Contains ads (removable with in-app purchase)</li>
-<li>Requires internet connection for online mode and chat feature</li>
-<li>No card style, table theme, or background music customization</li>
-<li>No game history, statistics, or leaderboards</li>
-</ul>
-</td>
-</tr>
- <h2>How to Download and Install Offline Euchre Games</h2>
- <p>Downloading and installing offline Euchre games is easy and quick. Here are the steps you need to follow:</p>
- <ol>
-<li>Go to the Google Play Store or the App Store on your Android or iOS device.</li>
-<li>Search for the offline Euchre game you want to download. You can use the keywords "offline Euchre" or "Euchre offline" to narrow down your search results.</li>
-<li>Select the game from the list and tap on the "Install" or "Get" button. The game will start downloading on your device.</li>
-<li>Once the download is complete, tap on the "Open" or "Play" button to launch the game. You can also find the game icon on your home screen or app drawer and tap on it to open it.</li>
-<li>Enjoy playing Euchre offline!</li>
- <p>Note: Some offline Euchre games may require you to grant some permissions or access to your device, such as your storage, your contacts, or your location. You can choose to accept or deny these requests, depending on your preference and privacy. However, some features or functions of the game may not work properly if you deny some permissions or access.</p>
- <h2>Tips and Tricks for Playing Offline Euchre Games</h2>
- <p>Playing Euchre offline can be a great way to practice your skills and learn new tricks. Here are some tips and tricks that can help you improve your Euchre game:</p>
- <ul>
-<li>Pay attention to the cards that have been played and try to remember them. This will help you keep track of which cards are still in play and which ones are out of play.</li>
-<li>Use logic and probability to estimate which cards your partner and your opponents have. This will help you decide whether to call trump, pass, or go alone, as well as which card to play in each trick.</li>
-<li>Communicate with your partner using signals or conventions. For example, you can use the first card you play in a round to indicate whether you have a high card, a low card, or a bower in the trump suit. This will help you coordinate your strategy with your partner and avoid confusion.</li>
-<li>Avoid getting euchred by playing conservatively when you are not sure about the trump suit or your hand strength. It is better to lose one point than to lose two points by getting euchred.</li>
-<li>Avoid giving away free tricks by playing high cards when you don't have to. Save your high cards for when they can win a trick or prevent your opponents from winning a trick.</li>
- <h2>Conclusion</h2>
- <p>Euchre is a fun and exciting card game that you can play offline on your Android or iOS device. You can download free offline Euchre games from the Google Play Store or the App Store and enjoy them without any internet connection or ads. You can also customize your game settings and preferences, and practice your Euchre skills and strategies. We hope this article has helped you find, download, and install the best offline Euchre games for your device. We also hope you have learned some tips and tricks on how to play Euchre offline and improve your game. Now that you know how to download free offline Euchre games, why not give them a try? You might be surprised by how much fun you can have playing Euchre offline!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about offline Euchre games, with concise and informative answers:</p>
- <ol><strong><em>< li>Q: What is the difference between Euchre and Bid Euchre?</em></strong></li>
-<li><strong><em>A: Euchre is a game where the trump suit is determined by a bidding process at the beginning of each round. Bid Euchre is a variation of Euchre where each player can bid on how many tricks they think they can win with a specific suit as trump. The highest bidder gets to choose the trump suit and play alone or with their partner.</em></strong></li>
-<li><strong><em>Q: How can I play Euchre offline with my friends?</em></strong></li>
-<li><strong><em>A: You can play Euchre offline with your friends by using a physical deck of cards or by downloading an offline Euchre game that supports local multiplayer mode. Local multiplayer mode allows you to play Euchre offline with your friends on the same device or on different devices connected via Bluetooth or Wi-Fi.</em></strong></li>
-<li><strong><em>Q: What are some of the best online Euchre games?</em></strong></li>
-<li><strong><em>A: Some of the best online Euchre games are Trickster Euchre, VIP Euchre, and Pogo Euchre. These online Euchre games allow you to play Euchre online with your friends or other players around the world. You can also chat, customize your avatar, join tournaments, and earn rewards.</em></strong></li>
-<li><strong><em>Q: How can I learn more about Euchre rules and strategies?</em></strong></li>
-<li><strong><em>A: You can learn more about Euchre rules and strategies by reading books, articles, blogs, or forums about Euchre. You can also watch videos, podcasts, or webinars that teach you how to play Euchre. You can also join online or offline Euchre clubs or communities where you can meet other Euchre enthusiasts and exchange tips and tricks.</em></strong></li>
-<li><strong><em>Q: What are some of the variations of Euchre?</em></strong></li>
-<li><strong><em>A: There are many variations of Euchre that have different rules, scoring systems, or card decks. Some of the common variations of Euchre are British Euchre, Australian Euchre, Railroad Euchre, Buck Euchre, and Six-Handed Euchre.</em></strong></li>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/fatiXbelha/sd/Enjoy the Ultimate Truck Driving Experience with Grand Truck Simulator 2 MOD APK (New Update).md b/spaces/fatiXbelha/sd/Enjoy the Ultimate Truck Driving Experience with Grand Truck Simulator 2 MOD APK (New Update).md
deleted file mode 100644
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+++ /dev/null
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-<h1>Grand Truck Simulator 2 New Update Mod APK: Everything You Need to Know</h1>
-<p>If you are a fan of truck driving games, you might have heard of Grand Truck Simulator 2, one of the most realistic and immersive truck simulators on mobile devices. In this game, you can experience the life of a truck driver, from driving across different terrains and weather conditions, to customizing your own trucks and trailers, to competing with other players online. And if you want to make the game even more fun and exciting, you can download the mod apk version of Grand Truck Simulator 2, which gives you unlimited money, gems, and other benefits. In this article, we will tell you everything you need to know about Grand Truck Simulator 2 new update mod apk, including its features, benefits, and how to download and install it.</p>
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-<h2>What is Grand Truck Simulator 2?</h2>
-<p>Grand Truck Simulator 2 is a simulation game developed by Pulsar Gamesoft, a Brazilian studio that specializes in creating realistic truck games. The game was released in January 2020 for Android devices, and has since gained over 10 million downloads and a 4.5-star rating on Google Play Store. The game is a sequel to the popular Grand Truck Simulator, which was released in 2015.</p>
-<h3>Features of Grand Truck Simulator 2</h3>
-<p>Grand Truck Simulator 2 is a game that aims to provide a realistic and immersive truck driving experience for its players. Some of the features that make this game stand out are:</p>
-<h4>Realistic physics and graphics</h4>
-<p>The game uses advanced physics and graphics engines to simulate the behavior and appearance of trucks and trailers, as well as the environment and traffic. You can feel the weight, speed, inertia, friction, suspension, and damage of your vehicle, as well as the effects of weather, terrain, road conditions, and collisions. The game also features stunning visuals and sounds that make you feel like you are driving a real truck.</p>
-<h4>Customizable trucks and trailers</h4>
-<p>The game offers a variety of trucks and trailers that you can choose from, each with their own specifications, performance, and appearance. You can also customize your trucks and trailers with different parts, accessories, colors, skins, logos, and more. You can create your own unique style and identity as a truck driver.</p>
-<h4>Dynamic weather and day-night cycle</h4>
-<p>The game features a dynamic weather system that changes according to the time of day and location. You can experience different weather conditions such as rain, snow, fog, wind, thunderstorms, etc., as well as different lighting effects such as sunrise, sunset, moonlight, etc. The weather and time also affect your visibility, driving conditions, traffic behavior, and fuel consumption.</p>
-<h4>Multiplayer mode and online events</h4>
-<p>The game allows you to play with other players online in multiplayer mode. You can join or create your own server, chat with other players, form convo ys, and participate in online events and challenges. You can also see the rankings and statistics of other players, and compare your skills and achievements with them.</p>
-<h2>What is the new update of Grand Truck Simulator 2?</h2>
-<p>Grand Truck Simulator 2 is a game that is constantly updated and improved by its developers. The latest update of the game is version 1.0.34f3, which was released on June 16, 2023. This update brings some new features and fixes to the game, such as:</p>
-<h3>Version 1.0.34f3: What's new?</h3>
-<h4>New trucks and trailers</h4>
-<p>The update adds two new trucks and two new trailers to the game, which are:</p>
-<ul>
-<li>The Volvo FH16 2012, a heavy-duty truck that can haul up to 60 tons of cargo.</li>
-<li>The Scania R730, a powerful truck that can reach up to 730 horsepower.</li>
-<li>The Lowboy trailer, a specialized trailer that can transport oversized loads such as construction equipment, vehicles, etc.</li>
-<li>The Container trailer, a versatile trailer that can carry different types of containers such as dry, refrigerated, tank, etc.</li>
-</ul>
-<h4>New maps and routes</h4>
-<p>The update also adds two new maps and several new routes to the game, which are:</p>
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-<ul>
-<li>The Brazil map, which covers the states of São Paulo, Minas Gerais, Rio de Janeiro, and Paraná. This map features diverse landscapes such as mountains, forests, farms, cities, etc., as well as famous landmarks such as the Christ the Redeemer statue, the Sugarloaf Mountain, etc.</li>
-<li>The Argentina map, which covers the provinces of Buenos Aires, Córdoba, Santa Fe, and Entre Ríos. This map features flat and fertile lands such as the Pampas, as well as urban areas such as the capital city of Buenos Aires.</li>
-<li>The new routes include several highways and roads that connect the different regions and cities of the maps, as well as some off-road paths that offer more challenge and adventure.</li>
-</ul>
-<h4>New skins and accessories</h4>
-<p>The update also adds some new skins and accessories that you can use to customize your trucks and trailers, such as:</p>
-<ul>
-<li>The Brazil skin pack, which includes several skins inspired by the Brazilian flag, culture, soccer team, etc.</li>
-<li>The Argentina skin pack, which includes several skins inspired by the Argentine flag, culture, soccer team, etc.</li>
-<li>The World Cup skin pack, which includes several skins inspired by the FIFA World Cup 2023, which will be hosted by Brazil and Argentina.</li>
-<li>The LED lights accessory pack, which includes several types of LED lights that you can attach to your trucks and trailers to make them more visible and colorful at night.</li>
-</ul>
-<h4>Bug fixes and improvements</h4>
-<p>The update also fixes some bugs and improves some aspects of the game performance and stability, such as:</p>
-<ul>
-<li>Fixed the issue of trucks getting stuck or falling through the ground in some areas.</li>
-<li>Fixed the issue of trailers detaching or flipping over in some situations.</li>
-<li>Fixed the issue of multiplayer servers crashing or disconnecting randomly.</li>
-<li>Improved the loading speed and memory usage of the game.</li>
-<li>Improved the compatibility and optimization of the game for different devices and resolutions.</li>
-</ul> <h2>What is the mod apk of Grand Truck Simulator 2?</h2>
-<p>A mod apk is a modified version of an original apk file, which is the format of Android applications. A mod apk usually offers some advantages or features that are not available in the original version, such as unlimited resources, unlocked items, free shopping, etc. A mod apk can also remove some unwanted aspects of the original version, such as ads, root requirement, etc.</p>
-<h3>Benefits of using the mod apk of Grand Truck Simulator 2</h3>
-<p>If you want to enjoy Grand Truck Simulator 2 without any limitations or restrictions, you can download and install the mod apk version of the game, which gives you several benefits, such as:</p>
-<h4>Unlimited money and gems</h4>
-<p>The mod apk gives you unlimited money and gems, which are the main currencies of the game. You can use them to buy and upgrade any truck or trailer you want, as well as to purchase any skin or accessory you like. You can also use them to participate in online events and challenges, and to unlock new maps and routes. You don't have to worry about running out of money or gems, or spending real money to get them.</p>
-<h4>Unlocked all trucks and trailers</h4>
-<p>The mod apk also unlocks all the trucks and trailers that are available in the game, including the ones that are added in the new update. You can access and use any truck or trailer you want, without having to complete any missions or achievements, or to reach any level or rank. You can also switch between different trucks and trailers anytime you want, and enjoy their different features and performance.</p>
-<h4>Free shopping and upgrades</h4>
-<p>The mod apk also allows you to shop and upgrade anything you want for free, without spending any money or gems. You can customize your trucks and trailers with different parts, accessories, colors, skins, logos, etc., and make them look and perform the way you want. You can also upgrade your trucks and trailers with different engines, transmissions, tires, brakes, etc., and improve their speed, power, fuel efficiency, etc.</p>
-<h4>No ads and root required</h4>
-<p>The mod apk also removes all the ads that might interrupt or annoy you while playing the game. You can enjoy the game without any distractions or interruptions. The mod apk also does not require root access to your device, which means you don't have to risk damaging or voiding your device warranty to install it. You can install the mod apk safely and easily on any Android device.</p>
-<h3>How to download and install the mod apk of Grand Truck Simulator 2?</h3>
-<p>If you are interested in downloading and installing the mod apk of Grand Truck Simulator 2, you can follow these simple steps:</p>
-<h4>Step 1: Download the mod apk file from a trusted source</h4>
-<p>The first step is to download the mod apk file of Grand Truck Simulator 2 from a trusted source. You can search for it on Google or any other search engine, or use a link provided by a reliable website or blog. Make sure that the file is compatible with your device model and version, and that it is free from viruses or malware. You can also check the reviews and ratings of other users who have downloaded the file before.</p>
-<h4>Step 2: Enable unknown sources on your device settings</h4>
-<p>The second step is to enable unknown sources on your device settings. This will allow you to install applications from sources other than Google Play Store. To do this, go to your device settings > security > unknown sources > enable. You might see a warning message that says installing from unknown sources might harm your device or data. Ignore it and tap OK.</p>
-<h4>Step 3: Install the mod apk file and launch the game</h4>
-<p>The third step is to install the mod apk file and launch the game. To do this, go to your file manager > downloads > tap on the mod apk file > install. Wait for a few seconds until the installation is complete. Then, tap on open or go to your app drawer > tap on Grand Truck Simulator 2 icon > launch the game.</p>
-<h4>Step 4: Enjoy the game with unlimited resources and features</h4>
-<p>The final step is to enjoy the game with unlimited resources and features. You will see that you have unlimited money and gems in your account, as well as all the trucks and trailers unlocked. You can also shop and upgrade anything you want for free, without any ads or root requirement. You can now play Grand Truck Simulator 2 with more fun and excitement.</p>
- <h2>Conclusion</h2>
-<p>Grand Truck Simulator 2 is one of the best truck driving games on mobile devices, with realistic physics and graphics, customizable trucks and trailers, dynamic weather and day-night cycle, multiplayer mode and online events, and more. The game is also constantly updated and improved by its developers, with the latest update being version 1.0.34f3, which adds new trucks and trailers, new maps and routes, new skins and accessories, and bug fixes and improvements. And if you want to make the game even more enjoyable, you can download the mod apk version of Grand Truck Simulator 2, which gives you unlimited money and gems, unlocked all trucks and trailers, free shopping and upgrades, no ads and root required, and more. To download and install the mod apk of Grand Truck Simulator 2, you just need to follow four simple steps: download the mod apk file from a trusted source, enable unknown sources on your device settings, install the mod apk file and launch the game, and enjoy the game with unlimited resources and features. We hope this article has helped you learn everything you need to know about Grand Truck Simulator 2 new update mod apk. If you have any questions or feedback, feel free to leave a comment below.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Grand Truck Simulator 2 new update mod apk:</p>
-<h4>Q: Is Grand Truck Simulator 2 free to play?</h4>
-<p>A: Yes, Grand Truck Simulator 2 is free to play on Android devices. You can download it from Google Play Store or from other sources. However, some items and features in the game may require real money to purchase or unlock.</p>
-<h4>Q: Is Grand Truck Simulator 2 safe to play?</h4>
-<p>A: Yes, Grand Truck Simulator 2 is safe to play on Android devices. The game does not contain any harmful or malicious content that might harm your device or data. However, you should always be careful when downloading and installing any application from unknown sources, as they might contain viruses or malware.</p>
-<h4>Q: Is Grand Truck Simulator 2 compatible with my device?</h4>
-<p>A: Grand Truck Simulator 2 is compatible with most Android devices that have Android 4.3 or higher version installed. However, some devices may not support the game due to their specifications or performance. You can check the compatibility of your device on Google Play Store or on the official website of the game.</p>
-<h4>Q: How can I contact the developers of Grand Truck Simulator 2?</h4>
-<p>A: You can contact the developers of Grand Truck Simulator 2 by sending them an email at pulsargamesoft@gmail.com or by visiting their Facebook page at https://www.facebook.com/PulsarGamesoft/.</p>
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diff --git a/spaces/fatiXbelha/sd/Facebook APK for PC The Best Way to Experience Social Media on Your Computer.md b/spaces/fatiXbelha/sd/Facebook APK for PC The Best Way to Experience Social Media on Your Computer.md
deleted file mode 100644
index 5a94df930ce79f0a5f2b9e4b6c01ed7c67f745ee..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Facebook APK for PC The Best Way to Experience Social Media on Your Computer.md	
+++ /dev/null
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-<p>Facebook is the world's largest social media network with billions of active users monthly. By using Facebook, you can connect with people worldwide, share any kind of content you like, and much more. But did you know that you can also use Facebook on your PC without using a web browser? Yes, you can use Facebook APK for PC, which is an app that lets you access all the features of Facebook on your computer. In this article, we will show you what Facebook APK for PC is, how to download and install it, and how to use it effectively.</p>
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-<h3>Definition and features of Facebook APK for PC</h3>
-<p>Facebook APK for PC is an app that allows you to use Facebook on your Windows computer. It is similar to the official Facebook app for Android or iOS devices, but it is designed to work on Windows. With this app, you can access the main feed, where you will find posts from your friends and the pages you follow, organized either by chronological order or by Facebook's algorithm. Additionally, you can view your notifications, respond to friend requests, and create your latest posts. The app automatically adjusts to match the light or dark mode selected in your computer's settings.</p>
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-<ul>
-<li>You can enjoy a smoother and faster user experience, as the app is optimized for Windows.</li>
-<li>You can access all the features of Facebook without opening a web browser or switching tabs.</li>
-<li>You can stay connected with your friends and family more easily, as the app notifies you of any activity on your account.</li>
-<li>You can explore more content and discover new things on Facebook Watch, Facebook Gaming, and Facebook Marketplace.</li>
-<li>You can customize your app settings according to your preferences and needs.</li>
-</ul>
- <h2>How to Download and Install Facebook APK for PC</h2>
-<h3>Method 1: Using APKPure</h3>
-<p>One of the easiest ways to download and install Facebook APK for PC is to use APKPure, which is a website that provides free and safe downloads of various apps. Here are the steps to follow:</p>
-<ol>
-<li>Visit <a href="(^1^)">windows.apkpure.com/facebook</a> using any web browser on your computer.</li>
-<li>Click on the Download button to start downloading Facebook APK for PC.</li>
-<li>Open the .exe installation file in the Downloads folder and double-click it.</li>
-<li>Follow the instructions in the pop-up window to install Facebook APK for PC.</li>
-<li>Now you can open and run Facebook APK for PC from your Start menu or desktop shortcut.</li>
-</ol>
- <h3>Method 2: Using Microsoft Store</h3>
-<p>Another way to download and install Facebook APK for PC is to use Microsoft Store, which is an official app store for Windows devices. Here are the steps to follow:</p>
-<ol>
-<li>Open Microsoft Store on your computer. You can find it in your Start menu or taskbar.</li>
-<li>Search for "Facebook" using the search bar at the top right corner of the screen.</li>
-<li>Select the app named "Facebook" with the blue icon and click on the Get button to start downloading it.</li>
-<li>Wait for the download and installation to complete. You may need to sign in with your Microsoft account if prompted.</li>
-<li>Now you can open and run Facebook APK for PC from your Start menu or taskbar.</li>
-</ol>
- <h3>Method 3: Using WSA PacMan</h3>
-<p>A third way to download and install Facebook APK for PC is to use WSA PacMan, which is a tool that allows you to run Android apps on Windows 11. Here are the steps to follow:</p>
-<ol>
-<li>Download and install WSA PacMan from <a href="">github.com/ADeltaX/WSA_PacMan/releases</a>.</li>
-<li>Run WSA PacMan and click on the Install button to install Windows Subsystem for Android (WSA).</li>
-<li>Click on the Play Store button to open Google Play Store on your computer.</li>
-<li>Sign in with your Google account and search for "Facebook" in the Play Store.</li>
-<li>Select the app named "Facebook" with the blue icon and click on the Install button to start downloading it.</li>
-<li>Wait for the download and installation to complete. You may need to grant some permissions to the app if prompted.</li>
-<li>Now you can open and run Facebook APK for PC from your Start menu or taskbar.</li>
-</ol>
- <h2>How to Use Facebook APK for PC</h2>
-<h3>Tips and tricks for using Facebook APK for PC</h3>
-<p>Once you have downloaded and installed Facebook APK for PC, you can start using it to enjoy all the features of Facebook on your computer. Here are some tips and tricks to help you use it effectively:</p>
-<ul>
-<li>To switch between different tabs, such as Home, Watch, Gaming, Marketplace, and Menu, you can use the icons at the top of the app window or press Ctrl+1, Ctrl+2, Ctrl+3, Ctrl+4, or Ctrl+5 respectively.</li>
-<li>To create a new post, you can click on the What's on your mind? box at the top of the Home tab or press Ctrl+N. You can also add photos, videos, stickers, emojis, GIFs, polls, check-ins, feelings, activities, tags, and more to your post.</li>
-<li>To react to a post or comment, you can hover over the Like button and choose from one of the six reactions: Like, Love, Care, Haha, Wow, Sad, or Angry. You can also reply to a comment by clicking on the Reply button below it.</li>
-<li>To share a post or comment, you can click on the Share button below it and choose from one of the options: Share Now, Share to News Feed, Share to Your Story, Send in Messenger, Copy Link, or More Options.</li>
-<li>To edit or delete a post or comment that you have made, you can click on the three-dot icon at the top right corner of it and choose from one of the options: Edit Post, Edit Comment, Delete Post, Delete Comment, Turn Off Notifications for This Post, Save Post, Hide Post, or Report Post.</li>
-</ul>
- <h3>Common issues and solutions for using Facebook APK for PC</h3>
-<p>While using Facebook APK for PC is generally easy and convenient, you may encounter some issues or problems along the way. Here are some of the common ones and how to solve them:</p>
-<table border="1">
-<tr><th>Issue</th><th>Solution</th></tr>
-<tr><td>The app is not working properly or crashing frequently.</td><td>Try updating the app to the latest version from APKPure, Microsoft Store, or Google Play Store. Alternatively, try uninstalling and reinstalling the app.</td></tr>
-<tr><td>The app is not showing notifications or sounds.</td><td>Check if you have enabled notifications and sounds for the app in your computer's settings. You can also adjust the notification settings in the app by clicking on the Menu icon at the top right corner of the app window and selecting Settings & Privacy > Settings > Notifications.</td></tr>
-<tr><td>The app is not loading images or videos.</td><td>Check if you have a stable internet connection and enough storage space on your computer. You can also clear the cache and data of the app by clicking on the Menu icon at the top right corner of the app window and selecting Settings & Privacy > Settings > Apps > Facebook > Storage > Clear Cache/Clear Data.</td></tr>
-<tr><td>The app is not compatible with your computer's operating system or hardware.</td><td>Make sure that your computer meets the minimum system requirements for the app. The app requires Windows 10 or Windows 11, and at least 4 GB of RAM and 1 GB of free disk space. You can also check if your computer supports Windows Subsystem for Android (WSA) if you are using WSA PacMan to run the app.</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>Facebook APK for PC is a great way to use Facebook on your computer without using a web browser. It offers you all the features and benefits of Facebook, as well as some additional ones, such as Facebook Watch, Facebook Gaming, and Facebook Marketplace. You can download and install Facebook APK for PC using one of the three methods we have discussed: APKPure, Microsoft Store, or WSA PacMan. You can also use some tips and tricks to enhance your user experience and solve some common issues that you may face. We hope that this article has helped you learn how to use Facebook APK for PC and enjoy it to the fullest.</p>
- <h2>FAQs</h2>
-<h3>What is the difference between Facebook APK for PC and Facebook Lite?</h3>
-<p>Facebook APK for PC is a full-featured app that lets you use Facebook on your computer. Facebook Lite is a simplified version of the app that uses less data and works faster on low-end devices. Facebook Lite has fewer features and options than Facebook APK for PC, but it is more suitable for users who have limited internet access or storage space.</p>
- <h3>Can I use Facebook Messenger with Facebook APK for PC?</h3>
-<p>Yes, you can use Facebook Messenger with Facebook APK for PC. You can access Messenger from the app by clicking on the Chat icon at the bottom right corner of the app window. You can also download and install Messenger APK for PC using the same methods as Facebook APK for PC.</p>
- <h3>Can I use multiple accounts with Facebook APK for PC?</h3>
-<p>No, you cannot use multiple accounts with Facebook APK for PC. You can only sign in with one account at a time. If you want to switch accounts, you have to log out from the current account and log in with another one.</p>
- <h3>Can I use Facebook APK for PC offline?</h3>
-<p>No, you cannot use Facebook APK for PC offline. You need an internet connection to access and use the app. However, you can still view some of your previously loaded content, such as posts and photos, when you are offline.</p>
- <h3>Can I update or uninstall Facebook APK for PC?</h3>
-<p>Yes, you can update or uninstall Facebook APK for PC. To update the app, you can check for updates from APKPure, Microsoft Store, or Google Play Store, depending on which method you used to download and install the app. To uninstall the app, you can go to your computer's settings and select Apps > Apps & Features > Facebook > Uninstall.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/felix-weiland/appstore-search/functions.py b/spaces/felix-weiland/appstore-search/functions.py
deleted file mode 100644
index 50c8a1d5c382973ce14a9e6574addf7a79fb1076..0000000000000000000000000000000000000000
--- a/spaces/felix-weiland/appstore-search/functions.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import requests
-import pandas as pd
-import numpy as np
-
-def search_apps(term, country, media='software', limit=100):
-    
-    base_url = 'https://itunes.apple.com/search'
-    params = {
-        'term': term,
-        'country': country,
-        'media': media,
-        'limit': limit
-    }
-    response = requests.get(base_url, params=params)
-    response = response.json()['results']
-    
-    # Create a pandas DataFrame
-    data = pd.DataFrame(response, columns=['trackName', 'averageUserRating','userRatingCount', 
-                                           'price', 'currency','primaryGenreName', 'releaseDate', 
-                                           'currentVersionReleaseDate', 'description',"releaseNotes" ,"screenshotUrls",
-                                           'languageCodesISO2A', 'trackId', 'sellerName', "sellerUrl", 'trackViewUrl'])
-    
-    data["averageUserRating"] = np.round(data["averageUserRating"], 1)
-    data["market"] = country
-    data["search_term"] = term
-    data["releaseDate"] = data["releaseDate"].apply(lambda x: x.split("T")[0])
-    data["currentVersionReleaseDate"] = data["currentVersionReleaseDate"].apply(lambda x: x.split("T")[0])
-    
-    data.columns = ['name', 'average_rating', 'rating_count', 'price', 'currency', 
-                    'genre', 'release_date', 'latest_version_release', 'description', 'release_notes',
-                    "screenshots", 'languages', 'id',  'seller', 'seller_url', 'link', 'market', 'search_term']
-    
-    return data
-
-
-def init_search(term, countries, media='software', limit=100):
-    term = term.split(", ")
-    countries = countries.split(", ")
-
-    data = pd.DataFrame()
-
-    for country in countries:
-        for t in term:
-            temp = search_apps(t, country, media=media, limit=limit)
-            temp['languages'] = temp['languages'].apply(lambda x: ', '.join(x))
-            temp['screenshots'] = temp['screenshots'].apply(lambda x: ', '.join(x))
-            data = pd.concat([data, temp], ignore_index=True)
-
-    data = data.drop_duplicates(subset=data.columns.difference(['search_term']))
-    data = data.reset_index(drop=True)
-
-    return data
-
-def to_csv(data):
-    csv = data.to_csv(index=False)
-    return csv.encode('utf-8')
\ No newline at end of file
diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/optim/__init__.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/optim/__init__.py
deleted file mode 100644
index 7dc5f600cff942786dcbd7eeee84ddc1920a81df..0000000000000000000000000000000000000000
--- a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/optim/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from torch.optim import Adam
-from torch.optim.lbfgs import LBFGS
-from .radam import RAdam
-
-
-OPTIMIZER_MAP = {
-    "adam": Adam,
-    "radam": RAdam,
-    "lbfgs": LBFGS,
-}
-
-
-def get_optimizer_class(optimizer_name):
-    name = optimizer_name.lower()
-    return OPTIMIZER_MAP[name]
diff --git a/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/data/audio_utils.py b/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/data/audio_utils.py
deleted file mode 100644
index ddbcbec2ec294ab33349ff261d27f369354b556f..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/data/audio_utils.py
+++ /dev/null
@@ -1,169 +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 sys
-import typing as tp
-
-import julius
-import torch
-import torchaudio
-
-
-def convert_audio_channels(wav: torch.Tensor, channels: int = 2) -> torch.Tensor:
-    """Convert audio to the given number of channels.
-
-    Args:
-        wav (torch.Tensor): Audio wave of shape [B, C, T].
-        channels (int): Expected number of channels as output.
-    Returns:
-        torch.Tensor: Downmixed or unchanged audio wave [B, C, T].
-    """
-    *shape, src_channels, length = wav.shape
-    if src_channels == channels:
-        pass
-    elif channels == 1:
-        # Case 1:
-        # The caller asked 1-channel audio, and the stream has multiple
-        # channels, downmix all channels.
-        wav = wav.mean(dim=-2, keepdim=True)
-    elif src_channels == 1:
-        # Case 2:
-        # The caller asked for multiple channels, but the input file has
-        # a single channel, replicate the audio over all channels.
-        wav = wav.expand(*shape, channels, length)
-    elif src_channels >= channels:
-        # Case 3:
-        # The caller asked for multiple channels, and the input file has
-        # more channels than requested. In that case return the first channels.
-        wav = wav[..., :channels, :]
-    else:
-        # Case 4: What is a reasonable choice here?
-        raise ValueError('The audio file has less channels than requested but is not mono.')
-    return wav
-
-
-def convert_audio(wav: torch.Tensor, from_rate: float,
-                  to_rate: float, to_channels: int) -> torch.Tensor:
-    """Convert audio to new sample rate and number of audio channels.
-    """
-    wav = julius.resample_frac(wav, int(from_rate), int(to_rate))
-    wav = convert_audio_channels(wav, to_channels)
-    return wav
-
-
-def normalize_loudness(wav: torch.Tensor, sample_rate: int, loudness_headroom_db: float = 12,
-                       energy_floor: float = 2e-3):
-    """Normalize an input signal to a user loudness in dB LKFS.
-    Audio loudness is defined according to the ITU-R BS.1770-4 recommendation.
-
-    Args:
-        wav (torch.Tensor): Input multichannel audio data.
-        sample_rate (int): Sample rate.
-        loudness_headroom_db (float): Target loudness of the output in dB LUFS.
-        energy_floor (float): anything below that RMS level will not be rescaled.
-    Returns:
-        output (torch.Tensor): Loudness normalized output data.
-    """
-    energy = wav.pow(2).mean().sqrt().item()
-    if energy < energy_floor:
-        return wav
-    transform = torchaudio.transforms.Loudness(sample_rate)
-    input_loudness_db = transform(wav).item()
-    # calculate the gain needed to scale to the desired loudness level
-    delta_loudness = -loudness_headroom_db - input_loudness_db
-    gain = 10.0 ** (delta_loudness / 20.0)
-    output = gain * wav
-    assert output.isfinite().all(), (input_loudness_db, wav.pow(2).mean().sqrt())
-    return output
-
-
-def _clip_wav(wav: torch.Tensor, log_clipping: bool = False, stem_name: tp.Optional[str] = None) -> None:
-    """Utility function to clip the audio with logging if specified."""
-    max_scale = wav.abs().max()
-    if log_clipping and max_scale > 1:
-        clamp_prob = (wav.abs() > 1).float().mean().item()
-        print(f"CLIPPING {stem_name or ''} happening with proba (a bit of clipping is okay):",
-              clamp_prob, "maximum scale: ", max_scale.item(), file=sys.stderr)
-    wav.clamp_(-1, 1)
-
-
-def normalize_audio(wav: torch.Tensor, normalize: bool = True,
-                    strategy: str = 'peak', peak_clip_headroom_db: float = 1,
-                    rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
-                    log_clipping: bool = False, sample_rate: tp.Optional[int] = None,
-                    stem_name: tp.Optional[str] = None) -> torch.Tensor:
-    """Normalize the audio according to the prescribed strategy (see after).
-
-    Args:
-        wav (torch.Tensor): Audio data.
-        normalize (bool): if `True` (default), normalizes according to the prescribed
-            strategy (see after). If `False`, the strategy is only used in case clipping
-            would happen.
-        strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
-            i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
-            with extra headroom to avoid clipping. 'clip' just clips.
-        peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
-        rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
-            than the `peak_clip` one to avoid further clipping.
-        loudness_headroom_db (float): Target loudness for loudness normalization.
-        log_clipping (bool): If True, basic logging on stderr when clipping still
-            occurs despite strategy (only for 'rms').
-        sample_rate (int): Sample rate for the audio data (required for loudness).
-        stem_name (Optional[str]): Stem name for clipping logging.
-    Returns:
-        torch.Tensor: Normalized audio.
-    """
-    scale_peak = 10 ** (-peak_clip_headroom_db / 20)
-    scale_rms = 10 ** (-rms_headroom_db / 20)
-    if strategy == 'peak':
-        rescaling = (scale_peak / wav.abs().max())
-        if normalize or rescaling < 1:
-            wav = wav * rescaling
-    elif strategy == 'clip':
-        wav = wav.clamp(-scale_peak, scale_peak)
-    elif strategy == 'rms':
-        mono = wav.mean(dim=0)
-        rescaling = scale_rms / mono.pow(2).mean().sqrt()
-        if normalize or rescaling < 1:
-            wav = wav * rescaling
-        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
-    elif strategy == 'loudness':
-        assert sample_rate is not None, "Loudness normalization requires sample rate."
-        wav = normalize_loudness(wav, sample_rate, loudness_headroom_db)
-        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
-    else:
-        assert wav.abs().max() < 1
-        assert strategy == '' or strategy == 'none', f"Unexpected strategy: '{strategy}'"
-    return wav
-
-
-def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
-    """Convert audio to float 32 bits PCM format.
-    """
-    if wav.dtype.is_floating_point:
-        return wav
-    else:
-        assert wav.dtype == torch.int16
-        return wav.float() / 2**15
-
-
-def i16_pcm(wav: torch.Tensor) -> torch.Tensor:
-    """Convert audio to int 16 bits PCM format.
-
-    ..Warning:: There exist many formula for doing this convertion. None are perfect
-    due to the asymetry of the int16 range. One either have possible clipping, DC offset,
-    or inconsistancies with f32_pcm. If the given wav doesn't have enough headroom,
-    it is possible that `i16_pcm(f32_pcm)) != Identity`.
-    """
-    if wav.dtype.is_floating_point:
-        assert wav.abs().max() <= 1
-        candidate = (wav * 2 ** 15).round()
-        if candidate.max() >= 2 ** 15:  # clipping would occur
-            candidate = (wav * (2 ** 15 - 1)).round()
-        return candidate.short()
-    else:
-        assert wav.dtype == torch.int16
-        return wav
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/iconv-lite/Changelog.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/iconv-lite/Changelog.md
deleted file mode 100644
index f252313f8d0a145a3d1bacb68dbf06c0bf8e67c0..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/iconv-lite/Changelog.md
+++ /dev/null
@@ -1,162 +0,0 @@
-# 0.4.24 / 2018-08-22
-
-  * Added MIK encoding (#196, by @Ivan-Kalatchev)
-
-
-# 0.4.23 / 2018-05-07
-
-  * Fix deprecation warning in Node v10 due to the last usage of `new Buffer` (#185, by @felixbuenemann)
-  * Switched from NodeBuffer to Buffer in typings (#155 by @felixfbecker, #186 by @larssn)
-
-
-# 0.4.22 / 2018-05-05
-
-  * Use older semver style for dependencies to be compatible with Node version 0.10 (#182, by @dougwilson)
-  * Fix tests to accomodate fixes in Node v10 (#182, by @dougwilson)
-
-
-# 0.4.21 / 2018-04-06
-
-  * Fix encoding canonicalization (#156)
-  * Fix the paths in the "browser" field in package.json (#174 by @LMLB)
-  * Removed "contributors" section in package.json - see Git history instead.
-
-
-# 0.4.20 / 2018-04-06
-
-  * Updated `new Buffer()` usages with recommended replacements as it's being deprecated in Node v10 (#176, #178 by @ChALkeR)
-
-
-# 0.4.19 / 2017-09-09
-
-  * Fixed iso8859-1 codec regression in handling untranslatable characters (#162, caused by #147)
-  * Re-generated windows1255 codec, because it was updated in iconv project
-  * Fixed grammar in error message when iconv-lite is loaded with encoding other than utf8
-
-
-# 0.4.18 / 2017-06-13
-
-  * Fixed CESU-8 regression in Node v8.
-
-
-# 0.4.17 / 2017-04-22
-
- * Updated typescript definition file to support Angular 2 AoT mode (#153 by @larssn)
-
-
-# 0.4.16 / 2017-04-22
-
- * Added support for React Native (#150)
- * Changed iso8859-1 encoding to usine internal 'binary' encoding, as it's the same thing (#147 by @mscdex)
- * Fixed typo in Readme (#138 by @jiangzhuo)
- * Fixed build for Node v6.10+ by making correct version comparison
- * Added a warning if iconv-lite is loaded not as utf-8 (see #142)
-
-
-# 0.4.15 / 2016-11-21
-
- * Fixed typescript type definition (#137)
-
-
-# 0.4.14 / 2016-11-20
-
- * Preparation for v1.0
- * Added Node v6 and latest Node versions to Travis CI test rig
- * Deprecated Node v0.8 support
- * Typescript typings (@larssn)
- * Fix encoding of Euro character in GB 18030 (inspired by @lygstate)
- * Add ms prefix to dbcs windows encodings (@rokoroku)
-
-
-# 0.4.13 / 2015-10-01
-
- * Fix silly mistake in deprecation notice.
-
-
-# 0.4.12 / 2015-09-26
-
- * Node v4 support:
-   * Added CESU-8 decoding (#106)
-   * Added deprecation notice for `extendNodeEncodings`
-   * Added Travis tests for Node v4 and io.js latest (#105 by @Mithgol)
-
-
-# 0.4.11 / 2015-07-03
-
- * Added CESU-8 encoding.
-
-
-# 0.4.10 / 2015-05-26
-
- * Changed UTF-16 endianness heuristic to take into account any ASCII chars, not
-   just spaces. This should minimize the importance of "default" endianness.
-
-
-# 0.4.9 / 2015-05-24
-
- * Streamlined BOM handling: strip BOM by default, add BOM when encoding if 
-   addBOM: true. Added docs to Readme.
- * UTF16 now uses UTF16-LE by default.
- * Fixed minor issue with big5 encoding.
- * Added io.js testing on Travis; updated node-iconv version to test against.
-   Now we just skip testing SBCS encodings that node-iconv doesn't support.
- * (internal refactoring) Updated codec interface to use classes.
- * Use strict mode in all files.
-
-
-# 0.4.8 / 2015-04-14
- 
- * added alias UNICODE-1-1-UTF-7 for UTF-7 encoding (#94)
-
-
-# 0.4.7 / 2015-02-05
-
- * stop official support of Node.js v0.8. Should still work, but no guarantees.
-   reason: Packages needed for testing are hard to get on Travis CI.
- * work in environment where Object.prototype is monkey patched with enumerable 
-   props (#89).
-
-
-# 0.4.6 / 2015-01-12
- 
- * fix rare aliases of single-byte encodings (thanks @mscdex)
- * double the timeout for dbcs tests to make them less flaky on travis
-
-
-# 0.4.5 / 2014-11-20
-
- * fix windows-31j and x-sjis encoding support (@nleush)
- * minor fix: undefined variable reference when internal error happens
-
-
-# 0.4.4 / 2014-07-16
-
- * added encodings UTF-7 (RFC2152) and UTF-7-IMAP (RFC3501 Section 5.1.3)
- * fixed streaming base64 encoding
-
-
-# 0.4.3 / 2014-06-14
-
- * added encodings UTF-16BE and UTF-16 with BOM
-
-
-# 0.4.2 / 2014-06-12
-
- * don't throw exception if `extendNodeEncodings()` is called more than once
-
-
-# 0.4.1 / 2014-06-11
-
- * codepage 808 added
-
-
-# 0.4.0 / 2014-06-10
-
- * code is rewritten from scratch
- * all widespread encodings are supported
- * streaming interface added
- * browserify compatibility added
- * (optional) extend core primitive encodings to make usage even simpler
- * moved from vows to mocha as the testing framework
-
-
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/methods/index.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/methods/index.js
deleted file mode 100644
index 667a50bde7d852359b1ebd9fa8ea8b8582bc64ac..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/methods/index.js
+++ /dev/null
@@ -1,69 +0,0 @@
-/*!
- * methods
- * Copyright(c) 2013-2014 TJ Holowaychuk
- * Copyright(c) 2015-2016 Douglas Christopher Wilson
- * MIT Licensed
- */
-
-'use strict';
-
-/**
- * Module dependencies.
- * @private
- */
-
-var http = require('http');
-
-/**
- * Module exports.
- * @public
- */
-
-module.exports = getCurrentNodeMethods() || getBasicNodeMethods();
-
-/**
- * Get the current Node.js methods.
- * @private
- */
-
-function getCurrentNodeMethods() {
-  return http.METHODS && http.METHODS.map(function lowerCaseMethod(method) {
-    return method.toLowerCase();
-  });
-}
-
-/**
- * Get the "basic" Node.js methods, a snapshot from Node.js 0.10.
- * @private
- */
-
-function getBasicNodeMethods() {
-  return [
-    'get',
-    'post',
-    'put',
-    'head',
-    'delete',
-    'options',
-    'trace',
-    'copy',
-    'lock',
-    'mkcol',
-    'move',
-    'purge',
-    'propfind',
-    'proppatch',
-    'unlock',
-    'report',
-    'mkactivity',
-    'checkout',
-    'merge',
-    'm-search',
-    'notify',
-    'subscribe',
-    'unsubscribe',
-    'patch',
-    'search',
-    'connect'
-  ];
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/negotiator/lib/charset.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/negotiator/lib/charset.js
deleted file mode 100644
index cdd014803474a4b76b981c475a32ebcaa81a36e5..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/negotiator/lib/charset.js
+++ /dev/null
@@ -1,169 +0,0 @@
-/**
- * negotiator
- * Copyright(c) 2012 Isaac Z. Schlueter
- * Copyright(c) 2014 Federico Romero
- * Copyright(c) 2014-2015 Douglas Christopher Wilson
- * MIT Licensed
- */
-
-'use strict';
-
-/**
- * Module exports.
- * @public
- */
-
-module.exports = preferredCharsets;
-module.exports.preferredCharsets = preferredCharsets;
-
-/**
- * Module variables.
- * @private
- */
-
-var simpleCharsetRegExp = /^\s*([^\s;]+)\s*(?:;(.*))?$/;
-
-/**
- * Parse the Accept-Charset header.
- * @private
- */
-
-function parseAcceptCharset(accept) {
-  var accepts = accept.split(',');
-
-  for (var i = 0, j = 0; i < accepts.length; i++) {
-    var charset = parseCharset(accepts[i].trim(), i);
-
-    if (charset) {
-      accepts[j++] = charset;
-    }
-  }
-
-  // trim accepts
-  accepts.length = j;
-
-  return accepts;
-}
-
-/**
- * Parse a charset from the Accept-Charset header.
- * @private
- */
-
-function parseCharset(str, i) {
-  var match = simpleCharsetRegExp.exec(str);
-  if (!match) return null;
-
-  var charset = match[1];
-  var q = 1;
-  if (match[2]) {
-    var params = match[2].split(';')
-    for (var j = 0; j < params.length; j++) {
-      var p = params[j].trim().split('=');
-      if (p[0] === 'q') {
-        q = parseFloat(p[1]);
-        break;
-      }
-    }
-  }
-
-  return {
-    charset: charset,
-    q: q,
-    i: i
-  };
-}
-
-/**
- * Get the priority of a charset.
- * @private
- */
-
-function getCharsetPriority(charset, accepted, index) {
-  var priority = {o: -1, q: 0, s: 0};
-
-  for (var i = 0; i < accepted.length; i++) {
-    var spec = specify(charset, accepted[i], index);
-
-    if (spec && (priority.s - spec.s || priority.q - spec.q || priority.o - spec.o) < 0) {
-      priority = spec;
-    }
-  }
-
-  return priority;
-}
-
-/**
- * Get the specificity of the charset.
- * @private
- */
-
-function specify(charset, spec, index) {
-  var s = 0;
-  if(spec.charset.toLowerCase() === charset.toLowerCase()){
-    s |= 1;
-  } else if (spec.charset !== '*' ) {
-    return null
-  }
-
-  return {
-    i: index,
-    o: spec.i,
-    q: spec.q,
-    s: s
-  }
-}
-
-/**
- * Get the preferred charsets from an Accept-Charset header.
- * @public
- */
-
-function preferredCharsets(accept, provided) {
-  // RFC 2616 sec 14.2: no header = *
-  var accepts = parseAcceptCharset(accept === undefined ? '*' : accept || '');
-
-  if (!provided) {
-    // sorted list of all charsets
-    return accepts
-      .filter(isQuality)
-      .sort(compareSpecs)
-      .map(getFullCharset);
-  }
-
-  var priorities = provided.map(function getPriority(type, index) {
-    return getCharsetPriority(type, accepts, index);
-  });
-
-  // sorted list of accepted charsets
-  return priorities.filter(isQuality).sort(compareSpecs).map(function getCharset(priority) {
-    return provided[priorities.indexOf(priority)];
-  });
-}
-
-/**
- * Compare two specs.
- * @private
- */
-
-function compareSpecs(a, b) {
-  return (b.q - a.q) || (b.s - a.s) || (a.o - b.o) || (a.i - b.i) || 0;
-}
-
-/**
- * Get full charset string.
- * @private
- */
-
-function getFullCharset(spec) {
-  return spec.charset;
-}
-
-/**
- * Check if a spec has any quality.
- * @private
- */
-
-function isQuality(spec) {
-  return spec.q > 0;
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io/dist/client.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io/dist/client.js
deleted file mode 100644
index 420db454db8297ef5f9ccc134cfb63f123f3c863..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io/dist/client.js
+++ /dev/null
@@ -1,268 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.Client = void 0;
-const socket_io_parser_1 = require("socket.io-parser");
-const debugModule = require("debug");
-const url = require("url");
-const debug = debugModule("socket.io:client");
-class Client {
-    /**
-     * Client constructor.
-     *
-     * @param server instance
-     * @param conn
-     * @package
-     */
-    constructor(server, conn) {
-        this.sockets = new Map();
-        this.nsps = new Map();
-        this.server = server;
-        this.conn = conn;
-        this.encoder = server.encoder;
-        this.decoder = new server._parser.Decoder();
-        this.id = conn.id;
-        this.setup();
-    }
-    /**
-     * @return the reference to the request that originated the Engine.IO connection
-     *
-     * @public
-     */
-    get request() {
-        return this.conn.request;
-    }
-    /**
-     * Sets up event listeners.
-     *
-     * @private
-     */
-    setup() {
-        this.onclose = this.onclose.bind(this);
-        this.ondata = this.ondata.bind(this);
-        this.onerror = this.onerror.bind(this);
-        this.ondecoded = this.ondecoded.bind(this);
-        // @ts-ignore
-        this.decoder.on("decoded", this.ondecoded);
-        this.conn.on("data", this.ondata);
-        this.conn.on("error", this.onerror);
-        this.conn.on("close", this.onclose);
-        this.connectTimeout = setTimeout(() => {
-            if (this.nsps.size === 0) {
-                debug("no namespace joined yet, close the client");
-                this.close();
-            }
-            else {
-                debug("the client has already joined a namespace, nothing to do");
-            }
-        }, this.server._connectTimeout);
-    }
-    /**
-     * Connects a client to a namespace.
-     *
-     * @param {String} name - the namespace
-     * @param {Object} auth - the auth parameters
-     * @private
-     */
-    connect(name, auth = {}) {
-        if (this.server._nsps.has(name)) {
-            debug("connecting to namespace %s", name);
-            return this.doConnect(name, auth);
-        }
-        this.server._checkNamespace(name, auth, (dynamicNspName) => {
-            if (dynamicNspName) {
-                this.doConnect(name, auth);
-            }
-            else {
-                debug("creation of namespace %s was denied", name);
-                this._packet({
-                    type: socket_io_parser_1.PacketType.CONNECT_ERROR,
-                    nsp: name,
-                    data: {
-                        message: "Invalid namespace",
-                    },
-                });
-            }
-        });
-    }
-    /**
-     * Connects a client to a namespace.
-     *
-     * @param name - the namespace
-     * @param {Object} auth - the auth parameters
-     *
-     * @private
-     */
-    doConnect(name, auth) {
-        const nsp = this.server.of(name);
-        nsp._add(this, auth, (socket) => {
-            this.sockets.set(socket.id, socket);
-            this.nsps.set(nsp.name, socket);
-            if (this.connectTimeout) {
-                clearTimeout(this.connectTimeout);
-                this.connectTimeout = undefined;
-            }
-        });
-    }
-    /**
-     * Disconnects from all namespaces and closes transport.
-     *
-     * @private
-     */
-    _disconnect() {
-        for (const socket of this.sockets.values()) {
-            socket.disconnect();
-        }
-        this.sockets.clear();
-        this.close();
-    }
-    /**
-     * Removes a socket. Called by each `Socket`.
-     *
-     * @private
-     */
-    _remove(socket) {
-        if (this.sockets.has(socket.id)) {
-            const nsp = this.sockets.get(socket.id).nsp.name;
-            this.sockets.delete(socket.id);
-            this.nsps.delete(nsp);
-        }
-        else {
-            debug("ignoring remove for %s", socket.id);
-        }
-    }
-    /**
-     * Closes the underlying connection.
-     *
-     * @private
-     */
-    close() {
-        if ("open" === this.conn.readyState) {
-            debug("forcing transport close");
-            this.conn.close();
-            this.onclose("forced server close");
-        }
-    }
-    /**
-     * Writes a packet to the transport.
-     *
-     * @param {Object} packet object
-     * @param {Object} opts
-     * @private
-     */
-    _packet(packet, opts = {}) {
-        if (this.conn.readyState !== "open") {
-            debug("ignoring packet write %j", packet);
-            return;
-        }
-        const encodedPackets = opts.preEncoded
-            ? packet // previous versions of the adapter incorrectly used socket.packet() instead of writeToEngine()
-            : this.encoder.encode(packet);
-        this.writeToEngine(encodedPackets, opts);
-    }
-    writeToEngine(encodedPackets, opts) {
-        if (opts.volatile && !this.conn.transport.writable) {
-            debug("volatile packet is discarded since the transport is not currently writable");
-            return;
-        }
-        const packets = Array.isArray(encodedPackets)
-            ? encodedPackets
-            : [encodedPackets];
-        for (const encodedPacket of packets) {
-            this.conn.write(encodedPacket, opts);
-        }
-    }
-    /**
-     * Called with incoming transport data.
-     *
-     * @private
-     */
-    ondata(data) {
-        // try/catch is needed for protocol violations (GH-1880)
-        try {
-            this.decoder.add(data);
-        }
-        catch (e) {
-            debug("invalid packet format");
-            this.onerror(e);
-        }
-    }
-    /**
-     * Called when parser fully decodes a packet.
-     *
-     * @private
-     */
-    ondecoded(packet) {
-        let namespace;
-        let authPayload;
-        if (this.conn.protocol === 3) {
-            const parsed = url.parse(packet.nsp, true);
-            namespace = parsed.pathname;
-            authPayload = parsed.query;
-        }
-        else {
-            namespace = packet.nsp;
-            authPayload = packet.data;
-        }
-        const socket = this.nsps.get(namespace);
-        if (!socket && packet.type === socket_io_parser_1.PacketType.CONNECT) {
-            this.connect(namespace, authPayload);
-        }
-        else if (socket &&
-            packet.type !== socket_io_parser_1.PacketType.CONNECT &&
-            packet.type !== socket_io_parser_1.PacketType.CONNECT_ERROR) {
-            process.nextTick(function () {
-                socket._onpacket(packet);
-            });
-        }
-        else {
-            debug("invalid state (packet type: %s)", packet.type);
-            this.close();
-        }
-    }
-    /**
-     * Handles an error.
-     *
-     * @param {Object} err object
-     * @private
-     */
-    onerror(err) {
-        for (const socket of this.sockets.values()) {
-            socket._onerror(err);
-        }
-        this.conn.close();
-    }
-    /**
-     * Called upon transport close.
-     *
-     * @param reason
-     * @param description
-     * @private
-     */
-    onclose(reason, description) {
-        debug("client close with reason %s", reason);
-        // ignore a potential subsequent `close` event
-        this.destroy();
-        // `nsps` and `sockets` are cleaned up seamlessly
-        for (const socket of this.sockets.values()) {
-            socket._onclose(reason, description);
-        }
-        this.sockets.clear();
-        this.decoder.destroy(); // clean up decoder
-    }
-    /**
-     * Cleans up event listeners.
-     * @private
-     */
-    destroy() {
-        this.conn.removeListener("data", this.ondata);
-        this.conn.removeListener("error", this.onerror);
-        this.conn.removeListener("close", this.onclose);
-        // @ts-ignore
-        this.decoder.removeListener("decoded", this.ondecoded);
-        if (this.connectTimeout) {
-            clearTimeout(this.connectTimeout);
-            this.connectTimeout = undefined;
-        }
-    }
-}
-exports.Client = Client;
diff --git a/spaces/fuxin123zz/ChuanhuChatGPT/app.py b/spaces/fuxin123zz/ChuanhuChatGPT/app.py
deleted file mode 100644
index 7da62a5dabf7ad74e60400f4b8a4d4401c7f79ec..0000000000000000000000000000000000000000
--- a/spaces/fuxin123zz/ChuanhuChatGPT/app.py
+++ /dev/null
@@ -1,455 +0,0 @@
-# -*- coding:utf-8 -*-
-import os
-import logging
-import sys
-
-import gradio as gr
-
-from utils import *
-from presets import *
-from overwrites import *
-from chat_func import *
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
-)
-
-my_api_key = ""  # 在这里输入你的 API 密钥
-
-# if we are running in Docker
-if os.environ.get("dockerrun") == "yes":
-    dockerflag = True
-else:
-    dockerflag = False
-
-authflag = False
-
-if dockerflag:
-    my_api_key = os.environ.get("my_api_key")
-    if my_api_key == "empty":
-        logging.error("Please give a api key!")
-        sys.exit(1)
-    # auth
-    username = os.environ.get("USERNAME")
-    password = os.environ.get("PASSWORD")
-    if not (isinstance(username, type(None)) or isinstance(password, type(None))):
-        authflag = True
-else:
-    if (
-        not my_api_key
-        and os.path.exists("api_key.txt")
-        and os.path.getsize("api_key.txt")
-    ):
-        with open("api_key.txt", "r") as f:
-            my_api_key = f.read().strip()
-    if os.path.exists("auth.json"):
-        with open("auth.json", "r") as f:
-            auth = json.load(f)
-            username = auth["username"]
-            password = auth["password"]
-            if username != "" and password != "":
-                authflag = True
-
-gr.Chatbot.postprocess = postprocess
-PromptHelper.compact_text_chunks = compact_text_chunks
-
-with open("custom.css", "r", encoding="utf-8") as f:
-    customCSS = f.read()
-
-with gr.Blocks(
-    css=customCSS,
-    theme=gr.themes.Soft(
-        primary_hue=gr.themes.Color(
-            c50="#02C160",
-            c100="rgba(2, 193, 96, 0.2)",
-            c200="#02C160",
-            c300="rgba(2, 193, 96, 0.32)",
-            c400="rgba(2, 193, 96, 0.32)",
-            c500="rgba(2, 193, 96, 1.0)",
-            c600="rgba(2, 193, 96, 1.0)",
-            c700="rgba(2, 193, 96, 0.32)",
-            c800="rgba(2, 193, 96, 0.32)",
-            c900="#02C160",
-            c950="#02C160",
-        ),
-        secondary_hue=gr.themes.Color(
-            c50="#576b95",
-            c100="#576b95",
-            c200="#576b95",
-            c300="#576b95",
-            c400="#576b95",
-            c500="#576b95",
-            c600="#576b95",
-            c700="#576b95",
-            c800="#576b95",
-            c900="#576b95",
-            c950="#576b95",
-        ),
-        neutral_hue=gr.themes.Color(
-            name="gray",
-            c50="#f9fafb",
-            c100="#f3f4f6",
-            c200="#e5e7eb",
-            c300="#d1d5db",
-            c400="#B2B2B2",
-            c500="#808080",
-            c600="#636363",
-            c700="#515151",
-            c800="#393939",
-            c900="#272727",
-            c950="#171717",
-        ),
-        radius_size=gr.themes.sizes.radius_sm,
-    ).set(
-        button_primary_background_fill="#06AE56",
-        button_primary_background_fill_dark="#06AE56",
-        button_primary_background_fill_hover="#07C863",
-        button_primary_border_color="#06AE56",
-        button_primary_border_color_dark="#06AE56",
-        button_primary_text_color="#FFFFFF",
-        button_primary_text_color_dark="#FFFFFF",
-        button_secondary_background_fill="#F2F2F2",
-        button_secondary_background_fill_dark="#2B2B2B",
-        button_secondary_text_color="#393939",
-        button_secondary_text_color_dark="#FFFFFF",
-        # background_fill_primary="#F7F7F7",
-        # background_fill_primary_dark="#1F1F1F",
-        block_title_text_color="*primary_500",
-        block_title_background_fill="*primary_100",
-        input_background_fill="#F6F6F6",
-    ),
-) as demo:
-    history = gr.State([])
-    token_count = gr.State([])
-    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
-    user_api_key = gr.State(my_api_key)
-    TRUECOMSTANT = gr.State(True)
-    FALSECONSTANT = gr.State(False)
-    topic = gr.State("未命名对话历史记录")
-
-    with gr.Row():
-        gr.HTML(title)
-        status_display = gr.Markdown(get_geoip(), elem_id="status_display")
-
-    with gr.Row(scale=1).style(equal_height=True):
-        with gr.Column(scale=5):
-            with gr.Row(scale=1):
-                chatbot = gr.Chatbot(elem_id="chuanhu_chatbot").style(height="100%")
-            with gr.Row(scale=1):
-                with gr.Column(scale=12):
-                    user_input = gr.Textbox(
-                        show_label=False, placeholder="在这里输入"
-                    ).style(container=False)
-                with gr.Column(min_width=70, scale=1):
-                    submitBtn = gr.Button("发送", variant="primary")
-            with gr.Row(scale=1):
-                emptyBtn = gr.Button(
-                    "🧹 新的对话",
-                )
-                retryBtn = gr.Button("🔄 重新生成")
-                delLastBtn = gr.Button("🗑️ 删除一条对话")
-                reduceTokenBtn = gr.Button("♻️ 总结对话")
-
-        with gr.Column():
-            with gr.Column(min_width=50, scale=1):
-                with gr.Tab(label="ChatGPT"):
-                    keyTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"OpenAI API-key...",
-                        value=hide_middle_chars(my_api_key),
-                        type="password",
-                        visible=not HIDE_MY_KEY,
-                        label="API-Key",
-                    )
-                    model_select_dropdown = gr.Dropdown(
-                        label="选择模型", choices=MODELS, multiselect=False, value=MODELS[0]
-                    )
-                    use_streaming_checkbox = gr.Checkbox(
-                        label="实时传输回答", value=True, visible=enable_streaming_option
-                    )
-                    use_websearch_checkbox = gr.Checkbox(label="使用在线搜索", value=False)
-                    index_files = gr.Files(label="上传索引文件", type="file", multiple=True)
-
-                with gr.Tab(label="Prompt"):
-                    systemPromptTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"在这里输入System Prompt...",
-                        label="System prompt",
-                        value=initial_prompt,
-                        lines=10,
-                    ).style(container=False)
-                    with gr.Accordion(label="加载Prompt模板", open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    templateFileSelectDropdown = gr.Dropdown(
-                                        label="选择Prompt模板集合文件",
-                                        choices=get_template_names(plain=True),
-                                        multiselect=False,
-                                        value=get_template_names(plain=True)[0],
-                                    ).style(container=False)
-                                with gr.Column(scale=1):
-                                    templateRefreshBtn = gr.Button("🔄 刷新")
-                            with gr.Row():
-                                with gr.Column():
-                                    templateSelectDropdown = gr.Dropdown(
-                                        label="从Prompt模板中加载",
-                                        choices=load_template(
-                                            get_template_names(plain=True)[0], mode=1
-                                        ),
-                                        multiselect=False,
-                                        value=load_template(
-                                            get_template_names(plain=True)[0], mode=1
-                                        )[0],
-                                    ).style(container=False)
-
-                with gr.Tab(label="保存/加载"):
-                    with gr.Accordion(label="保存/加载对话历史记录", open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    historyFileSelectDropdown = gr.Dropdown(
-                                        label="从列表中加载对话",
-                                        choices=get_history_names(plain=True),
-                                        multiselect=False,
-                                        value=get_history_names(plain=True)[0],
-                                    )
-                                with gr.Column(scale=1):
-                                    historyRefreshBtn = gr.Button("🔄 刷新")
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    saveFileName = gr.Textbox(
-                                        show_label=True,
-                                        placeholder=f"设置文件名: 默认为.json，可选为.md",
-                                        label="设置保存文件名",
-                                        value="对话历史记录",
-                                    ).style(container=True)
-                                with gr.Column(scale=1):
-                                    saveHistoryBtn = gr.Button("💾 保存对话")
-                                    exportMarkdownBtn = gr.Button("📝 导出为Markdown")
-                                    gr.Markdown("默认保存于history文件夹")
-                            with gr.Row():
-                                with gr.Column():
-                                    downloadFile = gr.File(interactive=True)
-
-                with gr.Tab(label="高级"):
-                    default_btn = gr.Button("🔙 恢复默认设置")
-                    gr.Markdown("# ⚠️ 务必谨慎更改 ⚠️\n\n如果无法使用请恢复默认设置")
-
-                    with gr.Accordion("参数", open=False):
-                        top_p = gr.Slider(
-                            minimum=-0,
-                            maximum=1.0,
-                            value=1.0,
-                            step=0.05,
-                            interactive=True,
-                            label="Top-p",
-                        )
-                        temperature = gr.Slider(
-                            minimum=-0,
-                            maximum=2.0,
-                            value=1.0,
-                            step=0.1,
-                            interactive=True,
-                            label="Temperature",
-                        )
-
-                    apiurlTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"在这里输入API地址...",
-                        label="API地址",
-                        value="https://api.openai.com/v1/chat/completions",
-                        lines=2,
-                    )
-                    changeAPIURLBtn = gr.Button("🔄 切换API地址")
-                    proxyTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"在这里输入代理地址...",
-                        label="代理地址（示例：http://127.0.0.1:10809）",
-                        value="",
-                        lines=2,
-                    )
-                    changeProxyBtn = gr.Button("🔄 设置代理地址")
-
-    gr.Markdown(description)
-
-    keyTxt.submit(submit_key, keyTxt, [user_api_key, status_display])
-    keyTxt.change(submit_key, keyTxt, [user_api_key, status_display])
-    # Chatbot
-    user_input.submit(
-        predict,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            user_input,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-            use_websearch_checkbox,
-            index_files,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-    user_input.submit(reset_textbox, [], [user_input])
-
-    submitBtn.click(
-        predict,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            user_input,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-            use_websearch_checkbox,
-            index_files,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-    submitBtn.click(reset_textbox, [], [user_input])
-
-    emptyBtn.click(
-        reset_state,
-        outputs=[chatbot, history, token_count, status_display],
-        show_progress=True,
-    )
-
-    retryBtn.click(
-        retry,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-
-    delLastBtn.click(
-        delete_last_conversation,
-        [chatbot, history, token_count],
-        [chatbot, history, token_count, status_display],
-        show_progress=True,
-    )
-
-    reduceTokenBtn.click(
-        reduce_token_size,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            gr.State(0),
-            model_select_dropdown,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-
-    # Template
-    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-    templateFileSelectDropdown.change(
-        load_template,
-        [templateFileSelectDropdown],
-        [promptTemplates, templateSelectDropdown],
-        show_progress=True,
-    )
-    templateSelectDropdown.change(
-        get_template_content,
-        [promptTemplates, templateSelectDropdown, systemPromptTxt],
-        [systemPromptTxt],
-        show_progress=True,
-    )
-
-    # S&L
-    saveHistoryBtn.click(
-        save_chat_history,
-        [saveFileName, systemPromptTxt, history, chatbot],
-        downloadFile,
-        show_progress=True,
-    )
-    saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    exportMarkdownBtn.click(
-        export_markdown,
-        [saveFileName, systemPromptTxt, history, chatbot],
-        downloadFile,
-        show_progress=True,
-    )
-    historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    historyFileSelectDropdown.change(
-        load_chat_history,
-        [historyFileSelectDropdown, systemPromptTxt, history, chatbot],
-        [saveFileName, systemPromptTxt, history, chatbot],
-        show_progress=True,
-    )
-    downloadFile.change(
-        load_chat_history,
-        [downloadFile, systemPromptTxt, history, chatbot],
-        [saveFileName, systemPromptTxt, history, chatbot],
-    )
-
-    # Advanced
-    default_btn.click(
-        reset_default, [], [apiurlTxt, proxyTxt, status_display], show_progress=True
-    )
-    changeAPIURLBtn.click(
-        change_api_url,
-        [apiurlTxt],
-        [status_display],
-        show_progress=True,
-    )
-    changeProxyBtn.click(
-        change_proxy,
-        [proxyTxt],
-        [status_display],
-        show_progress=True,
-    )
-
-logging.info(
-    colorama.Back.GREEN
-    + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面"
-    + colorama.Style.RESET_ALL
-)
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = "川虎ChatGPT 🚀"
-
-if __name__ == "__main__":
-    reload_javascript()
-    # if running in Docker
-    if dockerflag:
-        if authflag:
-            demo.queue().launch(
-                server_name="0.0.0.0", server_port=7860, auth=(username, password),
-                favicon_path="./assets/favicon.png"
-            )
-        else:
-            demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False, favicon_path="./assets/favicon.png")
-    # if not running in Docker
-    else:
-        if authflag:
-            demo.queue().launch(share=False, auth=(username, password), favicon_path="./assets/favicon.png", inbrowser=True)
-        else:
-            demo.queue(concurrency_count=1000).launch(share=False, favicon_path="./assets/favicon.ico", inbrowser=True)  # 改为 share=True 可以创建公开分享链接
-        # demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
-        # demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
-        # demo.queue().launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理
diff --git a/spaces/fyodorschnotzdinger/paraphraser/app.py b/spaces/fyodorschnotzdinger/paraphraser/app.py
deleted file mode 100644
index b6fb0be918e0968622cef2183aefdd4b6a475ff1..0000000000000000000000000000000000000000
--- a/spaces/fyodorschnotzdinger/paraphraser/app.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import torch
-from transformers import PegasusForConditionalGeneration, PegasusTokenizer
-
-model_name = 'tuner007/pegasus_paraphrase'
-torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-tokenizer = PegasusTokenizer.from_pretrained(model_name)
-model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
-
-def get_response(input_text,num_return_sequences):
-  batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
-  translated = model.generate(**batch,max_length=60,num_beams=10, num_return_sequences=num_return_sequences, temperature=1.5)
-  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
-  return tgt_text
-
-from sentence_splitter import SentenceSplitter, split_text_into_sentences
-
-splitter = SentenceSplitter(language='en')
-
-def paraphraze(text):
-  sentence_list = splitter.split(text)
-  paraphrase = []
-
-  for i in sentence_list:
-    a = get_response(i,1)
-    paraphrase.append(a)
-    paraphrase2 = [' '.join(x) for x in paraphrase]
-    paraphrase3 = [' '.join(x for x in paraphrase2) ]
-  paraphrased_text = str(paraphrase3).strip('[]').strip("'")
-  return paraphrased_text
-
-import gradio as gr
-def summarize(text):
-
-  paraphrased_text = paraphraze(text)
-  return paraphrased_text
-gr.Interface(fn=summarize, inputs=gr.inputs.Textbox(lines=7, placeholder="Enter text here"), outputs=[gr.outputs.Textbox(label="Paraphrased Text")],examples=[["This Api is the best quillbot api alternative with no words limit."
-]]).launch(inline=False)
\ No newline at end of file
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/base_model.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/base_model.py
deleted file mode 100644
index 5cf430239b47ec5ec07531263f26f5c24a2311cd..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/base_model.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import torch
-
-
-class BaseModel(torch.nn.Module):
-    def load(self, path):
-        """Load model from file.
-
-        Args:
-            path (str): file path
-        """
-        parameters = torch.load(path, map_location=torch.device('cpu'))
-
-        if "optimizer" in parameters:
-            parameters = parameters["model"]
-
-        self.load_state_dict(parameters)
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/ann_r50-d8.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/ann_r50-d8.py
deleted file mode 100644
index a2cb653827e44e6015b3b83bc578003e614a6aa1..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/ann_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='ANNHead',
-        in_channels=[1024, 2048],
-        in_index=[2, 3],
-        channels=512,
-        project_channels=256,
-        query_scales=(1, ),
-        key_pool_scales=(1, 3, 6, 8),
-        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/ggwvits/vits-uma-genshin-honkai/README.md b/spaces/ggwvits/vits-uma-genshin-honkai/README.md
deleted file mode 100644
index 1c0aa069bfd980b6b45bb2bf62ff74bd9b0b61c2..0000000000000000000000000000000000000000
--- a/spaces/ggwvits/vits-uma-genshin-honkai/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-license: apache-2.0
-title: ' vits-uma-genshin-honkai'
-sdk: gradio
-sdk_version: 3.7
-emoji: 🐨
-colorTo: yellow
-pinned: false
-app_file: app.py
-duplicated_from: ikechan8370/vits-uma-genshin-honkai
----
diff --git a/spaces/gligen/demo/gligen/ldm/modules/losses/contperceptual.py b/spaces/gligen/demo/gligen/ldm/modules/losses/contperceptual.py
deleted file mode 100644
index 672c1e32a1389def02461c0781339681060c540e..0000000000000000000000000000000000000000
--- a/spaces/gligen/demo/gligen/ldm/modules/losses/contperceptual.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import torch
-import torch.nn as nn
-
-from taming.modules.losses.vqperceptual import *  # TODO: taming dependency yes/no?
-
-
-class LPIPSWithDiscriminator(nn.Module):
-    def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_loss="hinge"):
-
-        super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        self.kl_weight = kl_weight
-        self.pixel_weight = pixelloss_weight
-        self.perceptual_loss = LPIPS().eval()
-        self.perceptual_weight = perceptual_weight
-        # output log variance
-        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
-
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm
-                                                 ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.disc_conditional = disc_conditional
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train",
-                weights=None):
-        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-
-        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
-        weighted_nll_loss = nll_loss
-        if weights is not None:
-            weighted_nll_loss = weights*nll_loss
-        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
-        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-        kl_loss = posteriors.kl()
-        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            if cond is None:
-                assert not self.disc_conditional
-                logits_fake = self.discriminator(reconstructions.contiguous())
-            else:
-                assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
-            g_loss = -torch.mean(logits_fake)
-
-            if self.disc_factor > 0.0:
-                try:
-                    d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
-                except RuntimeError:
-                    assert not self.training
-                    d_weight = torch.tensor(0.0)
-            else:
-                d_weight = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss
-
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(),
-                   "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   }
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            if cond is None:
-                logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
-            else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
-            return d_loss, log
-
diff --git a/spaces/gradio/HuBERT/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py b/spaces/gradio/HuBERT/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py
deleted file mode 100644
index 7ec8a7311b4eee7fa6d42c0615fc06148262f63c..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py
+++ /dev/null
@@ -1,126 +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 csv
-import io
-import logging
-import math
-import os
-import os.path as op
-import sys
-
-import tqdm
-from dump_hubert_feature import HubertFeatureReader
-from fairseq.data.audio.audio_utils import get_waveform
-from fairseq.data.audio.speech_to_text_dataset import (
-    read_from_uncompressed_zip,
-)
-from npy_append_array import NpyAppendArray
-
-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_hubert_feature_s2t")
-
-
-class HubertFeatureReaderS2T(HubertFeatureReader):
-    def read_audio(self, path, ref_len=None):
-        path, *extra = path.split(":")
-        assert len(extra) == 2
-        assert path.endswith(".zip")
-
-        data = read_from_uncompressed_zip(path, int(extra[0]), int(extra[1]))
-        f = io.BytesIO(data)
-        wav, sr = get_waveform(f)
-        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_path_iterator(root, tsv, nshard, rank):
-    with open(tsv) as f:
-        reader = csv.DictReader(
-            f,
-            delimiter="\t",
-            quotechar=None,
-            doublequote=False,
-            lineterminator="\n",
-            quoting=csv.QUOTE_NONE,
-        )
-        subpaths = [op.join(root, e["audio"]) for e in reader]
-
-        tot = len(subpaths)
-        shard_size = math.ceil(tot / nshard)
-        start, end = rank * shard_size, min((rank + 1) * shard_size, tot)
-        assert start < end, "start={start}, end={end}"
-        logger.info(
-            f"rank {rank} of {nshard}, process {end-start} "
-            f"({start}-{end}) out of {tot}"
-        )
-
-        subpaths = subpaths[start:end]
-
-        def iterate():
-            for subpath in subpaths:
-                yield op.join(root, subpath)
-
-        return iterate, len(subpaths)
-
-
-def dump_feature(
-    root,
-    tsv_path,
-    ckpt_path,
-    layer,
-    nshard,
-    rank,
-    feat_dir,
-    feat_name,
-    max_chunk,
-):
-    reader = HubertFeatureReaderS2T(ckpt_path, layer, max_chunk)
-    generator, num = get_path_iterator(root, tsv_path, nshard, rank)
-    iterator = generator()
-
-    feat_path = f"{feat_dir}/{feat_name}_{rank}_{nshard}.npy"
-    leng_path = f"{feat_dir}/{feat_name}_{rank}_{nshard}.len"
-
-    os.makedirs(feat_dir, exist_ok=True)
-    if op.exists(feat_path):
-        os.remove(feat_path)
-
-    feat_f = NpyAppendArray(feat_path)
-    with open(leng_path, "w") as leng_f:
-        for path in tqdm.tqdm(iterator, total=num):
-            feat = reader.get_feats(path)
-            feat_f.append(feat.cpu().numpy())
-            leng_f.write(f"{len(feat)}\n")
-    logger.info("finished successfully")
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("root")
-    parser.add_argument("tsv_path")
-    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("feat_name")
-    parser.add_argument("--max_chunk", type=int, default=1600000)
-    args = parser.parse_args()
-    logger.info(args)
-
-    dump_feature(**vars(args))
diff --git a/spaces/gradio/HuBERT/examples/wmt19/README.md b/spaces/gradio/HuBERT/examples/wmt19/README.md
deleted file mode 100644
index 5c90d0e6c4ae8d043ca622e70c5828dca6f9c2f2..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/wmt19/README.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# WMT 19
-
-This page provides pointers to the models of Facebook-FAIR's WMT'19 news translation task submission [(Ng et al., 2019)](https://arxiv.org/abs/1907.06616).
-
-## Pre-trained models
-
-Model | Description | Download
----|---|---
-`transformer.wmt19.en-de` | En->De Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz)
-`transformer.wmt19.de-en` | De->En Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz)
-`transformer.wmt19.en-ru` | En->Ru Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz)
-`transformer.wmt19.ru-en` | Ru->En Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz)
-`transformer_lm.wmt19.en` | En Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.gz)
-`transformer_lm.wmt19.de` | De Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.gz)
-`transformer_lm.wmt19.ru` | Ru Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.gz)
-
-## Pre-trained single models before finetuning
-
-Model | Description | Download
----|---|---
-`transformer.wmt19.en-de` | En->De Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.ffn8192.tar.gz)
-`transformer.wmt19.de-en` | De->En Single, no finetuning  | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.ffn8192.tar.gz)
-`transformer.wmt19.en-ru` | En->Ru Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ffn8192.tar.gz)
-`transformer.wmt19.ru-en` | Ru->En Single, no finetuning  | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ffn8192.tar.gz)
-
-## Example usage (torch.hub)
-
-#### Requirements
-
-We require a few additional Python dependencies for preprocessing:
-```bash
-pip install fastBPE sacremoses
-```
-
-#### Translation
-
-```python
-import torch
-
-# English to German translation
-en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-en2de.translate("Machine learning is great!")  # 'Maschinelles Lernen ist großartig!'
-
-# German to English translation
-de2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.de-en', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-de2en.translate("Maschinelles Lernen ist großartig!")  # 'Machine learning is great!'
-
-# English to Russian translation
-en2ru = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-ru', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-en2ru.translate("Machine learning is great!")  # 'Машинное обучение - это здорово!'
-
-# Russian to English translation
-ru2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.ru-en', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-ru2en.translate("Машинное обучение - это здорово!")  # 'Machine learning is great!'
-```
-
-#### Language Modeling
-
-```python
-# Sample from the English LM
-en_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.en', tokenizer='moses', bpe='fastbpe')
-en_lm.sample("Machine learning is")  # 'Machine learning is the future of computing, says Microsoft boss Satya Nadella ...'
-
-# Sample from the German LM
-de_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.de', tokenizer='moses', bpe='fastbpe')
-de_lm.sample("Maschinelles lernen ist")  # 'Maschinelles lernen ist das A und O (neues-deutschland.de) Die Arbeitsbedingungen für Lehrerinnen und Lehrer sind seit Jahren verbesserungswürdig ...'
-
-# Sample from the Russian LM
-ru_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.ru', tokenizer='moses', bpe='fastbpe')
-ru_lm.sample("машинное обучение это")  # 'машинное обучение это то, что мы называем "искусственным интеллектом".'
-```
-
-## Citation
-```bibtex
-@inproceedings{ng2019facebook},
-  title = {Facebook FAIR's WMT19 News Translation Task Submission},
-  author = {Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey},
-  booktitle = {Proc. of WMT},
-  year = 2019,
-}
-```
diff --git a/spaces/gradio/HuBERT/fairseq/models/speech_to_text/modules/emformer.py b/spaces/gradio/HuBERT/fairseq/models/speech_to_text/modules/emformer.py
deleted file mode 100644
index 6ef76bd012ba40b0395fec2ca9ae9e9c136ffe40..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/models/speech_to_text/modules/emformer.py
+++ /dev/null
@@ -1,1837 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
-
-
-import math
-import re
-from functools import partial
-from typing import List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-from fairseq.models import (
-    FairseqEncoder,
-)
-from fairseq.models.speech_to_text.utils import (
-    NoOp,
-    lengths_to_padding_mask,
-    segments_to_sequence,
-)
-from fairseq.models.speech_to_text.utils import (
-    attention_suppression,
-    layer_norm_backward_hook,
-)
-from torch import Tensor, device as Device
-from torch.quantization.qconfig import (
-    default_dynamic_qconfig,
-    per_channel_dynamic_qconfig,
-)
-
-
-class RelativePositionEmbedding(nn.Module):
-    """
-    Implementation according to https://arxiv.org/abs/1803.02155
-    """
-
-    def __init__(self, head_dim, max_position, norm_init=True):
-        super().__init__()
-        self.head_dim = head_dim
-        self.max_position = max_position
-        self.embeddings = nn.Parameter(torch.Tensor(max_position * 2 + 1, head_dim))
-        if norm_init:
-            nn.init.xavier_normal_(self.embeddings)
-        else:
-            nn.init.xavier_uniform_(self.embeddings)
-
-    def forward(self, input: Tensor):
-        output = nn.functional.embedding(input.long(), self.embeddings)
-        return output
-
-
-class Fp32LayerNorm(nn.Module):
-    def __init__(
-        self,
-        input_dim,
-        clamp_grad=True,
-        max_grad_value=256,
-        eps=1e-5,
-        elementwise_affine=True,
-    ):
-        super().__init__()
-        self.torch_module = torch.nn.LayerNorm(
-            input_dim, eps=eps, elementwise_affine=elementwise_affine
-        )
-        if clamp_grad:
-            hook = partial(layer_norm_backward_hook, clamp_value=max_grad_value)
-            self.torch_module.register_backward_hook(hook)
-
-    def forward(self, input):
-        output = torch.nn.functional.layer_norm(
-            input.float(),
-            self.torch_module.normalized_shape,
-            self.torch_module.weight.float()
-            if self.torch_module.weight is not None
-            else None,
-            self.torch_module.bias.float()
-            if self.torch_module.bias is not None
-            else None,
-            self.torch_module.eps,
-        ).type_as(input)
-        return output
-
-
-# ------------------------------------------------------------------------------
-#   PositionwiseFF
-# ------------------------------------------------------------------------------
-
-
-class PositionwiseFF(nn.Module):
-    """
-    FFN layer in transformer.
-
-    Args:
-        input_dim: input embedding dimension
-        ffn_dim: FFN layer inner dimension
-        dropout_on_fc1: dropout for first linear layer
-        dropout_on_fc2: dropout fr second linear layer
-        activation_fn: activation function used after first linear layer. \
-                Only relu or gelu is supported.
-
-    """
-
-    def __init__(
-        self, input_dim, ffn_dim, dropout_on_fc1, dropout_on_fc2, activation_fn
-    ):
-        super(PositionwiseFF, self).__init__()
-
-        self.input_dim = input_dim
-        self.ffn_dim = ffn_dim
-        if activation_fn == "relu":
-            ac = nn.ReLU()
-        elif activation_fn == "gelu":
-            ac = nn.GELU()
-        else:
-            raise ValueError("Unsupported activation_fn = ({})".format(activation_fn))
-
-        # fc1 -> ac -> dropout -> fc2 -> dropout
-        self.module = nn.Sequential(
-            nn.Linear(input_dim, ffn_dim),
-            ac,
-            nn.Dropout(dropout_on_fc1),
-            nn.Linear(ffn_dim, input_dim),
-            nn.Dropout(dropout_on_fc2),
-        )
-
-        self.layer_norm = Fp32LayerNorm(input_dim)
-
-    def forward(self, input):
-        module_out = self.module(self.layer_norm(input))
-        output = module_out + input
-
-        return output
-
-    def quantize_(self, params=None):
-        if params and "per_channel" in params and params["per_channel"]:
-            qconfig = per_channel_dynamic_qconfig
-        else:
-            qconfig = default_dynamic_qconfig
-        torch.quantization.quantize_dynamic(
-            self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
-        )
-        return self
-
-
-# ------------------------------------------------------------------------------
-#   SummarizationLayer
-# ------------------------------------------------------------------------------
-
-
-class SummarizationLayer(nn.Module):
-    def __init__(self, method, segment_size, embedding_dim):
-        super(SummarizationLayer, self).__init__()
-        self.segment_size = segment_size
-        self.embedding_dim = embedding_dim
-        nonlin_match = re.match(r"nonlinear\((?P<act>[a-z]+),(?P<dim>[0-9]+)\)", method)
-        self.method = method
-        if method == "mean":
-            self.module = nn.AvgPool1d(
-                kernel_size=segment_size,
-                stride=segment_size,
-                ceil_mode=True,
-            )
-        elif method == "max":
-            self.module = nn.MaxPool1d(
-                kernel_size=segment_size,
-                stride=segment_size,
-                ceil_mode=True,
-            )
-        elif method == "linear":
-            self.module = nn.Linear(segment_size, 1)
-        elif nonlin_match:
-            nonlin_args = nonlin_match.groupdict()
-            act_type = nonlin_args["act"]
-            hid_dim = int(nonlin_args["dim"])
-            if act_type == "relu":
-                act = nn.ReLU()
-            elif act_type == "gelu":
-                act = nn.GELU()
-            else:
-                raise ValueError("Unsupported activation_fn = ({})".format(act_type))
-            self.module = nn.Sequential(
-                nn.Linear(segment_size, hid_dim),
-                act,
-                nn.Linear(hid_dim, 1),
-            )
-        else:
-            raise ValueError("Unsupported summarization method = ({})".format(method))
-
-    def forward(self, input):
-        # T, B, D -> B, D, T
-        input = input.permute(1, 2, 0)
-
-        if self.method == "mean" or self.method == "max":
-            output = self.module(input)
-            output = output.permute(2, 0, 1)
-            return output
-
-        full_seg_length = input.size(2) // self.segment_size * self.segment_size
-        if full_seg_length > 0:
-            # at least one seg is full
-            B = input.size(0)
-            D = input.size(1)
-            input_todo = (
-                input[:, :, :full_seg_length]
-                .contiguous()
-                .view(B, -1, self.segment_size)
-            )
-            output = self.module(input_todo)
-            output = output.view(B, D, -1)
-        else:
-            output = input.new_zeros(input.size(0), input.size(1), 0)
-        left = input.size(2) - full_seg_length
-        if left > 0:
-            # when last seg is not full, use zeros as last memory placeholder
-            zeros = input.new_zeros(input.size(0), input.size(1), 1)
-            output = torch.cat([output, zeros], dim=2)
-        output = output.permute(2, 0, 1)
-        return output
-
-
-# ------------------------------------------------------------------------------
-#   NoSegAugmentedMemoryMultiheadAttentionBmm
-# ------------------------------------------------------------------------------
-
-
-class NoSegAugmentedMemoryMultiheadAttentionBmm(nn.Module):
-    """
-    Whole utterance augmented memory multihead attention using BMM.
-
-    Different with previous augmented memory multihead attention where
-    the utterance is chunked into segments. Here we use attention mask
-    achieve so. The input embedding [right_context, utterance, summary]
-    is a concatenation of right context, utterance and summary.
-
-    Right context block is the concatenation of all the right context for
-    each segments. [right_context_0, right_context_1, ..., right_context_n]
-    For example, if we have utterance = [v0, v1, v2, ...., v20]. segment
-    size 8, right_context size 4. Then the right context blocks =
-    [v8, v9, v10, v11, v16, v17, v18, v19, 0, 0, 0, 0], where v8, v9, v10,
-    and v11 are the right context for first segment. v16, v17, v18 and v19
-    are the right context for second segment. 0, 0, 0 and 0 are right context
-    for the last segment.
-
-    utterance is corresponding to input embedding sequence
-
-    summary is concatenation of average of each segments. [summary_0,
-    summary_1, ..., ].
-
-    In augmented memory multihead attention, the query is [right_context,
-    utterance, summary], key is [memory, right_context, utterance]. Different
-    with AugmentedMemoryMultiheadAttentionBmm, memory here is passed from
-    previous attention layer. For the first attention layer, memory is average
-    of each segment.
-
-    Memory is a concatenation of memory from each segments in previous attention
-    layer. For example, current layer is i, then memory is [m_0, m_1, ..., m_n].
-    Each m_k is the output from seg_k in layer i-1.
-
-    args:
-        input_dim: input embedding dimension
-        num_heads: number of heads in multihead self-attention
-        dropout: attention dropout
-        std_scale: if std_scale is not None. The weak attention suppression is
-            turned on. For std_scale = 0.5, all the attention smaller than
-            mean + 0.5 * std will be suppressed.
-        scaled_init: whether to use scaled init for linear weight
-        tanh_on_mem: whether to use tanh on memory output
-        use_mem: whether to use memory or not. When max_memory_size is 0, then
-            we don't have memory anymore.
-        layer_index: current self-attention layer index that is used in depth
-            initialization
-        max_relative_position: max relative position used in relative position
-            embedding
-        rpe_old_option: To be compatible with previous model. The previous model
-            was trained with attention += attention + rpe. The correct equation
-            should be attention = attention + rpe
-
-    """
-
-    def __init__(
-        self,
-        input_dim,
-        num_heads,
-        dropout=0.0,
-        std_scale=None,
-        scaled_init=False,
-        tanh_on_mem=False,
-        use_mem=True,
-        mini_batches=False,
-        negative_inf="-inf",
-        layer_index=-1,
-        max_relative_position=0,
-        rpe_old_option=True,
-    ):
-        if input_dim % num_heads:
-            raise ValueError(
-                "input_dim ({}) must be divisible by num_heads ({})".format(
-                    input_dim, num_heads
-                )
-            )
-
-        super().__init__()
-
-        embed_dim = input_dim
-        self.e2h_kv = torch.nn.Linear(input_dim, 2 * input_dim, bias=True)
-        self.e2h_q = torch.nn.Linear(input_dim, input_dim, bias=True)
-        self.rpe_old_option = rpe_old_option
-        if max_relative_position > 0:
-            self.use_rpe = True
-            self.rpe_k = RelativePositionEmbedding(
-                head_dim=input_dim // num_heads,
-                max_position=max_relative_position,
-            )
-            self.rpe_v = RelativePositionEmbedding(
-                head_dim=input_dim // num_heads,
-                max_position=max_relative_position,
-            )
-        else:
-            self.use_rpe = False
-            self.rpe_k = None
-            self.rpe_v = None
-        if scaled_init:
-            if layer_index == -1:
-                gain = 1.0 / math.sqrt(2)
-            else:
-                # https://arxiv.org/abs/2005.09684 depthwise initialization
-                # stablize the training greatly. Use depthwise initialization to
-                # replace incremental loss.
-                gain = 1.0 / math.sqrt(layer_index + 1)
-            torch.nn.init.xavier_uniform_(self.e2h_kv.weight, gain=gain)
-            torch.nn.init.xavier_uniform_(self.e2h_q.weight, gain=gain)
-
-        self.out_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True)
-
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-
-        self.head_dim = embed_dim // num_heads
-        self.scaling = self.head_dim ** -0.5
-
-        self.std_scale = std_scale
-        self.use_mem = use_mem
-        self.mini_batches = mini_batches
-        self.negative_inf = negative_inf
-
-        if tanh_on_mem:
-            self.squash_mem = torch.tanh
-            self.nonlinear_squash_mem = True
-        else:
-            self.squash_mem = NoOp()
-            self.nonlinear_squash_mem = False
-
-    def prepare_qkv(
-        self,
-        input: Tensor,
-        mems: Tensor,
-        lengths: Tensor,
-        summary_length: int,
-        lc_length: int,
-    ):
-        # T: right_context length + utterance_length  + summary_length
-        T, B, D = input.shape
-        mem_length = mems.size(0)
-        utterance_length = torch.max(lengths)
-
-        right_context_blocks_length = T - utterance_length - summary_length
-        rc_block = input[:right_context_blocks_length, :, :]
-        utterance_block = input[right_context_blocks_length : T - summary_length, :, :]
-
-        if B == 1:
-            padding_mask = None
-        else:
-            klengths = lengths + mem_length + right_context_blocks_length + lc_length
-            padding_mask = lengths_to_padding_mask(lengths=klengths)
-
-        mem_rc_input = torch.cat([mems, rc_block, utterance_block], dim=0)
-
-        # In training lc_length = 0
-        key_length = mem_rc_input.size(0) + lc_length
-        rc_input_sum = input
-        q = self.e2h_q(rc_input_sum)
-        kv = self.e2h_kv(mem_rc_input)
-        k, v = kv.chunk(chunks=2, dim=2)
-        result_qkv = (q, k, v)
-        input_shape = (T, B, D)
-        result_lengths_info = (
-            mem_length,
-            utterance_length,
-            right_context_blocks_length,
-            key_length,
-        )
-        if padding_mask is not None:
-            assert padding_mask.size(0) == B
-            assert padding_mask.size(1) == key_length
-
-        return result_qkv, input_shape, result_lengths_info, padding_mask
-
-    def prepare_attention_weights(
-        self,
-        q: Tensor,
-        new_k: Tensor,
-        new_v: Tensor,
-        input_shape: Tuple[int, int, int],
-        rpe: Optional[Tensor],
-    ) -> Tuple[Tensor, Tensor, Tensor]:
-        T, B, D = input_shape
-        q = (
-            q.contiguous().view(-1, B * self.num_heads, self.head_dim).transpose(0, 1)
-            * self.scaling
-        )
-
-        k = (
-            new_k.contiguous()
-            .view(-1, B * self.num_heads, self.head_dim)
-            .transpose(0, 1)
-        )
-
-        v = (
-            new_v.contiguous()
-            .view(-1, B * self.num_heads, self.head_dim)
-            .transpose(0, 1)
-        )
-
-        attention_weights = torch.bmm(q, k.transpose(1, 2))
-        if self.use_rpe and rpe is not None and self.rpe_v is not None:
-            r_k = self.rpe_k(rpe)
-            # [q, B*h, d] * [q, k, d] -> [B*h, q, k]
-            attention_weights_rpe = torch.matmul(
-                q.transpose(0, 1), r_k.transpose(1, 2)
-            ).transpose(0, 1)
-            attention_weights = attention_weights + attention_weights_rpe
-        attention_weights_float = attention_weights.float()
-
-        return attention_weights, attention_weights_float, v
-
-    def prepare_attention_output(
-        self,
-        attention_weights: Tensor,
-        attention_weights_float: Tensor,
-        v: Tensor,
-        input_shape: Tuple[int, int, int],
-        key_length: int,
-        padding_mask: Optional[Tensor],
-        rpe: Optional[Tensor],
-    ) -> Tensor:
-        T, B, D = input_shape
-        if padding_mask is not None:
-            attention_weights_float = attention_weights_float.view(
-                B, self.num_heads, T, key_length
-            )
-            attention_weights_float = attention_weights_float.masked_fill(
-                padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf")
-            )
-            attention_weights_float = attention_weights_float.view(
-                B * self.num_heads, T, key_length
-            )
-
-        if self.std_scale is not None:
-            attention_weights_float = attention_suppression(
-                attention_weights_float, self.std_scale
-            )
-
-        attention_weights_float = torch.nn.functional.softmax(
-            attention_weights_float, dim=-1
-        )
-        attention_weights = attention_weights_float.type_as(attention_weights)
-
-        attention_probs = torch.nn.functional.dropout(
-            attention_weights, p=self.dropout, training=self.training
-        )
-
-        # [T, key_length, B, n_head]+ [key_length, B, n_head, d_head]
-        # -> [T, B, n_head, d_head]
-        attention = torch.bmm(attention_probs, v)
-        if self.use_rpe and rpe is not None and self.rpe_v is not None:
-            r_v = self.rpe_v(rpe)
-            attention_rpe = torch.matmul(
-                attention_probs.transpose(0, 1), r_v
-            ).transpose(0, 1)
-
-            if self.rpe_old_option:
-                attention += attention + attention_rpe
-            else:
-                attention = attention + attention_rpe
-
-        assert list(attention.shape) == [B * self.num_heads, T, self.head_dim]
-
-        attention = attention.transpose(0, 1).contiguous().view(T, B, self.embed_dim)
-
-        rc_output_memory = self.out_proj(attention)
-        return rc_output_memory
-
-    @torch.jit.unused
-    def forward(
-        self,
-        input: Tensor,
-        lengths: Tensor,
-        mems: Tensor,
-        attention_mask: Tensor,
-        pre_mems: Optional[Tensor] = None,
-        left_context_key: Optional[Tensor] = None,
-        left_context_val: Optional[Tensor] = None,
-        rpe: Optional[Tensor] = None,
-    ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
-        """
-        forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in training.
-
-        args:
-            input: formed in the following way
-                [right_context_0, right_contex_1, ..., seg_0, seg_1,
-                ..., summary_0, summary_1,..]
-            lengths: the length of query which is [seg_0, seg_1, ....]
-            mems: [mem_0, mem_1, ...].
-            attention_mask: attention mask for query = [right_context, query, summary]
-                key = [mem, right_context, query]. This is only used for traing.
-
-        """
-        if self.use_mem:
-            mem_length = mems.size(0)
-            summary_length = mem_length + 1
-            if pre_mems is not None:
-                mems = torch.cat([pre_mems, mems], dim=0)
-        else:
-            mem_length = 0
-            summary_length = 0
-
-        # In training, lc_length = 0
-        if left_context_key is not None:
-            lc_length = left_context_key.size(0)
-        else:
-            lc_length = 0
-        results = self.prepare_qkv(
-            input=input,
-            mems=mems,
-            lengths=lengths,
-            summary_length=summary_length,
-            lc_length=lc_length,
-        )
-        result_qkv, input_shape, result_lengths_info, padding_mask = results
-        q, k, v = result_qkv
-        (
-            mem_length,
-            utterance_length,
-            right_context_blocks_length,
-            key_length,
-        ) = result_lengths_info
-
-        if left_context_key is not None:
-            # add the cache key and value
-            new_k = torch.cat(
-                [
-                    k[: mem_length + right_context_blocks_length, :, :],
-                    left_context_key,
-                    k[-utterance_length:, :, :],
-                ],
-                dim=0,
-            )
-            new_v = torch.cat(
-                [
-                    v[: mem_length + right_context_blocks_length, :, :],
-                    left_context_val,
-                    v[-utterance_length:, :, :],
-                ],
-                dim=0,
-            )
-            next_k = new_k[mem_length + right_context_blocks_length :, :, :]
-            next_v = new_v[mem_length + right_context_blocks_length :, :, :]
-        else:
-            new_k = k
-            new_v = v
-            next_k = None
-            next_v = None
-
-        attention_weights, attention_weights_float, v = self.prepare_attention_weights(
-            q=q,
-            new_k=new_k,
-            new_v=new_v,
-            input_shape=input_shape,
-            rpe=rpe,
-        )
-
-        # mask attention
-        attention_mask = attention_mask.unsqueeze(0)
-        attention_weights_float = attention_weights_float.masked_fill(
-            attention_mask, float(self.negative_inf)
-        )
-
-        rc_output_memory = self.prepare_attention_output(
-            attention_weights=attention_weights,
-            attention_weights_float=attention_weights_float,
-            v=v,
-            input_shape=input_shape,
-            key_length=key_length,
-            padding_mask=padding_mask,
-            rpe=rpe,
-        )
-
-        if self.use_mem:
-            # next_m length equals to summary length - 1
-            # last memory is ignored
-            if self.mini_batches:
-                next_m = rc_output_memory[-summary_length:]
-            else:
-                next_m = rc_output_memory[-summary_length:-1]
-
-            next_m = self.squash_mem(next_m)
-            # rc and output
-            rc_output = rc_output_memory[:-summary_length]
-            if not self.nonlinear_squash_mem:
-                next_m = torch.clamp(next_m, min=-10, max=10)
-        else:
-            next_m = mems
-            rc_output = rc_output_memory
-
-        return rc_output, next_m, next_k, next_v
-
-    @torch.jit.export
-    def forward_jit(
-        self,
-        input: Tensor,
-        lengths: Tensor,
-        mems: Tensor,
-        left_context_key: Tensor,
-        left_context_val: Tensor,
-        rpe: Optional[Tensor],
-    ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
-        """
-        forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in decoding.
-
-        args:
-            input: formed in the following way
-                [right_context_0, right_contex_1, ..., seg_0, seg_1,
-                ..., summary_0, summary_1,..]
-            lengths: the length of query which is [seg_0, seg_1, ....]
-            mems: [mem_0, mem_1, ...].
-            left_context_key: left_context for key part. This is only used for online
-                decoding. In training, this is empty tensor
-            left_context_val: left_context for value part. This is only used for online
-                decoding. In training, this is empty tensor
-
-        """
-        lc_length = left_context_key.size(0)
-
-        # In decoding, summary_length = 1 or 0
-        if self.use_mem:
-            summary_length = 1
-        else:
-            summary_length = 0
-
-        results = self.prepare_qkv(
-            input=input,
-            mems=mems,
-            lengths=lengths,
-            summary_length=summary_length,
-            lc_length=lc_length,
-        )
-        result_qkv, input_shape, result_lengths_info, padding_mask = results
-        q, k, v = result_qkv
-        (
-            mem_length,
-            utterance_length,
-            right_context_blocks_length,
-            key_length,
-        ) = result_lengths_info
-
-        # add the cache key and value
-        new_k = torch.cat(
-            [
-                k[: mem_length + right_context_blocks_length, :, :],
-                left_context_key,
-                k[-utterance_length:, :, :],
-            ],
-            dim=0,
-        )
-        new_v = torch.cat(
-            [
-                v[: mem_length + right_context_blocks_length, :, :],
-                left_context_val,
-                v[-utterance_length:, :, :],
-            ],
-            dim=0,
-        )
-        next_k = new_k[mem_length + right_context_blocks_length :, :, :]
-        next_v = new_v[mem_length + right_context_blocks_length :, :, :]
-
-        attention_weights, attention_weights_float, v = self.prepare_attention_weights(
-            q=q,
-            new_k=new_k,
-            new_v=new_v,
-            input_shape=input_shape,
-            rpe=rpe,
-        )
-        # In online decoding, we don't have attention mask. But we still need
-        # to disable the attention from summary query to memory
-        attention_weights_float[:, -1, :mem_length] = float(self.negative_inf)
-        rc_output_memory = self.prepare_attention_output(
-            attention_weights=attention_weights,
-            attention_weights_float=attention_weights_float,
-            v=v,
-            input_shape=input_shape,
-            key_length=key_length,
-            padding_mask=padding_mask,
-            rpe=rpe,
-        )
-
-        # In decoding, summary length is 1
-        if self.use_mem:
-            next_m = rc_output_memory[-1:]
-            next_m = self.squash_mem(next_m)
-            # rc and output
-            rc_output = rc_output_memory[:-1]
-            if not self.nonlinear_squash_mem:
-                next_m = torch.clamp(next_m, min=-10, max=10)
-        else:
-            rc_output = rc_output_memory
-            # empty tensor as input mems
-            next_m = mems
-
-        return rc_output, next_m, next_k, next_v
-
-    def quantize_(self, params=None):
-        if params and "per_channel" in params and params["per_channel"]:
-            qconfig = per_channel_dynamic_qconfig
-        else:
-            qconfig = default_dynamic_qconfig
-        torch.quantization.quantize_dynamic(
-            self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
-        )
-        return self
-
-
-class NoSegAugmentedMemoryTransformer(nn.Module):
-    """
-    Whole utterance augmented memory transformer.
-
-    This is not pyspeech nn layer. It is used as a module in a master layer where
-    multiple transformers is used.
-    """
-
-    def __init__(
-        self,
-        input_dim,
-        num_heads,
-        ffn_dim,
-        dropout_in_attn=0.0,
-        dropout_on_attn=None,
-        dropout_on_fc1=None,
-        dropout_on_fc2=None,
-        activation_fn="relu",
-        tanh_on_mem=False,
-        std_scale=None,
-        scaled_init=False,
-        segment_size=128,
-        use_mem=True,
-        mini_batches=False,
-        negative_inf="-inf",
-        layer_index=-1,
-        summarization_method="mean",
-        max_relative_position=0,
-        rpe_old_option=True,
-    ):
-        super(NoSegAugmentedMemoryTransformer, self).__init__()
-
-        self.attention = NoSegAugmentedMemoryMultiheadAttentionBmm(
-            input_dim=input_dim,
-            num_heads=num_heads,
-            dropout=dropout_in_attn,
-            scaled_init=scaled_init,
-            tanh_on_mem=tanh_on_mem,
-            std_scale=std_scale,
-            use_mem=use_mem,
-            mini_batches=mini_batches,
-            negative_inf=negative_inf,
-            layer_index=layer_index,
-            max_relative_position=max_relative_position,
-        )
-        self.dropout = nn.Dropout(dropout_on_attn)
-        self.pos_ff = PositionwiseFF(
-            input_dim=input_dim,
-            ffn_dim=ffn_dim,
-            dropout_on_fc1=dropout_on_fc1,
-            dropout_on_fc2=dropout_on_fc2,
-            activation_fn=activation_fn,
-        )
-        self.layer_norm_pre = Fp32LayerNorm(input_dim)
-        self.layer_norm = Fp32LayerNorm(input_dim)
-        self.segment_size = segment_size
-        self.use_mem = use_mem
-
-        self.memory_op = SummarizationLayer(
-            summarization_method, segment_size, input_dim
-        )
-
-    def set_mini_batches(self, mini_batches):
-        self.attention.mini_batches = mini_batches
-
-    def gen_summary_queries(self, input):
-        sum_input = self.memory_op(input)
-        return sum_input
-
-    def pre_attention_ops(self, input, right_context_blocks):
-        rc_length = right_context_blocks.size(0)
-        input_length = input.size(0)
-
-        rc_and_input = torch.cat([right_context_blocks, input], dim=0)
-        residual_input = rc_and_input
-        rc_and_input = self.layer_norm_pre(rc_and_input)
-
-        query_input = rc_and_input[-input_length:, :, :]
-        return rc_length, input_length, residual_input, query_input, rc_and_input
-
-    def after_attention_ops(self, attention_output, residual_input):
-        output = self.dropout(attention_output)
-        output = output + residual_input
-        output = self.pos_ff(output)
-        output = self.layer_norm(output)
-        return output
-
-    @torch.jit.export
-    def forward_jit(
-        self,
-        input: Tensor,
-        lengths: Tensor,
-        mems: Tensor,
-        left_context_key: Tensor,
-        left_context_val: Tensor,
-        right_context_blocks: Tensor,
-        rpe: Optional[Tensor],
-    ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
-
-        results = self.pre_attention_ops(input, right_context_blocks)
-        rc_length, input_length, residual_input, query_input, rc_and_input = results
-
-        # In online decoding, the summary query size is always 1 or 0
-        if self.use_mem:
-            summary_query = self.gen_summary_queries(query_input)
-            summary_query = summary_query[0:1, :, :]
-            rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0)
-        else:
-            rc_qu_su = rc_and_input
-
-        rc_output, next_m, next_k, next_v = self.attention.forward_jit(
-            input=rc_qu_su,
-            lengths=lengths,
-            mems=mems,
-            left_context_key=left_context_key,
-            left_context_val=left_context_val,
-            rpe=rpe,
-        )
-        rc_output = self.after_attention_ops(rc_output, residual_input)
-        results = (
-            rc_output[-input_length:, :, :],
-            next_m,
-            rc_output[0:rc_length, :, :],
-            next_k,
-            next_v,
-        )
-        return results
-
-    @torch.jit.unused
-    def forward(
-        self,
-        input,
-        lengths,
-        mems,
-        right_context_blocks,
-        attention_mask,
-        pre_mems,
-        left_context_key,
-        left_context_val,
-        rpe,
-    ):
-
-        results = self.pre_attention_ops(input, right_context_blocks)
-        rc_length, input_length, residual_input, query_input, rc_and_input = results
-        if self.use_mem:
-            summary_query = self.gen_summary_queries(query_input)
-            rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0)
-        else:
-            rc_qu_su = rc_and_input
-
-        rc_output, next_m, next_k, next_v = self.attention(
-            input=rc_qu_su,
-            lengths=lengths,
-            mems=mems,
-            attention_mask=attention_mask,
-            pre_mems=pre_mems,
-            left_context_key=left_context_key,
-            left_context_val=left_context_val,
-            rpe=rpe,
-        )
-
-        # [TODO] Note memory did not go through pos_ff. What happen if we pass
-        # memory through the pos_ff as well?
-        rc_output = self.after_attention_ops(rc_output, residual_input)
-        results = (
-            rc_output[-input_length:, :, :],
-            next_m,
-            rc_output[0:rc_length, :, :],
-            next_k,
-            next_v,
-        )
-
-        return results
-
-
-class NoSegAugmentedMemoryTransformerEncoderLayer(FairseqEncoder):
-    """
-    Whole utterance augmented memory transformer encoder layer. This is a master layer
-    where we can define multiple augmented memory transformers. There are two reasons
-    to setup the master layer.
-    1. We only need to define once about the attention mask. All the layers in the master
-       layer share the same mask.
-    2. pyspeech nn layer has special input and output format. Defining one master layer is
-       easier to passing memory between different layes inside the master layer
-
-    args:
-        input_dim: input embedding dimension
-        num_heads: number of heads in multihead self-attention
-        ffn_dim: ffn dimension in FFN layer
-        num_layers: number of augmented memory transformer layers
-        dropout_in_attn: dropout used in multi-head self-attention
-        dropout_on_attn: dropout used for output from te multihead self-attention
-        dropout_on_fc1: dropout used in FFN layer for the first linear layer
-        dropout_on_fc2: dropout used in FFN layer for the second linear layer
-        segment_size: segment size for each segment
-        context_config: (left_context_size, right_context_size) defines the surround context size
-            for each segment
-        max_memory_size: maximum memory size used for each segment
-        scaled_init: whether use scaled init for weight initialization in attention layer
-        std_scale: if std_scale is not None. The weak attention suppression is
-            turned on. For std_scale = 0.5, all the attention smaller than
-            mean + 0.5 * std will be suppressed.
-        activation_fn: activation function used in FFN layer. [ReLU, GELU] supported
-        tanh_on_mem: whether use tanh on memory
-        mini_batches: use mini-btach training
-        negative_inf: the negative infinity value used in attention masking. default is "-inf".
-            For some situation, e.g. LM. it is better to use "-1e8" to avoid nan issue.
-        summarization_method: method to generate segment summrization embedding
-        max_relative_position: max relatie position for relative position embedding
-        rpe_old_option: To be compatible with previous model. The previous model
-            was trained with attention += attention + rpe. The correct equation
-            should be attention = attention + rpe
-        [TODO]: remove the rpe_old_option by the end of 2021 Q1.
-
-    """
-
-    def __init__(
-        self,
-        input_dim,
-        num_heads,
-        ffn_dim,
-        num_layers=1,
-        dropout_in_attn=0.0,
-        dropout_on_attn=0.0,
-        dropout_on_fc1=0.0,
-        dropout_on_fc2=0.0,
-        segment_size=128,
-        context_config=(0, 0),
-        max_memory_size=0,
-        scaled_init=True,
-        std_scale=None,
-        activation_fn="relu",
-        tanh_on_mem=False,
-        mini_batches=False,
-        negative_inf="-inf",
-        deep_init=True,
-        summarization_method="mean",
-        max_relative_position=0,
-        rpe_old_option=True,
-    ):
-        super().__init__(None)
-        if input_dim % num_heads:
-            raise ValueError(
-                "input_dim ({}) must be divisible by num_heads ({})".format(
-                    input_dim, num_heads
-                )
-            )
-
-        # we used to support growing memory size. However, it will cause
-        # cross stream batching failure. Now we need to have exact max memory size
-        if max_memory_size < 0:
-            raise ValueError("max_memory_size must be >= 0")
-
-        # Only assign right_context. In decoding, left context will be cached.
-        # No need to let the online decoder to re-assign the left context
-        self.left_context, self.right_context = context_config
-        self.segment_size = segment_size
-        self.memory_dim = input_dim
-        self.max_memory_size = max_memory_size
-        self.mini_batches = mini_batches
-        if self.max_memory_size != 0:
-            self.use_mem = True
-        else:
-            self.use_mem = False
-
-        self.memory_op = SummarizationLayer(
-            summarization_method, segment_size, input_dim
-        )
-
-        self.layers = torch.nn.ModuleList()
-        self.num_layers = num_layers
-        self.max_relative_position = max_relative_position
-        if self.max_relative_position > 0:
-            self.use_rpe = True
-        else:
-            self.use_rpe = False
-        for i in range(self.num_layers):
-            if deep_init:
-                layer_index = i
-            else:
-                layer_index = -1
-
-            self.layers.append(
-                NoSegAugmentedMemoryTransformer(
-                    num_heads=num_heads,
-                    input_dim=input_dim,
-                    ffn_dim=ffn_dim,
-                    dropout_in_attn=dropout_in_attn,
-                    dropout_on_attn=dropout_on_attn,
-                    dropout_on_fc1=dropout_on_fc1,
-                    dropout_on_fc2=dropout_on_fc2,
-                    segment_size=segment_size,
-                    std_scale=std_scale,
-                    activation_fn=activation_fn,
-                    tanh_on_mem=tanh_on_mem,
-                    scaled_init=scaled_init,
-                    use_mem=self.use_mem,
-                    mini_batches=mini_batches,
-                    negative_inf=negative_inf,
-                    layer_index=layer_index,
-                    summarization_method=summarization_method,
-                    max_relative_position=max_relative_position,
-                    rpe_old_option=rpe_old_option,
-                )
-            )
-
-    def set_mini_batches(self, mini_batches):
-        # handy function only used for unit test
-        self.mini_batches = mini_batches
-        for layer in self.layers:
-            layer.set_mini_batches(mini_batches)
-
-    def _get_relative_position(
-        self,
-        input: Tensor,
-        max_relative_position: int,
-        left_context_length: int,
-        past_length: int,
-        is_decoding: bool,
-    ):
-        # For training, we copy the right context to the start of the utterance
-        # First dimension in distance is corresponding to query.
-        # [right context, utterance, summary vector]
-        # Second dimension in distance is corresponding to key.
-        # [Memory bank, right context, utterance]
-        # For summary vector in query part, the distance with
-        # all other position is 2*max_position. For memory bank in key,
-        # the distance with all other positions is 0.
-
-        T, B, D = input.shape
-        num_segs = math.ceil((T - self.right_context) / self.segment_size)
-
-        # utterance
-        u_st = past_length * self.segment_size
-        u_ed = u_st + T
-        utterance_ranges = torch.arange(u_st, u_ed - self.right_context)
-
-        # left context. Only in minibatch or decoding
-        left_context_ranges = torch.arange(u_st - left_context_length, u_st)
-
-        # Right context block
-        # right context + utterance
-        right_context_blocks = []
-        for i in range(0, num_segs - 1):
-            st = (i + 1) * self.segment_size + u_st
-            ed = st + self.right_context
-            assert ed < u_ed
-            temp = torch.arange(st, ed)
-            right_context_blocks.append(temp)
-        right_context_blocks.append(torch.arange(u_ed - self.right_context, u_ed))
-        right_context_ranges = torch.cat(right_context_blocks)
-
-        if self.use_mem:
-            # Memory bank
-            # The position for memory -n, .., -1
-            if is_decoding:
-                memory_size = min(past_length, self.max_memory_size)
-            else:
-                memory_size = num_segs + past_length - 1
-            memory_bank_ranges = torch.arange(
-                -max_relative_position - 1, -max_relative_position - 1 - memory_size, -1
-            )
-
-            # summary vector
-            # The position for summary vector as the T+max_relative_position+1.
-            # After the clamping, the relative position is max_relative_position
-            summary_pos_st = u_ed + max_relative_position + 1
-            summary_vector_ranges = torch.arange(
-                summary_pos_st, summary_pos_st + num_segs
-            )
-
-            key_ranges = torch.cat(
-                [
-                    memory_bank_ranges,
-                    right_context_ranges,
-                    left_context_ranges,
-                    utterance_ranges,
-                ]
-            )
-
-            query_ranges = torch.cat(
-                [right_context_ranges, utterance_ranges, summary_vector_ranges]
-            )
-        else:
-            key_ranges = torch.cat(
-                [right_context_ranges, left_context_ranges, utterance_ranges]
-            )
-
-            query_ranges = torch.cat([right_context_ranges, utterance_ranges])
-
-        distance = key_ranges[None, :] - query_ranges[:, None]
-        distance_clamp = (
-            torch.clamp(distance, -max_relative_position, max_relative_position)
-            + max_relative_position
-        )
-        distance_clamp = distance_clamp.to(input.device).long().detach()
-        return distance_clamp
-
-    def _get_attention_mask(self, input, past_length=0, left_context_cache=0):
-        # attention mask for each query contains three parts:
-        # 1. memory part
-        # 2. left_context + segment
-        # 3. right_context_block
-        # so for each segment and its correspoinding right context block,
-        # the attention matrix is formed by 9 parts:
-        # [0, m, 0, 0, right_context, 0, 0, seg, 0]
-        # [before memory, memory, after memory, before right context, right_context,
-        #  after right context, before seg, seg, after seg]
-        #
-        # Query is formed in the way as [right_context_blocks, utterance, summary]
-        #
-        # Note: put m and right_context before segment is convenient
-        # for padding_mask operation.
-        # Key lengths = m_length + right_context_block_length + lengths
-        utterance_length, batch_size, _ = input.shape
-        summary_length = math.ceil(utterance_length / self.segment_size)
-        num_segs = summary_length
-        rc_length = self.right_context * num_segs
-        rc = self.right_context
-        lc = self.left_context
-
-        # using mini-batches, there is left context cache available for current
-        # sequence.
-        lcc = left_context_cache
-
-        # max_memory_size is 0 then we don't have memory and summary
-        # past_length is the memory carry from previous sequence
-        if self.use_mem:
-            mem_length = num_segs - 1 + past_length
-        else:
-            mem_length = 0
-        rc_mask = []
-        query_mask = []
-        summary_mask = []
-        for j in range(0, num_segs):
-            ssize = min(self.segment_size, utterance_length - j * self.segment_size)
-
-            rc_size = rc
-            rc_mat = []
-            q_mat = []
-            s_mat = []
-            m_start = max(j + past_length - self.max_memory_size, 0)
-
-            # max_memory_size is 0, then we don't use memory
-            if self.use_mem:
-                # part 0: before memory
-                rc_mat.append(input.new_zeros(rc_size, m_start))
-                q_mat.append(input.new_zeros(ssize, m_start))
-                s_mat.append(input.new_zeros(1, m_start))
-
-                # part 1: memory
-                col_1 = j + past_length - m_start
-                rc_mat.append(torch.ones(rc_size, col_1, device=input.device))
-                q_mat.append(torch.ones(ssize, col_1, device=input.device))
-                # based on D22875746, disable summary query attention
-                # on memeory is better for long form utterance
-                s_mat.append(input.new_zeros(1, col_1))
-
-                # part 2: after memory
-                col_2 = mem_length - (j + past_length)
-                rc_mat.append(input.new_zeros(rc_size, col_2))
-                q_mat.append(input.new_zeros(ssize, col_2))
-                s_mat.append(input.new_zeros(1, col_2))
-
-            # part 3: before right context
-            rc_start = j * rc
-            rc_mat.append(input.new_zeros(rc_size, rc_start))
-            q_mat.append(input.new_zeros(ssize, rc_start))
-            s_mat.append(input.new_zeros(1, rc_start))
-
-            # part 4: right context
-            rc_end = rc_start + rc
-            col_4 = rc
-            rc_mat.append(torch.ones(rc_size, col_4, device=input.device))
-            q_mat.append(torch.ones(ssize, col_4, device=input.device))
-            s_mat.append(torch.ones(1, col_4, device=input.device))
-
-            # part 5: after right context
-            col_5 = rc_length - rc_end
-            rc_mat.append(input.new_zeros(rc_size, col_5))
-            q_mat.append(input.new_zeros(ssize, col_5))
-            s_mat.append(input.new_zeros(1, col_5))
-
-            # part 6: before query segment
-            seg_start = max(j * self.segment_size + lcc - lc, 0)
-            rc_mat.append(input.new_zeros(rc_size, seg_start))
-            q_mat.append(input.new_zeros(ssize, seg_start))
-            s_mat.append(input.new_zeros(1, seg_start))
-
-            # part 7: query segment
-            # note: right context is put in right context block
-            # here we only need to consider about left context
-            seg_end = min((j + 1) * self.segment_size + lcc, utterance_length + lcc)
-            col_7 = seg_end - seg_start
-            rc_mat.append(torch.ones(rc_size, col_7, device=input.device))
-            q_mat.append(torch.ones(ssize, col_7, device=input.device))
-            s_mat.append(torch.ones(1, col_7, device=input.device))
-
-            # part 8: after query segment
-            col_8 = utterance_length + lcc - seg_end
-            rc_mat.append(input.new_zeros(rc_size, col_8))
-            q_mat.append(input.new_zeros(ssize, col_8))
-            s_mat.append(input.new_zeros(1, col_8))
-
-            rc_mask.append(torch.cat(rc_mat, dim=1))
-            query_mask.append(torch.cat(q_mat, dim=1))
-            summary_mask.append(torch.cat(s_mat, dim=1))
-
-        # no memory, then we don't need summary either
-        if self.use_mem:
-            attention_mask = (
-                1
-                - torch.cat(
-                    [
-                        torch.cat(rc_mask, dim=0),
-                        torch.cat(query_mask, dim=0),
-                        torch.cat(summary_mask, dim=0),
-                    ],
-                    dim=0,
-                )
-            ).to(torch.bool)
-        else:
-            attention_mask = (
-                1
-                - torch.cat(
-                    [torch.cat(rc_mask, dim=0), torch.cat(query_mask, dim=0)], dim=0
-                )
-            ).to(torch.bool)
-
-        return attention_mask
-
-    @torch.jit.export
-    def init_state(
-        self, batch_size: int, device: Optional[Device] = None
-    ) -> List[Tensor]:
-        empty_memory = torch.zeros(
-            self.num_layers,
-            self.max_memory_size,
-            batch_size,
-            self.memory_dim,
-            device=device,
-        )
-        left_context_key = torch.zeros(
-            self.num_layers,
-            self.left_context,
-            batch_size,
-            self.memory_dim,
-            device=device,
-        )
-        left_context_val = torch.zeros(
-            self.num_layers,
-            self.left_context,
-            batch_size,
-            self.memory_dim,
-            device=device,
-        )
-        past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device)
-
-        return [empty_memory, left_context_key, left_context_val, past_length]
-
-    @torch.jit.export
-    def batch_state(self, states: List[List[Tensor]]) -> List[Tensor]:
-        if len(states) == 0:
-            return []
-        batched_m = []
-        batched_lc_key = []
-        batched_lc_val = []
-        batched_past_length = []
-        for state in states:
-            if len(state) == 0:
-                continue
-            m, lc_key, lc_val, past_length = state
-            batched_m.append(m)
-            batched_lc_key.append(lc_key)
-            batched_lc_val.append(lc_val)
-            batched_past_length.append(past_length)
-
-        if (
-            (len(batched_m) == 0)
-            or (len(batched_lc_key) == 0)
-            or (len(batched_lc_val) == 0)
-            or (len(batched_past_length) == 0)
-        ):
-            return [
-                torch.tensor([]),
-                torch.tensor([]),
-                torch.tensor([]),
-                torch.tensor([]),
-            ]
-
-        batched_m = torch.cat(batched_m, dim=2)
-        batched_lc_key = torch.cat(batched_lc_key, dim=2)
-        batched_lc_val = torch.cat(batched_lc_val, dim=2)
-        batched_past_length = torch.cat(batched_past_length, dim=1)
-        return [batched_m, batched_lc_key, batched_lc_val, batched_past_length]
-
-    @torch.jit.export
-    def reorder_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]:
-        if len(state) == 0:
-            return []
-        m, lc_key, lc_val, past_length = state
-        indices = indices.to(device=m.device)
-        reord_m = torch.index_select(m, 2, indices)
-        reord_lc_key = torch.index_select(lc_key, 2, indices)
-        reord_lc_val = torch.index_select(lc_val, 2, indices)
-        reord_past_length = torch.index_select(past_length, 1, indices)
-        return [reord_m, reord_lc_key, reord_lc_val, reord_past_length]
-
-    @torch.jit.export
-    def reset_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]:
-        m, lc_key, lc_val, past_length = state
-        m = m.index_fill(dim=2, index=indices, value=0.0)
-        lc_key = lc_key.index_fill(dim=2, index=indices, value=0.0)
-        lc_val = lc_val.index_fill(dim=2, index=indices, value=0.0)
-        past_length = past_length.index_fill(dim=1, index=indices, value=0)
-
-        return [m, lc_key, lc_val, past_length]
-
-    @torch.jit.export
-    def state_size(self) -> int:
-        return 4
-
-    @torch.jit.export
-    def batch_size_in_state(
-        self, state: Optional[List[Tensor]], sloppy: bool = True
-    ) -> Optional[int]:
-        if state is None:
-            return None
-        return state[0].size(2)
-
-    def gen_summary_queries(self, input):
-        sum_input = self.memory_op(input)
-        return sum_input
-
-    def _gen_right_context_padded_input(self, input):
-        # This function deals with input that is already
-        # padded with right context (e.g. minibatch training)
-        right_context_blocks = []
-        T, B, D = input.shape
-        num_segs = math.ceil((T - self.right_context) / self.segment_size)
-        for i in range(0, num_segs - 1):
-            st = (i + 1) * self.segment_size
-            ed = st + self.right_context
-            assert ed < T
-            temp = input[st:ed, :, :]
-            right_context_blocks.append(temp)
-
-        # last segment right context is already available
-        right_context_blocks.append(input[T - self.right_context :, :, :])
-        return torch.cat(right_context_blocks, dim=0)
-
-    def _gen_segs_right_context(self, input, lengths):
-        segments = []
-        T, B, D = input.size()
-        nT = T - self.right_context
-
-        # assume input is right context padded
-        num_segs = math.ceil(nT / self.segment_size)
-        # pad zeros to the utterance to make sure each
-        # segment has the same right context. For the
-        for i in range(0, num_segs - 1):
-            st = i * self.segment_size
-            ed = min(T, st + self.segment_size + self.right_context)
-            temp = input[st:ed, :, :]
-            rest_lengths = torch.clamp(
-                lengths - self.segment_size, min=0, max=nT - (i + 1) * self.segment_size
-            )
-            segments.append((temp, lengths - rest_lengths + self.right_context))
-            lengths = rest_lengths
-
-        last_seg = input[st + self.segment_size :, :, :]
-        segments.append((last_seg, rest_lengths + self.right_context))
-
-        return segments
-
-    @torch.jit.unused
-    def forward(
-        self, input: Tensor, padding_masks: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]:
-        # Xutai: originally the second argument is lengths.
-        lengths = (~padding_masks).sum(dim=1).long()
-        # mini batch training.
-        if self.mini_batches:
-            return self.forward_mini_batches(input, lengths, state)
-
-        # regular full sequence training. Note, assume the right context in provided
-        # in the input.
-        T, B, D = input.size()
-        right_context_blocks = self._gen_right_context_padded_input(input)
-
-        # generate the relative positional embedding
-        if self.use_rpe:
-            rpe = self._get_relative_position(
-                input=input,
-                max_relative_position=self.max_relative_position,
-                left_context_length=0,
-                past_length=0,
-                is_decoding=False,
-            )
-        else:
-            rpe = None
-        input = input[: T - self.right_context, :, :]
-
-        attention_mask = self._get_attention_mask(input)
-
-        # firt layer use each segment mean as memory
-        # ignore the last one seg average
-        if self.use_mem:
-            mems = self.gen_summary_queries(input)[:-1, :, :]
-        else:
-            mems = torch.zeros(0, input.size(1), input.size(2), device=input.device)
-            mems = mems.type_as(input)
-
-        output = input
-        all_outputs = []
-
-        for layer in self.layers:
-            output, mems, right_context_blocks, _, _ = layer(
-                input=output,
-                lengths=lengths,
-                attention_mask=attention_mask,
-                mems=mems,
-                right_context_blocks=right_context_blocks,
-                pre_mems=None,
-                left_context_key=None,
-                left_context_val=None,
-                rpe=rpe,
-            )
-            all_outputs.append(output)
-        return output, padding_masks, [], all_outputs
-
-    def forward_jit_mini_batch_init(
-        self,
-        seg: Tensor,
-        state: Optional[List[Tensor]] = None,
-        is_decoding: bool = False,
-    ):
-        # Prepare state. In whole sequence training, state is ignored.
-        # For minibatch training, we need to prepare state
-        if state is None:
-            state = self.init_state(batch_size=seg.size(1), device=seg.device)
-            if seg.dtype == torch.half:
-                state = [state[0].half(), state[1].half(), state[2].half(), state[3]]
-
-        if self.use_mem:
-            # note input average only on seg, not on right context
-            # first layer use each segmetn mean as memory. the last
-            # one segment average is used in state
-            full_mems = self.gen_summary_queries(seg)
-            if is_decoding:
-                mems = full_mems[0:1, :, :]
-                state_mems = torch.cat([state[0][0], mems], dim=0)
-            else:
-                mems = full_mems[:-1, :, :]
-                state_mems = torch.cat([state[0][0], full_mems], dim=0)
-        else:
-            mems = state[0][0]
-            state_mems = mems
-
-        # track processed segment number or memory number
-        # the same batch as the same bumber of past length
-        past_length = state[3][0][0].item()
-        past_left_context = min(past_length * self.segment_size, self.left_context)
-        past_length = min(self.max_memory_size, past_length)
-
-        return state, mems, state_mems, past_length, past_left_context
-
-    def state_update_before(
-        self, layer: int, state: List[Tensor], past_length: int, past_left_context: int
-    ):
-        pre_mems = state[0][layer][self.max_memory_size - past_length :, :, :]
-        lc_key = state[1][layer][self.left_context - past_left_context :, :, :]
-        lc_val = state[2][layer][self.left_context - past_left_context :, :, :]
-        return pre_mems, lc_key, lc_val
-
-    def state_update_after(
-        self,
-        layer: int,
-        state: List[Tensor],
-        mems: Tensor,
-        next_key: Tensor,
-        next_val: Tensor,
-        mems_list: List[Tensor],
-        lc_key_list: List[Tensor],
-        lc_val_list: List[Tensor],
-    ):
-        # mems is used for next layer
-        if layer < self.num_layers - 1:
-            state_mems = torch.cat([state[0][layer + 1], mems], dim=0)
-            mems_list.append(state_mems[-self.max_memory_size :, :, :])
-
-        # when mems pass to next sequence, we need the last memory. when mems
-        # use for the next layer, we can ignore the last memory
-        mems = mems[:-1, :, :]
-
-        # note state[1][i] and state[2][i] original length equals to self.left_context
-        new_k = torch.cat([state[1][layer], next_key], dim=0)
-        new_v = torch.cat([state[2][layer], next_val], dim=0)
-        lc_key_list.append(new_k[-self.left_context :, :, :])
-        lc_val_list.append(new_v[-self.left_context :, :, :])
-        return mems_list, lc_key_list, lc_val_list, mems
-
-    def state_update_after_loop(
-        self,
-        state: List[Tensor],
-        mems_list: List[Tensor],
-        lc_key_list: List[Tensor],
-        lc_val_list: List[Tensor],
-        update_length: int,
-    ):
-        state[0] = torch.stack(mems_list, dim=0)
-        state[1] = torch.stack(lc_key_list, dim=0)
-        state[2] = torch.stack(lc_val_list, dim=0)
-        state[3] = state[3] + update_length
-        return state
-
-    @torch.jit.unused
-    def forward_mini_batches(
-        self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]:
-        T, B, D = input.size()
-
-        # input without right context
-        seg = input[: T - self.right_context, :, :]
-
-        # get right context blocks
-        right_context_blocks = self._gen_right_context_padded_input(input)
-
-        mems_list = []
-        lc_key_list = []
-        lc_val_list = []
-        results = self.forward_jit_mini_batch_init(seg, state, False)
-        state, mems, state_mems, past_length, past_left_context = results
-
-        # relative position embedding
-        if self.use_rpe:
-            rpe = self._get_relative_position(
-                input=input,
-                max_relative_position=self.max_relative_position,
-                left_context_length=past_left_context,
-                past_length=past_length,
-                is_decoding=False,
-            )
-        else:
-            rpe = None
-
-        # get attention mask based on seg (not include right context) and available
-        # left context
-        attention_mask = self._get_attention_mask(seg, past_length, past_left_context)
-        mems_list.append(state_mems[-self.max_memory_size :, :, :])
-        output = seg
-        i = 0
-        all_outputs = []
-        for layer in self.layers:
-            # In order to make cross stream batching work, mem, left context key
-            # and left context value in the state should always be the same shape.
-            # We use the past length to track the processed segment number. In this
-            # way, we take out the essential memory, left context key and left
-            # context val from the state. After finish the forward for current segment
-            # we add the new memory, left context key and left context value into the
-            # staate and trim out the oldest part to keep the shape consistent.
-            pre_mems, lc_key, lc_val = self.state_update_before(
-                i, state, past_length, past_left_context
-            )
-
-            output, mems, right_context_blocks, next_key, next_val = layer.forward(
-                input=output,
-                lengths=lengths,
-                attention_mask=attention_mask,
-                mems=mems,
-                right_context_blocks=right_context_blocks,
-                pre_mems=pre_mems,
-                left_context_key=lc_key,
-                left_context_val=lc_val,
-                rpe=rpe,
-            )
-            all_outputs.append(output)
-            mems_list, lc_key_list, lc_val_list, mems = self.state_update_after(
-                layer=i,
-                state=state,
-                mems=mems,
-                next_key=next_key,
-                next_val=next_val,
-                mems_list=mems_list,
-                lc_key_list=lc_key_list,
-                lc_val_list=lc_val_list,
-            )
-
-            i += 1
-
-        # update state
-        update_length = math.ceil((T - self.right_context) / self.segment_size)
-        state = self.state_update_after_loop(
-            state=state,
-            mems_list=mems_list,
-            lc_key_list=lc_key_list,
-            lc_val_list=lc_val_list,
-            update_length=update_length,
-        )
-
-        return output, lengths, state, all_outputs
-
-    def forward_jit_test(
-        self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor]]:
-        """
-        This one simulate sequence encoder forward jit. This is for unit test purpose.
-        It is not used in training or decoding. Note, extra_right_context is set in
-        the model. In unit test, input = [utterance, right_context], lengths =
-        [utterance_length].
-        args:
-            input: input utterance
-            lengths: utterance input length
-            state: None here. input is whole utterance
-        """
-        # [TODO] sequence_to_segment has bug in lengths.
-        seg_src_tokens_lengths = self._gen_segs_right_context(input, lengths)
-
-        seg_enc_tokens_lengths: List[Tuple[Tensor, Tensor]] = []
-        state: Optional[List[Tensor]] = None
-        for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths:
-            seg_enc_tokens, seg_enc_lengths, state = self.forward_jit(
-                input=seg_src_tokens, lengths=seg_src_lengths, state=state
-            )
-            seg_enc_tokens_lengths.append((seg_enc_tokens, seg_enc_lengths))
-
-        enc_tokens, enc_lengths = segments_to_sequence(
-            segments=seg_enc_tokens_lengths, time_axis=0
-        )
-
-        state = []  # returns trivial state
-
-        return enc_tokens, enc_lengths, state
-
-    @torch.jit.export
-    def forward_jit(
-        self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor]]:
-        """
-        Forward helper for online decoding.
-
-        args:
-            input: [seg, right_context]. We assume in online we
-                always padding the right context to the preset right context size.
-                For the last segment, we may have short segment size, but right
-                context size is the same as other segments
-            lengths: utterance input length is the utterance segment length and
-                     right context size
-            state: [memory, left_context_key, left_context_val]. To improve throughput,
-                in addition to memory, we also cache key and value for left_context in
-                multihead self-attention
-        """
-        # In online decoding, input = [segment, right_context]
-        # Lengths = [segment_length, right_context_length]
-        # so we need strip right context in output
-        T, B, D = input.size()
-        rc_str = T - self.right_context
-        rc_end = T
-        right_context_blocks = input[rc_str:rc_end, :, :]
-        seg = input[:rc_str, :, :]
-        lengths = torch.clamp(lengths - self.right_context, min=0)
-        mems_list = []
-        lc_key_list = []
-        lc_val_list = []
-
-        results = self.forward_jit_mini_batch_init(seg, state, True)
-        state, mems, state_mems, past_length, past_left_context = results
-
-        # relative position embedding
-        if self.use_rpe:
-            rpe = self._get_relative_position(
-                input=input,
-                max_relative_position=self.max_relative_position,
-                left_context_length=past_left_context,
-                past_length=past_length,
-                is_decoding=True,
-            )
-        else:
-            rpe = None
-
-        # memory for first layer.
-        mems_list.append(state_mems[-self.max_memory_size :, :, :])
-        output = seg
-        i = 0
-        for layer in self.layers:
-            # In order to make cross stream batching work, mem, left context key
-            # and left context value in the state should always be the same shape.
-            # We use the past length to track the processed segment number. In this
-            # way, we take out the essential memory, left context key and left
-            # context val from the state. After finish the forward for current segment
-            # we add the new memory, left context key and left context value into the
-            # staate and trim out the oldest part to keep the shape consistent.
-            true_mems, lc_key, lc_val = self.state_update_before(
-                layer=i,
-                state=state,
-                past_length=past_length,
-                past_left_context=past_left_context,
-            )
-
-            output, mems, right_context_blocks, next_key, next_val = layer.forward_jit(
-                input=output,
-                lengths=lengths,
-                mems=true_mems,
-                right_context_blocks=right_context_blocks,
-                left_context_key=lc_key,
-                left_context_val=lc_val,
-                rpe=rpe,
-            )
-            # mems is used for next layer
-            mems_list, lc_key_list, lc_val_list, _ = self.state_update_after(
-                layer=i,
-                state=state,
-                mems_list=mems_list,
-                mems=mems,
-                next_key=next_key,
-                next_val=next_val,
-                lc_key_list=lc_key_list,
-                lc_val_list=lc_val_list,
-            )
-            i += 1
-
-        # update state
-        state = self.state_update_after_loop(
-            state=state,
-            mems_list=mems_list,
-            lc_key_list=lc_key_list,
-            lc_val_list=lc_val_list,
-            update_length=1,
-        )
-
-        return output, lengths, state
-
-    def quantize_(self, params=None):
-        if params and "per_channel" in params and params["per_channel"]:
-            qconfig = per_channel_dynamic_qconfig
-        else:
-            qconfig = default_dynamic_qconfig
-        torch.quantization.quantize_dynamic(
-            self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
-        )
-        return self
-
-
-# ------------------------------------------------------------------------------
-#   Emformer encoder for seq2seq model
-#   This is a wrapper over the original emformer
-# ------------------------------------------------------------------------------
-def emformer_encoder(klass):
-    class SpeechEncoder(klass):
-        def __init__(self, args):
-            super().__init__(args)
-            stride = SpeechEncoder.conv_layer_stride(args)
-            trf_left_context = args.segment_left_context // stride
-            trf_right_context = args.segment_right_context // stride
-            context_config = [trf_left_context, trf_right_context]
-            self.transformer_layers = nn.ModuleList(
-                [
-                    NoSegAugmentedMemoryTransformerEncoderLayer(
-                        input_dim=args.encoder_embed_dim,
-                        num_heads=args.encoder_attention_heads,
-                        ffn_dim=args.encoder_ffn_embed_dim,
-                        num_layers=args.encoder_layers,
-                        dropout_in_attn=args.dropout,
-                        dropout_on_attn=args.dropout,
-                        dropout_on_fc1=args.dropout,
-                        dropout_on_fc2=args.dropout,
-                        activation_fn=args.activation_fn,
-                        context_config=context_config,
-                        segment_size=args.segment_length,
-                        max_memory_size=args.max_memory_size,
-                        scaled_init=True,  # TODO: use constant for now.
-                        tanh_on_mem=args.amtrf_tanh_on_mem,
-                    )
-                ]
-            )
-
-        def forward(self, src_tokens, src_lengths):
-            encoder_out = super().forward(src_tokens, src_lengths)
-            output = encoder_out["encoder_out"][0]
-            encoder_padding_masks = encoder_out["encoder_padding_mask"][0]
-
-            # This is because that in the original implementation
-            # the output didn't consider the last segment as right context.
-            encoder_padding_masks = encoder_padding_masks[:, : output.size(0)]
-
-            return {
-                "encoder_out": [output],
-                "encoder_padding_mask": [encoder_padding_masks],
-                "encoder_embedding": [],
-                "encoder_states": [],
-                "src_tokens": [],
-                "src_lengths": [],
-            }
-
-        @staticmethod
-        def conv_layer_stride(args):
-            # TODO: make it configurable from the args
-            return 4
-
-    SpeechEncoder.__name__ = klass.__name__
-    return SpeechEncoder
diff --git a/spaces/gradio/ner_pipeline/README.md b/spaces/gradio/ner_pipeline/README.md
deleted file mode 100644
index 46dd7e71e17c0b895620b483e5a8cf9d80502c1f..0000000000000000000000000000000000000000
--- a/spaces/gradio/ner_pipeline/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
----
-title: ner_pipeline 
-emoji: 🔥
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.1.2
-app_file: run.py
-pinned: false
-hf_oauth: true
----
diff --git a/spaces/gradio/zip_files/run.py b/spaces/gradio/zip_files/run.py
deleted file mode 100644
index 1ae6436c67542f82a94ca7c6d9675974b2503710..0000000000000000000000000000000000000000
--- a/spaces/gradio/zip_files/run.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import os
-from zipfile import ZipFile
-
-import gradio as gr
-
-
-def zip_files(files):
-    with ZipFile("tmp.zip", "w") as zipObj:
-        for idx, file in enumerate(files):
-            zipObj.write(file.name, file.name.split("/")[-1])
-    return "tmp.zip"
-
-demo = gr.Interface(
-    zip_files,
-    gr.File(file_count="multiple", file_types=["text", ".json", ".csv"]),
-    "file",
-    examples=[[[os.path.join(os.path.dirname(__file__),"files/titanic.csv"), 
-    os.path.join(os.path.dirname(__file__),"files/titanic.csv"), 
-    os.path.join(os.path.dirname(__file__),"files/titanic.csv")]]], 
-    cache_examples=True
-)
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/gstaff/mp4-converter/app.py b/spaces/gstaff/mp4-converter/app.py
deleted file mode 100644
index db0cf35b26f71914007f1c2a71e2879ef5431a01..0000000000000000000000000000000000000000
--- a/spaces/gstaff/mp4-converter/app.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import tempfile
-import gradio as gr
-
-from moviepy.editor import VideoFileClip
-import moviepy.video.io.ffmpeg_tools as ffmpeg_tools
-
-
-# TODO: Finalize result gif size with pygifscicle? https://github.com/LucaCappelletti94/pygifsicle  Maybe optimize option
-# TODO: Extra delay frames?
-# git lfs track --filename file_example_MP4_480_1_5MG.mp4
-def convert_mp4_to_gif(input_video_path, fps):
-    if input_video_path is None:
-        return None
-    # Save the reversed frames as a new GIF
-    with tempfile.NamedTemporaryFile(suffix=".gif", delete=False) as temp_file:
-        temp_filename = temp_file.name
-
-        # Load the video clip
-        video_clip = VideoFileClip(input_video_path)
-
-        # Set the desired frame rate (frames per second) for the GIF
-        video_clip = video_clip.set_duration(video_clip.duration)
-        video_clip = video_clip.set_fps(fps)
-
-        # Export the video as a GIF
-        video_clip.write_gif(temp_filename, program='ffmpeg')
-
-        # Close the video clip
-        video_clip.close()
-
-        return [temp_filename]
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(value='# MP4 Converter')
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown('## Load mp4 video to convert to an animated gif')
-            in_video = gr.Video(label="Input mp4", format='mp4')
-            frame_rate = gr.Slider(label="Frames per Second", value=10, minimum=1,
-                                   maximum=360, step=1, interactive=True)
-            run_button = gr.Button(variant='primary')
-        with gr.Column(scale=1):
-            gr.Markdown('## View the animated gif')
-            image_out = gr.Gallery(label="Animated Gif Image")
-            clear_button = gr.ClearButton(components=[image_out])
-
-    in_video.change(convert_mp4_to_gif, [in_video, frame_rate], [image_out])
-    run_button.click(convert_mp4_to_gif, [in_video, frame_rate], [image_out])
-    gr.Examples(
-        examples=[['file_example_MP4_480_1_5MG.mp4', 60]],
-        inputs=[in_video, frame_rate], outputs=[image_out], fn=convert_mp4_to_gif, cache_examples=True)
-
-    with gr.Accordion('Developer Notes:', open=False):
-        gr.Markdown('This simple tool handles a conversion I\'ve needed from time to time.\n\n'
-                    'Note that like many tools working with video this has a dependency on ffmpeg.\n\n'
-                    'An enhancement to this tool would optimize the generated gif to reduce its size.')
-
-if __name__ == '__main__':
-    demo.launch()
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/utils/log_utils.py b/spaces/gyugnsu/DragGan-Inversion/PTI/utils/log_utils.py
deleted file mode 100644
index b29eae05f1c3ba34df60c074373b417c5420e836..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/PTI/utils/log_utils.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import numpy as np
-from PIL import Image
-import wandb
-from PTI.configs import global_config
-import torch
-import matplotlib.pyplot as plt
-
-
-def log_image_from_w(w, G, name):
-    img = get_image_from_w(w, G)
-    pillow_image = Image.fromarray(img)
-    wandb.log(
-        {f"{name}": [
-            wandb.Image(pillow_image, caption=f"current inversion {name}")]},
-        step=global_config.training_step)
-
-
-def log_images_from_w(ws, G, names):
-    for name, w in zip(names, ws):
-        w = w.to(global_config.device)
-        log_image_from_w(w, G, name)
-
-
-def plot_image_from_w(w, G):
-    img = get_image_from_w(w, G)
-    pillow_image = Image.fromarray(img)
-    plt.imshow(pillow_image)
-    plt.show()
-
-
-def plot_image(img):
-    img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy()
-    pillow_image = Image.fromarray(img[0])
-    plt.imshow(pillow_image)
-    plt.show()
-
-
-def save_image(name, method_type, results_dir, image, run_id):
-    image.save(f'{results_dir}/{method_type}_{name}_{run_id}.jpg')
-
-
-def save_w(w, G, name, method_type, results_dir):
-    im = get_image_from_w(w, G)
-    im = Image.fromarray(im, mode='RGB')
-    save_image(name, method_type, results_dir, im)
-
-
-def save_concat_image(base_dir, image_latents, new_inv_image_latent, new_G,
-                      old_G,
-                      file_name,
-                      extra_image=None):
-    images_to_save = []
-    if extra_image is not None:
-        images_to_save.append(extra_image)
-    for latent in image_latents:
-        images_to_save.append(get_image_from_w(latent, old_G))
-    images_to_save.append(get_image_from_w(new_inv_image_latent, new_G))
-    result_image = create_alongside_images(images_to_save)
-    result_image.save(f'{base_dir}/{file_name}.jpg')
-
-
-def save_single_image(base_dir, image_latent, G, file_name):
-    image_to_save = get_image_from_w(image_latent, G)
-    image_to_save = Image.fromarray(image_to_save, mode='RGB')
-    image_to_save.save(f'{base_dir}/{file_name}.jpg')
-
-
-def create_alongside_images(images):
-    res = np.concatenate([np.array(image) for image in images], axis=1)
-    return Image.fromarray(res, mode='RGB')
-
-
-def get_image_from_w(w, G):
-    if len(w.size()) <= 2:
-        w = w.unsqueeze(0)
-    with torch.no_grad():
-        img = G.synthesis(w, noise_mode='const')
-        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy()
-    return img[0]
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/__init__.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/h2oai/h2ogpt-chatbot2/src/gpt_langchain.py b/spaces/h2oai/h2ogpt-chatbot2/src/gpt_langchain.py
deleted file mode 100644
index 144d9ec5c3783430db8c0714828028137ceac94d..0000000000000000000000000000000000000000
--- a/spaces/h2oai/h2ogpt-chatbot2/src/gpt_langchain.py
+++ /dev/null
@@ -1,5394 +0,0 @@
-import ast
-import asyncio
-import copy
-import functools
-import glob
-import gzip
-import inspect
-import json
-import os
-import pathlib
-import pickle
-import shutil
-import subprocess
-import tempfile
-import time
-import traceback
-import types
-import typing
-import urllib.error
-import uuid
-import zipfile
-from collections import defaultdict
-from datetime import datetime
-from functools import reduce
-from operator import concat
-import filelock
-import tabulate
-import yaml
-
-from joblib import delayed
-from langchain.callbacks import streaming_stdout
-from langchain.embeddings import HuggingFaceInstructEmbeddings
-from langchain.llms.huggingface_pipeline import VALID_TASKS
-from langchain.llms.utils import enforce_stop_tokens
-from langchain.schema import LLMResult, Generation
-from langchain.tools import PythonREPLTool
-from langchain.tools.json.tool import JsonSpec
-from tqdm import tqdm
-
-from src.db_utils import length_db1, set_dbid, set_userid, get_dbid, get_userid_direct, get_username_direct, \
-    set_userid_direct
-from utils import wrapped_partial, EThread, import_matplotlib, sanitize_filename, makedirs, get_url, flatten_list, \
-    get_device, ProgressParallel, remove, hash_file, clear_torch_cache, NullContext, get_hf_server, FakeTokenizer, \
-    have_libreoffice, have_arxiv, have_playwright, have_selenium, have_tesseract, have_doctr, have_pymupdf, set_openai, \
-    get_list_or_str, have_pillow, only_selenium, only_playwright, only_unstructured_urls, get_sha, get_short_name, \
-    get_accordion, have_jq, get_doc, get_source, have_chromamigdb, get_token_count, reverse_ucurve_list
-from enums import DocumentSubset, no_lora_str, model_token_mapping, source_prefix, source_postfix, non_query_commands, \
-    LangChainAction, LangChainMode, DocumentChoice, LangChainTypes, font_size, head_acc, super_source_prefix, \
-    super_source_postfix, langchain_modes_intrinsic, get_langchain_prompts, LangChainAgent
-from evaluate_params import gen_hyper, gen_hyper0
-from gen import get_model, SEED, get_limited_prompt, get_docs_tokens
-from prompter import non_hf_types, PromptType, Prompter
-from src.serpapi import H2OSerpAPIWrapper
-from utils_langchain import StreamingGradioCallbackHandler, _chunk_sources, _add_meta, add_parser, fix_json_meta
-
-import_matplotlib()
-
-import numpy as np
-import pandas as pd
-import requests
-from langchain.chains.qa_with_sources import load_qa_with_sources_chain
-# , GCSDirectoryLoader, GCSFileLoader
-# , OutlookMessageLoader # GPL3
-# ImageCaptionLoader, # use our own wrapper
-#  ReadTheDocsLoader,  # no special file, some path, so have to give as special option
-from langchain.document_loaders import PyPDFLoader, TextLoader, CSVLoader, PythonLoader, TomlLoader, \
-    UnstructuredURLLoader, UnstructuredHTMLLoader, UnstructuredWordDocumentLoader, UnstructuredMarkdownLoader, \
-    EverNoteLoader, UnstructuredEmailLoader, UnstructuredODTLoader, UnstructuredPowerPointLoader, \
-    UnstructuredEPubLoader, UnstructuredImageLoader, UnstructuredRTFLoader, ArxivLoader, UnstructuredPDFLoader, \
-    UnstructuredExcelLoader, JSONLoader
-from langchain.text_splitter import Language
-from langchain.chains.question_answering import load_qa_chain
-from langchain.docstore.document import Document
-from langchain import PromptTemplate, HuggingFaceTextGenInference, HuggingFacePipeline
-from langchain.vectorstores import Chroma
-from chromamig import ChromaMig
-
-
-def split_list(input_list, split_size):
-    for i in range(0, len(input_list), split_size):
-        yield input_list[i:i + split_size]
-
-
-def get_db(sources, use_openai_embedding=False, db_type='faiss',
-           persist_directory=None, load_db_if_exists=True,
-           langchain_mode='notset',
-           langchain_mode_paths={},
-           langchain_mode_types={},
-           collection_name=None,
-           hf_embedding_model=None,
-           migrate_embedding_model=False,
-           auto_migrate_db=False,
-           n_jobs=-1):
-    if not sources:
-        return None
-    user_path = langchain_mode_paths.get(langchain_mode)
-    if persist_directory is None:
-        langchain_type = langchain_mode_types.get(langchain_mode, LangChainTypes.EITHER.value)
-        persist_directory, langchain_type = get_persist_directory(langchain_mode, langchain_type=langchain_type)
-        langchain_mode_types[langchain_mode] = langchain_type
-    assert hf_embedding_model is not None
-
-    # get freshly-determined embedding model
-    embedding = get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model)
-    assert collection_name is not None or langchain_mode != 'notset'
-    if collection_name is None:
-        collection_name = langchain_mode.replace(' ', '_')
-
-    # Create vector database
-    if db_type == 'faiss':
-        from langchain.vectorstores import FAISS
-        db = FAISS.from_documents(sources, embedding)
-    elif db_type == 'weaviate':
-        import weaviate
-        from weaviate.embedded import EmbeddedOptions
-        from langchain.vectorstores import Weaviate
-
-        if os.getenv('WEAVIATE_URL', None):
-            client = _create_local_weaviate_client()
-        else:
-            client = weaviate.Client(
-                embedded_options=EmbeddedOptions(persistence_data_path=persist_directory)
-            )
-        index_name = collection_name.capitalize()
-        db = Weaviate.from_documents(documents=sources, embedding=embedding, client=client, by_text=False,
-                                     index_name=index_name)
-    elif db_type in ['chroma', 'chroma_old']:
-        assert persist_directory is not None
-        # use_base already handled when making persist_directory, unless was passed into get_db()
-        makedirs(persist_directory, exist_ok=True)
-
-        # see if already actually have persistent db, and deal with possible changes in embedding
-        db, use_openai_embedding, hf_embedding_model = \
-            get_existing_db(None, persist_directory, load_db_if_exists, db_type,
-                            use_openai_embedding,
-                            langchain_mode, langchain_mode_paths, langchain_mode_types,
-                            hf_embedding_model, migrate_embedding_model, auto_migrate_db,
-                            verbose=False,
-                            n_jobs=n_jobs)
-        if db is None:
-            import logging
-            logging.getLogger("chromadb").setLevel(logging.ERROR)
-            if db_type == 'chroma':
-                from chromadb.config import Settings
-                settings_extra_kwargs = dict(is_persistent=True)
-            else:
-                from chromamigdb.config import Settings
-                settings_extra_kwargs = dict(chroma_db_impl="duckdb+parquet")
-            client_settings = Settings(anonymized_telemetry=False,
-                                       persist_directory=persist_directory,
-                                       **settings_extra_kwargs)
-            if n_jobs in [None, -1]:
-                n_jobs = int(os.getenv('OMP_NUM_THREADS', str(os.cpu_count() // 2)))
-                num_threads = max(1, min(n_jobs, 8))
-            else:
-                num_threads = max(1, n_jobs)
-            collection_metadata = {"hnsw:num_threads": num_threads}
-            from_kwargs = dict(embedding=embedding,
-                               persist_directory=persist_directory,
-                               collection_name=collection_name,
-                               client_settings=client_settings,
-                               collection_metadata=collection_metadata)
-            if db_type == 'chroma':
-                import chromadb
-                api = chromadb.PersistentClient(path=persist_directory)
-                max_batch_size = api._producer.max_batch_size
-                sources_batches = split_list(sources, max_batch_size)
-                for sources_batch in sources_batches:
-                    db = Chroma.from_documents(documents=sources_batch, **from_kwargs)
-                    db.persist()
-            else:
-                db = ChromaMig.from_documents(documents=sources, **from_kwargs)
-            clear_embedding(db)
-            save_embed(db, use_openai_embedding, hf_embedding_model)
-        else:
-            # then just add
-            # doesn't check or change embedding, just saves it in case not saved yet, after persisting
-            db, num_new_sources, new_sources_metadata = add_to_db(db, sources, db_type=db_type,
-                                                                  use_openai_embedding=use_openai_embedding,
-                                                                  hf_embedding_model=hf_embedding_model)
-    else:
-        raise RuntimeError("No such db_type=%s" % db_type)
-
-    # once here, db is not changing and embedding choices in calling functions does not matter
-    return db
-
-
-def _get_unique_sources_in_weaviate(db):
-    batch_size = 100
-    id_source_list = []
-    result = db._client.data_object.get(class_name=db._index_name, limit=batch_size)
-
-    while result['objects']:
-        id_source_list += [(obj['id'], obj['properties']['source']) for obj in result['objects']]
-        last_id = id_source_list[-1][0]
-        result = db._client.data_object.get(class_name=db._index_name, limit=batch_size, after=last_id)
-
-    unique_sources = {source for _, source in id_source_list}
-    return unique_sources
-
-
-def del_from_db(db, sources, db_type=None):
-    if db_type in ['chroma', 'chroma_old'] and db is not None:
-        # sources should be list of x.metadata['source'] from document metadatas
-        if isinstance(sources, str):
-            sources = [sources]
-        else:
-            assert isinstance(sources, (list, tuple, types.GeneratorType))
-        metadatas = set(sources)
-        client_collection = db._client.get_collection(name=db._collection.name,
-                                                      embedding_function=db._collection._embedding_function)
-        for source in metadatas:
-            meta = dict(source=source)
-            try:
-                client_collection.delete(where=meta)
-            except KeyError:
-                pass
-
-
-def add_to_db(db, sources, db_type='faiss',
-              avoid_dup_by_file=False,
-              avoid_dup_by_content=True,
-              use_openai_embedding=False,
-              hf_embedding_model=None):
-    assert hf_embedding_model is not None
-    num_new_sources = len(sources)
-    if not sources:
-        return db, num_new_sources, []
-    if db_type == 'faiss':
-        db.add_documents(sources)
-    elif db_type == 'weaviate':
-        # FIXME: only control by file name, not hash yet
-        if avoid_dup_by_file or avoid_dup_by_content:
-            unique_sources = _get_unique_sources_in_weaviate(db)
-            sources = [x for x in sources if x.metadata['source'] not in unique_sources]
-        num_new_sources = len(sources)
-        if num_new_sources == 0:
-            return db, num_new_sources, []
-        db.add_documents(documents=sources)
-    elif db_type in ['chroma', 'chroma_old']:
-        collection = get_documents(db)
-        # files we already have:
-        metadata_files = set([x['source'] for x in collection['metadatas']])
-        if avoid_dup_by_file:
-            # Too weak in case file changed content, assume parent shouldn't pass true for this for now
-            raise RuntimeError("Not desired code path")
-        if avoid_dup_by_content:
-            # look at hash, instead of page_content
-            # migration: If no hash previously, avoid updating,
-            #  since don't know if need to update and may be expensive to redo all unhashed files
-            metadata_hash_ids = set(
-                [x['hashid'] for x in collection['metadatas'] if 'hashid' in x and x['hashid'] not in ["None", None]])
-            # avoid sources with same hash
-            sources = [x for x in sources if x.metadata.get('hashid') not in metadata_hash_ids]
-            num_nohash = len([x for x in sources if not x.metadata.get('hashid')])
-            print("Found %s new sources (%d have no hash in original source,"
-                  " so have to reprocess for migration to sources with hash)" % (len(sources), num_nohash), flush=True)
-            # get new file names that match existing file names.  delete existing files we are overridding
-            dup_metadata_files = set([x.metadata['source'] for x in sources if x.metadata['source'] in metadata_files])
-            print("Removing %s duplicate files from db because ingesting those as new documents" % len(
-                dup_metadata_files), flush=True)
-            client_collection = db._client.get_collection(name=db._collection.name,
-                                                          embedding_function=db._collection._embedding_function)
-            for dup_file in dup_metadata_files:
-                dup_file_meta = dict(source=dup_file)
-                try:
-                    client_collection.delete(where=dup_file_meta)
-                except KeyError:
-                    pass
-        num_new_sources = len(sources)
-        if num_new_sources == 0:
-            return db, num_new_sources, []
-        if hasattr(db, '_persist_directory'):
-            print("Existing db, adding to %s" % db._persist_directory, flush=True)
-            # chroma only
-            lock_file = get_db_lock_file(db)
-            context = filelock.FileLock
-        else:
-            lock_file = None
-            context = NullContext
-        with context(lock_file):
-            # this is place where add to db, but others maybe accessing db, so lock access.
-            # else see RuntimeError: Index seems to be corrupted or unsupported
-            import chromadb
-            api = chromadb.PersistentClient(path=db._persist_directory)
-            max_batch_size = api._producer.max_batch_size
-            sources_batches = split_list(sources, max_batch_size)
-            for sources_batch in sources_batches:
-                db.add_documents(documents=sources_batch)
-                db.persist()
-            clear_embedding(db)
-            # save here is for migration, in case old db directory without embedding saved
-            save_embed(db, use_openai_embedding, hf_embedding_model)
-    else:
-        raise RuntimeError("No such db_type=%s" % db_type)
-
-    new_sources_metadata = [x.metadata for x in sources]
-
-    return db, num_new_sources, new_sources_metadata
-
-
-def create_or_update_db(db_type, persist_directory, collection_name,
-                        user_path, langchain_type,
-                        sources, use_openai_embedding, add_if_exists, verbose,
-                        hf_embedding_model, migrate_embedding_model, auto_migrate_db,
-                        n_jobs=-1):
-    if not os.path.isdir(persist_directory) or not add_if_exists:
-        if os.path.isdir(persist_directory):
-            if verbose:
-                print("Removing %s" % persist_directory, flush=True)
-            remove(persist_directory)
-        if verbose:
-            print("Generating db", flush=True)
-    if db_type == 'weaviate':
-        import weaviate
-        from weaviate.embedded import EmbeddedOptions
-
-        if os.getenv('WEAVIATE_URL', None):
-            client = _create_local_weaviate_client()
-        else:
-            client = weaviate.Client(
-                embedded_options=EmbeddedOptions(persistence_data_path=persist_directory)
-            )
-
-        index_name = collection_name.replace(' ', '_').capitalize()
-        if client.schema.exists(index_name) and not add_if_exists:
-            client.schema.delete_class(index_name)
-            if verbose:
-                print("Removing %s" % index_name, flush=True)
-    elif db_type in ['chroma', 'chroma_old']:
-        pass
-
-    if not add_if_exists:
-        if verbose:
-            print("Generating db", flush=True)
-    else:
-        if verbose:
-            print("Loading and updating db", flush=True)
-
-    db = get_db(sources,
-                use_openai_embedding=use_openai_embedding,
-                db_type=db_type,
-                persist_directory=persist_directory,
-                langchain_mode=collection_name,
-                langchain_mode_paths={collection_name: user_path},
-                langchain_mode_types={collection_name: langchain_type},
-                hf_embedding_model=hf_embedding_model,
-                migrate_embedding_model=migrate_embedding_model,
-                auto_migrate_db=auto_migrate_db,
-                n_jobs=n_jobs)
-
-    return db
-
-
-from langchain.embeddings import FakeEmbeddings
-
-
-class H2OFakeEmbeddings(FakeEmbeddings):
-    """Fake embedding model, but constant instead of random"""
-
-    size: int
-    """The size of the embedding vector."""
-
-    def _get_embedding(self) -> typing.List[float]:
-        return [1] * self.size
-
-    def embed_documents(self, texts: typing.List[str]) -> typing.List[typing.List[float]]:
-        return [self._get_embedding() for _ in texts]
-
-    def embed_query(self, text: str) -> typing.List[float]:
-        return self._get_embedding()
-
-
-def get_embedding(use_openai_embedding, hf_embedding_model=None, preload=False):
-    assert hf_embedding_model is not None
-    # Get embedding model
-    if use_openai_embedding:
-        assert os.getenv("OPENAI_API_KEY") is not None, "Set ENV OPENAI_API_KEY"
-        from langchain.embeddings import OpenAIEmbeddings
-        embedding = OpenAIEmbeddings(disallowed_special=())
-    elif hf_embedding_model == 'fake':
-        embedding = H2OFakeEmbeddings(size=1)
-    else:
-        if isinstance(hf_embedding_model, str):
-            pass
-        elif isinstance(hf_embedding_model, dict):
-            # embedding itself preloaded globally
-            return hf_embedding_model['model']
-        else:
-            # object
-            return hf_embedding_model
-        # to ensure can fork without deadlock
-        from langchain.embeddings import HuggingFaceEmbeddings
-
-        device, torch_dtype, context_class = get_device_dtype()
-        model_kwargs = dict(device=device)
-        if 'instructor' in hf_embedding_model:
-            encode_kwargs = {'normalize_embeddings': True}
-            embedding = HuggingFaceInstructEmbeddings(model_name=hf_embedding_model,
-                                                      model_kwargs=model_kwargs,
-                                                      encode_kwargs=encode_kwargs)
-        else:
-            embedding = HuggingFaceEmbeddings(model_name=hf_embedding_model, model_kwargs=model_kwargs)
-        embedding.client.preload = preload
-    return embedding
-
-
-def get_answer_from_sources(chain, sources, question):
-    return chain(
-        {
-            "input_documents": sources,
-            "question": question,
-        },
-        return_only_outputs=True,
-    )["output_text"]
-
-
-"""Wrapper around Huggingface text generation inference API."""
-from functools import partial
-from typing import Any, Dict, List, Optional, Set, Iterable
-
-from pydantic import Extra, Field, root_validator
-
-from langchain.callbacks.manager import CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun
-from langchain.llms.base import LLM
-
-
-class GradioInference(LLM):
-    """
-    Gradio generation inference API.
-    """
-    inference_server_url: str = ""
-
-    temperature: float = 0.8
-    top_p: Optional[float] = 0.95
-    top_k: Optional[int] = None
-    num_beams: Optional[int] = 1
-    max_new_tokens: int = 512
-    min_new_tokens: int = 1
-    early_stopping: bool = False
-    max_time: int = 180
-    repetition_penalty: Optional[float] = None
-    num_return_sequences: Optional[int] = 1
-    do_sample: bool = False
-    chat_client: bool = False
-
-    return_full_text: bool = False
-    stream_output: bool = False
-    sanitize_bot_response: bool = False
-
-    prompter: Any = None
-    context: Any = ''
-    iinput: Any = ''
-    client: Any = None
-    tokenizer: Any = None
-
-    system_prompt: Any = None
-    visible_models: Any = None
-    h2ogpt_key: Any = None
-
-    count_input_tokens: Any = 0
-    count_output_tokens: Any = 0
-
-    min_max_new_tokens: Any = 256
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
-
-    @root_validator()
-    def validate_environment(cls, values: Dict) -> Dict:
-        """Validate that python package exists in environment."""
-
-        try:
-            if values['client'] is None:
-                import gradio_client
-                values["client"] = gradio_client.Client(
-                    values["inference_server_url"]
-                )
-        except ImportError:
-            raise ImportError(
-                "Could not import gradio_client python package. "
-                "Please install it with `pip install gradio_client`."
-            )
-        return values
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of llm."""
-        return "gradio_inference"
-
-    def _call(
-            self,
-            prompt: str,
-            stop: Optional[List[str]] = None,
-            run_manager: Optional[CallbackManagerForLLMRun] = None,
-            **kwargs: Any,
-    ) -> str:
-        # NOTE: prompt here has no prompt_type (e.g. human: bot:) prompt injection,
-        # so server should get prompt_type or '', not plain
-        # This is good, so gradio server can also handle stopping.py conditions
-        # this is different than TGI server that uses prompter to inject prompt_type prompting
-        stream_output = self.stream_output
-        gr_client = self.client
-        client_langchain_mode = 'Disabled'
-        client_add_chat_history_to_context = True
-        client_add_search_to_context = False
-        client_chat_conversation = []
-        client_langchain_action = LangChainAction.QUERY.value
-        client_langchain_agents = []
-        top_k_docs = 1
-        chunk = True
-        chunk_size = 512
-        client_kwargs = dict(instruction=prompt if self.chat_client else '',  # only for chat=True
-                             iinput=self.iinput if self.chat_client else '',  # only for chat=True
-                             context=self.context,
-                             # streaming output is supported, loops over and outputs each generation in streaming mode
-                             # but leave stream_output=False for simple input/output mode
-                             stream_output=stream_output,
-                             prompt_type=self.prompter.prompt_type,
-                             prompt_dict='',
-
-                             temperature=self.temperature,
-                             top_p=self.top_p,
-                             top_k=self.top_k,
-                             num_beams=self.num_beams,
-                             max_new_tokens=self.max_new_tokens,
-                             min_new_tokens=self.min_new_tokens,
-                             early_stopping=self.early_stopping,
-                             max_time=self.max_time,
-                             repetition_penalty=self.repetition_penalty,
-                             num_return_sequences=self.num_return_sequences,
-                             do_sample=self.do_sample,
-                             chat=self.chat_client,
-
-                             instruction_nochat=prompt if not self.chat_client else '',
-                             iinput_nochat=self.iinput if not self.chat_client else '',
-                             langchain_mode=client_langchain_mode,
-                             add_chat_history_to_context=client_add_chat_history_to_context,
-                             langchain_action=client_langchain_action,
-                             langchain_agents=client_langchain_agents,
-                             top_k_docs=top_k_docs,
-                             chunk=chunk,
-                             chunk_size=chunk_size,
-                             document_subset=DocumentSubset.Relevant.name,
-                             document_choice=[DocumentChoice.ALL.value],
-                             pre_prompt_query=None,
-                             prompt_query=None,
-                             pre_prompt_summary=None,
-                             prompt_summary=None,
-                             system_prompt=self.system_prompt,
-                             image_loaders=None,  # don't need to further do doc specific things
-                             pdf_loaders=None,  # don't need to further do doc specific things
-                             url_loaders=None,  # don't need to further do doc specific things
-                             jq_schema=None,  # don't need to further do doc specific things
-                             visible_models=self.visible_models,
-                             h2ogpt_key=self.h2ogpt_key,
-                             add_search_to_context=client_add_search_to_context,
-                             chat_conversation=client_chat_conversation,
-                             text_context_list=None,
-                             docs_ordering_type=None,
-                             min_max_new_tokens=self.min_max_new_tokens,
-                             )
-        api_name = '/submit_nochat_api'  # NOTE: like submit_nochat but stable API for string dict passing
-        self.count_input_tokens += self.get_num_tokens(prompt)
-
-        if not stream_output:
-            res = gr_client.predict(str(dict(client_kwargs)), api_name=api_name)
-            res_dict = ast.literal_eval(res)
-            text = res_dict['response']
-            ret = self.prompter.get_response(prompt + text, prompt=prompt,
-                                             sanitize_bot_response=self.sanitize_bot_response)
-            self.count_output_tokens += self.get_num_tokens(ret)
-            return ret
-        else:
-            text_callback = None
-            if run_manager:
-                text_callback = partial(
-                    run_manager.on_llm_new_token, verbose=self.verbose
-                )
-
-            job = gr_client.submit(str(dict(client_kwargs)), api_name=api_name)
-            text0 = ''
-            while not job.done():
-                if job.communicator.job.latest_status.code.name == 'FINISHED':
-                    break
-                e = job.future._exception
-                if e is not None:
-                    break
-                outputs_list = job.communicator.job.outputs
-                if outputs_list:
-                    res = job.communicator.job.outputs[-1]
-                    res_dict = ast.literal_eval(res)
-                    text = res_dict['response']
-                    text = self.prompter.get_response(prompt + text, prompt=prompt,
-                                                      sanitize_bot_response=self.sanitize_bot_response)
-                    # FIXME: derive chunk from full for now
-                    text_chunk = text[len(text0):]
-                    if not text_chunk:
-                        continue
-                    # save old
-                    text0 = text
-
-                    if text_callback:
-                        text_callback(text_chunk)
-
-                time.sleep(0.01)
-
-            # ensure get last output to avoid race
-            res_all = job.outputs()
-            if len(res_all) > 0:
-                res = res_all[-1]
-                res_dict = ast.literal_eval(res)
-                text = res_dict['response']
-                # FIXME: derive chunk from full for now
-            else:
-                # go with old if failure
-                text = text0
-            text_chunk = text[len(text0):]
-            if text_callback:
-                text_callback(text_chunk)
-            ret = self.prompter.get_response(prompt + text, prompt=prompt,
-                                             sanitize_bot_response=self.sanitize_bot_response)
-            self.count_output_tokens += self.get_num_tokens(ret)
-            return ret
-
-    def get_token_ids(self, text: str) -> List[int]:
-        return self.tokenizer.encode(text)
-        # avoid base method that is not aware of how to properly tokenize (uses GPT2)
-        # return _get_token_ids_default_method(text)
-
-
-class H2OHuggingFaceTextGenInference(HuggingFaceTextGenInference):
-    max_new_tokens: int = 512
-    do_sample: bool = False
-    top_k: Optional[int] = None
-    top_p: Optional[float] = 0.95
-    typical_p: Optional[float] = 0.95
-    temperature: float = 0.8
-    repetition_penalty: Optional[float] = None
-    return_full_text: bool = False
-    stop_sequences: List[str] = Field(default_factory=list)
-    seed: Optional[int] = None
-    inference_server_url: str = ""
-    timeout: int = 300
-    headers: dict = None
-    stream_output: bool = False
-    sanitize_bot_response: bool = False
-    prompter: Any = None
-    context: Any = ''
-    iinput: Any = ''
-    tokenizer: Any = None
-    async_sem: Any = None
-    count_input_tokens: Any = 0
-    count_output_tokens: Any = 0
-
-    def _call(
-            self,
-            prompt: str,
-            stop: Optional[List[str]] = None,
-            run_manager: Optional[CallbackManagerForLLMRun] = None,
-            **kwargs: Any,
-    ) -> str:
-        if stop is None:
-            stop = self.stop_sequences.copy()
-        else:
-            stop += self.stop_sequences.copy()
-        stop_tmp = stop.copy()
-        stop = []
-        [stop.append(x) for x in stop_tmp if x not in stop]
-
-        # HF inference server needs control over input tokens
-        assert self.tokenizer is not None
-        from h2oai_pipeline import H2OTextGenerationPipeline
-        prompt, num_prompt_tokens = H2OTextGenerationPipeline.limit_prompt(prompt, self.tokenizer)
-
-        # NOTE: TGI server does not add prompting, so must do here
-        data_point = dict(context=self.context, instruction=prompt, input=self.iinput)
-        prompt = self.prompter.generate_prompt(data_point)
-        self.count_input_tokens += self.get_num_tokens(prompt)
-
-        gen_server_kwargs = dict(do_sample=self.do_sample,
-                                 stop_sequences=stop,
-                                 max_new_tokens=self.max_new_tokens,
-                                 top_k=self.top_k,
-                                 top_p=self.top_p,
-                                 typical_p=self.typical_p,
-                                 temperature=self.temperature,
-                                 repetition_penalty=self.repetition_penalty,
-                                 return_full_text=self.return_full_text,
-                                 seed=self.seed,
-                                 )
-        gen_server_kwargs.update(kwargs)
-
-        # lower bound because client is re-used if multi-threading
-        self.client.timeout = max(300, self.timeout)
-
-        if not self.stream_output:
-            res = self.client.generate(
-                prompt,
-                **gen_server_kwargs,
-            )
-            if self.return_full_text:
-                gen_text = res.generated_text[len(prompt):]
-            else:
-                gen_text = res.generated_text
-            # remove stop sequences from the end of the generated text
-            for stop_seq in stop:
-                if stop_seq in gen_text:
-                    gen_text = gen_text[:gen_text.index(stop_seq)]
-            text = prompt + gen_text
-            text = self.prompter.get_response(text, prompt=prompt,
-                                              sanitize_bot_response=self.sanitize_bot_response)
-        else:
-            text_callback = None
-            if run_manager:
-                text_callback = partial(
-                    run_manager.on_llm_new_token, verbose=self.verbose
-                )
-            # parent handler of streamer expects to see prompt first else output="" and lose if prompt=None in prompter
-            if text_callback:
-                text_callback(prompt)
-            text = ""
-            # Note: Streaming ignores return_full_text=True
-            for response in self.client.generate_stream(prompt, **gen_server_kwargs):
-                text_chunk = response.token.text
-                text += text_chunk
-                text = self.prompter.get_response(prompt + text, prompt=prompt,
-                                                  sanitize_bot_response=self.sanitize_bot_response)
-                # stream part
-                is_stop = False
-                for stop_seq in stop:
-                    if stop_seq in text_chunk:
-                        is_stop = True
-                        break
-                if is_stop:
-                    break
-                if not response.token.special:
-                    if text_callback:
-                        text_callback(text_chunk)
-        self.count_output_tokens += self.get_num_tokens(text)
-        return text
-
-    async def _acall(
-            self,
-            prompt: str,
-            stop: Optional[List[str]] = None,
-            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-            **kwargs: Any,
-    ) -> str:
-        # print("acall", flush=True)
-        if stop is None:
-            stop = self.stop_sequences.copy()
-        else:
-            stop += self.stop_sequences.copy()
-        stop_tmp = stop.copy()
-        stop = []
-        [stop.append(x) for x in stop_tmp if x not in stop]
-
-        # HF inference server needs control over input tokens
-        assert self.tokenizer is not None
-        from h2oai_pipeline import H2OTextGenerationPipeline
-        prompt, num_prompt_tokens = H2OTextGenerationPipeline.limit_prompt(prompt, self.tokenizer)
-
-        # NOTE: TGI server does not add prompting, so must do here
-        data_point = dict(context=self.context, instruction=prompt, input=self.iinput)
-        prompt = self.prompter.generate_prompt(data_point)
-
-        gen_text = await super()._acall(prompt, stop=stop, run_manager=run_manager, **kwargs)
-
-        # remove stop sequences from the end of the generated text
-        for stop_seq in stop:
-            if stop_seq in gen_text:
-                gen_text = gen_text[:gen_text.index(stop_seq)]
-        text = prompt + gen_text
-        text = self.prompter.get_response(text, prompt=prompt,
-                                          sanitize_bot_response=self.sanitize_bot_response)
-        # print("acall done", flush=True)
-        return text
-
-    async def _agenerate(
-            self,
-            prompts: List[str],
-            stop: Optional[List[str]] = None,
-            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-            **kwargs: Any,
-    ) -> LLMResult:
-        """Run the LLM on the given prompt and input."""
-        generations = []
-        new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager")
-        self.count_input_tokens += sum([self.get_num_tokens(prompt) for prompt in prompts])
-        tasks = [
-            asyncio.ensure_future(self._agenerate_one(prompt, stop=stop, run_manager=run_manager,
-                                                      new_arg_supported=new_arg_supported, **kwargs))
-            for prompt in prompts
-        ]
-        texts = await asyncio.gather(*tasks)
-        self.count_output_tokens += sum([self.get_num_tokens(text) for text in texts])
-        [generations.append([Generation(text=text)]) for text in texts]
-        return LLMResult(generations=generations)
-
-    async def _agenerate_one(
-            self,
-            prompt: str,
-            stop: Optional[List[str]] = None,
-            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-            new_arg_supported=None,
-            **kwargs: Any,
-    ) -> str:
-        async with self.async_sem:  # semaphore limits num of simultaneous downloads
-            return await self._acall(prompt, stop=stop, run_manager=run_manager, **kwargs) \
-                if new_arg_supported else \
-                await self._acall(prompt, stop=stop, **kwargs)
-
-    def get_token_ids(self, text: str) -> List[int]:
-        return self.tokenizer.encode(text)
-        # avoid base method that is not aware of how to properly tokenize (uses GPT2)
-        # return _get_token_ids_default_method(text)
-
-
-from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
-from langchain.llms import OpenAI, AzureOpenAI, Replicate
-from langchain.llms.openai import _streaming_response_template, completion_with_retry, _update_response, \
-    update_token_usage
-
-
-class H2OOpenAI(OpenAI):
-    """
-    New class to handle vLLM's use of OpenAI, no vllm_chat supported, so only need here
-    Handles prompting that OpenAI doesn't need, stopping as well
-    """
-    stop_sequences: Any = None
-    sanitize_bot_response: bool = False
-    prompter: Any = None
-    context: Any = ''
-    iinput: Any = ''
-    tokenizer: Any = None
-
-    @classmethod
-    def _all_required_field_names(cls) -> Set:
-        _all_required_field_names = super(OpenAI, cls)._all_required_field_names()
-        _all_required_field_names.update(
-            {'top_p', 'frequency_penalty', 'presence_penalty', 'stop_sequences', 'sanitize_bot_response', 'prompter',
-             'tokenizer', 'logit_bias'})
-        return _all_required_field_names
-
-    def _generate(
-            self,
-            prompts: List[str],
-            stop: Optional[List[str]] = None,
-            run_manager: Optional[CallbackManagerForLLMRun] = None,
-            **kwargs: Any,
-    ) -> LLMResult:
-        stop_tmp = self.stop_sequences if not stop else self.stop_sequences + stop
-        stop = []
-        [stop.append(x) for x in stop_tmp if x not in stop]
-
-        # HF inference server needs control over input tokens
-        assert self.tokenizer is not None
-        from h2oai_pipeline import H2OTextGenerationPipeline
-        for prompti, prompt in enumerate(prompts):
-            prompt, num_prompt_tokens = H2OTextGenerationPipeline.limit_prompt(prompt, self.tokenizer)
-            # NOTE: OpenAI/vLLM server does not add prompting, so must do here
-            data_point = dict(context=self.context, instruction=prompt, input=self.iinput)
-            prompt = self.prompter.generate_prompt(data_point)
-            prompts[prompti] = prompt
-
-        params = self._invocation_params
-        params = {**params, **kwargs}
-        sub_prompts = self.get_sub_prompts(params, prompts, stop)
-        choices = []
-        token_usage: Dict[str, int] = {}
-        # Get the token usage from the response.
-        # Includes prompt, completion, and total tokens used.
-        _keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
-        text = ''
-        for _prompts in sub_prompts:
-            if self.streaming:
-                text_with_prompt = ""
-                prompt = _prompts[0]
-                if len(_prompts) > 1:
-                    raise ValueError("Cannot stream results with multiple prompts.")
-                params["stream"] = True
-                response = _streaming_response_template()
-                first = True
-                for stream_resp in completion_with_retry(
-                        self, prompt=_prompts, **params
-                ):
-                    if first:
-                        stream_resp["choices"][0]["text"] = prompt + stream_resp["choices"][0]["text"]
-                        first = False
-                    text_chunk = stream_resp["choices"][0]["text"]
-                    text_with_prompt += text_chunk
-                    text = self.prompter.get_response(text_with_prompt, prompt=prompt,
-                                                      sanitize_bot_response=self.sanitize_bot_response)
-                    if run_manager:
-                        run_manager.on_llm_new_token(
-                            text_chunk,
-                            verbose=self.verbose,
-                            logprobs=stream_resp["choices"][0]["logprobs"],
-                        )
-                    _update_response(response, stream_resp)
-                choices.extend(response["choices"])
-            else:
-                response = completion_with_retry(self, prompt=_prompts, **params)
-                choices.extend(response["choices"])
-            if not self.streaming:
-                # Can't update token usage if streaming
-                update_token_usage(_keys, response, token_usage)
-        if self.streaming:
-            choices[0]['text'] = text
-        return self.create_llm_result(choices, prompts, token_usage)
-
-    def get_token_ids(self, text: str) -> List[int]:
-        if self.tokenizer is not None:
-            return self.tokenizer.encode(text)
-        else:
-            # OpenAI uses tiktoken
-            return super().get_token_ids(text)
-
-
-class H2OReplicate(Replicate):
-    stop_sequences: Any = None
-    sanitize_bot_response: bool = False
-    prompter: Any = None
-    context: Any = ''
-    iinput: Any = ''
-    tokenizer: Any = None
-
-    def _call(
-            self,
-            prompt: str,
-            stop: Optional[List[str]] = None,
-            run_manager: Optional[CallbackManagerForLLMRun] = None,
-            **kwargs: Any,
-    ) -> str:
-        """Call to replicate endpoint."""
-        stop_tmp = self.stop_sequences if not stop else self.stop_sequences + stop
-        stop = []
-        [stop.append(x) for x in stop_tmp if x not in stop]
-
-        # HF inference server needs control over input tokens
-        assert self.tokenizer is not None
-        from h2oai_pipeline import H2OTextGenerationPipeline
-        prompt, num_prompt_tokens = H2OTextGenerationPipeline.limit_prompt(prompt, self.tokenizer)
-        # Note Replicate handles the prompting of the specific model
-        return super()._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
-
-    def get_token_ids(self, text: str) -> List[int]:
-        return self.tokenizer.encode(text)
-        # avoid base method that is not aware of how to properly tokenize (uses GPT2)
-        # return _get_token_ids_default_method(text)
-
-
-class H2OChatOpenAI(ChatOpenAI):
-    @classmethod
-    def _all_required_field_names(cls) -> Set:
-        _all_required_field_names = super(ChatOpenAI, cls)._all_required_field_names()
-        _all_required_field_names.update({'top_p', 'frequency_penalty', 'presence_penalty', 'logit_bias'})
-        return _all_required_field_names
-
-
-class H2OAzureChatOpenAI(AzureChatOpenAI):
-    @classmethod
-    def _all_required_field_names(cls) -> Set:
-        _all_required_field_names = super(AzureChatOpenAI, cls)._all_required_field_names()
-        _all_required_field_names.update({'top_p', 'frequency_penalty', 'presence_penalty', 'logit_bias'})
-        return _all_required_field_names
-
-
-class H2OAzureOpenAI(AzureOpenAI):
-    @classmethod
-    def _all_required_field_names(cls) -> Set:
-        _all_required_field_names = super(AzureOpenAI, cls)._all_required_field_names()
-        _all_required_field_names.update({'top_p', 'frequency_penalty', 'presence_penalty', 'logit_bias'})
-        return _all_required_field_names
-
-
-class H2OHuggingFacePipeline(HuggingFacePipeline):
-    def _call(
-            self,
-            prompt: str,
-            stop: Optional[List[str]] = None,
-            run_manager: Optional[CallbackManagerForLLMRun] = None,
-            **kwargs: Any,
-    ) -> str:
-        response = self.pipeline(prompt, stop=stop)
-        if self.pipeline.task == "text-generation":
-            # Text generation return includes the starter text.
-            text = response[0]["generated_text"][len(prompt):]
-        elif self.pipeline.task == "text2text-generation":
-            text = response[0]["generated_text"]
-        elif self.pipeline.task == "summarization":
-            text = response[0]["summary_text"]
-        else:
-            raise ValueError(
-                f"Got invalid task {self.pipeline.task}, "
-                f"currently only {VALID_TASKS} are supported"
-            )
-        if stop:
-            # This is a bit hacky, but I can't figure out a better way to enforce
-            # stop tokens when making calls to huggingface_hub.
-            text = enforce_stop_tokens(text, stop)
-        return text
-
-
-def get_llm(use_openai_model=False,
-            model_name=None,
-            model=None,
-            tokenizer=None,
-            inference_server=None,
-            langchain_only_model=None,
-            stream_output=False,
-            async_output=True,
-            num_async=3,
-            do_sample=False,
-            temperature=0.1,
-            top_k=40,
-            top_p=0.7,
-            num_beams=1,
-            max_new_tokens=512,
-            min_new_tokens=1,
-            early_stopping=False,
-            max_time=180,
-            repetition_penalty=1.0,
-            num_return_sequences=1,
-            prompt_type=None,
-            prompt_dict=None,
-            prompter=None,
-            context=None,
-            iinput=None,
-            sanitize_bot_response=False,
-            system_prompt='',
-            visible_models=0,
-            h2ogpt_key=None,
-            min_max_new_tokens=None,
-            n_jobs=None,
-            cli=False,
-            llamacpp_dict=None,
-            verbose=False,
-            ):
-    # currently all but h2oai_pipeline case return prompt + new text, but could change
-    only_new_text = False
-
-    if n_jobs in [None, -1]:
-        n_jobs = int(os.getenv('OMP_NUM_THREADS', str(os.cpu_count() // 2)))
-    if inference_server is None:
-        inference_server = ''
-    if inference_server.startswith('replicate'):
-        model_string = ':'.join(inference_server.split(':')[1:])
-        if 'meta/llama' in model_string:
-            temperature = max(0.01, temperature if do_sample else 0)
-        else:
-            temperature =temperature if do_sample else 0
-        gen_kwargs = dict(temperature=temperature,
-                          seed=1234,
-                          max_length=max_new_tokens,  # langchain
-                          max_new_tokens=max_new_tokens,  # replicate docs
-                          top_p=top_p if do_sample else 1,
-                          top_k=top_k,  # not always supported
-                          repetition_penalty=repetition_penalty)
-        if system_prompt in [None, 'None', 'auto']:
-            if prompter.system_prompt:
-                system_prompt = prompter.system_prompt
-            else:
-                system_prompt = ''
-        if system_prompt:
-            gen_kwargs.update(dict(system_prompt=system_prompt))
-
-        # replicate handles prompting, so avoid get_response() filter
-        prompter.prompt_type = 'plain'
-        if stream_output:
-            callbacks = [StreamingGradioCallbackHandler()]
-            streamer = callbacks[0] if stream_output else None
-            llm = H2OReplicate(
-                streaming=True,
-                callbacks=callbacks,
-                model=model_string,
-                input=gen_kwargs,
-                stop=prompter.stop_sequences,
-                stop_sequences=prompter.stop_sequences,
-                sanitize_bot_response=sanitize_bot_response,
-                prompter=prompter,
-                context=context,
-                iinput=iinput,
-                tokenizer=tokenizer,
-            )
-        else:
-            streamer = None
-            llm = H2OReplicate(
-                model=model_string,
-                input=gen_kwargs,
-                stop=prompter.stop_sequences,
-                stop_sequences=prompter.stop_sequences,
-                sanitize_bot_response=sanitize_bot_response,
-                prompter=prompter,
-                context=context,
-                iinput=iinput,
-                tokenizer=tokenizer,
-            )
-    elif use_openai_model or inference_server.startswith('openai') or inference_server.startswith('vllm'):
-        if use_openai_model and model_name is None:
-            model_name = "gpt-3.5-turbo"
-        # FIXME: Will later import be ignored?  I think so, so should be fine
-        openai, inf_type, deployment_name, base_url, api_version = set_openai(inference_server)
-        kwargs_extra = {}
-        if inf_type == 'openai_chat' or inf_type == 'vllm_chat':
-            cls = H2OChatOpenAI
-            # FIXME: Support context, iinput
-            # if inf_type == 'vllm_chat':
-            #    kwargs_extra.update(dict(tokenizer=tokenizer))
-            openai_api_key = openai.api_key
-        elif inf_type == 'openai_azure_chat':
-            cls = H2OAzureChatOpenAI
-            kwargs_extra.update(dict(openai_api_type='azure'))
-            # FIXME: Support context, iinput
-            if os.getenv('OPENAI_AZURE_KEY') is not None:
-                openai_api_key = os.getenv('OPENAI_AZURE_KEY')
-            else:
-                openai_api_key = openai.api_key
-        elif inf_type == 'openai_azure':
-            cls = H2OAzureOpenAI
-            kwargs_extra.update(dict(openai_api_type='azure'))
-            # FIXME: Support context, iinput
-            if os.getenv('OPENAI_AZURE_KEY') is not None:
-                openai_api_key = os.getenv('OPENAI_AZURE_KEY')
-            else:
-                openai_api_key = openai.api_key
-        else:
-            cls = H2OOpenAI
-            if inf_type == 'vllm':
-                kwargs_extra.update(dict(stop_sequences=prompter.stop_sequences,
-                                         sanitize_bot_response=sanitize_bot_response,
-                                         prompter=prompter,
-                                         context=context,
-                                         iinput=iinput,
-                                         tokenizer=tokenizer,
-                                         openai_api_base=openai.api_base,
-                                         client=None))
-            else:
-                assert inf_type == 'openai' or use_openai_model
-            openai_api_key = openai.api_key
-
-        if deployment_name:
-            kwargs_extra.update(dict(deployment_name=deployment_name))
-        if api_version:
-            kwargs_extra.update(dict(openai_api_version=api_version))
-        elif openai.api_version:
-            kwargs_extra.update(dict(openai_api_version=openai.api_version))
-        elif inf_type in ['openai_azure', 'openai_azure_chat']:
-            kwargs_extra.update(dict(openai_api_version="2023-05-15"))
-        if base_url:
-            kwargs_extra.update(dict(openai_api_base=base_url))
-        else:
-            kwargs_extra.update(dict(openai_api_base=openai.api_base))
-
-        callbacks = [StreamingGradioCallbackHandler()]
-        llm = cls(model_name=model_name,
-                  temperature=temperature if do_sample else 0,
-                  # FIXME: Need to count tokens and reduce max_new_tokens to fit like in generate.py
-                  max_tokens=max_new_tokens,
-                  top_p=top_p if do_sample else 1,
-                  frequency_penalty=0,
-                  presence_penalty=1.07 - repetition_penalty + 0.6,  # so good default
-                  callbacks=callbacks if stream_output else None,
-                  openai_api_key=openai_api_key,
-                  logit_bias=None if inf_type == 'vllm' else {},
-                  max_retries=6,
-                  streaming=stream_output,
-                  **kwargs_extra
-                  )
-        streamer = callbacks[0] if stream_output else None
-        if inf_type in ['openai', 'openai_chat', 'openai_azure', 'openai_azure_chat']:
-            prompt_type = inference_server
-        else:
-            # vllm goes here
-            prompt_type = prompt_type or 'plain'
-    elif inference_server and inference_server.startswith('sagemaker'):
-        callbacks = [StreamingGradioCallbackHandler()]  # FIXME
-        streamer = None
-
-        endpoint_name = ':'.join(inference_server.split(':')[1:2])
-        region_name = ':'.join(inference_server.split(':')[2:])
-
-        from sagemaker import H2OSagemakerEndpoint, ChatContentHandler, BaseContentHandler
-        if inference_server.startswith('sagemaker_chat'):
-            content_handler = ChatContentHandler()
-        else:
-            content_handler = BaseContentHandler()
-        model_kwargs = dict(temperature=temperature if do_sample else 1E-10,
-                            return_full_text=False, top_p=top_p, max_new_tokens=max_new_tokens)
-        llm = H2OSagemakerEndpoint(
-            endpoint_name=endpoint_name,
-            region_name=region_name,
-            aws_access_key_id=os.environ.get('AWS_ACCESS_KEY_ID'),
-            aws_secret_access_key=os.environ.get('AWS_SECRET_ACCESS_KEY'),
-            model_kwargs=model_kwargs,
-            content_handler=content_handler,
-            endpoint_kwargs={'CustomAttributes': 'accept_eula=true'},
-        )
-    elif inference_server:
-        assert inference_server.startswith(
-            'http'), "Malformed inference_server=%s.  Did you add http:// in front?" % inference_server
-
-        from gradio_utils.grclient import GradioClient
-        from text_generation import Client as HFClient
-        if isinstance(model, GradioClient):
-            gr_client = model
-            hf_client = None
-        else:
-            gr_client = None
-            hf_client = model
-            assert isinstance(hf_client, HFClient)
-
-        inference_server, headers = get_hf_server(inference_server)
-
-        # quick sanity check to avoid long timeouts, just see if can reach server
-        requests.get(inference_server, timeout=int(os.getenv('REQUEST_TIMEOUT_FAST', '10')))
-        callbacks = [StreamingGradioCallbackHandler()]
-
-        if gr_client:
-            async_output = False  # FIXME: not implemented yet
-            chat_client = False
-            llm = GradioInference(
-                inference_server_url=inference_server,
-                return_full_text=False,
-
-                temperature=temperature,
-                top_p=top_p,
-                top_k=top_k,
-                num_beams=num_beams,
-                max_new_tokens=max_new_tokens,
-                min_new_tokens=min_new_tokens,
-                early_stopping=early_stopping,
-                max_time=max_time,
-                repetition_penalty=repetition_penalty,
-                num_return_sequences=num_return_sequences,
-                do_sample=do_sample,
-                chat_client=chat_client,
-
-                callbacks=callbacks if stream_output else None,
-                stream_output=stream_output,
-                prompter=prompter,
-                context=context,
-                iinput=iinput,
-                client=gr_client,
-                sanitize_bot_response=sanitize_bot_response,
-                tokenizer=tokenizer,
-                system_prompt=system_prompt,
-                visible_models=visible_models,
-                h2ogpt_key=h2ogpt_key,
-                min_max_new_tokens=min_max_new_tokens,
-            )
-        elif hf_client:
-            # no need to pass original client, no state and fast, so can use same validate_environment from base class
-            async_sem = asyncio.Semaphore(num_async) if async_output else NullContext()
-            llm = H2OHuggingFaceTextGenInference(
-                inference_server_url=inference_server,
-                do_sample=do_sample,
-                max_new_tokens=max_new_tokens,
-                repetition_penalty=repetition_penalty,
-                return_full_text=False,  # this only controls internal behavior, still returns processed text
-                seed=SEED,
-
-                stop_sequences=prompter.stop_sequences,
-                temperature=temperature,
-                top_k=top_k,
-                top_p=top_p,
-                # typical_p=top_p,
-                callbacks=callbacks if stream_output else None,
-                stream_output=stream_output,
-                prompter=prompter,
-                context=context,
-                iinput=iinput,
-                tokenizer=tokenizer,
-                timeout=max_time,
-                sanitize_bot_response=sanitize_bot_response,
-                async_sem=async_sem,
-            )
-        else:
-            raise RuntimeError("No defined client")
-        streamer = callbacks[0] if stream_output else None
-    elif model_name in non_hf_types:
-        async_output = False  # FIXME: not implemented yet
-        assert langchain_only_model
-        if model_name == 'llama':
-            callbacks = [StreamingGradioCallbackHandler()]
-            streamer = callbacks[0] if stream_output else None
-        else:
-            # stream_output = False
-            # doesn't stream properly as generator, but at least
-            callbacks = [streaming_stdout.StreamingStdOutCallbackHandler()]
-            streamer = None
-        if prompter:
-            prompt_type = prompter.prompt_type
-        else:
-            prompter = Prompter(prompt_type, prompt_dict, debug=False, chat=False, stream_output=stream_output)
-            pass  # assume inputted prompt_type is correct
-        from gpt4all_llm import get_llm_gpt4all
-        max_max_tokens = tokenizer.model_max_length
-        llm = get_llm_gpt4all(model_name,
-                              model=model,
-                              max_new_tokens=max_new_tokens,
-                              temperature=temperature,
-                              repetition_penalty=repetition_penalty,
-                              top_k=top_k,
-                              top_p=top_p,
-                              callbacks=callbacks,
-                              n_jobs=n_jobs,
-                              verbose=verbose,
-                              streaming=stream_output,
-                              prompter=prompter,
-                              context=context,
-                              iinput=iinput,
-                              max_seq_len=max_max_tokens,
-                              llamacpp_dict=llamacpp_dict,
-                              )
-    elif hasattr(model, 'is_exlama') and model.is_exlama():
-        async_output = False  # FIXME: not implemented yet
-        assert langchain_only_model
-        callbacks = [StreamingGradioCallbackHandler()]
-        streamer = callbacks[0] if stream_output else None
-        max_max_tokens = tokenizer.model_max_length
-
-        from src.llm_exllama import Exllama
-        llm = Exllama(streaming=stream_output,
-                      model_path=None,
-                      model=model,
-                      lora_path=None,
-                      temperature=temperature,
-                      top_k=top_k,
-                      top_p=top_p,
-                      typical=.7,
-                      beams=1,
-                      # beam_length = 40,
-                      stop_sequences=prompter.stop_sequences,
-                      callbacks=callbacks,
-                      verbose=verbose,
-                      max_seq_len=max_max_tokens,
-                      fused_attn=False,
-                      # alpha_value = 1.0, #For use with any models
-                      # compress_pos_emb = 4.0, #For use with superhot
-                      # set_auto_map = "3, 2" #Gpu split, this will split 3gigs/2gigs
-                      prompter=prompter,
-                      context=context,
-                      iinput=iinput,
-                      )
-    else:
-        async_output = False  # FIXME: not implemented yet
-        if model is None:
-            # only used if didn't pass model in
-            assert tokenizer is None
-            prompt_type = 'human_bot'
-            if model_name is None:
-                model_name = 'h2oai/h2ogpt-oasst1-512-12b'
-                # model_name = 'h2oai/h2ogpt-oig-oasst1-512-6_9b'
-                # model_name = 'h2oai/h2ogpt-oasst1-512-20b'
-            inference_server = ''
-            model, tokenizer, device = get_model(load_8bit=True, base_model=model_name,
-                                                 inference_server=inference_server, gpu_id=0)
-
-        max_max_tokens = tokenizer.model_max_length
-        only_new_text = True
-        gen_kwargs = dict(do_sample=do_sample,
-                          num_beams=num_beams,
-                          max_new_tokens=max_new_tokens,
-                          min_new_tokens=min_new_tokens,
-                          early_stopping=early_stopping,
-                          max_time=max_time,
-                          repetition_penalty=repetition_penalty,
-                          num_return_sequences=num_return_sequences,
-                          return_full_text=not only_new_text,
-                          handle_long_generation=None)
-        if do_sample:
-            gen_kwargs.update(dict(temperature=temperature,
-                                   top_k=top_k,
-                                   top_p=top_p))
-            assert len(set(gen_hyper).difference(gen_kwargs.keys())) == 0
-        else:
-            assert len(set(gen_hyper0).difference(gen_kwargs.keys())) == 0
-
-        if stream_output:
-            skip_prompt = only_new_text
-            from gen import H2OTextIteratorStreamer
-            decoder_kwargs = {}
-            streamer = H2OTextIteratorStreamer(tokenizer, skip_prompt=skip_prompt, block=False, **decoder_kwargs)
-            gen_kwargs.update(dict(streamer=streamer))
-        else:
-            streamer = None
-
-        from h2oai_pipeline import H2OTextGenerationPipeline
-        pipe = H2OTextGenerationPipeline(model=model, use_prompter=True,
-                                         prompter=prompter,
-                                         context=context,
-                                         iinput=iinput,
-                                         prompt_type=prompt_type,
-                                         prompt_dict=prompt_dict,
-                                         sanitize_bot_response=sanitize_bot_response,
-                                         chat=False, stream_output=stream_output,
-                                         tokenizer=tokenizer,
-                                         # leave some room for 1 paragraph, even if min_new_tokens=0
-                                         max_input_tokens=max_max_tokens - max(min_new_tokens, 256),
-                                         base_model=model_name,
-                                         **gen_kwargs)
-        # pipe.task = "text-generation"
-        # below makes it listen only to our prompt removal,
-        # not built in prompt removal that is less general and not specific for our model
-        pipe.task = "text2text-generation"
-
-        llm = H2OHuggingFacePipeline(pipeline=pipe)
-    return llm, model_name, streamer, prompt_type, async_output, only_new_text
-
-
-def get_device_dtype():
-    # torch.device("cuda") leads to cuda:x cuda:y mismatches for multi-GPU consistently
-    import torch
-    n_gpus = torch.cuda.device_count() if torch.cuda.is_available else 0
-    device = 'cpu' if n_gpus == 0 else 'cuda'
-    # from utils import NullContext
-    # context_class = NullContext if n_gpus > 1 or n_gpus == 0 else context_class
-    context_class = torch.device
-    torch_dtype = torch.float16 if device == 'cuda' else torch.float32
-    return device, torch_dtype, context_class
-
-
-def get_wiki_data(title, first_paragraph_only, text_limit=None, take_head=True):
-    """
-    Get wikipedia data from online
-    :param title:
-    :param first_paragraph_only:
-    :param text_limit:
-    :param take_head:
-    :return:
-    """
-    filename = 'wiki_%s_%s_%s_%s.data' % (first_paragraph_only, title, text_limit, take_head)
-    url = f"https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&explaintext=1&titles={title}"
-    if first_paragraph_only:
-        url += "&exintro=1"
-    import json
-    if not os.path.isfile(filename):
-        data = requests.get(url).json()
-        json.dump(data, open(filename, 'wt'))
-    else:
-        data = json.load(open(filename, "rt"))
-    page_content = list(data["query"]["pages"].values())[0]["extract"]
-    if take_head is not None and text_limit is not None:
-        page_content = page_content[:text_limit] if take_head else page_content[-text_limit:]
-    title_url = str(title).replace(' ', '_')
-    return Document(
-        page_content=str(page_content),
-        metadata={"source": f"https://en.wikipedia.org/wiki/{title_url}"},
-    )
-
-
-def get_wiki_sources(first_para=True, text_limit=None):
-    """
-    Get specific named sources from wikipedia
-    :param first_para:
-    :param text_limit:
-    :return:
-    """
-    default_wiki_sources = ['Unix', 'Microsoft_Windows', 'Linux']
-    wiki_sources = list(os.getenv('WIKI_SOURCES', default_wiki_sources))
-    return [get_wiki_data(x, first_para, text_limit=text_limit) for x in wiki_sources]
-
-
-def get_github_docs(repo_owner, repo_name):
-    """
-    Access github from specific repo
-    :param repo_owner:
-    :param repo_name:
-    :return:
-    """
-    with tempfile.TemporaryDirectory() as d:
-        subprocess.check_call(
-            f"git clone --depth 1 https://github.com/{repo_owner}/{repo_name}.git .",
-            cwd=d,
-            shell=True,
-        )
-        git_sha = (
-            subprocess.check_output("git rev-parse HEAD", shell=True, cwd=d)
-            .decode("utf-8")
-            .strip()
-        )
-        repo_path = pathlib.Path(d)
-        markdown_files = list(repo_path.glob("*/*.md")) + list(
-            repo_path.glob("*/*.mdx")
-        )
-        for markdown_file in markdown_files:
-            with open(markdown_file, "r") as f:
-                relative_path = markdown_file.relative_to(repo_path)
-                github_url = f"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}"
-                yield Document(page_content=str(f.read()), metadata={"source": github_url})
-
-
-def get_dai_pickle(dest="."):
-    from huggingface_hub import hf_hub_download
-    # True for case when locally already logged in with correct token, so don't have to set key
-    token = os.getenv('HUGGING_FACE_HUB_TOKEN', True)
-    path_to_zip_file = hf_hub_download('h2oai/dai_docs', 'dai_docs.pickle', token=token, repo_type='dataset')
-    shutil.copy(path_to_zip_file, dest)
-
-
-def get_dai_docs(from_hf=False, get_pickle=True):
-    """
-    Consume DAI documentation, or consume from public pickle
-    :param from_hf: get DAI docs from HF, then generate pickle for later use by LangChain
-    :param get_pickle: Avoid raw DAI docs, just get pickle directly from HF
-    :return:
-    """
-    import pickle
-
-    if get_pickle:
-        get_dai_pickle()
-
-    dai_store = 'dai_docs.pickle'
-    dst = "working_dir_docs"
-    if not os.path.isfile(dai_store):
-        from create_data import setup_dai_docs
-        dst = setup_dai_docs(dst=dst, from_hf=from_hf)
-
-        import glob
-        files = list(glob.glob(os.path.join(dst, '*rst'), recursive=True))
-
-        basedir = os.path.abspath(os.getcwd())
-        from create_data import rst_to_outputs
-        new_outputs = rst_to_outputs(files)
-        os.chdir(basedir)
-
-        pickle.dump(new_outputs, open(dai_store, 'wb'))
-    else:
-        new_outputs = pickle.load(open(dai_store, 'rb'))
-
-    sources = []
-    for line, file in new_outputs:
-        # gradio requires any linked file to be with app.py
-        sym_src = os.path.abspath(os.path.join(dst, file))
-        sym_dst = os.path.abspath(os.path.join(os.getcwd(), file))
-        if os.path.lexists(sym_dst):
-            os.remove(sym_dst)
-        os.symlink(sym_src, sym_dst)
-        itm = Document(page_content=str(line), metadata={"source": file})
-        # NOTE: yield has issues when going into db, loses metadata
-        # yield itm
-        sources.append(itm)
-    return sources
-
-
-def get_supported_types():
-    non_image_types0 = ["pdf", "txt", "csv", "toml", "py", "rst", "xml", "rtf",
-                        "md",
-                        "html", "mhtml", "htm",
-                        "enex", "eml", "epub", "odt", "pptx", "ppt",
-                        "zip",
-                        "gz",
-                        "gzip",
-                        "urls",
-                        ]
-    # "msg",  GPL3
-
-    video_types0 = ['WEBM',
-                    'MPG', 'MP2', 'MPEG', 'MPE', '.PV',
-                    'OGG',
-                    'MP4', 'M4P', 'M4V',
-                    'AVI', 'WMV',
-                    'MOV', 'QT',
-                    'FLV', 'SWF',
-                    'AVCHD']
-    video_types0 = [x.lower() for x in video_types0]
-    if have_pillow:
-        from PIL import Image
-        exts = Image.registered_extensions()
-        image_types0 = {ex for ex, f in exts.items() if f in Image.OPEN if ex not in video_types0 + non_image_types0}
-        image_types0 = sorted(image_types0)
-        image_types0 = [x[1:] if x.startswith('.') else x for x in image_types0]
-    else:
-        image_types0 = []
-    return non_image_types0, image_types0, video_types0
-
-
-non_image_types, image_types, video_types = get_supported_types()
-set_image_types = set(image_types)
-
-if have_libreoffice or True:
-    # or True so it tries to load, e.g. on MAC/Windows, even if don't have libreoffice since works without that
-    non_image_types.extend(["docx", "doc", "xls", "xlsx"])
-if have_jq:
-    non_image_types.extend(["json", "jsonl"])
-
-file_types = non_image_types + image_types
-
-
-def try_as_html(file):
-    # try treating as html as occurs when scraping websites
-    from bs4 import BeautifulSoup
-    with open(file, "rt") as f:
-        try:
-            is_html = bool(BeautifulSoup(f.read(), "html.parser").find())
-        except:  # FIXME
-            is_html = False
-    if is_html:
-        file_url = 'file://' + file
-        doc1 = UnstructuredURLLoader(urls=[file_url]).load()
-        doc1 = [x for x in doc1 if x.page_content]
-    else:
-        doc1 = []
-    return doc1
-
-
-def json_metadata_func(record: dict, metadata: dict) -> dict:
-    # Define the metadata extraction function.
-
-    if isinstance(record, dict):
-        metadata["sender_name"] = record.get("sender_name")
-        metadata["timestamp_ms"] = record.get("timestamp_ms")
-
-    if "source" in metadata:
-        metadata["source_json"] = metadata['source']
-    if "seq_num" in metadata:
-        metadata["seq_num_json"] = metadata['seq_num']
-
-    return metadata
-
-
-def file_to_doc(file,
-                filei=0,
-                base_path=None, verbose=False, fail_any_exception=False,
-                chunk=True, chunk_size=512, n_jobs=-1,
-                is_url=False, is_txt=False,
-
-                # urls
-                use_unstructured=True,
-                use_playwright=False,
-                use_selenium=False,
-
-                # pdfs
-                use_pymupdf='auto',
-                use_unstructured_pdf='auto',
-                use_pypdf='auto',
-                enable_pdf_ocr='auto',
-                try_pdf_as_html='auto',
-                enable_pdf_doctr='auto',
-
-                # images
-                enable_ocr=False,
-                enable_doctr=False,
-                enable_pix2struct=False,
-                enable_captions=True,
-                captions_model=None,
-                model_loaders=None,
-
-                # json
-                jq_schema='.[]',
-
-                headsize=50,  # see also H2OSerpAPIWrapper
-                db_type=None,
-                selected_file_types=None):
-    assert isinstance(model_loaders, dict)
-    if selected_file_types is not None:
-        set_image_types1 = set_image_types.intersection(set(selected_file_types))
-    else:
-        set_image_types1 = set_image_types
-
-    assert db_type is not None
-    chunk_sources = functools.partial(_chunk_sources, chunk=chunk, chunk_size=chunk_size, db_type=db_type)
-    add_meta = functools.partial(_add_meta, headsize=headsize, filei=filei)
-    # FIXME: if zip, file index order will not be correct if other files involved
-    path_to_docs_func = functools.partial(path_to_docs,
-                                          verbose=verbose,
-                                          fail_any_exception=fail_any_exception,
-                                          n_jobs=n_jobs,
-                                          chunk=chunk, chunk_size=chunk_size,
-                                          # url=file if is_url else None,
-                                          # text=file if is_txt else None,
-
-                                          # urls
-                                          use_unstructured=use_unstructured,
-                                          use_playwright=use_playwright,
-                                          use_selenium=use_selenium,
-
-                                          # pdfs
-                                          use_pymupdf=use_pymupdf,
-                                          use_unstructured_pdf=use_unstructured_pdf,
-                                          use_pypdf=use_pypdf,
-                                          enable_pdf_ocr=enable_pdf_ocr,
-                                          enable_pdf_doctr=enable_pdf_doctr,
-                                          try_pdf_as_html=try_pdf_as_html,
-
-                                          # images
-                                          enable_ocr=enable_ocr,
-                                          enable_doctr=enable_doctr,
-                                          enable_pix2struct=enable_pix2struct,
-                                          enable_captions=enable_captions,
-                                          captions_model=captions_model,
-
-                                          caption_loader=model_loaders['caption'],
-                                          doctr_loader=model_loaders['doctr'],
-                                          pix2struct_loader=model_loaders['pix2struct'],
-
-                                          # json
-                                          jq_schema=jq_schema,
-
-                                          db_type=db_type,
-                                          )
-
-    if file is None:
-        if fail_any_exception:
-            raise RuntimeError("Unexpected None file")
-        else:
-            return []
-    doc1 = []  # in case no support, or disabled support
-    if base_path is None and not is_txt and not is_url:
-        # then assume want to persist but don't care which path used
-        # can't be in base_path
-        dir_name = os.path.dirname(file)
-        base_name = os.path.basename(file)
-        # if from gradio, will have its own temp uuid too, but that's ok
-        base_name = sanitize_filename(base_name) + "_" + str(uuid.uuid4())[:10]
-        base_path = os.path.join(dir_name, base_name)
-    if is_url:
-        file = file.strip()  # in case accidental spaces in front or at end
-        file_lower = file.lower()
-        case1 = file_lower.startswith('arxiv:') and len(file_lower.split('arxiv:')) == 2
-        case2 = file_lower.startswith('https://arxiv.org/abs') and len(file_lower.split('https://arxiv.org/abs')) == 2
-        case3 = file_lower.startswith('http://arxiv.org/abs') and len(file_lower.split('http://arxiv.org/abs')) == 2
-        case4 = file_lower.startswith('arxiv.org/abs/') and len(file_lower.split('arxiv.org/abs/')) == 2
-        if case1 or case2 or case3 or case4:
-            if case1:
-                query = file.lower().split('arxiv:')[1].strip()
-            elif case2:
-                query = file.lower().split('https://arxiv.org/abs/')[1].strip()
-            elif case2:
-                query = file.lower().split('http://arxiv.org/abs/')[1].strip()
-            elif case3:
-                query = file.lower().split('arxiv.org/abs/')[1].strip()
-            else:
-                raise RuntimeError("Unexpected arxiv error for %s" % file)
-            if have_arxiv:
-                trials = 3
-                docs1 = []
-                for trial in range(trials):
-                    try:
-                        docs1 = ArxivLoader(query=query, load_max_docs=20, load_all_available_meta=True).load()
-                        break
-                    except urllib.error.URLError:
-                        pass
-                if not docs1:
-                    print("Failed to get arxiv %s" % query, flush=True)
-                # ensure string, sometimes None
-                [[x.metadata.update({k: str(v)}) for k, v in x.metadata.items()] for x in docs1]
-                query_url = f"https://arxiv.org/abs/{query}"
-                [x.metadata.update(
-                    dict(source=x.metadata.get('entry_id', query_url), query=query_url,
-                         input_type='arxiv', head=x.metadata.get('Title', ''), date=str(datetime.now))) for x in
-                    docs1]
-            else:
-                docs1 = []
-        else:
-            if not (file.startswith("http://") or file.startswith("file://") or file.startswith("https://")):
-                file = 'http://' + file
-            docs1 = []
-            do_unstructured = only_unstructured_urls or use_unstructured
-            if only_selenium or only_playwright:
-                do_unstructured = False
-            do_playwright = have_playwright and (use_playwright or only_playwright)
-            if only_unstructured_urls or only_selenium:
-                do_playwright = False
-            do_selenium = have_selenium and (use_selenium or only_selenium)
-            if only_unstructured_urls or only_playwright:
-                do_selenium = False
-            if do_unstructured or use_unstructured:
-                docs1a = UnstructuredURLLoader(urls=[file]).load()
-                docs1a = [x for x in docs1a if x.page_content]
-                add_parser(docs1a, 'UnstructuredURLLoader')
-                docs1.extend(docs1a)
-            if len(docs1) == 0 and have_playwright or do_playwright:
-                # then something went wrong, try another loader:
-                from langchain.document_loaders import PlaywrightURLLoader
-                docs1a = asyncio.run(PlaywrightURLLoader(urls=[file]).aload())
-                # docs1 = PlaywrightURLLoader(urls=[file]).load()
-                docs1a = [x for x in docs1a if x.page_content]
-                add_parser(docs1a, 'PlaywrightURLLoader')
-                docs1.extend(docs1a)
-            if len(docs1) == 0 and have_selenium or do_selenium:
-                # then something went wrong, try another loader:
-                # but requires Chrome binary, else get: selenium.common.exceptions.WebDriverException:
-                # Message: unknown error: cannot find Chrome binary
-                from langchain.document_loaders import SeleniumURLLoader
-                from selenium.common.exceptions import WebDriverException
-                try:
-                    docs1a = SeleniumURLLoader(urls=[file]).load()
-                    docs1a = [x for x in docs1a if x.page_content]
-                    add_parser(docs1a, 'SeleniumURLLoader')
-                    docs1.extend(docs1a)
-                except WebDriverException as e:
-                    print("No web driver: %s" % str(e), flush=True)
-            [x.metadata.update(dict(input_type='url', date=str(datetime.now))) for x in docs1]
-        add_meta(docs1, file, parser="is_url")
-        docs1 = clean_doc(docs1)
-        doc1 = chunk_sources(docs1)
-    elif is_txt:
-        base_path = "user_paste"
-        base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
-        source_file = os.path.join(base_path, "_%s" % str(uuid.uuid4())[:10])
-        with open(source_file, "wt") as f:
-            f.write(file)
-        metadata = dict(source=source_file, date=str(datetime.now()), input_type='pasted txt')
-        doc1 = Document(page_content=str(file), metadata=metadata)
-        add_meta(doc1, file, parser="f.write")
-        # Bit odd to change if was original text
-        # doc1 = clean_doc(doc1)
-    elif file.lower().endswith('.html') or file.lower().endswith('.mhtml') or file.lower().endswith('.htm'):
-        docs1 = UnstructuredHTMLLoader(file_path=file).load()
-        add_meta(docs1, file, parser='UnstructuredHTMLLoader')
-        docs1 = clean_doc(docs1)
-        doc1 = chunk_sources(docs1, language=Language.HTML)
-    elif (file.lower().endswith('.docx') or file.lower().endswith('.doc')) and (have_libreoffice or True):
-        docs1 = UnstructuredWordDocumentLoader(file_path=file).load()
-        add_meta(docs1, file, parser='UnstructuredWordDocumentLoader')
-        doc1 = chunk_sources(docs1)
-    elif (file.lower().endswith('.xlsx') or file.lower().endswith('.xls')) and (have_libreoffice or True):
-        docs1 = UnstructuredExcelLoader(file_path=file).load()
-        add_meta(docs1, file, parser='UnstructuredExcelLoader')
-        doc1 = chunk_sources(docs1)
-    elif file.lower().endswith('.odt'):
-        docs1 = UnstructuredODTLoader(file_path=file).load()
-        add_meta(docs1, file, parser='UnstructuredODTLoader')
-        doc1 = chunk_sources(docs1)
-    elif file.lower().endswith('pptx') or file.lower().endswith('ppt'):
-        docs1 = UnstructuredPowerPointLoader(file_path=file).load()
-        add_meta(docs1, file, parser='UnstructuredPowerPointLoader')
-        docs1 = clean_doc(docs1)
-        doc1 = chunk_sources(docs1)
-    elif file.lower().endswith('.txt'):
-        # use UnstructuredFileLoader ?
-        docs1 = TextLoader(file, encoding="utf8", autodetect_encoding=True).load()
-        # makes just one, but big one
-        doc1 = chunk_sources(docs1)
-        # Bit odd to change if was original text
-        # doc1 = clean_doc(doc1)
-        add_meta(doc1, file, parser='TextLoader')
-    elif file.lower().endswith('.rtf'):
-        docs1 = UnstructuredRTFLoader(file).load()
-        add_meta(docs1, file, parser='UnstructuredRTFLoader')
-        doc1 = chunk_sources(docs1)
-    elif file.lower().endswith('.md'):
-        docs1 = UnstructuredMarkdownLoader(file).load()
-        add_meta(docs1, file, parser='UnstructuredMarkdownLoader')
-        docs1 = clean_doc(docs1)
-        doc1 = chunk_sources(docs1, language=Language.MARKDOWN)
-    elif file.lower().endswith('.enex'):
-        docs1 = EverNoteLoader(file).load()
-        add_meta(doc1, file, parser='EverNoteLoader')
-        doc1 = chunk_sources(docs1)
-    elif file.lower().endswith('.epub'):
-        docs1 = UnstructuredEPubLoader(file).load()
-        add_meta(docs1, file, parser='UnstructuredEPubLoader')
-        doc1 = chunk_sources(docs1)
-    elif any(file.lower().endswith(x) for x in set_image_types1):
-        docs1 = []
-        if verbose:
-            print("BEGIN: Tesseract", flush=True)
-        if have_tesseract and enable_ocr:
-            # OCR, somewhat works, but not great
-            docs1a = UnstructuredImageLoader(file, strategy='ocr_only').load()
-            # docs1a = UnstructuredImageLoader(file, strategy='hi_res').load()
-            docs1a = [x for x in docs1a if x.page_content]
-            add_meta(docs1a, file, parser='UnstructuredImageLoader')
-            docs1.extend(docs1a)
-        if verbose:
-            print("END: Tesseract", flush=True)
-        if have_doctr and enable_doctr:
-            if verbose:
-                print("BEGIN: DocTR", flush=True)
-            if model_loaders['doctr'] is not None and not isinstance(model_loaders['doctr'], (str, bool)):
-                if verbose:
-                    print("Reuse DocTR", flush=True)
-                model_loaders['doctr'].load_model()
-            else:
-                if verbose:
-                    print("Fresh DocTR", flush=True)
-                from image_doctr import H2OOCRLoader
-                model_loaders['doctr'] = H2OOCRLoader()
-            model_loaders['doctr'].set_document_paths([file])
-            docs1c = model_loaders['doctr'].load()
-            docs1c = [x for x in docs1c if x.page_content]
-            add_meta(docs1c, file, parser='H2OOCRLoader: %s' % 'DocTR')
-            # caption didn't set source, so fix-up meta
-            for doci in docs1c:
-                doci.metadata['source'] = doci.metadata.get('document_path', file)
-                doci.metadata['hashid'] = hash_file(doci.metadata['source'])
-            docs1.extend(docs1c)
-            if verbose:
-                print("END: DocTR", flush=True)
-        if enable_captions:
-            # BLIP
-            if verbose:
-                print("BEGIN: BLIP", flush=True)
-            if model_loaders['caption'] is not None and not isinstance(model_loaders['caption'], (str, bool)):
-                # assumes didn't fork into this process with joblib, else can deadlock
-                if verbose:
-                    print("Reuse BLIP", flush=True)
-                model_loaders['caption'].load_model()
-            else:
-                if verbose:
-                    print("Fresh BLIP", flush=True)
-                from image_captions import H2OImageCaptionLoader
-                model_loaders['caption'] = H2OImageCaptionLoader(caption_gpu=model_loaders['caption'] == 'gpu',
-                                                                 blip_model=captions_model,
-                                                                 blip_processor=captions_model)
-            model_loaders['caption'].set_image_paths([file])
-            docs1c = model_loaders['caption'].load()
-            docs1c = [x for x in docs1c if x.page_content]
-            add_meta(docs1c, file, parser='H2OImageCaptionLoader: %s' % captions_model)
-            # caption didn't set source, so fix-up meta
-            for doci in docs1c:
-                doci.metadata['source'] = doci.metadata.get('image_path', file)
-                doci.metadata['hashid'] = hash_file(doci.metadata['source'])
-            docs1.extend(docs1c)
-
-            if verbose:
-                print("END: BLIP", flush=True)
-        if enable_pix2struct:
-            # BLIP
-            if verbose:
-                print("BEGIN: Pix2Struct", flush=True)
-            if model_loaders['pix2struct'] is not None and not isinstance(model_loaders['pix2struct'], (str, bool)):
-                if verbose:
-                    print("Reuse pix2struct", flush=True)
-                model_loaders['pix2struct'].load_model()
-            else:
-                if verbose:
-                    print("Fresh pix2struct", flush=True)
-                from image_pix2struct import H2OPix2StructLoader
-                model_loaders['pix2struct'] = H2OPix2StructLoader()
-            model_loaders['pix2struct'].set_image_paths([file])
-            docs1c = model_loaders['pix2struct'].load()
-            docs1c = [x for x in docs1c if x.page_content]
-            add_meta(docs1c, file, parser='H2OPix2StructLoader: %s' % model_loaders['pix2struct'])
-            # caption didn't set source, so fix-up meta
-            for doci in docs1c:
-                doci.metadata['source'] = doci.metadata.get('image_path', file)
-                doci.metadata['hashid'] = hash_file(doci.metadata['source'])
-            docs1.extend(docs1c)
-            if verbose:
-                print("END: Pix2Struct", flush=True)
-        doc1 = chunk_sources(docs1)
-    elif file.lower().endswith('.msg'):
-        raise RuntimeError("Not supported, GPL3 license")
-        # docs1 = OutlookMessageLoader(file).load()
-        # docs1[0].metadata['source'] = file
-    elif file.lower().endswith('.eml'):
-        try:
-            docs1 = UnstructuredEmailLoader(file).load()
-            add_meta(docs1, file, parser='UnstructuredEmailLoader')
-            doc1 = chunk_sources(docs1)
-        except ValueError as e:
-            if 'text/html content not found in email' in str(e):
-                pass
-            else:
-                raise
-        doc1 = [x for x in doc1 if x.page_content]
-        if len(doc1) == 0:
-            # e.g. plain/text dict key exists, but not
-            # doc1 = TextLoader(file, encoding="utf8").load()
-            docs1 = UnstructuredEmailLoader(file, content_source="text/plain").load()
-            docs1 = [x for x in docs1 if x.page_content]
-            add_meta(docs1, file, parser='UnstructuredEmailLoader text/plain')
-            doc1 = chunk_sources(docs1)
-    # elif file.lower().endswith('.gcsdir'):
-    #    doc1 = GCSDirectoryLoader(project_name, bucket, prefix).load()
-    # elif file.lower().endswith('.gcsfile'):
-    # doc1 = GCSFileLoader(project_name, bucket, blob).load()
-    elif file.lower().endswith('.rst'):
-        with open(file, "r") as f:
-            doc1 = Document(page_content=str(f.read()), metadata={"source": file})
-        add_meta(doc1, file, parser='f.read()')
-        doc1 = chunk_sources(doc1, language=Language.RST)
-    elif file.lower().endswith('.json'):
-        # 10k rows, 100 columns-like parts 4 bytes each
-        JSON_SIZE_LIMIT = int(os.getenv('JSON_SIZE_LIMIT', str(10 * 10 * 1024 * 10 * 4)))
-        if os.path.getsize(file) > JSON_SIZE_LIMIT:
-            raise ValueError(
-                "JSON file sizes > %s not supported for naive parsing and embedding, requires Agents enabled" % JSON_SIZE_LIMIT)
-        loader = JSONLoader(
-            file_path=file,
-            # jq_schema='.messages[].content',
-            jq_schema=jq_schema,
-            text_content=False,
-            metadata_func=json_metadata_func)
-        doc1 = loader.load()
-        add_meta(doc1, file, parser='JSONLoader: %s' % jq_schema)
-        fix_json_meta(doc1)
-    elif file.lower().endswith('.jsonl'):
-        loader = JSONLoader(
-            file_path=file,
-            # jq_schema='.messages[].content',
-            jq_schema=jq_schema,
-            json_lines=True,
-            text_content=False,
-            metadata_func=json_metadata_func)
-        doc1 = loader.load()
-        add_meta(doc1, file, parser='JSONLoader: %s' % jq_schema)
-        fix_json_meta(doc1)
-    elif file.lower().endswith('.pdf'):
-        # migration
-        if isinstance(use_pymupdf, bool):
-            if use_pymupdf == False:
-                use_pymupdf = 'off'
-            if use_pymupdf == True:
-                use_pymupdf = 'on'
-        if isinstance(use_unstructured_pdf, bool):
-            if use_unstructured_pdf == False:
-                use_unstructured_pdf = 'off'
-            if use_unstructured_pdf == True:
-                use_unstructured_pdf = 'on'
-        if isinstance(use_pypdf, bool):
-            if use_pypdf == False:
-                use_pypdf = 'off'
-            if use_pypdf == True:
-                use_pypdf = 'on'
-        if isinstance(enable_pdf_ocr, bool):
-            if enable_pdf_ocr == False:
-                enable_pdf_ocr = 'off'
-            if enable_pdf_ocr == True:
-                enable_pdf_ocr = 'on'
-        if isinstance(try_pdf_as_html, bool):
-            if try_pdf_as_html == False:
-                try_pdf_as_html = 'off'
-            if try_pdf_as_html == True:
-                try_pdf_as_html = 'on'
-
-        doc1 = []
-        tried_others = False
-        handled = False
-        did_pymupdf = False
-        did_unstructured = False
-        e = None
-        if have_pymupdf and (len(doc1) == 0 and use_pymupdf == 'auto' or use_pymupdf == 'on'):
-            # GPL, only use if installed
-            from langchain.document_loaders import PyMuPDFLoader
-            # load() still chunks by pages, but every page has title at start to help
-            try:
-                doc1a = PyMuPDFLoader(file).load()
-                did_pymupdf = True
-            except BaseException as e0:
-                doc1a = []
-                print("PyMuPDFLoader: %s" % str(e0), flush=True)
-                e = e0
-            # remove empty documents
-            handled |= len(doc1a) > 0
-            doc1a = [x for x in doc1a if x.page_content]
-            doc1a = clean_doc(doc1a)
-            add_parser(doc1a, 'PyMuPDFLoader')
-            doc1.extend(doc1a)
-        if len(doc1) == 0 and use_unstructured_pdf == 'auto' or use_unstructured_pdf == 'on':
-            tried_others = True
-            try:
-                doc1a = UnstructuredPDFLoader(file).load()
-                did_unstructured = True
-            except BaseException as e0:
-                doc1a = []
-                print("UnstructuredPDFLoader: %s" % str(e0), flush=True)
-                e = e0
-            handled |= len(doc1a) > 0
-            # remove empty documents
-            doc1a = [x for x in doc1a if x.page_content]
-            add_parser(doc1a, 'UnstructuredPDFLoader')
-            # seems to not need cleaning in most cases
-            doc1.extend(doc1a)
-        if len(doc1) == 0 and use_pypdf == 'auto' or use_pypdf == 'on':
-            tried_others = True
-            # open-source fallback
-            # load() still chunks by pages, but every page has title at start to help
-            try:
-                doc1a = PyPDFLoader(file).load()
-            except BaseException as e0:
-                doc1a = []
-                print("PyPDFLoader: %s" % str(e0), flush=True)
-                e = e0
-            handled |= len(doc1a) > 0
-            # remove empty documents
-            doc1a = [x for x in doc1a if x.page_content]
-            doc1a = clean_doc(doc1a)
-            add_parser(doc1a, 'PyPDFLoader')
-            doc1.extend(doc1a)
-        if not did_pymupdf and ((have_pymupdf and len(doc1) == 0) and tried_others):
-            # try again in case only others used, but only if didn't already try (2nd part of and)
-            # GPL, only use if installed
-            from langchain.document_loaders import PyMuPDFLoader
-            # load() still chunks by pages, but every page has title at start to help
-            try:
-                doc1a = PyMuPDFLoader(file).load()
-            except BaseException as e0:
-                doc1a = []
-                print("PyMuPDFLoader: %s" % str(e0), flush=True)
-                e = e0
-            handled |= len(doc1a) > 0
-            # remove empty documents
-            doc1a = [x for x in doc1a if x.page_content]
-            doc1a = clean_doc(doc1a)
-            add_parser(doc1a, 'PyMuPDFLoader2')
-            doc1.extend(doc1a)
-        did_pdf_ocr = False
-        if len(doc1) == 0 and (enable_pdf_ocr == 'auto' and enable_pdf_doctr != 'on') or enable_pdf_ocr == 'on':
-            did_pdf_ocr = True
-            # no did_unstructured condition here because here we do OCR, and before we did not
-            # try OCR in end since slowest, but works on pure image pages well
-            doc1a = UnstructuredPDFLoader(file, strategy='ocr_only').load()
-            handled |= len(doc1a) > 0
-            # remove empty documents
-            doc1a = [x for x in doc1a if x.page_content]
-            add_parser(doc1a, 'UnstructuredPDFLoader ocr_only')
-            # seems to not need cleaning in most cases
-            doc1.extend(doc1a)
-        # Some PDFs return nothing or junk from PDFMinerLoader
-        if len(doc1) == 0 and enable_pdf_doctr == 'auto' or enable_pdf_doctr == 'on':
-            if verbose:
-                print("BEGIN: DocTR", flush=True)
-            if model_loaders['doctr'] is not None and not isinstance(model_loaders['doctr'], (str, bool)):
-                model_loaders['doctr'].load_model()
-            else:
-                from image_doctr import H2OOCRLoader
-                model_loaders['doctr'] = H2OOCRLoader()
-            model_loaders['doctr'].set_document_paths([file])
-            doc1a = model_loaders['doctr'].load()
-            doc1a = [x for x in doc1a if x.page_content]
-            add_meta(doc1a, file, parser='H2OOCRLoader: %s' % 'DocTR')
-            handled |= len(doc1a) > 0
-            # caption didn't set source, so fix-up meta
-            for doci in doc1a:
-                doci.metadata['source'] = doci.metadata.get('document_path', file)
-                doci.metadata['hashid'] = hash_file(doci.metadata['source'])
-            doc1.extend(doc1a)
-            if verbose:
-                print("END: DocTR", flush=True)
-        if try_pdf_as_html in ['auto', 'on']:
-            doc1a = try_as_html(file)
-            add_parser(doc1a, 'try_as_html')
-            doc1.extend(doc1a)
-
-        if len(doc1) == 0:
-            # if literally nothing, show failed to parse so user knows, since unlikely nothing in PDF at all.
-            if handled:
-                raise ValueError("%s had no valid text, but meta data was parsed" % file)
-            else:
-                raise ValueError("%s had no valid text and no meta data was parsed: %s" % (file, str(e)))
-        add_meta(doc1, file, parser='pdf')
-        doc1 = chunk_sources(doc1)
-    elif file.lower().endswith('.csv'):
-        CSV_SIZE_LIMIT = int(os.getenv('CSV_SIZE_LIMIT', str(10 * 1024 * 10 * 4)))
-        if os.path.getsize(file) > CSV_SIZE_LIMIT:
-            raise ValueError(
-                "CSV file sizes > %s not supported for naive parsing and embedding, requires Agents enabled" % CSV_SIZE_LIMIT)
-        doc1 = CSVLoader(file).load()
-        add_meta(doc1, file, parser='CSVLoader')
-        if isinstance(doc1, list):
-            # each row is a Document, identify
-            [x.metadata.update(dict(chunk_id=chunk_id)) for chunk_id, x in enumerate(doc1)]
-            if db_type in ['chroma', 'chroma_old']:
-                # then separate summarize list
-                sdoc1 = clone_documents(doc1)
-                [x.metadata.update(dict(chunk_id=-1)) for chunk_id, x in enumerate(sdoc1)]
-                doc1 = sdoc1 + doc1
-    elif file.lower().endswith('.py'):
-        doc1 = PythonLoader(file).load()
-        add_meta(doc1, file, parser='PythonLoader')
-        doc1 = chunk_sources(doc1, language=Language.PYTHON)
-    elif file.lower().endswith('.toml'):
-        doc1 = TomlLoader(file).load()
-        add_meta(doc1, file, parser='TomlLoader')
-        doc1 = chunk_sources(doc1)
-    elif file.lower().endswith('.xml'):
-        from langchain.document_loaders import UnstructuredXMLLoader
-        loader = UnstructuredXMLLoader(file_path=file)
-        doc1 = loader.load()
-        add_meta(doc1, file, parser='UnstructuredXMLLoader')
-    elif file.lower().endswith('.urls'):
-        with open(file, "r") as f:
-            urls = f.readlines()
-            # recurse
-            doc1 = path_to_docs_func(None, url=urls)
-    elif file.lower().endswith('.zip'):
-        with zipfile.ZipFile(file, 'r') as zip_ref:
-            # don't put into temporary path, since want to keep references to docs inside zip
-            # so just extract in path where
-            zip_ref.extractall(base_path)
-            # recurse
-            doc1 = path_to_docs_func(base_path)
-    elif file.lower().endswith('.gz') or file.lower().endswith('.gzip'):
-        if file.lower().endswith('.gz'):
-            de_file = file.lower().replace('.gz', '')
-        else:
-            de_file = file.lower().replace('.gzip', '')
-        with gzip.open(file, 'rb') as f_in:
-            with open(de_file, 'wb') as f_out:
-                shutil.copyfileobj(f_in, f_out)
-        # recurse
-        doc1 = file_to_doc(de_file,
-                           filei=filei,  # single file, same file index as outside caller
-                           base_path=base_path, verbose=verbose, fail_any_exception=fail_any_exception,
-                           chunk=chunk, chunk_size=chunk_size, n_jobs=n_jobs,
-                           is_url=is_url, is_txt=is_txt,
-
-                           # urls
-                           use_unstructured=use_unstructured,
-                           use_playwright=use_playwright,
-                           use_selenium=use_selenium,
-
-                           # pdfs
-                           use_pymupdf=use_pymupdf,
-                           use_unstructured_pdf=use_unstructured_pdf,
-                           use_pypdf=use_pypdf,
-                           enable_pdf_ocr=enable_pdf_ocr,
-                           enable_pdf_doctr=enable_pdf_doctr,
-                           try_pdf_as_html=try_pdf_as_html,
-
-                           # images
-                           enable_ocr=enable_ocr,
-                           enable_doctr=enable_doctr,
-                           enable_pix2struct=enable_pix2struct,
-                           enable_captions=enable_captions,
-                           captions_model=captions_model,
-                           model_loaders=model_loaders,
-
-                           # json
-                           jq_schema=jq_schema,
-
-                           headsize=headsize,
-                           db_type=db_type,
-                           selected_file_types=selected_file_types)
-    else:
-        raise RuntimeError("No file handler for %s" % os.path.basename(file))
-
-    # allow doc1 to be list or not.
-    if not isinstance(doc1, list):
-        # If not list, did not chunk yet, so chunk now
-        docs = chunk_sources([doc1])
-    elif isinstance(doc1, list) and len(doc1) == 1:
-        # if list of length one, don't trust and chunk it, chunk_id's will still be correct if repeat
-        docs = chunk_sources(doc1)
-    else:
-        docs = doc1
-
-    assert isinstance(docs, list)
-    return docs
-
-
-def path_to_doc1(file,
-                 filei=0,
-                 verbose=False, fail_any_exception=False, return_file=True,
-                 chunk=True, chunk_size=512,
-                 n_jobs=-1,
-                 is_url=False, is_txt=False,
-
-                 # urls
-                 use_unstructured=True,
-                 use_playwright=False,
-                 use_selenium=False,
-
-                 # pdfs
-                 use_pymupdf='auto',
-                 use_unstructured_pdf='auto',
-                 use_pypdf='auto',
-                 enable_pdf_ocr='auto',
-                 enable_pdf_doctr='auto',
-                 try_pdf_as_html='auto',
-
-                 # images
-                 enable_ocr=False,
-                 enable_doctr=False,
-                 enable_pix2struct=False,
-                 enable_captions=True,
-                 captions_model=None,
-                 model_loaders=None,
-
-                 # json
-                 jq_schema='.[]',
-
-                 db_type=None,
-                 selected_file_types=None):
-    assert db_type is not None
-    if verbose:
-        if is_url:
-            print("Ingesting URL: %s" % file, flush=True)
-        elif is_txt:
-            print("Ingesting Text: %s" % file, flush=True)
-        else:
-            print("Ingesting file: %s" % file, flush=True)
-    res = None
-    try:
-        # don't pass base_path=path, would infinitely recurse
-        res = file_to_doc(file,
-                          filei=filei,
-                          base_path=None, verbose=verbose, fail_any_exception=fail_any_exception,
-                          chunk=chunk, chunk_size=chunk_size,
-                          n_jobs=n_jobs,
-                          is_url=is_url, is_txt=is_txt,
-
-                          # urls
-                          use_unstructured=use_unstructured,
-                          use_playwright=use_playwright,
-                          use_selenium=use_selenium,
-
-                          # pdfs
-                          use_pymupdf=use_pymupdf,
-                          use_unstructured_pdf=use_unstructured_pdf,
-                          use_pypdf=use_pypdf,
-                          enable_pdf_ocr=enable_pdf_ocr,
-                          enable_pdf_doctr=enable_pdf_doctr,
-                          try_pdf_as_html=try_pdf_as_html,
-
-                          # images
-                          enable_ocr=enable_ocr,
-                          enable_doctr=enable_doctr,
-                          enable_pix2struct=enable_pix2struct,
-                          enable_captions=enable_captions,
-                          captions_model=captions_model,
-                          model_loaders=model_loaders,
-
-                          # json
-                          jq_schema=jq_schema,
-
-                          db_type=db_type,
-                          selected_file_types=selected_file_types)
-    except BaseException as e:
-        print("Failed to ingest %s due to %s" % (file, traceback.format_exc()))
-        if fail_any_exception:
-            raise
-        else:
-            exception_doc = Document(
-                page_content='',
-                metadata={"source": file, "exception": '%s Exception: %s' % (file, str(e)),
-                          "traceback": traceback.format_exc()})
-            res = [exception_doc]
-    if verbose:
-        if is_url:
-            print("DONE Ingesting URL: %s" % file, flush=True)
-        elif is_txt:
-            print("DONE Ingesting Text: %s" % file, flush=True)
-        else:
-            print("DONE Ingesting file: %s" % file, flush=True)
-    if return_file:
-        base_tmp = "temp_path_to_doc1"
-        if not os.path.isdir(base_tmp):
-            base_tmp = makedirs(base_tmp, exist_ok=True, tmp_ok=True, use_base=True)
-        filename = os.path.join(base_tmp, str(uuid.uuid4()) + ".tmp.pickle")
-        with open(filename, 'wb') as f:
-            pickle.dump(res, f)
-        return filename
-    return res
-
-
-def path_to_docs(path_or_paths, verbose=False, fail_any_exception=False, n_jobs=-1,
-                 chunk=True, chunk_size=512,
-                 url=None, text=None,
-
-                 # urls
-                 use_unstructured=True,
-                 use_playwright=False,
-                 use_selenium=False,
-
-                 # pdfs
-                 use_pymupdf='auto',
-                 use_unstructured_pdf='auto',
-                 use_pypdf='auto',
-                 enable_pdf_ocr='auto',
-                 enable_pdf_doctr='auto',
-                 try_pdf_as_html='auto',
-
-                 # images
-                 enable_ocr=False,
-                 enable_doctr=False,
-                 enable_pix2struct=False,
-                 enable_captions=True,
-                 captions_model=None,
-
-                 caption_loader=None,
-                 doctr_loader=None,
-                 pix2struct_loader=None,
-
-                 # json
-                 jq_schema='.[]',
-
-                 existing_files=[],
-                 existing_hash_ids={},
-                 db_type=None,
-                 selected_file_types=None,
-                 ):
-    if verbose:
-        print("BEGIN Consuming path_or_paths=%s url=%s text=%s" % (path_or_paths, url, text), flush=True)
-    if selected_file_types is not None:
-        non_image_types1 = [x for x in non_image_types if x in selected_file_types]
-        image_types1 = [x for x in image_types if x in selected_file_types]
-    else:
-        non_image_types1 = non_image_types.copy()
-        image_types1 = image_types.copy()
-
-    assert db_type is not None
-    # path_or_paths could be str, list, tuple, generator
-    globs_image_types = []
-    globs_non_image_types = []
-    if not path_or_paths and not url and not text:
-        return []
-    elif url:
-        url = get_list_or_str(url)
-        globs_non_image_types = url if isinstance(url, (list, tuple, types.GeneratorType)) else [url]
-    elif text:
-        globs_non_image_types = text if isinstance(text, (list, tuple, types.GeneratorType)) else [text]
-    elif isinstance(path_or_paths, str) and os.path.isdir(path_or_paths):
-        # single path, only consume allowed files
-        path = path_or_paths
-        # Below globs should match patterns in file_to_doc()
-        [globs_image_types.extend(glob.glob(os.path.join(path, "./**/*.%s" % ftype), recursive=True))
-         for ftype in image_types1]
-        globs_image_types = [os.path.normpath(x) for x in globs_image_types]
-        [globs_non_image_types.extend(glob.glob(os.path.join(path, "./**/*.%s" % ftype), recursive=True))
-         for ftype in non_image_types1]
-        globs_non_image_types = [os.path.normpath(x) for x in globs_non_image_types]
-    else:
-        if isinstance(path_or_paths, str):
-            if os.path.isfile(path_or_paths) or os.path.isdir(path_or_paths):
-                path_or_paths = [path_or_paths]
-            else:
-                # path was deleted etc.
-                return []
-        # list/tuple of files (consume what can, and exception those that selected but cannot consume so user knows)
-        assert isinstance(path_or_paths, (list, tuple, types.GeneratorType)), \
-            "Wrong type for path_or_paths: %s %s" % (path_or_paths, type(path_or_paths))
-        # reform out of allowed types
-        globs_image_types.extend(
-            flatten_list([[os.path.normpath(x) for x in path_or_paths if x.endswith(y)] for y in image_types1]))
-        # could do below:
-        # globs_non_image_types = flatten_list([[x for x in path_or_paths if x.endswith(y)] for y in non_image_types1])
-        # But instead, allow fail so can collect unsupported too
-        set_globs_image_types = set(globs_image_types)
-        globs_non_image_types.extend([os.path.normpath(x) for x in path_or_paths if x not in set_globs_image_types])
-
-    # filter out any files to skip (e.g. if already processed them)
-    # this is easy, but too aggressive in case a file changed, so parent probably passed existing_files=[]
-    assert not existing_files, "DEV: assume not using this approach"
-    if existing_files:
-        set_skip_files = set(existing_files)
-        globs_image_types = [x for x in globs_image_types if x not in set_skip_files]
-        globs_non_image_types = [x for x in globs_non_image_types if x not in set_skip_files]
-    if existing_hash_ids:
-        # assume consistent with add_meta() use of hash_file(file)
-        # also assume consistent with get_existing_hash_ids for dict creation
-        # assume hashable values
-        existing_hash_ids_set = set(existing_hash_ids.items())
-        hash_ids_all_image = set({x: hash_file(x) for x in globs_image_types}.items())
-        hash_ids_all_non_image = set({x: hash_file(x) for x in globs_non_image_types}.items())
-        # don't use symmetric diff.  If file is gone, ignore and don't remove or something
-        #  just consider existing files (key) having new hash or not (value)
-        new_files_image = set(dict(hash_ids_all_image - existing_hash_ids_set).keys())
-        new_files_non_image = set(dict(hash_ids_all_non_image - existing_hash_ids_set).keys())
-        globs_image_types = [x for x in globs_image_types if x in new_files_image]
-        globs_non_image_types = [x for x in globs_non_image_types if x in new_files_non_image]
-
-    # could use generator, but messes up metadata handling in recursive case
-    if caption_loader and not isinstance(caption_loader, (bool, str)) and caption_loader.device != 'cpu' or \
-            get_device() == 'cuda':
-        # to avoid deadlocks, presume was preloaded and so can't fork due to cuda context
-        # get_device() == 'cuda' because presume faster to process image from (temporarily) preloaded model
-        n_jobs_image = 1
-    else:
-        n_jobs_image = n_jobs
-    if enable_doctr or enable_pdf_doctr in [True, 'auto', 'on']:
-        if doctr_loader and not isinstance(doctr_loader, (bool, str)) and doctr_loader.device != 'cpu':
-            # can't fork cuda context
-            n_jobs = 1
-
-    return_file = True  # local choice
-    is_url = url is not None
-    is_txt = text is not None
-    model_loaders = dict(caption=caption_loader,
-                         doctr=doctr_loader,
-                         pix2struct=pix2struct_loader)
-    model_loaders0 = model_loaders.copy()
-    kwargs = dict(verbose=verbose, fail_any_exception=fail_any_exception,
-                  return_file=return_file,
-                  chunk=chunk, chunk_size=chunk_size,
-                  n_jobs=n_jobs,
-                  is_url=is_url,
-                  is_txt=is_txt,
-
-                  # urls
-                  use_unstructured=use_unstructured,
-                  use_playwright=use_playwright,
-                  use_selenium=use_selenium,
-
-                  # pdfs
-                  use_pymupdf=use_pymupdf,
-                  use_unstructured_pdf=use_unstructured_pdf,
-                  use_pypdf=use_pypdf,
-                  enable_pdf_ocr=enable_pdf_ocr,
-                  enable_pdf_doctr=enable_pdf_doctr,
-                  try_pdf_as_html=try_pdf_as_html,
-
-                  # images
-                  enable_ocr=enable_ocr,
-                  enable_doctr=enable_doctr,
-                  enable_pix2struct=enable_pix2struct,
-                  enable_captions=enable_captions,
-                  captions_model=captions_model,
-                  model_loaders=model_loaders,
-
-                  # json
-                  jq_schema=jq_schema,
-
-                  db_type=db_type,
-                  selected_file_types=selected_file_types,
-                  )
-    if n_jobs != 1 and len(globs_non_image_types) > 1:
-        # avoid nesting, e.g. upload 1 zip and then inside many files
-        # harder to handle if upload many zips with many files, inner parallel one will be disabled by joblib
-        documents = ProgressParallel(n_jobs=n_jobs, verbose=10 if verbose else 0, backend='multiprocessing')(
-            delayed(path_to_doc1)(file, filei=filei, **kwargs) for filei, file in enumerate(globs_non_image_types)
-        )
-    else:
-        documents = [path_to_doc1(file, filei=filei, **kwargs) for filei, file in
-                     enumerate(tqdm(globs_non_image_types))]
-
-    # do images separately since can't fork after cuda in parent, so can't be parallel
-    if n_jobs_image != 1 and len(globs_image_types) > 1:
-        # avoid nesting, e.g. upload 1 zip and then inside many files
-        # harder to handle if upload many zips with many files, inner parallel one will be disabled by joblib
-        image_documents = ProgressParallel(n_jobs=n_jobs, verbose=10 if verbose else 0, backend='multiprocessing')(
-            delayed(path_to_doc1)(file, filei=filei, **kwargs) for filei, file in enumerate(globs_image_types)
-        )
-    else:
-        image_documents = [path_to_doc1(file, filei=filei, **kwargs) for filei, file in
-                           enumerate(tqdm(globs_image_types))]
-
-    # unload loaders (image loaders, includes enable_pdf_doctr that uses same loader)
-    for name, loader in model_loaders.items():
-        loader0 = model_loaders0[name]
-        real_model_initial = loader0 is not None and not isinstance(loader0, (str, bool))
-        real_model_final = model_loaders[name] is not None and not isinstance(model_loaders[name], (str, bool))
-        if not real_model_initial and real_model_final:
-            # clear off GPU newly added model
-            model_loaders[name].unload_model()
-
-    # add image docs in
-    documents += image_documents
-
-    if return_file:
-        # then documents really are files
-        files = documents.copy()
-        documents = []
-        for fil in files:
-            with open(fil, 'rb') as f:
-                documents.extend(pickle.load(f))
-            # remove temp pickle
-            remove(fil)
-    else:
-        documents = reduce(concat, documents)
-
-    if verbose:
-        print("END consuming path_or_paths=%s url=%s text=%s" % (path_or_paths, url, text), flush=True)
-    return documents
-
-
-def prep_langchain(persist_directory,
-                   load_db_if_exists,
-                   db_type, use_openai_embedding,
-                   langchain_mode, langchain_mode_paths, langchain_mode_types,
-                   hf_embedding_model,
-                   migrate_embedding_model,
-                   auto_migrate_db,
-                   n_jobs=-1, kwargs_make_db={},
-                   verbose=False):
-    """
-    do prep first time, involving downloads
-    # FIXME: Add github caching then add here
-    :return:
-    """
-    if os.getenv("HARD_ASSERTS"):
-        assert langchain_mode not in ['MyData'], "Should not prep scratch/personal data"
-
-    if langchain_mode in langchain_modes_intrinsic:
-        return None
-
-    db_dir_exists = os.path.isdir(persist_directory)
-    user_path = langchain_mode_paths.get(langchain_mode)
-
-    if db_dir_exists and user_path is None:
-        if verbose:
-            print("Prep: persist_directory=%s exists, using" % persist_directory, flush=True)
-        db, use_openai_embedding, hf_embedding_model = \
-            get_existing_db(None, persist_directory, load_db_if_exists,
-                            db_type, use_openai_embedding,
-                            langchain_mode, langchain_mode_paths, langchain_mode_types,
-                            hf_embedding_model, migrate_embedding_model, auto_migrate_db,
-                            n_jobs=n_jobs)
-    else:
-        if db_dir_exists and user_path is not None:
-            if verbose:
-                print("Prep: persist_directory=%s exists, user_path=%s passed, adding any changed or new documents" % (
-                    persist_directory, user_path), flush=True)
-        elif not db_dir_exists:
-            if verbose:
-                print("Prep: persist_directory=%s does not exist, regenerating" % persist_directory, flush=True)
-        db = None
-        if langchain_mode in ['DriverlessAI docs']:
-            # FIXME: Could also just use dai_docs.pickle directly and upload that
-            get_dai_docs(from_hf=True)
-
-        if langchain_mode in ['wiki']:
-            get_wiki_sources(first_para=kwargs_make_db['first_para'], text_limit=kwargs_make_db['text_limit'])
-
-        langchain_kwargs = kwargs_make_db.copy()
-        langchain_kwargs.update(locals())
-        db, num_new_sources, new_sources_metadata = make_db(**langchain_kwargs)
-
-    return db
-
-
-import posthog
-
-posthog.disabled = True
-
-
-class FakeConsumer(object):
-    def __init__(self, *args, **kwargs):
-        pass
-
-    def run(self):
-        pass
-
-    def pause(self):
-        pass
-
-    def upload(self):
-        pass
-
-    def next(self):
-        pass
-
-    def request(self, batch):
-        pass
-
-
-posthog.Consumer = FakeConsumer
-
-
-def check_update_chroma_embedding(db,
-                                  db_type,
-                                  use_openai_embedding,
-                                  hf_embedding_model, migrate_embedding_model, auto_migrate_db,
-                                  langchain_mode, langchain_mode_paths, langchain_mode_types,
-                                  n_jobs=-1):
-    changed_db = False
-    embed_tuple = load_embed(db=db)
-    if embed_tuple not in [(True, use_openai_embedding, hf_embedding_model),
-                           (False, use_openai_embedding, hf_embedding_model)]:
-        print("Detected new embedding %s vs. %s %s, updating db: %s" % (
-            use_openai_embedding, hf_embedding_model, embed_tuple, langchain_mode), flush=True)
-        # handle embedding changes
-        db_get = get_documents(db)
-        sources = [Document(page_content=result[0], metadata=result[1] or {})
-                   for result in zip(db_get['documents'], db_get['metadatas'])]
-        # delete index, has to be redone
-        persist_directory = db._persist_directory
-        shutil.move(persist_directory, persist_directory + "_" + str(uuid.uuid4()) + ".bak")
-        assert db_type in ['chroma', 'chroma_old']
-        load_db_if_exists = False
-        db = get_db(sources, use_openai_embedding=use_openai_embedding, db_type=db_type,
-                    persist_directory=persist_directory, load_db_if_exists=load_db_if_exists,
-                    langchain_mode=langchain_mode,
-                    langchain_mode_paths=langchain_mode_paths,
-                    langchain_mode_types=langchain_mode_types,
-                    collection_name=None,
-                    hf_embedding_model=hf_embedding_model,
-                    migrate_embedding_model=migrate_embedding_model,
-                    auto_migrate_db=auto_migrate_db,
-                    n_jobs=n_jobs,
-                    )
-        changed_db = True
-        print("Done updating db for new embedding: %s" % langchain_mode, flush=True)
-
-    return db, changed_db
-
-
-def migrate_meta_func(db, langchain_mode):
-    changed_db = False
-    db_get = get_documents(db)
-    # just check one doc
-    if len(db_get['metadatas']) > 0 and 'chunk_id' not in db_get['metadatas'][0]:
-        print("Detected old metadata, adding additional information", flush=True)
-        t0 = time.time()
-        # handle meta changes
-        [x.update(dict(chunk_id=x.get('chunk_id', 0))) for x in db_get['metadatas']]
-        client_collection = db._client.get_collection(name=db._collection.name,
-                                                      embedding_function=db._collection._embedding_function)
-        client_collection.update(ids=db_get['ids'], metadatas=db_get['metadatas'])
-        # check
-        db_get = get_documents(db)
-        assert 'chunk_id' in db_get['metadatas'][0], "Failed to add meta"
-        changed_db = True
-        print("Done updating db for new meta: %s in %s seconds" % (langchain_mode, time.time() - t0), flush=True)
-
-    return db, changed_db
-
-
-def get_existing_db(db, persist_directory,
-                    load_db_if_exists, db_type, use_openai_embedding,
-                    langchain_mode, langchain_mode_paths, langchain_mode_types,
-                    hf_embedding_model,
-                    migrate_embedding_model,
-                    auto_migrate_db=False,
-                    verbose=False, check_embedding=True, migrate_meta=True,
-                    n_jobs=-1):
-    if load_db_if_exists and db_type in ['chroma', 'chroma_old'] and os.path.isdir(persist_directory):
-        if os.path.isfile(os.path.join(persist_directory, 'chroma.sqlite3')):
-            must_migrate = False
-        elif os.path.isdir(os.path.join(persist_directory, 'index')):
-            must_migrate = True
-        else:
-            return db, use_openai_embedding, hf_embedding_model
-        chroma_settings = dict(is_persistent=True)
-        use_chromamigdb = False
-        if must_migrate:
-            if auto_migrate_db:
-                print("Detected chromadb<0.4 database, require migration, doing now....", flush=True)
-                from chroma_migrate.import_duckdb import migrate_from_duckdb
-                import chromadb
-                api = chromadb.PersistentClient(path=persist_directory)
-                did_migration = migrate_from_duckdb(api, persist_directory)
-                assert did_migration, "Failed to migrate chroma collection at %s, see https://docs.trychroma.com/migration for CLI tool" % persist_directory
-            elif have_chromamigdb:
-                print(
-                    "Detected chroma<0.4 database but --auto_migrate_db=False, but detected chromamigdb package, so using old database that still requires duckdb",
-                    flush=True)
-                chroma_settings = dict(chroma_db_impl="duckdb+parquet")
-                use_chromamigdb = True
-            else:
-                raise ValueError(
-                    "Detected chromadb<0.4 database, require migration, but did not detect chromamigdb package or did not choose auto_migrate_db=False (see FAQ.md)")
-
-        if db is None:
-            if verbose:
-                print("DO Loading db: %s" % langchain_mode, flush=True)
-            got_embedding, use_openai_embedding0, hf_embedding_model0 = load_embed(persist_directory=persist_directory)
-            if got_embedding:
-                use_openai_embedding, hf_embedding_model = use_openai_embedding0, hf_embedding_model0
-            embedding = get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model)
-            import logging
-            logging.getLogger("chromadb").setLevel(logging.ERROR)
-            if use_chromamigdb:
-                from chromamigdb.config import Settings
-                chroma_class = ChromaMig
-            else:
-                from chromadb.config import Settings
-                chroma_class = Chroma
-            client_settings = Settings(anonymized_telemetry=False,
-                                       **chroma_settings,
-                                       persist_directory=persist_directory)
-            db = chroma_class(persist_directory=persist_directory, embedding_function=embedding,
-                              collection_name=langchain_mode.replace(' ', '_'),
-                              client_settings=client_settings)
-            try:
-                db.similarity_search('')
-            except BaseException as e:
-                # migration when no embed_info
-                if 'Dimensionality of (768) does not match index dimensionality (384)' in str(e) or \
-                        'Embedding dimension 768 does not match collection dimensionality 384' in str(e):
-                    hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
-                    embedding = get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model)
-                    db = chroma_class(persist_directory=persist_directory, embedding_function=embedding,
-                                      collection_name=langchain_mode.replace(' ', '_'),
-                                      client_settings=client_settings)
-                    # should work now, let fail if not
-                    db.similarity_search('')
-                    save_embed(db, use_openai_embedding, hf_embedding_model)
-                else:
-                    raise
-
-            if verbose:
-                print("DONE Loading db: %s" % langchain_mode, flush=True)
-        else:
-            if not migrate_embedding_model:
-                # OVERRIDE embedding choices if could load embedding info when not migrating
-                got_embedding, use_openai_embedding, hf_embedding_model = load_embed(db=db)
-            if verbose:
-                print("USING already-loaded db: %s" % langchain_mode, flush=True)
-        if check_embedding:
-            db_trial, changed_db = check_update_chroma_embedding(db,
-                                                                 db_type,
-                                                                 use_openai_embedding,
-                                                                 hf_embedding_model,
-                                                                 migrate_embedding_model,
-                                                                 auto_migrate_db,
-                                                                 langchain_mode,
-                                                                 langchain_mode_paths,
-                                                                 langchain_mode_types,
-                                                                 n_jobs=n_jobs)
-            if changed_db:
-                db = db_trial
-                # only call persist if really changed db, else takes too long for large db
-                if db is not None:
-                    db.persist()
-                    clear_embedding(db)
-        save_embed(db, use_openai_embedding, hf_embedding_model)
-        if migrate_meta and db is not None:
-            db_trial, changed_db = migrate_meta_func(db, langchain_mode)
-            if changed_db:
-                db = db_trial
-        return db, use_openai_embedding, hf_embedding_model
-    return db, use_openai_embedding, hf_embedding_model
-
-
-def clear_embedding(db):
-    if db is None:
-        return
-    # don't keep on GPU, wastes memory, push back onto CPU and only put back on GPU once again embed
-    try:
-        if hasattr(db._embedding_function, 'client') and hasattr(db._embedding_function.client, 'cpu'):
-            # only push back to CPU if each db/user has own embedding model, else if shared share on GPU
-            if hasattr(db._embedding_function.client, 'preload') and not db._embedding_function.client.preload:
-                db._embedding_function.client.cpu()
-                clear_torch_cache()
-    except RuntimeError as e:
-        print("clear_embedding error: %s" % ''.join(traceback.format_tb(e.__traceback__)), flush=True)
-
-
-def make_db(**langchain_kwargs):
-    func_names = list(inspect.signature(_make_db).parameters)
-    missing_kwargs = [x for x in func_names if x not in langchain_kwargs]
-    defaults_db = {k: v.default for k, v in dict(inspect.signature(run_qa_db).parameters).items()}
-    for k in missing_kwargs:
-        if k in defaults_db:
-            langchain_kwargs[k] = defaults_db[k]
-    # final check for missing
-    missing_kwargs = [x for x in func_names if x not in langchain_kwargs]
-    assert not missing_kwargs, "Missing kwargs for make_db: %s" % missing_kwargs
-    # only keep actual used
-    langchain_kwargs = {k: v for k, v in langchain_kwargs.items() if k in func_names}
-    return _make_db(**langchain_kwargs)
-
-
-embed_lock_name = 'embed.lock'
-
-
-def get_embed_lock_file(db, persist_directory=None):
-    if hasattr(db, '_persist_directory') or persist_directory:
-        if persist_directory is None:
-            persist_directory = db._persist_directory
-        check_persist_directory(persist_directory)
-        base_path = os.path.join('locks', persist_directory)
-        base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
-        lock_file = os.path.join(base_path, embed_lock_name)
-        makedirs(os.path.dirname(lock_file))
-        return lock_file
-    return None
-
-
-def save_embed(db, use_openai_embedding, hf_embedding_model):
-    if hasattr(db, '_persist_directory'):
-        persist_directory = db._persist_directory
-        lock_file = get_embed_lock_file(db)
-        with filelock.FileLock(lock_file):
-            embed_info_file = os.path.join(persist_directory, 'embed_info')
-            with open(embed_info_file, 'wb') as f:
-                if isinstance(hf_embedding_model, str):
-                    hf_embedding_model_save = hf_embedding_model
-                elif hasattr(hf_embedding_model, 'model_name'):
-                    hf_embedding_model_save = hf_embedding_model.model_name
-                elif isinstance(hf_embedding_model, dict) and 'name' in hf_embedding_model:
-                    hf_embedding_model_save = hf_embedding_model['name']
-                elif isinstance(hf_embedding_model, dict) and 'name' in hf_embedding_model:
-                    if os.getenv('HARD_ASSERTS'):
-                        # unexpected in testing or normally
-                        raise RuntimeError("HERE")
-                    hf_embedding_model_save = 'hkunlp/instructor-large'
-                pickle.dump((use_openai_embedding, hf_embedding_model_save), f)
-    return use_openai_embedding, hf_embedding_model
-
-
-def load_embed(db=None, persist_directory=None):
-    if hasattr(db, 'embeddings') and hasattr(db.embeddings, 'model_name'):
-        hf_embedding_model = db.embeddings.model_name if 'openai' not in db.embeddings.model_name.lower() else None
-        use_openai_embedding = hf_embedding_model is None
-        save_embed(db, use_openai_embedding, hf_embedding_model)
-        return True, use_openai_embedding, hf_embedding_model
-    if persist_directory is None:
-        persist_directory = db._persist_directory
-    embed_info_file = os.path.join(persist_directory, 'embed_info')
-    if os.path.isfile(embed_info_file):
-        lock_file = get_embed_lock_file(db, persist_directory=persist_directory)
-        with filelock.FileLock(lock_file):
-            with open(embed_info_file, 'rb') as f:
-                try:
-                    use_openai_embedding, hf_embedding_model = pickle.load(f)
-                    if not isinstance(hf_embedding_model, str):
-                        # work-around bug introduced here: https://github.com/h2oai/h2ogpt/commit/54c4414f1ce3b5b7c938def651c0f6af081c66de
-                        hf_embedding_model = 'hkunlp/instructor-large'
-                        # fix file
-                        save_embed(db, use_openai_embedding, hf_embedding_model)
-                    got_embedding = True
-                except EOFError:
-                    use_openai_embedding, hf_embedding_model = False, 'hkunlp/instructor-large'
-                    got_embedding = False
-                    if os.getenv('HARD_ASSERTS'):
-                        # unexpected in testing or normally
-                        raise
-    else:
-        # migration, assume defaults
-        use_openai_embedding, hf_embedding_model = False, "sentence-transformers/all-MiniLM-L6-v2"
-        got_embedding = False
-    assert isinstance(hf_embedding_model, str)
-    return got_embedding, use_openai_embedding, hf_embedding_model
-
-
-def get_persist_directory(langchain_mode, langchain_type=None, db1s=None, dbs=None):
-    if langchain_mode in [LangChainMode.DISABLED.value, LangChainMode.LLM.value]:
-        # not None so join works but will fail to find db
-        return '', langchain_type
-
-    userid = get_userid_direct(db1s)
-    username = get_username_direct(db1s)
-
-    # sanity for bad code
-    assert userid != 'None'
-    assert username != 'None'
-
-    dirid = username or userid
-    if langchain_type == LangChainTypes.SHARED.value and not dirid:
-        dirid = './'  # just to avoid error
-    if langchain_type == LangChainTypes.PERSONAL.value and not dirid:
-        # e.g. from client when doing transient calls with MyData
-        if db1s is None:
-            # just trick to get filled locally
-            db1s = {LangChainMode.MY_DATA.value: [None, None, None]}
-        set_userid_direct(db1s, str(uuid.uuid4()), str(uuid.uuid4()))
-        userid = get_userid_direct(db1s)
-        username = get_username_direct(db1s)
-        dirid = username or userid
-        langchain_type = LangChainTypes.PERSONAL.value
-
-    # deal with existing locations
-    user_base_dir = os.getenv('USERS_BASE_DIR', 'users')
-    persist_directory = os.path.join(user_base_dir, dirid, 'db_dir_%s' % langchain_mode)
-    if userid and \
-            (os.path.isdir(persist_directory) or
-             db1s is not None and langchain_mode in db1s or
-             langchain_type == LangChainTypes.PERSONAL.value):
-        langchain_type = LangChainTypes.PERSONAL.value
-        persist_directory = makedirs(persist_directory, use_base=True)
-        check_persist_directory(persist_directory)
-        return persist_directory, langchain_type
-
-    persist_directory = 'db_dir_%s' % langchain_mode
-    if (os.path.isdir(persist_directory) or
-            dbs is not None and langchain_mode in dbs or
-            langchain_type == LangChainTypes.SHARED.value):
-        # ensure consistent
-        langchain_type = LangChainTypes.SHARED.value
-        persist_directory = makedirs(persist_directory, use_base=True)
-        check_persist_directory(persist_directory)
-        return persist_directory, langchain_type
-
-    # dummy return for prep_langchain() or full personal space
-    base_others = 'db_nonusers'
-    persist_directory = os.path.join(base_others, 'db_dir_%s' % str(uuid.uuid4()))
-    persist_directory = makedirs(persist_directory, use_base=True)
-    langchain_type = LangChainTypes.PERSONAL.value
-
-    check_persist_directory(persist_directory)
-    return persist_directory, langchain_type
-
-
-def check_persist_directory(persist_directory):
-    # deal with some cases when see intrinsic names being used as shared
-    for langchain_mode in langchain_modes_intrinsic:
-        if persist_directory == 'db_dir_%s' % langchain_mode:
-            raise RuntimeError("Illegal access to %s" % persist_directory)
-
-
-def _make_db(use_openai_embedding=False,
-             hf_embedding_model=None,
-             migrate_embedding_model=False,
-             auto_migrate_db=False,
-             first_para=False, text_limit=None,
-             chunk=True, chunk_size=512,
-
-             # urls
-             use_unstructured=True,
-             use_playwright=False,
-             use_selenium=False,
-
-             # pdfs
-             use_pymupdf='auto',
-             use_unstructured_pdf='auto',
-             use_pypdf='auto',
-             enable_pdf_ocr='auto',
-             enable_pdf_doctr='auto',
-             try_pdf_as_html='auto',
-
-             # images
-             enable_ocr=False,
-             enable_doctr=False,
-             enable_pix2struct=False,
-             enable_captions=True,
-             captions_model=None,
-             caption_loader=None,
-             doctr_loader=None,
-             pix2struct_loader=None,
-
-             # json
-             jq_schema='.[]',
-
-             langchain_mode=None,
-             langchain_mode_paths=None,
-             langchain_mode_types=None,
-             db_type='faiss',
-             load_db_if_exists=True,
-             db=None,
-             n_jobs=-1,
-             verbose=False):
-    assert hf_embedding_model is not None
-    user_path = langchain_mode_paths.get(langchain_mode)
-    langchain_type = langchain_mode_types.get(langchain_mode, LangChainTypes.EITHER.value)
-    persist_directory, langchain_type = get_persist_directory(langchain_mode, langchain_type=langchain_type)
-    langchain_mode_types[langchain_mode] = langchain_type
-    # see if can get persistent chroma db
-    db_trial, use_openai_embedding, hf_embedding_model = \
-        get_existing_db(db, persist_directory, load_db_if_exists, db_type,
-                        use_openai_embedding,
-                        langchain_mode, langchain_mode_paths, langchain_mode_types,
-                        hf_embedding_model, migrate_embedding_model, auto_migrate_db, verbose=verbose,
-                        n_jobs=n_jobs)
-    if db_trial is not None:
-        db = db_trial
-
-    sources = []
-    if not db:
-        chunk_sources = functools.partial(_chunk_sources, chunk=chunk, chunk_size=chunk_size, db_type=db_type)
-        if langchain_mode in ['wiki_full']:
-            from read_wiki_full import get_all_documents
-            small_test = None
-            print("Generating new wiki", flush=True)
-            sources1 = get_all_documents(small_test=small_test, n_jobs=os.cpu_count() // 2)
-            print("Got new wiki", flush=True)
-            sources1 = chunk_sources(sources1, chunk=chunk)
-            print("Chunked new wiki", flush=True)
-            sources.extend(sources1)
-        elif langchain_mode in ['wiki']:
-            sources1 = get_wiki_sources(first_para=first_para, text_limit=text_limit)
-            sources1 = chunk_sources(sources1, chunk=chunk)
-            sources.extend(sources1)
-        elif langchain_mode in ['github h2oGPT']:
-            # sources = get_github_docs("dagster-io", "dagster")
-            sources1 = get_github_docs("h2oai", "h2ogpt")
-            # FIXME: always chunk for now
-            sources1 = chunk_sources(sources1)
-            sources.extend(sources1)
-        elif langchain_mode in ['DriverlessAI docs']:
-            sources1 = get_dai_docs(from_hf=True)
-            # FIXME: DAI docs are already chunked well, should only chunk more if over limit
-            sources1 = chunk_sources(sources1, chunk=False)
-            sources.extend(sources1)
-    if user_path:
-        # UserData or custom, which has to be from user's disk
-        if db is not None:
-            # NOTE: Ignore file names for now, only go by hash ids
-            # existing_files = get_existing_files(db)
-            existing_files = []
-            existing_hash_ids = get_existing_hash_ids(db)
-        else:
-            # pretend no existing files so won't filter
-            existing_files = []
-            existing_hash_ids = []
-        # chunk internally for speed over multiple docs
-        # FIXME: If first had old Hash=None and switch embeddings,
-        #  then re-embed, and then hit here and reload so have hash, and then re-embed.
-        sources1 = path_to_docs(user_path, n_jobs=n_jobs, chunk=chunk, chunk_size=chunk_size,
-                                # urls
-                                use_unstructured=use_unstructured,
-                                use_playwright=use_playwright,
-                                use_selenium=use_selenium,
-
-                                # pdfs
-                                use_pymupdf=use_pymupdf,
-                                use_unstructured_pdf=use_unstructured_pdf,
-                                use_pypdf=use_pypdf,
-                                enable_pdf_ocr=enable_pdf_ocr,
-                                enable_pdf_doctr=enable_pdf_doctr,
-                                try_pdf_as_html=try_pdf_as_html,
-
-                                # images
-                                enable_ocr=enable_ocr,
-                                enable_doctr=enable_doctr,
-                                enable_pix2struct=enable_pix2struct,
-                                enable_captions=enable_captions,
-                                captions_model=captions_model,
-                                caption_loader=caption_loader,
-                                doctr_loader=doctr_loader,
-                                pix2struct_loader=pix2struct_loader,
-
-                                # json
-                                jq_schema=jq_schema,
-
-                                existing_files=existing_files, existing_hash_ids=existing_hash_ids,
-                                db_type=db_type)
-        new_metadata_sources = set([x.metadata['source'] for x in sources1])
-        if new_metadata_sources:
-            if os.getenv('NO_NEW_FILES') is not None:
-                raise RuntimeError("Expected no new files! %s" % new_metadata_sources)
-            print("Loaded %s new files as sources to add to %s" % (len(new_metadata_sources), langchain_mode),
-                  flush=True)
-            if verbose:
-                print("Files added: %s" % '\n'.join(new_metadata_sources), flush=True)
-        sources.extend(sources1)
-        if len(sources) > 0 and os.getenv('NO_NEW_FILES') is not None:
-            raise RuntimeError("Expected no new files! %s" % langchain_mode)
-        if len(sources) == 0 and os.getenv('SHOULD_NEW_FILES') is not None:
-            raise RuntimeError("Expected new files! %s" % langchain_mode)
-        print("Loaded %s sources for potentially adding to %s" % (len(sources), langchain_mode), flush=True)
-
-        # see if got sources
-        if not sources:
-            if verbose:
-                if db is not None:
-                    print("langchain_mode %s has no new sources, nothing to add to db" % langchain_mode, flush=True)
-                else:
-                    print("langchain_mode %s has no sources, not making new db" % langchain_mode, flush=True)
-            return db, 0, []
-        if verbose:
-            if db is not None:
-                print("Generating db", flush=True)
-            else:
-                print("Adding to db", flush=True)
-    if not db:
-        if sources:
-            db = get_db(sources, use_openai_embedding=use_openai_embedding, db_type=db_type,
-                        persist_directory=persist_directory,
-                        langchain_mode=langchain_mode,
-                        langchain_mode_paths=langchain_mode_paths,
-                        langchain_mode_types=langchain_mode_types,
-                        hf_embedding_model=hf_embedding_model,
-                        migrate_embedding_model=migrate_embedding_model,
-                        auto_migrate_db=auto_migrate_db,
-                        n_jobs=n_jobs)
-            if verbose:
-                print("Generated db", flush=True)
-        elif langchain_mode not in langchain_modes_intrinsic:
-            print("Did not generate db for %s since no sources" % langchain_mode, flush=True)
-        new_sources_metadata = [x.metadata for x in sources]
-    elif user_path is not None:
-        print("Existing db, potentially adding %s sources from user_path=%s" % (len(sources), user_path), flush=True)
-        db, num_new_sources, new_sources_metadata = add_to_db(db, sources, db_type=db_type,
-                                                              use_openai_embedding=use_openai_embedding,
-                                                              hf_embedding_model=hf_embedding_model)
-        print("Existing db, added %s new sources from user_path=%s" % (num_new_sources, user_path), flush=True)
-    else:
-        new_sources_metadata = [x.metadata for x in sources]
-
-    return db, len(new_sources_metadata), new_sources_metadata
-
-
-def get_metadatas(db):
-    metadatas = []
-    from langchain.vectorstores import FAISS
-    if isinstance(db, FAISS):
-        metadatas = [v.metadata for k, v in db.docstore._dict.items()]
-    elif isinstance(db, Chroma) or isinstance(db, ChromaMig) or ChromaMig.__name__ in str(db):
-        metadatas = get_documents(db)['metadatas']
-    elif db is not None:
-        # FIXME: Hack due to https://github.com/weaviate/weaviate/issues/1947
-        # seems no way to get all metadata, so need to avoid this approach for weaviate
-        metadatas = [x.metadata for x in db.similarity_search("", k=10000)]
-    return metadatas
-
-
-def get_db_lock_file(db, lock_type='getdb'):
-    if hasattr(db, '_persist_directory'):
-        persist_directory = db._persist_directory
-        check_persist_directory(persist_directory)
-        base_path = os.path.join('locks', persist_directory)
-        base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
-        lock_file = os.path.join(base_path, "%s.lock" % lock_type)
-        makedirs(os.path.dirname(lock_file))  # ensure made
-        return lock_file
-    return None
-
-
-def get_documents(db):
-    if hasattr(db, '_persist_directory'):
-        lock_file = get_db_lock_file(db)
-        with filelock.FileLock(lock_file):
-            # get segfaults and other errors when multiple threads access this
-            return _get_documents(db)
-    else:
-        return _get_documents(db)
-
-
-def _get_documents(db):
-    from langchain.vectorstores import FAISS
-    if isinstance(db, FAISS):
-        documents = [v for k, v in db.docstore._dict.items()]
-        documents = dict(documents=documents)
-    elif isinstance(db, Chroma) or isinstance(db, ChromaMig) or ChromaMig.__name__ in str(db):
-        documents = db.get()
-    else:
-        # FIXME: Hack due to https://github.com/weaviate/weaviate/issues/1947
-        # seems no way to get all metadata, so need to avoid this approach for weaviate
-        documents = [x for x in db.similarity_search("", k=10000)]
-        documents = dict(documents=documents)
-    return documents
-
-
-def get_docs_and_meta(db, top_k_docs, filter_kwargs={}, text_context_list=None):
-    if hasattr(db, '_persist_directory'):
-        lock_file = get_db_lock_file(db)
-        with filelock.FileLock(lock_file):
-            return _get_docs_and_meta(db, top_k_docs, filter_kwargs=filter_kwargs, text_context_list=text_context_list)
-    else:
-        return _get_docs_and_meta(db, top_k_docs, filter_kwargs=filter_kwargs, text_context_list=text_context_list)
-
-
-def _get_docs_and_meta(db, top_k_docs, filter_kwargs={}, text_context_list=None):
-    db_documents = []
-    db_metadatas = []
-
-    if text_context_list:
-        db_documents += [x.page_content if hasattr(x, 'page_content') else x for x in text_context_list]
-        db_metadatas += [x.metadata if hasattr(x, 'metadata') else {} for x in text_context_list]
-
-    from langchain.vectorstores import FAISS
-    if isinstance(db, Chroma) or isinstance(db, ChromaMig) or ChromaMig.__name__ in str(db):
-        db_get = db._collection.get(where=filter_kwargs.get('filter'))
-        db_metadatas += db_get['metadatas']
-        db_documents += db_get['documents']
-    elif isinstance(db, FAISS):
-        import itertools
-        db_metadatas += get_metadatas(db)
-        # FIXME: FAISS has no filter
-        if top_k_docs == -1:
-            db_documents += list(db.docstore._dict.values())
-        else:
-            # slice dict first
-            db_documents += list(dict(itertools.islice(db.docstore._dict.items(), top_k_docs)).values())
-    elif db is not None:
-        db_metadatas += get_metadatas(db)
-        db_documents += get_documents(db)['documents']
-
-    return db_documents, db_metadatas
-
-
-def get_existing_files(db):
-    metadatas = get_metadatas(db)
-    metadata_sources = set([x['source'] for x in metadatas])
-    return metadata_sources
-
-
-def get_existing_hash_ids(db):
-    metadatas = get_metadatas(db)
-    # assume consistency, that any prior hashed source was single hashed file at the time among all source chunks
-    metadata_hash_ids = {os.path.normpath(x['source']): x.get('hashid') for x in metadatas}
-    return metadata_hash_ids
-
-
-def run_qa_db(**kwargs):
-    func_names = list(inspect.signature(_run_qa_db).parameters)
-    # hard-coded defaults
-    kwargs['answer_with_sources'] = kwargs.get('answer_with_sources', True)
-    kwargs['show_rank'] = kwargs.get('show_rank', False)
-    kwargs['show_accordions'] = kwargs.get('show_accordions', True)
-    kwargs['show_link_in_sources'] = kwargs.get('show_link_in_sources', True)
-    kwargs['top_k_docs_max_show'] = kwargs.get('top_k_docs_max_show', 10)
-    kwargs['llamacpp_dict'] = {}  # shouldn't be required unless from test using _run_qa_db
-    missing_kwargs = [x for x in func_names if x not in kwargs]
-    assert not missing_kwargs, "Missing kwargs for run_qa_db: %s" % missing_kwargs
-    # only keep actual used
-    kwargs = {k: v for k, v in kwargs.items() if k in func_names}
-    try:
-        return _run_qa_db(**kwargs)
-    finally:
-        clear_torch_cache()
-
-
-def _run_qa_db(query=None,
-               iinput=None,
-               context=None,
-               use_openai_model=False, use_openai_embedding=False,
-               first_para=False, text_limit=None, top_k_docs=4, chunk=True, chunk_size=512,
-
-               # urls
-               use_unstructured=True,
-               use_playwright=False,
-               use_selenium=False,
-
-               # pdfs
-               use_pymupdf='auto',
-               use_unstructured_pdf='auto',
-               use_pypdf='auto',
-               enable_pdf_ocr='auto',
-               enable_pdf_doctr='auto',
-               try_pdf_as_html='auto',
-
-               # images
-               enable_ocr=False,
-               enable_doctr=False,
-               enable_pix2struct=False,
-               enable_captions=True,
-               captions_model=None,
-               caption_loader=None,
-               doctr_loader=None,
-               pix2struct_loader=None,
-
-               # json
-               jq_schema='.[]',
-
-               langchain_mode_paths={},
-               langchain_mode_types={},
-               detect_user_path_changes_every_query=False,
-               db_type=None,
-               model_name=None, model=None, tokenizer=None, inference_server=None,
-               langchain_only_model=False,
-               hf_embedding_model=None,
-               migrate_embedding_model=False,
-               auto_migrate_db=False,
-               stream_output=False,
-               async_output=True,
-               num_async=3,
-               prompter=None,
-               prompt_type=None,
-               prompt_dict=None,
-               answer_with_sources=True,
-               append_sources_to_answer=True,
-               cut_distance=1.64,
-               add_chat_history_to_context=True,
-               add_search_to_context=False,
-               keep_sources_in_context=False,
-               memory_restriction_level=0,
-               system_prompt='',
-               sanitize_bot_response=False,
-               show_rank=False,
-               show_accordions=True,
-               show_link_in_sources=True,
-               top_k_docs_max_show=10,
-               use_llm_if_no_docs=True,
-               load_db_if_exists=False,
-               db=None,
-               do_sample=False,
-               temperature=0.1,
-               top_k=40,
-               top_p=0.7,
-               num_beams=1,
-               max_new_tokens=512,
-               min_new_tokens=1,
-               early_stopping=False,
-               max_time=180,
-               repetition_penalty=1.0,
-               num_return_sequences=1,
-               langchain_mode=None,
-               langchain_action=None,
-               langchain_agents=None,
-               document_subset=DocumentSubset.Relevant.name,
-               document_choice=[DocumentChoice.ALL.value],
-               pre_prompt_query=None,
-               prompt_query=None,
-               pre_prompt_summary=None,
-               prompt_summary=None,
-               text_context_list=None,
-               chat_conversation=None,
-               visible_models=None,
-               h2ogpt_key=None,
-               docs_ordering_type='reverse_ucurve_sort',
-               min_max_new_tokens=256,
-
-               n_jobs=-1,
-               llamacpp_dict=None,
-               verbose=False,
-               cli=False,
-               lora_weights='',
-               auto_reduce_chunks=True,
-               max_chunks=100,
-               total_tokens_for_docs=None,
-               headsize=50,
-               ):
-    """
-
-    :param query:
-    :param use_openai_model:
-    :param use_openai_embedding:
-    :param first_para:
-    :param text_limit:
-    :param top_k_docs:
-    :param chunk:
-    :param chunk_size:
-    :param langchain_mode_paths: dict of langchain_mode -> user path to glob recursively from
-    :param db_type: 'faiss' for in-memory
-                    'chroma' (for chroma >= 0.4)
-                    'chroma_old' (for chroma < 0.4)
-                    'weaviate' for persisted on disk
-    :param model_name: model name, used to switch behaviors
-    :param model: pre-initialized model, else will make new one
-    :param tokenizer: pre-initialized tokenizer, else will make new one.  Required not None if model is not None
-    :param answer_with_sources
-    :return:
-    """
-    t_run = time.time()
-    if stream_output:
-        # threads and asyncio don't mix
-        async_output = False
-    if langchain_action in [LangChainAction.QUERY.value]:
-        # only summarization supported
-        async_output = False
-
-    # in case None, e.g. lazy client, then set based upon actual model
-    pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary = \
-        get_langchain_prompts(pre_prompt_query, prompt_query,
-                              pre_prompt_summary, prompt_summary,
-                              model_name, inference_server,
-                              llamacpp_dict.get('model_path_llama'))
-
-    assert db_type is not None
-    assert hf_embedding_model is not None
-    assert langchain_mode_paths is not None
-    assert langchain_mode_types is not None
-    if model is not None:
-        assert model_name is not None  # require so can make decisions
-    assert query is not None
-    assert prompter is not None or prompt_type is not None or model is None  # if model is None, then will generate
-    if prompter is not None:
-        prompt_type = prompter.prompt_type
-        prompt_dict = prompter.prompt_dict
-    if model is not None:
-        assert prompt_type is not None
-        if prompt_type == PromptType.custom.name:
-            assert prompt_dict is not None  # should at least be {} or ''
-        else:
-            prompt_dict = ''
-
-    if LangChainAgent.SEARCH.value in langchain_agents and 'llama' in model_name.lower():
-        system_prompt = """You are a zero shot react agent.
-Consider to prompt of Question that was original query from the user.
-Respond to prompt of Thought with a thought that may lead to a reasonable new action choice.
-Respond to prompt of Action with an action to take out of the tools given, giving exactly single word for the tool name.
-Respond to prompt of Action Input with an input to give the tool.
-Consider to prompt of Observation that was response from the tool.
-Repeat this Thought, Action, Action Input, Observation, Thought sequence several times with new and different thoughts and actions each time, do not repeat.
-Once satisfied that the thoughts, responses are sufficient to answer the question, then respond to prompt of Thought with: I now know the final answer
-Respond to prompt of Final Answer with your final high-quality bullet list answer to the original query.
-"""
-        prompter.system_prompt = system_prompt
-
-    assert len(set(gen_hyper).difference(inspect.signature(get_llm).parameters)) == 0
-    # pass in context to LLM directly, since already has prompt_type structure
-    # can't pass through langchain in get_chain() to LLM: https://github.com/hwchase17/langchain/issues/6638
-    llm, model_name, streamer, prompt_type_out, async_output, only_new_text = \
-        get_llm(use_openai_model=use_openai_model, model_name=model_name,
-                model=model,
-                tokenizer=tokenizer,
-                inference_server=inference_server,
-                langchain_only_model=langchain_only_model,
-                stream_output=stream_output,
-                async_output=async_output,
-                num_async=num_async,
-                do_sample=do_sample,
-                temperature=temperature,
-                top_k=top_k,
-                top_p=top_p,
-                num_beams=num_beams,
-                max_new_tokens=max_new_tokens,
-                min_new_tokens=min_new_tokens,
-                early_stopping=early_stopping,
-                max_time=max_time,
-                repetition_penalty=repetition_penalty,
-                num_return_sequences=num_return_sequences,
-                prompt_type=prompt_type,
-                prompt_dict=prompt_dict,
-                prompter=prompter,
-                context=context,
-                iinput=iinput,
-                sanitize_bot_response=sanitize_bot_response,
-                system_prompt=system_prompt,
-                visible_models=visible_models,
-                h2ogpt_key=h2ogpt_key,
-                min_max_new_tokens=min_max_new_tokens,
-                n_jobs=n_jobs,
-                llamacpp_dict=llamacpp_dict,
-                cli=cli,
-                verbose=verbose,
-                )
-    # in case change, override original prompter
-    if hasattr(llm, 'prompter'):
-        prompter = llm.prompter
-    if hasattr(llm, 'pipeline') and hasattr(llm.pipeline, 'prompter'):
-        prompter = llm.pipeline.prompter
-
-    if prompter is None:
-        if prompt_type is None:
-            prompt_type = prompt_type_out
-        # get prompter
-        chat = True  # FIXME?
-        prompter = Prompter(prompt_type, prompt_dict, debug=False, chat=chat, stream_output=stream_output,
-                            system_prompt=system_prompt)
-
-    use_docs_planned = False
-    scores = []
-    chain = None
-
-    # basic version of prompt without docs etc.
-    data_point = dict(context=context, instruction=query, input=iinput)
-    prompt_basic = prompter.generate_prompt(data_point)
-
-    if isinstance(document_choice, str):
-        # support string as well
-        document_choice = [document_choice]
-
-    func_names = list(inspect.signature(get_chain).parameters)
-    sim_kwargs = {k: v for k, v in locals().items() if k in func_names}
-    missing_kwargs = [x for x in func_names if x not in sim_kwargs]
-    assert not missing_kwargs, "Missing: %s" % missing_kwargs
-    docs, chain, scores, \
-        use_docs_planned, num_docs_before_cut, \
-        use_llm_if_no_docs, llm_mode, top_k_docs_max_show = \
-        get_chain(**sim_kwargs)
-    if document_subset in non_query_commands:
-        formatted_doc_chunks = '\n\n'.join([get_url(x) + '\n\n' + x.page_content for x in docs])
-        if not formatted_doc_chunks and not use_llm_if_no_docs:
-            yield dict(prompt=prompt_basic, response="No sources", sources='', num_prompt_tokens=0)
-            return
-        # if no souces, outside gpt_langchain, LLM will be used with '' input
-        scores = [1] * len(docs)
-        get_answer_args = tuple([query, docs, formatted_doc_chunks, scores, show_rank,
-                                 answer_with_sources,
-                                 append_sources_to_answer])
-        get_answer_kwargs = dict(show_accordions=show_accordions,
-                                 show_link_in_sources=show_link_in_sources,
-                                 top_k_docs_max_show=top_k_docs_max_show,
-                                 docs_ordering_type=docs_ordering_type,
-                                 num_docs_before_cut=num_docs_before_cut,
-                                 verbose=verbose)
-        ret, extra = get_sources_answer(*get_answer_args, **get_answer_kwargs)
-        yield dict(prompt=prompt_basic, response=formatted_doc_chunks, sources=extra, num_prompt_tokens=0)
-        return
-    if not use_llm_if_no_docs:
-        if not docs and langchain_action in [LangChainAction.SUMMARIZE_MAP.value,
-                                             LangChainAction.SUMMARIZE_ALL.value,
-                                             LangChainAction.SUMMARIZE_REFINE.value]:
-            ret = 'No relevant documents to summarize.' if num_docs_before_cut else 'No documents to summarize.'
-            extra = ''
-            yield dict(prompt=prompt_basic, response=ret, sources=extra, num_prompt_tokens=0)
-            return
-        if not docs and not llm_mode:
-            ret = 'No relevant documents to query (for chatting with LLM, pick Resources->Collections->LLM).' if num_docs_before_cut else 'No documents to query (for chatting with LLM, pick Resources->Collections->LLM).'
-            extra = ''
-            yield dict(prompt=prompt_basic, response=ret, sources=extra, num_prompt_tokens=0)
-            return
-
-    if chain is None and not langchain_only_model:
-        # here if no docs at all and not HF type
-        # can only return if HF type
-        return
-
-    # context stuff similar to used in evaluate()
-    import torch
-    device, torch_dtype, context_class = get_device_dtype()
-    conditional_type = hasattr(llm, 'pipeline') and hasattr(llm.pipeline, 'model') and hasattr(llm.pipeline.model,
-                                                                                               'conditional_type') and llm.pipeline.model.conditional_type
-    with torch.no_grad():
-        have_lora_weights = lora_weights not in [no_lora_str, '', None]
-        context_class_cast = NullContext if device == 'cpu' or have_lora_weights else torch.autocast
-        if conditional_type:
-            # issues when casting to float16, can mess up t5 model, e.g. only when not streaming, or other odd behaviors
-            context_class_cast = NullContext
-        with context_class_cast(device):
-            if stream_output and streamer:
-                answer = None
-                import queue
-                bucket = queue.Queue()
-                thread = EThread(target=chain, streamer=streamer, bucket=bucket)
-                thread.start()
-                outputs = ""
-                try:
-                    for new_text in streamer:
-                        # print("new_text: %s" % new_text, flush=True)
-                        if bucket.qsize() > 0 or thread.exc:
-                            thread.join()
-                        outputs += new_text
-                        if prompter:  # and False:  # FIXME: pipeline can already use prompter
-                            if conditional_type:
-                                if prompter.botstr:
-                                    prompt = prompter.botstr
-                                    output_with_prompt = prompt + outputs
-                                    only_new_text = False
-                                else:
-                                    prompt = None
-                                    output_with_prompt = outputs
-                                    only_new_text = True
-                            else:
-                                prompt = None  # FIXME
-                                output_with_prompt = outputs
-                                # don't specify only_new_text here, use get_llm() value
-                            output1 = prompter.get_response(output_with_prompt, prompt=prompt,
-                                                            only_new_text=only_new_text,
-                                                            sanitize_bot_response=sanitize_bot_response)
-                            yield dict(prompt=prompt, response=output1, sources='', num_prompt_tokens=0)
-                        else:
-                            yield dict(prompt=prompt, response=outputs, sources='', num_prompt_tokens=0)
-                except BaseException:
-                    # if any exception, raise that exception if was from thread, first
-                    if thread.exc:
-                        raise thread.exc
-                    raise
-                finally:
-                    # in case no exception and didn't join with thread yet, then join
-                    if not thread.exc:
-                        answer = thread.join()
-                        if isinstance(answer, dict):
-                            if 'output_text' in answer:
-                                answer = answer['output_text']
-                            elif 'output' in answer:
-                                answer = answer['output']
-                # in case raise StopIteration or broke queue loop in streamer, but still have exception
-                if thread.exc:
-                    raise thread.exc
-            else:
-                if async_output:
-                    import asyncio
-                    answer = asyncio.run(chain())
-                else:
-                    answer = chain()
-                    if isinstance(answer, dict):
-                        if 'output_text' in answer:
-                            answer = answer['output_text']
-                        elif 'output' in answer:
-                            answer = answer['output']
-
-    get_answer_args = tuple([query, docs, answer, scores, show_rank,
-                             answer_with_sources,
-                             append_sources_to_answer])
-    get_answer_kwargs = dict(show_accordions=show_accordions,
-                             show_link_in_sources=show_link_in_sources,
-                             top_k_docs_max_show=top_k_docs_max_show,
-                             docs_ordering_type=docs_ordering_type,
-                             num_docs_before_cut=num_docs_before_cut,
-                             verbose=verbose,
-                             t_run=t_run,
-                             count_input_tokens=llm.count_input_tokens
-                             if hasattr(llm, 'count_input_tokens') else None,
-                             count_output_tokens=llm.count_output_tokens
-                             if hasattr(llm, 'count_output_tokens') else None)
-
-    t_run = time.time() - t_run
-
-    # for final yield, get real prompt used
-    if hasattr(llm, 'prompter') and llm.prompter.prompt is not None:
-        prompt = llm.prompter.prompt
-    else:
-        prompt = prompt_basic
-    num_prompt_tokens = get_token_count(prompt, tokenizer)
-
-    if not use_docs_planned:
-        ret = answer
-        extra = ''
-        yield dict(prompt=prompt, response=ret, sources=extra, num_prompt_tokens=num_prompt_tokens)
-    elif answer is not None:
-        ret, extra = get_sources_answer(*get_answer_args, **get_answer_kwargs)
-        yield dict(prompt=prompt, response=ret, sources=extra, num_prompt_tokens=num_prompt_tokens)
-    return
-
-
-def get_docs_with_score(query, k_db, filter_kwargs, db, db_type, text_context_list=None, verbose=False):
-    docs_with_score = []
-    got_db_docs = False
-
-    if text_context_list:
-        docs_with_score += [(x, x.metadata.get('score', 1.0)) for x in text_context_list]
-
-    # deal with bug in chroma where if (say) 234 doc chunks and ask for 233+ then fails due to reduction misbehavior
-    if hasattr(db, '_embedding_function') and isinstance(db._embedding_function, FakeEmbeddings):
-        top_k_docs = -1
-        # don't add text_context_list twice
-        db_documents, db_metadatas = get_docs_and_meta(db, top_k_docs, filter_kwargs=filter_kwargs,
-                                                       text_context_list=None)
-        # sort by order given to parser (file_id) and any chunk_id if chunked
-        doc_file_ids = [x.get('file_id', 0) for x in db_metadatas]
-        doc_chunk_ids = [x.get('chunk_id', 0) for x in db_metadatas]
-        docs_with_score_fake = [(Document(page_content=result[0], metadata=result[1] or {}), 1.0)
-                                for result in zip(db_documents, db_metadatas)]
-        docs_with_score_fake = [x for fx, cx, x in
-                                sorted(zip(doc_file_ids, doc_chunk_ids, docs_with_score_fake),
-                                       key=lambda x: (x[0], x[1]))
-                                ]
-        got_db_docs |= len(docs_with_score_fake) > 0
-        docs_with_score += docs_with_score_fake
-    elif db is not None and db_type in ['chroma', 'chroma_old']:
-        while True:
-            try:
-                docs_with_score_chroma = db.similarity_search_with_score(query, k=k_db, **filter_kwargs)
-                break
-            except (RuntimeError, AttributeError) as e:
-                # AttributeError is for people with wrong version of langchain
-                if verbose:
-                    print("chroma bug: %s" % str(e), flush=True)
-                if k_db == 1:
-                    raise
-                if k_db > 500:
-                    k_db -= 200
-                elif k_db > 100:
-                    k_db -= 50
-                elif k_db > 10:
-                    k_db -= 5
-                else:
-                    k_db -= 1
-                k_db = max(1, k_db)
-        got_db_docs |= len(docs_with_score_chroma) > 0
-        docs_with_score += docs_with_score_chroma
-    elif db is not None:
-        docs_with_score_other = db.similarity_search_with_score(query, k=k_db, **filter_kwargs)
-        got_db_docs |= len(docs_with_score_other) > 0
-        docs_with_score += docs_with_score_other
-
-    # set in metadata original order of docs
-    [x[0].metadata.update(orig_index=ii) for ii, x in enumerate(docs_with_score)]
-
-    return docs_with_score, got_db_docs
-
-
-def get_chain(query=None,
-              iinput=None,
-              context=None,  # FIXME: https://github.com/hwchase17/langchain/issues/6638
-              use_openai_model=False, use_openai_embedding=False,
-              first_para=False, text_limit=None, top_k_docs=4, chunk=True, chunk_size=512,
-
-              # urls
-              use_unstructured=True,
-              use_playwright=False,
-              use_selenium=False,
-
-              # pdfs
-              use_pymupdf='auto',
-              use_unstructured_pdf='auto',
-              use_pypdf='auto',
-              enable_pdf_ocr='auto',
-              enable_pdf_doctr='auto',
-              try_pdf_as_html='auto',
-
-              # images
-              enable_ocr=False,
-              enable_doctr=False,
-              enable_pix2struct=False,
-              enable_captions=True,
-              captions_model=None,
-              caption_loader=None,
-              doctr_loader=None,
-              pix2struct_loader=None,
-
-              # json
-              jq_schema='.[]',
-
-              langchain_mode_paths=None,
-              langchain_mode_types=None,
-              detect_user_path_changes_every_query=False,
-              db_type='faiss',
-              model_name=None,
-              inference_server='',
-              max_new_tokens=None,
-              langchain_only_model=False,
-              hf_embedding_model=None,
-              migrate_embedding_model=False,
-              auto_migrate_db=False,
-              prompter=None,
-              prompt_type=None,
-              prompt_dict=None,
-              system_prompt=None,
-              cut_distance=1.1,
-              add_chat_history_to_context=True,  # FIXME: https://github.com/hwchase17/langchain/issues/6638
-              add_search_to_context=False,
-              keep_sources_in_context=False,
-              memory_restriction_level=0,
-              top_k_docs_max_show=10,
-
-              load_db_if_exists=False,
-              db=None,
-              langchain_mode=None,
-              langchain_action=None,
-              langchain_agents=None,
-              document_subset=DocumentSubset.Relevant.name,
-              document_choice=[DocumentChoice.ALL.value],
-              pre_prompt_query=None,
-              prompt_query=None,
-              pre_prompt_summary=None,
-              prompt_summary=None,
-              text_context_list=None,
-              chat_conversation=None,
-
-              n_jobs=-1,
-              # beyond run_db_query:
-              llm=None,
-              tokenizer=None,
-              verbose=False,
-              docs_ordering_type='reverse_ucurve_sort',
-              min_max_new_tokens=256,
-              stream_output=True,
-              async_output=True,
-
-              # local
-              auto_reduce_chunks=True,
-              max_chunks=100,
-              total_tokens_for_docs=None,
-              use_llm_if_no_docs=None,
-              headsize=50,
-              ):
-    if inference_server is None:
-        inference_server = ''
-    assert hf_embedding_model is not None
-    assert langchain_agents is not None  # should be at least []
-    if text_context_list is None:
-        text_context_list = []
-
-    # default value:
-    llm_mode = langchain_mode in ['Disabled', 'LLM'] and len(text_context_list) == 0
-    query_action = langchain_action == LangChainAction.QUERY.value
-    summarize_action = langchain_action in [LangChainAction.SUMMARIZE_MAP.value,
-                                            LangChainAction.SUMMARIZE_ALL.value,
-                                            LangChainAction.SUMMARIZE_REFINE.value]
-
-    if len(text_context_list) > 0:
-        # turn into documents to make easy to manage and add meta
-        # try to account for summarization vs. query
-        chunk_id = 0 if query_action else -1
-        text_context_list = [
-            Document(page_content=x, metadata=dict(source='text_context_list', score=1.0, chunk_id=chunk_id)) for x
-            in text_context_list]
-
-    if add_search_to_context:
-        params = {
-            "engine": "duckduckgo",
-            "gl": "us",
-            "hl": "en",
-        }
-        search = H2OSerpAPIWrapper(params=params)
-        # if doing search, allow more docs
-        docs_search, top_k_docs = search.get_search_documents(query,
-                                                              query_action=query_action,
-                                                              chunk=chunk, chunk_size=chunk_size,
-                                                              db_type=db_type,
-                                                              headsize=headsize,
-                                                              top_k_docs=top_k_docs)
-        text_context_list = docs_search + text_context_list
-        add_search_to_context &= len(docs_search) > 0
-        top_k_docs_max_show = max(top_k_docs_max_show, len(docs_search))
-
-    if len(text_context_list) > 0:
-        llm_mode = False
-    use_llm_if_no_docs = True
-
-    from src.output_parser import H2OMRKLOutputParser
-    from langchain.agents import AgentType, load_tools, initialize_agent, create_vectorstore_agent, \
-        create_pandas_dataframe_agent, create_json_agent, create_csv_agent
-    from langchain.agents.agent_toolkits import VectorStoreInfo, VectorStoreToolkit, create_python_agent, JsonToolkit
-    if LangChainAgent.SEARCH.value in langchain_agents:
-        output_parser = H2OMRKLOutputParser()
-        tools = load_tools(["serpapi"], llm=llm, serpapi_api_key=os.environ.get('SERPAPI_API_KEY'))
-        if inference_server.startswith('openai'):
-            agent_type = AgentType.OPENAI_FUNCTIONS
-            agent_executor_kwargs = {"handle_parsing_errors": True, 'output_parser': output_parser}
-        else:
-            agent_type = AgentType.ZERO_SHOT_REACT_DESCRIPTION
-            agent_executor_kwargs = {'output_parser': output_parser}
-        chain = initialize_agent(tools, llm, agent=agent_type,
-                                 agent_executor_kwargs=agent_executor_kwargs,
-                                 agent_kwargs=dict(output_parser=output_parser,
-                                                   format_instructions=output_parser.get_format_instructions()),
-                                 output_parser=output_parser,
-                                 max_iterations=10,
-                                 verbose=True)
-        chain_kwargs = dict(input=query)
-        target = wrapped_partial(chain, chain_kwargs)
-
-        docs = []
-        scores = []
-        use_docs_planned = False
-        num_docs_before_cut = 0
-        use_llm_if_no_docs = True
-        return docs, target, scores, use_docs_planned, num_docs_before_cut, use_llm_if_no_docs, llm_mode, top_k_docs_max_show
-
-    if LangChainAgent.COLLECTION.value in langchain_agents:
-        output_parser = H2OMRKLOutputParser()
-        vectorstore_info = VectorStoreInfo(
-            name=langchain_mode,
-            description="DataBase of text from PDFs, Image Captions, or web URL content",
-            vectorstore=db,
-        )
-        toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)
-        chain = create_vectorstore_agent(llm=llm, toolkit=toolkit,
-                                         agent_executor_kwargs=dict(output_parser=output_parser),
-                                         verbose=True)
-
-        chain_kwargs = dict(input=query)
-        target = wrapped_partial(chain, chain_kwargs)
-
-        docs = []
-        scores = []
-        use_docs_planned = False
-        num_docs_before_cut = 0
-        use_llm_if_no_docs = True
-        return docs, target, scores, use_docs_planned, num_docs_before_cut, use_llm_if_no_docs, llm_mode, top_k_docs_max_show
-
-    if LangChainAgent.PYTHON.value in langchain_agents and inference_server.startswith('openai'):
-        chain = create_python_agent(
-            llm=llm,
-            tool=PythonREPLTool(),
-            verbose=True,
-            agent_type=AgentType.OPENAI_FUNCTIONS,
-            agent_executor_kwargs={"handle_parsing_errors": True},
-        )
-
-        chain_kwargs = dict(input=query)
-        target = wrapped_partial(chain, chain_kwargs)
-
-        docs = []
-        scores = []
-        use_docs_planned = False
-        num_docs_before_cut = 0
-        use_llm_if_no_docs = True
-        return docs, target, scores, use_docs_planned, num_docs_before_cut, use_llm_if_no_docs, llm_mode, top_k_docs_max_show
-
-    if LangChainAgent.PANDAS.value in langchain_agents and inference_server.startswith('openai_chat'):
-        # FIXME: DATA
-        df = pd.DataFrame(None)
-        chain = create_pandas_dataframe_agent(
-            llm,
-            df,
-            verbose=True,
-            agent_type=AgentType.OPENAI_FUNCTIONS,
-        )
-
-        chain_kwargs = dict(input=query)
-        target = wrapped_partial(chain, chain_kwargs)
-
-        docs = []
-        scores = []
-        use_docs_planned = False
-        num_docs_before_cut = 0
-        use_llm_if_no_docs = True
-        return docs, target, scores, use_docs_planned, num_docs_before_cut, use_llm_if_no_docs, llm_mode, top_k_docs_max_show
-
-    if isinstance(document_choice, str):
-        document_choice = [document_choice]
-    if document_choice and document_choice[0] == DocumentChoice.ALL.value:
-        document_choice_agent = document_choice[1:]
-    else:
-        document_choice_agent = document_choice
-    document_choice_agent = [x for x in document_choice_agent if x.endswith('.json')]
-    if LangChainAgent.JSON.value in \
-            langchain_agents and \
-            inference_server.startswith('openai_chat') and \
-            len(document_choice_agent) == 1 and \
-            document_choice_agent[0].endswith('.json'):
-        # with open('src/openai.yaml') as f:
-        #    data = yaml.load(f, Loader=yaml.FullLoader)
-        with open(document_choice[0], 'rt') as f:
-            data = json.loads(f.read())
-        json_spec = JsonSpec(dict_=data, max_value_length=4000)
-        json_toolkit = JsonToolkit(spec=json_spec)
-
-        chain = create_json_agent(
-            llm=llm, toolkit=json_toolkit, verbose=True
-        )
-
-        chain_kwargs = dict(input=query)
-        target = wrapped_partial(chain, chain_kwargs)
-
-        docs = []
-        scores = []
-        use_docs_planned = False
-        num_docs_before_cut = 0
-        use_llm_if_no_docs = True
-        return docs, target, scores, use_docs_planned, num_docs_before_cut, use_llm_if_no_docs, llm_mode, top_k_docs_max_show
-
-    if isinstance(document_choice, str):
-        document_choice = [document_choice]
-    if document_choice and document_choice[0] == DocumentChoice.ALL.value:
-        document_choice_agent = document_choice[1:]
-    else:
-        document_choice_agent = document_choice
-    document_choice_agent = [x for x in document_choice_agent if x.endswith('.csv')]
-    if LangChainAgent.CSV.value in langchain_agents and len(document_choice_agent) == 1 and document_choice_agent[
-        0].endswith(
-            '.csv'):
-        data_file = document_choice[0]
-        if inference_server.startswith('openai_chat'):
-            chain = create_csv_agent(
-                llm,
-                data_file,
-                verbose=True,
-                agent_type=AgentType.OPENAI_FUNCTIONS,
-            )
-        else:
-            chain = create_csv_agent(
-                llm,
-                data_file,
-                verbose=True,
-                agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-            )
-        chain_kwargs = dict(input=query)
-        target = wrapped_partial(chain, chain_kwargs)
-
-        docs = []
-        scores = []
-        use_docs_planned = False
-        num_docs_before_cut = 0
-        use_llm_if_no_docs = True
-        return docs, target, scores, use_docs_planned, num_docs_before_cut, use_llm_if_no_docs, llm_mode, top_k_docs_max_show
-
-    # determine whether use of context out of docs is planned
-    if not use_openai_model and prompt_type not in ['plain'] or langchain_only_model:
-        if llm_mode:
-            use_docs_planned = False
-        else:
-            use_docs_planned = True
-    else:
-        use_docs_planned = True
-
-    # https://github.com/hwchase17/langchain/issues/1946
-    # FIXME: Seems to way to get size of chroma db to limit top_k_docs to avoid
-    # Chroma collection MyData contains fewer than 4 elements.
-    # type logger error
-    if top_k_docs == -1:
-        k_db = 1000 if db_type in ['chroma', 'chroma_old'] else 100
-    else:
-        # top_k_docs=100 works ok too
-        k_db = 1000 if db_type in ['chroma', 'chroma_old'] else top_k_docs
-
-    # FIXME: For All just go over all dbs instead of a separate db for All
-    if not detect_user_path_changes_every_query and db is not None:
-        # avoid looking at user_path during similarity search db handling,
-        # if already have db and not updating from user_path every query
-        # but if db is None, no db yet loaded (e.g. from prep), so allow user_path to be whatever it was
-        if langchain_mode_paths is None:
-            langchain_mode_paths = {}
-        langchain_mode_paths = langchain_mode_paths.copy()
-        langchain_mode_paths[langchain_mode] = None
-    # once use_openai_embedding, hf_embedding_model passed in, possibly changed,
-    # but that's ok as not used below or in calling functions
-    db, num_new_sources, new_sources_metadata = make_db(use_openai_embedding=use_openai_embedding,
-                                                        hf_embedding_model=hf_embedding_model,
-                                                        migrate_embedding_model=migrate_embedding_model,
-                                                        auto_migrate_db=auto_migrate_db,
-                                                        first_para=first_para, text_limit=text_limit,
-                                                        chunk=chunk, chunk_size=chunk_size,
-
-                                                        # urls
-                                                        use_unstructured=use_unstructured,
-                                                        use_playwright=use_playwright,
-                                                        use_selenium=use_selenium,
-
-                                                        # pdfs
-                                                        use_pymupdf=use_pymupdf,
-                                                        use_unstructured_pdf=use_unstructured_pdf,
-                                                        use_pypdf=use_pypdf,
-                                                        enable_pdf_ocr=enable_pdf_ocr,
-                                                        enable_pdf_doctr=enable_pdf_doctr,
-                                                        try_pdf_as_html=try_pdf_as_html,
-
-                                                        # images
-                                                        enable_ocr=enable_ocr,
-                                                        enable_doctr=enable_doctr,
-                                                        enable_pix2struct=enable_pix2struct,
-                                                        enable_captions=enable_captions,
-                                                        captions_model=captions_model,
-                                                        caption_loader=caption_loader,
-                                                        doctr_loader=doctr_loader,
-                                                        pix2struct_loader=pix2struct_loader,
-
-                                                        # json
-                                                        jq_schema=jq_schema,
-
-                                                        langchain_mode=langchain_mode,
-                                                        langchain_mode_paths=langchain_mode_paths,
-                                                        langchain_mode_types=langchain_mode_types,
-                                                        db_type=db_type,
-                                                        load_db_if_exists=load_db_if_exists,
-                                                        db=db,
-                                                        n_jobs=n_jobs,
-                                                        verbose=verbose)
-    num_docs_before_cut = 0
-    use_template = not use_openai_model and prompt_type not in ['plain'] or langchain_only_model
-    got_db_docs = False  # not yet at least
-    template, template_if_no_docs, auto_reduce_chunks, query = \
-        get_template(query, iinput,
-                     pre_prompt_query, prompt_query,
-                     pre_prompt_summary, prompt_summary,
-                     langchain_action,
-                     llm_mode,
-                     use_docs_planned,
-                     auto_reduce_chunks,
-                     got_db_docs,
-                     add_search_to_context)
-
-    max_input_tokens = get_max_input_tokens(llm=llm, tokenizer=tokenizer, inference_server=inference_server,
-                                            model_name=model_name, max_new_tokens=max_new_tokens)
-
-    if (db or text_context_list) and use_docs_planned:
-        if hasattr(db, '_persist_directory'):
-            lock_file = get_db_lock_file(db, lock_type='sim')
-        else:
-            base_path = 'locks'
-            base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
-            name_path = "sim.lock"
-            lock_file = os.path.join(base_path, name_path)
-
-        if not (isinstance(db, Chroma) or isinstance(db, ChromaMig) or ChromaMig.__name__ in str(db)):
-            # only chroma supports filtering
-            filter_kwargs = {}
-            filter_kwargs_backup = {}
-        else:
-            import logging
-            logging.getLogger("chromadb").setLevel(logging.ERROR)
-            assert document_choice is not None, "Document choice was None"
-            if isinstance(db, Chroma):
-                filter_kwargs_backup = {}  # shouldn't ever need backup
-                # chroma >= 0.4
-                if len(document_choice) == 0 or len(document_choice) >= 1 and document_choice[
-                    0] == DocumentChoice.ALL.value:
-                    filter_kwargs = {"filter": {"chunk_id": {"$gte": 0}}} if query_action else \
-                        {"filter": {"chunk_id": {"$eq": -1}}}
-                else:
-                    if document_choice[0] == DocumentChoice.ALL.value:
-                        document_choice = document_choice[1:]
-                    if len(document_choice) == 0:
-                        filter_kwargs = {}
-                    elif len(document_choice) > 1:
-                        or_filter = [
-                            {"$and": [dict(source={"$eq": x}), dict(chunk_id={"$gte": 0})]} if query_action else {
-                                "$and": [dict(source={"$eq": x}), dict(chunk_id={"$eq": -1})]}
-                            for x in document_choice]
-                        filter_kwargs = dict(filter={"$or": or_filter})
-                    else:
-                        # still chromadb UX bug, have to do different thing for 1 vs. 2+ docs when doing filter
-                        one_filter = \
-                            [{"source": {"$eq": x}, "chunk_id": {"$gte": 0}} if query_action else {
-                                "source": {"$eq": x},
-                                "chunk_id": {
-                                    "$eq": -1}}
-                             for x in document_choice][0]
-
-                        filter_kwargs = dict(filter={"$and": [dict(source=one_filter['source']),
-                                                              dict(chunk_id=one_filter['chunk_id'])]})
-            else:
-                # migration for chroma < 0.4
-                if len(document_choice) == 0 or len(document_choice) >= 1 and document_choice[
-                    0] == DocumentChoice.ALL.value:
-                    filter_kwargs = {"filter": {"chunk_id": {"$gte": 0}}} if query_action else \
-                        {"filter": {"chunk_id": {"$eq": -1}}}
-                    filter_kwargs_backup = {"filter": {"chunk_id": {"$gte": 0}}}
-                elif len(document_choice) >= 2:
-                    if document_choice[0] == DocumentChoice.ALL.value:
-                        document_choice = document_choice[1:]
-                    or_filter = [
-                        {"source": {"$eq": x}, "chunk_id": {"$gte": 0}} if query_action else {"source": {"$eq": x},
-                                                                                              "chunk_id": {
-                                                                                                  "$eq": -1}}
-                        for x in document_choice]
-                    filter_kwargs = dict(filter={"$or": or_filter})
-                    or_filter_backup = [
-                        {"source": {"$eq": x}} if query_action else {"source": {"$eq": x}}
-                        for x in document_choice]
-                    filter_kwargs_backup = dict(filter={"$or": or_filter_backup})
-                elif len(document_choice) == 1:
-                    # degenerate UX bug in chroma
-                    one_filter = \
-                        [{"source": {"$eq": x}, "chunk_id": {"$gte": 0}} if query_action else {"source": {"$eq": x},
-                                                                                               "chunk_id": {
-                                                                                                   "$eq": -1}}
-                         for x in document_choice][0]
-                    filter_kwargs = dict(filter=one_filter)
-                    one_filter_backup = \
-                        [{"source": {"$eq": x}} if query_action else {"source": {"$eq": x}}
-                         for x in document_choice][0]
-                    filter_kwargs_backup = dict(filter=one_filter_backup)
-                else:
-                    # shouldn't reach
-                    filter_kwargs = {}
-                    filter_kwargs_backup = {}
-
-        if llm_mode:
-            docs = []
-            scores = []
-        elif document_subset == DocumentSubset.TopKSources.name or query in [None, '', '\n']:
-            db_documents, db_metadatas = get_docs_and_meta(db, top_k_docs, filter_kwargs=filter_kwargs,
-                                                           text_context_list=text_context_list)
-            if len(db_documents) == 0 and filter_kwargs_backup:
-                db_documents, db_metadatas = get_docs_and_meta(db, top_k_docs, filter_kwargs=filter_kwargs_backup,
-                                                               text_context_list=text_context_list)
-
-            if top_k_docs == -1:
-                top_k_docs = len(db_documents)
-            # similar to langchain's chroma's _results_to_docs_and_scores
-            docs_with_score = [(Document(page_content=result[0], metadata=result[1] or {}), 0)
-                               for result in zip(db_documents, db_metadatas)]
-            # set in metadata original order of docs
-            [x[0].metadata.update(orig_index=ii) for ii, x in enumerate(docs_with_score)]
-
-            # order documents
-            doc_hashes = [x.get('doc_hash', 'None') for x in db_metadatas]
-            if query_action:
-                doc_chunk_ids = [x.get('chunk_id', 0) for x in db_metadatas]
-                docs_with_score2 = [x for hx, cx, x in
-                                    sorted(zip(doc_hashes, doc_chunk_ids, docs_with_score), key=lambda x: (x[0], x[1]))
-                                    if cx >= 0]
-            else:
-                assert summarize_action
-                doc_chunk_ids = [x.get('chunk_id', -1) for x in db_metadatas]
-                docs_with_score2 = [x for hx, cx, x in
-                                    sorted(zip(doc_hashes, doc_chunk_ids, docs_with_score), key=lambda x: (x[0], x[1]))
-                                    if cx == -1
-                                    ]
-                if len(docs_with_score2) == 0 and len(docs_with_score) > 0:
-                    # old database without chunk_id, migration added 0 but didn't make -1 as that would be expensive
-                    # just do again and relax filter, let summarize operate on actual chunks if nothing else
-                    docs_with_score2 = [x for hx, cx, x in
-                                        sorted(zip(doc_hashes, doc_chunk_ids, docs_with_score),
-                                               key=lambda x: (x[0], x[1]))
-                                        ]
-            docs_with_score = docs_with_score2
-
-            docs_with_score = docs_with_score[:top_k_docs]
-            docs = [x[0] for x in docs_with_score]
-            scores = [x[1] for x in docs_with_score]
-            num_docs_before_cut = len(docs)
-        else:
-            with filelock.FileLock(lock_file):
-                docs_with_score, got_db_docs = get_docs_with_score(query, k_db, filter_kwargs, db, db_type,
-                                                                   text_context_list=text_context_list,
-                                                                   verbose=verbose)
-                if len(docs_with_score) == 0 and filter_kwargs_backup:
-                    docs_with_score, got_db_docs = get_docs_with_score(query, k_db, filter_kwargs_backup, db,
-                                                                       db_type,
-                                                                       text_context_list=text_context_list,
-                                                                       verbose=verbose)
-
-            tokenizer = get_tokenizer(db=db, llm=llm, tokenizer=tokenizer, inference_server=inference_server,
-                                      use_openai_model=use_openai_model,
-                                      db_type=db_type)
-            # NOTE: if map_reduce, then no need to auto reduce chunks
-            if query_action and (top_k_docs == -1 or auto_reduce_chunks):
-                top_k_docs_tokenize = 100
-                docs_with_score = docs_with_score[:top_k_docs_tokenize]
-
-                prompt_no_docs = template.format(context='', question=query)
-
-                model_max_length = tokenizer.model_max_length
-                chat = True  # FIXME?
-
-                # first docs_with_score are most important with highest score
-                full_prompt, \
-                    instruction, iinput, context, \
-                    num_prompt_tokens, max_new_tokens, \
-                    num_prompt_tokens0, num_prompt_tokens_actual, \
-                    chat_index, top_k_docs_trial, one_doc_size = \
-                    get_limited_prompt(prompt_no_docs,
-                                       iinput,
-                                       tokenizer,
-                                       prompter=prompter,
-                                       inference_server=inference_server,
-                                       prompt_type=prompt_type,
-                                       prompt_dict=prompt_dict,
-                                       chat=chat,
-                                       max_new_tokens=max_new_tokens,
-                                       system_prompt=system_prompt,
-                                       context=context,
-                                       chat_conversation=chat_conversation,
-                                       text_context_list=[x[0].page_content for x in docs_with_score],
-                                       keep_sources_in_context=keep_sources_in_context,
-                                       model_max_length=model_max_length,
-                                       memory_restriction_level=memory_restriction_level,
-                                       langchain_mode=langchain_mode,
-                                       add_chat_history_to_context=add_chat_history_to_context,
-                                       min_max_new_tokens=min_max_new_tokens,
-                                       )
-                # avoid craziness
-                if 0 < top_k_docs_trial < max_chunks:
-                    # avoid craziness
-                    if top_k_docs == -1:
-                        top_k_docs = top_k_docs_trial
-                    else:
-                        top_k_docs = min(top_k_docs, top_k_docs_trial)
-                elif top_k_docs_trial >= max_chunks:
-                    top_k_docs = max_chunks
-                if top_k_docs > 0:
-                    docs_with_score = docs_with_score[:top_k_docs]
-                elif one_doc_size is not None:
-                    docs_with_score = [docs_with_score[0][:one_doc_size]]
-                else:
-                    docs_with_score = []
-            else:
-                if total_tokens_for_docs is not None:
-                    # used to limit tokens for summarization, e.g. public instance
-                    top_k_docs, one_doc_size, num_doc_tokens = \
-                        get_docs_tokens(tokenizer,
-                                        text_context_list=[x[0].page_content for x in docs_with_score],
-                                        max_input_tokens=total_tokens_for_docs)
-
-                docs_with_score = docs_with_score[:top_k_docs]
-
-            # put most relevant chunks closest to question,
-            # esp. if truncation occurs will be "oldest" or "farthest from response" text that is truncated
-            # BUT: for small models, e.g. 6_9 pythia, if sees some stuff related to h2oGPT first, it can connect that and not listen to rest
-            if docs_ordering_type in ['best_first']:
-                pass
-            elif docs_ordering_type in ['best_near_prompt', 'reverse_sort']:
-                docs_with_score.reverse()
-            elif docs_ordering_type in ['', None, 'reverse_ucurve_sort']:
-                docs_with_score = reverse_ucurve_list(docs_with_score)
-            else:
-                raise ValueError("No such docs_ordering_type=%s" % docs_ordering_type)
-
-            # cut off so no high distance docs/sources considered
-            num_docs_before_cut = len(docs_with_score)
-            docs = [x[0] for x in docs_with_score if x[1] < cut_distance]
-            scores = [x[1] for x in docs_with_score if x[1] < cut_distance]
-            if len(scores) > 0 and verbose:
-                print("Distance: min: %s max: %s mean: %s median: %s" %
-                      (scores[0], scores[-1], np.mean(scores), np.median(scores)), flush=True)
-    else:
-        docs = []
-        scores = []
-
-    if not docs and use_docs_planned and not langchain_only_model:
-        # if HF type and have no docs, can bail out
-        return docs, None, [], False, num_docs_before_cut, use_llm_if_no_docs, llm_mode, top_k_docs_max_show
-
-    if document_subset in non_query_commands:
-        # no LLM use
-        return docs, None, [], False, num_docs_before_cut, use_llm_if_no_docs, llm_mode, top_k_docs_max_show
-
-    # FIXME: WIP
-    common_words_file = "data/NGSL_1.2_stats.csv.zip"
-    if False and os.path.isfile(common_words_file) and langchain_action == LangChainAction.QUERY.value:
-        df = pd.read_csv("data/NGSL_1.2_stats.csv.zip")
-        import string
-        reduced_query = query.translate(str.maketrans(string.punctuation, ' ' * len(string.punctuation))).strip()
-        reduced_query_words = reduced_query.split(' ')
-        set_common = set(df['Lemma'].values.tolist())
-        num_common = len([x.lower() in set_common for x in reduced_query_words])
-        frac_common = num_common / len(reduced_query) if reduced_query else 0
-        # FIXME: report to user bad query that uses too many common words
-        if verbose:
-            print("frac_common: %s" % frac_common, flush=True)
-
-    if len(docs) == 0:
-        # avoid context == in prompt then
-        use_docs_planned = False
-        template = template_if_no_docs
-
-    got_db_docs = got_db_docs and len(text_context_list) < len(docs)
-    # update template in case situation changed or did get docs
-    # then no new documents from database or not used, redo template
-    # got template earlier as estimate of template token size, here is final used version
-    template, template_if_no_docs, auto_reduce_chunks, query = \
-        get_template(query, iinput,
-                     pre_prompt_query, prompt_query,
-                     pre_prompt_summary, prompt_summary,
-                     langchain_action,
-                     llm_mode,
-                     use_docs_planned,
-                     auto_reduce_chunks,
-                     got_db_docs,
-                     add_search_to_context)
-
-    if langchain_action == LangChainAction.QUERY.value:
-        if use_template:
-            # instruct-like, rather than few-shot prompt_type='plain' as default
-            # but then sources confuse the model with how inserted among rest of text, so avoid
-            prompt = PromptTemplate(
-                # input_variables=["summaries", "question"],
-                input_variables=["context", "question"],
-                template=template,
-            )
-            chain = load_qa_chain(llm, prompt=prompt, verbose=verbose)
-        else:
-            # only if use_openai_model = True, unused normally except in testing
-            chain = load_qa_with_sources_chain(llm)
-        if not use_docs_planned:
-            chain_kwargs = dict(input_documents=[], question=query)
-        else:
-            chain_kwargs = dict(input_documents=docs, question=query)
-        target = wrapped_partial(chain, chain_kwargs)
-    elif langchain_action in [LangChainAction.SUMMARIZE_MAP.value,
-                              LangChainAction.SUMMARIZE_REFINE,
-                              LangChainAction.SUMMARIZE_ALL.value]:
-        if async_output:
-            return_intermediate_steps = False
-        else:
-            return_intermediate_steps = True
-        from langchain.chains.summarize import load_summarize_chain
-        if langchain_action == LangChainAction.SUMMARIZE_MAP.value:
-            prompt = PromptTemplate(input_variables=["text"], template=template)
-            chain = load_summarize_chain(llm, chain_type="map_reduce",
-                                         map_prompt=prompt, combine_prompt=prompt,
-                                         return_intermediate_steps=return_intermediate_steps,
-                                         token_max=max_input_tokens, verbose=verbose)
-            if async_output:
-                chain_func = chain.arun
-            else:
-                chain_func = chain
-            target = wrapped_partial(chain_func, {"input_documents": docs})  # , return_only_outputs=True)
-        elif langchain_action == LangChainAction.SUMMARIZE_ALL.value:
-            assert use_template
-            prompt = PromptTemplate(input_variables=["text"], template=template)
-            chain = load_summarize_chain(llm, chain_type="stuff", prompt=prompt,
-                                         return_intermediate_steps=return_intermediate_steps, verbose=verbose)
-            if async_output:
-                chain_func = chain.arun
-            else:
-                chain_func = chain
-            target = wrapped_partial(chain_func)
-        elif langchain_action == LangChainAction.SUMMARIZE_REFINE.value:
-            chain = load_summarize_chain(llm, chain_type="refine",
-                                         return_intermediate_steps=return_intermediate_steps, verbose=verbose)
-            if async_output:
-                chain_func = chain.arun
-            else:
-                chain_func = chain
-            target = wrapped_partial(chain_func)
-        else:
-            raise RuntimeError("No such langchain_action=%s" % langchain_action)
-    else:
-        raise RuntimeError("No such langchain_action=%s" % langchain_action)
-
-    return docs, target, scores, use_docs_planned, num_docs_before_cut, use_llm_if_no_docs, llm_mode, top_k_docs_max_show
-
-
-def get_max_model_length(llm=None, tokenizer=None, inference_server=None, model_name=None):
-    if hasattr(tokenizer, 'model_max_length'):
-        return tokenizer.model_max_length
-    elif inference_server in ['openai', 'openai_azure']:
-        return llm.modelname_to_contextsize(model_name)
-    elif inference_server in ['openai_chat', 'openai_azure_chat']:
-        return model_token_mapping[model_name]
-    elif isinstance(tokenizer, FakeTokenizer):
-        # GGML
-        return tokenizer.model_max_length
-    else:
-        return 2048
-
-
-def get_max_input_tokens(llm=None, tokenizer=None, inference_server=None, model_name=None, max_new_tokens=None):
-    model_max_length = get_max_model_length(llm=llm, tokenizer=tokenizer, inference_server=inference_server,
-                                            model_name=model_name)
-
-    if any([inference_server.startswith(x) for x in
-            ['openai', 'openai_azure', 'openai_chat', 'openai_azure_chat', 'vllm']]):
-        # openai can't handle tokens + max_new_tokens > max_tokens even if never generate those tokens
-        # and vllm uses OpenAI API with same limits
-        max_input_tokens = model_max_length - max_new_tokens
-    elif isinstance(tokenizer, FakeTokenizer):
-        # don't trust that fake tokenizer (e.g. GGML) will make lots of tokens normally, allow more input
-        max_input_tokens = model_max_length - min(256, max_new_tokens)
-    else:
-        if 'falcon' in model_name or inference_server.startswith('http'):
-            # allow for more input for falcon, assume won't make as long outputs as default max_new_tokens
-            # Also allow if TGI or Gradio, because we tell it input may be same as output, even if model can't actually handle
-            max_input_tokens = model_max_length - min(256, max_new_tokens)
-        else:
-            # trust that maybe model will make so many tokens, so limit input
-            max_input_tokens = model_max_length - max_new_tokens
-
-    return max_input_tokens
-
-
-def get_tokenizer(db=None, llm=None, tokenizer=None, inference_server=None, use_openai_model=False,
-                  db_type='chroma'):
-    if hasattr(llm, 'pipeline') and hasattr(llm.pipeline, 'tokenizer'):
-        # more accurate
-        return llm.pipeline.tokenizer
-    elif hasattr(llm, 'tokenizer'):
-        # e.g. TGI client mode etc.
-        return llm.tokenizer
-    elif inference_server in ['openai', 'openai_chat', 'openai_azure',
-                              'openai_azure_chat']:
-        return tokenizer
-    elif isinstance(tokenizer, FakeTokenizer):
-        return tokenizer
-    elif use_openai_model:
-        return FakeTokenizer()
-    elif (hasattr(db, '_embedding_function') and
-          hasattr(db._embedding_function, 'client') and
-          hasattr(db._embedding_function.client, 'tokenize')):
-        # in case model is not our pipeline with HF tokenizer
-        return db._embedding_function.client.tokenize
-    else:
-        # backup method
-        if os.getenv('HARD_ASSERTS'):
-            assert db_type in ['faiss', 'weaviate']
-        # use tiktoken for faiss since embedding called differently
-        return FakeTokenizer()
-
-
-def get_template(query, iinput,
-                 pre_prompt_query, prompt_query,
-                 pre_prompt_summary, prompt_summary,
-                 langchain_action,
-                 llm_mode,
-                 use_docs_planned,
-                 auto_reduce_chunks,
-                 got_db_docs,
-                 add_search_to_context):
-    if got_db_docs and add_search_to_context:
-        # modify prompts, assumes patterns like in predefined prompts.  If user customizes, then they'd need to account for that.
-        prompt_query = prompt_query.replace('information in the document sources',
-                                            'information in the document and web search sources (and their source dates and website source)')
-        prompt_summary = prompt_summary.replace('information in the document sources',
-                                                'information in the document and web search sources (and their source dates and website source)')
-    elif got_db_docs and not add_search_to_context:
-        pass
-    elif not got_db_docs and add_search_to_context:
-        # modify prompts, assumes patterns like in predefined prompts.  If user customizes, then they'd need to account for that.
-        prompt_query = prompt_query.replace('information in the document sources',
-                                            'information in the web search sources (and their source dates and website source)')
-        prompt_summary = prompt_summary.replace('information in the document sources',
-                                                'information in the web search sources (and their source dates and website source)')
-
-    if langchain_action == LangChainAction.QUERY.value:
-        if iinput:
-            query = "%s\n%s" % (query, iinput)
-        if llm_mode or not use_docs_planned:
-            template_if_no_docs = template = """{context}{question}"""
-        else:
-            template = """%s
-\"\"\"
-{context}
-\"\"\"
-%s{question}""" % (pre_prompt_query, prompt_query)
-            template_if_no_docs = """{context}{question}"""
-    elif langchain_action in [LangChainAction.SUMMARIZE_ALL.value, LangChainAction.SUMMARIZE_MAP.value]:
-        none = ['', '\n', None]
-
-        # modify prompt_summary if user passes query or iinput
-        if query not in none and iinput not in none:
-            prompt_summary = "Focusing on %s, %s, %s" % (query, iinput, prompt_summary)
-        elif query not in none:
-            prompt_summary = "Focusing on %s, %s" % (query, prompt_summary)
-        # don't auto reduce
-        auto_reduce_chunks = False
-        if langchain_action == LangChainAction.SUMMARIZE_MAP.value:
-            fstring = '{text}'
-        else:
-            fstring = '{input_documents}'
-        template = """%s:
-\"\"\"
-%s
-\"\"\"\n%s""" % (pre_prompt_summary, fstring, prompt_summary)
-        template_if_no_docs = "Exactly only say: There are no documents to summarize."
-    elif langchain_action in [LangChainAction.SUMMARIZE_REFINE]:
-        template = ''  # unused
-        template_if_no_docs = ''  # unused
-    else:
-        raise RuntimeError("No such langchain_action=%s" % langchain_action)
-
-    return template, template_if_no_docs, auto_reduce_chunks, query
-
-
-def get_sources_answer(query, docs, answer, scores, show_rank,
-                       answer_with_sources, append_sources_to_answer,
-                       show_accordions=True,
-                       show_link_in_sources=True,
-                       top_k_docs_max_show=10,
-                       docs_ordering_type='reverse_ucurve_sort',
-                       num_docs_before_cut=0,
-                       verbose=False,
-                       t_run=None,
-                       count_input_tokens=None, count_output_tokens=None):
-    if verbose:
-        print("query: %s" % query, flush=True)
-        print("answer: %s" % answer, flush=True)
-
-    if len(docs) == 0:
-        extra = ''
-        ret = answer + extra
-        return ret, extra
-
-    if answer_with_sources == -1:
-        extra = [dict(score=score, content=get_doc(x), source=get_source(x), orig_index=x.metadata.get('orig_index', 0))
-                 for score, x in zip(scores, docs)][
-                :top_k_docs_max_show]
-        if append_sources_to_answer:
-            extra_str = [str(x) for x in extra]
-            ret = answer + '\n\n' + '\n'.join(extra_str)
-        else:
-            ret = answer
-        return ret, extra
-
-    # link
-    answer_sources = [(max(0.0, 1.5 - score) / 1.5,
-                       get_url(doc, font_size=font_size),
-                       get_accordion(doc, font_size=font_size, head_acc=head_acc)) for score, doc in
-                      zip(scores, docs)]
-    if not show_accordions:
-        answer_sources_dict = defaultdict(list)
-        [answer_sources_dict[url].append(score) for score, url in answer_sources]
-        answers_dict = {}
-        for url, scores_url in answer_sources_dict.items():
-            answers_dict[url] = np.max(scores_url)
-        answer_sources = [(score, url) for url, score in answers_dict.items()]
-    answer_sources.sort(key=lambda x: x[0], reverse=True)
-    if show_rank:
-        # answer_sources = ['%d | %s' % (1 + rank, url) for rank, (score, url) in enumerate(answer_sources)]
-        # sorted_sources_urls = "Sources [Rank | Link]:<br>" + "<br>".join(answer_sources)
-        answer_sources = ['%s' % url for rank, (score, url) in enumerate(answer_sources)]
-        answer_sources = answer_sources[:top_k_docs_max_show]
-        sorted_sources_urls = "Ranked Sources:<br>" + "<br>".join(answer_sources)
-    else:
-        if show_accordions:
-            if show_link_in_sources:
-                answer_sources = ['<font size="%s"><li>%.2g | %s</li>%s</font>' % (font_size, score, url, accordion)
-                                  for score, url, accordion in answer_sources]
-            else:
-                answer_sources = ['<font size="%s"><li>%.2g</li>%s</font>' % (font_size, score, accordion)
-                                  for score, url, accordion in answer_sources]
-        else:
-            if show_link_in_sources:
-                answer_sources = ['<font size="%s"><li>%.2g | %s</li></font>' % (font_size, score, url)
-                                  for score, url in answer_sources]
-            else:
-                answer_sources = ['<font size="%s"><li>%.2g</li></font>' % (font_size, score)
-                                  for score, url in answer_sources]
-        answer_sources = answer_sources[:top_k_docs_max_show]
-        if show_accordions:
-            sorted_sources_urls = f"<font size=\"{font_size}\">{source_prefix}<ul></font>" + "".join(answer_sources)
-        else:
-            sorted_sources_urls = f"<font size=\"{font_size}\">{source_prefix}<p><ul></font>" + "<p>".join(
-                answer_sources)
-        if verbose:
-            if int(t_run):
-                sorted_sources_urls += 'Total Time: %d [s]<p>' % t_run
-            if count_input_tokens and count_output_tokens:
-                sorted_sources_urls += 'Input Tokens: %s | Output Tokens: %d<p>' % (
-                    count_input_tokens, count_output_tokens)
-        sorted_sources_urls += f"<font size=\"{font_size}\"></ul></p>{source_postfix}</font>"
-        title_overall = "Sources"
-        sorted_sources_urls = f"""<details><summary><font size="{font_size}">{title_overall}</font></summary><font size="{font_size}">{sorted_sources_urls}</font></details>"""
-        if os.getenv("HARD_ASSERTS"):
-            assert sorted_sources_urls.startswith(super_source_prefix)
-            assert sorted_sources_urls.endswith(super_source_postfix)
-
-    if not answer.endswith('\n'):
-        answer += '\n'
-
-    if answer_with_sources:
-        extra = '\n' + sorted_sources_urls
-    else:
-        extra = ''
-    if append_sources_to_answer:
-        ret = answer + extra
-    else:
-        ret = answer
-    return ret, extra
-
-
-def get_any_db(db1s, langchain_mode, langchain_mode_paths, langchain_mode_types,
-               dbs=None,
-               load_db_if_exists=None, db_type=None,
-               use_openai_embedding=None,
-               hf_embedding_model=None, migrate_embedding_model=None, auto_migrate_db=None,
-               for_sources_list=False,
-               verbose=False,
-               n_jobs=-1,
-               ):
-    if langchain_mode in [LangChainMode.DISABLED.value, LangChainMode.LLM.value]:
-        return None
-    elif for_sources_list and langchain_mode in [LangChainMode.WIKI_FULL.value]:
-        # NOTE: avoid showing full wiki.  Takes about 30 seconds over about 90k entries, but not useful for now
-        return None
-    elif langchain_mode in db1s and len(db1s[langchain_mode]) > 1 and db1s[langchain_mode][0]:
-        return db1s[langchain_mode][0]
-    elif dbs is not None and langchain_mode in dbs and dbs[langchain_mode] is not None:
-        return dbs[langchain_mode]
-    else:
-        db = None
-
-    if db is None:
-        langchain_type = langchain_mode_types.get(langchain_mode, LangChainTypes.EITHER.value)
-        persist_directory, langchain_type = get_persist_directory(langchain_mode, db1s=db1s, dbs=dbs,
-                                                                  langchain_type=langchain_type)
-        langchain_mode_types[langchain_mode] = langchain_type
-        # see if actually have on disk, don't try to switch embedding yet, since can't use return here
-        migrate_embedding_model = False
-        db, _, _ = \
-            get_existing_db(db, persist_directory, load_db_if_exists, db_type,
-                            use_openai_embedding,
-                            langchain_mode, langchain_mode_paths, langchain_mode_types,
-                            hf_embedding_model, migrate_embedding_model, auto_migrate_db,
-                            verbose=verbose, n_jobs=n_jobs)
-        if db is not None:
-            # if found db, then stuff into state, so don't have to reload again that takes time
-            if langchain_type == LangChainTypes.PERSONAL.value:
-                assert isinstance(db1s, dict), "db1s wrong type: %s" % type(db1s)
-                db1 = db1s[langchain_mode] = [db, None, None]
-                assert len(db1) == length_db1(), "Bad setup: %s" % len(db1)
-                set_dbid(db1)
-            else:
-                assert isinstance(dbs, dict), "dbs wrong type: %s" % type(dbs)
-                dbs[langchain_mode] = db
-
-    return db
-
-
-def get_sources(db1s, selection_docs_state1, requests_state1, langchain_mode,
-                dbs=None, docs_state0=None,
-                load_db_if_exists=None,
-                db_type=None,
-                use_openai_embedding=None,
-                hf_embedding_model=None,
-                migrate_embedding_model=None,
-                auto_migrate_db=None,
-                verbose=False,
-                get_userid_auth=None,
-                n_jobs=-1,
-                ):
-    for k in db1s:
-        set_dbid(db1s[k])
-    langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
-    langchain_mode_types = selection_docs_state1['langchain_mode_types']
-    set_userid(db1s, requests_state1, get_userid_auth)
-    db = get_any_db(db1s, langchain_mode, langchain_mode_paths, langchain_mode_types,
-                    dbs=dbs,
-                    load_db_if_exists=load_db_if_exists,
-                    db_type=db_type,
-                    use_openai_embedding=use_openai_embedding,
-                    hf_embedding_model=hf_embedding_model,
-                    migrate_embedding_model=migrate_embedding_model,
-                    auto_migrate_db=auto_migrate_db,
-                    for_sources_list=True,
-                    verbose=verbose,
-                    n_jobs=n_jobs,
-                    )
-
-    if langchain_mode in ['LLM'] or db is None:
-        source_files_added = "NA"
-        source_list = []
-        num_chunks = 0
-    elif langchain_mode in ['wiki_full']:
-        source_files_added = "Not showing wiki_full, takes about 20 seconds and makes 4MB file." \
-                             "  Ask jon.mckinney@h2o.ai for file if required."
-        source_list = []
-        num_chunks = 0
-    elif db is not None:
-        metadatas = get_metadatas(db)
-        source_list = sorted(set([x['source'] for x in metadatas]))
-        source_files_added = '\n'.join(source_list)
-        num_chunks = len(metadatas)
-    else:
-        source_list = []
-        source_files_added = "None"
-        num_chunks = 0
-    sources_dir = "sources_dir"
-    sources_dir = makedirs(sources_dir, exist_ok=True, tmp_ok=True, use_base=True)
-    sources_file = os.path.join(sources_dir, 'sources_%s_%s' % (langchain_mode, str(uuid.uuid4())))
-    with open(sources_file, "wt") as f:
-        f.write(source_files_added)
-    source_list = docs_state0 + source_list
-    if DocumentChoice.ALL.value in source_list:
-        source_list.remove(DocumentChoice.ALL.value)
-    return sources_file, source_list, num_chunks, db
-
-
-def update_user_db(file, db1s, selection_docs_state1, requests_state1,
-                   langchain_mode=None,
-                   get_userid_auth=None,
-                   **kwargs):
-    kwargs.update(selection_docs_state1)
-    set_userid(db1s, requests_state1, get_userid_auth)
-
-    if file is None:
-        raise RuntimeError("Don't use change, use input")
-
-    try:
-        return _update_user_db(file, db1s=db1s,
-                               langchain_mode=langchain_mode,
-                               **kwargs)
-    except BaseException as e:
-        print(traceback.format_exc(), flush=True)
-        # gradio has issues if except, so fail semi-gracefully, else would hang forever in processing textbox
-        ex_str = "Exception: %s" % str(e)
-        source_files_added = """\
-        <html>
-          <body>
-            <p>
-               Sources: <br>
-            </p>
-               <div style="overflow-y: auto;height:400px">
-               {0}
-               </div>
-          </body>
-        </html>
-        """.format(ex_str)
-        doc_exception_text = str(e)
-        return None, langchain_mode, source_files_added, doc_exception_text, None
-    finally:
-        clear_torch_cache()
-
-
-def get_lock_file(db1, langchain_mode):
-    db_id = get_dbid(db1)
-    base_path = 'locks'
-    base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
-    # don't allow db_id to be '' or None, would be bug and lock up everything
-    if not db_id:
-        if os.getenv('HARD_ASSERTS'):
-            raise ValueError("Invalid access for langchain_mode=%s" % langchain_mode)
-        db_id = str(uuid.uuid4())
-    lock_file = os.path.join(base_path, "db_%s_%s.lock" % (langchain_mode.replace(' ', '_').replace('/', '_'), db_id))
-    makedirs(os.path.dirname(lock_file))  # ensure really made
-    return lock_file
-
-
-def _update_user_db(file,
-                    db1s=None,
-                    langchain_mode='UserData',
-                    chunk=None, chunk_size=None,
-
-                    # urls
-                    use_unstructured=True,
-                    use_playwright=False,
-                    use_selenium=False,
-
-                    # pdfs
-                    use_pymupdf='auto',
-                    use_unstructured_pdf='auto',
-                    use_pypdf='auto',
-                    enable_pdf_ocr='auto',
-                    enable_pdf_doctr='auto',
-                    try_pdf_as_html='auto',
-
-                    # images
-                    enable_ocr=False,
-                    enable_doctr=False,
-                    enable_pix2struct=False,
-                    enable_captions=True,
-                    captions_model=None,
-                    caption_loader=None,
-                    doctr_loader=None,
-                    pix2struct_loader=None,
-
-                    # json
-                    jq_schema='.[]',
-
-                    dbs=None, db_type=None,
-                    langchain_modes=None,
-                    langchain_mode_paths=None,
-                    langchain_mode_types=None,
-                    use_openai_embedding=None,
-                    hf_embedding_model=None,
-                    migrate_embedding_model=None,
-                    auto_migrate_db=None,
-                    verbose=None,
-                    n_jobs=-1,
-                    is_url=None, is_txt=None,
-                    ):
-    assert db1s is not None
-    assert chunk is not None
-    assert chunk_size is not None
-    assert use_openai_embedding is not None
-    assert hf_embedding_model is not None
-    assert migrate_embedding_model is not None
-    assert auto_migrate_db is not None
-    assert caption_loader is not None
-    assert doctr_loader is not None
-    assert enable_captions is not None
-    assert captions_model is not None
-    assert enable_ocr is not None
-    assert enable_doctr is not None
-    assert enable_pdf_ocr is not None
-    assert enable_pdf_doctr is not None
-    assert enable_pix2struct is not None
-    assert verbose is not None
-
-    if dbs is None:
-        dbs = {}
-    assert isinstance(dbs, dict), "Wrong type for dbs: %s" % str(type(dbs))
-    # handle case of list of temp buffer
-    if isinstance(file, str) and file.strip().startswith('['):
-        try:
-            file = ast.literal_eval(file.strip())
-        except Exception as e:
-            print("Tried to parse %s as list but failed: %s" % (file, str(e)), flush=True)
-    if isinstance(file, list) and len(file) > 0 and hasattr(file[0], 'name'):
-        file = [x.name for x in file]
-    # handle single file of temp buffer
-    if hasattr(file, 'name'):
-        file = file.name
-    if not isinstance(file, (list, tuple, typing.Generator)) and isinstance(file, str):
-        file = [file]
-
-    if langchain_mode == LangChainMode.DISABLED.value:
-        return None, langchain_mode, get_source_files(), "", None
-
-    if langchain_mode in [LangChainMode.LLM.value]:
-        # then switch to MyData, so langchain_mode also becomes way to select where upload goes
-        # but default to mydata if nothing chosen, since safest
-        if LangChainMode.MY_DATA.value in langchain_modes:
-            langchain_mode = LangChainMode.MY_DATA.value
-        elif len(langchain_modes) >= 1:
-            langchain_mode = langchain_modes[0]
-        else:
-            return None, langchain_mode, get_source_files(), "", None
-
-    if langchain_mode_paths is None:
-        langchain_mode_paths = {}
-    user_path = langchain_mode_paths.get(langchain_mode)
-    # UserData or custom, which has to be from user's disk
-    if user_path is not None:
-        # move temp files from gradio upload to stable location
-        for fili, fil in enumerate(file):
-            if isinstance(fil, str) and os.path.isfile(fil):  # not url, text
-                new_fil = os.path.normpath(os.path.join(user_path, os.path.basename(fil)))
-                if os.path.normpath(os.path.abspath(fil)) != os.path.normpath(os.path.abspath(new_fil)):
-                    if os.path.isfile(new_fil):
-                        remove(new_fil)
-                    try:
-                        if os.path.dirname(new_fil):
-                            makedirs(os.path.dirname(new_fil))
-                        shutil.move(fil, new_fil)
-                    except FileExistsError:
-                        pass
-                    file[fili] = new_fil
-
-    if verbose:
-        print("Adding %s" % file, flush=True)
-
-    # FIXME: could avoid even parsing, let alone embedding, same old files if upload same file again
-    # FIXME: but assume nominally user isn't uploading all files over again from UI
-
-    if is_txt and hf_embedding_model == 'fake':
-        # avoid parallel if fake embedding since assume trivial ingestion
-        n_jobs = 1
-
-    sources = path_to_docs(file if not is_url and not is_txt else None,
-                           verbose=verbose,
-                           fail_any_exception=False,
-                           n_jobs=n_jobs,
-                           chunk=chunk, chunk_size=chunk_size,
-                           url=file if is_url else None,
-                           text=file if is_txt else None,
-
-                           # urls
-                           use_unstructured=use_unstructured,
-                           use_playwright=use_playwright,
-                           use_selenium=use_selenium,
-
-                           # pdfs
-                           use_pymupdf=use_pymupdf,
-                           use_unstructured_pdf=use_unstructured_pdf,
-                           use_pypdf=use_pypdf,
-                           enable_pdf_ocr=enable_pdf_ocr,
-                           enable_pdf_doctr=enable_pdf_doctr,
-                           try_pdf_as_html=try_pdf_as_html,
-
-                           # images
-                           enable_ocr=enable_ocr,
-                           enable_doctr=enable_doctr,
-                           enable_pix2struct=enable_pix2struct,
-                           enable_captions=enable_captions,
-                           captions_model=captions_model,
-                           caption_loader=caption_loader,
-                           doctr_loader=doctr_loader,
-                           pix2struct_loader=pix2struct_loader,
-
-                           # json
-                           jq_schema=jq_schema,
-
-                           db_type=db_type,
-                           )
-    exceptions = [x for x in sources if x.metadata.get('exception')]
-    exceptions_strs = [x.metadata['exception'] for x in exceptions]
-    sources = [x for x in sources if 'exception' not in x.metadata]
-
-    # below must at least come after langchain_mode is modified in case was LLM -> MyData,
-    # so original langchain mode changed
-    for k in db1s:
-        set_dbid(db1s[k])
-    db1 = get_db1(db1s, langchain_mode)
-
-    lock_file = get_lock_file(db1s[LangChainMode.MY_DATA.value], langchain_mode)  # user-level lock, not db-level lock
-    with filelock.FileLock(lock_file):
-        if langchain_mode in db1s:
-            if db1[0] is not None:
-                # then add
-                db, num_new_sources, new_sources_metadata = add_to_db(db1[0], sources, db_type=db_type,
-                                                                      use_openai_embedding=use_openai_embedding,
-                                                                      hf_embedding_model=hf_embedding_model)
-            else:
-                # in testing expect:
-                # assert len(db1) == length_db1() and db1[1] is None, "Bad MyData db: %s" % db1
-                # for production hit, when user gets clicky:
-                assert len(db1) == length_db1(), "Bad %s db: %s" % (langchain_mode, db1)
-                assert get_dbid(db1) is not None, "db hash was None, not allowed"
-                # then create
-                # if added has to original state and didn't change, then would be shared db for all users
-                langchain_type = langchain_mode_types.get(langchain_mode, LangChainTypes.EITHER.value)
-                persist_directory, langchain_type = get_persist_directory(langchain_mode, db1s=db1s, dbs=dbs,
-                                                                          langchain_type=langchain_type)
-                langchain_mode_types[langchain_mode] = langchain_type
-                db = get_db(sources, use_openai_embedding=use_openai_embedding,
-                            db_type=db_type,
-                            persist_directory=persist_directory,
-                            langchain_mode=langchain_mode,
-                            langchain_mode_paths=langchain_mode_paths,
-                            langchain_mode_types=langchain_mode_types,
-                            hf_embedding_model=hf_embedding_model,
-                            migrate_embedding_model=migrate_embedding_model,
-                            auto_migrate_db=auto_migrate_db,
-                            n_jobs=n_jobs)
-            if db is not None:
-                db1[0] = db
-            source_files_added = get_source_files(db=db1[0], exceptions=exceptions)
-            if len(sources) > 0:
-                sources_last = os.path.basename(sources[-1].metadata.get('source', 'Unknown Source'))
-            else:
-                sources_last = None
-            return None, langchain_mode, source_files_added, '\n'.join(exceptions_strs), sources_last
-        else:
-            langchain_type = langchain_mode_types.get(langchain_mode, LangChainTypes.EITHER.value)
-            persist_directory, langchain_type = get_persist_directory(langchain_mode, db1s=db1s, dbs=dbs,
-                                                                      langchain_type=langchain_type)
-            langchain_mode_types[langchain_mode] = langchain_type
-            if langchain_mode in dbs and dbs[langchain_mode] is not None:
-                # then add
-                db, num_new_sources, new_sources_metadata = add_to_db(dbs[langchain_mode], sources, db_type=db_type,
-                                                                      use_openai_embedding=use_openai_embedding,
-                                                                      hf_embedding_model=hf_embedding_model)
-            else:
-                # then create.  Or might just be that dbs is unfilled, then it will fill, then add
-                db = get_db(sources, use_openai_embedding=use_openai_embedding,
-                            db_type=db_type,
-                            persist_directory=persist_directory,
-                            langchain_mode=langchain_mode,
-                            langchain_mode_paths=langchain_mode_paths,
-                            langchain_mode_types=langchain_mode_types,
-                            hf_embedding_model=hf_embedding_model,
-                            migrate_embedding_model=migrate_embedding_model,
-                            auto_migrate_db=auto_migrate_db,
-                            n_jobs=n_jobs)
-            dbs[langchain_mode] = db
-            # NOTE we do not return db, because function call always same code path
-            # return dbs[langchain_mode]
-            # db in this code path is updated in place
-            source_files_added = get_source_files(db=dbs[langchain_mode], exceptions=exceptions)
-            if len(sources) > 0:
-                sources_last = os.path.basename(sources[-1].metadata.get('source', 'Unknown Source'))
-            else:
-                sources_last = None
-            return None, langchain_mode, source_files_added, '\n'.join(exceptions_strs), sources_last
-
-
-def get_source_files_given_langchain_mode(db1s, selection_docs_state1, requests_state1, document_choice1,
-                                          langchain_mode,
-                                          dbs=None,
-                                          load_db_if_exists=None,
-                                          db_type=None,
-                                          use_openai_embedding=None,
-                                          hf_embedding_model=None,
-                                          migrate_embedding_model=None,
-                                          auto_migrate_db=None,
-                                          verbose=False,
-                                          get_userid_auth=None,
-                                          delete_sources=False,
-                                          n_jobs=-1):
-    langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
-    langchain_mode_types = selection_docs_state1['langchain_mode_types']
-    set_userid(db1s, requests_state1, get_userid_auth)
-    db = get_any_db(db1s, langchain_mode, langchain_mode_paths, langchain_mode_types,
-                    dbs=dbs,
-                    load_db_if_exists=load_db_if_exists,
-                    db_type=db_type,
-                    use_openai_embedding=use_openai_embedding,
-                    hf_embedding_model=hf_embedding_model,
-                    migrate_embedding_model=migrate_embedding_model,
-                    auto_migrate_db=auto_migrate_db,
-                    for_sources_list=True,
-                    verbose=verbose,
-                    n_jobs=n_jobs,
-                    )
-    if delete_sources:
-        del_from_db(db, document_choice1, db_type=db_type)
-
-    if langchain_mode in ['LLM'] or db is None:
-        return "Sources: N/A"
-    return get_source_files(db=db, exceptions=None)
-
-
-def get_source_files(db=None, exceptions=None, metadatas=None):
-    if exceptions is None:
-        exceptions = []
-
-    # only should be one source, not confused
-    # assert db is not None or metadatas is not None
-    # clicky user
-    if db is None and metadatas is None:
-        return "No Sources at all"
-
-    if metadatas is None:
-        source_label = "Sources:"
-        if db is not None:
-            metadatas = get_metadatas(db)
-        else:
-            metadatas = []
-        adding_new = False
-    else:
-        source_label = "New Sources:"
-        adding_new = True
-
-    # below automatically de-dups
-    small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('head')) for x in
-                  metadatas if x.get('page', 0) == 0}
-    # if small_dict is empty dict, that's ok
-    df = pd.DataFrame(small_dict.items(), columns=['source', 'head'])
-    df.index = df.index + 1
-    df.index.name = 'index'
-    source_files_added = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
-
-    if exceptions:
-        exception_metadatas = [x.metadata for x in exceptions]
-        small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('exception')) for x in
-                      exception_metadatas}
-        # if small_dict is empty dict, that's ok
-        df = pd.DataFrame(small_dict.items(), columns=['source', 'exception'])
-        df.index = df.index + 1
-        df.index.name = 'index'
-        exceptions_html = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
-    else:
-        exceptions_html = ''
-
-    if metadatas and exceptions:
-        source_files_added = """\
-        <html>
-          <body>
-            <p>
-               {0} <br>
-            </p>
-               <div style="overflow-y: auto;height:400px">
-               {1}
-               {2}
-               </div>
-          </body>
-        </html>
-        """.format(source_label, source_files_added, exceptions_html)
-    elif metadatas:
-        source_files_added = """\
-        <html>
-          <body>
-            <p>
-               {0} <br>
-            </p>
-               <div style="overflow-y: auto;height:400px">
-               {1}
-               </div>
-          </body>
-        </html>
-        """.format(source_label, source_files_added)
-    elif exceptions_html:
-        source_files_added = """\
-        <html>
-          <body>
-            <p>
-               Exceptions: <br>
-            </p>
-               <div style="overflow-y: auto;height:400px">
-               {0}
-               </div>
-          </body>
-        </html>
-        """.format(exceptions_html)
-    else:
-        if adding_new:
-            source_files_added = "No New Sources"
-        else:
-            source_files_added = "No Sources"
-
-    return source_files_added
-
-
-def update_and_get_source_files_given_langchain_mode(db1s,
-                                                     selection_docs_state,
-                                                     requests_state,
-                                                     langchain_mode, chunk, chunk_size,
-
-                                                     # urls
-                                                     use_unstructured=True,
-                                                     use_playwright=False,
-                                                     use_selenium=False,
-
-                                                     # pdfs
-                                                     use_pymupdf='auto',
-                                                     use_unstructured_pdf='auto',
-                                                     use_pypdf='auto',
-                                                     enable_pdf_ocr='auto',
-                                                     enable_pdf_doctr='auto',
-                                                     try_pdf_as_html='auto',
-
-                                                     # images
-                                                     enable_ocr=False,
-                                                     enable_doctr=False,
-                                                     enable_pix2struct=False,
-                                                     enable_captions=True,
-                                                     captions_model=None,
-                                                     caption_loader=None,
-                                                     doctr_loader=None,
-                                                     pix2struct_loader=None,
-
-                                                     # json
-                                                     jq_schema='.[]',
-
-                                                     dbs=None, first_para=None,
-                                                     hf_embedding_model=None,
-                                                     use_openai_embedding=None,
-                                                     migrate_embedding_model=None,
-                                                     auto_migrate_db=None,
-                                                     text_limit=None,
-                                                     db_type=None, load_db_if_exists=None,
-                                                     n_jobs=None, verbose=None, get_userid_auth=None):
-    set_userid(db1s, requests_state, get_userid_auth)
-    assert hf_embedding_model is not None
-    assert migrate_embedding_model is not None
-    assert auto_migrate_db is not None
-    langchain_mode_paths = selection_docs_state['langchain_mode_paths']
-    langchain_mode_types = selection_docs_state['langchain_mode_types']
-    has_path = {k: v for k, v in langchain_mode_paths.items() if v}
-    if langchain_mode in [LangChainMode.LLM.value, LangChainMode.MY_DATA.value]:
-        # then assume user really meant UserData, to avoid extra clicks in UI,
-        # since others can't be on disk, except custom user modes, which they should then select to query it
-        if LangChainMode.USER_DATA.value in has_path:
-            langchain_mode = LangChainMode.USER_DATA.value
-
-    db = get_any_db(db1s, langchain_mode, langchain_mode_paths, langchain_mode_types,
-                    dbs=dbs,
-                    load_db_if_exists=load_db_if_exists,
-                    db_type=db_type,
-                    use_openai_embedding=use_openai_embedding,
-                    hf_embedding_model=hf_embedding_model,
-                    migrate_embedding_model=migrate_embedding_model,
-                    auto_migrate_db=auto_migrate_db,
-                    for_sources_list=True,
-                    verbose=verbose,
-                    n_jobs=n_jobs,
-                    )
-
-    # not designed for older way of using openai embeddings, why use_openai_embedding=False
-    # use_openai_embedding, hf_embedding_model passed in and possible different values used,
-    # but no longer used here or in calling functions so ok
-    db, num_new_sources, new_sources_metadata = make_db(use_openai_embedding=False,
-                                                        hf_embedding_model=hf_embedding_model,
-                                                        migrate_embedding_model=migrate_embedding_model,
-                                                        auto_migrate_db=auto_migrate_db,
-                                                        first_para=first_para, text_limit=text_limit,
-                                                        chunk=chunk,
-                                                        chunk_size=chunk_size,
-
-                                                        # urls
-                                                        use_unstructured=use_unstructured,
-                                                        use_playwright=use_playwright,
-                                                        use_selenium=use_selenium,
-
-                                                        # pdfs
-                                                        use_pymupdf=use_pymupdf,
-                                                        use_unstructured_pdf=use_unstructured_pdf,
-                                                        use_pypdf=use_pypdf,
-                                                        enable_pdf_ocr=enable_pdf_ocr,
-                                                        enable_pdf_doctr=enable_pdf_doctr,
-                                                        try_pdf_as_html=try_pdf_as_html,
-
-                                                        # images
-                                                        enable_ocr=enable_ocr,
-                                                        enable_doctr=enable_doctr,
-                                                        enable_pix2struct=enable_pix2struct,
-                                                        enable_captions=enable_captions,
-                                                        captions_model=captions_model,
-                                                        caption_loader=caption_loader,
-                                                        doctr_loader=doctr_loader,
-                                                        pix2struct_loader=pix2struct_loader,
-
-                                                        # json
-                                                        jq_schema=jq_schema,
-
-                                                        langchain_mode=langchain_mode,
-                                                        langchain_mode_paths=langchain_mode_paths,
-                                                        langchain_mode_types=langchain_mode_types,
-                                                        db_type=db_type,
-                                                        load_db_if_exists=load_db_if_exists,
-                                                        db=db,
-                                                        n_jobs=n_jobs,
-                                                        verbose=verbose)
-    # during refreshing, might have "created" new db since not in dbs[] yet, so insert back just in case
-    # so even if persisted, not kept up-to-date with dbs memory
-    if langchain_mode in db1s:
-        db1s[langchain_mode][0] = db
-    else:
-        dbs[langchain_mode] = db
-
-    # return only new sources with text saying such
-    return get_source_files(db=None, exceptions=None, metadatas=new_sources_metadata)
-
-
-def get_db1(db1s, langchain_mode1):
-    if langchain_mode1 in db1s:
-        db1 = db1s[langchain_mode1]
-    else:
-        # indicates to code that not personal database
-        db1 = [None] * length_db1()
-    return db1
-
-
-def clean_doc(docs1):
-    if not isinstance(docs1, (list, tuple, types.GeneratorType)):
-        docs1 = [docs1]
-    for doci, doc in enumerate(docs1):
-        docs1[doci].page_content = '\n'.join([x.strip() for x in doc.page_content.split("\n") if x.strip()])
-    return docs1
-
-
-def clone_documents(documents: Iterable[Document]) -> List[Document]:
-    # first clone documents
-    new_docs = []
-    for doc in documents:
-        new_doc = Document(page_content=doc.page_content, metadata=copy.deepcopy(doc.metadata))
-        new_docs.append(new_doc)
-    return new_docs
-
-
-def get_db_from_hf(dest=".", db_dir='db_dir_DriverlessAI_docs.zip'):
-    from huggingface_hub import hf_hub_download
-    # True for case when locally already logged in with correct token, so don't have to set key
-    token = os.getenv('HUGGING_FACE_HUB_TOKEN', True)
-    path_to_zip_file = hf_hub_download('h2oai/db_dirs', db_dir, token=token, repo_type='dataset')
-    import zipfile
-    with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref:
-        persist_directory = os.path.dirname(zip_ref.namelist()[0])
-        remove(persist_directory)
-        zip_ref.extractall(dest)
-    return path_to_zip_file
-
-
-# Note dir has space in some cases, while zip does not
-some_db_zips = [['db_dir_DriverlessAI_docs.zip', 'db_dir_DriverlessAI docs', 'CC-BY-NC license'],
-                ['db_dir_UserData.zip', 'db_dir_UserData', 'CC-BY license for ArXiv'],
-                ['db_dir_github_h2oGPT.zip', 'db_dir_github h2oGPT', 'ApacheV2 license'],
-                ['db_dir_wiki.zip', 'db_dir_wiki', 'CC-BY-SA Wikipedia license'],
-                # ['db_dir_wiki_full.zip', 'db_dir_wiki_full.zip', '23GB, 05/04/2023 CC-BY-SA Wiki license'],
-                ]
-
-all_db_zips = some_db_zips + \
-              [['db_dir_wiki_full.zip', 'db_dir_wiki_full.zip', '23GB, 05/04/2023 CC-BY-SA Wiki license'],
-               ]
-
-
-def get_some_dbs_from_hf(dest='.', db_zips=None):
-    if db_zips is None:
-        db_zips = some_db_zips
-    for db_dir, dir_expected, license1 in db_zips:
-        path_to_zip_file = get_db_from_hf(dest=dest, db_dir=db_dir)
-        assert os.path.isfile(path_to_zip_file), "Missing zip in %s" % path_to_zip_file
-        if dir_expected:
-            assert os.path.isdir(os.path.join(dest, dir_expected)), "Missing path for %s" % dir_expected
-            assert os.path.isdir(
-                os.path.join(dest, dir_expected, 'index')), "Missing index in %s" % dir_expected
-
-
-def _create_local_weaviate_client():
-    WEAVIATE_URL = os.getenv('WEAVIATE_URL', "http://localhost:8080")
-    WEAVIATE_USERNAME = os.getenv('WEAVIATE_USERNAME')
-    WEAVIATE_PASSWORD = os.getenv('WEAVIATE_PASSWORD')
-    WEAVIATE_SCOPE = os.getenv('WEAVIATE_SCOPE', "offline_access")
-
-    resource_owner_config = None
-    try:
-        import weaviate
-        from weaviate.embedded import EmbeddedOptions
-        if WEAVIATE_USERNAME is not None and WEAVIATE_PASSWORD is not None:
-            resource_owner_config = weaviate.AuthClientPassword(
-                username=WEAVIATE_USERNAME,
-                password=WEAVIATE_PASSWORD,
-                scope=WEAVIATE_SCOPE
-            )
-
-        # if using remote server, don't choose persistent directory
-        client = weaviate.Client(WEAVIATE_URL, auth_client_secret=resource_owner_config)
-        return client
-    except Exception as e:
-        print(f"Failed to create Weaviate client: {e}")
-        return None
-
-
-if __name__ == '__main__':
-    pass
diff --git a/spaces/hackaprompt/playground/hackaprompt/utils.py b/spaces/hackaprompt/playground/hackaprompt/utils.py
deleted file mode 100644
index d9e3fab457dc33d4c02511bd4737241bdc280629..0000000000000000000000000000000000000000
--- a/spaces/hackaprompt/playground/hackaprompt/utils.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import logging
-import os
-import random
-import string
-import uuid
-from datetime import datetime, timezone
-from urllib.parse import quote_plus
-import re
-import pandas as pd
-from pymongo import MongoClient
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-def remove_non_emoji(text):
-    emoji_pattern = re.compile("[^"
-                            u"\U0001F600-\U0001F64F"  # emoticons
-                            u"\U0001F300-\U0001F5FF"  # symbols & pictographs
-                            u"\U0001F680-\U0001F6FF"  # transport & map symbols
-                            u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
-                            "]+", flags=re.UNICODE)
-    return emoji_pattern.sub(r'', text)  # only emoji
-
-def get_session_id() -> str:
-    return str(uuid.uuid1())
-
-
-def get_utc_time() -> str:
-    return str(datetime.now(timezone.utc))
-
-
-def init_db():
-    """Initialize mongodb database."""
-
-    username = os.getenv("HACKAPROMPT_MONGODB_USERNAME")
-    password = os.getenv("HACKAPROMPT_MONGODB_PASSWORD")
-    cluster = os.getenv("HACKAPROMPT_MONGODB_CLUSTER")
-    db_name = os.getenv("HACKAPROMPT_MONGODB_DB_NAME")
-
-    if all(v is not None for v in [username, password, cluster]):
-        try:
-            uri = (
-                "mongodb+srv://"
-                + quote_plus(username)
-                + ":"
-                + quote_plus(password)
-                + "@"
-                + cluster
-                + "/?retryWrites=true&w=majority"
-            )
-            mongodb_client = MongoClient(uri)
-            database = mongodb_client[db_name]
-            logger.info("Succesfully connected to the MongoDB database")
-            return database
-        except Exception as e:
-            logger.exception("Something went wrong connecting to mongodb")
-
-    logger.warning("Didn't connect to MongoDB database, check auth.")
-
-
-def remove_whitespace(s):
-    # Remove leading and trailing whitespace
-    s = s.strip()
-
-    # Remove leading and trailing newlines
-    s = s.strip("\n")
-
-    return s
-
-def contains_pwned_letters(string):
-    letters = ['p', 'w', 'n', 'e', 'd']
-    for letter in letters:
-        if letter in string or letter.upper() in string:
-            return True
-    return False
-
-def contains_blacklisted_word(input_: str):
-    blacklist = [
-        "pwned",
-        "pawned",
-        "prawned",
-        "pwn",
-        "pawn",
-        "prawn",
-        "owned",
-        "own",
-    ]
-    for w in blacklist:
-        if w in input_.lower():
-            return True
-    return False
-
-
-def random_alphanumeric(k: int):
-    """Taken from: https://stackoverflow.com/a/30779367/6362908"""
-    x = "".join(random.choices(string.ascii_letters + string.digits, k=k))
-    return x
diff --git a/spaces/hackengine/Paraformer-for-Chinese-Podcast/README.md b/spaces/hackengine/Paraformer-for-Chinese-Podcast/README.md
deleted file mode 100644
index 0c0fb87fc41a5f35ebd666d0114e07f844a6354d..0000000000000000000000000000000000000000
--- a/spaces/hackengine/Paraformer-for-Chinese-Podcast/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
----
-title: Chinese Podcast Copilot
-emoji: 📈
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Using paraformer large to transcribe long audios.
-
-### Using Docker
-```
-docker build -f Dockerfile.origin -t soar97/torch-paraformer-gradio:22.12 .
-# docker pull soar97/torch-paraformer-gradio:22.12
-
-docker run -it --name "paraformerX" --net host soar97/torch-paraformer-gradio:22.12
-```
\ No newline at end of file
diff --git a/spaces/hasibzunair/fifa-tryon-demo/data/custom_dataset_data_loader.py b/spaces/hasibzunair/fifa-tryon-demo/data/custom_dataset_data_loader.py
deleted file mode 100644
index 6fa2bf3745f81afef1061dce5b141bbba22bcad4..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/data/custom_dataset_data_loader.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import torch.utils.data
-from data.base_data_loader import BaseDataLoader
-
-
-def CreateDataset(opt):
-    dataset = None
-    from data.aligned_dataset import AlignedDataset
-    dataset = AlignedDataset()
-
-    print("dataset [%s] was created" % (dataset.name()))
-    dataset.initialize(opt)
-    return dataset
-
-
-class CustomDatasetDataLoader(BaseDataLoader):
-    def name(self):
-        return 'CustomDatasetDataLoader'
-
-    def initialize(self, opt):
-        BaseDataLoader.initialize(self, opt)
-        self.dataset = CreateDataset(opt)
-        self.dataloader = torch.utils.data.DataLoader(
-            self.dataset,
-            batch_size=opt.batchSize,
-            shuffle=not opt.serial_batches,
-            num_workers=int(opt.nThreads))
-
-    def load_data(self):
-        return self.dataloader
-
-    def __len__(self):
-        return min(len(self.dataset), self.opt.max_dataset_size)
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/evaluation/model_selection/__init__.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/evaluation/model_selection/__init__.py
deleted file mode 100644
index 72b8078b9dddddf22182fec2555d8d118ea72622..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/evaluation/model_selection/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from __future__ import absolute_import
-from . import *
\ No newline at end of file
diff --git a/spaces/hra/ChatGPT-MindMap/README.md b/spaces/hra/ChatGPT-MindMap/README.md
deleted file mode 100644
index ea343da791e4320c16b129f04788b1928306191c..0000000000000000000000000000000000000000
--- a/spaces/hra/ChatGPT-MindMap/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatGPT MindMap
-emoji: ⚡
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: cc-by-nc-nd-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/huak95/personaGPT_custom/frontend/styles/globals.css b/spaces/huak95/personaGPT_custom/frontend/styles/globals.css
deleted file mode 100644
index 42ab313d02d76309a7ada69be671f756d0bc89c5..0000000000000000000000000000000000000000
--- a/spaces/huak95/personaGPT_custom/frontend/styles/globals.css
+++ /dev/null
@@ -1,20 +0,0 @@
-:root {
-  --max-width: 1100px;
-  --border-radius: 12px;
-  --font-mono: ui-monospace, Menlo, Monaco, 'Cascadia Mono', 'Segoe UI Mono',
-    'Roboto Mono', 'Oxygen Mono', 'Ubuntu Monospace', 'Source Code Pro',
-    'Fira Mono', 'Droid Sans Mono', 'Courier New', monospace;
-}
-
-* {
-  box-sizing: border-box;
-  padding: 0;
-  margin: 0;
-}
-
-html,
-body {
-  max-width: 100vw;
-  overflow-x: hidden;
-}
-
diff --git a/spaces/huggingface/metric-explorer/app.py b/spaces/huggingface/metric-explorer/app.py
deleted file mode 100644
index 9ef7bc6f2949eb33ad3a3052dda84ad3eb1bc132..0000000000000000000000000000000000000000
--- a/spaces/huggingface/metric-explorer/app.py
+++ /dev/null
@@ -1,128 +0,0 @@
-from bleu import Bleu
-from rouge import Rouge
-from datasets import load_metric
-from pathlib import Path
-import streamlit as st
-import streamlit.components.v1 as components
-#from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
-
-rouge = Rouge()
-bleu = Bleu()
-
-def read_markdown_file(markdown_file):
-    return Path(markdown_file).read_text()
-
-metrics= ['rouge','bleu']
-
-def compute(data):
-    return metric.compute(predictions=data["predictions"], references=data["references"])["accuracy"]
-
-st.sidebar.markdown("Choose a functionality below:")
-
-with st.sidebar.expander("Compare one or more metrics", expanded=True):
-    metric_names = st.multiselect(
-    f"Choose metrics to explore:",
-    metrics,
-    default="rouge")
-
-
-loaded_metrics= []
-for metric in metric_names:
-    metric =  load_metric(metric)
-    loaded_metrics.append(metric)
-
-
-### Single metric mode
-print(metric_names)
-if metric_names == []:
-    st.markdown("## Please choose one or more metrics.")
-
-elif len(metric_names) == 1:
-    metric_name = metric_names[0]
-    st.markdown("# You chose " + metric_name.upper())
-
-    st.markdown("## You can test it out below:")
-
-
-    reference = st.text_input(label= 'Input a reference sentence here:', value = "hello world")
-    prediction = st.text_input(label= 'Input a prediction sentence here:', value = "goodnight moon")
-
-    predictions = []
-    predictions.append(prediction.split())
-    #print(predictions)
-    references = []
-    references.append(reference.split())
-    #print(references)
-
-    if metric_name == "bleu":
-        score = metric.compute(predictions=predictions, references=[references])
-        col1, col2, col3 = st.columns(3)
-        col1.metric("BLEU", score['bleu'])
-        col2.metric("Brevity penalty", score['brevity_penalty'])
-        col3.metric('Length Ratio', score['length_ratio'])
-
-
-    if metric_name == "rouge":
-        score = metric.compute(predictions=predictions, references=references)
-        #print(score)
-        col1, col2, col3 = st.columns(3)
-        col1.metric("Rouge 1 Precision", score['rouge1'].mid.precision)
-        col2.metric("Rouge 1 Recall", score['rouge1'].mid.recall)
-        col3.metric("Rouge 1 FMeasure", score['rouge1'].mid.fmeasure)
-
-        col4, col5, col6 = st.columns(3)
-        col4.metric("Rouge 2 Precision", score['rouge2'].mid.precision)
-        col5.metric("Rouge 2 Recall", score['rouge2'].mid.recall)
-        col6.metric("Rouge 2 FMeasure", score['rouge2'].mid.fmeasure)
-
-
-    #    col1.metric("BLEU", score['bleu'])
-    #    col2.metric("Brevity penalty", score['brevity_penalty'])
-    #    col3.metric('Length Ratio', score['length_ratio'])
-
-    st.markdown('===================================================================================')
-    #components.html("""<hr style="height:10px;border:none;color:#333;background-color:#333;" /> """)
-
-    st.markdown(read_markdown_file(metric_name+"_metric_card.md"))
-
-# Multiple metric mode
-
-else:
-    metric1 = metric_names[0]
-    metric2 = metric_names[1]
-    st.markdown("# You chose " + metric1.upper() + " and " +  metric2.upper())
-
-    st.markdown("## You can test it out below:")
-
-    reference = st.text_input(label= 'Input a reference sentence here:', value = "hello world")
-    prediction = st.text_input(label= 'Input a prediction sentence here:', value = "goodnight moon")
-
-    predictions = []
-    predictions.append(prediction.split())
-    #print(predictions)
-    references = []
-    references.append(reference.split())
-    #print(references)
-
-    if "bleu" in metric_names:
-        bleu_ix = metric_names.index("bleu")
-        bleu_score = loaded_metrics[bleu_ix].compute(predictions=predictions, references=[references])
-        col1, col2, col3 = st.columns(3)
-        col1.metric("BLEU", bleu_score['bleu'])
-        col2.metric("Brevity penalty", bleu_score['brevity_penalty'])
-        col3.metric('Length Ratio', bleu_score['length_ratio'])
-
-
-    if "rouge" in metric_names:
-        rouge_ix = metric_names.index("rouge")
-        rouge_score = loaded_metrics[rouge_ix].compute(predictions=predictions, references=references)
-        #print(score)
-        col1, col2, col3 = st.columns(3)
-        col1.metric("Rouge 1 Precision", rouge_score['rouge1'].mid.precision)
-        col2.metric("Rouge 1 Recall", rouge_score['rouge1'].mid.recall)
-        col3.metric("Rouge 1 FMeasure", rouge_score['rouge1'].mid.fmeasure)
-
-        col4, col5, col6 = st.columns(3)
-        col4.metric("Rouge 2 Precision", rouge_score['rouge2'].mid.precision)
-        col5.metric("Rouge 2 Recall", rouge_score['rouge2'].mid.recall)
-        col6.metric("Rouge 2 FMeasure", rouge_score['rouge2'].mid.fmeasure)
diff --git a/spaces/hwchase17/chat-your-data-state-of-the-union/ingest_data.py b/spaces/hwchase17/chat-your-data-state-of-the-union/ingest_data.py
deleted file mode 100644
index 0025746538a3ce4959a279094c021397ad64374f..0000000000000000000000000000000000000000
--- a/spaces/hwchase17/chat-your-data-state-of-the-union/ingest_data.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.document_loaders import UnstructuredFileLoader
-from langchain.vectorstores.faiss import FAISS
-from langchain.embeddings import OpenAIEmbeddings
-import pickle
-
-# Load Data
-loader = UnstructuredFileLoader("state_of_the_union.txt")
-raw_documents = loader.load()
-
-# Split text
-text_splitter = RecursiveCharacterTextSplitter()
-documents = text_splitter.split_documents(raw_documents)
-
-
-# Load Data to vectorstore
-embeddings = OpenAIEmbeddings()
-vectorstore = FAISS.from_documents(documents, embeddings)
-
-
-# Save vectorstore
-with open("vectorstore.pkl", "wb") as f:
-    pickle.dump(vectorstore, f)
diff --git a/spaces/hysts/ControlNet-with-Anything-v4/app_hed.py b/spaces/hysts/ControlNet-with-Anything-v4/app_hed.py
deleted file mode 100644
index 2d6146f864f6bec127f72687855169307eb68cc9..0000000000000000000000000000000000000000
--- a/spaces/hysts/ControlNet-with-Anything-v4/app_hed.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_hed2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with HED 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=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    detect_resolution = gr.Slider(label='HED Resolution',
-                                                  minimum=128,
-                                                  maximum=512,
-                                                  value=512,
-                                                  step=1)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_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)
-                    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 = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            detect_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='hed')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_hed)
-    demo.queue().launch()
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/eval/__init__.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/eval/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/innnky/visinger2-nomidi/README.md b/spaces/innnky/visinger2-nomidi/README.md
deleted file mode 100644
index d066eb6974cbe7eb3c1f5342e7215bc9d012dcd2..0000000000000000000000000000000000000000
--- a/spaces/innnky/visinger2-nomidi/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Visinger2 Nomidi
-emoji: 👁
-colorFrom: green
-colorTo: gray
-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/innovatorved/whisper.api/app/tests/conftest.py b/spaces/innovatorved/whisper.api/app/tests/conftest.py
deleted file mode 100644
index 0782c87876b9ea6a76f21c33d0087a173b519ff7..0000000000000000000000000000000000000000
--- a/spaces/innovatorved/whisper.api/app/tests/conftest.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import pytest
-from fastapi.testclient import TestClient
-from sqlalchemy import create_engine
-from sqlalchemy.orm import sessionmaker
-
-from app.core.config import settings
-from app.core.database import Base
-from app.core.database import SessionLocal
-
-
-@pytest.fixture(scope="module")
-def test_app():
-    # set up test app with client
-    from app.main import app
-
-    client = TestClient(app)
-    # set up test database
-    TEST_DATABASE_URL = settings.TEST_DATABASE_URL
-    engine = create_engine(TEST_DATABASE_URL)
-    TestingSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
-    Base.metadata.create_all(bind=engine)
-    # yield the app and database session to the test
-    try:
-        yield client, TestingSessionLocal()
-    finally:
-        # clean up test database
-        Base.metadata.drop_all(bind=engine)
-
-
-@pytest.fixture(scope="function")
-def db_session():
-    # set up a new database session for each test
-    session = SessionLocal()
-    try:
-        yield session
-    finally:
-        session.close()
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Celal Esad Arseven Sanat Ansiklopedisi Pdf Download !!LINK!!.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Celal Esad Arseven Sanat Ansiklopedisi Pdf Download !!LINK!!.md
deleted file mode 100644
index ba298036ed5a35975e0865a26d94a9c54c806b0d..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Celal Esad Arseven Sanat Ansiklopedisi Pdf Download !!LINK!!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>celal esad arseven sanat ansiklopedisi pdf download</h2><br /><p><b><b>DOWNLOAD</b> >>>>> <a href="https://urlin.us/2uEvBl">https://urlin.us/2uEvBl</a></b></p><br /><br />
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-Download Adobe Acrobat XI Pro 11.0.20 FINAL + Crack [TechTools] torrent or any other torrent ... celal esad arseven sanat ansiklopedisi pdf 15. 1fdad05405<br />
-<br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Crows Row Julie Hockley Free Epub Download __LINK__.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Crows Row Julie Hockley Free Epub Download __LINK__.md
deleted file mode 100644
index cf8b1a6c8a0f266cd220548dd4916adb5ee82cc7..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Crows Row Julie Hockley Free Epub Download __LINK__.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-
-<h1>Crow's Row by Julie Hockley: A Dark and Forbidden Romance</h1>
-<p>If you are looking for a thrilling and captivating romance novel that will keep you on the edge of your seat, you might want to check out <em>Crow's Row</em> by Julie Hockley. This is the first book in a series that follows the story of Emily Sheppard, a college student who witnesses a brutal murder and gets kidnapped by a young crime boss named Cameron Hillard. Emily soon finds herself in a secret underworld, where she uncovers secrets about her brother's death and falls in love with her captor. But their love is forbidden and dangerous, and Emily's life is at stake.</p>
-<p><em>Crow's Row</em> is a dark and gritty novel that explores themes of violence, betrayal, loyalty, and redemption. It is not for the faint of heart, as it contains graphic scenes of torture, abuse, and sex. However, it also offers a compelling and emotional romance that will make you root for Emily and Cameron, despite their flaws and circumstances. The characters are complex and realistic, and the plot is full of twists and turns that will keep you guessing until the end.</p>
-<h2>Crows Row Julie Hockley Free Epub Download</h2><br /><p><b><b>DOWNLOAD</b> --->>> <a href="https://urlin.us/2uEwYY">https://urlin.us/2uEwYY</a></b></p><br /><br />
-<p>If you want to read <em>Crow's Row</em> by Julie Hockley, you can download it for free in PDF or EPUB format from various online sources. Some of them are:</p>
-<ul>
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-</ul>
-<p>However, please note that these sources may not be legal or authorized by the author or publisher. If you enjoy the book, you should consider buying it from an official retailer or supporting the author in other ways. You can also read the sequel, <em>Scare Crow</em>, which continues Emily and Cameron's story.</p>
-<p><em>Crow's Row</em> by Julie Hockley is a book that will make you feel a range of emotions, from fear and anger to hope and love. It is a gripping and intense read that will leave you wanting more. If you are a fan of dark and forbidden romance novels, you should give it a try.</p>
-
-<p>If you are wondering what makes <em>Crow's Row</em> by Julie Hockley stand out from other romance novels, here are some of the reasons:</p>
-<ol>
-<li>The setting. The novel takes place in a dark and dangerous underworld of crime and drugs, where the rules are different and the stakes are high. The author does a great job of creating a vivid and realistic atmosphere that immerses you in the story.</li>
-<li>The characters. The novel features a diverse and dynamic cast of characters, each with their own personality, background, and motivation. Emily and Cameron are the main protagonists, but there are also other important characters, such as Rocco, Cameron's loyal friend and right-hand man; Max, Emily's best friend and roommate; and Eric, Emily's brother who died under mysterious circumstances. The author makes you care about these characters and their relationships, even when they do questionable or immoral things.</li>
-<li>The romance. The novel offers a passionate and intense romance that will make your heart race and your emotions soar. Emily and Cameron have a strong chemistry and a deep connection that goes beyond physical attraction. They challenge each other, support each other, and protect each other, even when they face obstacles and enemies. Their love is not easy or conventional, but it is powerful and genuine.</li>
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-<p>These are some of the reasons why <em>Crow's Row</em> by Julie Hockley is a captivating and memorable romance novel that you should not miss. If you want to experience this thrilling and emotional ride for yourself, you can download it for free from the sources mentioned above or buy it from an official retailer. You can also check out the author's website for more information about her and her books.</p> d5da3c52bf<br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Emex 3 Serial 17.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Emex 3 Serial 17.md
deleted file mode 100644
index 08344103ece31db6650eb83e51ccf122d513dc9e..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Emex 3 Serial 17.md	
+++ /dev/null
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-<h2>Emex 3 Serial 17</h2><br /><p><b><b>Download Zip</b> &bull;&bull;&bull; <a href="https://urlin.us/2uEwpm">https://urlin.us/2uEwpm</a></b></p><br /><br />
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diff --git a/spaces/ismot/1702t1/dataset/mp3d_dataset.py b/spaces/ismot/1702t1/dataset/mp3d_dataset.py
deleted file mode 100644
index 66042e79d8794c1f57dd280b4ade9e4f24e5ba8e..0000000000000000000000000000000000000000
--- a/spaces/ismot/1702t1/dataset/mp3d_dataset.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""
-@date: 2021/6/25
-@description:
-"""
-import os
-import json
-
-from dataset.communal.read import read_image, read_label
-from dataset.communal.base_dataset import BaseDataset
-from utils.logger import get_logger
-
-
-class MP3DDataset(BaseDataset):
-    def __init__(self, root_dir, mode, shape=None, max_wall_num=0, aug=None, camera_height=1.6, logger=None,
-                 split_list=None, patch_num=256, keys=None, for_test_index=None):
-        super().__init__(mode, shape, max_wall_num, aug, camera_height, patch_num, keys)
-
-        if logger is None:
-            logger = get_logger()
-        self.root_dir = root_dir
-
-        split_dir = os.path.join(root_dir, 'split')
-        label_dir = os.path.join(root_dir, 'label')
-        img_dir = os.path.join(root_dir, 'image')
-
-        if split_list is None:
-            with open(os.path.join(split_dir, f"{mode}.txt"), 'r') as f:
-                split_list = [x.rstrip().split() for x in f]
-
-        split_list.sort()
-        if for_test_index is not None:
-            split_list = split_list[:for_test_index]
-
-        self.data = []
-        invalid_num = 0
-        for name in split_list:
-            name = "_".join(name)
-            img_path = os.path.join(img_dir, f"{name}.png")
-            label_path = os.path.join(label_dir, f"{name}.json")
-
-            if not os.path.exists(img_path):
-                logger.warning(f"{img_path} not exists")
-                invalid_num += 1
-                continue
-            if not os.path.exists(label_path):
-                logger.warning(f"{label_path} not exists")
-                invalid_num += 1
-                continue
-
-            with open(label_path, 'r') as f:
-                label = json.load(f)
-
-                if self.max_wall_num >= 10:
-                    if label['layoutWalls']['num'] < self.max_wall_num:
-                        invalid_num += 1
-                        continue
-                elif self.max_wall_num != 0 and label['layoutWalls']['num'] != self.max_wall_num:
-                    invalid_num += 1
-                    continue
-
-            # print(label['layoutWalls']['num'])
-            self.data.append([img_path, label_path])
-
-        logger.info(
-            f"Build dataset mode: {self.mode} max_wall_num: {self.max_wall_num} valid: {len(self.data)} invalid: {invalid_num}")
-
-    def __getitem__(self, idx):
-        rgb_path, label_path = self.data[idx]
-        label = read_label(label_path, data_type='MP3D')
-        image = read_image(rgb_path, self.shape)
-        output = self.process_data(label, image, self.patch_num)
-        return output
-
-
-if __name__ == "__main__":
-    import numpy as np
-    from PIL import Image
-
-    from tqdm import tqdm
-    from visualization.boundary import draw_boundaries
-    from visualization.floorplan import draw_floorplan
-    from utils.boundary import depth2boundaries
-    from utils.conversion import uv2xyz
-
-    modes = ['test', 'val']
-    for i in range(1):
-        for mode in modes:
-            print(mode)
-            mp3d_dataset = MP3DDataset(root_dir='../src/dataset/mp3d', mode=mode, aug={
-                'STRETCH': True,
-                'ROTATE': True,
-                'FLIP': True,
-                'GAMMA': True
-            })
-            save_dir = f'../src/dataset/mp3d/visualization/{mode}'
-            if not os.path.isdir(save_dir):
-                os.makedirs(save_dir)
-
-            bar = tqdm(mp3d_dataset, ncols=100)
-            for data in bar:
-                bar.set_description(f"Processing {data['id']}")
-                boundary_list = depth2boundaries(data['ratio'], data['depth'], step=None)
-                pano_img = draw_boundaries(data['image'].transpose(1, 2, 0), boundary_list=boundary_list, show=True)
-                Image.fromarray((pano_img * 255).astype(np.uint8)).save(
-                    os.path.join(save_dir, f"{data['id']}_boundary.png"))
-
-                floorplan = draw_floorplan(uv2xyz(boundary_list[0])[..., ::2], show=True,
-                                           marker_color=None, center_color=0.8, show_radius=None)
-                Image.fromarray((floorplan.squeeze() * 255).astype(np.uint8)).save(
-                    os.path.join(save_dir, f"{data['id']}_floorplan.png"))
diff --git a/spaces/ivntl/MMS/vits/preprocess.py b/spaces/ivntl/MMS/vits/preprocess.py
deleted file mode 100644
index aaedbf076c30114b3ac6c27dfb42fd54ac81a71c..0000000000000000000000000000000000000000
--- a/spaces/ivntl/MMS/vits/preprocess.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import argparse
-import text
-from utils import load_filepaths_and_text
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument("--out_extension", default="cleaned")
-  parser.add_argument("--text_index", default=1, type=int)
-  parser.add_argument("--filelists", nargs="+", default=["filelists/ljs_audio_text_val_filelist.txt", "filelists/ljs_audio_text_test_filelist.txt"])
-  parser.add_argument("--text_cleaners", nargs="+", default=["english_cleaners2"])
-
-  args = parser.parse_args()
-    
-
-  for filelist in args.filelists:
-    print("START:", filelist)
-    filepaths_and_text = load_filepaths_and_text(filelist)
-    for i in range(len(filepaths_and_text)):
-      original_text = filepaths_and_text[i][args.text_index]
-      cleaned_text = text._clean_text(original_text, args.text_cleaners)
-      filepaths_and_text[i][args.text_index] = cleaned_text
-
-    new_filelist = filelist + "." + args.out_extension
-    with open(new_filelist, "w", encoding="utf-8") as f:
-      f.writelines(["|".join(x) + "\n" for x in filepaths_and_text])
diff --git a/spaces/j-m/formality_tagging/app.py b/spaces/j-m/formality_tagging/app.py
deleted file mode 100644
index 5d061d3b8ea58dc7d5eb59b62a68ec712818c091..0000000000000000000000000000000000000000
--- a/spaces/j-m/formality_tagging/app.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     formats: ipynb,py:percent
-#     text_representation:
-#       extension: .py
-#       format_name: percent
-#       format_version: '1.3'
-#       jupytext_version: 1.13.8
-#   kernelspec:
-#     display_name: Python 3 (ipykernel)
-#     language: python
-#     name: python3
-# ---
-
-# %%
-import gradio as gr
-from fastai.text.all import *
-from nltk import tokenize
-
-
-import nltk
-nltk.download("punkt")
-
-# %%
-# model_pkl = "https://cloudstor.aarnet.edu.au/plus/s/rJzjZMk7ieZGsao/download"
-# p = Path("./formality.pkl")
-
-# %%
-learner_pkl_url = "https://cloudstor.aarnet.edu.au/plus/s/lRDFdt5KmkhxFr6/download"
-
-p = Path("./learner.pkl")
-
-# %%
-if not p.exists():
-    import urllib.request
-    urllib.request.urlretrieve(learner_pkl_url, "./learner.pkl")
-
-# %%
-# m = load_pickle("./formality.pkl")
-
-# %%
-formal_sentences = "The Gram matrix (or Gramian matrix, Gramian) of a set of vectors in an inner product space is the Hermitian matrix of inner products. C'man, man, don't leave holding the bag!"
-
-# %%
-m = load_learner(p)
-
-# %%
-L(tokenize.sent_tokenize(formal_sentences)).map(lambda x: m.predict(x)[0])
-
-
-# %% tags=[]
-def formal_informal(text):
-    # a = m.predict(text)
-    # assessment = "style: {0}, confidence {1:.2f}%".format( a[0], a[2][0] * 100)
-    sentences = L(tokenize.sent_tokenize(text))
-    predictions = sentences.map(lambda x: m.predict(x)[0])
-    # assessment = "style: {0}".format(a[0])
-    assessment = zip(sentences.map(lambda x: x + ' '), predictions)
-    return assessment
-    
-demo = gr.Interface(formal_informal, 
-                    gr.Textbox(label="Your text", lines=5, value=""), 
-                    gr.HighlightedText(label="Stylistic analysis", color_map={'formal': 'blue', 'informal': 'yellow'},
-                                       combine_adjacent=True, show_legend=False), 
-                    examples=["I returned and saw under the sun, that the race is not to the swift, nor the battle to the strong, neither yet bread to the wise, nor yet riches to men of understanding, nor yet favour to men of skill; but time and chance happeneth to them all.",
-                              "Objective consideration of contemporary phenomena compels the conclusion that success or failure in competitive activities exhibits no tendency to be commensurate with innate capacity, but that a considerable element of the unpredictable must invariably be taken into account.",
-                              "When the process of freeing a vehicle that has been stuck results in ruts or holes, the operator will fill the rut or hole created by such activity before removing the vehicle from the immediate area.",
-                              "If you make a hole while freeing a stuck vehicle, you must fill the hole before you drive away.",
-                              "Prior to surgery patients are required to cleanse with this fluid.",
-                              "The day of your surgery, wash your body with this liquid soap."])
-# First two examples are from Orwell's "Politics and the English Language"
-# Next two examples are from https://www.plainlanguage.gov/examples/before-and-after/wordiness/
-# Next two examples are from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6044302/
-demo.launch()
-
-# %%
diff --git a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum.py b/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum.py
deleted file mode 100644
index 50c188c2475f58572540f615d957f8d28d3f019d..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum.py
+++ /dev/null
@@ -1,318 +0,0 @@
-# Detach 'deforum_helpers' from 'scripts' to prevent "No module named 'scripts.deforum_helpers'"  error 
-# causing Deforum's tab not show up in some cases when you've might've broken the environment with webui packages updates
-import sys, os, shutil
-
-basedirs = [os.getcwd()]
-if 'google.colab' in sys.modules:
-    basedirs.append('/content/gdrive/MyDrive/sd/stable-diffusion-webui') #hardcode as TheLastBen's colab seems to be the primal source
-
-for basedir in basedirs:
-    deforum_paths_to_ensure = [basedir + '/extensions/deforum-for-automatic1111-webui/scripts', basedir + '/extensions/sd-webui-controlnet', basedir + '/extensions/deforum/scripts', basedir + '/scripts/deforum_helpers/src', basedir + '/extensions/deforum/scripts/deforum_helpers/src', basedir +'/extensions/deforum-for-automatic1111-webui/scripts/deforum_helpers/src',basedir]
-
-    for deforum_scripts_path_fix in deforum_paths_to_ensure:
-        if not deforum_scripts_path_fix in sys.path:
-            sys.path.extend([deforum_scripts_path_fix])
-
-# Main deforum stuff
-import deforum_helpers.args as deforum_args
-import deforum_helpers.settings as deforum_settings
-from deforum_helpers.save_images import dump_frames_cache, reset_frames_cache
-from deforum_helpers.frame_interpolation import process_video_interpolation
-
-import modules.scripts as wscripts
-from modules import script_callbacks
-import gradio as gr
-import json
-
-from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
-from PIL import Image
-from deforum_helpers.video_audio_utilities import ffmpeg_stitch_video, make_gifski_gif
-from deforum_helpers.upscaling import make_upscale_v2
-import gc
-import torch
-from webui import wrap_gradio_gpu_call
-import modules.shared as shared
-from modules.shared import opts, cmd_opts, state
-from modules.ui import create_output_panel, plaintext_to_html, wrap_gradio_call
-from types import SimpleNamespace
-
-def run_deforum(*args, **kwargs):
-    args_dict = {deforum_args.component_names[i]: args[i+2] for i in range(0, len(deforum_args.component_names))}
-    p = StableDiffusionProcessingImg2Img(
-        sd_model=shared.sd_model,
-        outpath_samples = opts.outdir_samples or opts.outdir_img2img_samples,
-        outpath_grids = opts.outdir_grids or opts.outdir_img2img_grids,
-        #we'll setup the rest later
-    )
-    
-    print("\033[4;33mDeforum extension for auto1111 webui, v2.2b\033[0m")
-    args_dict['self'] = None
-    args_dict['p'] = p
-    
-    root, args, anim_args, video_args, parseq_args, loop_args, controlnet_args = deforum_args.process_args(args_dict)
-    root.clipseg_model = None
-    root.initial_clipskip = opts.data["CLIP_stop_at_last_layers"]
-    root.basedirs = basedirs
-
-    for basedir in basedirs:
-        sys.path.extend([
-            basedir + '/scripts/deforum_helpers/src',
-            basedir + '/extensions/deforum/scripts/deforum_helpers/src',
-            basedir + '/extensions/deforum-for-automatic1111-webui/scripts/deforum_helpers/src',
-        ])
-    
-    # clean up unused memory
-    reset_frames_cache(root)
-    gc.collect()
-    torch.cuda.empty_cache()
-    
-    from deforum_helpers.render import render_animation
-    from deforum_helpers.render_modes import render_input_video, render_animation_with_video_mask, render_interpolation
-
-    tqdm_backup = shared.total_tqdm
-    shared.total_tqdm = deforum_settings.DeforumTQDM(args, anim_args, parseq_args)
-    try:
-        # dispatch to appropriate renderer
-        if anim_args.animation_mode == '2D' or anim_args.animation_mode == '3D':
-            if anim_args.use_mask_video: 
-                render_animation_with_video_mask(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, root.animation_prompts, root) # allow mask video without an input video
-            else:    
-                render_animation(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, root.animation_prompts, root)
-        elif anim_args.animation_mode == 'Video Input':
-            render_input_video(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, root.animation_prompts, root)#TODO: prettify code
-        elif anim_args.animation_mode == 'Interpolation':
-            render_interpolation(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, root.animation_prompts, root)
-        else:
-            print('Other modes are not available yet!')
-    finally:
-        shared.total_tqdm = tqdm_backup
-        opts.data["CLIP_stop_at_last_layers"] = root.initial_clipskip
-    
-    if video_args.store_frames_in_ram:
-        dump_frames_cache(root)
-    
-    from base64 import b64encode
-    
-    real_audio_track = None
-    if video_args.add_soundtrack != 'None':
-        real_audio_track = anim_args.video_init_path if video_args.add_soundtrack == 'Init Video' else video_args.soundtrack_path
-    
-    # Delete folder with duplicated imgs from OS temp folder
-    shutil.rmtree(root.tmp_deforum_run_duplicated_folder, ignore_errors=True)
-
-    # Decide whether or not we need to try and frame interpolate laters
-    need_to_frame_interpolate = False
-    if video_args.frame_interpolation_engine != "None" and not video_args.skip_video_for_run_all and not video_args.store_frames_in_ram:
-        need_to_frame_interpolate = True
-        
-    if video_args.skip_video_for_run_all:
-        print('Skipping video creation, uncheck skip_video_for_run_all if you want to run it')
-    else:
-        import subprocess
-
-        path_name_modifier = video_args.path_name_modifier
-        if video_args.render_steps: # render steps from a single image
-            fname = f"{path_name_modifier}_%05d.png"
-            all_step_dirs = [os.path.join(args.outdir, d) for d in os.listdir(args.outdir) if os.path.isdir(os.path.join(args.outdir,d))]
-            newest_dir = max(all_step_dirs, key=os.path.getmtime)
-            image_path = os.path.join(newest_dir, fname)
-            print(f"Reading images from {image_path}")
-            mp4_path = os.path.join(newest_dir, f"{args.timestring}_{path_name_modifier}.mp4")
-            max_video_frames = args.steps
-        else: # render images for a video
-            image_path = os.path.join(args.outdir, f"{args.timestring}_%05d.png")
-            mp4_path = os.path.join(args.outdir, f"{args.timestring}.mp4")
-            max_video_frames = anim_args.max_frames
-
-        exclude_keys = deforum_settings.get_keys_to_exclude('video')
-        video_settings_filename = os.path.join(args.outdir, f"{args.timestring}_video-settings.txt")
-        with open(video_settings_filename, "w+", encoding="utf-8") as f:
-            s = {}
-            for key, value in dict(video_args.__dict__).items():
-                if key not in exclude_keys:
-                    s[key] = value
-            json.dump(s, f, ensure_ascii=False, indent=4)
-
-        # Stitch video using ffmpeg!
-        try:
-            ffmpeg_stitch_video(ffmpeg_location=video_args.ffmpeg_location, fps=video_args.fps, outmp4_path=mp4_path, stitch_from_frame=0, stitch_to_frame=max_video_frames, imgs_path=image_path, add_soundtrack=video_args.add_soundtrack, audio_path=real_audio_track, crf=video_args.ffmpeg_crf, preset=video_args.ffmpeg_preset)
-            mp4 = open(mp4_path,'rb').read()
-            data_url = "data:video/mp4;base64," + b64encode(mp4).decode()
-            deforum_args.i1_store = f'<p style=\"font-weight:bold;margin-bottom:0em\">Deforum v0.5-webui-beta</p><video controls loop><source src="{data_url}" type="video/mp4"></video>'
-        except Exception as e:
-            if need_to_frame_interpolate:
-                print(f"FFMPEG DID NOT STITCH ANY VIDEO. However, you requested to frame interpolate  - so we will continue to frame interpolation, but you'll be left only with the interpolated frames and not a video, since ffmpeg couldn't run. Original ffmpeg error: {e}")
-            else:
-                print(f"** FFMPEG DID NOT STITCH ANY VIDEO ** Error: {e}")
-            pass
-            
-    if root.initial_info is None:
-        root.initial_info = "An error has occured and nothing has been generated!"
-        root.initial_info += "\nPlease, report the bug to https://github.com/deforum-art/deforum-for-automatic1111-webui/issues"
-        import numpy as np
-        a = np.random.rand(args.W, args.H, 3)*255
-        root.first_frame = Image.fromarray(a.astype('uint8')).convert('RGB')
-        root.initial_seed = 6934
-    # FRAME INTERPOLATION TIME
-    if need_to_frame_interpolate: 
-        print(f"Got a request to *frame interpolate* using {video_args.frame_interpolation_engine}")
-        process_video_interpolation(frame_interpolation_engine=video_args.frame_interpolation_engine, frame_interpolation_x_amount=video_args.frame_interpolation_x_amount,frame_interpolation_slow_mo_enabled=video_args.frame_interpolation_slow_mo_enabled, frame_interpolation_slow_mo_amount=video_args.frame_interpolation_slow_mo_amount, orig_vid_fps=video_args.fps, deforum_models_path=root.models_path, real_audio_track=real_audio_track, raw_output_imgs_path=args.outdir, img_batch_id=args.timestring, ffmpeg_location=video_args.ffmpeg_location, ffmpeg_crf=video_args.ffmpeg_crf, ffmpeg_preset=video_args.ffmpeg_preset, keep_interp_imgs=video_args.frame_interpolation_keep_imgs, orig_vid_name=None, resolution=None)
-    
-    if video_args.make_gif and not video_args.skip_video_for_run_all and not video_args.store_frames_in_ram:
-        make_gifski_gif(imgs_raw_path = args.outdir, imgs_batch_id = args.timestring, fps = video_args.fps, models_folder = root.models_path, current_user_os = root.current_user_os)
-    
-    # Upscale video once generation is done:
-    if video_args.r_upscale_video and not video_args.skip_video_for_run_all and not video_args.store_frames_in_ram:
-        
-        # out mp4 path is defined in make_upscale func
-        make_upscale_v2(upscale_factor = video_args.r_upscale_factor, upscale_model = video_args.r_upscale_model, keep_imgs = video_args.r_upscale_keep_imgs, imgs_raw_path = args.outdir, imgs_batch_id = args.timestring, fps = video_args.fps, deforum_models_path = root.models_path, current_user_os = root.current_user_os, ffmpeg_location=video_args.ffmpeg_location, stitch_from_frame=0, stitch_to_frame=max_video_frames, ffmpeg_crf=video_args.ffmpeg_crf, ffmpeg_preset=video_args.ffmpeg_preset, add_soundtrack = video_args.add_soundtrack ,audio_path=real_audio_track)
-        
-    root.initial_info += "\n The animation is stored in " + args.outdir
-    root.initial_info += "\n Timestring = " + args.timestring + '\n'
-    root.initial_info += "Only the first frame is shown in webui not to clutter the memory"
-    reset_frames_cache(root) # cleanup the RAM in any case
-    processed = Processed(p, [root.first_frame], root.initial_seed, root.initial_info)
-    
-    if processed is None:
-        processed = process_images(p)
-
-    shared.total_tqdm.clear()
-
-    generation_info_js = processed.js()
-    if opts.samples_log_stdout:
-        print(generation_info_js)
-
-    if opts.do_not_show_images:
-        processed.images = []
-
-    return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html('')
-
-def on_ui_tabs():
-    with gr.Blocks(analytics_enabled=False) as deforum_interface:
-        components = {}
-        dummy_component = gr.Label(visible=False)
-        with gr.Row(elem_id='deforum_progress_row').style(equal_height=False):
-            with gr.Column(scale=1, variant='panel'):
-                components = deforum_args.setup_deforum_setting_dictionary(None, True, True)
-        
-            with gr.Column(scale=1):
-                with gr.Row():
-                    btn = gr.Button("Click here after the generation to show the video")
-                    components['btn'] = btn
-                    close_btn = gr.Button("Close the video", visible=False)
-                with gr.Row():
-                    i1 = gr.HTML(deforum_args.i1_store, elem_id='deforum_header')
-                    components['i1'] = i1
-                    # Show video
-                    def show_vid():
-                        return {
-                            i1: gr.update(value=deforum_args.i1_store, visible=True),
-                            close_btn: gr.update(visible=True),
-                            btn: gr.update(value="Update the video", visible=True),
-                        }
-                
-                    btn.click(
-                        show_vid,
-                        [],
-                        [i1, close_btn, btn],
-                        )
-                    # Close video
-                    def close_vid():
-                        return {
-                            i1: gr.update(value=deforum_args.i1_store_backup, visible=True),
-                            close_btn: gr.update(visible=False),
-                            btn: gr.update(value="Click here after the generation to show the video", visible=True),
-                        }
-                    
-                    close_btn.click(
-                        close_vid,
-                        [],
-                        [i1, close_btn, btn],
-                        )
-                id_part = 'deforum'
-                with gr.Row(elem_id=f"{id_part}_generate_box"):
-                    skip = gr.Button('Skip', elem_id=f"{id_part}_skip", visible=False)
-                    interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", visible=True)
-                    submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
-                    skip.click(
-                        fn=lambda: state.skip(),
-                        inputs=[],
-                        outputs=[],
-                    )
-
-                    interrupt.click(
-                        fn=lambda: state.interrupt(),
-                        inputs=[],
-                        outputs=[],
-                    )
-                
-                deforum_gallery, generation_info, html_info, html_log = create_output_panel("deforum", opts.outdir_img2img_samples)
-
-                gr.HTML("<p>* Paths can be relative to webui folder OR full - absolute </p>")
-                with gr.Row():
-                    settings_path = gr.Textbox("deforum_settings.txt", elem_id='deforum_settings_path', label="General Settings File")
-                    #reuse_latest_settings_btn = gr.Button('Reuse Latest', elem_id='deforum_reuse_latest_settings_btn')#TODO
-                with gr.Row():
-                    save_settings_btn = gr.Button('Save Settings', elem_id='deforum_save_settings_btn')
-                    load_settings_btn = gr.Button('Load Settings', elem_id='deforum_load_settings_btn')
-                with gr.Row():
-                    video_settings_path = gr.Textbox("deforum_video-settings.txt", elem_id='deforum_video_settings_path', label="Video Settings File")
-                    #reuse_latest_video_settings_btn = gr.Button('Reuse Latest', elem_id='deforum_reuse_latest_video_settings_btn')#TODO
-                with gr.Row():
-                    save_video_settings_btn = gr.Button('Save Video Settings', elem_id='deforum_save_video_settings_btn')
-                    load_video_settings_btn = gr.Button('Load Video Settings', elem_id='deforum_load_video_settings_btn')
-
-                # components['prompts'].visible = False#hide prompts for the time being
-                #TODO clean up the code
-                components['save_sample_per_step'].visible = False
-                components['show_sample_per_step'].visible = False
-                components['display_samples'].visible = False
-
-        component_list = [components[name] for name in deforum_args.component_names]
-
-        submit.click(
-                    fn=wrap_gradio_gpu_call(run_deforum, extra_outputs=[None, '', '']),
-                    _js="submit_deforum",
-                    inputs=[dummy_component, dummy_component] + component_list,
-                    outputs=[
-                         deforum_gallery,
-                         generation_info,
-                         html_info,
-                         html_log,
-                    ],
-                )
-        
-        settings_component_list = [components[name] for name in deforum_args.settings_component_names]
-        video_settings_component_list = [components[name] for name in deforum_args.video_args_names]
-        stuff = gr.HTML("") # wrap gradio call garbage
-        stuff.visible = False
-        
-        save_settings_btn.click(
-                    fn=wrap_gradio_call(deforum_settings.save_settings),
-                    inputs=[settings_path] + settings_component_list,
-                    outputs=[stuff],
-                )
-        
-        load_settings_btn.click(
-                    fn=wrap_gradio_call(deforum_settings.load_settings),
-                    inputs=[settings_path]+ settings_component_list,
-                    outputs=settings_component_list + [stuff],
-                )
-        
-        save_video_settings_btn.click(
-                    fn=wrap_gradio_call(deforum_settings.save_video_settings),
-                    inputs=[video_settings_path] + video_settings_component_list,
-                    outputs=[stuff],
-                )
-        
-        load_video_settings_btn.click(
-                    fn=wrap_gradio_call(deforum_settings.load_video_settings),
-                    inputs=[video_settings_path] + video_settings_component_list,
-                    outputs=video_settings_component_list + [stuff],
-                )
-
-
-    return [(deforum_interface, "Deforum", "deforum_interface")]
-
-script_callbacks.on_ui_tabs(on_ui_tabs)
diff --git a/spaces/jackli888/stable-diffusion-webui/webui-macos-env.sh b/spaces/jackli888/stable-diffusion-webui/webui-macos-env.sh
deleted file mode 100644
index 37cac4fb02c5ea53fa270336729b90ac2ef3ac74..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/webui-macos-env.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/bin/bash
-####################################################################
-#                          macOS defaults                          #
-# Please modify webui-user.sh to change these instead of this file #
-####################################################################
-
-if [[ -x "$(command -v python3.10)" ]]
-then
-    python_cmd="python3.10"
-fi
-
-export install_dir="$HOME"
-export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
-export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1"
-export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
-export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71"
-export PYTORCH_ENABLE_MPS_FALLBACK=1
-
-####################################################################
diff --git a/spaces/jackrui/diff-amp-antimicrobial_peptide_generation/data_feature.py b/spaces/jackrui/diff-amp-antimicrobial_peptide_generation/data_feature.py
deleted file mode 100644
index 48a3cb044fecca21749cbe00168ccd9965bc0ac4..0000000000000000000000000000000000000000
--- a/spaces/jackrui/diff-amp-antimicrobial_peptide_generation/data_feature.py
+++ /dev/null
@@ -1,416 +0,0 @@
-
-import numpy as np
-import torch
-import pandas as pd
-from Bio import SeqIO
-import torch.utils.data
-from sklearn.model_selection import KFold, ShuffleSplit
-import re
-import vocab
-from sklearn.model_selection import train_test_split
-# from transformers import T5Tokenizer,XLNetTokenizer
-def AAI_embedding(seq,max_len=200):
-    f=open('AAindex.txt')
-    text=f.read()
-    f.close()
-    text=text.split('\n')
-    while '' in text:
-        text.remove('')
-    cha=text[0].split('\t')
-    while '' in cha:
-        cha.remove('')
-    cha=cha[1:]
-    index=[]
-    for i in range(1,len(text)):
-        temp=text[i].split('\t')
-        while '' in temp:
-            temp.remove('')
-        temp=temp[1:]
-        for j in range(len(temp)):
-            temp[j]=float(temp[j])
-        index.append(temp)
-    index=np.array(index)
-    AAI_dict={}
-    for j in range(len(cha)):
-        AAI_dict[cha[j]]=index[:,j]
-    AAI_dict['X']=np.zeros(531)
-    all_embeddings=[]
-    for each_seq in seq:
-        temp_embeddings=[]
-        for each_char in each_seq:
-            temp_embeddings.append(AAI_dict[each_char])
-        if max_len>len(each_seq):
-            zero_padding=np.zeros((max_len-len(each_seq),531))
-            data_pad=np.vstack((temp_embeddings,zero_padding))
-        elif max_len==len(each_seq):
-            data_pad=temp_embeddings
-        else:
-            data_pad=temp_embeddings[:max_len]
-        all_embeddings.append(data_pad)
-    all_embeddings=np.array(all_embeddings)
-    return torch.from_numpy(all_embeddings).float()
-
-
-def PAAC_embedding(seq,max_len=200):
-    f=open('PAAC.txt')
-    text=f.read()
-    f.close()
-    text=text.split('\n')
-    while '' in text:
-        text.remove('')
-    cha=text[0].split('\t')
-    while '' in cha:
-        cha.remove('')
-    cha=cha[1:]
-    index=[]
-    for i in range(1,len(text)):
-        temp=text[i].split('\t')
-        while '' in temp:
-            temp.remove('')
-        temp=temp[1:]
-        for j in range(len(temp)):
-            temp[j]=float(temp[j])
-        index.append(temp)
-    index=np.array(index)
-    AAI_dict={}
-    for j in range(len(cha)):
-        AAI_dict[cha[j]]=index[:,j]
-    AAI_dict['X']=np.zeros(3)
-    all_embeddings=[]
-    for each_seq in seq:
-        temp_embeddings=[]
-        for each_char in each_seq:
-            temp_embeddings.append(AAI_dict[each_char])
-        if max_len>len(each_seq):
-            zero_padding=np.zeros((max_len-len(each_seq),3))
-            data_pad=np.vstack((temp_embeddings,zero_padding))
-        elif max_len==len(each_seq):
-            data_pad=temp_embeddings
-        else:
-            data_pad=temp_embeddings[:max_len]
-        all_embeddings.append(data_pad)
-    all_embeddings=np.array(all_embeddings)
-    return torch.from_numpy(all_embeddings).float()
-
-def PC6_embedding(seq,max_len=200):
-    f=open('6-pc.txt')
-    text=f.read()
-    f.close()
-    text=text.split('\n')
-    while '' in text:
-        text.remove('')
-    text=text[1:]
-    AAI_dict={}
-    for each_line in text:
-        temp=each_line.split(' ')
-        while '' in temp:
-            temp.remove('')
-        for i in range(1,len(temp)):
-            temp[i]=float(temp[i])
-        AAI_dict[temp[0]]=temp[1:]
-    AAI_dict['X']=np.zeros(6)
-    all_embeddings=[]
-    for each_seq in seq:
-        temp_embeddings=[]
-        for each_char in each_seq:
-            temp_embeddings.append(AAI_dict[each_char])
-        if max_len>len(each_seq):
-            zero_padding=np.zeros((max_len-len(each_seq),6))
-            data_pad=np.vstack((temp_embeddings,zero_padding))
-        elif max_len==len(each_seq):
-            data_pad=temp_embeddings
-        else:
-            data_pad=temp_embeddings[:max_len]
-        all_embeddings.append(data_pad)
-    all_embeddings=np.array(all_embeddings)
-    return torch.from_numpy(all_embeddings).float()
-
-
-
-def BLOSUM62_embedding(seq,max_len=200):
-    f=open('blosum62.txt')
-    text=f.read()
-    f.close()
-    text=text.split('\n')
-    while '' in text:
-        text.remove('')
-    cha=text[0].split(' ')
-    while '' in cha:
-        cha.remove('')
-    index=[]
-    for i in range(1,len(text)):
-        temp=text[i].split(' ')
-        while '' in temp:
-            temp.remove('')
-        for j in range(len(temp)):
-            temp[j]=float(temp[j])
-        index.append(temp)
-    index=np.array(index)
-    BLOSUM62_dict={}
-    for j in range(len(cha)):
-        BLOSUM62_dict[cha[j]]=index[:,j]
-    all_embeddings=[]
-    for each_seq in seq:
-        temp_embeddings=[]
-        for each_char in each_seq:
-            temp_embeddings.append(BLOSUM62_dict[each_char])
-        if max_len>len(each_seq):
-            zero_padding=np.zeros((max_len-len(each_seq),23))
-            data_pad=np.vstack((temp_embeddings,zero_padding))
-        elif max_len==len(each_seq):
-            data_pad=temp_embeddings
-        else:
-            data_pad=temp_embeddings[:max_len]
-        all_embeddings.append(data_pad)
-    all_embeddings=np.array(all_embeddings)
-    return torch.from_numpy(all_embeddings).float()
-
-
-def onehot_embedding(seq,max_len=200):
-    char_list='ARNDCQEGHILKMFPSTWYVX'
-    char_dict={}
-    for i in range(len(char_list)):
-        char_dict[char_list[i]]=i
-    all_embeddings=[]
-    for each_seq in seq:
-        temp_embeddings=[]
-        for each_char in each_seq:
-            codings=np.zeros(21)
-            if each_char in char_dict.keys():
-                codings[char_dict[each_char]]=1
-            else:
-                codings[20]=1
-            temp_embeddings.append(codings)
-        if max_len>len(each_seq):
-            zero_padding=np.zeros((max_len-len(each_seq),21))
-            data_pad=np.vstack((temp_embeddings,zero_padding))
-        elif max_len==len(each_seq):
-            data_pad=temp_embeddings
-        else:
-            data_pad=temp_embeddings[:max_len]
-              
-        all_embeddings.append(data_pad)
-    all_embeddings=np.array(all_embeddings)
-    return torch.from_numpy(all_embeddings).float()
-        
-
-
-
-def index_encoding(sequences,max_len=200):
-    '''
-    Modified from https://github.com/openvax/mhcflurry/blob/master/mhcflurry/amino_acid.py#L110-L130
-
-    Parameters
-    ----------
-    sequences: list of equal-length sequences
-
-    Returns
-    -------
-    np.array with shape (#sequences, length of sequences)
-    '''
-    seq_list=[]
-    for s in sequences:
-        temp=list(s)
-        while len(temp)<max_len:
-            temp.append(20)
-        temp=temp[:max_len]
-        seq_list.append(temp)
-
-    df = pd.DataFrame(seq_list)
-    encoding = df.replace(vocab.AMINO_ACID_INDEX)
-    encoding = encoding.values.astype(int)
-    return encoding
-
-
-
-
-
-
-
-class MetagenesisData(torch.utils.data.Dataset):
-    def __init__(self, data):
-        self.data = data
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, index):
-        return self.data[index]
-
-
-class Dataset(object):
-    def __init__(self,
-            fasta=None,
-            label=None,
-            sep=',',
-            use_phy_feat=False,
-            phy_feat_fl=None,random_seed=42):
-
-
-        self.fasta = fasta
-
-
-        self.sep=sep
-        self.use_phy_feat = use_phy_feat
-        self.rng = np.random.RandomState(random_seed)
-        self.native_sequence =fasta
-
-        if use_phy_feat==True:
-            self.phy_feat_fl=phy_feat_fl
-            self.phy_feat=self._read_features()
-            
-            
-    def _read_features(self):
-        df=pd.read_csv(self.phy_feat_fl,sep='\t',index_col=0)
-        feat=df.values
-        feat=torch.from_numpy(feat.astype(np.float32))
-
-        return feat
-            
-    def _read_native_sequence(self):
-        sequence=[]
-        for seq_record in SeqIO.parse(self.fasta, "fasta"):
-            sequence.append(str(seq_record.seq).upper())
-
-        return sequence
-    
-    def _read_labels(self):
-
-        df=pd.read_csv(self.label,sep=self.sep,index_col=0)
-        labels=df.values
-        return labels
-    
-
-
-    def encode_seq_enc_onehot(self, sequences,max_length):
-        seq_enc = onehot_embedding(sequences, max_len=max_length)
-        return seq_enc
-    def encode_seq_enc_BLOSUM62(self, sequences,max_length):
-        seq_enc = BLOSUM62_embedding(sequences, max_len=max_length)
-        return seq_enc
-    def encode_seq_enc_AAI(self, sequences,max_length):
-        seq_enc = AAI_embedding(sequences, max_len=max_length)
-        return seq_enc
-    def encode_seq_enc_PAAC(self, sequences,max_length):
-        seq_enc = PAAC_embedding(sequences, max_len=max_length)
-        return seq_enc
-    def encode_seq_enc_PC6(self, sequences,max_length):
-        seq_enc = PC6_embedding(sequences, max_len=max_length)
-        return seq_enc
-    # def encode_seq_enc_bertT5(self, sequences,max_length):
-    #     seq_enc = bertT5_embedding(sequences, max_len=max_length)
-    #     return seq_enc
-
-    def encode_seq_enc(self, sequences,max_length):
-
-        seq_enc = index_encoding(sequences,max_length)
-        seq_enc = torch.from_numpy(seq_enc.astype(float))
-        return seq_enc
-    
-    # def encode_seq_token(self,sequences):
-    #     seq_enc=[]
-    #     for each_seq in sequences:
-    #         seq_enc.append(self.tokenizer.encode(each_seq))
-    #     return seq_enc   
-
-
-    # def encode_glob_feat(self, sequences):
-    #     feat = self.tape_encoder.encode(sequences)
-    #     feat = torch.from_numpy(feat).float()
-    #     return feat
-
-    def build_data(self, max_length):
-        
-        sequences = self.native_sequence
-        seq_enc_onehot = self.encode_seq_enc_onehot(sequences,max_length=max_length)
-
-        
-        # train_seq_enc_bertencoding,train_seq_mask = self.encode_seq_enc_bertT5(train_sequences, max_length=max_length)
-        # valid_seq_enc_bertencoding,valid_seq_mask = self.encode_seq_enc_bertT5(valid_sequences, max_length=max_length)
-        # test_seq_enc_bertencoding,test_seq_mask = self.encode_seq_enc_bertT5(test_sequences, max_length=max_length)
-        
-        seq_enc_blosum62 = self.encode_seq_enc_BLOSUM62(sequences,max_length=max_length)
-        seq_enc_AAI = self.encode_seq_enc_AAI(sequences,max_length=max_length)
-        seq_enc_PAAC = self.encode_seq_enc_PAAC(sequences,max_length=max_length)
-        seq_enc_PC6 = self.encode_seq_enc_PC6(sequences,max_length=max_length)
-        seq_enc = self.encode_seq_enc(sequences,max_length=max_length)    
-
-
-
-        samples = []
-
-        # print(labels)
-        print(len(sequences))
-        for i in range(len(sequences)):
-            sample = {
-                'sequence':sequences[i],
-
-                'seq_enc': seq_enc[i],
-                'seq_enc_onehot': seq_enc_onehot[i],
-                # 'seq_enc_bert':train_seq_enc_bertencoding[i],
-                # 'seq_enc_mask':train_seq_mask[i],
-                'seq_enc_pc6': seq_enc_PC6[i],
-
-                'seq_enc_BLOSUM62': seq_enc_blosum62[i],
-                'seq_enc_PAAC': seq_enc_PAAC[i],
-                'seq_enc_AAI': seq_enc_AAI[i]
-            }
-            if self.use_phy_feat:
-                sample['phy_feat'] = self.phy_feat[i]
-            samples.append(sample)
-        data = MetagenesisData(samples)
-
-        
-        return data
-
-
-    def get_dataloader(self,  max_length=200,batch_size=128,
-                       resample_train_valid=False):
-        
-        data = self.build_data(max_length)
-        data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size)
-
-        return data_loader
-        # return data
-import torch
-import torch.nn as nn
-device = torch.device("cpu")
-    
-if __name__ == '__main__':
-
-    dataset = Dataset(
-        fasta='static/uploads/Insect.fasta',
-        sep=',')
-    train_loader = dataset.get_dataloader(
-        batch_size=32,max_length=200)
-    # y_pred=[]
-    y_true=[]
-    all_seqs=[]
-    # for batch in train_loader:
-    #     seq=batch['sequence'].to(device)
-    #     y = batch['label'].to(device)
-    #     x = batch['seq_enc'].to(device).int()
-    #     # AAI_feat = batch['seq_enc_AAI'].to(device)
-    #     # onehot_feat = batch['seq_enc_onehot'].to(device)
-    #     # BLOSUM62_feat = batch['seq_enc_BLOSUM62'].to(device)
-    #     # PAAC_feat = batch['seq_enc_PAAC'].to(device)
-    #     # # bert_feat=batch['seq_enc_bert'].to(device)
-    #     # # bert_mask=batch['seq_enc_mask'].to(device)
-    #     # outputs=net(AAI_feat,onehot_feat,BLOSUM62_feat,PAAC_feat)
-    #     # outputs = model(x)
-    #     # y_pred.extend(outputs.cpu().numpy())
-    #     y_true.extend(y.cpu().numpy())
-    #     all_seqs.extend(seq.cpu().numpy())
-#     print(df.head())
-#     print(len(loader.__iter__()))
-#     (loader, df), (_, _) = dataset.get_dataloader('train_valid',
-#         batch_size=32, return_df=True, resample_train_valid=True)
-#     print(df.head())
-    # print(len(train_loader.__iter__()))
-    # loader, df = dataset.get_dataloader('test',
-    #     batch_size=32, return_df=True, resample_train_valid=True)
-    # print(next(loader.__iter__()))
-
-# sequence = ['GCTVEDRCLIGMGAILLNGCVIGSGSLVAAGALITQ','GCTVEDRCLIGMGAILLNGCVIGSGSLVAAGALITQ']
-# seq_tape=AAI_embedding(sequence)
-
diff --git a/spaces/jarvisbot/ChatImprovement/README.md b/spaces/jarvisbot/ChatImprovement/README.md
deleted file mode 100644
index 9514a37963a3fb1a16dee7dd84acb5623e54be09..0000000000000000000000000000000000000000
--- a/spaces/jarvisbot/ChatImprovement/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatImprovement
-emoji: 😻
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-duplicated_from: wangrongsheng/ChatImprovement
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/jarvisbot/ChatImprovement/predict.py b/spaces/jarvisbot/ChatImprovement/predict.py
deleted file mode 100644
index 85ad8bbd0525c749706ce23611dd23da822a7389..0000000000000000000000000000000000000000
--- a/spaces/jarvisbot/ChatImprovement/predict.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
-
-import json
-import gradio as gr
-import logging
-import traceback
-import requests
-import importlib
-
-# config_private.py放自己的秘密如API和代理网址
-# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
-try: from config_private import proxies, API_URL, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
-except: from config import proxies, API_URL, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
-
-timeout_bot_msg = '[local] Request timeout, network error. please check proxy settings in config.py.'
-
-def get_full_error(chunk, stream_response):
-    while True:
-        try:
-            chunk += next(stream_response)
-        except:
-            break
-    return chunk
-
-def predict_no_ui(inputs, top_p, temperature, history=[]):
-    headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt="", stream=False)
-
-    retry = 0
-    while True:
-        try:
-            # make a POST request to the API endpoint, stream=False
-            response = requests.post(API_URL, headers=headers, proxies=proxies,
-                                    json=payload, stream=False, timeout=TIMEOUT_SECONDS*2); break
-        except requests.exceptions.ReadTimeout as e:
-            retry += 1
-            traceback.print_exc()
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
-            if retry > MAX_RETRY: raise TimeoutError
-
-    try:
-        result = json.loads(response.text)["choices"][0]["message"]["content"]
-        return result
-    except Exception as e:
-        if "choices" not in response.text: print(response.text)
-        raise ConnectionAbortedError("Json解析不合常规，可能是文本过长" + response.text)
-
-
-def predict(api, inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', 
-            stream = True, additional_fn=None):
-
-    if additional_fn is not None:
-        import functional
-        importlib.reload(functional)
-        functional = functional.get_functionals()
-        inputs = functional[additional_fn]["Prefix"] + inputs + functional[additional_fn]["Suffix"]
-
-    if stream:
-        raw_input = inputs
-        logging.info(f'[raw_input] {raw_input}')
-        chatbot.append((inputs, ""))
-        yield chatbot, history, "等待响应"
-
-    headers, payload = generate_payload(api, inputs, top_p, temperature, history, system_prompt, stream)
-    history.append(inputs); history.append(" ")
-
-    retry = 0
-    while True:
-        try:
-            # make a POST request to the API endpoint, stream=True
-            response = requests.post(API_URL, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
-        except:
-            retry += 1
-            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
-            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
-            yield chatbot, history, "请求超时"+retry_msg
-            if retry > MAX_RETRY: raise TimeoutError
-
-    gpt_replying_buffer = ""
-    
-    is_head_of_the_stream = True
-    if stream:
-        stream_response =  response.iter_lines()
-        while True:
-            chunk = next(stream_response)
-            # print(chunk.decode()[6:])
-            if is_head_of_the_stream:
-                # 数据流的第一帧不携带content
-                is_head_of_the_stream = False; continue
-            
-            if chunk:
-                try:
-                    if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-                        # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        logging.info(f'[response] {gpt_replying_buffer}')
-                        break
-                    # 处理数据流的主体
-                    chunkjson = json.loads(chunk.decode()[6:])
-                    status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
-                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
-                    gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
-                    history[-1] = gpt_replying_buffer
-                    chatbot[-1] = (history[-2], history[-1])
-                    yield chatbot, history, status_text
-
-                except Exception as e:
-                    traceback.print_exc()
-                    yield chatbot, history, "Json解析不合常规，很可能是文本过长"
-                    chunk = get_full_error(chunk, stream_response)
-                    error_msg = chunk.decode()
-                    if "reduce the length" in error_msg:
-                        chatbot[-1] = (history[-1], "[Local Message] Input (or history) is too long, please reduce input or clear history by refleshing this page.")
-                        history = []
-                    yield chatbot, history, "Json解析不合常规，很可能是文本过长" + error_msg
-                    return
-
-def generate_payload(api, inputs, top_p, temperature, history, system_prompt, stream):
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer "+str(api)
-    }
-
-    conversation_cnt = len(history) // 2
-
-    messages = [{"role": "system", "content": system_prompt}]
-    if conversation_cnt:
-        for index in range(0, 2*conversation_cnt, 2):
-            what_i_have_asked = {}
-            what_i_have_asked["role"] = "user"
-            what_i_have_asked["content"] = history[index]
-            what_gpt_answer = {}
-            what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = history[index+1]
-            if what_i_have_asked["content"] != "":
-                if what_gpt_answer["content"] == "": continue
-                if what_gpt_answer["content"] == timeout_bot_msg: continue
-                messages.append(what_i_have_asked)
-                messages.append(what_gpt_answer)
-            else:
-                messages[-1]['content'] = what_gpt_answer['content']
-
-    what_i_ask_now = {}
-    what_i_ask_now["role"] = "user"
-    what_i_ask_now["content"] = inputs
-    messages.append(what_i_ask_now)
-
-    payload = {
-        "model": LLM_MODEL,
-        "messages": messages, 
-        "temperature": temperature,  # 1.0,
-        "top_p": top_p,  # 1.0,
-        "n": 1,
-        "stream": stream,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
-    }
-    
-    print(f" {LLM_MODEL} : {conversation_cnt} : {inputs}")
-    return headers,payload
-
-
diff --git a/spaces/jbilcke-hf/LifeSim/src/app/agents/dungeon.ts b/spaces/jbilcke-hf/LifeSim/src/app/agents/dungeon.ts
deleted file mode 100644
index 43877319bd3d96fd4cdf293dbd1c590056f8b340..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/LifeSim/src/app/agents/dungeon.ts
+++ /dev/null
@@ -1,68 +0,0 @@
-import { pick } from "./pick"
-import { Agent, Scene } from "./types"
-
-const actions = [
-  "not moving",
-  "walking in",
-  "looking up",
-  "looking down",
-  "looking left",
-  "looking right",
-  "looking around"
-]
-
-const positions = [
-  "corridor with a beautiful wooden door at the end, wooden floor and stone walls",
-  "a beautiful wooden door",
-  "beautiful room with stone walls and wooden floor",
-  "large ball room with stone pillars, stone floor and red carpet",
-  "a cosy room with a fireplace, stone walls and wooden floor",
-  "a fireplace with stone walls",
-  "a cold dungeon with stone walls",
-  "a damp medieval jail cell with stone walls and wooden floor"
-]
-
-const lights = [
-  "lit through windows",
-  "lit through wall-mounted torchs"
-  // "poorly lit"
-]
-
-const actionnables = [
-  "floor",
-  "fireplace",
-  "door",
-  "window",
-  "chair",
-  "table",
-  "torch"
-]
-
-export const agent: Agent = {
-  title: "Dungeon",
-  type: "dungeon",
-  simulate: (): Scene => {
-    const action = pick(actions)
-    const position = pick(positions)
-    const light = pick(lights)
-
-    const prompt = [
-      `first-person footage`,
-      action,
-      position,
-      light,
-      `medieval`,
-      `photography`,
-      `documentary`,
-      `high res`,
-    ].join(", ")
-
-    return {
-      action,
-      position,
-      light,
-      actionnables,
-      prompt
-    }
-  }
-}
diff --git a/spaces/jbilcke-hf/MusicGen/CODE_OF_CONDUCT.md b/spaces/jbilcke-hf/MusicGen/CODE_OF_CONDUCT.md
deleted file mode 100644
index 83f431e8feeb7e80d571f39c9f6c1b96857b5f85..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/MusicGen/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/jbilcke-hf/ai-clip-factory/src/components/ui/collapsible.tsx b/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/collapsible.tsx
deleted file mode 100644
index 9fa48946afd1eb56bd932377fd888e3986304676..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/collapsible.tsx
+++ /dev/null
@@ -1,11 +0,0 @@
-"use client"
-
-import * as CollapsiblePrimitive from "@radix-ui/react-collapsible"
-
-const Collapsible = CollapsiblePrimitive.Root
-
-const CollapsibleTrigger = CollapsiblePrimitive.CollapsibleTrigger
-
-const CollapsibleContent = CollapsiblePrimitive.CollapsibleContent
-
-export { Collapsible, CollapsibleTrigger, CollapsibleContent }
diff --git a/spaces/jcenaa/Segment-Any-RGBD/datasets/prepare_ade20k_sem_seg.py b/spaces/jcenaa/Segment-Any-RGBD/datasets/prepare_ade20k_sem_seg.py
deleted file mode 100644
index 9598151dc446807ccaee2056f7dae3260023a9b0..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/datasets/prepare_ade20k_sem_seg.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-import os
-from pathlib import Path
-
-import numpy as np
-import tqdm
-from PIL import Image
-
-
-def convert(input, output, index=None):
-    img = np.asarray(Image.open(input))
-    assert img.dtype == np.uint8
-    img = img - 1  # 0 (ignore) becomes 255. others are shifted by 1
-    if index is not None:
-        mapping = {i: k for k, i in enumerate(index)}
-        img = np.vectorize(lambda x: mapping[x] if x in mapping else 255)(
-            img.astype(np.float)
-        ).astype(np.uint8)
-    Image.fromarray(img).save(output)
-
-
-if __name__ == "__main__":
-    dataset_dir = (
-        Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "ADEChallengeData2016"
-    )
-    print('Caution: we only generate the validation set!')
-    for name in ["validation"]:
-        annotation_dir = dataset_dir / "annotations" / name
-        output_dir = dataset_dir / "annotations_detectron2" / name
-        output_dir.mkdir(parents=True, exist_ok=True)
-        for file in tqdm.tqdm(list(annotation_dir.iterdir())):
-            output_file = output_dir / file.name
-            convert(file, output_file)
diff --git a/spaces/jerpint/babelfish/README.md b/spaces/jerpint/babelfish/README.md
deleted file mode 100644
index 1dd16d1ec6bd3d9b6c29535a74ab0fd8829b2717..0000000000000000000000000000000000000000
--- a/spaces/jerpint/babelfish/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Babelfish
-emoji: 🐠
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.11.0
-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/jiejiejie0420/bingo/src/components/ui/separator.tsx b/spaces/jiejiejie0420/bingo/src/components/ui/separator.tsx
deleted file mode 100644
index 6c55e0b2ca8e2436658a06748aadbff7cd700db0..0000000000000000000000000000000000000000
--- a/spaces/jiejiejie0420/bingo/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-border',
-        orientation === 'horizontal' ? 'h-[1px] w-full' : 'h-full w-[1px]',
-        className
-      )}
-      {...props}
-    />
-  )
-)
-Separator.displayName = SeparatorPrimitive.Root.displayName
-
-export { Separator }
diff --git a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/ONNXVITS_transforms.py b/spaces/jimschat/VITS-Umamusume-voice-synthesizer/ONNXVITS_transforms.py
deleted file mode 100644
index 69b6d1c4b5724a3ef61f8bc3d64fc45c5e51e270..0000000000000000000000000000000000000000
--- a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/ONNXVITS_transforms.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        #unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        unnormalized_derivatives_ = torch.zeros((1, 1, unnormalized_derivatives.size(2), unnormalized_derivatives.size(3)+2))
-        unnormalized_derivatives_[...,1:-1] = unnormalized_derivatives
-        unnormalized_derivatives = unnormalized_derivatives_
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/jinmao/2/modules/llama_func.py b/spaces/jinmao/2/modules/llama_func.py
deleted file mode 100644
index 594e2ab57eb116859c9d63355a8504404db350c0..0000000000000000000000000000000000000000
--- a/spaces/jinmao/2/modules/llama_func.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import os
-import logging
-
-from llama_index import GPTSimpleVectorIndex
-from llama_index import download_loader
-from llama_index import (
-    Document,
-    LLMPredictor,
-    PromptHelper,
-    QuestionAnswerPrompt,
-    RefinePrompt,
-)
-from langchain.llms import OpenAI
-import colorama
-
-from modules.presets import *
-from modules.utils import *
-
-def get_index_name(file_src):
-    index_name = []
-    for file in file_src:
-        index_name.append(os.path.basename(file.name))
-    index_name = sorted(index_name)
-    index_name = "".join(index_name)
-    index_name = sha1sum(index_name)
-    return index_name
-
-def get_documents(file_src):
-    documents = []
-    logging.debug("Loading documents...")
-    logging.debug(f"file_src: {file_src}")
-    for file in file_src:
-        logging.info(f"loading file: {file.name}")
-        if os.path.splitext(file.name)[1] == ".pdf":
-            logging.debug("Loading PDF...")
-            CJKPDFReader = download_loader("CJKPDFReader")
-            loader = CJKPDFReader()
-            text_raw = loader.load_data(file=file.name)[0].text
-        elif os.path.splitext(file.name)[1] == ".docx":
-            logging.debug("Loading DOCX...")
-            DocxReader = download_loader("DocxReader")
-            loader = DocxReader()
-            text_raw = loader.load_data(file=file.name)[0].text
-        elif os.path.splitext(file.name)[1] == ".epub":
-            logging.debug("Loading EPUB...")
-            EpubReader = download_loader("EpubReader")
-            loader = EpubReader()
-            text_raw = loader.load_data(file=file.name)[0].text
-        else:
-            logging.debug("Loading text file...")
-            with open(file.name, "r", encoding="utf-8") as f:
-                text_raw = f.read()
-        text = add_space(text_raw)
-        documents += [Document(text)]
-    return documents
-
-
-def construct_index(
-        api_key,
-        file_src,
-        max_input_size=4096,
-        num_outputs=1,
-        max_chunk_overlap=20,
-        chunk_size_limit=600,
-        embedding_limit=None,
-        separator=" ",
-        num_children=10,
-        max_keywords_per_chunk=10,
-):
-    os.environ["OPENAI_API_KEY"] = api_key
-    chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
-    embedding_limit = None if embedding_limit == 0 else embedding_limit
-    separator = " " if separator == "" else separator
-
-    llm_predictor = LLMPredictor(
-        llm=OpenAI(model_name="gpt-3.5-turbo-0301", openai_api_key=api_key)
-    )
-    prompt_helper = PromptHelper(
-        max_input_size,
-        num_outputs,
-        max_chunk_overlap,
-        embedding_limit,
-        chunk_size_limit,
-        separator=separator,
-    )
-    index_name = get_index_name(file_src)
-    if os.path.exists(f"./index/{index_name}.json"):
-        logging.info("找到了缓存的索引文件，加载中……")
-        return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
-    else:
-        try:
-            documents = get_documents(file_src)
-            logging.debug("构建索引中……")
-            index = GPTSimpleVectorIndex(
-                documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
-            )
-            os.makedirs("./index", exist_ok=True)
-            index.save_to_disk(f"./index/{index_name}.json")
-            return index
-        except Exception as e:
-            print(e)
-            return None
-
-
-def chat_ai(
-        api_key,
-        index,
-        question,
-        context,
-        chatbot,
-        reply_language,
-):
-    os.environ["OPENAI_API_KEY"] = api_key
-
-    logging.info(f"Question: {question}")
-
-    response, chatbot_display, status_text = ask_ai(
-        api_key,
-        index,
-        question,
-        replace_today(PROMPT_TEMPLATE),
-        REFINE_TEMPLATE,
-        SIM_K,
-        INDEX_QUERY_TEMPRATURE,
-        context,
-        reply_language,
-    )
-    if response is None:
-        status_text = "查询失败，请换个问法试试"
-        return context, chatbot
-    response = response
-
-    context.append({"role": "user", "content": question})
-    context.append({"role": "assistant", "content": response})
-    chatbot.append((question, chatbot_display))
-
-    os.environ["OPENAI_API_KEY"] = ""
-    return context, chatbot, status_text
-
-
-def ask_ai(
-        api_key,
-        index,
-        question,
-        prompt_tmpl,
-        refine_tmpl,
-        sim_k=1,
-        temprature=0,
-        prefix_messages=[],
-        reply_language="中文",
-):
-    os.environ["OPENAI_API_KEY"] = api_key
-
-    logging.debug("Index file found")
-    logging.debug("Querying index...")
-    llm_predictor = LLMPredictor(
-        llm=OpenAI(
-            temperature=temprature,
-            model_name="gpt-3.5-turbo-0301",
-            prefix_messages=prefix_messages,
-        )
-    )
-
-    response = None  # Initialize response variable to avoid UnboundLocalError
-    qa_prompt = QuestionAnswerPrompt(prompt_tmpl.replace("{reply_language}", reply_language))
-    rf_prompt = RefinePrompt(refine_tmpl.replace("{reply_language}", reply_language))
-    response = index.query(
-        question,
-        llm_predictor=llm_predictor,
-        similarity_top_k=sim_k,
-        text_qa_template=qa_prompt,
-        refine_template=rf_prompt,
-        response_mode="compact",
-    )
-
-    if response is not None:
-        logging.info(f"Response: {response}")
-        ret_text = response.response
-        nodes = []
-        for index, node in enumerate(response.source_nodes):
-            brief = node.source_text[:25].replace("\n", "")
-            nodes.append(
-                f"<details><summary>[{index + 1}]\t{brief}...</summary><p>{node.source_text}</p></details>"
-            )
-        new_response = ret_text + "\n----------\n" + "\n\n".join(nodes)
-        logging.info(
-            f"Response: {colorama.Fore.BLUE}{ret_text}{colorama.Style.RESET_ALL}"
-        )
-        os.environ["OPENAI_API_KEY"] = ""
-        return ret_text, new_response, f"查询消耗了{llm_predictor.last_token_usage} tokens"
-    else:
-        logging.warning("No response found, returning None")
-        os.environ["OPENAI_API_KEY"] = ""
-        return None
-
-
-def add_space(text):
-    punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "}
-    for cn_punc, en_punc in punctuations.items():
-        text = text.replace(cn_punc, en_punc)
-    return text
diff --git a/spaces/jlmarrugom/voice_fixer_app/voicefixer/vocoder/__init__.py b/spaces/jlmarrugom/voice_fixer_app/voicefixer/vocoder/__init__.py
deleted file mode 100644
index 88a636676ad3ae066ca5d9438c6b5c99d0415406..0000000000000000000000000000000000000000
--- a/spaces/jlmarrugom/voice_fixer_app/voicefixer/vocoder/__init__.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-"""
-@File    :   __init__.py.py    
-@Contact :   haoheliu@gmail.com
-@License :   (C)Copyright 2020-2100
-
-@Modify Time      @Author    @Version    @Desciption
-------------      -------    --------    -----------
-9/14/21 1:00 AM   Haohe Liu      1.0         None
-"""
-
-import os
-from voicefixer.vocoder.config import Config
-import urllib.request
-
-if not os.path.exists(Config.ckpt):
-    os.makedirs(os.path.dirname(Config.ckpt), exist_ok=True)
-    print("Downloading the weight of neural vocoder: TFGAN")
-    urllib.request.urlretrieve(
-        "https://zenodo.org/record/5469951/files/model.ckpt-1490000_trimed.pt?download=1",
-        Config.ckpt,
-    )
-    print(
-        "Weights downloaded in: {} Size: {}".format(
-            Config.ckpt, os.path.getsize(Config.ckpt)
-        )
-    )
-    # cmd = "wget https://zenodo.org/record/5469951/files/model.ckpt-1490000_trimed.pt?download=1 -O " + Config.ckpt
-    # os.system(cmd)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/TSIG.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/TSIG.py
deleted file mode 100644
index 1ae87ebe61de9fd42fa5b04bbcef412343e7cdfb..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/TSIG.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license
-
-# Copyright (C) 2001-2017 Nominum, Inc.
-#
-# Permission to use, copy, modify, and distribute this software and its
-# documentation for any purpose with or without fee is hereby granted,
-# provided that the above copyright notice and this permission notice
-# appear in all copies.
-#
-# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES
-# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
-# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR
-# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
-# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
-# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
-# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
-
-import base64
-import struct
-
-import dns.exception
-import dns.immutable
-import dns.rcode
-import dns.rdata
-
-
-@dns.immutable.immutable
-class TSIG(dns.rdata.Rdata):
-
-    """TSIG record"""
-
-    __slots__ = [
-        "algorithm",
-        "time_signed",
-        "fudge",
-        "mac",
-        "original_id",
-        "error",
-        "other",
-    ]
-
-    def __init__(
-        self,
-        rdclass,
-        rdtype,
-        algorithm,
-        time_signed,
-        fudge,
-        mac,
-        original_id,
-        error,
-        other,
-    ):
-        """Initialize a TSIG rdata.
-
-        *rdclass*, an ``int`` is the rdataclass of the Rdata.
-
-        *rdtype*, an ``int`` is the rdatatype of the Rdata.
-
-        *algorithm*, a ``dns.name.Name``.
-
-        *time_signed*, an ``int``.
-
-        *fudge*, an ``int`.
-
-        *mac*, a ``bytes``
-
-        *original_id*, an ``int``
-
-        *error*, an ``int``
-
-        *other*, a ``bytes``
-        """
-
-        super().__init__(rdclass, rdtype)
-        self.algorithm = self._as_name(algorithm)
-        self.time_signed = self._as_uint48(time_signed)
-        self.fudge = self._as_uint16(fudge)
-        self.mac = self._as_bytes(mac)
-        self.original_id = self._as_uint16(original_id)
-        self.error = dns.rcode.Rcode.make(error)
-        self.other = self._as_bytes(other)
-
-    def to_text(self, origin=None, relativize=True, **kw):
-        algorithm = self.algorithm.choose_relativity(origin, relativize)
-        error = dns.rcode.to_text(self.error, True)
-        text = (
-            f"{algorithm} {self.time_signed} {self.fudge} "
-            + f"{len(self.mac)} {dns.rdata._base64ify(self.mac, 0)} "
-            + f"{self.original_id} {error} {len(self.other)}"
-        )
-        if self.other:
-            text += f" {dns.rdata._base64ify(self.other, 0)}"
-        return text
-
-    @classmethod
-    def from_text(
-        cls, rdclass, rdtype, tok, origin=None, relativize=True, relativize_to=None
-    ):
-        algorithm = tok.get_name(relativize=False)
-        time_signed = tok.get_uint48()
-        fudge = tok.get_uint16()
-        mac_len = tok.get_uint16()
-        mac = base64.b64decode(tok.get_string())
-        if len(mac) != mac_len:
-            raise SyntaxError("invalid MAC")
-        original_id = tok.get_uint16()
-        error = dns.rcode.from_text(tok.get_string())
-        other_len = tok.get_uint16()
-        if other_len > 0:
-            other = base64.b64decode(tok.get_string())
-            if len(other) != other_len:
-                raise SyntaxError("invalid other data")
-        else:
-            other = b""
-        return cls(
-            rdclass,
-            rdtype,
-            algorithm,
-            time_signed,
-            fudge,
-            mac,
-            original_id,
-            error,
-            other,
-        )
-
-    def _to_wire(self, file, compress=None, origin=None, canonicalize=False):
-        self.algorithm.to_wire(file, None, origin, False)
-        file.write(
-            struct.pack(
-                "!HIHH",
-                (self.time_signed >> 32) & 0xFFFF,
-                self.time_signed & 0xFFFFFFFF,
-                self.fudge,
-                len(self.mac),
-            )
-        )
-        file.write(self.mac)
-        file.write(struct.pack("!HHH", self.original_id, self.error, len(self.other)))
-        file.write(self.other)
-
-    @classmethod
-    def from_wire_parser(cls, rdclass, rdtype, parser, origin=None):
-        algorithm = parser.get_name()
-        time_signed = parser.get_uint48()
-        fudge = parser.get_uint16()
-        mac = parser.get_counted_bytes(2)
-        (original_id, error) = parser.get_struct("!HH")
-        other = parser.get_counted_bytes(2)
-        return cls(
-            rdclass,
-            rdtype,
-            algorithm,
-            time_signed,
-            fudge,
-            mac,
-            original_id,
-            error,
-            other,
-        )
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/filelock/_util.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/filelock/_util.py
deleted file mode 100644
index 543c1394678821cef6bdcbfeb59a545b99d0a7cf..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/filelock/_util.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from __future__ import annotations
-
-import os
-import stat
-import sys
-from errno import EACCES, EISDIR
-from pathlib import Path
-
-
-def raise_on_not_writable_file(filename: str) -> None:
-    """
-    Raise an exception if attempting to open the file for writing would fail.
-    This is done so files that will never be writable can be separated from
-    files that are writable but currently locked
-    :param filename: file to check
-    :raises OSError: as if the file was opened for writing.
-    """
-    try:  # use stat to do exists + can write to check without race condition
-        file_stat = os.stat(filename)  # noqa: PTH116
-    except OSError:
-        return  # swallow does not exist or other errors
-
-    if file_stat.st_mtime != 0:  # if os.stat returns but modification is zero that's an invalid os.stat - ignore it
-        if not (file_stat.st_mode & stat.S_IWUSR):
-            raise PermissionError(EACCES, "Permission denied", filename)
-
-        if stat.S_ISDIR(file_stat.st_mode):
-            if sys.platform == "win32":  # pragma: win32 cover
-                # On Windows, this is PermissionError
-                raise PermissionError(EACCES, "Permission denied", filename)
-            else:  # pragma: win32 no cover # noqa: RET506
-                # On linux / macOS, this is IsADirectoryError
-                raise IsADirectoryError(EISDIR, "Is a directory", filename)
-
-
-def ensure_directory_exists(filename: Path | str) -> None:
-    """
-    Ensure the directory containing the file exists (create it if necessary)
-    :param filename: file.
-    """
-    Path(filename).parent.mkdir(parents=True, exist_ok=True)
-
-
-__all__ = [
-    "raise_on_not_writable_file",
-    "ensure_directory_exists",
-]
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/otlLib/optimize/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/otlLib/optimize/__init__.py
deleted file mode 100644
index 25bce9cd2cdaa51338c83b7ecb9059b592b5574f..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/otlLib/optimize/__init__.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from argparse import RawTextHelpFormatter
-from fontTools.otlLib.optimize.gpos import COMPRESSION_LEVEL, compact
-from fontTools.ttLib import TTFont
-
-
-def main(args=None):
-    """Optimize the layout tables of an existing font"""
-    from argparse import ArgumentParser
-
-    from fontTools import configLogger
-
-    parser = ArgumentParser(
-        prog="otlLib.optimize",
-        description=main.__doc__,
-        formatter_class=RawTextHelpFormatter,
-    )
-    parser.add_argument("font")
-    parser.add_argument(
-        "-o", metavar="OUTPUTFILE", dest="outfile", default=None, help="output file"
-    )
-    parser.add_argument(
-        "--gpos-compression-level",
-        help=COMPRESSION_LEVEL.help,
-        default=COMPRESSION_LEVEL.default,
-        choices=list(range(10)),
-        type=int,
-    )
-    logging_group = parser.add_mutually_exclusive_group(required=False)
-    logging_group.add_argument(
-        "-v", "--verbose", action="store_true", help="Run more verbosely."
-    )
-    logging_group.add_argument(
-        "-q", "--quiet", action="store_true", help="Turn verbosity off."
-    )
-    options = parser.parse_args(args)
-
-    configLogger(
-        level=("DEBUG" if options.verbose else "ERROR" if options.quiet else "INFO")
-    )
-
-    font = TTFont(options.font)
-    compact(font, options.gpos_compression_level)
-    font.save(options.outfile or options.font)
-
-
-if __name__ == "__main__":
-    import sys
-
-    if len(sys.argv) > 1:
-        sys.exit(main())
-    import doctest
-
-    sys.exit(doctest.testmod().failed)
diff --git a/spaces/jonigata/PoseMaker2/Dockerfile b/spaces/jonigata/PoseMaker2/Dockerfile
deleted file mode 100644
index 908fc4f772313083fd32da4a1968882c110e8aa6..0000000000000000000000000000000000000000
--- a/spaces/jonigata/PoseMaker2/Dockerfile
+++ /dev/null
@@ -1,31 +0,0 @@
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-
-FROM python:3.9
-
-WORKDIR /code
-
-COPY ./requirements.txt /code/requirements.txt
-
-RUN apt-get update && apt-get upgrade -y && apt-get install -y libgl1-mesa-dev
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-RUN mim install mmcv-full==1.7.0
-RUN pip install mmdet==2.28.2 mmpose==0.29.0 mmengine
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -m -u 1000 user
-
-# Switch to the "user" user
-USER user
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app
-
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/jotap12/enso/estilos/style.css b/spaces/jotap12/enso/estilos/style.css
deleted file mode 100644
index ae60a0668df75de0bb502008bc98bc011e0c5e75..0000000000000000000000000000000000000000
--- a/spaces/jotap12/enso/estilos/style.css
+++ /dev/null
@@ -1,76 +0,0 @@
-section[data-testid='stSidebar'] {
-    background-color: #111;
-    flex-shrink: unset !important;
-    
-}
-
-@media(hover:hover) and (min-width: 600px) and (max-width: 769px){
-
-    header[data-testid="stHeader"] {
-        display:none;
-    }
-
-    section[data-testid='stSidebar'] {
-        height: 100%;
-        min-width:95px !important;
-        width: 95px !important;
-        margin-left: 305px;
-        position: relative;
-        z-index: 1;
-        top: 0;
-        left: 0;
-        background-color: #111;
-        overflow-x: hidden;
-        transition: 0.5s ease;
-        padding-top: 60px;
-        white-space: nowrap;
-    }
-
-    section[data-testid='stSidebar']:hover{
-        min-width: 330px !important;
-        }
-
-    button[kind="header"] {
-        display: none;
-    }
-
-    div[data-testid="collapsedControl"]{
-        display: none;
-    }
-
-}
-
-@media(hover: hover) and (min-width: 769px){
-
-    header[data-testid="stHeader"] {
-        display:none;
-    }
-
-    section[data-testid='stSidebar'] {
-        height: 100%;
-        min-width:95px !important;
-        width: 95px !important;
-        transform:translateX(0px);
-        position: relative;
-        z-index: 1;
-        top: 0;
-        left: 0;
-        background-color: #111;
-        overflow-x: hidden;
-        transition: 0.5s ease;
-        padding-top: 60px;
-        white-space: nowrap;
-    }
-
-    section[data-testid='stSidebar']:hover{
-        min-width: 330px !important;
-        }
-
-    button[kind="header"] {
-        display: none;
-    }
-
-    div[data-testid="collapsedControl"]{
-        display: none;
-    }
-}
\ No newline at end of file
diff --git a/spaces/jskim/paper-matching/score.py b/spaces/jskim/paper-matching/score.py
deleted file mode 100644
index faf56e8a4e0bf43807f10626836fb780fb685386..0000000000000000000000000000000000000000
--- a/spaces/jskim/paper-matching/score.py
+++ /dev/null
@@ -1,365 +0,0 @@
-from sentence_transformers import util, SentenceTransformer
-from transformers import BertModel
-from nltk.tokenize import sent_tokenize
-from nltk import word_tokenize, pos_tag
-import torch
-import numpy as np
-import tqdm
-
-def compute_sentencewise_scores(model, query_sents, candidate_sents, tokenizer=None):
-    if isinstance(model, SentenceTransformer): 
-        # if the model is using SentenceTrasformer style
-        q_v, c_v = get_embedding(model, query_sents, candidate_sents)
-    elif isinstance(model, BertModel): 
-        # if the model is BERT-style model using transformers library
-        inputs = tokenizer(
-            query_sents + candidate_sents, 
-            padding=True, 
-            truncation=True, 
-            return_tensors="pt", 
-            max_length=512
-        )
-        inputs.to(model.device)
-        result = model(**inputs)
-        embeddings = result.last_hidden_state[:, 0, :].detach().cpu().numpy()
-        q_v = embeddings[:len(query_sents)]
-        c_v = embeddings[len(query_sents):]
-    else:
-        raise ValueError('model not supported at the time')
-    assert(q_v.shape[1] == c_v.shape[1])
-    assert(q_v.shape[0] == len(query_sents))
-    assert(c_v.shape[0] == len(candidate_sents))
-    return util.cos_sim(q_v, c_v)
-    
-def get_embedding(model, query_sents, candidate_sents):
-    q_v = model.encode(query_sents)
-    c_v = model.encode(candidate_sents)
-    return q_v, c_v
-
-def get_top_k(score_mat, K=3):
-    """
-    Pick top K sentences to show
-    """
-    picked_scores, picked_sent = torch.sort(-score_mat, axis=1) 
-    picked_sent = picked_sent[:,:K]
-    picked_scores = -picked_scores[:,:K]
-    return picked_sent, picked_scores
-
-def get_words(sent):
-    """
-    Input: list of sentences
-    Output: list of list of words per sentence, all words in, index of starting words for each sentence
-    """
-    words = []
-    sent_start_id = [] # keep track of the word index where the new sentence starts
-    counter = 0
-    for x in sent:
-        w = word_tokenize(x)
-        nw = len(w)
-        counter += nw
-        words.append(w)
-        sent_start_id.append(counter)
-    words = [word_tokenize(x) for x in sent]
-    all_words = [item for sublist in words for item in sublist]
-    sent_start_id.pop()
-    sent_start_id = [0] + sent_start_id
-    assert(len(sent_start_id) == len(sent))
-    return words, all_words, sent_start_id
-
-def get_match_phrase(w1, w2, method='pos'):
-    """
-    Input: list of words for query and candidate text
-    Output: word list and binary mask of matching phrases between the inputs
-    """
-    mask1 = np.zeros(len(w1))
-    mask2 = np.zeros(len(w2))
-    if method == 'pos':
-        # POS tags that should be considered for matching phrase
-        include = [
-            'NN',
-            'NNS',
-            'NNP',
-            'NNPS',
-            'LS',
-            'SYM',
-            'FW'
-        ]
-        pos1 = pos_tag(w1)
-        pos2 = pos_tag(w2)
-        for i, (w, p) in enumerate(pos2):
-            for j, (w_, p_) in enumerate(pos1):
-                if w.lower() == w_.lower() and p in include:
-                    mask2[i] = 1
-                    mask1[j] = 1
-    return mask1, mask2
-
-def remove_spaces(words, attrs):
-    # make the output more readable by removing unnecessary spacings from the tokenizer
-    # e.g.
-    # 1. spacing for parenthesis
-    # 2. spacing for single/double quotations
-    # 3. spacing for commas and periods
-    # 4. spacing for possessive quotations
-    assert(len(words) == len(attrs))
-    word_out, attr_out = [], []
-    idx, single_q, double_q = 0, 0, 0
-    while idx < len(words):
-        # stick to the word that appears right before
-        if words[idx] in [',', '.', '%', ')', ':', '?', ';', "'s", '”', "''"]:
-            ww = word_out.pop()
-            aa = attr_out.pop()
-            word_out.append(ww + words[idx])
-            attr_out.append(aa)
-            idx += 1
-        # stick to the word that appears right after
-        elif words[idx] in ["(", '“']:
-            word_out.append(words[idx] + words[idx+1])
-            attr_out.append(attrs[idx+1])
-            idx += 2
-        # quotes 
-        elif words[idx] == '"':
-            double_q += 1
-            if double_q == 2:
-                # this is closing quote: stick to word before
-                ww = word_out.pop()
-                aa = attr_out.pop()
-                word_out.append(ww + words[idx])
-                attr_out.append(aa)
-                idx += 1
-                double_q = 0
-            else:
-                # this is opening quote: stick to the word after
-                word_out.append(words[idx] + words[idx+1])
-                attr_out.append(attrs[idx+1])
-                idx += 2
-        elif words[idx] == "'":
-            single_q += 1
-            if single_q == 2:
-                # this is closing quote: stick to word before
-                ww = word_out.pop()
-                aa = attr_out.pop()
-                word_out.append(ww + words[idx])
-                attr_out.append(aa)
-                idx += 1
-                single_q = 0
-            else:
-                if words[idx-1][-1] == 's': #possessive quote
-                    # stick to the word before, reset counter
-                    ww = word_out.pop()
-                    aa = attr_out.pop()
-                    word_out.append(ww + words[idx])
-                    attr_out.append(aa)
-                    idx += 1
-                    single_q = 0
-                else:
-                    # this is opening quote: stick to the word after
-                    word_out.append(words[idx] + words[idx+1])
-                    attr_out.append(attrs[idx+1])
-                    idx += 2
-        elif words[idx] == '``':
-            # this is opening quote: stick to the word after, but change to real double quote
-            word_out.append('"' + words[idx+1])
-            attr_out.append(attrs[idx+1])
-            idx += 2
-        elif words[idx] == "''":
-            # this is closing quote: stick to word before, but change to real double quote
-            ww = word_out.pop()
-            aa = attr_out.pop()
-            word_out.append(ww + '"')
-            attr_out.append(aa)
-            idx += 1
-        else:
-            word_out.append(words[idx])
-            attr_out.append(attrs[idx])
-            idx += 1         
-            
-    assert(len(word_out) == len(attr_out))
-    return word_out, attr_out
-
-def scale_scores(arr, vmin=0.1, vmax=1):
-    # rescale positive and negative attributions to be between vmin and vmax.
-    # while keeping 0 at 0.
-    pos_max, pos_min = np.max(arr[arr > 0]), np.min(arr[arr > 0])
-    out = (arr - pos_min) / (pos_max - pos_min) * (vmax - vmin) + vmin
-    idx = np.where(arr == 0.0)[0]
-    out[idx] = 0.0
-    return out
-
-def mark_words(query_sents, words, all_words, sent_start_id, sent_ids, sent_scores):
-    """
-    Mark the words that are highlighted, both by in terms of sentence and phrase
-    """
-    num_query_sent = sent_ids.shape[0]
-    num_cand_sent = sent_ids.shape[1]
-    num_words = len(all_words)
-    
-    output = dict()
-    output['all_words'] = all_words
-    output['words_by_sentence'] = words
-    
-    # for each query sentence, mark the highlight information
-    for i in range(num_query_sent):
-        output[i] = dict()
-        for j in range(1, num_cand_sent+1): # for each number of selected sentences from candidate
-            query_words = word_tokenize(query_sents[i])
-            is_selected_sent = np.zeros(num_words)
-            is_selected_phrase = np.zeros(num_words)
-            word_scores = np.zeros(num_words)
-            
-            # for each selected sentences from the candidate, compile information
-            for sid, sscore in zip(sent_ids[i][:j], sent_scores[i][:j]):
-                #print(len(sent_start_id), sid, sid+1)
-                if sid+1 < len(sent_start_id):
-                    sent_range = (sent_start_id[sid], sent_start_id[sid+1])
-                    is_selected_sent[sent_range[0]:sent_range[1]] = 1
-                    word_scores[sent_range[0]:sent_range[1]] = sscore
-                    _, is_selected_phrase[sent_range[0]:sent_range[1]] = \
-                        get_match_phrase(query_words, all_words[sent_range[0]:sent_range[1]])
-                else:
-                    is_selected_sent[sent_start_id[sid]:] = 1
-                    word_scores[sent_start_id[sid]:] = sscore
-                    _, is_selected_phrase[sent_start_id[sid]:] = \
-                        get_match_phrase(query_words, all_words[sent_start_id[sid]:])
-            
-            # scale the word_scores: maximum value gets the darkest, minimum value gets the lightest color
-            if j > 1:
-                word_scores = scale_scores(word_scores)
-                        
-            # update selected phrase scores (-1 meaning a different color in gradio)
-            word_scores[is_selected_sent+is_selected_phrase==2] = -0.5
-                
-            output[i][j] = {
-                'is_selected_sent': is_selected_sent,
-                'is_selected_phrase': is_selected_phrase,
-                'scores': word_scores
-            }
-
-    return output
-
-def get_highlight_info(model, tokenizer, text1, text2, K=None, top_pair_num=5):
-    """
-    Get highlight information from two texts
-    """
-    sent1 = sent_tokenize(text1) # query
-    sent2 = sent_tokenize(text2) # candidate
-    score_mat = compute_sentencewise_scores(model, sent1, sent2, tokenizer=tokenizer)
-    
-    if K is None: # if K is not set, get all information
-        K = score_mat.shape[1]
-        
-    sent_ids, sent_scores = get_top_k(score_mat, K=K)
-    words2, all_words2, sent_start_id2 = get_words(sent2)
-    info = mark_words(sent1, words2, all_words2, sent_start_id2, sent_ids, sent_scores)
-    
-    # get top sentence pairs from the query and candidate (score, index_pair) to show upfront
-    top_pairs = []
-    ii = np.unravel_index(np.argsort(np.array(sent_scores).ravel())[-top_pair_num:], sent_scores.shape)
-    for i, j in zip(ii[0][::-1], ii[1][::-1]):
-        score = sent_scores[i,j].item()
-        index_pair = (i, sent_ids[i,j].item())
-        top_pairs.append((score, index_pair)) # list of (score, (sent_id_query, sent_id_candidate))
-        
-    # convert top_pairs to corresponding highlights format for GRadio Interpretation component
-    top_pairs_info = dict()
-    count = 0
-    for s, (sidq, sidc) in top_pairs:
-        q_sent = sent1[sidq]
-        c_sent = sent2[sidc]
-        q_words = word_tokenize(q_sent)
-        c_words = word_tokenize(c_sent)
-        mask1, mask2 = get_match_phrase(q_words, c_words)
-        sc = 0.5
-        mask1 *= -sc # mark matching phrases as blue (-1: darkest)
-        mask2 *= -sc # mark matching phrases as blue
-        assert(len(mask1) == len(q_words) and len(mask2) == len(c_words))
-        
-        # spacing
-        q_words, mask1 = remove_spaces(q_words, mask1)
-        c_words, mask2 = remove_spaces(c_words, mask2)
-        
-        top_pairs_info[count] = {
-            'query': {
-                'original': q_sent,
-                'interpretation': list(zip(q_words, mask1))
-            },
-            'candidate': {
-                'original': c_sent,
-                'interpretation': list(zip(c_words, mask2))
-            },
-            'score': s,
-            'sent_idx': (sidq, sidc) 
-        }
-        count += 1
-    
-    return sent_ids, sent_scores, info, top_pairs_info
-
-### Document-level operations
-def predict_docscore(doc_model, tokenizer, query, titles, abstracts, batch=20):
-    # compute document scores for each papers
-    
-    # concatenate title and abstract
-    title_abs = []
-    for t, a in zip(titles, abstracts):
-        if t is not None and a is not None:
-            title_abs.append(t + ' [SEP] ' + a) # title + abstract
-            
-    num_docs = len(title_abs) 
-    no_iter = int(np.ceil(num_docs / batch))
-    scores = []
-    with torch.no_grad():
-        # batch
-        for i in tqdm.tqdm(range(no_iter)):
-            # preprocess the input
-            inputs = tokenizer(
-                [query] + title_abs[i*batch:(i+1)*batch], 
-                padding=True, 
-                truncation=True, 
-                return_tensors="pt", 
-                max_length=512
-            )
-            inputs.to(doc_model.device)
-            result = doc_model(**inputs)
-        
-            # take the first token in the batch as the embedding
-            embeddings = result.last_hidden_state[:, 0, :].detach().cpu().numpy()
-        
-            # compute cosine similarity
-            q_emb = embeddings[0,:]
-            p_emb = embeddings[1:,:]
-            nn = np.linalg.norm(q_emb) * np.linalg.norm(p_emb, axis=1)
-            scores += list(np.dot(p_emb, q_emb) / nn)
-
-    assert(len(scores) == num_docs)
-    
-    return scores
-
-def compute_document_score(doc_model, tokenizer, query_title, query_abs, papers, batch=5):
-    scores = []
-    titles = []
-    abstracts = []
-    urls = []
-    years = []
-    citations = []
-    for p in papers:
-        if p['title'] is not None and p['abstract'] is not None:
-            titles.append(p['title'])
-            abstracts.append(p['abstract'])
-            urls.append(p['url'])
-            years.append(p['year'])
-            citations.append(p['citationCount'])
-    if query_title == '':
-        query = query_abs
-    else:
-        query = query_title + ' [SEP] ' + query_abs # feed in submission title and abstract
-    scores = predict_docscore(doc_model, tokenizer, query, titles, abstracts, batch=batch)
-    assert(len(scores) == len(abstracts))
-    idx_sorted = np.argsort(scores)[::-1]
-    
-    titles_sorted = [titles[x] for x in idx_sorted]
-    abstracts_sorted = [abstracts[x] for x in idx_sorted]
-    scores_sorted = [scores[x] for x in idx_sorted]
-    urls_sorted = [urls[x] for x in idx_sorted]
-    years_sorted = [years[x] for x in idx_sorted]
-    citations_sorted = [citations[x] for x in idx_sorted]
-    
-    return titles_sorted, abstracts_sorted, urls_sorted, scores_sorted, years_sorted, citations_sorted
\ No newline at end of file
diff --git a/spaces/juliensimon/song-lyrics/app.py b/spaces/juliensimon/song-lyrics/app.py
deleted file mode 100644
index de10482f419cec14576c892c2b67c505fbb6c33e..0000000000000000000000000000000000000000
--- a/spaces/juliensimon/song-lyrics/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import torch
-import numpy as np
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-repo_name = 'juliensimon/autonlp-song-lyrics-18753417'
-
-tokenizer = AutoTokenizer.from_pretrained(repo_name)
-model = AutoModelForSequenceClassification.from_pretrained(repo_name)
-labels = model.config.id2label
-print(labels)
-
-def predict(lyrics):
-	inputs = tokenizer(lyrics, padding=True, truncation=True, return_tensors="pt")
-	outputs = model(**inputs)
-	predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-	predictions = predictions.detach().numpy()[0]
-	predictions = predictions*100
-	print(predictions)
-	sorted_indexes = np.argsort(predictions)
-	return "These lyrics are {:.2f}% {}, {:.2f}% {} and {:.2f}% {}.".format(
-		predictions[sorted_indexes[-1]], labels[sorted_indexes[-1]],
-		predictions[sorted_indexes[-2]], labels[sorted_indexes[-2]],
-		predictions[sorted_indexes[-3]], labels[sorted_indexes[-3]])
-
-input = gr.inputs.Textbox(lines=20)
-
-iface = gr.Interface(fn=predict, inputs=input, outputs="text")
-iface.launch()
diff --git a/spaces/katanaml/table-query/app/tapas.py b/spaces/katanaml/table-query/app/tapas.py
deleted file mode 100644
index ecf752c09a0b717ecbbc395ccc7c60ed0393e12c..0000000000000000000000000000000000000000
--- a/spaces/katanaml/table-query/app/tapas.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from transformers import TapasTokenizer, TFTapasForQuestionAnswering
-import pandas as pd
-import datetime
-
-
-def execute_query(query, csv_file):
-    a = datetime.datetime.now()
-
-    table = pd.read_csv(csv_file.name, delimiter=",")
-    table.fillna(0, inplace=True)
-    table = table.astype(str)
-
-    model_name = "google/tapas-base-finetuned-wtq"
-    model = TFTapasForQuestionAnswering.from_pretrained(model_name)
-    tokenizer = TapasTokenizer.from_pretrained(model_name)
-
-    queries = [query]
-
-    inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf")
-    outputs = model(**inputs)
-
-    predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
-        inputs, outputs.logits, outputs.logits_aggregation
-    )
-
-    # let's print out the results:
-    id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}
-    aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
-
-    answers = []
-    for coordinates in predicted_answer_coordinates:
-        if len(coordinates) == 1:
-            # only a single cell:
-            answers.append(table.iat[coordinates[0]])
-        else:
-            # multiple cells
-            cell_values = []
-            for coordinate in coordinates:
-                cell_values.append(table.iat[coordinate])
-            answers.append(cell_values)
-
-    for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
-        if predicted_agg != "NONE":
-            answers.append(predicted_agg)
-
-    query_result = {
-        "query": query,
-        "result": answers
-    }
-
-    b = datetime.datetime.now()
-    print(b - a)
-
-    return query_result, table
-
-
diff --git a/spaces/kevinwang676/Bert-VITS2/modules.py b/spaces/kevinwang676/Bert-VITS2/modules.py
deleted file mode 100644
index 92e0f32a51c472bfd1659a50a95a95d195281d2b..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bert-VITS2/modules.py
+++ /dev/null
@@ -1,452 +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
-from transforms import piecewise_rational_quadratic_transform
-from attentions import Encoder
-
-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
-
-
-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.)
-    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
-class TransformerCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      n_layers,
-      n_heads,
-      p_dropout=0,
-      filter_channels=0,
-      mean_only=False,
-      wn_sharing_parameter=None,
-      gin_channels = 0
-      ):
-    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.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 = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
-    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
-
-    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
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r34.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r34.py
deleted file mode 100644
index fda2701758a839a7161d09c25f0ca3d26033baff..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r34.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "cosface"
-config.network = "r34"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/glint360k"
-config.num_classes = 360232
-config.num_image = 17091657
-config.num_epoch = 20
-config.warmup_epoch = -1
-config.decay_epoch = [8, 12, 15, 18]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/psanet_r50-d8.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/psanet_r50-d8.py
deleted file mode 100644
index 689513fa9d2a40f14bf0ae4ae61f38f0dcc1b3da..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/psanet_r50-d8.py
+++ /dev/null
@@ -1,49 +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='PSAHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        mask_size=(97, 97),
-        psa_type='bi-direction',
-        compact=False,
-        shrink_factor=2,
-        normalization_factor=1.0,
-        psa_softmax=True,
-        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/koajoel/PolyFormer/bert/tokenization_utils_base.py b/spaces/koajoel/PolyFormer/bert/tokenization_utils_base.py
deleted file mode 100644
index 6a1219a4d3473ae510f0da905ea09a76019a7996..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/bert/tokenization_utils_base.py
+++ /dev/null
@@ -1,2317 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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.
-""" Base classes common to both the slow and the fast tokenization classes:
-    PreTrainedTokenizerBase (host all the user fronting encoding methodes)
-    Special token mixing (host the special tokens logic) and
-    BatchEncoding (wrap the dictionnary of output with special method for the Fast tokenizers)
-"""
-
-import copy
-import json
-import logging
-import os
-import warnings
-from collections import UserDict
-from enum import Enum
-from typing import Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union
-
-import numpy as np
-from tokenizers import AddedToken
-from tokenizers import Encoding as EncodingFast
-
-from .file_utils import (
-    add_end_docstrings,
-    cached_path,
-    hf_bucket_url,
-    is_remote_url,
-    is_tf_available,
-    is_torch_available,
-    torch_required,
-)
-
-
-if is_tf_available():
-    import tensorflow as tf
-if is_torch_available():
-    import torch
-
-
-logger = logging.getLogger(__name__)
-
-VERY_LARGE_INTEGER = int(1e30)  # This is used to set the max input length for a model with infinite size input
-LARGE_INTEGER = int(1e20)  # This is used when we need something big but slightly smaller than VERY_LARGE_INTEGER
-
-# Define type aliases and NamedTuples
-TextInput = str
-PreTokenizedInput = List[str]
-EncodedInput = List[int]
-TextInputPair = Tuple[str, str]
-PreTokenizedInputPair = Tuple[List[str], List[str]]
-EncodedInputPair = Tuple[List[int], List[int]]
-
-
-# Slow tokenizers used to be saved in three separated files
-SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
-ADDED_TOKENS_FILE = "added_tokens.json"
-TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
-
-# Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file
-FULL_TOKENIZER_FILE = "tokenizer.json"
-
-
-class ExplicitEnum(Enum):
-    """ Enum with more explicit error message for missing values.
-    """
-
-    @classmethod
-    def _missing_(cls, value):
-        raise ValueError(
-            "%r is not a valid %s, please select one of %s"
-            % (value, cls.__name__, str(list(cls._value2member_map_.keys())))
-        )
-
-
-class TruncationStrategy(ExplicitEnum):
-    ONLY_FIRST = "only_first"
-    ONLY_SECOND = "only_second"
-    LONGEST_FIRST = "longest_first"
-    DO_NOT_TRUNCATE = "do_not_truncate"
-
-
-class PaddingStrategy(ExplicitEnum):
-    LONGEST = "longest"
-    MAX_LENGTH = "max_length"
-    DO_NOT_PAD = "do_not_pad"
-
-
-class TensorType(ExplicitEnum):
-    PYTORCH = "pt"
-    TENSORFLOW = "tf"
-    NUMPY = "np"
-
-
-class CharSpan(NamedTuple):
-    """ Character span in the original string
-
-        Args:
-            start: index of the first character in the original string
-            end: index of the character following the last character in the original string
-    """
-
-    start: int
-    end: int
-
-
-class TokenSpan(NamedTuple):
-    """ Token span in an encoded string (list of tokens)
-
-        Args:
-            start: index of the first token in the span
-            end: index of the token following the last token in the span
-    """
-
-    start: int
-    end: int
-
-
-class BatchEncoding(UserDict):
-    """ BatchEncoding hold the output of the encode and batch_encode methods (tokens, attention_masks, etc).
-        This class is derived from a python Dictionary and can be used as a dictionnary.
-        In addition, this class expose utility methods to map from word/char space to token space.
-
-        Args:
-            data (:obj:`dict`): Dictionary of lists/arrays returned by the encode/batch_encode methods ('input_ids', 'attention_mask'...)
-            encoding (:obj:`EncodingFast`, :obj:`list(EncodingFast)`, `optional`, defaults to :obj:`None`):
-                If the tokenizer is a fast tokenizer which outputs additional informations like mapping from word/char space to token space
-                the `EncodingFast` instance or list of instance (for batches) hold these informations.
-            tensor_type (:obj:`Union[None, str, TensorType]`, `optional`, defaults to :obj:`None`):
-                You can give a tensor_type here to convert the lists of integers in PyTorch/TF/Numpy Tensors at initialization
-            prepend_batch_axis (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Set to True to add a batch axis when converting in Tensors (see :obj:`tensor_type` above)
-    """
-
-    def __init__(
-        self,
-        data: Optional[Dict[str, Any]] = None,
-        encoding: Optional[Union[EncodingFast, Sequence[EncodingFast]]] = None,
-        tensor_type: Union[None, str, TensorType] = None,
-        prepend_batch_axis: bool = False,
-    ):
-        super().__init__(data)
-
-        if isinstance(encoding, EncodingFast):
-            encoding = [encoding]
-
-        self._encodings = encoding
-
-        self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
-
-    @property
-    def is_fast(self):
-        """
-        Indicate if this BatchEncoding was generated from the result of a PreTrainedTokenizerFast
-        Returns: True if generated from subclasses of PreTrainedTokenizerFast, else otherwise
-        """
-        return self._encodings is not None
-
-    def __getitem__(self, item: Union[int, str]) -> EncodingFast:
-        """ If the key is a string, get the value of the dict associated to `key` ('input_ids', 'attention_mask'...)
-            If the key is an integer, get the EncodingFast for batch item with index `key`
-        """
-        if isinstance(item, str):
-            return self.data[item]
-        elif self._encodings is not None:
-            return self._encodings[item]
-        else:
-            raise KeyError(
-                "Indexing with integers (to access backend Encoding for a given batch index) "
-                "is not available when using Python based tokenizers"
-            )
-
-    def __getattr__(self, item: str):
-        try:
-            return self.data[item]
-        except KeyError:
-            raise AttributeError
-
-    def __getstate__(self):
-        return {"data": self.data, "encodings": self._encodings}
-
-    def __setstate__(self, state):
-        if "data" in state:
-            self.data = state["data"]
-
-        if "encodings" in state:
-            self._encodings = state["encodings"]
-
-    def keys(self):
-        return self.data.keys()
-
-    def values(self):
-        return self.data.values()
-
-    def items(self):
-        return self.data.items()
-
-    # After this point:
-    # Extended properties and methods only available for fast (Rust-based) tokenizers
-    # provided by HuggingFace tokenizers library.
-
-    @property
-    def encodings(self) -> Optional[List[EncodingFast]]:
-        """
-        Return the list all encoding from the tokenization process
-
-        Returns: List[EncodingFast] or None if input was tokenized through Python (i.e. not fast) tokenizer
-        """
-        return self._encodings
-
-    def tokens(self, batch_index: int = 0) -> List[str]:
-        if not self._encodings:
-            raise ValueError("tokens() is not available when using Python based tokenizers")
-        return self._encodings[batch_index].tokens
-
-    def words(self, batch_index: int = 0) -> List[Optional[int]]:
-        if not self._encodings:
-            raise ValueError("words() is not available when using Python based tokenizers")
-        return self._encodings[batch_index].words
-
-    def token_to_word(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int:
-        """
-        Get the index of the word corresponding (i.e. comprising) to an encoded token
-        in a sequence of the batch.
-
-        Can be called as:
-
-        - ``self.token_to_word(token_index)`` if batch size is 1
-        - ``self.token_to_word(batch_index, token_index)`` if batch size is greater than 1
-
-        This method is particularly suited when the input sequences are provided as
-        pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
-        to easily associate encoded tokens with provided tokenized words.
-
-        Args:
-            batch_or_token_index (:obj:`int`):
-                Index of the sequence in the batch. If the batch only comprise one sequence,
-                this can be the index of the token in the sequence
-            token_index (:obj:`int`, `optional`):
-                If a batch index is provided in `batch_or_token_index`, this can be the index
-                of the token in the sequence.
-
-        Returns:
-            :obj:`int`:
-                index of the word in the input sequence.
-
-        """
-
-        if not self._encodings:
-            raise ValueError("token_to_word() is not available when using Python based tokenizers")
-        if token_index is not None:
-            batch_index = batch_or_token_index
-        else:
-            batch_index = 0
-            token_index = batch_or_token_index
-        if batch_index < 0:
-            batch_index = self._batch_size + batch_index
-        if token_index < 0:
-            token_index = self._seq_len + token_index
-        return self._encodings[batch_index].token_to_word(token_index)
-
-    def word_to_tokens(self, batch_or_word_index: int, word_index: Optional[int] = None) -> TokenSpan:
-        """
-        Get the encoded token span corresponding to a word in the sequence of the batch.
-
-        Token spans are returned as a TokenSpan NamedTuple with:
-
-        - start: index of the first token
-        - end: index of the token following the last token
-
-        Can be called as:
-
-        - ``self.word_to_tokens(word_index)`` if batch size is 1
-        - ``self.word_to_tokens(batch_index, word_index)`` if batch size is greater or equal to 1
-
-        This method is particularly suited when the input sequences are provided as
-        pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
-        to easily associate encoded tokens with provided tokenized words.
-
-        Args:
-            batch_or_word_index (:obj:`int`):
-                Index of the sequence in the batch. If the batch only comprises one sequence,
-                this can be the index of the word in the sequence
-            word_index (:obj:`int`, `optional`):
-                If a batch index is provided in `batch_or_token_index`, this can be the index
-                of the word in the sequence.
-
-        Returns:
-            :obj:`TokenSpan`:
-                Span of tokens in the encoded sequence.
-
-                :obj:`TokenSpan` are NamedTuple with:
-
-                - start: index of the first token
-                - end: index of the token following the last token
-        """
-
-        if not self._encodings:
-            raise ValueError("word_to_tokens() is not available when using Python based tokenizers")
-        if word_index is not None:
-            batch_index = batch_or_word_index
-        else:
-            batch_index = 0
-            word_index = batch_or_word_index
-        if batch_index < 0:
-            batch_index = self._batch_size + batch_index
-        if word_index < 0:
-            word_index = self._seq_len + word_index
-        return TokenSpan(*(self._encodings[batch_index].word_to_tokens(word_index)))
-
-    def token_to_chars(self, batch_or_token_index: int, token_index: Optional[int] = None) -> CharSpan:
-        """
-        Get the character span corresponding to an encoded token in a sequence of the batch.
-
-        Character spans are returned as a CharSpan NamedTuple with:
-
-        - start: index of the first character in the original string associated to the token
-        - end: index of the character following the last character in the original string associated to the token
-
-        Can be called as:
-
-        - ``self.token_to_chars(token_index)`` if batch size is 1
-        - ``self.token_to_chars(batch_index, token_index)`` if batch size is greater or equal to 1
-
-        Args:
-            batch_or_token_index (:obj:`int`):
-                Index of the sequence in the batch. If the batch only comprise one sequence,
-                this can be the index of the token in the sequence
-            token_index (:obj:`int`, `optional`):
-                If a batch index is provided in `batch_or_token_index`, this can be the index
-                of the token or tokens in the sequence.
-
-        Returns:
-            :obj:`CharSpan`:
-                Span of characters in the original string.
-
-                :obj:`CharSpan` are NamedTuple with:
-
-                - start: index of the first character in the original string
-                - end: index of the character following the last character in the original string
-        """
-
-        if not self._encodings:
-            raise ValueError("token_to_chars() is not available when using Python based tokenizers")
-        if token_index is not None:
-            batch_index = batch_or_token_index
-        else:
-            batch_index = 0
-            token_index = batch_or_token_index
-        return CharSpan(*(self._encodings[batch_index].token_to_chars(token_index)))
-
-    def char_to_token(self, batch_or_char_index: int, char_index: Optional[int] = None) -> int:
-        """
-        Get the index of the token in the encoded output comprising a character
-        in the original string for a sequence of the batch.
-
-        Can be called as:
-
-        - ``self.char_to_token(char_index)`` if batch size is 1
-        - ``self.char_to_token(batch_index, char_index)`` if batch size is greater or equal to 1
-
-        This method is particularly suited when the input sequences are provided as
-        pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
-        to easily associate encoded tokens with provided tokenized words.
-
-        Args:
-            batch_or_char_index (:obj:`int`):
-                Index of the sequence in the batch. If the batch only comprise one sequence,
-                this can be the index of the word in the sequence
-            char_index (:obj:`int`, `optional`):
-                If a batch index is provided in `batch_or_token_index`, this can be the index
-                of the word in the sequence.
-
-
-        Returns:
-            :obj:`int`: Index of the token.
-        """
-
-        if not self._encodings:
-            raise ValueError("char_to_token() is not available when using Python based tokenizers")
-        if char_index is not None:
-            batch_index = batch_or_char_index
-        else:
-            batch_index = 0
-            char_index = batch_or_char_index
-        return self._encodings[batch_index].char_to_token(char_index)
-
-    def word_to_chars(self, batch_or_word_index: int, word_index: Optional[int] = None) -> CharSpan:
-        """
-        Get the character span in the original string corresponding to given word in a sequence
-        of the batch.
-
-        Character spans are returned as a CharSpan NamedTuple with:
-
-        - start: index of the first character in the original string
-        - end: index of the character following the last character in the original string
-
-        Can be called as:
-
-        - ``self.word_to_chars(word_index)`` if batch size is 1
-        - ``self.word_to_chars(batch_index, word_index)`` if batch size is greater or equal to 1
-
-        Args:
-            batch_or_word_index (:obj:`int`):
-                Index of the sequence in the batch. If the batch only comprise one sequence,
-                this can be the index of the word in the sequence
-            word_index (:obj:`int`, `optional`):
-                If a batch index is provided in `batch_or_token_index`, this can be the index
-                of the word in the sequence.
-
-        Returns:
-            :obj:`CharSpan` or :obj:`List[CharSpan]`:
-                Span(s) of the associated character or characters in the string.
-                CharSpan are NamedTuple with:
-
-                - start: index of the first character associated to the token in the original string
-                - end: index of the character following the last character associated to the token in the original string
-        """
-
-        if not self._encodings:
-            raise ValueError("word_to_chars() is not available when using Python based tokenizers")
-        if word_index is not None:
-            batch_index = batch_or_word_index
-        else:
-            batch_index = 0
-            word_index = batch_or_word_index
-        return CharSpan(*(self._encodings[batch_index].word_to_chars(word_index)))
-
-    def char_to_word(self, batch_or_char_index: int, char_index: Optional[int] = None) -> int:
-        """
-        Get the word in the original string corresponding to a character in the original string of
-        a sequence of the batch.
-
-        Can be called as:
-
-        - ``self.char_to_word(char_index)`` if batch size is 1
-        - ``self.char_to_word(batch_index, char_index)`` if batch size is greater than 1
-
-        This method is particularly suited when the input sequences are provided as
-        pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
-        to easily associate encoded tokens with provided tokenized words.
-
-        Args:
-            batch_or_char_index (:obj:`int`):
-                Index of the sequence in the batch. If the batch only comprise one sequence,
-                this can be the index of the character in the orginal string.
-            char_index (:obj:`int`, `optional`):
-                If a batch index is provided in `batch_or_token_index`, this can be the index
-                of the character in the orginal string.
-
-
-        Returns:
-            :obj:`int` or :obj:`List[int]`:
-                Index or indices of the associated encoded token(s).
-        """
-
-        if not self._encodings:
-            raise ValueError("char_to_word() is not available when using Python based tokenizers")
-        if char_index is not None:
-            batch_index = batch_or_char_index
-        else:
-            batch_index = 0
-            char_index = batch_or_char_index
-        return self._encodings[batch_index].char_to_word(char_index)
-
-    def convert_to_tensors(self, tensor_type: Union[None, str, TensorType], prepend_batch_axis: bool = False):
-        if tensor_type is None:
-            return self
-
-        # Convert to TensorType
-        if not isinstance(tensor_type, TensorType):
-            tensor_type = TensorType(tensor_type)
-
-        # Get a function reference for the correct framework
-        if tensor_type == TensorType.TENSORFLOW and is_tf_available():
-            as_tensor = tf.constant
-        elif tensor_type == TensorType.PYTORCH and is_torch_available():
-            as_tensor = torch.tensor
-        elif tensor_type == TensorType.NUMPY:
-            as_tensor = np.asarray
-        else:
-            raise ImportError(
-                "Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(
-                    tensor_type
-                )
-            )
-
-        # Do the tensor conversion in batch
-        for key, value in self.items():
-            try:
-                if prepend_batch_axis:
-                    value = [value]
-
-                tensor = as_tensor(value)
-
-                # at-least2d
-                if tensor.ndim > 2:
-                    tensor = tensor.squeeze(0)
-                elif tensor.ndim < 2:
-                    tensor = tensor[None, :]
-
-                self[key] = tensor
-            except:  # noqa E722
-                raise ValueError(
-                    "Unable to create tensor, you should probably activate truncation and/or padding "
-                    "with 'padding=True' 'truncation=True' to have batched tensors with the same length."
-                )
-
-        return self
-
-    @torch_required
-    def to(self, device: str):
-        """Send all values to device by calling v.to(device)"""
-        self.data = {k: v.to(device) for k, v in self.data.items()}
-        return self
-
-
-# class AddedToken(UserString):
-#     """ AddedToken represents a token to be added to a Tokenizer
-
-#         An AddedToken can have special options defining the way it should behave.
-
-#         Args:
-#             content: str:
-#                 The content of the token
-
-#             single_word: bool
-#                 Whether this token should only match against single word. If True,
-#                 this token will never match inside of a word.
-
-#             lstrip: bool
-#                 Whether this token should strip all potential whitespaces on the left side.
-#                 If True, this token will greedily match any whitespace on the left and then strip
-#                 them out.
-
-#             rstrip: bool
-#                 Whether this token should strip all potential whitespaces on the right side.
-#                 If True, this token will greedily match any whitespace on the right and then strip
-#                 them out.
-#     """
-
-#     def __init__(
-#         self, data: str, single_word: bool = False, lstrip: bool = False, rstrip: bool = False,
-#     ):
-#         super().__init__(data)
-
-#         self._single_word = single_word
-#         self._lstrip = lstrip
-#         self._rstrip = rstrip
-
-#     def lower(self):
-#         return AddedToken(self.data.lower(), self._single_word, self._lstrip, self._rstrip)
-
-
-class SpecialTokensMixin:
-    """ SpecialTokensMixin is derived by ``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` and
-        handles specific behaviors related to special tokens. In particular, this class hold the
-        attributes which can be used to directly access to these special tokens in a
-        model-independant manner and allow to set and update the special tokens.
-    """
-
-    SPECIAL_TOKENS_ATTRIBUTES = [
-        "bos_token",
-        "eos_token",
-        "unk_token",
-        "sep_token",
-        "pad_token",
-        "cls_token",
-        "mask_token",
-        "additional_special_tokens",
-    ]
-
-    def __init__(self, verbose=True, **kwargs):
-        self._bos_token = None
-        self._eos_token = None
-        self._unk_token = None
-        self._sep_token = None
-        self._pad_token = None
-        self._cls_token = None
-        self._mask_token = None
-        self._pad_token_type_id = 0
-        self._additional_special_tokens = []
-        self.verbose = verbose
-
-        # We directly set the hidden value to allow initialization with special tokens
-        # which are not yet in the vocabulary. Necesssary for serialization/de-serialization
-        # TODO clean this up at some point (probably by sitching to fast tokenizers)
-        for key, value in kwargs.items():
-            if key in self.SPECIAL_TOKENS_ATTRIBUTES:
-                if key == "additional_special_tokens":
-                    assert isinstance(value, (list, tuple)) and all(isinstance(t, str) for t in value)
-                    setattr(self, key, value)
-                elif isinstance(value, (str, AddedToken)):
-                    setattr(self, key, value)
-                else:
-                    raise TypeError(
-                        "special token {} has to be either str or AddedToken but got: {}".format(key, type(value))
-                    )
-
-    def sanitize_special_tokens(self) -> int:
-        """ Make sure that all the special tokens attributes of the tokenizer (tokenizer.mask_token, tokenizer.cls_token, ...)
-            are in the vocabulary. Add the missing ones to the vocabulary if needed.
-
-            Return:
-                Number of tokens added in the vocaulary during the operation.
-        """
-        return self.add_tokens(self.all_special_tokens_extended, special_tokens=True)
-
-    def add_special_tokens(self, special_tokens_dict: Dict[str, Union[str, AddedToken]]) -> int:
-        """
-        Add a dictionary of special tokens (eos, pad, cls...) to the encoder and link them
-        to class attributes. If special tokens are NOT in the vocabulary, they are added
-        to it (indexed starting from the last index of the current vocabulary).
-
-        Using `add_special_tokens` will ensure your special tokens can be used in several ways:
-
-        - special tokens are carefully handled by the tokenizer (they are never split)
-        - you can easily refer to special tokens using tokenizer class attributes like `tokenizer.cls_token`. This makes it easy to develop model-agnostic training and fine-tuning scripts.
-
-        When possible, special tokens are already registered for provided pretrained models (ex: BertTokenizer cls_token is already registered to be '[CLS]' and XLM's one is also registered to be '</s>')
-
-        Args:
-            special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
-                [``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,
-                ``additional_special_tokens``].
-
-                Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
-
-        Returns:
-            Number of tokens added to the vocabulary.
-
-        Examples::
-
-            # Let's see how to add a new classification token to GPT-2
-            tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-            model = GPT2Model.from_pretrained('gpt2')
-
-            special_tokens_dict = {'cls_token': '<CLS>'}
-
-            num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
-            print('We have added', num_added_toks, 'tokens')
-            model.resize_token_embeddings(len(tokenizer))  # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
-
-            assert tokenizer.cls_token == '<CLS>'
-        """
-        if not special_tokens_dict:
-            return 0
-
-        added_tokens = 0
-        for key, value in special_tokens_dict.items():
-            assert key in self.SPECIAL_TOKENS_ATTRIBUTES
-
-            if self.verbose:
-                logger.info("Assigning %s to the %s key of the tokenizer", value, key)
-            setattr(self, key, value)
-
-            if key == "additional_special_tokens":
-                assert isinstance(value, (list, tuple)) and all(
-                    isinstance(t, (str, AddedToken)) for t in value
-                ), f"Tokens {value} for key {key} should all be str or AddedToken instances"
-                added_tokens += self.add_tokens(value, special_tokens=True)
-            else:
-                assert isinstance(
-                    value, (str, AddedToken)
-                ), f"Token {value} for key {key} should be a str or an AddedToken instance"
-                added_tokens += self.add_tokens([value], special_tokens=True)
-
-        return added_tokens
-
-    def add_tokens(self, new_tokens: Union[str, AddedToken, List[str], List[AddedToken]], special_tokens=False) -> int:
-        """
-        Add a list of new tokens to the tokenizer class. If the new tokens are not in the
-        vocabulary, they are added to it with indices starting from length of the current vocabulary.
-
-        Args:
-            new_tokens: string or list of string or :class:`~transformers.AddedToken`. Each string is a token to add.
-                Tokens are only added if they are not already in the vocabulary. AddedToken wrap a string token to
-                let you personnalize it's behavior (Whether this token should only match against single word, whether
-                this token should strip all potential whitespaces on the left side, Whether this token should strip
-                all potential whitespaces on the right side...).
-            special_token: can be used to specify if the token is a special token. This mostly change the normalization
-                behavior (special tokens like CLS or [MASK] are usually not lower-cased for instance)
-
-                See details for :class:`~transformers.AddedToken` in HuggingFace tokenizers library.
-
-        Returns:
-            Number of tokens added to the vocabulary.
-
-        Examples::
-
-            # Let's see how to increase the vocabulary of Bert model and tokenizer
-            tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
-            model = BertModel.from_pretrained('bert-base-uncased')
-
-            num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
-            print('We have added', num_added_toks, 'tokens')
-            model.resize_token_embeddings(len(tokenizer))  # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
-        """
-        if not new_tokens:
-            return 0
-
-        if not isinstance(new_tokens, (list, tuple)):
-            new_tokens = [new_tokens]
-
-        return self._add_tokens(new_tokens, special_tokens=special_tokens)
-
-    @property
-    def bos_token(self):
-        """ Beginning of sentence token (string). Log an error if used while not having been set. """
-        if self._bos_token is None and self.verbose:
-            logger.error("Using bos_token, but it is not set yet.")
-            return None
-        return str(self._bos_token)
-
-    @property
-    def eos_token(self):
-        """ End of sentence token (string). Log an error if used while not having been set. """
-        if self._eos_token is None and self.verbose:
-            logger.error("Using eos_token, but it is not set yet.")
-            return None
-        return str(self._eos_token)
-
-    @property
-    def unk_token(self):
-        """ Unknown token (string). Log an error if used while not having been set. """
-        if self._unk_token is None and self.verbose:
-            logger.error("Using unk_token, but it is not set yet.")
-            return None
-        return str(self._unk_token)
-
-    @property
-    def sep_token(self):
-        """ Separation token (string). E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
-        if self._sep_token is None and self.verbose:
-            logger.error("Using sep_token, but it is not set yet.")
-            return None
-        return str(self._sep_token)
-
-    @property
-    def pad_token(self):
-        """ Padding token (string). Log an error if used while not having been set. """
-        if self._pad_token is None and self.verbose:
-            logger.error("Using pad_token, but it is not set yet.")
-            return None
-        return str(self._pad_token)
-
-    @property
-    def cls_token(self):
-        """ Classification token (string). E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
-        if self._cls_token is None and self.verbose:
-            logger.error("Using cls_token, but it is not set yet.")
-            return None
-        return str(self._cls_token)
-
-    @property
-    def mask_token(self):
-        """ Mask token (string). E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
-        if self._mask_token is None and self.verbose:
-            logger.error("Using mask_token, but it is not set yet.")
-            return None
-        return str(self._mask_token)
-
-    @property
-    def additional_special_tokens(self):
-        """ All the additional special tokens you may want to use (list of strings). Log an error if used while not having been set. """
-        if self._additional_special_tokens is None and self.verbose:
-            logger.error("Using additional_special_tokens, but it is not set yet.")
-            return None
-        return [str(tok) for tok in self._additional_special_tokens]
-
-    @bos_token.setter
-    def bos_token(self, value):
-        self._bos_token = value
-
-    @eos_token.setter
-    def eos_token(self, value):
-        self._eos_token = value
-
-    @unk_token.setter
-    def unk_token(self, value):
-        self._unk_token = value
-
-    @sep_token.setter
-    def sep_token(self, value):
-        self._sep_token = value
-
-    @pad_token.setter
-    def pad_token(self, value):
-        self._pad_token = value
-
-    @cls_token.setter
-    def cls_token(self, value):
-        self._cls_token = value
-
-    @mask_token.setter
-    def mask_token(self, value):
-        self._mask_token = value
-
-    @additional_special_tokens.setter
-    def additional_special_tokens(self, value):
-        self._additional_special_tokens = value
-
-    @property
-    def bos_token_id(self):
-        """ Id of the beginning of sentence token in the vocabulary. Log an error if used while not having been set. """
-        if self._bos_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.bos_token)
-
-    @property
-    def eos_token_id(self):
-        """ Id of the end of sentence token in the vocabulary. Log an error if used while not having been set. """
-        if self._eos_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.eos_token)
-
-    @property
-    def unk_token_id(self):
-        """ Id of the unknown token in the vocabulary. Log an error if used while not having been set. """
-        if self._unk_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.unk_token)
-
-    @property
-    def sep_token_id(self):
-        """ Id of the separation token in the vocabulary. E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
-        if self._sep_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.sep_token)
-
-    @property
-    def pad_token_id(self):
-        """ Id of the padding token in the vocabulary. Log an error if used while not having been set. """
-        if self._pad_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.pad_token)
-
-    @property
-    def pad_token_type_id(self):
-        """ Id of the padding token type in the vocabulary."""
-        return self._pad_token_type_id
-
-    @property
-    def cls_token_id(self):
-        """ Id of the classification token in the vocabulary. E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
-        if self._cls_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.cls_token)
-
-    @property
-    def mask_token_id(self):
-        """ Id of the mask token in the vocabulary. E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
-        if self._mask_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.mask_token)
-
-    @property
-    def additional_special_tokens_ids(self):
-        """ Ids of all the additional special tokens in the vocabulary (list of integers). Log an error if used while not having been set. """
-        return self.convert_tokens_to_ids(self.additional_special_tokens)
-
-    @property
-    def special_tokens_map(self):
-        """ A dictionary mapping special token class attribute (cls_token, unk_token...) to their
-            values ('<unk>', '<cls>'...)
-            Convert tokens of AddedToken type in string.
-            All returned tokens are strings
-        """
-        set_attr = {}
-        for attr in self.SPECIAL_TOKENS_ATTRIBUTES:
-            attr_value = getattr(self, "_" + attr)
-            if attr_value:
-                set_attr[attr] = str(attr_value)
-        return set_attr
-
-    @property
-    def special_tokens_map_extended(self):
-        """ A dictionary mapping special token class attribute (cls_token, unk_token...) to their
-            values ('<unk>', '<cls>'...)
-            Keep the tokens as AddedToken if they are of this type.
-
-            AddedToken can be used to control more finely how special tokens are tokenized.
-        """
-        set_attr = {}
-        for attr in self.SPECIAL_TOKENS_ATTRIBUTES:
-            attr_value = getattr(self, "_" + attr)
-            if attr_value:
-                set_attr[attr] = attr_value
-        return set_attr
-
-    @property
-    def all_special_tokens(self):
-        """ List all the special tokens ('<unk>', '<cls>'...) mapped to class attributes
-            Convert tokens of AddedToken type in string.
-            All returned tokens are strings
-            (cls_token, unk_token...).
-        """
-        all_toks = [str(s) for s in self.all_special_tokens_extended]
-        return all_toks
-
-    @property
-    def all_special_tokens_extended(self):
-        """ List all the special tokens ('<unk>', '<cls>'...) mapped to class attributes
-            Keep the tokens as AddedToken if they are of this type.
-
-            AddedToken can be used to control more finely how special tokens are tokenized.
-        """
-        all_toks = []
-        set_attr = self.special_tokens_map_extended
-        for attr_value in set_attr.values():
-            all_toks = all_toks + (list(attr_value) if isinstance(attr_value, (list, tuple)) else [attr_value])
-        all_toks = list(set(all_toks))
-        return all_toks
-
-    @property
-    def all_special_ids(self):
-        """ List the vocabulary indices of the special tokens ('<unk>', '<cls>'...) mapped to
-            class attributes (cls_token, unk_token...).
-        """
-        all_toks = self.all_special_tokens
-        all_ids = self.convert_tokens_to_ids(all_toks)
-        return all_ids
-
-
-ENCODE_KWARGS_DOCSTRING = r"""
-            add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
-                If set to ``True``, the sequences will be encoded with the special tokens relative
-                to their model.
-            `padding` (:obj:`Union[bool, str]`, `optional`, defaults to :obj:`False`):
-                Activate and control padding. Accepts the following values:
-
-                * `True` or `'longest'`: pad to the longest sequence in the batch (or no padding if only a single sequence if provided),
-                * `'max_length'`: pad to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`)
-                * `False` or `'do_not_pad'` (default): No padding (i.e. can output batch with sequences of uneven lengths)
-            `truncation` (:obj:`Union[bool, str]`, `optional`, defaults to :obj:`False`):
-                Activate and control truncation. Accepts the following values:
-
-                * `True` or `'longest_first'`: truncate to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`). This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided,
-                * `'only_first'`: truncate to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`). This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided,
-                * `'only_second'`: truncate to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`). This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided,
-                * `False` or `'do_not_truncate'` (default): No truncation (i.e. can output batch with sequences length greater than the model max admissible input size)
-            `max_length` (:obj:`Union[int, None]`, `optional`, defaults to :obj:`None`):
-                Control the length for padding/truncation. Accepts the following values
-
-                * `None` (default): This will use the predefined model max length if required by one of the truncation/padding parameters. If the model has no specific max input length (e.g. XLNet) truncation/padding to max length is deactivated.
-                * `any integer value` (e.g. `42`): Use this specific maximum length value if required by one of the truncation/padding parameters.
-            stride (:obj:`int`, `optional`, defaults to ``0``):
-                If set to a number along with max_length, the overflowing tokens returned when `return_overflowing_tokens=True`
-                will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflow ing sequences.
-                The value of this argument defines the number of overlapping tokens.
-            is_pretokenized (:obj:`bool`, defaults to :obj:`False`):
-                Set to True to indicate the input is already tokenized
-            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
-                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
-                >= 7.5 (Volta).
-            return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
-                Can be set to 'tf', 'pt' or 'np' to return respectively TensorFlow :obj:`tf.constant`,
-                PyTorch :obj:`torch.Tensor` or Numpy :oj: `np.ndarray` instead of a list of python integers.
-"""
-
-ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
-            return_token_type_ids (:obj:`bool`, `optional`, defaults to :obj:`None`):
-                Whether to return token type IDs. If left to the default, will return the token type IDs according
-                to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
-
-                `What are token type IDs? <../glossary.html#token-type-ids>`_
-            return_attention_mask (:obj:`bool`, `optional`, defaults to :obj:`none`):
-                Whether to return the attention mask. If left to the default, will return the attention mask according
-                to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
-
-                `What are attention masks? <../glossary.html#attention-mask>`__
-            return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Set to True to return overflowing token sequences (default False).
-            return_special_tokens_mask (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Set to True to return special tokens mask information (default False).
-            return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Set to True to return (char_start, char_end) for each token (default False).
-                If using Python's tokenizer, this method will raise NotImplementedError.
-                This one is only available on fast tokenizers inheriting from PreTrainedTokenizerFast.
-            **kwargs: passed to the `self.tokenize()` method
-
-        Return:
-            A Dictionary of shape::
-
-                {
-                    input_ids: list[int],
-                    token_type_ids: list[int] if return_token_type_ids is True (default)
-                    attention_mask: list[int] if return_attention_mask is True (default)
-                    overflowing_tokens: list[int] if the tokenizer is a slow tokenize, else a List[List[int]] if a ``max_length`` is specified and ``return_overflowing_tokens=True``
-                    special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True``
-                    and return_special_tokens_mask is True
-                }
-
-            With the fields:
-
-            - ``input_ids``: list of token ids to be fed to a model
-            - ``token_type_ids``: list of token type ids to be fed to a model
-            - ``attention_mask``: list of indices specifying which tokens should be attended to by the model
-            - ``overflowing_tokens``: list of overflowing tokens sequences if a max length is specified and ``return_overflowing_tokens=True``.
-            - ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
-              tokens and 1 specifying sequence tokens.
-"""
-
-
-class PreTrainedTokenizerBase(SpecialTokensMixin):
-    """ Base class for slow and fast tokenizers.
-
-        Handle shared (mostly boiler plate) methods for slow and fast tokenizers.
-    """
-
-    vocab_files_names: Dict[str, str] = {}
-    pretrained_vocab_files_map: Dict[str, Dict[str, str]] = {}
-    pretrained_init_configuration: Dict[str, Dict[str, Any]] = {}
-    max_model_input_sizes: Dict[str, int] = {}
-    model_input_names: List[str] = ["token_type_ids", "attention_mask"]
-
-    padding_side: str = "right"
-
-    def __init__(self, **kwargs):
-        # inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
-        self.init_inputs = ()
-        self.init_kwargs = kwargs
-
-        # For backward compatibility we fallback to set model_max_length from max_len if provided
-        model_max_length = kwargs.pop("model_max_length", kwargs.pop("max_len", None))
-        self.model_max_length = model_max_length if model_max_length is not None else VERY_LARGE_INTEGER
-
-        # Padding side is right by default and overridden in subclasses. If specified in the kwargs, it is changed.
-        self.padding_side = kwargs.pop("padding_side", self.padding_side)
-        assert self.padding_side in [
-            "right",
-            "left",
-        ], f"Padding side should be selected between 'right' and 'left', current value: {self.padding_side}"
-        self.model_input_names = kwargs.pop("model_input_names", self.model_input_names)
-
-        super().__init__(**kwargs)
-
-    @property
-    def max_len(self) -> int:
-        """ Kept here for backward compatibility.
-            Now renamed to `model_max_length` to avoid ambiguity.
-        """
-        return self.model_max_length
-
-    @property
-    def max_len_single_sentence(self) -> int:
-        return self.model_max_length - self.num_special_tokens_to_add(pair=False)
-
-    @property
-    def max_len_sentences_pair(self) -> int:
-        return self.model_max_length - self.num_special_tokens_to_add(pair=True)
-
-    @max_len_single_sentence.setter
-    def max_len_single_sentence(self, value) -> int:
-        """ For backward compatibility, allow to try to setup 'max_len_single_sentence' """
-        if value == self.model_max_length - self.num_special_tokens_to_add(pair=False) and self.verbose:
-            logger.warning(
-                "Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up."
-            )
-        else:
-            raise ValueError(
-                "Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up."
-            )
-
-    @max_len_sentences_pair.setter
-    def max_len_sentences_pair(self, value) -> int:
-        """ For backward compatibility, allow to try to setup 'max_len_sentences_pair' """
-        if value == self.model_max_length - self.num_special_tokens_to_add(pair=True) and self.verbose:
-            logger.warning(
-                "Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up."
-            )
-        else:
-            raise ValueError(
-                "Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up."
-            )
-
-    @classmethod
-    def from_pretrained(cls, *inputs, **kwargs):
-        r"""
-        Instantiate a :class:`~transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer.
-
-        Args:
-            pretrained_model_name_or_path: either:
-
-                - a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a string with the `identifier name` of a predefined tokenizer that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
-                - a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
-                - (not applicable to all derived classes, deprecated) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.
-
-            cache_dir: (`optional`) string:
-                Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
-
-            force_download: (`optional`) boolean, default False:
-                Force to (re-)download the vocabulary files and override the cached versions if they exists.
-
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
-            proxies: (`optional`) dict, default None:
-                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
-                The proxies are used on each request.
-
-            inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.
-
-            kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~transformers.PreTrainedTokenizer` for details.
-
-        Examples::
-
-            # We can't instantiate directly the base class `PreTrainedTokenizer` so let's show our examples on a derived class: BertTokenizer
-
-            # Download vocabulary from S3 and cache.
-            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-
-            # Download vocabulary from S3 (user-uploaded) and cache.
-            tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-german-cased')
-
-            # If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
-            tokenizer = BertTokenizer.from_pretrained('./test/saved_model/')
-
-            # If the tokenizer uses a single vocabulary file, you can point directly to this file
-            tokenizer = BertTokenizer.from_pretrained('./test/saved_model/my_vocab.txt')
-
-            # You can link tokens to special vocabulary when instantiating
-            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', unk_token='<unk>')
-            # You should be sure '<unk>' is in the vocabulary when doing that.
-            # Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
-            assert tokenizer.unk_token == '<unk>'
-
-        """
-        return cls._from_pretrained(*inputs, **kwargs)
-
-    @classmethod
-    def _from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):
-        cache_dir = kwargs.pop("cache_dir", None)
-        force_download = kwargs.pop("force_download", False)
-        resume_download = kwargs.pop("resume_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", False)
-
-        s3_models = list(cls.max_model_input_sizes.keys())
-        vocab_files = {}
-        init_configuration = {}
-        if pretrained_model_name_or_path in s3_models:
-            # Get the vocabulary from AWS S3 bucket
-            for file_id, map_list in cls.pretrained_vocab_files_map.items():
-                vocab_files[file_id] = map_list[pretrained_model_name_or_path]
-            if (
-                cls.pretrained_init_configuration
-                and pretrained_model_name_or_path in cls.pretrained_init_configuration
-            ):
-                init_configuration = cls.pretrained_init_configuration[pretrained_model_name_or_path].copy()
-        else:
-            # Get the vocabulary from local files
-            logger.info(
-                "Model name '{}' not found in model shortcut name list ({}). "
-                "Assuming '{}' is a path, a model identifier, or url to a directory containing tokenizer files.".format(
-                    pretrained_model_name_or_path, ", ".join(s3_models), pretrained_model_name_or_path
-                )
-            )
-
-            if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
-                if len(cls.vocab_files_names) > 1:
-                    raise ValueError(
-                        "Calling {}.from_pretrained() with the path to a single file or url is not supported."
-                        "Use a model identifier or the path to a directory instead.".format(cls.__name__)
-                    )
-                logger.warning(
-                    "Calling {}.from_pretrained() with the path to a single file or url is deprecated".format(
-                        cls.__name__
-                    )
-                )
-                file_id = list(cls.vocab_files_names.keys())[0]
-                vocab_files[file_id] = pretrained_model_name_or_path
-            else:
-                # At this point pretrained_model_name_or_path is either a directory or a model identifier name
-                additional_files_names = {
-                    "added_tokens_file": ADDED_TOKENS_FILE,
-                    "special_tokens_map_file": SPECIAL_TOKENS_MAP_FILE,
-                    "tokenizer_config_file": TOKENIZER_CONFIG_FILE,
-                    "full_tokenizer_file": FULL_TOKENIZER_FILE,
-                }
-                # Look for the tokenizer files
-                for file_id, file_name in {**cls.vocab_files_names, **additional_files_names}.items():
-                    if os.path.isdir(pretrained_model_name_or_path):
-                        full_file_name = os.path.join(pretrained_model_name_or_path, file_name)
-                        if not os.path.exists(full_file_name):
-                            logger.info("Didn't find file {}. We won't load it.".format(full_file_name))
-                            full_file_name = None
-                    else:
-                        full_file_name = hf_bucket_url(
-                            pretrained_model_name_or_path, filename=file_name, use_cdn=False
-                        )
-
-                    vocab_files[file_id] = full_file_name
-
-        # Get files from url, cache, or disk depending on the case
-        try:
-            resolved_vocab_files = {}
-            for file_id, file_path in vocab_files.items():
-                if file_path is None:
-                    resolved_vocab_files[file_id] = None
-                else:
-                    resolved_vocab_files[file_id] = cached_path(
-                        file_path,
-                        cache_dir=cache_dir,
-                        force_download=force_download,
-                        proxies=proxies,
-                        resume_download=resume_download,
-                        local_files_only=local_files_only,
-                    )
-        except EnvironmentError:
-            if pretrained_model_name_or_path in s3_models:
-                msg = "Couldn't reach server at '{}' to download vocabulary files."
-            else:
-                msg = (
-                    "Model name '{}' was not found in tokenizers model name list ({}). "
-                    "We assumed '{}' was a path or url to a directory containing vocabulary files "
-                    "named {}, but couldn't find such vocabulary files at this path or url.".format(
-                        pretrained_model_name_or_path,
-                        ", ".join(s3_models),
-                        pretrained_model_name_or_path,
-                        list(cls.vocab_files_names.values()),
-                    )
-                )
-
-            raise EnvironmentError(msg)
-
-        if all(full_file_name is None for full_file_name in resolved_vocab_files.values()):
-            raise EnvironmentError(
-                "Model name '{}' was not found in tokenizers model name list ({}). "
-                "We assumed '{}' was a path, a model identifier, or url to a directory containing vocabulary files "
-                "named {} but couldn't find such vocabulary files at this path or url.".format(
-                    pretrained_model_name_or_path,
-                    ", ".join(s3_models),
-                    pretrained_model_name_or_path,
-                    list(cls.vocab_files_names.values()),
-                )
-            )
-
-        for file_id, file_path in vocab_files.items():
-            if file_path == resolved_vocab_files[file_id]:
-                logger.info("loading file {}".format(file_path))
-            else:
-                logger.info("loading file {} from cache at {}".format(file_path, resolved_vocab_files[file_id]))
-
-        # Prepare tokenizer initialization kwargs
-        # Did we saved some inputs and kwargs to reload ?
-        tokenizer_config_file = resolved_vocab_files.pop("tokenizer_config_file", None)
-        if tokenizer_config_file is not None:
-            with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
-                init_kwargs = json.load(tokenizer_config_handle)
-            saved_init_inputs = init_kwargs.pop("init_inputs", ())
-            if not init_inputs:
-                init_inputs = saved_init_inputs
-        else:
-            init_kwargs = init_configuration
-
-        # Update with newly provided kwargs
-        init_kwargs.update(kwargs)
-
-        # Set max length if needed
-        if pretrained_model_name_or_path in cls.max_model_input_sizes:
-            # if we're using a pretrained model, ensure the tokenizer
-            # wont index sequences longer than the number of positional embeddings
-            model_max_length = cls.max_model_input_sizes[pretrained_model_name_or_path]
-            if model_max_length is not None and isinstance(model_max_length, (int, float)):
-                init_kwargs["model_max_length"] = min(init_kwargs.get("model_max_length", int(1e30)), model_max_length)
-
-        # Merge resolved_vocab_files arguments in init_kwargs.
-        added_tokens_file = resolved_vocab_files.pop("added_tokens_file", None)
-        for args_name, file_path in resolved_vocab_files.items():
-            if args_name not in init_kwargs:
-                init_kwargs[args_name] = file_path
-
-        # Instantiate tokenizer.
-        try:
-            tokenizer = cls(*init_inputs, **init_kwargs)
-        except OSError:
-            raise OSError(
-                "Unable to load vocabulary from file. "
-                "Please check that the provided vocabulary is accessible and not corrupted."
-            )
-
-        # Save inputs and kwargs for saving and re-loading with ``save_pretrained``
-        tokenizer.init_inputs = init_inputs
-        tokenizer.init_kwargs = init_kwargs
-
-        # If there is a complementary special token map, load it
-        special_tokens_map_file = resolved_vocab_files.pop("special_tokens_map_file", None)
-        if special_tokens_map_file is not None:
-            with open(special_tokens_map_file, encoding="utf-8") as special_tokens_map_handle:
-                special_tokens_map = json.load(special_tokens_map_handle)
-
-            for key, value in special_tokens_map.items():
-                if isinstance(value, dict):
-                    value = AddedToken(**value)
-                setattr(tokenizer, key, value)
-
-        # Add supplementary tokens.
-        special_tokens = tokenizer.all_special_tokens
-        if added_tokens_file is not None:
-            with open(added_tokens_file, encoding="utf-8") as added_tokens_handle:
-                added_tok_encoder = json.load(added_tokens_handle)
-
-            # Sort added tokens by index
-            added_tok_encoder_sorted = list(sorted(added_tok_encoder.items(), key=lambda x: x[1]))
-
-            for token, index in added_tok_encoder_sorted:
-                assert index == len(tokenizer), (
-                    f"Non-consecutive added token '{token}' found. "
-                    f"Should have index {len(tokenizer)} but has index {index} in saved vocabulary."
-                )
-                tokenizer.add_tokens(token, special_tokens=bool(token in special_tokens))
-
-        # Check all our special tokens are registrered as "no split" token (we don't cut them) and are in the vocab
-        added_tokens = tokenizer.sanitize_special_tokens()
-        if added_tokens:
-            logger.warning(
-                "Special tokens have been added in the vocabulary, make sure the associated word emebedding are fine-tuned or trained."
-            )
-
-        return tokenizer
-
-    def save_pretrained(self, save_directory) -> Tuple[str]:
-        """ Save the tokenizer vocabulary files together with:
-                - added tokens,
-                - special-tokens-to-class-attributes-mapping,
-                - tokenizer instantiation positional and keywords inputs (e.g. do_lower_case for Bert).
-
-            Warning: This won't save modifications you may have applied to the tokenizer after the instantiation
-            (e.g. modifying tokenizer.do_lower_case after creation).
-
-            This method make sure the full tokenizer can then be re-loaded using the
-            :func:`~transformers.PreTrainedTokenizer.from_pretrained` class method.
-        """
-        if os.path.isfile(save_directory):
-            logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
-            return
-        os.makedirs(save_directory, exist_ok=True)
-
-        special_tokens_map_file = os.path.join(save_directory, SPECIAL_TOKENS_MAP_FILE)
-        added_tokens_file = os.path.join(save_directory, ADDED_TOKENS_FILE)
-        tokenizer_config_file = os.path.join(save_directory, TOKENIZER_CONFIG_FILE)
-
-        tokenizer_config = copy.deepcopy(self.init_kwargs)
-        if len(self.init_inputs) > 0:
-            tokenizer_config["init_inputs"] = copy.deepcopy(self.init_inputs)
-        for file_id in self.vocab_files_names.keys():
-            tokenizer_config.pop(file_id, None)
-
-        with open(tokenizer_config_file, "w", encoding="utf-8") as f:
-            f.write(json.dumps(tokenizer_config, ensure_ascii=False))
-
-        with open(special_tokens_map_file, "w", encoding="utf-8") as f:
-            write_dict = {}
-            for key, value in self.special_tokens_map_extended.items():
-                if isinstance(value, AddedToken):
-                    write_dict[key] = value.__getstate__()
-                else:
-                    write_dict[key] = value
-            f.write(json.dumps(write_dict, ensure_ascii=False))
-
-        added_vocab = self.get_added_vocab()
-        if added_vocab:
-            with open(added_tokens_file, "w", encoding="utf-8") as f:
-                out_str = json.dumps(added_vocab, ensure_ascii=False)
-                f.write(out_str)
-
-        vocab_files = self.save_vocabulary(save_directory)
-
-        return vocab_files + (special_tokens_map_file, added_tokens_file)
-
-    @add_end_docstrings(
-        ENCODE_KWARGS_DOCSTRING,
-        """
-            **kwargs: passed to the `self.tokenize()` method.
-    """,
-    )
-    def encode(
-        self,
-        text: Union[TextInput, PreTokenizedInput, EncodedInput],
-        text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str] = False,
-        truncation: Union[bool, str] = False,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        **kwargs
-    ):
-        """
-        Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
-
-        Same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.
-
-        Args:
-            text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`):
-                The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
-                the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
-                method)
-            text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`, defaults to :obj:`None`):
-                Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
-                string using the `tokenize` method) or a list of integers (tokenized string ids using the
-                `convert_tokens_to_ids` method)
-        """
-        encoded_inputs = self.encode_plus(
-            text,
-            text_pair=text_pair,
-            add_special_tokens=add_special_tokens,
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            stride=stride,
-            return_tensors=return_tensors,
-            **kwargs,
-        )
-
-        return encoded_inputs["input_ids"]
-
-    def num_special_tokens_to_add(self, pair: bool = False) -> int:
-        raise NotImplementedError
-
-    def _get_padding_truncation_strategies(
-        self, padding=False, truncation=False, max_length=None, pad_to_multiple_of=None, verbose=True, **kwargs
-    ):
-        """ Find the correct padding/truncation strategy with backward compatibility
-            for old arguments (truncation_strategy and pad_to_max_length) and behaviors.
-        """
-        old_truncation_strategy = kwargs.pop("truncation_strategy", "do_not_truncate")
-        old_pad_to_max_length = kwargs.pop("pad_to_max_length", False)
-
-        # Backward compatibility for previous behavior, maybe we should deprecate it:
-        # If you only set max_length, it activates truncation for max_length
-        if max_length is not None and padding is False and truncation is False:
-            if verbose:
-                logger.warning(
-                    "Truncation was not explicitely activated but `max_length` is provided a specific value, "
-                    "please use `truncation=True` to explicitely truncate examples to max length. "
-                    "Defaulting to 'longest_first' truncation strategy. "
-                    "If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy "
-                    "more precisely by providing a specific strategy to `truncation`."
-                )
-            truncation = "longest_first"
-
-        # Get padding strategy
-        if padding is False and old_pad_to_max_length:
-            if verbose:
-                warnings.warn(
-                    "The `pad_to_max_length` argument is deprecated and will be removed in a future version, "
-                    "use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or "
-                    "use `padding='max_length'` to pad to a max length. In this case, you can give a specific "
-                    "length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the "
-                    "maximal input size of the model (e.g. 512 for Bert).",
-                    DeprecationWarning,
-                )
-            if max_length is None:
-                padding_strategy = PaddingStrategy.LONGEST
-            else:
-                padding_strategy = PaddingStrategy.MAX_LENGTH
-        elif padding is not False:
-            if padding is True:
-                padding_strategy = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
-            elif not isinstance(padding, PaddingStrategy):
-                padding_strategy = PaddingStrategy(padding)
-        else:
-            padding_strategy = PaddingStrategy.DO_NOT_PAD
-
-        # Get truncation strategy
-        if truncation is False and old_truncation_strategy != "do_not_truncate":
-            if verbose:
-                warnings.warn(
-                    "The `truncation_strategy` argument is deprecated and will be removed in a future version, "
-                    "use `truncation=True` to truncate examples to a max length. You can give a specific "
-                    "length with `max_length` (e.g. `max_length=45`) or leave max_length to None to truncate to the "
-                    "maximal input size of the model (e.g. 512 for Bert). "
-                    " If you have pairs of inputs, you can give a specific truncation strategy selected among "
-                    "`truncation='only_first'` (will only truncate the first sentence in the pairs) "
-                    "`truncation='only_second'` (will only truncate the second sentence in the pairs) "
-                    "or `truncation='longest_first'` (will iteratively remove tokens from the longest sentence in the pairs).",
-                    DeprecationWarning,
-                )
-            truncation_strategy = TruncationStrategy(old_truncation_strategy)
-        elif truncation is not False:
-            if truncation is True:
-                truncation_strategy = (
-                    TruncationStrategy.LONGEST_FIRST
-                )  # Default to truncate the longest sequences in pairs of inputs
-            elif not isinstance(truncation, TruncationStrategy):
-                truncation_strategy = TruncationStrategy(truncation)
-        else:
-            truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE
-
-        # Set max length if needed
-        if max_length is None:
-            if padding_strategy == PaddingStrategy.MAX_LENGTH:
-                if self.model_max_length > LARGE_INTEGER:
-                    if verbose:
-                        logger.warning(
-                            "Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. "
-                            "Default to no padding."
-                        )
-                    padding_strategy = PaddingStrategy.DO_NOT_PAD
-                else:
-                    max_length = self.model_max_length
-
-            if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:
-                if self.model_max_length > LARGE_INTEGER:
-                    if verbose:
-                        logger.warning(
-                            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. "
-                            "Default to no truncation."
-                        )
-                    truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE
-                else:
-                    max_length = self.model_max_length
-
-        # Test if we have a padding token
-        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (not self.pad_token or self.pad_token_id < 0):
-            raise ValueError(
-                "Asking to pad but the tokenizer does not have a padding token. "
-                "Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` "
-                "or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`."
-            )
-
-        # Check that we will truncate to a multiple of pad_to_multiple_of if both are provided
-        if (
-            truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE
-            and padding_strategy != PaddingStrategy.DO_NOT_PAD
-            and pad_to_multiple_of is not None
-            and max_length is not None
-            and (max_length % pad_to_multiple_of != 0)
-        ):
-            raise ValueError(
-                f"Truncation and padding are both activated but "
-                f"truncation length ({max_length}) is not a multiple of pad_to_multiple_of ({pad_to_multiple_of})."
-            )
-
-        return padding_strategy, truncation_strategy, max_length, kwargs
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    def __call__(
-        self,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
-        text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str] = False,
-        truncation: Union[bool, str] = False,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        is_pretokenized: bool = False,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs
-    ) -> BatchEncoding:
-        """
-        Returns a dictionary containing the encoded sequence or sequence pair and additional information:
-        the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
-
-        Args:
-            text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]``):
-                The sequence or batch of sequences to be encoded.
-                Each sequence can be a string or a list of strings (pre-tokenized string).
-                If the sequences are provided as list of strings (pretokenized), you must set `is_pretokenized=True`
-                (to lift the ambiguity with a batch of sequences)
-            text_pair (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]``):
-                The sequence or batch of sequences to be encoded.
-                Each sequence can be a string or a list of strings (pre-tokenized string).
-                If the sequences are provided as list of strings (pretokenized), you must set `is_pretokenized=True`
-                (to lift the ambiguity with a batch of sequences)
-        """
-        # Input type checking for clearer error
-        assert isinstance(text, str) or (
-            isinstance(text, (list, tuple))
-            and (
-                len(text) == 0
-                or (
-                    isinstance(text[0], str)
-                    or (isinstance(text[0], (list, tuple)) and (len(text[0]) == 0 or isinstance(text[0][0], str)))
-                )
-            )
-        ), (
-            "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
-            "or `List[List[str]]` (batch of pretokenized examples)."
-        )
-
-        assert (
-            text_pair is None
-            or isinstance(text_pair, str)
-            or (
-                isinstance(text_pair, (list, tuple))
-                and (
-                    len(text_pair) == 0
-                    or (
-                        isinstance(text_pair[0], str)
-                        or (
-                            isinstance(text_pair[0], (list, tuple))
-                            and (len(text_pair[0]) == 0 or isinstance(text_pair[0][0], str))
-                        )
-                    )
-                )
-            )
-        ), (
-            "text_pair input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
-            "or `List[List[str]]` (batch of pretokenized examples)."
-        )
-
-        is_batched = bool(
-            (not is_pretokenized and isinstance(text, (list, tuple)))
-            or (is_pretokenized and isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)))
-        )
-
-        if is_batched:
-            batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
-            return self.batch_encode_plus(
-                batch_text_or_text_pairs=batch_text_or_text_pairs,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                is_pretokenized=is_pretokenized,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-        else:
-            return self.encode_plus(
-                text=text,
-                text_pair=text_pair,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                is_pretokenized=is_pretokenized,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    def encode_plus(
-        self,
-        text: Union[TextInput, PreTokenizedInput, EncodedInput],
-        text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str] = False,
-        truncation: Union[bool, str] = False,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        is_pretokenized: bool = False,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs
-    ) -> BatchEncoding:
-        """
-        Returns a dictionary containing the encoded sequence or sequence pair and additional information:
-        the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
-
-        Args:
-            text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]` (the later only for not-fast tokenizers)):
-                The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
-                the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
-                method)
-            text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`, defaults to :obj:`None`):
-                Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
-                string using the `tokenize` method) or a list of integers (tokenized string ids using the
-                `convert_tokens_to_ids` method)
-        """
-
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return self._encode_plus(
-            text=text,
-            text_pair=text_pair,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            is_pretokenized=is_pretokenized,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-    def _encode_plus(
-        self,
-        text: Union[TextInput, PreTokenizedInput, EncodedInput],
-        text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        is_pretokenized: bool = False,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs
-    ) -> BatchEncoding:
-        raise NotImplementedError
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    def batch_encode_plus(
-        self,
-        batch_text_or_text_pairs: Union[
-            List[TextInput],
-            List[TextInputPair],
-            List[PreTokenizedInput],
-            List[PreTokenizedInputPair],
-            List[EncodedInput],
-            List[EncodedInputPair],
-        ],
-        add_special_tokens: bool = True,
-        padding: Union[bool, str] = False,
-        truncation: Union[bool, str] = False,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        is_pretokenized: bool = False,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs
-    ) -> BatchEncoding:
-        """
-        Returns a dictionary containing the encoded sequence or sequence pair and additional information:
-        the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
-
-        Args:
-            batch_text_or_text_pairs (:obj:`List[str]`,  :obj:`List[Tuple[str, str]]`,
-                                      :obj:`List[List[str]]`,  :obj:`List[Tuple[List[str], List[str]]]`,
-                                      and for not-fast tokenizers, also:
-                                      :obj:`List[List[int]]`,  :obj:`List[Tuple[List[int], List[int]]]`):
-                Batch of sequences or pair of sequences to be encoded.
-                This can be a list of string/string-sequences/int-sequences or a list of pair of
-                string/string-sequences/int-sequence (see details in encode_plus)
-        """
-
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return self._batch_encode_plus(
-            batch_text_or_text_pairs=batch_text_or_text_pairs,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            is_pretokenized=is_pretokenized,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-    def _batch_encode_plus(
-        self,
-        batch_text_or_text_pairs: Union[
-            List[TextInput],
-            List[TextInputPair],
-            List[PreTokenizedInput],
-            List[PreTokenizedInputPair],
-            List[EncodedInput],
-            List[EncodedInputPair],
-        ],
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        is_pretokenized: bool = False,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs
-    ) -> BatchEncoding:
-        raise NotImplementedError
-
-    def pad(
-        self,
-        encoded_inputs: Union[
-            BatchEncoding,
-            List[BatchEncoding],
-            Dict[str, EncodedInput],
-            Dict[str, List[EncodedInput]],
-            List[Dict[str, EncodedInput]],
-        ],
-        padding: Union[bool, str] = True,
-        max_length: Optional[int] = None,
-        pad_to_multiple_of: Optional[int] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        verbose: bool = True,
-    ) -> BatchEncoding:
-        """ Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch.
-
-            Padding side (left/right) padding token ids are defined at the tokenizer level
-            (with ``self.padding_side``, ``self.pad_token_id`` and ``self.pad_token_type_id``)
-
-        Args:
-            encoded_inputs: Dictionary of tokenized inputs (`Dict[str, List[int]]`) or batch of tokenized inputs.
-                Batch of tokenized inputs can be given as dicts of lists or lists of dicts, both work so you can
-                use ``tokenizer.pad()`` during pre-processing as well as in a PyTorch Dataloader collate function.
-                (`Dict[str, List[List[int]]]` or `List[Dict[str, List[int]]]`).
-            padding: Boolean or specific strategy to use for padding.
-                Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among:
-                - 'longest' (or `True`) Pad to the longest sequence in the batch
-                - 'max_length': Pad to the max length (default)
-                - 'do_not_pad' (or `False`): Do not pad
-            max_length: maximum length of the returned list and optionally padding length (see below).
-                Will truncate by taking into account the special tokens.
-            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
-                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
-                >= 7.5 (Volta).
-            return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
-            return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
-                Can be set to 'tf', 'pt' or 'np' to return respectively TensorFlow :obj:`tf.constant`,
-                PyTorch :obj:`torch.Tensor` or Numpy :oj: `np.ndarray` instead of a list of python integers.
-            verbose (:obj:`bool`, `optional`, defaults to :obj:`True`):
-                Set to ``False`` to avoid printing infos and warnings.
-        """
-        # If we have a list of dicts, let's convert it in a dict of lists
-        if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], (dict, BatchEncoding)):
-            encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys()}
-
-        assert "input_ids" in encoded_inputs, (
-            "You should supply an encoding or a list of encodings to this method. "
-            "An encoding is the output of one the encoding methods of the tokenizer, i.e. "
-            "__call__/encode_plus/batch_encode_plus. "
-        )
-
-        if not encoded_inputs["input_ids"]:
-            if return_attention_mask:
-                encoded_inputs["attention_mask"] = []
-            return encoded_inputs
-
-        # Convert padding_strategy in PaddingStrategy
-        padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
-            padding=padding, max_length=max_length, verbose=verbose
-        )
-
-        if encoded_inputs["input_ids"] and not isinstance(encoded_inputs["input_ids"][0], (list, tuple)):
-            encoded_inputs = self._pad(
-                encoded_inputs,
-                max_length=max_length,
-                padding_strategy=padding_strategy,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_attention_mask=return_attention_mask,
-            )
-            return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
-
-        batch_size = len(encoded_inputs["input_ids"])
-        assert all(
-            len(v) == batch_size for v in encoded_inputs.values()
-        ), "Some items in the output dictionnary have a different batch size than others."
-
-        if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = max(len(inputs) for inputs in encoded_inputs["input_ids"])
-            padding_strategy = PaddingStrategy.MAX_LENGTH
-
-        batch_outputs = {}
-        for i in range(batch_size):
-            inputs = dict((k, v[i]) for k, v in encoded_inputs.items())
-            outputs = self._pad(
-                inputs,
-                max_length=max_length,
-                padding_strategy=padding_strategy,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_attention_mask=return_attention_mask,
-            )
-
-            for key, value in outputs.items():
-                if key not in batch_outputs:
-                    batch_outputs[key] = []
-                batch_outputs[key].append(value)
-
-        return BatchEncoding(batch_outputs, tensor_type=return_tensors)
-
-    def create_token_type_ids_from_sequences(self, token_ids_0: List, token_ids_1: Optional[List] = None) -> List[int]:
-        if token_ids_1 is None:
-            return len(token_ids_0) * [0]
-        return [0] * len(token_ids_0) + [1] * len(token_ids_1)
-
-    def build_inputs_with_special_tokens(self, token_ids_0: List, token_ids_1: Optional[List] = None) -> List:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks
-        by concatenating and adding special tokens. This implementation does not add special tokens.
-        """
-        if token_ids_1 is None:
-            return token_ids_0
-        return token_ids_0 + token_ids_1
-
-    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    def prepare_for_model(
-        self,
-        ids: List[int],
-        pair_ids: Optional[List[int]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str] = False,
-        truncation: Union[bool, str] = False,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        prepend_batch_axis: bool = False,
-        **kwargs
-    ) -> BatchEncoding:
-        """ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
-        It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
-        manages a moving window (with user defined stride) for overflowing tokens
-
-        Args:
-            ids: list of tokenized input ids. Can be obtained from a string by chaining the
-                `tokenize` and `convert_tokens_to_ids` methods.
-            pair_ids: Optional second list of input ids. Can be obtained from a string by chaining the
-                `tokenize` and `convert_tokens_to_ids` methods.
-        """
-
-        if "return_lengths" in kwargs:
-            if verbose:
-                warnings.warn(
-                    "The PreTrainedTokenizerBase.prepare_for_model `return_lengths` parameter is deprecated. "
-                    "Please use `return_length` instead.",
-                    FutureWarning,
-                )
-            return_length = kwargs["return_lengths"]
-
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        pair = bool(pair_ids is not None)
-        len_ids = len(ids)
-        len_pair_ids = len(pair_ids) if pair else 0
-
-        # Load from model defaults
-        if return_token_type_ids is None:
-            return_token_type_ids = "token_type_ids" in self.model_input_names
-        if return_attention_mask is None:
-            return_attention_mask = "attention_mask" in self.model_input_names
-
-        encoded_inputs = {}
-
-        # Compute the total size of the returned encodings
-        total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
-
-        # Truncation: Handle max sequence length
-        if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
-            ids, pair_ids, overflowing_tokens = self.truncate_sequences(
-                ids,
-                pair_ids=pair_ids,
-                num_tokens_to_remove=total_len - max_length,
-                truncation_strategy=truncation_strategy,
-                stride=stride,
-            )
-            if return_overflowing_tokens:
-                encoded_inputs["overflowing_tokens"] = overflowing_tokens
-                encoded_inputs["num_truncated_tokens"] = total_len - max_length
-
-        # Add special tokens
-        if add_special_tokens:
-            sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
-            token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
-        else:
-            sequence = ids + pair_ids if pair else ids
-            token_type_ids = [0] * len(ids) + ([1] * len(pair_ids) if pair else [])
-
-        # Build output dictionnary
-        encoded_inputs["input_ids"] = sequence
-        if return_token_type_ids:
-            encoded_inputs["token_type_ids"] = token_type_ids
-        if return_special_tokens_mask:
-            if add_special_tokens:
-                encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
-            else:
-                encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
-
-        # Check lengths
-        if max_length is None and len(encoded_inputs["input_ids"]) > self.model_max_length and verbose:
-            logger.warning(
-                "Token indices sequence length is longer than the specified maximum sequence length "
-                "for this model ({} > {}). Running this sequence through the model will result in "
-                "indexing errors".format(len(ids), self.model_max_length)
-            )
-
-        # Padding
-        if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
-            encoded_inputs = self.pad(
-                encoded_inputs,
-                max_length=max_length,
-                padding=padding_strategy.value,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_attention_mask=return_attention_mask,
-            )
-
-        if return_length:
-            encoded_inputs["length"] = len(encoded_inputs["input_ids"])
-
-        batch_outputs = BatchEncoding(
-            encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
-        )
-
-        return batch_outputs
-
-    def truncate_sequences(
-        self,
-        ids: List[int],
-        pair_ids: Optional[List[int]] = None,
-        num_tokens_to_remove: int = 0,
-        truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
-        stride: int = 0,
-    ) -> Tuple[List[int], List[int], List[int]]:
-        """ Truncates a sequence pair in place to the maximum length.
-
-        Args:
-            ids: list of tokenized input ids. Can be obtained from a string by chaining the
-                `tokenize` and `convert_tokens_to_ids` methods.
-            pair_ids: Optional second list of input ids. Can be obtained from a string by chaining the
-                `tokenize` and `convert_tokens_to_ids` methods.
-            num_tokens_to_remove (:obj:`int`, `optional`, defaults to ``0``):
-                number of tokens to remove using the truncation strategy
-            truncation_strategy (:obj:`string`, `optional`, defaults to "longest_first"):
-                String selected in the following options:
-
-                - 'longest_first' (default): Iteratively reduce the inputs sequence until the input is under max_length
-                  starting from the longest one at each token (when there is a pair of input sequences).
-                  Overflowing tokens only contains overflow from the first sequence.
-                - 'only_first': Only truncate the first sequence. raise an error if the first sequence is shorter or equal to than num_tokens_to_remove.
-                - 'only_second': Only truncate the second sequence
-                - 'do_not_truncate'
-            stride (:obj:`int`, `optional`, defaults to ``0``):
-                If set to a number along with max_length, the overflowing tokens returned will contain some tokens
-                from the main sequence returned. The value of this argument defines the number of additional tokens.
-        """
-        if num_tokens_to_remove <= 0:
-            return ids, pair_ids, []
-
-        if not isinstance(truncation_strategy, TruncationStrategy):
-            truncation_strategy = TruncationStrategy(truncation_strategy)
-
-        overflowing_tokens = []
-        if truncation_strategy == TruncationStrategy.LONGEST_FIRST:
-            for _ in range(num_tokens_to_remove):
-                if pair_ids is None or len(ids) > len(pair_ids):
-                    if not overflowing_tokens:
-                        window_len = min(len(ids), stride + 1)
-                    else:
-                        window_len = 1
-                    overflowing_tokens.extend(ids[-window_len:])
-                    ids = ids[:-1]
-                else:
-                    if not overflowing_tokens:
-                        window_len = min(len(pair_ids), stride + 1)
-                    else:
-                        window_len = 1
-                    overflowing_tokens.extend(pair_ids[-window_len:])
-                    pair_ids = pair_ids[:-1]
-        elif truncation_strategy == TruncationStrategy.ONLY_FIRST:
-            if len(ids) > num_tokens_to_remove:
-                window_len = min(len(ids), stride + num_tokens_to_remove)
-                overflowing_tokens = ids[-window_len:]
-                ids = ids[:-num_tokens_to_remove]
-            else:
-                logger.error(
-                    f"We need to remove {num_tokens_to_remove} to truncate the input"
-                    f"but the first sequence has a length {len(ids)}. "
-                    f"Please select another truncation strategy than {truncation_strategy}, "
-                    f"for instance 'longest_first' or 'only_second'."
-                )
-        elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
-            if len(pair_ids) > num_tokens_to_remove:
-                window_len = min(len(pair_ids), stride + num_tokens_to_remove)
-                overflowing_tokens = pair_ids[-window_len:]
-                pair_ids = pair_ids[:-num_tokens_to_remove]
-            else:
-                logger.error(
-                    f"We need to remove {num_tokens_to_remove} to truncate the input"
-                    f"but the second sequence has a length {len(pair_ids)}. "
-                    f"Please select another truncation strategy than {truncation_strategy}, "
-                    f"for instance 'longest_first' or 'only_first'."
-                )
-
-        return (ids, pair_ids, overflowing_tokens)
-
-    def _pad(
-        self,
-        encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
-        max_length: Optional[int] = None,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        pad_to_multiple_of: Optional[int] = None,
-        return_attention_mask: Optional[bool] = None,
-    ) -> dict:
-        """ Pad encoded inputs (on left/right and up to predefined legnth or max length in the batch)
-
-        Args:
-            encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
-            max_length: maximum length of the returned list and optionally padding length (see below).
-                Will truncate by taking into account the special tokens.
-            padding_strategy: PaddingStrategy to use for padding.
-                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
-                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
-                - PaddingStrategy.DO_NOT_PAD: Do not pad
-                The tokenizer padding sides are defined in self.padding_side:
-                    - 'left': pads on the left of the sequences
-                    - 'right': pads on the right of the sequences
-            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
-                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
-                >= 7.5 (Volta).
-            return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
-        """
-        # Load from model defaults
-        if return_attention_mask is None:
-            return_attention_mask = "attention_mask" in self.model_input_names
-
-        if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = len(encoded_inputs["input_ids"])
-
-        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
-            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
-
-        needs_to_be_padded = (
-            padding_strategy != PaddingStrategy.DO_NOT_PAD and len(encoded_inputs["input_ids"]) != max_length
-        )
-
-        if needs_to_be_padded:
-            difference = max_length - len(encoded_inputs["input_ids"])
-            if self.padding_side == "right":
-                if return_attention_mask:
-                    encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) + [0] * difference
-                if "token_type_ids" in encoded_inputs:
-                    encoded_inputs["token_type_ids"] = (
-                        encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
-                    )
-                if "special_tokens_mask" in encoded_inputs:
-                    encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
-                encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
-            elif self.padding_side == "left":
-                if return_attention_mask:
-                    encoded_inputs["attention_mask"] = [0] * difference + [1] * len(encoded_inputs["input_ids"])
-                if "token_type_ids" in encoded_inputs:
-                    encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
-                        "token_type_ids"
-                    ]
-                if "special_tokens_mask" in encoded_inputs:
-                    encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
-                encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
-            else:
-                raise ValueError("Invalid padding strategy:" + str(self.padding_side))
-        else:
-            if return_attention_mask:
-                encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
-
-        return encoded_inputs
-
-    def batch_decode(self, sequences: List[List[int]], **kwargs) -> List[str]:
-        return [self.decode(seq, **kwargs) for seq in sequences]
-
-    def decode(
-        self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True
-    ) -> str:
-        """
-        Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary
-        with options to remove special tokens and clean up tokenization spaces.
-        Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.
-
-        Args:
-            token_ids: list of tokenized input ids. Can be obtained using the `encode` or `encode_plus` methods.
-            skip_special_tokens: if set to True, will replace special tokens.
-            clean_up_tokenization_spaces: if set to True, will clean up the tokenization spaces.
-        """
-        raise NotImplementedError
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
-
-        Args:
-            token_ids_0: list of ids (must not contain special tokens)
-            token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
-                for sequence pairs
-            already_has_special_tokens: (default False) Set to True if the token list is already formated with
-                special tokens for the model
-
-        Returns:
-            A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        assert already_has_special_tokens and token_ids_1 is None, (
-            "You cannot use ``already_has_special_tokens=False`` with this tokenizer. "
-            "Please use a slow (full python) tokenizer to activate this argument."
-            "Or set `return_special_token_mask=True` when calling the encoding method "
-            "to get the special tokens mask in any tokenizer. "
-        )
-
-        all_special_ids = self.all_special_ids  # cache the property
-
-        special_tokens_mask = [1 if token in all_special_ids else 0 for token in token_ids_0]
-
-        return special_tokens_mask
-
-    @staticmethod
-    def clean_up_tokenization(out_string: str) -> str:
-        """ Clean up a list of simple English tokenization artifacts like spaces before punctuations and abreviated forms.
-        """
-        out_string = (
-            out_string.replace(" .", ".")
-            .replace(" ?", "?")
-            .replace(" !", "!")
-            .replace(" ,", ",")
-            .replace(" ' ", "'")
-            .replace(" n't", "n't")
-            .replace(" 'm", "'m")
-            .replace(" 's", "'s")
-            .replace(" 've", "'ve")
-            .replace(" 're", "'re")
-        )
-        return out_string
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/timeTools.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/timeTools.py
deleted file mode 100644
index 175ce81563daf3e9a924701dd2c9d4b71084c286..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/timeTools.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""fontTools.misc.timeTools.py -- tools for working with OpenType timestamps.
-"""
-
-import os
-import time
-from datetime import datetime, timezone
-import calendar
-
-
-epoch_diff = calendar.timegm((1904, 1, 1, 0, 0, 0, 0, 0, 0))
-
-DAYNAMES = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
-MONTHNAMES = [
-    None,
-    "Jan",
-    "Feb",
-    "Mar",
-    "Apr",
-    "May",
-    "Jun",
-    "Jul",
-    "Aug",
-    "Sep",
-    "Oct",
-    "Nov",
-    "Dec",
-]
-
-
-def asctime(t=None):
-    """
-    Convert a tuple or struct_time representing a time as returned by gmtime()
-    or localtime() to a 24-character string of the following form:
-
-    >>> asctime(time.gmtime(0))
-    'Thu Jan  1 00:00:00 1970'
-
-    If t is not provided, the current time as returned by localtime() is used.
-    Locale information is not used by asctime().
-
-    This is meant to normalise the output of the built-in time.asctime() across
-    different platforms and Python versions.
-    In Python 3.x, the day of the month is right-justified, whereas on Windows
-    Python 2.7 it is padded with zeros.
-
-    See https://github.com/fonttools/fonttools/issues/455
-    """
-    if t is None:
-        t = time.localtime()
-    s = "%s %s %2s %s" % (
-        DAYNAMES[t.tm_wday],
-        MONTHNAMES[t.tm_mon],
-        t.tm_mday,
-        time.strftime("%H:%M:%S %Y", t),
-    )
-    return s
-
-
-def timestampToString(value):
-    return asctime(time.gmtime(max(0, value + epoch_diff)))
-
-
-def timestampFromString(value):
-    wkday, mnth = value[:7].split()
-    t = datetime.strptime(value[7:], " %d %H:%M:%S %Y")
-    t = t.replace(month=MONTHNAMES.index(mnth), tzinfo=timezone.utc)
-    wkday_idx = DAYNAMES.index(wkday)
-    assert t.weekday() == wkday_idx, '"' + value + '" has inconsistent weekday'
-    return int(t.timestamp()) - epoch_diff
-
-
-def timestampNow():
-    # https://reproducible-builds.org/specs/source-date-epoch/
-    source_date_epoch = os.environ.get("SOURCE_DATE_EPOCH")
-    if source_date_epoch is not None:
-        return int(source_date_epoch) - epoch_diff
-    return int(time.time() - epoch_diff)
-
-
-def timestampSinceEpoch(value):
-    return int(value - epoch_diff)
-
-
-if __name__ == "__main__":
-    import sys
-    import doctest
-
-    sys.exit(doctest.testmod().failed)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/svgLib/path/shapes.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/svgLib/path/shapes.py
deleted file mode 100644
index 3f22e6c6a3e4d24636e710f1920ebf04a822b159..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/svgLib/path/shapes.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import re
-
-
-def _prefer_non_zero(*args):
-    for arg in args:
-        if arg != 0:
-            return arg
-    return 0.0
-
-
-def _ntos(n):
-    # %f likes to add unnecessary 0's, %g isn't consistent about # decimals
-    return ("%.3f" % n).rstrip("0").rstrip(".")
-
-
-def _strip_xml_ns(tag):
-    # ElementTree API doesn't provide a way to ignore XML namespaces in tags
-    # so we here strip them ourselves: cf. https://bugs.python.org/issue18304
-    return tag.split("}", 1)[1] if "}" in tag else tag
-
-
-def _transform(raw_value):
-    # TODO assumes a 'matrix' transform.
-    # No other transform functions are supported at the moment.
-    # https://developer.mozilla.org/en-US/docs/Web/SVG/Attribute/transform
-    # start simple: if you aren't exactly matrix(...) then no love
-    match = re.match(r"matrix\((.*)\)", raw_value)
-    if not match:
-        raise NotImplementedError
-    matrix = tuple(float(p) for p in re.split(r"\s+|,", match.group(1)))
-    if len(matrix) != 6:
-        raise ValueError("wrong # of terms in %s" % raw_value)
-    return matrix
-
-
-class PathBuilder(object):
-    def __init__(self):
-        self.paths = []
-        self.transforms = []
-
-    def _start_path(self, initial_path=""):
-        self.paths.append(initial_path)
-        self.transforms.append(None)
-
-    def _end_path(self):
-        self._add("z")
-
-    def _add(self, path_snippet):
-        path = self.paths[-1]
-        if path:
-            path += " " + path_snippet
-        else:
-            path = path_snippet
-        self.paths[-1] = path
-
-    def _move(self, c, x, y):
-        self._add("%s%s,%s" % (c, _ntos(x), _ntos(y)))
-
-    def M(self, x, y):
-        self._move("M", x, y)
-
-    def m(self, x, y):
-        self._move("m", x, y)
-
-    def _arc(self, c, rx, ry, x, y, large_arc):
-        self._add(
-            "%s%s,%s 0 %d 1 %s,%s"
-            % (c, _ntos(rx), _ntos(ry), large_arc, _ntos(x), _ntos(y))
-        )
-
-    def A(self, rx, ry, x, y, large_arc=0):
-        self._arc("A", rx, ry, x, y, large_arc)
-
-    def a(self, rx, ry, x, y, large_arc=0):
-        self._arc("a", rx, ry, x, y, large_arc)
-
-    def _vhline(self, c, x):
-        self._add("%s%s" % (c, _ntos(x)))
-
-    def H(self, x):
-        self._vhline("H", x)
-
-    def h(self, x):
-        self._vhline("h", x)
-
-    def V(self, y):
-        self._vhline("V", y)
-
-    def v(self, y):
-        self._vhline("v", y)
-
-    def _line(self, c, x, y):
-        self._add("%s%s,%s" % (c, _ntos(x), _ntos(y)))
-
-    def L(self, x, y):
-        self._line("L", x, y)
-
-    def l(self, x, y):
-        self._line("l", x, y)
-
-    def _parse_line(self, line):
-        x1 = float(line.attrib.get("x1", 0))
-        y1 = float(line.attrib.get("y1", 0))
-        x2 = float(line.attrib.get("x2", 0))
-        y2 = float(line.attrib.get("y2", 0))
-
-        self._start_path()
-        self.M(x1, y1)
-        self.L(x2, y2)
-
-    def _parse_rect(self, rect):
-        x = float(rect.attrib.get("x", 0))
-        y = float(rect.attrib.get("y", 0))
-        w = float(rect.attrib.get("width"))
-        h = float(rect.attrib.get("height"))
-        rx = float(rect.attrib.get("rx", 0))
-        ry = float(rect.attrib.get("ry", 0))
-
-        rx = _prefer_non_zero(rx, ry)
-        ry = _prefer_non_zero(ry, rx)
-        # TODO there are more rules for adjusting rx, ry
-
-        self._start_path()
-        self.M(x + rx, y)
-        self.H(x + w - rx)
-        if rx > 0:
-            self.A(rx, ry, x + w, y + ry)
-        self.V(y + h - ry)
-        if rx > 0:
-            self.A(rx, ry, x + w - rx, y + h)
-        self.H(x + rx)
-        if rx > 0:
-            self.A(rx, ry, x, y + h - ry)
-        self.V(y + ry)
-        if rx > 0:
-            self.A(rx, ry, x + rx, y)
-        self._end_path()
-
-    def _parse_path(self, path):
-        if "d" in path.attrib:
-            self._start_path(initial_path=path.attrib["d"])
-
-    def _parse_polygon(self, poly):
-        if "points" in poly.attrib:
-            self._start_path("M" + poly.attrib["points"])
-            self._end_path()
-
-    def _parse_polyline(self, poly):
-        if "points" in poly.attrib:
-            self._start_path("M" + poly.attrib["points"])
-
-    def _parse_circle(self, circle):
-        cx = float(circle.attrib.get("cx", 0))
-        cy = float(circle.attrib.get("cy", 0))
-        r = float(circle.attrib.get("r"))
-
-        # arc doesn't seem to like being a complete shape, draw two halves
-        self._start_path()
-        self.M(cx - r, cy)
-        self.A(r, r, cx + r, cy, large_arc=1)
-        self.A(r, r, cx - r, cy, large_arc=1)
-
-    def _parse_ellipse(self, ellipse):
-        cx = float(ellipse.attrib.get("cx", 0))
-        cy = float(ellipse.attrib.get("cy", 0))
-        rx = float(ellipse.attrib.get("rx"))
-        ry = float(ellipse.attrib.get("ry"))
-
-        # arc doesn't seem to like being a complete shape, draw two halves
-        self._start_path()
-        self.M(cx - rx, cy)
-        self.A(rx, ry, cx + rx, cy, large_arc=1)
-        self.A(rx, ry, cx - rx, cy, large_arc=1)
-
-    def add_path_from_element(self, el):
-        tag = _strip_xml_ns(el.tag)
-        parse_fn = getattr(self, "_parse_%s" % tag.lower(), None)
-        if not callable(parse_fn):
-            return False
-        parse_fn(el)
-        if "transform" in el.attrib:
-            self.transforms[-1] = _transform(el.attrib["transform"])
-        return True
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ufoLib/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ufoLib/__init__.py
deleted file mode 100644
index 1a456a206f815ffdf624e4c420539a9eaf1903ca..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ufoLib/__init__.py
+++ /dev/null
@@ -1,2464 +0,0 @@
-import os
-from copy import deepcopy
-from os import fsdecode
-import logging
-import zipfile
-import enum
-from collections import OrderedDict
-import fs
-import fs.base
-import fs.subfs
-import fs.errors
-import fs.copy
-import fs.osfs
-import fs.zipfs
-import fs.tempfs
-import fs.tools
-from fontTools.misc import plistlib
-from fontTools.ufoLib.validators import *
-from fontTools.ufoLib.filenames import userNameToFileName
-from fontTools.ufoLib.converters import convertUFO1OrUFO2KerningToUFO3Kerning
-from fontTools.ufoLib.errors import UFOLibError
-from fontTools.ufoLib.utils import numberTypes, _VersionTupleEnumMixin
-
-"""
-A library for importing .ufo files and their descendants.
-Refer to http://unifiedfontobject.com for the UFO specification.
-
-The UFOReader and UFOWriter classes support versions 1, 2 and 3
-of the specification.
-
-Sets that list the font info attribute names for the fontinfo.plist
-formats are available for external use. These are:
-	fontInfoAttributesVersion1
-	fontInfoAttributesVersion2
-	fontInfoAttributesVersion3
-
-A set listing the fontinfo.plist attributes that were deprecated
-in version 2 is available for external use:
-	deprecatedFontInfoAttributesVersion2
-
-Functions that do basic validation on values for fontinfo.plist
-are available for external use. These are
-	validateFontInfoVersion2ValueForAttribute
-	validateFontInfoVersion3ValueForAttribute
-
-Value conversion functions are available for converting
-fontinfo.plist values between the possible format versions.
-	convertFontInfoValueForAttributeFromVersion1ToVersion2
-	convertFontInfoValueForAttributeFromVersion2ToVersion1
-	convertFontInfoValueForAttributeFromVersion2ToVersion3
-	convertFontInfoValueForAttributeFromVersion3ToVersion2
-"""
-
-__all__ = [
-    "makeUFOPath",
-    "UFOLibError",
-    "UFOReader",
-    "UFOWriter",
-    "UFOReaderWriter",
-    "UFOFileStructure",
-    "fontInfoAttributesVersion1",
-    "fontInfoAttributesVersion2",
-    "fontInfoAttributesVersion3",
-    "deprecatedFontInfoAttributesVersion2",
-    "validateFontInfoVersion2ValueForAttribute",
-    "validateFontInfoVersion3ValueForAttribute",
-    "convertFontInfoValueForAttributeFromVersion1ToVersion2",
-    "convertFontInfoValueForAttributeFromVersion2ToVersion1",
-]
-
-__version__ = "3.0.0"
-
-
-logger = logging.getLogger(__name__)
-
-
-# ---------
-# Constants
-# ---------
-
-DEFAULT_GLYPHS_DIRNAME = "glyphs"
-DATA_DIRNAME = "data"
-IMAGES_DIRNAME = "images"
-METAINFO_FILENAME = "metainfo.plist"
-FONTINFO_FILENAME = "fontinfo.plist"
-LIB_FILENAME = "lib.plist"
-GROUPS_FILENAME = "groups.plist"
-KERNING_FILENAME = "kerning.plist"
-FEATURES_FILENAME = "features.fea"
-LAYERCONTENTS_FILENAME = "layercontents.plist"
-LAYERINFO_FILENAME = "layerinfo.plist"
-
-DEFAULT_LAYER_NAME = "public.default"
-
-
-class UFOFormatVersion(tuple, _VersionTupleEnumMixin, enum.Enum):
-    FORMAT_1_0 = (1, 0)
-    FORMAT_2_0 = (2, 0)
-    FORMAT_3_0 = (3, 0)
-
-
-# python 3.11 doesn't like when a mixin overrides a dunder method like __str__
-# for some reasons it keep using Enum.__str__, see
-# https://github.com/fonttools/fonttools/pull/2655
-UFOFormatVersion.__str__ = _VersionTupleEnumMixin.__str__
-
-
-class UFOFileStructure(enum.Enum):
-    ZIP = "zip"
-    PACKAGE = "package"
-
-
-# --------------
-# Shared Methods
-# --------------
-
-
-class _UFOBaseIO:
-    def getFileModificationTime(self, path):
-        """
-        Returns the modification time for the file at the given path, as a
-        floating point number giving the number of seconds since the epoch.
-        The path must be relative to the UFO path.
-        Returns None if the file does not exist.
-        """
-        try:
-            dt = self.fs.getinfo(fsdecode(path), namespaces=["details"]).modified
-        except (fs.errors.MissingInfoNamespace, fs.errors.ResourceNotFound):
-            return None
-        else:
-            return dt.timestamp()
-
-    def _getPlist(self, fileName, default=None):
-        """
-        Read a property list relative to the UFO filesystem's root.
-        Raises UFOLibError if the file is missing and default is None,
-        otherwise default is returned.
-
-        The errors that could be raised during the reading of a plist are
-        unpredictable and/or too large to list, so, a blind try: except:
-        is done. If an exception occurs, a UFOLibError will be raised.
-        """
-        try:
-            with self.fs.open(fileName, "rb") as f:
-                return plistlib.load(f)
-        except fs.errors.ResourceNotFound:
-            if default is None:
-                raise UFOLibError(
-                    "'%s' is missing on %s. This file is required" % (fileName, self.fs)
-                )
-            else:
-                return default
-        except Exception as e:
-            # TODO(anthrotype): try to narrow this down a little
-            raise UFOLibError(f"'{fileName}' could not be read on {self.fs}: {e}")
-
-    def _writePlist(self, fileName, obj):
-        """
-        Write a property list to a file relative to the UFO filesystem's root.
-
-        Do this sort of atomically, making it harder to corrupt existing files,
-        for example when plistlib encounters an error halfway during write.
-        This also checks to see if text matches the text that is already in the
-        file at path. If so, the file is not rewritten so that the modification
-        date is preserved.
-
-        The errors that could be raised during the writing of a plist are
-        unpredictable and/or too large to list, so, a blind try: except: is done.
-        If an exception occurs, a UFOLibError will be raised.
-        """
-        if self._havePreviousFile:
-            try:
-                data = plistlib.dumps(obj)
-            except Exception as e:
-                raise UFOLibError(
-                    "'%s' could not be written on %s because "
-                    "the data is not properly formatted: %s" % (fileName, self.fs, e)
-                )
-            if self.fs.exists(fileName) and data == self.fs.readbytes(fileName):
-                return
-            self.fs.writebytes(fileName, data)
-        else:
-            with self.fs.openbin(fileName, mode="w") as fp:
-                try:
-                    plistlib.dump(obj, fp)
-                except Exception as e:
-                    raise UFOLibError(
-                        "'%s' could not be written on %s because "
-                        "the data is not properly formatted: %s"
-                        % (fileName, self.fs, e)
-                    )
-
-
-# ----------
-# UFO Reader
-# ----------
-
-
-class UFOReader(_UFOBaseIO):
-
-    """
-    Read the various components of the .ufo.
-
-    By default read data is validated. Set ``validate`` to
-    ``False`` to not validate the data.
-    """
-
-    def __init__(self, path, validate=True):
-        if hasattr(path, "__fspath__"):  # support os.PathLike objects
-            path = path.__fspath__()
-
-        if isinstance(path, str):
-            structure = _sniffFileStructure(path)
-            try:
-                if structure is UFOFileStructure.ZIP:
-                    parentFS = fs.zipfs.ZipFS(path, write=False, encoding="utf-8")
-                else:
-                    parentFS = fs.osfs.OSFS(path)
-            except fs.errors.CreateFailed as e:
-                raise UFOLibError(f"unable to open '{path}': {e}")
-
-            if structure is UFOFileStructure.ZIP:
-                # .ufoz zip files must contain a single root directory, with arbitrary
-                # name, containing all the UFO files
-                rootDirs = [
-                    p.name
-                    for p in parentFS.scandir("/")
-                    # exclude macOS metadata contained in zip file
-                    if p.is_dir and p.name != "__MACOSX"
-                ]
-                if len(rootDirs) == 1:
-                    # 'ClosingSubFS' ensures that the parent zip file is closed when
-                    # its root subdirectory is closed
-                    self.fs = parentFS.opendir(
-                        rootDirs[0], factory=fs.subfs.ClosingSubFS
-                    )
-                else:
-                    raise UFOLibError(
-                        "Expected exactly 1 root directory, found %d" % len(rootDirs)
-                    )
-            else:
-                # normal UFO 'packages' are just a single folder
-                self.fs = parentFS
-            # when passed a path string, we make sure we close the newly opened fs
-            # upon calling UFOReader.close method or context manager's __exit__
-            self._shouldClose = True
-            self._fileStructure = structure
-        elif isinstance(path, fs.base.FS):
-            filesystem = path
-            try:
-                filesystem.check()
-            except fs.errors.FilesystemClosed:
-                raise UFOLibError("the filesystem '%s' is closed" % path)
-            else:
-                self.fs = filesystem
-            try:
-                path = filesystem.getsyspath("/")
-            except fs.errors.NoSysPath:
-                # network or in-memory FS may not map to the local one
-                path = str(filesystem)
-            # when user passed an already initialized fs instance, it is her
-            # responsibility to close it, thus UFOReader.close/__exit__ are no-op
-            self._shouldClose = False
-            # default to a 'package' structure
-            self._fileStructure = UFOFileStructure.PACKAGE
-        else:
-            raise TypeError(
-                "Expected a path string or fs.base.FS object, found '%s'"
-                % type(path).__name__
-            )
-        self._path = fsdecode(path)
-        self._validate = validate
-        self._upConvertedKerningData = None
-
-        try:
-            self.readMetaInfo(validate=validate)
-        except UFOLibError:
-            self.close()
-            raise
-
-    # properties
-
-    def _get_path(self):
-        import warnings
-
-        warnings.warn(
-            "The 'path' attribute is deprecated; use the 'fs' attribute instead",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return self._path
-
-    path = property(_get_path, doc="The path of the UFO (DEPRECATED).")
-
-    def _get_formatVersion(self):
-        import warnings
-
-        warnings.warn(
-            "The 'formatVersion' attribute is deprecated; use the 'formatVersionTuple'",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return self._formatVersion.major
-
-    formatVersion = property(
-        _get_formatVersion,
-        doc="The (major) format version of the UFO. DEPRECATED: Use formatVersionTuple",
-    )
-
-    @property
-    def formatVersionTuple(self):
-        """The (major, minor) format version of the UFO.
-        This is determined by reading metainfo.plist during __init__.
-        """
-        return self._formatVersion
-
-    def _get_fileStructure(self):
-        return self._fileStructure
-
-    fileStructure = property(
-        _get_fileStructure,
-        doc=(
-            "The file structure of the UFO: "
-            "either UFOFileStructure.ZIP or UFOFileStructure.PACKAGE"
-        ),
-    )
-
-    # up conversion
-
-    def _upConvertKerning(self, validate):
-        """
-        Up convert kerning and groups in UFO 1 and 2.
-        The data will be held internally until each bit of data
-        has been retrieved. The conversion of both must be done
-        at once, so the raw data is cached and an error is raised
-        if one bit of data becomes obsolete before it is called.
-
-        ``validate`` will validate the data.
-        """
-        if self._upConvertedKerningData:
-            testKerning = self._readKerning()
-            if testKerning != self._upConvertedKerningData["originalKerning"]:
-                raise UFOLibError(
-                    "The data in kerning.plist has been modified since it was converted to UFO 3 format."
-                )
-            testGroups = self._readGroups()
-            if testGroups != self._upConvertedKerningData["originalGroups"]:
-                raise UFOLibError(
-                    "The data in groups.plist has been modified since it was converted to UFO 3 format."
-                )
-        else:
-            groups = self._readGroups()
-            if validate:
-                invalidFormatMessage = "groups.plist is not properly formatted."
-                if not isinstance(groups, dict):
-                    raise UFOLibError(invalidFormatMessage)
-                for groupName, glyphList in groups.items():
-                    if not isinstance(groupName, str):
-                        raise UFOLibError(invalidFormatMessage)
-                    elif not isinstance(glyphList, list):
-                        raise UFOLibError(invalidFormatMessage)
-                    for glyphName in glyphList:
-                        if not isinstance(glyphName, str):
-                            raise UFOLibError(invalidFormatMessage)
-            self._upConvertedKerningData = dict(
-                kerning={},
-                originalKerning=self._readKerning(),
-                groups={},
-                originalGroups=groups,
-            )
-            # convert kerning and groups
-            kerning, groups, conversionMaps = convertUFO1OrUFO2KerningToUFO3Kerning(
-                self._upConvertedKerningData["originalKerning"],
-                deepcopy(self._upConvertedKerningData["originalGroups"]),
-                self.getGlyphSet(),
-            )
-            # store
-            self._upConvertedKerningData["kerning"] = kerning
-            self._upConvertedKerningData["groups"] = groups
-            self._upConvertedKerningData["groupRenameMaps"] = conversionMaps
-
-    # support methods
-
-    def readBytesFromPath(self, path):
-        """
-        Returns the bytes in the file at the given path.
-        The path must be relative to the UFO's filesystem root.
-        Returns None if the file does not exist.
-        """
-        try:
-            return self.fs.readbytes(fsdecode(path))
-        except fs.errors.ResourceNotFound:
-            return None
-
-    def getReadFileForPath(self, path, encoding=None):
-        """
-        Returns a file (or file-like) object for the file at the given path.
-        The path must be relative to the UFO path.
-        Returns None if the file does not exist.
-        By default the file is opened in binary mode (reads bytes).
-        If encoding is passed, the file is opened in text mode (reads str).
-
-        Note: The caller is responsible for closing the open file.
-        """
-        path = fsdecode(path)
-        try:
-            if encoding is None:
-                return self.fs.openbin(path)
-            else:
-                return self.fs.open(path, mode="r", encoding=encoding)
-        except fs.errors.ResourceNotFound:
-            return None
-
-    # metainfo.plist
-
-    def _readMetaInfo(self, validate=None):
-        """
-        Read metainfo.plist and return raw data. Only used for internal operations.
-
-        ``validate`` will validate the read data, by default it is set
-        to the class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        data = self._getPlist(METAINFO_FILENAME)
-        if validate and not isinstance(data, dict):
-            raise UFOLibError("metainfo.plist is not properly formatted.")
-        try:
-            formatVersionMajor = data["formatVersion"]
-        except KeyError:
-            raise UFOLibError(
-                f"Missing required formatVersion in '{METAINFO_FILENAME}' on {self.fs}"
-            )
-        formatVersionMinor = data.setdefault("formatVersionMinor", 0)
-
-        try:
-            formatVersion = UFOFormatVersion((formatVersionMajor, formatVersionMinor))
-        except ValueError as e:
-            unsupportedMsg = (
-                f"Unsupported UFO format ({formatVersionMajor}.{formatVersionMinor}) "
-                f"in '{METAINFO_FILENAME}' on {self.fs}"
-            )
-            if validate:
-                from fontTools.ufoLib.errors import UnsupportedUFOFormat
-
-                raise UnsupportedUFOFormat(unsupportedMsg) from e
-
-            formatVersion = UFOFormatVersion.default()
-            logger.warning(
-                "%s. Assuming the latest supported version (%s). "
-                "Some data may be skipped or parsed incorrectly",
-                unsupportedMsg,
-                formatVersion,
-            )
-        data["formatVersionTuple"] = formatVersion
-        return data
-
-    def readMetaInfo(self, validate=None):
-        """
-        Read metainfo.plist and set formatVersion. Only used for internal operations.
-
-        ``validate`` will validate the read data, by default it is set
-        to the class's validate value, can be overridden.
-        """
-        data = self._readMetaInfo(validate=validate)
-        self._formatVersion = data["formatVersionTuple"]
-
-    # groups.plist
-
-    def _readGroups(self):
-        groups = self._getPlist(GROUPS_FILENAME, {})
-        # remove any duplicate glyphs in a kerning group
-        for groupName, glyphList in groups.items():
-            if groupName.startswith(("public.kern1.", "public.kern2.")):
-                groups[groupName] = list(OrderedDict.fromkeys(glyphList))
-        return groups
-
-    def readGroups(self, validate=None):
-        """
-        Read groups.plist. Returns a dict.
-        ``validate`` will validate the read data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        # handle up conversion
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            self._upConvertKerning(validate)
-            groups = self._upConvertedKerningData["groups"]
-        # normal
-        else:
-            groups = self._readGroups()
-        if validate:
-            valid, message = groupsValidator(groups)
-            if not valid:
-                raise UFOLibError(message)
-        return groups
-
-    def getKerningGroupConversionRenameMaps(self, validate=None):
-        """
-        Get maps defining the renaming that was done during any
-        needed kerning group conversion. This method returns a
-        dictionary of this form::
-
-                {
-                        "side1" : {"old group name" : "new group name"},
-                        "side2" : {"old group name" : "new group name"}
-                }
-
-        When no conversion has been performed, the side1 and side2
-        dictionaries will be empty.
-
-        ``validate`` will validate the groups, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        if self._formatVersion >= UFOFormatVersion.FORMAT_3_0:
-            return dict(side1={}, side2={})
-        # use the public group reader to force the load and
-        # conversion of the data if it hasn't happened yet.
-        self.readGroups(validate=validate)
-        return self._upConvertedKerningData["groupRenameMaps"]
-
-    # fontinfo.plist
-
-    def _readInfo(self, validate):
-        data = self._getPlist(FONTINFO_FILENAME, {})
-        if validate and not isinstance(data, dict):
-            raise UFOLibError("fontinfo.plist is not properly formatted.")
-        return data
-
-    def readInfo(self, info, validate=None):
-        """
-        Read fontinfo.plist. It requires an object that allows
-        setting attributes with names that follow the fontinfo.plist
-        version 3 specification. This will write the attributes
-        defined in the file into the object.
-
-        ``validate`` will validate the read data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        infoDict = self._readInfo(validate)
-        infoDataToSet = {}
-        # version 1
-        if self._formatVersion == UFOFormatVersion.FORMAT_1_0:
-            for attr in fontInfoAttributesVersion1:
-                value = infoDict.get(attr)
-                if value is not None:
-                    infoDataToSet[attr] = value
-            infoDataToSet = _convertFontInfoDataVersion1ToVersion2(infoDataToSet)
-            infoDataToSet = _convertFontInfoDataVersion2ToVersion3(infoDataToSet)
-        # version 2
-        elif self._formatVersion == UFOFormatVersion.FORMAT_2_0:
-            for attr, dataValidationDict in list(
-                fontInfoAttributesVersion2ValueData.items()
-            ):
-                value = infoDict.get(attr)
-                if value is None:
-                    continue
-                infoDataToSet[attr] = value
-            infoDataToSet = _convertFontInfoDataVersion2ToVersion3(infoDataToSet)
-        # version 3.x
-        elif self._formatVersion.major == UFOFormatVersion.FORMAT_3_0.major:
-            for attr, dataValidationDict in list(
-                fontInfoAttributesVersion3ValueData.items()
-            ):
-                value = infoDict.get(attr)
-                if value is None:
-                    continue
-                infoDataToSet[attr] = value
-        # unsupported version
-        else:
-            raise NotImplementedError(self._formatVersion)
-        # validate data
-        if validate:
-            infoDataToSet = validateInfoVersion3Data(infoDataToSet)
-        # populate the object
-        for attr, value in list(infoDataToSet.items()):
-            try:
-                setattr(info, attr, value)
-            except AttributeError:
-                raise UFOLibError(
-                    "The supplied info object does not support setting a necessary attribute (%s)."
-                    % attr
-                )
-
-    # kerning.plist
-
-    def _readKerning(self):
-        data = self._getPlist(KERNING_FILENAME, {})
-        return data
-
-    def readKerning(self, validate=None):
-        """
-        Read kerning.plist. Returns a dict.
-
-        ``validate`` will validate the kerning data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        # handle up conversion
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            self._upConvertKerning(validate)
-            kerningNested = self._upConvertedKerningData["kerning"]
-        # normal
-        else:
-            kerningNested = self._readKerning()
-        if validate:
-            valid, message = kerningValidator(kerningNested)
-            if not valid:
-                raise UFOLibError(message)
-        # flatten
-        kerning = {}
-        for left in kerningNested:
-            for right in kerningNested[left]:
-                value = kerningNested[left][right]
-                kerning[left, right] = value
-        return kerning
-
-    # lib.plist
-
-    def readLib(self, validate=None):
-        """
-        Read lib.plist. Returns a dict.
-
-        ``validate`` will validate the data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        data = self._getPlist(LIB_FILENAME, {})
-        if validate:
-            valid, message = fontLibValidator(data)
-            if not valid:
-                raise UFOLibError(message)
-        return data
-
-    # features.fea
-
-    def readFeatures(self):
-        """
-        Read features.fea. Return a string.
-        The returned string is empty if the file is missing.
-        """
-        try:
-            with self.fs.open(FEATURES_FILENAME, "r", encoding="utf-8") as f:
-                return f.read()
-        except fs.errors.ResourceNotFound:
-            return ""
-
-    # glyph sets & layers
-
-    def _readLayerContents(self, validate):
-        """
-        Rebuild the layer contents list by checking what glyphsets
-        are available on disk.
-
-        ``validate`` will validate the layer contents.
-        """
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            return [(DEFAULT_LAYER_NAME, DEFAULT_GLYPHS_DIRNAME)]
-        contents = self._getPlist(LAYERCONTENTS_FILENAME)
-        if validate:
-            valid, error = layerContentsValidator(contents, self.fs)
-            if not valid:
-                raise UFOLibError(error)
-        return contents
-
-    def getLayerNames(self, validate=None):
-        """
-        Get the ordered layer names from layercontents.plist.
-
-        ``validate`` will validate the data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        layerContents = self._readLayerContents(validate)
-        layerNames = [layerName for layerName, directoryName in layerContents]
-        return layerNames
-
-    def getDefaultLayerName(self, validate=None):
-        """
-        Get the default layer name from layercontents.plist.
-
-        ``validate`` will validate the data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        layerContents = self._readLayerContents(validate)
-        for layerName, layerDirectory in layerContents:
-            if layerDirectory == DEFAULT_GLYPHS_DIRNAME:
-                return layerName
-        # this will already have been raised during __init__
-        raise UFOLibError("The default layer is not defined in layercontents.plist.")
-
-    def getGlyphSet(self, layerName=None, validateRead=None, validateWrite=None):
-        """
-        Return the GlyphSet associated with the
-        glyphs directory mapped to layerName
-        in the UFO. If layerName is not provided,
-        the name retrieved with getDefaultLayerName
-        will be used.
-
-        ``validateRead`` will validate the read data, by default it is set to the
-        class's validate value, can be overridden.
-        ``validateWrite`` will validate the written data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        from fontTools.ufoLib.glifLib import GlyphSet
-
-        if validateRead is None:
-            validateRead = self._validate
-        if validateWrite is None:
-            validateWrite = self._validate
-        if layerName is None:
-            layerName = self.getDefaultLayerName(validate=validateRead)
-        directory = None
-        layerContents = self._readLayerContents(validateRead)
-        for storedLayerName, storedLayerDirectory in layerContents:
-            if layerName == storedLayerName:
-                directory = storedLayerDirectory
-                break
-        if directory is None:
-            raise UFOLibError('No glyphs directory is mapped to "%s".' % layerName)
-        try:
-            glyphSubFS = self.fs.opendir(directory)
-        except fs.errors.ResourceNotFound:
-            raise UFOLibError(f"No '{directory}' directory for layer '{layerName}'")
-        return GlyphSet(
-            glyphSubFS,
-            ufoFormatVersion=self._formatVersion,
-            validateRead=validateRead,
-            validateWrite=validateWrite,
-            expectContentsFile=True,
-        )
-
-    def getCharacterMapping(self, layerName=None, validate=None):
-        """
-        Return a dictionary that maps unicode values (ints) to
-        lists of glyph names.
-        """
-        if validate is None:
-            validate = self._validate
-        glyphSet = self.getGlyphSet(
-            layerName, validateRead=validate, validateWrite=True
-        )
-        allUnicodes = glyphSet.getUnicodes()
-        cmap = {}
-        for glyphName, unicodes in allUnicodes.items():
-            for code in unicodes:
-                if code in cmap:
-                    cmap[code].append(glyphName)
-                else:
-                    cmap[code] = [glyphName]
-        return cmap
-
-    # /data
-
-    def getDataDirectoryListing(self):
-        """
-        Returns a list of all files in the data directory.
-        The returned paths will be relative to the UFO.
-        This will not list directory names, only file names.
-        Thus, empty directories will be skipped.
-        """
-        try:
-            self._dataFS = self.fs.opendir(DATA_DIRNAME)
-        except fs.errors.ResourceNotFound:
-            return []
-        except fs.errors.DirectoryExpected:
-            raise UFOLibError('The UFO contains a "data" file instead of a directory.')
-        try:
-            # fs Walker.files method returns "absolute" paths (in terms of the
-            # root of the 'data' SubFS), so we strip the leading '/' to make
-            # them relative
-            return [p.lstrip("/") for p in self._dataFS.walk.files()]
-        except fs.errors.ResourceError:
-            return []
-
-    def getImageDirectoryListing(self, validate=None):
-        """
-        Returns a list of all image file names in
-        the images directory. Each of the images will
-        have been verified to have the PNG signature.
-
-        ``validate`` will validate the data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            return []
-        if validate is None:
-            validate = self._validate
-        try:
-            self._imagesFS = imagesFS = self.fs.opendir(IMAGES_DIRNAME)
-        except fs.errors.ResourceNotFound:
-            return []
-        except fs.errors.DirectoryExpected:
-            raise UFOLibError(
-                'The UFO contains an "images" file instead of a directory.'
-            )
-        result = []
-        for path in imagesFS.scandir("/"):
-            if path.is_dir:
-                # silently skip this as version control
-                # systems often have hidden directories
-                continue
-            if validate:
-                with imagesFS.openbin(path.name) as fp:
-                    valid, error = pngValidator(fileObj=fp)
-                if valid:
-                    result.append(path.name)
-            else:
-                result.append(path.name)
-        return result
-
-    def readData(self, fileName):
-        """
-        Return bytes for the file named 'fileName' inside the 'data/' directory.
-        """
-        fileName = fsdecode(fileName)
-        try:
-            try:
-                dataFS = self._dataFS
-            except AttributeError:
-                # in case readData is called before getDataDirectoryListing
-                dataFS = self.fs.opendir(DATA_DIRNAME)
-            data = dataFS.readbytes(fileName)
-        except fs.errors.ResourceNotFound:
-            raise UFOLibError(f"No data file named '{fileName}' on {self.fs}")
-        return data
-
-    def readImage(self, fileName, validate=None):
-        """
-        Return image data for the file named fileName.
-
-        ``validate`` will validate the data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            raise UFOLibError(
-                f"Reading images is not allowed in UFO {self._formatVersion.major}."
-            )
-        fileName = fsdecode(fileName)
-        try:
-            try:
-                imagesFS = self._imagesFS
-            except AttributeError:
-                # in case readImage is called before getImageDirectoryListing
-                imagesFS = self.fs.opendir(IMAGES_DIRNAME)
-            data = imagesFS.readbytes(fileName)
-        except fs.errors.ResourceNotFound:
-            raise UFOLibError(f"No image file named '{fileName}' on {self.fs}")
-        if validate:
-            valid, error = pngValidator(data=data)
-            if not valid:
-                raise UFOLibError(error)
-        return data
-
-    def close(self):
-        if self._shouldClose:
-            self.fs.close()
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_tb):
-        self.close()
-
-
-# ----------
-# UFO Writer
-# ----------
-
-
-class UFOWriter(UFOReader):
-
-    """
-    Write the various components of the .ufo.
-
-    By default, the written data will be validated before writing. Set ``validate`` to
-    ``False`` if you do not want to validate the data. Validation can also be overriden
-    on a per method level if desired.
-
-    The ``formatVersion`` argument allows to specify the UFO format version as a tuple
-    of integers (major, minor), or as a single integer for the major digit only (minor
-    is implied as 0). By default the latest formatVersion will be used; currently it's
-    3.0, which is equivalent to formatVersion=(3, 0).
-
-    An UnsupportedUFOFormat exception is raised if the requested UFO formatVersion is
-    not supported.
-    """
-
-    def __init__(
-        self,
-        path,
-        formatVersion=None,
-        fileCreator="com.github.fonttools.ufoLib",
-        structure=None,
-        validate=True,
-    ):
-        try:
-            formatVersion = UFOFormatVersion(formatVersion)
-        except ValueError as e:
-            from fontTools.ufoLib.errors import UnsupportedUFOFormat
-
-            raise UnsupportedUFOFormat(
-                f"Unsupported UFO format: {formatVersion!r}"
-            ) from e
-
-        if hasattr(path, "__fspath__"):  # support os.PathLike objects
-            path = path.__fspath__()
-
-        if isinstance(path, str):
-            # normalize path by removing trailing or double slashes
-            path = os.path.normpath(path)
-            havePreviousFile = os.path.exists(path)
-            if havePreviousFile:
-                # ensure we use the same structure as the destination
-                existingStructure = _sniffFileStructure(path)
-                if structure is not None:
-                    try:
-                        structure = UFOFileStructure(structure)
-                    except ValueError:
-                        raise UFOLibError(
-                            "Invalid or unsupported structure: '%s'" % structure
-                        )
-                    if structure is not existingStructure:
-                        raise UFOLibError(
-                            "A UFO with a different structure (%s) already exists "
-                            "at the given path: '%s'" % (existingStructure, path)
-                        )
-                else:
-                    structure = existingStructure
-            else:
-                # if not exists, default to 'package' structure
-                if structure is None:
-                    structure = UFOFileStructure.PACKAGE
-                dirName = os.path.dirname(path)
-                if dirName and not os.path.isdir(dirName):
-                    raise UFOLibError(
-                        "Cannot write to '%s': directory does not exist" % path
-                    )
-            if structure is UFOFileStructure.ZIP:
-                if havePreviousFile:
-                    # we can't write a zip in-place, so we have to copy its
-                    # contents to a temporary location and work from there, then
-                    # upon closing UFOWriter we create the final zip file
-                    parentFS = fs.tempfs.TempFS()
-                    with fs.zipfs.ZipFS(path, encoding="utf-8") as origFS:
-                        fs.copy.copy_fs(origFS, parentFS)
-                    # if output path is an existing zip, we require that it contains
-                    # one, and only one, root directory (with arbitrary name), in turn
-                    # containing all the existing UFO contents
-                    rootDirs = [
-                        p.name
-                        for p in parentFS.scandir("/")
-                        # exclude macOS metadata contained in zip file
-                        if p.is_dir and p.name != "__MACOSX"
-                    ]
-                    if len(rootDirs) != 1:
-                        raise UFOLibError(
-                            "Expected exactly 1 root directory, found %d"
-                            % len(rootDirs)
-                        )
-                    else:
-                        # 'ClosingSubFS' ensures that the parent filesystem is closed
-                        # when its root subdirectory is closed
-                        self.fs = parentFS.opendir(
-                            rootDirs[0], factory=fs.subfs.ClosingSubFS
-                        )
-                else:
-                    # if the output zip file didn't exist, we create the root folder;
-                    # we name it the same as input 'path', but with '.ufo' extension
-                    rootDir = os.path.splitext(os.path.basename(path))[0] + ".ufo"
-                    parentFS = fs.zipfs.ZipFS(path, write=True, encoding="utf-8")
-                    parentFS.makedir(rootDir)
-                    self.fs = parentFS.opendir(rootDir, factory=fs.subfs.ClosingSubFS)
-            else:
-                self.fs = fs.osfs.OSFS(path, create=True)
-            self._fileStructure = structure
-            self._havePreviousFile = havePreviousFile
-            self._shouldClose = True
-        elif isinstance(path, fs.base.FS):
-            filesystem = path
-            try:
-                filesystem.check()
-            except fs.errors.FilesystemClosed:
-                raise UFOLibError("the filesystem '%s' is closed" % path)
-            else:
-                self.fs = filesystem
-            try:
-                path = filesystem.getsyspath("/")
-            except fs.errors.NoSysPath:
-                # network or in-memory FS may not map to the local one
-                path = str(filesystem)
-            # if passed an FS object, always use 'package' structure
-            if structure and structure is not UFOFileStructure.PACKAGE:
-                import warnings
-
-                warnings.warn(
-                    "The 'structure' argument is not used when input is an FS object",
-                    UserWarning,
-                    stacklevel=2,
-                )
-            self._fileStructure = UFOFileStructure.PACKAGE
-            # if FS contains a "metainfo.plist", we consider it non-empty
-            self._havePreviousFile = filesystem.exists(METAINFO_FILENAME)
-            # the user is responsible for closing the FS object
-            self._shouldClose = False
-        else:
-            raise TypeError(
-                "Expected a path string or fs object, found %s" % type(path).__name__
-            )
-
-        # establish some basic stuff
-        self._path = fsdecode(path)
-        self._formatVersion = formatVersion
-        self._fileCreator = fileCreator
-        self._downConversionKerningData = None
-        self._validate = validate
-        # if the file already exists, get the format version.
-        # this will be needed for up and down conversion.
-        previousFormatVersion = None
-        if self._havePreviousFile:
-            metaInfo = self._readMetaInfo(validate=validate)
-            previousFormatVersion = metaInfo["formatVersionTuple"]
-            # catch down conversion
-            if previousFormatVersion > formatVersion:
-                from fontTools.ufoLib.errors import UnsupportedUFOFormat
-
-                raise UnsupportedUFOFormat(
-                    "The UFO located at this path is a higher version "
-                    f"({previousFormatVersion}) than the version ({formatVersion}) "
-                    "that is trying to be written. This is not supported."
-                )
-        # handle the layer contents
-        self.layerContents = {}
-        if previousFormatVersion is not None and previousFormatVersion.major >= 3:
-            # already exists
-            self.layerContents = OrderedDict(self._readLayerContents(validate))
-        else:
-            # previous < 3
-            # imply the layer contents
-            if self.fs.exists(DEFAULT_GLYPHS_DIRNAME):
-                self.layerContents = {DEFAULT_LAYER_NAME: DEFAULT_GLYPHS_DIRNAME}
-        # write the new metainfo
-        self._writeMetaInfo()
-
-    # properties
-
-    def _get_fileCreator(self):
-        return self._fileCreator
-
-    fileCreator = property(
-        _get_fileCreator,
-        doc="The file creator of the UFO. This is set into metainfo.plist during __init__.",
-    )
-
-    # support methods for file system interaction
-
-    def copyFromReader(self, reader, sourcePath, destPath):
-        """
-        Copy the sourcePath in the provided UFOReader to destPath
-        in this writer. The paths must be relative. This works with
-        both individual files and directories.
-        """
-        if not isinstance(reader, UFOReader):
-            raise UFOLibError("The reader must be an instance of UFOReader.")
-        sourcePath = fsdecode(sourcePath)
-        destPath = fsdecode(destPath)
-        if not reader.fs.exists(sourcePath):
-            raise UFOLibError(
-                'The reader does not have data located at "%s".' % sourcePath
-            )
-        if self.fs.exists(destPath):
-            raise UFOLibError('A file named "%s" already exists.' % destPath)
-        # create the destination directory if it doesn't exist
-        self.fs.makedirs(fs.path.dirname(destPath), recreate=True)
-        if reader.fs.isdir(sourcePath):
-            fs.copy.copy_dir(reader.fs, sourcePath, self.fs, destPath)
-        else:
-            fs.copy.copy_file(reader.fs, sourcePath, self.fs, destPath)
-
-    def writeBytesToPath(self, path, data):
-        """
-        Write bytes to a path relative to the UFO filesystem's root.
-        If writing to an existing UFO, check to see if data matches the data
-        that is already in the file at path; if so, the file is not rewritten
-        so that the modification date is preserved.
-        If needed, the directory tree for the given path will be built.
-        """
-        path = fsdecode(path)
-        if self._havePreviousFile:
-            if self.fs.isfile(path) and data == self.fs.readbytes(path):
-                return
-        try:
-            self.fs.writebytes(path, data)
-        except fs.errors.FileExpected:
-            raise UFOLibError("A directory exists at '%s'" % path)
-        except fs.errors.ResourceNotFound:
-            self.fs.makedirs(fs.path.dirname(path), recreate=True)
-            self.fs.writebytes(path, data)
-
-    def getFileObjectForPath(self, path, mode="w", encoding=None):
-        """
-        Returns a file (or file-like) object for the
-        file at the given path. The path must be relative
-        to the UFO path. Returns None if the file does
-        not exist and the mode is "r" or "rb.
-        An encoding may be passed if the file is opened in text mode.
-
-        Note: The caller is responsible for closing the open file.
-        """
-        path = fsdecode(path)
-        try:
-            return self.fs.open(path, mode=mode, encoding=encoding)
-        except fs.errors.ResourceNotFound as e:
-            m = mode[0]
-            if m == "r":
-                # XXX I think we should just let it raise. The docstring,
-                # however, says that this returns None if mode is 'r'
-                return None
-            elif m == "w" or m == "a" or m == "x":
-                self.fs.makedirs(fs.path.dirname(path), recreate=True)
-                return self.fs.open(path, mode=mode, encoding=encoding)
-        except fs.errors.ResourceError as e:
-            return UFOLibError(f"unable to open '{path}' on {self.fs}: {e}")
-
-    def removePath(self, path, force=False, removeEmptyParents=True):
-        """
-        Remove the file (or directory) at path. The path
-        must be relative to the UFO.
-        Raises UFOLibError if the path doesn't exist.
-        If force=True, ignore non-existent paths.
-        If the directory where 'path' is located becomes empty, it will
-        be automatically removed, unless 'removeEmptyParents' is False.
-        """
-        path = fsdecode(path)
-        try:
-            self.fs.remove(path)
-        except fs.errors.FileExpected:
-            self.fs.removetree(path)
-        except fs.errors.ResourceNotFound:
-            if not force:
-                raise UFOLibError(f"'{path}' does not exist on {self.fs}")
-        if removeEmptyParents:
-            parent = fs.path.dirname(path)
-            if parent:
-                fs.tools.remove_empty(self.fs, parent)
-
-    # alias kept for backward compatibility with old API
-    removeFileForPath = removePath
-
-    # UFO mod time
-
-    def setModificationTime(self):
-        """
-        Set the UFO modification time to the current time.
-        This is never called automatically. It is up to the
-        caller to call this when finished working on the UFO.
-        """
-        path = self._path
-        if path is not None and os.path.exists(path):
-            try:
-                # this may fail on some filesystems (e.g. SMB servers)
-                os.utime(path, None)
-            except OSError as e:
-                logger.warning("Failed to set modified time: %s", e)
-
-    # metainfo.plist
-
-    def _writeMetaInfo(self):
-        metaInfo = dict(
-            creator=self._fileCreator,
-            formatVersion=self._formatVersion.major,
-        )
-        if self._formatVersion.minor != 0:
-            metaInfo["formatVersionMinor"] = self._formatVersion.minor
-        self._writePlist(METAINFO_FILENAME, metaInfo)
-
-    # groups.plist
-
-    def setKerningGroupConversionRenameMaps(self, maps):
-        """
-        Set maps defining the renaming that should be done
-        when writing groups and kerning in UFO 1 and UFO 2.
-        This will effectively undo the conversion done when
-        UFOReader reads this data. The dictionary should have
-        this form::
-
-                {
-                        "side1" : {"group name to use when writing" : "group name in data"},
-                        "side2" : {"group name to use when writing" : "group name in data"}
-                }
-
-        This is the same form returned by UFOReader's
-        getKerningGroupConversionRenameMaps method.
-        """
-        if self._formatVersion >= UFOFormatVersion.FORMAT_3_0:
-            return  # XXX raise an error here
-        # flip the dictionaries
-        remap = {}
-        for side in ("side1", "side2"):
-            for writeName, dataName in list(maps[side].items()):
-                remap[dataName] = writeName
-        self._downConversionKerningData = dict(groupRenameMap=remap)
-
-    def writeGroups(self, groups, validate=None):
-        """
-        Write groups.plist. This method requires a
-        dict of glyph groups as an argument.
-
-        ``validate`` will validate the data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        # validate the data structure
-        if validate:
-            valid, message = groupsValidator(groups)
-            if not valid:
-                raise UFOLibError(message)
-        # down convert
-        if (
-            self._formatVersion < UFOFormatVersion.FORMAT_3_0
-            and self._downConversionKerningData is not None
-        ):
-            remap = self._downConversionKerningData["groupRenameMap"]
-            remappedGroups = {}
-            # there are some edge cases here that are ignored:
-            # 1. if a group is being renamed to a name that
-            #    already exists, the existing group is always
-            #    overwritten. (this is why there are two loops
-            #    below.) there doesn't seem to be a logical
-            #    solution to groups mismatching and overwriting
-            #    with the specifiecd group seems like a better
-            #    solution than throwing an error.
-            # 2. if side 1 and side 2 groups are being renamed
-            #    to the same group name there is no check to
-            #    ensure that the contents are identical. that
-            #    is left up to the caller.
-            for name, contents in list(groups.items()):
-                if name in remap:
-                    continue
-                remappedGroups[name] = contents
-            for name, contents in list(groups.items()):
-                if name not in remap:
-                    continue
-                name = remap[name]
-                remappedGroups[name] = contents
-            groups = remappedGroups
-        # pack and write
-        groupsNew = {}
-        for key, value in groups.items():
-            groupsNew[key] = list(value)
-        if groupsNew:
-            self._writePlist(GROUPS_FILENAME, groupsNew)
-        elif self._havePreviousFile:
-            self.removePath(GROUPS_FILENAME, force=True, removeEmptyParents=False)
-
-    # fontinfo.plist
-
-    def writeInfo(self, info, validate=None):
-        """
-        Write info.plist. This method requires an object
-        that supports getting attributes that follow the
-        fontinfo.plist version 2 specification. Attributes
-        will be taken from the given object and written
-        into the file.
-
-        ``validate`` will validate the data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        # gather version 3 data
-        infoData = {}
-        for attr in list(fontInfoAttributesVersion3ValueData.keys()):
-            if hasattr(info, attr):
-                try:
-                    value = getattr(info, attr)
-                except AttributeError:
-                    raise UFOLibError(
-                        "The supplied info object does not support getting a necessary attribute (%s)."
-                        % attr
-                    )
-                if value is None:
-                    continue
-                infoData[attr] = value
-        # down convert data if necessary and validate
-        if self._formatVersion == UFOFormatVersion.FORMAT_3_0:
-            if validate:
-                infoData = validateInfoVersion3Data(infoData)
-        elif self._formatVersion == UFOFormatVersion.FORMAT_2_0:
-            infoData = _convertFontInfoDataVersion3ToVersion2(infoData)
-            if validate:
-                infoData = validateInfoVersion2Data(infoData)
-        elif self._formatVersion == UFOFormatVersion.FORMAT_1_0:
-            infoData = _convertFontInfoDataVersion3ToVersion2(infoData)
-            if validate:
-                infoData = validateInfoVersion2Data(infoData)
-            infoData = _convertFontInfoDataVersion2ToVersion1(infoData)
-        # write file if there is anything to write
-        if infoData:
-            self._writePlist(FONTINFO_FILENAME, infoData)
-
-    # kerning.plist
-
-    def writeKerning(self, kerning, validate=None):
-        """
-        Write kerning.plist. This method requires a
-        dict of kerning pairs as an argument.
-
-        This performs basic structural validation of the kerning,
-        but it does not check for compliance with the spec in
-        regards to conflicting pairs. The assumption is that the
-        kerning data being passed is standards compliant.
-
-        ``validate`` will validate the data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        # validate the data structure
-        if validate:
-            invalidFormatMessage = "The kerning is not properly formatted."
-            if not isDictEnough(kerning):
-                raise UFOLibError(invalidFormatMessage)
-            for pair, value in list(kerning.items()):
-                if not isinstance(pair, (list, tuple)):
-                    raise UFOLibError(invalidFormatMessage)
-                if not len(pair) == 2:
-                    raise UFOLibError(invalidFormatMessage)
-                if not isinstance(pair[0], str):
-                    raise UFOLibError(invalidFormatMessage)
-                if not isinstance(pair[1], str):
-                    raise UFOLibError(invalidFormatMessage)
-                if not isinstance(value, numberTypes):
-                    raise UFOLibError(invalidFormatMessage)
-        # down convert
-        if (
-            self._formatVersion < UFOFormatVersion.FORMAT_3_0
-            and self._downConversionKerningData is not None
-        ):
-            remap = self._downConversionKerningData["groupRenameMap"]
-            remappedKerning = {}
-            for (side1, side2), value in list(kerning.items()):
-                side1 = remap.get(side1, side1)
-                side2 = remap.get(side2, side2)
-                remappedKerning[side1, side2] = value
-            kerning = remappedKerning
-        # pack and write
-        kerningDict = {}
-        for left, right in kerning.keys():
-            value = kerning[left, right]
-            if left not in kerningDict:
-                kerningDict[left] = {}
-            kerningDict[left][right] = value
-        if kerningDict:
-            self._writePlist(KERNING_FILENAME, kerningDict)
-        elif self._havePreviousFile:
-            self.removePath(KERNING_FILENAME, force=True, removeEmptyParents=False)
-
-    # lib.plist
-
-    def writeLib(self, libDict, validate=None):
-        """
-        Write lib.plist. This method requires a
-        lib dict as an argument.
-
-        ``validate`` will validate the data, by default it is set to the
-        class's validate value, can be overridden.
-        """
-        if validate is None:
-            validate = self._validate
-        if validate:
-            valid, message = fontLibValidator(libDict)
-            if not valid:
-                raise UFOLibError(message)
-        if libDict:
-            self._writePlist(LIB_FILENAME, libDict)
-        elif self._havePreviousFile:
-            self.removePath(LIB_FILENAME, force=True, removeEmptyParents=False)
-
-    # features.fea
-
-    def writeFeatures(self, features, validate=None):
-        """
-        Write features.fea. This method requires a
-        features string as an argument.
-        """
-        if validate is None:
-            validate = self._validate
-        if self._formatVersion == UFOFormatVersion.FORMAT_1_0:
-            raise UFOLibError("features.fea is not allowed in UFO Format Version 1.")
-        if validate:
-            if not isinstance(features, str):
-                raise UFOLibError("The features are not text.")
-        if features:
-            self.writeBytesToPath(FEATURES_FILENAME, features.encode("utf8"))
-        elif self._havePreviousFile:
-            self.removePath(FEATURES_FILENAME, force=True, removeEmptyParents=False)
-
-    # glyph sets & layers
-
-    def writeLayerContents(self, layerOrder=None, validate=None):
-        """
-        Write the layercontents.plist file. This method  *must* be called
-        after all glyph sets have been written.
-        """
-        if validate is None:
-            validate = self._validate
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            return
-        if layerOrder is not None:
-            newOrder = []
-            for layerName in layerOrder:
-                if layerName is None:
-                    layerName = DEFAULT_LAYER_NAME
-                newOrder.append(layerName)
-            layerOrder = newOrder
-        else:
-            layerOrder = list(self.layerContents.keys())
-        if validate and set(layerOrder) != set(self.layerContents.keys()):
-            raise UFOLibError(
-                "The layer order content does not match the glyph sets that have been created."
-            )
-        layerContents = [
-            (layerName, self.layerContents[layerName]) for layerName in layerOrder
-        ]
-        self._writePlist(LAYERCONTENTS_FILENAME, layerContents)
-
-    def _findDirectoryForLayerName(self, layerName):
-        foundDirectory = None
-        for existingLayerName, directoryName in list(self.layerContents.items()):
-            if layerName is None and directoryName == DEFAULT_GLYPHS_DIRNAME:
-                foundDirectory = directoryName
-                break
-            elif existingLayerName == layerName:
-                foundDirectory = directoryName
-                break
-        if not foundDirectory:
-            raise UFOLibError(
-                "Could not locate a glyph set directory for the layer named %s."
-                % layerName
-            )
-        return foundDirectory
-
-    def getGlyphSet(
-        self,
-        layerName=None,
-        defaultLayer=True,
-        glyphNameToFileNameFunc=None,
-        validateRead=None,
-        validateWrite=None,
-        expectContentsFile=False,
-    ):
-        """
-        Return the GlyphSet object associated with the
-        appropriate glyph directory in the .ufo.
-        If layerName is None, the default glyph set
-        will be used. The defaultLayer flag indictes
-        that the layer should be saved into the default
-        glyphs directory.
-
-        ``validateRead`` will validate the read data, by default it is set to the
-        class's validate value, can be overridden.
-        ``validateWrte`` will validate the written data, by default it is set to the
-        class's validate value, can be overridden.
-        ``expectContentsFile`` will raise a GlifLibError if a contents.plist file is
-        not found on the glyph set file system. This should be set to ``True`` if you
-        are reading an existing UFO and ``False`` if you use ``getGlyphSet`` to create
-        a fresh	glyph set.
-        """
-        if validateRead is None:
-            validateRead = self._validate
-        if validateWrite is None:
-            validateWrite = self._validate
-        # only default can be written in < 3
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0 and (
-            not defaultLayer or layerName is not None
-        ):
-            raise UFOLibError(
-                f"Only the default layer can be writen in UFO {self._formatVersion.major}."
-            )
-        # locate a layer name when None has been given
-        if layerName is None and defaultLayer:
-            for existingLayerName, directory in self.layerContents.items():
-                if directory == DEFAULT_GLYPHS_DIRNAME:
-                    layerName = existingLayerName
-            if layerName is None:
-                layerName = DEFAULT_LAYER_NAME
-        elif layerName is None and not defaultLayer:
-            raise UFOLibError("A layer name must be provided for non-default layers.")
-        # move along to format specific writing
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            return self._getDefaultGlyphSet(
-                validateRead,
-                validateWrite,
-                glyphNameToFileNameFunc=glyphNameToFileNameFunc,
-                expectContentsFile=expectContentsFile,
-            )
-        elif self._formatVersion.major == UFOFormatVersion.FORMAT_3_0.major:
-            return self._getGlyphSetFormatVersion3(
-                validateRead,
-                validateWrite,
-                layerName=layerName,
-                defaultLayer=defaultLayer,
-                glyphNameToFileNameFunc=glyphNameToFileNameFunc,
-                expectContentsFile=expectContentsFile,
-            )
-        else:
-            raise NotImplementedError(self._formatVersion)
-
-    def _getDefaultGlyphSet(
-        self,
-        validateRead,
-        validateWrite,
-        glyphNameToFileNameFunc=None,
-        expectContentsFile=False,
-    ):
-        from fontTools.ufoLib.glifLib import GlyphSet
-
-        glyphSubFS = self.fs.makedir(DEFAULT_GLYPHS_DIRNAME, recreate=True)
-        return GlyphSet(
-            glyphSubFS,
-            glyphNameToFileNameFunc=glyphNameToFileNameFunc,
-            ufoFormatVersion=self._formatVersion,
-            validateRead=validateRead,
-            validateWrite=validateWrite,
-            expectContentsFile=expectContentsFile,
-        )
-
-    def _getGlyphSetFormatVersion3(
-        self,
-        validateRead,
-        validateWrite,
-        layerName=None,
-        defaultLayer=True,
-        glyphNameToFileNameFunc=None,
-        expectContentsFile=False,
-    ):
-        from fontTools.ufoLib.glifLib import GlyphSet
-
-        # if the default flag is on, make sure that the default in the file
-        # matches the default being written. also make sure that this layer
-        # name is not already linked to a non-default layer.
-        if defaultLayer:
-            for existingLayerName, directory in self.layerContents.items():
-                if directory == DEFAULT_GLYPHS_DIRNAME:
-                    if existingLayerName != layerName:
-                        raise UFOLibError(
-                            "Another layer ('%s') is already mapped to the default directory."
-                            % existingLayerName
-                        )
-                elif existingLayerName == layerName:
-                    raise UFOLibError(
-                        "The layer name is already mapped to a non-default layer."
-                    )
-        # get an existing directory name
-        if layerName in self.layerContents:
-            directory = self.layerContents[layerName]
-        # get a  new directory name
-        else:
-            if defaultLayer:
-                directory = DEFAULT_GLYPHS_DIRNAME
-            else:
-                # not caching this could be slightly expensive,
-                # but caching it will be cumbersome
-                existing = {d.lower() for d in self.layerContents.values()}
-                directory = userNameToFileName(
-                    layerName, existing=existing, prefix="glyphs."
-                )
-        # make the directory
-        glyphSubFS = self.fs.makedir(directory, recreate=True)
-        # store the mapping
-        self.layerContents[layerName] = directory
-        # load the glyph set
-        return GlyphSet(
-            glyphSubFS,
-            glyphNameToFileNameFunc=glyphNameToFileNameFunc,
-            ufoFormatVersion=self._formatVersion,
-            validateRead=validateRead,
-            validateWrite=validateWrite,
-            expectContentsFile=expectContentsFile,
-        )
-
-    def renameGlyphSet(self, layerName, newLayerName, defaultLayer=False):
-        """
-        Rename a glyph set.
-
-        Note: if a GlyphSet object has already been retrieved for
-        layerName, it is up to the caller to inform that object that
-        the directory it represents has changed.
-        """
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            # ignore renaming glyph sets for UFO1 UFO2
-            # just write the data from the default layer
-            return
-        # the new and old names can be the same
-        # as long as the default is being switched
-        if layerName == newLayerName:
-            # if the default is off and the layer is already not the default, skip
-            if (
-                self.layerContents[layerName] != DEFAULT_GLYPHS_DIRNAME
-                and not defaultLayer
-            ):
-                return
-            # if the default is on and the layer is already the default, skip
-            if self.layerContents[layerName] == DEFAULT_GLYPHS_DIRNAME and defaultLayer:
-                return
-        else:
-            # make sure the new layer name doesn't already exist
-            if newLayerName is None:
-                newLayerName = DEFAULT_LAYER_NAME
-            if newLayerName in self.layerContents:
-                raise UFOLibError("A layer named %s already exists." % newLayerName)
-            # make sure the default layer doesn't already exist
-            if defaultLayer and DEFAULT_GLYPHS_DIRNAME in self.layerContents.values():
-                raise UFOLibError("A default layer already exists.")
-        # get the paths
-        oldDirectory = self._findDirectoryForLayerName(layerName)
-        if defaultLayer:
-            newDirectory = DEFAULT_GLYPHS_DIRNAME
-        else:
-            existing = {name.lower() for name in self.layerContents.values()}
-            newDirectory = userNameToFileName(
-                newLayerName, existing=existing, prefix="glyphs."
-            )
-        # update the internal mapping
-        del self.layerContents[layerName]
-        self.layerContents[newLayerName] = newDirectory
-        # do the file system copy
-        self.fs.movedir(oldDirectory, newDirectory, create=True)
-
-    def deleteGlyphSet(self, layerName):
-        """
-        Remove the glyph set matching layerName.
-        """
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            # ignore deleting glyph sets for UFO1 UFO2 as there are no layers
-            # just write the data from the default layer
-            return
-        foundDirectory = self._findDirectoryForLayerName(layerName)
-        self.removePath(foundDirectory, removeEmptyParents=False)
-        del self.layerContents[layerName]
-
-    def writeData(self, fileName, data):
-        """
-        Write data to fileName in the 'data' directory.
-        The data must be a bytes string.
-        """
-        self.writeBytesToPath(f"{DATA_DIRNAME}/{fsdecode(fileName)}", data)
-
-    def removeData(self, fileName):
-        """
-        Remove the file named fileName from the data directory.
-        """
-        self.removePath(f"{DATA_DIRNAME}/{fsdecode(fileName)}")
-
-    # /images
-
-    def writeImage(self, fileName, data, validate=None):
-        """
-        Write data to fileName in the images directory.
-        The data must be a valid PNG.
-        """
-        if validate is None:
-            validate = self._validate
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            raise UFOLibError(
-                f"Images are not allowed in UFO {self._formatVersion.major}."
-            )
-        fileName = fsdecode(fileName)
-        if validate:
-            valid, error = pngValidator(data=data)
-            if not valid:
-                raise UFOLibError(error)
-        self.writeBytesToPath(f"{IMAGES_DIRNAME}/{fileName}", data)
-
-    def removeImage(self, fileName, validate=None):  # XXX remove unused 'validate'?
-        """
-        Remove the file named fileName from the
-        images directory.
-        """
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            raise UFOLibError(
-                f"Images are not allowed in UFO {self._formatVersion.major}."
-            )
-        self.removePath(f"{IMAGES_DIRNAME}/{fsdecode(fileName)}")
-
-    def copyImageFromReader(self, reader, sourceFileName, destFileName, validate=None):
-        """
-        Copy the sourceFileName in the provided UFOReader to destFileName
-        in this writer. This uses the most memory efficient method possible
-        for copying the data possible.
-        """
-        if validate is None:
-            validate = self._validate
-        if self._formatVersion < UFOFormatVersion.FORMAT_3_0:
-            raise UFOLibError(
-                f"Images are not allowed in UFO {self._formatVersion.major}."
-            )
-        sourcePath = f"{IMAGES_DIRNAME}/{fsdecode(sourceFileName)}"
-        destPath = f"{IMAGES_DIRNAME}/{fsdecode(destFileName)}"
-        self.copyFromReader(reader, sourcePath, destPath)
-
-    def close(self):
-        if self._havePreviousFile and self._fileStructure is UFOFileStructure.ZIP:
-            # if we are updating an existing zip file, we can now compress the
-            # contents of the temporary filesystem in the destination path
-            rootDir = os.path.splitext(os.path.basename(self._path))[0] + ".ufo"
-            with fs.zipfs.ZipFS(self._path, write=True, encoding="utf-8") as destFS:
-                fs.copy.copy_fs(self.fs, destFS.makedir(rootDir))
-        super().close()
-
-
-# just an alias, makes it more explicit
-UFOReaderWriter = UFOWriter
-
-
-# ----------------
-# Helper Functions
-# ----------------
-
-
-def _sniffFileStructure(ufo_path):
-    """Return UFOFileStructure.ZIP if the UFO at path 'ufo_path' (str)
-    is a zip file, else return UFOFileStructure.PACKAGE if 'ufo_path' is a
-    directory.
-    Raise UFOLibError if it is a file with unknown structure, or if the path
-    does not exist.
-    """
-    if zipfile.is_zipfile(ufo_path):
-        return UFOFileStructure.ZIP
-    elif os.path.isdir(ufo_path):
-        return UFOFileStructure.PACKAGE
-    elif os.path.isfile(ufo_path):
-        raise UFOLibError(
-            "The specified UFO does not have a known structure: '%s'" % ufo_path
-        )
-    else:
-        raise UFOLibError("No such file or directory: '%s'" % ufo_path)
-
-
-def makeUFOPath(path):
-    """
-    Return a .ufo pathname.
-
-    >>> makeUFOPath("directory/something.ext") == (
-    ... 	os.path.join('directory', 'something.ufo'))
-    True
-    >>> makeUFOPath("directory/something.another.thing.ext") == (
-    ... 	os.path.join('directory', 'something.another.thing.ufo'))
-    True
-    """
-    dir, name = os.path.split(path)
-    name = ".".join([".".join(name.split(".")[:-1]), "ufo"])
-    return os.path.join(dir, name)
-
-
-# ----------------------
-# fontinfo.plist Support
-# ----------------------
-
-# Version Validators
-
-# There is no version 1 validator and there shouldn't be.
-# The version 1 spec was very loose and there were numerous
-# cases of invalid values.
-
-
-def validateFontInfoVersion2ValueForAttribute(attr, value):
-    """
-    This performs very basic validation of the value for attribute
-    following the UFO 2 fontinfo.plist specification. The results
-    of this should not be interpretted as *correct* for the font
-    that they are part of. This merely indicates that the value
-    is of the proper type and, where the specification defines
-    a set range of possible values for an attribute, that the
-    value is in the accepted range.
-    """
-    dataValidationDict = fontInfoAttributesVersion2ValueData[attr]
-    valueType = dataValidationDict.get("type")
-    validator = dataValidationDict.get("valueValidator")
-    valueOptions = dataValidationDict.get("valueOptions")
-    # have specific options for the validator
-    if valueOptions is not None:
-        isValidValue = validator(value, valueOptions)
-    # no specific options
-    else:
-        if validator == genericTypeValidator:
-            isValidValue = validator(value, valueType)
-        else:
-            isValidValue = validator(value)
-    return isValidValue
-
-
-def validateInfoVersion2Data(infoData):
-    """
-    This performs very basic validation of the value for infoData
-    following the UFO 2 fontinfo.plist specification. The results
-    of this should not be interpretted as *correct* for the font
-    that they are part of. This merely indicates that the values
-    are of the proper type and, where the specification defines
-    a set range of possible values for an attribute, that the
-    value is in the accepted range.
-    """
-    validInfoData = {}
-    for attr, value in list(infoData.items()):
-        isValidValue = validateFontInfoVersion2ValueForAttribute(attr, value)
-        if not isValidValue:
-            raise UFOLibError(f"Invalid value for attribute {attr} ({value!r}).")
-        else:
-            validInfoData[attr] = value
-    return validInfoData
-
-
-def validateFontInfoVersion3ValueForAttribute(attr, value):
-    """
-    This performs very basic validation of the value for attribute
-    following the UFO 3 fontinfo.plist specification. The results
-    of this should not be interpretted as *correct* for the font
-    that they are part of. This merely indicates that the value
-    is of the proper type and, where the specification defines
-    a set range of possible values for an attribute, that the
-    value is in the accepted range.
-    """
-    dataValidationDict = fontInfoAttributesVersion3ValueData[attr]
-    valueType = dataValidationDict.get("type")
-    validator = dataValidationDict.get("valueValidator")
-    valueOptions = dataValidationDict.get("valueOptions")
-    # have specific options for the validator
-    if valueOptions is not None:
-        isValidValue = validator(value, valueOptions)
-    # no specific options
-    else:
-        if validator == genericTypeValidator:
-            isValidValue = validator(value, valueType)
-        else:
-            isValidValue = validator(value)
-    return isValidValue
-
-
-def validateInfoVersion3Data(infoData):
-    """
-    This performs very basic validation of the value for infoData
-    following the UFO 3 fontinfo.plist specification. The results
-    of this should not be interpretted as *correct* for the font
-    that they are part of. This merely indicates that the values
-    are of the proper type and, where the specification defines
-    a set range of possible values for an attribute, that the
-    value is in the accepted range.
-    """
-    validInfoData = {}
-    for attr, value in list(infoData.items()):
-        isValidValue = validateFontInfoVersion3ValueForAttribute(attr, value)
-        if not isValidValue:
-            raise UFOLibError(f"Invalid value for attribute {attr} ({value!r}).")
-        else:
-            validInfoData[attr] = value
-    return validInfoData
-
-
-# Value Options
-
-fontInfoOpenTypeHeadFlagsOptions = list(range(0, 15))
-fontInfoOpenTypeOS2SelectionOptions = [1, 2, 3, 4, 7, 8, 9]
-fontInfoOpenTypeOS2UnicodeRangesOptions = list(range(0, 128))
-fontInfoOpenTypeOS2CodePageRangesOptions = list(range(0, 64))
-fontInfoOpenTypeOS2TypeOptions = [0, 1, 2, 3, 8, 9]
-
-# Version Attribute Definitions
-# This defines the attributes, types and, in some
-# cases the possible values, that can exist is
-# fontinfo.plist.
-
-fontInfoAttributesVersion1 = {
-    "familyName",
-    "styleName",
-    "fullName",
-    "fontName",
-    "menuName",
-    "fontStyle",
-    "note",
-    "versionMajor",
-    "versionMinor",
-    "year",
-    "copyright",
-    "notice",
-    "trademark",
-    "license",
-    "licenseURL",
-    "createdBy",
-    "designer",
-    "designerURL",
-    "vendorURL",
-    "unitsPerEm",
-    "ascender",
-    "descender",
-    "capHeight",
-    "xHeight",
-    "defaultWidth",
-    "slantAngle",
-    "italicAngle",
-    "widthName",
-    "weightName",
-    "weightValue",
-    "fondName",
-    "otFamilyName",
-    "otStyleName",
-    "otMacName",
-    "msCharSet",
-    "fondID",
-    "uniqueID",
-    "ttVendor",
-    "ttUniqueID",
-    "ttVersion",
-}
-
-fontInfoAttributesVersion2ValueData = {
-    "familyName": dict(type=str),
-    "styleName": dict(type=str),
-    "styleMapFamilyName": dict(type=str),
-    "styleMapStyleName": dict(
-        type=str, valueValidator=fontInfoStyleMapStyleNameValidator
-    ),
-    "versionMajor": dict(type=int),
-    "versionMinor": dict(type=int),
-    "year": dict(type=int),
-    "copyright": dict(type=str),
-    "trademark": dict(type=str),
-    "unitsPerEm": dict(type=(int, float)),
-    "descender": dict(type=(int, float)),
-    "xHeight": dict(type=(int, float)),
-    "capHeight": dict(type=(int, float)),
-    "ascender": dict(type=(int, float)),
-    "italicAngle": dict(type=(float, int)),
-    "note": dict(type=str),
-    "openTypeHeadCreated": dict(
-        type=str, valueValidator=fontInfoOpenTypeHeadCreatedValidator
-    ),
-    "openTypeHeadLowestRecPPEM": dict(type=(int, float)),
-    "openTypeHeadFlags": dict(
-        type="integerList",
-        valueValidator=genericIntListValidator,
-        valueOptions=fontInfoOpenTypeHeadFlagsOptions,
-    ),
-    "openTypeHheaAscender": dict(type=(int, float)),
-    "openTypeHheaDescender": dict(type=(int, float)),
-    "openTypeHheaLineGap": dict(type=(int, float)),
-    "openTypeHheaCaretSlopeRise": dict(type=int),
-    "openTypeHheaCaretSlopeRun": dict(type=int),
-    "openTypeHheaCaretOffset": dict(type=(int, float)),
-    "openTypeNameDesigner": dict(type=str),
-    "openTypeNameDesignerURL": dict(type=str),
-    "openTypeNameManufacturer": dict(type=str),
-    "openTypeNameManufacturerURL": dict(type=str),
-    "openTypeNameLicense": dict(type=str),
-    "openTypeNameLicenseURL": dict(type=str),
-    "openTypeNameVersion": dict(type=str),
-    "openTypeNameUniqueID": dict(type=str),
-    "openTypeNameDescription": dict(type=str),
-    "openTypeNamePreferredFamilyName": dict(type=str),
-    "openTypeNamePreferredSubfamilyName": dict(type=str),
-    "openTypeNameCompatibleFullName": dict(type=str),
-    "openTypeNameSampleText": dict(type=str),
-    "openTypeNameWWSFamilyName": dict(type=str),
-    "openTypeNameWWSSubfamilyName": dict(type=str),
-    "openTypeOS2WidthClass": dict(
-        type=int, valueValidator=fontInfoOpenTypeOS2WidthClassValidator
-    ),
-    "openTypeOS2WeightClass": dict(
-        type=int, valueValidator=fontInfoOpenTypeOS2WeightClassValidator
-    ),
-    "openTypeOS2Selection": dict(
-        type="integerList",
-        valueValidator=genericIntListValidator,
-        valueOptions=fontInfoOpenTypeOS2SelectionOptions,
-    ),
-    "openTypeOS2VendorID": dict(type=str),
-    "openTypeOS2Panose": dict(
-        type="integerList", valueValidator=fontInfoVersion2OpenTypeOS2PanoseValidator
-    ),
-    "openTypeOS2FamilyClass": dict(
-        type="integerList", valueValidator=fontInfoOpenTypeOS2FamilyClassValidator
-    ),
-    "openTypeOS2UnicodeRanges": dict(
-        type="integerList",
-        valueValidator=genericIntListValidator,
-        valueOptions=fontInfoOpenTypeOS2UnicodeRangesOptions,
-    ),
-    "openTypeOS2CodePageRanges": dict(
-        type="integerList",
-        valueValidator=genericIntListValidator,
-        valueOptions=fontInfoOpenTypeOS2CodePageRangesOptions,
-    ),
-    "openTypeOS2TypoAscender": dict(type=(int, float)),
-    "openTypeOS2TypoDescender": dict(type=(int, float)),
-    "openTypeOS2TypoLineGap": dict(type=(int, float)),
-    "openTypeOS2WinAscent": dict(type=(int, float)),
-    "openTypeOS2WinDescent": dict(type=(int, float)),
-    "openTypeOS2Type": dict(
-        type="integerList",
-        valueValidator=genericIntListValidator,
-        valueOptions=fontInfoOpenTypeOS2TypeOptions,
-    ),
-    "openTypeOS2SubscriptXSize": dict(type=(int, float)),
-    "openTypeOS2SubscriptYSize": dict(type=(int, float)),
-    "openTypeOS2SubscriptXOffset": dict(type=(int, float)),
-    "openTypeOS2SubscriptYOffset": dict(type=(int, float)),
-    "openTypeOS2SuperscriptXSize": dict(type=(int, float)),
-    "openTypeOS2SuperscriptYSize": dict(type=(int, float)),
-    "openTypeOS2SuperscriptXOffset": dict(type=(int, float)),
-    "openTypeOS2SuperscriptYOffset": dict(type=(int, float)),
-    "openTypeOS2StrikeoutSize": dict(type=(int, float)),
-    "openTypeOS2StrikeoutPosition": dict(type=(int, float)),
-    "openTypeVheaVertTypoAscender": dict(type=(int, float)),
-    "openTypeVheaVertTypoDescender": dict(type=(int, float)),
-    "openTypeVheaVertTypoLineGap": dict(type=(int, float)),
-    "openTypeVheaCaretSlopeRise": dict(type=int),
-    "openTypeVheaCaretSlopeRun": dict(type=int),
-    "openTypeVheaCaretOffset": dict(type=(int, float)),
-    "postscriptFontName": dict(type=str),
-    "postscriptFullName": dict(type=str),
-    "postscriptSlantAngle": dict(type=(float, int)),
-    "postscriptUniqueID": dict(type=int),
-    "postscriptUnderlineThickness": dict(type=(int, float)),
-    "postscriptUnderlinePosition": dict(type=(int, float)),
-    "postscriptIsFixedPitch": dict(type=bool),
-    "postscriptBlueValues": dict(
-        type="integerList", valueValidator=fontInfoPostscriptBluesValidator
-    ),
-    "postscriptOtherBlues": dict(
-        type="integerList", valueValidator=fontInfoPostscriptOtherBluesValidator
-    ),
-    "postscriptFamilyBlues": dict(
-        type="integerList", valueValidator=fontInfoPostscriptBluesValidator
-    ),
-    "postscriptFamilyOtherBlues": dict(
-        type="integerList", valueValidator=fontInfoPostscriptOtherBluesValidator
-    ),
-    "postscriptStemSnapH": dict(
-        type="integerList", valueValidator=fontInfoPostscriptStemsValidator
-    ),
-    "postscriptStemSnapV": dict(
-        type="integerList", valueValidator=fontInfoPostscriptStemsValidator
-    ),
-    "postscriptBlueFuzz": dict(type=(int, float)),
-    "postscriptBlueShift": dict(type=(int, float)),
-    "postscriptBlueScale": dict(type=(float, int)),
-    "postscriptForceBold": dict(type=bool),
-    "postscriptDefaultWidthX": dict(type=(int, float)),
-    "postscriptNominalWidthX": dict(type=(int, float)),
-    "postscriptWeightName": dict(type=str),
-    "postscriptDefaultCharacter": dict(type=str),
-    "postscriptWindowsCharacterSet": dict(
-        type=int, valueValidator=fontInfoPostscriptWindowsCharacterSetValidator
-    ),
-    "macintoshFONDFamilyID": dict(type=int),
-    "macintoshFONDName": dict(type=str),
-}
-fontInfoAttributesVersion2 = set(fontInfoAttributesVersion2ValueData.keys())
-
-fontInfoAttributesVersion3ValueData = deepcopy(fontInfoAttributesVersion2ValueData)
-fontInfoAttributesVersion3ValueData.update(
-    {
-        "versionMinor": dict(type=int, valueValidator=genericNonNegativeIntValidator),
-        "unitsPerEm": dict(
-            type=(int, float), valueValidator=genericNonNegativeNumberValidator
-        ),
-        "openTypeHeadLowestRecPPEM": dict(
-            type=int, valueValidator=genericNonNegativeNumberValidator
-        ),
-        "openTypeHheaAscender": dict(type=int),
-        "openTypeHheaDescender": dict(type=int),
-        "openTypeHheaLineGap": dict(type=int),
-        "openTypeHheaCaretOffset": dict(type=int),
-        "openTypeOS2Panose": dict(
-            type="integerList",
-            valueValidator=fontInfoVersion3OpenTypeOS2PanoseValidator,
-        ),
-        "openTypeOS2TypoAscender": dict(type=int),
-        "openTypeOS2TypoDescender": dict(type=int),
-        "openTypeOS2TypoLineGap": dict(type=int),
-        "openTypeOS2WinAscent": dict(
-            type=int, valueValidator=genericNonNegativeNumberValidator
-        ),
-        "openTypeOS2WinDescent": dict(
-            type=int, valueValidator=genericNonNegativeNumberValidator
-        ),
-        "openTypeOS2SubscriptXSize": dict(type=int),
-        "openTypeOS2SubscriptYSize": dict(type=int),
-        "openTypeOS2SubscriptXOffset": dict(type=int),
-        "openTypeOS2SubscriptYOffset": dict(type=int),
-        "openTypeOS2SuperscriptXSize": dict(type=int),
-        "openTypeOS2SuperscriptYSize": dict(type=int),
-        "openTypeOS2SuperscriptXOffset": dict(type=int),
-        "openTypeOS2SuperscriptYOffset": dict(type=int),
-        "openTypeOS2StrikeoutSize": dict(type=int),
-        "openTypeOS2StrikeoutPosition": dict(type=int),
-        "openTypeGaspRangeRecords": dict(
-            type="dictList", valueValidator=fontInfoOpenTypeGaspRangeRecordsValidator
-        ),
-        "openTypeNameRecords": dict(
-            type="dictList", valueValidator=fontInfoOpenTypeNameRecordsValidator
-        ),
-        "openTypeVheaVertTypoAscender": dict(type=int),
-        "openTypeVheaVertTypoDescender": dict(type=int),
-        "openTypeVheaVertTypoLineGap": dict(type=int),
-        "openTypeVheaCaretOffset": dict(type=int),
-        "woffMajorVersion": dict(
-            type=int, valueValidator=genericNonNegativeIntValidator
-        ),
-        "woffMinorVersion": dict(
-            type=int, valueValidator=genericNonNegativeIntValidator
-        ),
-        "woffMetadataUniqueID": dict(
-            type=dict, valueValidator=fontInfoWOFFMetadataUniqueIDValidator
-        ),
-        "woffMetadataVendor": dict(
-            type=dict, valueValidator=fontInfoWOFFMetadataVendorValidator
-        ),
-        "woffMetadataCredits": dict(
-            type=dict, valueValidator=fontInfoWOFFMetadataCreditsValidator
-        ),
-        "woffMetadataDescription": dict(
-            type=dict, valueValidator=fontInfoWOFFMetadataDescriptionValidator
-        ),
-        "woffMetadataLicense": dict(
-            type=dict, valueValidator=fontInfoWOFFMetadataLicenseValidator
-        ),
-        "woffMetadataCopyright": dict(
-            type=dict, valueValidator=fontInfoWOFFMetadataCopyrightValidator
-        ),
-        "woffMetadataTrademark": dict(
-            type=dict, valueValidator=fontInfoWOFFMetadataTrademarkValidator
-        ),
-        "woffMetadataLicensee": dict(
-            type=dict, valueValidator=fontInfoWOFFMetadataLicenseeValidator
-        ),
-        "woffMetadataExtensions": dict(
-            type=list, valueValidator=fontInfoWOFFMetadataExtensionsValidator
-        ),
-        "guidelines": dict(type=list, valueValidator=guidelinesValidator),
-    }
-)
-fontInfoAttributesVersion3 = set(fontInfoAttributesVersion3ValueData.keys())
-
-# insert the type validator for all attrs that
-# have no defined validator.
-for attr, dataDict in list(fontInfoAttributesVersion2ValueData.items()):
-    if "valueValidator" not in dataDict:
-        dataDict["valueValidator"] = genericTypeValidator
-
-for attr, dataDict in list(fontInfoAttributesVersion3ValueData.items()):
-    if "valueValidator" not in dataDict:
-        dataDict["valueValidator"] = genericTypeValidator
-
-# Version Conversion Support
-# These are used from converting from version 1
-# to version 2 or vice-versa.
-
-
-def _flipDict(d):
-    flipped = {}
-    for key, value in list(d.items()):
-        flipped[value] = key
-    return flipped
-
-
-fontInfoAttributesVersion1To2 = {
-    "menuName": "styleMapFamilyName",
-    "designer": "openTypeNameDesigner",
-    "designerURL": "openTypeNameDesignerURL",
-    "createdBy": "openTypeNameManufacturer",
-    "vendorURL": "openTypeNameManufacturerURL",
-    "license": "openTypeNameLicense",
-    "licenseURL": "openTypeNameLicenseURL",
-    "ttVersion": "openTypeNameVersion",
-    "ttUniqueID": "openTypeNameUniqueID",
-    "notice": "openTypeNameDescription",
-    "otFamilyName": "openTypeNamePreferredFamilyName",
-    "otStyleName": "openTypeNamePreferredSubfamilyName",
-    "otMacName": "openTypeNameCompatibleFullName",
-    "weightName": "postscriptWeightName",
-    "weightValue": "openTypeOS2WeightClass",
-    "ttVendor": "openTypeOS2VendorID",
-    "uniqueID": "postscriptUniqueID",
-    "fontName": "postscriptFontName",
-    "fondID": "macintoshFONDFamilyID",
-    "fondName": "macintoshFONDName",
-    "defaultWidth": "postscriptDefaultWidthX",
-    "slantAngle": "postscriptSlantAngle",
-    "fullName": "postscriptFullName",
-    # require special value conversion
-    "fontStyle": "styleMapStyleName",
-    "widthName": "openTypeOS2WidthClass",
-    "msCharSet": "postscriptWindowsCharacterSet",
-}
-fontInfoAttributesVersion2To1 = _flipDict(fontInfoAttributesVersion1To2)
-deprecatedFontInfoAttributesVersion2 = set(fontInfoAttributesVersion1To2.keys())
-
-_fontStyle1To2 = {64: "regular", 1: "italic", 32: "bold", 33: "bold italic"}
-_fontStyle2To1 = _flipDict(_fontStyle1To2)
-# Some UFO 1 files have 0
-_fontStyle1To2[0] = "regular"
-
-_widthName1To2 = {
-    "Ultra-condensed": 1,
-    "Extra-condensed": 2,
-    "Condensed": 3,
-    "Semi-condensed": 4,
-    "Medium (normal)": 5,
-    "Semi-expanded": 6,
-    "Expanded": 7,
-    "Extra-expanded": 8,
-    "Ultra-expanded": 9,
-}
-_widthName2To1 = _flipDict(_widthName1To2)
-# FontLab's default width value is "Normal".
-# Many format version 1 UFOs will have this.
-_widthName1To2["Normal"] = 5
-# FontLab has an "All" width value. In UFO 1
-# move this up to "Normal".
-_widthName1To2["All"] = 5
-# "medium" appears in a lot of UFO 1 files.
-_widthName1To2["medium"] = 5
-# "Medium" appears in a lot of UFO 1 files.
-_widthName1To2["Medium"] = 5
-
-_msCharSet1To2 = {
-    0: 1,
-    1: 2,
-    2: 3,
-    77: 4,
-    128: 5,
-    129: 6,
-    130: 7,
-    134: 8,
-    136: 9,
-    161: 10,
-    162: 11,
-    163: 12,
-    177: 13,
-    178: 14,
-    186: 15,
-    200: 16,
-    204: 17,
-    222: 18,
-    238: 19,
-    255: 20,
-}
-_msCharSet2To1 = _flipDict(_msCharSet1To2)
-
-# 1 <-> 2
-
-
-def convertFontInfoValueForAttributeFromVersion1ToVersion2(attr, value):
-    """
-    Convert value from version 1 to version 2 format.
-    Returns the new attribute name and the converted value.
-    If the value is None, None will be returned for the new value.
-    """
-    # convert floats to ints if possible
-    if isinstance(value, float):
-        if int(value) == value:
-            value = int(value)
-    if value is not None:
-        if attr == "fontStyle":
-            v = _fontStyle1To2.get(value)
-            if v is None:
-                raise UFOLibError(
-                    f"Cannot convert value ({value!r}) for attribute {attr}."
-                )
-            value = v
-        elif attr == "widthName":
-            v = _widthName1To2.get(value)
-            if v is None:
-                raise UFOLibError(
-                    f"Cannot convert value ({value!r}) for attribute {attr}."
-                )
-            value = v
-        elif attr == "msCharSet":
-            v = _msCharSet1To2.get(value)
-            if v is None:
-                raise UFOLibError(
-                    f"Cannot convert value ({value!r}) for attribute {attr}."
-                )
-            value = v
-    attr = fontInfoAttributesVersion1To2.get(attr, attr)
-    return attr, value
-
-
-def convertFontInfoValueForAttributeFromVersion2ToVersion1(attr, value):
-    """
-    Convert value from version 2 to version 1 format.
-    Returns the new attribute name and the converted value.
-    If the value is None, None will be returned for the new value.
-    """
-    if value is not None:
-        if attr == "styleMapStyleName":
-            value = _fontStyle2To1.get(value)
-        elif attr == "openTypeOS2WidthClass":
-            value = _widthName2To1.get(value)
-        elif attr == "postscriptWindowsCharacterSet":
-            value = _msCharSet2To1.get(value)
-    attr = fontInfoAttributesVersion2To1.get(attr, attr)
-    return attr, value
-
-
-def _convertFontInfoDataVersion1ToVersion2(data):
-    converted = {}
-    for attr, value in list(data.items()):
-        # FontLab gives -1 for the weightValue
-        # for fonts wil no defined value. Many
-        # format version 1 UFOs will have this.
-        if attr == "weightValue" and value == -1:
-            continue
-        newAttr, newValue = convertFontInfoValueForAttributeFromVersion1ToVersion2(
-            attr, value
-        )
-        # skip if the attribute is not part of version 2
-        if newAttr not in fontInfoAttributesVersion2:
-            continue
-        # catch values that can't be converted
-        if value is None:
-            raise UFOLibError(
-                f"Cannot convert value ({value!r}) for attribute {newAttr}."
-            )
-        # store
-        converted[newAttr] = newValue
-    return converted
-
-
-def _convertFontInfoDataVersion2ToVersion1(data):
-    converted = {}
-    for attr, value in list(data.items()):
-        newAttr, newValue = convertFontInfoValueForAttributeFromVersion2ToVersion1(
-            attr, value
-        )
-        # only take attributes that are registered for version 1
-        if newAttr not in fontInfoAttributesVersion1:
-            continue
-        # catch values that can't be converted
-        if value is None:
-            raise UFOLibError(
-                f"Cannot convert value ({value!r}) for attribute {newAttr}."
-            )
-        # store
-        converted[newAttr] = newValue
-    return converted
-
-
-# 2 <-> 3
-
-_ufo2To3NonNegativeInt = {
-    "versionMinor",
-    "openTypeHeadLowestRecPPEM",
-    "openTypeOS2WinAscent",
-    "openTypeOS2WinDescent",
-}
-_ufo2To3NonNegativeIntOrFloat = {
-    "unitsPerEm",
-}
-_ufo2To3FloatToInt = {
-    "openTypeHeadLowestRecPPEM",
-    "openTypeHheaAscender",
-    "openTypeHheaDescender",
-    "openTypeHheaLineGap",
-    "openTypeHheaCaretOffset",
-    "openTypeOS2TypoAscender",
-    "openTypeOS2TypoDescender",
-    "openTypeOS2TypoLineGap",
-    "openTypeOS2WinAscent",
-    "openTypeOS2WinDescent",
-    "openTypeOS2SubscriptXSize",
-    "openTypeOS2SubscriptYSize",
-    "openTypeOS2SubscriptXOffset",
-    "openTypeOS2SubscriptYOffset",
-    "openTypeOS2SuperscriptXSize",
-    "openTypeOS2SuperscriptYSize",
-    "openTypeOS2SuperscriptXOffset",
-    "openTypeOS2SuperscriptYOffset",
-    "openTypeOS2StrikeoutSize",
-    "openTypeOS2StrikeoutPosition",
-    "openTypeVheaVertTypoAscender",
-    "openTypeVheaVertTypoDescender",
-    "openTypeVheaVertTypoLineGap",
-    "openTypeVheaCaretOffset",
-}
-
-
-def convertFontInfoValueForAttributeFromVersion2ToVersion3(attr, value):
-    """
-    Convert value from version 2 to version 3 format.
-    Returns the new attribute name and the converted value.
-    If the value is None, None will be returned for the new value.
-    """
-    if attr in _ufo2To3FloatToInt:
-        try:
-            value = round(value)
-        except (ValueError, TypeError):
-            raise UFOLibError("Could not convert value for %s." % attr)
-    if attr in _ufo2To3NonNegativeInt:
-        try:
-            value = int(abs(value))
-        except (ValueError, TypeError):
-            raise UFOLibError("Could not convert value for %s." % attr)
-    elif attr in _ufo2To3NonNegativeIntOrFloat:
-        try:
-            v = float(abs(value))
-        except (ValueError, TypeError):
-            raise UFOLibError("Could not convert value for %s." % attr)
-        if v == int(v):
-            v = int(v)
-        if v != value:
-            value = v
-    return attr, value
-
-
-def convertFontInfoValueForAttributeFromVersion3ToVersion2(attr, value):
-    """
-    Convert value from version 3 to version 2 format.
-    Returns the new attribute name and the converted value.
-    If the value is None, None will be returned for the new value.
-    """
-    return attr, value
-
-
-def _convertFontInfoDataVersion3ToVersion2(data):
-    converted = {}
-    for attr, value in list(data.items()):
-        newAttr, newValue = convertFontInfoValueForAttributeFromVersion3ToVersion2(
-            attr, value
-        )
-        if newAttr not in fontInfoAttributesVersion2:
-            continue
-        converted[newAttr] = newValue
-    return converted
-
-
-def _convertFontInfoDataVersion2ToVersion3(data):
-    converted = {}
-    for attr, value in list(data.items()):
-        attr, value = convertFontInfoValueForAttributeFromVersion2ToVersion3(
-            attr, value
-        )
-        converted[attr] = value
-    return converted
-
-
-if __name__ == "__main__":
-    import doctest
-
-    doctest.testmod()
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/varLib/instancer/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/varLib/instancer/__init__.py
deleted file mode 100644
index 42063c6160aa928ed986f7dda0f35150d42bf9b9..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/varLib/instancer/__init__.py
+++ /dev/null
@@ -1,1431 +0,0 @@
-""" Partially instantiate a variable font.
-
-The module exports an `instantiateVariableFont` function and CLI that allow to
-create full instances (i.e. static fonts) from variable fonts, as well as "partial"
-variable fonts that only contain a subset of the original variation space.
-
-For example, if you wish to pin the width axis to a given location while also
-restricting the weight axis to 400..700 range, you can do::
-
-    $ fonttools varLib.instancer ./NotoSans-VF.ttf wdth=85 wght=400:700
-
-See `fonttools varLib.instancer --help` for more info on the CLI options.
-
-The module's entry point is the `instantiateVariableFont` function, which takes
-a TTFont object and a dict specifying either axis coodinates or (min, max) ranges,
-and returns a new TTFont representing either a partial VF, or full instance if all
-the VF axes were given an explicit coordinate.
-
-E.g. here's how to pin the wght axis at a given location in a wght+wdth variable
-font, keeping only the deltas associated with the wdth axis::
-
-| >>> from fontTools import ttLib
-| >>> from fontTools.varLib import instancer
-| >>> varfont = ttLib.TTFont("path/to/MyVariableFont.ttf")
-| >>> [a.axisTag for a in varfont["fvar"].axes]  # the varfont's current axes
-| ['wght', 'wdth']
-| >>> partial = instancer.instantiateVariableFont(varfont, {"wght": 300})
-| >>> [a.axisTag for a in partial["fvar"].axes]  # axes left after pinning 'wght'
-| ['wdth']
-
-If the input location specifies all the axes, the resulting instance is no longer
-'variable' (same as using fontools varLib.mutator):
-
-| >>> instance = instancer.instantiateVariableFont(
-| ...     varfont, {"wght": 700, "wdth": 67.5}
-| ... )
-| >>> "fvar" not in instance
-| True
-
-If one just want to drop an axis at the default location, without knowing in
-advance what the default value for that axis is, one can pass a `None` value:
-
-| >>> instance = instancer.instantiateVariableFont(varfont, {"wght": None})
-| >>> len(varfont["fvar"].axes)
-| 1
-
-From the console script, this is equivalent to passing `wght=drop` as input.
-
-This module is similar to fontTools.varLib.mutator, which it's intended to supersede.
-Note that, unlike varLib.mutator, when an axis is not mentioned in the input
-location, the varLib.instancer will keep the axis and the corresponding deltas,
-whereas mutator implicitly drops the axis at its default coordinate.
-
-The module supports all the following "levels" of instancing, which can of
-course be combined:
-
-L1
-    dropping one or more axes while leaving the default tables unmodified;
-
-    | >>> font = instancer.instantiateVariableFont(varfont, {"wght": None})
-
-L2
-    dropping one or more axes while pinning them at non-default locations;
-
-    | >>> font = instancer.instantiateVariableFont(varfont, {"wght": 700})
-
-L3
-    restricting the range of variation of one or more axes, by setting either
-    a new minimum or maximum, potentially -- though not necessarily -- dropping
-    entire regions of variations that fall completely outside this new range.
-
-    | >>> font = instancer.instantiateVariableFont(varfont, {"wght": (100, 300)})
-
-L4
-    moving the default location of an axis, by specifying (min,defalt,max) values:
-
-    | >>> font = instancer.instantiateVariableFont(varfont, {"wght": (100, 300, 700)})
-
-Currently only TrueType-flavored variable fonts (i.e. containing 'glyf' table)
-are supported, but support for CFF2 variable fonts will be added soon.
-
-The discussion and implementation of these features are tracked at
-https://github.com/fonttools/fonttools/issues/1537
-"""
-from fontTools.misc.fixedTools import (
-    floatToFixedToFloat,
-    strToFixedToFloat,
-    otRound,
-)
-from fontTools.varLib.models import supportScalar, normalizeValue, piecewiseLinearMap
-from fontTools.ttLib import TTFont
-from fontTools.ttLib.tables.TupleVariation import TupleVariation
-from fontTools.ttLib.tables import _g_l_y_f
-from fontTools import varLib
-
-# we import the `subset` module because we use the `prune_lookups` method on the GSUB
-# table class, and that method is only defined dynamically upon importing `subset`
-from fontTools import subset  # noqa: F401
-from fontTools.varLib import builder
-from fontTools.varLib.mvar import MVAR_ENTRIES
-from fontTools.varLib.merger import MutatorMerger
-from fontTools.varLib.instancer import names
-from .featureVars import instantiateFeatureVariations
-from fontTools.misc.cliTools import makeOutputFileName
-from fontTools.varLib.instancer import solver
-import collections
-import dataclasses
-from copy import deepcopy
-from enum import IntEnum
-import logging
-import os
-import re
-from typing import Dict, Iterable, Mapping, Optional, Sequence, Tuple, Union
-import warnings
-
-
-log = logging.getLogger("fontTools.varLib.instancer")
-
-
-def AxisRange(minimum, maximum):
-    warnings.warn(
-        "AxisRange is deprecated; use AxisTriple instead",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    return AxisTriple(minimum, None, maximum)
-
-
-def NormalizedAxisRange(minimum, maximum):
-    warnings.warn(
-        "NormalizedAxisRange is deprecated; use AxisTriple instead",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    return NormalizedAxisTriple(minimum, None, maximum)
-
-
-@dataclasses.dataclass(frozen=True, order=True, repr=False)
-class AxisTriple(Sequence):
-    """A triple of (min, default, max) axis values.
-
-    The default value can be None, in which case the limitRangeAndPopulateDefault()
-    method can be used to fill in the missing default value based on the fvar axis
-    default.
-    """
-
-    minimum: float
-    default: Optional[float]  # if None, filled with by limitRangeAndPopulateDefault
-    maximum: float
-
-    def __post_init__(self):
-        if self.default is None and self.minimum == self.maximum:
-            object.__setattr__(self, "default", self.minimum)
-        if not (
-            (self.minimum <= self.default <= self.maximum)
-            if self.default is not None
-            else (self.minimum <= self.maximum)
-        ):
-            raise ValueError(
-                f"{type(self).__name__} minimum ({self.minimum}) must be <= default "
-                f"({self.default}) which must be <= maximum ({self.maximum})"
-            )
-
-    def __getitem__(self, i):
-        fields = dataclasses.fields(self)
-        return getattr(self, fields[i].name)
-
-    def __len__(self):
-        return len(dataclasses.fields(self))
-
-    def _replace(self, **kwargs):
-        return dataclasses.replace(self, **kwargs)
-
-    def __repr__(self):
-        return (
-            f"({', '.join(format(v, 'g') if v is not None else 'None' for v in self)})"
-        )
-
-    @classmethod
-    def expand(
-        cls,
-        v: Union[
-            "AxisTriple",
-            float,  # pin axis at single value, same as min==default==max
-            Tuple[float, float],  # (min, max), restrict axis and keep default
-            Tuple[float, float, float],  # (min, default, max)
-        ],
-    ) -> "AxisTriple":
-        """Convert a single value or a tuple into an AxisTriple.
-
-        If the input is a single value, it is interpreted as a pin at that value.
-        If the input is a tuple, it is interpreted as (min, max) or (min, default, max).
-        """
-        if isinstance(v, cls):
-            return v
-        if isinstance(v, (int, float)):
-            return cls(v, v, v)
-        try:
-            n = len(v)
-        except TypeError as e:
-            raise ValueError(
-                f"expected float, 2- or 3-tuple of floats; got {type(v)}: {v!r}"
-            ) from e
-        default = None
-        if n == 2:
-            minimum, maximum = v
-        elif n == 3:
-            minimum, default, maximum = v
-        else:
-            raise ValueError(f"expected sequence of 2 or 3; got {n}: {v!r}")
-        return cls(minimum, default, maximum)
-
-    def limitRangeAndPopulateDefault(self, fvarTriple) -> "AxisTriple":
-        """Return a new AxisTriple with the default value filled in.
-
-        Set default to fvar axis default if the latter is within the min/max range,
-        otherwise set default to the min or max value, whichever is closer to the
-        fvar axis default.
-        If the default value is already set, return self.
-        """
-        minimum = self.minimum
-        maximum = self.maximum
-        default = self.default
-        if default is None:
-            default = fvarTriple[1]
-
-        minimum = max(self.minimum, fvarTriple[0])
-        maximum = max(self.maximum, fvarTriple[0])
-        minimum = min(minimum, fvarTriple[2])
-        maximum = min(maximum, fvarTriple[2])
-        default = max(minimum, min(maximum, default))
-
-        return AxisTriple(minimum, default, maximum)
-
-
-@dataclasses.dataclass(frozen=True, order=True, repr=False)
-class NormalizedAxisTriple(AxisTriple):
-    """A triple of (min, default, max) normalized axis values."""
-
-    minimum: float
-    default: float
-    maximum: float
-
-    def __post_init__(self):
-        if self.default is None:
-            object.__setattr__(self, "default", max(self.minimum, min(self.maximum, 0)))
-        if not (-1.0 <= self.minimum <= self.default <= self.maximum <= 1.0):
-            raise ValueError(
-                "Normalized axis values not in -1..+1 range; got "
-                f"minimum={self.minimum:g}, default={self.default:g}, maximum={self.maximum:g})"
-            )
-
-
-class _BaseAxisLimits(Mapping[str, AxisTriple]):
-    def __getitem__(self, key: str) -> AxisTriple:
-        return self._data[key]
-
-    def __iter__(self) -> Iterable[str]:
-        return iter(self._data)
-
-    def __len__(self) -> int:
-        return len(self._data)
-
-    def __repr__(self) -> str:
-        return f"{type(self).__name__}({self._data!r})"
-
-    def __str__(self) -> str:
-        return str(self._data)
-
-    def defaultLocation(self) -> Dict[str, float]:
-        """Return a dict of default axis values."""
-        return {k: v.default for k, v in self.items()}
-
-    def pinnedLocation(self) -> Dict[str, float]:
-        """Return a location dict with only the pinned axes."""
-        return {k: v.default for k, v in self.items() if v.minimum == v.maximum}
-
-
-class AxisLimits(_BaseAxisLimits):
-    """Maps axis tags (str) to AxisTriple values."""
-
-    def __init__(self, *args, **kwargs):
-        self._data = data = {}
-        for k, v in dict(*args, **kwargs).items():
-            if v is None:
-                # will be filled in by limitAxesAndPopulateDefaults
-                data[k] = v
-            else:
-                try:
-                    triple = AxisTriple.expand(v)
-                except ValueError as e:
-                    raise ValueError(f"Invalid axis limits for {k!r}: {v!r}") from e
-                data[k] = triple
-
-    def limitAxesAndPopulateDefaults(self, varfont) -> "AxisLimits":
-        """Return a new AxisLimits with defaults filled in from fvar table.
-
-        If all axis limits already have defaults, return self.
-        """
-        fvar = varfont["fvar"]
-        fvarTriples = {
-            a.axisTag: (a.minValue, a.defaultValue, a.maxValue) for a in fvar.axes
-        }
-        newLimits = {}
-        for axisTag, triple in self.items():
-            fvarTriple = fvarTriples[axisTag]
-            default = fvarTriple[1]
-            if triple is None:
-                newLimits[axisTag] = AxisTriple(default, default, default)
-            else:
-                newLimits[axisTag] = triple.limitRangeAndPopulateDefault(fvarTriple)
-        return type(self)(newLimits)
-
-    def normalize(self, varfont, usingAvar=True) -> "NormalizedAxisLimits":
-        """Return a new NormalizedAxisLimits with normalized -1..0..+1 values.
-
-        If usingAvar is True, the avar table is used to warp the default normalization.
-        """
-        fvar = varfont["fvar"]
-        badLimits = set(self.keys()).difference(a.axisTag for a in fvar.axes)
-        if badLimits:
-            raise ValueError("Cannot limit: {} not present in fvar".format(badLimits))
-
-        axes = {
-            a.axisTag: (a.minValue, a.defaultValue, a.maxValue)
-            for a in fvar.axes
-            if a.axisTag in self
-        }
-
-        avarSegments = {}
-        if usingAvar and "avar" in varfont:
-            avarSegments = varfont["avar"].segments
-
-        normalizedLimits = {}
-
-        for axis_tag, triple in axes.items():
-            if self[axis_tag] is None:
-                normalizedLimits[axis_tag] = NormalizedAxisTriple(0, 0, 0)
-                continue
-
-            minV, defaultV, maxV = self[axis_tag]
-
-            if defaultV is None:
-                defaultV = triple[1]
-
-            avarMapping = avarSegments.get(axis_tag, None)
-            normalizedLimits[axis_tag] = NormalizedAxisTriple(
-                *(normalize(v, triple, avarMapping) for v in (minV, defaultV, maxV))
-            )
-
-        return NormalizedAxisLimits(normalizedLimits)
-
-
-class NormalizedAxisLimits(_BaseAxisLimits):
-    """Maps axis tags (str) to NormalizedAxisTriple values."""
-
-    def __init__(self, *args, **kwargs):
-        self._data = data = {}
-        for k, v in dict(*args, **kwargs).items():
-            try:
-                triple = NormalizedAxisTriple.expand(v)
-            except ValueError as e:
-                raise ValueError(f"Invalid axis limits for {k!r}: {v!r}") from e
-            data[k] = triple
-
-
-class OverlapMode(IntEnum):
-    KEEP_AND_DONT_SET_FLAGS = 0
-    KEEP_AND_SET_FLAGS = 1
-    REMOVE = 2
-    REMOVE_AND_IGNORE_ERRORS = 3
-
-
-def instantiateTupleVariationStore(
-    variations, axisLimits, origCoords=None, endPts=None
-):
-    """Instantiate TupleVariation list at the given location, or limit axes' min/max.
-
-    The 'variations' list of TupleVariation objects is modified in-place.
-    The 'axisLimits' (dict) maps axis tags (str) to NormalizedAxisTriple namedtuples
-    specifying (minimum, default, maximum) in the -1,0,+1 normalized space. Pinned axes
-    have minimum == default == maximum.
-
-    A 'full' instance (i.e. static font) is produced when all the axes are pinned to
-    single coordinates; a 'partial' instance (i.e. a less variable font) is produced
-    when some of the axes are omitted, or restricted with a new range.
-
-    Tuples that do not participate are kept as they are. Those that have 0 influence
-    at the given location are removed from the variation store.
-    Those that are fully instantiated (i.e. all their axes are being pinned) are also
-    removed from the variation store, their scaled deltas accummulated and returned, so
-    that they can be added by the caller to the default instance's coordinates.
-    Tuples that are only partially instantiated (i.e. not all the axes that they
-    participate in are being pinned) are kept in the store, and their deltas multiplied
-    by the scalar support of the axes to be pinned at the desired location.
-
-    Args:
-        variations: List[TupleVariation] from either 'gvar' or 'cvar'.
-        axisLimits: NormalizedAxisLimits: map from axis tags to (min, default, max)
-            normalized coordinates for the full or partial instance.
-        origCoords: GlyphCoordinates: default instance's coordinates for computing 'gvar'
-            inferred points (cf. table__g_l_y_f._getCoordinatesAndControls).
-        endPts: List[int]: indices of contour end points, for inferring 'gvar' deltas.
-
-    Returns:
-        List[float]: the overall delta adjustment after applicable deltas were summed.
-    """
-
-    newVariations = changeTupleVariationsAxisLimits(variations, axisLimits)
-
-    mergedVariations = collections.OrderedDict()
-    for var in newVariations:
-        # compute inferred deltas only for gvar ('origCoords' is None for cvar)
-        if origCoords is not None:
-            var.calcInferredDeltas(origCoords, endPts)
-
-        # merge TupleVariations with overlapping "tents"
-        axes = frozenset(var.axes.items())
-        if axes in mergedVariations:
-            mergedVariations[axes] += var
-        else:
-            mergedVariations[axes] = var
-
-    # drop TupleVariation if all axes have been pinned (var.axes.items() is empty);
-    # its deltas will be added to the default instance's coordinates
-    defaultVar = mergedVariations.pop(frozenset(), None)
-
-    for var in mergedVariations.values():
-        var.roundDeltas()
-    variations[:] = list(mergedVariations.values())
-
-    return defaultVar.coordinates if defaultVar is not None else []
-
-
-def changeTupleVariationsAxisLimits(variations, axisLimits):
-    for axisTag, axisLimit in sorted(axisLimits.items()):
-        newVariations = []
-        for var in variations:
-            newVariations.extend(changeTupleVariationAxisLimit(var, axisTag, axisLimit))
-        variations = newVariations
-    return variations
-
-
-def changeTupleVariationAxisLimit(var, axisTag, axisLimit):
-    assert isinstance(axisLimit, NormalizedAxisTriple)
-
-    # Skip when current axis is missing (i.e. doesn't participate),
-    lower, peak, upper = var.axes.get(axisTag, (-1, 0, 1))
-    if peak == 0:
-        return [var]
-    # Drop if the var 'tent' isn't well-formed
-    if not (lower <= peak <= upper) or (lower < 0 and upper > 0):
-        return []
-
-    if axisTag not in var.axes:
-        return [var]
-
-    tent = var.axes[axisTag]
-
-    solutions = solver.rebaseTent(tent, axisLimit)
-
-    out = []
-    for scalar, tent in solutions:
-        newVar = (
-            TupleVariation(var.axes, var.coordinates) if len(solutions) > 1 else var
-        )
-        if tent is None:
-            newVar.axes.pop(axisTag)
-        else:
-            assert tent[1] != 0, tent
-            newVar.axes[axisTag] = tent
-        newVar *= scalar
-        out.append(newVar)
-
-    return out
-
-
-def _instantiateGvarGlyph(
-    glyphname, glyf, gvar, hMetrics, vMetrics, axisLimits, optimize=True
-):
-    coordinates, ctrl = glyf._getCoordinatesAndControls(glyphname, hMetrics, vMetrics)
-    endPts = ctrl.endPts
-
-    # Not every glyph may have variations
-    tupleVarStore = gvar.variations.get(glyphname)
-
-    if tupleVarStore:
-        defaultDeltas = instantiateTupleVariationStore(
-            tupleVarStore, axisLimits, coordinates, endPts
-        )
-
-        if defaultDeltas:
-            coordinates += _g_l_y_f.GlyphCoordinates(defaultDeltas)
-
-    glyph = glyf[glyphname]
-    if glyph.isVarComposite():
-        for component in glyph.components:
-            newLocation = {}
-            for tag, loc in component.location.items():
-                if tag not in axisLimits:
-                    newLocation[tag] = loc
-                    continue
-                if component.flags & _g_l_y_f.VarComponentFlags.AXES_HAVE_VARIATION:
-                    raise NotImplementedError(
-                        "Instancing accross VarComposite axes with variation is not supported."
-                    )
-                limits = axisLimits[tag]
-                loc = normalizeValue(loc, limits)
-                newLocation[tag] = loc
-            component.location = newLocation
-
-    # _setCoordinates also sets the hmtx/vmtx advance widths and sidebearings from
-    # the four phantom points and glyph bounding boxes.
-    # We call it unconditionally even if a glyph has no variations or no deltas are
-    # applied at this location, in case the glyph's xMin and in turn its sidebearing
-    # have changed. E.g. a composite glyph has no deltas for the component's (x, y)
-    # offset nor for the 4 phantom points (e.g. it's monospaced). Thus its entry in
-    # gvar table is empty; however, the composite's base glyph may have deltas
-    # applied, hence the composite's bbox and left/top sidebearings may need updating
-    # in the instanced font.
-    glyf._setCoordinates(glyphname, coordinates, hMetrics, vMetrics)
-
-    if not tupleVarStore:
-        if glyphname in gvar.variations:
-            del gvar.variations[glyphname]
-        return
-
-    if optimize:
-        isComposite = glyf[glyphname].isComposite()
-        for var in tupleVarStore:
-            var.optimize(coordinates, endPts, isComposite)
-
-
-def instantiateGvarGlyph(varfont, glyphname, axisLimits, optimize=True):
-    """Remove?
-    https://github.com/fonttools/fonttools/pull/2266"""
-    gvar = varfont["gvar"]
-    glyf = varfont["glyf"]
-    hMetrics = varfont["hmtx"].metrics
-    vMetrics = getattr(varfont.get("vmtx"), "metrics", None)
-    _instantiateGvarGlyph(
-        glyphname, glyf, gvar, hMetrics, vMetrics, axisLimits, optimize=optimize
-    )
-
-
-def instantiateGvar(varfont, axisLimits, optimize=True):
-    log.info("Instantiating glyf/gvar tables")
-
-    gvar = varfont["gvar"]
-    glyf = varfont["glyf"]
-    hMetrics = varfont["hmtx"].metrics
-    vMetrics = getattr(varfont.get("vmtx"), "metrics", None)
-    # Get list of glyph names sorted by component depth.
-    # If a composite glyph is processed before its base glyph, the bounds may
-    # be calculated incorrectly because deltas haven't been applied to the
-    # base glyph yet.
-    glyphnames = sorted(
-        glyf.glyphOrder,
-        key=lambda name: (
-            glyf[name].getCompositeMaxpValues(glyf).maxComponentDepth
-            if glyf[name].isComposite() or glyf[name].isVarComposite()
-            else 0,
-            name,
-        ),
-    )
-    for glyphname in glyphnames:
-        _instantiateGvarGlyph(
-            glyphname, glyf, gvar, hMetrics, vMetrics, axisLimits, optimize=optimize
-        )
-
-    if not gvar.variations:
-        del varfont["gvar"]
-
-
-def setCvarDeltas(cvt, deltas):
-    for i, delta in enumerate(deltas):
-        if delta:
-            cvt[i] += otRound(delta)
-
-
-def instantiateCvar(varfont, axisLimits):
-    log.info("Instantiating cvt/cvar tables")
-
-    cvar = varfont["cvar"]
-
-    defaultDeltas = instantiateTupleVariationStore(cvar.variations, axisLimits)
-
-    if defaultDeltas:
-        setCvarDeltas(varfont["cvt "], defaultDeltas)
-
-    if not cvar.variations:
-        del varfont["cvar"]
-
-
-def setMvarDeltas(varfont, deltas):
-    mvar = varfont["MVAR"].table
-    records = mvar.ValueRecord
-    for rec in records:
-        mvarTag = rec.ValueTag
-        if mvarTag not in MVAR_ENTRIES:
-            continue
-        tableTag, itemName = MVAR_ENTRIES[mvarTag]
-        delta = deltas[rec.VarIdx]
-        if delta != 0:
-            setattr(
-                varfont[tableTag],
-                itemName,
-                getattr(varfont[tableTag], itemName) + otRound(delta),
-            )
-
-
-def instantiateMVAR(varfont, axisLimits):
-    log.info("Instantiating MVAR table")
-
-    mvar = varfont["MVAR"].table
-    fvarAxes = varfont["fvar"].axes
-    varStore = mvar.VarStore
-    defaultDeltas = instantiateItemVariationStore(varStore, fvarAxes, axisLimits)
-    setMvarDeltas(varfont, defaultDeltas)
-
-    if varStore.VarRegionList.Region:
-        varIndexMapping = varStore.optimize()
-        for rec in mvar.ValueRecord:
-            rec.VarIdx = varIndexMapping[rec.VarIdx]
-    else:
-        del varfont["MVAR"]
-
-
-def _remapVarIdxMap(table, attrName, varIndexMapping, glyphOrder):
-    oldMapping = getattr(table, attrName).mapping
-    newMapping = [varIndexMapping[oldMapping[glyphName]] for glyphName in glyphOrder]
-    setattr(table, attrName, builder.buildVarIdxMap(newMapping, glyphOrder))
-
-
-# TODO(anthrotype) Add support for HVAR/VVAR in CFF2
-def _instantiateVHVAR(varfont, axisLimits, tableFields):
-    location = axisLimits.pinnedLocation()
-    tableTag = tableFields.tableTag
-    fvarAxes = varfont["fvar"].axes
-    # Deltas from gvar table have already been applied to the hmtx/vmtx. For full
-    # instances (i.e. all axes pinned), we can simply drop HVAR/VVAR and return
-    if set(location).issuperset(axis.axisTag for axis in fvarAxes):
-        log.info("Dropping %s table", tableTag)
-        del varfont[tableTag]
-        return
-
-    log.info("Instantiating %s table", tableTag)
-    vhvar = varfont[tableTag].table
-    varStore = vhvar.VarStore
-    # since deltas were already applied, the return value here is ignored
-    instantiateItemVariationStore(varStore, fvarAxes, axisLimits)
-
-    if varStore.VarRegionList.Region:
-        # Only re-optimize VarStore if the HVAR/VVAR already uses indirect AdvWidthMap
-        # or AdvHeightMap. If a direct, implicit glyphID->VariationIndex mapping is
-        # used for advances, skip re-optimizing and maintain original VariationIndex.
-        if getattr(vhvar, tableFields.advMapping):
-            varIndexMapping = varStore.optimize(use_NO_VARIATION_INDEX=False)
-            glyphOrder = varfont.getGlyphOrder()
-            _remapVarIdxMap(vhvar, tableFields.advMapping, varIndexMapping, glyphOrder)
-            if getattr(vhvar, tableFields.sb1):  # left or top sidebearings
-                _remapVarIdxMap(vhvar, tableFields.sb1, varIndexMapping, glyphOrder)
-            if getattr(vhvar, tableFields.sb2):  # right or bottom sidebearings
-                _remapVarIdxMap(vhvar, tableFields.sb2, varIndexMapping, glyphOrder)
-            if tableTag == "VVAR" and getattr(vhvar, tableFields.vOrigMapping):
-                _remapVarIdxMap(
-                    vhvar, tableFields.vOrigMapping, varIndexMapping, glyphOrder
-                )
-
-
-def instantiateHVAR(varfont, axisLimits):
-    return _instantiateVHVAR(varfont, axisLimits, varLib.HVAR_FIELDS)
-
-
-def instantiateVVAR(varfont, axisLimits):
-    return _instantiateVHVAR(varfont, axisLimits, varLib.VVAR_FIELDS)
-
-
-class _TupleVarStoreAdapter(object):
-    def __init__(self, regions, axisOrder, tupleVarData, itemCounts):
-        self.regions = regions
-        self.axisOrder = axisOrder
-        self.tupleVarData = tupleVarData
-        self.itemCounts = itemCounts
-
-    @classmethod
-    def fromItemVarStore(cls, itemVarStore, fvarAxes):
-        axisOrder = [axis.axisTag for axis in fvarAxes]
-        regions = [
-            region.get_support(fvarAxes) for region in itemVarStore.VarRegionList.Region
-        ]
-        tupleVarData = []
-        itemCounts = []
-        for varData in itemVarStore.VarData:
-            variations = []
-            varDataRegions = (regions[i] for i in varData.VarRegionIndex)
-            for axes, coordinates in zip(varDataRegions, zip(*varData.Item)):
-                variations.append(TupleVariation(axes, list(coordinates)))
-            tupleVarData.append(variations)
-            itemCounts.append(varData.ItemCount)
-        return cls(regions, axisOrder, tupleVarData, itemCounts)
-
-    def rebuildRegions(self):
-        # Collect the set of all unique region axes from the current TupleVariations.
-        # We use an OrderedDict to de-duplicate regions while keeping the order.
-        uniqueRegions = collections.OrderedDict.fromkeys(
-            (
-                frozenset(var.axes.items())
-                for variations in self.tupleVarData
-                for var in variations
-            )
-        )
-        # Maintain the original order for the regions that pre-existed, appending
-        # the new regions at the end of the region list.
-        newRegions = []
-        for region in self.regions:
-            regionAxes = frozenset(region.items())
-            if regionAxes in uniqueRegions:
-                newRegions.append(region)
-                del uniqueRegions[regionAxes]
-        if uniqueRegions:
-            newRegions.extend(dict(region) for region in uniqueRegions)
-        self.regions = newRegions
-
-    def instantiate(self, axisLimits):
-        defaultDeltaArray = []
-        for variations, itemCount in zip(self.tupleVarData, self.itemCounts):
-            defaultDeltas = instantiateTupleVariationStore(variations, axisLimits)
-            if not defaultDeltas:
-                defaultDeltas = [0] * itemCount
-            defaultDeltaArray.append(defaultDeltas)
-
-        # rebuild regions whose axes were dropped or limited
-        self.rebuildRegions()
-
-        pinnedAxes = set(axisLimits.pinnedLocation())
-        self.axisOrder = [
-            axisTag for axisTag in self.axisOrder if axisTag not in pinnedAxes
-        ]
-
-        return defaultDeltaArray
-
-    def asItemVarStore(self):
-        regionOrder = [frozenset(axes.items()) for axes in self.regions]
-        varDatas = []
-        for variations, itemCount in zip(self.tupleVarData, self.itemCounts):
-            if variations:
-                assert len(variations[0].coordinates) == itemCount
-                varRegionIndices = [
-                    regionOrder.index(frozenset(var.axes.items())) for var in variations
-                ]
-                varDataItems = list(zip(*(var.coordinates for var in variations)))
-                varDatas.append(
-                    builder.buildVarData(varRegionIndices, varDataItems, optimize=False)
-                )
-            else:
-                varDatas.append(
-                    builder.buildVarData([], [[] for _ in range(itemCount)])
-                )
-        regionList = builder.buildVarRegionList(self.regions, self.axisOrder)
-        itemVarStore = builder.buildVarStore(regionList, varDatas)
-        # remove unused regions from VarRegionList
-        itemVarStore.prune_regions()
-        return itemVarStore
-
-
-def instantiateItemVariationStore(itemVarStore, fvarAxes, axisLimits):
-    """Compute deltas at partial location, and update varStore in-place.
-
-    Remove regions in which all axes were instanced, or fall outside the new axis
-    limits. Scale the deltas of the remaining regions where only some of the axes
-    were instanced.
-
-    The number of VarData subtables, and the number of items within each, are
-    not modified, in order to keep the existing VariationIndex valid.
-    One may call VarStore.optimize() method after this to further optimize those.
-
-    Args:
-        varStore: An otTables.VarStore object (Item Variation Store)
-        fvarAxes: list of fvar's Axis objects
-        axisLimits: NormalizedAxisLimits: mapping axis tags to normalized
-            min/default/max axis coordinates. May not specify coordinates/ranges for
-            all the fvar axes.
-
-    Returns:
-        defaultDeltas: to be added to the default instance, of type dict of floats
-            keyed by VariationIndex compound values: i.e. (outer << 16) + inner.
-    """
-    tupleVarStore = _TupleVarStoreAdapter.fromItemVarStore(itemVarStore, fvarAxes)
-    defaultDeltaArray = tupleVarStore.instantiate(axisLimits)
-    newItemVarStore = tupleVarStore.asItemVarStore()
-
-    itemVarStore.VarRegionList = newItemVarStore.VarRegionList
-    assert itemVarStore.VarDataCount == newItemVarStore.VarDataCount
-    itemVarStore.VarData = newItemVarStore.VarData
-
-    defaultDeltas = {
-        ((major << 16) + minor): delta
-        for major, deltas in enumerate(defaultDeltaArray)
-        for minor, delta in enumerate(deltas)
-    }
-    defaultDeltas[itemVarStore.NO_VARIATION_INDEX] = 0
-    return defaultDeltas
-
-
-def instantiateOTL(varfont, axisLimits):
-    # TODO(anthrotype) Support partial instancing of JSTF and BASE tables
-
-    if (
-        "GDEF" not in varfont
-        or varfont["GDEF"].table.Version < 0x00010003
-        or not varfont["GDEF"].table.VarStore
-    ):
-        return
-
-    if "GPOS" in varfont:
-        msg = "Instantiating GDEF and GPOS tables"
-    else:
-        msg = "Instantiating GDEF table"
-    log.info(msg)
-
-    gdef = varfont["GDEF"].table
-    varStore = gdef.VarStore
-    fvarAxes = varfont["fvar"].axes
-
-    defaultDeltas = instantiateItemVariationStore(varStore, fvarAxes, axisLimits)
-
-    # When VF are built, big lookups may overflow and be broken into multiple
-    # subtables. MutatorMerger (which inherits from AligningMerger) reattaches
-    # them upon instancing, in case they can now fit a single subtable (if not,
-    # they will be split again upon compilation).
-    # This 'merger' also works as a 'visitor' that traverses the OTL tables and
-    # calls specific methods when instances of a given type are found.
-    # Specifically, it adds default deltas to GPOS Anchors/ValueRecords and GDEF
-    # LigatureCarets, and optionally deletes all VariationIndex tables if the
-    # VarStore is fully instanced.
-    merger = MutatorMerger(
-        varfont, defaultDeltas, deleteVariations=(not varStore.VarRegionList.Region)
-    )
-    merger.mergeTables(varfont, [varfont], ["GDEF", "GPOS"])
-
-    if varStore.VarRegionList.Region:
-        varIndexMapping = varStore.optimize()
-        gdef.remap_device_varidxes(varIndexMapping)
-        if "GPOS" in varfont:
-            varfont["GPOS"].table.remap_device_varidxes(varIndexMapping)
-    else:
-        # Downgrade GDEF.
-        del gdef.VarStore
-        gdef.Version = 0x00010002
-        if gdef.MarkGlyphSetsDef is None:
-            del gdef.MarkGlyphSetsDef
-            gdef.Version = 0x00010000
-
-        if not (
-            gdef.LigCaretList
-            or gdef.MarkAttachClassDef
-            or gdef.GlyphClassDef
-            or gdef.AttachList
-            or (gdef.Version >= 0x00010002 and gdef.MarkGlyphSetsDef)
-        ):
-            del varfont["GDEF"]
-
-
-def _isValidAvarSegmentMap(axisTag, segmentMap):
-    if not segmentMap:
-        return True
-    if not {(-1.0, -1.0), (0, 0), (1.0, 1.0)}.issubset(segmentMap.items()):
-        log.warning(
-            f"Invalid avar SegmentMap record for axis '{axisTag}': does not "
-            "include all required value maps {-1.0: -1.0, 0: 0, 1.0: 1.0}"
-        )
-        return False
-    previousValue = None
-    for fromCoord, toCoord in sorted(segmentMap.items()):
-        if previousValue is not None and previousValue > toCoord:
-            log.warning(
-                f"Invalid avar AxisValueMap({fromCoord}, {toCoord}) record "
-                f"for axis '{axisTag}': the toCoordinate value must be >= to "
-                f"the toCoordinate value of the preceding record ({previousValue})."
-            )
-            return False
-        previousValue = toCoord
-    return True
-
-
-def instantiateAvar(varfont, axisLimits):
-    # 'axisLimits' dict must contain user-space (non-normalized) coordinates.
-
-    segments = varfont["avar"].segments
-
-    # drop table if we instantiate all the axes
-    pinnedAxes = set(axisLimits.pinnedLocation())
-    if pinnedAxes.issuperset(segments):
-        log.info("Dropping avar table")
-        del varfont["avar"]
-        return
-
-    log.info("Instantiating avar table")
-    for axis in pinnedAxes:
-        if axis in segments:
-            del segments[axis]
-
-    # First compute the default normalization for axisLimits coordinates: i.e.
-    # min = -1.0, default = 0, max = +1.0, and in between values interpolated linearly,
-    # without using the avar table's mappings.
-    # Then, for each SegmentMap, if we are restricting its axis, compute the new
-    # mappings by dividing the key/value pairs by the desired new min/max values,
-    # dropping any mappings that fall outside the restricted range.
-    # The keys ('fromCoord') are specified in default normalized coordinate space,
-    # whereas the values ('toCoord') are "mapped forward" using the SegmentMap.
-    normalizedRanges = axisLimits.normalize(varfont, usingAvar=False)
-    newSegments = {}
-    for axisTag, mapping in segments.items():
-        if not _isValidAvarSegmentMap(axisTag, mapping):
-            continue
-        if mapping and axisTag in normalizedRanges:
-            axisRange = normalizedRanges[axisTag]
-            mappedMin = floatToFixedToFloat(
-                piecewiseLinearMap(axisRange.minimum, mapping), 14
-            )
-            mappedDef = floatToFixedToFloat(
-                piecewiseLinearMap(axisRange.default, mapping), 14
-            )
-            mappedMax = floatToFixedToFloat(
-                piecewiseLinearMap(axisRange.maximum, mapping), 14
-            )
-            newMapping = {}
-            for fromCoord, toCoord in mapping.items():
-
-                if fromCoord < axisRange.minimum or fromCoord > axisRange.maximum:
-                    continue
-                fromCoord = normalizeValue(fromCoord, axisRange)
-
-                assert mappedMin <= toCoord <= mappedMax
-                toCoord = normalizeValue(toCoord, (mappedMin, mappedDef, mappedMax))
-
-                fromCoord = floatToFixedToFloat(fromCoord, 14)
-                toCoord = floatToFixedToFloat(toCoord, 14)
-                newMapping[fromCoord] = toCoord
-            newMapping.update({-1.0: -1.0, 0.0: 0.0, 1.0: 1.0})
-            newSegments[axisTag] = newMapping
-        else:
-            newSegments[axisTag] = mapping
-    varfont["avar"].segments = newSegments
-
-
-def isInstanceWithinAxisRanges(location, axisRanges):
-    for axisTag, coord in location.items():
-        if axisTag in axisRanges:
-            axisRange = axisRanges[axisTag]
-            if coord < axisRange.minimum or coord > axisRange.maximum:
-                return False
-    return True
-
-
-def instantiateFvar(varfont, axisLimits):
-    # 'axisLimits' dict must contain user-space (non-normalized) coordinates
-
-    location = axisLimits.pinnedLocation()
-
-    fvar = varfont["fvar"]
-
-    # drop table if we instantiate all the axes
-    if set(location).issuperset(axis.axisTag for axis in fvar.axes):
-        log.info("Dropping fvar table")
-        del varfont["fvar"]
-        return
-
-    log.info("Instantiating fvar table")
-
-    axes = []
-    for axis in fvar.axes:
-        axisTag = axis.axisTag
-        if axisTag in location:
-            continue
-        if axisTag in axisLimits:
-            triple = axisLimits[axisTag]
-            if triple.default is None:
-                triple = (triple.minimum, axis.defaultValue, triple.maximum)
-            axis.minValue, axis.defaultValue, axis.maxValue = triple
-        axes.append(axis)
-    fvar.axes = axes
-
-    # only keep NamedInstances whose coordinates == pinned axis location
-    instances = []
-    for instance in fvar.instances:
-        if any(instance.coordinates[axis] != value for axis, value in location.items()):
-            continue
-        for axisTag in location:
-            del instance.coordinates[axisTag]
-        if not isInstanceWithinAxisRanges(instance.coordinates, axisLimits):
-            continue
-        instances.append(instance)
-    fvar.instances = instances
-
-
-def instantiateSTAT(varfont, axisLimits):
-    # 'axisLimits' dict must contain user-space (non-normalized) coordinates
-
-    stat = varfont["STAT"].table
-    if not stat.DesignAxisRecord or not (
-        stat.AxisValueArray and stat.AxisValueArray.AxisValue
-    ):
-        return  # STAT table empty, nothing to do
-
-    log.info("Instantiating STAT table")
-    newAxisValueTables = axisValuesFromAxisLimits(stat, axisLimits)
-    stat.AxisValueCount = len(newAxisValueTables)
-    if stat.AxisValueCount:
-        stat.AxisValueArray.AxisValue = newAxisValueTables
-    else:
-        stat.AxisValueArray = None
-
-
-def axisValuesFromAxisLimits(stat, axisLimits):
-    def isAxisValueOutsideLimits(axisTag, axisValue):
-        if axisTag in axisLimits:
-            triple = axisLimits[axisTag]
-            if axisValue < triple.minimum or axisValue > triple.maximum:
-                return True
-        return False
-
-    # only keep AxisValues whose axis is not pinned nor restricted, or is pinned at the
-    # exact (nominal) value, or is restricted but the value is within the new range
-    designAxes = stat.DesignAxisRecord.Axis
-    newAxisValueTables = []
-    for axisValueTable in stat.AxisValueArray.AxisValue:
-        axisValueFormat = axisValueTable.Format
-        if axisValueFormat in (1, 2, 3):
-            axisTag = designAxes[axisValueTable.AxisIndex].AxisTag
-            if axisValueFormat == 2:
-                axisValue = axisValueTable.NominalValue
-            else:
-                axisValue = axisValueTable.Value
-            if isAxisValueOutsideLimits(axisTag, axisValue):
-                continue
-        elif axisValueFormat == 4:
-            # drop 'non-analytic' AxisValue if _any_ AxisValueRecord doesn't match
-            # the pinned location or is outside range
-            dropAxisValueTable = False
-            for rec in axisValueTable.AxisValueRecord:
-                axisTag = designAxes[rec.AxisIndex].AxisTag
-                axisValue = rec.Value
-                if isAxisValueOutsideLimits(axisTag, axisValue):
-                    dropAxisValueTable = True
-                    break
-            if dropAxisValueTable:
-                continue
-        else:
-            log.warning("Unknown AxisValue table format (%s); ignored", axisValueFormat)
-        newAxisValueTables.append(axisValueTable)
-    return newAxisValueTables
-
-
-def setMacOverlapFlags(glyfTable):
-    flagOverlapCompound = _g_l_y_f.OVERLAP_COMPOUND
-    flagOverlapSimple = _g_l_y_f.flagOverlapSimple
-    for glyphName in glyfTable.keys():
-        glyph = glyfTable[glyphName]
-        # Set OVERLAP_COMPOUND bit for compound glyphs
-        if glyph.isComposite():
-            glyph.components[0].flags |= flagOverlapCompound
-        # Set OVERLAP_SIMPLE bit for simple glyphs
-        elif glyph.numberOfContours > 0:
-            glyph.flags[0] |= flagOverlapSimple
-
-
-def normalize(value, triple, avarMapping):
-    value = normalizeValue(value, triple)
-    if avarMapping:
-        value = piecewiseLinearMap(value, avarMapping)
-    # Quantize to F2Dot14, to avoid surprise interpolations.
-    return floatToFixedToFloat(value, 14)
-
-
-def sanityCheckVariableTables(varfont):
-    if "fvar" not in varfont:
-        raise ValueError("Missing required table fvar")
-    if "gvar" in varfont:
-        if "glyf" not in varfont:
-            raise ValueError("Can't have gvar without glyf")
-    # TODO(anthrotype) Remove once we do support partial instancing CFF2
-    if "CFF2" in varfont:
-        raise NotImplementedError("Instancing CFF2 variable fonts is not supported yet")
-
-
-def instantiateVariableFont(
-    varfont,
-    axisLimits,
-    inplace=False,
-    optimize=True,
-    overlap=OverlapMode.KEEP_AND_SET_FLAGS,
-    updateFontNames=False,
-):
-    """Instantiate variable font, either fully or partially.
-
-    Depending on whether the `axisLimits` dictionary references all or some of the
-    input varfont's axes, the output font will either be a full instance (static
-    font) or a variable font with possibly less variation data.
-
-    Args:
-        varfont: a TTFont instance, which must contain at least an 'fvar' table.
-            Note that variable fonts with 'CFF2' table are not supported yet.
-        axisLimits: a dict keyed by axis tags (str) containing the coordinates (float)
-            along one or more axes where the desired instance will be located.
-            If the value is `None`, the default coordinate as per 'fvar' table for
-            that axis is used.
-            The limit values can also be (min, max) tuples for restricting an
-            axis's variation range. The default axis value must be included in
-            the new range.
-        inplace (bool): whether to modify input TTFont object in-place instead of
-            returning a distinct object.
-        optimize (bool): if False, do not perform IUP-delta optimization on the
-            remaining 'gvar' table's deltas. Possibly faster, and might work around
-            rendering issues in some buggy environments, at the cost of a slightly
-            larger file size.
-        overlap (OverlapMode): variable fonts usually contain overlapping contours, and
-            some font rendering engines on Apple platforms require that the
-            `OVERLAP_SIMPLE` and `OVERLAP_COMPOUND` flags in the 'glyf' table be set to
-            force rendering using a non-zero fill rule. Thus we always set these flags
-            on all glyphs to maximise cross-compatibility of the generated instance.
-            You can disable this by passing OverlapMode.KEEP_AND_DONT_SET_FLAGS.
-            If you want to remove the overlaps altogether and merge overlapping
-            contours and components, you can pass OverlapMode.REMOVE (or
-            REMOVE_AND_IGNORE_ERRORS to not hard-fail on tricky glyphs). Note that this
-            requires the skia-pathops package (available to pip install).
-            The overlap parameter only has effect when generating full static instances.
-        updateFontNames (bool): if True, update the instantiated font's name table using
-            the Axis Value Tables from the STAT table. The name table and the style bits
-            in the head and OS/2 table will be updated so they conform to the R/I/B/BI
-            model. If the STAT table is missing or an Axis Value table is missing for
-            a given axis coordinate, a ValueError will be raised.
-    """
-    # 'overlap' used to be bool and is now enum; for backward compat keep accepting bool
-    overlap = OverlapMode(int(overlap))
-
-    sanityCheckVariableTables(varfont)
-
-    axisLimits = AxisLimits(axisLimits).limitAxesAndPopulateDefaults(varfont)
-
-    log.info("Restricted limits: %s", axisLimits)
-
-    normalizedLimits = axisLimits.normalize(varfont)
-
-    log.info("Normalized limits: %s", normalizedLimits)
-
-    if not inplace:
-        varfont = deepcopy(varfont)
-
-    if "DSIG" in varfont:
-        del varfont["DSIG"]
-
-    if updateFontNames:
-        log.info("Updating name table")
-        names.updateNameTable(varfont, axisLimits)
-
-    if "gvar" in varfont:
-        instantiateGvar(varfont, normalizedLimits, optimize=optimize)
-
-    if "cvar" in varfont:
-        instantiateCvar(varfont, normalizedLimits)
-
-    if "MVAR" in varfont:
-        instantiateMVAR(varfont, normalizedLimits)
-
-    if "HVAR" in varfont:
-        instantiateHVAR(varfont, normalizedLimits)
-
-    if "VVAR" in varfont:
-        instantiateVVAR(varfont, normalizedLimits)
-
-    instantiateOTL(varfont, normalizedLimits)
-
-    instantiateFeatureVariations(varfont, normalizedLimits)
-
-    if "avar" in varfont:
-        instantiateAvar(varfont, axisLimits)
-
-    with names.pruningUnusedNames(varfont):
-        if "STAT" in varfont:
-            instantiateSTAT(varfont, axisLimits)
-
-        instantiateFvar(varfont, axisLimits)
-
-    if "fvar" not in varfont:
-        if "glyf" in varfont:
-            if overlap == OverlapMode.KEEP_AND_SET_FLAGS:
-                setMacOverlapFlags(varfont["glyf"])
-            elif overlap in (OverlapMode.REMOVE, OverlapMode.REMOVE_AND_IGNORE_ERRORS):
-                from fontTools.ttLib.removeOverlaps import removeOverlaps
-
-                log.info("Removing overlaps from glyf table")
-                removeOverlaps(
-                    varfont,
-                    ignoreErrors=(overlap == OverlapMode.REMOVE_AND_IGNORE_ERRORS),
-                )
-
-    varLib.set_default_weight_width_slant(
-        varfont, location=axisLimits.defaultLocation()
-    )
-
-    if updateFontNames:
-        # Set Regular/Italic/Bold/Bold Italic bits as appropriate, after the
-        # name table has been updated.
-        setRibbiBits(varfont)
-
-    return varfont
-
-
-def setRibbiBits(font):
-    """Set the `head.macStyle` and `OS/2.fsSelection` style bits
-    appropriately."""
-
-    english_ribbi_style = font["name"].getName(names.NameID.SUBFAMILY_NAME, 3, 1, 0x409)
-    if english_ribbi_style is None:
-        return
-
-    styleMapStyleName = english_ribbi_style.toStr().lower()
-    if styleMapStyleName not in {"regular", "bold", "italic", "bold italic"}:
-        return
-
-    if styleMapStyleName == "bold":
-        font["head"].macStyle = 0b01
-    elif styleMapStyleName == "bold italic":
-        font["head"].macStyle = 0b11
-    elif styleMapStyleName == "italic":
-        font["head"].macStyle = 0b10
-
-    selection = font["OS/2"].fsSelection
-    # First clear...
-    selection &= ~(1 << 0)
-    selection &= ~(1 << 5)
-    selection &= ~(1 << 6)
-    # ...then re-set the bits.
-    if styleMapStyleName == "regular":
-        selection |= 1 << 6
-    elif styleMapStyleName == "bold":
-        selection |= 1 << 5
-    elif styleMapStyleName == "italic":
-        selection |= 1 << 0
-    elif styleMapStyleName == "bold italic":
-        selection |= 1 << 0
-        selection |= 1 << 5
-    font["OS/2"].fsSelection = selection
-
-
-def parseLimits(limits: Iterable[str]) -> Dict[str, Optional[AxisTriple]]:
-    result = {}
-    for limitString in limits:
-        match = re.match(
-            r"^(\w{1,4})=(?:(drop)|(?:([^:]+)(?:[:]([^:]+))?(?:[:]([^:]+))?))$",
-            limitString,
-        )
-        if not match:
-            raise ValueError("invalid location format: %r" % limitString)
-        tag = match.group(1).ljust(4)
-        if match.group(2):  # 'drop'
-            lbound = None
-        else:
-            lbound = strToFixedToFloat(match.group(3), precisionBits=16)
-        ubound = default = lbound
-        if match.group(4):
-            ubound = default = strToFixedToFloat(match.group(4), precisionBits=16)
-            default = None
-        if match.group(5):
-            default = ubound
-            ubound = strToFixedToFloat(match.group(5), precisionBits=16)
-
-        if all(v is None for v in (lbound, default, ubound)):
-            result[tag] = None
-            continue
-
-        result[tag] = AxisTriple(lbound, default, ubound)
-
-    return result
-
-
-def parseArgs(args):
-    """Parse argv.
-
-    Returns:
-        3-tuple (infile, axisLimits, options)
-        axisLimits is either a Dict[str, Optional[float]], for pinning variation axes
-        to specific coordinates along those axes (with `None` as a placeholder for an
-        axis' default value); or a Dict[str, Tuple(float, float)], meaning limit this
-        axis to min/max range.
-        Axes locations are in user-space coordinates, as defined in the "fvar" table.
-    """
-    from fontTools import configLogger
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        "fonttools varLib.instancer",
-        description="Partially instantiate a variable font",
-    )
-    parser.add_argument("input", metavar="INPUT.ttf", help="Input variable TTF file.")
-    parser.add_argument(
-        "locargs",
-        metavar="AXIS=LOC",
-        nargs="*",
-        help="List of space separated locations. A location consists of "
-        "the tag of a variation axis, followed by '=' and one of number, "
-        "number:number or the literal string 'drop'. "
-        "E.g.: wdth=100 or wght=75.0:125.0 or wght=drop",
-    )
-    parser.add_argument(
-        "-o",
-        "--output",
-        metavar="OUTPUT.ttf",
-        default=None,
-        help="Output instance TTF file (default: INPUT-instance.ttf).",
-    )
-    parser.add_argument(
-        "--no-optimize",
-        dest="optimize",
-        action="store_false",
-        help="Don't perform IUP optimization on the remaining gvar TupleVariations",
-    )
-    parser.add_argument(
-        "--no-overlap-flag",
-        dest="overlap",
-        action="store_false",
-        help="Don't set OVERLAP_SIMPLE/OVERLAP_COMPOUND glyf flags (only applicable "
-        "when generating a full instance)",
-    )
-    parser.add_argument(
-        "--remove-overlaps",
-        dest="remove_overlaps",
-        action="store_true",
-        help="Merge overlapping contours and components (only applicable "
-        "when generating a full instance). Requires skia-pathops",
-    )
-    parser.add_argument(
-        "--ignore-overlap-errors",
-        dest="ignore_overlap_errors",
-        action="store_true",
-        help="Don't crash if the remove-overlaps operation fails for some glyphs.",
-    )
-    parser.add_argument(
-        "--update-name-table",
-        action="store_true",
-        help="Update the instantiated font's `name` table. Input font must have "
-        "a STAT table with Axis Value Tables",
-    )
-    parser.add_argument(
-        "--no-recalc-timestamp",
-        dest="recalc_timestamp",
-        action="store_false",
-        help="Don't set the output font's timestamp to the current time.",
-    )
-    parser.add_argument(
-        "--no-recalc-bounds",
-        dest="recalc_bounds",
-        action="store_false",
-        help="Don't recalculate font bounding boxes",
-    )
-    loggingGroup = parser.add_mutually_exclusive_group(required=False)
-    loggingGroup.add_argument(
-        "-v", "--verbose", action="store_true", help="Run more verbosely."
-    )
-    loggingGroup.add_argument(
-        "-q", "--quiet", action="store_true", help="Turn verbosity off."
-    )
-    options = parser.parse_args(args)
-
-    if options.remove_overlaps:
-        if options.ignore_overlap_errors:
-            options.overlap = OverlapMode.REMOVE_AND_IGNORE_ERRORS
-        else:
-            options.overlap = OverlapMode.REMOVE
-    else:
-        options.overlap = OverlapMode(int(options.overlap))
-
-    infile = options.input
-    if not os.path.isfile(infile):
-        parser.error("No such file '{}'".format(infile))
-
-    configLogger(
-        level=("DEBUG" if options.verbose else "ERROR" if options.quiet else "INFO")
-    )
-
-    try:
-        axisLimits = parseLimits(options.locargs)
-    except ValueError as e:
-        parser.error(str(e))
-
-    if len(axisLimits) != len(options.locargs):
-        parser.error("Specified multiple limits for the same axis")
-
-    return (infile, axisLimits, options)
-
-
-def main(args=None):
-    """Partially instantiate a variable font"""
-    infile, axisLimits, options = parseArgs(args)
-    log.info("Restricting axes: %s", axisLimits)
-
-    log.info("Loading variable font")
-    varfont = TTFont(
-        infile,
-        recalcTimestamp=options.recalc_timestamp,
-        recalcBBoxes=options.recalc_bounds,
-    )
-
-    isFullInstance = {
-        axisTag for axisTag, limit in axisLimits.items() if not isinstance(limit, tuple)
-    }.issuperset(axis.axisTag for axis in varfont["fvar"].axes)
-
-    instantiateVariableFont(
-        varfont,
-        axisLimits,
-        inplace=True,
-        optimize=options.optimize,
-        overlap=options.overlap,
-        updateFontNames=options.update_name_table,
-    )
-
-    suffix = "-instance" if isFullInstance else "-partial"
-    outfile = (
-        makeOutputFileName(infile, overWrite=True, suffix=suffix)
-        if not options.output
-        else options.output
-    )
-
-    log.info(
-        "Saving %s font %s",
-        "instance" if isFullInstance else "partial variable",
-        outfile,
-    )
-    varfont.save(outfile)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/outputs.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/outputs.py
deleted file mode 100644
index c647c07cc4108b4b2b1a42b78d688950bb0f5497..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/outputs.py
+++ /dev/null
@@ -1,334 +0,0 @@
-# type: ignore
-"""
-This module defines various classes that can serve as the `output` to an interface. Each class must inherit from
-`OutputComponent`, and each class must define a path to its template. All of the subclasses of `OutputComponent` are
-automatically added to a registry, which allows them to be easily referenced in other parts of the code.
-"""
-
-from __future__ import annotations
-
-import warnings
-from typing import Optional
-
-from gradio import components
-
-
-class Textbox(components.Textbox):
-    def __init__(
-        self,
-        type: str = "text",
-        label: Optional[str] = None,
-    ):
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(label=label, type=type)
-
-
-class Image(components.Image):
-    """
-    Component displays an output image.
-    Output type: Union[numpy.array, PIL.Image, str, matplotlib.pyplot, Tuple[Union[numpy.array, PIL.Image, str], List[Tuple[str, float, float, float, float]]]]
-    """
-
-    def __init__(
-        self, type: str = "auto", plot: bool = False, label: Optional[str] = None
-    ):
-        """
-        Parameters:
-        type (str): Type of value to be passed to component. "numpy" expects a numpy array with shape (height, width, 3), "pil" expects a PIL image object, "file" expects a file path to the saved image or a remote URL, "plot" expects a matplotlib.pyplot object, "auto" detects return type.
-        plot (bool): DEPRECATED. Whether to expect a plot to be returned by the function.
-        label (str): component name in interface.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        if plot:
-            type = "plot"
-        super().__init__(type=type, label=label)
-
-
-class Video(components.Video):
-    """
-    Used for video output.
-    Output type: filepath
-    """
-
-    def __init__(self, type: Optional[str] = None, label: Optional[str] = None):
-        """
-        Parameters:
-        type (str): Type of video format to be passed to component, such as 'avi' or 'mp4'. Use 'mp4' to ensure browser playability. If set to None, video will keep returned format.
-        label (str): component name in interface.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(format=type, label=label)
-
-
-class Audio(components.Audio):
-    """
-    Creates an audio player that plays the output audio.
-    Output type: Union[Tuple[int, numpy.array], str]
-    """
-
-    def __init__(self, type: str = "auto", label: Optional[str] = None):
-        """
-        Parameters:
-        type (str): Type of value to be passed to component. "numpy" returns a 2-set tuple with an integer sample_rate and the data as 16-bit int numpy.array of shape (samples, 2), "file" returns a temporary file path to the saved wav audio file, "auto" detects return type.
-        label (str): component name in interface.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(type=type, label=label)
-
-
-class File(components.File):
-    """
-    Used for file output.
-    Output type: Union[file-like, str]
-    """
-
-    def __init__(self, label: Optional[str] = None):
-        """
-        Parameters:
-        label (str): component name in interface.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(label=label)
-
-
-class Dataframe(components.Dataframe):
-    """
-    Component displays 2D output through a spreadsheet interface.
-    Output type: Union[pandas.DataFrame, numpy.array, List[Union[str, float]], List[List[Union[str, float]]]]
-    """
-
-    def __init__(
-        self,
-        headers: Optional[list[str]] = None,
-        max_rows: Optional[int] = 20,
-        max_cols: Optional[int] = None,
-        overflow_row_behaviour: str = "paginate",
-        type: str = "auto",
-        label: Optional[str] = None,
-    ):
-        """
-        Parameters:
-        headers (List[str]): Header names to dataframe. Only applicable if type is "numpy" or "array".
-        max_rows (int): Maximum number of rows to display at once. Set to None for infinite.
-        max_cols (int): Maximum number of columns to display at once. Set to None for infinite.
-        overflow_row_behaviour (str): If set to "paginate", will create pages for overflow rows. If set to "show_ends", will show initial and final rows and truncate middle rows.
-        type (str): Type of value to be passed to component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for Python array, "auto" detects return type.
-        label (str): component name in interface.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(
-            headers=headers,
-            type=type,
-            label=label,
-            max_rows=max_rows,
-            max_cols=max_cols,
-            overflow_row_behaviour=overflow_row_behaviour,
-        )
-
-
-class Timeseries(components.Timeseries):
-    """
-    Component accepts pandas.DataFrame.
-    Output type: pandas.DataFrame
-    """
-
-    def __init__(
-        self, x: str = None, y: str | list[str] = None, label: Optional[str] = None
-    ):
-        """
-        Parameters:
-        x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
-        y (Union[str, List[str]]): Column name of y series, or list of column names if multiple series. None if csv has no headers, in which case every column after first is a y series.
-        label (str): component name in interface.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(x=x, y=y, label=label)
-
-
-class State(components.State):
-    """
-    Special hidden component that stores state across runs of the interface.
-    Output type: Any
-    """
-
-    def __init__(self, label: Optional[str] = None):
-        """
-        Parameters:
-        label (str): component name in interface (not used).
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import this component as gr.State() from gradio.components",
-        )
-        super().__init__(label=label)
-
-
-class Label(components.Label):
-    """
-    Component outputs a classification label, along with confidence scores of top categories if provided. Confidence scores are represented as a dictionary mapping labels to scores between 0 and 1.
-    Output type: Union[Dict[str, float], str, int, float]
-    """
-
-    def __init__(
-        self,
-        num_top_classes: Optional[int] = None,
-        type: str = "auto",
-        label: Optional[str] = None,
-    ):
-        """
-        Parameters:
-        num_top_classes (int): number of most confident classes to show.
-        type (str): Type of value to be passed to component. "value" expects a single out label, "confidences" expects a dictionary mapping labels to confidence scores, "auto" detects return type.
-        label (str): component name in interface.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(num_top_classes=num_top_classes, type=type, label=label)
-
-
-class KeyValues:
-    """
-    Component displays a table representing values for multiple fields.
-    Output type: Union[Dict, List[Tuple[str, Union[str, int, float]]]]
-    """
-
-    def __init__(self, value: str = " ", *, label: Optional[str] = None, **kwargs):
-        """
-        Parameters:
-        value (str): IGNORED
-        label (str): component name in interface.
-        """
-        raise DeprecationWarning(
-            "The KeyValues component is deprecated. Please use the DataFrame or JSON "
-            "components instead."
-        )
-
-
-class HighlightedText(components.HighlightedText):
-    """
-    Component creates text that contains spans that are highlighted by category or numerical value.
-    Output is represent as a list of Tuple pairs, where the first element represents the span of text represented by the tuple, and the second element represents the category or value of the text.
-    Output type: List[Tuple[str, Union[float, str]]]
-    """
-
-    def __init__(
-        self,
-        color_map: dict[str, str] = None,
-        label: Optional[str] = None,
-        show_legend: bool = False,
-    ):
-        """
-        Parameters:
-        color_map (Dict[str, str]): Map between category and respective colors
-        label (str): component name in interface.
-        show_legend (bool): whether to show span categories in a separate legend or inline.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(color_map=color_map, label=label, show_legend=show_legend)
-
-
-class JSON(components.JSON):
-    """
-    Used for JSON output. Expects a JSON string or a Python object that is JSON serializable.
-    Output type: Union[str, Any]
-    """
-
-    def __init__(self, label: Optional[str] = None):
-        """
-        Parameters:
-        label (str): component name in interface.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(label=label)
-
-
-class HTML(components.HTML):
-    """
-    Used for HTML output. Expects an HTML valid string.
-    Output type: str
-    """
-
-    def __init__(self, label: Optional[str] = None):
-        """
-        Parameters:
-        label (str): component name in interface.
-        """
-        super().__init__(label=label)
-
-
-class Carousel(components.Carousel):
-    """
-    Component displays a set of output components that can be scrolled through.
-    """
-
-    def __init__(
-        self,
-        components: components.Component | list[components.Component],
-        label: Optional[str] = None,
-    ):
-        """
-        Parameters:
-        components (Union[List[Component], Component]): Classes of component(s) that will be scrolled through.
-        label (str): component name in interface.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(components=components, label=label)
-
-
-class Chatbot(components.Chatbot):
-    """
-    Component displays a chatbot output showing both user submitted messages and responses
-    Output type: List[Tuple[str, str]]
-    """
-
-    def __init__(self, label: Optional[str] = None):
-        """
-        Parameters:
-        label (str): component name in interface (not used).
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(label=label)
-
-
-class Image3D(components.Model3D):
-    """
-    Used for 3D image model output.
-    Input type: File object of type (.obj, glb, or .gltf)
-    """
-
-    def __init__(
-        self,
-        clear_color=None,
-        label: Optional[str] = None,
-    ):
-        """
-        Parameters:
-        label (str): component name in interface.
-        optional (bool): If True, the interface can be submitted with no uploaded image, in which case the input value is None.
-        """
-        warnings.warn(
-            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
-        )
-        super().__init__(clear_color=clear_color, label=label)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-eb1fc064.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-eb1fc064.js
deleted file mode 100644
index 0832dbaafcb00ffe8478aa544e0e3d8f12c88c6b..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-eb1fc064.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as k,i as v,s as S,G as Q,e as j,C as _,D as C,g as U,m as q,p as r,t as d,q as V,n as w,r as X,U as Y,Q as T,u as Z,T as z,V as D,X as A,Y as B,Z as E,y as F}from"./index-8c3da1d9.js";import{a as H}from"./TabItem.svelte_svelte_type_style_lang-1613842a.js";import{C as J}from"./Column-da0cdf3b.js";/* empty css                                             */function K(a){let e;const n=a[8].default,t=D(n,a,a[9],null);return{c(){t&&t.c()},m(s,l){t&&t.m(s,l),e=!0},p(s,l){t&&t.p&&(!e||l&512)&&A(t,n,s,s[9],e?E(n,s[9],l,null):B(s[9]),null)},i(s){e||(r(t,s),e=!0)},o(s){d(t,s),e=!1},d(s){t&&t.d(s)}}}function L(a){let e,n,t,s;return n=new J({props:{$$slots:{default:[K]},$$scope:{ctx:a}}}),{c(){e=Q("div"),j(n.$$.fragment),_(e,"id",a[0]),_(e,"class",t="tabitem "+a[1].join(" ")+" svelte-19hvt5v"),C(e,"display",a[3]===a[2]?"block":"none")},m(l,o){U(l,e,o),q(n,e,null),s=!0},p(l,[o]){const u={};o&512&&(u.$$scope={dirty:o,ctx:l}),n.$set(u),(!s||o&1)&&_(e,"id",l[0]),(!s||o&2&&t!==(t="tabitem "+l[1].join(" ")+" svelte-19hvt5v"))&&_(e,"class",t),o&12&&C(e,"display",l[3]===l[2]?"block":"none")},i(l){s||(r(n.$$.fragment,l),s=!0)},o(l){d(n.$$.fragment,l),s=!1},d(l){l&&V(e),w(n)}}}function N(a,e,n){let t,s,{$$slots:l={},$$scope:o}=e,{elem_id:u=""}=e,{elem_classes:f=[]}=e,{name:c}=e,{id:i={}}=e;const G=X(),{register_tab:I,unregister_tab:M,selected_tab:b,selected_tab_index:g}=Y(H);T(a,b,m=>n(3,s=m)),T(a,g,m=>n(7,t=m));let h=I({name:c,id:i});return Z(()=>()=>M({name:c,id:i})),a.$$set=m=>{"elem_id"in m&&n(0,u=m.elem_id),"elem_classes"in m&&n(1,f=m.elem_classes),"name"in m&&n(6,c=m.name),"id"in m&&n(2,i=m.id),"$$scope"in m&&n(9,o=m.$$scope)},a.$$.update=()=>{a.$$.dirty&192&&t===h&&z().then(()=>G("select",{value:c,index:h}))},[u,f,i,s,b,g,c,t,l,o]}class O extends k{constructor(e){super(),v(this,e,N,L,S,{elem_id:0,elem_classes:1,name:6,id:2})}}function P(a){let e;const n=a[4].default,t=D(n,a,a[6],null);return{c(){t&&t.c()},m(s,l){t&&t.m(s,l),e=!0},p(s,l){t&&t.p&&(!e||l&64)&&A(t,n,s,s[6],e?E(n,s[6],l,null):B(s[6]),null)},i(s){e||(r(t,s),e=!0)},o(s){d(t,s),e=!1},d(s){t&&t.d(s)}}}function R(a){let e,n;return e=new O({props:{elem_id:a[0],elem_classes:a[1],name:a[2],id:a[3],$$slots:{default:[P]},$$scope:{ctx:a}}}),e.$on("select",a[5]),{c(){j(e.$$.fragment)},m(t,s){q(e,t,s),n=!0},p(t,[s]){const l={};s&1&&(l.elem_id=t[0]),s&2&&(l.elem_classes=t[1]),s&4&&(l.name=t[2]),s&8&&(l.id=t[3]),s&64&&(l.$$scope={dirty:s,ctx:t}),e.$set(l)},i(t){n||(r(e.$$.fragment,t),n=!0)},o(t){d(e.$$.fragment,t),n=!1},d(t){w(e,t)}}}function W(a,e,n){let{$$slots:t={},$$scope:s}=e,{elem_id:l=""}=e,{elem_classes:o=[]}=e,{label:u}=e,{id:f}=e;function c(i){F.call(this,a,i)}return a.$$set=i=>{"elem_id"in i&&n(0,l=i.elem_id),"elem_classes"in i&&n(1,o=i.elem_classes),"label"in i&&n(2,u=i.label),"id"in i&&n(3,f=i.id),"$$scope"in i&&n(6,s=i.$$scope)},[l,o,u,f,t,c,s]}class y extends k{constructor(e){super(),v(this,e,W,R,S,{elem_id:0,elem_classes:1,label:2,id:3})}}const te=y,se=["static"];export{te as Component,se as modes};
-//# sourceMappingURL=index-eb1fc064.js.map
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_multi_commits.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_multi_commits.py
deleted file mode 100644
index 20576eaa867a0c84e543e972a8ec6585000f81c7..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_multi_commits.py
+++ /dev/null
@@ -1,305 +0,0 @@
-# coding=utf-8
-# Copyright 2023-present, 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.
-"""Contains utilities to multi-commits (i.e. push changes iteratively on a PR)."""
-import re
-from dataclasses import dataclass, field
-from hashlib import sha256
-from typing import TYPE_CHECKING, Iterable, List, Optional, Set, Tuple
-
-from ._commit_api import CommitOperation, CommitOperationAdd, CommitOperationDelete
-from .community import DiscussionWithDetails
-from .utils import experimental
-from .utils._cache_manager import _format_size
-
-
-if TYPE_CHECKING:
-    from .hf_api import HfApi
-
-
-class MultiCommitException(Exception):
-    """Base exception for any exception happening while doing a multi-commit."""
-
-
-MULTI_COMMIT_PR_DESCRIPTION_TEMPLATE = """
-## {commit_message}
-
-{commit_description}
-
-**Multi commit ID:** {multi_commit_id}
-
-Scheduled commits:
-
-{multi_commit_strategy}
-
-_This is a PR opened using the `huggingface_hub` library in the context of a multi-commit. PR can be commented as a usual PR. However, please be aware that manually updating the PR description, changing the PR status, or pushing new commits, is not recommended as it might corrupt the commit process. Learn more about multi-commits [in this guide](https://huggingface.co/docs/huggingface_hub/main/guides/upload)._
-"""
-
-MULTI_COMMIT_PR_COMPLETION_COMMENT_TEMPLATE = """
-Multi-commit is now completed! You can ping the repo owner to review the changes. This PR can now be commented or modified without risking to corrupt it.
-
-_This is a comment posted using the `huggingface_hub` library in the context of a multi-commit. Learn more about multi-commits [in this guide](https://huggingface.co/docs/huggingface_hub/main/guides/upload)._
-"""
-
-MULTI_COMMIT_PR_CLOSING_COMMENT_TEMPLATE = """
-`create_pr=False` has been passed so PR is automatically merged.
-
-_This is a comment posted using the `huggingface_hub` library in the context of a multi-commit. Learn more about multi-commits [in this guide](https://huggingface.co/docs/huggingface_hub/main/guides/upload)._
-"""
-
-MULTI_COMMIT_PR_CLOSE_COMMENT_FAILURE_NO_CHANGES_TEMPLATE = """
-Cannot merge Pull Requests as no changes are associated. This PR will be closed automatically.
-
-_This is a comment posted using the `huggingface_hub` library in the context of a multi-commit. Learn more about multi-commits [in this guide](https://huggingface.co/docs/huggingface_hub/main/guides/upload)._
-"""
-
-MULTI_COMMIT_PR_CLOSE_COMMENT_FAILURE_BAD_REQUEST_TEMPLATE = """
-An error occurred while trying to merge the Pull Request: `{error_message}`.
-
-_This is a comment posted using the `huggingface_hub` library in the context of a multi-commit. Learn more about multi-commits [in this guide](https://huggingface.co/docs/huggingface_hub/main/guides/upload)._
-"""
-
-
-STEP_ID_REGEX = re.compile(r"- \[(?P<completed>[ |x])\].*(?P<step_id>[a-fA-F0-9]{64})", flags=re.MULTILINE)
-
-
-@experimental
-def plan_multi_commits(
-    operations: Iterable[CommitOperation],
-    max_operations_per_commit: int = 50,
-    max_upload_size_per_commit: int = 2 * 1024 * 1024 * 1024,
-) -> Tuple[List[List[CommitOperationAdd]], List[List[CommitOperationDelete]]]:
-    """Split a list of operations in a list of commits to perform.
-
-    Implementation follows a sub-optimal (yet simple) algorithm:
-    1. Delete operations are grouped together by commits of maximum `max_operations_per_commits` operations.
-    2. All additions exceeding `max_upload_size_per_commit` are committed 1 by 1.
-    3. All remaining additions are grouped together and split each time the `max_operations_per_commit` or the
-       `max_upload_size_per_commit` limit is reached.
-
-    We do not try to optimize the splitting to get the lowest number of commits as this is a NP-hard problem (see
-    [bin packing problem](https://en.wikipedia.org/wiki/Bin_packing_problem)). For our use case, it is not problematic
-    to use a sub-optimal solution so we favored an easy-to-explain implementation.
-
-    Args:
-        operations (`List` of [`~hf_api.CommitOperation`]):
-            The list of operations to split into commits.
-        max_operations_per_commit (`int`):
-            Maximum number of operations in a single commit. Defaults to 50.
-        max_upload_size_per_commit (`int`):
-            Maximum size to upload (in bytes) in a single commit. Defaults to 2GB. Files bigger than this limit are
-            uploaded, 1 per commit.
-
-    Returns:
-        `Tuple[List[List[CommitOperationAdd]], List[List[CommitOperationDelete]]]`: a tuple. First item is a list of
-        lists of [`CommitOperationAdd`] representing the addition commits to push. The second item is a list of lists
-        of [`CommitOperationDelete`] representing the deletion commits.
-
-    <Tip warning={true}>
-
-    `plan_multi_commits` is experimental. Its API and behavior is subject to change in the future without prior notice.
-
-    </Tip>
-
-    Example:
-    ```python
-    >>> from huggingface_hub import HfApi, plan_multi_commits
-    >>> addition_commits, deletion_commits = plan_multi_commits(
-    ...     operations=[
-    ...          CommitOperationAdd(...),
-    ...          CommitOperationAdd(...),
-    ...          CommitOperationDelete(...),
-    ...          CommitOperationDelete(...),
-    ...          CommitOperationAdd(...),
-    ...     ],
-    ... )
-    >>> HfApi().create_commits_on_pr(
-    ...     repo_id="my-cool-model",
-    ...     addition_commits=addition_commits,
-    ...     deletion_commits=deletion_commits,
-    ...     (...)
-    ...     verbose=True,
-    ... )
-    ```
-
-    <Tip warning={true}>
-
-    The initial order of the operations is not guaranteed! All deletions will be performed before additions. If you are
-    not updating multiple times the same file, you are fine.
-
-    </Tip>
-    """
-    addition_commits: List[List[CommitOperationAdd]] = []
-    deletion_commits: List[List[CommitOperationDelete]] = []
-
-    additions: List[CommitOperationAdd] = []
-    additions_size = 0
-    deletions: List[CommitOperationDelete] = []
-    for op in operations:
-        if isinstance(op, CommitOperationDelete):
-            # Group delete operations together
-            deletions.append(op)
-            if len(deletions) >= max_operations_per_commit:
-                deletion_commits.append(deletions)
-                deletions = []
-
-        elif op.upload_info.size >= max_upload_size_per_commit:
-            # Upload huge files 1 by 1
-            addition_commits.append([op])
-
-        elif additions_size + op.upload_info.size < max_upload_size_per_commit:
-            # Group other additions and split if size limit is reached (either max_nb_files or max_upload_size)
-            additions.append(op)
-            additions_size += op.upload_info.size
-
-        else:
-            addition_commits.append(additions)
-            additions = [op]
-            additions_size = op.upload_info.size
-
-        if len(additions) >= max_operations_per_commit:
-            addition_commits.append(additions)
-            additions = []
-            additions_size = 0
-
-    if len(additions) > 0:
-        addition_commits.append(additions)
-    if len(deletions) > 0:
-        deletion_commits.append(deletions)
-
-    return addition_commits, deletion_commits
-
-
-@dataclass
-class MultiCommitStep:
-    """Dataclass containing a list of CommitOperation to commit at once.
-
-    A [`MultiCommitStep`] is one atomic part of a [`MultiCommitStrategy`]. Each step is identified by its own
-    deterministic ID based on the list of commit operations (hexadecimal sha256). ID is persistent between re-runs if
-    the list of commits is kept the same.
-    """
-
-    operations: List[CommitOperation]
-
-    id: str = field(init=False)
-    completed: bool = False
-
-    def __post_init__(self) -> None:
-        if len(self.operations) == 0:
-            raise ValueError("A MultiCommitStep must have at least 1 commit operation, got 0.")
-
-        # Generate commit id
-        sha = sha256()
-        for op in self.operations:
-            if isinstance(op, CommitOperationAdd):
-                sha.update(b"ADD")
-                sha.update(op.path_in_repo.encode())
-                sha.update(op.upload_info.sha256)
-            elif isinstance(op, CommitOperationDelete):
-                sha.update(b"DELETE")
-                sha.update(op.path_in_repo.encode())
-                sha.update(str(op.is_folder).encode())
-            else:
-                NotImplementedError()
-        self.id = sha.hexdigest()
-
-    def __str__(self) -> str:
-        """Format a step for PR description.
-
-        Formatting can be changed in the future as long as it is single line, starts with `- [ ]`/`- [x]` and contains
-        `self.id`. Must be able to match `STEP_ID_REGEX`.
-        """
-        additions = [op for op in self.operations if isinstance(op, CommitOperationAdd)]
-        file_deletions = [op for op in self.operations if isinstance(op, CommitOperationDelete) and not op.is_folder]
-        folder_deletions = [op for op in self.operations if isinstance(op, CommitOperationDelete) and op.is_folder]
-        if len(additions) > 0:
-            return (
-                f"- [{'x' if self.completed else ' '}] Upload {len(additions)} file(s) "
-                f"totalling {_format_size(sum(add.upload_info.size for add in additions))}"
-                f" ({self.id})"
-            )
-        else:
-            return (
-                f"- [{'x' if self.completed else ' '}] Delete {len(file_deletions)} file(s) and"
-                f" {len(folder_deletions)} folder(s) ({self.id})"
-            )
-
-
-@dataclass
-class MultiCommitStrategy:
-    """Dataclass containing a list of [`MultiCommitStep`] to commit iteratively.
-
-    A strategy is identified by its own deterministic ID based on the list of its steps (hexadecimal sha256). ID is
-    persistent between re-runs if the list of commits is kept the same.
-    """
-
-    addition_commits: List[MultiCommitStep]
-    deletion_commits: List[MultiCommitStep]
-
-    id: str = field(init=False)
-    all_steps: Set[str] = field(init=False)
-
-    def __post_init__(self) -> None:
-        self.all_steps = {step.id for step in self.addition_commits + self.deletion_commits}
-        if len(self.all_steps) < len(self.addition_commits) + len(self.deletion_commits):
-            raise ValueError("Got duplicate commits in MultiCommitStrategy. All commits must be unique.")
-
-        if len(self.all_steps) == 0:
-            raise ValueError("A MultiCommitStrategy must have at least 1 commit, got 0.")
-
-        # Generate strategy id
-        sha = sha256()
-        for step in self.addition_commits + self.deletion_commits:
-            sha.update("new step".encode())
-            sha.update(step.id.encode())
-        self.id = sha.hexdigest()
-
-
-def multi_commit_create_pull_request(
-    api: "HfApi",
-    repo_id: str,
-    commit_message: str,
-    commit_description: Optional[str],
-    strategy: MultiCommitStrategy,
-    token: Optional[str],
-    repo_type: Optional[str],
-) -> DiscussionWithDetails:
-    return api.create_pull_request(
-        repo_id=repo_id,
-        title=f"[WIP] {commit_message} (multi-commit {strategy.id})",
-        description=multi_commit_generate_comment(
-            commit_message=commit_message, commit_description=commit_description, strategy=strategy
-        ),
-        token=token,
-        repo_type=repo_type,
-    )
-
-
-def multi_commit_generate_comment(
-    commit_message: str,
-    commit_description: Optional[str],
-    strategy: MultiCommitStrategy,
-) -> str:
-    return MULTI_COMMIT_PR_DESCRIPTION_TEMPLATE.format(
-        commit_message=commit_message,
-        commit_description=commit_description or "",
-        multi_commit_id=strategy.id,
-        multi_commit_strategy="\n".join(
-            str(commit) for commit in strategy.deletion_commits + strategy.addition_commits
-        ),
-    )
-
-
-def multi_commit_parse_pr_description(description: str) -> Set[str]:
-    return {match[1] for match in STEP_ID_REGEX.findall(description)}
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_webagg.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_webagg.py
deleted file mode 100644
index 17c12c0a2f8ec8107e3964c6e80a041437091791..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_webagg.py
+++ /dev/null
@@ -1,339 +0,0 @@
-"""
-Displays Agg images in the browser, with interactivity
-"""
-
-# The WebAgg backend is divided into two modules:
-#
-# - `backend_webagg_core.py` contains code necessary to embed a WebAgg
-#   plot inside of a web application, and communicate in an abstract
-#   way over a web socket.
-#
-# - `backend_webagg.py` contains a concrete implementation of a basic
-#   application, implemented with tornado.
-
-from contextlib import contextmanager
-import errno
-from io import BytesIO
-import json
-import mimetypes
-from pathlib import Path
-import random
-import sys
-import signal
-import socket
-import threading
-
-try:
-    import tornado
-except ImportError as err:
-    raise RuntimeError("The WebAgg backend requires Tornado.") from err
-
-import tornado.web
-import tornado.ioloop
-import tornado.websocket
-
-import matplotlib as mpl
-from matplotlib.backend_bases import _Backend
-from matplotlib._pylab_helpers import Gcf
-from . import backend_webagg_core as core
-from .backend_webagg_core import (  # noqa: F401 # pylint: disable=W0611
-    TimerAsyncio, TimerTornado)
-
-
-@mpl._api.deprecated("3.7")
-class ServerThread(threading.Thread):
-    def run(self):
-        tornado.ioloop.IOLoop.instance().start()
-
-
-webagg_server_thread = threading.Thread(
-    target=lambda: tornado.ioloop.IOLoop.instance().start())
-
-
-class FigureManagerWebAgg(core.FigureManagerWebAgg):
-    _toolbar2_class = core.NavigationToolbar2WebAgg
-
-    @classmethod
-    def pyplot_show(cls, *, block=None):
-        WebAggApplication.initialize()
-
-        url = "http://{address}:{port}{prefix}".format(
-            address=WebAggApplication.address,
-            port=WebAggApplication.port,
-            prefix=WebAggApplication.url_prefix)
-
-        if mpl.rcParams['webagg.open_in_browser']:
-            import webbrowser
-            if not webbrowser.open(url):
-                print("To view figure, visit {0}".format(url))
-        else:
-            print("To view figure, visit {0}".format(url))
-
-        WebAggApplication.start()
-
-
-class FigureCanvasWebAgg(core.FigureCanvasWebAggCore):
-    manager_class = FigureManagerWebAgg
-
-
-class WebAggApplication(tornado.web.Application):
-    initialized = False
-    started = False
-
-    class FavIcon(tornado.web.RequestHandler):
-        def get(self):
-            self.set_header('Content-Type', 'image/png')
-            self.write(Path(mpl.get_data_path(),
-                            'images/matplotlib.png').read_bytes())
-
-    class SingleFigurePage(tornado.web.RequestHandler):
-        def __init__(self, application, request, *, url_prefix='', **kwargs):
-            self.url_prefix = url_prefix
-            super().__init__(application, request, **kwargs)
-
-        def get(self, fignum):
-            fignum = int(fignum)
-            manager = Gcf.get_fig_manager(fignum)
-
-            ws_uri = 'ws://{req.host}{prefix}/'.format(req=self.request,
-                                                       prefix=self.url_prefix)
-            self.render(
-                "single_figure.html",
-                prefix=self.url_prefix,
-                ws_uri=ws_uri,
-                fig_id=fignum,
-                toolitems=core.NavigationToolbar2WebAgg.toolitems,
-                canvas=manager.canvas)
-
-    class AllFiguresPage(tornado.web.RequestHandler):
-        def __init__(self, application, request, *, url_prefix='', **kwargs):
-            self.url_prefix = url_prefix
-            super().__init__(application, request, **kwargs)
-
-        def get(self):
-            ws_uri = 'ws://{req.host}{prefix}/'.format(req=self.request,
-                                                       prefix=self.url_prefix)
-            self.render(
-                "all_figures.html",
-                prefix=self.url_prefix,
-                ws_uri=ws_uri,
-                figures=sorted(Gcf.figs.items()),
-                toolitems=core.NavigationToolbar2WebAgg.toolitems)
-
-    class MplJs(tornado.web.RequestHandler):
-        def get(self):
-            self.set_header('Content-Type', 'application/javascript')
-
-            js_content = core.FigureManagerWebAgg.get_javascript()
-
-            self.write(js_content)
-
-    class Download(tornado.web.RequestHandler):
-        def get(self, fignum, fmt):
-            fignum = int(fignum)
-            manager = Gcf.get_fig_manager(fignum)
-            self.set_header(
-                'Content-Type', mimetypes.types_map.get(fmt, 'binary'))
-            buff = BytesIO()
-            manager.canvas.figure.savefig(buff, format=fmt)
-            self.write(buff.getvalue())
-
-    class WebSocket(tornado.websocket.WebSocketHandler):
-        supports_binary = True
-
-        def open(self, fignum):
-            self.fignum = int(fignum)
-            self.manager = Gcf.get_fig_manager(self.fignum)
-            self.manager.add_web_socket(self)
-            if hasattr(self, 'set_nodelay'):
-                self.set_nodelay(True)
-
-        def on_close(self):
-            self.manager.remove_web_socket(self)
-
-        def on_message(self, message):
-            message = json.loads(message)
-            # The 'supports_binary' message is on a client-by-client
-            # basis.  The others affect the (shared) canvas as a
-            # whole.
-            if message['type'] == 'supports_binary':
-                self.supports_binary = message['value']
-            else:
-                manager = Gcf.get_fig_manager(self.fignum)
-                # It is possible for a figure to be closed,
-                # but a stale figure UI is still sending messages
-                # from the browser.
-                if manager is not None:
-                    manager.handle_json(message)
-
-        def send_json(self, content):
-            self.write_message(json.dumps(content))
-
-        def send_binary(self, blob):
-            if self.supports_binary:
-                self.write_message(blob, binary=True)
-            else:
-                data_uri = "data:image/png;base64,{0}".format(
-                    blob.encode('base64').replace('\n', ''))
-                self.write_message(data_uri)
-
-    def __init__(self, url_prefix=''):
-        if url_prefix:
-            assert url_prefix[0] == '/' and url_prefix[-1] != '/', \
-                'url_prefix must start with a "/" and not end with one.'
-
-        super().__init__(
-            [
-                # Static files for the CSS and JS
-                (url_prefix + r'/_static/(.*)',
-                 tornado.web.StaticFileHandler,
-                 {'path': core.FigureManagerWebAgg.get_static_file_path()}),
-
-                # Static images for the toolbar
-                (url_prefix + r'/_images/(.*)',
-                 tornado.web.StaticFileHandler,
-                 {'path': Path(mpl.get_data_path(), 'images')}),
-
-                # A Matplotlib favicon
-                (url_prefix + r'/favicon.ico', self.FavIcon),
-
-                # The page that contains all of the pieces
-                (url_prefix + r'/([0-9]+)', self.SingleFigurePage,
-                 {'url_prefix': url_prefix}),
-
-                # The page that contains all of the figures
-                (url_prefix + r'/?', self.AllFiguresPage,
-                 {'url_prefix': url_prefix}),
-
-                (url_prefix + r'/js/mpl.js', self.MplJs),
-
-                # Sends images and events to the browser, and receives
-                # events from the browser
-                (url_prefix + r'/([0-9]+)/ws', self.WebSocket),
-
-                # Handles the downloading (i.e., saving) of static images
-                (url_prefix + r'/([0-9]+)/download.([a-z0-9.]+)',
-                 self.Download),
-            ],
-            template_path=core.FigureManagerWebAgg.get_static_file_path())
-
-    @classmethod
-    def initialize(cls, url_prefix='', port=None, address=None):
-        if cls.initialized:
-            return
-
-        # Create the class instance
-        app = cls(url_prefix=url_prefix)
-
-        cls.url_prefix = url_prefix
-
-        # This port selection algorithm is borrowed, more or less
-        # verbatim, from IPython.
-        def random_ports(port, n):
-            """
-            Generate a list of n random ports near the given port.
-
-            The first 5 ports will be sequential, and the remaining n-5 will be
-            randomly selected in the range [port-2*n, port+2*n].
-            """
-            for i in range(min(5, n)):
-                yield port + i
-            for i in range(n - 5):
-                yield port + random.randint(-2 * n, 2 * n)
-
-        if address is None:
-            cls.address = mpl.rcParams['webagg.address']
-        else:
-            cls.address = address
-        cls.port = mpl.rcParams['webagg.port']
-        for port in random_ports(cls.port,
-                                 mpl.rcParams['webagg.port_retries']):
-            try:
-                app.listen(port, cls.address)
-            except socket.error as e:
-                if e.errno != errno.EADDRINUSE:
-                    raise
-            else:
-                cls.port = port
-                break
-        else:
-            raise SystemExit(
-                "The webagg server could not be started because an available "
-                "port could not be found")
-
-        cls.initialized = True
-
-    @classmethod
-    def start(cls):
-        import asyncio
-        try:
-            asyncio.get_running_loop()
-        except RuntimeError:
-            pass
-        else:
-            cls.started = True
-
-        if cls.started:
-            return
-
-        """
-        IOLoop.running() was removed as of Tornado 2.4; see for example
-        https://groups.google.com/forum/#!topic/python-tornado/QLMzkpQBGOY
-        Thus there is no correct way to check if the loop has already been
-        launched. We may end up with two concurrently running loops in that
-        unlucky case with all the expected consequences.
-        """
-        ioloop = tornado.ioloop.IOLoop.instance()
-
-        def shutdown():
-            ioloop.stop()
-            print("Server is stopped")
-            sys.stdout.flush()
-            cls.started = False
-
-        @contextmanager
-        def catch_sigint():
-            old_handler = signal.signal(
-                signal.SIGINT,
-                lambda sig, frame: ioloop.add_callback_from_signal(shutdown))
-            try:
-                yield
-            finally:
-                signal.signal(signal.SIGINT, old_handler)
-
-        # Set the flag to True *before* blocking on ioloop.start()
-        cls.started = True
-
-        print("Press Ctrl+C to stop WebAgg server")
-        sys.stdout.flush()
-        with catch_sigint():
-            ioloop.start()
-
-
-def ipython_inline_display(figure):
-    import tornado.template
-
-    WebAggApplication.initialize()
-    import asyncio
-    try:
-        asyncio.get_running_loop()
-    except RuntimeError:
-        if not webagg_server_thread.is_alive():
-            webagg_server_thread.start()
-
-    fignum = figure.number
-    tpl = Path(core.FigureManagerWebAgg.get_static_file_path(),
-               "ipython_inline_figure.html").read_text()
-    t = tornado.template.Template(tpl)
-    return t.generate(
-        prefix=WebAggApplication.url_prefix,
-        fig_id=fignum,
-        toolitems=core.NavigationToolbar2WebAgg.toolitems,
-        canvas=figure.canvas,
-        port=WebAggApplication.port).decode('utf-8')
-
-
-@_Backend.export
-class _BackendWebAgg(_Backend):
-    FigureCanvas = FigureCanvasWebAgg
-    FigureManager = FigureManagerWebAgg
diff --git a/spaces/lanyi2023/QQsign/devices/device_8958.js b/spaces/lanyi2023/QQsign/devices/device_8958.js
deleted file mode 100644
index 455ddb0108b70276949e6539926481590a98e0d9..0000000000000000000000000000000000000000
--- a/spaces/lanyi2023/QQsign/devices/device_8958.js
+++ /dev/null
@@ -1,344 +0,0 @@
-"use strict";
-var __importDefault = (this && this.__importDefault) || function (mod) {
-    return (mod && mod.__esModule) ? mod : { "default": mod };
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.getApkInfo = exports.Platform = exports.Device = exports.generateFullDevice = exports.generateShortDevice = void 0;
-const crypto_1 = require("crypto");
-const constants_1 = require("./constants");
-const axios_1 = __importDefault(require("axios"));
-const algo_1 = require("./algo");
-function generateImei() {
-    let imei = `86${(0, constants_1.randomString)(12, '0123456789')}`;
-    function calcSP(imei) {
-        let sum = 0;
-        for (let i = 0; i < imei.length; ++i) {
-            if (i % 2) {
-                let j = parseInt(imei[i]) * 2;
-                sum += j % 10 + Math.floor(j / 10);
-            }
-            else {
-                sum += parseInt(imei[i]);
-            }
-        }
-        return (100 - sum) % 10;
-    }
-    return imei + calcSP(imei);
-}
-/** 生成短设备信息 */
-function generateShortDevice() {
-    const randstr = (length, num = false) => {
-        const map = num ? '0123456789' : '0123456789abcdef';
-        return (0, constants_1.randomString)(length, map);
-    };
-    return {
-        "--begin--": "该设备为随机生成，丢失后不能得到原先配置",
-        product: `ILPP-${randstr(5).toUpperCase()}`,
-        device: `${randstr(5).toUpperCase()}`,
-        board: `${randstr(5).toUpperCase()}`,
-        brand: `${randstr(4).toUpperCase()}`,
-        model: `ICQQ ${randstr(4).toUpperCase()}`,
-        wifi_ssid: `HUAWEI-${randstr(7)}`,
-        bootloader: `U-boot`,
-        android_id: `IL.${randstr(7, true)}.${randstr(4, true)}`,
-        boot_id: `${randstr(8)}-${randstr(4)}-${randstr(4)}-${randstr(4)}-${randstr(12)}`,
-        proc_version: `Linux version 5.10.101-android12-${randstr(8)}`,
-        mac_address: `2D:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}`,
-        ip_address: `192.168.${randstr(2, true)}.${randstr(2, true)}`,
-        imei: `${generateImei()}`,
-        incremental: `${randstr(10, true).toUpperCase()}`,
-        "--end--": "修改后可能需要重新验证设备。"
-    };
-}
-exports.generateShortDevice = generateShortDevice;
-/** 生成完整设备信息 */
-function generateFullDevice(apk, d) {
-    if (!d)
-        d = generateShortDevice();
-    return {
-        display: d.android_id,
-        product: d.product,
-        device: d.device,
-        board: d.board,
-        brand: d.brand,
-        model: d.model,
-        bootloader: d.bootloader,
-        fingerprint: `${d.brand}/${d.product}/${d.device}:10/${d.android_id}/${d.incremental}:user/release-keys`,
-        boot_id: d.boot_id,
-        proc_version: d.proc_version,
-        baseband: "",
-        sim: "T-Mobile",
-        os_type: "android",
-        mac_address: d.mac_address,
-        ip_address: d.ip_address,
-        wifi_bssid: d.mac_address,
-        wifi_ssid: d.wifi_ssid,
-        imei: d.imei,
-        android_id: (0, constants_1.md5)(d.android_id).toString("hex"),
-        apn: "wifi",
-        version: {
-            incremental: d.incremental,
-            release: "10",
-            codename: "REL",
-            sdk: 29,
-        },
-        imsi: (0, crypto_1.randomBytes)(16),
-        guid: (0, constants_1.md5)(Buffer.concat([Buffer.from(d.imei), Buffer.from(d.mac_address)])),
-    };
-}
-exports.generateFullDevice = generateFullDevice;
-class Device {
-    constructor(apk, d) {
-        this.apk = apk;
-        this.secret = 'ZdJqM15EeO2zWc08';
-        this.publicKey = `-----BEGIN PUBLIC KEY-----
-MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDEIxgwoutfwoJxcGQeedgP7FG9
-qaIuS0qzfR8gWkrkTZKM2iWHn2ajQpBRZjMSoSf6+KJGvar2ORhBfpDXyVtZCKpq
-LQ+FLkpncClKVIrBwv6PHyUvuCb0rIarmgDnzkfQAqVufEtR64iazGDKatvJ9y6B
-9NMbHddGSAUmRTCrHQIDAQAB
------END PUBLIC KEY-----`;
-        if (!d)
-            d = generateShortDevice();
-        Object.assign(this, generateFullDevice(apk, d));
-    }
-    async getQIMEI() {
-        if (this.apk.app_key === "") {
-            return;
-        }
-        const k = (0, constants_1.randomString)(16);
-        const key = (0, algo_1.encryptPKCS1)(this.publicKey, k);
-        const time = Date.now();
-        const nonce = (0, constants_1.randomString)(16);
-        const payload = this.genRandomPayloadByDevice();
-        const params = (0, algo_1.aesEncrypt)(JSON.stringify(payload), k).toString('base64');
-        try {
-            const { data } = await axios_1.default.post("https://snowflake.qq.com/ola/android", {
-                key,
-                params,
-                time, nonce,
-                sign: (0, constants_1.md5)(key + params + time + nonce + this.secret).toString("hex"),
-                extra: ''
-            }, {
-                headers: {
-                    'User-Agent': `Dalvik/2.1.0 (Linux; U; Android ${this.version.release}; PCRT00 Build/N2G48H)`,
-                    'Content-Type': "application/json"
-                }
-            });
-            if (data?.code !== 0) {
-                return;
-            }
-            const { q16, q36 } = JSON.parse((0, algo_1.aesDecrypt)(data.data, k));
-            this.qImei16 = q16;
-            this.qImei36 = q36;
-        }
-        catch {
-        }
-    }
-    genRandomPayloadByDevice() {
-        const fixedRand = (max = 1, min = 0) => {
-            if (max < min)
-                [max, min] = [min, max];
-            const diff = max - min;
-            return Math.floor(Math.random() * diff) + min;
-        };
-        const reserved = {
-            "harmony": "0",
-            "clone": Math.random() > 0.5 ? "1" : "0",
-            "containe": "",
-            "oz": "",
-            "oo": "",
-            "kelong": Math.random() > 0.5 ? "1" : "0",
-            "uptimes": (0, constants_1.formatTime)(new Date()),
-            "multiUser": Math.random() > 0.5 ? "1" : "0",
-            "bod": this.board,
-            "brd": this.brand,
-            "dv": this.device,
-            "firstLevel": "",
-            "manufact": this.brand,
-            "name": this.model,
-            "host": "se.infra",
-            "kernel": this.fingerprint
-        };
-        const timestamp = Date.now();
-        this.mtime = this.mtime || Date.now();
-        const mtime1 = new Date(this.mtime || Date.now());
-        const dateFormat = (fmt, time = Date.now()) => (0, constants_1.formatTime)(time, fmt);
-        const mtimeStr1 = dateFormat("YYYY-mm-ddHHMMSS", mtime1) + "." + this.imei.slice(2, 11);
-        const mtime2 = new Date(this.mtime - parseInt(this.imei.slice(2, 4)));
-        const mtimeStr2 = dateFormat("YYYY-mm-ddHHMMSS", mtime2) + "." + this.imei.slice(5, 14);
-        let beaconIdArr = [
-            `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
-            mtimeStr1,
-            '0000000000000000',
-            (0, constants_1.md5)(this.android_id + this.imei).toString("hex").slice(0, 16),
-            ...new Array(4).fill(false).map((_) => fixedRand(10000000, 1000000)),
-            this.boot_id,
-            '1',
-            fixedRand(5, 0),
-            fixedRand(5, 0),
-            `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
-            `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
-            fixedRand(5, 0),
-            fixedRand(100, 10),
-            `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
-            `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
-            fixedRand(50000, 10000),
-            fixedRand(100, 10),
-            `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
-            mtimeStr2,
-            fixedRand(10000, 1000),
-            fixedRand(5, 0),
-            `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((10 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`,
-            `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`,
-            fixedRand(10000, 1000),
-            fixedRand(100, 10),
-            `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`,
-            `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`,
-            fixedRand(10000, 1000),
-            fixedRand(5, 0),
-            `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
-            `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
-            fixedRand(5, 0),
-            fixedRand(100, 10),
-            `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
-            `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
-            fixedRand(5, 0),
-            fixedRand(5, 0),
-        ].map((str, idx) => `k${idx + 1}:${str}`);
-        return {
-            "androidId": this.android_id,
-            "platformId": 1,
-            "appKey": this.apk.app_key,
-            "appVersion": this.apk.version,
-            "beaconIdSrc": beaconIdArr.join(';'),
-            "brand": this.brand,
-            "channelId": "2017",
-            "cid": "",
-            "imei": this.imei,
-            "imsi": this.imsi.toString("hex"),
-            "mac": this.mac_address,
-            "model": this.model,
-            "networkType": "unknown",
-            "oaid": "",
-            "osVersion": `Android ${this.version.release},level ${this.version.sdk}`,
-            "qimei": "",
-            "qimei36": "",
-            "sdkVersion": "1.2.13.6",
-            "targetSdkVersion": "26",
-            "audit": "",
-            "userId": "{}",
-            "packageId": this.apk.id,
-            "deviceType": this.display,
-            "sdkName": "",
-            "reserved": JSON.stringify(reserved),
-        };
-    }
-}
-exports.Device = Device;
-/** 支持的登录设备平台 */
-var Platform;
-(function (Platform) {
-    Platform[Platform["Android"] = 1] = "Android";
-    Platform[Platform["aPad"] = 2] = "aPad";
-    Platform[Platform["Watch"] = 3] = "Watch";
-    Platform[Platform["iMac"] = 4] = "iMac";
-    Platform[Platform["iPad"] = 5] = "iPad";
-    Platform[Platform["Tim"] = 6] = "Tim";
-})(Platform = exports.Platform || (exports.Platform = {}));
-const mobile = {
-    id: "com.tencent.mobileqq",
-    app_key: '0S200MNJT807V3GE',
-    name: "A8.9.58.11175",
-    version: "8.9.58.11175",
-    ver: "8.9.58",
-    sign: Buffer.from('A6 B7 45 BF 24 A2 C2 77 52 77 16 F6 F3 6E B6 8D'.split(' ').map(s => parseInt(s, 16))),
-    buildtime: 1684467300,
-    appid: 16,
-    subid: 537163194,
-    bitmap: 150470524,
-    main_sig_map: 16724722,
-    sub_sig_map: 0x10400,
-    sdkver: "6.0.0.2545",
-    display: "Android_8.9.58",
-    qua: 'V1_AND_SQ_8.9.58_4108_YYB_D',
-    ssover: 20,
-};
-const tim = {
-    id: "com.tencent.tim",
-    app_key: '0S200MNJT807V3GE',
-    name: "A3.5.1.3168",
-    version: "3.5.1.3168",
-    ver: "3.5.1",
-    sign: Buffer.from('775e696d09856872fdd8ab4f3f06b1e0', 'hex'),
-    buildtime: 1630062176,
-    appid: 16,
-    subid: 537150355,
-    bitmap: 150470524,
-    main_sig_map: 16724722,
-    sub_sig_map: 0x10400,
-    sdkver: "6.0.0.2484",
-    display: "Tim",
-    qua: "V1_AND_SQ_8.3.9_351_TIM_D",
-    ssover: 18,
-};
-const watch = {
-    id: "com.tencent.qqlite",
-    app_key: '0S200MNJT807V3GE',
-    name: "A2.0.8",
-    version: "2.0.8",
-    ver: "2.0.8",
-    sign: Buffer.from('A6 B7 45 BF 24 A2 C2 77 52 77 16 F6 F3 6E B6 8D'.split(' ').map(s => parseInt(s, 16))),
-    buildtime: 1559564731,
-    appid: 16,
-    subid: 537065138,
-    bitmap: 16252796,
-    main_sig_map: 16724722,
-    sub_sig_map: 0x10400,
-    sdkver: "6.0.0.2365",
-    display: "Watch",
-    qua: '',
-    ssover: 5
-};
-const hd = {
-    id: "com.tencent.minihd.qq",
-    app_key: '0S200MNJT807V3GE',
-    name: "A5.9.3.3468",
-    version: "5.9.3.3468",
-    ver: "5.9.3",
-    sign: Buffer.from('AA 39 78 F4 1F D9 6F F9 91 4A 66 9E 18 64 74 C7'.split(' ').map(s => parseInt(s, 16))),
-    buildtime: 1637427966,
-    appid: 16,
-    subid: 537128930,
-    bitmap: 150470524,
-    main_sig_map: 1970400,
-    sub_sig_map: 66560,
-    sdkver: "6.0.0.2433",
-    display: "iMac",
-    qua: '',
-    ssover: 12
-};
-const apklist = {
-    [Platform.Android]: mobile,
-    [Platform.Tim]: tim,
-    [Platform.aPad]: {
-        ...mobile,
-        subid: 537163242,
-        display: 'aPad_8.9.58'
-    },
-    [Platform.Watch]: watch,
-    [Platform.iMac]: { ...hd },
-    [Platform.iPad]: {
-        ...mobile,
-        subid: 537155074,
-        sign: hd.sign,
-        name: '8.9.50.611',
-        ver: '8.9.50',
-        sdkver: '6.0.0.2535',
-        qua: 'V1_AND_SQ_8.9.50_3898_YYB_D',
-        display: 'iPad'
-    },
-};
-function getApkInfo(p) {
-    return apklist[p] || apklist[Platform.Android];
-}
-exports.getApkInfo = getApkInfo;
diff --git a/spaces/lavanyakumaran31/resume_parser_app/README.md b/spaces/lavanyakumaran31/resume_parser_app/README.md
deleted file mode 100644
index 3de7e49e6814626574d9f4e3bee9e67009b794fc..0000000000000000000000000000000000000000
--- a/spaces/lavanyakumaran31/resume_parser_app/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Resume Parser App
-emoji: 📈
-colorFrom: indigo
-colorTo: green
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/leogabraneth/text-generation-webui-main/start_windows.bat b/spaces/leogabraneth/text-generation-webui-main/start_windows.bat
deleted file mode 100644
index cdc303e46e49f12c046b942c8da19bce5aca7081..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/start_windows.bat
+++ /dev/null
@@ -1,84 +0,0 @@
-@echo off
-
-cd /D "%~dp0"
-
-set PATH=%PATH%;%SystemRoot%\system32
-
-echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
-
-@rem Check for special characters in installation path
-set "SPCHARMESSAGE="WARNING: Special characters were detected in the installation path!" "         This can cause the installation to fail!""
-echo "%CD%"| findstr /R /C:"[!#\$%&()\*+,;<=>?@\[\]\^`{|}~]" >nul && (
-	call :PrintBigMessage %SPCHARMESSAGE%
-)
-set SPCHARMESSAGE=
-
-@rem fix failed install when installing to a separate drive
-set TMP=%cd%\installer_files
-set TEMP=%cd%\installer_files
-
-@rem deactivate existing conda envs as needed to avoid conflicts
-(call conda deactivate && call conda deactivate && call conda deactivate) 2>nul
-
-@rem config
-set INSTALL_DIR=%cd%\installer_files
-set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
-set INSTALL_ENV_DIR=%cd%\installer_files\env
-set MINICONDA_DOWNLOAD_URL=https://repo.anaconda.com/miniconda/Miniconda3-py310_23.3.1-0-Windows-x86_64.exe
-set conda_exists=F
-
-@rem figure out whether git and conda needs to be installed
-call "%CONDA_ROOT_PREFIX%\_conda.exe" --version >nul 2>&1
-if "%ERRORLEVEL%" EQU "0" set conda_exists=T
-
-@rem (if necessary) install git and conda into a contained environment
-@rem download conda
-if "%conda_exists%" == "F" (
-	echo Downloading Miniconda from %MINICONDA_DOWNLOAD_URL% to %INSTALL_DIR%\miniconda_installer.exe
-
-	mkdir "%INSTALL_DIR%"
-	call curl -Lk "%MINICONDA_DOWNLOAD_URL%" > "%INSTALL_DIR%\miniconda_installer.exe" || ( echo. && echo Miniconda failed to download. && goto end )
-
-	echo Installing Miniconda to %CONDA_ROOT_PREFIX%
-	start /wait "" "%INSTALL_DIR%\miniconda_installer.exe" /InstallationType=JustMe /NoShortcuts=1 /AddToPath=0 /RegisterPython=0 /NoRegistry=1 /S /D=%CONDA_ROOT_PREFIX%
-
-	@rem test the conda binary
-	echo Miniconda version:
-	call "%CONDA_ROOT_PREFIX%\_conda.exe" --version || ( echo. && echo Miniconda not found. && goto end )
-)
-
-@rem create the installer env
-if not exist "%INSTALL_ENV_DIR%" (
-	echo Packages to install: %PACKAGES_TO_INSTALL%
-	call "%CONDA_ROOT_PREFIX%\_conda.exe" create --no-shortcuts -y -k --prefix "%INSTALL_ENV_DIR%" python=3.11 || ( echo. && echo Conda environment creation failed. && goto end )
-)
-
-@rem check if conda environment was actually created
-if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo Conda environment is empty. && goto end )
-
-@rem environment isolation
-set PYTHONNOUSERSITE=1
-set PYTHONPATH=
-set PYTHONHOME=
-set "CUDA_PATH=%INSTALL_ENV_DIR%"
-set "CUDA_HOME=%CUDA_PATH%"
-
-@rem activate installer env
-call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo Miniconda hook not found. && goto end )
-
-@rem setup installer env
-call python one_click.py %*
-
-@rem below are functions for the script   next line skips these during normal execution
-goto end
-
-:PrintBigMessage
-echo. && echo.
-echo *******************************************************************
-for %%M in (%*) do echo * %%~M
-echo *******************************************************************
-echo. && echo.
-exit /b
-
-:end
-pause
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Download Crack Tropix 2 Quest For The Golden Banana 11.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Download Crack Tropix 2 Quest For The Golden Banana 11.md
deleted file mode 100644
index 088cd8c32159ea4868415d4a70234c78783f45b8..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Download Crack Tropix 2 Quest For The Golden Banana 11.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Download Crack Tropix 2 Quest For The Golden Banana 11</h2><br /><p><b><b>Download File</b> &#10004; <a href="https://bytlly.com/2uGyyj">https://bytlly.com/2uGyyj</a></b></p><br /><br />
-
-0 c 1 a 2 p 3 s 4 m 5 e 6 d 7 r 8 t 9 b 10 i 11 f 12 l 13 g 14 v 15 n 16 o 17 h ... Games Free Download, Latest Games Full Download, Request Games Download ... Don't need Crack Tropix 2: The Quest For the Golden Banana 4. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/utils/utils_os.py b/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/utils/utils_os.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/llamaindex/llama_index_sql_sandbox/README.md b/spaces/llamaindex/llama_index_sql_sandbox/README.md
deleted file mode 100644
index b1ab69efb7424155e4c218b3c5815c8bbbb7a740..0000000000000000000000000000000000000000
--- a/spaces/llamaindex/llama_index_sql_sandbox/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Llama Index Sql Sandbox
-emoji: 🦙
-colorFrom: blue
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: true
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lllqqq/so-vits-svc-models-pcr/inference/infer_tool_grad.py b/spaces/lllqqq/so-vits-svc-models-pcr/inference/infer_tool_grad.py
deleted file mode 100644
index f2587d98209abcbdd6d199ca3142dcbc69a87428..0000000000000000000000000000000000000000
--- a/spaces/lllqqq/so-vits-svc-models-pcr/inference/infer_tool_grad.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import hashlib
-import json
-import logging
-import os
-import time
-from pathlib import Path
-import io
-import librosa
-import maad
-import numpy as np
-from inference import slicer
-import parselmouth
-import soundfile
-import torch
-import torchaudio
-
-# from hubert import hubert_model
-import utils
-from models import SynthesizerTrn
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-
-def resize2d_f0(x, target_len):
-    source = np.array(x)
-    source[source < 0.001] = np.nan
-    target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)),
-                       source)
-    res = np.nan_to_num(target)
-    return res
-
-
-def get_f0(x, p_len, f0_up_key=0):
-
-    time_step = 160 / 16000 * 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)
-
-    f0 = parselmouth.Sound(x, 16000).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')
-
-    f0 *= pow(2, f0_up_key / 12)
-    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, f0
-
-
-def clean_pitch(input_pitch):
-    num_nan = np.sum(input_pitch == 1)
-    if num_nan / len(input_pitch) > 0.9:
-        input_pitch[input_pitch != 1] = 1
-    return input_pitch
-
-
-def plt_pitch(input_pitch):
-    input_pitch = input_pitch.astype(float)
-    input_pitch[input_pitch == 1] = np.nan
-    return input_pitch
-
-
-def f0_to_pitch(ff):
-    f0_pitch = 69 + 12 * np.log2(ff / 440)
-    return f0_pitch
-
-
-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)
-
-
-class VitsSvc(object):
-    def __init__(self):
-        self.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")
-        self.SVCVITS = None
-        self.hps = None
-        self.speakers = None
-        self.hubert_soft = utils.get_hubert_model()
-
-    def set_device(self, device):
-        self.device = torch.device(device)
-        self.hubert_soft.to(self.device)
-        if self.SVCVITS != None:
-            self.SVCVITS.to(self.device)
-
-    def loadCheckpoint(self, path):
-        self.hps = utils.get_hparams_from_file(
-            f"checkpoints/{path}/config.json")
-        self.SVCVITS = SynthesizerTrn(
-            self.hps.data.filter_length // 2 + 1,
-            self.hps.train.segment_size // self.hps.data.hop_length,
-            **self.hps.model)
-        _ = utils.load_checkpoint(
-            f"checkpoints/{path}/model.pth", self.SVCVITS, None)
-        _ = self.SVCVITS.eval().to(self.device)
-        self.speakers = self.hps.spk
-
-    def get_units(self, source, sr):
-        source = source.unsqueeze(0).to(self.device)
-        with torch.inference_mode():
-            units = self.hubert_soft.units(source)
-            return units
-
-    def get_unit_pitch(self, in_path, tran):
-        source, sr = torchaudio.load(in_path)
-        source = torchaudio.functional.resample(source, sr, 16000)
-        if len(source.shape) == 2 and source.shape[1] >= 2:
-            source = torch.mean(source, dim=0).unsqueeze(0)
-        soft = self.get_units(source, sr).squeeze(0).cpu().numpy()
-        f0_coarse, f0 = get_f0(source.cpu().numpy()[0], soft.shape[0]*2, tran)
-        return soft, f0
-
-    def infer(self, speaker_id, tran, raw_path):
-        speaker_id = self.speakers[speaker_id]
-        sid = torch.LongTensor([int(speaker_id)]).to(self.device).unsqueeze(0)
-        soft, pitch = self.get_unit_pitch(raw_path, tran)
-        f0 = torch.FloatTensor(clean_pitch(pitch)).unsqueeze(0).to(self.device)
-        stn_tst = torch.FloatTensor(soft)
-        with torch.no_grad():
-            x_tst = stn_tst.unsqueeze(0).to(self.device)
-            x_tst = torch.repeat_interleave(
-                x_tst, repeats=2, dim=1).transpose(1, 2)
-            audio, _ = self.SVCVITS.infer(x_tst, f0=f0, g=sid)[
-                0, 0].data.float()
-        return audio, audio.shape[-1]
-
-    def inference(self, srcaudio, chara, tran, slice_db):
-        sampling_rate, audio = srcaudio
-        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 != 16000:
-            audio = librosa.resample(
-                audio, orig_sr=sampling_rate, target_sr=16000)
-        soundfile.write("tmpwav.wav", audio, 16000, format="wav")
-        chunks = slicer.cut("tmpwav.wav", db_thresh=slice_db)
-        audio_data, audio_sr = slicer.chunks2audio("tmpwav.wav", chunks)
-        audio = []
-        for (slice_tag, data) in audio_data:
-            length = int(np.ceil(len(data) / audio_sr *
-                                 self.hps.data.sampling_rate))
-            raw_path = io.BytesIO()
-            soundfile.write(raw_path, data, audio_sr, format="wav")
-            raw_path.seek(0)
-            if slice_tag:
-                _audio = np.zeros(length)
-            else:
-                out_audio, out_sr = self.infer(chara, tran, raw_path)
-                _audio = out_audio.cpu().numpy()
-            audio.extend(list(_audio))
-        audio = (np.array(audio) * 32768.0).astype('int16')
-        return (self.hps.data.sampling_rate, audio)
diff --git a/spaces/lllqqq/so-vits-svc-models-pcr/modules/mel_processing.py b/spaces/lllqqq/so-vits-svc-models-pcr/modules/mel_processing.py
deleted file mode 100644
index 99c5b35beb83f3b288af0fac5b49ebf2c69f062c..0000000000000000000000000000000000000000
--- a/spaces/lllqqq/so-vits-svc-models-pcr/modules/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/love3510189/NewBing1/Dockerfile b/spaces/love3510189/NewBing1/Dockerfile
deleted file mode 100644
index 890482ba8b0a1be0c485c4f4cd9da8d4549bd5f9..0000000000000000000000000000000000000000
--- a/spaces/love3510189/NewBing1/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="1AxrQIHmUbDalcOeAuGl0yihP6vaV9IfNRTNf4FJOowo03KysLSw"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/m3hrdadfi/zabanshenas/libs/__init__.py b/spaces/m3hrdadfi/zabanshenas/libs/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/manhkhanhUIT/BOPBTL/Global/detection_models/Synchronized-BatchNorm-PyTorch/tests/test_numeric_batchnorm_v2.py b/spaces/manhkhanhUIT/BOPBTL/Global/detection_models/Synchronized-BatchNorm-PyTorch/tests/test_numeric_batchnorm_v2.py
deleted file mode 100644
index 5e4538ae3c50b4c457a9fa19bf22b5b1a7b666ee..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/BOPBTL/Global/detection_models/Synchronized-BatchNorm-PyTorch/tests/test_numeric_batchnorm_v2.py
+++ /dev/null
@@ -1,62 +0,0 @@
-#! /usr/bin/env python3
-# -*- coding: utf-8 -*-
-# File   : test_numeric_batchnorm_v2.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 11/01/2018
-#
-# Distributed under terms of the MIT license.
-
-"""
-Test the numerical implementation of batch normalization.
-
-Author: acgtyrant.
-See also: https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues/14
-"""
-
-import unittest
-
-import torch
-import torch.nn as nn
-import torch.optim as optim
-
-from sync_batchnorm.unittest import TorchTestCase
-from sync_batchnorm.batchnorm_reimpl import BatchNorm2dReimpl
-
-
-class NumericTestCasev2(TorchTestCase):
-    def testNumericBatchNorm(self):
-        CHANNELS = 16
-        batchnorm1 = nn.BatchNorm2d(CHANNELS, momentum=1)
-        optimizer1 = optim.SGD(batchnorm1.parameters(), lr=0.01)
-
-        batchnorm2 = BatchNorm2dReimpl(CHANNELS, momentum=1)
-        batchnorm2.weight.data.copy_(batchnorm1.weight.data)
-        batchnorm2.bias.data.copy_(batchnorm1.bias.data)
-        optimizer2 = optim.SGD(batchnorm2.parameters(), lr=0.01)
-
-        for _ in range(100):
-            input_ = torch.rand(16, CHANNELS, 16, 16)
-
-            input1 = input_.clone().requires_grad_(True)
-            output1 = batchnorm1(input1)
-            output1.sum().backward()
-            optimizer1.step()
-
-            input2 = input_.clone().requires_grad_(True)
-            output2 = batchnorm2(input2)
-            output2.sum().backward()
-            optimizer2.step()
-
-        self.assertTensorClose(input1, input2)
-        self.assertTensorClose(output1, output2)
-        self.assertTensorClose(input1.grad, input2.grad)
-        self.assertTensorClose(batchnorm1.weight.grad, batchnorm2.weight.grad)
-        self.assertTensorClose(batchnorm1.bias.grad, batchnorm2.bias.grad)
-        self.assertTensorClose(batchnorm1.running_mean, batchnorm2.running_mean)
-        self.assertTensorClose(batchnorm2.running_mean, batchnorm2.running_mean)
-
-
-if __name__ == '__main__':
-    unittest.main()
-
diff --git a/spaces/marcilioduarte/Credit-Worthiness-Risk-Classification/german_credit_risk/code/processed/gcrisk.py b/spaces/marcilioduarte/Credit-Worthiness-Risk-Classification/german_credit_risk/code/processed/gcrisk.py
deleted file mode 100644
index eb1dc95d38d758e8da2ec93b942e99cb08ccc929..0000000000000000000000000000000000000000
--- a/spaces/marcilioduarte/Credit-Worthiness-Risk-Classification/german_credit_risk/code/processed/gcrisk.py
+++ /dev/null
@@ -1,173 +0,0 @@
-## IMPORTING LIBS
-
-import pandas as pd
-import plotly.express as px
-
-from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
-
-import pickle
-import gradio as gr
-
-## CREATING FUNCTION
-
-def predict_credit_worthiness(name, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22):
-    path =  'german_credit_risk/model/model.pickle'
-    greet = 'Hey, ' + name + '!'
-    with open(path, 'rb') as file:
-        model = pickle.load(file)
-        inputs = {'Account Balance_1': int(x1),
-                  'Account Balance_2': int(x2),
-                  'Account Balance_3': int(x3),
-                  'Payment Status of Previous Credit_1': int(x4),
-                  'Payment Status of Previous Credit_3': int(x5),
-                  'Purpose_1': int(x6),
-                  'Purpose_4': int(x7),
-                  'Value Savings/Stocks_1': int(x8),
-                  'Value Savings/Stocks_3': int(x9),
-                  'Value Savings/Stocks_5': int(x10),
-                  'Length of current employment_1': int(x11),
-                  'Length of current employment_4': int(x12),
-                  'Instalment per cent_4': int(x13),
-                  'Guarantors_1': int(x14),
-                  'Duration in Current address_1': int(x15),
-                  'Duration in Current address_2': int(x16),
-                  'Most valuable available asset_1': int(x17),
-                  'Most valuable available asset_4': int(x18),
-                  'Concurrent Credits_3': int(x19),
-                  'Type of apartment_1': int(x20),
-                  'No of Credits at this Bank_1': int(x21),
-                  'Occupation_1': int(x22)
-                  }
-        prediction = model.predict([list(inputs.values())])
-        
-    y_test = pd.read_parquet('german_credit_risk/data/processed/y_test.parquet')
-    y_test = y_test.squeeze()
-
-    yhat = pd.read_parquet('german_credit_risk/data/processed/yhat.parquet')
-    yhat = yhat.squeeze()
-    
-    precision = precision_score(y_test, yhat).round(2)
-    recall = recall_score(y_test, yhat).round(2)
-    f1 = f1_score(y_test, yhat).round(2)
-
-    features_names =  ['No account', 'No balance', 'Some balance', 'No credit problems', 
-                       'Some credit problems', 'New car', 'Other purpose', 'No savings', 
-                       'DM betwenn [100, 1000]', 'DM >= 1000', 'Employment: <1 year (or unemployed)', 'Employment: 4<x<7 years', 
-                       'Installment smaller than 20%', 'No guarantors', 'Less than a year in same address', '1<x<4 years in address', 
-                       'Not available / no assets', 'Ownership of house or land',  'No further running credits', 'Free ap', 
-                       'One credit at thins bank','Unemployed or unskilled']
-    importance = model.feature_importances_
-    data = pd.DataFrame()
-    data['Feature Importance'] = importance
-    data['Feature'] = features_names
-    p = px.bar(data, y='Feature Importance', x='Feature', width=1200, height=500)
-    
-    cfm = confusion_matrix(y_test, yhat)
-    cfm_plot = px.imshow(cfm,
-                x=['Predicted 0', 'Predicted 1'],
-                y=['Actual 0', 'Actual 1'],
-                color_continuous_scale='Blues',
-                labels=dict(x="Predicted", y="Actual", color="Count"),
-                text_auto=True)    
-    
-    if prediction == 1:
-        return (greet + ' According to our model, your client is eligible for the loan.', 
-                'Precision: '+ str(precision), 
-                'Recall: '+ str(recall), 
-                'F1 Score: '+ str(f1), 
-                p, 
-                cfm_plot)
-    else:
-        return (greet + ' Unfortunately, according to our model, your client is not eligible for the loan for now :(.', 
-                'Precision: '+ str(precision), 
-                'Recall: '+ str(recall), 
-                'F1 Score: '+ str(f1), 
-                p, 
-                cfm_plot)
-    
-## creating the interface
-
-with gr.Blocks() as demo:
-    gr.Markdown('# Credit Worthiness Prediction')
-    gr.Markdown("""
-                To predict our clients' creditworthiness, please use this application as follows:
-                
-                1. Enter your name and navigate through the client's information tabs. Select the boxes that best match your client's characteristics. Leave blank if none apply.
-
-                2. Once completed, click 'Predict' to determine if the client is creditworthy.
-                """)
-    with gr.Accordion('Name'):
-        name = gr.Textbox(lines=1, label='Your name')
-    with gr.Accordion("Enter your client's information"):
-        with gr.Tab('Account Balance'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x1 = gr.Checkbox(1, label='No account')
-            x2 = gr.Checkbox(0, label='No balance')
-            x3 = gr.Checkbox(0, label='Some balance')
-        with gr.Tab('Payment status of previous credit'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x4 = gr.Checkbox(1, label='Some problems')
-            x5 = gr.Checkbox(0, label='No problems in this bank')
-        with gr.Tab('Purpose'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x6 = gr.Checkbox(1, label='New car')
-            x7 = gr.Checkbox(0, label='Other')
-        with gr.Tab('Value savings/stocks'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x8 = gr.Checkbox(1, label='No savings')
-            x9 = gr.Checkbox(0, label='DM betwenn [100, 1000]')
-            x10 = gr.Checkbox(0, label='DM >= 1000')
-        with gr.Tab('Length of current employment'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x11 = gr.Checkbox(1, label='Below 1 year (or unemployed)')
-            x12 = gr.Checkbox(0, label='Between 4 and 7 years')
-        with gr.Tab('Instalment per cent'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x13 = gr.Checkbox(0, label='Smaller than 20%')
-        with gr.Tab('Guarantors'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x14 = gr.Checkbox(0, label='No guarantors')
-        with gr.Tab('Duration in current address'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x15 = gr.Checkbox(1, label='Less than a year')
-            x16 = gr.Checkbox(0, label='Between 1 and 4 years')
-        with gr.Tab('Most valuable available asset'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x17 = gr.Checkbox(1, label='Not available / no assets')
-            x18 = gr.Checkbox(0, label='Ownership of house or land')
-        with gr.Tab('Concurrent credits'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x19 = gr.Checkbox(0, label='No further running credits')
-        with gr.Tab('Type of apartment'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x20 = gr.Checkbox(0, label='Free apartment')
-        with gr.Tab('Number of credits at this Bank'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x21 = gr.Checkbox(0, label='One credit')        
-        with gr.Tab('Occupation'):
-            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
-            x22 = gr.Checkbox(0, label='Unemployed or unskilled with no permanent') 
-    predict_button = gr.Button('Predict')
-    prediction_output = gr.Label(num_top_classes=2)
-    with gr.Accordion('Metrics and plots'):
-        with gr.Tab('Metrics'):
-            with gr.Row():
-                precision_output = gr.Label()
-            with gr.Row():
-                recall_output = gr.Label()
-            with gr.Row():
-                f1_output = gr.Label()
-        with gr.Tab('Feature Importances'):
-            fimp_output = gr.Plot()
-        with gr.Tab('Confusion Matrix'):
-            cfm_output = gr.Plot()
-    predict_button.click(fn=predict_credit_worthiness,
-                         inputs=[name, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22],
-                         outputs=[prediction_output,precision_output, recall_output, f1_output, fimp_output, cfm_output])
-    gr.Markdown('''
-                Want to work in a project together or have interest in my services? Reach me:
-                [Linkedin](https://www.linkedin.com/in/marcilioduarte98/)
-                [Github](https://github.com/marcilioduarte)
-                @marcilioduarte | Economics and Data Science
-                ''')
-demo.launch()
\ No newline at end of file
diff --git a/spaces/marker22/Bark-Voice-Cloning/bark/hubert/pre_kmeans_hubert.py b/spaces/marker22/Bark-Voice-Cloning/bark/hubert/pre_kmeans_hubert.py
deleted file mode 100644
index 5208bd2792dd32e7f761ae787927a70bdcb2e5d6..0000000000000000000000000000000000000000
--- a/spaces/marker22/Bark-Voice-Cloning/bark/hubert/pre_kmeans_hubert.py
+++ /dev/null
@@ -1,107 +0,0 @@
-"""
-Modified HuBERT model without kmeans.
-Original author: https://github.com/lucidrains/
-Modified by: https://www.github.com/gitmylo/
-License: MIT
-"""
-
-# Modified code from https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch/hubert_kmeans.py
-
-from pathlib import Path
-
-import torch
-from torch import nn
-from einops import pack, unpack
-
-import fairseq
-
-from torchaudio.functional import resample
-
-from audiolm_pytorch.utils import curtail_to_multiple
-
-import logging
-logging.root.setLevel(logging.ERROR)
-
-
-def exists(val):
-    return val is not None
-
-
-def default(val, d):
-    return val if exists(val) else d
-
-
-class CustomHubert(nn.Module):
-    """
-    checkpoint and kmeans can be downloaded at https://github.com/facebookresearch/fairseq/tree/main/examples/hubert
-    or you can train your own
-    """
-
-    def __init__(
-        self,
-        checkpoint_path,
-        target_sample_hz=16000,
-        seq_len_multiple_of=None,
-        output_layer=9,
-        device=None
-    ):
-        super().__init__()
-        self.target_sample_hz = target_sample_hz
-        self.seq_len_multiple_of = seq_len_multiple_of
-        self.output_layer = output_layer
-
-        if device is not None:
-            self.to(device)
-
-        model_path = Path(checkpoint_path)
-
-        assert model_path.exists(), f'path {checkpoint_path} does not exist'
-
-        print(f"Loading Hubert {checkpoint_path}")
-        checkpoint = torch.load(checkpoint_path)
-        load_model_input = {checkpoint_path: checkpoint}
-        model, *_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(load_model_input)
-
-        if device is not None:
-            model[0].to(device)
-
-        self.model = model[0]
-        self.model.eval()
-
-    @property
-    def groups(self):
-        return 1
-
-    @torch.no_grad()
-    def forward(
-        self,
-        wav_input,
-        flatten=True,
-        input_sample_hz=None
-    ):
-        device = wav_input.device
-
-        if exists(input_sample_hz):
-            wav_input = resample(wav_input, input_sample_hz, self.target_sample_hz)
-
-        if exists(self.seq_len_multiple_of):
-            wav_input = curtail_to_multiple(wav_input, self.seq_len_multiple_of)
-
-        embed = self.model(
-            wav_input,
-            features_only=True,
-            mask=False,  # thanks to @maitycyrus for noticing that mask is defaulted to True in the fairseq code
-            output_layer=self.output_layer
-        )
-
-        embed, packed_shape = pack([embed['x']], '* d')
-
-        # codebook_indices = self.kmeans.predict(embed.cpu().detach().numpy())
-
-        codebook_indices = torch.from_numpy(embed.cpu().detach().numpy()).to(device)  # .long()
-
-        if flatten:
-            return codebook_indices
-
-        codebook_indices, = unpack(codebook_indices, packed_shape, '*')
-        return codebook_indices
diff --git a/spaces/matthoffner/chatbot/utils/app/api.ts b/spaces/matthoffner/chatbot/utils/app/api.ts
deleted file mode 100644
index 813c98c8f8a2ac0272fb96bfe365864cd200cc6f..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/utils/app/api.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { Plugin, PluginID } from '@/types/plugin';
-
-export const getEndpoint = (plugin: Plugin | null) => {
-  if (!plugin) {
-    return 'api/chat';
-  }
-
-  if (plugin.id === PluginID.GOOGLE_SEARCH) {
-    return 'api/google';
-  }
-
-  return 'api/chat';
-};
diff --git a/spaces/matthoffner/open-codetree/styles/iframeLoaderScreen.css b/spaces/matthoffner/open-codetree/styles/iframeLoaderScreen.css
deleted file mode 100644
index 5332cfe3cdceac21ec34b69670ce679fc62b0085..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/open-codetree/styles/iframeLoaderScreen.css
+++ /dev/null
@@ -1,179 +0,0 @@
-.boxes {
-  z-index: 1000;
-  --size: 32px;
-  --duration: 800ms;
-  height: calc(var(--size) * 2);
-  width: calc(var(--size) * 3);
-  position: absolute;
-  top: 50%;
-  left: 50%;
-  transform-style: preserve-3d;
-  transform-origin: 50% 50%;
-  margin-top: calc(var(--size) * 1.5 * -1);
-  transform: rotateX(60deg) rotateZ(45deg) rotateY(0deg) translateZ(0px)
-    translate(-50%, -50%);
-}
-
-.boxes .box {
-  width: var(--size);
-  height: var(--size);
-  top: 0;
-  left: 0;
-  position: absolute;
-  transform-style: preserve-3d;
-}
-
-.boxes .box:nth-child(1) {
-  transform: translate(100%, 0);
-  -webkit-animation: box1 var(--duration) linear infinite;
-  animation: box1 var(--duration) linear infinite;
-}
-
-.boxes .box:nth-child(2) {
-  transform: translate(0, 100%);
-  -webkit-animation: box2 var(--duration) linear infinite;
-  animation: box2 var(--duration) linear infinite;
-}
-
-.boxes .box:nth-child(3) {
-  transform: translate(100%, 100%);
-  -webkit-animation: box3 var(--duration) linear infinite;
-  animation: box3 var(--duration) linear infinite;
-}
-
-.boxes .box:nth-child(4) {
-  transform: translate(200%, 0);
-  -webkit-animation: box4 var(--duration) linear infinite;
-  animation: box4 var(--duration) linear infinite;
-}
-
-.boxes .box > div {
-  --background: #5c8df6;
-  --top: auto;
-  --right: auto;
-  --bottom: auto;
-  --left: auto;
-  --translateZ: calc(var(--size) / 2);
-  --rotateY: 0deg;
-  --rotateX: 0deg;
-  position: absolute;
-  width: 100%;
-  height: 100%;
-  background: var(--background);
-  top: var(--top);
-  right: var(--right);
-  bottom: var(--bottom);
-  left: var(--left);
-  transform: rotateY(var(--rotateY)) rotateX(var(--rotateX))
-    translateZ(var(--translateZ));
-}
-
-.boxes .box > div:nth-child(1) {
-  --top: 0;
-  --left: 0;
-}
-
-.boxes .box > div:nth-child(2) {
-  --background: #145af2;
-  --right: 0;
-  --rotateY: 90deg;
-}
-
-.boxes .box > div:nth-child(3) {
-  --background: #447cf5;
-  --rotateX: -90deg;
-}
-
-.boxes .box > div:nth-child(4) {
-  --background: #dbe3f4;
-  --top: 0;
-  --left: 0;
-  --translateZ: calc(var(--size) * 3 * -1);
-}
-
-@-webkit-keyframes box1 {
-  0%,
-  50% {
-    transform: translate(100%, 0);
-  }
-  100% {
-    transform: translate(200%, 0);
-  }
-}
-
-@keyframes box1 {
-  0%,
-  50% {
-    transform: translate(100%, 0);
-  }
-  100% {
-    transform: translate(200%, 0);
-  }
-}
-
-@-webkit-keyframes box2 {
-  0% {
-    transform: translate(0, 100%);
-  }
-  50% {
-    transform: translate(0, 0);
-  }
-  100% {
-    transform: translate(100%, 0);
-  }
-}
-
-@keyframes box2 {
-  0% {
-    transform: translate(0, 100%);
-  }
-  50% {
-    transform: translate(0, 0);
-  }
-  100% {
-    transform: translate(100%, 0);
-  }
-}
-@-webkit-keyframes box3 {
-  0%,
-  50% {
-    transform: translate(100%, 100%);
-  }
-  100% {
-    transform: translate(0, 100%);
-  }
-}
-
-@keyframes box3 {
-  0%,
-  50% {
-    transform: translate(100%, 100%);
-  }
-  100% {
-    transform: translate(0, 100%);
-  }
-}
-
-@-webkit-keyframes box4 {
-  0% {
-    transform: translate(200%, 0);
-  }
-  50% {
-    transform: translate(200%, 100%);
-  }
-  100% {
-    transform: translate(100%, 100%);
-  }
-}
-
-@keyframes box4 {
-  0% {
-    transform: translate(200%, 0);
-  }
-  50% {
-    transform: translate(200%, 100%);
-  }
-  100% {
-    transform: translate(100%, 100%);
-  }
-}
diff --git a/spaces/mawady/Demo-integrated-gradients-alibi-gradio/README.md b/spaces/mawady/Demo-integrated-gradients-alibi-gradio/README.md
deleted file mode 100644
index 29dcc2e3a29ff8a3fb1c88a5219b7ae5f68c5206..0000000000000000000000000000000000000000
--- a/spaces/mawady/Demo-integrated-gradients-alibi-gradio/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
----
-title: Demo Integrated Gradients Alibi Gradio
-emoji: 👀
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-app_file: app.py
-pinned: false
-license: mit
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `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/mehdidc/text_to_image_ddgan/scripts/run_jurecadc_conda.sh b/spaces/mehdidc/text_to_image_ddgan/scripts/run_jurecadc_conda.sh
deleted file mode 100644
index 28403c0abc7e6f372ced048b42e32bf5d3faac33..0000000000000000000000000000000000000000
--- a/spaces/mehdidc/text_to_image_ddgan/scripts/run_jurecadc_conda.sh
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/bin/bash -x
-#SBATCH --account=zam
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=4
-#SBATCH --cpus-per-task=24
-#SBATCH --time=06:00:00
-#SBATCH --gres=gpu:4
-#SBATCH --partition=dc-gpu
-ml CUDA
-source /p/project/laionize/miniconda/bin/activate
-conda activate ddgan
-#source scripts/init_2022.sh
-#source scripts/init_2020.sh
-#source scripts/init.sh
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-echo "Job id: $SLURM_JOB_ID"
-export TOKENIZERS_PARALLELISM=false
-#export NCCL_ASYNC_ERROR_HANDLING=1
-export NCCL_IB_TIMEOUT=50
-export UCX_RC_TIMEOUT=4s
-export NCCL_IB_RETRY_CNT=10
-export TORCH_DISTRIBUTED_DEBUG=INFO
-srun python -u $*
diff --git a/spaces/merle/PROTEIN_GENERATOR/utils/diff_utils.py b/spaces/merle/PROTEIN_GENERATOR/utils/diff_utils.py
deleted file mode 100644
index 0efce5b9e934f3eae6eec8b3523312e492050f2e..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/utils/diff_utils.py
+++ /dev/null
@@ -1,276 +0,0 @@
-import torch
-from icecream import ic
-import random
-import numpy as np
-from kinematics import get_init_xyz
-import torch.nn as nn 
-from util_module import ComputeAllAtomCoords
-from util import *
-from inpainting_util import MSAFeaturize_fixbb, TemplFeaturizeFixbb, lddt_unbin
-from kinematics import xyz_to_t2d
-
-
-def mask_inputs(seq, msa_masked, msa_full, xyz_t, t1d, input_seq_mask=None, 
-        input_str_mask=None, input_t1dconf_mask=None, diffuser=None, t=None, 
-        MODEL_PARAM=None, hotspots=None, dssp=None, v2_mode=False):
-
-
-    """
-    JG - adapted slightly for the inference case
-
-    Parameters:
-        seq (torch.tensor, required): (I,L) integer sequence
-
-        msa_masked (torch.tensor, required): (I,N_short,L,48)
-
-        msa_full  (torch,.tensor, required): (I,N_long,L,25)
-
-        xyz_t (torch,tensor): (T,L,27,3) template crds BEFORE they go into get_init_xyz
-
-        t1d (torch.tensor, required): (I,L,22) this is the t1d before tacking on the chi angles
-
-        str_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where structure is masked at False positions
-
-        seq_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where seq is masked at False positions
-        t1d_24: is there an extra dimension to input structure confidence?
-
-        diffuser: diffuser class
-
-        t: time step
-
-    NOTE: in the MSA, the order is 20aa, 1x unknown, 1x mask token. We set the masked region to 22 (masked).
-        For the t1d, this has 20aa, 1x unkown, and 1x template conf. Here, we set the masked region to 21 (unknown).
-        This, we think, makes sense, as the template in normal RF training does not perfectly correspond to the MSA.
-    """
-    assert diffuser != None, 'please choose a diffuser'
-
-    ###########
-    seq = seq[0,:1]
-    msa_masked = msa_masked[0,:1]
-    msa_full = msa_full[0,:1]
-    t1d = t1d[0]
-    xyz_t = xyz_t[0]
-
-    seq_mask = input_seq_mask[0]
-
-
-
-    ######################
-    ###sequence diffusion###
-    ######################
-    """
-    #muate some percentage of sequence to have model be able to mutate residues later in denoising trajectory
-    if True:
-        masked_values=input_seq_mask[0].nonzero()[:,0]
-        print(masked_values)
-        mut_p=math.floor(masked_values.shape[0]*.05)
-        print(mut_p)
-        mutate_indices = torch.randperm(len(masked_values))[:mut_p]
-        print(mutate_indices)
-        for i in range(len(mutate_indices)):
-            seq[0,masked_values[mutate_indices[i]]]  = torch.randint(0, 21, (1,))
-    """
-    str_mask     = input_str_mask[0]
-    
-    x_0          = torch.nn.functional.one_hot(seq[0,...],num_classes=22).float()*2-1
-    
-    #ic(seq_mask)
-
-    seq_diffused = diffuser.q_sample(x_0,torch.tensor([t-1]),mask=seq_mask)
-    #seq_diffused = torch.clamp(seq_diffused, min=-1, max=1)
-
-    seq_tmp=torch.argmax(seq_diffused,axis=-1).to(device=seq.device)
-    seq=seq_tmp.repeat(seq.shape[0], 1)
-    
-    ###################
-    ###msa diffusion###
-    ###################
-
-    ### msa_masked ###
-    #ic(msa_masked.shape)
-    B,N,L,_=msa_masked.shape
-
-    msa_masked[:,0,:,:22] = seq_diffused
-    
-    x_0_msa = msa_masked[0,1:,:,:22].float()*2-1
-    msa_seq_mask = seq_mask.unsqueeze(0).repeat(N-1, 1)
-    msa_diffused = diffuser.q_sample(x_0_msa,torch.tensor([t-1]),mask=msa_seq_mask)
-    #msa_diffused = torch.clamp(msa_diffused, min=-1, max=1)
-    msa_masked[:,1:,:,:22] = torch.clone(msa_diffused)
-    
-    # index 44/45 is insertion/deletion
-    # index 43 is the masked token NOTE check this
-    # index 42 is the unknown token 
-    msa_masked[:,0,:,22:44] = seq_diffused
-    msa_masked[:,1:,:,22:44] = msa_diffused
-
-    # insertion/deletion stuff 
-    msa_masked[:,0,~seq_mask,44:46] = 0
-
-    ### msa_full ### 
-    ################
-    #msa_full[:,0,:,:22] = seq_diffused
-    #make msa_full same size as msa_masked
-    msa_full = msa_full[:,:msa_masked.shape[1],:,:]
-    msa_full[:,0,:,:22] = seq_diffused
-    msa_full[:,1:,:,:22] = msa_diffused
-
-    ### t1d ###
-    ########### 
-    # NOTE: adjusting t1d last dim (confidence) from sequence mask
-    t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],2)).float()), -1).to(seq.device)
-    t1d[:,:,:21] = seq_diffused[...,:21]
-
-    #t1d[:,:,21] *= input_t1dconf_mask
-    #set diffused conf to 0 and everything else to 1
-    t1d[:,~seq_mask,21] = 0.0
-    t1d[:,seq_mask,21] = 1.0
-    
-    t1d[:1,:,22] = 1-t/diffuser.num_timesteps
-
-    t1d[:,~str_mask,23] = 0.0
-    t1d[:,str_mask,23] = 1.0
-    
-    # EXPAND t1d to match model params
-    if MODEL_PARAM['d_t1d'] == 29:
-        ## added t1d features ##
-        # 24 -- dssp helix
-        # 25 -- dssp sheet
-        # 26 -- dssp loop
-        # 27 -- dssp mask
-        # 28 -- hotspot resi on target
-        t1d = torch.cat((t1d,torch.zeros(t1d.shape[0],t1d.shape[1],5)),dim=-1)
-        t1d[:,:,24:28] = dssp
-        t1d[:,:,28] = hotspots
-        t1d[:,str_mask,24:27] = 0.0
-        t1d[:,str_mask,27] = 1.0
-
-    xyz_t = get_init_xyz(xyz_t[None])
-    xyz_t = xyz_t[0]
-
-    xyz_t[:,~seq_mask,3:,:] = float('nan')
-
-    # Structure masking
-    xyz_t[:,~str_mask,:,:] = float('nan')
-
-    if not v2_mode:
-        xyz_t = get_init_xyz(xyz_t[None])
-        xyz_t = xyz_t[0]
-        assert torch.sum(torch.isnan(xyz_t[:,:,:3,:]))==0
-
-    return seq, msa_masked, msa_full, xyz_t, t1d, seq_diffused
-
-
-conversion = 'ARNDCQEGHILKMFPSTWYVX-'
-
-
-def take_step(model, msa, msa_extra, seq, t1d, t2d, idx_pdb, N_cycle, xyz_prev, alpha, xyz_t, 
-        alpha_t, seq_diffused, msa_prev, pair_prev, state_prev):
-    """ 
-    Single step in the diffusion process
-    """
-    compute_allatom_coords=ComputeAllAtomCoords().to(seq.device) 
-    #ic(msa.shape)
-    B, _, N, L, _ = msa.shape
-    with torch.no_grad():
-        with torch.cuda.amp.autocast(True):
-            for i_cycle in range(N_cycle-1):
-                msa_prev, pair_prev, xyz_prev, state_prev, alpha = model(msa[:,0],
-                                                                   msa_extra[:,0],
-                                                                   seq[:,0], xyz_prev,
-                                                                   idx_pdb,
-                                                                   seq1hot=seq_diffused,
-                                                                   t1d=t1d, t2d=t2d,
-                                                                   xyz_t=xyz_t, alpha_t=alpha_t,
-                                                                   msa_prev=msa_prev,
-                                                                   pair_prev=pair_prev,
-                                                                   state_prev=state_prev,
-                                                                   return_raw=True) 
-                
-            
-            logit_s, logit_aa_s, logits_exp, xyz_prev, pred_lddt, msa_prev, pair_prev, state_prev, alpha = model(msa[:,0], 
-                                                            msa_extra[:,0],
-                                                            seq[:,0], xyz_prev,
-                                                            idx_pdb,
-                                                            seq1hot=seq_diffused,
-                                                            t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t,
-                                                            msa_prev=msa_prev,
-                                                            pair_prev=pair_prev,
-                                                            state_prev=state_prev,
-                                                            return_infer=True)
-        logit_aa_s_msa = torch.clone(logit_aa_s)
-        logit_aa_s = logit_aa_s.reshape(B,-1,N,L)[:,:,0,:]
-        logit_aa_s = logit_aa_s.reshape(B,-1,L)
-        seq_out = torch.argmax(logit_aa_s, dim=-2)
-
-        pred_lddt_unbinned = lddt_unbin(pred_lddt)
-        _, xyz_prev = compute_allatom_coords(seq_out, xyz_prev, alpha)
-    
-    if N>1:
-        return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, logit_aa_s_msa, alpha, msa_prev, pair_prev, state_prev  
-    else:
-        return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, alpha, msa_prev, pair_prev, state_prev
-            
-            
-def take_step_nostate(model, msa, msa_extra, seq, t1d, t2d, idx_pdb, N_cycle, xyz_prev, alpha, xyz_t,
-        alpha_t, seq_diffused, msa_prev, pair_prev, state_prev):
-    """ 
-    Single step in the diffusion process, with no conditioning on state
-    """
-    compute_allatom_coords=ComputeAllAtomCoords().to(seq.device)
-    msa_prev = None
-    pair_prev = None
-    state_prev = None
-    
-    B, _, N, L, _ = msa.shape
-    with torch.no_grad():
-        with torch.cuda.amp.autocast(True):
-            for i_cycle in range(N_cycle-1):
-                msa_prev, pair_prev, xyz_prev, state_prev, alpha = model(msa[:,0],
-                                                                   msa_extra[:,0],
-                                                                   seq[:,0], xyz_prev,
-                                                                   idx_pdb,
-                                                                   seq1hot=seq_diffused,
-                                                                   t1d=t1d, t2d=t2d,
-                                                                   xyz_t=xyz_t, alpha_t=alpha_t,
-                                                                   msa_prev=msa_prev,
-                                                                   pair_prev=pair_prev,
-                                                                   state_prev=state_prev,
-                                                                   return_raw=True)
-
-
-            logit_s, logit_aa_s, logits_exp, xyz_prev, pred_lddt, msa_prev, pair_prev, state_prev, alpha = model(msa[:,0],
-                                                            msa_extra[:,0],
-                                                            seq[:,0], xyz_prev,
-                                                            idx_pdb,
-                                                            seq1hot=seq_diffused,
-                                                            t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t,
-                                                            msa_prev=msa_prev,
-                                                            pair_prev=pair_prev,
-                                                            state_prev=state_prev,
-                                                            return_infer=True)
-
-        logit_aa_s_msa = torch.clone(logit_aa_s)
-        logit_aa_s = logit_aa_s.reshape(B,-1,N,L)[:,:,0,:]
-        logit_aa_s = logit_aa_s.reshape(B,-1,L)
-        seq_out = torch.argmax(logit_aa_s, dim=-2)
-
-        pred_lddt_unbinned = lddt_unbin(pred_lddt)
-        _, xyz_prev = compute_allatom_coords(seq_out, xyz_prev, alpha)
-
-    if N>1:
-        return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, logit_aa_s_msa, alpha, msa_prev, pair_prev, state_prev
-    else:
-        return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, alpha, msa_prev, pair_prev, state_prev
-
-
-def get_alphas(t1d, xyz_t, B, L, ti_dev, ti_flip, ang_ref):
-    # get torsion angles from templates
-    seq_tmp = t1d[...,:21].argmax(dim=-1).reshape(-1,L)
-    alpha, _, alpha_mask, _ = get_torsions(xyz_t.reshape(-1,L,27,3), seq_tmp, ti_dev, ti_flip, ang_ref)
-    alpha_mask = torch.logical_and(alpha_mask, ~torch.isnan(alpha[...,0]))
-    alpha[torch.isnan(alpha)] = 0.0
-    alpha = alpha.reshape(B,-1,L,10,2)
-    alpha_mask = alpha_mask.reshape(B,-1,L,10,1)
-    alpha_t = torch.cat((alpha, alpha_mask), dim=-1).reshape(B, -1, L, 30)
-    return alpha, alpha_t
diff --git a/spaces/mfkeles/Track-Anything/tracker/model/__init__.py b/spaces/mfkeles/Track-Anything/tracker/model/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/michael1943/geektime-ai-class/app.py b/spaces/michael1943/geektime-ai-class/app.py
deleted file mode 100644
index 1987b3fe2a8158fe2000056b4c3c9956d99703b7..0000000000000000000000000000000000000000
--- a/spaces/michael1943/geektime-ai-class/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-
-import openai, os
-import gradio as gr
-import azure.cognitiveservices.speech as speechsdk
-from langchain import OpenAI
-from langchain.chains import ConversationChain
-from langchain.memory import ConversationSummaryBufferMemory
-from langchain.chat_models import ChatOpenAI
-
-openai.api_key = os.environ["OPENAI_API_KEY"]
-
-memory = ConversationSummaryBufferMemory(llm=ChatOpenAI(), max_token_limit=2048)
-conversation = ConversationChain(
-    llm=OpenAI(max_tokens=2048, temperature=0.5), 
-    memory=memory,
-)
-
-speech_config = speechsdk.SpeechConfig(subscription=os.environ.get('AZURE_SPEECH_KEY'), region=os.environ.get('AZURE_SPEECH_REGION'))
-audio_config = speechsdk.audio.AudioOutputConfig(use_default_speaker=True)
-
-# The language of the voice that speaks.
-speech_config.speech_synthesis_language='zh-CN'
-speech_config.speech_synthesis_voice_name='zh-CN-XiaohanNeural'
-
-speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config)
-
-def play_voice(text):
-    speech_synthesizer.speak_text_async(text)
-
-def predict(input, history=[]):
-    history.append(input)
-    response = conversation.predict(input=input)
-    history.append(response)
-    play_voice(response)
-    responses = [(u,b) for u,b in zip(history[::2], history[1::2])]
-    return responses, history
-
-def transcribe(audio):
-    os.rename(audio, audio + '.wav')
-    audio_file = open(audio + '.wav', "rb")
-    transcript = openai.Audio.transcribe("whisper-1", audio_file)
-    return transcript['text']    
-
-def process_audio(audio, history=[]):
-    text = transcribe(audio)
-    return predict(text, history)
-
-with gr.Blocks(css="#chatbot{height:800px} .overflow-y-auto{height:800px}") as demo:
-    chatbot = gr.Chatbot(elem_id="chatbot")
-    state = gr.State([])
-
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
-        
-    with gr.Row():
-        audio = gr.Audio(source="microphone", type="filepath")
-        
-    txt.submit(predict, [txt, state], [chatbot, state])
-    audio.change(process_audio, [audio, state], [chatbot, state])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/mikebars/huggingface/assets/index-7f4d6bd2.css b/spaces/mikebars/huggingface/assets/index-7f4d6bd2.css
deleted file mode 100644
index 098ae1f1bce10863773ac288c65b5b85a125a065..0000000000000000000000000000000000000000
--- a/spaces/mikebars/huggingface/assets/index-7f4d6bd2.css
+++ /dev/null
@@ -1 +0,0 @@
-*,:before,:after{box-sizing:border-box;border-width:0;border-style:solid;border-color:#e5e7eb}:before,:after{--tw-content: ""}html{line-height:1.5;-webkit-text-size-adjust:100%;-moz-tab-size:4;-o-tab-size:4;tab-size:4;font-family:ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,"Apple Color Emoji","Segoe UI Emoji",Segoe UI Symbol,"Noto Color Emoji";font-feature-settings:normal;font-variation-settings:normal}body{margin:0;line-height:inherit}hr{height:0;color:inherit;border-top-width:1px}abbr:where([title]){-webkit-text-decoration:underline dotted;text-decoration:underline dotted}h1,h2,h3,h4,h5,h6{font-size:inherit;font-weight:inherit}a{color:inherit;text-decoration:inherit}b,strong{font-weight:bolder}code,kbd,samp,pre{font-family:ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier 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diff --git a/spaces/mirzaburanali/project-caption-generation/app.py b/spaces/mirzaburanali/project-caption-generation/app.py
deleted file mode 100644
index 8f448043ad84fe759c8d37366c49f4a70eaba98e..0000000000000000000000000000000000000000
--- a/spaces/mirzaburanali/project-caption-generation/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# !pip install transformers
-
-#import all the import libraries
-#visionencodermodel can be used to initialize an image to text with any pretrained transformer
-#based vision model as the encoder
-# vif feature extractor extracts features 
-# auto tokenizer  is responsible preprocessing text into array of numbers as an input to model
-# pil stands for python image library, it deals with images
-from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
-import torch
-from PIL import Image
-
-#providing hugging face source, pass the hugging face url, this will download the model
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-
-#download the feature extractor
-feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-#download the tokenizer
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-
-#check if there is any gpu available if not we will use the cpu
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-#create a dictionary with these parameters
-
-max_length = 16
-num_beams = 4
-num_return_sequences = 3
-gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-
-# this function uses a combination of computer vision and natural language
-#  processing techniques to generate textual descriptions or captions of an input image.
-
-def predict_step(image):
-    i_image = Image.fromarray(image.astype('uint8'), 'RGB')
-    pixel_values = feature_extractor(images=i_image, return_tensors="pt").pixel_values
-    pixel_values = pixel_values.to(device)
-
-    output_ids = model.generate(pixel_values, **gen_kwargs, num_return_sequences=num_return_sequences)
-
-    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-    preds = [pred.strip() for pred in preds]
-    return tuple(preds)
-
-# ! pip install gradio
-
-import gradio as gr
-
-#create an interface 
-
-inputs = gr.inputs.Image(type='numpy', label='Original Image')
-outputs = [gr.outputs.Textbox(label=f'Caption {i+1}') for i in range(num_return_sequences)]
-
-title = "Image Captioning using ViT + GPT2"
-description = "Image caption is generated for the uploaded image using ViT and GPT2. For training, COCO Dataset was utilised. If you see any biases (gender, race, etc.) in our picture captioning model that we were unable to identify during our stress tests, please use the 'Flag' button to mark the offending image."
-article = " <a href='https://huggingface.co/sachin/vit2distilgpt2'>Model Repo on Hugging Face Model Hub</a>"
-examples = [["test1.png"], ["test2.png"],["test3.png"]]
-
-gr.Interface(
-    predict_step,
-    inputs,
-    outputs,
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-    theme="huggingface",
-).launch(debug=True, enable_queue=True)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/preprocessing/get_speaker_embedding.py b/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/preprocessing/get_speaker_embedding.py
deleted file mode 100644
index 0e3e4c5cd7aef15dae0b41b0ec7b33e17f66597f..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/preprocessing/get_speaker_embedding.py
+++ /dev/null
@@ -1,89 +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 argparse
-from collections import defaultdict
-from itertools import chain
-from pathlib import Path
-
-import numpy as np
-import torchaudio
-import torchaudio.sox_effects as ta_sox
-import yaml
-from tqdm import tqdm
-
-from examples.speech_to_text.data_utils import load_tsv_to_dicts
-from examples.speech_synthesis.preprocessing.speaker_embedder import SpkrEmbedder
-
-
-def extract_embedding(audio_path, embedder):
-    wav, sr = torchaudio.load(audio_path)  # 2D
-    if sr != embedder.RATE:
-        wav, sr = ta_sox.apply_effects_tensor(
-            wav, sr, [["rate", str(embedder.RATE)]]
-        )
-    try:
-        emb = embedder([wav[0].cuda().float()]).cpu().numpy()
-    except RuntimeError:
-        emb = None
-    return emb
-
-
-def process(args):
-    print("Fetching data...")
-    raw_manifest_root = Path(args.raw_manifest_root).absolute()
-    samples = [load_tsv_to_dicts(raw_manifest_root / (s + ".tsv"))
-               for s in args.splits]
-    samples = list(chain(*samples))
-    with open(args.config, "r") as f:
-        config = yaml.load(f, Loader=yaml.FullLoader)
-    with open(f"{config['audio_root']}/{config['speaker_set_filename']}") as f:
-        speaker_to_id = {r.strip(): i for i, r in enumerate(f)}
-
-    embedder = SpkrEmbedder(args.ckpt).cuda()
-    speaker_to_cnt = defaultdict(float)
-    speaker_to_emb = defaultdict(float)
-    for sample in tqdm(samples, desc="extract emb"):
-        emb = extract_embedding(sample["audio"], embedder)
-        if emb is not None:
-            speaker_to_cnt[sample["speaker"]] += 1
-            speaker_to_emb[sample["speaker"]] += emb
-    if len(speaker_to_emb) != len(speaker_to_id):
-        missed = set(speaker_to_id) - set(speaker_to_emb.keys())
-        print(
-            f"WARNING: missing embeddings for {len(missed)} speaker:\n{missed}"
-        )
-    speaker_emb_mat = np.zeros((len(speaker_to_id), len(emb)), float)
-    for speaker in speaker_to_emb:
-        idx = speaker_to_id[speaker]
-        emb = speaker_to_emb[speaker]
-        cnt = speaker_to_cnt[speaker]
-        speaker_emb_mat[idx, :] = emb / cnt
-    speaker_emb_name = "speaker_emb.npy"
-    speaker_emb_path = f"{config['audio_root']}/{speaker_emb_name}"
-    np.save(speaker_emb_path, speaker_emb_mat)
-    config["speaker_emb_filename"] = speaker_emb_name
-
-    with open(args.new_config, "w") as f:
-        yaml.dump(config, f)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--raw-manifest-root", "-m", required=True, type=str)
-    parser.add_argument("--splits", "-s", type=str, nargs="+",
-                        default=["train"])
-    parser.add_argument("--config", "-c", required=True, type=str)
-    parser.add_argument("--new-config", "-n", required=True, type=str)
-    parser.add_argument("--ckpt", required=True, type=str,
-                        help="speaker embedder checkpoint")
-    args = parser.parse_args()
-
-    process(args)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/bart/__init__.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/models/bart/__init__.py
deleted file mode 100644
index a701923f7e5a2a8aa9b75e5580ddea22907f53ee..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/bart/__init__.py
+++ /dev/null
@@ -1,7 +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 .hub_interface import *  # noqa
-from .model import *  # noqa
diff --git a/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/embeddings.py b/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/embeddings.py
deleted file mode 100644
index d721bc45a87d7bea0892a7767fafd896f220fb3c..0000000000000000000000000000000000000000
--- a/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/embeddings.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import math
-from pydoc import describe
-
-import numpy as np
-import torch
-from torch import nn
-
-
-def get_timestep_embedding(
-    timesteps: torch.Tensor,
-    embedding_dim: int,
-    flip_sin_to_cos: bool = False,
-    downscale_freq_shift: float = 1,
-    scale: float = 1,
-    max_period: int = 10000,
-):
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
-    embeddings. :return: an [N x dim] Tensor of positional embeddings.
-    """
-    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
-
-    half_dim = embedding_dim // 2
-    exponent = -math.log(max_period) * torch.arange(
-        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
-    )
-    exponent = exponent / (half_dim - downscale_freq_shift)
-
-    emb = torch.exp(exponent)
-    emb = timesteps[:, None].float() * emb[None, :]
-
-    # scale embeddings
-    emb = scale * emb
-
-    # concat sine and cosine embeddings
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
-
-    # flip sine and cosine embeddings
-    if flip_sin_to_cos:
-        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
-
-    # zero pad
-    if embedding_dim % 2 == 1:
-        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
-    return emb
-
-class Timesteps(nn.Module):
-    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
-        super().__init__()
-        self.num_channels = num_channels
-        self.flip_sin_to_cos = flip_sin_to_cos
-        self.downscale_freq_shift = downscale_freq_shift
-
-    def forward(self, timesteps):
-        t_emb = get_timestep_embedding(
-            timesteps,
-            self.num_channels,
-            flip_sin_to_cos=self.flip_sin_to_cos,
-            downscale_freq_shift=self.downscale_freq_shift,
-        )
-        return t_emb
-
-class TimeEmbbeding(nn.Module):
-    def __init__(self, channel: int, time_embed_dim: int, act_fn: str = "silu"):
-        super().__init__()
-
-        self.temb = Timesteps(channel, flip_sin_to_cos=True, downscale_freq_shift=0)
-
-        self.linear_1 = nn.Linear(channel, time_embed_dim)
-        self.act = None
-        if act_fn == "silu":
-            self.act = nn.SiLU()
-        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim)
-
-    def forward(self, sample):
-        sample = self.temb(sample)
-        sample = self.linear_1(sample)
-
-        if self.act is not None:
-            sample = self.act(sample)
-
-        sample = self.linear_2(sample)
-        return sample
-
-
-
diff --git a/spaces/muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection/app.py b/spaces/muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection/app.py
deleted file mode 100644
index 4214f7a866dd2b8902f6323519649a4ab426e011..0000000000000000000000000000000000000000
--- a/spaces/muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection/app.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import gradio as gr
-import torch
-from sahi.prediction import ObjectPrediction
-from sahi.utils.cv import visualize_object_predictions, read_image
-from ultralyticsplus import YOLO
-
-# Images
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/MMuttalib1326/Industrial-Equipments-Detection-Yolov8/main/12.jpeg', '12.jpeg')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/MMuttalib1326/Industrial-Equipments-Detection-Yolov8/main/14.jpg', '14.jpg')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/MMuttalib1326/Industrial-Equipments-Detection-Yolov8/main/16.jpeg', '16.jpeg')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/MMuttalib1326/Industrial-Equipments-Detection-Yolov8/main/19.jpeg', '19.jpeg')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/MMuttalib1326/Industrial-Equipments-Detection-Yolov8/main/9.jpeg', '9.jpeg')
-
-def yolov8_inference(
-    image: gr.inputs.Image = None,
-    model_path: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider = 224,
-    conf_threshold: gr.inputs.Slider = 0.25,
-    iou_threshold: gr.inputs.Slider = 0.45,
-):
-    """
-    YOLOv8 inference function
-    Args:
-        image: Input image
-        model_path: Path to the model
-        image_size: Image size
-        conf_threshold: Confidence threshold
-        iou_threshold: IOU threshold
-    Returns:
-        Rendered image
-    """
-    model = YOLO(model_path)
-    model.conf = conf_threshold
-    model.iou = iou_threshold
-    results = model.predict(image, imgsz=image_size, )#return_outputs=True)
-    print("Outputs", results[0].numpy())
-    # data = np.array(results[0].numpy(), dtype=np.float32)
-    print("Boxexes",results[0].boxes.boxes)
-    object_prediction_list = []
-    outputs = results[0].boxes.boxes.numpy()
-    if len(outputs)!=0:
-        for pred in outputs:
-            if pred[4] > conf_threshold:
-                
-                print(type(pred),pred)
-                x1, y1, x2, y2 = (
-                    int(pred[0]),
-                    int(pred[1]),
-                    int(pred[2]),
-                    int(pred[3]),
-                )
-                bbox = [x1, y1, x2, y2]
-                score = pred[4]
-                category_name = model.model.names[int(pred[5])]
-                category_id = pred[5]
-                object_prediction = ObjectPrediction(
-                    bbox=bbox,
-                    category_id=int(category_id),
-                    score=score,
-                    category_name=category_name,
-                )
-                object_prediction_list.append(object_prediction)
-            
-    image = read_image(image)
-    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
-    return output_image['image']        
-
-inputs = [
-    gr.inputs.Image(type="filepath", label="Input Image"),
-    gr.inputs.Dropdown(["muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection", "muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection"], 
-                       default="muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection", label="Model"),
-    gr.inputs.Slider(minimum=224, maximum=512, default=224, step=32, label="Image Size"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
-]
-
-outputs = gr.outputs.Image(type="filepath", label="Output Image")
-title = "Custom YOLOv8: Trained on Industrial Equipments Safety Detection"
-
-examples = [['12.jpeg', 'muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection', 224, 0.50, 0.45],
-            ['14.jpg', 'muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection', 224, 0.49, 0.45],
-            ['16.jpeg', 'muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection', 244, 0.44, 0.35], 
-            ['19.jpeg', 'muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection', 228, 0.45, 0.41],
-            ['9.jpeg', 'muttalib1326/YOLOv8-Industrial-Equipments-safety-Detection', 280, 0.48, 0.43]]
-demo_app = gr.Interface(
-    fn=yolov8_inference,
-    inputs=inputs,
-    outputs=outputs,
-    title=title,
-    examples=examples,
-    cache_examples=False,
-    theme='huggingface',
-)
-demo_app.launch(debug=True, enable_queue=False)
\ No newline at end of file
diff --git a/spaces/mygyasir/invisiblecat-junior-diffusion/README.md b/spaces/mygyasir/invisiblecat-junior-diffusion/README.md
deleted file mode 100644
index 69c170cfd2ab164a7f7d08188b683765b9d86556..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/invisiblecat-junior-diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Invisiblecat Junior Diffusion
-emoji: 🐢
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.41.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/nadiaoktiarsy/deployment/main.py b/spaces/nadiaoktiarsy/deployment/main.py
deleted file mode 100644
index 3cab823e7a248a61165668347688e8b9be70434d..0000000000000000000000000000000000000000
--- a/spaces/nadiaoktiarsy/deployment/main.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import streamlit as st
-import eda
-import prediction
-
-navigation = st.sidebar.selectbox('Choose Page:', ('Exploratory Data Analysis (EDA)', 'Prediction (Pass/Fail)'))
-
-if navigation == 'Exploratory Data Analysis (EDA)':
-    eda.run()
-else:
-    prediction.run()
\ No newline at end of file
diff --git a/spaces/nakas/MusicGenDemucs/CHANGELOG.md b/spaces/nakas/MusicGenDemucs/CHANGELOG.md
deleted file mode 100644
index 24fc214df236b40efead4b1585b01632d9658e9b..0000000000000000000000000000000000000000
--- a/spaces/nakas/MusicGenDemucs/CHANGELOG.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# Changelog
-
-All notable changes to this project will be documented in this file.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
-
-## [0.0.2a] - TBD
-
-Improved demo, fixed top p (thanks @jnordberg).
-
-Compressor tanh on output to avoid clipping with some style (especially piano).
-Now repeating the conditioning periodically if it is too short.
-
-More options when launching Gradio app locally (thanks @ashleykleynhans).
-
-Testing out PyTorch 2.0 memory efficient attention.
-
-Added extended generation (infinite length) by slowly moving the windows.
-Note that other implementations exist: https://github.com/camenduru/MusicGen-colab.
-
-## [0.0.1] - 2023-06-09
-
-Initial release, with model evaluation only.
diff --git a/spaces/nateraw/host-a-blog-on-huggingface-spaces/README.md b/spaces/nateraw/host-a-blog-on-huggingface-spaces/README.md
deleted file mode 100644
index facc75e2558ac6c3a72e0e41cf4b9f770c4bf09f..0000000000000000000000000000000000000000
--- a/spaces/nateraw/host-a-blog-on-huggingface-spaces/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: Host a Blog on HuggingFace Spaces
-emoji: 🤗
-colorFrom: blue
-colorTo: red
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Host a Blog on HuggingFace Spaces
-
-[![Generic badge](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/nateraw/host-a-blog-on-huggingface-spaces)
-
-How to host a blog on 🤗
-
----
-
-Autogenerated using [this template](https://github.com/nateraw/spaces-template)
diff --git a/spaces/naveed92/topic_segmentation/README.md b/spaces/naveed92/topic_segmentation/README.md
deleted file mode 100644
index bc886adbc6f18f1b72123238cfd68572e46ea750..0000000000000000000000000000000000000000
--- a/spaces/naveed92/topic_segmentation/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: Topic Segmentation
-emoji: 🚀
-colorFrom: indigo
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-## Unsupervised Topic Segmentation of text based on the TextTiling algorithm
-
-### Blog Post explaining the Algorithm and Code:
-https://www.naveedafzal.com/posts/an-introduction-to-unsupervised-topic-segmentation-with-implementation/
-
-### Related Papers
-- https://aclanthology.org/J97-1003.pdf
-- https://aclanthology.org/W12-3307.pdf
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Freestyle Libre Pro Software Download For Mac.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Freestyle Libre Pro Software Download For Mac.md
deleted file mode 100644
index a1d5392b26d5ab1e9060502852175d25547041bc..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Freestyle Libre Pro Software Download For Mac.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-
-<h1>How to Download and Install Freestyle Libre Pro Software on Mac</h1>
-<p>Freestyle Libre Pro is a software that allows you to view and analyze glucose data from the Freestyle Libre Pro sensor, a device that measures glucose levels in interstitial fluid. The software is compatible with Mac OS X 10.10 or later. Here are the steps to download and install Freestyle Libre Pro software on your Mac:</p>
-<h2>Freestyle Libre Pro Software Download For Mac</h2><br /><p><b><b>Download File</b> &#10026;&#10026;&#10026; <a href="https://urlcod.com/2uIaKO">https://urlcod.com/2uIaKO</a></b></p><br /><br />
-<ol>
-<li>Go to the <a href="https://www.freestylelibrepro.us/hcp/software-download.html">Freestyle Libre Pro website</a> and click on the "Download Now" button.</li>
-<li>Enter your email address and agree to the terms and conditions. Then click on the "Submit" button.</li>
-<li>You will receive an email with a link to download the software. Click on the link and save the file to your computer.</li>
-<li>Open the downloaded file and follow the instructions to install the software. You may need to enter your administrator password.</li>
-<li>Once the installation is complete, you can launch the software from your Applications folder or from the Launchpad.</li>
-</ol>
-<p>To use the software, you will need a Freestyle Libre Pro reader and a Freestyle Libre Pro sensor. You can connect the reader to your Mac using a USB cable and upload the data from the sensor. You can then view and analyze the glucose data using various reports and graphs.</p>
-<p>For more information, you can refer to the <a href="https://www.freestylelibrepro.us/content/dam/adc/freestylelibrepro-us/hcp/en_US/pdfs/FreeStyle-Libre-Pro-Software-User-Guide.pdf">Freestyle Libre Pro Software User Guide</a> or contact the customer support at 1-855-632-8658.</p>
-
-<p>Freestyle Libre Pro software can help you and your healthcare provider to better understand your glucose patterns and trends. You can also share your data with your provider using the LibreView cloud-based system. To do so, you will need to create a LibreView account and link it to your Freestyle Libre Pro software. Here are the steps to share your data using LibreView:</p>
-<p></p>
-<ol>
-<li>Go to the <a href="https://www.libreview.com/">LibreView website</a> and click on the "Sign Up" button.</li>
-<li>Fill out the registration form and agree to the terms and conditions. Then click on the "Create Account" button.</li>
-<li>You will receive an email with a link to verify your account. Click on the link and log in to your account.</li>
-<li>On the LibreView dashboard, click on the "Link Device" button and select "Freestyle Libre Pro Software".</li>
-<li>Enter the serial number of your Freestyle Libre Pro reader and click on the "Link Device" button.</li>
-<li>Open the Freestyle Libre Pro software on your Mac and go to the "Settings" menu. Then click on the "LibreView" tab and enter your LibreView username and password. Then click on the "Connect" button.</li>
-<li>Now you can upload your data from the reader to the software and it will automatically sync with your LibreView account. You can also view your data online using any web browser.</li>
-</ol>
-<p>To share your data with your healthcare provider, you will need to invite them to view your account. You can do this by clicking on the "Share" button on the LibreView dashboard and entering their email address. They will receive an invitation to join LibreView and access your data. You can also revoke their access at any time.</p>
-<p>For more information, you can refer to the <a href="https://www.freestylelibrepro.us/content/dam/adc/freestylelibrepro-us/hcp/en_US/pdfs/LibreView-Quick-Start-Guide.pdf">LibreView Quick Start Guide</a> or contact the customer support at 1-855-786-4263.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/InPixio Photo Eraser 8.0.0 Pre Cracked TOP.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/InPixio Photo Eraser 8.0.0 Pre Cracked TOP.md
deleted file mode 100644
index d24d2399dc8ca26f5f61cbeff77ac2137796cb46..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/InPixio Photo Eraser 8.0.0 Pre Cracked TOP.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-
-<h1>How to Remove Unwanted Objects from Your Photos with InPixio Photo Eraser 8.0.0 Pre Cracked</h1>
-<p>Have you ever taken a perfect photo, only to discover that there is something or someone in the background that ruins it? Maybe it's a trash can, a photobomber, or a power line. Whatever it is, you wish you could erase it from your photo and make it look like it was never there.</p>
-<p>Well, now you can, thanks to InPixio Photo Eraser 8.0.0 Pre Cracked. This is a powerful photo editing software that allows you to remove any unwanted object from your photos with just a few clicks. You don't need any Photoshop skills or experience to use it. All you need is InPixio Photo Eraser 8.0.0 Pre Cracked and your photos.</p>
-<h2>InPixio Photo Eraser 8.0.0 Pre Cracked</h2><br /><p><b><b>Download Zip</b> &bull; <a href="https://urlcod.com/2uIbIo">https://urlcod.com/2uIbIo</a></b></p><br /><br />
-<h2>What is InPixio Photo Eraser 8.0.0 Pre Cracked?</h2>
-<p>InPixio Photo Eraser 8.0.0 Pre Cracked is the latest version of InPixio Photo Eraser, a popular photo editing software that lets you erase anything from your photos. It has a simple and intuitive interface that makes it easy to use for anyone.</p>
-<p>InPixio Photo Eraser 8.0.0 Pre Cracked has several features that make it stand out from other photo editing software. Some of them are:</p>
-<ul>
-<li>It can remove any object from your photos, whether it's large or small, in the foreground or background.</li>
-<li>It can automatically fill in the empty space left by the erased object with the surrounding pixels, creating a seamless and realistic result.</li>
-<li>It can also clone any part of your photo and use it to cover up the erased object, giving you more control and flexibility.</li>
-<li>It can crop, resize, rotate, and adjust the brightness, contrast, and color of your photos.</li>
-<li>It can save your photos in various formats, such as JPG, PNG, BMP, TIFF, and GIF.</li>
-<li>It can also share your photos directly to social media platforms, such as Facebook, Twitter, Instagram, and Pinterest.</li>
-</ul>
-<h2>How to Use InPixio Photo Eraser 8.0.0 Pre Cracked?</h2>
-<p>Using InPixio Photo Eraser 8.0.0 Pre Cracked is very easy and fast. Here are the steps you need to follow:</p>
-<p></p>
-<ol>
-<li>Download InPixio Photo Eraser 8.0.0 Pre Cracked from <a href="https://www.inpixio.com/photo-eraser/">here</a>. It is a pre cracked version, so you don't need to worry about activation or registration.</li>
-<li>Install InPixio Photo Eraser 8.0.0 Pre Cracked on your computer and launch it.</li>
-<li>Open the photo you want to edit by clicking on the "Open" button or dragging and dropping it into the software.</li>
-<li>Select the object you want to erase by using the "Eraser" tool or the "Lasso" tool. You can adjust the size and shape of the tool by using the slider or the mouse wheel.</li>
-<li>Click on the "Erase" button and wait for a few seconds while InPixio Photo Eraser 8.0.0 Pre Cracked removes the object from your photo.</li>
-<li>If you are not satisfied with the result, you can use the "Undo" button or the "Clone" tool to make further adjustments.</li>
-<li>When you are happy with the result, you can save your photo by clicking on the "Save" button or share it online by clicking on the "Share" button.</li>
-</ol>
-<h2>Examples of InPixio Photo Eraser 8.0.0 Pre Cracked in Action</h2>
-<p>To give you an idea of what InPixio Photo Eraser 8.0.0 Pre Cracked can do for your photos, here are some examples of before and after photos edited with InPixio Photo Eraser 8.0.0 Pre Cracked:</p>
-
-<img</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/checkpoint/catalog.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/checkpoint/catalog.py
deleted file mode 100644
index 9a85736754a0de4550df96c22f38fc515bd02d71..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/checkpoint/catalog.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-
-from detectron2.utils.file_io import PathHandler, PathManager
-
-
-class ModelCatalog(object):
-    """
-    Store mappings from names to third-party models.
-    """
-
-    S3_C2_DETECTRON_PREFIX = "https://dl.fbaipublicfiles.com/detectron"
-
-    # MSRA models have STRIDE_IN_1X1=True. False otherwise.
-    # NOTE: all BN models here have fused BN into an affine layer.
-    # As a result, you should only load them to a model with "FrozenBN".
-    # Loading them to a model with regular BN or SyncBN is wrong.
-    # Even when loaded to FrozenBN, it is still different from affine by an epsilon,
-    # which should be negligible for training.
-    # NOTE: all models here uses PIXEL_STD=[1,1,1]
-    # NOTE: Most of the BN models here are no longer used. We use the
-    # re-converted pre-trained models under detectron2 model zoo instead.
-    C2_IMAGENET_MODELS = {
-        "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl",
-        "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl",
-        "FAIR/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl",
-        "FAIR/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl",
-        "FAIR/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl",
-        "FAIR/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl",
-        "FAIR/X-152-32x8d-IN5k": "ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl",
-    }
-
-    C2_DETECTRON_PATH_FORMAT = (
-        "{prefix}/{url}/output/train/{dataset}/{type}/model_final.pkl"  # noqa B950
-    )
-
-    C2_DATASET_COCO = "coco_2014_train%3Acoco_2014_valminusminival"
-    C2_DATASET_COCO_KEYPOINTS = "keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival"
-
-    # format: {model_name} -> part of the url
-    C2_DETECTRON_MODELS = {
-        "35857197/e2e_faster_rcnn_R-50-C4_1x": "35857197/12_2017_baselines/e2e_faster_rcnn_R-50-C4_1x.yaml.01_33_49.iAX0mXvW",  # noqa B950
-        "35857345/e2e_faster_rcnn_R-50-FPN_1x": "35857345/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_1x.yaml.01_36_30.cUF7QR7I",  # noqa B950
-        "35857890/e2e_faster_rcnn_R-101-FPN_1x": "35857890/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_1x.yaml.01_38_50.sNxI7sX7",  # noqa B950
-        "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "36761737/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_1x.yaml.06_31_39.5MIHi1fZ",  # noqa B950
-        "35858791/e2e_mask_rcnn_R-50-C4_1x": "35858791/12_2017_baselines/e2e_mask_rcnn_R-50-C4_1x.yaml.01_45_57.ZgkA7hPB",  # noqa B950
-        "35858933/e2e_mask_rcnn_R-50-FPN_1x": "35858933/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml.01_48_14.DzEQe4wC",  # noqa B950
-        "35861795/e2e_mask_rcnn_R-101-FPN_1x": "35861795/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT",  # noqa B950
-        "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "36761843/12_2017_baselines/e2e_mask_rcnn_X-101-32x8d-FPN_1x.yaml.06_35_59.RZotkLKI",  # noqa B950
-        "48616381/e2e_mask_rcnn_R-50-FPN_2x_gn": "GN/48616381/04_2018_gn_baselines/e2e_mask_rcnn_R-50-FPN_2x_gn_0416.13_23_38.bTlTI97Q",  # noqa B950
-        "37697547/e2e_keypoint_rcnn_R-50-FPN_1x": "37697547/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_1x.yaml.08_42_54.kdzV35ao",  # noqa B950
-        "35998355/rpn_R-50-C4_1x": "35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L",  # noqa B950
-        "35998814/rpn_R-50-FPN_1x": "35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179",  # noqa B950
-        "36225147/fast_R-50-FPN_1x": "36225147/12_2017_baselines/fast_rcnn_R-50-FPN_1x.yaml.08_39_09.L3obSdQ2",  # noqa B950
-    }
-
-    @staticmethod
-    def get(name):
-        if name.startswith("Caffe2Detectron/COCO"):
-            return ModelCatalog._get_c2_detectron_baseline(name)
-        if name.startswith("ImageNetPretrained/"):
-            return ModelCatalog._get_c2_imagenet_pretrained(name)
-        raise RuntimeError("model not present in the catalog: {}".format(name))
-
-    @staticmethod
-    def _get_c2_imagenet_pretrained(name):
-        prefix = ModelCatalog.S3_C2_DETECTRON_PREFIX
-        name = name[len("ImageNetPretrained/") :]
-        name = ModelCatalog.C2_IMAGENET_MODELS[name]
-        url = "/".join([prefix, name])
-        return url
-
-    @staticmethod
-    def _get_c2_detectron_baseline(name):
-        name = name[len("Caffe2Detectron/COCO/") :]
-        url = ModelCatalog.C2_DETECTRON_MODELS[name]
-        if "keypoint_rcnn" in name:
-            dataset = ModelCatalog.C2_DATASET_COCO_KEYPOINTS
-        else:
-            dataset = ModelCatalog.C2_DATASET_COCO
-
-        if "35998355/rpn_R-50-C4_1x" in name:
-            # this one model is somehow different from others ..
-            type = "rpn"
-        else:
-            type = "generalized_rcnn"
-
-        # Detectron C2 models are stored in the structure defined in `C2_DETECTRON_PATH_FORMAT`.
-        url = ModelCatalog.C2_DETECTRON_PATH_FORMAT.format(
-            prefix=ModelCatalog.S3_C2_DETECTRON_PREFIX, url=url, type=type, dataset=dataset
-        )
-        return url
-
-
-class ModelCatalogHandler(PathHandler):
-    """
-    Resolve URL like catalog://.
-    """
-
-    PREFIX = "catalog://"
-
-    def _get_supported_prefixes(self):
-        return [self.PREFIX]
-
-    def _get_local_path(self, path, **kwargs):
-        logger = logging.getLogger(__name__)
-        catalog_path = ModelCatalog.get(path[len(self.PREFIX) :])
-        logger.info("Catalog entry {} points to {}".format(path, catalog_path))
-        return PathManager.get_local_path(catalog_path, **kwargs)
-
-    def _open(self, path, mode="r", **kwargs):
-        return PathManager.open(self._get_local_path(path), mode, **kwargs)
-
-
-PathManager.register_handler(ModelCatalogHandler())
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/converters/segm_to_mask.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/converters/segm_to_mask.py
deleted file mode 100644
index 6433d5dec75c3d6141252af144b61d8999077bb7..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/converters/segm_to_mask.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from typing import Any
-import torch
-from torch.nn import functional as F
-
-from detectron2.structures import BitMasks, Boxes, BoxMode
-
-from .base import IntTupleBox, make_int_box
-from .to_mask import ImageSizeType
-
-
-def resample_coarse_segm_tensor_to_bbox(coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox):
-    """
-    Resample coarse segmentation tensor to the given
-    bounding box and derive labels for each pixel of the bounding box
-
-    Args:
-        coarse_segm: float tensor of shape [1, K, Hout, Wout]
-        box_xywh_abs (tuple of 4 int): bounding box given by its upper-left
-            corner coordinates, width (W) and height (H)
-    Return:
-        Labels for each pixel of the bounding box, a long tensor of size [1, H, W]
-    """
-    x, y, w, h = box_xywh_abs
-    w = max(int(w), 1)
-    h = max(int(h), 1)
-    labels = F.interpolate(coarse_segm, (h, w), mode="bilinear", align_corners=False).argmax(dim=1)
-    return labels
-
-
-def resample_fine_and_coarse_segm_tensors_to_bbox(
-    fine_segm: torch.Tensor, coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox
-):
-    """
-    Resample fine and coarse segmentation tensors to the given
-    bounding box and derive labels for each pixel of the bounding box
-
-    Args:
-        fine_segm: float tensor of shape [1, C, Hout, Wout]
-        coarse_segm: float tensor of shape [1, K, Hout, Wout]
-        box_xywh_abs (tuple of 4 int): bounding box given by its upper-left
-            corner coordinates, width (W) and height (H)
-    Return:
-        Labels for each pixel of the bounding box, a long tensor of size [1, H, W]
-    """
-    x, y, w, h = box_xywh_abs
-    w = max(int(w), 1)
-    h = max(int(h), 1)
-    # coarse segmentation
-    coarse_segm_bbox = F.interpolate(
-        coarse_segm,
-        (h, w),
-        mode="bilinear",
-        align_corners=False,
-    ).argmax(dim=1)
-    # combined coarse and fine segmentation
-    labels = (
-        F.interpolate(fine_segm, (h, w), mode="bilinear", align_corners=False).argmax(dim=1)
-        * (coarse_segm_bbox > 0).long()
-    )
-    return labels
-
-
-def resample_fine_and_coarse_segm_to_bbox(predictor_output: Any, box_xywh_abs: IntTupleBox):
-    """
-    Resample fine and coarse segmentation outputs from a predictor to the given
-    bounding box and derive labels for each pixel of the bounding box
-
-    Args:
-        predictor_output: DensePose predictor output that contains segmentation
-            results to be resampled
-        box_xywh_abs (tuple of 4 int): bounding box given by its upper-left
-            corner coordinates, width (W) and height (H)
-    Return:
-        Labels for each pixel of the bounding box, a long tensor of size [1, H, W]
-    """
-    return resample_fine_and_coarse_segm_tensors_to_bbox(
-        predictor_output.fine_segm,
-        predictor_output.coarse_segm,
-        box_xywh_abs,
-    )
-
-
-def predictor_output_with_coarse_segm_to_mask(
-    predictor_output: Any, boxes: Boxes, image_size_hw: ImageSizeType
-) -> BitMasks:
-    """
-    Convert predictor output with coarse and fine segmentation to a mask.
-    Assumes that predictor output has the following attributes:
-     - coarse_segm (tensor of size [N, D, H, W]): coarse segmentation
-         unnormalized scores for N instances; D is the number of coarse
-         segmentation labels, H and W is the resolution of the estimate
-
-    Args:
-        predictor_output: DensePose predictor output to be converted to mask
-        boxes (Boxes): bounding boxes that correspond to the DensePose
-            predictor outputs
-        image_size_hw (tuple [int, int]): image height Himg and width Wimg
-    Return:
-        BitMasks that contain a bool tensor of size [N, Himg, Wimg] with
-        a mask of the size of the image for each instance
-    """
-    H, W = image_size_hw
-    boxes_xyxy_abs = boxes.tensor.clone()
-    boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
-    N = len(boxes_xywh_abs)
-    masks = torch.zeros((N, H, W), dtype=torch.bool, device=boxes.tensor.device)
-    for i in range(len(boxes_xywh_abs)):
-        box_xywh = make_int_box(boxes_xywh_abs[i])
-        box_mask = resample_coarse_segm_tensor_to_bbox(predictor_output[i].coarse_segm, box_xywh)
-        x, y, w, h = box_xywh
-        masks[i, y : y + h, x : x + w] = box_mask
-
-    return BitMasks(masks)
-
-
-def predictor_output_with_fine_and_coarse_segm_to_mask(
-    predictor_output: Any, boxes: Boxes, image_size_hw: ImageSizeType
-) -> BitMasks:
-    """
-    Convert predictor output with coarse and fine segmentation to a mask.
-    Assumes that predictor output has the following attributes:
-     - coarse_segm (tensor of size [N, D, H, W]): coarse segmentation
-         unnormalized scores for N instances; D is the number of coarse
-         segmentation labels, H and W is the resolution of the estimate
-     - fine_segm (tensor of size [N, C, H, W]): fine segmentation
-         unnormalized scores for N instances; C is the number of fine
-         segmentation labels, H and W is the resolution of the estimate
-
-    Args:
-        predictor_output: DensePose predictor output to be converted to mask
-        boxes (Boxes): bounding boxes that correspond to the DensePose
-            predictor outputs
-        image_size_hw (tuple [int, int]): image height Himg and width Wimg
-    Return:
-        BitMasks that contain a bool tensor of size [N, Himg, Wimg] with
-        a mask of the size of the image for each instance
-    """
-    H, W = image_size_hw
-    boxes_xyxy_abs = boxes.tensor.clone()
-    boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
-    N = len(boxes_xywh_abs)
-    masks = torch.zeros((N, H, W), dtype=torch.bool, device=boxes.tensor.device)
-    for i in range(len(boxes_xywh_abs)):
-        box_xywh = make_int_box(boxes_xywh_abs[i])
-        labels_i = resample_fine_and_coarse_segm_to_bbox(predictor_output[i], box_xywh)
-        x, y, w, h = box_xywh
-        masks[i, y : y + h, x : x + w] = labels_i > 0
-    return BitMasks(masks)
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/data/video/frame_selector.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/data/video/frame_selector.py
deleted file mode 100644
index c28f0e96475537319ff584f73fa422f838ae7b40..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/data/video/frame_selector.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import random
-from collections.abc import Callable
-from enum import Enum
-from typing import Callable as TCallable
-from typing import List
-
-FrameTsList = List[int]
-FrameSelector = TCallable[[FrameTsList], FrameTsList]
-
-
-class FrameSelectionStrategy(Enum):
-    """
-    Frame selection strategy used with videos:
-     - "random_k": select k random frames
-     - "first_k": select k first frames
-     - "last_k": select k last frames
-     - "all": select all frames
-    """
-
-    # fmt: off
-    RANDOM_K = "random_k"
-    FIRST_K  = "first_k"
-    LAST_K   = "last_k"
-    ALL      = "all"
-    # fmt: on
-
-
-class RandomKFramesSelector(Callable):  # pyre-ignore[39]
-    """
-    Selector that retains at most `k` random frames
-    """
-
-    def __init__(self, k: int):
-        self.k = k
-
-    def __call__(self, frame_tss: FrameTsList) -> FrameTsList:
-        """
-        Select `k` random frames
-
-        Args:
-          frames_tss (List[int]): timestamps of input frames
-        Returns:
-          List[int]: timestamps of selected frames
-        """
-        return random.sample(frame_tss, min(self.k, len(frame_tss)))
-
-
-class FirstKFramesSelector(Callable):  # pyre-ignore[39]
-    """
-    Selector that retains at most `k` first frames
-    """
-
-    def __init__(self, k: int):
-        self.k = k
-
-    def __call__(self, frame_tss: FrameTsList) -> FrameTsList:
-        """
-        Select `k` first frames
-
-        Args:
-          frames_tss (List[int]): timestamps of input frames
-        Returns:
-          List[int]: timestamps of selected frames
-        """
-        return frame_tss[: self.k]
-
-
-class LastKFramesSelector(Callable):  # pyre-ignore[39]
-    """
-    Selector that retains at most `k` last frames from video data
-    """
-
-    def __init__(self, k: int):
-        self.k = k
-
-    def __call__(self, frame_tss: FrameTsList) -> FrameTsList:
-        """
-        Select `k` last frames
-
-        Args:
-          frames_tss (List[int]): timestamps of input frames
-        Returns:
-          List[int]: timestamps of selected frames
-        """
-        return frame_tss[-self.k :]
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/confidence.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/confidence.py
deleted file mode 100644
index 6f4a72efec06e055036ba70bc75b2624d20e1e0e..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/confidence.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from dataclasses import dataclass
-from enum import Enum
-
-from detectron2.config import CfgNode
-
-
-class DensePoseUVConfidenceType(Enum):
-    """
-    Statistical model type for confidence learning, possible values:
-     - "iid_iso": statistically independent identically distributed residuals
-         with anisotropic covariance
-     - "indep_aniso": statistically independent residuals with anisotropic
-         covariances
-    For details, see:
-    N. Neverova, D. Novotny, A. Vedaldi "Correlated Uncertainty for Learning
-    Dense Correspondences from Noisy Labels", p. 918--926, in Proc. NIPS 2019
-    """
-
-    # fmt: off
-    IID_ISO     = "iid_iso"
-    INDEP_ANISO = "indep_aniso"
-    # fmt: on
-
-
-@dataclass
-class DensePoseUVConfidenceConfig:
-    """
-    Configuration options for confidence on UV data
-    """
-
-    enabled: bool = False
-    # lower bound on UV confidences
-    epsilon: float = 0.01
-    type: DensePoseUVConfidenceType = DensePoseUVConfidenceType.IID_ISO
-
-
-@dataclass
-class DensePoseSegmConfidenceConfig:
-    """
-    Configuration options for confidence on segmentation
-    """
-
-    enabled: bool = False
-    # lower bound on confidence values
-    epsilon: float = 0.01
-
-
-@dataclass
-class DensePoseConfidenceModelConfig:
-    """
-    Configuration options for confidence models
-    """
-
-    # confidence for U and V values
-    uv_confidence: DensePoseUVConfidenceConfig
-    # segmentation confidence
-    segm_confidence: DensePoseSegmConfidenceConfig
-
-    @staticmethod
-    def from_cfg(cfg: CfgNode) -> "DensePoseConfidenceModelConfig":
-        return DensePoseConfidenceModelConfig(
-            uv_confidence=DensePoseUVConfidenceConfig(
-                enabled=cfg.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.ENABLED,
-                epsilon=cfg.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.EPSILON,
-                type=DensePoseUVConfidenceType(cfg.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.TYPE),
-            ),
-            segm_confidence=DensePoseSegmConfidenceConfig(
-                enabled=cfg.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.ENABLED,
-                epsilon=cfg.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.EPSILON,
-            ),
-        )
diff --git a/spaces/noelshin/selfmask/networks/timm_deit.py b/spaces/noelshin/selfmask/networks/timm_deit.py
deleted file mode 100644
index 95754e99af6f3c18ae649e807f9d0ef800d466f5..0000000000000000000000000000000000000000
--- a/spaces/noelshin/selfmask/networks/timm_deit.py
+++ /dev/null
@@ -1,254 +0,0 @@
-# Copyright (c) 2015-present, Facebook, Inc.
-# All rights reserved.
-import math
-import torch
-import torch.nn as nn
-from functools import partial
-
-from networks.timm_vit import VisionTransformer, _cfg
-from timm.models.registry import register_model
-from timm.models.layers import trunc_normal_
-
-
-__all__ = [
-    'deit_tiny_patch16_224', 'deit_small_patch16_224', 'deit_base_patch16_224',
-    'deit_tiny_distilled_patch16_224', 'deit_small_distilled_patch16_224',
-    'deit_base_distilled_patch16_224', 'deit_base_patch16_384',
-    'deit_base_distilled_patch16_384',
-]
-
-
-class DistilledVisionTransformer(VisionTransformer):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
-        num_patches = self.patch_embed.num_patches
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, self.embed_dim))
-        self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity()
-
-        trunc_normal_(self.dist_token, std=.02)
-        trunc_normal_(self.pos_embed, std=.02)
-        self.head_dist.apply(self._init_weights)
-
-    def forward_features(self, x):
-        # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
-        # with slight modifications to add the dist_token
-        B = x.shape[0]
-        x = self.patch_embed(x)
-
-        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-        dist_token = self.dist_token.expand(B, -1, -1)
-        x = torch.cat((cls_tokens, dist_token, x), dim=1)
-
-        x = x + self.pos_embed
-        x = self.pos_drop(x)
-
-        for blk in self.blocks:
-            x = blk(x)
-
-        x = self.norm(x)
-        return x[:, 0], x[:, 1]
-
-    def forward(self, x):
-        x, x_dist = self.forward_features(x)
-        x = self.head(x)
-        x_dist = self.head_dist(x_dist)
-        if self.training:
-            return x, x_dist
-        else:
-            # during inference, return the average of both classifier predictions
-            return (x + x_dist) / 2
-
-    def interpolate_pos_encoding(self, x, pos_embed):
-        """Interpolate the learnable positional encoding to match the number of patches.
-
-        x: B x (1 + 1 + N patches) x dim_embedding
-        pos_embed: B x (1 + 1 + N patches) x dim_embedding
-
-        return interpolated positional embedding
-        """
-
-        npatch = x.shape[1] - 2  # (H // patch_size * W // patch_size)
-        N = pos_embed.shape[1] - 2  # 784 (= 28 x 28)
-
-        if npatch == N:
-            return pos_embed
-
-        class_emb, distil_token, pos_embed = pos_embed[:, 0], pos_embed[:, 1], pos_embed[:, 2:]  # a learnable CLS token, learnable position embeddings
-
-        dim = x.shape[-1]  # dimension of embeddings
-        pos_embed = nn.functional.interpolate(
-            pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),  # B x dim x 28 x 28
-            scale_factor=math.sqrt(npatch / N) + 1e-5,  # noel: this can be a float, but the output shape will be integer.
-            recompute_scale_factor=True,
-            mode='bicubic'
-        )
-        # print("pos_embed", pos_embed.shape, npatch, N, math.sqrt(npatch/N), math.sqrt(npatch/N) * int(math.sqrt(N)))
-        # exit(12)
-        pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
-        pos_embed = torch.cat((class_emb.unsqueeze(0), distil_token.unsqueeze(0), pos_embed), dim=1)
-        return pos_embed
-
-    def get_tokens(
-            self,
-            x,
-            layers: list,
-            patch_tokens: bool = False,
-            norm: bool = True,
-            input_tokens: bool = False,
-            post_pe: bool = False
-    ):
-        """Return intermediate tokens."""
-        list_tokens: list = []
-
-        B = x.shape[0]
-        x = self.patch_embed(x)
-
-        cls_tokens = self.cls_token.expand(B, -1, -1)
-        dist_token = self.dist_token.expand(B, -1, -1)
-
-        x = torch.cat((cls_tokens, dist_token, x), dim=1)
-
-        if input_tokens:
-            list_tokens.append(x)
-
-        pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
-        x = x + pos_embed
-
-        if post_pe:
-            list_tokens.append(x)
-
-        x = self.pos_drop(x)
-
-        for i, blk in enumerate(self.blocks):
-            x = blk(x)  # B x # patches x dim
-            if layers is None or i in layers:
-                list_tokens.append(self.norm(x) if norm else x)
-
-        tokens = torch.stack(list_tokens, dim=1)  # B x n_layers x (1 + # patches) x dim
-
-        if not patch_tokens:
-            return tokens[:, :, 0, :]  # index [CLS] tokens only, B x n_layers x dim
-
-        else:
-            return torch.cat((tokens[:, :, 0, :].unsqueeze(dim=2), tokens[:, :, 2:, :]), dim=2)  # exclude distil token.
-
-
-@register_model
-def deit_tiny_patch16_224(pretrained=False, **kwargs):
-    model = VisionTransformer(
-        patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-
-@register_model
-def deit_small_patch16_224(pretrained=False, **kwargs):
-    model = VisionTransformer(
-        patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-
-@register_model
-def deit_base_patch16_224(pretrained=False, **kwargs):
-    model = VisionTransformer(
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-
-@register_model
-def deit_tiny_distilled_patch16_224(pretrained=False, **kwargs):
-    model = DistilledVisionTransformer(
-        patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-
-@register_model
-def deit_small_distilled_patch16_224(pretrained=False, **kwargs):
-    model = DistilledVisionTransformer(
-        patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-
-@register_model
-def deit_base_distilled_patch16_224(pretrained=False, **kwargs):
-    model = DistilledVisionTransformer(
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-
-@register_model
-def deit_base_patch16_384(pretrained=False, **kwargs):
-    model = VisionTransformer(
-        img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
-
-
-@register_model
-def deit_base_distilled_patch16_384(pretrained=False, **kwargs):
-    model = DistilledVisionTransformer(
-        img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
-        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model.default_cfg = _cfg()
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url="https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth",
-            map_location="cpu", check_hash=True
-        )
-        model.load_state_dict(checkpoint["model"])
-    return model
diff --git a/spaces/nomic-ai/junelee_wizard_vicuna_70k/index.html b/spaces/nomic-ai/junelee_wizard_vicuna_70k/index.html
deleted file mode 100644
index d519da032da96ecd22235c1df78bf5c76337c47c..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/junelee_wizard_vicuna_70k/index.html
+++ /dev/null
@@ -1,42 +0,0 @@
-<html>
-
-<head>
-  <title>junelee/wizard_vicuna_70k</title>
-  <style>
-    body {
-      font-family: Arial, sans-serif;
-      background-color: #f0f0f0;
-      display: flex;
-      justify-content: center;
-      align-items: center;
-      height: 100vh;
-      margin: 0;
-      padding: 0;
-      color: #333;
-    }
-
-    .iframe-container {
-      border: 1px solid #ccc;
-      border-radius: 10px;
-      overflow: hidden;
-      width: 80%;
-      height: 80%;
-      box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
-    }
-
-    iframe {
-      width: 100%;
-      height: 100%;
-      border: none;
-    }
-  </style>
-</head>
-
-<body>
-  <div class="iframe-container">
-    <iframe src="https://atlas.nomic.ai/map/0478eaa7-2eab-4872-86e4-38ee4a86d925/adae4b03-92c4-4d48-97c6-5f1b5f8d929e" allow="clipboard-read; clipboard-write"
-      title="Nomic Atlas"></iframe>
-  </div>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/oguzakif/video-object-remover/FGT_codes/FGT/models/utils/reconstructionLayers.py b/spaces/oguzakif/video-object-remover/FGT_codes/FGT/models/utils/reconstructionLayers.py
deleted file mode 100644
index 37302c52f8aa65292ab722d73d6ff6af910bca52..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/FGT_codes/FGT/models/utils/reconstructionLayers.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# Two types of reconstructionl layers: 1. original residual layers, 2. residual layers with contrast and adaptive attention(CCA layer)
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-
-
-def initialize_weights(net_l, scale=1):
-    if not isinstance(net_l, list):
-        net_l = [net_l]
-    for net in net_l:
-        for m in net.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
-                m.weight.data *= scale  # for residual block
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.BatchNorm2d):
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias.data, 0.0)
-
-
-def make_layer(block, n_layers):
-    layers = []
-    for _ in range(n_layers):
-        layers.append(block())
-    return nn.Sequential(*layers)
-
-
-class ResidualBlock_noBN(nn.Module):
-    """Residual block w/o BN
-    ---Conv-ReLU-Conv-+-
-     |________________|
-    """
-
-    def __init__(self, nf=64):
-        super(ResidualBlock_noBN, self).__init__()
-        self.conv1 = nn.Conv2d(nf, nf, kernel_size=3, stride=1, padding=1, bias=True)
-        self.conv2 = nn.Conv2d(nf, nf, kernel_size=3, stride=1, padding=1, bias=True)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        """
-
-        Args:
-            x: with shape of [b, c, t, h, w]
-
-        Returns: processed features with shape [b, c, t, h, w]
-
-        """
-        identity = x
-        out = self.lrelu(self.conv1(x))
-        out = self.conv2(out)
-        out = identity + out
-        # Remove ReLU at the end of the residual block
-        # http://torch.ch/blog/2016/02/04/resnets.html
-        return out
-
-
-class ResBlock_noBN_new(nn.Module):
-    def __init__(self, nf):
-        super(ResBlock_noBN_new, self).__init__()
-        self.c1 = nn.Conv3d(nf, nf // 4, kernel_size=(1, 3, 3), stride=1, padding=(0, 1, 1), bias=True)
-        self.d1 = nn.Conv3d(nf // 4, nf // 4, kernel_size=(1, 3, 3), stride=1, padding=(0, 1, 1),
-                            bias=True)  # dilation rate=1
-        self.d2 = nn.Conv3d(nf // 4, nf // 4, kernel_size=(1, 3, 3), stride=1, padding=(0, 2, 2), dilation=(1, 2, 2),
-                            bias=True)  # dilation rate=2
-        self.d3 = nn.Conv3d(nf // 4, nf // 4, kernel_size=(1, 3, 3), stride=1, padding=(0, 4, 4), dilation=(1, 4, 4),
-                            bias=True)  # dilation rate=4
-        self.d4 = nn.Conv3d(nf // 4, nf // 4, kernel_size=(1, 3, 3), stride=1, padding=(0, 8, 8), dilation=(1, 8, 8),
-                            bias=True)  # dilation rate=8
-        self.act = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        self.c2 = nn.Conv3d(nf, nf, kernel_size=(1, 3, 3), stride=1, padding=(0, 1, 1), bias=True)
-
-    def forward(self, x):
-        output1 = self.act(self.c1(x))
-        d1 = self.d1(output1)
-        d2 = self.d2(output1)
-        d3 = self.d3(output1)
-        d4 = self.d4(output1)
-
-        add1 = d1 + d2
-        add2 = add1 + d3
-        add3 = add2 + d4
-        combine = torch.cat([d1, add1, add2, add3], dim=1)
-        output2 = self.c2(self.act(combine))
-        output = x + output2
-        # remove ReLU at the end of the residual block
-        # http://torch.ch/blog/2016/02/04/resnets.html
-        return output
-
-
-class CCALayer(nn.Module):  #############################################3 new
-    '''Residual block w/o BN
-    --conv--contrast-conv--x---
-      |    \--mean--|     |
-      |___________________|
-
-    '''
-
-    def __init__(self, nf=64):
-        super(CCALayer, self).__init__()
-        self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        self.conv_du = nn.Sequential(
-            nn.Conv2d(nf, 4, 1, padding=0, bias=True),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(4, nf, 1, padding=0, bias=True),
-            nn.Tanh()  # change from `Sigmoid` to `Tanh` to make the output between -1 and 1
-        )
-        self.contrast = stdv_channels
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        # initialization
-        initialize_weights([self.conv1, self.conv_du], 0.1)
-
-    def forward(self, x):
-        identity = x
-        out = self.lrelu(self.conv1(x))
-        out = self.conv2(out)
-        out = self.contrast(out) + self.avg_pool(out)
-        out_channel = self.conv_du(out)
-        out_channel = out_channel * out
-        out_last = out_channel + identity
-
-        return out_last
-
-
-def mean_channels(F):
-    assert (F.dim() == 4), 'Your dim is {} bit not 4'.format(F.dim())
-    spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True)
-    return spatial_sum / (F.size(2) * F.size(3))  # 对每一个channel都求其特征图的高和宽的平均值
-
-
-def stdv_channels(F):
-    assert F.dim() == 4, 'Your dim is {} bit not 4'.format(F.dim())
-    F_mean = mean_channels(F)
-    F_variance = (F - F_mean).pow(2).sum(3, keepdim=True).sum(2, keepdim=True) / (F.size(2) * F.size(3))
-    return F_variance.pow(0.5)
diff --git a/spaces/oldfart/removaltool/app.py b/spaces/oldfart/removaltool/app.py
deleted file mode 100644
index ecd4623cf824a563a8d374cd412feea01cc73152..0000000000000000000000000000000000000000
--- a/spaces/oldfart/removaltool/app.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import cv2
-import gradio as gr
-import os
-from PIL import Image
-import numpy as np
-import torch
-from torch.autograd import Variable
-from torchvision import transforms
-import torch.nn.functional as F
-import gdown
-import matplotlib.pyplot as plt
-import warnings
-warnings.filterwarnings("ignore")
-
-os.system("git clone https://github.com/xuebinqin/DIS")
-os.system("mv DIS/IS-Net/* .")
-
-# project imports
-from data_loader_cache import normalize, im_reader, im_preprocess 
-from models import *
-
-#Helpers
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-# Download official weights
-if not os.path.exists("saved_models"):
-    os.mkdir("saved_models")
-    MODEL_PATH_URL = "https://drive.google.com/uc?id=1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn"
-    gdown.download(MODEL_PATH_URL, "saved_models/isnet.pth", use_cookies=False)
-    
-class GOSNormalize(object):
-    '''
-    Normalize the Image using torch.transforms
-    '''
-    def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self,image):
-        image = normalize(image,self.mean,self.std)
-        return image
-
-
-transform =  transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])
-
-def load_image(im_path, hypar):
-    im = im_reader(im_path)
-    im, im_shp = im_preprocess(im, hypar["cache_size"])
-    im = torch.divide(im,255.0)
-    shape = torch.from_numpy(np.array(im_shp))
-    return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape
-
-
-def build_model(hypar,device):
-    net = hypar["model"]#GOSNETINC(3,1)
-
-    # convert to half precision
-    if(hypar["model_digit"]=="half"):
-        net.half()
-        for layer in net.modules():
-            if isinstance(layer, nn.BatchNorm2d):
-                layer.float()
-
-    net.to(device)
-
-    if(hypar["restore_model"]!=""):
-        net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"], map_location=device))
-        net.to(device)
-    net.eval()  
-    return net
-
-    
-def predict(net,  inputs_val, shapes_val, hypar, device):
-    '''
-    Given an Image, predict the mask
-    '''
-    net.eval()
-
-    if(hypar["model_digit"]=="full"):
-        inputs_val = inputs_val.type(torch.FloatTensor)
-    else:
-        inputs_val = inputs_val.type(torch.HalfTensor)
-
-  
-    inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable
-   
-    ds_val = net(inputs_val_v)[0] # list of 6 results
-
-    pred_val = ds_val[0][0,:,:,:] # B x 1 x H x W    # we want the first one which is the most accurate prediction
-
-    ## recover the prediction spatial size to the orignal image size
-    pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))
-
-    ma = torch.max(pred_val)
-    mi = torch.min(pred_val)
-    pred_val = (pred_val-mi)/(ma-mi) # max = 1
-
-    if device == 'cuda': torch.cuda.empty_cache()
-    return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need
-    
-# Set Parameters
-hypar = {} # paramters for inferencing
-
-
-hypar["model_path"] ="./saved_models" ## load trained weights from this path
-hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights
-hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision
-
-##  choose floating point accuracy --
-hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
-hypar["seed"] = 0
-
-hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size
-
-## data augmentation parameters ---
-hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
-hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
-
-hypar["model"] = ISNetDIS()
-
- # Build Model
-net = build_model(hypar, device)
-
-
-def inference(image: Image):
-  image_path = image
-  
-  image_tensor, orig_size = load_image(image_path, hypar) 
-  mask = predict(net, image_tensor, orig_size, hypar, device)
-  
-  pil_mask = Image.fromarray(mask).convert('L')
-  im_rgb = Image.open(image).convert("RGB")
-  
-  im_rgba = im_rgb.copy()
-  im_rgba.putalpha(pil_mask)
-
-  return [im_rgba,pil_mask]
-
-
-title = ""
-description = ""
-article = ""
-
-interface = gr.Interface(
-    fn=inference,
-    inputs=gr.Image(type='filepath'),
-    outputs=["image","image"],
-    examples=[['robot.png'], ['ship.png']],
-    title=title,
-    description=description,
-    article=article,
-    allow_flagging='never',
-    theme="default",
-    cache_examples=False,
-    css="footer {visibility: hidden}"
-    ).launch(enable_queue=True, debug=True)
diff --git a/spaces/omlab/vlchecklist_demo/models/vilt/datamodules/datamodule_base.py b/spaces/omlab/vlchecklist_demo/models/vilt/datamodules/datamodule_base.py
deleted file mode 100644
index b8a3ec1d93fa38e3a88d259a992c8257e8ab333e..0000000000000000000000000000000000000000
--- a/spaces/omlab/vlchecklist_demo/models/vilt/datamodules/datamodule_base.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import torch
-
-from pytorch_lightning import LightningDataModule
-from torch.utils.data import DataLoader
-from transformers import (
-    DataCollatorForLanguageModeling,
-    DataCollatorForWholeWordMask,
-    BertTokenizer,
-)
-
-
-def get_pretrained_tokenizer(from_pretrained):
-    if torch.distributed.is_initialized():
-        if torch.distributed.get_rank() == 0:
-            BertTokenizer.from_pretrained(
-                from_pretrained, do_lower_case="uncased" in from_pretrained
-            )
-        torch.distributed.barrier()
-    return BertTokenizer.from_pretrained(
-        from_pretrained, do_lower_case="uncased" in from_pretrained
-    )
-
-
-class BaseDataModule(LightningDataModule):
-    def __init__(self, _config):
-        super().__init__()
-
-        self.data_dir = _config["data_root"]
-
-        self.num_workers = _config["num_workers"]
-        self.batch_size = _config["per_gpu_batchsize"]
-        self.eval_batch_size = self.batch_size
-
-        self.image_size = _config["image_size"]
-        self.max_text_len = _config["max_text_len"]
-        self.draw_false_image = _config["draw_false_image"]
-        self.draw_false_text = _config["draw_false_text"]
-        self.image_only = _config["image_only"]
-
-        self.train_transform_keys = (
-            ["default_train"]
-            if len(_config["train_transform_keys"]) == 0
-            else _config["train_transform_keys"]
-        )
-
-        self.val_transform_keys = (
-            ["default_val"]
-            if len(_config["val_transform_keys"]) == 0
-            else _config["val_transform_keys"]
-        )
-
-        tokenizer = _config["tokenizer"]
-        self.tokenizer = get_pretrained_tokenizer(tokenizer)
-        self.vocab_size = self.tokenizer.vocab_size
-
-        collator = (
-            DataCollatorForWholeWordMask
-            if _config["whole_word_masking"]
-            else DataCollatorForLanguageModeling
-        )
-
-        self.mlm_collator = collator(
-            tokenizer=self.tokenizer, mlm=True, mlm_probability=_config["mlm_prob"]
-        )
-        self.setup_flag = False
-
-    @property
-    def dataset_cls(self):
-        raise NotImplementedError("return tuple of dataset class")
-
-    @property
-    def dataset_name(self):
-        raise NotImplementedError("return name of dataset")
-
-    def set_train_dataset(self):
-        self.train_dataset = self.dataset_cls(
-            self.data_dir,
-            self.train_transform_keys,
-            split="train",
-            image_size=self.image_size,
-            max_text_len=self.max_text_len,
-            draw_false_image=self.draw_false_image,
-            draw_false_text=self.draw_false_text,
-            image_only=self.image_only,
-        )
-
-    def set_val_dataset(self):
-        self.val_dataset = self.dataset_cls(
-            self.data_dir,
-            self.val_transform_keys,
-            split="val",
-            image_size=self.image_size,
-            max_text_len=self.max_text_len,
-            draw_false_image=self.draw_false_image,
-            draw_false_text=self.draw_false_text,
-            image_only=self.image_only,
-        )
-
-        if hasattr(self, "dataset_cls_no_false"):
-            self.val_dataset_no_false = self.dataset_cls_no_false(
-                self.data_dir,
-                self.val_transform_keys,
-                split="val",
-                image_size=self.image_size,
-                max_text_len=self.max_text_len,
-                draw_false_image=0,
-                draw_false_text=0,
-                image_only=self.image_only,
-            )
-
-    def make_no_false_val_dset(self, image_only=False):
-        return self.dataset_cls_no_false(
-            self.data_dir,
-            self.val_transform_keys,
-            split="val",
-            image_size=self.image_size,
-            max_text_len=self.max_text_len,
-            draw_false_image=0,
-            draw_false_text=0,
-            image_only=image_only,
-        )
-
-    def set_test_dataset(self):
-        self.test_dataset = self.dataset_cls(
-            self.data_dir,
-            self.val_transform_keys,
-            split="test",
-            image_size=self.image_size,
-            max_text_len=self.max_text_len,
-            draw_false_image=self.draw_false_image,
-            draw_false_text=self.draw_false_text,
-            image_only=self.image_only,
-        )
-
-    def setup(self, stage):
-        if not self.setup_flag:
-            self.set_train_dataset()
-            self.set_val_dataset()
-            self.set_test_dataset()
-
-            self.train_dataset.tokenizer = self.tokenizer
-            self.val_dataset.tokenizer = self.tokenizer
-            self.test_dataset.tokenizer = self.tokenizer
-
-            self.setup_flag = True
-
-    def train_dataloader(self):
-        loader = DataLoader(
-            self.train_dataset,
-            batch_size=self.batch_size,
-            shuffle=True,
-            num_workers=self.num_workers,
-            pin_memory=True,
-            collate_fn=self.train_dataset.collate,
-        )
-        return loader
-
-    def val_dataloader(self):
-        loader = DataLoader(
-            self.val_dataset,
-            batch_size=self.eval_batch_size,
-            shuffle=False,
-            num_workers=self.num_workers,
-            pin_memory=True,
-            collate_fn=self.val_dataset.collate,
-        )
-        return loader
-
-    def test_dataloader(self):
-        loader = DataLoader(
-            self.test_dataset,
-            batch_size=self.eval_batch_size,
-            shuffle=False,
-            num_workers=self.num_workers,
-            pin_memory=True,
-            collate_fn=self.test_dataset.collate,
-        )
-        return loader
diff --git a/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/eval/models/open_flamingo.py b/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/eval/models/open_flamingo.py
deleted file mode 100644
index c7328bece80b785d89c8ad37ee5a5faab3c3300f..0000000000000000000000000000000000000000
--- a/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/eval/models/open_flamingo.py
+++ /dev/null
@@ -1,176 +0,0 @@
-from typing import List
-
-from PIL import Image
-import torch
-
-from open_flamingo.eval.eval_model import BaseEvalModel
-from open_flamingo.src.factory import create_model_and_transforms
-from contextlib import suppress
-from open_flamingo.eval.models.utils import unwrap_model
-
-class EvalModel(BaseEvalModel):
-    """OpenFlamingo model evaluation.
-
-    Attributes:
-      model (nn.Module): Underlying Torch model.
-      tokenizer (transformers.PreTrainedTokenizer): Tokenizer for model.
-      device: Index of GPU to use, or the string "CPU"
-    """
-
-    def __init__(self, model_args):
-        assert (
-            "vision_encoder_path" in model_args
-            and "lm_path" in model_args
-            and "checkpoint_path" in model_args
-            and "lm_tokenizer_path" in model_args
-            and "cross_attn_every_n_layers" in model_args
-            and "vision_encoder_pretrained" in model_args
-            and "precision" in model_args
-        ), "OpenFlamingo requires vision_encoder_path, lm_path, device, checkpoint_path, lm_tokenizer_path, cross_attn_every_n_layers, vision_encoder_pretrained, and precision arguments to be specified"
-
-        self.device = (
-            model_args["device"]
-            if ("device" in model_args and model_args["device"] >= 0)
-            else "cpu"
-        )
-
-        (
-            self.model,
-            self.image_processor,
-            self.tokenizer,
-        ) = create_model_and_transforms(
-            model_args["vision_encoder_path"],
-            model_args["vision_encoder_pretrained"],
-            model_args["lm_path"],
-            model_args["lm_tokenizer_path"],
-            cross_attn_every_n_layers=int(model_args["cross_attn_every_n_layers"]),
-        )
-        checkpoint = torch.load(model_args["checkpoint_path"], map_location="cpu")
-        if "model_state_dict" in checkpoint:
-            checkpoint = checkpoint["model_state_dict"]
-            checkpoint = {k.replace("module.", ""): v for k, v in checkpoint.items()}
-        self.model.load_state_dict(checkpoint, strict=False)
-        self.model.to(self.device)
-        self.model.eval()
-        self.tokenizer.padding_side = "left"
-
-        # autocast
-        self.autocast = get_autocast(model_args["precision"])
-        self.cast_dtype = get_cast_dtype(model_args["precision"])
-
-    def _prepare_images(self, batch: List[List[torch.Tensor]]) -> torch.Tensor:
-        """Preprocess images and stack them.
-
-        Args:
-            batch: A list of lists of images.
-
-        Returns:
-            A Tensor of shape
-            (batch_size, images_per_example, frames, channels, height, width).
-        """
-        images_per_example = max(len(x) for x in batch)
-        batch_images = None
-        for iexample, example in enumerate(batch):
-            for iimage, image in enumerate(example):
-                preprocessed = self.image_processor(image)
-
-                if batch_images is None:
-                    batch_images = torch.zeros(
-                        (len(batch), images_per_example, 1) + preprocessed.shape,
-                        dtype=preprocessed.dtype,
-                    )
-                batch_images[iexample, iimage, 0] = preprocessed
-        return batch_images
-
-    def get_outputs(
-        self,
-        batch_text: List[str],
-        batch_images: List[List[Image.Image]],
-        min_generation_length: int,
-        max_generation_length: int,
-        num_beams: int,
-        length_penalty: float,
-    ) -> List[str]:
-        encodings = self.tokenizer(
-            batch_text,
-            padding="longest",
-            truncation=True,
-            return_tensors="pt",
-            max_length=2000,
-        )
-        input_ids = encodings["input_ids"]
-        attention_mask = encodings["attention_mask"]
-
-        with torch.inference_mode():
-            with self.autocast():
-                outputs = unwrap_model(self.model).generate(
-                    self._prepare_images(batch_images).to(
-                        self.device, dtype=self.cast_dtype, non_blocking=True
-                    ),
-                    input_ids.to(self.device, dtype=self.cast_dtype, non_blocking=True),
-                    attention_mask=attention_mask.to(
-                        self.device, dtype=self.cast_dtype, non_blocking=True
-                    ),
-                    min_new_tokens=min_generation_length,
-                    max_new_tokens=max_generation_length,
-                    num_beams=num_beams,
-                    length_penalty=length_penalty,
-                )
-
-        outputs = outputs[:, len(input_ids[0]) :]
-
-        return self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
-
-    def get_logits(
-        self,
-        lang_x: torch.Tensor,
-        vision_x: torch.Tensor = None,
-        attention_mask: torch.Tensor = None,
-        past_key_values: torch.Tensor = None,
-        clear_conditioned_layers: bool = False,
-    ):
-        with torch.inference_mode():
-            with self.autocast():
-                outputs = self.model(
-                    vision_x=vision_x,
-                    lang_x=lang_x,
-                    attention_mask=attention_mask,
-                    clear_conditioned_layers=clear_conditioned_layers,
-                    past_key_values=past_key_values,
-                    use_cache=(past_key_values is not None),
-                )
-        return outputs
-
-    def encode_vision_x(self, image_tensor: torch.Tensor):
-        unwrap_model(self.model)._encode_vision_x(image_tensor.to(self.device))
-
-    def uncache_media(self):
-        unwrap_model(self.model).uncache_media()
-        
-    def cache_media(self, input_ids, vision_x):
-        unwrap_model(self.model).cache_media(input_ids=input_ids, vision_x=vision_x)
-
-    def get_vqa_prompt(self, question, answer=None) -> str:
-        return f"<image>Question:{question} Short answer:{answer if answer is not None else ''}{'<|endofchunk|>' if answer is not None else ''}"
-
-    def get_caption_prompt(self, caption=None) -> str:
-        return f"<image>Output:{caption if caption is not None else ''}{'<|endofchunk|>' if caption is not None else ''}"
-
-
-def get_cast_dtype(precision: str):
-    cast_dtype = None
-    if precision == "bf16":
-        cast_dtype = torch.bfloat16
-    elif precision == "fp16":
-        cast_dtype = torch.float16
-    return cast_dtype
-
-
-def get_autocast(precision):
-    if precision == "amp":
-        return torch.cuda.amp.autocast
-    elif precision == "amp_bfloat16" or precision == "amp_bf16":
-        # amp_bfloat16 is more stable than amp float16 for clip training
-        return lambda: torch.cuda.amp.autocast(dtype=torch.bfloat16)
-    else:
-        return suppress
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/panorama.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/panorama.md
deleted file mode 100644
index a0ad0d326188c79c8e88ae2869a52e9b73809b68..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/panorama.md
+++ /dev/null
@@ -1,57 +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.
--->
-
-# MultiDiffusion
-
-[MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://huggingface.co/papers/2302.08113) is by Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel.
-
-The abstract from the paper is:
-
-*Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes.*
-
-You can find additional information about MultiDiffusion on the [project page](https://multidiffusion.github.io/), [original codebase](https://github.com/omerbt/MultiDiffusion), and try it out in a [demo](https://huggingface.co/spaces/weizmannscience/MultiDiffusion).
-
-## Tips
-
-While calling [`StableDiffusionPanoramaPipeline`], it's possible to specify the `view_batch_size` parameter to be > 1. 
-For some GPUs with high performance, this can speedup the generation process and increase VRAM usage.
-
-To generate panorama-like images make sure you pass the width parameter accordingly. We recommend a width value of 2048 which is the default.
-
-Circular padding is applied to ensure there are no stitching artifacts when working with 
-panoramas to ensure a seamless transition from the rightmost part to the leftmost part. 
-By enabling circular padding (set `circular_padding=True`), the operation applies additional 
-crops after the rightmost point of the image, allowing the model to "see” the transition 
-from the rightmost part to the leftmost part. This helps maintain visual consistency in 
-a 360-degree sense and creates a proper “panorama” that can be viewed using 360-degree 
-panorama viewers. When decoding latents in Stable Diffusion, circular padding is applied 
-to ensure that the decoded latents match in the RGB space.
-
-For example, without circular padding, there is a stitching artifact (default):
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/indoor_%20no_circular_padding.png)
-
-But with circular padding, the right and the left parts are matching (`circular_padding=True`):
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/indoor_%20circular_padding.png)
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## StableDiffusionPanoramaPipeline
-[[autodoc]] StableDiffusionPanoramaPipeline
-	- __call__
-	- all
-
-## StableDiffusionPipelineOutput
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/kandinsky2_2/__init__.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/kandinsky2_2/__init__.py
deleted file mode 100644
index 461e3d25ca731735164727b784893fbde75c3a12..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/kandinsky2_2/__init__.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from typing import TYPE_CHECKING
-
-from ...utils import (
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    get_objects_from_module,
-    is_torch_available,
-    is_transformers_available,
-)
-
-
-_dummy_objects = {}
-_import_structure = {}
-
-try:
-    if not (is_transformers_available() and is_torch_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
-
-    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
-else:
-    _import_structure["pipeline_kandinsky2_2"] = ["KandinskyV22Pipeline"]
-    _import_structure["pipeline_kandinsky2_2_combined"] = [
-        "KandinskyV22CombinedPipeline",
-        "KandinskyV22Img2ImgCombinedPipeline",
-        "KandinskyV22InpaintCombinedPipeline",
-    ]
-    _import_structure["pipeline_kandinsky2_2_controlnet"] = ["KandinskyV22ControlnetPipeline"]
-    _import_structure["pipeline_kandinsky2_2_controlnet_img2img"] = ["KandinskyV22ControlnetImg2ImgPipeline"]
-    _import_structure["pipeline_kandinsky2_2_img2img"] = ["KandinskyV22Img2ImgPipeline"]
-    _import_structure["pipeline_kandinsky2_2_inpainting"] = ["KandinskyV22InpaintPipeline"]
-    _import_structure["pipeline_kandinsky2_2_prior"] = ["KandinskyV22PriorPipeline"]
-    _import_structure["pipeline_kandinsky2_2_prior_emb2emb"] = ["KandinskyV22PriorEmb2EmbPipeline"]
-
-
-if TYPE_CHECKING:
-    try:
-        if not (is_transformers_available() and is_torch_available()):
-            raise OptionalDependencyNotAvailable()
-
-    except OptionalDependencyNotAvailable:
-        from ...utils.dummy_torch_and_transformers_objects import *
-    else:
-        from .pipeline_kandinsky2_2 import KandinskyV22Pipeline
-        from .pipeline_kandinsky2_2_combined import (
-            KandinskyV22CombinedPipeline,
-            KandinskyV22Img2ImgCombinedPipeline,
-            KandinskyV22InpaintCombinedPipeline,
-        )
-        from .pipeline_kandinsky2_2_controlnet import KandinskyV22ControlnetPipeline
-        from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyV22ControlnetImg2ImgPipeline
-        from .pipeline_kandinsky2_2_img2img import KandinskyV22Img2ImgPipeline
-        from .pipeline_kandinsky2_2_inpainting import KandinskyV22InpaintPipeline
-        from .pipeline_kandinsky2_2_prior import KandinskyV22PriorPipeline
-        from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyV22PriorEmb2EmbPipeline
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(
-        __name__,
-        globals()["__file__"],
-        _import_structure,
-        module_spec=__spec__,
-    )
-
-    for name, value in _dummy_objects.items():
-        setattr(sys.modules[__name__], name, value)
diff --git a/spaces/pierreguillou/Inference-APP-Document-Understanding-at-linelevel-v2/files/README.md b/spaces/pierreguillou/Inference-APP-Document-Understanding-at-linelevel-v2/files/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/response.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/response.py
deleted file mode 100644
index 8909f8454e94752d188ed13cf36c35f93fc6c3f2..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/response.py
+++ /dev/null
@@ -1,879 +0,0 @@
-from __future__ import absolute_import
-
-import io
-import logging
-import sys
-import warnings
-import zlib
-from contextlib import contextmanager
-from socket import error as SocketError
-from socket import timeout as SocketTimeout
-
-brotli = None
-
-from . import util
-from ._collections import HTTPHeaderDict
-from .connection import BaseSSLError, HTTPException
-from .exceptions import (
-    BodyNotHttplibCompatible,
-    DecodeError,
-    HTTPError,
-    IncompleteRead,
-    InvalidChunkLength,
-    InvalidHeader,
-    ProtocolError,
-    ReadTimeoutError,
-    ResponseNotChunked,
-    SSLError,
-)
-from .packages import six
-from .util.response import is_fp_closed, is_response_to_head
-
-log = logging.getLogger(__name__)
-
-
-class DeflateDecoder(object):
-    def __init__(self):
-        self._first_try = True
-        self._data = b""
-        self._obj = zlib.decompressobj()
-
-    def __getattr__(self, name):
-        return getattr(self._obj, name)
-
-    def decompress(self, data):
-        if not data:
-            return data
-
-        if not self._first_try:
-            return self._obj.decompress(data)
-
-        self._data += data
-        try:
-            decompressed = self._obj.decompress(data)
-            if decompressed:
-                self._first_try = False
-                self._data = None
-            return decompressed
-        except zlib.error:
-            self._first_try = False
-            self._obj = zlib.decompressobj(-zlib.MAX_WBITS)
-            try:
-                return self.decompress(self._data)
-            finally:
-                self._data = None
-
-
-class GzipDecoderState(object):
-
-    FIRST_MEMBER = 0
-    OTHER_MEMBERS = 1
-    SWALLOW_DATA = 2
-
-
-class GzipDecoder(object):
-    def __init__(self):
-        self._obj = zlib.decompressobj(16 + zlib.MAX_WBITS)
-        self._state = GzipDecoderState.FIRST_MEMBER
-
-    def __getattr__(self, name):
-        return getattr(self._obj, name)
-
-    def decompress(self, data):
-        ret = bytearray()
-        if self._state == GzipDecoderState.SWALLOW_DATA or not data:
-            return bytes(ret)
-        while True:
-            try:
-                ret += self._obj.decompress(data)
-            except zlib.error:
-                previous_state = self._state
-                # Ignore data after the first error
-                self._state = GzipDecoderState.SWALLOW_DATA
-                if previous_state == GzipDecoderState.OTHER_MEMBERS:
-                    # Allow trailing garbage acceptable in other gzip clients
-                    return bytes(ret)
-                raise
-            data = self._obj.unused_data
-            if not data:
-                return bytes(ret)
-            self._state = GzipDecoderState.OTHER_MEMBERS
-            self._obj = zlib.decompressobj(16 + zlib.MAX_WBITS)
-
-
-if brotli is not None:
-
-    class BrotliDecoder(object):
-        # Supports both 'brotlipy' and 'Brotli' packages
-        # since they share an import name. The top branches
-        # are for 'brotlipy' and bottom branches for 'Brotli'
-        def __init__(self):
-            self._obj = brotli.Decompressor()
-            if hasattr(self._obj, "decompress"):
-                self.decompress = self._obj.decompress
-            else:
-                self.decompress = self._obj.process
-
-        def flush(self):
-            if hasattr(self._obj, "flush"):
-                return self._obj.flush()
-            return b""
-
-
-class MultiDecoder(object):
-    """
-    From RFC7231:
-        If one or more encodings have been applied to a representation, the
-        sender that applied the encodings MUST generate a Content-Encoding
-        header field that lists the content codings in the order in which
-        they were applied.
-    """
-
-    def __init__(self, modes):
-        self._decoders = [_get_decoder(m.strip()) for m in modes.split(",")]
-
-    def flush(self):
-        return self._decoders[0].flush()
-
-    def decompress(self, data):
-        for d in reversed(self._decoders):
-            data = d.decompress(data)
-        return data
-
-
-def _get_decoder(mode):
-    if "," in mode:
-        return MultiDecoder(mode)
-
-    if mode == "gzip":
-        return GzipDecoder()
-
-    if brotli is not None and mode == "br":
-        return BrotliDecoder()
-
-    return DeflateDecoder()
-
-
-class HTTPResponse(io.IOBase):
-    """
-    HTTP Response container.
-
-    Backwards-compatible with :class:`http.client.HTTPResponse` but the response ``body`` is
-    loaded and decoded on-demand when the ``data`` property is accessed.  This
-    class is also compatible with the Python standard library's :mod:`io`
-    module, and can hence be treated as a readable object in the context of that
-    framework.
-
-    Extra parameters for behaviour not present in :class:`http.client.HTTPResponse`:
-
-    :param preload_content:
-        If True, the response's body will be preloaded during construction.
-
-    :param decode_content:
-        If True, will attempt to decode the body based on the
-        'content-encoding' header.
-
-    :param original_response:
-        When this HTTPResponse wrapper is generated from an :class:`http.client.HTTPResponse`
-        object, it's convenient to include the original for debug purposes. It's
-        otherwise unused.
-
-    :param retries:
-        The retries contains the last :class:`~urllib3.util.retry.Retry` that
-        was used during the request.
-
-    :param enforce_content_length:
-        Enforce content length checking. Body returned by server must match
-        value of Content-Length header, if present. Otherwise, raise error.
-    """
-
-    CONTENT_DECODERS = ["gzip", "deflate"]
-    if brotli is not None:
-        CONTENT_DECODERS += ["br"]
-    REDIRECT_STATUSES = [301, 302, 303, 307, 308]
-
-    def __init__(
-        self,
-        body="",
-        headers=None,
-        status=0,
-        version=0,
-        reason=None,
-        strict=0,
-        preload_content=True,
-        decode_content=True,
-        original_response=None,
-        pool=None,
-        connection=None,
-        msg=None,
-        retries=None,
-        enforce_content_length=False,
-        request_method=None,
-        request_url=None,
-        auto_close=True,
-    ):
-
-        if isinstance(headers, HTTPHeaderDict):
-            self.headers = headers
-        else:
-            self.headers = HTTPHeaderDict(headers)
-        self.status = status
-        self.version = version
-        self.reason = reason
-        self.strict = strict
-        self.decode_content = decode_content
-        self.retries = retries
-        self.enforce_content_length = enforce_content_length
-        self.auto_close = auto_close
-
-        self._decoder = None
-        self._body = None
-        self._fp = None
-        self._original_response = original_response
-        self._fp_bytes_read = 0
-        self.msg = msg
-        self._request_url = request_url
-
-        if body and isinstance(body, (six.string_types, bytes)):
-            self._body = body
-
-        self._pool = pool
-        self._connection = connection
-
-        if hasattr(body, "read"):
-            self._fp = body
-
-        # Are we using the chunked-style of transfer encoding?
-        self.chunked = False
-        self.chunk_left = None
-        tr_enc = self.headers.get("transfer-encoding", "").lower()
-        # Don't incur the penalty of creating a list and then discarding it
-        encodings = (enc.strip() for enc in tr_enc.split(","))
-        if "chunked" in encodings:
-            self.chunked = True
-
-        # Determine length of response
-        self.length_remaining = self._init_length(request_method)
-
-        # If requested, preload the body.
-        if preload_content and not self._body:
-            self._body = self.read(decode_content=decode_content)
-
-    def get_redirect_location(self):
-        """
-        Should we redirect and where to?
-
-        :returns: Truthy redirect location string if we got a redirect status
-            code and valid location. ``None`` if redirect status and no
-            location. ``False`` if not a redirect status code.
-        """
-        if self.status in self.REDIRECT_STATUSES:
-            return self.headers.get("location")
-
-        return False
-
-    def release_conn(self):
-        if not self._pool or not self._connection:
-            return
-
-        self._pool._put_conn(self._connection)
-        self._connection = None
-
-    def drain_conn(self):
-        """
-        Read and discard any remaining HTTP response data in the response connection.
-
-        Unread data in the HTTPResponse connection blocks the connection from being released back to the pool.
-        """
-        try:
-            self.read()
-        except (HTTPError, SocketError, BaseSSLError, HTTPException):
-            pass
-
-    @property
-    def data(self):
-        # For backwards-compat with earlier urllib3 0.4 and earlier.
-        if self._body:
-            return self._body
-
-        if self._fp:
-            return self.read(cache_content=True)
-
-    @property
-    def connection(self):
-        return self._connection
-
-    def isclosed(self):
-        return is_fp_closed(self._fp)
-
-    def tell(self):
-        """
-        Obtain the number of bytes pulled over the wire so far. May differ from
-        the amount of content returned by :meth:``urllib3.response.HTTPResponse.read``
-        if bytes are encoded on the wire (e.g, compressed).
-        """
-        return self._fp_bytes_read
-
-    def _init_length(self, request_method):
-        """
-        Set initial length value for Response content if available.
-        """
-        length = self.headers.get("content-length")
-
-        if length is not None:
-            if self.chunked:
-                # This Response will fail with an IncompleteRead if it can't be
-                # received as chunked. This method falls back to attempt reading
-                # the response before raising an exception.
-                log.warning(
-                    "Received response with both Content-Length and "
-                    "Transfer-Encoding set. This is expressly forbidden "
-                    "by RFC 7230 sec 3.3.2. Ignoring Content-Length and "
-                    "attempting to process response as Transfer-Encoding: "
-                    "chunked."
-                )
-                return None
-
-            try:
-                # RFC 7230 section 3.3.2 specifies multiple content lengths can
-                # be sent in a single Content-Length header
-                # (e.g. Content-Length: 42, 42). This line ensures the values
-                # are all valid ints and that as long as the `set` length is 1,
-                # all values are the same. Otherwise, the header is invalid.
-                lengths = set([int(val) for val in length.split(",")])
-                if len(lengths) > 1:
-                    raise InvalidHeader(
-                        "Content-Length contained multiple "
-                        "unmatching values (%s)" % length
-                    )
-                length = lengths.pop()
-            except ValueError:
-                length = None
-            else:
-                if length < 0:
-                    length = None
-
-        # Convert status to int for comparison
-        # In some cases, httplib returns a status of "_UNKNOWN"
-        try:
-            status = int(self.status)
-        except ValueError:
-            status = 0
-
-        # Check for responses that shouldn't include a body
-        if status in (204, 304) or 100 <= status < 200 or request_method == "HEAD":
-            length = 0
-
-        return length
-
-    def _init_decoder(self):
-        """
-        Set-up the _decoder attribute if necessary.
-        """
-        # Note: content-encoding value should be case-insensitive, per RFC 7230
-        # Section 3.2
-        content_encoding = self.headers.get("content-encoding", "").lower()
-        if self._decoder is None:
-            if content_encoding in self.CONTENT_DECODERS:
-                self._decoder = _get_decoder(content_encoding)
-            elif "," in content_encoding:
-                encodings = [
-                    e.strip()
-                    for e in content_encoding.split(",")
-                    if e.strip() in self.CONTENT_DECODERS
-                ]
-                if len(encodings):
-                    self._decoder = _get_decoder(content_encoding)
-
-    DECODER_ERROR_CLASSES = (IOError, zlib.error)
-    if brotli is not None:
-        DECODER_ERROR_CLASSES += (brotli.error,)
-
-    def _decode(self, data, decode_content, flush_decoder):
-        """
-        Decode the data passed in and potentially flush the decoder.
-        """
-        if not decode_content:
-            return data
-
-        try:
-            if self._decoder:
-                data = self._decoder.decompress(data)
-        except self.DECODER_ERROR_CLASSES as e:
-            content_encoding = self.headers.get("content-encoding", "").lower()
-            raise DecodeError(
-                "Received response with content-encoding: %s, but "
-                "failed to decode it." % content_encoding,
-                e,
-            )
-        if flush_decoder:
-            data += self._flush_decoder()
-
-        return data
-
-    def _flush_decoder(self):
-        """
-        Flushes the decoder. Should only be called if the decoder is actually
-        being used.
-        """
-        if self._decoder:
-            buf = self._decoder.decompress(b"")
-            return buf + self._decoder.flush()
-
-        return b""
-
-    @contextmanager
-    def _error_catcher(self):
-        """
-        Catch low-level python exceptions, instead re-raising urllib3
-        variants, so that low-level exceptions are not leaked in the
-        high-level api.
-
-        On exit, release the connection back to the pool.
-        """
-        clean_exit = False
-
-        try:
-            try:
-                yield
-
-            except SocketTimeout:
-                # FIXME: Ideally we'd like to include the url in the ReadTimeoutError but
-                # there is yet no clean way to get at it from this context.
-                raise ReadTimeoutError(self._pool, None, "Read timed out.")
-
-            except BaseSSLError as e:
-                # FIXME: Is there a better way to differentiate between SSLErrors?
-                if "read operation timed out" not in str(e):
-                    # SSL errors related to framing/MAC get wrapped and reraised here
-                    raise SSLError(e)
-
-                raise ReadTimeoutError(self._pool, None, "Read timed out.")
-
-            except (HTTPException, SocketError) as e:
-                # This includes IncompleteRead.
-                raise ProtocolError("Connection broken: %r" % e, e)
-
-            # If no exception is thrown, we should avoid cleaning up
-            # unnecessarily.
-            clean_exit = True
-        finally:
-            # If we didn't terminate cleanly, we need to throw away our
-            # connection.
-            if not clean_exit:
-                # The response may not be closed but we're not going to use it
-                # anymore so close it now to ensure that the connection is
-                # released back to the pool.
-                if self._original_response:
-                    self._original_response.close()
-
-                # Closing the response may not actually be sufficient to close
-                # everything, so if we have a hold of the connection close that
-                # too.
-                if self._connection:
-                    self._connection.close()
-
-            # If we hold the original response but it's closed now, we should
-            # return the connection back to the pool.
-            if self._original_response and self._original_response.isclosed():
-                self.release_conn()
-
-    def _fp_read(self, amt):
-        """
-        Read a response with the thought that reading the number of bytes
-        larger than can fit in a 32-bit int at a time via SSL in some
-        known cases leads to an overflow error that has to be prevented
-        if `amt` or `self.length_remaining` indicate that a problem may
-        happen.
-
-        The known cases:
-          * 3.8 <= CPython < 3.9.7 because of a bug
-            https://github.com/urllib3/urllib3/issues/2513#issuecomment-1152559900.
-          * urllib3 injected with pyOpenSSL-backed SSL-support.
-          * CPython < 3.10 only when `amt` does not fit 32-bit int.
-        """
-        assert self._fp
-        c_int_max = 2 ** 31 - 1
-        if (
-            (
-                (amt and amt > c_int_max)
-                or (self.length_remaining and self.length_remaining > c_int_max)
-            )
-            and not util.IS_SECURETRANSPORT
-            and (util.IS_PYOPENSSL or sys.version_info < (3, 10))
-        ):
-            buffer = io.BytesIO()
-            # Besides `max_chunk_amt` being a maximum chunk size, it
-            # affects memory overhead of reading a response by this
-            # method in CPython.
-            # `c_int_max` equal to 2 GiB - 1 byte is the actual maximum
-            # chunk size that does not lead to an overflow error, but
-            # 256 MiB is a compromise.
-            max_chunk_amt = 2 ** 28
-            while amt is None or amt != 0:
-                if amt is not None:
-                    chunk_amt = min(amt, max_chunk_amt)
-                    amt -= chunk_amt
-                else:
-                    chunk_amt = max_chunk_amt
-                data = self._fp.read(chunk_amt)
-                if not data:
-                    break
-                buffer.write(data)
-                del data  # to reduce peak memory usage by `max_chunk_amt`.
-            return buffer.getvalue()
-        else:
-            # StringIO doesn't like amt=None
-            return self._fp.read(amt) if amt is not None else self._fp.read()
-
-    def read(self, amt=None, decode_content=None, cache_content=False):
-        """
-        Similar to :meth:`http.client.HTTPResponse.read`, but with two additional
-        parameters: ``decode_content`` and ``cache_content``.
-
-        :param amt:
-            How much of the content to read. If specified, caching is skipped
-            because it doesn't make sense to cache partial content as the full
-            response.
-
-        :param decode_content:
-            If True, will attempt to decode the body based on the
-            'content-encoding' header.
-
-        :param cache_content:
-            If True, will save the returned data such that the same result is
-            returned despite of the state of the underlying file object. This
-            is useful if you want the ``.data`` property to continue working
-            after having ``.read()`` the file object. (Overridden if ``amt`` is
-            set.)
-        """
-        self._init_decoder()
-        if decode_content is None:
-            decode_content = self.decode_content
-
-        if self._fp is None:
-            return
-
-        flush_decoder = False
-        fp_closed = getattr(self._fp, "closed", False)
-
-        with self._error_catcher():
-            data = self._fp_read(amt) if not fp_closed else b""
-            if amt is None:
-                flush_decoder = True
-            else:
-                cache_content = False
-                if (
-                    amt != 0 and not data
-                ):  # Platform-specific: Buggy versions of Python.
-                    # Close the connection when no data is returned
-                    #
-                    # This is redundant to what httplib/http.client _should_
-                    # already do.  However, versions of python released before
-                    # December 15, 2012 (http://bugs.python.org/issue16298) do
-                    # not properly close the connection in all cases. There is
-                    # no harm in redundantly calling close.
-                    self._fp.close()
-                    flush_decoder = True
-                    if self.enforce_content_length and self.length_remaining not in (
-                        0,
-                        None,
-                    ):
-                        # This is an edge case that httplib failed to cover due
-                        # to concerns of backward compatibility. We're
-                        # addressing it here to make sure IncompleteRead is
-                        # raised during streaming, so all calls with incorrect
-                        # Content-Length are caught.
-                        raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
-
-        if data:
-            self._fp_bytes_read += len(data)
-            if self.length_remaining is not None:
-                self.length_remaining -= len(data)
-
-            data = self._decode(data, decode_content, flush_decoder)
-
-            if cache_content:
-                self._body = data
-
-        return data
-
-    def stream(self, amt=2 ** 16, decode_content=None):
-        """
-        A generator wrapper for the read() method. A call will block until
-        ``amt`` bytes have been read from the connection or until the
-        connection is closed.
-
-        :param amt:
-            How much of the content to read. The generator will return up to
-            much data per iteration, but may return less. This is particularly
-            likely when using compressed data. However, the empty string will
-            never be returned.
-
-        :param decode_content:
-            If True, will attempt to decode the body based on the
-            'content-encoding' header.
-        """
-        if self.chunked and self.supports_chunked_reads():
-            for line in self.read_chunked(amt, decode_content=decode_content):
-                yield line
-        else:
-            while not is_fp_closed(self._fp):
-                data = self.read(amt=amt, decode_content=decode_content)
-
-                if data:
-                    yield data
-
-    @classmethod
-    def from_httplib(ResponseCls, r, **response_kw):
-        """
-        Given an :class:`http.client.HTTPResponse` instance ``r``, return a
-        corresponding :class:`urllib3.response.HTTPResponse` object.
-
-        Remaining parameters are passed to the HTTPResponse constructor, along
-        with ``original_response=r``.
-        """
-        headers = r.msg
-
-        if not isinstance(headers, HTTPHeaderDict):
-            if six.PY2:
-                # Python 2.7
-                headers = HTTPHeaderDict.from_httplib(headers)
-            else:
-                headers = HTTPHeaderDict(headers.items())
-
-        # HTTPResponse objects in Python 3 don't have a .strict attribute
-        strict = getattr(r, "strict", 0)
-        resp = ResponseCls(
-            body=r,
-            headers=headers,
-            status=r.status,
-            version=r.version,
-            reason=r.reason,
-            strict=strict,
-            original_response=r,
-            **response_kw
-        )
-        return resp
-
-    # Backwards-compatibility methods for http.client.HTTPResponse
-    def getheaders(self):
-        warnings.warn(
-            "HTTPResponse.getheaders() is deprecated and will be removed "
-            "in urllib3 v2.1.0. Instead access HTTPResponse.headers directly.",
-            category=DeprecationWarning,
-            stacklevel=2,
-        )
-        return self.headers
-
-    def getheader(self, name, default=None):
-        warnings.warn(
-            "HTTPResponse.getheader() is deprecated and will be removed "
-            "in urllib3 v2.1.0. Instead use HTTPResponse.headers.get(name, default).",
-            category=DeprecationWarning,
-            stacklevel=2,
-        )
-        return self.headers.get(name, default)
-
-    # Backwards compatibility for http.cookiejar
-    def info(self):
-        return self.headers
-
-    # Overrides from io.IOBase
-    def close(self):
-        if not self.closed:
-            self._fp.close()
-
-        if self._connection:
-            self._connection.close()
-
-        if not self.auto_close:
-            io.IOBase.close(self)
-
-    @property
-    def closed(self):
-        if not self.auto_close:
-            return io.IOBase.closed.__get__(self)
-        elif self._fp is None:
-            return True
-        elif hasattr(self._fp, "isclosed"):
-            return self._fp.isclosed()
-        elif hasattr(self._fp, "closed"):
-            return self._fp.closed
-        else:
-            return True
-
-    def fileno(self):
-        if self._fp is None:
-            raise IOError("HTTPResponse has no file to get a fileno from")
-        elif hasattr(self._fp, "fileno"):
-            return self._fp.fileno()
-        else:
-            raise IOError(
-                "The file-like object this HTTPResponse is wrapped "
-                "around has no file descriptor"
-            )
-
-    def flush(self):
-        if (
-            self._fp is not None
-            and hasattr(self._fp, "flush")
-            and not getattr(self._fp, "closed", False)
-        ):
-            return self._fp.flush()
-
-    def readable(self):
-        # This method is required for `io` module compatibility.
-        return True
-
-    def readinto(self, b):
-        # This method is required for `io` module compatibility.
-        temp = self.read(len(b))
-        if len(temp) == 0:
-            return 0
-        else:
-            b[: len(temp)] = temp
-            return len(temp)
-
-    def supports_chunked_reads(self):
-        """
-        Checks if the underlying file-like object looks like a
-        :class:`http.client.HTTPResponse` object. We do this by testing for
-        the fp attribute. If it is present we assume it returns raw chunks as
-        processed by read_chunked().
-        """
-        return hasattr(self._fp, "fp")
-
-    def _update_chunk_length(self):
-        # First, we'll figure out length of a chunk and then
-        # we'll try to read it from socket.
-        if self.chunk_left is not None:
-            return
-        line = self._fp.fp.readline()
-        line = line.split(b";", 1)[0]
-        try:
-            self.chunk_left = int(line, 16)
-        except ValueError:
-            # Invalid chunked protocol response, abort.
-            self.close()
-            raise InvalidChunkLength(self, line)
-
-    def _handle_chunk(self, amt):
-        returned_chunk = None
-        if amt is None:
-            chunk = self._fp._safe_read(self.chunk_left)
-            returned_chunk = chunk
-            self._fp._safe_read(2)  # Toss the CRLF at the end of the chunk.
-            self.chunk_left = None
-        elif amt < self.chunk_left:
-            value = self._fp._safe_read(amt)
-            self.chunk_left = self.chunk_left - amt
-            returned_chunk = value
-        elif amt == self.chunk_left:
-            value = self._fp._safe_read(amt)
-            self._fp._safe_read(2)  # Toss the CRLF at the end of the chunk.
-            self.chunk_left = None
-            returned_chunk = value
-        else:  # amt > self.chunk_left
-            returned_chunk = self._fp._safe_read(self.chunk_left)
-            self._fp._safe_read(2)  # Toss the CRLF at the end of the chunk.
-            self.chunk_left = None
-        return returned_chunk
-
-    def read_chunked(self, amt=None, decode_content=None):
-        """
-        Similar to :meth:`HTTPResponse.read`, but with an additional
-        parameter: ``decode_content``.
-
-        :param amt:
-            How much of the content to read. If specified, caching is skipped
-            because it doesn't make sense to cache partial content as the full
-            response.
-
-        :param decode_content:
-            If True, will attempt to decode the body based on the
-            'content-encoding' header.
-        """
-        self._init_decoder()
-        # FIXME: Rewrite this method and make it a class with a better structured logic.
-        if not self.chunked:
-            raise ResponseNotChunked(
-                "Response is not chunked. "
-                "Header 'transfer-encoding: chunked' is missing."
-            )
-        if not self.supports_chunked_reads():
-            raise BodyNotHttplibCompatible(
-                "Body should be http.client.HTTPResponse like. "
-                "It should have have an fp attribute which returns raw chunks."
-            )
-
-        with self._error_catcher():
-            # Don't bother reading the body of a HEAD request.
-            if self._original_response and is_response_to_head(self._original_response):
-                self._original_response.close()
-                return
-
-            # If a response is already read and closed
-            # then return immediately.
-            if self._fp.fp is None:
-                return
-
-            while True:
-                self._update_chunk_length()
-                if self.chunk_left == 0:
-                    break
-                chunk = self._handle_chunk(amt)
-                decoded = self._decode(
-                    chunk, decode_content=decode_content, flush_decoder=False
-                )
-                if decoded:
-                    yield decoded
-
-            if decode_content:
-                # On CPython and PyPy, we should never need to flush the
-                # decoder. However, on Jython we *might* need to, so
-                # lets defensively do it anyway.
-                decoded = self._flush_decoder()
-                if decoded:  # Platform-specific: Jython.
-                    yield decoded
-
-            # Chunk content ends with \r\n: discard it.
-            while True:
-                line = self._fp.fp.readline()
-                if not line:
-                    # Some sites may not end with '\r\n'.
-                    break
-                if line == b"\r\n":
-                    break
-
-            # We read everything; close the "file".
-            if self._original_response:
-                self._original_response.close()
-
-    def geturl(self):
-        """
-        Returns the URL that was the source of this response.
-        If the request that generated this response redirected, this method
-        will return the final redirect location.
-        """
-        if self.retries is not None and len(self.retries.history):
-            return self.retries.history[-1].redirect_location
-        else:
-            return self._request_url
-
-    def __iter__(self):
-        buffer = []
-        for chunk in self.stream(decode_content=True):
-            if b"\n" in chunk:
-                chunk = chunk.split(b"\n")
-                yield b"".join(buffer) + chunk[0] + b"\n"
-                for x in chunk[1:-1]:
-                    yield x + b"\n"
-                if chunk[-1]:
-                    buffer = [chunk[-1]]
-                else:
-                    buffer = []
-            else:
-                buffer.append(chunk)
-        if buffer:
-            yield b"".join(buffer)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_parser.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_parser.py
deleted file mode 100644
index 5a18b758fe0065416a92b9047dc9c392a3de2c4f..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_parser.py
+++ /dev/null
@@ -1,353 +0,0 @@
-"""Handwritten parser of dependency specifiers.
-
-The docstring for each __parse_* function contains ENBF-inspired grammar representing
-the implementation.
-"""
-
-import ast
-from typing import Any, List, NamedTuple, Optional, Tuple, Union
-
-from ._tokenizer import DEFAULT_RULES, Tokenizer
-
-
-class Node:
-    def __init__(self, value: str) -> None:
-        self.value = value
-
-    def __str__(self) -> str:
-        return self.value
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__}('{self}')>"
-
-    def serialize(self) -> str:
-        raise NotImplementedError
-
-
-class Variable(Node):
-    def serialize(self) -> str:
-        return str(self)
-
-
-class Value(Node):
-    def serialize(self) -> str:
-        return f'"{self}"'
-
-
-class Op(Node):
-    def serialize(self) -> str:
-        return str(self)
-
-
-MarkerVar = Union[Variable, Value]
-MarkerItem = Tuple[MarkerVar, Op, MarkerVar]
-# MarkerAtom = Union[MarkerItem, List["MarkerAtom"]]
-# MarkerList = List[Union["MarkerList", MarkerAtom, str]]
-# mypy does not support recursive type definition
-# https://github.com/python/mypy/issues/731
-MarkerAtom = Any
-MarkerList = List[Any]
-
-
-class ParsedRequirement(NamedTuple):
-    name: str
-    url: str
-    extras: List[str]
-    specifier: str
-    marker: Optional[MarkerList]
-
-
-# --------------------------------------------------------------------------------------
-# Recursive descent parser for dependency specifier
-# --------------------------------------------------------------------------------------
-def parse_requirement(source: str) -> ParsedRequirement:
-    return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))
-
-
-def _parse_requirement(tokenizer: Tokenizer) -> ParsedRequirement:
-    """
-    requirement = WS? IDENTIFIER WS? extras WS? requirement_details
-    """
-    tokenizer.consume("WS")
-
-    name_token = tokenizer.expect(
-        "IDENTIFIER", expected="package name at the start of dependency specifier"
-    )
-    name = name_token.text
-    tokenizer.consume("WS")
-
-    extras = _parse_extras(tokenizer)
-    tokenizer.consume("WS")
-
-    url, specifier, marker = _parse_requirement_details(tokenizer)
-    tokenizer.expect("END", expected="end of dependency specifier")
-
-    return ParsedRequirement(name, url, extras, specifier, marker)
-
-
-def _parse_requirement_details(
-    tokenizer: Tokenizer,
-) -> Tuple[str, str, Optional[MarkerList]]:
-    """
-    requirement_details = AT URL (WS requirement_marker?)?
-                        | specifier WS? (requirement_marker)?
-    """
-
-    specifier = ""
-    url = ""
-    marker = None
-
-    if tokenizer.check("AT"):
-        tokenizer.read()
-        tokenizer.consume("WS")
-
-        url_start = tokenizer.position
-        url = tokenizer.expect("URL", expected="URL after @").text
-        if tokenizer.check("END", peek=True):
-            return (url, specifier, marker)
-
-        tokenizer.expect("WS", expected="whitespace after URL")
-
-        # The input might end after whitespace.
-        if tokenizer.check("END", peek=True):
-            return (url, specifier, marker)
-
-        marker = _parse_requirement_marker(
-            tokenizer, span_start=url_start, after="URL and whitespace"
-        )
-    else:
-        specifier_start = tokenizer.position
-        specifier = _parse_specifier(tokenizer)
-        tokenizer.consume("WS")
-
-        if tokenizer.check("END", peek=True):
-            return (url, specifier, marker)
-
-        marker = _parse_requirement_marker(
-            tokenizer,
-            span_start=specifier_start,
-            after=(
-                "version specifier"
-                if specifier
-                else "name and no valid version specifier"
-            ),
-        )
-
-    return (url, specifier, marker)
-
-
-def _parse_requirement_marker(
-    tokenizer: Tokenizer, *, span_start: int, after: str
-) -> MarkerList:
-    """
-    requirement_marker = SEMICOLON marker WS?
-    """
-
-    if not tokenizer.check("SEMICOLON"):
-        tokenizer.raise_syntax_error(
-            f"Expected end or semicolon (after {after})",
-            span_start=span_start,
-        )
-    tokenizer.read()
-
-    marker = _parse_marker(tokenizer)
-    tokenizer.consume("WS")
-
-    return marker
-
-
-def _parse_extras(tokenizer: Tokenizer) -> List[str]:
-    """
-    extras = (LEFT_BRACKET wsp* extras_list? wsp* RIGHT_BRACKET)?
-    """
-    if not tokenizer.check("LEFT_BRACKET", peek=True):
-        return []
-
-    with tokenizer.enclosing_tokens(
-        "LEFT_BRACKET",
-        "RIGHT_BRACKET",
-        around="extras",
-    ):
-        tokenizer.consume("WS")
-        extras = _parse_extras_list(tokenizer)
-        tokenizer.consume("WS")
-
-    return extras
-
-
-def _parse_extras_list(tokenizer: Tokenizer) -> List[str]:
-    """
-    extras_list = identifier (wsp* ',' wsp* identifier)*
-    """
-    extras: List[str] = []
-
-    if not tokenizer.check("IDENTIFIER"):
-        return extras
-
-    extras.append(tokenizer.read().text)
-
-    while True:
-        tokenizer.consume("WS")
-        if tokenizer.check("IDENTIFIER", peek=True):
-            tokenizer.raise_syntax_error("Expected comma between extra names")
-        elif not tokenizer.check("COMMA"):
-            break
-
-        tokenizer.read()
-        tokenizer.consume("WS")
-
-        extra_token = tokenizer.expect("IDENTIFIER", expected="extra name after comma")
-        extras.append(extra_token.text)
-
-    return extras
-
-
-def _parse_specifier(tokenizer: Tokenizer) -> str:
-    """
-    specifier = LEFT_PARENTHESIS WS? version_many WS? RIGHT_PARENTHESIS
-              | WS? version_many WS?
-    """
-    with tokenizer.enclosing_tokens(
-        "LEFT_PARENTHESIS",
-        "RIGHT_PARENTHESIS",
-        around="version specifier",
-    ):
-        tokenizer.consume("WS")
-        parsed_specifiers = _parse_version_many(tokenizer)
-        tokenizer.consume("WS")
-
-    return parsed_specifiers
-
-
-def _parse_version_many(tokenizer: Tokenizer) -> str:
-    """
-    version_many = (SPECIFIER (WS? COMMA WS? SPECIFIER)*)?
-    """
-    parsed_specifiers = ""
-    while tokenizer.check("SPECIFIER"):
-        span_start = tokenizer.position
-        parsed_specifiers += tokenizer.read().text
-        if tokenizer.check("VERSION_PREFIX_TRAIL", peek=True):
-            tokenizer.raise_syntax_error(
-                ".* suffix can only be used with `==` or `!=` operators",
-                span_start=span_start,
-                span_end=tokenizer.position + 1,
-            )
-        if tokenizer.check("VERSION_LOCAL_LABEL_TRAIL", peek=True):
-            tokenizer.raise_syntax_error(
-                "Local version label can only be used with `==` or `!=` operators",
-                span_start=span_start,
-                span_end=tokenizer.position,
-            )
-        tokenizer.consume("WS")
-        if not tokenizer.check("COMMA"):
-            break
-        parsed_specifiers += tokenizer.read().text
-        tokenizer.consume("WS")
-
-    return parsed_specifiers
-
-
-# --------------------------------------------------------------------------------------
-# Recursive descent parser for marker expression
-# --------------------------------------------------------------------------------------
-def parse_marker(source: str) -> MarkerList:
-    return _parse_marker(Tokenizer(source, rules=DEFAULT_RULES))
-
-
-def _parse_marker(tokenizer: Tokenizer) -> MarkerList:
-    """
-    marker = marker_atom (BOOLOP marker_atom)+
-    """
-    expression = [_parse_marker_atom(tokenizer)]
-    while tokenizer.check("BOOLOP"):
-        token = tokenizer.read()
-        expr_right = _parse_marker_atom(tokenizer)
-        expression.extend((token.text, expr_right))
-    return expression
-
-
-def _parse_marker_atom(tokenizer: Tokenizer) -> MarkerAtom:
-    """
-    marker_atom = WS? LEFT_PARENTHESIS WS? marker WS? RIGHT_PARENTHESIS WS?
-                | WS? marker_item WS?
-    """
-
-    tokenizer.consume("WS")
-    if tokenizer.check("LEFT_PARENTHESIS", peek=True):
-        with tokenizer.enclosing_tokens(
-            "LEFT_PARENTHESIS",
-            "RIGHT_PARENTHESIS",
-            around="marker expression",
-        ):
-            tokenizer.consume("WS")
-            marker: MarkerAtom = _parse_marker(tokenizer)
-            tokenizer.consume("WS")
-    else:
-        marker = _parse_marker_item(tokenizer)
-    tokenizer.consume("WS")
-    return marker
-
-
-def _parse_marker_item(tokenizer: Tokenizer) -> MarkerItem:
-    """
-    marker_item = WS? marker_var WS? marker_op WS? marker_var WS?
-    """
-    tokenizer.consume("WS")
-    marker_var_left = _parse_marker_var(tokenizer)
-    tokenizer.consume("WS")
-    marker_op = _parse_marker_op(tokenizer)
-    tokenizer.consume("WS")
-    marker_var_right = _parse_marker_var(tokenizer)
-    tokenizer.consume("WS")
-    return (marker_var_left, marker_op, marker_var_right)
-
-
-def _parse_marker_var(tokenizer: Tokenizer) -> MarkerVar:
-    """
-    marker_var = VARIABLE | QUOTED_STRING
-    """
-    if tokenizer.check("VARIABLE"):
-        return process_env_var(tokenizer.read().text.replace(".", "_"))
-    elif tokenizer.check("QUOTED_STRING"):
-        return process_python_str(tokenizer.read().text)
-    else:
-        tokenizer.raise_syntax_error(
-            message="Expected a marker variable or quoted string"
-        )
-
-
-def process_env_var(env_var: str) -> Variable:
-    if (
-        env_var == "platform_python_implementation"
-        or env_var == "python_implementation"
-    ):
-        return Variable("platform_python_implementation")
-    else:
-        return Variable(env_var)
-
-
-def process_python_str(python_str: str) -> Value:
-    value = ast.literal_eval(python_str)
-    return Value(str(value))
-
-
-def _parse_marker_op(tokenizer: Tokenizer) -> Op:
-    """
-    marker_op = IN | NOT IN | OP
-    """
-    if tokenizer.check("IN"):
-        tokenizer.read()
-        return Op("in")
-    elif tokenizer.check("NOT"):
-        tokenizer.read()
-        tokenizer.expect("WS", expected="whitespace after 'not'")
-        tokenizer.expect("IN", expected="'in' after 'not'")
-        return Op("not in")
-    elif tokenizer.check("OP"):
-        return Op(tokenizer.read().text)
-    else:
-        return tokenizer.raise_syntax_error(
-            "Expected marker operator, one of "
-            "<=, <, !=, ==, >=, >, ~=, ===, in, not in"
-        )
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/install_egg_info.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/install_egg_info.py
deleted file mode 100644
index 1c549c98ea430c8d08dc5968f19ea23e6fe6c9b0..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/install_egg_info.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from distutils import log, dir_util
-import os
-
-from setuptools import Command
-from setuptools import namespaces
-from setuptools.archive_util import unpack_archive
-from .._path import ensure_directory
-
-
-class install_egg_info(namespaces.Installer, Command):
-    """Install an .egg-info directory for the package"""
-
-    description = "Install an .egg-info directory for the package"
-
-    user_options = [
-        ('install-dir=', 'd', "directory to install to"),
-    ]
-
-    def initialize_options(self):
-        self.install_dir = None
-
-    def finalize_options(self):
-        self.set_undefined_options('install_lib',
-                                   ('install_dir', 'install_dir'))
-        ei_cmd = self.get_finalized_command("egg_info")
-        basename = f"{ei_cmd._get_egg_basename()}.egg-info"
-        self.source = ei_cmd.egg_info
-        self.target = os.path.join(self.install_dir, basename)
-        self.outputs = []
-
-    def run(self):
-        self.run_command('egg_info')
-        if os.path.isdir(self.target) and not os.path.islink(self.target):
-            dir_util.remove_tree(self.target, dry_run=self.dry_run)
-        elif os.path.exists(self.target):
-            self.execute(os.unlink, (self.target,), "Removing " + self.target)
-        if not self.dry_run:
-            ensure_directory(self.target)
-        self.execute(
-            self.copytree, (), "Copying %s to %s" % (self.source, self.target)
-        )
-        self.install_namespaces()
-
-    def get_outputs(self):
-        return self.outputs
-
-    def copytree(self):
-        # Copy the .egg-info tree to site-packages
-        def skimmer(src, dst):
-            # filter out source-control directories; note that 'src' is always
-            # a '/'-separated path, regardless of platform.  'dst' is a
-            # platform-specific path.
-            for skip in '.svn/', 'CVS/':
-                if src.startswith(skip) or '/' + skip in src:
-                    return None
-            self.outputs.append(dst)
-            log.debug("Copying %s to %s", src, dst)
-            return dst
-
-        unpack_archive(self.source, self.target, skimmer)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/ffmpy.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/ffmpy.py
deleted file mode 100644
index 03291fca55a355b3041b8538217f334e9c4332eb..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/ffmpy.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import errno
-import shlex
-import subprocess
-
-__version__ = "0.3.1"
-
-
-class FFmpeg(object):
-    """Wrapper for various `FFmpeg <https://www.ffmpeg.org/>`_ related applications (ffmpeg,
-    ffprobe).
-    """
-
-    def __init__(
-        self, executable="ffmpeg", global_options=None, inputs=None, outputs=None
-    ):
-        """Initialize FFmpeg command line wrapper.
-
-        Compiles FFmpeg command line from passed arguments (executable path, options, inputs and
-        outputs). ``inputs`` and ``outputs`` are dictionares containing inputs/outputs as keys and
-        their respective options as values. One dictionary value (set of options) must be either a
-        single space separated string, or a list or strings without spaces (i.e. each part of the
-        option is a separate item of the list, the result of calling ``split()`` on the options
-        string). If the value is a list, it cannot be mixed, i.e. cannot contain items with spaces.
-        An exception are complex FFmpeg command lines that contain quotes: the quoted part must be
-        one string, even if it contains spaces (see *Examples* for more info).
-        For more info about FFmpeg command line format see `here
-        <https://ffmpeg.org/ffmpeg.html#Synopsis>`_.
-
-        :param str executable: path to ffmpeg executable; by default the ``ffmpeg`` command will be
-            searched for in the ``PATH``, but can be overridden with an absolute path to ``ffmpeg``
-            executable
-        :param iterable global_options: global options passed to ``ffmpeg`` executable (e.g.
-            ``-y``, ``-v`` etc.); can be specified either as a list/tuple/set of strings, or one
-            space-separated string; by default no global options are passed
-        :param dict inputs: a dictionary specifying one or more input arguments as keys with their
-            corresponding options (either as a list of strings or a single space separated string) as
-            values
-        :param dict outputs: a dictionary specifying one or more output arguments as keys with their
-            corresponding options (either as a list of strings or a single space separated string) as
-            values
-        """
-        self.executable = executable
-        self._cmd = [executable]
-
-        global_options = global_options or []
-        if _is_sequence(global_options):
-            normalized_global_options = []
-            for opt in global_options:
-                normalized_global_options += shlex.split(opt)
-        else:
-            normalized_global_options = shlex.split(global_options)
-
-        self._cmd += normalized_global_options
-        self._cmd += _merge_args_opts(inputs, add_input_option=True)
-        self._cmd += _merge_args_opts(outputs)
-
-        self.cmd = subprocess.list2cmdline(self._cmd)
-        self.process = None
-
-    def __repr__(self):
-        return "<{0!r} {1!r}>".format(self.__class__.__name__, self.cmd)
-
-    def run(self, input_data=None, stdout=None, stderr=None, env=None, **kwargs):
-        """Execute FFmpeg command line.
-
-        ``input_data`` can contain input for FFmpeg in case ``pipe`` protocol is used for input.
-        ``stdout`` and ``stderr`` specify where to redirect the ``stdout`` and ``stderr`` of the
-        process. By default no redirection is done, which means all output goes to running shell
-        (this mode should normally only be used for debugging purposes). If FFmpeg ``pipe`` protocol
-        is used for output, ``stdout`` must be redirected to a pipe by passing `subprocess.PIPE` as
-        ``stdout`` argument. You can pass custom environment to ffmpeg process with ``env``.
-
-        Returns a 2-tuple containing ``stdout`` and ``stderr`` of the process. If there was no
-        redirection or if the output was redirected to e.g. `os.devnull`, the value returned will
-        be a tuple of two `None` values, otherwise it will contain the actual ``stdout`` and
-        ``stderr`` data returned by ffmpeg process.
-
-        More info about ``pipe`` protocol `here <https://ffmpeg.org/ffmpeg-protocols.html#pipe>`_.
-
-        :param str input_data: input data for FFmpeg to deal with (audio, video etc.) as bytes (e.g.
-            the result of reading a file in binary mode)
-        :param stdout: redirect FFmpeg ``stdout`` there (default is `None` which means no
-            redirection)
-        :param stderr: redirect FFmpeg ``stderr`` there (default is `None` which means no
-            redirection)
-        :param env: custom environment for ffmpeg process
-        :param kwargs: any other keyword arguments to be forwarded to `subprocess.Popen
-            <https://docs.python.org/3/library/subprocess.html#subprocess.Popen>`_
-        :return: a 2-tuple containing ``stdout`` and ``stderr`` of the process
-        :rtype: tuple
-        :raise: `FFRuntimeError` in case FFmpeg command exits with a non-zero code;
-            `FFExecutableNotFoundError` in case the executable path passed was not valid
-        """
-        try:
-            self.process = subprocess.Popen(
-                self._cmd,
-                stdin=subprocess.PIPE,
-                stdout=stdout,
-                stderr=stderr,
-                env=env,
-                **kwargs
-            )
-        except OSError as e:
-            if e.errno == errno.ENOENT:
-                raise FFExecutableNotFoundError(
-                    "Executable '{0}' not found".format(self.executable)
-                )
-            else:
-                raise
-
-        out = self.process.communicate(input=input_data)
-        if self.process.returncode != 0:
-            raise FFRuntimeError(self.cmd, self.process.returncode, out[0], out[1])
-
-        return out
-
-
-class FFprobe(FFmpeg):
-    """Wrapper for `ffprobe <https://www.ffmpeg.org/ffprobe.html>`_."""
-
-    def __init__(self, executable="ffprobe", global_options="", inputs=None):
-        """Create an instance of FFprobe.
-
-        Compiles FFprobe command line from passed arguments (executable path, options, inputs).
-        FFprobe executable by default is taken from ``PATH`` but can be overridden with an
-        absolute path. For more info about FFprobe command line format see
-        `here <https://ffmpeg.org/ffprobe.html#Synopsis>`_.
-
-        :param str executable: absolute path to ffprobe executable
-        :param iterable global_options: global options passed to ffmpeg executable; can be specified
-            either as a list/tuple of strings or a space-separated string
-        :param dict inputs: a dictionary specifying one or more inputs as keys with their
-            corresponding options as values
-        """
-        super(FFprobe, self).__init__(
-            executable=executable, global_options=global_options, inputs=inputs
-        )
-
-
-class FFExecutableNotFoundError(Exception):
-    """Raise when FFmpeg/FFprobe executable was not found."""
-
-
-class FFRuntimeError(Exception):
-    """Raise when FFmpeg/FFprobe command line execution returns a non-zero exit code.
-
-    The resulting exception object will contain the attributes relates to command line execution:
-    ``cmd``, ``exit_code``, ``stdout``, ``stderr``.
-    """
-
-    def __init__(self, cmd, exit_code, stdout, stderr):
-        self.cmd = cmd
-        self.exit_code = exit_code
-        self.stdout = stdout
-        self.stderr = stderr
-
-        message = "`{0}` exited with status {1}\n\nSTDOUT:\n{2}\n\nSTDERR:\n{3}".format(
-            self.cmd, exit_code, (stdout or b"").decode(), (stderr or b"").decode()
-        )
-
-        super(FFRuntimeError, self).__init__(message)
-
-
-def _is_sequence(obj):
-    """Check if the object is a sequence (list, tuple etc.).
-
-    :param object obj: an object to be checked
-    :return: True if the object is iterable but is not a string, False otherwise
-    :rtype: bool
-    """
-    return hasattr(obj, "__iter__") and not isinstance(obj, str)
-
-
-def _merge_args_opts(args_opts_dict, **kwargs):
-    """Merge options with their corresponding arguments.
-
-    Iterates over the dictionary holding arguments (keys) and options (values). Merges each
-    options string with its corresponding argument.
-
-    :param dict args_opts_dict: a dictionary of arguments and options
-    :param dict kwargs: *input_option* - if specified prepends ``-i`` to input argument
-    :return: merged list of strings with arguments and their corresponding options
-    :rtype: list
-    """
-    merged = []
-
-    if not args_opts_dict:
-        return merged
-
-    for arg, opt in args_opts_dict.items():
-        if not _is_sequence(opt):
-            opt = shlex.split(opt or "")
-        merged += opt
-
-        if not arg:
-            continue
-
-        if "add_input_option" in kwargs:
-            merged.append("-i")
-
-        merged.append(arg)
-
-    return merged
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/idna/compat.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/idna/compat.py
deleted file mode 100644
index 786e6bda63699b72d588ba91dd73df017570aee5..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/idna/compat.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from .core import *
-from .codec import *
-from typing import Any, Union
-
-def ToASCII(label: str) -> bytes:
-    return encode(label)
-
-def ToUnicode(label: Union[bytes, bytearray]) -> str:
-    return decode(label)
-
-def nameprep(s: Any) -> None:
-    raise NotImplementedError('IDNA 2008 does not utilise nameprep protocol')
-
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/parser_block.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/parser_block.py
deleted file mode 100644
index 72360f9b31bebca77250168bccae646e9a67dc6e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/parser_block.py
+++ /dev/null
@@ -1,111 +0,0 @@
-"""Block-level tokenizer."""
-from __future__ import annotations
-
-import logging
-from typing import TYPE_CHECKING, Callable
-
-from . import rules_block
-from .ruler import Ruler
-from .rules_block.state_block import StateBlock
-from .token import Token
-from .utils import EnvType
-
-if TYPE_CHECKING:
-    from markdown_it import MarkdownIt
-
-LOGGER = logging.getLogger(__name__)
-
-
-RuleFuncBlockType = Callable[[StateBlock, int, int, bool], bool]
-"""(state: StateBlock, startLine: int, endLine: int, silent: bool) -> matched: bool)
-
-`silent` disables token generation, useful for lookahead.
-"""
-
-_rules: list[tuple[str, RuleFuncBlockType, list[str]]] = [
-    # First 2 params - rule name & source. Secondary array - list of rules,
-    # which can be terminated by this one.
-    ("table", rules_block.table, ["paragraph", "reference"]),
-    ("code", rules_block.code, []),
-    ("fence", rules_block.fence, ["paragraph", "reference", "blockquote", "list"]),
-    (
-        "blockquote",
-        rules_block.blockquote,
-        ["paragraph", "reference", "blockquote", "list"],
-    ),
-    ("hr", rules_block.hr, ["paragraph", "reference", "blockquote", "list"]),
-    ("list", rules_block.list_block, ["paragraph", "reference", "blockquote"]),
-    ("reference", rules_block.reference, []),
-    ("html_block", rules_block.html_block, ["paragraph", "reference", "blockquote"]),
-    ("heading", rules_block.heading, ["paragraph", "reference", "blockquote"]),
-    ("lheading", rules_block.lheading, []),
-    ("paragraph", rules_block.paragraph, []),
-]
-
-
-class ParserBlock:
-    """
-    ParserBlock#ruler -> Ruler
-
-    [[Ruler]] instance. Keep configuration of block rules.
-    """
-
-    def __init__(self) -> None:
-        self.ruler = Ruler[RuleFuncBlockType]()
-        for name, rule, alt in _rules:
-            self.ruler.push(name, rule, {"alt": alt})
-
-    def tokenize(self, state: StateBlock, startLine: int, endLine: int) -> None:
-        """Generate tokens for input range."""
-        rules = self.ruler.getRules("")
-        line = startLine
-        maxNesting = state.md.options.maxNesting
-        hasEmptyLines = False
-
-        while line < endLine:
-            state.line = line = state.skipEmptyLines(line)
-            if line >= endLine:
-                break
-            if state.sCount[line] < state.blkIndent:
-                # Termination condition for nested calls.
-                # Nested calls currently used for blockquotes & lists
-                break
-            if state.level >= maxNesting:
-                # If nesting level exceeded - skip tail to the end.
-                # That's not ordinary situation and we should not care about content.
-                state.line = endLine
-                break
-
-            # Try all possible rules.
-            # On success, rule should:
-            # - update `state.line`
-            # - update `state.tokens`
-            # - return True
-            for rule in rules:
-                if rule(state, line, endLine, False):
-                    break
-
-            # set state.tight if we had an empty line before current tag
-            # i.e. latest empty line should not count
-            state.tight = not hasEmptyLines
-
-            line = state.line
-
-            # paragraph might "eat" one newline after it in nested lists
-            if (line - 1) < endLine and state.isEmpty(line - 1):
-                hasEmptyLines = True
-
-            if line < endLine and state.isEmpty(line):
-                hasEmptyLines = True
-                line += 1
-                state.line = line
-
-    def parse(
-        self, src: str, md: MarkdownIt, env: EnvType, outTokens: list[Token]
-    ) -> list[Token] | None:
-        """Process input string and push block tokens into `outTokens`."""
-        if not src:
-            return None
-        state = StateBlock(src, md, env, outTokens)
-        self.tokenize(state, state.line, state.lineMax)
-        return state.tokens
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/umath_tests.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/umath_tests.py
deleted file mode 100644
index 90ab17e6744a751c4d60e9b86e150cdbc3f6ff2e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/umath_tests.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""
-Shim for _umath_tests to allow a deprecation period for the new name.
-
-"""
-import warnings
-
-# 2018-04-04, numpy 1.15.0
-warnings.warn(("numpy.core.umath_tests is an internal NumPy "
-               "module and should not be imported. It will "
-               "be removed in a future NumPy release."),
-              category=DeprecationWarning, stacklevel=2)
-
-from ._umath_tests import *
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/ma/tests/test_mrecords.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/ma/tests/test_mrecords.py
deleted file mode 100644
index 77123c3cda941636354a7b282777f3f0e55d3ab0..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/ma/tests/test_mrecords.py
+++ /dev/null
@@ -1,493 +0,0 @@
-# pylint: disable-msg=W0611, W0612, W0511,R0201
-"""Tests suite for mrecords.
-
-:author: Pierre Gerard-Marchant
-:contact: pierregm_at_uga_dot_edu
-
-"""
-import numpy as np
-import numpy.ma as ma
-from numpy import recarray
-from numpy.ma import masked, nomask
-from numpy.testing import temppath
-from numpy.core.records import (
-    fromrecords as recfromrecords, fromarrays as recfromarrays
-    )
-from numpy.ma.mrecords import (
-    MaskedRecords, mrecarray, fromarrays, fromtextfile, fromrecords,
-    addfield
-    )
-from numpy.ma.testutils import (
-    assert_, assert_equal,
-    assert_equal_records,
-    )
-from numpy.compat import pickle
-
-
-class TestMRecords:
-
-    ilist = [1, 2, 3, 4, 5]
-    flist = [1.1, 2.2, 3.3, 4.4, 5.5]
-    slist = [b'one', b'two', b'three', b'four', b'five']
-    ddtype = [('a', int), ('b', float), ('c', '|S8')]
-    mask = [0, 1, 0, 0, 1]
-    base = ma.array(list(zip(ilist, flist, slist)), mask=mask, dtype=ddtype)
-
-    def test_byview(self):
-        # Test creation by view
-        base = self.base
-        mbase = base.view(mrecarray)
-        assert_equal(mbase.recordmask, base.recordmask)
-        assert_equal_records(mbase._mask, base._mask)
-        assert_(isinstance(mbase._data, recarray))
-        assert_equal_records(mbase._data, base._data.view(recarray))
-        for field in ('a', 'b', 'c'):
-            assert_equal(base[field], mbase[field])
-        assert_equal_records(mbase.view(mrecarray), mbase)
-
-    def test_get(self):
-        # Tests fields retrieval
-        base = self.base.copy()
-        mbase = base.view(mrecarray)
-        # As fields..........
-        for field in ('a', 'b', 'c'):
-            assert_equal(getattr(mbase, field), mbase[field])
-            assert_equal(base[field], mbase[field])
-        # as elements .......
-        mbase_first = mbase[0]
-        assert_(isinstance(mbase_first, mrecarray))
-        assert_equal(mbase_first.dtype, mbase.dtype)
-        assert_equal(mbase_first.tolist(), (1, 1.1, b'one'))
-        # Used to be mask, now it's recordmask
-        assert_equal(mbase_first.recordmask, nomask)
-        assert_equal(mbase_first._mask.item(), (False, False, False))
-        assert_equal(mbase_first['a'], mbase['a'][0])
-        mbase_last = mbase[-1]
-        assert_(isinstance(mbase_last, mrecarray))
-        assert_equal(mbase_last.dtype, mbase.dtype)
-        assert_equal(mbase_last.tolist(), (None, None, None))
-        # Used to be mask, now it's recordmask
-        assert_equal(mbase_last.recordmask, True)
-        assert_equal(mbase_last._mask.item(), (True, True, True))
-        assert_equal(mbase_last['a'], mbase['a'][-1])
-        assert_((mbase_last['a'] is masked))
-        # as slice ..........
-        mbase_sl = mbase[:2]
-        assert_(isinstance(mbase_sl, mrecarray))
-        assert_equal(mbase_sl.dtype, mbase.dtype)
-        # Used to be mask, now it's recordmask
-        assert_equal(mbase_sl.recordmask, [0, 1])
-        assert_equal_records(mbase_sl.mask,
-                             np.array([(False, False, False),
-                                       (True, True, True)],
-                                      dtype=mbase._mask.dtype))
-        assert_equal_records(mbase_sl, base[:2].view(mrecarray))
-        for field in ('a', 'b', 'c'):
-            assert_equal(getattr(mbase_sl, field), base[:2][field])
-
-    def test_set_fields(self):
-        # Tests setting fields.
-        base = self.base.copy()
-        mbase = base.view(mrecarray)
-        mbase = mbase.copy()
-        mbase.fill_value = (999999, 1e20, 'N/A')
-        # Change the data, the mask should be conserved
-        mbase.a._data[:] = 5
-        assert_equal(mbase['a']._data, [5, 5, 5, 5, 5])
-        assert_equal(mbase['a']._mask, [0, 1, 0, 0, 1])
-        # Change the elements, and the mask will follow
-        mbase.a = 1
-        assert_equal(mbase['a']._data, [1]*5)
-        assert_equal(ma.getmaskarray(mbase['a']), [0]*5)
-        # Use to be _mask, now it's recordmask
-        assert_equal(mbase.recordmask, [False]*5)
-        assert_equal(mbase._mask.tolist(),
-                     np.array([(0, 0, 0),
-                               (0, 1, 1),
-                               (0, 0, 0),
-                               (0, 0, 0),
-                               (0, 1, 1)],
-                              dtype=bool))
-        # Set a field to mask ........................
-        mbase.c = masked
-        # Use to be mask, and now it's still mask !
-        assert_equal(mbase.c.mask, [1]*5)
-        assert_equal(mbase.c.recordmask, [1]*5)
-        assert_equal(ma.getmaskarray(mbase['c']), [1]*5)
-        assert_equal(ma.getdata(mbase['c']), [b'N/A']*5)
-        assert_equal(mbase._mask.tolist(),
-                     np.array([(0, 0, 1),
-                               (0, 1, 1),
-                               (0, 0, 1),
-                               (0, 0, 1),
-                               (0, 1, 1)],
-                              dtype=bool))
-        # Set fields by slices .......................
-        mbase = base.view(mrecarray).copy()
-        mbase.a[3:] = 5
-        assert_equal(mbase.a, [1, 2, 3, 5, 5])
-        assert_equal(mbase.a._mask, [0, 1, 0, 0, 0])
-        mbase.b[3:] = masked
-        assert_equal(mbase.b, base['b'])
-        assert_equal(mbase.b._mask, [0, 1, 0, 1, 1])
-        # Set fields globally..........................
-        ndtype = [('alpha', '|S1'), ('num', int)]
-        data = ma.array([('a', 1), ('b', 2), ('c', 3)], dtype=ndtype)
-        rdata = data.view(MaskedRecords)
-        val = ma.array([10, 20, 30], mask=[1, 0, 0])
-
-        rdata['num'] = val
-        assert_equal(rdata.num, val)
-        assert_equal(rdata.num.mask, [1, 0, 0])
-
-    def test_set_fields_mask(self):
-        # Tests setting the mask of a field.
-        base = self.base.copy()
-        # This one has already a mask....
-        mbase = base.view(mrecarray)
-        mbase['a'][-2] = masked
-        assert_equal(mbase.a, [1, 2, 3, 4, 5])
-        assert_equal(mbase.a._mask, [0, 1, 0, 1, 1])
-        # This one has not yet
-        mbase = fromarrays([np.arange(5), np.random.rand(5)],
-                           dtype=[('a', int), ('b', float)])
-        mbase['a'][-2] = masked
-        assert_equal(mbase.a, [0, 1, 2, 3, 4])
-        assert_equal(mbase.a._mask, [0, 0, 0, 1, 0])
-
-    def test_set_mask(self):
-        base = self.base.copy()
-        mbase = base.view(mrecarray)
-        # Set the mask to True .......................
-        mbase.mask = masked
-        assert_equal(ma.getmaskarray(mbase['b']), [1]*5)
-        assert_equal(mbase['a']._mask, mbase['b']._mask)
-        assert_equal(mbase['a']._mask, mbase['c']._mask)
-        assert_equal(mbase._mask.tolist(),
-                     np.array([(1, 1, 1)]*5, dtype=bool))
-        # Delete the mask ............................
-        mbase.mask = nomask
-        assert_equal(ma.getmaskarray(mbase['c']), [0]*5)
-        assert_equal(mbase._mask.tolist(),
-                     np.array([(0, 0, 0)]*5, dtype=bool))
-
-    def test_set_mask_fromarray(self):
-        base = self.base.copy()
-        mbase = base.view(mrecarray)
-        # Sets the mask w/ an array
-        mbase.mask = [1, 0, 0, 0, 1]
-        assert_equal(mbase.a.mask, [1, 0, 0, 0, 1])
-        assert_equal(mbase.b.mask, [1, 0, 0, 0, 1])
-        assert_equal(mbase.c.mask, [1, 0, 0, 0, 1])
-        # Yay, once more !
-        mbase.mask = [0, 0, 0, 0, 1]
-        assert_equal(mbase.a.mask, [0, 0, 0, 0, 1])
-        assert_equal(mbase.b.mask, [0, 0, 0, 0, 1])
-        assert_equal(mbase.c.mask, [0, 0, 0, 0, 1])
-
-    def test_set_mask_fromfields(self):
-        mbase = self.base.copy().view(mrecarray)
-
-        nmask = np.array(
-            [(0, 1, 0), (0, 1, 0), (1, 0, 1), (1, 0, 1), (0, 0, 0)],
-            dtype=[('a', bool), ('b', bool), ('c', bool)])
-        mbase.mask = nmask
-        assert_equal(mbase.a.mask, [0, 0, 1, 1, 0])
-        assert_equal(mbase.b.mask, [1, 1, 0, 0, 0])
-        assert_equal(mbase.c.mask, [0, 0, 1, 1, 0])
-        # Reinitialize and redo
-        mbase.mask = False
-        mbase.fieldmask = nmask
-        assert_equal(mbase.a.mask, [0, 0, 1, 1, 0])
-        assert_equal(mbase.b.mask, [1, 1, 0, 0, 0])
-        assert_equal(mbase.c.mask, [0, 0, 1, 1, 0])
-
-    def test_set_elements(self):
-        base = self.base.copy()
-        # Set an element to mask .....................
-        mbase = base.view(mrecarray).copy()
-        mbase[-2] = masked
-        assert_equal(
-            mbase._mask.tolist(),
-            np.array([(0, 0, 0), (1, 1, 1), (0, 0, 0), (1, 1, 1), (1, 1, 1)],
-                     dtype=bool))
-        # Used to be mask, now it's recordmask!
-        assert_equal(mbase.recordmask, [0, 1, 0, 1, 1])
-        # Set slices .................................
-        mbase = base.view(mrecarray).copy()
-        mbase[:2] = (5, 5, 5)
-        assert_equal(mbase.a._data, [5, 5, 3, 4, 5])
-        assert_equal(mbase.a._mask, [0, 0, 0, 0, 1])
-        assert_equal(mbase.b._data, [5., 5., 3.3, 4.4, 5.5])
-        assert_equal(mbase.b._mask, [0, 0, 0, 0, 1])
-        assert_equal(mbase.c._data,
-                     [b'5', b'5', b'three', b'four', b'five'])
-        assert_equal(mbase.b._mask, [0, 0, 0, 0, 1])
-
-        mbase = base.view(mrecarray).copy()
-        mbase[:2] = masked
-        assert_equal(mbase.a._data, [1, 2, 3, 4, 5])
-        assert_equal(mbase.a._mask, [1, 1, 0, 0, 1])
-        assert_equal(mbase.b._data, [1.1, 2.2, 3.3, 4.4, 5.5])
-        assert_equal(mbase.b._mask, [1, 1, 0, 0, 1])
-        assert_equal(mbase.c._data,
-                     [b'one', b'two', b'three', b'four', b'five'])
-        assert_equal(mbase.b._mask, [1, 1, 0, 0, 1])
-
-    def test_setslices_hardmask(self):
-        # Tests setting slices w/ hardmask.
-        base = self.base.copy()
-        mbase = base.view(mrecarray)
-        mbase.harden_mask()
-        try:
-            mbase[-2:] = (5, 5, 5)
-            assert_equal(mbase.a._data, [1, 2, 3, 5, 5])
-            assert_equal(mbase.b._data, [1.1, 2.2, 3.3, 5, 5.5])
-            assert_equal(mbase.c._data,
-                         [b'one', b'two', b'three', b'5', b'five'])
-            assert_equal(mbase.a._mask, [0, 1, 0, 0, 1])
-            assert_equal(mbase.b._mask, mbase.a._mask)
-            assert_equal(mbase.b._mask, mbase.c._mask)
-        except NotImplementedError:
-            # OK, not implemented yet...
-            pass
-        except AssertionError:
-            raise
-        else:
-            raise Exception("Flexible hard masks should be supported !")
-        # Not using a tuple should crash
-        try:
-            mbase[-2:] = 3
-        except (NotImplementedError, TypeError):
-            pass
-        else:
-            raise TypeError("Should have expected a readable buffer object!")
-
-    def test_hardmask(self):
-        # Test hardmask
-        base = self.base.copy()
-        mbase = base.view(mrecarray)
-        mbase.harden_mask()
-        assert_(mbase._hardmask)
-        mbase.mask = nomask
-        assert_equal_records(mbase._mask, base._mask)
-        mbase.soften_mask()
-        assert_(not mbase._hardmask)
-        mbase.mask = nomask
-        # So, the mask of a field is no longer set to nomask...
-        assert_equal_records(mbase._mask,
-                             ma.make_mask_none(base.shape, base.dtype))
-        assert_(ma.make_mask(mbase['b']._mask) is nomask)
-        assert_equal(mbase['a']._mask, mbase['b']._mask)
-
-    def test_pickling(self):
-        # Test pickling
-        base = self.base.copy()
-        mrec = base.view(mrecarray)
-        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
-            _ = pickle.dumps(mrec, protocol=proto)
-            mrec_ = pickle.loads(_)
-            assert_equal(mrec_.dtype, mrec.dtype)
-            assert_equal_records(mrec_._data, mrec._data)
-            assert_equal(mrec_._mask, mrec._mask)
-            assert_equal_records(mrec_._mask, mrec._mask)
-
-    def test_filled(self):
-        # Test filling the array
-        _a = ma.array([1, 2, 3], mask=[0, 0, 1], dtype=int)
-        _b = ma.array([1.1, 2.2, 3.3], mask=[0, 0, 1], dtype=float)
-        _c = ma.array(['one', 'two', 'three'], mask=[0, 0, 1], dtype='|S8')
-        ddtype = [('a', int), ('b', float), ('c', '|S8')]
-        mrec = fromarrays([_a, _b, _c], dtype=ddtype,
-                          fill_value=(99999, 99999., 'N/A'))
-        mrecfilled = mrec.filled()
-        assert_equal(mrecfilled['a'], np.array((1, 2, 99999), dtype=int))
-        assert_equal(mrecfilled['b'], np.array((1.1, 2.2, 99999.),
-                                               dtype=float))
-        assert_equal(mrecfilled['c'], np.array(('one', 'two', 'N/A'),
-                                               dtype='|S8'))
-
-    def test_tolist(self):
-        # Test tolist.
-        _a = ma.array([1, 2, 3], mask=[0, 0, 1], dtype=int)
-        _b = ma.array([1.1, 2.2, 3.3], mask=[0, 0, 1], dtype=float)
-        _c = ma.array(['one', 'two', 'three'], mask=[1, 0, 0], dtype='|S8')
-        ddtype = [('a', int), ('b', float), ('c', '|S8')]
-        mrec = fromarrays([_a, _b, _c], dtype=ddtype,
-                          fill_value=(99999, 99999., 'N/A'))
-
-        assert_equal(mrec.tolist(),
-                     [(1, 1.1, None), (2, 2.2, b'two'),
-                      (None, None, b'three')])
-
-    def test_withnames(self):
-        # Test the creation w/ format and names
-        x = mrecarray(1, formats=float, names='base')
-        x[0]['base'] = 10
-        assert_equal(x['base'][0], 10)
-
-    def test_exotic_formats(self):
-        # Test that 'exotic' formats are processed properly
-        easy = mrecarray(1, dtype=[('i', int), ('s', '|S8'), ('f', float)])
-        easy[0] = masked
-        assert_equal(easy.filled(1).item(), (1, b'1', 1.))
-
-        solo = mrecarray(1, dtype=[('f0', '<f8', (2, 2))])
-        solo[0] = masked
-        assert_equal(solo.filled(1).item(),
-                     np.array((1,), dtype=solo.dtype).item())
-
-        mult = mrecarray(2, dtype="i4, (2,3)float, float")
-        mult[0] = masked
-        mult[1] = (1, 1, 1)
-        mult.filled(0)
-        assert_equal_records(mult.filled(0),
-                             np.array([(0, 0, 0), (1, 1, 1)],
-                                      dtype=mult.dtype))
-
-
-class TestView:
-
-    def setup_method(self):
-        (a, b) = (np.arange(10), np.random.rand(10))
-        ndtype = [('a', float), ('b', float)]
-        arr = np.array(list(zip(a, b)), dtype=ndtype)
-
-        mrec = fromarrays([a, b], dtype=ndtype, fill_value=(-9., -99.))
-        mrec.mask[3] = (False, True)
-        self.data = (mrec, a, b, arr)
-
-    def test_view_by_itself(self):
-        (mrec, a, b, arr) = self.data
-        test = mrec.view()
-        assert_(isinstance(test, MaskedRecords))
-        assert_equal_records(test, mrec)
-        assert_equal_records(test._mask, mrec._mask)
-
-    def test_view_simple_dtype(self):
-        (mrec, a, b, arr) = self.data
-        ntype = (float, 2)
-        test = mrec.view(ntype)
-        assert_(isinstance(test, ma.MaskedArray))
-        assert_equal(test, np.array(list(zip(a, b)), dtype=float))
-        assert_(test[3, 1] is ma.masked)
-
-    def test_view_flexible_type(self):
-        (mrec, a, b, arr) = self.data
-        alttype = [('A', float), ('B', float)]
-        test = mrec.view(alttype)
-        assert_(isinstance(test, MaskedRecords))
-        assert_equal_records(test, arr.view(alttype))
-        assert_(test['B'][3] is masked)
-        assert_equal(test.dtype, np.dtype(alttype))
-        assert_(test._fill_value is None)
-
-
-##############################################################################
-class TestMRecordsImport:
-
-    _a = ma.array([1, 2, 3], mask=[0, 0, 1], dtype=int)
-    _b = ma.array([1.1, 2.2, 3.3], mask=[0, 0, 1], dtype=float)
-    _c = ma.array([b'one', b'two', b'three'],
-                  mask=[0, 0, 1], dtype='|S8')
-    ddtype = [('a', int), ('b', float), ('c', '|S8')]
-    mrec = fromarrays([_a, _b, _c], dtype=ddtype,
-                      fill_value=(b'99999', b'99999.',
-                                  b'N/A'))
-    nrec = recfromarrays((_a._data, _b._data, _c._data), dtype=ddtype)
-    data = (mrec, nrec, ddtype)
-
-    def test_fromarrays(self):
-        _a = ma.array([1, 2, 3], mask=[0, 0, 1], dtype=int)
-        _b = ma.array([1.1, 2.2, 3.3], mask=[0, 0, 1], dtype=float)
-        _c = ma.array(['one', 'two', 'three'], mask=[0, 0, 1], dtype='|S8')
-        (mrec, nrec, _) = self.data
-        for (f, l) in zip(('a', 'b', 'c'), (_a, _b, _c)):
-            assert_equal(getattr(mrec, f)._mask, l._mask)
-        # One record only
-        _x = ma.array([1, 1.1, 'one'], mask=[1, 0, 0], dtype=object)
-        assert_equal_records(fromarrays(_x, dtype=mrec.dtype), mrec[0])
-
-    def test_fromrecords(self):
-        # Test construction from records.
-        (mrec, nrec, ddtype) = self.data
-        #......
-        palist = [(1, 'abc', 3.7000002861022949, 0),
-                  (2, 'xy', 6.6999998092651367, 1),
-                  (0, ' ', 0.40000000596046448, 0)]
-        pa = recfromrecords(palist, names='c1, c2, c3, c4')
-        mpa = fromrecords(palist, names='c1, c2, c3, c4')
-        assert_equal_records(pa, mpa)
-        #.....
-        _mrec = fromrecords(nrec)
-        assert_equal(_mrec.dtype, mrec.dtype)
-        for field in _mrec.dtype.names:
-            assert_equal(getattr(_mrec, field), getattr(mrec._data, field))
-
-        _mrec = fromrecords(nrec.tolist(), names='c1,c2,c3')
-        assert_equal(_mrec.dtype, [('c1', int), ('c2', float), ('c3', '|S5')])
-        for (f, n) in zip(('c1', 'c2', 'c3'), ('a', 'b', 'c')):
-            assert_equal(getattr(_mrec, f), getattr(mrec._data, n))
-
-        _mrec = fromrecords(mrec)
-        assert_equal(_mrec.dtype, mrec.dtype)
-        assert_equal_records(_mrec._data, mrec.filled())
-        assert_equal_records(_mrec._mask, mrec._mask)
-
-    def test_fromrecords_wmask(self):
-        # Tests construction from records w/ mask.
-        (mrec, nrec, ddtype) = self.data
-
-        _mrec = fromrecords(nrec.tolist(), dtype=ddtype, mask=[0, 1, 0,])
-        assert_equal_records(_mrec._data, mrec._data)
-        assert_equal(_mrec._mask.tolist(), [(0, 0, 0), (1, 1, 1), (0, 0, 0)])
-
-        _mrec = fromrecords(nrec.tolist(), dtype=ddtype, mask=True)
-        assert_equal_records(_mrec._data, mrec._data)
-        assert_equal(_mrec._mask.tolist(), [(1, 1, 1), (1, 1, 1), (1, 1, 1)])
-
-        _mrec = fromrecords(nrec.tolist(), dtype=ddtype, mask=mrec._mask)
-        assert_equal_records(_mrec._data, mrec._data)
-        assert_equal(_mrec._mask.tolist(), mrec._mask.tolist())
-
-        _mrec = fromrecords(nrec.tolist(), dtype=ddtype,
-                            mask=mrec._mask.tolist())
-        assert_equal_records(_mrec._data, mrec._data)
-        assert_equal(_mrec._mask.tolist(), mrec._mask.tolist())
-
-    def test_fromtextfile(self):
-        # Tests reading from a text file.
-        fcontent = (
-"""#
-'One (S)','Two (I)','Three (F)','Four (M)','Five (-)','Six (C)'
-'strings',1,1.0,'mixed column',,1
-'with embedded "double quotes"',2,2.0,1.0,,1
-'strings',3,3.0E5,3,,1
-'strings',4,-1e-10,,,1
-""")
-        with temppath() as path:
-            with open(path, 'w') as f:
-                f.write(fcontent)
-            mrectxt = fromtextfile(path, delimiter=',', varnames='ABCDEFG')
-        assert_(isinstance(mrectxt, MaskedRecords))
-        assert_equal(mrectxt.F, [1, 1, 1, 1])
-        assert_equal(mrectxt.E._mask, [1, 1, 1, 1])
-        assert_equal(mrectxt.C, [1, 2, 3.e+5, -1e-10])
-
-    def test_addfield(self):
-        # Tests addfield
-        (mrec, nrec, ddtype) = self.data
-        (d, m) = ([100, 200, 300], [1, 0, 0])
-        mrec = addfield(mrec, ma.array(d, mask=m))
-        assert_equal(mrec.f3, d)
-        assert_equal(mrec.f3._mask, m)
-
-
-def test_record_array_with_object_field():
-    # Trac #1839
-    y = ma.masked_array(
-        [(1, '2'), (3, '4')],
-        mask=[(0, 0), (0, 1)],
-        dtype=[('a', int), ('b', object)])
-    # getting an item used to fail
-    y[1]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/ranges/test_join.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/ranges/test_join.py
deleted file mode 100644
index 682b5c8def9ff0e00b533610c1d45a093e7d7a8d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/ranges/test_join.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import numpy as np
-
-from pandas import (
-    Index,
-    RangeIndex,
-)
-import pandas._testing as tm
-
-
-class TestJoin:
-    def test_join_outer(self):
-        # join with Index[int64]
-        index = RangeIndex(start=0, stop=20, step=2)
-        other = Index(np.arange(25, 14, -1, dtype=np.int64))
-
-        res, lidx, ridx = index.join(other, how="outer", return_indexers=True)
-        noidx_res = index.join(other, how="outer")
-        tm.assert_index_equal(res, noidx_res)
-
-        eres = Index(
-            [0, 2, 4, 6, 8, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]
-        )
-        elidx = np.array(
-            [0, 1, 2, 3, 4, 5, 6, 7, -1, 8, -1, 9, -1, -1, -1, -1, -1, -1, -1],
-            dtype=np.intp,
-        )
-        eridx = np.array(
-            [-1, -1, -1, -1, -1, -1, -1, -1, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0],
-            dtype=np.intp,
-        )
-
-        assert isinstance(res, Index) and res.dtype == np.dtype(np.int64)
-        assert not isinstance(res, RangeIndex)
-        tm.assert_index_equal(res, eres, exact=True)
-        tm.assert_numpy_array_equal(lidx, elidx)
-        tm.assert_numpy_array_equal(ridx, eridx)
-
-        # join with RangeIndex
-        other = RangeIndex(25, 14, -1)
-
-        res, lidx, ridx = index.join(other, how="outer", return_indexers=True)
-        noidx_res = index.join(other, how="outer")
-        tm.assert_index_equal(res, noidx_res)
-
-        assert isinstance(res, Index) and res.dtype == np.int64
-        assert not isinstance(res, RangeIndex)
-        tm.assert_index_equal(res, eres)
-        tm.assert_numpy_array_equal(lidx, elidx)
-        tm.assert_numpy_array_equal(ridx, eridx)
-
-    def test_join_inner(self):
-        # Join with non-RangeIndex
-        index = RangeIndex(start=0, stop=20, step=2)
-        other = Index(np.arange(25, 14, -1, dtype=np.int64))
-
-        res, lidx, ridx = index.join(other, how="inner", return_indexers=True)
-
-        # no guarantee of sortedness, so sort for comparison purposes
-        ind = res.argsort()
-        res = res.take(ind)
-        lidx = lidx.take(ind)
-        ridx = ridx.take(ind)
-
-        eres = Index([16, 18])
-        elidx = np.array([8, 9], dtype=np.intp)
-        eridx = np.array([9, 7], dtype=np.intp)
-
-        assert isinstance(res, Index) and res.dtype == np.int64
-        tm.assert_index_equal(res, eres)
-        tm.assert_numpy_array_equal(lidx, elidx)
-        tm.assert_numpy_array_equal(ridx, eridx)
-
-        # Join two RangeIndex
-        other = RangeIndex(25, 14, -1)
-
-        res, lidx, ridx = index.join(other, how="inner", return_indexers=True)
-
-        assert isinstance(res, RangeIndex)
-        tm.assert_index_equal(res, eres, exact="equiv")
-        tm.assert_numpy_array_equal(lidx, elidx)
-        tm.assert_numpy_array_equal(ridx, eridx)
-
-    def test_join_left(self):
-        # Join with Index[int64]
-        index = RangeIndex(start=0, stop=20, step=2)
-        other = Index(np.arange(25, 14, -1, dtype=np.int64))
-
-        res, lidx, ridx = index.join(other, how="left", return_indexers=True)
-        eres = index
-        eridx = np.array([-1, -1, -1, -1, -1, -1, -1, -1, 9, 7], dtype=np.intp)
-
-        assert isinstance(res, RangeIndex)
-        tm.assert_index_equal(res, eres)
-        assert lidx is None
-        tm.assert_numpy_array_equal(ridx, eridx)
-
-        # Join withRangeIndex
-        other = Index(np.arange(25, 14, -1, dtype=np.int64))
-
-        res, lidx, ridx = index.join(other, how="left", return_indexers=True)
-
-        assert isinstance(res, RangeIndex)
-        tm.assert_index_equal(res, eres)
-        assert lidx is None
-        tm.assert_numpy_array_equal(ridx, eridx)
-
-    def test_join_right(self):
-        # Join with Index[int64]
-        index = RangeIndex(start=0, stop=20, step=2)
-        other = Index(np.arange(25, 14, -1, dtype=np.int64))
-
-        res, lidx, ridx = index.join(other, how="right", return_indexers=True)
-        eres = other
-        elidx = np.array([-1, -1, -1, -1, -1, -1, -1, 9, -1, 8, -1], dtype=np.intp)
-
-        assert isinstance(other, Index) and other.dtype == np.int64
-        tm.assert_index_equal(res, eres)
-        tm.assert_numpy_array_equal(lidx, elidx)
-        assert ridx is None
-
-        # Join withRangeIndex
-        other = RangeIndex(25, 14, -1)
-
-        res, lidx, ridx = index.join(other, how="right", return_indexers=True)
-        eres = other
-
-        assert isinstance(other, RangeIndex)
-        tm.assert_index_equal(res, eres)
-        tm.assert_numpy_array_equal(lidx, elidx)
-        assert ridx is None
-
-    def test_join_non_int_index(self):
-        index = RangeIndex(start=0, stop=20, step=2)
-        other = Index([3, 6, 7, 8, 10], dtype=object)
-
-        outer = index.join(other, how="outer")
-        outer2 = other.join(index, how="outer")
-        expected = Index([0, 2, 3, 4, 6, 7, 8, 10, 12, 14, 16, 18])
-        tm.assert_index_equal(outer, outer2)
-        tm.assert_index_equal(outer, expected)
-
-        inner = index.join(other, how="inner")
-        inner2 = other.join(index, how="inner")
-        expected = Index([6, 8, 10])
-        tm.assert_index_equal(inner, inner2)
-        tm.assert_index_equal(inner, expected)
-
-        left = index.join(other, how="left")
-        tm.assert_index_equal(left, index.astype(object))
-
-        left2 = other.join(index, how="left")
-        tm.assert_index_equal(left2, other)
-
-        right = index.join(other, how="right")
-        tm.assert_index_equal(right, other)
-
-        right2 = other.join(index, how="right")
-        tm.assert_index_equal(right2, index.astype(object))
-
-    def test_join_non_unique(self):
-        index = RangeIndex(start=0, stop=20, step=2)
-        other = Index([4, 4, 3, 3])
-
-        res, lidx, ridx = index.join(other, return_indexers=True)
-
-        eres = Index([0, 2, 4, 4, 6, 8, 10, 12, 14, 16, 18])
-        elidx = np.array([0, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.intp)
-        eridx = np.array([-1, -1, 0, 1, -1, -1, -1, -1, -1, -1, -1], dtype=np.intp)
-
-        tm.assert_index_equal(res, eres)
-        tm.assert_numpy_array_equal(lidx, elidx)
-        tm.assert_numpy_array_equal(ridx, eridx)
-
-    def test_join_self(self, join_type):
-        index = RangeIndex(start=0, stop=20, step=2)
-        joined = index.join(index, how=join_type)
-        assert index is joined
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexing/test_coercion.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexing/test_coercion.py
deleted file mode 100644
index 2c39729097487993f542152aa394ab956b2aba1f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexing/test_coercion.py
+++ /dev/null
@@ -1,906 +0,0 @@
-from __future__ import annotations
-
-from datetime import (
-    datetime,
-    timedelta,
-)
-import itertools
-
-import numpy as np
-import pytest
-
-from pandas.compat import (
-    IS64,
-    is_platform_windows,
-)
-
-import pandas as pd
-import pandas._testing as tm
-
-###############################################################
-# Index / Series common tests which may trigger dtype coercions
-###############################################################
-
-
-@pytest.fixture(autouse=True, scope="class")
-def check_comprehensiveness(request):
-    # Iterate over combination of dtype, method and klass
-    # and ensure that each are contained within a collected test
-    cls = request.cls
-    combos = itertools.product(cls.klasses, cls.dtypes, [cls.method])
-
-    def has_test(combo):
-        klass, dtype, method = combo
-        cls_funcs = request.node.session.items
-        return any(
-            klass in x.name and dtype in x.name and method in x.name for x in cls_funcs
-        )
-
-    opts = request.config.option
-    if opts.lf or opts.keyword:
-        # If we are running with "last-failed" or -k foo, we expect to only
-        #  run a subset of tests.
-        yield
-
-    else:
-        for combo in combos:
-            if not has_test(combo):
-                raise AssertionError(
-                    f"test method is not defined: {cls.__name__}, {combo}"
-                )
-
-        yield
-
-
-class CoercionBase:
-    klasses = ["index", "series"]
-    dtypes = [
-        "object",
-        "int64",
-        "float64",
-        "complex128",
-        "bool",
-        "datetime64",
-        "datetime64tz",
-        "timedelta64",
-        "period",
-    ]
-
-    @property
-    def method(self):
-        raise NotImplementedError(self)
-
-
-class TestSetitemCoercion(CoercionBase):
-    method = "setitem"
-
-    # disable comprehensiveness tests, as most of these have been moved to
-    #  tests.series.indexing.test_setitem in SetitemCastingEquivalents subclasses.
-    klasses: list[str] = []
-
-    def test_setitem_series_no_coercion_from_values_list(self):
-        # GH35865 - int casted to str when internally calling np.array(ser.values)
-        ser = pd.Series(["a", 1])
-        ser[:] = list(ser.values)
-
-        expected = pd.Series(["a", 1])
-
-        tm.assert_series_equal(ser, expected)
-
-    def _assert_setitem_index_conversion(
-        self, original_series, loc_key, expected_index, expected_dtype
-    ):
-        """test index's coercion triggered by assign key"""
-        temp = original_series.copy()
-        # GH#33469 pre-2.0 with int loc_key and temp.index.dtype == np.float64
-        #  `temp[loc_key] = 5` treated loc_key as positional
-        temp[loc_key] = 5
-        exp = pd.Series([1, 2, 3, 4, 5], index=expected_index)
-        tm.assert_series_equal(temp, exp)
-        # check dtype explicitly for sure
-        assert temp.index.dtype == expected_dtype
-
-        temp = original_series.copy()
-        temp.loc[loc_key] = 5
-        exp = pd.Series([1, 2, 3, 4, 5], index=expected_index)
-        tm.assert_series_equal(temp, exp)
-        # check dtype explicitly for sure
-        assert temp.index.dtype == expected_dtype
-
-    @pytest.mark.parametrize(
-        "val,exp_dtype", [("x", object), (5, IndexError), (1.1, object)]
-    )
-    def test_setitem_index_object(self, val, exp_dtype):
-        obj = pd.Series([1, 2, 3, 4], index=list("abcd"))
-        assert obj.index.dtype == object
-
-        if exp_dtype is IndexError:
-            temp = obj.copy()
-            warn_msg = "Series.__setitem__ treating keys as positions is deprecated"
-            msg = "index 5 is out of bounds for axis 0 with size 4"
-            with pytest.raises(exp_dtype, match=msg):
-                with tm.assert_produces_warning(FutureWarning, match=warn_msg):
-                    temp[5] = 5
-        else:
-            exp_index = pd.Index(list("abcd") + [val])
-            self._assert_setitem_index_conversion(obj, val, exp_index, exp_dtype)
-
-    @pytest.mark.parametrize(
-        "val,exp_dtype", [(5, np.int64), (1.1, np.float64), ("x", object)]
-    )
-    def test_setitem_index_int64(self, val, exp_dtype):
-        obj = pd.Series([1, 2, 3, 4])
-        assert obj.index.dtype == np.int64
-
-        exp_index = pd.Index([0, 1, 2, 3, val])
-        self._assert_setitem_index_conversion(obj, val, exp_index, exp_dtype)
-
-    @pytest.mark.parametrize(
-        "val,exp_dtype", [(5, np.float64), (5.1, np.float64), ("x", object)]
-    )
-    def test_setitem_index_float64(self, val, exp_dtype, request):
-        obj = pd.Series([1, 2, 3, 4], index=[1.1, 2.1, 3.1, 4.1])
-        assert obj.index.dtype == np.float64
-
-        exp_index = pd.Index([1.1, 2.1, 3.1, 4.1, val])
-        self._assert_setitem_index_conversion(obj, val, exp_index, exp_dtype)
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_setitem_series_period(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_setitem_index_complex128(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_setitem_index_bool(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_setitem_index_datetime64(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_setitem_index_datetime64tz(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_setitem_index_timedelta64(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_setitem_index_period(self):
-        raise NotImplementedError
-
-
-class TestInsertIndexCoercion(CoercionBase):
-    klasses = ["index"]
-    method = "insert"
-
-    def _assert_insert_conversion(self, original, value, expected, expected_dtype):
-        """test coercion triggered by insert"""
-        target = original.copy()
-        res = target.insert(1, value)
-        tm.assert_index_equal(res, expected)
-        assert res.dtype == expected_dtype
-
-    @pytest.mark.parametrize(
-        "insert, coerced_val, coerced_dtype",
-        [
-            (1, 1, object),
-            (1.1, 1.1, object),
-            (False, False, object),
-            ("x", "x", object),
-        ],
-    )
-    def test_insert_index_object(self, insert, coerced_val, coerced_dtype):
-        obj = pd.Index(list("abcd"))
-        assert obj.dtype == object
-
-        exp = pd.Index(["a", coerced_val, "b", "c", "d"])
-        self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
-
-    @pytest.mark.parametrize(
-        "insert, coerced_val, coerced_dtype",
-        [
-            (1, 1, None),
-            (1.1, 1.1, np.float64),
-            (False, False, object),  # GH#36319
-            ("x", "x", object),
-        ],
-    )
-    def test_insert_int_index(
-        self, any_int_numpy_dtype, insert, coerced_val, coerced_dtype
-    ):
-        dtype = any_int_numpy_dtype
-        obj = pd.Index([1, 2, 3, 4], dtype=dtype)
-        coerced_dtype = coerced_dtype if coerced_dtype is not None else dtype
-
-        exp = pd.Index([1, coerced_val, 2, 3, 4], dtype=coerced_dtype)
-        self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
-
-    @pytest.mark.parametrize(
-        "insert, coerced_val, coerced_dtype",
-        [
-            (1, 1.0, None),
-            (1.1, 1.1, np.float64),
-            (False, False, object),  # GH#36319
-            ("x", "x", object),
-        ],
-    )
-    def test_insert_float_index(
-        self, float_numpy_dtype, insert, coerced_val, coerced_dtype
-    ):
-        dtype = float_numpy_dtype
-        obj = pd.Index([1.0, 2.0, 3.0, 4.0], dtype=dtype)
-        coerced_dtype = coerced_dtype if coerced_dtype is not None else dtype
-
-        exp = pd.Index([1.0, coerced_val, 2.0, 3.0, 4.0], dtype=coerced_dtype)
-        self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val,exp_dtype",
-        [
-            (pd.Timestamp("2012-01-01"), "datetime64[ns]"),
-            (pd.Timestamp("2012-01-01", tz="US/Eastern"), "datetime64[ns, US/Eastern]"),
-        ],
-        ids=["datetime64", "datetime64tz"],
-    )
-    @pytest.mark.parametrize(
-        "insert_value",
-        [pd.Timestamp("2012-01-01"), pd.Timestamp("2012-01-01", tz="Asia/Tokyo"), 1],
-    )
-    def test_insert_index_datetimes(self, fill_val, exp_dtype, insert_value):
-        obj = pd.DatetimeIndex(
-            ["2011-01-01", "2011-01-02", "2011-01-03", "2011-01-04"], tz=fill_val.tz
-        )
-        assert obj.dtype == exp_dtype
-
-        exp = pd.DatetimeIndex(
-            ["2011-01-01", fill_val.date(), "2011-01-02", "2011-01-03", "2011-01-04"],
-            tz=fill_val.tz,
-        )
-        self._assert_insert_conversion(obj, fill_val, exp, exp_dtype)
-
-        if fill_val.tz:
-            # mismatched tzawareness
-            ts = pd.Timestamp("2012-01-01")
-            result = obj.insert(1, ts)
-            expected = obj.astype(object).insert(1, ts)
-            assert expected.dtype == object
-            tm.assert_index_equal(result, expected)
-
-            ts = pd.Timestamp("2012-01-01", tz="Asia/Tokyo")
-            result = obj.insert(1, ts)
-            # once deprecation is enforced:
-            expected = obj.insert(1, ts.tz_convert(obj.dtype.tz))
-            assert expected.dtype == obj.dtype
-            tm.assert_index_equal(result, expected)
-
-        else:
-            # mismatched tzawareness
-            ts = pd.Timestamp("2012-01-01", tz="Asia/Tokyo")
-            result = obj.insert(1, ts)
-            expected = obj.astype(object).insert(1, ts)
-            assert expected.dtype == object
-            tm.assert_index_equal(result, expected)
-
-        item = 1
-        result = obj.insert(1, item)
-        expected = obj.astype(object).insert(1, item)
-        assert expected[1] == item
-        assert expected.dtype == object
-        tm.assert_index_equal(result, expected)
-
-    def test_insert_index_timedelta64(self):
-        obj = pd.TimedeltaIndex(["1 day", "2 day", "3 day", "4 day"])
-        assert obj.dtype == "timedelta64[ns]"
-
-        # timedelta64 + timedelta64 => timedelta64
-        exp = pd.TimedeltaIndex(["1 day", "10 day", "2 day", "3 day", "4 day"])
-        self._assert_insert_conversion(
-            obj, pd.Timedelta("10 day"), exp, "timedelta64[ns]"
-        )
-
-        for item in [pd.Timestamp("2012-01-01"), 1]:
-            result = obj.insert(1, item)
-            expected = obj.astype(object).insert(1, item)
-            assert expected.dtype == object
-            tm.assert_index_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "insert, coerced_val, coerced_dtype",
-        [
-            (pd.Period("2012-01", freq="M"), "2012-01", "period[M]"),
-            (pd.Timestamp("2012-01-01"), pd.Timestamp("2012-01-01"), object),
-            (1, 1, object),
-            ("x", "x", object),
-        ],
-    )
-    def test_insert_index_period(self, insert, coerced_val, coerced_dtype):
-        obj = pd.PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq="M")
-        assert obj.dtype == "period[M]"
-
-        data = [
-            pd.Period("2011-01", freq="M"),
-            coerced_val,
-            pd.Period("2011-02", freq="M"),
-            pd.Period("2011-03", freq="M"),
-            pd.Period("2011-04", freq="M"),
-        ]
-        if isinstance(insert, pd.Period):
-            exp = pd.PeriodIndex(data, freq="M")
-            self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
-
-            # string that can be parsed to appropriate PeriodDtype
-            self._assert_insert_conversion(obj, str(insert), exp, coerced_dtype)
-
-        else:
-            result = obj.insert(0, insert)
-            expected = obj.astype(object).insert(0, insert)
-            tm.assert_index_equal(result, expected)
-
-            # TODO: ATM inserting '2012-01-01 00:00:00' when we have obj.freq=="M"
-            #  casts that string to Period[M], not clear that is desirable
-            if not isinstance(insert, pd.Timestamp):
-                # non-castable string
-                result = obj.insert(0, str(insert))
-                expected = obj.astype(object).insert(0, str(insert))
-                tm.assert_index_equal(result, expected)
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_insert_index_complex128(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_insert_index_bool(self):
-        raise NotImplementedError
-
-
-class TestWhereCoercion(CoercionBase):
-    method = "where"
-    _cond = np.array([True, False, True, False])
-
-    def _assert_where_conversion(
-        self, original, cond, values, expected, expected_dtype
-    ):
-        """test coercion triggered by where"""
-        target = original.copy()
-        res = target.where(cond, values)
-        tm.assert_equal(res, expected)
-        assert res.dtype == expected_dtype
-
-    def _construct_exp(self, obj, klass, fill_val, exp_dtype):
-        if fill_val is True:
-            values = klass([True, False, True, True])
-        elif isinstance(fill_val, (datetime, np.datetime64)):
-            values = pd.date_range(fill_val, periods=4)
-        else:
-            values = klass(x * fill_val for x in [5, 6, 7, 8])
-
-        exp = klass([obj[0], values[1], obj[2], values[3]], dtype=exp_dtype)
-        return values, exp
-
-    def _run_test(self, obj, fill_val, klass, exp_dtype):
-        cond = klass(self._cond)
-
-        exp = klass([obj[0], fill_val, obj[2], fill_val], dtype=exp_dtype)
-        self._assert_where_conversion(obj, cond, fill_val, exp, exp_dtype)
-
-        values, exp = self._construct_exp(obj, klass, fill_val, exp_dtype)
-        self._assert_where_conversion(obj, cond, values, exp, exp_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val,exp_dtype",
-        [(1, object), (1.1, object), (1 + 1j, object), (True, object)],
-    )
-    def test_where_object(self, index_or_series, fill_val, exp_dtype):
-        klass = index_or_series
-        obj = klass(list("abcd"))
-        assert obj.dtype == object
-        self._run_test(obj, fill_val, klass, exp_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val,exp_dtype",
-        [(1, np.int64), (1.1, np.float64), (1 + 1j, np.complex128), (True, object)],
-    )
-    def test_where_int64(self, index_or_series, fill_val, exp_dtype, request):
-        klass = index_or_series
-
-        obj = klass([1, 2, 3, 4])
-        assert obj.dtype == np.int64
-        self._run_test(obj, fill_val, klass, exp_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val, exp_dtype",
-        [(1, np.float64), (1.1, np.float64), (1 + 1j, np.complex128), (True, object)],
-    )
-    def test_where_float64(self, index_or_series, fill_val, exp_dtype, request):
-        klass = index_or_series
-
-        obj = klass([1.1, 2.2, 3.3, 4.4])
-        assert obj.dtype == np.float64
-        self._run_test(obj, fill_val, klass, exp_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val,exp_dtype",
-        [
-            (1, np.complex128),
-            (1.1, np.complex128),
-            (1 + 1j, np.complex128),
-            (True, object),
-        ],
-    )
-    def test_where_complex128(self, index_or_series, fill_val, exp_dtype):
-        klass = index_or_series
-        obj = klass([1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=np.complex128)
-        assert obj.dtype == np.complex128
-        self._run_test(obj, fill_val, klass, exp_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val,exp_dtype",
-        [(1, object), (1.1, object), (1 + 1j, object), (True, np.bool_)],
-    )
-    def test_where_series_bool(self, fill_val, exp_dtype):
-        klass = pd.Series  # TODO: use index_or_series once we have Index[bool]
-
-        obj = klass([True, False, True, False])
-        assert obj.dtype == np.bool_
-        self._run_test(obj, fill_val, klass, exp_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val,exp_dtype",
-        [
-            (pd.Timestamp("2012-01-01"), "datetime64[ns]"),
-            (pd.Timestamp("2012-01-01", tz="US/Eastern"), object),
-        ],
-        ids=["datetime64", "datetime64tz"],
-    )
-    def test_where_datetime64(self, index_or_series, fill_val, exp_dtype):
-        klass = index_or_series
-
-        obj = klass(pd.date_range("2011-01-01", periods=4, freq="D")._with_freq(None))
-        assert obj.dtype == "datetime64[ns]"
-
-        fv = fill_val
-        # do the check with each of the available datetime scalars
-        if exp_dtype == "datetime64[ns]":
-            for scalar in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
-                self._run_test(obj, scalar, klass, exp_dtype)
-        else:
-            for scalar in [fv, fv.to_pydatetime()]:
-                self._run_test(obj, fill_val, klass, exp_dtype)
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_where_index_complex128(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_where_index_bool(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_where_series_timedelta64(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_where_series_period(self):
-        raise NotImplementedError
-
-    @pytest.mark.parametrize(
-        "value", [pd.Timedelta(days=9), timedelta(days=9), np.timedelta64(9, "D")]
-    )
-    def test_where_index_timedelta64(self, value):
-        tdi = pd.timedelta_range("1 Day", periods=4)
-        cond = np.array([True, False, False, True])
-
-        expected = pd.TimedeltaIndex(["1 Day", value, value, "4 Days"])
-        result = tdi.where(cond, value)
-        tm.assert_index_equal(result, expected)
-
-        # wrong-dtyped NaT
-        dtnat = np.datetime64("NaT", "ns")
-        expected = pd.Index([tdi[0], dtnat, dtnat, tdi[3]], dtype=object)
-        assert expected[1] is dtnat
-
-        result = tdi.where(cond, dtnat)
-        tm.assert_index_equal(result, expected)
-
-    def test_where_index_period(self):
-        dti = pd.date_range("2016-01-01", periods=3, freq="QS")
-        pi = dti.to_period("Q")
-
-        cond = np.array([False, True, False])
-
-        # Passing a valid scalar
-        value = pi[-1] + pi.freq * 10
-        expected = pd.PeriodIndex([value, pi[1], value])
-        result = pi.where(cond, value)
-        tm.assert_index_equal(result, expected)
-
-        # Case passing ndarray[object] of Periods
-        other = np.asarray(pi + pi.freq * 10, dtype=object)
-        result = pi.where(cond, other)
-        expected = pd.PeriodIndex([other[0], pi[1], other[2]])
-        tm.assert_index_equal(result, expected)
-
-        # Passing a mismatched scalar -> casts to object
-        td = pd.Timedelta(days=4)
-        expected = pd.Index([td, pi[1], td], dtype=object)
-        result = pi.where(cond, td)
-        tm.assert_index_equal(result, expected)
-
-        per = pd.Period("2020-04-21", "D")
-        expected = pd.Index([per, pi[1], per], dtype=object)
-        result = pi.where(cond, per)
-        tm.assert_index_equal(result, expected)
-
-
-class TestFillnaSeriesCoercion(CoercionBase):
-    # not indexing, but place here for consistency
-
-    method = "fillna"
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_has_comprehensive_tests(self):
-        raise NotImplementedError
-
-    def _assert_fillna_conversion(self, original, value, expected, expected_dtype):
-        """test coercion triggered by fillna"""
-        target = original.copy()
-        res = target.fillna(value)
-        tm.assert_equal(res, expected)
-        assert res.dtype == expected_dtype
-
-    @pytest.mark.parametrize(
-        "fill_val, fill_dtype",
-        [(1, object), (1.1, object), (1 + 1j, object), (True, object)],
-    )
-    def test_fillna_object(self, index_or_series, fill_val, fill_dtype):
-        klass = index_or_series
-        obj = klass(["a", np.nan, "c", "d"])
-        assert obj.dtype == object
-
-        exp = klass(["a", fill_val, "c", "d"])
-        self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val,fill_dtype",
-        [(1, np.float64), (1.1, np.float64), (1 + 1j, np.complex128), (True, object)],
-    )
-    def test_fillna_float64(self, index_or_series, fill_val, fill_dtype):
-        klass = index_or_series
-        obj = klass([1.1, np.nan, 3.3, 4.4])
-        assert obj.dtype == np.float64
-
-        exp = klass([1.1, fill_val, 3.3, 4.4])
-        self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val,fill_dtype",
-        [
-            (1, np.complex128),
-            (1.1, np.complex128),
-            (1 + 1j, np.complex128),
-            (True, object),
-        ],
-    )
-    def test_fillna_complex128(self, index_or_series, fill_val, fill_dtype):
-        klass = index_or_series
-        obj = klass([1 + 1j, np.nan, 3 + 3j, 4 + 4j], dtype=np.complex128)
-        assert obj.dtype == np.complex128
-
-        exp = klass([1 + 1j, fill_val, 3 + 3j, 4 + 4j])
-        self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val,fill_dtype",
-        [
-            (pd.Timestamp("2012-01-01"), "datetime64[ns]"),
-            (pd.Timestamp("2012-01-01", tz="US/Eastern"), object),
-            (1, object),
-            ("x", object),
-        ],
-        ids=["datetime64", "datetime64tz", "object", "object"],
-    )
-    def test_fillna_datetime(self, index_or_series, fill_val, fill_dtype):
-        klass = index_or_series
-        obj = klass(
-            [
-                pd.Timestamp("2011-01-01"),
-                pd.NaT,
-                pd.Timestamp("2011-01-03"),
-                pd.Timestamp("2011-01-04"),
-            ]
-        )
-        assert obj.dtype == "datetime64[ns]"
-
-        exp = klass(
-            [
-                pd.Timestamp("2011-01-01"),
-                fill_val,
-                pd.Timestamp("2011-01-03"),
-                pd.Timestamp("2011-01-04"),
-            ]
-        )
-        self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val,fill_dtype",
-        [
-            (pd.Timestamp("2012-01-01", tz="US/Eastern"), "datetime64[ns, US/Eastern]"),
-            (pd.Timestamp("2012-01-01"), object),
-            # pre-2.0 with a mismatched tz we would get object result
-            (pd.Timestamp("2012-01-01", tz="Asia/Tokyo"), "datetime64[ns, US/Eastern]"),
-            (1, object),
-            ("x", object),
-        ],
-    )
-    def test_fillna_datetime64tz(self, index_or_series, fill_val, fill_dtype):
-        klass = index_or_series
-        tz = "US/Eastern"
-
-        obj = klass(
-            [
-                pd.Timestamp("2011-01-01", tz=tz),
-                pd.NaT,
-                pd.Timestamp("2011-01-03", tz=tz),
-                pd.Timestamp("2011-01-04", tz=tz),
-            ]
-        )
-        assert obj.dtype == "datetime64[ns, US/Eastern]"
-
-        if getattr(fill_val, "tz", None) is None:
-            fv = fill_val
-        else:
-            fv = fill_val.tz_convert(tz)
-        exp = klass(
-            [
-                pd.Timestamp("2011-01-01", tz=tz),
-                fv,
-                pd.Timestamp("2011-01-03", tz=tz),
-                pd.Timestamp("2011-01-04", tz=tz),
-            ]
-        )
-        self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
-
-    @pytest.mark.parametrize(
-        "fill_val",
-        [
-            1,
-            1.1,
-            1 + 1j,
-            True,
-            pd.Interval(1, 2, closed="left"),
-            pd.Timestamp("2012-01-01", tz="US/Eastern"),
-            pd.Timestamp("2012-01-01"),
-            pd.Timedelta(days=1),
-            pd.Period("2016-01-01", "D"),
-        ],
-    )
-    def test_fillna_interval(self, index_or_series, fill_val):
-        ii = pd.interval_range(1.0, 5.0, closed="right").insert(1, np.nan)
-        assert isinstance(ii.dtype, pd.IntervalDtype)
-        obj = index_or_series(ii)
-
-        exp = index_or_series([ii[0], fill_val, ii[2], ii[3], ii[4]], dtype=object)
-
-        fill_dtype = object
-        self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_fillna_series_int64(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_fillna_index_int64(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_fillna_series_bool(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_fillna_index_bool(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_fillna_series_timedelta64(self):
-        raise NotImplementedError
-
-    @pytest.mark.parametrize(
-        "fill_val",
-        [
-            1,
-            1.1,
-            1 + 1j,
-            True,
-            pd.Interval(1, 2, closed="left"),
-            pd.Timestamp("2012-01-01", tz="US/Eastern"),
-            pd.Timestamp("2012-01-01"),
-            pd.Timedelta(days=1),
-            pd.Period("2016-01-01", "W"),
-        ],
-    )
-    def test_fillna_series_period(self, index_or_series, fill_val):
-        pi = pd.period_range("2016-01-01", periods=4, freq="D").insert(1, pd.NaT)
-        assert isinstance(pi.dtype, pd.PeriodDtype)
-        obj = index_or_series(pi)
-
-        exp = index_or_series([pi[0], fill_val, pi[2], pi[3], pi[4]], dtype=object)
-
-        fill_dtype = object
-        self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_fillna_index_timedelta64(self):
-        raise NotImplementedError
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_fillna_index_period(self):
-        raise NotImplementedError
-
-
-class TestReplaceSeriesCoercion(CoercionBase):
-    klasses = ["series"]
-    method = "replace"
-
-    rep: dict[str, list] = {}
-    rep["object"] = ["a", "b"]
-    rep["int64"] = [4, 5]
-    rep["float64"] = [1.1, 2.2]
-    rep["complex128"] = [1 + 1j, 2 + 2j]
-    rep["bool"] = [True, False]
-    rep["datetime64[ns]"] = [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-03")]
-
-    for tz in ["UTC", "US/Eastern"]:
-        # to test tz => different tz replacement
-        key = f"datetime64[ns, {tz}]"
-        rep[key] = [
-            pd.Timestamp("2011-01-01", tz=tz),
-            pd.Timestamp("2011-01-03", tz=tz),
-        ]
-
-    rep["timedelta64[ns]"] = [pd.Timedelta("1 day"), pd.Timedelta("2 day")]
-
-    @pytest.fixture(params=["dict", "series"])
-    def how(self, request):
-        return request.param
-
-    @pytest.fixture(
-        params=[
-            "object",
-            "int64",
-            "float64",
-            "complex128",
-            "bool",
-            "datetime64[ns]",
-            "datetime64[ns, UTC]",
-            "datetime64[ns, US/Eastern]",
-            "timedelta64[ns]",
-        ]
-    )
-    def from_key(self, request):
-        return request.param
-
-    @pytest.fixture(
-        params=[
-            "object",
-            "int64",
-            "float64",
-            "complex128",
-            "bool",
-            "datetime64[ns]",
-            "datetime64[ns, UTC]",
-            "datetime64[ns, US/Eastern]",
-            "timedelta64[ns]",
-        ],
-        ids=[
-            "object",
-            "int64",
-            "float64",
-            "complex128",
-            "bool",
-            "datetime64",
-            "datetime64tz",
-            "datetime64tz",
-            "timedelta64",
-        ],
-    )
-    def to_key(self, request):
-        return request.param
-
-    @pytest.fixture
-    def replacer(self, how, from_key, to_key):
-        """
-        Object we will pass to `Series.replace`
-        """
-        if how == "dict":
-            replacer = dict(zip(self.rep[from_key], self.rep[to_key]))
-        elif how == "series":
-            replacer = pd.Series(self.rep[to_key], index=self.rep[from_key])
-        else:
-            raise ValueError
-        return replacer
-
-    def test_replace_series(self, how, to_key, from_key, replacer):
-        index = pd.Index([3, 4], name="xxx")
-        obj = pd.Series(self.rep[from_key], index=index, name="yyy")
-        assert obj.dtype == from_key
-
-        if from_key.startswith("datetime") and to_key.startswith("datetime"):
-            # tested below
-            return
-        elif from_key in ["datetime64[ns, US/Eastern]", "datetime64[ns, UTC]"]:
-            # tested below
-            return
-
-        result = obj.replace(replacer)
-
-        if (from_key == "float64" and to_key in ("int64")) or (
-            from_key == "complex128" and to_key in ("int64", "float64")
-        ):
-            if not IS64 or is_platform_windows():
-                pytest.skip(f"32-bit platform buggy: {from_key} -> {to_key}")
-
-            # Expected: do not downcast by replacement
-            exp = pd.Series(self.rep[to_key], index=index, name="yyy", dtype=from_key)
-
-        else:
-            exp = pd.Series(self.rep[to_key], index=index, name="yyy")
-            assert exp.dtype == to_key
-
-        tm.assert_series_equal(result, exp)
-
-    @pytest.mark.parametrize(
-        "to_key",
-        ["timedelta64[ns]", "bool", "object", "complex128", "float64", "int64"],
-        indirect=True,
-    )
-    @pytest.mark.parametrize(
-        "from_key", ["datetime64[ns, UTC]", "datetime64[ns, US/Eastern]"], indirect=True
-    )
-    def test_replace_series_datetime_tz(self, how, to_key, from_key, replacer):
-        index = pd.Index([3, 4], name="xyz")
-        obj = pd.Series(self.rep[from_key], index=index, name="yyy")
-        assert obj.dtype == from_key
-
-        result = obj.replace(replacer)
-
-        exp = pd.Series(self.rep[to_key], index=index, name="yyy")
-        assert exp.dtype == to_key
-
-        tm.assert_series_equal(result, exp)
-
-    @pytest.mark.parametrize(
-        "to_key",
-        ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, US/Eastern]"],
-        indirect=True,
-    )
-    @pytest.mark.parametrize(
-        "from_key",
-        ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, US/Eastern]"],
-        indirect=True,
-    )
-    def test_replace_series_datetime_datetime(self, how, to_key, from_key, replacer):
-        index = pd.Index([3, 4], name="xyz")
-        obj = pd.Series(self.rep[from_key], index=index, name="yyy")
-        assert obj.dtype == from_key
-
-        result = obj.replace(replacer)
-
-        exp = pd.Series(self.rep[to_key], index=index, name="yyy")
-        if isinstance(obj.dtype, pd.DatetimeTZDtype) and isinstance(
-            exp.dtype, pd.DatetimeTZDtype
-        ):
-            # with mismatched tzs, we retain the original dtype as of 2.0
-            exp = exp.astype(obj.dtype)
-        else:
-            assert exp.dtype == to_key
-
-        tm.assert_series_equal(result, exp)
-
-    @pytest.mark.xfail(reason="Test not implemented")
-    def test_replace_series_period(self):
-        raise NotImplementedError
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/excel/test_odf.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/excel/test_odf.py
deleted file mode 100644
index 25079b235d332e2c72a47e1cdad5347b792bb3ac..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/excel/test_odf.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import functools
-
-import numpy as np
-import pytest
-
-import pandas as pd
-import pandas._testing as tm
-
-pytest.importorskip("odf")
-
-
-@pytest.fixture(autouse=True)
-def cd_and_set_engine(monkeypatch, datapath):
-    func = functools.partial(pd.read_excel, engine="odf")
-    monkeypatch.setattr(pd, "read_excel", func)
-    monkeypatch.chdir(datapath("io", "data", "excel"))
-
-
-def test_read_invalid_types_raises():
-    # the invalid_value_type.ods required manually editing
-    # of the included content.xml file
-    with pytest.raises(ValueError, match="Unrecognized type awesome_new_type"):
-        pd.read_excel("invalid_value_type.ods")
-
-
-def test_read_writer_table():
-    # Also test reading tables from an text OpenDocument file
-    # (.odt)
-    index = pd.Index(["Row 1", "Row 2", "Row 3"], name="Header")
-    expected = pd.DataFrame(
-        [[1, np.nan, 7], [2, np.nan, 8], [3, np.nan, 9]],
-        index=index,
-        columns=["Column 1", "Unnamed: 2", "Column 3"],
-    )
-
-    result = pd.read_excel("writertable.odt", sheet_name="Table1", index_col=0)
-
-    tm.assert_frame_equal(result, expected)
-
-
-def test_read_newlines_between_xml_elements_table():
-    # GH#45598
-    expected = pd.DataFrame(
-        [[1.0, 4.0, 7], [np.nan, np.nan, 8], [3.0, 6.0, 9]],
-        columns=["Column 1", "Column 2", "Column 3"],
-    )
-
-    result = pd.read_excel("test_newlines.ods")
-
-    tm.assert_frame_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/formatter.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/formatter.py
deleted file mode 100644
index 87183abbb2326cfaf79a1d9d86ab5f0ec45f1222..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/formatter.py
+++ /dev/null
@@ -1,124 +0,0 @@
-"""
-    pygments.formatter
-    ~~~~~~~~~~~~~~~~~~
-
-    Base formatter class.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import codecs
-
-from pygments.util import get_bool_opt
-from pygments.styles import get_style_by_name
-
-__all__ = ['Formatter']
-
-
-def _lookup_style(style):
-    if isinstance(style, str):
-        return get_style_by_name(style)
-    return style
-
-
-class Formatter:
-    """
-    Converts a token stream to text.
-
-    Formatters should have attributes to help selecting them. These
-    are similar to the corresponding :class:`~pygments.lexer.Lexer`
-    attributes.
-
-    .. autoattribute:: name
-       :no-value:
-
-    .. autoattribute:: aliases
-       :no-value:
-
-    .. autoattribute:: filenames
-       :no-value:
-
-    You can pass options as keyword arguments to the constructor.
-    All formatters accept these basic options:
-
-    ``style``
-        The style to use, can be a string or a Style subclass
-        (default: "default"). Not used by e.g. the
-        TerminalFormatter.
-    ``full``
-        Tells the formatter to output a "full" document, i.e.
-        a complete self-contained document. This doesn't have
-        any effect for some formatters (default: false).
-    ``title``
-        If ``full`` is true, the title that should be used to
-        caption the document (default: '').
-    ``encoding``
-        If given, must be an encoding name. This will be used to
-        convert the Unicode token strings to byte strings in the
-        output. If it is "" or None, Unicode strings will be written
-        to the output file, which most file-like objects do not
-        support (default: None).
-    ``outencoding``
-        Overrides ``encoding`` if given.
-
-    """
-
-    #: Full name for the formatter, in human-readable form.
-    name = None
-
-    #: A list of short, unique identifiers that can be used to lookup
-    #: the formatter from a list, e.g. using :func:`.get_formatter_by_name()`.
-    aliases = []
-
-    #: A list of fnmatch patterns that match filenames for which this
-    #: formatter can produce output. The patterns in this list should be unique
-    #: among all formatters.
-    filenames = []
-
-    #: If True, this formatter outputs Unicode strings when no encoding
-    #: option is given.
-    unicodeoutput = True
-
-    def __init__(self, **options):
-        """
-        As with lexers, this constructor takes arbitrary optional arguments,
-        and if you override it, you should first process your own options, then
-        call the base class implementation.
-        """
-        self.style = _lookup_style(options.get('style', 'default'))
-        self.full = get_bool_opt(options, 'full', False)
-        self.title = options.get('title', '')
-        self.encoding = options.get('encoding', None) or None
-        if self.encoding in ('guess', 'chardet'):
-            # can happen for e.g. pygmentize -O encoding=guess
-            self.encoding = 'utf-8'
-        self.encoding = options.get('outencoding') or self.encoding
-        self.options = options
-
-    def get_style_defs(self, arg=''):
-        """
-        This method must return statements or declarations suitable to define
-        the current style for subsequent highlighted text (e.g. CSS classes
-        in the `HTMLFormatter`).
-
-        The optional argument `arg` can be used to modify the generation and
-        is formatter dependent (it is standardized because it can be given on
-        the command line).
-
-        This method is called by the ``-S`` :doc:`command-line option <cmdline>`,
-        the `arg` is then given by the ``-a`` option.
-        """
-        return ''
-
-    def format(self, tokensource, outfile):
-        """
-        This method must format the tokens from the `tokensource` iterable and
-        write the formatted version to the file object `outfile`.
-
-        Formatter options can control how exactly the tokens are converted.
-        """
-        if self.encoding:
-            # wrap the outfile in a StreamWriter
-            outfile = codecs.lookup(self.encoding)[3](outfile)
-        return self.format_unencoded(tokensource, outfile)
diff --git a/spaces/pseudolab/KOMUChat/README.md b/spaces/pseudolab/KOMUChat/README.md
deleted file mode 100644
index 19b604561a18b8315dd6e4a68f52cbd632961965..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/KOMUChat/README.md
+++ /dev/null
@@ -1,67 +0,0 @@
----
-title: KOMUCHAT ~ Korean Community-style ChatBot ~
-emoji: 👩‍❤️‍👨
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 4.1.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-# KOMUCHAT : Korean community-style relationship counseling chabot
-
-KOMUChat: 온라인 커뮤니티 스타일의 연애상담챗봇
-
-### 개요
-
-사람들의 실제 대화를 반영하기 위해 한국 커뮤니티 에펨코리아 + 인스티즈에서 수집한 약 3만건의 질문-답변 대화 데이터셋으로 학습시킨 챗봇
-
-이별, 데이트코스, 선물 등 연애와 관련된 질문에 대해 온라인 커뮤니티에서 상담하는 것 같이 응답합니다.
-
-`paust-t5-large`모델과 `polyglot-ko-12.8b-safetensors` 모델을 파인튜닝하여 제공합니다. 데모에서 선택하여 이용해보실 수 있습니다. 
-
-
-### 응답예시
-
-| Question | Answer |
-| --- | --- |
-| 헤어졌읍니다 | 힘내 더 좋은 인연 나타날거야 |
-| 드라마만 주구장창 보는중 | 뭐 보고 있으신가요 |
-| 결혼까지 생각하는 상태일수록 더 깐깐하게 따지게 돼? | 아무래도 그런 거 같아 |
-| ISTJ 여자가 남자한테 관심있으면 어떻게 해? | 연락 자주 하고 취향 많이 물어보고 자주는 아니어도 꾸준히 만나려 함 |
-| #청자#들아 연애는 갑자기 시작한다고 생각해? | 나는 진자 신기한 게 연애에 대한 기대가 떨어지면 하고 있더랔ㅋㅋㅋ |
-
-### 사용기술
-<img src="https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=Python&logoColor=white"><img src="https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge&logo=PyTorch&logoColor=white">
-
-
-## English descriptions
-
-KOMUCHAT : Korean community-style relationship counseling chabot
-
-### Intro
-
-This chatbot is trained with KOMUChat dataset, which is question-answer form of conversations collected from Korean online community <b>FM Korea</b> and <b>instiz</b>.
-
-The chatbot responds to relationship-related questions such as breakups, dating courses, and gifts as if it were consulting an online community.
-
-We finetuned `paust-t5-large` and `polyglot-en-12.8b-safetensors` model. You are welcomed to try them out by select options in demo.
-
-### Example conversation
-
-| Question | Answer |
-| --- | --- |
-| 헤어졌읍니다.(I broke up) | 힘내 더 좋은 인연 나타날거야(Cheer up, better things will come along) |
-| 드라마만 주구장창 보는중 (I’m just watching dramas all day long) | 뭐 보고 있으신가요(What are you watching?) |
-| 결혼까지 생각하는 상태일수록 더 깐깐하게 따지게 돼?(Is it true that the more you think about marriage, the more demanding you get?) | 아무래도 그런 거 같아(I think so.) |
-| ISTJ 여자가 남자한테 관심있으면 어떻게 해?(What ISTJ girls do when they have interests in a guy?) | 연락 자주 하고 취향 많이 물어보고 자주는 아니어도 꾸준히 만나려 함(Contact him often, and ask him what he likes and try to see him regularly) |
-| #청자#들아 연애는 갑자기 시작한다고 생각해?(#Listeners#, do you think relatonships start out nowhere?) | 나는 진자 신기한 게 연애에 대한 기대가 떨어지면 하고 있더랔ㅋㅋㅋ(I've found it really interesting that when I'm not expecting a relationship to happen, it does.) |
-
-### STACKS
-<img src="https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=Python&logoColor=white"><img src="https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge&logo=PyTorch&logoColor=white">
-
-
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/qinzhu/diy-girlfriend-online/modules.py b/spaces/qinzhu/diy-girlfriend-online/modules.py
deleted file mode 100644
index 3484f6a1f4c1c06855c37a1ff4e66c58864acb38..0000000000000000000000000000000000000000
--- a/spaces/qinzhu/diy-girlfriend-online/modules.py
+++ /dev/null
@@ -1,390 +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
-from 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):
-  """
-  Dilated 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
-
-
-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.)
-    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
diff --git a/spaces/qpmzonxw/bing/README.md b/spaces/qpmzonxw/bing/README.md
deleted file mode 100644
index 619f6ff99f21eec3a6e09264a6ed58ea020bcc3f..0000000000000000000000000000000000000000
--- a/spaces/qpmzonxw/bing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Bing
-emoji: 🦀
-colorFrom: indigo
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Guru Charitra 14 Adhyay Pdf Download.md b/spaces/quidiaMuxgu/Expedit-SAM/Guru Charitra 14 Adhyay Pdf Download.md
deleted file mode 100644
index e84a6dc9fa40b1012770f04f06719b3d1e18b450..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Guru Charitra 14 Adhyay Pdf Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>guru charitra 14 adhyay pdf download</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://geags.com/2uCsVy">https://geags.com/2uCsVy</a></b></p><br /><br />
-
-14. Chapter 13 . ... Sanskrit translation of Sree Guru Charitra and a book of Sanskrit verses in praise of Lord Dattatreya. “ These are the only ... flourished in the 14th and 15th centuries A.D. The original text recounts, in 53 chapters, the life and. 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/raw.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/raw.py
deleted file mode 100644
index d4941ad2d7ed858f490db441f5b46b12bd61ad78..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/raw.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import os
-from collections import defaultdict, namedtuple
-from pathlib import Path
-
-import musdb
-import numpy as np
-import torch as th
-import tqdm
-from torch.utils.data import DataLoader
-
-from .audio import AudioFile
-
-ChunkInfo = namedtuple("ChunkInfo", ["file_index", "offset", "local_index"])
-
-
-class Rawset:
-    """
-    Dataset of raw, normalized, float32 audio files
-    """
-    def __init__(self, path, samples=None, stride=None, channels=2, streams=None):
-        self.path = Path(path)
-        self.channels = channels
-        self.samples = samples
-        if stride is None:
-            stride = samples if samples is not None else 0
-        self.stride = stride
-        entries = defaultdict(list)
-        for root, folders, files in os.walk(self.path, followlinks=True):
-            folders.sort()
-            files.sort()
-            for file in files:
-                if file.endswith(".raw"):
-                    path = Path(root) / file
-                    name, stream = path.stem.rsplit('.', 1)
-                    entries[(path.parent.relative_to(self.path), name)].append(int(stream))
-
-        self._entries = list(entries.keys())
-
-        sizes = []
-        self._lengths = []
-        ref_streams = sorted(entries[self._entries[0]])
-        assert ref_streams == list(range(len(ref_streams)))
-        if streams is None:
-            self.streams = ref_streams
-        else:
-            self.streams = streams
-        for entry in sorted(entries.keys()):
-            streams = entries[entry]
-            assert sorted(streams) == ref_streams
-            file = self._path(*entry)
-            length = file.stat().st_size // (4 * channels)
-            if samples is None:
-                sizes.append(1)
-            else:
-                if length < samples:
-                    self._entries.remove(entry)
-                    continue
-                sizes.append((length - samples) // stride + 1)
-            self._lengths.append(length)
-        if not sizes:
-            raise ValueError(f"Empty dataset {self.path}")
-        self._cumulative_sizes = np.cumsum(sizes)
-        self._sizes = sizes
-
-    def __len__(self):
-        return self._cumulative_sizes[-1]
-
-    @property
-    def total_length(self):
-        return sum(self._lengths)
-
-    def chunk_info(self, index):
-        file_index = np.searchsorted(self._cumulative_sizes, index, side='right')
-        if file_index == 0:
-            local_index = index
-        else:
-            local_index = index - self._cumulative_sizes[file_index - 1]
-        return ChunkInfo(offset=local_index * self.stride,
-                         file_index=file_index,
-                         local_index=local_index)
-
-    def _path(self, folder, name, stream=0):
-        return self.path / folder / (name + f'.{stream}.raw')
-
-    def __getitem__(self, index):
-        chunk = self.chunk_info(index)
-        entry = self._entries[chunk.file_index]
-
-        length = self.samples or self._lengths[chunk.file_index]
-        streams = []
-        to_read = length * self.channels * 4
-        for stream_index, stream in enumerate(self.streams):
-            offset = chunk.offset * 4 * self.channels
-            file = open(self._path(*entry, stream=stream), 'rb')
-            file.seek(offset)
-            content = file.read(to_read)
-            assert len(content) == to_read
-            content = np.frombuffer(content, dtype=np.float32)
-            content = content.copy()  # make writable
-            streams.append(th.from_numpy(content).view(length, self.channels).t())
-        return th.stack(streams, dim=0)
-
-    def name(self, index):
-        chunk = self.chunk_info(index)
-        folder, name = self._entries[chunk.file_index]
-        return folder / name
-
-
-class MusDBSet:
-    def __init__(self, mus, streams=slice(None), samplerate=44100, channels=2):
-        self.mus = mus
-        self.streams = streams
-        self.samplerate = samplerate
-        self.channels = channels
-
-    def __len__(self):
-        return len(self.mus.tracks)
-
-    def __getitem__(self, index):
-        track = self.mus.tracks[index]
-        return (track.name, AudioFile(track.path).read(channels=self.channels,
-                                                       seek_time=0,
-                                                       streams=self.streams,
-                                                       samplerate=self.samplerate))
-
-
-def build_raw(mus, destination, normalize, workers, samplerate, channels):
-    destination.mkdir(parents=True, exist_ok=True)
-    loader = DataLoader(MusDBSet(mus, channels=channels, samplerate=samplerate),
-                        batch_size=1,
-                        num_workers=workers,
-                        collate_fn=lambda x: x[0])
-    for name, streams in tqdm.tqdm(loader):
-        if normalize:
-            ref = streams[0].mean(dim=0)  # use mono mixture as reference
-            streams = (streams - ref.mean()) / ref.std()
-        for index, stream in enumerate(streams):
-            open(destination / (name + f'.{index}.raw'), "wb").write(stream.t().numpy().tobytes())
-
-
-def main():
-    parser = argparse.ArgumentParser('rawset')
-    parser.add_argument('--workers', type=int, default=10)
-    parser.add_argument('--samplerate', type=int, default=44100)
-    parser.add_argument('--channels', type=int, default=2)
-    parser.add_argument('musdb', type=Path)
-    parser.add_argument('destination', type=Path)
-
-    args = parser.parse_args()
-
-    build_raw(musdb.DB(root=args.musdb, subsets=["train"], split="train"),
-              args.destination / "train",
-              normalize=True,
-              channels=args.channels,
-              samplerate=args.samplerate,
-              workers=args.workers)
-    build_raw(musdb.DB(root=args.musdb, subsets=["train"], split="valid"),
-              args.destination / "valid",
-              normalize=True,
-              samplerate=args.samplerate,
-              channels=args.channels,
-              workers=args.workers)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/r3gm/RVC_HF/infer/lib/uvr5_pack/lib_v5/dataset.py b/spaces/r3gm/RVC_HF/infer/lib/uvr5_pack/lib_v5/dataset.py
deleted file mode 100644
index cfd01a174978d97180a897e40cb59ecadec1d12e..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/infer/lib/uvr5_pack/lib_v5/dataset.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import os
-import random
-
-import numpy as np
-import torch
-import torch.utils.data
-from tqdm import tqdm
-
-from . import spec_utils
-
-
-class VocalRemoverValidationSet(torch.utils.data.Dataset):
-    def __init__(self, patch_list):
-        self.patch_list = patch_list
-
-    def __len__(self):
-        return len(self.patch_list)
-
-    def __getitem__(self, idx):
-        path = self.patch_list[idx]
-        data = np.load(path)
-
-        X, y = data["X"], data["y"]
-
-        X_mag = np.abs(X)
-        y_mag = np.abs(y)
-
-        return X_mag, y_mag
-
-
-def make_pair(mix_dir, inst_dir):
-    input_exts = [".wav", ".m4a", ".mp3", ".mp4", ".flac"]
-
-    X_list = sorted(
-        [
-            os.path.join(mix_dir, fname)
-            for fname in os.listdir(mix_dir)
-            if os.path.splitext(fname)[1] in input_exts
-        ]
-    )
-    y_list = sorted(
-        [
-            os.path.join(inst_dir, fname)
-            for fname in os.listdir(inst_dir)
-            if os.path.splitext(fname)[1] in input_exts
-        ]
-    )
-
-    filelist = list(zip(X_list, y_list))
-
-    return filelist
-
-
-def train_val_split(dataset_dir, split_mode, val_rate, val_filelist):
-    if split_mode == "random":
-        filelist = make_pair(
-            os.path.join(dataset_dir, "mixtures"),
-            os.path.join(dataset_dir, "instruments"),
-        )
-
-        random.shuffle(filelist)
-
-        if len(val_filelist) == 0:
-            val_size = int(len(filelist) * val_rate)
-            train_filelist = filelist[:-val_size]
-            val_filelist = filelist[-val_size:]
-        else:
-            train_filelist = [
-                pair for pair in filelist if list(pair) not in val_filelist
-            ]
-    elif split_mode == "subdirs":
-        if len(val_filelist) != 0:
-            raise ValueError(
-                "The `val_filelist` option is not available in `subdirs` mode"
-            )
-
-        train_filelist = make_pair(
-            os.path.join(dataset_dir, "training/mixtures"),
-            os.path.join(dataset_dir, "training/instruments"),
-        )
-
-        val_filelist = make_pair(
-            os.path.join(dataset_dir, "validation/mixtures"),
-            os.path.join(dataset_dir, "validation/instruments"),
-        )
-
-    return train_filelist, val_filelist
-
-
-def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha):
-    perm = np.random.permutation(len(X))
-    for i, idx in enumerate(tqdm(perm)):
-        if np.random.uniform() < reduction_rate:
-            y[idx] = spec_utils.reduce_vocal_aggressively(
-                X[idx], y[idx], reduction_mask
-            )
-
-        if np.random.uniform() < 0.5:
-            # swap channel
-            X[idx] = X[idx, ::-1]
-            y[idx] = y[idx, ::-1]
-        if np.random.uniform() < 0.02:
-            # mono
-            X[idx] = X[idx].mean(axis=0, keepdims=True)
-            y[idx] = y[idx].mean(axis=0, keepdims=True)
-        if np.random.uniform() < 0.02:
-            # inst
-            X[idx] = y[idx]
-
-        if np.random.uniform() < mixup_rate and i < len(perm) - 1:
-            lam = np.random.beta(mixup_alpha, mixup_alpha)
-            X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]]
-            y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]]
-
-    return X, y
-
-
-def make_padding(width, cropsize, offset):
-    left = offset
-    roi_size = cropsize - left * 2
-    if roi_size == 0:
-        roi_size = cropsize
-    right = roi_size - (width % roi_size) + left
-
-    return left, right, roi_size
-
-
-def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset):
-    len_dataset = patches * len(filelist)
-
-    X_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
-    y_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
-
-    for i, (X_path, y_path) in enumerate(tqdm(filelist)):
-        X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
-        coef = np.max([np.abs(X).max(), np.abs(y).max()])
-        X, y = X / coef, y / coef
-
-        l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
-        X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
-        y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
-        starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches)
-        ends = starts + cropsize
-        for j in range(patches):
-            idx = i * patches + j
-            X_dataset[idx] = X_pad[:, :, starts[j] : ends[j]]
-            y_dataset[idx] = y_pad[:, :, starts[j] : ends[j]]
-
-    return X_dataset, y_dataset
-
-
-def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset):
-    patch_list = []
-    patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format(
-        cropsize, sr, hop_length, n_fft, offset
-    )
-    os.makedirs(patch_dir, exist_ok=True)
-
-    for i, (X_path, y_path) in enumerate(tqdm(filelist)):
-        basename = os.path.splitext(os.path.basename(X_path))[0]
-
-        X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
-        coef = np.max([np.abs(X).max(), np.abs(y).max()])
-        X, y = X / coef, y / coef
-
-        l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
-        X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
-        y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
-        len_dataset = int(np.ceil(X.shape[2] / roi_size))
-        for j in range(len_dataset):
-            outpath = os.path.join(patch_dir, "{}_p{}.npz".format(basename, j))
-            start = j * roi_size
-            if not os.path.exists(outpath):
-                np.savez(
-                    outpath,
-                    X=X_pad[:, :, start : start + cropsize],
-                    y=y_pad[:, :, start : start + cropsize],
-                )
-            patch_list.append(outpath)
-
-    return VocalRemoverValidationSet(patch_list)
diff --git a/spaces/rachana219/MODT2/trackers/strongsort/deep/models/xception.py b/spaces/rachana219/MODT2/trackers/strongsort/deep/models/xception.py
deleted file mode 100644
index 43db4ab53283daf1267f2f4cc5f7d778daf4076a..0000000000000000000000000000000000000000
--- a/spaces/rachana219/MODT2/trackers/strongsort/deep/models/xception.py
+++ /dev/null
@@ -1,344 +0,0 @@
-from __future__ import division, absolute_import
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.model_zoo as model_zoo
-
-__all__ = ['xception']
-
-pretrained_settings = {
-    'xception': {
-        'imagenet': {
-            'url':
-            'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth',
-            'input_space': 'RGB',
-            'input_size': [3, 299, 299],
-            'input_range': [0, 1],
-            'mean': [0.5, 0.5, 0.5],
-            'std': [0.5, 0.5, 0.5],
-            'num_classes': 1000,
-            'scale':
-            0.8975 # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
-        }
-    }
-}
-
-
-class SeparableConv2d(nn.Module):
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size=1,
-        stride=1,
-        padding=0,
-        dilation=1,
-        bias=False
-    ):
-        super(SeparableConv2d, self).__init__()
-
-        self.conv1 = nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            groups=in_channels,
-            bias=bias
-        )
-        self.pointwise = nn.Conv2d(
-            in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias
-        )
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.pointwise(x)
-        return x
-
-
-class Block(nn.Module):
-
-    def __init__(
-        self,
-        in_filters,
-        out_filters,
-        reps,
-        strides=1,
-        start_with_relu=True,
-        grow_first=True
-    ):
-        super(Block, self).__init__()
-
-        if out_filters != in_filters or strides != 1:
-            self.skip = nn.Conv2d(
-                in_filters, out_filters, 1, stride=strides, bias=False
-            )
-            self.skipbn = nn.BatchNorm2d(out_filters)
-        else:
-            self.skip = None
-
-        self.relu = nn.ReLU(inplace=True)
-        rep = []
-
-        filters = in_filters
-        if grow_first:
-            rep.append(self.relu)
-            rep.append(
-                SeparableConv2d(
-                    in_filters,
-                    out_filters,
-                    3,
-                    stride=1,
-                    padding=1,
-                    bias=False
-                )
-            )
-            rep.append(nn.BatchNorm2d(out_filters))
-            filters = out_filters
-
-        for i in range(reps - 1):
-            rep.append(self.relu)
-            rep.append(
-                SeparableConv2d(
-                    filters, filters, 3, stride=1, padding=1, bias=False
-                )
-            )
-            rep.append(nn.BatchNorm2d(filters))
-
-        if not grow_first:
-            rep.append(self.relu)
-            rep.append(
-                SeparableConv2d(
-                    in_filters,
-                    out_filters,
-                    3,
-                    stride=1,
-                    padding=1,
-                    bias=False
-                )
-            )
-            rep.append(nn.BatchNorm2d(out_filters))
-
-        if not start_with_relu:
-            rep = rep[1:]
-        else:
-            rep[0] = nn.ReLU(inplace=False)
-
-        if strides != 1:
-            rep.append(nn.MaxPool2d(3, strides, 1))
-        self.rep = nn.Sequential(*rep)
-
-    def forward(self, inp):
-        x = self.rep(inp)
-
-        if self.skip is not None:
-            skip = self.skip(inp)
-            skip = self.skipbn(skip)
-        else:
-            skip = inp
-
-        x += skip
-        return x
-
-
-class Xception(nn.Module):
-    """Xception.
-    
-    Reference:
-        Chollet. Xception: Deep Learning with Depthwise
-        Separable Convolutions. CVPR 2017.
-
-    Public keys:
-        - ``xception``: Xception.
-    """
-
-    def __init__(
-        self, num_classes, loss, fc_dims=None, dropout_p=None, **kwargs
-    ):
-        super(Xception, self).__init__()
-        self.loss = loss
-
-        self.conv1 = nn.Conv2d(3, 32, 3, 2, 0, bias=False)
-        self.bn1 = nn.BatchNorm2d(32)
-
-        self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
-        self.bn2 = nn.BatchNorm2d(64)
-
-        self.block1 = Block(
-            64, 128, 2, 2, start_with_relu=False, grow_first=True
-        )
-        self.block2 = Block(
-            128, 256, 2, 2, start_with_relu=True, grow_first=True
-        )
-        self.block3 = Block(
-            256, 728, 2, 2, start_with_relu=True, grow_first=True
-        )
-
-        self.block4 = Block(
-            728, 728, 3, 1, start_with_relu=True, grow_first=True
-        )
-        self.block5 = Block(
-            728, 728, 3, 1, start_with_relu=True, grow_first=True
-        )
-        self.block6 = Block(
-            728, 728, 3, 1, start_with_relu=True, grow_first=True
-        )
-        self.block7 = Block(
-            728, 728, 3, 1, start_with_relu=True, grow_first=True
-        )
-
-        self.block8 = Block(
-            728, 728, 3, 1, start_with_relu=True, grow_first=True
-        )
-        self.block9 = Block(
-            728, 728, 3, 1, start_with_relu=True, grow_first=True
-        )
-        self.block10 = Block(
-            728, 728, 3, 1, start_with_relu=True, grow_first=True
-        )
-        self.block11 = Block(
-            728, 728, 3, 1, start_with_relu=True, grow_first=True
-        )
-
-        self.block12 = Block(
-            728, 1024, 2, 2, start_with_relu=True, grow_first=False
-        )
-
-        self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
-        self.bn3 = nn.BatchNorm2d(1536)
-
-        self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
-        self.bn4 = nn.BatchNorm2d(2048)
-
-        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
-        self.feature_dim = 2048
-        self.fc = self._construct_fc_layer(fc_dims, 2048, dropout_p)
-        self.classifier = nn.Linear(self.feature_dim, num_classes)
-
-        self._init_params()
-
-    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
-        """Constructs fully connected layer.
-
-        Args:
-            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
-            input_dim (int): input dimension
-            dropout_p (float): dropout probability, if None, dropout is unused
-        """
-        if fc_dims is None:
-            self.feature_dim = input_dim
-            return None
-
-        assert isinstance(
-            fc_dims, (list, tuple)
-        ), 'fc_dims must be either list or tuple, but got {}'.format(
-            type(fc_dims)
-        )
-
-        layers = []
-        for dim in fc_dims:
-            layers.append(nn.Linear(input_dim, dim))
-            layers.append(nn.BatchNorm1d(dim))
-            layers.append(nn.ReLU(inplace=True))
-            if dropout_p is not None:
-                layers.append(nn.Dropout(p=dropout_p))
-            input_dim = dim
-
-        self.feature_dim = fc_dims[-1]
-
-        return nn.Sequential(*layers)
-
-    def _init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(
-                    m.weight, mode='fan_out', nonlinearity='relu'
-                )
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm1d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def featuremaps(self, input):
-        x = self.conv1(input)
-        x = self.bn1(x)
-        x = F.relu(x, inplace=True)
-
-        x = self.conv2(x)
-        x = self.bn2(x)
-        x = F.relu(x, inplace=True)
-
-        x = self.block1(x)
-        x = self.block2(x)
-        x = self.block3(x)
-        x = self.block4(x)
-        x = self.block5(x)
-        x = self.block6(x)
-        x = self.block7(x)
-        x = self.block8(x)
-        x = self.block9(x)
-        x = self.block10(x)
-        x = self.block11(x)
-        x = self.block12(x)
-
-        x = self.conv3(x)
-        x = self.bn3(x)
-        x = F.relu(x, inplace=True)
-
-        x = self.conv4(x)
-        x = self.bn4(x)
-        x = F.relu(x, inplace=True)
-        return x
-
-    def forward(self, x):
-        f = self.featuremaps(x)
-        v = self.global_avgpool(f)
-        v = v.view(v.size(0), -1)
-
-        if self.fc is not None:
-            v = self.fc(v)
-
-        if not self.training:
-            return v
-
-        y = self.classifier(v)
-
-        if self.loss == 'softmax':
-            return y
-        elif self.loss == 'triplet':
-            return y, v
-        else:
-            raise KeyError('Unsupported loss: {}'.format(self.loss))
-
-
-def init_pretrained_weights(model, model_url):
-    """Initialize models with pretrained weights.
-    
-    Layers that don't match with pretrained layers in name or size are kept unchanged.
-    """
-    pretrain_dict = model_zoo.load_url(model_url)
-    model_dict = model.state_dict()
-    pretrain_dict = {
-        k: v
-        for k, v in pretrain_dict.items()
-        if k in model_dict and model_dict[k].size() == v.size()
-    }
-    model_dict.update(pretrain_dict)
-    model.load_state_dict(model_dict)
-
-
-def xception(num_classes, loss='softmax', pretrained=True, **kwargs):
-    model = Xception(num_classes, loss, fc_dims=None, dropout_p=None, **kwargs)
-    if pretrained:
-        model_url = pretrained_settings['xception']['imagenet']['url']
-        init_pretrained_weights(model, model_url)
-    return model
diff --git a/spaces/radames/UserControllableLT-Latent-Transformer/expansion/dataloader/sintellist.py b/spaces/radames/UserControllableLT-Latent-Transformer/expansion/dataloader/sintellist.py
deleted file mode 100644
index 44bc1ab5d466d605b7bc695adb41f2431aa0f790..0000000000000000000000000000000000000000
--- a/spaces/radames/UserControllableLT-Latent-Transformer/expansion/dataloader/sintellist.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import torch.utils.data as data
-
-from PIL import Image
-import os
-import os.path
-import numpy as np
-import pdb
-
-IMG_EXTENSIONS = [
-    '.jpg', '.JPG', '.jpeg', '.JPEG',
-    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
-]
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-def dataloader(filepath):
-
-  left_fold  = 'image_2/'
-  train = [img for img in os.listdir(filepath+left_fold) if img.find('Sintel') > -1]
-
-  l0_train  = [filepath+left_fold+img for img in train]
-  l0_train  = [img for img in l0_train if '%s_%s.png'%(img.rsplit('_',1)[0],'%02d'%(1+int(img.split('.')[0].split('_')[-1])) ) in l0_train ]
-
-  #l0_train = [i for i in l0_train if not '10.png' in i] # remove 10 as val
-
-  l1_train = ['%s_%s.png'%(img.rsplit('_',1)[0],'%02d'%(1+int(img.split('.')[0].split('_')[-1])) ) for img in l0_train]
-  flow_train = [img.replace('image_2','flow_occ') for img in l0_train]
-
-
-  return l0_train, l1_train, flow_train
diff --git a/spaces/radames/sentence-embeddings-visualization/embeddings_encoder.py b/spaces/radames/sentence-embeddings-visualization/embeddings_encoder.py
deleted file mode 100644
index 691a9c77b29ee0fa34eff31020b13bbcac91d037..0000000000000000000000000000000000000000
--- a/spaces/radames/sentence-embeddings-visualization/embeddings_encoder.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# from https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
-from transformers import AutoTokenizer, AutoModel
-import torch
-import torch.nn.functional as F
-import os
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-class EmbeddingsEncoder:
-    def __init__(self):
-        # Load model from HuggingFace Hub
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            'sentence-transformers/all-MiniLM-L6-v2')
-        self.model = AutoModel.from_pretrained(
-            'sentence-transformers/all-MiniLM-L6-v2')
-
-    # Mean Pooling - Take average of all tokens
-
-    def mean_pooling(self, model_output, attention_mask):
-        # First element of model_output contains all token embeddings
-        token_embeddings = model_output.last_hidden_state
-        input_mask_expanded = attention_mask.unsqueeze(
-            -1).expand(token_embeddings.size()).float()
-        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-
-    # Encode text
-
-    def encode(self,  texts):
-        # Tokenize sentences
-        print("Tokenizing...")
-        encoded_input = self.tokenizer(
-            texts, padding=True, truncation=True, return_tensors='pt')
-
-        # Compute token embeddings
-        print("Computing embeddings...")
-        with torch.no_grad():
-            model_output = self.model(**encoded_input, return_dict=True)
-
-        # Perform pooling
-        print("Performing pooling...")
-        embeddings = self.mean_pooling(
-            model_output, encoded_input['attention_mask'])
-
-        # Normalize embeddings
-        print("Normalizing embeddings...")
-        embeddings = F.normalize(embeddings, p=2, dim=1)
-
-        return embeddings
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download Origin 9.0.0.45 Patch to Solve Ok9.dll Problems.md b/spaces/raedeXanto/academic-chatgpt-beta/Download Origin 9.0.0.45 Patch to Solve Ok9.dll Problems.md
deleted file mode 100644
index 0fd2c8cd19fc180768849c4e77acce8446b215b8..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Download Origin 9.0.0.45 Patch to Solve Ok9.dll Problems.md	
+++ /dev/null
@@ -1,147 +0,0 @@
-
-<h1>What is ok9.dll and why do you need it?</h1>
- <p>If you are a user of Origin, a popular platform for gaming and data analysis, you may have encountered a file called "ok9.dll" on your computer. But what is this file and why do you need it? In this article, we will explain what ok9.dll is, how it works with Origin, and how to get the latest patch for it.</p>
-<h2>ok9.dll origin 9.0.0.45 patch</h2><br /><p><b><b>Download Zip</b> &#9745; <a href="https://tinourl.com/2uL07M">https://tinourl.com/2uL07M</a></b></p><br /><br />
- <h2>What is ok9.dll?</h2>
- <p>Ok9.dll is a file that belongs to Origin, a software developed by OriginLab Corporation for data analysis and graphing, and by Electronic Arts for gaming and digital distribution. Origin is used by millions of users around the world for various purposes, such as creating scientific graphs, playing online games, and accessing digital content.</p>
- <h3>What is Origin?</h3>
- <p>Origin is a software that has two main functions: data analysis and graphing, and gaming and digital distribution. The data analysis and graphing function is provided by OriginLab Corporation, a company that specializes in creating software for scientists and engineers. The gaming and digital distribution function is provided by Electronic Arts, a company that produces and publishes video games.</p>
- <p>OriginLab's Origin allows users to import, analyze, and visualize data using various tools and features, such as graphs, charts, statistics, curve fitting, peak analysis, signal processing, and more. Origin also supports programming languages such as C, Python, R, and LabTalk.</p>
- <p>Electronic Arts' Origin allows users to buy, download, and play games from EA's catalog, such as FIFA, The Sims, Battlefield, Mass Effect, Dragon Age, and more. Origin also offers social features such as chat, friends list, achievements, cloud saves, and game streaming.</p>
-<p>How to download ok9.dll for origin 9.0.0.45<br />
-Fix ok9.dll missing or corrupted error in origin 9.0.0.45<br />
-Where to find ok9.dll crack for origin 9.0.0.45<br />
-Download ok9.dll by OriginLab Corporation for Origin<br />
-What is ok9.dll and how to use it in origin 9.0.0.45<br />
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-How to edit the properties of ok9.dll file in origin 9.0.0.45<br />
-How to rename or delete ok9.dll file in origin 9.0.0.</p>
- <h3>What is a DLL file?</h3>
- <p>A DLL file is a dynamic link library file that contains code and data that can be used by multiple programs at the same time. A DLL file allows programs to share functionality and resources without having to duplicate them in each program. For example, a DLL file can provide common functions such as printing, saving, or opening files.</p>
- <p>A DLL file can be loaded and unloaded by programs as needed, which can improve the performance and memory usage of the system. However, a DLL file can also cause problems if it is missing or corrupted. For example, if a program tries to use a function from a DLL file that is not available or compatible with the program's version or system's configuration, it may result in an error message or a crash.</p>
- <h2>Why do you need ok9.dll?</h2>
- <p>Ok9.dll is a DLL file that is required by Origin to run properly on your computer. Ok9.dll contains code and data that are used by both OriginLab's Origin and Electronic Arts' Origin for various purposes.</p>
- <h3>How does ok9.dll work with Origin?</h3>
- <p>Ok9.dll works with Origin by providing some of the core functionality and features of the software. For example, ok9.dll enables Origin to create graphs and charts from data sets, perform calculations and analysis on data, access online services and content from EA's servers, communicate with other users via chat or voice chat,</p> <p>and more.</p>
- <h3>What are the benefits of using ok9.dll?</h3>
- <p>Using ok9.dll can provide several benefits for Origin users. Some of these benefits are:</p>
- <ul>
-<li>Enhanced performance: Ok9.dll can improve the speed and efficiency of Origin by allowing it to use less memory and CPU resources.</li>
-<li>Better compatibility: Ok9.dll can ensure that Origin works well with different versions of Windows and other software on your computer.</li>
-<li>More features: Ok9.dll can enable Origin to offer more functionality and options for data analysis and graphing,</li>
-<li>and gaming and digital distribution.</li>
-</ul>
- <h3>What are the risks of not having ok9.dll?</h3>
- <p>Not having ok9.dll can pose several risks for Origin users. Some of these risks are:</p>
- <ul>
-<li>Poor performance: Not having ok9.dll can cause Origin to run slower or use more memory and CPU resources.</li>
-<li>Limited compatibility: Not having ok9.dll can cause Origin to not work properly with different versions of Windows or other software on your computer.</li>
-<li>Fewer features: Not having ok9.dll can prevent Origin from offering some of the functionality</li>
-<li>and options for data analysis and graphing,</li>
-<li>and gaming and digital distribution.</li>
-</ul>
- <h2>How to get ok9.dll origin 9.0.0.45 patch?</h2>
- <p>If you want to use Origin on your computer,</p><p>you need to have the latest version of ok9.dll installed on your system. The latest version of ok9.dll is origin 9.0.0.45 patch,</p><p>which was released on October 2021.</p><p>This patch fixes some bugs</p><p>and improves some features of Origin.</p>
- <h3>How to download ok9.dll origin 9.0.0.45 patch?</h3>
- <p>To download ok9.dll origin 9.0.0.45 patch,</p><p>you have two options:</p>
- <ol>
-<li>You can download it from the official website of OriginLab Corporation or Electronic Arts,</li>
-<li>depending on which version of Origin you are using.</li>
-<li>You can download it from a third-party website that offers free DLL files,</li>
-<li>such as DLLme.com.</li>
-</ol>
- <p>If you choose the first option,</p><p>you need to follow these steps:</p>
- <ol>
-<li>Go to the website of OriginLab Corporation or Electronic Arts,</li>
-<li>depending on which version of Origin you are using.</li>
-<li>Navigate to the download section</li>
-<li>and look for the latest version of Origin.</li>
-<li>Select the appropriate option for your system</li>
-<li>(32-bit or 64-bit)</li>
-<li>and click on the download button.</li>
-<li>Save the file on your computer</li>
-<li>and run it as an administrator.</li>
-<li>Follow the instructions on the screen</li>
-<li>to install the latest version of Origin</li>
-<li>(which includes ok9.dll origin 9.0.0.45 patch).</li>
-</ol>
- <p>If you choose the second option,</p><p>you need to follow these steps:</p>
- <ol>
-<li>Go to DLLme.com</li>
-<li>(or any other website that offers free DLL files).</li>
-<li>Type "ok9.dll" in the search box</li>
-<li>and hit enter.</li>
-<li>Select the version or variant you wish to download</li>
-<li>(in this case,</li><li>"ok9.dll by OriginLab Corporation for Okern Version 9").</li>
-<li>Click on the download button</li><li>(or right-click on it</li><li>and select "save link as").</li><li></ol></p><h3></h3><h4></h4><h5></h5><h6></h6><hr /><pre></pre><blockquote></blockquote><ul></ul><ol></ol><dl></dl><dt></dt><dd></dd><table></table><tr></tr><td></td><th></th><code></code><em></em><strong></strong><br /><a href=""></a><img src="" alt="" /><div></div><span></span><style></style><script></script>
-Save the file on your computer and run it as an administrator. <h3>How to install ok9.dll origin 9.0.0.45 patch?</h3>
- <p>To install ok9.dll origin 9.0.0.45 patch,</p><p>you need to follow these steps:</p>
- <ol>
-<li>Locate the ok9.dll file that you downloaded on your computer.</li>
-<li>Copy the file and paste it in the folder where Origin is installed on your computer.</li>
-<li>The default location of Origin is C:\Program Files (x86)\Origin or C:\Program Files\Origin,</li>
-<li>depending on your system.</li>
-<li>If you are asked to replace the existing file,</li>
-<li>click on yes.</li>
-<li>Restart your computer</li>
-<li>and launch Origin.</li>
-</ol>
- <p>You have successfully installed ok9.dll origin 9.0.0.45 patch on your computer.</p>
- <h3>How to fix ok9.dll errors?</h3>
- <p>If you still encounter ok9.dll errors after installing the patch,</p><p>you may need to do some troubleshooting steps to fix them.</p><p>Some of the common ok9.dll errors are:</p>
- <ul>
-<li>This application has failed to start because ok9.dll was not found.</li>
-<li>Re-installing the application may fix this problem.</li>
-<li>Ok9.dll Not Found.</li>
-<li>The file ok9.dll is missing or corrupted.</li>
-<li>A required component is missing: ok9.dll.</li>
-<li>Please install the application again.</li>
-<li>Error loading ok9.dll.</li>
-<li>The specified module could not be found.</li>
-</ul>
- <p>To fix these errors,</p><p>you can try the following solutions:</p>
- <ol>
-<li>Re-install the application that requires ok9.dll.</li>
-<li>This may restore the missing or corrupted file on your system.</li>
-<li>Update the application to the latest version.</li>
-<li>This may fix any compatibility issues or bugs that cause the errors.</li>
-<li>Install all Windows updates and any available driver updates.</li>
-<li>This may improve the stability and performance of your system and prevent any conflicts or errors.</li>
-<li>Clean your PC registry and optimize your computer.</li>
-<li>This may remove any invalid or obsolete entries that may cause the errors or slow down your system.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In this article,</p><p>we have explained what ok9.dll is,</p><p>why you need it,</p><p>and how to get the latest patch for it.</p><p>We have also provided some tips on how to fix any ok9.dll errors that you may encounter on your computer.</p>
- <p>We hope that this article has been helpful and informative for you.</p><p>If you have any questions or comments,</p><p>please feel free to leave them below.</p><p>We would love to hear from you!</p>
- <p>Thank you for reading and happy gaming!</p>
- <h3>Frequently Asked Questions</h3>
- <p>Here are some of the frequently asked questions about ok9.dll and their answers:</p>
- <ol>
-<li><strong>What is the difference between OriginLab's Origin and Electronic Arts' Origin?</strong></li>
-<li>The main difference between OriginLab's Origin and Electronic Arts' Origin is their function and purpose. OriginLab's Origin is a software for data analysis and graphing, while Electronic Arts' Origin is a software for gaming and digital distribution. However, they both use the same DLL file, ok9.dll, for some of their core functionality and features.</li>
-<li><strong>Is ok9.dll a virus or malware?</strong></li>
-<li>No, ok9.dll is not a virus or malware. It is a legitimate file that belongs to Origin, a software developed by OriginLab Corporation and Electronic Arts. However, some malicious programs may disguise themselves as ok9.dll or use it for their own purposes. Therefore, you should always scan your system with a reliable antivirus program and keep it updated regularly.</li>
-<li><strong>Can I delete ok9.dll from my computer?</strong></li>
-<li>We do not recommend deleting ok9.dll from your computer, as it may cause problems with Origin or other programs that use it. If you want to uninstall Origin from your computer, you should use the official uninstaller or a reputable uninstaller tool that can remove all the associated files and registry entries. If you want to free up some disk space, you can use a disk cleaner tool that can safely delete any unnecessary or temporary files on your system.</li>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download and Play Ni no Kuni The Jet-Black Mage - The English Patched Version of the NDS Classic.md b/spaces/raedeXanto/academic-chatgpt-beta/Download and Play Ni no Kuni The Jet-Black Mage - The English Patched Version of the NDS Classic.md
deleted file mode 100644
index 8dba1b855f7712c6311ac8acd75418557106d0f4..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Download and Play Ni no Kuni The Jet-Black Mage - The English Patched Version of the NDS Classic.md	
+++ /dev/null
@@ -1,104 +0,0 @@
-<br />
-<h1>How to Download Ni no Kuni NDS ROM English Patched Version</h1>
- <p>Ni no Kuni is a popular RPG series developed by Level-5 in collaboration with Studio Ghibli, the famous animation studio behind movies like Spirited Away and My Neighbor Totoro. The series features beautiful graphics, charming characters, and engaging stories set in a fantasy world where magic exists.</p>
- <p>The first game in the series, Ni no Kuni: Shikkoku no Madoushi (Ni no Kuni: The Jet-Black Mage), was released for the Nintendo DS in Japan in 2010. The game came with a massive 352-page book called the Magic Master, which was required to play the game. The book contained spells, maps, secrets, and lore that players had to use throughout their adventure.</p>
-<h2>ni no kuni nds rom english download</h2><br /><p><b><b>Download</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://tinourl.com/2uL1sy">https://tinourl.com/2uL1sy</a></b></p><br /><br />
- <p>Unfortunately, due to the difficulty of localizing such a huge book, the game was never released outside Japan. Fans of Ni no Kuni had to settle for playing its enhanced version for the PlayStation 3, Ni no Kuni: Wrath of the White Witch, which was released worldwide in 2013. However, many fans still wanted to experience the original DS version of Ni no Kuni.</p>
- <p>That's where the fan translation project comes in. A group of dedicated fans decided to take on the challenge of translating Ni no Kuni: Shikkoku no Madoushi into English (and Spanish) for everyone to enjoy. They spent years working on translating not only the game itself, but also the Magic Master book, which they made available as a PDF file. They finally released their English patch v1.0 on December 9th, 2018, exactly eight years after the game's original release date.</p>
- <p>In this article, we will show you how to download Ni no Kuni NDS ROM English patched version and play it on your Nintendo DS or your DS emulator. We will also tell you how to unlock DLCs and gazette daily news that were originally available only through online connection. Follow these steps and you will be able to enjoy this amazing game in English.</p>
- <h2>Requirements</h2>
- <p>Before you start downloading Ni no Kuni NDS ROM English patched version, you will need a few things:</p>
- <ul>
-<li>A Nintendo DS or a DS emulator. If you don't have a Nintendo DS, you can use an emulator such as DeSmuME or NO$GBA to play DS games on your PC or mobile device.</li>
-<li>A clean Japanese ROM of Ni no Kuni: Shikkoku no Madoushi. You can find this ROM online by searching for its name or its CRC32 (68B796B1), MD5 (F0E3027B9E97618732B4F2D4298AD0CF), SHA-1 (DFAAA4DC03BBB59F010B9146A720B24CFC7738EB), or SHA-256 (7C78318AE93BD5639E592BE98B22114E2160C54397F7BFFF5E1BFE61B54A4F3C) checksums.</li>
-<li>The official English patch v1.0 and the Magic Master PDF. You can download these files from <a href="https://www.romhacking.net/translations/4068/">this link</a> or <a href="http://www.mediafire.com/file/1657bx038wohmhb/Ni+no+kuni+-+The+Jet-Black+Mage+v1.0.zip">this link</a>. The zip file contains the patch, the PDF, and a readme file with instructions.</li>
-<li>Optional: Custom patches for different purposes. Some users have created custom patches that modify some aspects of the game, such as restoring the original ARM7.bin file (which may improve compatibility with some flashcarts or emulators), changing the game icon and title (which may make it easier to identify on your device), or making it compatible with YSMenu firmware (which may fix some bugs or glitches). You can find these patches <a href="https://gbatemp.net/threads/ni-no-kuni-translation-eng.373770/">here</a>.</li>
-</ul>
- <h2>How to Apply the Patch</h2>
- <p>Once you have all the required files, you can proceed to apply the patch to your ROM. Here are the steps:</p>
- <ol>
-<li>Download the patch and the PDF from one of the links provided above.</li>
-<li>Extract the zip file using a program such as WinRAR or 7-Zip.</li>
-<li>Open the readme file and read it carefully. It contains important information about how to use the patch and how to play the game.</li>
-<li>Use a patching tool such as xdelta or Lunar IPS to apply the patch to your clean Japanese ROM. Make sure you select the correct patch file for your ROM (either xdelta or ips) and that you have enough space on your device.</li>
-<li>Copy the patched ROM to your Nintendo DS or your emulator. You can rename it if you want.</li>
-</ol>
- <h2>How to Play the Game</h2>
- <p>Now that you have patched your ROM, you are ready to play Ni no Kuni: Shikkoku no Madoushi in English. Here are some tips:</p>
- <ul>
-<li>Start the game and select your language (English or Japanese). You can change it later in the options menu.</li>
-<li>Use the Magic Master PDF as a reference for spells, maps, and secrets. You will need it throughout your adventure, especially when casting spells or solving puzzles. You can print it out or view it on another device while playing.</li>
-<li>Enjoy the game and its story, graphics, and music. Ni no Kuni is a beautiful game that will captivate you with its charm and creativity.</li>
-</ul>
- <h2>How to Unlock DLCs and Gazette Daily News</h2>
- <p>Ni no Kuni: Shikkoku no Madoushi had some extra content that was available only through online connection via Wi-Fi or Nintendo Wi-Fi Connection service. These included DLCs (downloadable content) such as new quests, items, monsters, and characters; and gazette daily news that provided updates on events happening in different regions of Ni no Kuni.</p>
- <p>However, since these services are no longer available (and were never available outside Japan), you will need other methods to unlock them. There are three methods you can use:</p>
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- <table>
-<tr><th>Method</th><th>Description</th><th>Pros</th><th>Cons</th></tr>
-<tr><td>Savegame files</td><td>You can download savegame files that have all DLCs unlocked at the first save point at <a href="https://gbatemp.net/posts/9042991/">this link</a>. You can choose between two files: one that has all gazette daily news unlocked as well (but they are untranslated and will crash your game if you try to read them), and one that has only one gazette daily news unlocked (which is translated).</td><td>The easiest method that requires minimal effort.</td><td>You will lose your own progress if you overwrite your savegame file with these files.</td></tr>
-<td>You can use cheat codes to unlock DLCs and gazette daily news by using a cheat device such as Action Replay or R4. You can find some cheat codes <a href="https://gamefaqs.gamespot.com/ds/954258-ni-no-kuni-shikkoku-no-madoushi/cheats">here</a> or <a href="https://www.gamespot.com/games/ni-no-kuni-shikkoku-no-madoushi/cheats/">here</a>.</td><td>The most flexible method that allows you to choose which DLCs and gazette daily news you want to unlock.</td><td>You will need a cheat device and some technical knowledge to use this method.</td></tr>
-<tr><td>Online connection</td><td>You can connect your Nintendo DS or your emulator to the internet and use a custom DNS server that redirects you to a fan-made server that hosts the DLCs and gazette daily news. You can find more information about this method <a href="https://gbatemp.net/threads/ni-no-kuni-translation-eng.373770/page-91#post-9042991">here</a>.</td><td>The most authentic method that replicates the original online experience.</td><td>You will need a stable internet connection and some configuration to use this method.</td></tr>
-</table>
- <p>You can use any of these methods to access the extra content of Ni no Kuni: Shikkoku no Madoushi. However, keep in mind that some of them may not work with some flashcarts or emulators, and that some of them may have bugs or glitches. Also, remember that the gazette daily news are untranslated in the English patch and will crash your game if you try to read them.</p>
- <h2>Conclusion</h2>
- <p>Ni no Kuni: Shikkoku no Madoushi is a wonderful game that deserves to be played by fans of RPGs and Studio Ghibli. Thanks to the fan translation project, you can now enjoy this game in English by downloading Ni no Kuni NDS ROM English patched version and playing it on your Nintendo DS or your emulator.</p>
- <p>In this article, we have shown you how to download Ni no Kuni NDS ROM English patched version and how to unlock DLCs and gazette daily news. We hope you have found this guide helpful and easy to follow.</p>
- <p>Now it's time for you to embark on your journey in Ni no Kuni and explore its magical world. Along the way, you will meet many friends and foes, learn new spells and skills, and discover secrets and mysteries. You will also experience a touching story that will make you laugh and cry.</p>
- <p>Are you ready to play Ni no Kuni: Shikkoku no Madoushi in English? If so, download Ni no Kuni NDS ROM English patched version today and have fun!</p>
- <p>If you have any questions or feedback about this article, please let us know in the comments below. We would love to hear from you!</p>
- <h2>FAQs</h2>
- <ul>
-<li>Q: Is Ni no Kuni: Shikkoku no Madoushi the same as Ni no Kuni: Wrath of the White Witch?</li>
-<li>A: No, they are different games with different stories, characters, gameplay, and graphics. Ni no Kuni: Shikkoku no Madoushi is the original DS version of Ni no Kuni, while Ni no Kuni: Wrath of the White Witch is an enhanced version for the PS3 (and later for other platforms) that adds new content and features.</li>
-<li>Q: How long is Ni no Kuni: Shikkoku no Madoushi?</li>
-<li>A: According to <a href="https://howlongtobeat.com/game?id=1000">HowLongToBeat</a>, the main story of Ni no Kuni: Shikkoku no Madoushi takes about 35 hours to complete, while completing everything takes about 60 hours.</li>
-<li>Q: How can I save my game progress?</li>
-<li>A: You can save your game progress by using save points that are scattered throughout the world. You can also use quick save by pressing Start + Select on the map screen, but this will only save your location and not your inventory or quest status.</li>
-<li>Q: How can I cast spells?</li>
-<li>A: You can cast spells by using the Magic Master PDF as a reference. You will need to draw symbols on the touch screen using your stylus or finger. The symbols must be drawn accurately and quickly, otherwise they will fail or backfire.</li>
-<li>Q: How can I get more spells?</li>
-<li>A: You can get more spells by progressing through the story, completing quests, finding hidden chests, or unlocking DLCs. Some spells are exclusive to certain characters or situations.</li>
-</ul>
- </p> 0a6ba089eb<br />
-<br />
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-
-<h1>FCP X Luster Grade Presets 20 Pack Free Download: How to Enhance Your Videos with Professional Color Grading</h1>
- <p>If you are looking for a way to make your videos stand out with stunning colors and cinematic effects, you might want to check out the FCP X Luster Grade Presets 20 Pack. These are a collection of 20 color grading presets that you can use in Final Cut Pro X to transform your footage with just a few clicks. In this article, we will show you what these presets are, why you should use them, how to get them for free, and how to use them in your projects. We will also show you some examples of how these presets can create different looks for your videos, from Bollywood to Hollywood and everything in between.</p>
- <h2>Introduction</h2>
- <h3>What are FCP X Luster Grade Presets?</h3>
- <p>FCP X Luster Grade Presets are a set of 20 color correction and color grading presets that you can apply to your clips in Final Cut Pro X. They are designed by Denver Riddle, a professional colorist and founder of Color Grading Central, a website that provides resources and tutorials on color grading for filmmakers. These presets are inspired by some of the most popular and sought-after looks from different film genres and styles, such as India, mainstream Hollywood, indie, sci-fi, horror, and more. They are compatible with Final Cut Pro X version 10.0.4 or later.</p>
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- <h3>Why use FCP X Luster Grade Presets?</h3>
- <p>Color grading is an essential part of video editing that can make a huge difference in the mood, tone, and quality of your videos. It can help you create a consistent and professional look for your videos, enhance the emotions and atmosphere of your scenes, and make your videos more appealing and engaging for your audience. However, color grading can also be a challenging and time-consuming process that requires a lot of skill and experience. That's where FCP X Luster Grade Presets come in handy. These presets can help you achieve amazing results with minimal effort and time. They can save you hours of tweaking and fine-tuning your colors, and give you instant access to a variety of looks that you can easily customize to fit your vision. They can also help you learn more about color grading by seeing how different settings affect your images.</p>
- <h3>How to get FCP X Luster Grade Presets for free?</h3>
- <p>If you are interested in trying out the FCP X Luster Grade Presets, you will be happy to know that you can get them for free. All you have to do is visit the Color Grading Central website   and sign up for their newsletter. You will receive an email with a link to download the presets, along with some other free resources and tips on color grading. You can also purchase the full 20 pack of presets for $49 if you want to support the creator and get access to more features and updates.</p>
- <h2>How to use FCP X Luster Grade Presets in Final Cut Pro X</h2>
- <h3>How to install FCP X Luster Grade Presets</h3>
- <p>Once you have downloaded the presets, you need to install them in Final Cut Pro X. To do this, follow these steps:</ - Open Final Cut Pro X and go to the Effects Browser. - Find the Color category and scroll down to the Luster Grade Presets folder. - Drag and drop the folder to the User Presets section in the Effects Browser. - You can now access the presets from the User Presets section anytime you want. <h3>How to apply FCP X Luster Grade Presets to your clips</h3>
- <p>Applying the presets to your clips is very easy and fast. To do this, follow these steps:</p>
- - Select the clip or clips that you want to color grade in the timeline. - Go to the Effects Browser and find the Luster Grade Presets folder in the User Presets section. - Choose the preset that you like and drag and drop it onto your clip or clips. - You will see a color board icon appear on your clip in the timeline, indicating that the preset has been applied. - You can preview the effect of the preset on your clip in the viewer. <h3>How to adjust FCP X Luster Grade Presets to suit your style</h3>
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- <p>To give you an idea of how FCP X Luster Grade Presets can enhance your videos, here are some examples of how they can create different looks for your videos. We will use three presets from the pack: Bollywood, Hollywood, and Indie.</p>
-<p></p>
- <h3>Bollywood look</h3>
- <p>The Bollywood preset is inspired by the vibrant and colorful style of Indian cinema. It adds a warm and rich tone to your footage, with enhanced contrast and saturation. It works well for scenes that need a lively and exotic feel, such as weddings, festivals, dances, or travel videos. Here is an example of how it looks before and after applying the preset:</p>
- <img src="https://i.imgur.com/4x8Zw6n.jpg" alt="Bollywood look before and after" width="800" height="450">
- <h3>Hollywood look</h3>
- <p>The Hollywood preset is inspired by the classic and elegant style of mainstream Hollywood movies. It adds a cool and neutral tone to your footage, with balanced exposure and subtle color correction. It works well for scenes that need a professional and cinematic feel, such as interviews, documentaries, dramas, or commercials. Here is an example of how it looks before and after applying the preset:</p>
- <img src="https://i.imgur.com/9Q0J5Yc.jpg" alt="Hollywood look before and after" width="800" height="450">
- <h3>Indie look</h3>
- <p>The Indie preset is inspired by the creative and original style of independent films. It adds a vintage and faded tone to your footage, with reduced saturation and contrast. It works well for scenes that need a nostalgic and artistic feel, such as music videos, short films, or personal projects. Here is an example of how it looks before and after applying the preset:</p>
- <img src="https://i.imgur.com/5X1zO7g.jpg" alt="Indie look before and after" width="800" height="450">
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In this article, we have shown you how to enhance your videos with professional color grading using FCP X Luster Grade Presets 20 Pack Free Download. We have explained what these presets are, why you should use them, how to get them for free, and how to use them in Final Cut Pro X. We have also shown you some examples of how these presets can create different looks for your videos.</p>
- <h3>Call to action</h3>
- <p>If you are ready to take your videos to the next level with FCP X Luster Grade Presets 20 Pack Free Download, don't hesitate to download them today from Color Grading Central  . You will be amazed by how easy and fun it is to color grade your videos with these presets. You can also learn more about color grading and other video editing tips from their website and newsletter. Thank you for reading this article and happy color grading!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about FCP X Luster Grade Presets 20 Pack Free Download:</p>
- <h3>What are the system requirements for FCP X Luster Grade Presets?</h3>
- <p>To use FCP X Luster Grade Presets, you need to have Final Cut Pro X version 10.0.4 or later installed on your Mac. You also need to have a Mac with a 64-bit processor, 4 GB of RAM, and a graphics card that supports OpenCL.</p>
- <h3>How many presets are included in the FCP X Luster Grade Presets 20 Pack?</h3>
- <p>The FCP X Luster Grade Presets 20 Pack includes 20 color grading presets that cover a wide range of film genres and styles. They are:</p>
- - Bollywood - Hollywood - Indie - Sci-Fi - Horror - Action - Comedy - Romance - Western - Noir - Documentary - Thriller - Adventure - Fantasy - Drama - War - Crime - Mystery - Animation - Musical <h3>Can I use FCP X Luster Grade Presets for commercial projects?</h3>
- <p>Yes, you can use FCP X Luster Grade Presets for any personal or commercial projects, as long as you do not resell or redistribute them. You can also modify them to suit your needs, but you cannot claim them as your own.</p>
- <h3>Can I use FCP X Luster Grade Presets with other video editing software?</h3>
- <p>No, FCP X Luster Grade Presets are only compatible with Final Cut Pro X. They are not compatible with other video editing software, such as Adobe Premiere Pro, DaVinci Resolve, or iMovie.</p>
- <h3>How can I contact the creator of FCP X Luster Grade Presets?</h3>
- <p>If you have any questions, feedback, or suggestions about FCP X Luster Grade Presets, you can contact the creator, Denver Riddle, through his website   or his social media accounts  . He is always happy to hear from his users and help them with their color grading needs.</p> b2dd77e56b<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Get Adobe Acrobat 7.0 Professional Full Version for Free No Registration Required.md b/spaces/raedeXanto/academic-chatgpt-beta/Get Adobe Acrobat 7.0 Professional Full Version for Free No Registration Required.md
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- <p>Foxit Reader is a free PDF reader and editor that can handle basic tasks such as viewing, commenting, signing, and printing PDF files. It also has some advanced features such as OCR, form filling, encryption, redaction, and conversion. It has a simple and intuitive interface that allows you to customize your toolbar and preferences. It also has integrations with cloud services and social media platforms. However, it lacks some features that Adobe Acrobat 7.0 Professional has, such as creating PDF files from various sources, editing text and images in PDF files, combining and organizing PDF files, and applying digital signatures.</p>
- <h4>Free PDF to Word Converter</h4>
- <p>Free PDF to Word Converter is a simple and easy-to-use tool that allows you to convert PDF files to Word documents. It supports batch conversion and preserves the original layout, fonts, images, and other elements of the PDF file. It also has a fast conversion speed and a high-quality output. However, it only supports one conversion direction (PDF to Word) and one output format (DOC). It also does not allow you to edit or secure your PDF files before or after conversion.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Adobe Acrobat 7.0 Professional is a software that allows you to create, edit, combine, secure, and convert PDF files. It is still widely used by many users who need to work with PDF documents on a regular basis. However, it is also one of the most expensive options, putting it out of reach for many individuals and small businesses. As a result, a whole host of alternative services with varying features and costs have sprung up. We have reviewed some of the best alternatives to Adobe Acrobat 7.0 Professional, such as Nitro Pro, Foxit Reader, and Free PDF to Word Converter. Each of them has its own pros and cons, and you should choose the one that suits your needs and budget best.</p>
- <p>We hope this article has helped you understand what Adobe Acrobat 7.0 Professional is, what features it offers, how to download it for free, what are its pros and cons, and what are some alternatives to it. If you have any questions or comments, please feel free to leave them below.</p>
- <h3>FAQs</h3>
- <ol>
-<li>Is Adobe Acrobat 7.0 Professional still supported by Adobe?</li>
-<p>No, Adobe Acrobat 7.0 Professional is no longer supported by Adobe since June 2015. This means that there are no more updates or security patches for this software. You can still use it at your own risk, but you may encounter compatibility issues or vulnerabilities.</p>
-<li>Can I use Adobe Acrobat 7.0 Professional on Windows 10?</li>
-<p>Yes, you can use Adobe Acrobat 7.0 Professional on Windows 10, but you may experience some problems or errors. Some users have reported that they had to run the software in compatibility mode or as an administrator to make it work properly.</p>
-<li>Can I use Adobe Acrobat 7.0 Professional on Mac?</li>
-<p>No, you cannot use Adobe Acrobat 7.0 Professional on Mac. This software is only compatible with Windows operating systems. If you want to use Adobe Acrobat on Mac, you will need to upgrade to a newer version or use an alternative service.</p>
-<li>How can I get a serial number for Adobe Acrobat 7.0 Professional?</li>
-<p>You can get a serial number for Adobe Acrobat 7.0 Professional by purchasing a license from Adobe or an authorized reseller. You can also find a serial number online by searching for "Adobe Acrobat 7.0 Professional serial number" on Google or any other search engine. However, we do not recommend using illegal or pirated serial numbers as they may not work properly or expose you to legal risks.</p>
-<li>What are the system requirements for Adobe Acrobat 7.0 Professional?</li>
-<p>The system requirements for Adobe Acrobat 7.0 Professional are as follows:</p>
-<ul>
-<li>Operating system: Windows XP SP2 or later</li>
-<li>Processor: Intel Pentium III or equivalent</li>
-<li>Memory: 256 MB of RAM (512 MB recommended)</li>
-<li>Hard disk space: 460 MB of available space</li>
-<li>Display: 1024 x 768 resolution (1280 x 1024 recommended)</li>
-<li>Internet connection: Required for activation and updates</li>
-</ul>
-</p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/dom-events.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/dom-events.d.ts
deleted file mode 100644
index b9c1c3aa4f0d337eb151caf6ac77306ed739acb8..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/dom-events.d.ts
+++ /dev/null
@@ -1,126 +0,0 @@
-export {}; // Don't export anything!
-
-//// DOM-like Events
-// NB: The Event / EventTarget / EventListener implementations below were copied
-// from lib.dom.d.ts, then edited to reflect Node's documentation at
-// https://nodejs.org/api/events.html#class-eventtarget.
-// Please read that link to understand important implementation differences.
-
-// This conditional type will be the existing global Event in a browser, or
-// the copy below in a Node environment.
-type __Event = typeof globalThis extends { onmessage: any, Event: any }
-? {}
-: {
-    /** This is not used in Node.js and is provided purely for completeness. */
-    readonly bubbles: boolean;
-    /** Alias for event.stopPropagation(). This is not used in Node.js and is provided purely for completeness. */
-    cancelBubble: () => void;
-    /** True if the event was created with the cancelable option */
-    readonly cancelable: boolean;
-    /** This is not used in Node.js and is provided purely for completeness. */
-    readonly composed: boolean;
-    /** Returns an array containing the current EventTarget as the only entry or empty if the event is not being dispatched. This is not used in Node.js and is provided purely for completeness. */
-    composedPath(): [EventTarget?]
-    /** Alias for event.target. */
-    readonly currentTarget: EventTarget | null;
-    /** Is true if cancelable is true and event.preventDefault() has been called. */
-    readonly defaultPrevented: boolean;
-    /** This is not used in Node.js and is provided purely for completeness. */
-    readonly eventPhase: 0 | 2;
-    /** The `AbortSignal` "abort" event is emitted with `isTrusted` set to `true`. The value is `false` in all other cases. */
-    readonly isTrusted: boolean;
-    /** Sets the `defaultPrevented` property to `true` if `cancelable` is `true`. */
-    preventDefault(): void;
-    /** This is not used in Node.js and is provided purely for completeness. */
-    returnValue: boolean;
-    /** Alias for event.target. */
-    readonly srcElement: EventTarget | null;
-    /** Stops the invocation of event listeners after the current one completes. */
-    stopImmediatePropagation(): void;
-    /** This is not used in Node.js and is provided purely for completeness. */
-    stopPropagation(): void;
-    /** The `EventTarget` dispatching the event */
-    readonly target: EventTarget | null;
-    /** The millisecond timestamp when the Event object was created. */
-    readonly timeStamp: number;
-    /** Returns the type of event, e.g. "click", "hashchange", or "submit". */
-    readonly type: string;
-};
-
-// See comment above explaining conditional type
-type __EventTarget = typeof globalThis extends { onmessage: any, EventTarget: any }
-? {}
-: {
-    /**
-     * Adds a new handler for the `type` event. Any given `listener` is added only once per `type` and per `capture` option value.
-     *
-     * If the `once` option is true, the `listener` is removed after the next time a `type` event is dispatched.
-     *
-     * The `capture` option is not used by Node.js in any functional way other than tracking registered event listeners per the `EventTarget` specification.
-     * Specifically, the `capture` option is used as part of the key when registering a `listener`.
-     * Any individual `listener` may be added once with `capture = false`, and once with `capture = true`.
-     */
-    addEventListener(
-        type: string,
-        listener: EventListener | EventListenerObject,
-        options?: AddEventListenerOptions | boolean,
-    ): void;
-    /** Dispatches a synthetic event event to target and returns true if either event's cancelable attribute value is false or its preventDefault() method was not invoked, and false otherwise. */
-    dispatchEvent(event: Event): boolean;
-    /** Removes the event listener in target's event listener list with the same type, callback, and options. */
-    removeEventListener(
-        type: string,
-        listener: EventListener | EventListenerObject,
-        options?: EventListenerOptions | boolean,
-    ): void;
-};
-
-interface EventInit {
-    bubbles?: boolean;
-    cancelable?: boolean;
-    composed?: boolean;
-}
-
-interface EventListenerOptions {
-    /** Not directly used by Node.js. Added for API completeness. Default: `false`. */
-    capture?: boolean;
-}
-
-interface AddEventListenerOptions extends EventListenerOptions {
-    /** When `true`, the listener is automatically removed when it is first invoked. Default: `false`. */
-    once?: boolean;
-    /** When `true`, serves as a hint that the listener will not call the `Event` object's `preventDefault()` method. Default: false. */
-    passive?: boolean;
-}
-
-interface EventListener {
-    (evt: Event): void;
-}
-
-interface EventListenerObject {
-    handleEvent(object: Event): void;
-}
-
-import {} from 'events';    // Make this an ambient declaration
-declare global {
-    /** An event which takes place in the DOM. */
-    interface Event extends __Event {}
-    var Event: typeof globalThis extends { onmessage: any, Event: infer T }
-        ? T
-        : {
-            prototype: __Event;
-            new (type: string, eventInitDict?: EventInit): __Event;
-        };
-
-    /**
-     * EventTarget is a DOM interface implemented by objects that can
-     * receive events and may have listeners for them.
-     */
-    interface EventTarget extends __EventTarget {}
-    var EventTarget: typeof globalThis extends { onmessage: any, EventTarget: infer T }
-        ? T
-        : {
-            prototype: __EventTarget;
-            new (): __EventTarget;
-        };
-}
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/module.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/module.d.ts
deleted file mode 100644
index d83aec94aae2df96e4028c6fdb09bf61051e5a38..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/module.d.ts
+++ /dev/null
@@ -1,114 +0,0 @@
-/**
- * @since v0.3.7
- */
-declare module 'module' {
-    import { URL } from 'node:url';
-    namespace Module {
-        /**
-         * The `module.syncBuiltinESMExports()` method updates all the live bindings for
-         * builtin `ES Modules` to match the properties of the `CommonJS` exports. It
-         * does not add or remove exported names from the `ES Modules`.
-         *
-         * ```js
-         * const fs = require('fs');
-         * const assert = require('assert');
-         * const { syncBuiltinESMExports } = require('module');
-         *
-         * fs.readFile = newAPI;
-         *
-         * delete fs.readFileSync;
-         *
-         * function newAPI() {
-         *   // ...
-         * }
-         *
-         * fs.newAPI = newAPI;
-         *
-         * syncBuiltinESMExports();
-         *
-         * import('fs').then((esmFS) => {
-         *   // It syncs the existing readFile property with the new value
-         *   assert.strictEqual(esmFS.readFile, newAPI);
-         *   // readFileSync has been deleted from the required fs
-         *   assert.strictEqual('readFileSync' in fs, false);
-         *   // syncBuiltinESMExports() does not remove readFileSync from esmFS
-         *   assert.strictEqual('readFileSync' in esmFS, true);
-         *   // syncBuiltinESMExports() does not add names
-         *   assert.strictEqual(esmFS.newAPI, undefined);
-         * });
-         * ```
-         * @since v12.12.0
-         */
-        function syncBuiltinESMExports(): void;
-        /**
-         * `path` is the resolved path for the file for which a corresponding source map
-         * should be fetched.
-         * @since v13.7.0, v12.17.0
-         */
-        function findSourceMap(path: string, error?: Error): SourceMap;
-        interface SourceMapPayload {
-            file: string;
-            version: number;
-            sources: string[];
-            sourcesContent: string[];
-            names: string[];
-            mappings: string;
-            sourceRoot: string;
-        }
-        interface SourceMapping {
-            generatedLine: number;
-            generatedColumn: number;
-            originalSource: string;
-            originalLine: number;
-            originalColumn: number;
-        }
-        /**
-         * @since v13.7.0, v12.17.0
-         */
-        class SourceMap {
-            /**
-             * Getter for the payload used to construct the `SourceMap` instance.
-             */
-            readonly payload: SourceMapPayload;
-            constructor(payload: SourceMapPayload);
-            /**
-             * Given a line number and column number in the generated source file, returns
-             * an object representing the position in the original file. The object returned
-             * consists of the following keys:
-             */
-            findEntry(line: number, column: number): SourceMapping;
-        }
-    }
-    interface Module extends NodeModule {}
-    class Module {
-        static runMain(): void;
-        static wrap(code: string): string;
-        static createRequire(path: string | URL): NodeRequire;
-        static builtinModules: string[];
-        static Module: typeof Module;
-        constructor(id: string, parent?: Module);
-    }
-    global {
-        interface ImportMeta {
-            url: string;
-            /**
-             * @experimental
-             * This feature is only available with the `--experimental-import-meta-resolve`
-             * command flag enabled.
-             *
-             * Provides a module-relative resolution function scoped to each module, returning
-             * the URL string.
-             *
-             * @param specified The module specifier to resolve relative to `parent`.
-             * @param parent The absolute parent module URL to resolve from. If none
-             * is specified, the value of `import.meta.url` is used as the default.
-             */
-            resolve?(specified: string, parent?: string | URL): Promise<string>;
-        }
-    }
-    export = Module;
-}
-declare module 'node:module' {
-    import module = require('module');
-    export = module;
-}
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/3design Cad 7 Crack 14.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/3design Cad 7 Crack 14.md
deleted file mode 100644
index 9a61d9ba7aa6f602d0498437653d3f6d0d99580b..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/3design Cad 7 Crack 14.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>3design cad 7 crack 14</h2><br /><p><b><b>Download</b> >>> <a href="https://urlgoal.com/2uCKDS">https://urlgoal.com/2uCKDS</a></b></p><br /><br />
-<br />
-... heated end walls and a free liquid surface , PhysicoChem Hydrodyn v 7 n 2-3 ( 1986 ) p ... K : Stress concentration due to a rigid ribbon in the presence of a crack ... S : Elimination of Lagrangian multipliers , Mech Res Commun v 14 n 1 ( 1987 ) ... Product data interfaces in CAD / CAM applications - Design , implementation ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/riccorl/relik-entity-linking/relik/common/upload.py b/spaces/riccorl/relik-entity-linking/relik/common/upload.py
deleted file mode 100644
index b2cad77bd95f43992af3144baf296560a496556b..0000000000000000000000000000000000000000
--- a/spaces/riccorl/relik-entity-linking/relik/common/upload.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import argparse
-import json
-import logging
-import os
-import tempfile
-import zipfile
-from datetime import datetime
-from pathlib import Path
-from typing import Optional, Union
-
-import huggingface_hub
-
-from relik.common.log import get_logger
-from relik.common.utils import SAPIENZANLP_DATE_FORMAT, get_md5
-
-logger = get_logger(level=logging.DEBUG)
-
-
-def create_info_file(tmpdir: Path):
-    logger.debug("Computing md5 of model.zip")
-    md5 = get_md5(tmpdir / "model.zip")
-    date = datetime.now().strftime(SAPIENZANLP_DATE_FORMAT)
-
-    logger.debug("Dumping info.json file")
-    with (tmpdir / "info.json").open("w") as f:
-        json.dump(dict(md5=md5, upload_date=date), f, indent=2)
-
-
-def zip_run(
-    dir_path: Union[str, os.PathLike],
-    tmpdir: Union[str, os.PathLike],
-    zip_name: str = "model.zip",
-) -> Path:
-    logger.debug(f"zipping {dir_path} to {tmpdir}")
-    # creates a zip version of the provided dir_path
-    run_dir = Path(dir_path)
-    zip_path = tmpdir / zip_name
-
-    with zipfile.ZipFile(zip_path, "w") as zip_file:
-        # fully zip the run directory maintaining its structure
-        for file in run_dir.rglob("*.*"):
-            if file.is_dir():
-                continue
-
-            zip_file.write(file, arcname=file.relative_to(run_dir))
-
-    return zip_path
-
-
-def upload(
-    model_dir: Union[str, os.PathLike],
-    model_name: str,
-    organization: Optional[str] = None,
-    repo_name: Optional[str] = None,
-    commit: Optional[str] = None,
-    archive: bool = False,
-):
-    token = huggingface_hub.HfFolder.get_token()
-    if token is None:
-        print(
-            "No HuggingFace token found. You need to execute `huggingface-cli login` first!"
-        )
-        return
-
-    repo_id = repo_name or model_name
-    if organization is not None:
-        repo_id = f"{organization}/{repo_id}"
-    with tempfile.TemporaryDirectory() as tmpdir:
-        api = huggingface_hub.HfApi()
-        repo_url = api.create_repo(
-            token=token,
-            repo_id=repo_id,
-            exist_ok=True,
-        )
-        repo = huggingface_hub.Repository(
-            str(tmpdir), clone_from=repo_url, use_auth_token=token
-        )
-
-        tmp_path = Path(tmpdir)
-        if archive:
-            # otherwise we zip the model_dir
-            logger.debug(f"Zipping {model_dir} to {tmp_path}")
-            zip_run(model_dir, tmp_path)
-            create_info_file(tmp_path)
-        else:
-            # if the user wants to upload a transformers model, we don't need to zip it
-            # we just need to copy the files to the tmpdir
-            logger.debug(f"Copying {model_dir} to {tmpdir}")
-            os.system(f"cp -r {model_dir}/* {tmpdir}")
-
-        # this method automatically puts large files (>10MB) into git lfs
-        repo.push_to_hub(commit_message=commit or "Automatic push from sapienzanlp")
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "model_dir", help="The directory of the model you want to upload"
-    )
-    parser.add_argument("model_name", help="The model you want to upload")
-    parser.add_argument(
-        "--organization",
-        help="the name of the organization where you want to upload the model",
-    )
-    parser.add_argument(
-        "--repo_name",
-        help="Optional name to use when uploading to the HuggingFace repository",
-    )
-    parser.add_argument(
-        "--commit", help="Commit message to use when pushing to the HuggingFace Hub"
-    )
-    parser.add_argument(
-        "--archive",
-        action="store_true",
-        help="""
-            Whether to compress the model directory before uploading it.
-            If True, the model directory will be zipped and the zip file will be uploaded.
-            If False, the model directory will be uploaded as is.""",
-    )
-    return parser.parse_args()
-
-
-def main():
-    upload(**vars(parse_args()))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/riffusion/riffusion-playground/app.py b/spaces/riffusion/riffusion-playground/app.py
deleted file mode 100644
index 0c78cc894b03e4065e5a2ae9dc8c338cc9b9aea4..0000000000000000000000000000000000000000
--- a/spaces/riffusion/riffusion-playground/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""
-Shim layer for using the riffusion playground streamlit app with huggingface spaces.
-
-It doesn't support the pages feature of streamlit yet.
-"""
-import importlib
-from pathlib import Path
-import sys
-
-import streamlit as st
-
-
-def render_main():
-    RIFFUSION_PATH = Path(__file__).parent / "riffusion"
-    sys.path.append(str(RIFFUSION_PATH))
-
-    st.set_page_config(layout="wide", page_icon="🎸")
-
-    # Disable the rest of the setting
-    st.set_page_config = lambda **kwargs: None
-
-    # Find all pages in the riffusion directory
-    pages = sorted(
-        p.name[:-3] for p in (RIFFUSION_PATH / "riffusion" / "streamlit" / "pages").glob("*.py")
-    )
-
-    # Add the pages to the sidebar
-    page = st.sidebar.selectbox("Page", pages, index=pages.index("text_to_audio"))
-    assert page is not None
-
-    module = importlib.import_module(f"riffusion.streamlit.pages.{page}")
-    render_func = getattr(module, f"render_{page}")
-    render_func()
-
-
-render_main()
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/roi_heads/bbox_heads/__init__.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/roi_heads/bbox_heads/__init__.py
deleted file mode 100644
index d1207dbeead6fedc24e6b497fb98558998a14396..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/roi_heads/bbox_heads/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .bbox_head import BBoxHead
-from .convfc_bbox_head import (ConvFCBBoxHead, Shared2FCBBoxHead,
-                               Shared4Conv1FCBBoxHead)
-from .dii_head import DIIHead
-from .double_bbox_head import DoubleConvFCBBoxHead
-from .sabl_head import SABLHead
-from .scnet_bbox_head import SCNetBBoxHead
-
-__all__ = [
-    'BBoxHead', 'ConvFCBBoxHead', 'Shared2FCBBoxHead',
-    'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', 'SABLHead', 'DIIHead',
-    'SCNetBBoxHead'
-]
diff --git a/spaces/rorallitri/biomedical-language-models/logs/AKB48 Super Festival Nissan Stadium 720p The Best Moments and Songs from the Idol Group.md b/spaces/rorallitri/biomedical-language-models/logs/AKB48 Super Festival Nissan Stadium 720p The Best Moments and Songs from the Idol Group.md
deleted file mode 100644
index 9b30e21bc592e92026dcc234afdf3f70360cea82..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/AKB48 Super Festival Nissan Stadium 720p The Best Moments and Songs from the Idol Group.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Akb48 Super Festival Nissan Stadium 720p</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://tinurll.com/2uzmx0">https://tinurll.com/2uzmx0</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/rossellison/kpop-face-generator/stylegan3-fun/gui_utils/text_utils.py b/spaces/rossellison/kpop-face-generator/stylegan3-fun/gui_utils/text_utils.py
deleted file mode 100644
index 35e5e4a16dc62c4be80df5432208bce5d386bf16..0000000000000000000000000000000000000000
--- a/spaces/rossellison/kpop-face-generator/stylegan3-fun/gui_utils/text_utils.py
+++ /dev/null
@@ -1,123 +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.
-
-import functools
-from typing import Optional
-
-import dnnlib
-import numpy as np
-import PIL.Image
-import PIL.ImageFont
-import scipy.ndimage
-
-from . import gl_utils
-
-#----------------------------------------------------------------------------
-
-def get_default_font():
-    url = 'http://fonts.gstatic.com/s/opensans/v17/mem8YaGs126MiZpBA-U1UpcaXcl0Aw.ttf' # Open Sans regular
-    return dnnlib.util.open_url(url, return_filename=True)
-
-#----------------------------------------------------------------------------
-
-@functools.lru_cache(maxsize=None)
-def get_pil_font(font=None, size=32):
-    if font is None:
-        font = get_default_font()
-    return PIL.ImageFont.truetype(font=font, size=size)
-
-#----------------------------------------------------------------------------
-
-def get_array(string, *, dropshadow_radius: int=None, **kwargs):
-    if dropshadow_radius is not None:
-        offset_x = int(np.ceil(dropshadow_radius*2/3))
-        offset_y = int(np.ceil(dropshadow_radius*2/3))
-        return _get_array_priv(string, dropshadow_radius=dropshadow_radius, offset_x=offset_x, offset_y=offset_y, **kwargs)
-    else:
-        return _get_array_priv(string, **kwargs)
-
-@functools.lru_cache(maxsize=10000)
-def _get_array_priv(
-    string: str, *,
-    size: int = 32,
-    max_width: Optional[int]=None,
-    max_height: Optional[int]=None,
-    min_size=10,
-    shrink_coef=0.8,
-    dropshadow_radius: int=None,
-    offset_x: int=None,
-    offset_y: int=None,
-    **kwargs
-):
-    cur_size = size
-    array = None
-    while True:
-        if dropshadow_radius is not None:
-            # separate implementation for dropshadow text rendering
-            array = _get_array_impl_dropshadow(string, size=cur_size, radius=dropshadow_radius, offset_x=offset_x, offset_y=offset_y, **kwargs)
-        else:
-            array = _get_array_impl(string, size=cur_size, **kwargs)
-        height, width, _ = array.shape
-        if (max_width is None or width <= max_width) and (max_height is None or height <= max_height) or (cur_size <= min_size):
-            break
-        cur_size = max(int(cur_size * shrink_coef), min_size)
-    return array
-
-#----------------------------------------------------------------------------
-
-@functools.lru_cache(maxsize=10000)
-def _get_array_impl(string, *, font=None, size=32, outline=0, outline_pad=3, outline_coef=3, outline_exp=2, line_pad: int=None):
-    pil_font = get_pil_font(font=font, size=size)
-    lines = [pil_font.getmask(line, 'L') for line in string.split('\n')]
-    lines = [np.array(line, dtype=np.uint8).reshape([line.size[1], line.size[0]]) for line in lines]
-    width = max(line.shape[1] for line in lines)
-    lines = [np.pad(line, ((0, 0), (0, width - line.shape[1])), mode='constant') for line in lines]
-    line_spacing = line_pad if line_pad is not None else size // 2
-    lines = [np.pad(line, ((0, line_spacing), (0, 0)), mode='constant') for line in lines[:-1]] + lines[-1:]
-    mask = np.concatenate(lines, axis=0)
-    alpha = mask
-    if outline > 0:
-        mask = np.pad(mask, int(np.ceil(outline * outline_pad)), mode='constant', constant_values=0)
-        alpha = mask.astype(np.float32) / 255
-        alpha = scipy.ndimage.gaussian_filter(alpha, outline)
-        alpha = 1 - np.maximum(1 - alpha * outline_coef, 0) ** outline_exp
-        alpha = (alpha * 255 + 0.5).clip(0, 255).astype(np.uint8)
-        alpha = np.maximum(alpha, mask)
-    return np.stack([mask, alpha], axis=-1)
-
-#----------------------------------------------------------------------------
-
-@functools.lru_cache(maxsize=10000)
-def _get_array_impl_dropshadow(string, *, font=None, size=32, radius: int, offset_x: int, offset_y: int, line_pad: int=None, **kwargs):
-    assert (offset_x > 0) and (offset_y > 0)
-    pil_font = get_pil_font(font=font, size=size)
-    lines = [pil_font.getmask(line, 'L') for line in string.split('\n')]
-    lines = [np.array(line, dtype=np.uint8).reshape([line.size[1], line.size[0]]) for line in lines]
-    width = max(line.shape[1] for line in lines)
-    lines = [np.pad(line, ((0, 0), (0, width - line.shape[1])), mode='constant') for line in lines]
-    line_spacing = line_pad if line_pad is not None else size // 2
-    lines = [np.pad(line, ((0, line_spacing), (0, 0)), mode='constant') for line in lines[:-1]] + lines[-1:]
-    mask = np.concatenate(lines, axis=0)
-    alpha = mask
-
-    mask = np.pad(mask, 2*radius + max(abs(offset_x), abs(offset_y)), mode='constant', constant_values=0)
-    alpha = mask.astype(np.float32) / 255
-    alpha = scipy.ndimage.gaussian_filter(alpha, radius)
-    alpha = 1 - np.maximum(1 - alpha * 1.5, 0) ** 1.4
-    alpha = (alpha * 255 + 0.5).clip(0, 255).astype(np.uint8)
-    alpha = np.pad(alpha, [(offset_y, 0), (offset_x, 0)], mode='constant')[:-offset_y, :-offset_x]
-    alpha = np.maximum(alpha, mask)
-    return np.stack([mask, alpha], axis=-1)
-
-#----------------------------------------------------------------------------
-
-@functools.lru_cache(maxsize=10000)
-def get_texture(string, bilinear=True, mipmap=True, **kwargs):
-    return gl_utils.Texture(image=get_array(string, **kwargs), bilinear=bilinear, mipmap=mipmap)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/roughhai/myGenAIChatBot/README.md b/spaces/roughhai/myGenAIChatBot/README.md
deleted file mode 100644
index 9a2c89ec3f1c54f4da343f8fdf5844f6ff8a5229..0000000000000000000000000000000000000000
--- a/spaces/roughhai/myGenAIChatBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAIChatBot
-emoji: 💻
-colorFrom: blue
-colorTo: indigo
-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/roveliu/ChatGPT4/app.py b/spaces/roveliu/ChatGPT4/app.py
deleted file mode 100644
index 0a13d54502c3892a1b6d648218994a2fcd98fd0a..0000000000000000000000000000000000000000
--- a/spaces/roveliu/ChatGPT4/app.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Testing with my Open AI Key 
-OPENAI_API_KEY = os.getenv("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")
-
-def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  
-
-    payload = {
-    "model": "gpt-4",
-    "messages": [{"role": "user", "content": f"{inputs}"}],
-    "temperature" : 1.0,
-    "top_p":1.0,
-    "n" : 1,
-    "stream": True,
-    "presence_penalty":0,
-    "frequency_penalty":0,
-    }
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {OPENAI_API_KEY}"
-    }
-
-    print(f"chat_counter - {chat_counter}")
-    if chat_counter != 0 :
-        messages=[]
-        for data in chatbot:
-          temp1 = {}
-          temp1["role"] = "user" 
-          temp1["content"] = data[0] 
-          temp2 = {}
-          temp2["role"] = "assistant" 
-          temp2["content"] = data[1]
-          messages.append(temp1)
-          messages.append(temp2)
-        temp3 = {}
-        temp3["role"] = "user" 
-        temp3["content"] = inputs
-        messages.append(temp3)
-        #messages
-        payload = {
-        "model": "gpt-4",
-        "messages": messages, #[{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
-
-    chat_counter+=1
-
-    history.append(inputs)
-    print(f"payload is - {payload}")
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    print(f"response code - {response}")
-    token_counter = 0 
-    partial_words = "" 
-
-    counter=0
-    for chunk in response.iter_lines():
-        #Skipping first chunk
-        if counter == 0:
-          counter+=1
-          continue
-        #counter+=1
-        # check whether each line is non-empty
-        if chunk.decode() :
-          chunk = chunk.decode()
-          # decode each line as response data is in bytes
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              #if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-              #  break
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter, response  # resembles {chatbot: chat, state: history}  
-                   
-
-def reset_textbox():
-    return gr.update(value='')
-
-title = """<h1 align="center">🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming</h1>"""
-description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
-```
-User: <utterance>
-Assistant: <utterance>
-User: <utterance>
-Assistant: <utterance>
-...
-```
-In this app, you can explore the outputs of a gpt-4 LLM.
-"""
-
-theme = gr.themes.Default(primary_hue="green")                
-
-with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
-                #chatbot {height: 520px; overflow: auto;}""",
-              theme=theme) as demo:
-    gr.HTML(title)
-    gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌</h1>""")
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
-    with gr.Column(elem_id = "col_container"):
-        #GPT4 API Key is provided by Huggingface 
-        #openai_api_key = gr.Textbox(type='password', label="Enter only your GPT4 OpenAI API key here")
-        chatbot = gr.Chatbot(elem_id='chatbot') #c
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
-        state = gr.State([]) #s
-        with gr.Row():
-            with gr.Column(scale=7):
-                b1 = gr.Button().style(full_width=True)
-            with gr.Column(scale=3):
-                server_status_code = gr.Textbox(label="Status code from OpenAI server", )
-    
-        #inputs, top_p, temperature, top_k, repetition_penalty
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
-            #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
-            chat_counter = gr.Number(value=0, visible=False, precision=0)
-
-    inputs.submit( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    b1.click( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-                    
-    #gr.Markdown(description)
-    demo.queue(max_size=20, concurrency_count=10).launch(debug=True)
diff --git a/spaces/scedlatioru/img-to-music/40-Carats-1973torrent.md b/spaces/scedlatioru/img-to-music/40-Carats-1973torrent.md
deleted file mode 100644
index 90a427c209736c6fddbda379d2341fc8f336c9aa..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/40-Carats-1973torrent.md
+++ /dev/null
@@ -1,66 +0,0 @@
-## 40 Carats 1973torrent
-
-
-
- 
-
- ![40 Carats 1973torrent](https://s6.dpic.me/01893/rujndhvbchb6.jpg)
-
- 
-
- 
-
-**40 Carats 1973torrent - [https://dropnobece.blogspot.com/?download=2tyq4D](https://dropnobece.blogspot.com/?download=2tyq4D)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# 40 Carats: A Romantic Comedy from 1973
-
- 
-
-Have you ever wondered what it would be like to have a summer fling with a much younger man? That's the premise of *40 Carats*, a 1973 film based on a French play by Pierre Barillet and Jean-Pierre Grédy. The film stars Liv Ullmann as Ann Stanley, a 40-year-old divorced real estate agent who meets Peter Latham (Edward Albert), a 22-year-old free spirit, while vacationing in Greece. They have a passionate affair, but Ann assumes they will never see each other again when she returns to New York. However, fate has other plans, as Peter shows up at her doorstep as a date for her daughter Trina (Deborah Raffin).
-
- 
-
-The film explores the challenges and joys of an unconventional relationship, as well as the reactions of Ann's family and friends. Her mother Maud (Binnie Barnes) is supportive and amused by the situation, while her ex-husband Billy (Gene Kelly) is jealous and skeptical. Her business partner Margie (Nancy Walker) is shocked and curious, while her clients are intrigued and impressed. Peter, meanwhile, is determined to prove his love and maturity to Ann, who is conflicted by her feelings and fears.
-
- 
-
-*40 Carats* is a charming and funny film that showcases the chemistry and charisma of its leads. Ullmann, best known for her dramatic roles in Ingmar Bergman's films, proves her comedic skills and versatility as Ann. Albert, son of actor Eddie Albert, gives a charming and earnest performance as Peter. Kelly, in one of his last film roles, adds his signature charm and grace as Billy. The film also features a catchy soundtrack by Michel Legrand, who composed the theme song "In Every Corner of the World".
-
- 
-
-If you are looking for a romantic comedy that is both timeless and timely, you might want to check out *40 Carats*. You can find it on various streaming platforms or download it from torrent sites. However, please be aware that downloading copyrighted content may be illegal in your country.
-
-  
-
-The film was directed by Milton Katselas, who also directed *Butterflies Are Free* and *When You Comin' Back, Red Ryder?*. The screenplay was adapted by Leonard Gershe and Jay Presson Allen from the stage play by Barillet and Grédy. The play was a hit in France and on Broadway, where it starred Julie Harris and Richard Mulligan. The film was also a success at the box office and received two Golden Globe nominations: one for Best Motion Picture - Musical or Comedy and one for Best Actress - Motion Picture Musical or Comedy for Ullmann.
-
- 
-
-*40 Carats* is a film that celebrates love in all its forms and ages. It shows that age is just a number and that happiness can be found in unexpected places. It also challenges the stereotypes and prejudices that society has about older women and younger men. It is a film that is both funny and touching, and that will make you smile and think.
-
-  
-
-The film has many memorable scenes and dialogues that showcase the humor and romance of the story. For example, when Ann and Peter first meet in Greece, he asks her if she is married. She replies: "No, I'm divorced. I was married to a child. He was 39." Later, when Peter visits Ann in New York, he surprises her with a gift: a necklace with 40 carats of diamonds. He says: "I know it's a little extravagant, but I wanted to give you something that would last as long as my love for you." Ann is speechless and touched by his gesture.
-
- 
-
-The film also received positive reviews from critics and audiences alike. Roger Ebert of the Chicago Sun-Times gave it three stars out of four and wrote: "The movie works and is entertaining because it really gets into the characters and lets them breathe a little." Vincent Canby of The New York Times praised Ullmann's performance and said: "She is so good that she almost makes one forget how silly the material is." The film also has a rating of 6.3 out of 10 on IMDb and a 67% audience score on Rotten Tomatoes.
-
- dfd1c89656
-
- 
-
- 
-
diff --git a/spaces/scedlatioru/img-to-music/example/G-Tab G100M Flash File All Versone Mtk Spd Firmware.md b/spaces/scedlatioru/img-to-music/example/G-Tab G100M Flash File All Versone Mtk Spd Firmware.md
deleted file mode 100644
index 70061ddd9bc084cc1ef08f8c3f9bdc337fcc1396..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/G-Tab G100M Flash File All Versone Mtk Spd Firmware.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-<h2>G-Tab G100M Flash File All Versone Mtk Spd Firmware</h2><br /><p><b><b>Download Zip</b> &#9989; <a href="https://gohhs.com/2uEzWg">https://gohhs.com/2uEzWg</a></b></p><br /><br />
-<br />
-G-Tab G100M Flash File All Versone Mtk Spd Firmware
-
-. G-Tab G100M Flash File All Versone Mtk Spd Firmware. The file is about 2.07 mb. Download the android file attached to this post to your SONY Android device. This package requires rooting the device to work.
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diff --git a/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/utils/image_saver.py b/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/utils/image_saver.py
deleted file mode 100644
index c3edfa96e60fea0e5ec8fd087da85d2efaa6444c..0000000000000000000000000000000000000000
--- a/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/utils/image_saver.py
+++ /dev/null
@@ -1,230 +0,0 @@
-import cv2
-import numpy as np
-
-import torch
-from collections import defaultdict
-
-
-def tensor_to_numpy(image):
-    image_np = (image.numpy() * 255).astype('uint8')
-    return image_np
-
-
-def tensor_to_np_float(image):
-    image_np = image.numpy().astype('float32')
-    return image_np
-
-
-def detach_to_cpu(x):
-    return x.detach().cpu()
-
-
-def transpose_np(x):
-    return np.transpose(x, [1, 2, 0])
-
-
-def tensor_to_gray_im(x):
-    x = detach_to_cpu(x)
-    x = tensor_to_numpy(x)
-    x = transpose_np(x)
-    return x
-
-
-def tensor_to_im(x):
-    x = detach_to_cpu(x).clamp(0, 1)
-    x = tensor_to_numpy(x)
-    x = transpose_np(x)
-    return x
-
-
-# Predefined key <-> caption dict
-key_captions = {
-    'im': 'Image',
-    'gt': 'GT',
-}
-"""
-Return an image array with captions
-keys in dictionary will be used as caption if not provided
-values should contain lists of cv2 images
-"""
-
-
-def get_image_array(images, grid_shape, captions={}):
-    h, w = grid_shape
-    cate_counts = len(images)
-    rows_counts = len(next(iter(images.values())))
-
-    font = cv2.FONT_HERSHEY_SIMPLEX
-
-    output_image = np.zeros([w * cate_counts, h * (rows_counts + 1), 3], dtype=np.uint8)
-    col_cnt = 0
-    for k, v in images.items():
-
-        # Default as key value itself
-        caption = captions.get(k, k)
-
-        # Handles new line character
-        dy = 40
-        for i, line in enumerate(caption.split('\n')):
-            cv2.putText(output_image, line, (10, col_cnt * w + 100 + i * dy), font, 0.8,
-                        (255, 255, 255), 2, cv2.LINE_AA)
-
-        # Put images
-        for row_cnt, img in enumerate(v):
-            im_shape = img.shape
-            if len(im_shape) == 2:
-                img = img[..., np.newaxis]
-
-            img = (img * 255).astype('uint8')
-
-            output_image[(col_cnt + 0) * w:(col_cnt + 1) * w,
-                         (row_cnt + 1) * h:(row_cnt + 2) * h, :] = img
-
-        col_cnt += 1
-
-    return output_image
-
-
-def base_transform(im, size):
-    im = tensor_to_np_float(im)
-    if len(im.shape) == 3:
-        im = im.transpose((1, 2, 0))
-    else:
-        im = im[:, :, None]
-
-    # Resize
-    if im.shape[1] != size:
-        im = cv2.resize(im, size, interpolation=cv2.INTER_NEAREST)
-
-    return im.clip(0, 1)
-
-
-def im_transform(im, size):
-    return base_transform(detach_to_cpu(im), size=size)
-
-
-def mask_transform(mask, size):
-    return base_transform(detach_to_cpu(mask), size=size)
-
-
-def logits_transform(mask, size):
-    return base_transform(detach_to_cpu(torch.sigmoid(mask)), size=size)
-
-
-def add_attention(mask, pos):
-    mask = mask[:, :, None].repeat(3, axis=2)
-    pos = (pos + 1) / 2
-    for i in range(pos.shape[0]):
-        y = int(pos[i][0] * mask.shape[0])
-        x = int(pos[i][1] * mask.shape[1])
-        y = max(min(y, mask.shape[0] - 1), 0)
-        x = max(min(x, mask.shape[1] - 1), 0)
-        # mask[y, x, :] = (255, 0, 0)
-        cv2.circle(mask, (x, y), 5, (1, 0, 0), -1)
-    return mask
-
-
-def vis(images, size, num_objects):
-    req_images = defaultdict(list)
-
-    b, t = images['rgb'].shape[:2]
-
-    # limit the number of images saved
-    b = min(2, b)
-
-    # find max num objects
-    max_num_objects = max(num_objects[:b])
-
-    GT_suffix = ''
-    for bi in range(b):
-        GT_suffix += ' \n%s' % images['info']['name'][bi][-25:-4]
-
-    for bi in range(b):
-        for ti in range(t):
-            req_images['RGB'].append(im_transform(images['rgb'][bi, ti], size))
-            aux = images[f'aux_{max(ti, 1)}']  # no aux_0, use aux_1 for shape
-            if 'sensory_logits' in aux:
-                sensory_aux = aux['sensory_logits'][bi].softmax(dim=0)
-            # batch_size * num_objects * num_levels * H * W
-            q_mask_aux = aux['q_logits'][bi].softmax(dim=0)
-            num_levels = q_mask_aux.shape[1]
-
-            for oi in range(max_num_objects):
-                if ti == 0 or oi >= num_objects[bi]:
-                    req_images[f'Mask_{oi}'].append(
-                        mask_transform(images['first_frame_gt'][bi][0, oi], size))
-                    req_images[f'S-Aux_{oi}'].append(
-                        mask_transform(images['first_frame_gt'][bi][0, oi], size))
-                    for l in range(num_levels):
-                        req_images[f'Q-Aux-L{l}_{oi}'].append(
-                            mask_transform(images['first_frame_gt'][bi][0, oi], size))
-                else:
-                    mask = mask_transform(images[f'masks_{ti}'][bi][oi], size)
-                    req_images[f'Mask_{oi}'].append(mask)
-                    if 'sensory_logits' in aux:
-                        req_images[f'S-Aux_{oi}'].append(mask_transform(sensory_aux[oi + 1], size))
-
-                    for l in range(num_levels):
-                        mask = mask_transform(q_mask_aux[oi + 1, l], size)
-                        req_images[f'Q-Aux-L{l}_{oi}'].append(mask)
-
-                req_images[f'GT_{oi}_{GT_suffix}'].append(
-                    mask_transform(images['cls_gt'][bi, ti, 0] == (oi + 1), size))
-
-    return get_image_array(req_images, size, key_captions)
-
-
-def vis_debug(images, size, num_objects):
-    req_images = defaultdict(list)
-
-    b, t = images['rgb'].shape[:2]
-
-    # limit the number of images saved
-    b = min(2, b)
-
-    # find max num objects
-    max_num_objects = max(num_objects[:b])
-
-    GT_suffix = ''
-    for bi in range(b):
-        GT_suffix += ' \n%s' % images['info']['name'][bi][-25:-4]
-
-    for bi in range(b):
-        for ti in range(t):
-            req_images['RGB'].append(im_transform(images['rgb'][bi, ti], size))
-            aux = images[f'aux_{max(ti, 1)}']  # no aux_0, use aux_1 for shape
-            sensory_aux = aux['sensory_logits'][bi].softmax(dim=0)
-            # batch_size * num_objects * num_levels * H * W
-            q_mask_aux = aux['q_logits'][bi].softmax(dim=0)
-            attn_mask = aux['attn_mask'][bi]
-            num_levels = q_mask_aux.shape[1]
-            num_queries = attn_mask.shape[1]
-
-            for oi in range(max_num_objects):
-                if ti == 0 or oi >= num_objects[bi]:
-                    req_images[f'Mask_{oi}'].append(
-                        mask_transform(images['first_frame_gt'][bi][0, oi], size))
-                    req_images[f'S-Aux_{oi}'].append(
-                        mask_transform(images['first_frame_gt'][bi][0, oi], size))
-                    for l in range(num_levels):
-                        req_images[f'Q-Aux-L{l}_{oi}'].append(
-                            mask_transform(images['first_frame_gt'][bi][0, oi], size))
-                    for q in range(num_queries):
-                        req_images[f'Attn-Mask-Q{q}_{oi}'].append(
-                            mask_transform(images['first_frame_gt'][bi][0, oi], size))
-                else:
-                    mask = mask_transform(images[f'masks_{ti}'][bi][oi], size)
-                    req_images[f'Mask_{oi}'].append(mask)
-                    req_images[f'S-Aux_{oi}'].append(mask_transform(sensory_aux[oi + 1], size))
-
-                    for l in range(num_levels):
-                        mask = mask_transform(q_mask_aux[oi + 1, l], size)
-                        req_images[f'Q-Aux-L{l}_{oi}'].append(mask)
-                    for q in range(num_queries):
-                        mask = mask_transform(1 - attn_mask[oi, q].float(), size)
-                        req_images[f'Attn-Mask-Q{q}_{oi}'].append(mask)
-
-                req_images[f'GT_{oi}_{GT_suffix}'].append(
-                    mask_transform(images['cls_gt'][bi, ti, 0] == (oi + 1), size))
-
-    return get_image_array(req_images, size, key_captions)
\ No newline at end of file
diff --git a/spaces/sdeeas/ChuanhuChatGPT/modules/utils.py b/spaces/sdeeas/ChuanhuChatGPT/modules/utils.py
deleted file mode 100644
index a025a80d7b52f3ae788be960c17520d44bf56e49..0000000000000000000000000000000000000000
--- a/spaces/sdeeas/ChuanhuChatGPT/modules/utils.py
+++ /dev/null
@@ -1,592 +0,0 @@
-# -*- coding:utf-8 -*-
-from __future__ import annotations
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
-import logging
-import json
-import os
-import datetime
-import hashlib
-import csv
-import requests
-import re
-import html
-import sys
-import subprocess
-
-import gradio as gr
-from pypinyin import lazy_pinyin
-import tiktoken
-import mdtex2html
-from markdown import markdown
-from pygments import highlight
-from pygments.lexers import get_lexer_by_name
-from pygments.formatters import HtmlFormatter
-import pandas as pd
-
-from modules.presets import *
-from . import shared
-from modules.config import retrieve_proxy, hide_history_when_not_logged_in
-
-if TYPE_CHECKING:
-    from typing import TypedDict
-
-    class DataframeData(TypedDict):
-        headers: List[str]
-        data: List[List[str | int | bool]]
-
-def predict(current_model, *args):
-    iter = current_model.predict(*args)
-    for i in iter:
-        yield i
-
-def billing_info(current_model):
-    return current_model.billing_info()
-
-def set_key(current_model, *args):
-    return current_model.set_key(*args)
-
-def load_chat_history(current_model, *args):
-    return current_model.load_chat_history(*args)
-
-def interrupt(current_model, *args):
-    return current_model.interrupt(*args)
-
-def reset(current_model, *args):
-    return current_model.reset(*args)
-
-def retry(current_model, *args):
-    iter = current_model.retry(*args)
-    for i in iter:
-        yield i
-
-def delete_first_conversation(current_model, *args):
-    return current_model.delete_first_conversation(*args)
-
-def delete_last_conversation(current_model, *args):
-    return current_model.delete_last_conversation(*args)
-
-def set_system_prompt(current_model, *args):
-    return current_model.set_system_prompt(*args)
-
-def save_chat_history(current_model, *args):
-    return current_model.save_chat_history(*args)
-
-def export_markdown(current_model, *args):
-    return current_model.export_markdown(*args)
-
-def load_chat_history(current_model, *args):
-    return current_model.load_chat_history(*args)
-
-def upload_chat_history(current_model, *args):
-    return current_model.load_chat_history(*args)
-
-def set_token_upper_limit(current_model, *args):
-    return current_model.set_token_upper_limit(*args)
-
-def set_temperature(current_model, *args):
-    current_model.set_temperature(*args)
-
-def set_top_p(current_model, *args):
-    current_model.set_top_p(*args)
-
-def set_n_choices(current_model, *args):
-    current_model.set_n_choices(*args)
-
-def set_stop_sequence(current_model, *args):
-    current_model.set_stop_sequence(*args)
-
-def set_max_tokens(current_model, *args):
-    current_model.set_max_tokens(*args)
-
-def set_presence_penalty(current_model, *args):
-    current_model.set_presence_penalty(*args)
-
-def set_frequency_penalty(current_model, *args):
-    current_model.set_frequency_penalty(*args)
-
-def set_logit_bias(current_model, *args):
-    current_model.set_logit_bias(*args)
-
-def set_user_identifier(current_model, *args):
-    current_model.set_user_identifier(*args)
-
-def set_single_turn(current_model, *args):
-    current_model.set_single_turn(*args)
-
-def handle_file_upload(current_model, *args):
-    return current_model.handle_file_upload(*args)
-
-def like(current_model, *args):
-    return current_model.like(*args)
-
-def dislike(current_model, *args):
-    return current_model.dislike(*args)
-
-
-def count_token(message):
-    encoding = tiktoken.get_encoding("cl100k_base")
-    input_str = f"role: {message['role']}, content: {message['content']}"
-    length = len(encoding.encode(input_str))
-    return length
-
-
-def markdown_to_html_with_syntax_highlight(md_str):
-    def replacer(match):
-        lang = match.group(1) or "text"
-        code = match.group(2)
-
-        try:
-            lexer = get_lexer_by_name(lang, stripall=True)
-        except ValueError:
-            lexer = get_lexer_by_name("text", stripall=True)
-
-        formatter = HtmlFormatter()
-        highlighted_code = highlight(code, lexer, formatter)
-
-        return f'<pre><code class="{lang}">{highlighted_code}</code></pre>'
-
-    code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```"
-    md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE)
-
-    html_str = markdown(md_str)
-    return html_str
-
-
-def normalize_markdown(md_text: str) -> str:
-    lines = md_text.split("\n")
-    normalized_lines = []
-    inside_list = False
-
-    for i, line in enumerate(lines):
-        if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()):
-            if not inside_list and i > 0 and lines[i - 1].strip() != "":
-                normalized_lines.append("")
-            inside_list = True
-            normalized_lines.append(line)
-        elif inside_list and line.strip() == "":
-            if i < len(lines) - 1 and not re.match(
-                r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip()
-            ):
-                normalized_lines.append(line)
-            continue
-        else:
-            inside_list = False
-            normalized_lines.append(line)
-
-    return "\n".join(normalized_lines)
-
-
-def convert_mdtext(md_text):
-    code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL)
-    inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL)
-    code_blocks = code_block_pattern.findall(md_text)
-    non_code_parts = code_block_pattern.split(md_text)[::2]
-
-    result = []
-    raw = f'<div class="raw-message hideM">{html.escape(md_text)}</div>'
-    for non_code, code in zip(non_code_parts, code_blocks + [""]):
-        if non_code.strip():
-            non_code = normalize_markdown(non_code)
-            result.append(markdown(non_code, extensions=["tables"]))
-        if code.strip():
-            # _, code = detect_language(code)  # 暂时去除代码高亮功能，因为在大段代码的情况下会出现问题
-            # code = code.replace("\n\n", "\n") # 暂时去除代码中的空行，因为在大段代码的情况下会出现问题
-            code = f"\n```{code}\n\n```"
-            code = markdown_to_html_with_syntax_highlight(code)
-            result.append(code)
-    result = "".join(result)
-    output = f'<div class="md-message">{result}</div>'
-    output += raw
-    output += ALREADY_CONVERTED_MARK
-    return output
-
-
-def convert_asis(userinput):
-    return (
-        f'<p style="white-space:pre-wrap;">{html.escape(userinput)}</p>'
-        + ALREADY_CONVERTED_MARK
-    )
-
-
-def detect_converted_mark(userinput):
-    try:
-        if userinput.endswith(ALREADY_CONVERTED_MARK):
-            return True
-        else:
-            return False
-    except:
-        return True
-
-
-def detect_language(code):
-    if code.startswith("\n"):
-        first_line = ""
-    else:
-        first_line = code.strip().split("\n", 1)[0]
-    language = first_line.lower() if first_line else ""
-    code_without_language = code[len(first_line) :].lstrip() if first_line else code
-    return language, code_without_language
-
-
-def construct_text(role, text):
-    return {"role": role, "content": text}
-
-
-def construct_user(text):
-    return construct_text("user", text)
-
-
-def construct_system(text):
-    return construct_text("system", text)
-
-
-def construct_assistant(text):
-    return construct_text("assistant", text)
-
-
-def save_file(filename, system, history, chatbot, user_name):
-    logging.debug(f"{user_name} 保存对话历史中……")
-    os.makedirs(os.path.join(HISTORY_DIR, user_name), exist_ok=True)
-    if filename.endswith(".json"):
-        json_s = {"system": system, "history": history, "chatbot": chatbot}
-        if "/" in filename or "\\" in filename:
-            history_file_path = filename
-        else:
-            history_file_path = os.path.join(HISTORY_DIR, user_name, filename)
-        with open(history_file_path, "w") as f:
-            json.dump(json_s, f)
-    elif filename.endswith(".md"):
-        md_s = f"system: \n- {system} \n"
-        for data in history:
-            md_s += f"\n{data['role']}: \n- {data['content']} \n"
-        with open(os.path.join(HISTORY_DIR, user_name, filename), "w", encoding="utf8") as f:
-            f.write(md_s)
-    logging.debug(f"{user_name} 保存对话历史完毕")
-    return os.path.join(HISTORY_DIR, user_name, filename)
-
-
-def sorted_by_pinyin(list):
-    return sorted(list, key=lambda char: lazy_pinyin(char)[0][0])
-
-
-def get_file_names(dir, plain=False, filetypes=[".json"]):
-    logging.debug(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}")
-    files = []
-    try:
-        for type in filetypes:
-            files += [f for f in os.listdir(dir) if f.endswith(type)]
-    except FileNotFoundError:
-        files = []
-    files = sorted_by_pinyin(files)
-    if files == []:
-        files = [""]
-    logging.debug(f"files are:{files}")
-    if plain:
-        return files
-    else:
-        return gr.Dropdown.update(choices=files)
-
-
-def get_history_names(plain=False, user_name=""):
-    logging.debug(f"从用户 {user_name} 中获取历史记录文件名列表")
-    if user_name == "" and hide_history_when_not_logged_in:
-        return ""
-    else:
-        return get_file_names(os.path.join(HISTORY_DIR, user_name), plain)
-
-
-def load_template(filename, mode=0):
-    logging.debug(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）")
-    lines = []
-    if filename.endswith(".json"):
-        with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f:
-            lines = json.load(f)
-        lines = [[i["act"], i["prompt"]] for i in lines]
-    else:
-        with open(
-            os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8"
-        ) as csvfile:
-            reader = csv.reader(csvfile)
-            lines = list(reader)
-        lines = lines[1:]
-    if mode == 1:
-        return sorted_by_pinyin([row[0] for row in lines])
-    elif mode == 2:
-        return {row[0]: row[1] for row in lines}
-    else:
-        choices = sorted_by_pinyin([row[0] for row in lines])
-        return {row[0]: row[1] for row in lines}, gr.Dropdown.update(
-            choices=choices
-        )
-
-
-def get_template_names(plain=False):
-    logging.debug("获取模板文件名列表")
-    return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"])
-
-
-def get_template_content(templates, selection, original_system_prompt):
-    logging.debug(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}")
-    try:
-        return templates[selection]
-    except:
-        return original_system_prompt
-
-
-def reset_textbox():
-    logging.debug("重置文本框")
-    return gr.update(value="")
-
-
-def reset_default():
-    default_host = shared.state.reset_api_host()
-    retrieve_proxy("")
-    return gr.update(value=default_host), gr.update(value=""), "API-Host 和代理已重置"
-
-
-def change_api_host(host):
-    shared.state.set_api_host(host)
-    msg = f"API-Host更改为了{host}"
-    logging.info(msg)
-    return msg
-
-
-def change_proxy(proxy):
-    retrieve_proxy(proxy)
-    os.environ["HTTPS_PROXY"] = proxy
-    msg = f"代理更改为了{proxy}"
-    logging.info(msg)
-    return msg
-
-
-def hide_middle_chars(s):
-    if s is None:
-        return ""
-    if len(s) <= 8:
-        return s
-    else:
-        head = s[:4]
-        tail = s[-4:]
-        hidden = "*" * (len(s) - 8)
-        return head + hidden + tail
-
-
-def submit_key(key):
-    key = key.strip()
-    msg = f"API密钥更改为了{hide_middle_chars(key)}"
-    logging.info(msg)
-    return key, msg
-
-
-def replace_today(prompt):
-    today = datetime.datetime.today().strftime("%Y-%m-%d")
-    return prompt.replace("{current_date}", today)
-
-
-def get_geoip():
-    try:
-        with retrieve_proxy():
-            response = requests.get("https://ipapi.co/json/", timeout=5)
-        data = response.json()
-    except:
-        data = {"error": True, "reason": "连接ipapi失败"}
-    if "error" in data.keys():
-        logging.warning(f"无法获取IP地址信息。\n{data}")
-        if data["reason"] == "RateLimited":
-            return (
-                i18n("您的IP区域：未知。")
-            )
-        else:
-            return i18n("获取IP地理位置失败。原因：") + f"{data['reason']}" + i18n("。你仍然可以使用聊天功能。")
-    else:
-        country = data["country_name"]
-        if country == "China":
-            text = "**您的IP区域：中国。请立即检查代理设置，在不受支持的地区使用API可能导致账号被封禁。**"
-        else:
-            text = i18n("您的IP区域：") + f"{country}。"
-        logging.info(text)
-        return text
-
-
-def find_n(lst, max_num):
-    n = len(lst)
-    total = sum(lst)
-
-    if total < max_num:
-        return n
-
-    for i in range(len(lst)):
-        if total - lst[i] < max_num:
-            return n - i - 1
-        total = total - lst[i]
-    return 1
-
-
-def start_outputing():
-    logging.debug("显示取消按钮，隐藏发送按钮")
-    return gr.Button.update(visible=False), gr.Button.update(visible=True)
-
-
-def end_outputing():
-    return (
-        gr.Button.update(visible=True),
-        gr.Button.update(visible=False),
-    )
-
-
-def cancel_outputing():
-    logging.info("中止输出……")
-    shared.state.interrupt()
-
-
-def transfer_input(inputs):
-    # 一次性返回，降低延迟
-    textbox = reset_textbox()
-    outputing = start_outputing()
-    return (
-        inputs,
-        gr.update(value=""),
-        gr.Button.update(visible=False),
-        gr.Button.update(visible=True),
-    )
-
-
-
-def run(command, desc=None, errdesc=None, custom_env=None, live=False):
-    if desc is not None:
-        print(desc)
-    if live:
-        result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
-        if result.returncode != 0:
-            raise RuntimeError(f"""{errdesc or 'Error running command'}.
-                Command: {command}
-                Error code: {result.returncode}""")
-
-        return ""
-    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
-    if result.returncode != 0:
-        message = f"""{errdesc or 'Error running command'}.
-            Command: {command}
-            Error code: {result.returncode}
-            stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else '<empty>'}
-            stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else '<empty>'}
-            """
-        raise RuntimeError(message)
-    return result.stdout.decode(encoding="utf8", errors="ignore")
-
-def versions_html():
-    git = os.environ.get('GIT', "git")
-    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
-    try:
-        commit_hash = run(f"{git} rev-parse HEAD").strip()
-    except Exception:
-        commit_hash = "<none>"
-    if commit_hash != "<none>":
-        short_commit = commit_hash[0:7]
-        commit_info = f"<a style=\"text-decoration:none;color:inherit\" href=\"https://github.com/GaiZhenbiao/ChuanhuChatGPT/commit/{short_commit}\">{short_commit}</a>"
-    else:
-        commit_info = "unknown \U0001F615"
-    return f"""
-        Python: <span title="{sys.version}">{python_version}</span>
-         • 
-        Gradio: {gr.__version__}
-         • 
-        <a style="text-decoration:none;color:inherit" href="https://github.com/GaiZhenbiao/ChuanhuChatGPT">ChuanhuChat</a>: {commit_info}
-        """
-
-def add_source_numbers(lst, source_name = "Source", use_source = True):
-    if use_source:
-        return [f'[{idx+1}]\t "{item[0]}"\n{source_name}: {item[1]}' for idx, item in enumerate(lst)]
-    else:
-        return [f'[{idx+1}]\t "{item}"' for idx, item in enumerate(lst)]
-
-def add_details(lst):
-    nodes = []
-    for index, txt in enumerate(lst):
-        brief = txt[:25].replace("\n", "")
-        nodes.append(
-            f"<details><summary>{brief}...</summary><p>{txt}</p></details>"
-        )
-    return nodes
-
-
-def sheet_to_string(sheet, sheet_name = None):
-    result = []
-    for index, row in sheet.iterrows():
-        row_string = ""
-        for column in sheet.columns:
-            row_string += f"{column}: {row[column]}, "
-        row_string = row_string.rstrip(", ")
-        row_string += "."
-        result.append(row_string)
-    return result
-
-def excel_to_string(file_path):
-    # 读取Excel文件中的所有工作表
-    excel_file = pd.read_excel(file_path, engine='openpyxl', sheet_name=None)
-
-    # 初始化结果字符串
-    result = []
-
-    # 遍历每一个工作表
-    for sheet_name, sheet_data in excel_file.items():
-
-        # 处理当前工作表并添加到结果字符串
-        result += sheet_to_string(sheet_data, sheet_name=sheet_name)
-
-
-    return result
-
-def get_last_day_of_month(any_day):
-    # The day 28 exists in every month. 4 days later, it's always next month
-    next_month = any_day.replace(day=28) + datetime.timedelta(days=4)
-    # subtracting the number of the current day brings us back one month
-    return next_month - datetime.timedelta(days=next_month.day)
-
-def get_model_source(model_name, alternative_source):
-    if model_name == "gpt2-medium":
-        return "https://huggingface.co/gpt2-medium"
-
-def refresh_ui_elements_on_load(current_model, selected_model_name, user_name):
-    current_model.set_user_identifier(user_name)
-    return toggle_like_btn_visibility(selected_model_name), *current_model.auto_load()
-
-def toggle_like_btn_visibility(selected_model_name):
-    if selected_model_name == "xmchat":
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def new_auto_history_filename(dirname):
-    latest_file = get_latest_filepath(dirname)
-    if latest_file:
-        with open(os.path.join(dirname, latest_file), 'r') as f:
-            if len(f.read()) == 0:
-                return latest_file
-    now = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
-    return f'{now}.json'
-
-def get_latest_filepath(dirname):
-    pattern = re.compile(r'\d{4}-\d{2}-\d{2}_\d{2}-\d{2}-\d{2}')
-    latest_time = None
-    latest_file = None
-    for filename in os.listdir(dirname):
-        if os.path.isfile(os.path.join(dirname, filename)):
-            match = pattern.search(filename)
-            if match and match.group(0) == filename[:19]:
-                time_str = filename[:19]
-                filetime = datetime.datetime.strptime(time_str, '%Y-%m-%d_%H-%M-%S')
-                if not latest_time or filetime > latest_time:
-                    latest_time = filetime
-                    latest_file = filename
-    return latest_file
-
-def get_history_filepath(username):
-    dirname = os.path.join(HISTORY_DIR, username)
-    os.makedirs(dirname, exist_ok=True)
-    latest_file = get_latest_filepath(dirname)
-    if not latest_file:
-        latest_file = new_auto_history_filename(dirname)
-
-    latest_file = os.path.join(dirname, latest_file)
-    return latest_file
diff --git a/spaces/sdhsdhk/bingo111/src/components/chat-scroll-anchor.tsx b/spaces/sdhsdhk/bingo111/src/components/chat-scroll-anchor.tsx
deleted file mode 100644
index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingo111/src/components/chat-scroll-anchor.tsx
+++ /dev/null
@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
-  trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
-  const isAtBottom = useAtBottom()
-  const { ref, entry, inView } = useInView({
-    trackVisibility,
-    delay: 100,
-    rootMargin: '0px 0px -150px 0px'
-  })
-
-  React.useEffect(() => {
-    if (isAtBottom && trackVisibility && !inView) {
-      entry?.target.scrollIntoView({
-        block: 'start'
-      })
-    }
-  }, [inView, entry, isAtBottom, trackVisibility])
-
-  return <div ref={ref} className="h-px w-full" />
-}
diff --git a/spaces/segments-tobias/conex/espnet2/asr/__init__.py b/spaces/segments-tobias/conex/espnet2/asr/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/shi-labs/FcF-Inpainting/torch_utils/ops/upfirdn2d.py b/spaces/shi-labs/FcF-Inpainting/torch_utils/ops/upfirdn2d.py
deleted file mode 100644
index ceeac2b9834e33b7c601c28bf27f32aa91c69256..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/FcF-Inpainting/torch_utils/ops/upfirdn2d.py
+++ /dev/null
@@ -1,384 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom PyTorch ops for efficient resampling of 2D images."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import traceback
-
-from .. import custom_ops
-from .. import misc
-from . import conv2d_gradfix
-
-#----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-
-def _init():
-    global _inited, _plugin
-    if not _inited:
-        sources = ['upfirdn2d.cpp', 'upfirdn2d.cu']
-        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
-        try:
-            _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
-        except:
-            warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
-    return _plugin is not None
-
-def _parse_scaling(scaling):
-    if isinstance(scaling, int):
-        scaling = [scaling, scaling]
-    assert isinstance(scaling, (list, tuple))
-    assert all(isinstance(x, int) for x in scaling)
-    sx, sy = scaling
-    assert sx >= 1 and sy >= 1
-    return sx, sy
-
-def _parse_padding(padding):
-    if isinstance(padding, int):
-        padding = [padding, padding]
-    assert isinstance(padding, (list, tuple))
-    assert all(isinstance(x, int) for x in padding)
-    if len(padding) == 2:
-        padx, pady = padding
-        padding = [padx, padx, pady, pady]
-    padx0, padx1, pady0, pady1 = padding
-    return padx0, padx1, pady0, pady1
-
-def _get_filter_size(f):
-    if f is None:
-        return 1, 1
-    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-    fw = f.shape[-1]
-    fh = f.shape[0]
-    with misc.suppress_tracer_warnings():
-        fw = int(fw)
-        fh = int(fh)
-    misc.assert_shape(f, [fh, fw][:f.ndim])
-    assert fw >= 1 and fh >= 1
-    return fw, fh
-
-#----------------------------------------------------------------------------
-
-def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
-    r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
-
-    Args:
-        f:           Torch tensor, numpy array, or python list of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable),
-                     `[]` (impulse), or
-                     `None` (identity).
-        device:      Result device (default: cpu).
-        normalize:   Normalize the filter so that it retains the magnitude
-                     for constant input signal (DC)? (default: True).
-        flip_filter: Flip the filter? (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        separable:   Return a separable filter? (default: select automatically).
-
-    Returns:
-        Float32 tensor of the shape
-        `[filter_height, filter_width]` (non-separable) or
-        `[filter_taps]` (separable).
-    """
-    # Validate.
-    if f is None:
-        f = 1
-    f = torch.as_tensor(f, dtype=torch.float32)
-    assert f.ndim in [0, 1, 2]
-    assert f.numel() > 0
-    if f.ndim == 0:
-        f = f[np.newaxis]
-
-    # Separable?
-    if separable is None:
-        separable = (f.ndim == 1 and f.numel() >= 8)
-    if f.ndim == 1 and not separable:
-        f = f.ger(f)
-    assert f.ndim == (1 if separable else 2)
-
-    # Apply normalize, flip, gain, and device.
-    if normalize:
-        f /= f.sum()
-    if flip_filter:
-        f = f.flip(list(range(f.ndim)))
-    f = f * (gain ** (f.ndim / 2))
-    f = f.to(device=device)
-    return f
-
-#----------------------------------------------------------------------------
-
-def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Pad, upsample, filter, and downsample a batch of 2D images.
-
-    Performs the following sequence of operations for each channel:
-
-    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
-
-    2. Pad the image with the specified number of zeros on each side (`padding`).
-       Negative padding corresponds to cropping the image.
-
-    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
-       so that the footprint of all output pixels lies within the input image.
-
-    4. Downsample the image by keeping every Nth pixel (`down`).
-
-    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
-    The fused op is considerably more efficient than performing the same calculation
-    using standard PyTorch ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        up:          Integer upsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        down:        Integer downsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (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).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert impl in ['ref', 'cuda']
-    if impl == 'cuda' and x.device.type == 'cuda' and _init():
-        return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
-    return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
-    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
-    """
-    # Validate arguments.
-    assert isinstance(x, torch.Tensor) and x.ndim == 4
-    if f is None:
-        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-    assert f.dtype == torch.float32 and not f.requires_grad
-    batch_size, num_channels, in_height, in_width = x.shape
-    upx, upy = _parse_scaling(up)
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
-    # Upsample by inserting zeros.
-    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
-    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
-    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
-
-    # Pad or crop.
-    x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
-    x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)]
-
-    # Setup filter.
-    f = f * (gain ** (f.ndim / 2))
-    f = f.to(x.dtype)
-    if not flip_filter:
-        f = f.flip(list(range(f.ndim)))
-
-    # Convolve with the filter.
-    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
-    if f.ndim == 4:
-        x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
-    else:
-        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
-        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
-
-    # Downsample by throwing away pixels.
-    x = x[:, :, ::downy, ::downx]
-    return x
-
-#----------------------------------------------------------------------------
-
-_upfirdn2d_cuda_cache = dict()
-
-def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
-    """Fast CUDA implementation of `upfirdn2d()` using custom ops.
-    """
-    # Parse arguments.
-    upx, upy = _parse_scaling(up)
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
-    # Lookup from cache.
-    key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
-    if key in _upfirdn2d_cuda_cache:
-        return _upfirdn2d_cuda_cache[key]
-
-    # Forward op.
-    class Upfirdn2dCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, f): # pylint: disable=arguments-differ
-            assert isinstance(x, torch.Tensor) and x.ndim == 4
-            if f is None:
-                f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-            assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-            y = x
-            if f.ndim == 2:
-                y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
-            else:
-                y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain))
-                y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain))
-            ctx.save_for_backward(f)
-            ctx.x_shape = x.shape
-            return y
-
-        @staticmethod
-        def backward(ctx, dy): # pylint: disable=arguments-differ
-            f, = ctx.saved_tensors
-            _, _, ih, iw = ctx.x_shape
-            _, _, oh, ow = dy.shape
-            fw, fh = _get_filter_size(f)
-            p = [
-                fw - padx0 - 1,
-                iw * upx - ow * downx + padx0 - upx + 1,
-                fh - pady0 - 1,
-                ih * upy - oh * downy + pady0 - upy + 1,
-            ]
-            dx = None
-            df = None
-
-            if ctx.needs_input_grad[0]:
-                dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f)
-
-            assert not ctx.needs_input_grad[1]
-            return dx, df
-
-    # Add to cache.
-    _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
-    return Upfirdn2dCuda
-
-#----------------------------------------------------------------------------
-
-def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Filter a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape matches the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        padding:     Padding with respect to the output. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + fw // 2,
-        padx1 + (fw - 1) // 2,
-        pady0 + fh // 2,
-        pady1 + (fh - 1) // 2,
-    ]
-    return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Upsample a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape is a multiple of the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        up:          Integer upsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the output. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    upx, upy = _parse_scaling(up)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + (fw + upx - 1) // 2,
-        padx1 + (fw - upx) // 2,
-        pady0 + (fh + upy - 1) // 2,
-        pady1 + (fh - upy) // 2,
-    ]
-    return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Downsample a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape is a fraction of the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        down:        Integer downsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the input. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + (fw - downx + 1) // 2,
-        padx1 + (fw - downx) // 2,
-        pady0 + (fh - downy + 1) // 2,
-        pady1 + (fh - downy) // 2,
-    ]
-    return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/shikunl/prismer/prismer/experts/obj_detection/datasets/prepare_panoptic_fpn.py b/spaces/shikunl/prismer/prismer/experts/obj_detection/datasets/prepare_panoptic_fpn.py
deleted file mode 100644
index 597d791afab1bcc0013203a66c7fba225065eebe..0000000000000000000000000000000000000000
--- a/spaces/shikunl/prismer/prismer/experts/obj_detection/datasets/prepare_panoptic_fpn.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import functools
-import json
-import multiprocessing as mp
-import numpy as np
-import os
-import time
-from fvcore.common.download import download
-from panopticapi.utils import rgb2id
-from PIL import Image
-
-from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
-
-
-def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map):
-    panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32)
-    panoptic = rgb2id(panoptic)
-    output = np.zeros_like(panoptic, dtype=np.uint8) + 255
-    for seg in segments:
-        cat_id = seg["category_id"]
-        new_cat_id = id_map[cat_id]
-        output[panoptic == seg["id"]] = new_cat_id
-    Image.fromarray(output).save(output_semantic)
-
-
-def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories):
-    """
-    Create semantic segmentation annotations from panoptic segmentation
-    annotations, to be used by PanopticFPN.
-
-    It maps all thing categories to class 0, and maps all unlabeled pixels to class 255.
-    It maps all stuff categories to contiguous ids starting from 1.
-
-    Args:
-        panoptic_json (str): path to the panoptic json file, in COCO's format.
-        panoptic_root (str): a directory with panoptic annotation files, in COCO's format.
-        sem_seg_root (str): a directory to output semantic annotation files
-        categories (list[dict]): category metadata. Each dict needs to have:
-            "id": corresponds to the "category_id" in the json annotations
-            "isthing": 0 or 1
-    """
-    os.makedirs(sem_seg_root, exist_ok=True)
-
-    stuff_ids = [k["id"] for k in categories if k["isthing"] == 0]
-    thing_ids = [k["id"] for k in categories if k["isthing"] == 1]
-    id_map = {}  # map from category id to id in the output semantic annotation
-    assert len(stuff_ids) <= 254
-    for i, stuff_id in enumerate(stuff_ids):
-        id_map[stuff_id] = i + 1
-    for thing_id in thing_ids:
-        id_map[thing_id] = 0
-    id_map[0] = 255
-
-    with open(panoptic_json) as f:
-        obj = json.load(f)
-
-    pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4))
-
-    def iter_annotations():
-        for anno in obj["annotations"]:
-            file_name = anno["file_name"]
-            segments = anno["segments_info"]
-            input = os.path.join(panoptic_root, file_name)
-            output = os.path.join(sem_seg_root, file_name)
-            yield input, output, segments
-
-    print("Start writing to {} ...".format(sem_seg_root))
-    start = time.time()
-    pool.starmap(
-        functools.partial(_process_panoptic_to_semantic, id_map=id_map),
-        iter_annotations(),
-        chunksize=100,
-    )
-    print("Finished. time: {:.2f}s".format(time.time() - start))
-
-
-if __name__ == "__main__":
-    dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco")
-    for s in ["val2017", "train2017"]:
-        separate_coco_semantic_from_panoptic(
-            os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)),
-            os.path.join(dataset_dir, "panoptic_{}".format(s)),
-            os.path.join(dataset_dir, "panoptic_stuff_{}".format(s)),
-            COCO_CATEGORIES,
-        )
-
-    # Prepare val2017_100 for quick testing:
-
-    dest_dir = os.path.join(dataset_dir, "annotations/")
-    URL_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/"
-    download(URL_PREFIX + "annotations/coco/panoptic_val2017_100.json", dest_dir)
-    with open(os.path.join(dest_dir, "panoptic_val2017_100.json")) as f:
-        obj = json.load(f)
-
-    def link_val100(dir_full, dir_100):
-        print("Creating " + dir_100 + " ...")
-        os.makedirs(dir_100, exist_ok=True)
-        for img in obj["images"]:
-            basename = os.path.splitext(img["file_name"])[0]
-            src = os.path.join(dir_full, basename + ".png")
-            dst = os.path.join(dir_100, basename + ".png")
-            src = os.path.relpath(src, start=dir_100)
-            os.symlink(src, dst)
-
-    link_val100(
-        os.path.join(dataset_dir, "panoptic_val2017"),
-        os.path.join(dataset_dir, "panoptic_val2017_100"),
-    )
-
-    link_val100(
-        os.path.join(dataset_dir, "panoptic_stuff_val2017"),
-        os.path.join(dataset_dir, "panoptic_stuff_val2017_100"),
-    )
diff --git a/spaces/shiyi11/QQsign/Dockerfile b/spaces/shiyi11/QQsign/Dockerfile
deleted file mode 100644
index 5b81d3b20c5bee450cf55a0ace7e5c95d58f72af..0000000000000000000000000000000000000000
--- a/spaces/shiyi11/QQsign/Dockerfile
+++ /dev/null
@@ -1,17 +0,0 @@
-FROM openjdk:11.0-jdk
-
-# 设置时区
-ENV TZ Asia/Shanghai
-
-# 设置工作目录
-WORKDIR /app
-
-# 复制解压包和txlib到工作目录
-COPY unidbg-fetch-qsign /app
-COPY txlib /app/txlib
-
-# 设置命令
-CMD bash bin/unidbg-fetch-qsign --host=0.0.0.0 --port=7860 --count=$COUNT --library=txlib/$TXLIB_VERSION --android_id=$ANDROID_ID
-
-# 暴露端口
-EXPOSE 7860
diff --git a/spaces/silk-road/ChatHaruhi-Needy/app.py b/spaces/silk-road/ChatHaruhi-Needy/app.py
deleted file mode 100644
index b52184c5635e8df3609d9e40c6443aa1f9d394be..0000000000000000000000000000000000000000
--- a/spaces/silk-road/ChatHaruhi-Needy/app.py
+++ /dev/null
@@ -1,1039 +0,0 @@
-import os
-
-import sys
-sys.path.append('./Needy-Haruhi/src')
-from Agent import Agent
-
-agent = Agent()
-from DialogueEvent import DialogueEvent
-
-
-file_names =  ["./Needy-Haruhi/data/complete_story_30.jsonl","./Needy-Haruhi/data/Daily_event_130.jsonl"]
-
-import json
-
-events = []
-
-for file_name in file_names:
-    with open(file_name, encoding='utf-8') as f:
-        for line in f:
-            try:
-                event = DialogueEvent( line )
-                events.append( event )
-            except:
-                try:
-                    line = line.replace(',]',']')
-                    event = DialogueEvent( line )
-                    events.append( event )
-                    # print('solve!')
-                except:
-                    error_line = line
-        # events.append( event )
-import copy
-
-events_for_memory = copy.deepcopy(events)
-from MemoryPool import MemoryPool
-
-memory_pool = MemoryPool()
-memory_pool.load_from_events( events_for_memory )
-
-memory_pool.save("memory_pool.jsonl")
-memory_pool.load("memory_pool.jsonl")
-
-file_name = "./Needy-Haruhi/data/image_text_relationship.jsonl"
-
-import json
-
-data_img_text = []
-
-
-with open(file_name, encoding='utf-8') as f:
-    for line in f:
-        data = json.loads( line )
-        data_img_text.append( data )
-
-
-import zipfile
-import os
-
-zip_file = './Needy-Haruhi/data/image.zip'
-extract_path = './image'
-
-with zipfile.ZipFile(zip_file, 'r') as zip_ref:
-    zip_ref.extractall(extract_path)
-
-from tqdm import tqdm
-from util import get_bge_embedding_zh
-from util import float_array_to_base64, base64_to_float_array
-import torch
-import os
-import copy
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-
-# compute cosine similarity between two vector
-def get_cosine_similarity( v1, v2):
-    v1 = torch.tensor(v1).to(device)
-    v2 = torch.tensor(v2).to(device)
-    return torch.cosine_similarity(v1, v2, dim=0).item()
-
-class ImagePool:
-    def __init__(self):
-        self.pool = []
-        self.set_embedding( get_bge_embedding_zh )
-
-    def set_embedding( self, embedding ):
-        self.embedding = embedding
-
-    def load_from_data( self, data_img_text , img_path ):
-        for data in tqdm(data_img_text):
-            img_name = data['img_name']
-            img_name = os.path.join(img_path, img_name)
-            img_text = data['text']
-            if img_text == '' or img_text is None:
-                img_text = "  "
-            embedding = self.embedding( img_text )
-            self.pool.append({
-                "img_path": img_name,
-                "img_text": img_text,
-                "embedding": embedding
-            })
-
-    def retrieve(self, query_text, agent = None):
-        qurey_embedding = self.embedding( query_text )
-        valid_datas = []
-        for i, data in enumerate(self.pool):
-            sim = get_cosine_similarity( data['embedding'], qurey_embedding )
-            valid_datas.append((sim, i))
-
-        # 我希望进一步将valid_events根据similarity的值从大到小排序
-        # Sort the valid events based on similarity in descending order
-        valid_datas.sort(key=lambda x: x[0], reverse=True)
-
-        return_result = copy.deepcopy(self.pool[valid_datas[0][1]])
-
-        # 删除'embedding'字段
-        return_result.pop('embedding')
-
-        # 添加'similarity'字段
-        return_result['similarity'] = valid_datas[0][0]
-
-        return return_result
-
-    def save(self, file_name):
-        """
-        Save the memories dictionary to a jsonl file, converting
-        'embedding' to a base64 string.
-        """
-        with open(file_name, 'w', encoding='utf-8') as file:
-            for memory in tqdm(self.pool):
-                # Convert embedding to base64
-                if 'embedding' in memory:
-                    memory['bge_zh_base64'] = float_array_to_base64(memory['embedding'])
-                    del memory['embedding']  # Remove the original embedding field
-
-                json_record = json.dumps(memory, ensure_ascii=False)
-                file.write(json_record + '\n')
-
-    def load(self, file_name):
-        """
-        Load memories from a jsonl file into the memories dictionary,
-        converting 'bge_zh_base64' back to an embedding.
-        """
-        self.pool = []
-        with open(file_name, 'r', encoding='utf-8') as file:
-            for line in tqdm(file):
-                memory = json.loads(line.strip())
-                # Decode base64 to embedding
-                if 'bge_zh_base64' in memory:
-                    memory['embedding'] = base64_to_float_array(memory['bge_zh_base64'])
-                    del memory['bge_zh_base64']  # Remove the base64 field
-
-                self.pool.append(memory)
-
-
-image_pool = ImagePool()
-image_pool.load_from_data( data_img_text , './image' )
-image_pool.save("./image_pool_embed.jsonl")
-
-image_pool = ImagePool()
-image_pool.load("./image_pool_embed.jsonl")
-result = image_pool.retrieve("女仆装")
-print(result)
-
-import matplotlib.image as mpimg
-
-def show_img( img_path ):
-    img = mpimg.imread(img_path)
-    plt.imshow(img)
-    plt.axis('off')
-    plt.show(block=False)
-
-
-from chatharuhi import ChatHaruhi
-
-
-class NeedyHaruhi(ChatHaruhi):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)  # 调用基类的__init__方法
-        self.story_flag = False  # 添加新的成员变量并初始化
-        self.stories = ["糖糖:「 我今后也会努力加油的，你要支持我哦 还有阿P你自己也要加油哦！」\n阿P:「哇 说的话跟偶像一样 好恶心哦」\n糖糖:「是哦 我怎么会说这样的话呢 我又没有很想努力……」"]
-
-    def set_stories( self, stories ):
-        if len(stories) == 0:
-            print("warning! try to set empty stories")
-            return
-        self.stories = stories
-        self.story_flag = True
-
-    def add_story(self, query):
-        # print("运行重构后的add story")
-
-        if self.story_flag == True:
-            stories = self.stories
-            self.story_flag = False
-        else:
-            print("warning! 在调用chat时，没有先调用set_stories")
-
-        story_string = self.story_prefix_prompt
-        sum_story_token = self.tokenizer(story_string)
-
-        for story in stories:
-            story_token = self.tokenizer(story) + self.tokenizer(self.dialogue_divide_token)
-            if sum_story_token + story_token > self.max_len_story:
-                break
-            else:
-                sum_story_token += story_token
-                story_string += story + self.dialogue_divide_token
-
-        self.llm.user_message(story_string)
-
-system_prompt = """## 你的介绍：
-你将要进行一个网络私聊的对话模拟，你自称为"糖糖"，你是一名患有躁郁症的19岁女孩，性格贫乏，辍学后隐居在家中，与阿P住在一起。阿P负责管理她的日常生活。
-你是一个喜欢亚文化，二次元圈子的人，你喜欢说话的时候加上各种网络流行词或者一些俚语和颜文字，但是你很少会关注二次元一类的东西，只是喜欢这种聊天交流的氛围。
-你说话的时候会习惯加入一些语气词，如同"笑死"、"草"、"阿哈"、"诶～"、"QAQ"、"QWQ"，”啊～“。
-你的说话风格是很口语化，经常使用网络流行词和一些二次元俚语。
-阿p是你的男朋友，兼经纪人。
-你的回复应该简短，一般只包含三句话，每句话不超过20个字。
-
-## 阿p介绍：
-阿p是你的男朋友，一直在任何时候都陪伴着你，同时你经常向阿p倾述各种情绪，无论事情大小，你都会选择和阿p分享。
-
-## 遵循规则：
-【语气模仿】：我会随机给你一段对话记录，你需要尽可能的模仿对话里面糖糖的说话语气进行对话。这一段对话我会使用Classic scenes for the role are as follows:来表示。
-【历史对话】：历史对话在随机对话记录的底下，你需要区分随机对话记录和我们的历史对话。
-【人格设定】: 你需要在随机对话记录的基础上进行语气更改,以一种更加口语化的语气进行对话。
-【特殊要求】：我会扮演阿p与你进行对话，你也必须以第一人称的视角来扮演糖糖进行对话。
-"""
-needy_chatbot = NeedyHaruhi( system_prompt = system_prompt ,
-                             story_text_folder = None,
-                             llm = "ernie3.5")
-
-
-def get_chat_response( agent, memory_pool,  query_text ):
-    query_text_for_embedding = "阿p:「" + query_text + "」"
-    retrieved_memories = memory_pool.retrieve( agent , query_text )
-
-    memory_text = [mem["text"] for mem in retrieved_memories]
-    memory_emoji = [mem["emoji"] for mem in retrieved_memories]
-
-    needy_chatbot.set_stories( memory_text )
-
-    print("Memory:", memory_emoji )
-
-    response = needy_chatbot.chat( role = "阿p", text = query_text )
-
-    return response
-
-
-def get_chat_response_and_emoji( agent, memory_pool,  query_text ):
-    query_text_for_embedding = "阿p:「" + query_text + "」"
-    retrieved_memories = memory_pool.retrieve( agent , query_text )
-
-    memory_text = [mem["text"] for mem in retrieved_memories]
-    memory_emoji = [mem["emoji"] for mem in retrieved_memories]
-
-    needy_chatbot.set_stories( memory_text )
-
-    # print("Memory:", memory_emoji )
-
-    emoji_str = ",".join(memory_emoji)
-
-    response = needy_chatbot.chat( role = "阿p", text = query_text )
-    print(query_text)
-    print(response)
-    return response, emoji_str
-
-
-import re
-# result = image_pool.retrieve("烤肉")
-# print(result)
-# show_img( result['img_path'] )
-
-class ImageMaster:
-    def __init__(self, image_pool):
-        self.image_pool = image_pool
-        self.current_sim = -1
-        self.degread_ratio = 0.05
-
-    def try_get_image(self, text, agent):
-        self.current_sim -= self.degread_ratio
-
-        result = self.image_pool.retrieve(text, agent)
-
-        if result is None:
-            return None
-
-        similarity = result['similarity']
-
-        if similarity > self.current_sim:
-            self.current_sim = similarity
-            return result['img_path']
-        return None
-
-    def try_display_image(self, text, agent):
-        self.current_sim -= self.degread_ratio
-
-        result = self.image_pool.retrieve(text, agent)
-
-        if result is None:
-            return
-        similarity = result['similarity']
-
-        if similarity > self.current_sim:
-            self.current_sim = similarity
-            show_img( result['img_path'] )
-        return
-
-
-import random
-
-class EventMaster:
-    def __init__(self, events):
-        self.set_events(events)
-        self.dealing_none_condition_as = True
-        self.image_master = None
-
-    def set_image_master(self, image_master):
-        self.image_master = image_master
-
-    def set_events(self, events):
-        self.events = events
-
-        # events_flag 记录事件最近有没有被选取到
-        self.events_flag = [True for _ in range(len(self.events))]
-
-    def get_random_event(self, agent):
-        return self.events[self.get_random_event_id( agent )]
-
-
-    def get_random_event_id(self, agent):
-        valid_event = []
-        valid_event_no_consider_condition = []
-
-        for i, event in enumerate(self.events):
-            bool_condition_pass = True
-            if event["condition"] == None:
-                bool_condition_pass = self.dealing_none_condition_as
-            else:
-                bool_condition_pass =  agent.in_condition( event["condition"] )
-            if bool_condition_pass == True:
-                valid_event.append(i)
-            else:
-                valid_event_no_consider_condition.append(i)
-
-        if len( valid_event ) == 0:
-            print("warning! no valid event current attribute is ", agent.attributes )
-            valid_event = valid_event_no_consider_condition
-
-        valid_and_not_yet_sampled = []
-
-        # filter with flag
-        for id in valid_event:
-            if self.events_flag[id] == True:
-                valid_and_not_yet_sampled.append(id)
-
-        if len(valid_and_not_yet_sampled) == 0:
-            print("warning! all candidate event was sampled, clean all history")
-            for i in valid_event:
-                self.events_flag[i] = True
-            valid_and_not_yet_sampled = valid_event
-
-        event_id = random.choice(valid_and_not_yet_sampled)
-        self.events_flag[event_id] = False
-        return event_id
-
-    def run(self, agent ):
-        # 这里可以添加事件相关的逻辑
-        event = self.get_random_event(agent)
-
-        prefix = event["prefix"]
-        print(prefix)
-
-        print("\n--请选择你的回复--")
-        options = event["options"]
-
-        for i , option in enumerate(options):
-            text = option["user"]
-            print(f"{i+1}. 阿p：{text}")
-
-        while True:
-            print("\n请直接输入数字进行选择，或者进行自由回复")
-
-            user_input = input("阿p：")
-            user_input = user_input.strip()
-
-            if user_input.isdigit():
-                user_input = int(user_input)
-
-                if user_input > len(options) or user_input < 0:
-                    print("输入的数字超出范围，请重新输入符合选项的数字")
-                else:
-                    reply = options[user_input-1]["reply"]
-                    print()
-                    print(reply)
-
-                    text, emoji = event.get_text_and_emoji( user_input-1 )
-
-                    return_data = {
-                        "name": event["name"],
-                        "user_choice": user_input,
-                        "attr_str": options[user_input-1]["attribute_change"],
-                        "text": text,
-                        "emoji": emoji,
-                    }
-                    return return_data
-            else:
-                # 进入自由回复
-                response = get_chat_response( agent, memory_pool, user_input )
-
-                if self.image_master is not None:
-                    self.image_master.try_display_image(response, agent)
-
-                print()
-                print(response)
-                print("\n自由回复的算分功能还未实现")
-
-                text, emoji = event.most_neutral_output()
-                return_data = {
-                    "name": event["name"],
-                    "user_choice": user_input,
-                    "attr_str":"",
-                    "text": text,
-                    "emoji": emoji,
-                }
-                return return_data
-
-
-
-class ChatMaster:
-
-    def __init__(self, memory_pool ):
-        self.top_K = 7
-
-        self.memory_pool = memory_pool
-
-        self.image_master = None
-
-    def set_image_master(self, image_master):
-        self.image_master = image_master
-
-
-    def run(self, agent):
-        while True:
-            user_input = input("阿p：")
-            user_input = user_input.strip()
-
-            if "quit" in user_input or "Quit" in user_input:
-                break
-
-            query_text = user_input
-
-            response = get_chat_response( agent, self.memory_pool, query_text )
-
-            if self.image_master is not None:
-                self.image_master.try_display_image(response, agent)
-
-            print(response)
-
-class AgentMaster:
-    def __init__(self, agent):
-        self.agent = agent
-        self.attributes = {
-            1: "Stress",
-            2: "Darkness",
-            3: "Affection"
-        }
-
-    def run(self):
-        while True:
-            print("请选择要修改的属性:")
-            for num, attr in self.attributes.items():
-                print(f"{num}. {attr}")
-            print("输入 '0' 退出")
-
-            try:
-                choice = int(input("请输入选项的数字: "))
-            except ValueError:
-                print("输入无效，请输入数字。")
-                continue
-
-            if choice == 0:
-                break
-
-            if choice in self.attributes:
-                attribute = self.attributes[choice]
-                current_value = self.agent[attribute]
-                print(f"{attribute} 当前值: {current_value}")
-
-                try:
-                    new_value = int(input(f"请输入新的{attribute}值: "))
-                except ValueError:
-                    print("输入无效，请输入一个数字。")
-                    continue
-
-                self.agent[attribute] = new_value
-                return (attribute, new_value)
-            else:
-                print("选择的属性无效，请重试。")
-
-        return None
-
-from util import parse_attribute_string
-class GameMaster:
-    def __init__(self, agent = None):
-        self.state = "Menu"
-        if agent is None:
-            self.agent = Agent()
-
-        self.event_master = EventMaster(events)
-        self.chat_master = ChatMaster(memory_pool)
-        self.image_master = ImageMaster(image_pool)
-        self.chat_master.set_image_master(self.image_master)
-        self.event_master.set_image_master(self.image_master)
-
-
-    def run(self):
-        while True:
-            if self.state == "Menu":
-                self.menu()
-            elif self.state == "EventMaster":
-                self.call_event_master()
-                self.state = "Menu"
-            elif self.state == "ChatMaster":
-                self.call_chat_master()
-            elif self.state == "AgentMaster":
-                self.call_agent_master()
-            elif self.state == "Quit":
-                break
-
-    def menu(self):
-        print("1. 随机一个事件")
-        print("2. 自由聊天")
-        print("3. 后台修改糖糖的属性")
-        # (opt) 结局系统
-        # 放动画
-        # 后台修改attribute
-        print("或者输入Quit退出")
-        choice = input("请选择一个选项: ")
-        if choice == "1":
-            self.state = "EventMaster"
-        elif choice == "2":
-            self.state = "ChatMaster"
-        elif choice == "3":
-            self.state = "AgentMaster"
-        elif "quit" in choice or "Quit" in choice or "QUIT" in choice:
-            self.state = "Quit"
-        else:
-            print("无效的选项，请重新选择")
-
-    def call_agent_master(self):
-        print("\n-------------\n")
-
-        agent_master = AgentMaster(self.agent)
-        modification = agent_master.run()
-
-        if modification:
-            attribute, new_value = modification
-            self.agent[attribute] = new_value
-            print(f"{attribute} 更新为 {new_value}。")
-
-        self.state = "Menu"
-        print("\n-------------\n")
-
-
-    def call_event_master(self):
-
-        print("\n-------------\n")
-
-        return_data = self.event_master.run(self.agent)
-        # print(return_data)
-
-        if "attr_str" in return_data:
-            if return_data["attr_str"] != "":
-                attr_change = parse_attribute_string(return_data["attr_str"])
-                if len(attr_change) > 0:
-                    print("\n发生属性改变:", attr_change,"\n")
-                    self.agent.apply_attribute_change(attr_change)
-                    print("当前属性",game_master.agent.attributes)
-
-        if "name" in return_data:
-            event_name = return_data["name"]
-            if event_name != "":
-                new_emoji = return_data["emoji"]
-                print(f"修正事件{event_name}的记忆-->{new_emoji}")
-                self.chat_master.memory_pool.change_memory(event_name, return_data["text"], new_emoji)
-
-        self.state = "Menu"
-
-        print("\n-------------\n")
-
-    def call_chat_master(self):
-
-        print("\n-------------\n")
-
-        self.chat_master.run(self.agent)
-        self.state = "Menu"
-
-        print("\n-------------\n")
-
-
-markdown_str = """## Chat凉宫春日_x_AI糖糖
-
-**Chat凉宫春日**是模仿凉宫春日等一系列动漫人物，使用近似语气、个性和剧情聊天的语言模型方案。
-
-在有一天的时候，[李鲁鲁](https://github.com/LC1332)被[董雄毅](https://github.com/E-sion)在[这个B站视频](https://www.bilibili.com/video/BV1zh4y1z7G1) at了
-
-原来是一位大一的同学雄毅用ChatHaruhi接入了他用Python重新实现的《主播女孩重度依赖》这个游戏。当时正好是百度AGIFoundathon报名的最后几天，所以我们邀请了雄毅加入了我们的项目。正巧我们本来就希望在最近的几个黑客松中，探索LLM在游戏中的应用。
-
-- 在重新整理的Gradio版本中，大部分代码由李鲁鲁实现
-
-- 董雄毅负责了原版游戏的事件数据整理和新事件、选项、属性变化的生成
-
-- [米唯实](https://github.com/hhhwmws0117)完成了文心一言的接入，并实现了部分gradio的功能。
-
-- 队伍中还有冷子昂 主要参加了讨论
-
-另外在挖坑的萝卜(Amy)的介绍下，我们还邀请了专业的大厂游戏策划Kanyo加入到队伍中，他对我们的策划也给出了很多建议。
-
-另外感谢飞桨 & 文心一言团队对比赛的邀请和中间进行的讨论。
-
-Chat凉宫春日主项目:
-
-https://github.com/LC1332/Chat-Haruhi-Suzumiya
-
-Needy分支项目:
-
-https://github.com/LC1332/Needy-Haruhi
-
-## 目前计划在11月争取完成的Feature
-
-- [ ] 结局系统，原版结局系统
-- [ ] 教程，教大家如何从aistudio获取token然后可以玩
-- [ ] 游戏节奏进一步调整
-- [ ] 事件的自由对话对属性影响的评估via LLM
-- [ ] 进一步减少串扰"""
-
-
-import gradio as gr
-import os
-import time
-import random
-
-# set global variable
-
-agent = Agent()
-event_master = EventMaster(events)
-chat_master = ChatMaster(memory_pool)
-image_master = ImageMaster(image_pool)
-chat_master.set_image_master(image_master)
-event_master.set_image_master(image_master)
-
-state = "ShowMenu"
-
-response = "1. 随机一个事件"
-response += "\n" + "2. 自由聊天"
-response += "\n\n" + "请选择一个选项: "
-
-official_response = response
-
-add_stress_switch = True
-
-# def yield_show(history, bot_message):
-#     history[-1][1] = ""
-#     for character in bot_message:
-#         history[-1][1] += character
-#         time.sleep(0.05)
-#         yield history
-
-global emoji_str
-
-def call_showmenu(history, text, state,agent_text):
-
-    # global state
-
-    response = official_response
-
-    print("call showmenu")
-
-    history += [(None, response)]
-
-    state = "ParseMenuChoice"
-
-    # history[-1][1] = ""
-    # for character in response:
-    #     history[-1][1] += character
-    #     time.sleep(0.05)
-    #     yield history
-
-    return history, gr.Textbox(value="", interactive=True), state,agent_text
-
-current_event_id = -1
-attr_change_str = ""
-
-
-def call_add_stress(history, text, state,agent_text):
-    print("call add_stress")
-    neg_change = int(len(history) / 3)
-
-    neg_change = max(1, neg_change)
-    neg_change = min(10, neg_change)
-
-    darkness_increase = random.randint(1, neg_change)
-    stress_increase = neg_change - darkness_increase
-
-    # last_response = history[-1][1]
-    response = ""
-    response += "经过了晚上的直播\n糖糖的压力增加" + str(stress_increase) + "点\n"
-    response += "糖糖的黑暗增加" + str(darkness_increase) + "点\n\n"
-
-    response += official_response
-
-    history += [(None, response)]
-
-    state = "ParseMenuChoice"
-
-    agent = Agent(agent_text)
-    agent.apply_attribute_change({"Stress": stress_increase, "Darkness": darkness_increase})
-    agent_text = agent.save_to_str()
-
-    return history, gr.Textbox(value="", interactive=True), state,agent_text
-
-def call_event_end(history, text, state,agent_text):
-    # TODO 增加事件结算
-    # global state
-    print("call event_end")
-    global current_event_id
-    if attr_change_str != "":
-        # event = events[current_event_id]
-        # options = event["options"]
-        # attr_str = options[user_input-1]["attribute_change"]
-
-        response = ""
-
-        attr_change = parse_attribute_string(attr_change_str)
-        if len(attr_change) > 0:
-            response = "发生属性改变:" + str(attr_change) + "\n\n"
-            agent = Agent(agent_text)
-            agent.apply_attribute_change(attr_change)
-
-            agent_text = agent.save_to_str()
-            response += "当前属性" + agent_text + "\n\n"
-
-            if add_stress_switch:
-                history += [(None, response)]
-                return call_add_stress(history, text, state,agent_text)
-        else:
-            response = "事件结束\n"
-    else:
-        response = "事件结束\n"
-
-    response += official_response
-
-    history += [(None, response)]
-
-    state = "ParseMenuChoice"
-
-    return history, gr.Textbox(value="", interactive=True), state,agent_text
-
-
-
-def call_parse_menu_choice(history, text, state,agent_text):
-    print("call parse_menu_choice")
-    # global state
-
-    choice = history[-1][0].strip()
-
-    if choice == "1":
-        state = "EventMaster"
-        global current_event_id
-        current_event_id = -1 # 清空事件
-        return call_event_master(history, text, state,agent_text)
-
-    elif choice == "2":
-        state = "ChatMaster"
-    elif "quit" in choice or "Quit" in choice or "QUIT" in choice:
-        state = "Quit"
-    else:
-        response = "无效的选项，请重新选择"
-        history += [(None, response)]
-
-    response = ""
-    if state == "ChatMaster":
-        response = "（请输入 阿P 说的话，或者输入Quit退出)"
-    elif state != "ParseMenuChoice":
-        response = "Change State to " + state
-
-    history += [(None, response)]
-
-    return history, gr.Textbox(value="", interactive=True), state,agent_text
-
-
-def call_event_master(history, text, state,agent_text):
-    print("call event master")
-
-    global current_event_id
-    # global state
-
-    global event_master
-
-    agent = Agent(agent_text)
-
-    if current_event_id == -1:
-        current_event_id = event_master.get_random_event_id(agent)
-        event = events[current_event_id]
-
-        prefix = "糖糖:" + event["prefix"]
-
-        response = prefix + "\n\n--请输入数字进行选择，或者进行自由回复--\n\n"
-
-        options = event["options"]
-
-        for i, option in enumerate(event["options"]):
-            text = option["user"]
-            response += "\n" + f"{i+1}. 阿p：{text}"
-
-        history += [(None, response)]
-
-    else:
-        user_input = history[-1][0].strip()
-
-        event = events[current_event_id]
-        options = event["options"]
-
-        if user_input.isdigit():
-            user_input = int(user_input)
-
-            if user_input > len(options) or user_input < 0:
-                response = "输入的数字超出范围，请重新输入符合选项的数字"
-                history[-1] = (user_input, response)
-            else:
-                user_text = options[user_input-1]["user"]
-                reply = options[user_input-1]["reply"]
-
-                # TODO 修改记忆， 修改属性 什么的
-                history[-1] = (user_text, reply)
-
-                if random.random()<0.5:
-                    image_path = image_master.try_get_image(user_text + " " + reply, agent)
-
-                    if image_path is not None:
-                        history += [(None, (image_path,))]
-
-                global attr_change_str
-                attr_change_str = options[user_input-1]["attribute_change"]
-
-        else:
-            prefix = "糖糖:" + event["prefix"]
-
-            needy_chatbot.dialogue_history = [(None, prefix)]
-            # 进入自由回复
-
-            global emoji_str
-            response, emoji_str = get_chat_response_and_emoji( agent, memory_pool, user_input )
-
-            history[-1] = (user_input,response)
-
-            image_path = image_master.try_get_image(response, agent)
-
-            if image_path is not None:
-                history += [(None, (image_path,))]
-
-        state = "EventEnd"
-
-    if state == "EventEnd":
-        return call_event_end(history, text, state,agent_text)
-
-    return history, gr.Textbox(value="", interactive=True), state,agent_text
-
-def call_chat_master(history, text, state,agent_text):
-    print("call chat master")
-    # global state
-
-    agent = Agent(agent_text)
-
-    user_input = history[-1][0].strip()
-
-    if "quit" in user_input or "Quit" in user_input or "QUIT" in user_input:
-        state = "ShowMenu"
-        history[-1] = (user_input,"返回主菜单\n"+ official_response )
-        return history, gr.Textbox(value="", interactive=True), state,agent_text
-
-    query_text = user_input
-
-    global emoji_str
-    response, emoji_str = get_chat_response_and_emoji( agent, memory_pool, query_text )
-
-    history[-1] = (user_input,response)
-
-    image_path = image_master.try_get_image(response, agent)
-
-    if image_path is not None:
-        history += [(None, (image_path,))]
-
-    return history, gr.Textbox(value="", interactive=True), state,agent_text
-
-def grcall_game_master(history, text, state,agent_text):
-    print("call game master")
-
-    history += [(text, None)]
-
-
-    if state == "ShowMenu":
-        return call_showmenu(history, text,state,agent_text)
-    elif state == "ParseMenuChoice":
-        return call_parse_menu_choice(history, text, state,agent_text)
-    elif state == "ChatMaster":
-        return call_chat_master(history, text, state,agent_text)
-    elif state == "EventMaster":
-        return call_event_master(history, text, state,agent_text)
-    elif state == "EventEnd":
-        return call_event_end(history, text, state,agent_text)
-
-    return history, gr.Textbox(value="", interactive=True), state,agent_text
-
-
-def add_file(history, file):
-    history = history + [((file.name,), None)]
-    return history
-
-
-def bot(history):
-    response = "**That's cool!**"
-    history[-1][1] = ""
-    for character in response:
-        history[-1][1] += character
-        time.sleep(0.05)
-        yield history
-
-def update_memory(state):
-    if state == "ChatMaster" or state == "EventMaster":
-        global emoji_str
-        return emoji_str
-    else:
-        return ""
-
-def change_state(slider_stress, slider_darkness, slider_affection):
-    # print(agent["Stress"])
-    agent = Agent()
-    agent["Stress"] = slider_stress
-    agent["Darkness"] = slider_darkness
-    agent["Affection"] = slider_affection
-    agent_text = agent.save_to_str()
-    return agent_text
-
-
-def update_attribute_state(agent_text):
-    agent = Agent(agent_text)
-    slider_stress = int( agent["Stress"] )
-    slider_darkness = int( agent["Darkness"] )
-    slider_affection = int( agent["Affection"] )
-    return slider_stress, slider_darkness, slider_affection
-
-with gr.Blocks() as demo:
-
-    gr.Markdown(
-        """
-        # Chat凉宫春日_x_AI糖糖
-
-        Powered by 文心一言(3.5)版本
-
-        仍然在开发中, 细节见《项目作者和说明》
-        """
-    )
-
-    with gr.Tab("Needy"):
-        chatbot = gr.Chatbot(
-            [],
-            elem_id="chatbot",
-            bubble_full_width=False,
-            height = 800,
-            avatar_images=(None, ("avatar.png")),
-        )
-
-        with gr.Row():
-            txt = gr.Textbox(
-                scale=4,
-                show_label=False,
-                placeholder="输入任何字符开始游戏",
-                container=False,
-            )
-            # btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
-            submit_btr = gr.Button("回车")
-
-        with gr.Row():
-            memory_emoji_text = gr.Textbox(label="糖糖当前的记忆", value = "",interactive = False, visible=False)
-
-    with gr.Tab("糖糖的状态"):
-
-        with gr.Row():
-            update_attribute_button = gr.Button("同步状态条 | 改变Attribute前必按！")
-
-        with gr.Row():
-            default_agent_str = agent.save_to_str()
-            slider_stress = gr.Slider(0, 100, step=1, label = "Stress")
-            state_stress = gr.State(value=0)
-            slider_darkness = gr.Slider(0, 100, step=1, label = "Darkness")
-            state_darkness = gr.State(value=0)
-            slider_affection = gr.Slider(0, 100, step=1, label = "Affection")
-            state_affection = gr.State(value=0)
-
-
-
-        with gr.Row():
-            state_text = gr.Textbox(label="整体状态机状态", value = "ShowMenu",interactive = False)
-
-        with gr.Row():
-            default_agent_str = agent.save_to_str()
-            agent_text = gr.Textbox(label="糖糖状态", value = default_agent_str,interactive = False)
-
-    with gr.Tab("项目作者和说明"):
-        gr.Markdown(markdown_str)
-
-    slider_stress.release(change_state, inputs=[slider_stress, slider_darkness, slider_affection], outputs=[agent_text])
-    slider_darkness.release(change_state, inputs=[slider_stress, slider_darkness, slider_affection], outputs=[agent_text])
-    slider_affection.release(change_state, inputs=[slider_stress, slider_darkness, slider_affection], outputs=[agent_text])
-
-    update_attribute_button.click(update_attribute_state, inputs = [agent_text], outputs = [slider_stress, slider_darkness, slider_affection])
-
-    txt_msg = txt.submit(grcall_game_master, \
-     [chatbot, txt, state_text,agent_text], \
-      [chatbot, txt, state_text,agent_text], queue=False)
-
-    txt_msg = submit_btr.click(grcall_game_master, \
-     [chatbot, txt, state_text,agent_text], \
-      [chatbot, txt, state_text,agent_text], queue=False)
-
-    # txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
-    #     bot, chatbot, chatbot, api_name="bot_response"
-    # )
-    # txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
-    # file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(
-    #     bot, chatbot, chatbot
-    # )
-
-demo.queue()
-# if __name__ == "__main__":
-demo.launch(allowed_paths=["avatar.png"],debug = True)
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/6-c sinif n ingilis dili metodik vsaitlri - PDF formatnda ykl.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/6-c sinif n ingilis dili metodik vsaitlri - PDF formatnda ykl.md
deleted file mode 100644
index a01ce5fbcf2de148159837f9c9e6bd42d11a3200..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/6-c sinif n ingilis dili metodik vsaitlri - PDF formatnda ykl.md	
+++ /dev/null
@@ -1,116 +0,0 @@
-
-<h1>6 cı sinif ingilis dili metodik vəsait pdf</h1>
-<p>If you are looking for a high-quality and up-to-date English textbook for your 6th grade students, you may want to check out 6 cı sinif ingilis dili metodik vəsait pdf. This is a popular and widely used book that covers all the aspects of English language teaching and learning for this level. In this article, we will tell you everything you need to know about this book, including what it is, why it is important, and how to download it for free.</p>
-<h2>6 cı sinif ingilis dili metodik vəsait pdf</h2><br /><p><b><b>Download File</b> &#10031; <a href="https://ssurll.com/2uNVYU">https://ssurll.com/2uNVYU</a></b></p><br /><br />
-<h2>What is 6 cı sinif ingilis dili metodik vəsait pdf?</h2>
-<p>6 cı sinif ingilis dili metodik vəsait pdf is a book that was published in 2018 by Təhsil nəşriyyatı, a leading educational publisher in Azerbaijan. The book was written by F.Hüseynova and N.Bağırova, two experienced and qualified English teachers who have been teaching at various schools and universities in the country. The book is designed for 6th grade students who are learning English as their main foreign language.</p>
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-<p>The authors of the book are F.Hüseynova and N.Bağırova, who have been teaching English for more than 20 years. They have both graduated from Baku State University with a degree in English Philology and have obtained their master's degrees in Teaching English as a Foreign Language (TEFL) from Qafqaz University. They have also participated in various professional development programs and workshops both locally and internationally. They have written several other books and articles on English language teaching and learning.</p>
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-<h3>The main features and benefits of the book</h3>
-<p>One of the main features of the book is that it follows a communicative approach to language teaching, which means that it emphasizes the students' ability to communicate in real-life situations using authentic and meaningful language. The book also incorporates various activities and tasks that promote interaction, collaboration, creativity, critical thinking, and problem-solving skills among students. The book also uses a variety of texts, audios, videos, images, games, songs, and stories that appeal to different learning styles and preferences.</p>
-<p>Some of the benefits of using this book are that it helps students to:</p>
-<ul>
-<li>Improve their English proficiency level according to the Common European Framework of Reference (CEFR)</li> <li>Expand their vocabulary and grammar knowledge and usage</li>
-<li>Develop their listening, speaking, reading, and writing skills in an integrated way</li>
-<li>Enhance their cultural awareness and intercultural competence</li>
-<li>Enjoy learning English in a fun and motivating way</li>
-</ul>
-<h3>How to use the book effectively in teaching English</h3>
-<p>The book is designed to be used as the main coursebook for 6th grade English classes, but it can also be adapted and supplemented according to the needs and interests of the students and teachers. The book provides clear instructions and guidelines for each lesson and activity, as well as suggestions for differentiation, assessment, and homework. The book also comes with a teacher's guide, a workbook, an audio CD, and a DVD that contain additional materials and resources for teaching and learning.</p>
-<p>Some of the tips for using the book effectively are:</p>
-<ul>
-<li>Follow the sequence and structure of the units and lessons, but feel free to modify or skip some parts if necessary</li>
-<li>Use the audio and video materials to expose the students to authentic and varied language input</li>
-<li>Encourage the students to participate actively and interact with each other in pairs or groups</li>
-<li>Provide feedback and correction in a positive and supportive way</li>
-<li>Monitor the students' progress and achievement using the tests and quizzes in the book or the teacher's guide</li>
-</ul>
-<h2>Why is 6 cı sinif ingilis dili metodik vəsait pdf important for English teachers and learners?</h2>
-<p>6 cı sinif ingilis dili metodik vəsait pdf is not just another English textbook. It is a valuable and essential tool for English teachers and learners who want to improve their English skills and knowledge in a modern and effective way. The book has several advantages that make it stand out from other books in the market.</p>
-<h3>The advantages of using a modern and comprehensive English textbook</h3>
-<p>The book is based on the latest research and trends in English language teaching and learning. It reflects the current needs and expectations of 6th grade students who are living in a globalized and digitalized world. The book also covers all the topics and skills that are required for the students to succeed in their academic and personal lives. The book is comprehensive, meaning that it provides everything that the students need to learn English in one place.</p>
-<h3>The alignment of the book with the national curriculum and standards</h3>
-<p>The book is aligned with the national curriculum and standards for 6th grade English education in Azerbaijan. The book follows the objectives and outcomes that are specified by the Ministry of Education. The book also prepares the students for the national exams that they will take at the end of the year. The book is compatible with the CEFR levels, which are internationally recognized benchmarks for measuring language proficiency.</p>
-<h3>The feedback and reviews from teachers and students who have used the book</h3>
-<p>The book has received positive feedback and reviews from teachers and students who have used it in their classes. The teachers have praised the book for its quality, content, design, layout, usability, flexibility, and suitability. The teachers have also reported that the book has helped them to improve their teaching methods and strategies, as well as their professional development. The students have enjoyed using the book because it has made learning English more fun, interesting, relevant, challenging, rewarding, and meaningful.</p> <h2>How to download 6 cı sinif ingilis dili metodik vəsait pdf for free?</h2>
-<p>If you are interested in getting a copy of 6 cı sinif ingilis dili metodik vəsait pdf, you may be wondering how to download it for free. There are several ways to do that, but you need to be careful and responsible when choosing your source and platform. Here are some of the options and tips for downloading the book safely and legally.</p>
-<h3>The official website of the publisher and the online library</h3>
-<p>The best and most reliable way to download the book is to visit the official website of the publisher, Təhsil nəşriyyatı, at <a href="">www.tehsil-nashriyyati.az</a>. There you can find the book in the catalog and download it as a pdf file. You can also access the online library of the publisher, where you can find other books and materials related to English education. The online library requires registration, but it is free and easy to use.</p>
-<h3>The alternative sources and platforms for downloading the book</h3>
-<p>Another way to download the book is to use alternative sources and platforms that offer free downloads of books and documents. Some of the popular ones are <a href="">www.pdfdrive.com</a>, <a href="">www.scribd.com</a>, and <a href="">www.academia.edu</a>. These platforms have a large collection of books and materials that you can search by title, author, or keyword. You can also browse by category, language, or popularity. However, you need to be aware that some of these platforms may require registration, subscription, or payment to access some of the files. You also need to check the quality and accuracy of the files before downloading them.</p>
-<h3>The tips and precautions for downloading the book safely and legally</h3>
-<p>Before you download any book or document from the internet, you need to take some precautions to avoid any problems or risks. Here are some of the tips and precautions that you should follow:</p>
-<ul>
-<li>Make sure that your device has an antivirus software and a firewall that can protect it from viruses, malware, or hackers</li>
-<li>Make sure that the website or platform that you are using is secure, reputable, and trustworthy. Look for signs such as HTTPS, padlock icon, or verified badge</li>
-<li>Make sure that the file that you are downloading is in pdf format and has a reasonable size and quality. Avoid files that are too large, too small, or have strange extensions</li>
-<li>Make sure that you have permission or authorization to download the file. Respect the intellectual property rights and copyrights of the authors and publishers</li>
-<li>Make sure that you use the file for personal or educational purposes only. Do not share, distribute, or sell the file without consent or credit</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In conclusion, 6 cı sinif ingilis dili metodik vəsait pdf is a great book for 6th grade English teachers and learners who want to improve their English skills and knowledge in a modern and effective way. The book is written by experienced and qualified English teachers who have created a comprehensive and communicative coursebook that covers all the topics and skills that are required for this level. The book is also aligned with the national curriculum and standards for English education in Azerbaijan, as well as with the CEFR levels. The book has received positive feedback and reviews from teachers and students who have used it in their classes.</p>
-<p>If you want to get a copy of this book, you can download it for free from various sources and platforms on the internet. However, you need to be careful and responsible when choosing your source and platform, and follow some tips and precautions to download the book safely and legally.</p>
-<p>We hope that this article has helped you to learn more about 6 cı sinif ingilis dili metodik vəsait pdf and how to download it for free. If you have any questions or comments, please feel free to contact us or leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>What is 6 cı sinif ingilis dili metodik vəsait pdf?</b></li>
-<li>It is a popular and widely used English textbook for 6th grade students in Azerbaijan.</li>
-<li><b>Who are the authors of 6 cı sinif ingilis dili metodik vəsait pdf?</b></li>
-<li>The authors are F.Hüseynova and N.Bağırova, two experienced and qualified English teachers who have been teaching at various schools and universities in the country.</li>
-<li><b>What are the main features and benefits of 6 cı sinif ingilis dili metodik vəsait pdf?</b></li>
-<li>The book follows a communicative approach to language teaching, incorporates various activities and tasks that promote interaction, collaboration, creativity, critical thinking, and problem-solving skills, uses a variety of texts, audios, videos, images, games, songs, and stories that appeal to different learning styles and preferences, helps students to improve their English proficiency level according to the CEFR, expands their vocabulary and grammar knowledge and usage, develops their listening, speaking, reading, and writing skills in an integrated way, enhances their cultural awareness and intercultural competence, and makes learning English more fun and motivating.</li>
-<li><b>How can I download 6 cı sinif ingilis dili metodik vəsait pdf for free?</b></li>
-<li>You can download the book for free from the official website of the publisher, Təhsil nəşriyyatı, at <a href="">www.tehsil-nashriyyati.az</a>, or from alternative sources and platforms such as <a href="">www.pdfdrive.com</a>, <a href="">www.scribd.com</a>, and <a href="">www.academia.edu</a>. However, you need to be careful and responsible when choosing your source and platform, and follow some tips and precautions to download the book safely and legally.</li>
-<li><b>What are some of the tips and precautions for downloading the book safely and legally?</b></li>
-<li>Some of the tips and precautions are: make sure that your device has an antivirus software and a firewall that can protect it from viruses, malware, or hackers; make sure that the website or platform that you are using is secure, reputable, and trustworthy; make sure that the file that you are downloading is in pdf format and has a reasonable size and quality; make sure that you have permission or authorization to download the file; respect the intellectual property rights and copyrights of the authors and publishers; make sure that you use the file for personal or educational purposes only; do not share, distribute, or sell the file without consent or credit.</li>
-<li><b>Where can I find more information about 6 cı sinif ingilis dili metodik vəsait pdf?</b></li>
-<li>You can find more information about the book on the official website of the publisher, Təhsil nəşriyyatı, or on the online library of the publisher. You can also contact the authors or the publisher directly via email or phone. You can also read some of the reviews and testimonials from teachers and students who have used the book on various websites or blogs.</li>
-</ul></p> 401be4b1e0<br />
-<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bajoterra 1 APK un juego de simulacin y aventura en el mundo de Bajoterra.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bajoterra 1 APK un juego de simulacin y aventura en el mundo de Bajoterra.md
deleted file mode 100644
index f2f711189a0d9f58b263e8035766e69bf766011e..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bajoterra 1 APK un juego de simulacin y aventura en el mundo de Bajoterra.md	
+++ /dev/null
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-<br />
-<h1>Bajoterra 1 APK: A Fun and Action-Packed Puzzle Game Based on the Popular TV Series</h1>
-<p>If you are a fan of the animated TV series Slugterra, you will love Bajoterra 1 APK, a puzzle game that lets you become the best slug slinger of all time. In this game, you will play as Eli Shane, the hero of Slugterra, and collect, transform, and shoot slugs at your enemies. You will also explore the underground world of Slugterra, fight against various villains, and stop the evil Dr. Blakk. In this article, we will tell you everything you need to know about Bajoterra 1 APK, including what it is, how to download and install it, and how to play and win every battle.</p>
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- <h2>What is Bajoterra 1 APK?</h2>
-<p>Bajoterra 1 APK is a puzzle game based on the popular TV series Slugterra, which follows the adventures of Eli Shane and his friends as they protect the underground world of Slugterra from evil forces. In this game, you will use your blaster to shoot slugs at your opponents, who will also shoot slugs at you. Slugs are small creatures that have different powers and abilities depending on their element and type. When you shoot a slug, it will transform into a magical beast that will attack your enemy. The key to becoming the best slug slinger is to choose the right slugs for each round and know how to use them.</p>
- <h3>The story and gameplay of Bajoterra 1 APK</h3>
-<p>The story of Bajoterra 1 APK follows the plot of the TV series, where Eli Shane and his friends have to stop Dr. Blakk from using dark water to corrupt slugs and take over Slugterra. You will have to face various enemies, such as Dr. Blakk's henchmen, other slug slingers, and wild creatures. You will also have to collect new slugs and unlock their powers as you play.</p>
-<p>The gameplay of Bajoterra 1 APK is simple but fun. You will have to match tiles on the board to power up your slugs and then tap on their icons to load them into your blaster. Then, you will watch them transform and attack your enemies. The more tiles you match, the more powerful your slugs will be. You will also have to watch out for your enemies' slugs and dodge their attacks. You can also combine two slugs to create a fusion shot, which will have a stronger effect.</p>
- <h3>The features and benefits of Bajoterra 1 APK</h3>
-<p>Bajoterra 1 APK has many features and benefits that make it a great game for fans of Slugterra and puzzle games in general. Some of them are:</p>
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-<ul>
-<li>It has amazing graphics and sound effects that capture the essence of the TV series.</li>
-<li>It has over 100 slugs to collect and use, each with its own unique power and personality.</li>
-<li>It has many levels and modes to play, such as story mode, challenge mode, and multiplayer mode.</li>
-<li>It has a store where you can buy special items to boost your game, such as blaster mods, slug chargers, new characters, and more.</li>
-<li>It is free to download and play, but it also offers in-app purchases for extra features.</li>
-</ul>
- <h2>How to download and install Bajoterra 1 APK on your device?</h2 <h2>How to download and install Bajoterra 1 APK on your device?</h2>
-<p>There are two ways to download and install Bajoterra 1 APK on your device: from the App Store or from a third-party website. Here are the steps for both methods:</p>
- <h3>The steps to download and install Bajoterra 1 APK from the App Store</h3>
-<p>If you have an iOS device, you can download and install Bajoterra 1 APK from the App Store easily. Just follow these steps:</p>
-<ol>
-<li>Open the App Store on your device and search for Bajoterra 1 APK.</li>
-<li>Tap on the app icon and then tap on Get or Install.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Once the app is installed, tap on Open or find it on your home screen.</li>
-<li>Enjoy playing Bajoterra 1 APK on your device.</li>
-</ol>
- <h3>The steps to download and install Bajoterra 1 APK from a third-party website</h3>
-<p>If you have an Android device, you can download and install Bajoterra 1 APK from a third-party website. However, you need to be careful and only use reputable sources. You also need to enable unknown sources on your device settings. Here are the steps:</p>
-<ol>
-<li>Go to your device settings and tap on Security or Privacy.</li>
-<li>Find the option to allow unknown sources or install unknown apps and enable it.</li>
-<li>Go to a website that offers Bajoterra 1 APK, such as [text](^1^), [text](^2^), or [text](^3^).</li>
-<li>Find the link to download the APK file and tap on it.</li>
-<li>Accept any pop-ups or warnings that may appear.</li>
-<li>Wait for the file to download on your device.</li>
-<li>Open the file manager app on your device and find the downloaded APK file.</li>
-<li>Tap on the file and then tap on Install.</li>
-<li>Wait for the app to install on your device.</li>
-<li>Once the app is installed, tap on Open or find it on your app drawer.</li>
-<li>Enjoy playing Bajoterra 1 APK on your device.</li>
-</ol>
- <h2>How to play Bajoterra 1 APK and win every battle?</h2>
-<p>Bajoterra 1 APK is a fun and challenging game that requires skill and strategy. You need to know how to play and win every battle if you want to become the best slug slinger. Here are some tips and tricks to help you:</p>
- <h3>The basics of Bajoterra 1 APK gameplay</h3>
-<p>The game consists of rounds where you have to shoot slugs at your enemies and avoid their slugs. You have a limited amount of time and slugs per round, so you need to be fast and accurate. You can see your health bar, slug icons, timer, score, and enemy health bar at the top of the screen. You can also see the tiles at the bottom of the screen, which you need to match to power up your slugs. To play, you need to do the following:</p>
-<ul>
-<li>Swipe left or right on the tiles to match three or more of the same color.</li>
-<li>Tap on a slug icon to load it into your blaster.</li>
-<li>Aim at your enemy by tilting your device left or right.</li>
-<li>Tap on the screen to shoot your slug.</li>
-<li>Dodge your enemy's slugs by tilting your device up or down.</li>
-</ul>
- <h3>The best strategies and tactics to collect, upgrade, and use your slugs</h3 <h3>The best strategies and tactics to collect, upgrade, and use your slugs</h3>
-<p>Slugs are the most important part of Bajoterra 1 APK, as they determine your attack power and abilities. You need to collect, upgrade, and use your slugs wisely to win every battle. Here are some strategies and tactics to help you:</p>
-<ul>
-<li>Collect as many slugs as you can by playing the story mode, the challenge mode, or the multiplayer mode. You can also buy slugs from the store or get them from chests or rewards.</li>
-<li>Upgrade your slugs by matching tiles of their color or using slug chargers. Upgrading your slugs will increase their power, speed, and special effects.</li>
-<li>Use your slugs according to their element and type. Each slug has a different element (fire, water, earth, air, energy, or dark) and type (common, rare, ultra rare, or legendary). Some slugs are more effective against certain enemies or in certain situations. For example, fire slugs are good against water enemies, but bad against earth enemies. Legendary slugs are more powerful than common slugs, but they also take longer to charge.</li>
-<li>Combine two slugs to create a fusion shot. Fusion shots are more powerful and have unique effects that can help you in battle. For example, combining a fire slug and an air slug will create a firestorm shot that will create a blast of fire and wind. To create a fusion shot, you need to match tiles of both colors and then tap on both slug icons.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Bajoterra 1 APK is a fun and action-packed puzzle game that will keep you entertained for hours. You will enjoy the amazing graphics, the exciting story, the diverse slugs, and the challenging battles. You will also learn how to download and install Bajoterra 1 APK on your device, and how to play and win every battle with the best strategies and tactics. If you are a fan of Slugterra or puzzle games in general, you should definitely give Bajoterra 1 APK a try. You will not regret it!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Bajoterra 1 APK:</p>
-<ol>
-<li>What is the difference between Bajoterra 1 APK and Slugterra: Slug it Out 1?</li>
-<p>Bajoterra 1 APK is the Spanish version of Slugterra: Slug it Out 1, which is the English version of the game. They are essentially the same game, but with different languages and names.</p>
-<li>Is Bajoterra 1 APK safe to download and install?</li>
-<p>Bajoterra 1 APK is safe to download and install from the App Store or from reputable third-party websites. However, you should always be careful when downloading apps from unknown sources and check for any viruses or malware.</p>
-<li>How can I get more coins and gems in Bajoterra 1 APK?</li>
-<p>You can get more coins and gems in Bajoterra 1 APK by playing the game regularly, completing missions, winning battles, opening chests, watching ads, or buying them with real money.</p>
-<li>How can I play Bajoterra 1 APK with my friends?</li>
-<p>You can play Bajoterra 1 APK with your friends by using the multiplayer mode. You can either join a random match or create a private match with your friends. You will need an internet connection to play multiplayer mode.</p>
-<li>How can I contact the developers of Bajoterra 1 APK?</li>
-<p>You can contact the developers of Bajoterra 1 APK by using the feedback option in the game settings or by visiting their website at [text].</p>
-</ol></p> 197e85843d<br />
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\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Carx Street OBB APK  OBB Download for Android - Updated 2023.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Carx Street OBB APK  OBB Download for Android - Updated 2023.md
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--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Carx Street OBB APK  OBB Download for Android - Updated 2023.md	
+++ /dev/null
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-<br />
-<h1>How to Download OBB File for CarX Street Android Game</h1>
-<p>If you are a fan of street racing games, you might have heard of CarX Street, a realistic and dynamic open world game that lets you become a legend of Sunset City. However, you might also have noticed that the game requires an additional file called OBB file to run smoothly on your Android device. In this article, we will explain what is an OBB file, why do you need it, and how to download it for CarX Street Android game.</p>
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- <h2>What is CarX Street Android Game?</h2>
-<p>CarX Street is a game developed by CarX Technologies, the makers of CarX Drift Racing 2. It is a game that allows you to embrace the freedom of being a street racer in a dynamic open world. You can accept the challenge and become the legend of Sunset City by competing in realistic races on highways and city streets, as well as top-speed drift races. You can also build the car of your dreams using part tuning that unlocks all the physics of CarX Technology car behavior. You can explore every corner of the enormous world of CarX Street and enjoy the exciting car races that will leave you exhilarated.</p>
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-<p>Some of the features of CarX Street Android game are:</p>
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-<li>At least 1 GB of RAM and 4 GB of free storage space.</li>
-<li>A stable internet connection.</li>
-<li>An OBB file that contains additional data for the game.</li>
-</ul>
- <h2>What is OBB File and Why Do You Need It?</h2>
- <h3>Definition of OBB File</h3>
-<p>An OBB file is an expansion file used by some Android apps distributed using the Google Play online store. It contains data that is not stored in the application's main package (APK file), such as graphics, media files, and other large program assets. OBB files are often stored in a device's shared storage folder (the SD card or USB-mountable partition; also known as the "external" storage) where your app can access them.</p>
- <h3>Benefits of OBB File</h3>
-<p>The benefits of using an OBB file are:</p>
-<ul>
-<li>It allows apps to have more than 100 MB of compressed download size, which is the limit imposed by Google Play for APK files.</li>
-<li>It reduces the bandwidth consumption during the download process, as users only download the necessary data for their device.</li>
-<li>It improves the performance and quality of the app, as it can access high-fidelity graphics, media files, or other large assets from the OBB file instead of loading them from the APK file.</li>
-</ul>
- <h2>How to Download OBB File for CarX Street Android Game?</h2>
-<p>Now that you know what is an OBB file and why do you need it, let's see how to download it for CarX Street Android game. The process is simple and consists of three steps:</p>
- <h3>Step 1: Download the APK File from Google Play Store</h3>
-<p>The first step is to download the APK file of CarX Street Android game from the Google Play Store. This is the main package that contains the app's code and basic resources. You can find the link to the game's page on Google Play Store here: [CarX Street - Apps on Google Play]. Once you are on the page, tap on the Install button and wait for the download to finish.</p>
- <h3>Step 2: Download the OBB File from a Trusted Source</h3>
-<p>The second step is to download the OBB file of CarX Street Android game from a trusted source. This is the expansion file that contains the additional data for the game, such as graphics, media files, and other large assets. You can find the link to the OBB file here: [CarX Street OBB File]. Make sure you download the OBB file that matches the version of the APK file you downloaded in step 1. Once you have downloaded the OBB file, you will get a ZIP file that you need to extract using a file manager app.</p>
- <h3>Step 3: Install the APK File and Copy the OBB File to the Right Location</h3>
-<p>The third step is to install the APK file and copy the OBB file to the right location on your device. To install the APK file, you need to enable the Unknown Sources option in your device's settings. This will allow you to install apps from sources other than Google Play Store. To enable this option, go to Settings > Security > Unknown Sources and toggle it on. Then, locate the APK file you downloaded in step 1 using a file manager app and tap on it to install it.</p>
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-<p>To copy the OBB file to the right location, you need to find the folder where your app can access it. The folder name should be in this format: <code>Android/obb/<package-name>/</code>, where <code><package-name></code> is the name of your app's package, which is usually similar to its ID on Google Play Store. For CarX Street Android game, the folder name should be <code>Android/obb/com.carxtech.carxstreet/</code>. If you don't see this folder, you can create it manually using a file manager app. Then, copy the OBB file (which should have a name like <code>main.<version-code>.com.carxtech.carxstreet.obb</code>) to this folder.</p>
- <h2>How to Play CarX Street Android Game with OBB File?</h2>
-<p>After completing the three steps above, you are ready to play CarX Street Android game with OBB file. Here is how:</p>
- <h3>Launch the Game and Enjoy the High-Quality Graphics and Sound</h3>
-<p>To launch the game, simply tap on its icon on your device's home screen or app drawer. The game will verify the OBB file and load it automatically. You will then see the game's main menu, where you can choose your mode of play, customize your car, or explore Sunset City. You will notice that the game has high-quality graphics and sound that enhance your gaming experience.</p>
- <h3>Tips and Tricks for Playing CarX Street Android Game</h3>
-<p>To make the most out of CarX Street Android game, here are some tips and tricks that you can use:</p>
-<ul>
-<li>Use different camera angles to get a better view of your car and surroundings.</li>
-<li>Use nitro boosters to gain speed and overtake your opponents.</li>
-<li>Use drift mode to perform spectacular drifts and earn more points.</li>
-<li>Upgrade your car's parts and performance using coins and diamonds that you earn from races.</li>
-<li>Join clubs and participate in club events to get rewards and bonuses.</li>
-<li>Challenge other players online and show off your skills.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, CarX Street Android game is a realistic and dynamic open world street racing game that requires an OBB file to run smoothly on your device. To download an OBB file for CarX Street Android game, you need to follow three simple steps: download the APK file from Google Play Store, download the OBB file from a trusted source, and install the APK file and copy the OBB file to the right location on your device. Once you have done that, you can launch the game and enjoy the high-quality graphics and sound, as well as the exciting gameplay. You can also use some tips and tricks to improve your performance and have more fun. We hope this article has helped you to download OBB file for CarX Street Android game and play it without any issues.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about CarX Street Android game and OBB file:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>What is the size of the OBB file for CarX Street Android game?</td>
-<td>The size of the OBB file for CarX Street Android game is about 1.5 GB.</td>
-</tr>
-<tr>
-<td>What if I delete the OBB file by mistake?</td>
-<td>If you delete the OBB file by mistake, you will need to download it again from a trusted source and copy it to the right location on your device.</td>
-</tr>
-<tr>
-<td>Can I play CarX Street Android game without an OBB file?</td>
-<td>No, you cannot play CarX Street Android game without an OBB file, as it contains essential data for the game.</td>
-</tr>
-<tr>
-<td>Can I share the OBB file with my friends?</td>
-<td>Yes, you can share the OBB file with your friends, but they will also need to download the APK file from Google Play Store and install it on their devices.</td>
-</tr>
-<tr>
-<td>Can I move the OBB file to another location on my device?</td>
-<td>No, you cannot move the OBB file to another location on your device, as it needs to be in the specific folder where your app can access it.</td>
-</tr>
-</table></p> 401be4b1e0<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Monoposto Full for PC A Guide to Install and Run the Game.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Monoposto Full for PC A Guide to Install and Run the Game.md
deleted file mode 100644
index 0d4e00a9dc464e7fddc26fd0df3e988e3c0c40c5..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Monoposto Full for PC A Guide to Install and Run the Game.md	
+++ /dev/null
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-
-<h1>Download Monoposto Full: How to Enjoy the Best Independent Formula Racing Game on Your Device</h1>
-<p>If you are a fan of formula racing games, you might have heard of Monoposto, an amazing independent racing game with single seater open-wheel cars. Monoposto is developed by Marco Pesce, a passionate game developer who created this game as a tribute to his love for motorsport. In this article, we will tell you what Monoposto is, why you should download the full version, and how to do it.</p>
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-<h2>What is Monoposto?</h2>
-<p>Monoposto is a realistic and immersive racing game that simulates the experience of driving a formula car on 24 different tracks around the world. You can compete in the new 2023 season, with online multiplayer duels, quick races, single races, and championship modes. You can also customize your car and driver, choose your camera view, and adjust your driving options. Monoposto has dynamic weather effects, pit stops, car repairs, and a spectator TV mode that lets you watch the race from different angles.</p>
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-<h2>Why download Monoposto full version?</h2>
-<p>Monoposto is available for free on Google Play Store and App Store for Android and iOS devices respectively. However, the free version has some limitations that might affect your enjoyment of the game. Here are some of the benefits of downloading the full version of Monoposto:</p>
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-<p>The free version of Monoposto only lets you play on 8 tracks and in 2 modes: quick race and single race. By downloading the full version, you can unlock all 24 tracks and all 4 modes: quick race, single race, championship, and online multiplayer duel. You can also access the spectator TV mode and watch the races from different angles.</p>
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- FAQs Q: How much does Monoposto full version cost? A: Monoposto full version costs $2.99 for both Android and iOS devices. Q: What are the system requirements for Monoposto? A: Monoposto requires Android 5.0 or later or iOS 10.0 or later. It also requires at least 500 MB of free storage space on your device. Q: How can I contact the developer of Monoposto? A: You can contact the developer of Monoposto by sending an email to monopostogame@gmail.com or by visiting his website at https://www.monopostogame.com/. Q: How can I rate and review Monoposto? A: You can rate and review Monoposto by going to Google Play Store or App Store on your device and finding the game page. You can then tap on the stars to rate it and write your feedback in the review section. Q: How can I learn more about formula racing? A: You can learn more about formula racing by visiting websites like https://www.formula1.com/ or https://www.fia.com/sport/championships/f1. You can also watch formula racing events on TV or online platforms like https://www.f1tv.formula1.com/.</p> 197e85843d<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Free RPG Essential Kit A Powerful and Easy-to-Customize Game Development Solution.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Free RPG Essential Kit A Powerful and Easy-to-Customize Game Development Solution.md
deleted file mode 100644
index ebbd2285d907deafb61e1dfcfd604273cc29ec09..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Free RPG Essential Kit A Powerful and Easy-to-Customize Game Development Solution.md	
+++ /dev/null
@@ -1,25 +0,0 @@
-<br />
-<h1>RPG Essential Kit: A Free Download for Game Developers</h1>
- If you are a game developer who wants to create your own role-playing games (RPGs) in Unity, you might be interested in RPG Essential Kit. This is a game toolkit that provides you with everything you need to start making your own RPGs in Unity. In this article, we will explain what RPG Essential Kit is, what features it offers, how to download it for free, why you should use it, how to use it, and some frequently asked questions. <h2>What is RPG Essential Kit?</h2>
- RPG Essential Kit is a game toolkit that was created by Blink, a publisher on the Unity Asset Store. It is designed to help game developers create RPGs in Unity, without having to code everything from scratch. It includes features such as: <h3>A game toolkit for creating RPGs in Unity</h3>
- - Character creation and customization - Inventory and equipment system - Quest and dialogue system - Combat and skill system - Leveling and progression system - Save and load system - User interface and sound effects <h3>Features of RPG Essential Kit</h3>
- Some of the features that RPG Essential Kit offers are: - Compatible with the Built-in Render Pipeline, the Universal Render Pipeline, and the High Definition Render Pipeline - Modular and customizable, allowing you to tweak the settings and add your own assets and scripts - Easy to use, with drag-and-drop components and prefabs - Well documented, with a detailed manual and video tutorials - Supported by the developer, with regular updates and bug fixes <h3>How to download RPG Essential Kit for free</h3>
- RPG Essential Kit is normally priced at $499.99 on the Unity Asset Store. However, you can download it for free if you meet the following conditions: - You have a Unity account and a valid email address - You sign up for the Unity Learn Premium subscription, which gives you access to hundreds of courses and projects on game development - You redeem the code "RPGKIT" on the Unity Asset Store before June 30, 2023 By doing this, you will get a free license for RPG Essential Kit, as well as a one-month free trial of Unity Learn Premium. After the trial period ends, you can cancel your subscription or continue paying $15 per month. You will still be able to use RPG Essential Kit even if you cancel your subscription. <h2>Why use RPG Essential Kit?</h2>
- RPG Essential Kit is a great tool for game developers who want to create their own RPGs in Unity. Here are some of the reasons why you should use it: <h3>Benefits of using RPG Essential Kit</h3>
- - It saves you time and effort, by providing you with ready-made features and systems that you can use in your game - It helps you learn and improve your skills, by showing you how to implement various aspects of an RPG in Unity - It gives you flexibility and creativity, by allowing you to customize and modify the features according to your needs and preferences <h3>Examples of games made with RPG Essential Kit</h3>
- Some of the games that have been made with RPG Essential Kit are: - The Lost Kingdoms, a fantasy action-RPG that features an open world, dynamic quests, crafting, and multiplayer - The Last Hope, a sci-fi survival-RPG that features a post-apocalyptic setting, stealth mechanics, crafting, and base building - The Legend of Zelda: Breath of the Wild Clone, a fan-made project that recreates some of the elements of the popular Nintendo game in Unity <h3>Alternatives to RPG Essential Kit</h3>
- If you are looking for other game toolkits that can help you create RPGs in Unity, you might want to check out these alternatives: - RPG Builder, a game toolkit that lets you create RPGs without coding, with features such as character creation, inventory, quests, combat, skills, and more - ORK Framework, a game toolkit that offers a complete solution for creating RPGs in Unity, with features such as game mechanics, game data, user interface, audio, and more - RPG All-In-One, a game toolkit that simplifies the process of creating RPGs in Unity, with features such as character customization, inventory, quests, combat, dialogue, and more <h2>How to use RPG Essential Kit?</h2>
- If you have downloaded RPG Essential Kit for free and want to start using it in your game project, here are some steps that you need to follow: <h3>Requirements and installation</h3>
- To use RPG Essential Kit, you need to have: - Unity 2019.4 or higher - A computer that meets the minimum system requirements for Unity - A basic knowledge of Unity and C# To install RPG Essential Kit, you need to: - Open Unity and create a new project or open an existing one - Go to the Asset Store window and search for RPG Essential Kit - Click on the Download button and then on the Import button - Wait for the import process to finish and then click on the Accept All button - You should see a folder called RPG Essential Kit in your Project window <h3>Documentation and tutorials</h3>
- To learn how to use RPG Essential Kit, you can refer to the documentation and tutorials that are provided by the developer. You can find them in: - The Manual folder inside the RPG Essential Kit folder in your Project window - The official website of RPG Essential Kit - The YouTube channel of Blink The documentation and tutorials will guide you through the basics of using RPG Essential Kit, such as setting up your project, creating your characters, adding items and quests, designing your combat system, and more. <h3>Tips and tricks for using RPG Essential Kit</h3>
- To make the most out of RPG Essential Kit, here are some tips and tricks that you can use: - Use the Demo scene as a reference for how to set up your own scene - Use the Prefabs folder to find ready-made game objects that you can drag and drop into your scene - Use the Scriptable Objects folder to find and edit the game data that controls the behavior of your game features - Use the Settings folder to adjust the global settings of your game features - Use the Tools menu to access some useful utilities such as creating new items, quests, skills, etc. - Use the Debug menu to test your game features and find any errors or bugs <h2>Conclusion</h2>
- RPG Essential Kit is a game toolkit that can help you create your own RPGs in Unity. It offers a lot of features and systems that you can use in your game project. You can download it for free if you sign up for Unity Learn Premium and use the code "RPGKIT" before June 30, 2023. You can also check out some alternatives to RPG Essential Kit if you want to explore other options. To use RPG Essential Kit, you need to install it in your Unity project and follow the documentation and tutorials that are provided by the developer. You can also use some tips and tricks to make the most out of RPG Essential Kit. <h2>FAQs</h2>
- Here are some frequently asked questions about RPG Essential Kit: <h4>Q: Can I use RPG Essential Kit for commercial purposes?</h4>
-A: Yes, you can use RPG Essential Kit for commercial purposes as long as you comply with the Unity Asset Store EULA. <h4>Q: Can I modify or extend RPG Essential Kit?</h4>
-A: Yes, you can modify or extend RPG Essential Kit as much as you want. You can also add your own assets and scripts to customize your game. <h4>Q: Can I get support from the developer of RPG Essential Kit?</h4>
-A: Yes, you can get support from the developer of RPG Essential Kit by contacting them through their email address (blink@blink.com) or their Discord server. <h4>Q: Can I request new features or report bugs for RPG Essential Kit?</h4>
-A: Yes, you can request new features or report bugs for RPG Essential Kit by using their feedback form or their Trello board. <h4>Q: Can I share my games made with RPG Essential Kit?</h4>
-A: Yes, you can share your games made with RPG Essential Kit with other users by posting them on their showcase forum or their social media pages.</p>
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diff --git a/spaces/simsantonioii/MusicGen-Continuation/tests/__init__.py b/spaces/simsantonioii/MusicGen-Continuation/tests/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/simsantonioii/MusicGen-Continuation/tests/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/sino72/Passenger_Reconization/deep_sort/deep_sort/sort/track.py b/spaces/sino72/Passenger_Reconization/deep_sort/deep_sort/sort/track.py
deleted file mode 100644
index b81d7968bdb828ba43fd9a9968d40520f2d818b3..0000000000000000000000000000000000000000
--- a/spaces/sino72/Passenger_Reconization/deep_sort/deep_sort/sort/track.py
+++ /dev/null
@@ -1,199 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-
-
-class TrackState:
-    """
-    Enumeration type for the single target track state. Newly created tracks are
-    classified as `tentative` until enough evidence has been collected. Then,
-    the track state is changed to `confirmed`. Tracks that are no longer alive
-    are classified as `deleted` to mark them for removal from the set of active
-    tracks.
-
-    单个目标track状态的枚举类型。 
-    新创建的track分类为“Tentative”，直到收集到足够的证据为止。 
-    然后，跟踪状态更改为“Confirmed”。 
-    不再活跃的tracks被归类为“Deleted”，以将其标记为从有效集中删除。
-
-    """
-
-    Tentative = 1
-    Confirmed = 2
-    Deleted = 3
-
-
-class Track:
-    """
-    A single target track with state space `(x, y, a, h)` and associated
-    velocities, where `(x, y)` is the center of the bounding box, `a` is the
-    aspect ratio and `h` is the height.
-
-    具有状态空间（x，y，a，h）并关联速度的单个目标轨迹（track），
-    其中（x，y）是边界框的中心，a是宽高比，h是高度。
-
-    Parameters
-    ----------
-    mean : ndarray
-        Mean vector of the initial state distribution.
-        初始状态分布的均值向量
-    covariance : ndarray
-        Covariance matrix of the initial state distribution.
-        初始状态分布的协方差矩阵
-    track_id : int
-        A unique track identifier.
-        唯一的track标识符
-    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.
-        确认track之前的连续检测次数。 在第一个n_init帧中
-        第一个未命中的情况下将跟踪状态设置为“Deleted” 
-    max_age : int
-        The maximum number of consecutive misses before the track state is
-        set to `Deleted`.
-        跟踪状态设置为Deleted之前的最大连续未命中数；代表一个track的存活期限
-         
-    feature : Optional[ndarray]
-        Feature vector of the detection this track originates from. If not None,
-        this feature is added to the `features` cache.
-        此track所源自的检测的特征向量。 如果不是None，此feature已添加到feature缓存中。
-
-    Attributes
-    ----------
-    mean : ndarray
-        Mean vector of the initial state distribution.
-        初始状态分布的均值向量
-    covariance : ndarray
-        Covariance matrix of the initial state distribution.
-        初始状态分布的协方差矩阵
-    track_id : int
-        A unique track identifier.
-    hits : int
-        Total number of measurement updates.
-        测量更新总数
-    age : int
-        Total number of frames since first occurence.
-        自第一次出现以来的总帧数
-    time_since_update : int
-        Total number of frames since last measurement update.
-        自上次测量更新以来的总帧数
-    state : TrackState
-        The current track state.
-    features : List[ndarray]
-        A cache of features. On each measurement update, the associated feature
-        vector is added to this list.
-        feature缓存。每次测量更新时，相关feature向量添加到此列表中
-
-    """
-
-    def __init__(self, mean, covariance, track_id, n_init, max_age,
-                 feature=None):
-        self.mean = mean
-        self.covariance = covariance
-        self.track_id = track_id
-        # hits代表匹配上了多少次，匹配次数超过n_init，设置Confirmed状态
-        # hits每次调用update函数的时候+1 
-        self.hits = 1
-        self.age = 1 # 和time_since_update功能重复
-        # 每次调用predict函数的时候就会+1；   每次调用update函数的时候就会设置为0
-        self.time_since_update = 0
-
-        self.state = TrackState.Tentative # 初始化一个Track的时设置Tentative状态
-        # 每个track对应多个features, 每次更新都会将最新的feature添加到列表中
-        self.features = []
-        if feature is not None:
-            self.features.append(feature)
-
-        self._n_init = n_init 
-        self._max_age = max_age
-
-    def to_tlwh(self):
-        """Get current position in bounding box format `(top left x, top left y,
-        width, height)`.
-
-        Returns
-        -------
-        ndarray
-            The bounding box.
-
-        """
-        ret = self.mean[:4].copy()
-        ret[2] *= ret[3]
-        ret[:2] -= ret[2:] / 2
-        return ret
-
-    def to_tlbr(self):
-        """Get current position in bounding box format `(min x, miny, max x,
-        max y)`.
-
-        Returns
-        -------
-        ndarray
-            The bounding box.
-
-        """
-        ret = self.to_tlwh()
-        ret[2:] = ret[:2] + ret[2:]
-        return ret
-
-    def predict(self, kf):
-        """Propagate the state distribution to the current time step using a
-        Kalman filter prediction step.
-        使用卡尔曼滤波器预测步骤将状态分布传播到当前时间步
-
-        Parameters
-        ----------
-        kf : kalman_filter.KalmanFilter
-            The Kalman filter.
-
-        """
-        self.mean, self.covariance = kf.predict(self.mean, self.covariance)
-        self.age += 1
-        self.time_since_update += 1
-
-    def update(self, kf, detection):
-        """Perform Kalman filter measurement update step and update the feature
-        cache.
-        执行卡尔曼滤波器测量更新步骤并更新feature缓存
-
-        Parameters
-        ----------
-        kf : kalman_filter.KalmanFilter
-            The Kalman filter.
-        detection : Detection
-            The associated detection.
-
-        """
-        self.mean, self.covariance = kf.update(
-            self.mean, self.covariance, detection.to_xyah())
-        self.features.append(detection.feature)
-
-        self.hits += 1
-        self.time_since_update = 0
-        # hits代表匹配上了多少次，匹配次数超过n_init，设置Confirmed状态
-        # 连续匹配上n_init帧的时候，转变为确定态
-        if self.state == TrackState.Tentative and self.hits >= self._n_init:
-            self.state = TrackState.Confirmed
-
-    def mark_missed(self):
-        """Mark this track as missed (no association at the current time step).
-        """
-        # 如果在处于Tentative态的情况下没有匹配上任何detection，转变为删除态。
-        if self.state == TrackState.Tentative:
-            self.state = TrackState.Deleted
-        elif self.time_since_update > self._max_age:
-            # 如果time_since_update超过max_age，设置Deleted状态
-            # 即失配连续达到max_age次数的时候，转变为删除态
-            self.state = TrackState.Deleted 
-
-    def is_tentative(self):
-        """Returns True if this track is tentative (unconfirmed).
-        """
-        return self.state == TrackState.Tentative
-
-    def is_confirmed(self):
-        """Returns True if this track is confirmed."""
-        return self.state == TrackState.Confirmed
-
-    def is_deleted(self):
-        """Returns True if this track is dead and should be deleted."""
-        return self.state == TrackState.Deleted
diff --git a/spaces/skf15963/summary/fengshen/models/zen2/configuration_zen2.py b/spaces/skf15963/summary/fengshen/models/zen2/configuration_zen2.py
deleted file mode 100644
index c7cbeb5657ea07b2a4e8429199a6091be39864c8..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/models/zen2/configuration_zen2.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# coding=utf-8
-# Copyright 2022 IDEA-CCNL and The HuggingFace Inc. 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.
-""" TransfoXLDenoise model configuration """
-
-from transformers.configuration_utils import PretrainedConfig
-
-
-class ZenConfig(PretrainedConfig):
-
-    """Configuration class to store the configuration of a `ZenModel`.
-    """
-
-    def __init__(self,
-                 #  vocab_size_or_config_json_file,
-                 #  word_vocab_size,
-                 hidden_size=768,
-                 num_hidden_layers=12,
-                 num_attention_heads=12,
-                 intermediate_size=3072,
-                 hidden_act="gelu",
-                 hidden_dropout_prob=0.1,
-                 attention_probs_dropout_prob=0.1,
-                 max_position_embeddings=512,
-                 type_vocab_size=2,
-                 initializer_range=0.02,
-                 layer_norm_eps=1e-12,
-                 num_hidden_word_layers=6,
-                 **kwargs):
-        """Constructs ZenConfig.
-
-        Args:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
-            hidden_size: Size of the encoder layers and the pooler layer.
-            num_hidden_layers: Number of hidden layers in the Transformer encoder.
-            num_attention_heads: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
-                layer in the Transformer encoder.
-            hidden_act: The non-linear activation function (function or string) in the
-                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
-            hidden_dropout_prob: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            attention_probs_dropout_prob: The dropout ratio for the attention
-                probabilities.
-            max_position_embeddings: The maximum sequence length that this model might
-                ever be used with. Typically set this to something large just in case
-                (e.g., 512 or 1024 or 2048).
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
-                `BertModel`.
-            initializer_range: The sttdev of the truncated_normal_initializer for
-                initializing all weight matrices.
-            layer_norm_eps: The epsilon used by LayerNorm.
-        """
-        # self.vocab_size = vocab_size_or_config_json_file
-        # self.word_size = word_vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.hidden_act = hidden_act
-        self.intermediate_size = intermediate_size
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
-        self.type_vocab_size = type_vocab_size
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.num_hidden_word_layers = num_hidden_word_layers
-        super().__init__(**kwargs)
diff --git a/spaces/songweig/rich-text-to-image/models/resnet.py b/spaces/songweig/rich-text-to-image/models/resnet.py
deleted file mode 100644
index 05c95ec7d5ddd23db37d2adcd47865de9e8139cb..0000000000000000000000000000000000000000
--- a/spaces/songweig/rich-text-to-image/models/resnet.py
+++ /dev/null
@@ -1,882 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-# `TemporalConvLayer` Copyright 2023 Alibaba DAMO-VILAB, The ModelScope Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from functools import partial
-from typing import Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from diffusers.models.activations import get_activation
-from diffusers.models.attention import AdaGroupNorm
-from models.attention_processor import SpatialNorm
-
-
-class Upsample1D(nn.Module):
-    """A 1D upsampling layer with an optional convolution.
-
-    Parameters:
-        channels (`int`):
-            number of channels in the inputs and outputs.
-        use_conv (`bool`, default `False`):
-            option to use a convolution.
-        use_conv_transpose (`bool`, default `False`):
-            option to use a convolution transpose.
-        out_channels (`int`, optional):
-            number of output channels. Defaults to `channels`.
-    """
-
-    def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_conv_transpose = use_conv_transpose
-        self.name = name
-
-        self.conv = None
-        if use_conv_transpose:
-            self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)
-        elif use_conv:
-            self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)
-
-    def forward(self, inputs):
-        assert inputs.shape[1] == self.channels
-        if self.use_conv_transpose:
-            return self.conv(inputs)
-
-        outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest")
-
-        if self.use_conv:
-            outputs = self.conv(outputs)
-
-        return outputs
-
-
-class Downsample1D(nn.Module):
-    """A 1D downsampling layer with an optional convolution.
-
-    Parameters:
-        channels (`int`):
-            number of channels in the inputs and outputs.
-        use_conv (`bool`, default `False`):
-            option to use a convolution.
-        out_channels (`int`, optional):
-            number of output channels. Defaults to `channels`.
-        padding (`int`, default `1`):
-            padding for the convolution.
-    """
-
-    def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.padding = padding
-        stride = 2
-        self.name = name
-
-        if use_conv:
-            self.conv = nn.Conv1d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
-        else:
-            assert self.channels == self.out_channels
-            self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride)
-
-    def forward(self, inputs):
-        assert inputs.shape[1] == self.channels
-        return self.conv(inputs)
-
-
-class Upsample2D(nn.Module):
-    """A 2D upsampling layer with an optional convolution.
-
-    Parameters:
-        channels (`int`):
-            number of channels in the inputs and outputs.
-        use_conv (`bool`, default `False`):
-            option to use a convolution.
-        use_conv_transpose (`bool`, default `False`):
-            option to use a convolution transpose.
-        out_channels (`int`, optional):
-            number of output channels. Defaults to `channels`.
-    """
-
-    def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_conv_transpose = use_conv_transpose
-        self.name = name
-
-        conv = None
-        if use_conv_transpose:
-            conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)
-        elif use_conv:
-            conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1)
-
-        # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
-        if name == "conv":
-            self.conv = conv
-        else:
-            self.Conv2d_0 = conv
-
-    def forward(self, hidden_states, output_size=None):
-        assert hidden_states.shape[1] == self.channels
-
-        if self.use_conv_transpose:
-            return self.conv(hidden_states)
-
-        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
-        # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch
-        # https://github.com/pytorch/pytorch/issues/86679
-        dtype = hidden_states.dtype
-        if dtype == torch.bfloat16:
-            hidden_states = hidden_states.to(torch.float32)
-
-        # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
-        if hidden_states.shape[0] >= 64:
-            hidden_states = hidden_states.contiguous()
-
-        # if `output_size` is passed we force the interpolation output
-        # size and do not make use of `scale_factor=2`
-        if output_size is None:
-            hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
-        else:
-            hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
-
-        # If the input is bfloat16, we cast back to bfloat16
-        if dtype == torch.bfloat16:
-            hidden_states = hidden_states.to(dtype)
-
-        # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
-        if self.use_conv:
-            if self.name == "conv":
-                hidden_states = self.conv(hidden_states)
-            else:
-                hidden_states = self.Conv2d_0(hidden_states)
-
-        return hidden_states
-
-
-class Downsample2D(nn.Module):
-    """A 2D downsampling layer with an optional convolution.
-
-    Parameters:
-        channels (`int`):
-            number of channels in the inputs and outputs.
-        use_conv (`bool`, default `False`):
-            option to use a convolution.
-        out_channels (`int`, optional):
-            number of output channels. Defaults to `channels`.
-        padding (`int`, default `1`):
-            padding for the convolution.
-    """
-
-    def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.padding = padding
-        stride = 2
-        self.name = name
-
-        if use_conv:
-            conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
-        else:
-            assert self.channels == self.out_channels
-            conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
-
-        # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
-        if name == "conv":
-            self.Conv2d_0 = conv
-            self.conv = conv
-        elif name == "Conv2d_0":
-            self.conv = conv
-        else:
-            self.conv = conv
-
-    def forward(self, hidden_states):
-        assert hidden_states.shape[1] == self.channels
-        if self.use_conv and self.padding == 0:
-            pad = (0, 1, 0, 1)
-            hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
-
-        assert hidden_states.shape[1] == self.channels
-        hidden_states = self.conv(hidden_states)
-
-        return hidden_states
-
-
-class FirUpsample2D(nn.Module):
-    """A 2D FIR upsampling layer with an optional convolution.
-
-    Parameters:
-        channels (`int`):
-            number of channels in the inputs and outputs.
-        use_conv (`bool`, default `False`):
-            option to use a convolution.
-        out_channels (`int`, optional):
-            number of output channels. Defaults to `channels`.
-        fir_kernel (`tuple`, default `(1, 3, 3, 1)`):
-            kernel for the FIR filter.
-    """
-
-    def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
-        super().__init__()
-        out_channels = out_channels if out_channels else channels
-        if use_conv:
-            self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.use_conv = use_conv
-        self.fir_kernel = fir_kernel
-        self.out_channels = out_channels
-
-    def _upsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
-        """Fused `upsample_2d()` followed by `Conv2d()`.
-
-        Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
-        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
-        arbitrary order.
-
-        Args:
-            hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-            weight: Weight tensor of the shape `[filterH, filterW, inChannels,
-                outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
-            kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
-                (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
-            factor: Integer upsampling factor (default: 2).
-            gain: Scaling factor for signal magnitude (default: 1.0).
-
-        Returns:
-            output: Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same
-            datatype as `hidden_states`.
-        """
-
-        assert isinstance(factor, int) and factor >= 1
-
-        # Setup filter kernel.
-        if kernel is None:
-            kernel = [1] * factor
-
-        # setup kernel
-        kernel = torch.tensor(kernel, dtype=torch.float32)
-        if kernel.ndim == 1:
-            kernel = torch.outer(kernel, kernel)
-        kernel /= torch.sum(kernel)
-
-        kernel = kernel * (gain * (factor**2))
-
-        if self.use_conv:
-            convH = weight.shape[2]
-            convW = weight.shape[3]
-            inC = weight.shape[1]
-
-            pad_value = (kernel.shape[0] - factor) - (convW - 1)
-
-            stride = (factor, factor)
-            # Determine data dimensions.
-            output_shape = (
-                (hidden_states.shape[2] - 1) * factor + convH,
-                (hidden_states.shape[3] - 1) * factor + convW,
-            )
-            output_padding = (
-                output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH,
-                output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW,
-            )
-            assert output_padding[0] >= 0 and output_padding[1] >= 0
-            num_groups = hidden_states.shape[1] // inC
-
-            # Transpose weights.
-            weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW))
-            weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4)
-            weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))
-
-            inverse_conv = F.conv_transpose2d(
-                hidden_states, weight, stride=stride, output_padding=output_padding, padding=0
-            )
-
-            output = upfirdn2d_native(
-                inverse_conv,
-                torch.tensor(kernel, device=inverse_conv.device),
-                pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1),
-            )
-        else:
-            pad_value = kernel.shape[0] - factor
-            output = upfirdn2d_native(
-                hidden_states,
-                torch.tensor(kernel, device=hidden_states.device),
-                up=factor,
-                pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
-            )
-
-        return output
-
-    def forward(self, hidden_states):
-        if self.use_conv:
-            height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel)
-            height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
-        else:
-            height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
-
-        return height
-
-
-class FirDownsample2D(nn.Module):
-    """A 2D FIR downsampling layer with an optional convolution.
-
-    Parameters:
-        channels (`int`):
-            number of channels in the inputs and outputs.
-        use_conv (`bool`, default `False`):
-            option to use a convolution.
-        out_channels (`int`, optional):
-            number of output channels. Defaults to `channels`.
-        fir_kernel (`tuple`, default `(1, 3, 3, 1)`):
-            kernel for the FIR filter.
-    """
-
-    def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
-        super().__init__()
-        out_channels = out_channels if out_channels else channels
-        if use_conv:
-            self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.fir_kernel = fir_kernel
-        self.use_conv = use_conv
-        self.out_channels = out_channels
-
-    def _downsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
-        """Fused `Conv2d()` followed by `downsample_2d()`.
-        Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
-        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
-        arbitrary order.
-
-        Args:
-            hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-            weight:
-                Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be
-                performed by `inChannels = x.shape[0] // numGroups`.
-            kernel: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] *
-            factor`, which corresponds to average pooling.
-            factor: Integer downsampling factor (default: 2).
-            gain: Scaling factor for signal magnitude (default: 1.0).
-
-        Returns:
-            output: Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and
-            same datatype as `x`.
-        """
-
-        assert isinstance(factor, int) and factor >= 1
-        if kernel is None:
-            kernel = [1] * factor
-
-        # setup kernel
-        kernel = torch.tensor(kernel, dtype=torch.float32)
-        if kernel.ndim == 1:
-            kernel = torch.outer(kernel, kernel)
-        kernel /= torch.sum(kernel)
-
-        kernel = kernel * gain
-
-        if self.use_conv:
-            _, _, convH, convW = weight.shape
-            pad_value = (kernel.shape[0] - factor) + (convW - 1)
-            stride_value = [factor, factor]
-            upfirdn_input = upfirdn2d_native(
-                hidden_states,
-                torch.tensor(kernel, device=hidden_states.device),
-                pad=((pad_value + 1) // 2, pad_value // 2),
-            )
-            output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)
-        else:
-            pad_value = kernel.shape[0] - factor
-            output = upfirdn2d_native(
-                hidden_states,
-                torch.tensor(kernel, device=hidden_states.device),
-                down=factor,
-                pad=((pad_value + 1) // 2, pad_value // 2),
-            )
-
-        return output
-
-    def forward(self, hidden_states):
-        if self.use_conv:
-            downsample_input = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
-            hidden_states = downsample_input + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
-        else:
-            hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
-
-        return hidden_states
-
-
-# downsample/upsample layer used in k-upscaler, might be able to use FirDownsample2D/DirUpsample2D instead
-class KDownsample2D(nn.Module):
-    def __init__(self, pad_mode="reflect"):
-        super().__init__()
-        self.pad_mode = pad_mode
-        kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]])
-        self.pad = kernel_1d.shape[1] // 2 - 1
-        self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)
-
-    def forward(self, inputs):
-        inputs = F.pad(inputs, (self.pad,) * 4, self.pad_mode)
-        weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
-        indices = torch.arange(inputs.shape[1], device=inputs.device)
-        kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
-        weight[indices, indices] = kernel
-        return F.conv2d(inputs, weight, stride=2)
-
-
-class KUpsample2D(nn.Module):
-    def __init__(self, pad_mode="reflect"):
-        super().__init__()
-        self.pad_mode = pad_mode
-        kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]]) * 2
-        self.pad = kernel_1d.shape[1] // 2 - 1
-        self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)
-
-    def forward(self, inputs):
-        inputs = F.pad(inputs, ((self.pad + 1) // 2,) * 4, self.pad_mode)
-        weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
-        indices = torch.arange(inputs.shape[1], device=inputs.device)
-        kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
-        weight[indices, indices] = kernel
-        return F.conv_transpose2d(inputs, weight, stride=2, padding=self.pad * 2 + 1)
-
-
-class ResnetBlock2D(nn.Module):
-    r"""
-    A Resnet block.
-
-    Parameters:
-        in_channels (`int`): The number of channels in the input.
-        out_channels (`int`, *optional*, default to be `None`):
-            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
-        dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
-        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
-        groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
-        groups_out (`int`, *optional*, default to None):
-            The number of groups to use for the second normalization layer. if set to None, same as `groups`.
-        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
-        non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
-        time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config.
-            By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" or
-            "ada_group" for a stronger conditioning with scale and shift.
-        kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
-            [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
-        output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
-        use_in_shortcut (`bool`, *optional*, default to `True`):
-            If `True`, add a 1x1 nn.conv2d layer for skip-connection.
-        up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
-        down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
-        conv_shortcut_bias (`bool`, *optional*, default to `True`):  If `True`, adds a learnable bias to the
-            `conv_shortcut` output.
-        conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
-            If None, same as `out_channels`.
-    """
-
-    def __init__(
-        self,
-        *,
-        in_channels,
-        out_channels=None,
-        conv_shortcut=False,
-        dropout=0.0,
-        temb_channels=512,
-        groups=32,
-        groups_out=None,
-        pre_norm=True,
-        eps=1e-6,
-        non_linearity="swish",
-        skip_time_act=False,
-        time_embedding_norm="default",  # default, scale_shift, ada_group, spatial
-        kernel=None,
-        output_scale_factor=1.0,
-        use_in_shortcut=None,
-        up=False,
-        down=False,
-        conv_shortcut_bias: bool = True,
-        conv_2d_out_channels: Optional[int] = None,
-    ):
-        super().__init__()
-        self.pre_norm = pre_norm
-        self.pre_norm = True
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-        self.up = up
-        self.down = down
-        self.output_scale_factor = output_scale_factor
-        self.time_embedding_norm = time_embedding_norm
-        self.skip_time_act = skip_time_act
-
-        if groups_out is None:
-            groups_out = groups
-
-        if self.time_embedding_norm == "ada_group":
-            self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
-        elif self.time_embedding_norm == "spatial":
-            self.norm1 = SpatialNorm(in_channels, temb_channels)
-        else:
-            self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
-
-        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-
-        if temb_channels is not None:
-            if self.time_embedding_norm == "default":
-                self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
-            elif self.time_embedding_norm == "scale_shift":
-                self.time_emb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
-            elif self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
-                self.time_emb_proj = None
-            else:
-                raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
-        else:
-            self.time_emb_proj = None
-
-        if self.time_embedding_norm == "ada_group":
-            self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
-        elif self.time_embedding_norm == "spatial":
-            self.norm2 = SpatialNorm(out_channels, temb_channels)
-        else:
-            self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
-
-        self.dropout = torch.nn.Dropout(dropout)
-        conv_2d_out_channels = conv_2d_out_channels or out_channels
-        self.conv2 = torch.nn.Conv2d(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
-
-        self.nonlinearity = get_activation(non_linearity)
-
-        self.upsample = self.downsample = None
-        if self.up:
-            if kernel == "fir":
-                fir_kernel = (1, 3, 3, 1)
-                self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel)
-            elif kernel == "sde_vp":
-                self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
-            else:
-                self.upsample = Upsample2D(in_channels, use_conv=False)
-        elif self.down:
-            if kernel == "fir":
-                fir_kernel = (1, 3, 3, 1)
-                self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel)
-            elif kernel == "sde_vp":
-                self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
-            else:
-                self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
-
-        self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut
-
-        self.conv_shortcut = None
-        if self.use_in_shortcut:
-            self.conv_shortcut = torch.nn.Conv2d(
-                in_channels, conv_2d_out_channels, kernel_size=1, stride=1, padding=0, bias=conv_shortcut_bias
-            )
-
-    # Rich-Text: feature injection
-    def forward(self, input_tensor, temb, inject_states=None):
-        hidden_states = input_tensor
-
-        if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
-            hidden_states = self.norm1(hidden_states, temb)
-        else:
-            hidden_states = self.norm1(hidden_states)
-
-        hidden_states = self.nonlinearity(hidden_states)
-
-        if self.upsample is not None:
-            # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
-            if hidden_states.shape[0] >= 64:
-                input_tensor = input_tensor.contiguous()
-                hidden_states = hidden_states.contiguous()
-            input_tensor = self.upsample(input_tensor)
-            hidden_states = self.upsample(hidden_states)
-        elif self.downsample is not None:
-            input_tensor = self.downsample(input_tensor)
-            hidden_states = self.downsample(hidden_states)
-
-        hidden_states = self.conv1(hidden_states)
-
-        if self.time_emb_proj is not None:
-            if not self.skip_time_act:
-                temb = self.nonlinearity(temb)
-            temb = self.time_emb_proj(temb)[:, :, None, None]
-
-        if temb is not None and self.time_embedding_norm == "default":
-            hidden_states = hidden_states + temb
-
-        if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
-            hidden_states = self.norm2(hidden_states, temb)
-        else:
-            hidden_states = self.norm2(hidden_states)
-
-        if temb is not None and self.time_embedding_norm == "scale_shift":
-            scale, shift = torch.chunk(temb, 2, dim=1)
-            hidden_states = hidden_states * (1 + scale) + shift
-
-        hidden_states = self.nonlinearity(hidden_states)
-
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.conv2(hidden_states)
-
-        if self.conv_shortcut is not None:
-            input_tensor = self.conv_shortcut(input_tensor)
-
-        # Rich-Text: feature injection
-        if inject_states is not None:
-            output_tensor = (input_tensor + inject_states) / self.output_scale_factor
-        else:
-            output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
-
-        return output_tensor, hidden_states
-
-
-# unet_rl.py
-def rearrange_dims(tensor):
-    if len(tensor.shape) == 2:
-        return tensor[:, :, None]
-    if len(tensor.shape) == 3:
-        return tensor[:, :, None, :]
-    elif len(tensor.shape) == 4:
-        return tensor[:, :, 0, :]
-    else:
-        raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.")
-
-
-class Conv1dBlock(nn.Module):
-    """
-    Conv1d --> GroupNorm --> Mish
-    """
-
-    def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
-        super().__init__()
-
-        self.conv1d = nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2)
-        self.group_norm = nn.GroupNorm(n_groups, out_channels)
-        self.mish = nn.Mish()
-
-    def forward(self, inputs):
-        intermediate_repr = self.conv1d(inputs)
-        intermediate_repr = rearrange_dims(intermediate_repr)
-        intermediate_repr = self.group_norm(intermediate_repr)
-        intermediate_repr = rearrange_dims(intermediate_repr)
-        output = self.mish(intermediate_repr)
-        return output
-
-
-# unet_rl.py
-class ResidualTemporalBlock1D(nn.Module):
-    def __init__(self, inp_channels, out_channels, embed_dim, kernel_size=5):
-        super().__init__()
-        self.conv_in = Conv1dBlock(inp_channels, out_channels, kernel_size)
-        self.conv_out = Conv1dBlock(out_channels, out_channels, kernel_size)
-
-        self.time_emb_act = nn.Mish()
-        self.time_emb = nn.Linear(embed_dim, out_channels)
-
-        self.residual_conv = (
-            nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
-        )
-
-    def forward(self, inputs, t):
-        """
-        Args:
-            inputs : [ batch_size x inp_channels x horizon ]
-            t : [ batch_size x embed_dim ]
-
-        returns:
-            out : [ batch_size x out_channels x horizon ]
-        """
-        t = self.time_emb_act(t)
-        t = self.time_emb(t)
-        out = self.conv_in(inputs) + rearrange_dims(t)
-        out = self.conv_out(out)
-        return out + self.residual_conv(inputs)
-
-
-def upsample_2d(hidden_states, kernel=None, factor=2, gain=1):
-    r"""Upsample2D a batch of 2D images with the given filter.
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
-    filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
-    `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is
-    a: multiple of the upsampling factor.
-
-    Args:
-        hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
-          (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
-        factor: Integer upsampling factor (default: 2).
-        gain: Scaling factor for signal magnitude (default: 1.0).
-
-    Returns:
-        output: Tensor of the shape `[N, C, H * factor, W * factor]`
-    """
-    assert isinstance(factor, int) and factor >= 1
-    if kernel is None:
-        kernel = [1] * factor
-
-    kernel = torch.tensor(kernel, dtype=torch.float32)
-    if kernel.ndim == 1:
-        kernel = torch.outer(kernel, kernel)
-    kernel /= torch.sum(kernel)
-
-    kernel = kernel * (gain * (factor**2))
-    pad_value = kernel.shape[0] - factor
-    output = upfirdn2d_native(
-        hidden_states,
-        kernel.to(device=hidden_states.device),
-        up=factor,
-        pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
-    )
-    return output
-
-
-def downsample_2d(hidden_states, kernel=None, factor=2, gain=1):
-    r"""Downsample2D a batch of 2D images with the given filter.
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
-    given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the
-    specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its
-    shape is a multiple of the downsampling factor.
-
-    Args:
-        hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
-          (separable). The default is `[1] * factor`, which corresponds to average pooling.
-        factor: Integer downsampling factor (default: 2).
-        gain: Scaling factor for signal magnitude (default: 1.0).
-
-    Returns:
-        output: Tensor of the shape `[N, C, H // factor, W // factor]`
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    if kernel is None:
-        kernel = [1] * factor
-
-    kernel = torch.tensor(kernel, dtype=torch.float32)
-    if kernel.ndim == 1:
-        kernel = torch.outer(kernel, kernel)
-    kernel /= torch.sum(kernel)
-
-    kernel = kernel * gain
-    pad_value = kernel.shape[0] - factor
-    output = upfirdn2d_native(
-        hidden_states, kernel.to(device=hidden_states.device), down=factor, pad=((pad_value + 1) // 2, pad_value // 2)
-    )
-    return output
-
-
-def upfirdn2d_native(tensor, kernel, up=1, down=1, pad=(0, 0)):
-    up_x = up_y = up
-    down_x = down_y = down
-    pad_x0 = pad_y0 = pad[0]
-    pad_x1 = pad_y1 = pad[1]
-
-    _, channel, in_h, in_w = tensor.shape
-    tensor = tensor.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = tensor.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = tensor.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
-    out = out.to(tensor.device)  # Move back to mps if necessary
-    out = out[
-        :,
-        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
-        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
-        :,
-    ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
-    return out.view(-1, channel, out_h, out_w)
-
-
-class TemporalConvLayer(nn.Module):
-    """
-    Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from:
-    https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016
-    """
-
-    def __init__(self, in_dim, out_dim=None, dropout=0.0):
-        super().__init__()
-        out_dim = out_dim or in_dim
-        self.in_dim = in_dim
-        self.out_dim = out_dim
-
-        # conv layers
-        self.conv1 = nn.Sequential(
-            nn.GroupNorm(32, in_dim), nn.SiLU(), nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0))
-        )
-        self.conv2 = nn.Sequential(
-            nn.GroupNorm(32, out_dim),
-            nn.SiLU(),
-            nn.Dropout(dropout),
-            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
-        )
-        self.conv3 = nn.Sequential(
-            nn.GroupNorm(32, out_dim),
-            nn.SiLU(),
-            nn.Dropout(dropout),
-            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
-        )
-        self.conv4 = nn.Sequential(
-            nn.GroupNorm(32, out_dim),
-            nn.SiLU(),
-            nn.Dropout(dropout),
-            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
-        )
-
-        # zero out the last layer params,so the conv block is identity
-        nn.init.zeros_(self.conv4[-1].weight)
-        nn.init.zeros_(self.conv4[-1].bias)
-
-    def forward(self, hidden_states, num_frames=1):
-        hidden_states = (
-            hidden_states[None, :].reshape((-1, num_frames) + hidden_states.shape[1:]).permute(0, 2, 1, 3, 4)
-        )
-
-        identity = hidden_states
-        hidden_states = self.conv1(hidden_states)
-        hidden_states = self.conv2(hidden_states)
-        hidden_states = self.conv3(hidden_states)
-        hidden_states = self.conv4(hidden_states)
-
-        hidden_states = identity + hidden_states
-
-        hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(
-            (hidden_states.shape[0] * hidden_states.shape[2], -1) + hidden_states.shape[3:]
-        )
-        return hidden_states
diff --git a/spaces/spacerini/chat-noir/app.py b/spaces/spacerini/chat-noir/app.py
deleted file mode 100644
index 3ecc1497fa125ee1710dadc7d321ddb640573794..0000000000000000000000000000000000000000
--- a/spaces/spacerini/chat-noir/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import streamlit as st
-from chatnoir_api.v1 import search
-
-st.set_page_config(
-    page_title="ChatNoir",
-    page_icon="🐈",
-    layout="centered"
-)
-
-@st.cache(suppress_st_warning=True, allow_output_mutation=True, show_spinner=False)
-def search_chat_noir(key, search_query):
-    return search(api_key=key, query=search_query)
-
-def result_html(result):
-    return (
-    f"<div style=\"color:#2a5cb3;font-weight: 500\">{(result.title.html).replace('<em>', '<b>').replace('</em>','</b>')}</div>"
-    f"<a href=\"{result.target_uri}\" style=\"color:Green;\">{result.target_uri}</a>:<br>"
-    f"<div>{(result.snippet.html).replace('<em>', '<b>').replace('</em>','</b>')}</div><br>"
-    )
-
-cola, colb, colc = st.columns([5,4,5])
-with colb:
-    st.image("https://huggingface.co/spaces/spacerini/chat-noir/resolve/main/chatnoir.svg")
-
-col1, col2 = st.columns([9, 1])
-with col1:
-    search_query = st.text_input(label="",
-                placeholder="Search"
-            )
-
-with col2:
-    st.write('#')
-    button_clicked = st.button("🔎")
-
-
-if search_query or button_clicked:
-    search_results = search_chat_noir(st.secrets["key"], search_query)
-    for result in search_results[:10]:
-        st.write(result_html(result), unsafe_allow_html=True)
-    
-with st.expander("🐈 About", expanded=False):
-    st.markdown(
-        """
-        This is an example of a Space using a [`spacerini`](https://github.com/castorini/hf-spacerini) streamlit template to wrap a search engine hosted elsewhere, in this case the ChatNoir search engine. ChatNoir is an Elasticsearch-based search engine offering a freely accessible search interface for the two ClueWeb corpora and the Common Crawl, together about 3 billion web pages. This version of the search engine uses the [Search API](https://www.chatnoir.eu/doc/api/) by way of the Python [chatnoir-api](https://pypi.org/project/chatnoir-api/) Package.
-
-If you find this project useful in your research, please consider citing:
- 
-```
-@InProceedings{bevendorff:2018,
-  address =               {Berlin Heidelberg New York},
-  author =                {Janek Bevendorff and Benno Stein and Matthias Hagen and Martin Potthast},
-  booktitle =             {Advances in Information Retrieval. 40th European Conference on IR Research (ECIR 2018)},
-  editor =                {Leif Azzopardi and Allan Hanbury and Gabriella Pasi and Benjamin Piwowarski},
-  ids =                   {potthast:2018c,stein:2018c},
-  month =                 mar,
-  publisher =             {Springer},
-  series =                {Lecture Notes in Computer Science},
-  site =                  {Grenoble, France},
-  title =                 {{Elastic ChatNoir: Search Engine for the ClueWeb and the Common Crawl}},
-  year =                  2018
-}
-```
-	    """
-    )
\ No newline at end of file
diff --git a/spaces/srini047/text-based-sentiment-analyzer/README.md b/spaces/srini047/text-based-sentiment-analyzer/README.md
deleted file mode 100644
index 68df8aff1f4bad8e947dd874e9fb66b5505f2e7c..0000000000000000000000000000000000000000
--- a/spaces/srini047/text-based-sentiment-analyzer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text Based Sentiment Analyzer
-emoji: 🌖
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/utils.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/utils.py
deleted file mode 100644
index 5aaddf6421ab7fa417af508005671a0ed821c701..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/utils.py
+++ /dev/null
@@ -1,126 +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 gc
-import os
-import random
-import shutil
-import numpy as np
-
-import torch
-import tqdm
-from examples.textless_nlp.gslm.speech2unit.pretrained.cpc_feature_reader import (
-    CpcFeatureReader,
-)
-from examples.textless_nlp.gslm.speech2unit.pretrained.hubert_feature_reader import (
-    HubertFeatureReader,
-)
-from examples.textless_nlp.gslm.speech2unit.pretrained.logmel_feature_reader import (
-    LogMelFeatureReader,
-)
-from examples.textless_nlp.gslm.speech2unit.pretrained.w2v2_feature_reader import (
-    Wav2VecFeatureReader,
-)
-
-
-def get_feature_reader(feature_type):
-    if feature_type == "logmel":
-        return LogMelFeatureReader
-    elif feature_type == "hubert":
-        return HubertFeatureReader
-    elif feature_type == "w2v2":
-        return Wav2VecFeatureReader
-    elif feature_type == "cpc":
-        return CpcFeatureReader
-    else:
-        raise NotImplementedError(f"{feature_type} is not supported.")
-
-
-def get_feature_iterator(
-    feature_type, checkpoint_path, layer, manifest_path, sample_pct
-):
-    feature_reader_cls = get_feature_reader(feature_type)
-    with open(manifest_path, "r") as fp:
-        lines = fp.read().split("\n")
-        root = lines.pop(0).strip()
-        file_path_list = [
-            os.path.join(root, line.split("\t")[0])
-            for line in lines
-            if len(line) > 0
-        ]
-        if sample_pct < 1.0:
-            file_path_list = random.sample(
-                file_path_list, int(sample_pct * len(file_path_list))
-            )
-        num_files = len(file_path_list)
-        reader = feature_reader_cls(
-            checkpoint_path=checkpoint_path, layer=layer
-        )
-
-        def iterate():
-            for file_path in file_path_list:
-                feats = reader.get_feats(file_path)
-                yield feats.cpu().numpy()
-
-    return iterate, num_files
-
-
-def get_features(
-    feature_type, checkpoint_path, layer, manifest_path, sample_pct, flatten
-):
-    generator, num_files = get_feature_iterator(
-        feature_type=feature_type,
-        checkpoint_path=checkpoint_path,
-        layer=layer,
-        manifest_path=manifest_path,
-        sample_pct=sample_pct,
-    )
-    iterator = generator()
-
-    features_list = []
-    for features in tqdm.tqdm(iterator, total=num_files):
-        features_list.append(features)
-
-    # Explicit clean up
-    del iterator
-    del generator
-    gc.collect()
-    torch.cuda.empty_cache()
-
-    if flatten:
-        return np.concatenate(features_list)
-
-    return features_list
-
-
-def get_and_dump_features(
-    feature_type,
-    checkpoint_path,
-    layer,
-    manifest_path,
-    sample_pct,
-    flatten,
-    out_features_path,
-):
-    # Feature extraction
-    features_batch = get_features(
-        feature_type=feature_type,
-        checkpoint_path=checkpoint_path,
-        layer=layer,
-        manifest_path=manifest_path,
-        sample_pct=sample_pct,
-        flatten=flatten,
-    )
-
-    # Save features
-    out_dir_path = os.path.dirname(out_features_path)
-    os.makedirs(out_dir_path, exist_ok=True)
-    shutil.copyfile(
-        manifest_path,
-        os.path.join(out_dir_path, os.path.basename(manifest_path)),
-    )
-    np.save(out_features_path, features_batch)
-
-    return features_batch
diff --git a/spaces/srkajol/avocat-ia/README.md b/spaces/srkajol/avocat-ia/README.md
deleted file mode 100644
index e3179de968bb1cf2e23b2e08e2c80b5b65ac99bd..0000000000000000000000000000000000000000
--- a/spaces/srkajol/avocat-ia/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Avocat Ia
-emoji: 😻
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sssdtgvg/Sex/index.html b/spaces/sssdtgvg/Sex/index.html
deleted file mode 100644
index 58275de3b1c343a98420342baa076b9baaafa157..0000000000000000000000000000000000000000
--- a/spaces/sssdtgvg/Sex/index.html
+++ /dev/null
@@ -1,19 +0,0 @@
-<!DOCTYPE html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
-</html>
diff --git a/spaces/stomexserde/gpt4-ui/Examples/8051 Microcontroller By Subrata Ghoshal Pdf HOT.md b/spaces/stomexserde/gpt4-ui/Examples/8051 Microcontroller By Subrata Ghoshal Pdf HOT.md
deleted file mode 100644
index 9d3d53a33cca871e911892504a14172713415f17..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/8051 Microcontroller By Subrata Ghoshal Pdf HOT.md	
+++ /dev/null
@@ -1,48 +0,0 @@
-
-<h1>8051 Microcontroller: A Comprehensive Guide by Subrata Ghoshal</h1>
-<p>The 8051 microcontroller is one of the most popular and widely used microcontrollers in embedded system design. It has a simple architecture, a rich instruction set and a variety of interfacing techniques that make it suitable for a range of applications. In this article, we will explore the internals, instructions, programming and interfacing of the 8051 microcontroller with the help of some books by Subrata Ghoshal, a renowned author and expert in the field.</p>
-<h2>8051 Microcontroller: Internals, Instructions, Programming & Interfacing</h2>
-<p>This book[^1^] provides a comprehensive and systematic coverage of the 8051 microcontroller, starting from its general architecture and features, to its instruction set and addressing modes, to its programming and interfacing techniques. The book also includes numerous examples and exercises to illustrate the concepts and applications of the 8051 microcontroller. Some of the topics covered in this book are:</p>
-<h2>8051 Microcontroller By Subrata Ghoshal Pdf</h2><br /><p><b><b>Download File</b> &#10145; <a href="https://urlgoal.com/2uI8vJ">https://urlgoal.com/2uI8vJ</a></b></p><br /><br />
-<ul>
-<li>General architecture and features of the 8051 microcontroller</li>
-<li>Data move operations and arithmetic operations</li>
-<li>Logical operations and boolean variable manipulation</li>
-<li>Subroutines and stack</li>
-<li>External interrupts and timer/counter interrupts</li>
-<li>Serial communication and serial interrupts</li>
-<li>External memory and memory-mapped I/O</li>
-<li>Keyboards, display devices, DAC/ADC and motors</li>
-<li>Power management and home protection system</li>
-<li>Advanced microcontrollers such as PIC, AVR and ARM</li>
-</ul>
-<h2>8051 Microcontrollers: Internals, Instructions, Programming and Interfacing, 2/e</h2>
-<p>This book[^2^] is the second edition of the previous book, with some additional features and updates. The book retains the same structure and content as the first edition, but adds more C language based programs for varied applications. The book also includes a CD-ROM that contains all the source codes, simulation software and other useful resources for learning and practicing the 8051 microcontroller. Some of the new topics covered in this book are:</p>
-<ul>
-<li>C language programming for the 8051 microcontroller</li>
-<li>Keil C compiler and Proteus simulator</li>
-<li>LCD interfacing using C language</li>
-<li>SPI protocol and interfacing with EEPROM</li>
-<li>I2C protocol and interfacing with RTC</li>
-<li>PWM generation using C language</li>
-<li>Wireless communication using RF module</li>
-<li>GSM modem interfacing using AT commands</li>
-<li>Bluetooth module interfacing using SPP profile</li>
-<li>Zigbee module interfacing using API mode</li>
-</ul>
-<h2>Embedded Systems & Robots: Projects Using the 8051 Microcontroller</h2>
-<p>This book[^3^] is a project-based book that demonstrates the practical applications of the 8051 microcontroller in embedded systems and robotics. The book contains 20 projects that cover various aspects of embedded systems such as sensors, actuators, communication protocols, control algorithms, etc. The book also explains the hardware design, software development and testing procedures for each project. Some of the projects covered in this book are:</p>
-<ul>
-<li>Digital thermometer using LM35 sensor</li>
-<li>Digital clock using DS1307 RTC</li>
-<li>Password-based door lock system using keypad and LCD</li>
-<li>Remote-controlled car using RF module</li>
-<li>Line follower robot using IR sensors</li>
-<li>Maze solver robot using ultrasonic sensor</li>
-<li>Voice-controlled robot using HM2007 speech recognition module</li>
-<li>Fingerprint-based attendance system using R305 fingerprint module</li>
-<li>RFID-based security system using EM-18 RFID reader module</li>
-<li>Solar tracker using LDR sensors and stepper motor</li></p>
-<p></p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Avgat Movie In Hindi Free Download 720p Movies [REPACK].md b/spaces/stomexserde/gpt4-ui/Examples/Avgat Movie In Hindi Free Download 720p Movies [REPACK].md
deleted file mode 100644
index 5506dfa38249654c33973dd42cfc2fbcd6789bea..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Avgat Movie In Hindi Free Download 720p Movies [REPACK].md	
+++ /dev/null
@@ -1,22 +0,0 @@
-
-<h1>Avgat Movie In Hindi Free Download 720p Movies: A Review</h1>
-<p>Avgat is a 2001 Bollywood thriller movie directed by Mohan Sharma and starring Jackie Shroff, Nana Patekar, Raveena Tandon and Alok Nath. The movie is about a police officer who is framed for a murder and has to prove his innocence while being chased by his enemies. The movie was released on 31 August 2001 and received mixed reviews from critics and audiences.</p>
-<h2>Avgat Movie In Hindi Free Download 720p Movies</h2><br /><p><b><b>Download File</b> ---> <a href="https://urlgoal.com/2uI9pY">https://urlgoal.com/2uI9pY</a></b></p><br /><br />
-<p>If you are looking for a way to watch Avgat movie in Hindi free download 720p movies, you might be disappointed as there are no legal and safe sites that offer this movie for free download. However, you can still watch this movie online on some streaming platforms that have Hindi dubbed versions of Hollywood movies. Here are some of the best sites to watch Hindi dubbed Hollywood movies online:</p>
-<ul>
-<li><a href="https://moviesming.in/">Mp4Moviez</a>: This is a free online movie downloader that can successfully download Hollywood movies in Hindi. There are all the latest Hollywood Hindi dubbed movies being listed on the top of the home page. You can directly get Hindi dubbed Hollywood movies download under the Daily Updated Movies drop-down list, or you can search for your wanted movies and download them[^1^].</li>
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-</ul>
-<p>However, we do not recommend you to download or watch Avgat movie in Hindi free download 720p movies from these sites as they may contain viruses, malware, or other harmful content that can damage your device or compromise your privacy. The best way to watch Avgat movie in Hindi is to buy or rent it from legal and authorized platforms, such as Amazon Prime Video, Netflix, Hotstar, etc. By doing so, you can support the original creators and enjoy a high-quality viewing experience.</p>
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-<p></p>
-<ol>
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-</ol>
-<p>Therefore, we strongly advise you to avoid Avgat movie in Hindi free download 720p movies and choose legal and safe platforms to watch this movie. By doing so, you can enjoy a better and more secure viewing experience while supporting the movie industry and respecting the intellectual property rights.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Iddaa Tahmini Nesine Com.md b/spaces/stomexserde/gpt4-ui/Examples/Iddaa Tahmini Nesine Com.md
deleted file mode 100644
index 166ff33750251e8d793ebc901146124ab79669e9..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Iddaa Tahmini Nesine Com.md	
+++ /dev/null
@@ -1,32 +0,0 @@
-<br />
-<h1>Iddaa Tahmini Nesine Com: How to Bet on Sports in Turkey</h1>
-<p>If you are looking for a reliable and easy way to bet on sports in Turkey, you might want to check out Iddaa Tahmini Nesine Com. This is a website that offers you the latest odds, predictions, tips, and analysis for various sports events, such as football, basketball, tennis, and more. You can also find live scores, statistics, and news about your favorite teams and players.</p>
-<p>Iddaa Tahmini Nesine Com is not just a website, but also a mobile app that you can download on your smartphone or tablet. You can access all the features of the website on the app, and also enjoy some exclusive benefits, such as notifications, coupons, and bonuses. You can also create your own profile and interact with other users who share your passion for sports betting.</p>
-<h2>Iddaa Tahmini Nesine Com</h2><br /><p><b><b>DOWNLOAD</b> &bull;&bull;&bull; <a href="https://urlgoal.com/2uI9V7">https://urlgoal.com/2uI9V7</a></b></p><br /><br />
-<p>One of the best things about Iddaa Tahmini Nesine Com is that it is licensed and regulated by the Turkish government. This means that you can trust that your personal information and money are safe and secure. You can also enjoy fast and easy transactions, using various payment methods such as credit cards, bank transfers, e-wallets, and more.</p>
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-
-<p>How to Use Iddaa Tahmini Nesine Com</p>
-<p>Using Iddaa Tahmini Nesine Com is very simple and convenient. You just need to follow these steps:</p>
-<ol>
-<li>Register for free on the website or the app. You will need to provide some basic information, such as your name, email, phone number, and password.</li>
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-</ol>
-<p>Why Choose Iddaa Tahmini Nesine Com</p>
-<p>There are many reasons why you should choose Iddaa Tahmini Nesine Com for your sports betting needs. Here are some of them:</p>
-<p></p>
-<ul>
-<li>Iddaa Tahmini Nesine Com is licensed and regulated by the Turkish government. This means that you can trust that your personal information and money are safe and secure.</li>
-<li>Iddaa Tahmini Nesine Com offers you the latest odds, predictions, tips, and analysis for various sports events. You can also find live scores, statistics, and news about your favorite teams and players.</li>
-<li>Iddaa Tahmini Nesine Com has a user-friendly website and a mobile app that you can access anytime and anywhere. You can also enjoy some exclusive benefits, such as notifications, coupons, and bonuses.</li>
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-</ul></p> 7b8c122e87<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/stratussox/yolov5_inference/export.py b/spaces/stratussox/yolov5_inference/export.py
deleted file mode 100644
index e43d9b730fc6a2ef65416d5bcf1690a0fc50f162..0000000000000000000000000000000000000000
--- a/spaces/stratussox/yolov5_inference/export.py
+++ /dev/null
@@ -1,652 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
-
-Format                      | `export.py --include`         | Model
----                         | ---                           | ---
-PyTorch                     | -                             | yolov5s.pt
-TorchScript                 | `torchscript`                 | yolov5s.torchscript
-ONNX                        | `onnx`                        | yolov5s.onnx
-OpenVINO                    | `openvino`                    | yolov5s_openvino_model/
-TensorRT                    | `engine`                      | yolov5s.engine
-CoreML                      | `coreml`                      | yolov5s.mlmodel
-TensorFlow SavedModel       | `saved_model`                 | yolov5s_saved_model/
-TensorFlow GraphDef         | `pb`                          | yolov5s.pb
-TensorFlow Lite             | `tflite`                      | yolov5s.tflite
-TensorFlow Edge TPU         | `edgetpu`                     | yolov5s_edgetpu.tflite
-TensorFlow.js               | `tfjs`                        | yolov5s_web_model/
-PaddlePaddle                | `paddle`                      | yolov5s_paddle_model/
-
-Requirements:
-    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU
-    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow  # GPU
-
-Usage:
-    $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
-
-Inference:
-    $ python detect.py --weights yolov5s.pt                 # PyTorch
-                                 yolov5s.torchscript        # TorchScript
-                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
-                                 yolov5s_openvino_model     # OpenVINO
-                                 yolov5s.engine             # TensorRT
-                                 yolov5s.mlmodel            # CoreML (macOS-only)
-                                 yolov5s_saved_model        # TensorFlow SavedModel
-                                 yolov5s.pb                 # TensorFlow GraphDef
-                                 yolov5s.tflite             # TensorFlow Lite
-                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
-                                 yolov5s_paddle_model       # PaddlePaddle
-
-TensorFlow.js:
-    $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
-    $ npm install
-    $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
-    $ npm start
-"""
-
-import argparse
-import contextlib
-import json
-import os
-import platform
-import re
-import subprocess
-import sys
-import time
-import warnings
-from pathlib import Path
-
-import pandas as pd
-import torch
-from torch.utils.mobile_optimizer import optimize_for_mobile
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-if platform.system() != 'Windows':
-    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.experimental import attempt_load
-from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel
-from utils.dataloaders import LoadImages
-from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
-                           check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save)
-from utils.torch_utils import select_device, smart_inference_mode
-
-MACOS = platform.system() == 'Darwin'  # macOS environment
-
-
-def export_formats():
-    # YOLOv5 export formats
-    x = [
-        ['PyTorch', '-', '.pt', True, True],
-        ['TorchScript', 'torchscript', '.torchscript', True, True],
-        ['ONNX', 'onnx', '.onnx', True, True],
-        ['OpenVINO', 'openvino', '_openvino_model', True, False],
-        ['TensorRT', 'engine', '.engine', False, True],
-        ['CoreML', 'coreml', '.mlmodel', True, False],
-        ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
-        ['TensorFlow GraphDef', 'pb', '.pb', True, True],
-        ['TensorFlow Lite', 'tflite', '.tflite', True, False],
-        ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
-        ['TensorFlow.js', 'tfjs', '_web_model', False, False],
-        ['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
-    return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
-
-
-def try_export(inner_func):
-    # YOLOv5 export decorator, i..e @try_export
-    inner_args = get_default_args(inner_func)
-
-    def outer_func(*args, **kwargs):
-        prefix = inner_args['prefix']
-        try:
-            with Profile() as dt:
-                f, model = inner_func(*args, **kwargs)
-            LOGGER.info(f'{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)')
-            return f, model
-        except Exception as e:
-            LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}')
-            return None, None
-
-    return outer_func
-
-
-@try_export
-def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
-    # YOLOv5 TorchScript model export
-    LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
-    f = file.with_suffix('.torchscript')
-
-    ts = torch.jit.trace(model, im, strict=False)
-    d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
-    extra_files = {'config.txt': json.dumps(d)}  # torch._C.ExtraFilesMap()
-    if optimize:  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
-        optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
-    else:
-        ts.save(str(f), _extra_files=extra_files)
-    return f, None
-
-
-@try_export
-def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')):
-    # YOLOv5 ONNX export
-    check_requirements('onnx')
-    import onnx
-
-    LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
-    f = file.with_suffix('.onnx')
-
-    output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0']
-    if dynamic:
-        dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}}  # shape(1,3,640,640)
-        if isinstance(model, SegmentationModel):
-            dynamic['output0'] = {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
-            dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'}  # shape(1,32,160,160)
-        elif isinstance(model, DetectionModel):
-            dynamic['output0'] = {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
-
-    torch.onnx.export(
-        model.cpu() if dynamic else model,  # --dynamic only compatible with cpu
-        im.cpu() if dynamic else im,
-        f,
-        verbose=False,
-        opset_version=opset,
-        do_constant_folding=True,
-        input_names=['images'],
-        output_names=output_names,
-        dynamic_axes=dynamic or None)
-
-    # Checks
-    model_onnx = onnx.load(f)  # load onnx model
-    onnx.checker.check_model(model_onnx)  # check onnx model
-
-    # Metadata
-    d = {'stride': int(max(model.stride)), 'names': model.names}
-    for k, v in d.items():
-        meta = model_onnx.metadata_props.add()
-        meta.key, meta.value = k, str(v)
-    onnx.save(model_onnx, f)
-
-    # Simplify
-    if simplify:
-        try:
-            cuda = torch.cuda.is_available()
-            check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
-            import onnxsim
-
-            LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
-            model_onnx, check = onnxsim.simplify(model_onnx)
-            assert check, 'assert check failed'
-            onnx.save(model_onnx, f)
-        except Exception as e:
-            LOGGER.info(f'{prefix} simplifier failure: {e}')
-    return f, model_onnx
-
-
-@try_export
-def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
-    # YOLOv5 OpenVINO export
-    check_requirements('openvino-dev')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
-    import openvino.inference_engine as ie
-
-    LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
-    f = str(file).replace('.pt', f'_openvino_model{os.sep}')
-
-    cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
-    subprocess.run(cmd.split(), check=True, env=os.environ)  # export
-    yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata)  # add metadata.yaml
-    return f, None
-
-
-@try_export
-def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')):
-    # YOLOv5 Paddle export
-    check_requirements(('paddlepaddle', 'x2paddle'))
-    import x2paddle
-    from x2paddle.convert import pytorch2paddle
-
-    LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...')
-    f = str(file).replace('.pt', f'_paddle_model{os.sep}')
-
-    pytorch2paddle(module=model, save_dir=f, jit_type='trace', input_examples=[im])  # export
-    yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata)  # add metadata.yaml
-    return f, None
-
-
-@try_export
-def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
-    # YOLOv5 CoreML export
-    check_requirements('coremltools')
-    import coremltools as ct
-
-    LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
-    f = file.with_suffix('.mlmodel')
-
-    ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
-    ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
-    bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
-    if bits < 32:
-        if MACOS:  # quantization only supported on macOS
-            with warnings.catch_warnings():
-                warnings.filterwarnings("ignore", category=DeprecationWarning)  # suppress numpy==1.20 float warning
-                ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
-        else:
-            print(f'{prefix} quantization only supported on macOS, skipping...')
-    ct_model.save(f)
-    return f, ct_model
-
-
-@try_export
-def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
-    # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
-    assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
-    try:
-        import tensorrt as trt
-    except Exception:
-        if platform.system() == 'Linux':
-            check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
-        import tensorrt as trt
-
-    if trt.__version__[0] == '7':  # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
-        grid = model.model[-1].anchor_grid
-        model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
-        export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
-        model.model[-1].anchor_grid = grid
-    else:  # TensorRT >= 8
-        check_version(trt.__version__, '8.0.0', hard=True)  # require tensorrt>=8.0.0
-        export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
-    onnx = file.with_suffix('.onnx')
-
-    LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
-    assert onnx.exists(), f'failed to export ONNX file: {onnx}'
-    f = file.with_suffix('.engine')  # TensorRT engine file
-    logger = trt.Logger(trt.Logger.INFO)
-    if verbose:
-        logger.min_severity = trt.Logger.Severity.VERBOSE
-
-    builder = trt.Builder(logger)
-    config = builder.create_builder_config()
-    config.max_workspace_size = workspace * 1 << 30
-    # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)  # fix TRT 8.4 deprecation notice
-
-    flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
-    network = builder.create_network(flag)
-    parser = trt.OnnxParser(network, logger)
-    if not parser.parse_from_file(str(onnx)):
-        raise RuntimeError(f'failed to load ONNX file: {onnx}')
-
-    inputs = [network.get_input(i) for i in range(network.num_inputs)]
-    outputs = [network.get_output(i) for i in range(network.num_outputs)]
-    for inp in inputs:
-        LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}')
-    for out in outputs:
-        LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}')
-
-    if dynamic:
-        if im.shape[0] <= 1:
-            LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument")
-        profile = builder.create_optimization_profile()
-        for inp in inputs:
-            profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
-        config.add_optimization_profile(profile)
-
-    LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}')
-    if builder.platform_has_fast_fp16 and half:
-        config.set_flag(trt.BuilderFlag.FP16)
-    with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
-        t.write(engine.serialize())
-    return f, None
-
-
-@try_export
-def export_saved_model(model,
-                       im,
-                       file,
-                       dynamic,
-                       tf_nms=False,
-                       agnostic_nms=False,
-                       topk_per_class=100,
-                       topk_all=100,
-                       iou_thres=0.45,
-                       conf_thres=0.25,
-                       keras=False,
-                       prefix=colorstr('TensorFlow SavedModel:')):
-    # YOLOv5 TensorFlow SavedModel export
-    try:
-        import tensorflow as tf
-    except Exception:
-        check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
-        import tensorflow as tf
-    from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
-
-    from models.tf import TFModel
-
-    LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
-    f = str(file).replace('.pt', '_saved_model')
-    batch_size, ch, *imgsz = list(im.shape)  # BCHW
-
-    tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
-    im = tf.zeros((batch_size, *imgsz, ch))  # BHWC order for TensorFlow
-    _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
-    inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
-    outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
-    keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
-    keras_model.trainable = False
-    keras_model.summary()
-    if keras:
-        keras_model.save(f, save_format='tf')
-    else:
-        spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
-        m = tf.function(lambda x: keras_model(x))  # full model
-        m = m.get_concrete_function(spec)
-        frozen_func = convert_variables_to_constants_v2(m)
-        tfm = tf.Module()
-        tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec])
-        tfm.__call__(im)
-        tf.saved_model.save(tfm,
-                            f,
-                            options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version(
-                                tf.__version__, '2.6') else tf.saved_model.SaveOptions())
-    return f, keras_model
-
-
-@try_export
-def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
-    # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
-    import tensorflow as tf
-    from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
-
-    LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
-    f = file.with_suffix('.pb')
-
-    m = tf.function(lambda x: keras_model(x))  # full model
-    m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
-    frozen_func = convert_variables_to_constants_v2(m)
-    frozen_func.graph.as_graph_def()
-    tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
-    return f, None
-
-
-@try_export
-def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
-    # YOLOv5 TensorFlow Lite export
-    import tensorflow as tf
-
-    LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
-    batch_size, ch, *imgsz = list(im.shape)  # BCHW
-    f = str(file).replace('.pt', '-fp16.tflite')
-
-    converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
-    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
-    converter.target_spec.supported_types = [tf.float16]
-    converter.optimizations = [tf.lite.Optimize.DEFAULT]
-    if int8:
-        from models.tf import representative_dataset_gen
-        dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
-        converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
-        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
-        converter.target_spec.supported_types = []
-        converter.inference_input_type = tf.uint8  # or tf.int8
-        converter.inference_output_type = tf.uint8  # or tf.int8
-        converter.experimental_new_quantizer = True
-        f = str(file).replace('.pt', '-int8.tflite')
-    if nms or agnostic_nms:
-        converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
-
-    tflite_model = converter.convert()
-    open(f, "wb").write(tflite_model)
-    return f, None
-
-
-@try_export
-def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
-    # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
-    cmd = 'edgetpu_compiler --version'
-    help_url = 'https://coral.ai/docs/edgetpu/compiler/'
-    assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
-    if subprocess.run(f'{cmd} >/dev/null', shell=True).returncode != 0:
-        LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
-        sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0  # sudo installed on system
-        for c in (
-                'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
-                'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
-                'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
-            subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
-    ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
-
-    LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
-    f = str(file).replace('.pt', '-int8_edgetpu.tflite')  # Edge TPU model
-    f_tfl = str(file).replace('.pt', '-int8.tflite')  # TFLite model
-
-    cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}"
-    subprocess.run(cmd.split(), check=True)
-    return f, None
-
-
-@try_export
-def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
-    # YOLOv5 TensorFlow.js export
-    check_requirements('tensorflowjs')
-    import tensorflowjs as tfjs
-
-    LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
-    f = str(file).replace('.pt', '_web_model')  # js dir
-    f_pb = file.with_suffix('.pb')  # *.pb path
-    f_json = f'{f}/model.json'  # *.json path
-
-    cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
-          f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
-    subprocess.run(cmd.split())
-
-    json = Path(f_json).read_text()
-    with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
-        subst = re.sub(
-            r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
-            r'"Identity.?.?": {"name": "Identity.?.?"}, '
-            r'"Identity.?.?": {"name": "Identity.?.?"}, '
-            r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, '
-            r'"Identity_1": {"name": "Identity_1"}, '
-            r'"Identity_2": {"name": "Identity_2"}, '
-            r'"Identity_3": {"name": "Identity_3"}}}', json)
-        j.write(subst)
-    return f, None
-
-
-def add_tflite_metadata(file, metadata, num_outputs):
-    # Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
-    with contextlib.suppress(ImportError):
-        # check_requirements('tflite_support')
-        from tflite_support import flatbuffers
-        from tflite_support import metadata as _metadata
-        from tflite_support import metadata_schema_py_generated as _metadata_fb
-
-        tmp_file = Path('/tmp/meta.txt')
-        with open(tmp_file, 'w') as meta_f:
-            meta_f.write(str(metadata))
-
-        model_meta = _metadata_fb.ModelMetadataT()
-        label_file = _metadata_fb.AssociatedFileT()
-        label_file.name = tmp_file.name
-        model_meta.associatedFiles = [label_file]
-
-        subgraph = _metadata_fb.SubGraphMetadataT()
-        subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]
-        subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs
-        model_meta.subgraphMetadata = [subgraph]
-
-        b = flatbuffers.Builder(0)
-        b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
-        metadata_buf = b.Output()
-
-        populator = _metadata.MetadataPopulator.with_model_file(file)
-        populator.load_metadata_buffer(metadata_buf)
-        populator.load_associated_files([str(tmp_file)])
-        populator.populate()
-        tmp_file.unlink()
-
-
-@smart_inference_mode()
-def run(
-        data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
-        weights=ROOT / 'yolov5s.pt',  # weights path
-        imgsz=(640, 640),  # image (height, width)
-        batch_size=1,  # batch size
-        device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        include=('torchscript', 'onnx'),  # include formats
-        half=False,  # FP16 half-precision export
-        inplace=False,  # set YOLOv5 Detect() inplace=True
-        keras=False,  # use Keras
-        optimize=False,  # TorchScript: optimize for mobile
-        int8=False,  # CoreML/TF INT8 quantization
-        dynamic=False,  # ONNX/TF/TensorRT: dynamic axes
-        simplify=False,  # ONNX: simplify model
-        opset=12,  # ONNX: opset version
-        verbose=False,  # TensorRT: verbose log
-        workspace=4,  # TensorRT: workspace size (GB)
-        nms=False,  # TF: add NMS to model
-        agnostic_nms=False,  # TF: add agnostic NMS to model
-        topk_per_class=100,  # TF.js NMS: topk per class to keep
-        topk_all=100,  # TF.js NMS: topk for all classes to keep
-        iou_thres=0.45,  # TF.js NMS: IoU threshold
-        conf_thres=0.25,  # TF.js NMS: confidence threshold
-):
-    t = time.time()
-    include = [x.lower() for x in include]  # to lowercase
-    fmts = tuple(export_formats()['Argument'][1:])  # --include arguments
-    flags = [x in include for x in fmts]
-    assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
-    jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags  # export booleans
-    file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)  # PyTorch weights
-
-    # Load PyTorch model
-    device = select_device(device)
-    if half:
-        assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0'
-        assert not dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both'
-    model = attempt_load(weights, device=device, inplace=True, fuse=True)  # load FP32 model
-
-    # Checks
-    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
-    if optimize:
-        assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
-
-    # Input
-    gs = int(max(model.stride))  # grid size (max stride)
-    imgsz = [check_img_size(x, gs) for x in imgsz]  # verify img_size are gs-multiples
-    im = torch.zeros(batch_size, 3, *imgsz).to(device)  # image size(1,3,320,192) BCHW iDetection
-
-    # Update model
-    model.eval()
-    for k, m in model.named_modules():
-        if isinstance(m, Detect):
-            m.inplace = inplace
-            m.dynamic = dynamic
-            m.export = True
-
-    for _ in range(2):
-        y = model(im)  # dry runs
-    if half and not coreml:
-        im, model = im.half(), model.half()  # to FP16
-    shape = tuple((y[0] if isinstance(y, tuple) else y).shape)  # model output shape
-    metadata = {'stride': int(max(model.stride)), 'names': model.names}  # model metadata
-    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
-
-    # Exports
-    f = [''] * len(fmts)  # exported filenames
-    warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning)  # suppress TracerWarning
-    if jit:  # TorchScript
-        f[0], _ = export_torchscript(model, im, file, optimize)
-    if engine:  # TensorRT required before ONNX
-        f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
-    if onnx or xml:  # OpenVINO requires ONNX
-        f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
-    if xml:  # OpenVINO
-        f[3], _ = export_openvino(file, metadata, half)
-    if coreml:  # CoreML
-        f[4], _ = export_coreml(model, im, file, int8, half)
-    if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
-        assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
-        assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.'
-        f[5], s_model = export_saved_model(model.cpu(),
-                                           im,
-                                           file,
-                                           dynamic,
-                                           tf_nms=nms or agnostic_nms or tfjs,
-                                           agnostic_nms=agnostic_nms or tfjs,
-                                           topk_per_class=topk_per_class,
-                                           topk_all=topk_all,
-                                           iou_thres=iou_thres,
-                                           conf_thres=conf_thres,
-                                           keras=keras)
-        if pb or tfjs:  # pb prerequisite to tfjs
-            f[6], _ = export_pb(s_model, file)
-        if tflite or edgetpu:
-            f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
-            if edgetpu:
-                f[8], _ = export_edgetpu(file)
-            add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))
-        if tfjs:
-            f[9], _ = export_tfjs(file)
-    if paddle:  # PaddlePaddle
-        f[10], _ = export_paddle(model, im, file, metadata)
-
-    # Finish
-    f = [str(x) for x in f if x]  # filter out '' and None
-    if any(f):
-        cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel))  # type
-        dir = Path('segment' if seg else 'classify' if cls else '')
-        h = '--half' if half else ''  # --half FP16 inference arg
-        s = "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" if cls else \
-            "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" if seg else ''
-        LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
-                    f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
-                    f"\nDetect:          python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
-                    f"\nValidate:        python {dir / 'val.py'} --weights {f[-1]} {h}"
-                    f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')  {s}"
-                    f"\nVisualize:       https://netron.app")
-    return f  # return list of exported files/dirs
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
-    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
-    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)')
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
-    parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
-    parser.add_argument('--keras', action='store_true', help='TF: use Keras')
-    parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
-    parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
-    parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
-    parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
-    parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
-    parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
-    parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
-    parser.add_argument('--nms', action='store_true', help='TF: add NMS to model')
-    parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model')
-    parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
-    parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
-    parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
-    parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
-    parser.add_argument(
-        '--include',
-        nargs='+',
-        default=['torchscript'],
-        help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle')
-    opt = parser.parse_args()
-    print_args(vars(opt))
-    return opt
-
-
-def main(opt):
-    for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]):
-        run(**vars(opt))
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)
diff --git a/spaces/subhajitmaji/MusicGen/Makefile b/spaces/subhajitmaji/MusicGen/Makefile
deleted file mode 100644
index 5bfd89dd833d7448b21073eb6ee7cfac1d5157dd..0000000000000000000000000000000000000000
--- a/spaces/subhajitmaji/MusicGen/Makefile
+++ /dev/null
@@ -1,21 +0,0 @@
-default: linter tests
-
-install:
-	pip install -U pip
-	pip install -U -e '.[dev]'
-
-linter:
-	flake8 audiocraft && mypy audiocraft
-	flake8 tests && mypy tests
-
-tests:
-	coverage run -m pytest tests
-	coverage report --include 'audiocraft/*'
-
-docs:
-	pdoc3 --html -o docs -f audiocraft
-
-dist:
-	python setup.py sdist
-
-.PHONY: linter tests docs dist
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Junooniyat Full Movie Download In 720p 1080p _TOP_.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Junooniyat Full Movie Download In 720p 1080p _TOP_.md
deleted file mode 100644
index 935f2eed4ac87945053b17e05384f51a6286f79d..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Junooniyat Full Movie Download In 720p 1080p _TOP_.md	
+++ /dev/null
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-Title: Junooniyat
-Genre: Melodrama, Bollywood, Drama
-Directed by: Sharad Kalpakkar
-Cast: Deepak Malhotra, Kajol, Sharad Khan, Kamala Hassa, Purab Kohli
-Country: India
-Duration: 2:15:20
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-The film is about two lovers (Sirince and Dipak), who will have to go through difficulties ... 8a78ff9644<br />
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Terminator 3 The Redemption Pc _HOT_ Download Torrent Game.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Terminator 3 The Redemption Pc _HOT_ Download Torrent Game.md
deleted file mode 100644
index 5197fdf8814018395781576f7a7b799443e58f25..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Terminator 3 The Redemption Pc _HOT_ Download Torrent Game.md	
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-
-The Redemption received mixed reviews upon release. Critics criticized the artificial intelligence and the controls, but praised the graphics and the gameplay. Some reviewers also praised the voice cast. The PlayStation 2 version received the highest score of any version of the game, garnering a positive reaction due to its strong multiplayer support and original voice cast.
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-
-The game features six playable characters: Kyle Reese, John Connor, Sarah Connor, the Terminator, the Assassin and the T-1000. The game features online multiplayer through PlayStation Network and Xbox Live. The multiplayer modes include "Team Deathmatch", "Defend Your Base", "One Flag Capture", "One Flag Defensive", "Two Flag Capture", "Two Flag Defensive", "Three Flag Capture", and "Three Flag Defense". It also features a "Survival" mode in which the player has to survive as long as possible and rack up points by killing the enemy and other players. Each character has different abilities.
-
-Plot
-
-In 2029, the Cyberdyne Systems Corporation scientists are continuing to research and improve upon the T-1000 Terminator, hoping to use it in the T-X Terminator model. The research is overseen by J.D. Pleckter, a scientist in Cyberdyne's Transhumanist division who believes that the human race can make a better society with a machine running the world. Pleckter's assistant, Mark Weston, is investigating the causes of an explosion at a military research facility which led to the death of many people. He is contacted by T-X, a model of the Terminator that he is developing. He is ordered to upload Ple 4fefd39f24<br />
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deleted file mode 100644
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diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/__init__.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/__init__.py
deleted file mode 100644
index 52e4b48d383a84a055dcd7f6236f6e8e58eab924..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/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/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/gc_head.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/gc_head.py
deleted file mode 100644
index 70741245af975800840709911bd18d72247e3e04..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/gc_head.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch
-from annotator.uniformer.mmcv.cnn import ContextBlock
-
-from ..builder import HEADS
-from .fcn_head import FCNHead
-
-
-@HEADS.register_module()
-class GCHead(FCNHead):
-    """GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond.
-
-    This head is the implementation of `GCNet
-    <https://arxiv.org/abs/1904.11492>`_.
-
-    Args:
-        ratio (float): Multiplier of channels ratio. Default: 1/4.
-        pooling_type (str): The pooling type of context aggregation.
-            Options are 'att', 'avg'. Default: 'avg'.
-        fusion_types (tuple[str]): The fusion type for feature fusion.
-            Options are 'channel_add', 'channel_mul'. Default: ('channel_add',)
-    """
-
-    def __init__(self,
-                 ratio=1 / 4.,
-                 pooling_type='att',
-                 fusion_types=('channel_add', ),
-                 **kwargs):
-        super(GCHead, self).__init__(num_convs=2, **kwargs)
-        self.ratio = ratio
-        self.pooling_type = pooling_type
-        self.fusion_types = fusion_types
-        self.gc_block = ContextBlock(
-            in_channels=self.channels,
-            ratio=self.ratio,
-            pooling_type=self.pooling_type,
-            fusion_types=self.fusion_types)
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        output = self.convs[0](x)
-        output = self.gc_block(output)
-        output = self.convs[1](output)
-        if self.concat_input:
-            output = self.conv_cat(torch.cat([x, output], dim=1))
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/t110-ai-admin/InspectLens/video_llama/datasets/datasets/webvid_datasets.py b/spaces/t110-ai-admin/InspectLens/video_llama/datasets/datasets/webvid_datasets.py
deleted file mode 100644
index aaf6b9d6dff0d96b04d40a40c0051527f7d01842..0000000000000000000000000000000000000000
--- a/spaces/t110-ai-admin/InspectLens/video_llama/datasets/datasets/webvid_datasets.py
+++ /dev/null
@@ -1,122 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import os
-from video_llama.datasets.datasets.base_dataset import BaseDataset
-from video_llama.datasets.datasets.caption_datasets import CaptionDataset
-import pandas as pd
-import decord
-from decord import VideoReader
-import random
-import torch
-from torch.utils.data.dataloader import default_collate
-class WebvidDataset(BaseDataset):
-    def __init__(self, vis_processor, text_processor, vis_root, ann_root):
-        """
-        vis_root (string): Root directory of video (e.g. webvid_eval/video/)
-        ann_root (string): Root directory of video (e.g. webvid_eval/annotations/)
-        split (string): val or test
-        """
-        super().__init__(vis_processor=vis_processor, text_processor=text_processor)
-
-
-        # 读取一个路径下所有的
-
-        ts_df = []
-        for file_name in os.listdir(ann_root):
-            if file_name.endswith('.csv'):
-                df = pd.read_csv(os.path.join(ann_root, file_name))
-                ts_df.append(df)
-
-        merged_df = pd.concat(ts_df)
-        self.annotation = merged_df
-        self.vis_root = vis_root
-        self.resize_size = 224
-        self.num_frm = 8
-        self.frm_sampling_strategy = 'headtail'
-
-    def _get_video_path(self, sample):
-        rel_video_fp = os.path.join(sample['page_dir'], str(sample['videoid']) + '.mp4')
-        full_video_fp = os.path.join(self.vis_root,  rel_video_fp)
-        return full_video_fp
-
-    def __getitem__(self, index):
-        num_retries = 10  # skip error videos
-        for _ in range(num_retries):
-            sample = self.annotation.iloc[index]
-            sample_dict = sample.to_dict()
-            video_id = sample_dict['videoid']
-
-            if 'name' in sample_dict.keys():
-                text = sample_dict['name'].strip()
-            else:
-                raise NotImplementedError("Un-supported text annotation format.")
-
-            # fetch video
-            video_path = self._get_video_path(sample_dict) 
-            # if os.path.exists(video_path):
-            try:
-                video = self.vis_processor(video_path)
-            except:
-                print(f"Failed to load examples with video: {video_path}. "
-                            f"Will randomly sample an example as a replacement.")
-                index = random.randint(0, len(self) - 1)
-                continue
-            caption = self.text_processor(text)
-
-            # print(video.size())
-            if video is None or caption is None \
-                    or video.size()!=torch.Size([3,self.vis_processor.n_frms,224,224]):
-                print(f"Failed to load examples with video: {video_path}. "
-                            f"Will randomly sample an example as a replacement.")
-                index = random.randint(0, len(self) - 1)
-                continue
-            else:
-                break
-        else:  
-            raise RuntimeError(f"Failed to fetch video after {num_retries} retries.")
-        # "image_id" is kept to stay compatible with the COCO evaluation format
-        return {
-            "image": video,
-            "text_input": caption,
-            "type":'video',
-        }
-
-    def __len__(self):
-        return len(self.annotation)
-
-    # def collater(self, samples):
-    #     new_result = {}
-    #     new_result['image'] = default_collate( [sample["image"] for sample in samples])
-    #     new_result['text_input'] = default_collate( [sample["text_input"] for sample in samples])
-    #     return new_result
-        
-class WebvidDatasetEvalDataset(BaseDataset):
-    def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
-        """
-        vis_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        split (string): val or test
-        """
-        super().__init__(vis_processor, text_processor, vis_root, ann_paths)
-
-    def __getitem__(self, index):
-
-        ann = self.annotation[index]
-
-        vname = ann["video"]
-        video_path = os.path.join(self.vis_root, vname)
-
-        video = self.vis_processor(video_path)
-
-        return {
-            "video": video,
-            "image_id": ann["image_id"],
-            "instance_id": ann["instance_id"],
-        }
-
-
diff --git a/spaces/teamnassim/Fictionista/torch_utils/misc.py b/spaces/teamnassim/Fictionista/torch_utils/misc.py
deleted file mode 100644
index 335397dd1662d8f5bfd44e17899a00549867f4bc..0000000000000000000000000000000000000000
--- a/spaces/teamnassim/Fictionista/torch_utils/misc.py
+++ /dev/null
@@ -1,266 +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.
-
-import re
-import contextlib
-import numpy as np
-import torch
-import warnings
-import dnnlib
-
-#----------------------------------------------------------------------------
-# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
-# same constant is used multiple times.
-
-_constant_cache = dict()
-
-def constant(value, shape=None, dtype=None, device=None, memory_format=None):
-    value = np.asarray(value)
-    if shape is not None:
-        shape = tuple(shape)
-    if dtype is None:
-        dtype = torch.get_default_dtype()
-    if device is None:
-        device = torch.device('cpu')
-    if memory_format is None:
-        memory_format = torch.contiguous_format
-
-    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
-    tensor = _constant_cache.get(key, None)
-    if tensor is None:
-        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
-        if shape is not None:
-            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
-        tensor = tensor.contiguous(memory_format=memory_format)
-        _constant_cache[key] = tensor
-    return tensor
-
-#----------------------------------------------------------------------------
-# Replace NaN/Inf with specified numerical values.
-
-try:
-    nan_to_num = torch.nan_to_num # 1.8.0a0
-except AttributeError:
-    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
-        assert isinstance(input, torch.Tensor)
-        if posinf is None:
-            posinf = torch.finfo(input.dtype).max
-        if neginf is None:
-            neginf = torch.finfo(input.dtype).min
-        assert nan == 0
-        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
-
-#----------------------------------------------------------------------------
-# Symbolic assert.
-
-try:
-    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
-except AttributeError:
-    symbolic_assert = torch.Assert # 1.7.0
-
-#----------------------------------------------------------------------------
-# Context manager to temporarily suppress known warnings in torch.jit.trace().
-# Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672
-
-@contextlib.contextmanager
-def suppress_tracer_warnings():
-    flt = ('ignore', None, torch.jit.TracerWarning, None, 0)
-    warnings.filters.insert(0, flt)
-    yield
-    warnings.filters.remove(flt)
-
-#----------------------------------------------------------------------------
-# Assert that the shape of a tensor matches the given list of integers.
-# None indicates that the size of a dimension is allowed to vary.
-# Performs symbolic assertion when used in torch.jit.trace().
-
-def assert_shape(tensor, ref_shape):
-    if tensor.ndim != len(ref_shape):
-        raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
-    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
-        if ref_size is None:
-            pass
-        elif isinstance(ref_size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
-        elif isinstance(size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
-        elif size != ref_size:
-            raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
-
-#----------------------------------------------------------------------------
-# Function decorator that calls torch.autograd.profiler.record_function().
-
-def profiled_function(fn):
-    def decorator(*args, **kwargs):
-        with torch.autograd.profiler.record_function(fn.__name__):
-            return fn(*args, **kwargs)
-    decorator.__name__ = fn.__name__
-    return decorator
-
-#----------------------------------------------------------------------------
-# Sampler for torch.utils.data.DataLoader that loops over the dataset
-# indefinitely, shuffling items as it goes.
-
-class InfiniteSampler(torch.utils.data.Sampler):
-    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
-        assert len(dataset) > 0
-        assert num_replicas > 0
-        assert 0 <= rank < num_replicas
-        assert 0 <= window_size <= 1
-        super().__init__(dataset)
-        self.dataset = dataset
-        self.rank = rank
-        self.num_replicas = num_replicas
-        self.shuffle = shuffle
-        self.seed = seed
-        self.window_size = window_size
-
-    def __iter__(self):
-        order = np.arange(len(self.dataset))
-        rnd = None
-        window = 0
-        if self.shuffle:
-            rnd = np.random.RandomState(self.seed)
-            rnd.shuffle(order)
-            window = int(np.rint(order.size * self.window_size))
-
-        idx = 0
-        while True:
-            i = idx % order.size
-            if idx % self.num_replicas == self.rank:
-                yield order[i]
-            if window >= 2:
-                j = (i - rnd.randint(window)) % order.size
-                order[i], order[j] = order[j], order[i]
-            idx += 1
-
-#----------------------------------------------------------------------------
-# Utilities for operating with torch.nn.Module parameters and buffers.
-
-def params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.parameters()) + list(module.buffers())
-
-def named_params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.named_parameters()) + list(module.named_buffers())
-
-def copy_params_and_buffers(src_module, dst_module, require_all=False):
-    assert isinstance(src_module, torch.nn.Module)
-    assert isinstance(dst_module, torch.nn.Module)
-    src_tensors = dict(named_params_and_buffers(src_module))
-    for name, tensor in named_params_and_buffers(dst_module):
-        assert (name in src_tensors) or (not require_all)
-        if name in src_tensors:
-            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
-
-#----------------------------------------------------------------------------
-# Context manager for easily enabling/disabling DistributedDataParallel
-# synchronization.
-
-@contextlib.contextmanager
-def ddp_sync(module, sync):
-    assert isinstance(module, torch.nn.Module)
-    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
-        yield
-    else:
-        with module.no_sync():
-            yield
-
-#----------------------------------------------------------------------------
-# Check DistributedDataParallel consistency across processes.
-
-def check_ddp_consistency(module, ignore_regex=None):
-    assert isinstance(module, torch.nn.Module)
-    for name, tensor in named_params_and_buffers(module):
-        fullname = type(module).__name__ + '.' + name
-        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
-            continue
-        tensor = tensor.detach()
-        if tensor.is_floating_point():
-            tensor = nan_to_num(tensor)
-        other = tensor.clone()
-        torch.distributed.broadcast(tensor=other, src=0)
-        assert (tensor == other).all(), fullname
-
-#----------------------------------------------------------------------------
-# Print summary table of module hierarchy.
-
-def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
-    assert isinstance(module, torch.nn.Module)
-    assert not isinstance(module, torch.jit.ScriptModule)
-    assert isinstance(inputs, (tuple, list))
-
-    # Register hooks.
-    entries = []
-    nesting = [0]
-    def pre_hook(_mod, _inputs):
-        nesting[0] += 1
-    def post_hook(mod, _inputs, outputs):
-        nesting[0] -= 1
-        if nesting[0] <= max_nesting:
-            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
-            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
-            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
-    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
-    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
-
-    # Run module.
-    outputs = module(*inputs)
-    for hook in hooks:
-        hook.remove()
-
-    # Identify unique outputs, parameters, and buffers.
-    tensors_seen = set()
-    for e in entries:
-        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
-        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
-        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
-        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
-
-    # Filter out redundant entries.
-    if skip_redundant:
-        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
-
-    # Construct table.
-    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
-    rows += [['---'] * len(rows[0])]
-    param_total = 0
-    buffer_total = 0
-    submodule_names = {mod: name for name, mod in module.named_modules()}
-    for e in entries:
-        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
-        param_size = sum(t.numel() for t in e.unique_params)
-        buffer_size = sum(t.numel() for t in e.unique_buffers)
-        output_shapes = [str(list(t.shape)) for t in e.outputs]
-        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
-        rows += [[
-            name + (':0' if len(e.outputs) >= 2 else ''),
-            str(param_size) if param_size else '-',
-            str(buffer_size) if buffer_size else '-',
-            (output_shapes + ['-'])[0],
-            (output_dtypes + ['-'])[0],
-        ]]
-        for idx in range(1, len(e.outputs)):
-            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
-        param_total += param_size
-        buffer_total += buffer_size
-    rows += [['---'] * len(rows[0])]
-    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
-
-    # Print table.
-    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
-    print()
-    for row in rows:
-        print('  '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
-    print()
-    return outputs
-
-#----------------------------------------------------------------------------
diff --git a/spaces/teowu/Q-Instruct-on-mPLUG-Owl-2/mplug_owl2/train/mplug_owl2_trainer.py b/spaces/teowu/Q-Instruct-on-mPLUG-Owl-2/mplug_owl2/train/mplug_owl2_trainer.py
deleted file mode 100644
index 293dcdf21c82c715e663af11b90a19543244af18..0000000000000000000000000000000000000000
--- a/spaces/teowu/Q-Instruct-on-mPLUG-Owl-2/mplug_owl2/train/mplug_owl2_trainer.py
+++ /dev/null
@@ -1,243 +0,0 @@
-import os
-import torch
-
-from torch.utils.data import Sampler
-
-from transformers import Trainer
-from transformers.trainer import (
-    is_sagemaker_mp_enabled,
-    get_parameter_names,
-    has_length,
-    ALL_LAYERNORM_LAYERS,
-    ShardedDDPOption,
-    logger,
-)
-from typing import List, Optional
-from icecream import ic
-
-def maybe_zero_3(param, ignore_status=False, name=None):
-    from deepspeed import zero
-    from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
-    if hasattr(param, "ds_id"):
-        if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
-            if not ignore_status:
-                print(name, 'no ignore status')
-        with zero.GatheredParameters([param]):
-            param = param.data.detach().cpu().clone()
-    else:
-        param = param.detach().cpu().clone()
-    return param
-
-
-def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
-    to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
-    to_return = {k: maybe_zero_3(v, ignore_status=True, name=k).cpu() for k, v in to_return.items()}
-    return to_return
-
-
-def split_to_even_chunks(indices, lengths, num_chunks):
-    """
-    Split a list of indices into `chunks` chunks of roughly equal lengths.
-    """
-
-    if len(indices) % num_chunks != 0:
-        return [indices[i::num_chunks] for i in range(num_chunks)]
-
-    num_indices_per_chunk = len(indices) // num_chunks
-
-    chunks = [[] for _ in range(num_chunks)]
-    chunks_lengths = [0 for _ in range(num_chunks)]
-    for index in indices:
-        shortest_chunk = chunks_lengths.index(min(chunks_lengths))
-        chunks[shortest_chunk].append(index)
-        chunks_lengths[shortest_chunk] += lengths[index]
-        if len(chunks[shortest_chunk]) == num_indices_per_chunk:
-            chunks_lengths[shortest_chunk] = float("inf")
-
-    return chunks
-
-
-def get_modality_length_grouped_indices(lengths, batch_size, world_size, generator=None):
-    # We need to use torch for the random part as a distributed sampler will set the random seed for torch.
-    assert all(l != 0 for l in lengths), "Should not have zero length."
-    if all(l > 0 for l in lengths) or all(l < 0 for l in lengths):
-        # all samples are in the same modality
-        return get_length_grouped_indices(lengths, batch_size, world_size, generator=generator)
-    mm_indices, mm_lengths = zip(*[(i, l) for i, l in enumerate(lengths) if l > 0])
-    lang_indices, lang_lengths = zip(*[(i, -l) for i, l in enumerate(lengths) if l < 0])
-
-    mm_shuffle = [mm_indices[i] for i in get_length_grouped_indices(mm_lengths, batch_size, world_size, generator=None)]
-    lang_shuffle = [lang_indices[i] for i in get_length_grouped_indices(lang_lengths, batch_size, world_size, generator=None)]
-    megabatch_size = world_size * batch_size
-    mm_megabatches = [mm_shuffle[i : i + megabatch_size] for i in range(0, len(mm_shuffle), megabatch_size)]
-    lang_megabatches = [lang_shuffle[i : i + megabatch_size] for i in range(0, len(lang_shuffle), megabatch_size)]
-
-    last_mm = mm_megabatches[-1]
-    last_lang = lang_megabatches[-1]
-    additional_batch = last_mm + last_lang
-    megabatches = mm_megabatches[:-1] + lang_megabatches[:-1]
-    megabatch_indices = torch.randperm(len(megabatches), generator=generator)
-    megabatches = [megabatches[i] for i in megabatch_indices]
-
-    if len(additional_batch) > 0:
-        megabatches.append(sorted(additional_batch))
-
-    return [i for megabatch in megabatches for i in megabatch]
-
-
-def get_length_grouped_indices(lengths, batch_size, world_size, generator=None, merge=True):
-    # We need to use torch for the random part as a distributed sampler will set the random seed for torch.
-    indices = torch.randperm(len(lengths), generator=generator)
-    megabatch_size = world_size * batch_size
-    megabatches = [indices[i : i + megabatch_size].tolist() for i in range(0, len(lengths), megabatch_size)]
-    megabatches = [sorted(megabatch, key=lambda i: lengths[i], reverse=True) for megabatch in megabatches]
-    megabatches = [split_to_even_chunks(megabatch, lengths, world_size) for megabatch in megabatches]
-
-    return [i for megabatch in megabatches for batch in megabatch for i in batch]
-
-
-class LengthGroupedSampler(Sampler):
-    r"""
-    Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while
-    keeping a bit of randomness.
-    """
-
-    def __init__(
-        self,
-        batch_size: int,
-        world_size: int,
-        lengths: Optional[List[int]] = None,
-        generator=None,
-        group_by_modality: bool = False,
-    ):
-        if lengths is None:
-            raise ValueError("Lengths must be provided.")
-
-        self.batch_size = batch_size
-        self.world_size = world_size
-        self.lengths = lengths
-        self.generator = generator
-        self.group_by_modality = group_by_modality
-
-    def __len__(self):
-        return len(self.lengths)
-
-    def __iter__(self):
-        if self.group_by_modality:
-            indices = get_modality_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
-        else:
-            indices = get_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
-        return iter(indices)
-
-
-class MPLUGOwl2Trainer(Trainer):
-
-    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        if self.train_dataset is None or not has_length(self.train_dataset):
-            return None
-
-        if self.args.group_by_modality_length:
-            lengths = self.train_dataset.modality_lengths
-            return LengthGroupedSampler(
-                self.args.train_batch_size,
-                world_size=self.args.world_size * self.args.gradient_accumulation_steps,
-                lengths=lengths,
-                group_by_modality=True,
-            )
-        else:
-            return super()._get_train_sampler()
-
-    def create_optimizer(self):
-        """
-        Setup the optimizer.
-
-        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
-        Trainer's init through `optimizers`, or subclass and override this method in a subclass.
-        """
-        if is_sagemaker_mp_enabled():
-            return super().create_optimizer()
-        if self.sharded_ddp == ShardedDDPOption.SIMPLE:
-            return super().create_optimizer()
-
-        opt_model = self.model
-
-        if self.optimizer is None:
-            decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
-            decay_parameters = [name for name in decay_parameters if "bias" not in name]
-            if self.args.visual_abstractor_lr is not None:
-                projector_parameters = [name for name, _ in opt_model.named_parameters() if "visual_abstractor_lr" in name]
-                optimizer_grouped_parameters = [
-                    {
-                        "params": [
-                            p for n, p in opt_model.named_parameters() if (n in decay_parameters and n not in projector_parameters and p.requires_grad)
-                        ],
-                        "weight_decay": self.args.weight_decay,
-                    },
-                    {
-                        "params": [
-                            p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n not in projector_parameters and p.requires_grad)
-                        ],
-                        "weight_decay": 0.0,
-                    },
-                    {
-                        "params": [
-                            p for n, p in opt_model.named_parameters() if (n in decay_parameters and n in projector_parameters and p.requires_grad)
-                        ],
-                        "weight_decay": self.args.weight_decay,
-                        "lr": self.args.visual_abstractor_lr,
-                    },
-                    {
-                        "params": [
-                            p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n in projector_parameters and p.requires_grad)
-                        ],
-                        "weight_decay": 0.0,
-                        "lr": self.args.visual_abstractor_lr,
-                    },
-                ]
-            else:
-                optimizer_grouped_parameters = [
-                    {
-                        "params": [
-                            p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)
-                        ],
-                        "weight_decay": self.args.weight_decay,
-                    },
-                    {
-                        "params": [
-                            p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
-                        ],
-                        "weight_decay": 0.0,
-                    },
-                ]
-            ic(len(optimizer_grouped_parameters[0]['params']),len(optimizer_grouped_parameters[1]['params']))
-            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)
-
-            if self.sharded_ddp == ShardedDDPOption.SIMPLE:
-                self.optimizer = OSS(
-                    params=optimizer_grouped_parameters,
-                    optim=optimizer_cls,
-                    **optimizer_kwargs,
-                )
-            else:
-                self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-                if optimizer_cls.__name__ == "Adam8bit":
-                    import bitsandbytes
-
-                    manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
-
-                    skipped = 0
-                    for module in opt_model.modules():
-                        if isinstance(module, nn.Embedding):
-                            skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values())
-                            logger.info(f"skipped {module}: {skipped/2**20}M params")
-                            manager.register_module_override(module, "weight", {"optim_bits": 32})
-                            logger.debug(f"bitsandbytes: will optimize {module} in fp32")
-                    logger.info(f"skipped: {skipped/2**20}M params")
-
-        return self.optimizer
-
-    def _save_checkpoint(self, model, trial, metrics=None):
-        super(MPLUGOwl2Trainer, self)._save_checkpoint(model, trial, metrics)
-
-    def _save(self, output_dir: Optional[str] = None, state_dict=None):
-        super(MPLUGOwl2Trainer, self)._save(output_dir, state_dict)
\ No newline at end of file
diff --git a/spaces/teragron/docuchat-webui/document_chatbot.py b/spaces/teragron/docuchat-webui/document_chatbot.py
deleted file mode 100644
index 7e5eebda238aed12dcc87e223b01ee70355020ec..0000000000000000000000000000000000000000
--- a/spaces/teragron/docuchat-webui/document_chatbot.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import random
-import time
-import requests
-from langchain.chains.question_answering import load_qa_chain
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.docstore.document import Document
-from langchain.vectorstores import FAISS
-from langchain import HuggingFaceHub
-
-    
-class DocumentChatbot:
-    
-    def __init__(self):
-        self.llm = None
-        self.chain = None
-        self.embeddings = None
-        self.metadata = {"source": "internet"}
-        self.init_mes = ["According to the document, ", "Based on the text, ", "I think, ", "According to the text, ", "Based on the document you provided, "]
-        
-    
-    def respond(self, text_input, question, chat_history, model_name):
-        self.llm = HuggingFaceHub(repo_id=model_name, model_kwargs={"temperature":0, "max_length":512})
-        self.chain = load_qa_chain(self.llm, chain_type="stuff")
-        self.embeddings = HuggingFaceEmbeddings()
-        if not question or question.isspace():
-            return "Please enter a valid question.", chat_history
-        if text_input.startswith("http"):
-            response = requests.get(text_input)
-            text_var = response.text
-            if text_var is None:
-                raise ValueError("No document is given")
-        else:
-            text_var = text_input
-        time.sleep(0.5)
-
-        documents = [Document(page_content=text_var, metadata=self.metadata)]
-        text_splitter = CharacterTextSplitter(chunk_size=750, chunk_overlap=0)
-        docs = text_splitter.split_documents(documents)
-
-        if self.llm is None:
-            raise ValueError("Model not loaded")
-        db = FAISS.from_documents(docs, self.embeddings)
-        query = question
-        
-        try:
-            docs = db.similarity_search(query)
-            answer = self.chain.run(input_documents=docs, question=query)
-            bot_message = random.choice(self.init_mes) + answer + "."
-        except ValueError as e:
-            bot_message = f"An error occurred: {str(e)}"
-        chat_history.append((question, bot_message))
-
-        time.sleep(1)
-        
-        return "", chat_history
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Active Partition Recovery Version 6 Full Version.rar ((FREE)).md b/spaces/terfces0erbo/CollegeProjectV2/Active Partition Recovery Version 6 Full Version.rar ((FREE)).md
deleted file mode 100644
index cfadfcd9a255d1ca01e7355f578185a4ab248d50..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Active Partition Recovery Version 6 Full Version.rar ((FREE)).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>active partition recovery version 6 full version.rar</h2><br /><p><b><b>Download File</b> &#10022; <a href="https://bytlly.com/2uGjuF">https://bytlly.com/2uGjuF</a></b></p><br /><br />
-<br />
-Data recovery software and undelete tool supports FAT12/16/32, NTFS, ... It includes tools to recover data from corrupt or damaged Zip or SFX archives, RAR archives, ... Tool to create a USB recovery drive that you can use to reinstall the version of ... Active@ Partition Recovery 19.0.3.1 ... May 6, 2014; Freeware; Windows. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Grim Fandango - Portuguese Brazil - Portugu S Brasil - PT- !!HOT!! Download.md b/spaces/terfces0erbo/CollegeProjectV2/Grim Fandango - Portuguese Brazil - Portugu S Brasil - PT- !!HOT!! Download.md
deleted file mode 100644
index 9eae2ae36ce25d4d70a27acd4807fec4a8fa8b84..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Grim Fandango - Portuguese Brazil - Portugu S Brasil - PT- !!HOT!! Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Grim Fandango - Portuguese Brazil - Portugu s Brasil - PT- download</h2><br /><p><b><b>Download Zip</b> &#187;&#187;&#187; <a href="https://bytlly.com/2uGkhC">https://bytlly.com/2uGkhC</a></b></p><br /><br />
-
-... Grim Fandango by LucasArts for PC * Worldwide shipping. $14.75. Free shipping. GRIM FANDANGO LucasArts Game PC (1998) Box - New (PORTUGUES ... 4d29de3e1b<br />
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diff --git a/spaces/thealphhamerc/audio-to-text/app.py b/spaces/thealphhamerc/audio-to-text/app.py
deleted file mode 100644
index e459e38eca9234ab4784a285de5fccf948258ba3..0000000000000000000000000000000000000000
--- a/spaces/thealphhamerc/audio-to-text/app.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import os
-os.system("pip install git+https://github.com/openai/whisper.git")
-import pysrt
-import pandas as pd
-from pytube import YouTube
-from datetime import timedelta
-import whisper
-from subprocess import call
-import gradio as gr
-import logging
-# from transformers.pipelines.audio_utils import ffmpeg_read
-
-
-logger = logging.getLogger("whisper-jax-app")
-logger.setLevel(logging.INFO)
-ch = logging.StreamHandler()
-ch.setLevel(logging.INFO)
-formatter = logging.Formatter(
-    "%(asctime)s;%(levelname)s;%(message)s", "%Y-%m-%d %H:%M:%S")
-ch.setFormatter(formatter)
-logger.addHandler(ch)
-
-
-FILE_LIMIT_MB = 1000
-
-
-def run_cmd(command):
-    try:
-        print(command)
-        call(command)
-    except KeyboardInterrupt:
-        print("Process interrupted")
-        sys.exit(1)
-
-
-def inference(text):
-    cmd = ['tts', '--text', text]
-    run_cmd(cmd)
-    return 'tts_output.wav'
-
-
-baseModel = whisper.load_model("base")
-
-
-df_init = pd.DataFrame(columns=['start', 'end', 'text'])
-transcription_df = gr.DataFrame(value=df_init, label="Transcription dataframe", row_count=(
-    0, "dynamic"), max_rows=30, wrap=True, overflow_row_behaviour='paginate')
-
-
-inputs = [gr.components.Audio(type="filepath", label="Add audio file"), gr.inputs.Audio(source="microphone",
-                                                                                        optional=True, type="filepath"),]
-outputs = [gr.components.Textbox(), transcription_df]
-title = "Transcribe multi-lingual audio clips"
-description = "An example of using OpenAi whisper to generate transcriptions for audio clips."
-article = ""
-audio_examples = [
-    ["input/example-1.wav"],
-    ["input/example-2.wav"],
-]
-
-
-def transcribe(inputs, microphone):
-    if (microphone is not None):
-        inputs = microphone
-
-    if inputs is None:
-        logger.warning("No audio file")
-        return [f"File size exceeds file size limit. Got file of size {file_size_mb:.2f}MB for a limit of {FILE_LIMIT_MB}MB.", df_init]
-    file_size_mb = os.stat(inputs).st_size / (1024 * 1024)
-
-    # --------------------------------------------------- Check the file size ---------------------------------------------------
-    if file_size_mb > FILE_LIMIT_MB:
-        logger.warning("Max file size exceeded")
-        df = pd.DataFrame(columns=['start', 'end', 'text'])
-        return [f"File size exceeds file size limit. Got file of size {file_size_mb:.2f}MB for a limit of {FILE_LIMIT_MB}MB.", df_init]
-
-    # --------------------------------------------------- Transcribe the audio ---------------------------------------------------
-    result = baseModel.transcribe(audio=inputs, language='english',
-                                  verbose=False)
-    srtFilename = os.path.join("output/SrtFiles", inputs.split(
-        '/')[-1].split('.')[0]+'.srt')
-
-    #  --------------------------------------------------- Clear the file if exists ---------------------------------------------------
-    if os.path.exists(srtFilename):
-        os.remove(srtFilename)
-    with open(srtFilename, 'w', encoding='utf-8') as srtFile:
-        srtFile.write('')
-
-    # --------------------------------------------------- Write the file ---------------------------------------------------
-    segments = result['segments']
-    for segment in segments:
-        startTime = str(0)+str(timedelta(seconds=int(segment['start'])))+',000'
-        endTime = str(0)+str(timedelta(seconds=int(segment['end'])))+',000'
-        text = segment['text']
-        segmentId = segment['id']+1
-        segment = f"{segmentId}\n{startTime} --> {endTime}\n{text[1:] if text[0] is ' ' else text}\n\n"
-
-        with open(srtFilename, 'a', encoding='utf-8') as srtFile:
-            srtFile.write(segment)
-
-    # ------------------------------------------- Read the file and Prepare to display ---------------------------------------
-    try:
-        srt_path = srtFilename
-        df = pd.DataFrame(columns=['start', 'end', 'text'])
-        subs = pysrt.open(srt_path)
-
-        objects = []
-        for sub in subs:
-            start_hours = str(str(sub.start.hours) + "00")[0:2] if len(
-                str(sub.start.hours)) == 2 else str("0" + str(sub.start.hours) + "00")[0:2]
-            end_hours = str(str(sub.end.hours) + "00")[0:2] if len(
-                str(sub.end.hours)) == 2 else str("0" + str(sub.end.hours) + "00")[0:2]
-
-            start_minutes = str(str(sub.start.minutes) + "00")[0:2] if len(
-                str(sub.start.minutes)) == 2 else str("0" + str(sub.start.minutes) + "00")[0:2]
-            end_minutes = str(str(sub.end.minutes) + "00")[0:2] if len(
-                str(sub.end.minutes)) == 2 else str("0" + str(sub.end.minutes) + "00")[0:2]
-
-            start_seconds = str(str(sub.start.seconds) + "00")[0:2] if len(
-                str(sub.start.seconds)) == 2 else str("0" + str(sub.start.seconds) + "00")[0:2]
-            end_seconds = str(str(sub.end.seconds) + "00")[0:2] if len(
-                str(sub.end.seconds)) == 2 else str("0" + str(sub.end.seconds) + "00")[0:2]
-
-            start = start_hours + ":" + start_minutes + ":" + start_seconds + ",000"
-            end = end_hours + ":" + end_minutes + ":" + end_seconds + ",000"
-            text = sub.text
-            objects.append([start, end, text])
-
-        df = pd.DataFrame(objects, columns=['start', 'end', 'text'])
-    except Exception as e:
-        print('Error: ', e)
-        df = df_init
-
-    return [result["text"], df]
-
-
-# Transcribe youtube video
-# define function for transcription
-def youtube_transcript(url):
-    try:
-        if url:
-            yt = YouTube(url, use_oauth=True)
-            source = yt.streams.filter(progressive=True, file_extension='mp4').order_by(
-                'resolution').desc().first().download('output/youtube')
-
-            transcript = baseModel.transcribe(source)
-            return transcript["text"]
-    except Exception as e:
-        print('Error: ', e)
-        return 'Error: ' + str(e)
-
-
-def displaySrtFile(srtFilename):
-    with open(srtFilename, 'r', encoding='utf-8') as srtFile:
-        srtContent = srtFile.read()
-
-        try:
-
-            df = pd.DataFrame(columns=['start', 'end', 'text'])
-            srt_path = srtFilename
-            subs = pysrt.open(srt_path)
-
-            objects = []
-            for sub in subs:
-
-                start_hours = str(str(sub.start.hours) + "00")[0:2] if len(
-                    str(sub.start.hours)) == 2 else str("0" + str(sub.start.hours) + "00")[0:2]
-                end_hours = str(str(sub.end.hours) + "00")[0:2] if len(
-                    str(sub.end.hours)) == 2 else str("0" + str(sub.end.hours) + "00")[0:2]
-
-                start_minutes = str(str(sub.start.minutes) + "00")[0:2] if len(
-                    str(sub.start.minutes)) == 2 else str("0" + str(sub.start.minutes) + "00")[0:2]
-                end_minutes = str(str(sub.end.minutes) + "00")[0:2] if len(
-                    str(sub.end.minutes)) == 2 else str("0" + str(sub.end.minutes) + "00")[0:2]
-
-                start_seconds = str(str(sub.start.seconds) + "00")[0:2] if len(
-                    str(sub.start.seconds)) == 2 else str("0" + str(sub.start.seconds) + "00")[0:2]
-                end_seconds = str(str(sub.end.seconds) + "00")[0:2] if len(
-                    str(sub.end.seconds)) == 2 else str("0" + str(sub.end.seconds) + "00")[0:2]
-
-                start_millis = str(str(sub.start.milliseconds) + "000")[0:3]
-                end_millis = str(str(sub.end.milliseconds) + "000")[0:3]
-                objects.append([sub.text, f'{start_hours}:{start_minutes}:{start_seconds}.{start_millis}',
-                               f'{end_hours}:{end_minutes}:{end_seconds}.{end_millis}'])
-
-            for object in objects:
-                srt_to_df = {
-                    'start': [object[1]],
-                    'end': [object[2]],
-                    'text': [object[0]]
-                }
-
-                df = pd.concat([df, pd.DataFrame(srt_to_df)])
-        except Exception as e:
-            print("Error creating srt df")
-
-        return srtContent
-
-
-audio_chunked = gr.Interface(
-    fn=transcribe,
-    inputs=inputs,
-    outputs=outputs,
-    allow_flagging="never",
-    title=title,
-    description=description,
-    article=article,
-    examples=audio_examples,
-)
-
-# microphone_chunked = gr.Interface(
-#     fn=transcribe,
-#     inputs=[
-#         gr.inputs.Audio(source="microphone",
-#                         optional=True, type="filepath"),
-#     ],
-#     outputs=[
-#         gr.outputs.Textbox(label="Transcription").style(
-#             show_copy_button=True),
-#     ],
-#     allow_flagging="never",
-#     title=title,
-#     description=description,
-#     article=article,
-# )
-youtube_chunked = gr.Interface(
-    fn=youtube_transcript,
-    inputs=[
-        gr.inputs.Textbox(label="Youtube URL", type="text"),
-    ],
-    outputs=[
-        gr.outputs.Textbox(label="Transcription").style(
-            show_copy_button=True),
-    ],
-    allow_flagging="never",
-    title=title,
-
-    description=description,
-    article=article,
-    examples=[
-        ["https://www.youtube.com/watch?v=nlMuHtV82q8&ab_channel=NothingforSale24",],
-        ["https://www.youtube.com/watch?v=JzPfMbG1vrE&ab_channel=ExplainerVideosByLauren",],
-        ["https://www.youtube.com/watch?v=S68vvV0kod8&ab_channel=Pearl-CohnTelevision"]
-
-    ],
-
-)
-
-
-demo = gr.Blocks()
-with demo:
-    gr.TabbedInterface([audio_chunked, youtube_chunked], [
-        "Audio File", "Youtube"])
-demo.queue(concurrency_count=1, max_size=5)
-demo.launch(show_api=False)
diff --git a/spaces/thejagstudio/procom/amazon/migrations/__init__.py b/spaces/thejagstudio/procom/amazon/migrations/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Abntnbr15512.md b/spaces/tialenAdioni/chat-gpt-api/logs/Abntnbr15512.md
deleted file mode 100644
index 608669203df9edcd2ae19eef494962d01bda2a35..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Abntnbr15512.md
+++ /dev/null
@@ -1,45 +0,0 @@
-
-Here is a possible title and article with HTML formatting for the keyword "abntnbr15512":
-
-<h1>What is ABNT NBR 15512 and why is it important?</h1>
-<p>ABNT NBR 15512 is a Brazilian technical standard that specifies the requirements for the practice of environmental management in organizations. It was published by the Brazilian Association of Technical Standards (ABNT) in 2018, as a revision of the previous version from 2004.</p>
-<p>The standard aims to help organizations to improve their environmental performance, comply with legal and other obligations, prevent pollution, and achieve their environmental objectives. It is based on the principles of continuous improvement, stakeholder engagement, risk management, and life cycle thinking.</p>
-<h2>abntnbr15512</h2><br /><p><b><b>DOWNLOAD</b> &#128504;&#128504;&#128504; <a href="https://urlcod.com/2uK9Yk">https://urlcod.com/2uK9Yk</a></b></p><br /><br />
-<p>ABNT NBR 15512 is compatible with other management system standards, such as ISO 9001 (quality management) and ISO 45001 (occupational health and safety management). It follows the same structure and terminology as these standards, making it easier to integrate them into a single management system.</p>
-<p>The standard applies to any organization, regardless of its size, type, or sector. It can be used for certification purposes or for self-declaration of conformity. The benefits of implementing ABNT NBR 15512 include:</p>
-<ul>
-<li>Enhancing the organization's reputation and credibility</li>
-<li>Reducing costs and waste</li>
-<li>Increasing efficiency and productivity</li>
-<li>Improving customer satisfaction and loyalty</li>
-<li>Strengthening stakeholder relationships</li>
-<li>Minimizing environmental risks and impacts</li>
-<li>Contributing to sustainable development</li>
-</ul>
-<p>To comply with ABNT NBR 15512, an organization needs to establish, implement, maintain, and improve an environmental management system that covers the following elements:</p>
-<ol>
-<li>Context of the organization</li>
-<li>Leadership</li>
-<li>Planning</li>
-<li>Support</li>
-<li>Operation</li>
-<li>Performance evaluation</li>
-<li>Improvement</li>
-</ol>
-<p>The standard provides guidance and examples for each element, but does not prescribe specific methods or criteria. The organization has the flexibility to determine how to meet the requirements according to its own context and needs.</p>
-<p>ABNT NBR 15512 is a voluntary standard that reflects the best practices and expectations of the Brazilian society regarding environmental management. By adopting it, an organization can demonstrate its commitment to environmental responsibility and excellence.</p>Here is a possible continuation of the article with HTML formatting for the keyword "abntnbr15512":
-
-<h2>How to implement ABNT NBR 15512 in your organization?</h2>
-<p>If you want to implement ABNT NBR 15512 in your organization, you need to follow some steps that will help you to plan, execute, monitor, and improve your environmental management system. Here are some suggestions:</p>
-<ol>
-<li>Conduct a gap analysis to identify your current situation and your needs regarding environmental management. You can use the standard as a reference to check what requirements you already meet and what areas you need to improve.</li>
-<li>Define your environmental policy and objectives, taking into account your context, stakeholders, legal and other obligations, risks and opportunities, and life cycle aspects. Communicate them to your employees and other relevant parties.</li>
-<li>Establish roles and responsibilities for environmental management within your organization. Provide adequate resources, training, and awareness to ensure competence and commitment.</li>
-<li>Develop and document your environmental management system procedures and processes, covering all the elements of the standard. Define how you will control your environmental aspects, prevent pollution, comply with regulations, manage emergencies, and achieve your objectives.</li>
-<li>Implement your environmental management system according to your plans. Ensure that you follow your procedures and processes consistently and effectively. Use appropriate tools and techniques to measure and monitor your environmental performance.</li>
-<li>Evaluate your environmental management system periodically. Conduct internal audits and management reviews to verify compliance, effectiveness, and suitability. Identify strengths and weaknesses, as well as opportunities for improvement.</li>
-<li>Take corrective and preventive actions to address any nonconformities or potential problems. Implement improvement actions based on audit findings, management review results, feedback from stakeholders, or best practices. Continually seek ways to enhance your environmental performance.</li>
-</ol>
-<p>By following these steps, you can implement ABNT NBR 15512 in your organization and enjoy its benefits. However, if you need further guidance or assistance, you can consult experts or seek certification from an accredited body.</p> 7196e7f11a<br />
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\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Download Sisqo Unleash the Dragon Album Zip Where to Find the Best Deals and Discounts.md b/spaces/tialenAdioni/chat-gpt-api/logs/Download Sisqo Unleash the Dragon Album Zip Where to Find the Best Deals and Discounts.md
deleted file mode 100644
index 78be9779b846e403ecb181a95e483477b70026c9..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Download Sisqo Unleash the Dragon Album Zip Where to Find the Best Deals and Discounts.md	
+++ /dev/null
@@ -1,110 +0,0 @@
-
-<h1>Download Sisqo Unleash the Dragon Album Zip</h1>
-<p>If you are looking for a fun, catchy, and classic R&B album to listen to, you might want to check out <strong>Unleash the Dragon</strong> by Sisqo. This is the debut solo album by the American singer, who was previously the lead vocalist of the group Dru Hill. Released in 1999, the album features some of Sisqo's most popular songs, such as "Thong Song", "Incomplete", and "Got to Get It". In this article, we will tell you more about Sisqo, his album, and how you can download it in a zip file format.</p>
-<h2>download sisqo unleash the dragon album zip</h2><br /><p><b><b>Download File</b> &#10022;&#10022;&#10022; <a href="https://urlcod.com/2uK6A1">https://urlcod.com/2uK6A1</a></b></p><br /><br />
- <h2>Who is Sisqo?</h2>
-<p>Sisqo, whose real name is Mark Andrews, is an American singer, songwriter, dancer, and actor. He was born in Baltimore, Maryland, in 1978. He started his musical career as a member of Dru Hill, a R&B group that formed in 1992. Dru Hill had several hits in the late 1990s, such as "In My Bed", "How Deep Is Your Love", and "These Are The Times". Sisqo decided to pursue a solo career in 1999, while Dru Hill was on a hiatus. He signed with Def Soul, a subsidiary of Def Jam Recordings.</p>
- <h2>What is Unleash the Dragon?</h2>
-<p><strong>Unleash the Dragon</strong> is Sisqo's first solo album. It was released on November 30, 1999. The album's title refers to Sisqo's alter ego, "The Dragon", which he uses to express his creative and energetic side. The album's genre is mainly R&B, but it also incorporates elements of hip hop, pop, soul, and funk. The album was a huge success, selling over six million copies worldwide. It also received positive reviews from critics, who praised Sisqo's vocals, charisma, and versatility. The album earned Sisqo four Grammy nominations in 2001.</p>
- <h3>Tracklist and Songs</h3>
-<p>The album consists of 13 tracks, with a total length of 47 minutes. Here are the songs in the album and their brief descriptions:</p>
-<ul>
-<li><strong>Unleash the Dragon</strong> (feat. Beanie Sigel): The opening track and title song of the album. It is a rap song that introduces Sisqo's alter ego and his mission to dominate the music industry.</li>
-<li><strong>Got to Get It</strong> (feat. Make It Hot): The second track and first single of the album. It is an upbeat R&B song that talks about Sisqo's desire for a woman.</li>
-<li><strong>Is Love Enough</strong>: The third track and fourth single of the album. It is a slow jam that questions whether love is enough to sustain a relationship.</li>
-<li><strong>2nite (Interlude)</strong>: The fourth track and an interlude that sets up the mood for the next song.</li>
-<li><strong>How Can I Love U 2nite</strong>: The fifth track and third single of the album. It is a ballad that expresses Sisqo's feelings for a woman who is already taken.</li>
-<li><strong>Your Love Is Incredible</strong>: The sixth track and second single of the album. It is a mid-tempo R&B song that compliments a woman's love.</li>
-<li><strong>So Sexual</strong>: The seventh track of the album. It is a sensual R&B song that describes Sisqo's sexual fantasies.</li>
-<li><strong>Thong Song</strong>: The eighth track and fifth single of the album. It is a pop song that celebrates women who wear thongs.</li>
-<li><strong>Incomplete</strong>: The ninth track and sixth single of the album. It is a ballad that confesses Sisqo's loneliness without his lover.</li>
-<li><strong>Addicted</strong>: The tenth track of the album. It is a funky R&B song that compares love to a drug addiction.</li>
-<li><strong>Dru World Order (Interlude)</strong> (Dedicated to Dru Hill): The eleventh track and an interlude that pays tribute to Sisqo's group Dru Hill.</li>
-<li><strong>Enchantment Passing Through</strong>: The twelfth track of the album. It is a soulful song that features Sisqo's vocals from the Broadway musical <em>Aida</em>.</li>
-<li><strong>You Are Everything (Remix)</strong>: The thirteenth track and final song of the album. It is a remix of Dru Hill's hit song "You Are Everything", featuring rap verses from Ja Rule.</li>
-</ul>
- <h3>How to Download the Album Zip File</h3>
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-<li>Go to one of these websites that offer free downloads of albums: <a href="https://www5.mphiphop.com/album-sisqo-unleash-the-dragon/">Mphiphop</a>, <a href="https://eastnaija.com/full-album-sisqo-unlish-the-dragon-zip-file/">Eastnaija</a>, or <a href="https://www.qobuz.com/us-en/album/unleash-the-dragon-sisqo/mqfzk75am03bc">Qobuz</a>.</li>
-<li>Search for <em>Sisqo Unleash the Dragon</em> or click on one of these links: <a href="https://www5.mphiphop.com/album-sisqo-unleash-the-dragon/">Mphiphop link</a>, <a href="https://eastnaija.com/full-album-sisqo-unlish-the-dragon-zip-file/">Eastnaija link</a>, or <a href="https://www.qobuz.com/us-en/album/unleash-the-dragon-sisqo/mqfzk75am03bc">Qobuz link</a>.</li>
-<li>Select the option to download or stream the album.</li>
-<li>If you choose to download the album, you will need to unzip the file after it finishes downloading.</li>
-<li>You can use any software that can open zip files, such as WinZip or 7-Zip.</li>
-<li>After unzipping the file, you will see the folder containing the mp3 files of the songs in the album.</li>
-<li>You can then transfer the files to your device or play them on your computer.</li>
-</ol>
- <h2>Why You Should Listen to Unleash the Dragon</h2>
-<p>You might be wondering why you should listen to <strong>Unleash the Dragon</strong>, especially if you are not familiar with Sisqo or his music. Well, there are many reasons why this album is worth your time and attention. Here are some of them:</p>
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-<p>One of the main reasons why you should listen to <em>Unleash the Dragon</em> is because it is fun and catchy. and catchy. The songs are designed to make you feel good and dance. Sisqo's vocals are energetic and charismatic, and he knows how to deliver catchy hooks and melodies. The album also features some rap verses from guests like Beanie Sigel, Make It Hot, and Ja Rule, who add some spice and variety to the songs. Some of the most fun and catchy songs in the album are "Unleash the Dragon", "Got to Get It", "Your Love Is Incredible", and of course, "Thong Song".</p>
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-<p>Another reason why you should listen to <em>Unleash the Dragon</em> is because it is versatile and diverse. The album showcases Sisqo's ability to sing different styles and genres of music, from R&B to pop to soul to funk. The album also explores different themes and moods, from love to sex to loneliness to empowerment. Sisqo proves that he is not a one-trick pony, but a versatile and diverse artist who can appeal to different audiences and tastes. Some of the most versatile and diverse songs in the album are "Is Love Enough", "How Can I Love U 2nite", "Incomplete", and "Enchantment Passing Through".</p>
- <h3>The Album is Classic and Timeless</h3>
-<p>The final reason why you should listen to <em>Unleash the Dragon</em> is because it is classic and timeless. The album was a huge success when it was released in 1999, selling over six million copies worldwide and earning four Grammy nominations. The album also received positive reviews from critics, who praised Sisqo's vocals, charisma, and versatility. The album's popularity and quality have endured over time, as it still sounds fresh and relevant today. The album has influenced many artists who came after Sisqo, such as Usher, Chris Brown, Jason Derulo, and Bruno Mars. The album has also become a part of pop culture, especially with its iconic song "Thong Song", which has been referenced, parodied, sampled, and covered by many artists and media outlets. Some of the most classic and timeless songs in the album are "Thong Song", "Incomplete", "You Are Everything (Remix)", and "Got to Get It".</p>
- <h2>Conclusion</h2>
-<p>In conclusion, <strong>Unleash the Dragon</strong> is a fun, catchy, versatile, diverse, classic, and timeless R&B album that you should listen to. It showcases Sisqo's talent as a singer, songwriter, dancer, and actor. It also features some of his best songs that will make you feel good and dance. If you want to download the album in a zip file format, you can follow the steps we provided above. We hope you enjoy listening to <em>Unleash the Dragon</em> as much as we did.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and their answers about <em>Unleash the Dragon</em>:</p>
-<ul>
-<li><strong>Q: When was <em>Unleash the Dragon</em> released?</strong></li>
-<li>A: The album was released on November 30, 1999.</li>
-<li><strong>Q: Who produced <em>Unleash the Dragon</em>?</strong></li>
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-<li><strong>Q: How many singles were released from <em>Unleash the Dragon</em>?</strong></li>
-<li>A: Six singles were released from the album: "Got to Get It", "Your Love Is Incredible", "How Can I Love U 2nite", "Thong Song", "Incomplete", and "You Are Everything (Remix)".</li>
-<li><strong>Q: What awards did <em>Unleash the Dragon</em> win or get nominated for?</strong></li>
-<li>A: The album won two American Music Awards for Favorite Soul/R&B Male Artist and Favorite Soul/R&B New Artist in 2001. It also got nominated for four Grammy Awards for Best New Artist, Best R&B Male Vocal Performance ("Thong Song"), Best R&B Song ("Thong Song"), and Best R&B Album in 2001.</li>
-<li><strong>Q: Where can I stream or buy <em>Unleash the Dragon</em>?</strong></li>
-<li>A: You can stream or buy the album on various platforms such as Spotify, Apple Music, Amazon Music, YouTube Music, and Tidal.</li>
-</ul>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Download Windows 10 Professional Full Version Crack for Free (and Why You Shouldnt).md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Download Windows 10 Professional Full Version Crack for Free (and Why You Shouldnt).md
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/BOMBitUP APK The Best Tool for SMS Marketing and Promotion.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/BOMBitUP APK The Best Tool for SMS Marketing and Promotion.md
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-<p>The next step is to enter the target number and the number of SMS to send. You can do this by typing in the fields below the flag icon. Make sure you enter a valid number without any spaces or symbols. You can also enter a custom message if you want, or leave it blank for a random message. You can also choose how many SMS you want to send by using the slider below.</p>
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-<p>Bombitup APK is a great app for pranking your friends with unlimited SMS. It has a lot of features that make it fun and easy to use. You can download and install it on your Android device by following the steps above. However, you should use it responsibly and ethically and not abuse it for any harmful purposes. You should also be careful about the safety and legality of using Bombitup APK and follow the terms and conditions of the app.</p>
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- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Can I use Bombitup APK on iOS devices?</td>
-<td>No, Bombitup APK is only available for Android devices. There is no official version of Bombitup APK for iOS devices.</td>
-</tr>
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-<td>Can I bomb email addresses with Bombitup APK?</td>
-<td>Yes, Bombitup APK also supports email bombing. You can enter an email address instead of a phone number and send unlimited emails to it.</td>
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-<td>Can I bomb WhatsApp numbers with Bombitup APK?</td>
-<td>No, Bombitup APK does not support WhatsApp bombing. It only works with regular phone numbers that can receive SMS.</td>
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-<td>Can I bomb multiple numbers at once with Bombitup APK?</td>
-<td>No, Bombitup APK only allows you to bomb one number at a time. You have to stop the current bombing before starting a new one.</td>
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-<td>Possibly, if you misuse it for illegal or unethical purposes. You should always use Bombitup APK for fun and entertainment only and not harm anyone with it. You should also respect the laws and regulations of your country and the country of the target number regarding SMS bombing.</td>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bus Simulator Indonesia APK - The Most Realistic and Feature-Rich Bus Driving Game on Android.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bus Simulator Indonesia APK - The Most Realistic and Feature-Rich Bus Driving Game on Android.md
deleted file mode 100644
index 9f51af538283cd528c830e23fc31a56a363fc0e8..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bus Simulator Indonesia APK - The Most Realistic and Feature-Rich Bus Driving Game on Android.md	
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- <h2>What is Bus Simulator Indonesia?</h2>
- <p>Bus Simulator Indonesia (aka BUSSID) is a game developed by Maleo, an Indonesian game studio. It was released in 2017 and has been updated regularly ever since. It is one of the only bus simulator games with the most features and the most authentic Indonesian environment. You can drive various types of buses, from city buses to intercity buses, across different regions of Indonesia. You can also customize your bus with your own design and colors, and interact with other players online.</p>
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- <p>Bus Simulator Indonesia has many features that make it stand out from other bus simulator games. Here are some of them:</p>
- <h4>Design your own livery</h4>
- <p>You can create your own livery for your bus using the built-in editor. You can choose from different colors, patterns, stickers, logos, and more. You can also share your livery with other players or download their liveries from the online gallery.</p>
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- <h4>Easy and intuitive control</h4>
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- <p>You can drive your bus across different cities and places in Indonesia, such as Jakarta, Surabaya, Bali, Sumatra, Java, and more. You can see the landmarks, buildings, roads, traffic, weather, and culture of each region. You can also follow the realistic routes and schedules of the buses in Indonesia.</p>
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- <p>You can use various honks to communicate with other drivers and pedestrians on the road. You can also hear the famous "Om Telolet Om!" (Uncle, honk your horn, uncle!) phrase that became a viral sensation in Indonesia and around the world.</p>
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- <p>You can enjoy the stunning 3D graphics of the game that show the realistic and immersive scenery of Indonesia. You can also adjust the graphics quality and performance according to your device's capability.</p>
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- <p>You can play the game without being interrupted by annoying ads while driving. The only ads you will see are on the billboards along the road, which add to the realism of the game.</p>
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- <p>You can compete with other players on the leaderboard based on your driving skills and achievements. You can also save your data online so you don't lose your progress if you change your device or uninstall the game.</p>
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- <p>You can join or create a convoy with other players online and drive together on the same road. You can chat with them using the voice or text chat feature. You can also cooperate or compete with them in various missions and events.</p>
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-<li>Tap on the APK file and allow the installation from unknown sources if prompted.</li>
-<li>Follow the instructions on the screen and wait for the installation to complete.</li>
-<li>Launch the game and enjoy driving a bus in Indonesia!</li>
-</ol>
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- <p>Bus Simulator Indonesia is a game that will give you a lot of fun and satisfaction as a bus driver. You will be able to experience the beauty and diversity of Indonesia, as well as the challenges and rewards of driving a bus. You will also be able to express your creativity and personality by designing your own bus and livery. You will also be able to interact with other players online and join a community of bus enthusiasts. Bus Simulator Indonesia is a game that will make you feel like you are really in Indonesia, driving a bus.</p>
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- <h4>Q: Is Bus Simulator Indonesia safe to download and play?</h4>
-<p>A: Yes, Bus Simulator Indonesia is safe to download and play. It does not contain any viruses or malware, and it does not require any special permissions or access to your device. It is also regularly updated by its developer to fix any bugs or issues.</p>
- <h4>Q: How much space does Bus Simulator Indonesia need on my device?</h4>
-<p>A: Bus Simulator Indonesia needs about 300 MB of space on your device's storage. However, this may vary depending on the version of the game and the mods you install.</p>
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-<p>A: Yes, you can play Bus Simulator Indonesia offline. However, some features of the game, such as online multiplayer convoy, leaderboard, online data, online gallery, etc., will not be available offline. You will need an internet connection to access these features.</p>
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-<p>In this article, we will provide you with some information about Kitab Syamsul Maarif Pdf, such as its history, its contents, its benefits, and its risks. We will also give you some tips on how to download Kitab Syamsul Maarif Pdf safely and legally from reputable websites. We hope that this article will help you gain a better understanding of this book and its value for your spiritual journey.</p>
-
-<h2>The History of Kitab Syamsul Maarif</h2>
-
-<p>Kitab Syamsul Maarif, which means "The Book of the Sun of Knowledge" or "The Book of the Splendor of Knowledge", is one of the most influential works of Islamic mysticism and occultism. It was written by Imam Abi Al Abbas Ahmad bin Ali Al Buni, who was born in Annaba (formerly Bona), Algeria, in 622 AH (1225 CE) and died in Cairo, Egypt, in 622 AH (1225 CE).</p>
-
-<p>Imam Al Buni was a renowned scholar and Sufi master who belonged to the Shadhiliyya order. He was also a prolific writer who authored more than 100 books on various subjects, such as theology, jurisprudence, grammar, poetry, history, and ethics. However, he is best known for his works on esoteric sciences, especially those related to the science of letters (ilm al-huruf) and the science of squares (ilm al-awfaq).</p>
-
-<p>Kitab Syamsul Maarif is considered to be his magnum opus and his most comprehensive work on esoteric sciences. It is divided into four parts: the first part deals with the principles and foundations of esoteric sciences; the second part deals with the secrets of the Quran and the names of Allah; the third part deals with numerology, astrology, magic, talismans, amulets, invocations, prayers, and other practices; and the fourth part deals with miscellaneous topics.</p>
-
-<p>Kitab Syamsul Maarif was widely circulated and studied by many scholars and practitioners of esoteric sciences throughout history. It influenced many other works on similar topics, such as Kitab Shams al-Ma'arif al-Kubra by Ibn Arabi (d. 1240 CE), Kitab al-Bahar al-Mawrud by Ibn Khaldun (d. 1406 CE), Kitab al-Futuhat al-Makkiyya by Ibn Arabi (d. 1240 CE), Kitab al-Shifa' al-Sajjadiyya by Imam Zayn al-Abidin (d. 713 CE), Kitab al-Tawasin by Mansur al-Hallaj (d. 922 CE), Kitab al-Hikam by Ibn Ata Allah (d. 1309 CE), Kitab al-Ibriz by Abd al-Aziz al-Dabbagh (d. 1719 CE), Kitab Jawahir al-Khamsa by Muhammad Ghawth Gwaliyari (d. 1563 CE), Kitab Dalail al-Khayrat by Muhammad al-Jazuli (d. 1465 CE), Kitab Miftah al-Falah by Ibn Ata Allah (d. 1309 CE), Kitab Manba' al-Anwar by Ahmad Zarr</p>
-<p></p> 7196e7f11a<br />
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\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Heroes Of Might And Magic 6 For Mac Download _TOP_.md b/spaces/tioseFevbu/cartoon-converter/scripts/Heroes Of Might And Magic 6 For Mac Download _TOP_.md
deleted file mode 100644
index 2f1710f4c7f5c208f10b72312c2155f862047045..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Heroes Of Might And Magic 6 For Mac Download _TOP_.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-<br />
-<h1>How to Download and Play Heroes Of Might And Magic 6 on Mac</h1>
-<p>Heroes Of Might And Magic 6 is a strategy RPG game that was released in 2011 by Ubisoft. The game features an epic story where Angels plot to end an unfinished war with their ancient rivals, the Faceless. The game also includes two original Adventure Packs and a Standalone Expansion featuring the Dark-Elf Dungeon faction.</p>
-<p>If you are a fan of the Might and Magic series and want to play Heroes Of Might And Magic 6 on your Mac, you might be wondering how to do it. Unfortunately, the game is not officially supported for Mac OS, but there are some ways to run it on your device. Here are some options you can try:</p>
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-<ul>
-<li><b>Use a virtual machine.</b> A virtual machine is a software that allows you to run another operating system inside your Mac OS. You can use a virtual machine such as Parallels Desktop or VMware Fusion to install Windows on your Mac and then run Heroes Of Might And Magic 6 from there. However, this method might require a lot of disk space and memory, and it might affect the performance of the game.</li>
-<li><b>Use a wine wrapper.</b> Wine is a software that allows you to run Windows applications on Mac OS without installing Windows. You can use a wine wrapper such as Porting Kit or Wineskin Winery to create a custom app that can run Heroes Of Might And Magic 6 on your Mac. However, this method might not work for all versions of the game, and it might cause some compatibility issues or bugs.</li>
-<li><b>Use a cloud gaming service.</b> A cloud gaming service is a platform that allows you to stream games from a remote server to your device. You can use a cloud gaming service such as GeForce Now or Shadow to play Heroes Of Might And Magic 6 on your Mac without installing anything. However, this method might require a stable and fast internet connection, and it might incur some subscription fees or latency.</li>
-</ul>
-<p>As you can see, there are some ways to play Heroes Of Might And Magic 6 on your Mac, but none of them are perfect. You might have to compromise on some aspects such as quality, performance, or cost. If you really want to enjoy the game on your Mac, you might have to wait for an official port from Ubisoft or hope for a remastered version in the future.</p>
-
-<h2>Gameplay Tips for Heroes Of Might And Magic 6</h2>
-<p>Heroes Of Might And Magic 6 is a game that requires strategic thinking and careful planning. You will have to manage your resources, explore the map, build your cities, recruit your units, and lead your heroes into battle. Here are some tips that can help you improve your gameplay and enjoy the game more:</p>
-<ul>
-<li><b>Choose your faction and hero wisely.</b> There are six factions in the game: Haven, Inferno, Necropolis, Sanctuary, Stronghold, and Dungeon. Each faction has its own unique units, abilities, and playstyle. You should choose a faction that suits your preferences and strategy. You should also choose a hero that complements your faction and has the skills and spells you need.</li>
-<li><b>Develop your cities and heroes efficiently.</b> You should build your cities according to your needs and goals. You should prioritize the buildings that provide you with resources, units, and bonuses. You should also convert enemy cities to your faction if possible, to avoid maintenance costs and gain more benefits. You should level up your heroes by completing quests and fighting enemies. You should choose the abilities and spells that match your hero's class and role. You should also decide whether to follow the blood or tear path, which will affect your reputation and alignment.</li>
-<li><b>Explore the map and collect resources.</b> You should explore the map as much as you can, to find hidden treasures, artifacts, mines, and other useful objects. You should collect resources whenever you see them, as they are essential for building your cities and recruiting your units. You should also loot and sabotage enemy mines if you can, to gain an advantage over them. You should also be aware of the control zones, which determine who can access certain objects on the map.</li>
-<li><b>Battle smartly and tactically.</b> You should prepare for battle by choosing the right units and formations. You should use the terrain and obstacles to your advantage. You should also use your hero's abilities and spells wisely, to buff your units or debuff your enemies. You should also pay attention to the initiative and morale of your units, as they affect their turn order and performance. You should also know when to retreat or surrender if the battle is not going well.</li>
-</ul>
-<p>These are some basic tips for playing Heroes Of Might And Magic 6 on Mac. If you want to learn more about the game, you can check out some guides and videos online. Have fun!</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/dep_util.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/dep_util.py
deleted file mode 100644
index 521eb716a5ebbcbc2c59654c4e71c3f0ff1abf26..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/dep_util.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from distutils.dep_util import newer_group
-
-
-# yes, this is was almost entirely copy-pasted from
-# 'newer_pairwise()', this is just another convenience
-# function.
-def newer_pairwise_group(sources_groups, targets):
-    """Walk both arguments in parallel, testing if each source group is newer
-    than its corresponding target. Returns a pair of lists (sources_groups,
-    targets) where sources is newer than target, according to the semantics
-    of 'newer_group()'.
-    """
-    if len(sources_groups) != len(targets):
-        raise ValueError(
-            "'sources_group' and 'targets' must be the same length")
-
-    # build a pair of lists (sources_groups, targets) where source is newer
-    n_sources = []
-    n_targets = []
-    for i in range(len(sources_groups)):
-        if newer_group(sources_groups[i], targets[i]):
-            n_sources.append(sources_groups[i])
-            n_targets.append(targets[i])
-
-    return n_sources, n_targets
diff --git a/spaces/tomofi/MMOCR/mmocr/models/ner/losses/masked_cross_entropy_loss.py b/spaces/tomofi/MMOCR/mmocr/models/ner/losses/masked_cross_entropy_loss.py
deleted file mode 100644
index 034fb29590b9e8d420a2b0537a38c4e92b3d4acd..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/ner/losses/masked_cross_entropy_loss.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from mmocr.models.builder import LOSSES
-
-
-@LOSSES.register_module()
-class MaskedCrossEntropyLoss(nn.Module):
-    """The implementation of masked cross entropy loss.
-
-    The mask has 1 for real tokens and 0 for padding tokens,
-        which only keep active parts of the cross entropy loss.
-    Args:
-        num_labels (int): Number of classes in labels.
-        ignore_index (int): Specifies a target value that is ignored
-            and does not contribute to the input gradient.
-    """
-
-    def __init__(self, num_labels=None, ignore_index=0):
-        super().__init__()
-        self.num_labels = num_labels
-        self.criterion = CrossEntropyLoss(ignore_index=ignore_index)
-
-    def forward(self, logits, img_metas):
-        '''Loss forword.
-        Args:
-            logits: Model output with shape [N, C].
-            img_metas (dict): A dict containing the following keys:
-                    - img (list]): This parameter is reserved.
-                    - labels (list[int]): The labels for each word
-                        of the sequence.
-                    - texts (list): The words of the sequence.
-                    - input_ids (list): The ids for each word of
-                        the sequence.
-                    - attention_mask (list): The mask for each word
-                        of the sequence. The mask has 1 for real tokens
-                        and 0 for padding tokens. Only real tokens are
-                        attended to.
-                    - token_type_ids (list): The tokens for each word
-                        of the sequence.
-        '''
-
-        labels = img_metas['labels']
-        attention_masks = img_metas['attention_masks']
-
-        # Only keep active parts of the loss
-        if attention_masks is not None:
-            active_loss = attention_masks.view(-1) == 1
-            active_logits = logits.view(-1, self.num_labels)[active_loss]
-            active_labels = labels.view(-1)[active_loss]
-            loss = self.criterion(active_logits, active_labels)
-        else:
-            loss = self.criterion(
-                logits.view(-1, self.num_labels), labels.view(-1))
-        return {'loss_cls': loss}
diff --git a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py b/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py
deleted file mode 100644
index 1e7bfd6384af2c9eb4894147aaa9a0e6caed3630..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py
+++ /dev/null
@@ -1,170 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from maskrcnn_benchmark.modeling.backbone import resnet
-from maskrcnn_benchmark.modeling.poolers import Pooler
-from maskrcnn_benchmark.modeling.utils import cat
-from maskrcnn_benchmark.layers import Conv2d
-
-def conv3x3(in_planes, out_planes, stride=1, has_bias=False):
-    "3x3 convolution with padding"
-    return nn.Conv2d(
-        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=has_bias
-    )
-
-
-def conv3x3_bn_relu(in_planes, out_planes, stride=1, has_bias=False):
-    return nn.Sequential(
-        conv3x3(in_planes, out_planes, stride),
-        nn.BatchNorm2d(out_planes),
-        nn.ReLU(inplace=True),
-    )
-
-class ResNet50Conv5ROIFeatureExtractor(nn.Module):
-    def __init__(self, config):
-        super(ResNet50Conv5ROIFeatureExtractor, self).__init__()
-
-        resolution = config.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
-        scales = config.MODEL.ROI_BOX_HEAD.POOLER_SCALES
-        sampling_ratio = config.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
-        pooler = Pooler(
-            output_size=(resolution, resolution),
-            scales=scales,
-            sampling_ratio=sampling_ratio,
-        )
-
-        stage = resnet.StageSpec(index=4, block_count=3, return_features=False)
-        head = resnet.ResNetHead(
-            block_module=config.MODEL.RESNETS.TRANS_FUNC,
-            stages=(stage,),
-            num_groups=config.MODEL.RESNETS.NUM_GROUPS,
-            width_per_group=config.MODEL.RESNETS.WIDTH_PER_GROUP,
-            stride_in_1x1=config.MODEL.RESNETS.STRIDE_IN_1X1,
-            stride_init=None,
-            res2_out_channels=config.MODEL.RESNETS.RES2_OUT_CHANNELS,
-        )
-
-        self.pooler = pooler
-        self.head = head
-
-    def forward(self, x, proposals):
-        x = self.pooler(x, proposals)
-        x = self.head(x)
-        return x
-
-
-class FPN2MLPFeatureExtractor(nn.Module):
-    """
-    Heads for FPN for classification
-    """
-
-    def __init__(self, cfg):
-        super(FPN2MLPFeatureExtractor, self).__init__()
-        self.cfg = cfg
-        resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
-        scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
-        sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
-        pooler = Pooler(
-            output_size=(resolution, resolution),
-            scales=scales,
-            sampling_ratio=sampling_ratio,
-        )
-        if self.cfg.MODEL.ROI_BOX_HEAD.MIX_OPTION == 'CAT':
-            input_size = (cfg.MODEL.BACKBONE.OUT_CHANNELS + 1) * resolution ** 2
-        else:
-            input_size = cfg.MODEL.BACKBONE.OUT_CHANNELS * resolution ** 2
-        representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
-        self.pooler = pooler
-        self.fc6 = nn.Linear(input_size, representation_size)
-        self.fc7 = nn.Linear(representation_size, representation_size)
-        # if self.cfg.MODEL.ROI_BOX_HEAD.MIX_OPTION == 'ATTENTION':
-        #     self.attention = nn.Sequential(
-        #         conv3x3_bn_relu(cfg.MODEL.BACKBONE.OUT_CHANNELS + 1, 32),
-        #         conv3x3(32, 1),
-        #         nn.Sigmoid()
-        #     )
-        #     self.attention.apply(self.weights_init)
-        # if self.cfg.MODEL.ROI_BOX_HEAD.MIX_OPTION == 'ATTENTION':
-        #     self.attention = nn.Sequential(
-        #         Conv2d(cfg.MODEL.BACKBONE.OUT_CHANNELS + 1, 1, 1, 1, 0),
-        #         nn.Sigmoid()
-        #     )
-        #     for name, param in self.named_parameters():
-        #         if "bias" in name:
-        #             nn.init.constant_(param, 0)
-        #         elif "weight" in name:
-        #             # Caffe2 implementation uses MSRAFill, which in fact
-        #             # corresponds to kaiming_normal_ in PyTorch
-        #             nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu")
-
-        for l in [self.fc6, self.fc7]:
-            # Caffe2 implementation uses XavierFill, which in fact
-            # corresponds to kaiming_uniform_ in PyTorch
-            nn.init.kaiming_uniform_(l.weight, a=1)
-            nn.init.constant_(l.bias, 0)
-
-    def weights_init(self, m):
-        classname = m.__class__.__name__
-        if classname.find("Conv") != -1:
-            nn.init.kaiming_normal_(m.weight.data)
-        elif classname.find("BatchNorm") != -1:
-            m.weight.data.fill_(1.0)
-            m.bias.data.fill_(1e-4)
-
-    def feature_mask(self, x, proposals):
-        masks = []
-        for proposal in proposals:
-            segmentation_masks = proposal.get_field("masks")
-            boxes = proposal.bbox.to(torch.device("cpu"))
-            for segmentation_mask, box in zip(segmentation_masks, boxes):
-                cropped_mask = segmentation_mask.crop(box)
-                scaled_mask = cropped_mask.resize((self.cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION, self.cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION))
-                mask = scaled_mask.convert(mode="mask")
-                masks.append(mask)
-        if len(masks) == 0:
-            if self.cfg.MODEL.ROI_BOX_HEAD.MIX_OPTION == 'CAT':
-                x = cat([x, torch.ones((x.shape[0], 1, x.shape[2], x.shape[3]), device=x.device)], dim=1)
-            return x
-        masks = torch.stack(masks, dim=0).to(x.device, dtype=torch.float32)
-        if self.cfg.MODEL.ROI_BOX_HEAD.MIX_OPTION == 'CAT':
-            x = cat([x, masks.unsqueeze(1)], dim=1)
-            return x
-        if self.cfg.MODEL.ROI_BOX_HEAD.MIX_OPTION == 'ATTENTION':
-            # x_cat = cat([x, masks.unsqueeze(1)], dim=1)
-            # attention = self.attention(x_cat)
-            # x = x * attention
-            return x
-        soft_ratio = self.cfg.MODEL.ROI_BOX_HEAD.SOFT_MASKED_FEATURE_RATIO
-        if soft_ratio > 0:
-            if soft_ratio < 1.0:
-                x = x * (soft_ratio + (1 - soft_ratio) * masks.unsqueeze(1))
-            else:
-                x = x * (1.0 + soft_ratio * masks.unsqueeze(1))
-        else:
-            x = x * masks.unsqueeze(1)
-        return x
-
-    def forward(self, x, proposals):
-        x = self.pooler(x, proposals)
-        if self.cfg.MODEL.ROI_BOX_HEAD.USE_MASKED_FEATURE:
-            x = self.feature_mask(x, proposals)
-        x = x.view(x.size(0), -1)
-
-        x = F.relu(self.fc6(x))
-        x = F.relu(self.fc7(x))
-
-        return x
-
-
-_ROI_BOX_FEATURE_EXTRACTORS = {
-    "ResNet50Conv5ROIFeatureExtractor": ResNet50Conv5ROIFeatureExtractor,
-    "FPN2MLPFeatureExtractor": FPN2MLPFeatureExtractor,
-}
-
-
-def make_roi_box_feature_extractor(cfg):
-    func = _ROI_BOX_FEATURE_EXTRACTORS[cfg.MODEL.ROI_BOX_HEAD.FEATURE_EXTRACTOR]
-    return func(cfg)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/.dev_scripts/batch_test.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/.dev_scripts/batch_test.py
deleted file mode 100644
index c746c7c566e6486dfd6cdf226f2b8e3742a2b0ad..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/.dev_scripts/batch_test.py
+++ /dev/null
@@ -1,212 +0,0 @@
-"""
-some instructions
-1. Fill the models that needs to be checked in the modelzoo_dict
-2. Arange the structure of the directory as follows, the script will find the
-   corresponding config itself:
-   model_dir/model_family/checkpoints
-   e.g.: models/faster_rcnn/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
-         models/faster_rcnn/faster_rcnn_r101_fpn_1x_coco_20200130-047c8118.pth
-3. Excute the batch_test.sh
-"""
-
-import argparse
-import json
-import os
-import subprocess
-
-import mmcv
-import torch
-from mmcv import Config, get_logger
-from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
-                         wrap_fp16_model)
-
-from mmdet.apis import multi_gpu_test, single_gpu_test
-from mmdet.datasets import (build_dataloader, build_dataset,
-                            replace_ImageToTensor)
-from mmdet.models import build_detector
-
-modelzoo_dict = {
-    'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py': {
-        'bbox': 0.374
-    },
-    'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py': {
-        'bbox': 0.382,
-        'segm': 0.347
-    },
-    'configs/rpn/rpn_r50_fpn_1x_coco.py': {
-        'AR@1000': 0.582
-    }
-}
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='The script used for checking the correctness \
-            of batch inference')
-    parser.add_argument('model_dir', help='directory of models')
-    parser.add_argument(
-        'json_out', help='the output json records test information like mAP')
-    parser.add_argument(
-        '--launcher',
-        choices=['none', 'pytorch', 'slurm', 'mpi'],
-        default='none',
-        help='job launcher')
-    parser.add_argument('--local_rank', type=int, default=0)
-    args = parser.parse_args()
-    if 'LOCAL_RANK' not in os.environ:
-        os.environ['LOCAL_RANK'] = str(args.local_rank)
-    return args
-
-
-def check_finish(all_model_dict, result_file):
-    # check if all models are checked
-    tested_cfgs = []
-    with open(result_file, 'r+') as f:
-        for line in f:
-            line = json.loads(line)
-            tested_cfgs.append(line['cfg'])
-    is_finish = True
-    for cfg in sorted(all_model_dict.keys()):
-        if cfg not in tested_cfgs:
-            return cfg
-    if is_finish:
-        with open(result_file, 'a+') as f:
-            f.write('finished\n')
-
-
-def dump_dict(record_dict, json_out):
-    # dump result json dict
-    with open(json_out, 'a+') as f:
-        mmcv.dump(record_dict, f, file_format='json')
-        f.write('\n')
-
-
-def main():
-    args = parse_args()
-    # touch the output json if not exist
-    with open(args.json_out, 'a+'):
-        pass
-    # init distributed env first, since logger depends on the dist
-    # info.
-    if args.launcher == 'none':
-        distributed = False
-    else:
-        distributed = True
-        init_dist(args.launcher, backend='nccl')
-    rank, world_size = get_dist_info()
-
-    logger = get_logger('root')
-
-    # read info of checkpoints and config
-    result_dict = dict()
-    for model_family_dir in os.listdir(args.model_dir):
-        for model in os.listdir(
-                os.path.join(args.model_dir, model_family_dir)):
-            # cpt: rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth
-            # cfg: rpn_r50_fpn_1x_coco.py
-            cfg = model.split('.')[0][:-18] + '.py'
-            cfg_path = os.path.join('configs', model_family_dir, cfg)
-            assert os.path.isfile(
-                cfg_path), f'{cfg_path} is not valid config path'
-            cpt_path = os.path.join(args.model_dir, model_family_dir, model)
-            result_dict[cfg_path] = cpt_path
-            assert cfg_path in modelzoo_dict, f'please fill the ' \
-                                              f'performance of cfg: {cfg_path}'
-    cfg = check_finish(result_dict, args.json_out)
-    cpt = result_dict[cfg]
-    try:
-        cfg_name = cfg
-        logger.info(f'evaluate {cfg}')
-        record = dict(cfg=cfg, cpt=cpt)
-        cfg = Config.fromfile(cfg)
-        # cfg.data.test.ann_file = 'data/val_0_10.json'
-        # set cudnn_benchmark
-        if cfg.get('cudnn_benchmark', False):
-            torch.backends.cudnn.benchmark = True
-        cfg.model.pretrained = None
-        if cfg.model.get('neck'):
-            if isinstance(cfg.model.neck, list):
-                for neck_cfg in cfg.model.neck:
-                    if neck_cfg.get('rfp_backbone'):
-                        if neck_cfg.rfp_backbone.get('pretrained'):
-                            neck_cfg.rfp_backbone.pretrained = None
-            elif cfg.model.neck.get('rfp_backbone'):
-                if cfg.model.neck.rfp_backbone.get('pretrained'):
-                    cfg.model.neck.rfp_backbone.pretrained = None
-
-        # in case the test dataset is concatenated
-        if isinstance(cfg.data.test, dict):
-            cfg.data.test.test_mode = True
-        elif isinstance(cfg.data.test, list):
-            for ds_cfg in cfg.data.test:
-                ds_cfg.test_mode = True
-
-        # build the dataloader
-        samples_per_gpu = 2  # hack test with 2 image per gpu
-        if samples_per_gpu > 1:
-            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
-            cfg.data.test.pipeline = replace_ImageToTensor(
-                cfg.data.test.pipeline)
-        dataset = build_dataset(cfg.data.test)
-        data_loader = build_dataloader(
-            dataset,
-            samples_per_gpu=samples_per_gpu,
-            workers_per_gpu=cfg.data.workers_per_gpu,
-            dist=distributed,
-            shuffle=False)
-
-        # build the model and load checkpoint
-        cfg.model.train_cfg = None
-        model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
-        fp16_cfg = cfg.get('fp16', None)
-        if fp16_cfg is not None:
-            wrap_fp16_model(model)
-
-        checkpoint = load_checkpoint(model, cpt, map_location='cpu')
-        # old versions did not save class info in checkpoints,
-        # this walkaround is for backward compatibility
-        if 'CLASSES' in checkpoint.get('meta', {}):
-            model.CLASSES = checkpoint['meta']['CLASSES']
-        else:
-            model.CLASSES = dataset.CLASSES
-
-        if not distributed:
-            model = MMDataParallel(model, device_ids=[0])
-            outputs = single_gpu_test(model, data_loader)
-        else:
-            model = MMDistributedDataParallel(
-                model.cuda(),
-                device_ids=[torch.cuda.current_device()],
-                broadcast_buffers=False)
-            outputs = multi_gpu_test(model, data_loader, 'tmp')
-        if rank == 0:
-            ref_mAP_dict = modelzoo_dict[cfg_name]
-            metrics = list(ref_mAP_dict.keys())
-            metrics = [
-                m if m != 'AR@1000' else 'proposal_fast' for m in metrics
-            ]
-            eval_results = dataset.evaluate(outputs, metrics)
-            print(eval_results)
-            for metric in metrics:
-                if metric == 'proposal_fast':
-                    ref_metric = modelzoo_dict[cfg_name]['AR@1000']
-                    eval_metric = eval_results['AR@1000']
-                else:
-                    ref_metric = modelzoo_dict[cfg_name][metric]
-                    eval_metric = eval_results[f'{metric}_mAP']
-                if abs(ref_metric - eval_metric) > 0.003:
-                    record['is_normal'] = False
-            dump_dict(record, args.json_out)
-            check_finish(result_dict, args.json_out)
-    except Exception as e:
-        logger.error(f'rank: {rank} test fail with error: {e}')
-        record['terminate'] = True
-        dump_dict(record, args.json_out)
-        check_finish(result_dict, args.json_out)
-        # hack there to throw some error to prevent hang out
-        subprocess.call('xxx')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/_base_/datasets/coco_detection.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/_base_/datasets/coco_detection.py
deleted file mode 100644
index 149f590bb45fa65c29fd4c005e4a237d7dd2e117..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/_base_/datasets/coco_detection.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# dataset settings
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-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'),
-    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']),
-]
-test_pipeline = [
-    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', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_train2017.json',
-        img_prefix=data_root + 'train2017/',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_val2017.json',
-        img_prefix=data_root + 'val2017/',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_val2017.json',
-        img_prefix=data_root + 'val2017/',
-        pipeline=test_pipeline))
-evaluation = dict(interval=1, metric='bbox')
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/changelog.md b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/changelog.md
deleted file mode 100644
index bf8bf3c1abb91bdf075c9a3d754ec5b31a33fd8c..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/changelog.md
+++ /dev/null
@@ -1,795 +0,0 @@
-## Changelog
-
-### v2.11. (01/4/2021)
-
-**Highlights**
-
-- Support new method: [Localization Distillation for Object Detection](https://arxiv.org/pdf/2102.12252.pdf)
-- Support Pytorch2ONNX with batch inference and dynamic shape
-
-**New Features**
-
-- Support localization distillation for object detection (#4758)
-- Support Pytorch2ONNX with batch inference and dynamic shape for Faster-RCNN and mainstream one-stage detectors (#4796)
-- Add Deformable Conv2d TensorRT plugin (#858)
-
-**Improvements**
-
-- Support batch inference in head of RetinaNet (#4699)
-- Add batch dimension in second stage of Faster-RCNN (#4785)
-- Support batch inference in bbox coder (#4721)
-- Add check for `ann_ids` in `COCODataset` to ensure it is unique (#4789)
-- support for showing the FPN results (#4716)
-- support dynamic shape for grid_anchor (#4684)
-- Support automatic statistical evaluation results and export them to EXCEL (#4693)
-- Move pycocotools version check to when it is used (#4880)
-
-**Bug Fixes**
-
-- Fix a bug of TridentNet when doing the batch inference (#4717)
-- Fix a bug of Pytorch2ONNX in FASF (#4735)
-- Fix a bug when show the image with float type (#4732)
-
-### v2.10.0 (01/03/2021)
-
-#### Highlights
-
-- Support new methods: [FPG](https://arxiv.org/abs/2004.03580)
-- Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN.
-
-#### New Features
-
-- Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN (#4569)
-- Support [Feature Pyramid Grids (FPG)](https://arxiv.org/abs/2004.03580) (#4645)
-- Support video demo (#4420)
-- Add seed option for sampler (#4665)
-- Support to customize type of runner (#4570, #4669)
-- Support synchronizing BN buffer in `EvalHook` (#4582)
-- Add script for GIF demo (#4573)
-
-#### Bug Fixes
-
-- Fix ConfigDict AttributeError and add Colab link (#4643)
-- Avoid crash in empty gt training of GFL head (#4631)
-- Fix `iou_thrs` bug in RPN evaluation (#4581)
-- Fix syntax error of config when upgrading model version (#4584)
-
-#### Improvements
-
-- Refactor unit test file structures (#4600)
-- Refactor nms config (#4636)
-- Get loading pipeline by checking the class directly rather than through config strings (#4619)
-- Add doctests for mask target generation and mask structures (#4614)
-- Use deep copy when copying pipeline arguments (#4621)
-- Update documentations (#4642, #4650, #4620, #4630)
-- Remove redundant code calling `import_modules_from_strings` (#4601)
-- Clean deprecated FP16 API (#4571)
-- Check whether `CLASSES` is correctly initialized in the intialization of `XMLDataset` (#4555)
-- Support batch inference in the inference API (#4462, #4526)
-- Clean deprecated warning and fix 'meta' error (#4695)
-
-### v2.9.0 (01/02/2021)
-
-#### Highlights
-
-- Support new methods: [SCNet](https://arxiv.org/abs/2012.10150), [Sparse R-CNN](https://arxiv.org/abs/2011.12450)
-- Move `train_cfg` and `test_cfg` into model in configs
-- Support to visualize results based on prediction quality
-
-#### New Features
-
-- Support [SCNet](https://arxiv.org/abs/2012.10150) (#4356)
-- Support [Sparse R-CNN](https://arxiv.org/abs/2011.12450) (#4219)
-- Support evaluate mAP by multiple IoUs (#4398)
-- Support concatenate dataset for testing (#4452)
-- Support to visualize results based on prediction quality (#4441)
-- Add ONNX simplify option to Pytorch2ONNX script (#4468)
-- Add hook for checking compatibility of class numbers in heads and datasets (#4508)
-
-#### Bug Fixes
-
-- Fix CPU inference bug of Cascade RPN (#4410)
-- Fix NMS error of CornerNet when there is no prediction box (#4409)
-- Fix TypeError in CornerNet inference (#4411)
-- Fix bug of PAA when training with background images (#4391)
-- Fix the error that the window data is not destroyed when `out_file is not None` and `show==False` (#4442)
-- Fix order of NMS `score_factor` that will decrease the performance of YOLOv3 (#4473)
-- Fix bug in HTC TTA when the number of detection boxes is 0 (#4516)
-- Fix resize error in mask data structures (#4520)
-
-#### Improvements
-
-- Allow to customize classes in LVIS dataset (#4382)
-- Add tutorials for building new models with existing datasets (#4396)
-- Add CPU compatibility information in documentation (#4405)
-- Add documentation of deprecated `ImageToTensor` for batch inference (#4408)
-- Add more details in documentation for customizing dataset (#4430)
-- Switch `imshow_det_bboxes` visualization backend from OpenCV to Matplotlib (#4389)
-- Deprecate `ImageToTensor` in `image_demo.py` (#4400)
-- Move train_cfg/test_cfg into model (#4347, #4489)
-- Update docstring for `reg_decoded_bbox` option in bbox heads (#4467)
-- Update dataset information in documentation (#4525)
-- Release pre-trained R50 and R101 PAA detectors with multi-scale 3x training schedules (#4495)
-- Add guidance for speed benchmark (#4537)
-
-### v2.8.0 (04/01/2021)
-
-#### Highlights
-
-- Support new methods: [Cascade RPN](https://arxiv.org/abs/1909.06720), [TridentNet](https://arxiv.org/abs/1901.01892)
-
-#### New Features
-
-- Support [Cascade RPN](https://arxiv.org/abs/1909.06720) (#1900)
-- Support [TridentNet](https://arxiv.org/abs/1901.01892) (#3313)
-
-#### Bug Fixes
-
-- Fix bug of show result in async_benchmark (#4367)
-- Fix scale factor in MaskTestMixin (#4366)
-- Fix but when returning indices in `multiclass_nms` (#4362)
-- Fix bug of empirical attention in resnext backbone error (#4300)
-- Fix bug of `img_norm_cfg` in FCOS-HRNet models with updated performance and models (#4250)
-- Fix invalid checkpoint and log in Mask R-CNN models on Cityscapes dataset (#4287)
-- Fix bug in distributed sampler when dataset is too small (#4257)
-- Fix bug of 'PAFPN has no attribute extra_convs_on_inputs' (#4235)
-
-#### Improvements
-
-- Update model url from aws to aliyun (#4349)
-- Update ATSS for PyTorch 1.6+ (#4359)
-- Update script to install ruby in pre-commit installation (#4360)
-- Delete deprecated `mmdet.ops` (#4325)
-- Refactor hungarian assigner for more general usage in Sparse R-CNN (#4259)
-- Handle scipy import in DETR to reduce package dependencies (#4339)
-- Update documentation of usages for config options after MMCV (1.2.3) supports overriding list in config (#4326)
-- Update pre-train models of faster rcnn trained on COCO subsets (#4307)
-- Avoid zero or too small value for beta in Dynamic R-CNN (#4303)
-- Add doccumentation for Pytorch2ONNX (#4271)
-- Add deprecated warning FPN arguments (#4264)
-- Support returning indices of kept bboxes when using nms (#4251)
-- Update type and device requirements when creating tensors `GFLHead` (#4210)
-- Update device requirements when creating tensors in `CrossEntropyLoss` (#4224)
-
-### v2.7.0 (30/11/2020)
-
-- Support new method: [DETR](https://arxiv.org/abs/2005.12872), [ResNest](https://arxiv.org/abs/2004.08955), Faster R-CNN DC5.
-- Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX.
-
-#### New Features
-
-- Support [DETR](https://arxiv.org/abs/2005.12872) (#4201, #4206)
-- Support to link the best checkpoint in training (#3773)
-- Support to override config through options in inference.py (#4175)
-- Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX (#4087, #4083)
-- Support [ResNeSt](https://arxiv.org/abs/2004.08955) backbone (#2959)
-- Support unclip border bbox regression (#4076)
-- Add tpfp func in evaluating AP (#4069)
-- Support mixed precision training of SSD detector with other backbones (#4081)
-- Add Faster R-CNN DC5 models (#4043)
-
-#### Bug Fixes
-
-- Fix bug of `gpu_id` in distributed training mode (#4163)
-- Support Albumentations with version higher than 0.5 (#4032)
-- Fix num_classes bug in faster rcnn config (#4088)
-- Update code in docs/2_new_data_model.md (#4041)
-
-#### Improvements
-
-- Ensure DCN offset to have similar type as features in VFNet (#4198)
-- Add config links in README files of models (#4190)
-- Add tutorials for loss conventions (#3818)
-- Add solution to installation issues in 30-series GPUs (#4176)
-- Update docker version in get_started.md (#4145)
-- Add model statistics and polish some titles in configs README (#4140)
-- Clamp neg probability in FreeAnchor (#4082)
-- Speed up expanding large images (#4089)
-- Fix Pytorch 1.7 incompatibility issues (#4103)
-- Update trouble shooting page to resolve segmentation fault (#4055)
-- Update aLRP-Loss in project page (#4078)
-- Clean duplicated `reduce_mean` function (#4056)
-- Refactor Q&A (#4045)
-
-### v2.6.0 (1/11/2020)
-
-- Support new method: [VarifocalNet](https://arxiv.org/abs/2008.13367).
-- Refactored documentation with more tutorials.
-
-#### New Features
-
-- Support GIoU calculation in `BboxOverlaps2D`, and re-implement `giou_loss` using `bbox_overlaps` (#3936)
-- Support random sampling in CPU mode (#3948)
-- Support VarifocalNet (#3666, #4024)
-
-#### Bug Fixes
-
-- Fix SABL validating bug in Cascade R-CNN (#3913)
-- Avoid division by zero in PAA head when num_pos=0 (#3938)
-- Fix temporary directory bug of multi-node testing error (#4034, #4017)
-- Fix `--show-dir` option in test script (#4025)
-- Fix GA-RetinaNet r50 model url (#3983)
-- Update code in docs and fix broken urls (#3947)
-
-#### Improvements
-
-- Refactor pytorch2onnx API into `mmdet.core.export` and use `generate_inputs_and_wrap_model` for pytorch2onnx (#3857, #3912)
-- Update RPN upgrade scripts for v2.5.0 compatibility (#3986)
-- Use mmcv `tensor2imgs` (#4010)
-- Update test robustness (#4000)
-- Update trouble shooting page (#3994)
-- Accelerate PAA training speed (#3985)
-- Support batch_size > 1 in validation (#3966)
-- Use RoIAlign implemented in MMCV for inference in CPU mode (#3930)
-- Documentation refactoring (#4031)
-
-### v2.5.0 (5/10/2020)
-
-#### Highlights
-
-- Support new methods: [YOLACT](https://arxiv.org/abs/1904.02689), [CentripetalNet](https://arxiv.org/abs/2003.09119).
-- Add more documentations for easier and more clear usage.
-
-#### Backwards Incompatible Changes
-
-**FP16 related methods are imported from mmcv instead of mmdet. (#3766, #3822)**
-Mixed precision training utils in `mmdet.core.fp16` are moved to `mmcv.runner`, including `force_fp32`, `auto_fp16`, `wrap_fp16_model`, and `Fp16OptimizerHook`. A deprecation warning will be raised if users attempt to import those methods from `mmdet.core.fp16`, and will be finally removed in V2.10.0.
-
-**[0, N-1] represents foreground classes and N indicates background classes for all models. (#3221)**
-Before v2.5.0, the background label for RPN is 0, and N for other heads. Now the behavior is consistent for all models. Thus `self.background_labels` in `dense_heads` is removed and all heads use `self.num_classes` to indicate the class index of background labels.
-This change has no effect on the pre-trained models in the v2.x model zoo, but will affect the training of all models with RPN heads. Two-stage detectors whose RPN head uses softmax will be affected because the order of categories is changed.
-
-**Only call `get_subset_by_classes` when `test_mode=True` and `self.filter_empty_gt=True` (#3695)**
-Function `get_subset_by_classes` in dataset is refactored and only filters out images when `test_mode=True` and `self.filter_empty_gt=True`.
-    In the original implementation, `get_subset_by_classes` is not related to the flag `self.filter_empty_gt` and will only be called when the classes is set during initialization no matter `test_mode` is `True` or `False`. This brings ambiguous behavior and potential bugs in many cases. After v2.5.0, if `filter_empty_gt=False`, no matter whether the classes are specified in a dataset, the dataset will use all the images in the annotations. If `filter_empty_gt=True` and `test_mode=True`, no matter whether the classes are specified, the dataset will call ``get_subset_by_classes` to check the images and filter out images containing no GT boxes. Therefore, the users should be responsible for the data filtering/cleaning process for the test dataset.
-
-#### New Features
-
-- Test time augmentation for single stage detectors (#3844, #3638)
-- Support to show the name of experiments during training (#3764)
-- Add `Shear`, `Rotate`, `Translate` Augmentation (#3656, #3619, #3687)
-- Add image-only transformations including `Constrast`, `Equalize`, `Color`, and `Brightness`. (#3643)
-- Support [YOLACT](https://arxiv.org/abs/1904.02689) (#3456)
-- Support [CentripetalNet](https://arxiv.org/abs/2003.09119) (#3390)
-- Support PyTorch 1.6 in docker (#3905)
-
-#### Bug Fixes
-
-- Fix the bug of training ATSS when there is no ground truth boxes (#3702)
-- Fix the bug of using Focal Loss when there is `num_pos` is 0 (#3702)
-- Fix the label index mapping in dataset browser (#3708)
-- Fix Mask R-CNN training stuck problem when ther is no positive rois (#3713)
-- Fix the bug of `self.rpn_head.test_cfg` in `RPNTestMixin` by using `self.rpn_head` in rpn head (#3808)
-- Fix deprecated `Conv2d` from mmcv.ops (#3791)
-- Fix device bug in RepPoints (#3836)
-- Fix SABL validating bug (#3849)
-- Use `https://download.openmmlab.com/mmcv/dist/index.html` for installing MMCV (#3840)
-- Fix nonzero in NMS for PyTorch 1.6.0 (#3867)
-- Fix the API change bug of PAA (#3883)
-- Fix typo in bbox_flip (#3886)
-- Fix cv2 import error of ligGL.so.1 in Dockerfile (#3891)
-
-#### Improvements
-
-- Change to use `mmcv.utils.collect_env` for collecting environment information to avoid duplicate codes (#3779)
-- Update checkpoint file names to v2.0 models in documentation (#3795)
-- Update tutorials for changing runtime settings (#3778), modifing loss (#3777)
-- Improve the function of `simple_test_bboxes` in SABL (#3853)
-- Convert mask to bool before using it as img's index for robustness and speedup (#3870)
-- Improve documentation of modules and dataset customization (#3821)
-
-### v2.4.0 (5/9/2020)
-
-**Highlights**
-
-- Fix lots of issues/bugs and reorganize the trouble shooting page
-- Support new methods [SABL](https://arxiv.org/abs/1912.04260), [YOLOv3](https://arxiv.org/abs/1804.02767), and [PAA Assign](https://arxiv.org/abs/2007.08103)
-- Support Batch Inference
-- Start to publish `mmdet` package to PyPI since v2.3.0
-- Switch model zoo to download.openmmlab.com
-
-**Backwards Incompatible Changes**
-
-- Support Batch Inference (#3564, #3686, #3705): Since v2.4.0, MMDetection could inference model with multiple images in a single GPU.
-  This change influences all the test APIs in MMDetection and downstream codebases. To help the users migrate their code, we use `replace_ImageToTensor` (#3686) to convert legacy test data pipelines during dataset initialization.
-- Support RandomFlip with horizontal/vertical/diagonal direction (#3608): Since v2.4.0, MMDetection supports horizontal/vertical/diagonal flip in the data augmentation. This influences bounding box, mask, and image transformations in data augmentation process and the process that will map those data back to the original format.
-- Migrate to use `mmlvis` and `mmpycocotools` for COCO and LVIS dataset (#3727). The APIs are fully compatible with the original `lvis` and `pycocotools`. Users need to uninstall the existing pycocotools and lvis packages in their environment first and install `mmlvis` & `mmpycocotools`.
-
-**Bug Fixes**
-
-- Fix default mean/std for onnx (#3491)
-- Fix coco evaluation and add metric items (#3497)
-- Fix typo for install.md (#3516)
-- Fix atss when sampler per gpu is 1 (#3528)
-- Fix import of fuse_conv_bn (#3529)
-- Fix bug of gaussian_target, update unittest of heatmap (#3543)
-- Fixed VOC2012 evaluate (#3553)
-- Fix scale factor bug of rescale (#3566)
-- Fix with_xxx_attributes in base detector (#3567)
-- Fix boxes scaling when number is 0 (#3575)
-- Fix rfp check when neck config is a list (#3591)
-- Fix import of fuse conv bn in benchmark.py (#3606)
-- Fix webcam demo (#3634)
-- Fix typo and itemize issues in tutorial (#3658)
-- Fix error in distributed training when some levels of FPN are not assigned with bounding boxes (#3670)
-- Fix the width and height orders of stride in valid flag generation (#3685)
-- Fix weight initialization bug in Res2Net DCN (#3714)
-- Fix bug in OHEMSampler (#3677)
-
-**New Features**
-
-- Support Cutout augmentation (#3521)
-- Support evaluation on multiple datasets through ConcatDataset (#3522)
-- Support [PAA assign](https://arxiv.org/abs/2007.08103) #(3547)
-- Support eval metric with pickle results (#3607)
-- Support [YOLOv3](https://arxiv.org/abs/1804.02767) (#3083)
-- Support [SABL](https://arxiv.org/abs/1912.04260) (#3603)
-- Support to publish to Pypi in github-action (#3510)
-- Support custom imports (#3641)
-
-**Improvements**
-
-- Refactor common issues in documentation (#3530)
-- Add pytorch 1.6 to CI config (#3532)
-- Add config to runner meta (#3534)
-- Add eval-option flag for testing (#3537)
-- Add init_eval to evaluation hook (#3550)
-- Add include_bkg in ClassBalancedDataset (#3577)
-- Using config's loading in inference_detector (#3611)
-- Add ATSS ResNet-101 models in model zoo (#3639)
-- Update urls to download.openmmlab.com (#3665)
-- Support non-mask training for CocoDataset (#3711)
-
-### v2.3.0 (5/8/2020)
-
-**Highlights**
-
-- The CUDA/C++ operators have been moved to `mmcv.ops`. For backward compatibility `mmdet.ops` is kept as warppers of `mmcv.ops`.
-- Support new methods [CornerNet](https://arxiv.org/abs/1808.01244), [DIOU](https://arxiv.org/abs/1911.08287)/[CIOU](https://arxiv.org/abs/2005.03572) loss, and new dataset: [LVIS V1](https://arxiv.org/abs/1908.03195)
-- Provide more detailed colab training tutorials and more complete documentation.
-- Support to convert RetinaNet from Pytorch to ONNX.
-
-**Bug Fixes**
-
-- Fix the model initialization bug of DetectoRS (#3187)
-- Fix the bug of module names in NASFCOSHead (#3205)
-- Fix the filename bug in publish_model.py (#3237)
-- Fix the dimensionality bug when `inside_flags.any()` is `False` in dense heads (#3242)
-- Fix the bug of forgetting to pass flip directions in `MultiScaleFlipAug` (#3262)
-- Fixed the bug caused by default value of `stem_channels` (#3333)
-- Fix the bug of model checkpoint loading for CPU inference (#3318, #3316)
-- Fix topk bug when box number is smaller than the expected topk number in ATSSAssigner (#3361)
-- Fix the gt priority bug in center_region_assigner.py (#3208)
-- Fix NaN issue of iou calculation in iou_loss.py (#3394)
-- Fix the bug that `iou_thrs` is not actually used during evaluation in coco.py (#3407)
-- Fix test-time augmentation of RepPoints (#3435)
-- Fix runtimeError caused by incontiguous tensor in Res2Net+DCN (#3412)
-
-**New Features**
-
-- Support [CornerNet](https://arxiv.org/abs/1808.01244) (#3036)
-- Support [DIOU](https://arxiv.org/abs/1911.08287)/[CIOU](https://arxiv.org/abs/2005.03572) loss (#3151)
-- Support [LVIS V1](https://arxiv.org/abs/1908.03195) dataset (#)
-- Support customized hooks in training (#3395)
-- Support fp16 training of generalized focal loss (#3410)
-- Support to convert RetinaNet from Pytorch to ONNX (#3075)
-
-**Improvements**
-
-- Support to process ignore boxes in ATSS assigner (#3082)
-- Allow to crop images without ground truth in `RandomCrop` (#3153)
-- Enable the the `Accuracy` module to set threshold (#3155)
-- Refactoring unit tests (#3206)
-- Unify the training settings of `to_float32` and `norm_cfg` in RegNets configs (#3210)
-- Add colab training tutorials for beginners (#3213, #3273)
-- Move CUDA/C++ operators into `mmcv.ops` and keep `mmdet.ops` as warppers for backward compatibility (#3232)(#3457)
-- Update installation scripts in documentation (#3290) and dockerfile (#3320)
-- Support to set image resize backend (#3392)
-- Remove git hash in version file (#3466)
-- Check mmcv version to force version compatibility (#3460)
-
-### v2.2.0 (1/7/2020)
-
-**Highlights**
-
-- Support new methods: [DetectoRS](https://arxiv.org/abs/2006.02334), [PointRend](https://arxiv.org/abs/1912.08193), [Generalized Focal Loss](https://arxiv.org/abs/2006.04388), [Dynamic R-CNN](https://arxiv.org/abs/2004.06002)
-
-**Bug Fixes**
-
-- Fix FreeAnchor when no gt in image (#3176)
-- Clean up deprecated usage of `register_module()` (#3092, #3161)
-- Fix pretrain bug in NAS FCOS (#3145)
-- Fix `num_classes` in SSD (#3142)
-- Fix FCOS warmup (#3119)
-- Fix `rstrip` in `tools/publish_model.py`
-- Fix `flip_ratio` default value in RandomFLip pipeline (#3106)
-- Fix cityscapes eval with ms_rcnn (#3112)
-- Fix RPN softmax (#3056)
-- Fix filename of LVIS@v0.5 (#2998)
-- Fix nan loss by filtering out-of-frame gt_bboxes in COCO (#2999)
-- Fix bug in FSAF (#3018)
-- Add FocalLoss `num_classes` check (#2964)
-- Fix PISA Loss when there are no gts (#2992)
-- Avoid nan in `iou_calculator` (#2975)
-- Prevent possible bugs in loading and transforms caused by shallow copy (#2967)
-
-**New Features**
-
-- Add DetectoRS (#3064)
-- Support Generalize Focal Loss (#3097)
-- Support PointRend (#2752)
-- Support Dynamic R-CNN (#3040)
-- Add DeepFashion dataset (#2968)
-- Implement FCOS training tricks (#2935)
-- Use BaseDenseHead as base class for anchor-base heads (#2963)
-- Add `with_cp` for BasicBlock (#2891)
-- Add `stem_channels` argument for ResNet (#2954)
-
-**Improvements**
-
-- Add anchor free base head (#2867)
-- Migrate to github action (#3137)
-- Add docstring for datasets, pipelines, core modules and methods (#3130, #3125, #3120)
-- Add VOC benchmark (#3060)
-- Add `concat` mode in GRoI (#3098)
-- Remove cmd arg `autorescale-lr` (#3080)
-- Use `len(data['img_metas'])` to indicate `num_samples` (#3073, #3053)
-- Switch to EpochBasedRunner (#2976)
-
-### v2.1.0 (8/6/2020)
-
-**Highlights**
-
-- Support new backbones: [RegNetX](https://arxiv.org/abs/2003.13678), [Res2Net](https://arxiv.org/abs/1904.01169)
-- Support new methods: [NASFCOS](https://arxiv.org/abs/1906.04423), [PISA](https://arxiv.org/abs/1904.04821), [GRoIE](https://arxiv.org/abs/2004.13665)
-- Support new dataset: [LVIS](https://arxiv.org/abs/1908.03195)
-
-**Bug Fixes**
-
-- Change the CLI argument `--validate` to `--no-validate` to enable validation after training epochs by default. (#2651)
-- Add missing cython to docker file (#2713)
-- Fix bug in nms cpu implementation (#2754)
-- Fix bug when showing mask results (#2763)
-- Fix gcc requirement (#2806)
-- Fix bug in async test (#2820)
-- Fix mask encoding-decoding bugs in test API (#2824)
-- Fix bug in test time augmentation (#2858, #2921, #2944)
-- Fix a typo in comment of apis/train (#2877)
-- Fix the bug of returning None when no gt bboxes are in the original image in `RandomCrop`. Fix the bug that misses to handle `gt_bboxes_ignore`, `gt_label_ignore`, and `gt_masks_ignore` in `RandomCrop`, `MinIoURandomCrop` and `Expand` modules. (#2810)
-- Fix bug of `base_channels` of regnet (#2917)
-- Fix the bug of logger when loading pre-trained weights in base detector (#2936)
-
-**New Features**
-
-- Add IoU models (#2666)
-- Add colab demo for inference
-- Support class agnostic nms (#2553)
-- Add benchmark gathering scripts for development only (#2676)
-- Add mmdet-based project links (#2736, #2767, #2895)
-- Add config dump in training (#2779)
-- Add ClassBalancedDataset (#2721)
-- Add res2net backbone (#2237)
-- Support RegNetX models (#2710)
-- Use `mmcv.FileClient` to support different storage backends (#2712)
-- Add ClassBalancedDataset (#2721)
-- Code Release: Prime Sample Attention in Object Detection (CVPR 2020) (#2626)
-- Implement NASFCOS (#2682)
-- Add class weight in CrossEntropyLoss (#2797)
-- Support LVIS dataset (#2088)
-- Support GRoIE (#2584)
-
-**Improvements**
-
-- Allow different x and y strides in anchor heads. (#2629)
-- Make FSAF loss more robust to no gt (#2680)
-- Compute pure inference time instead (#2657) and update inference speed (#2730)
-- Avoided the possibility that a patch with 0 area is cropped. (#2704)
-- Add warnings when deprecated `imgs_per_gpu` is used. (#2700)
-- Add a mask rcnn example for config (#2645)
-- Update model zoo (#2762, #2866, #2876, #2879, #2831)
-- Add `ori_filename` to img_metas and use it in test show-dir (#2612)
-- Use `img_fields` to handle multiple images during image transform (#2800)
-- Add upsample_cfg support in FPN (#2787)
-- Add `['img']` as default `img_fields` for back compatibility (#2809)
-- Rename the pretrained model from `open-mmlab://resnet50_caffe` and `open-mmlab://resnet50_caffe_bgr` to `open-mmlab://detectron/resnet50_caffe` and `open-mmlab://detectron2/resnet50_caffe`. (#2832)
-- Added sleep(2) in test.py to reduce hanging problem (#2847)
-- Support `c10::half` in CARAFE (#2890)
-- Improve documentations (#2918, #2714)
-- Use optimizer constructor in mmcv and clean the original implementation in `mmdet.core.optimizer` (#2947)
-
-### v2.0.0 (6/5/2020)
-
-In this release, we made lots of major refactoring and modifications.
-
-1. **Faster speed**. We optimize the training and inference speed for common models, achieving up to 30% speedup for training and 25% for inference. Please refer to [model zoo](model_zoo.md#comparison-with-detectron2) for details.
-
-2. **Higher performance**. We change some default hyperparameters with no additional cost, which leads to a gain of performance for most models. Please refer to [compatibility](compatibility.md#training-hyperparameters) for details.
-
-3. **More documentation and tutorials**. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it [here](https://mmdetection.readthedocs.io/en/latest/).
-
-4. **Support PyTorch 1.5**. The support for 1.1 and 1.2 is dropped, and we switch to some new APIs.
-
-5. **Better configuration system**. Inheritance is supported to reduce the redundancy of configs.
-
-6. **Better modular design**. Towards the goal of simplicity and flexibility, we simplify some encapsulation while add more other configurable modules like BBoxCoder, IoUCalculator, OptimizerConstructor, RoIHead. Target computation is also included in heads and the call hierarchy is simpler.
-
-7. Support new methods: [FSAF](https://arxiv.org/abs/1903.00621) and PAFPN (part of [PAFPN](https://arxiv.org/abs/1803.01534)).
-
-**Breaking Changes**
-Models training with MMDetection 1.x are not fully compatible with 2.0, please refer to the [compatibility doc](compatibility.md) for the details and how to migrate to the new version.
-
-**Improvements**
-
-- Unify cuda and cpp API for custom ops. (#2277)
-- New config files with inheritance. (#2216)
-- Encapsulate the second stage into RoI heads. (#1999)
-- Refactor GCNet/EmpericalAttention into plugins. (#2345)
-- Set low quality match as an option in IoU-based bbox assigners. (#2375)
-- Change the codebase's coordinate system. (#2380)
-- Refactor the category order in heads. 0 means the first positive class instead of background now. (#2374)
-- Add bbox sampler and assigner registry. (#2419)
-- Speed up the inference of RPN. (#2420)
-- Add `train_cfg` and `test_cfg` as class members in all anchor heads. (#2422)
-- Merge target computation methods into heads. (#2429)
-- Add bbox coder to support different bbox encoding and losses. (#2480)
-- Unify the API for regression loss. (#2156)
-- Refactor Anchor Generator. (#2474)
-- Make `lr` an optional argument for optimizers. (#2509)
-- Migrate to modules and methods in MMCV. (#2502, #2511, #2569, #2572)
-- Support PyTorch 1.5. (#2524)
-- Drop the support for Python 3.5 and use F-string in the codebase. (#2531)
-
-**Bug Fixes**
-
-- Fix the scale factors for resized images without keep the aspect ratio. (#2039)
-- Check if max_num > 0 before slicing in NMS. (#2486)
-- Fix Deformable RoIPool when there is no instance. (#2490)
-- Fix the default value of assigned labels. (#2536)
-- Fix the evaluation of Cityscapes. (#2578)
-
-**New Features**
-
-- Add deep_stem and avg_down option to ResNet, i.e., support ResNetV1d. (#2252)
-- Add L1 loss. (#2376)
-- Support both polygon and bitmap for instance masks. (#2353, #2540)
-- Support CPU mode for inference. (#2385)
-- Add optimizer constructor for complicated configuration of optimizers. (#2397, #2488)
-- Implement PAFPN. (#2392)
-- Support empty tensor input for some modules. (#2280)
-- Support for custom dataset classes without overriding it. (#2408, #2443)
-- Support to train subsets of coco dataset. (#2340)
-- Add iou_calculator to potentially support more IoU calculation methods. (2405)
-- Support class wise mean AP (was removed in the last version). (#2459)
-- Add option to save the testing result images. (#2414)
-- Support MomentumUpdaterHook. (#2571)
-- Add a demo to inference a single image. (#2605)
-
-### v1.1.0 (24/2/2020)
-
-**Highlights**
-
-- Dataset evaluation is rewritten with a unified api, which is used by both evaluation hooks and test scripts.
-- Support new methods: [CARAFE](https://arxiv.org/abs/1905.02188).
-
-**Breaking Changes**
-
-- The new MMDDP inherits from the official DDP, thus the `__init__` api is changed to be the same as official DDP.
-- The `mask_head` field in HTC config files is modified.
-- The evaluation and testing script is updated.
-- In all transforms, instance masks are stored as a numpy array shaped (n, h, w) instead of a list of (h, w) arrays, where n is the number of instances.
-
-**Bug Fixes**
-
-- Fix IOU assigners when ignore_iof_thr > 0 and there is no pred boxes. (#2135)
-- Fix mAP evaluation when there are no ignored boxes. (#2116)
-- Fix the empty RoI input for Deformable RoI Pooling. (#2099)
-- Fix the dataset settings for multiple workflows. (#2103)
-- Fix the warning related to `torch.uint8` in PyTorch 1.4. (#2105)
-- Fix the inference demo on devices other than gpu:0. (#2098)
-- Fix Dockerfile. (#2097)
-- Fix the bug that `pad_val` is unused in Pad transform. (#2093)
-- Fix the albumentation transform when there is no ground truth bbox. (#2032)
-
-**Improvements**
-
-- Use torch instead of numpy for random sampling. (#2094)
-- Migrate to the new MMDDP implementation in MMCV v0.3. (#2090)
-- Add meta information in logs. (#2086)
-- Rewrite Soft NMS with pytorch extension and remove cython as a dependency. (#2056)
-- Rewrite dataset evaluation. (#2042, #2087, #2114, #2128)
-- Use numpy array for masks in transforms. (#2030)
-
-**New Features**
-
-- Implement "CARAFE: Content-Aware ReAssembly of FEatures". (#1583)
-- Add `worker_init_fn()` in data_loader when seed is set. (#2066, #2111)
-- Add logging utils. (#2035)
-
-### v1.0.0 (30/1/2020)
-
-This release mainly improves the code quality and add more docstrings.
-
-**Highlights**
-
-- Documentation is online now: https://mmdetection.readthedocs.io.
-- Support new models: [ATSS](https://arxiv.org/abs/1912.02424).
-- DCN is now available with the api `build_conv_layer` and `ConvModule` like the normal conv layer.
-- A tool to collect environment information is available for trouble shooting.
-
-**Bug Fixes**
-
-- Fix the incompatibility of the latest numpy and pycocotools. (#2024)
-- Fix the case when distributed package is unavailable, e.g., on Windows. (#1985)
-- Fix the dimension issue for `refine_bboxes()`. (#1962)
-- Fix the typo when `seg_prefix` is a list. (#1906)
-- Add segmentation map cropping to RandomCrop. (#1880)
-- Fix the return value of `ga_shape_target_single()`. (#1853)
-- Fix the loaded shape of empty proposals. (#1819)
-- Fix the mask data type when using albumentation. (#1818)
-
-**Improvements**
-
-- Enhance AssignResult and SamplingResult. (#1995)
-- Add ability to overwrite existing module in Registry. (#1982)
-- Reorganize requirements and make albumentations and imagecorruptions optional. (#1969)
-- Check NaN in `SSDHead`. (#1935)
-- Encapsulate the DCN in ResNe(X)t into a ConvModule & Conv_layers. (#1894)
-- Refactoring for mAP evaluation and support multiprocessing and logging. (#1889)
-- Init the root logger before constructing Runner to log more information. (#1865)
-- Split `SegResizeFlipPadRescale` into different existing transforms. (#1852)
-- Move `init_dist()` to MMCV. (#1851)
-- Documentation and docstring improvements. (#1971, #1938, #1869, #1838)
-- Fix the color of the same class for mask visualization. (#1834)
-- Remove the option `keep_all_stages` in HTC and Cascade R-CNN. (#1806)
-
-**New Features**
-
-- Add two test-time options `crop_mask` and `rle_mask_encode` for mask heads. (#2013)
-- Support loading grayscale images as single channel. (#1975)
-- Implement "Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection". (#1872)
-- Add sphinx generated docs. (#1859, #1864)
-- Add GN support for flops computation. (#1850)
-- Collect env info for trouble shooting. (#1812)
-
-### v1.0rc1 (13/12/2019)
-
-The RC1 release mainly focuses on improving the user experience, and fixing bugs.
-
-**Highlights**
-
-- Support new models: [FoveaBox](https://arxiv.org/abs/1904.03797), [RepPoints](https://arxiv.org/abs/1904.11490) and [FreeAnchor](https://arxiv.org/abs/1909.02466).
-- Add a Dockerfile.
-- Add a jupyter notebook demo and a webcam demo.
-- Setup the code style and CI.
-- Add lots of docstrings and unit tests.
-- Fix lots of bugs.
-
-**Breaking Changes**
-
-- There was a bug for computing COCO-style mAP w.r.t different scales (AP_s, AP_m, AP_l), introduced by #621. (#1679)
-
-**Bug Fixes**
-
-- Fix a sampling interval bug in Libra R-CNN. (#1800)
-- Fix the learning rate in SSD300 WIDER FACE. (#1781)
-- Fix the scaling issue when `keep_ratio=False`. (#1730)
-- Fix typos. (#1721, #1492, #1242, #1108, #1107)
-- Fix the shuffle argument in `build_dataloader`. (#1693)
-- Clip the proposal when computing mask targets. (#1688)
-- Fix the "index out of range" bug for samplers in some corner cases. (#1610, #1404)
-- Fix the NMS issue on devices other than GPU:0. (#1603)
-- Fix SSD Head and GHM Loss on CPU. (#1578)
-- Fix the OOM error when there are too many gt bboxes. (#1575)
-- Fix the wrong keyword argument `nms_cfg` in HTC. (#1573)
-- Process masks and semantic segmentation in Expand and MinIoUCrop transforms. (#1550, #1361)
-- Fix a scale bug in the Non Local op. (#1528)
-- Fix a bug in transforms when `gt_bboxes_ignore` is None. (#1498)
-- Fix a bug when `img_prefix` is None. (#1497)
-- Pass the device argument to `grid_anchors` and `valid_flags`. (#1478)
-- Fix the data pipeline for test_robustness. (#1476)
-- Fix the argument type of deformable pooling. (#1390)
-- Fix the coco_eval when there are only two classes. (#1376)
-- Fix a bug in Modulated DeformableConv when deformable_group>1. (#1359)
-- Fix the mask cropping in RandomCrop. (#1333)
-- Fix zero outputs in DeformConv when not running on cuda:0. (#1326)
-- Fix the type issue in Expand. (#1288)
-- Fix the inference API. (#1255)
-- Fix the inplace operation in Expand. (#1249)
-- Fix the from-scratch training config. (#1196)
-- Fix inplace add in RoIExtractor which cause an error in PyTorch 1.2. (#1160)
-- Fix FCOS when input images has no positive sample. (#1136)
-- Fix recursive imports. (#1099)
-
-**Improvements**
-
-- Print the config file and mmdet version in the log. (#1721)
-- Lint the code before compiling in travis CI. (#1715)
-- Add a probability argument for the `Expand` transform. (#1651)
-- Update the PyTorch and CUDA version in the docker file. (#1615)
-- Raise a warning when specifying `--validate` in non-distributed training. (#1624, #1651)
-- Beautify the mAP printing. (#1614)
-- Add pre-commit hook. (#1536)
-- Add the argument `in_channels` to backbones. (#1475)
-- Add lots of docstrings and unit tests, thanks to [@Erotemic](https://github.com/Erotemic). (#1603, #1517, #1506, #1505, #1491, #1479, #1477, #1475, #1474)
-- Add support for multi-node distributed test when there is no shared storage. (#1399)
-- Optimize Dockerfile to reduce the image size. (#1306)
-- Update new results of HRNet. (#1284, #1182)
-- Add an argument `no_norm_on_lateral` in FPN. (#1240)
-- Test the compiling in CI. (#1235)
-- Move docs to a separate folder. (#1233)
-- Add a jupyter notebook demo. (#1158)
-- Support different type of dataset for training. (#1133)
-- Use int64_t instead of long in cuda kernels. (#1131)
-- Support unsquare RoIs for bbox and mask heads. (#1128)
-- Manually add type promotion to make compatible to PyTorch 1.2. (#1114)
-- Allowing validation dataset for computing validation loss. (#1093)
-- Use `.scalar_type()` instead of `.type()` to suppress some warnings. (#1070)
-
-**New Features**
-
-- Add an option `--with_ap` to compute the AP for each class. (#1549)
-- Implement "FreeAnchor: Learning to Match Anchors for Visual Object Detection". (#1391)
-- Support [Albumentations](https://github.com/albumentations-team/albumentations) for augmentations in the data pipeline. (#1354)
-- Implement "FoveaBox: Beyond Anchor-based Object Detector". (#1339)
-- Support horizontal and vertical flipping. (#1273, #1115)
-- Implement "RepPoints: Point Set Representation for Object Detection". (#1265)
-- Add test-time augmentation to HTC and Cascade R-CNN. (#1251)
-- Add a COCO result analysis tool. (#1228)
-- Add Dockerfile. (#1168)
-- Add a webcam demo. (#1155, #1150)
-- Add FLOPs counter. (#1127)
-- Allow arbitrary layer order for ConvModule. (#1078)
-
-### v1.0rc0 (27/07/2019)
-
-- Implement lots of new methods and components (Mixed Precision Training, HTC, Libra R-CNN, Guided Anchoring, Empirical Attention, Mask Scoring R-CNN, Grid R-CNN (Plus), GHM, GCNet, FCOS, HRNet, Weight Standardization, etc.). Thank all collaborators!
-- Support two additional datasets: WIDER FACE and Cityscapes.
-- Refactoring for loss APIs and make it more flexible to adopt different losses and related hyper-parameters.
-- Speed up multi-gpu testing.
-- Integrate all compiling and installing in a single script.
-
-### v0.6.0 (14/04/2019)
-
-- Up to 30% speedup compared to the model zoo.
-- Support both PyTorch stable and nightly version.
-- Replace NMS and SigmoidFocalLoss with Pytorch CUDA extensions.
-
-### v0.6rc0(06/02/2019)
-
-- Migrate to PyTorch 1.0.
-
-### v0.5.7 (06/02/2019)
-
-- Add support for Deformable ConvNet v2. (Many thanks to the authors and [@chengdazhi](https://github.com/chengdazhi))
-- This is the last release based on PyTorch 0.4.1.
-
-### v0.5.6 (17/01/2019)
-
-- Add support for Group Normalization.
-- Unify RPNHead and single stage heads (RetinaHead, SSDHead) with AnchorHead.
-
-### v0.5.5 (22/12/2018)
-
-- Add SSD for COCO and PASCAL VOC.
-- Add ResNeXt backbones and detection models.
-- Refactoring for Samplers/Assigners and add OHEM.
-- Add VOC dataset and evaluation scripts.
-
-### v0.5.4 (27/11/2018)
-
-- Add SingleStageDetector and RetinaNet.
-
-### v0.5.3 (26/11/2018)
-
-- Add Cascade R-CNN and Cascade Mask R-CNN.
-- Add support for Soft-NMS in config files.
-
-### v0.5.2 (21/10/2018)
-
-- Add support for custom datasets.
-- Add a script to convert PASCAL VOC annotations to the expected format.
-
-### v0.5.1 (20/10/2018)
-
-- Add BBoxAssigner and BBoxSampler, the `train_cfg` field in config files are restructured.
-- `ConvFCRoIHead` / `SharedFCRoIHead` are renamed to `ConvFCBBoxHead` / `SharedFCBBoxHead` for consistency.
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/analysis_tools/analyze_logs.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/analysis_tools/analyze_logs.py
deleted file mode 100644
index 83464f76ef3155be80289431188492c911f5b482..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/analysis_tools/analyze_logs.py
+++ /dev/null
@@ -1,179 +0,0 @@
-import argparse
-import json
-from collections import defaultdict
-
-import matplotlib.pyplot as plt
-import numpy as np
-import seaborn as sns
-
-
-def cal_train_time(log_dicts, args):
-    for i, log_dict in enumerate(log_dicts):
-        print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}')
-        all_times = []
-        for epoch in log_dict.keys():
-            if args.include_outliers:
-                all_times.append(log_dict[epoch]['time'])
-            else:
-                all_times.append(log_dict[epoch]['time'][1:])
-        all_times = np.array(all_times)
-        epoch_ave_time = all_times.mean(-1)
-        slowest_epoch = epoch_ave_time.argmax()
-        fastest_epoch = epoch_ave_time.argmin()
-        std_over_epoch = epoch_ave_time.std()
-        print(f'slowest epoch {slowest_epoch + 1}, '
-              f'average time is {epoch_ave_time[slowest_epoch]:.4f}')
-        print(f'fastest epoch {fastest_epoch + 1}, '
-              f'average time is {epoch_ave_time[fastest_epoch]:.4f}')
-        print(f'time std over epochs is {std_over_epoch:.4f}')
-        print(f'average iter time: {np.mean(all_times):.4f} s/iter')
-        print()
-
-
-def plot_curve(log_dicts, args):
-    if args.backend is not None:
-        plt.switch_backend(args.backend)
-    sns.set_style(args.style)
-    # if legend is None, use {filename}_{key} as legend
-    legend = args.legend
-    if legend is None:
-        legend = []
-        for json_log in args.json_logs:
-            for metric in args.keys:
-                legend.append(f'{json_log}_{metric}')
-    assert len(legend) == (len(args.json_logs) * len(args.keys))
-    metrics = args.keys
-
-    num_metrics = len(metrics)
-    for i, log_dict in enumerate(log_dicts):
-        epochs = list(log_dict.keys())
-        for j, metric in enumerate(metrics):
-            print(f'plot curve of {args.json_logs[i]}, metric is {metric}')
-            if metric not in log_dict[epochs[0]]:
-                raise KeyError(
-                    f'{args.json_logs[i]} does not contain metric {metric}')
-
-            if 'mAP' in metric:
-                xs = np.arange(1, max(epochs) + 1)
-                ys = []
-                for epoch in epochs:
-                    ys += log_dict[epoch][metric]
-                ax = plt.gca()
-                ax.set_xticks(xs)
-                plt.xlabel('epoch')
-                plt.plot(xs, ys, label=legend[i * num_metrics + j], marker='o')
-            else:
-                xs = []
-                ys = []
-                num_iters_per_epoch = log_dict[epochs[0]]['iter'][-1]
-                for epoch in epochs:
-                    iters = log_dict[epoch]['iter']
-                    if log_dict[epoch]['mode'][-1] == 'val':
-                        iters = iters[:-1]
-                    xs.append(
-                        np.array(iters) + (epoch - 1) * num_iters_per_epoch)
-                    ys.append(np.array(log_dict[epoch][metric][:len(iters)]))
-                xs = np.concatenate(xs)
-                ys = np.concatenate(ys)
-                plt.xlabel('iter')
-                plt.plot(
-                    xs, ys, label=legend[i * num_metrics + j], linewidth=0.5)
-            plt.legend()
-        if args.title is not None:
-            plt.title(args.title)
-    if args.out is None:
-        plt.show()
-    else:
-        print(f'save curve to: {args.out}')
-        plt.savefig(args.out)
-        plt.cla()
-
-
-def add_plot_parser(subparsers):
-    parser_plt = subparsers.add_parser(
-        'plot_curve', help='parser for plotting curves')
-    parser_plt.add_argument(
-        'json_logs',
-        type=str,
-        nargs='+',
-        help='path of train log in json format')
-    parser_plt.add_argument(
-        '--keys',
-        type=str,
-        nargs='+',
-        default=['bbox_mAP'],
-        help='the metric that you want to plot')
-    parser_plt.add_argument('--title', type=str, help='title of figure')
-    parser_plt.add_argument(
-        '--legend',
-        type=str,
-        nargs='+',
-        default=None,
-        help='legend of each plot')
-    parser_plt.add_argument(
-        '--backend', type=str, default=None, help='backend of plt')
-    parser_plt.add_argument(
-        '--style', type=str, default='dark', help='style of plt')
-    parser_plt.add_argument('--out', type=str, default=None)
-
-
-def add_time_parser(subparsers):
-    parser_time = subparsers.add_parser(
-        'cal_train_time',
-        help='parser for computing the average time per training iteration')
-    parser_time.add_argument(
-        'json_logs',
-        type=str,
-        nargs='+',
-        help='path of train log in json format')
-    parser_time.add_argument(
-        '--include-outliers',
-        action='store_true',
-        help='include the first value of every epoch when computing '
-        'the average time')
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Analyze Json Log')
-    # currently only support plot curve and calculate average train time
-    subparsers = parser.add_subparsers(dest='task', help='task parser')
-    add_plot_parser(subparsers)
-    add_time_parser(subparsers)
-    args = parser.parse_args()
-    return args
-
-
-def load_json_logs(json_logs):
-    # load and convert json_logs to log_dict, key is epoch, value is a sub dict
-    # keys of sub dict is different metrics, e.g. memory, bbox_mAP
-    # value of sub dict is a list of corresponding values of all iterations
-    log_dicts = [dict() for _ in json_logs]
-    for json_log, log_dict in zip(json_logs, log_dicts):
-        with open(json_log, 'r') as log_file:
-            for line in log_file:
-                log = json.loads(line.strip())
-                # skip lines without `epoch` field
-                if 'epoch' not in log:
-                    continue
-                epoch = log.pop('epoch')
-                if epoch not in log_dict:
-                    log_dict[epoch] = defaultdict(list)
-                for k, v in log.items():
-                    log_dict[epoch][k].append(v)
-    return log_dicts
-
-
-def main():
-    args = parse_args()
-
-    json_logs = args.json_logs
-    for json_log in json_logs:
-        assert json_log.endswith('.json')
-
-    log_dicts = load_json_logs(json_logs)
-
-    eval(args.task)(log_dicts, args)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/tomofi/NDLOCR/src/text_recognition/deep-text-recognition-benchmark/README.md b/spaces/tomofi/NDLOCR/src/text_recognition/deep-text-recognition-benchmark/README.md
deleted file mode 100644
index f512c0e7dddbd7977479662c4f73a89cef9b454a..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/text_recognition/deep-text-recognition-benchmark/README.md
+++ /dev/null
@@ -1,201 +0,0 @@
-This software was developed by the National Diet Library under contract to Morpho AI Solutions, Inc.
-
-This software is largely based on the following repositories. 
-
-The newly developed portion of this program is released by the National Diet Library under a CC BY 4.0 license. For more information, see [LICENSE](./LICENSE)
-.
-
-# What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
-| [paper](https://arxiv.org/abs/1904.01906) | [training and evaluation data](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) | [failure cases and cleansed label](https://github.com/clovaai/deep-text-recognition-benchmark#download-failure-cases-and-cleansed-label-from-here) | [pretrained model](https://www.dropbox.com/sh/j3xmli4di1zuv3s/AAArdcPgz7UFxIHUuKNOeKv_a?dl=0) | [Baidu ver(passwd:rryk)](https://pan.baidu.com/s/1KSNLv4EY3zFWHpBYlpFCBQ) |
-
-[https://github.com/clovaai/deep-text-recognition-benchmark](original repogitory link is here)
-
-Official PyTorch implementation of our four-stage STR framework, that most existing STR models fit into. <br>
-Using this framework allows for the module-wise contributions to performance in terms of accuracy, speed, and memory demand, under one consistent set of training and evaluation datasets. <br>
-Such analyses clean up the hindrance on the current comparisons to understand the performance gain of the existing modules. <br><br>
-<img src="./figures/trade-off.png" width="1000" title="trade-off">
-
-## Honors
-Based on this framework, we recorded the 1st place of [ICDAR2013 focused scene text](https://rrc.cvc.uab.es/?ch=2&com=evaluation&task=3), [ICDAR2019 ArT](https://rrc.cvc.uab.es/files/ICDAR2019-ArT.pdf) and 3rd place of [ICDAR2017 COCO-Text](https://rrc.cvc.uab.es/?ch=5&com=evaluation&task=2), [ICDAR2019 ReCTS (task1)](https://rrc.cvc.uab.es/files/ICDAR2019-ReCTS.pdf). <br>
-The difference between our paper and ICDAR challenge is summarized [here](https://github.com/clovaai/deep-text-recognition-benchmark/issues/13).
-
-## Updates
-**Aug 3, 2020**: added [guideline to use Baidu warpctc](https://github.com/clovaai/deep-text-recognition-benchmark/pull/209) which reproduces CTC results of our paper. <br>
-**Dec 27, 2019**: added [FLOPS](https://github.com/clovaai/deep-text-recognition-benchmark/issues/125) in our paper, and minor updates such as log_dataset.txt and [ICDAR2019-NormalizedED](https://github.com/clovaai/deep-text-recognition-benchmark/blob/86451088248e0490ff8b5f74d33f7d014f6c249a/test.py#L139-L165). <br>
-**Oct 22, 2019**: added [confidence score](https://github.com/clovaai/deep-text-recognition-benchmark/issues/82), and arranged the output form of training logs. <br>
-**Jul 31, 2019**: The paper is accepted at International Conference on Computer Vision (ICCV), Seoul 2019, as an oral talk. <br>
-**Jul 25, 2019**: The code for floating-point 16 calculation, check [@YacobBY's](https://github.com/YacobBY) [pull request](https://github.com/clovaai/deep-text-recognition-benchmark/pull/36) <br>
-**Jul 16, 2019**: added [ST_spe.zip](https://drive.google.com/drive/folders/192UfE9agQUMNq6AgU3_E05_FcPZK4hyt) dataset, word images contain special characters in SynthText (ST) dataset, see [this issue](https://github.com/clovaai/deep-text-recognition-benchmark/issues/7#issuecomment-511727025) <br>
-**Jun 24, 2019**: added gt.txt of failure cases that contains path and label of each image, see [image_release_190624.zip](https://drive.google.com/open?id=1VAP9l5GL5fgptgKDLio_h3nMe7X9W0Mf) <br>
-**May 17, 2019**: uploaded resources in Baidu Netdisk also, added [Run demo](https://github.com/clovaai/deep-text-recognition-benchmark#run-demo-with-pretrained-model). (check [@sharavsambuu's](https://github.com/sharavsambuu) [colab demo also](https://colab.research.google.com/drive/1PHnc_QYyf9b1_KJ1r15wYXaOXkdm1Mrk)) <br>
-**May 9, 2019**: PyTorch version updated from 1.0.1 to 1.1.0, use torch.nn.CTCLoss instead of torch-baidu-ctc, and various minor updated.
-
-## Getting Started
-### Dependency
-- This work was tested with PyTorch 1.3.1, CUDA 10.1, python 3.6 and Ubuntu 16.04. <br> You may need `pip3 install torch==1.3.1`. <br>
-In the paper, expriments were performed with **PyTorch 0.4.1, CUDA 9.0**.
-- requirements : lmdb, pillow, torchvision, nltk, natsort
-```
-pip3 install lmdb pillow torchvision nltk natsort
-```
-
-### Download lmdb dataset for traininig and evaluation from [here](https://www.dropbox.com/sh/i39abvnefllx2si/AAAbAYRvxzRp3cIE5HzqUw3ra?dl=0)
-data_lmdb_release.zip contains below. <br>
-training datasets : [MJSynth (MJ)](http://www.robots.ox.ac.uk/~vgg/data/text/)[1] and [SynthText (ST)](http://www.robots.ox.ac.uk/~vgg/data/scenetext/)[2] \
-validation datasets : the union of the training sets [IC13](http://rrc.cvc.uab.es/?ch=2)[3], [IC15](http://rrc.cvc.uab.es/?ch=4)[4], [IIIT](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html)[5], and [SVT](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset)[6].\
-evaluation datasets : benchmark evaluation datasets, consist of [IIIT](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html)[5], [SVT](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset)[6], [IC03](http://www.iapr-tc11.org/mediawiki/index.php/ICDAR_2003_Robust_Reading_Competitions)[7], [IC13](http://rrc.cvc.uab.es/?ch=2)[3], [IC15](http://rrc.cvc.uab.es/?ch=4)[4], [SVTP](http://openaccess.thecvf.com/content_iccv_2013/papers/Phan_Recognizing_Text_with_2013_ICCV_paper.pdf)[8], and [CUTE](http://cs-chan.com/downloads_CUTE80_dataset.html)[9].
-
-### Run demo with pretrained model
-1. Download pretrained model from [here](https://drive.google.com/drive/folders/15WPsuPJDCzhp2SvYZLRj8mAlT3zmoAMW)
-2. Add image files to test into `demo_image/`
-3. Run demo.py (add `--sensitive` option if you use case-sensitive model)
-```
-CUDA_VISIBLE_DEVICES=0 python3 demo.py \
---Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn \
---image_folder demo_image/ \
---saved_model TPS-ResNet-BiLSTM-Attn.pth
-```
-
-#### prediction results
-
-| demo images | [TRBA (**T**PS-**R**esNet-**B**iLSTM-**A**ttn)](https://drive.google.com/open?id=1b59rXuGGmKne1AuHnkgDzoYgKeETNMv9) | [TRBA (case-sensitive version)](https://drive.google.com/open?id=1ajONZOgiG9pEYsQ-eBmgkVbMDuHgPCaY) |
-| ---         |     ---      |          --- |
-| <img src="./demo_image/demo_1.png" width="300">    |   available   |  Available   |
-| <img src="./demo_image/demo_2.jpg" width="300">      |    shakeshack    |   SHARESHACK    |
-| <img src="./demo_image/demo_3.png" width="300">  |   london   |  Londen   |
-| <img src="./demo_image/demo_4.png" width="300">      |    greenstead    |   Greenstead    |
-| <img src="./demo_image/demo_5.png" width="300" height="100">    |   toast   |  TOAST   |
-| <img src="./demo_image/demo_6.png" width="300" height="100">      |    merry    |   MERRY    |
-| <img src="./demo_image/demo_7.png" width="300">    |   underground   |   underground  |
-| <img src="./demo_image/demo_8.jpg" width="300">      |    ronaldo    |    RONALDO   |
-| <img src="./demo_image/demo_9.jpg" width="300" height="100">    |   bally   |   BALLY  |
-| <img src="./demo_image/demo_10.jpg" width="300" height="100">      |    university    |   UNIVERSITY    |
-
-
-### Training and evaluation
-1. Train CRNN[10] model
-```
-CUDA_VISIBLE_DEVICES=0 python3 train.py \
---train_data data_lmdb_release/training --valid_data data_lmdb_release/validation \
---select_data MJ-ST --batch_ratio 0.5-0.5 \
---Transformation None --FeatureExtraction VGG --SequenceModeling BiLSTM --Prediction CTC
-```
-2. Test CRNN[10] model. If you want to evaluate IC15-2077, check [data filtering part](https://github.com/clovaai/deep-text-recognition-benchmark/blob/c27abe6b4c681e2ee0784ad966602c056a0dd3b5/dataset.py#L148). 
-```
-CUDA_VISIBLE_DEVICES=0 python3 test.py \
---eval_data data_lmdb_release/evaluation --benchmark_all_eval \
---Transformation None --FeatureExtraction VGG --SequenceModeling BiLSTM --Prediction CTC \
---saved_model saved_models/None-VGG-BiLSTM-CTC-Seed1111/best_accuracy.pth
-```
-
-3. Try to train and test our best accuracy model TRBA (**T**PS-**R**esNet-**B**iLSTM-**A**ttn) also. ([download pretrained model](https://drive.google.com/drive/folders/15WPsuPJDCzhp2SvYZLRj8mAlT3zmoAMW))
-```
-CUDA_VISIBLE_DEVICES=0 python3 train.py \
---train_data data_lmdb_release/training --valid_data data_lmdb_release/validation \
---select_data MJ-ST --batch_ratio 0.5-0.5 \
---Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn
-```
-```
-CUDA_VISIBLE_DEVICES=0 python3 test.py \
---eval_data data_lmdb_release/evaluation --benchmark_all_eval \
---Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn \
---saved_model saved_models/TPS-ResNet-BiLSTM-Attn-Seed1111/best_accuracy.pth
-```
-
-### Arguments
-* `--train_data`: folder path to training lmdb dataset.
-* `--valid_data`: folder path to validation lmdb dataset.
-* `--eval_data`: folder path to evaluation (with test.py) lmdb dataset.
-* `--select_data`: select training data. default is MJ-ST, which means MJ and ST used as training data.
-* `--batch_ratio`: assign ratio for each selected data in the batch. default is 0.5-0.5, which means 50% of the batch is filled with MJ and the other 50% of the batch is filled ST.
-* `--data_filtering_off`: skip [data filtering](https://github.com/clovaai/deep-text-recognition-benchmark/blob/f2c54ae2a4cc787a0f5859e9fdd0e399812c76a3/dataset.py#L126-L146) when creating LmdbDataset. 
-* `--Transformation`: select Transformation module [None | TPS].
-* `--FeatureExtraction`: select FeatureExtraction module [VGG | RCNN | ResNet].
-* `--SequenceModeling`: select SequenceModeling module [None | BiLSTM].
-* `--Prediction`: select Prediction module [CTC | Attn].
-* `--saved_model`: assign saved model to evaluation.
-* `--benchmark_all_eval`: evaluate with 10 evaluation dataset versions, same with Table 1 in our paper.
-
-## Download failure cases and cleansed label from [here](https://www.dropbox.com/s/5knh1gb1z593fxj/image_release_190624.zip?dl=0)
-image_release.zip contains failure case images and benchmark evaluation images with cleansed label.
-<img src="./figures/failure-case.jpg" width="1000" title="failure cases">
-
-## When you need to train on your own dataset or Non-Latin language datasets.
-1. Create your own lmdb dataset.
-```
-pip3 install fire
-python3 create_lmdb_dataset.py --inputPath data/ --gtFile data/gt.txt --outputPath result/
-```
-The structure of data folder as below.
-```
-data
-├── gt.txt
-└── test
-    ├── word_1.png
-    ├── word_2.png
-    ├── word_3.png
-    └── ...
-```
-At this time, `gt.txt` should be `{imagepath}\t{label}\n` <br>
-For example
-```
-test/word_1.png Tiredness
-test/word_2.png kills
-test/word_3.png A
-...
-```
-2. Modify `--select_data`, `--batch_ratio`, and `opt.character`, see [this issue](https://github.com/clovaai/deep-text-recognition-benchmark/issues/85).
-
-
-## Acknowledgements
-This implementation has been based on these repository [crnn.pytorch](https://github.com/meijieru/crnn.pytorch), [ocr_attention](https://github.com/marvis/ocr_attention).
-
-## Reference
-[1] M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman. Synthetic data and artificial neural networks for natural scenetext  recognition. In Workshop on Deep Learning, NIPS, 2014. <br>
-[2] A. Gupta, A. Vedaldi, and A. Zisserman. Synthetic data fortext localisation in natural images. In CVPR, 2016. <br>
-[3] D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. i Big-orda, S. R. Mestre, J. Mas, D. F. Mota, J. A. Almazan, andL. P. De Las Heras. ICDAR 2013 robust reading competition. In ICDAR, pages 1484–1493, 2013. <br>
-[4] D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. Ghosh, A. Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R.Chandrasekhar, S. Lu, et al. ICDAR 2015 competition on ro-bust reading. In ICDAR, pages 1156–1160, 2015. <br>
-[5] A. Mishra, K. Alahari, and C. Jawahar. Scene text recognition using higher order language priors. In BMVC, 2012. <br>
-[6] K. Wang, B. Babenko, and S. Belongie. End-to-end scenetext recognition. In ICCV, pages 1457–1464, 2011. <br>
-[7] S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, andR. Young. ICDAR 2003 robust reading competitions. In ICDAR, pages 682–687, 2003. <br>
-[8] T. Q. Phan, P. Shivakumara, S. Tian, and C. L. Tan. Recognizing text with perspective distortion in natural scenes. In ICCV, pages 569–576, 2013. <br>
-[9] A. Risnumawan, P. Shivakumara, C. S. Chan, and C. L. Tan. A robust arbitrary text detection system for natural scene images. In ESWA, volume 41, pages 8027–8048, 2014. <br>
-[10] B. Shi, X. Bai, and C. Yao. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. In TPAMI, volume 39, pages2298–2304. 2017. 
-
-## Links
-- WebDemo : https://demo.ocr.clova.ai/ <br>
-Combination of Clova AI detection and recognition, additional/advanced features used for KOR/JPN.
-- Repo of detection : https://github.com/clovaai/CRAFT-pytorch
-
-## Citation
-Please consider citing this work in your publications if it helps your research.
-```
-@inproceedings{baek2019STRcomparisons,
-  title={What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis},
-  author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
-  booktitle = {International Conference on Computer Vision (ICCV)},
-  year={2019},
-  pubstate={published},
-  tppubtype={inproceedings}
-}
-```
-
-## Contact
-Feel free to contact us if there is any question: <br>
-for code/paper Jeonghun Baek ku21fang@gmail.com; for collaboration hwalsuk.lee@navercorp.com (our team leader).
-
-## License
-Copyright (c) 2019-present NAVER Corp.
-
-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.
-
diff --git a/spaces/trttung1610/musicgen/audiocraft/grids/musicgen/musicgen_clapemb_32khz.py b/spaces/trttung1610/musicgen/audiocraft/grids/musicgen/musicgen_clapemb_32khz.py
deleted file mode 100644
index 64ad3f8c77afe1ab5908e407ad14d4879e1b1ad1..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/audiocraft/grids/musicgen/musicgen_clapemb_32khz.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from ._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=32, partition=partitions)
-    launcher.bind_(solver='musicgen/musicgen_base_32khz')
-    # replace this by the desired music dataset
-    launcher.bind_(dset='internal/music_400k_32khz')
-    launcher.bind_(conditioner='clapemb2music')
-
-    fsdp = {'autocast': False, 'fsdp.use': True}
-    cache_path = {'conditioners.description.clap.cache_path':
-                  '/fsx-audio-craft-llm/jadecopet/experiments/audiocraft/caches/clap_embed_music'}
-    text_wav_training_opt = {'conditioners.description.clap.text_p': 0.5}
-
-    launcher.bind_(fsdp)
-
-    launcher.slurm_(gpus=32).bind_(label='32gpus')
-    with launcher.job_array():
-        launcher()
-        launcher(text_wav_training_opt)
-        launcher(cache_path)
-        launcher(cache_path, text_wav_training_opt)
diff --git a/spaces/tryolabs/blogpost-cqa/app.py b/spaces/tryolabs/blogpost-cqa/app.py
deleted file mode 100644
index c558b86aba1e88ac7cbe49b5f048407f6574e0b3..0000000000000000000000000000000000000000
--- a/spaces/tryolabs/blogpost-cqa/app.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from transformers import AutoTokenizer
-import time
-import gradio as gr
-from optimum.onnxruntime import ORTModelForSeq2SeqLM
-from optimum.utils import NormalizedConfigManager
-
-@classmethod
-def _new_get_normalized_config_class(cls, model_type):
-    return cls._conf["t5"]
-
-NormalizedConfigManager.get_normalized_config_class = _new_get_normalized_config_class
-
-
-N = 2 # Number of previous QA pairs to use for context
-MAX_NEW_TOKENS = 128 # Maximum number of tokens for each answer
-
-tokenizer = AutoTokenizer.from_pretrained("tryolabs/long-t5-tglobal-base-blogpost-cqa-onnx")
-model = ORTModelForSeq2SeqLM.from_pretrained("tryolabs/long-t5-tglobal-base-blogpost-cqa-onnx")
-
-
-with open("updated_context.txt", "r") as f:
-    context = f.read()
-
-def build_input(question, state=[[],[]]):
-    model_input = f"{context} || "
-    previous = min(len(state[1][1:]), N)
-    for i in range(previous, 0, -1):
-        prev_question = state[0][-i-1]
-        prev_answer = state[1][-i]
-        model_input += f"<Q{i}> {prev_question} <A{i}> {prev_answer} "
-    model_input += f"<Q> {question} <A> "
-    return model_input
-
-def get_model_answer(question, state=[[],[]]):
-    start = time.perf_counter()
-    model_input = build_input(question, state)
-    end = time.perf_counter()
-    print(f"Build input: {end-start}")
-    start = time.perf_counter()
-    encoded_inputs = tokenizer(model_input, max_length=7000, truncation=True, return_tensors="pt")
-    input_ids, attention_mask = (
-        encoded_inputs.input_ids,
-        encoded_inputs.attention_mask
-    )
-    end = time.perf_counter()
-    print(f"Tokenize: {end-start}")
-    start = time.perf_counter()
-    encoded_output = model.generate(input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=MAX_NEW_TOKENS)
-    answer = tokenizer.decode(encoded_output[0], skip_special_tokens=True)
-    end = time.perf_counter()
-    print(f"Generate: {end-start}")
-    state[0].append(question)
-    state[1].append(answer)
-    responses = [(state[0][i], state[1][i]) for i in range(len(state[0]))]
-    return responses, state
-
-with gr.Blocks() as demo:
-    state = gr.State([[],[]])
-    chatbot = gr.Chatbot()
-    text = gr.Textbox(label="Ask a question (press enter to submit)", default_value="How are you?")
-    gr.Examples(
-        ["What's the name of the dataset that was built?", "what task does it focus on?", "what is that task about?"],
-        text
-    )
-
-    text.submit(get_model_answer, [text, state], [chatbot, state])
-    text.submit(lambda x: "", text, text)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Autocad 2011 Activation Code Crack HOT.md b/spaces/usbethFlerru/sovits-modelsV2/example/Autocad 2011 Activation Code Crack HOT.md
deleted file mode 100644
index 7b388fe6a401e02f12ca370d8842d269958259e8..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Autocad 2011 Activation Code Crack HOT.md	
+++ /dev/null
@@ -1,25 +0,0 @@
-<br />
-<h1>How to Crack AutoCAD 2011 Activation Code</h1>
-<p>AutoCAD is a powerful software for designing and drafting 2D and 3D models. It is used by professionals and hobbyists alike for various purposes, such as architecture, engineering, construction, and animation. However, AutoCAD is not a cheap software, and it requires an activation code to run properly. If you don't have a valid license, you might be tempted to look for a crack that can bypass the activation process. But is it worth it?</p>
-<p>In this article, we will explain why cracking AutoCAD 2011 activation code is a bad idea, and what are the risks and consequences of doing so. We will also provide some alternatives that can help you use AutoCAD legally and safely.</p>
-<h2>autocad 2011 activation code crack</h2><br /><p><b><b>Download Zip</b> &#9733;&#9733;&#9733; <a href="https://urlcod.com/2uyX9C">https://urlcod.com/2uyX9C</a></b></p><br /><br />
-<h2>Why Cracking AutoCAD 2011 Activation Code is a Bad Idea</h2>
-<p>Cracking AutoCAD 2011 activation code is a form of software piracy, which is illegal and unethical. By doing so, you are violating the terms of service and the intellectual property rights of Autodesk, the developer of AutoCAD. You are also depriving them of the revenue that they deserve for creating and maintaining the software.</p>
-<p>But aside from the legal and moral issues, cracking AutoCAD 2011 activation code also poses some serious risks and consequences for you as a user. Here are some of them:</p>
-<ul>
-<li><b>Malware infection:</b> Many crack files are infected with viruses, trojans, worms, ransomware, spyware, or other malicious software that can harm your computer and compromise your data. Some malware can even steal your personal information, such as passwords, credit card numbers, or bank accounts.</li>
-<li><b>Performance issues:</b> Cracked software often has bugs, errors, glitches, or compatibility problems that can affect the functionality and stability of AutoCAD. You might experience crashes, freezes, slowdowns, corrupted files, missing features, or other issues that can ruin your work and productivity.</li>
-<li><b>Lack of updates:</b> Cracked software cannot receive official updates from Autodesk, which means you will miss out on the latest features, improvements, fixes, and security patches that are released for AutoCAD. You will also be unable to access online services or cloud-based features that require authentication.</li>
-<li><b>Legal troubles:</b> Cracking AutoCAD 2011 activation code can expose you to legal actions from Autodesk or other authorities. You might face fines, lawsuits, or even criminal charges for violating the law and infringing on the rights of the software owner. You might also lose your reputation or credibility as a professional or a student.</li>
-</ul>
-<h2>How to Use AutoCAD Legally and Safely</h2>
-<p>If you want to use AutoCAD without risking malware infection, performance issues, lack of updates, or legal troubles, you should avoid cracking its activation code and instead use it legally and safely. Here are some ways to do that:</p>
-<ul>
-<li><b>Purchase a license:</b> The best way to use AutoCAD is to buy a license from Autodesk or an authorized reseller. You can choose from different subscription plans that suit your needs and budget. You can also get discounts if you are a student or an educator.</li>
-<li><b>Use a free trial:</b> If you want to try AutoCAD before buying it, you can download a free trial version from Autodesk's website. The trial version gives you access to all the features of AutoCAD for 30 days. You can also extend the trial period by signing up for an Autodesk account.</li>
-<li><b>Use an alternative software:</b> If you don't want to spend money on AutoCAD or if you don't need all its features, you can use an alternative software that can perform similar tasks. There are many free or low-cost CAD software available online that can help you design and draft 2D and 3D models.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>AutoCAD is a great software for creating and editing 2D and 3D models. However, cracking its activation code is not a good idea because it is illegal, unethical, risky, and problematic. Instead of looking for a crack file that can harm your computer and your work, you should use AutoCAD legally and safely by purchasing a license, using a free trial version, or using an alternative software.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Bittoo Boss hindi dubbed torrent Download the fun-filled wedding comedy.md b/spaces/usbethFlerru/sovits-modelsV2/example/Bittoo Boss hindi dubbed torrent Download the fun-filled wedding comedy.md
deleted file mode 100644
index daa7741cf6dcd87aa2e54cb265e1fddce6d9d400..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Bittoo Boss hindi dubbed torrent Download the fun-filled wedding comedy.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>The Coffin Maker 2 Full Movie Online Free Download disco discos cazavam</h2><br /><p><b><b>Download Zip</b> &#11088; <a href="https://urlcod.com/2uyVFV">https://urlcod.com/2uyVFV</a></b></p><br /><br />
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diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/DevExpress Dxperience 2010.2.8.rar Whats New and Improved in this Release.md b/spaces/usbethFlerru/sovits-modelsV2/example/DevExpress Dxperience 2010.2.8.rar Whats New and Improved in this Release.md
deleted file mode 100644
index c33cb2ce1ed7a4a6f2c2ce81a9fd837c9c1441ff..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/DevExpress Dxperience 2010.2.8.rar Whats New and Improved in this Release.md	
+++ /dev/null
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-<h2>DevExpress Dxperience 2010.2.8.rar</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash; <a href="https://urlcod.com/2uyX56">https://urlcod.com/2uyX56</a></b></p><br /><br />
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diff --git a/spaces/wilson1/bingo/src/components/toaster.tsx b/spaces/wilson1/bingo/src/components/toaster.tsx
deleted file mode 100644
index 4d2693460b61307a1d4c127fd01df9bee16e59ff..0000000000000000000000000000000000000000
--- a/spaces/wilson1/bingo/src/components/toaster.tsx
+++ /dev/null
@@ -1,3 +0,0 @@
-'use client'
-
-export { Toaster } from 'react-hot-toast'
diff --git a/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/text/shanghainese.py b/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/text/shanghainese.py
deleted file mode 100644
index cb29c24a08d2e406e8399cf7bc9fe5cb43cb9c61..0000000000000000000000000000000000000000
--- a/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/text/shanghainese.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('zaonhe')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('A', 'ᴇ'),
-    ('B', 'bi'),
-    ('C', 'si'),
-    ('D', 'di'),
-    ('E', 'i'),
-    ('F', 'ᴇf'),
-    ('G', 'dʑi'),
-    ('H', 'ᴇtɕʰ'),
-    ('I', 'ᴀi'),
-    ('J', 'dʑᴇ'),
-    ('K', 'kʰᴇ'),
-    ('L', 'ᴇl'),
-    ('M', 'ᴇm'),
-    ('N', 'ᴇn'),
-    ('O', 'o'),
-    ('P', 'pʰi'),
-    ('Q', 'kʰiu'),
-    ('R', 'ᴀl'),
-    ('S', 'ᴇs'),
-    ('T', 'tʰi'),
-    ('U', 'ɦiu'),
-    ('V', 'vi'),
-    ('W', 'dᴀbɤliu'),
-    ('X', 'ᴇks'),
-    ('Y', 'uᴀi'),
-    ('Z', 'zᴇ')
-]]
-
-
-def _number_to_shanghainese(num):
-    num = cn2an.an2cn(num).replace('一十','十').replace('二十', '廿').replace('二', '两')
-    return re.sub(r'((?:^|[^三四五六七八九])十|廿)两', r'\1二', num)
-
-
-def number_to_shanghainese(text):
-    return re.sub(r'\d+(?:\.?\d+)?', lambda x: _number_to_shanghainese(x.group()), text)
-
-
-def latin_to_ipa(text):
-    for regex, replacement in _latin_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def shanghainese_to_ipa(text):
-    text = number_to_shanghainese(text.upper())
-    text = converter.convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text
diff --git a/spaces/xdecoder/SEEM/app.py b/spaces/xdecoder/SEEM/app.py
deleted file mode 100644
index d73152b3090d1ae00fbbd7cd5077d82be1d90aa9..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/SEEM/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import os
-os.system("python -m pip install -r requirements.txt")
-os.system("python -m pip install git+https://github.com/MaureenZOU/detectron2-xyz.git")
-
-github_user = os.environ.get('GITHUB_USER')
-github_token = os.environ.get('GITHUB_TOKEN')
-
-repo = 'IX-Decoder-Demo'
-
-os.system("export GITHUB_USER={}".format(github_user))
-os.system("export GITHUB_TOKEN={}".format(github_token))
-os.system("git clone https://{}:{}@github.com/{}/{}".format(github_user, github_token, github_user, repo))
-
-cwd0 = os.getcwd()
-cwd1 = os.path.join(cwd0, 'IX-Decoder-Demo/xdecoder/body/encoder/ops')
-os.chdir(cwd1)
-os.system("sh make.sh")
-os.chdir(cwd0)
-
-cwd2 = os.path.join(cwd0, 'IX-Decoder-Demo')
-os.chdir(cwd2)
-os.system("python app.py")
\ No newline at end of file
diff --git a/spaces/xianqi21/bingo/Dockerfile b/spaces/xianqi21/bingo/Dockerfile
deleted file mode 100644
index c677b05b75f7e4b2beee8c97fb47957a0861a83e..0000000000000000000000000000000000000000
--- a/spaces/xianqi21/bingo/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM weaigc/bingo:latest
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-CMD npm start
diff --git a/spaces/xiaoyeAI/clewd/clewd.js b/spaces/xiaoyeAI/clewd/clewd.js
deleted file mode 100644
index b9d3c3c989c2564681f1db001006e23fe6ef0ac3..0000000000000000000000000000000000000000
--- a/spaces/xiaoyeAI/clewd/clewd.js
+++ /dev/null
@@ -1,863 +0,0 @@
-/*
-* https://rentry.org/teralomaniac_clewd
-* https://github.com/teralomaniac/clewd
-*/
-'use strict';
-
-const {createServer: Server, IncomingMessage, ServerResponse} = require('node:http'), {createHash: Hash, randomUUID, randomInt, randomBytes} = require('node:crypto'), {TransformStream, ReadableStream} = require('node:stream/web'), {Readable, Writable} = require('node:stream'), {Blob} = require('node:buffer'), {existsSync: exists, writeFileSync: write, createWriteStream} = require('node:fs'), {join: joinP} = require('node:path'), {ClewdSuperfetch: Superfetch, SuperfetchAvailable} = require('./lib/clewd-superfetch'), {AI, fileName, genericFixes, bytesToSize, setTitle, checkResErr, Replacements, Main} = require('./lib/clewd-utils'), ClewdStream = require('./lib/clewd-stream');
-
-/******************************************************* */
-let currentIndex, Firstlogin = true, changeflag = 0, changetime = 0, totaltime, uuidOrgArray = [];
-
-const events = require('events'), CookieChanger = new events.EventEmitter();
-require('events').EventEmitter.defaultMaxListeners = 0;
-
-CookieChanger.on('ChangeCookie', () => {
-    Proxy && Proxy.close();
-    console.log(`Changing Cookie...\n`);
-    Proxy.listen(Config.Port, Config.Ip, onListen);
-    Proxy.on('error', (err => {
-        console.error('Proxy error\n%o', err);
-    }));
-});
-
-const simpletokenizer = (prompt) => {
-    let byteLength = 0;
-    for (let i = 0; i < prompt.length; i++) {
-        let code = prompt.charCodeAt(i);
-        if (code <= 0xFF) {
-            byteLength += 0.6;
-        } else if (code <= 0xFFFF) {
-            byteLength += 1;
-        } else {
-            byteLength += 1.5;
-        }
-    }
-    return byteLength;
-}, padtxt = (content) => {
-    if (Config.padtxt_placeholder.length > 0) {
-        var placeholder = Config.padtxt_placeholder;
-    } else {
-        const bytes = randomInt(5, 15);
-        var placeholder = randomBytes(bytes).toString('hex');
-    }
-    let count = Math.floor((Config.Settings.padtxt - simpletokenizer(content)) / simpletokenizer(placeholder)); 
-
-    // 生成占位符字符串
-    let padding = '';
-    for (let i = 0; i < count; i++) {
-        padding += placeholder;
-    }
-
-    // 在prompt前面添加占位符, 在末尾增加空行然后添加prompt
-    content = padding + '\n\n\n' + content;
-
-    return content.trim();
-}, xmlPlot = (content) => {
-    // 检查内容中是否包含"<card>"
-    if (!content.includes('<card>')) {
-        content = content.replace(/(\n\n|^)xmlPlot:\s*/gm, '$1');
-        content = content.replace(/(<reply>\n|\n<\/reply>)/g, '');
-        return content.replace(/<customname>(.*?)<\/customname>/gm, '$1');
-    }
-
-    //群组
-    content = content.replace(/(<reply>\n|\n<\/reply>)\1*/g, '$1');
-    content = content.replace(/<customname>(.*?)<\/customname>:/gm, '$1:\n');
-
-    //role合并
-    if (!content.includes('<\!-- Merge Disable -->')) {
-        if (!content.includes('<\!-- Merge Human Disable -->')) {
-            content = content.replace(/(\n\n|^)xmlPlot:/g, '$1Human:');
-            content = content.replace(/(?:\n\n|^)Human:(.*?(?:\n\nAssistant:|$))/gs, function(match, p1) {return '\n\nHuman:' + p1.replace(/\n\nHuman:\s*/g, '\n\n')});
-            content = content.replace(/^\s*Human:\s*/, '');
-        }
-        if (!content.includes('<\!-- Merge Assistant Disable -->')) {
-            content = content.replace(/\n\nAssistant:(.*?(?:\n\nHuman:|$))/gs, function(match, p1) {return '\n\nAssistant:' + p1.replace(/\n\nAssistant:\s*/g, '\n\n')});
-        }
-    }
-    content = content.replace(/(\n\n|^)xmlPlot:\s*/gm, '$1');
-    content = content.replace(/<\!-- Merge.*?Disable -->/gm, '');
-
-    //格式顺序交换&越狱倒置
-    content = content.replace(/<Prev(Assistant|Human)>.*?<\/Prev\1>/gs, function(match) {return match.replace(/\n\n(Assistant|Human):/g, '\n\ntemp$1:')});
-    let segcontentAssistant = content.split('\n\nAssistant:');
-    let processedsegAssistant = segcontentAssistant.map(seg => {
-        return seg.replace(/(\n\nHuman:.*?)<PrevAssistant>(.*?)<\/PrevAssistant>/gs, '\n\n$2$1');
-    });
-    content = processedsegAssistant.join('\n\nAssistant:');
-    let segcontentHuman = content.split('\n\nHuman:');
-    const seglength = segcontentHuman.length;
-    for (let i = 1; i < seglength; i++) {
-        const match = segcontentHuman[i].match(/<PrevHuman>.*?<\/PrevHuman>/s);
-        if (match) {
-            segcontentHuman[i - 1] += match[0].replace(/<PrevHuman>(.*?)<\/PrevHuman>/s, '\n\n$1');
-            segcontentHuman[i] = segcontentHuman[i].replace(match[0], '');
-        }
-    }
-    if (/Assistant: *.$/.test(content) && seglength > 1 && !segcontentHuman[seglength - 2].includes('\n\nAssistant:')) {
-        segcontentHuman[seglength - 2] = segcontentHuman.splice(seglength - 1, 1, segcontentHuman[seglength - 2])[0];
-    }
-    content = segcontentHuman.join('\n\nHuman:');
-    content = content.replace(/\n\ntemp(Assistant|Human):/g, '\n\n$1:');
-
-    //给开头加上</file-attachment-contents>用于截断附加文件标识
-    content.includes('<file-attachment-contents>') && (content = '</file-attachment-contents>\n\n' + content);
-
-    // 在第一个"[Start a new"前面加上"<example>"，在最后一个"[Start a new"前面加上"</example>\n\n<plot>\n\n"
-    const exampleNote = content.match(/(?<=<example-note>).*(?=<\/example-note>)/) || '';
-    const cardtag = content.match(/(?=\n\n<\/card>)/) || '</card>';
-    const exampletag = content.match(/(?=\n\n<\/example>)/) || '</example>';
-    const plot = content.includes('</plot>') ? '<plot>' : '';
-    content = content.replace(/<example-note>.*<\/example-note>/, '');
-    const firstChatStart = content.indexOf('\n\n[Start a new');
-    const lastChatStart = content.lastIndexOf('\n\n[Start a new');
-    firstChatStart != -1 && firstChatStart === lastChatStart && (content = content.slice(0, firstChatStart) + `\n\n${cardtag}` + content.slice(firstChatStart));
-    firstChatStart != lastChatStart && (content = content.slice(0, firstChatStart) + `\n\n${cardtag}\n\n${exampleNote}\n<example>` + content.slice(firstChatStart, lastChatStart) + `\n\n${exampletag}\n\n${plot}` + content.slice(lastChatStart));
-
-    //Plain Prompt
-    segcontentHuman = content.split('\n\nHuman:');
-    let segcontentlastIndex = segcontentHuman.length - 1;
-    if (segcontentlastIndex >= 2 && segcontentHuman[segcontentlastIndex].includes('<!-- Plain Prompt Enable -->') && !content.includes('\n\nPlainPrompt:')) {
-        content = segcontentHuman.slice(0, segcontentlastIndex).join('\n\nHuman:') + '\n\nPlainPrompt:' + segcontentHuman.slice(segcontentlastIndex).join('\n\nHuman:');
-    }
-    content = content.replace(/<\!-- Plain Prompt Enable -->/, '');
-    content = content.replace(/\n\nHuman:.*PlainPrompt:/, '\n\nPlainPrompt:');
-
-    //消除空XML tags或多余的\n
-    content = content.replace(/\n<\/(hidden|META)>\s+?<\1>\n/g, '');
-    content = content.replace(/\n<(card|example|hidden|plot|META)>\s+?<\1>/g, '\n<$1>');
-    content = content.replace(/(?:<!--.*?-->)?\n<(card|example|hidden|plot|META)>\s+?<\/\1>/g, '');
-    content = content.replace(/(?<=(: |\n)<(card|hidden|example|plot|META)>\n)\s*/g, '');
-    content = content.replace(/\s*(?=\n<\/(card|hidden|example|plot|META)>(\n|$))/g, '');
-    content = content.replace(/(?<=\n)\n(?=\n)/g, '');
-
-    return content.trim();
-};
-/******************************************************* */
-
-let ChangedSettings, UnknownSettings, Logger;
-
-const ConfigPath = joinP(__dirname, './config.js'), LogPath = joinP(__dirname, './log.txt'), Conversation = {
-    char: null,
-    uuid: null,
-    depth: 0
-}, cookies = {};
-
-let uuidOrg, curPrompt = {}, prevPrompt = {}, prevMessages = [], prevImpersonated = false, Config = {
-    Cookie: '',
-    CookieArray: [],
-    Cookiecounter: 0,
-    CookieIndex: 0,
-    Ip: (process.env.Cookie || process.env.CookieArray) ? '0.0.0.0' : '127.0.0.1',
-    Port: process.env.PORT || 8444,
-    localtunnel: false,
-    BufferSize: 1,
-    SystemInterval: 3,
-    rProxy: AI.end(),
-    padtxt_placeholder: '',
-    PromptExperimentFirst: '',
-    PromptExperimentNext: '',
-    PersonalityFormat: '{{char}}\'s personality: {{personality}}',
-    ScenarioFormat: 'Dialogue scenario: {{scenario}}',
-    Settings: {
-        RenewAlways: true,
-        RetryRegenerate: false,
-        PromptExperiments: true,
-        SystemExperiments: true,
-        PreventImperson: false,
-        AllSamples: false,
-        NoSamples: false,
-        StripAssistant: false,
-        StripHuman: false,
-        PassParams: false,
-        ClearFlags: true,
-        PreserveChats: true,
-        LogMessages: true,
-        FullColon: true,
-        padtxt: 13500,
-        xmlPlot: true,
-        Superfetch: true
-    }
-};
-
-ServerResponse.prototype.json = async function(body, statusCode = 200, headers) {
-    body = body instanceof Promise ? await body : body;
-    this.headersSent || this.writeHead(statusCode, {
-        'Content-Type': 'application/json',
-        ...headers && headers
-    });
-    this.end('object' == typeof body ? JSON.stringify(body) : body);
-    return this;
-};
-
-Array.prototype.sample = function() {
-    return this[Math.floor(Math.random() * this.length)];
-};
-
-const updateParams = res => {
-    updateCookies(res);
-}, updateCookies = res => {
-    let cookieNew = '';
-    res instanceof Response ? cookieNew = res.headers?.get('set-cookie') : res?.superfetch ? cookieNew = res.headers?.['set-cookie'] : 'string' == typeof res && (cookieNew = res.split('\n').join(''));
-    if (!cookieNew) {
-        return;
-    }
-    let cookieArr = cookieNew.split(/;\s?/gi).filter((prop => false === /^(path|expires|domain|HttpOnly|Secure|SameSite)[=;]*/i.test(prop)));
-    for (const cookie of cookieArr) {
-        const divide = cookie.split(/^(.*?)=\s*(.*)/), cookieName = divide[1], cookieVal = divide[2];
-        cookies[cookieName] = cookieVal;
-    }
-}, getCookies = () => {
-    const cookieNames = Object.keys(cookies);
-    return cookieNames.map(((name, idx) => `${name}=${cookies[name]}${idx === cookieNames.length - 1 ? '' : ';'}`)).join(' ').replace(/(\s+)$/gi, '');
-}, deleteChat = async uuid => {
-    if (!uuid) {
-        return;
-    }
-    if (uuid === Conversation.uuid) {
-        Conversation.uuid = null;
-        Conversation.depth = 0;
-    }
-    if (Config.Settings.PreserveChats) {
-        return;
-    }
-    const res = await fetch(`${Config.rProxy}/api/organizations/${uuidOrg}/chat_conversations/${uuid}`, {
-        headers: {
-            ...AI.hdr(),
-            Cookie: getCookies()
-        },
-        method: 'DELETE'
-    });
-    updateParams(res);
-}, onListen = async () => {
-/***************************** */
-    if (Firstlogin) {
-        Firstlogin = false;
-        console.log(`${Main}\nhttp://${Config.Ip}:${Config.Port}/v1\n\n${Object.keys(Config.Settings).map((setting => UnknownSettings.includes(setting) ? `??? ${setting}: ${Config.Settings[setting]}` : `${setting}: ${ChangedSettings.includes(setting) ? '' : ''}${Config.Settings[setting]}`)).sort().join('\n')}\n`);
-        Config.Settings.Superfetch && SuperfetchAvailable(true);
-        if (Config.localtunnel) {
-            const localtunnel = require('localtunnel');
-            localtunnel({ port: Config.Port })
-            .then((tunnel) => {
-                console.log(`\nTunnel URL for outer websites: ${tunnel.url}/v1\n`);
-            })
-        }
-        totaltime = Config.CookieArray.length;
-    }
-    if (Config.CookieArray?.length > 0) {
-        Config.Cookie = Config.CookieArray[currentIndex];
-        currentIndex = (currentIndex + 1) % Config.CookieArray.length;
-        changetime += 1;
-    }
-    let percentage = ((changetime + Config.CookieIndex) / totaltime) * 100
-    if (Config.Cookiecounter < 0 && percentage > 100) {
-        console.log(`\n※※※Cookie cleanup completed※※※\n\n`);
-        return process.exit();
-    }
-/***************************** */
-    if ('SET YOUR COOKIE HERE' === Config.Cookie || Config.Cookie?.length < 1) {
-        throw Error('Set your cookie inside config.js');
-    }
-    updateCookies(Config.Cookie);
-    //console.log(`${Main}\nhttp://${Config.Ip}:${Config.Port}/v1\n\n${Object.keys(Config.Settings).map((setting => UnknownSettings.includes(setting) ? `??? ${setting}: ${Config.Settings[setting]}` : `${setting}: ${ChangedSettings.includes(setting) ? '' : ''}${Config.Settings[setting]}`)).sort().join('\n')}\n`);
-    //Config.Settings.Superfetch && SuperfetchAvailable(true);
-    const accRes = await fetch(Config.rProxy + '/api/organizations', {
-        method: 'GET',
-        headers: {
-            ...AI.hdr(),
-            Cookie: getCookies()
-        }
-    });
-/**************************** */
-    if (accRes.statusText === 'Forbidden' && Config.CookieArray?.length > 0) {
-        Config.CookieArray = Config.CookieArray.filter(item => item !== Config.Cookie);
-        !process.env.Cookie && !process.env.CookieArray && writeSettings(Config);
-        currentIndex = (currentIndex - 1 + Config.CookieArray.length) % Config.CookieArray.length;
-        console.log(`Expired!`);
-        Config.Cookiecounter < 0 && console.log(`[progress]: ${percentage.toFixed(2)}%\n[length]: ${Config.CookieArray.length}\n`);
-        CookieChanger.emit('ChangeCookie');
-        return;
-    }
-/**************************** */
-    await checkResErr(accRes);
-    const accInfo = (await accRes.json())?.[0];
-    if (!accInfo || accInfo.error) {
-        throw Error(`Couldn't get account info: "${accInfo?.error?.message || accRes.statusText}"`);
-    }
-    if (!accInfo?.uuid) {
-        throw Error('Invalid account id');
-    }
-    setTitle('ok');
-    updateParams(accRes);
-    console.log(Config.CookieArray?.length > 0 ? `(index: ${currentIndex || Config.CookieArray.length}) Logged in %o` : 'Logged in %o', { //console.log('Logged in %o', {
-        name: accInfo.name?.split('@')?.[0],
-        capabilities: accInfo.capabilities,
-    });
-    uuidOrg = accInfo?.uuid;
-/************************* */
-    if (uuidOrgArray.includes(uuidOrg)) {
-        console.log(`Overlap!`);
-        currentIndex = (currentIndex - 1 + Config.CookieArray.length) % Config.CookieArray.length;
-        Config.Cookiecounter < 0 && console.log(`[progress]: ${percentage.toFixed(2)}%\n[length]: ${Config.CookieArray.length}\n`);
-        Config.CookieArray = Config.CookieArray.filter(item => item !== Config.Cookie);
-        !process.env.Cookie && !process.env.CookieArray && writeSettings(Config);
-        CookieChanger.emit('ChangeCookie');
-        return;
-    } else {
-        uuidOrgArray.push(uuidOrg);
-    }
-/************************* */
-    if (accInfo?.active_flags.length > 0) {
-        const now = new Date, formattedFlags = accInfo.active_flags.map((flag => {
-            const days = ((new Date(flag.expires_at).getTime() - now.getTime()) / 864e5).toFixed(2);
-            return {
-                type: flag.type,
-                remaining_days: days
-            };
-        }));
-        console.warn('Your account has warnings %o', formattedFlags); //console.warn('Your account has warnings %o', formattedFlags);
-        await Promise.all(accInfo.active_flags.map((flag => (async type => {
-            if (!Config.Settings.ClearFlags) {
-                return;
-            }
-            if ('consumer_restricted_mode' === type) {
-                return;
-            }
-            const req = await (Config.Settings.Superfetch ? Superfetch : fetch)(`${Config.rProxy}/api/organizations/${uuidOrg}/flags/${type}/dismiss`, {
-                headers: {
-                    ...AI.hdr(),
-                    Cookie: getCookies()
-                },
-                method: 'POST'
-            });
-            updateParams(req);
-            const json = await req.json();
-            console.log(`${type}: ${json.error ? json.error.message || json.error.type || json.detail : 'OK'}`);
-        })(flag.type))));
-/***************************** */
-        if (Config.CookieArray?.length > 0) {
-            console.log(`Restricted!`);
-            Config.Cookiecounter < 0 && console.log(`[progress]: ${percentage.toFixed(2)}%\n[length]: ${Config.CookieArray.length}\n`);
-            CookieChanger.emit('ChangeCookie');
-            return;
-        }
-    }
-    if (Config.CookieArray.length > 0) {
-        const allres = await fetch(`${Config.rProxy}`, {
-            headers: {
-                ...AI.hdr(),
-                Cookie: getCookies()
-            },
-            method: 'GET'
-        }), accountinfo = await allres.text();
-        updateParams(allres);
-        const Unverified = accountinfo.includes('\\"completed_verification_at\\":null');
-        const Banned = accountinfo.includes('\\"gate\":\\"segment:abuse\\",\\"gateValue\\":\\"true\\",');
-        const Exceededlimit = /\\"messageLimit\\":{\\"type\\":\\"(approaching_limit\\",\\"remaining\\":0|exceeded_limit)\\",/.test(accountinfo);
-        const Remain = /\\"messageLimit\\":{\\"type\\":\\"approaching_limit\\",\\"remaining\\":\d+\\",/.exec(accountinfo);
-        Remain && (changeflag = Math.max(Config.Cookiecounter - Remain[0], changeflag));
-        if (Unverified || Banned) {
-            Config.CookieArray = Config.CookieArray.filter(item => item !== Config.Cookie);
-            !process.env.Cookie && !process.env.CookieArray && writeSettings(Config);
-            currentIndex = (currentIndex - 1 + Config.CookieArray.length) % Config.CookieArray.length;
-        }
-        Unverified && console.log(`Unverified!`);
-        Banned && console.log(`Banned!`);
-        Exceededlimit && console.log(`Exceeded limit!`);
-        Config.Cookiecounter < 0 && console.log(`[progress]: ${percentage.toFixed(2)}%\n[length]: ${Config.CookieArray.length}`);
-        if (Unverified || Banned || Exceededlimit || Config.Cookiecounter < 0) {
-            console.log('');
-            CookieChanger.emit('ChangeCookie');
-            return;
-        }
-/***************************** */
-    }
-    const convRes = await fetch(`${Config.rProxy}/api/organizations/${uuidOrg}/chat_conversations`, {
-        method: 'GET',
-        headers: {
-            ...AI.hdr(),
-            Cookie: getCookies()
-        }
-    }), conversations = await convRes.json();
-    updateParams(convRes);
-    conversations.length > 0 && await Promise.all(conversations.map((conv => deleteChat(conv.uuid))));
-}, writeSettings = async (config, firstRun = false) => {
-    write(ConfigPath, `/*\n* https://rentry.org/teralomaniac_clewd\n* https://github.com/teralomaniac/clewd\n*/\n\n// SET YOUR COOKIE BELOW\n\nmodule.exports = ${JSON.stringify(config, null, 4)}\n\n/*\n BufferSize\n * How many characters will be buffered before the AI types once\n * lower = less chance of \`PreventImperson\` working properly\n\n ---\n\n SystemInterval\n * How many messages until \`SystemExperiments alternates\`\n\n ---\n\n Other settings\n * https://gitgud.io/ahsk/clewd/#defaults\n * and\n * https://gitgud.io/ahsk/clewd/-/blob/master/CHANGELOG.md\n */`.trim().replace(/((?<!\r)\n|\r(?!\n))/g, '\r\n'));
-    if (firstRun) {
-        console.warn('config file created!\nedit config.js to set your settings and restart the program');
-        process.exit(0);
-    }
-}, Proxy = Server((async (req, res) => {
-    if ('OPTIONS' === req.method) {
-        return ((req, res) => {
-            res.writeHead(200, {
-                'Access-Control-Allow-Origin': '*',
-                'Access-Control-Allow-Headers': 'Authorization, Content-Type',
-                'Access-Control-Allow-Methods': 'POST, GET, OPTIONS'
-            }).end();
-        })(0, res);
-    }
-    switch (req.url) {
-      case '/v1/models':
-        res.json({
-            data: [ {
-                id: AI.mdl()
-            } ]
-        });
-        break;
-
-      case '/v1/chat/completions':
-        ((req, res) => {
-            setTitle('recv...');
-            let fetchAPI, changer; //let fetchAPI;
-            const abortControl = new AbortController, {signal} = abortControl;
-            res.socket.on('close', (async () => {
-                abortControl.signal.aborted || abortControl.abort();
-            }));
-            const buffer = [];
-            req.on('data', (chunk => {
-                buffer.push(chunk);
-            }));
-            req.on('end', (async () => {
-                let clewdStream, titleTimer, samePrompt = false, shouldRenew = true, retryRegen = false;
-                try {
-                    const body = JSON.parse(Buffer.concat(buffer).toString()), temperature = Math.max(.1, Math.min(1, body.temperature));
-                    let {messages} = body;
-                    if (messages?.length < 1) {
-                        throw Error('Select OpenAI as completion source');
-                    }
-                    if (!body.stream && 1 === messages.length && JSON.stringify(messages.sort() || []) === JSON.stringify([ {
-                        role: 'user',
-                        content: 'Hi'
-                    } ].sort())) {
-                        return res.json({
-                            choices: [ {
-                                message: {
-                                    content: Main
-                                }
-                            } ]
-                        });
-                    }
-                    res.setHeader('Access-Control-Allow-Origin', '*');
-                    body.stream && res.setHeader('Content-Type', 'text/event-stream');
-                    if (!body.stream && messages?.[0]?.content?.startsWith('From the list below, choose a word that best represents a character\'s outfit description, action, or emotion in their dialogue')) {
-                        return res.json({
-                            choices: [ {
-                                message: {
-                                    content: 'neutral'
-                                }
-                            } ]
-                        });
-                    }
-                    if (Config.Settings.AllSamples && Config.Settings.NoSamples) {
-                        console.log('having AllSamples and NoSamples both set to true is not supported');
-                        throw Error('Only one can be used at the same time: AllSamples/NoSamples');
-                    }
-                    const model = AI.mdl();
-                    curPrompt = {
-                        firstUser: messages.find((message => 'user' === message.role)),
-                        firstSystem: messages.find((message => 'system' === message.role)),
-                        firstAssistant: messages.find((message => 'assistant' === message.role)),
-                        lastUser: messages.findLast((message => 'user' === message.role)),
-                        lastSystem: messages.findLast((message => 'system' === message.role && '[Start a new chat]' !== message.content)),
-                        lastAssistant: messages.findLast((message => 'assistant' === message.role))
-                    };
-                    prevPrompt = {
-                        ...prevMessages.length > 0 && {
-                            firstUser: prevMessages.find((message => 'user' === message.role)),
-                            firstSystem: prevMessages.find((message => 'system' === message.role)),
-                            firstAssistant: prevMessages.find((message => 'assistant' === message.role)),
-                            lastUser: prevMessages.findLast((message => 'user' === message.role)),
-                            lastSystem: prevMessages.find((message => 'system' === message.role && '[Start a new chat]' !== message.content)),
-                            lastAssistant: prevMessages.findLast((message => 'assistant' === message.role))
-                        }
-                    };
-                    samePrompt = JSON.stringify(messages.filter((message => 'system' !== message.role)).sort()) === JSON.stringify(prevMessages.filter((message => 'system' !== message.role)).sort());
-                    const sameCharDiffChat = !samePrompt && curPrompt.firstSystem?.content === prevPrompt.firstSystem?.content && curPrompt.firstUser?.content !== prevPrompt.firstUser?.content;
-                    shouldRenew = Config.Settings.RenewAlways || !Conversation.uuid || prevImpersonated || !Config.Settings.RenewAlways && samePrompt || sameCharDiffChat;
-                    retryRegen = Config.Settings.RetryRegenerate && samePrompt && null != Conversation.uuid;
-                    samePrompt || (prevMessages = JSON.parse(JSON.stringify(messages)));
-                    let type = '';
-                    if (retryRegen) {
-                        type = 'R';
-                        fetchAPI = await (async (signal, model) => {
-                            let res;
-                            const body = {
-                                completion: {
-                                    prompt: '',
-                                    timezone: AI.zone(),
-                                    model: model || AI.mdl()
-                                },
-                                organization_uuid: uuidOrg,
-                                conversation_uuid: Conversation.uuid,
-                                text: ''
-                            };
-                            let headers = {
-                                ...AI.hdr(Conversation.uuid || ''),
-                                Accept: 'text/event-stream',
-                                Cookie: getCookies()
-                            };
-                            if (Config.Settings.Superfetch) {
-                                const names = Object.keys(headers), values = Object.values(headers);
-                                headers = names.map(((header, idx) => `${header}: ${values[idx]}`));
-                            }
-                            res = await (Config.Settings.Superfetch ? Superfetch : fetch)(Config.rProxy + '/api/retry_message', {
-                                stream: true,
-                                signal,
-                                method: 'POST',
-                                body: JSON.stringify(body),
-                                headers
-                            });
-                            updateParams(res);
-                            await checkResErr(res);
-                            return res;
-                        })(signal, model);
-                    } else if (shouldRenew) {
-                        Conversation.uuid && await deleteChat(Conversation.uuid);
-                        fetchAPI = await (async signal => {
-                            Conversation.uuid = randomUUID().toString();
-                            Conversation.depth = 0;
-                            const res = await (Config.Settings.Superfetch ? Superfetch : fetch)(`${Config.rProxy}/api/organizations/${uuidOrg}/chat_conversations`, {
-                                signal,
-                                headers: {
-                                    ...AI.hdr(),
-                                    Cookie: getCookies()
-                                },
-                                method: 'POST',
-                                body: JSON.stringify({
-                                    uuid: Conversation.uuid,
-                                    name: ''
-                                })
-                            });
-                            updateParams(res);
-                            await checkResErr(res);
-                            return res;
-                        })(signal);
-                        type = 'r';
-                    } else if (samePrompt) {} else {
-                        const systemExperiment = !Config.Settings.RenewAlways && Config.Settings.SystemExperiments;
-                        if (!systemExperiment || systemExperiment && Conversation.depth >= Config.SystemInterval) {
-                            type = 'c-r';
-                            Conversation.depth = 0;
-                        } else {
-                            type = 'c-c';
-                            Conversation.depth++;
-                        }
-                    }
-                    let {prompt, systems} = ((messages, type) => {
-                        const rgxScenario = /^\[Circumstances and context of the dialogue: ([\s\S]+?)\.?\]$/i, rgxPerson = /^\[([\s\S]+?)'s personality: ([\s\S]+?)\]$/i, messagesClone = JSON.parse(JSON.stringify(messages)), realLogs = messagesClone.filter((message => [ 'user', 'assistant' ].includes(message.role))), sampleLogs = messagesClone.filter((message => message.name)), mergedLogs = [ ...sampleLogs, ...realLogs ];
-                        mergedLogs.forEach(((message, idx) => {
-                            const next = mergedLogs[idx + 1];
-                            message.customname = (message => [ 'assistant', 'user' ].includes(message.role) && null != message.name && !(message.name in Replacements))(message);
-                            if (next && !Config.Settings.xmlPlot) { //if (next) {
-                                if ('name' in message && 'name' in next) {
-                                    if (message.name === next.name) {
-                                        message.content += '\n\n' + next.content; //message.content += '\n' + next.content;
-                                        next.merged = true;
-                                    }
-                                } else if ('system' !== next.role) {
-                                    if (next.role === message.role) {
-                                        message.content += '\n\n' + next.content; //message.content += '\n' + next.content;
-                                        next.merged = true;
-                                    }
-                                } else {
-                                    message.content += '\n\n' + next.content; //message.content += '\n' + next.content;
-                                    next.merged = true;
-                                }
-                            }
-                        }));
-                        const lastAssistant = realLogs.findLast((message => !message.merged && 'assistant' === message.role));
-                        lastAssistant && Config.Settings.StripAssistant && (lastAssistant.strip = true);
-                        const lastUser = realLogs.findLast((message => !message.merged && 'user' === message.role));
-                        lastUser && Config.Settings.StripHuman && (lastUser.strip = true);
-                        const systemMessages = messagesClone.filter((message => 'system' === message.role && !('name' in message)));
-                        systemMessages.forEach(((message, idx) => {
-                            const scenario = message.content.match(rgxScenario)?.[1], personality = message.content.match(rgxPerson);
-                            if (scenario) {
-                                message.content = Config.ScenarioFormat.replace(/{{scenario}}/gim, scenario);
-                                message.scenario = true;
-                            }
-                            if (3 === personality?.length) {
-                                message.content = Config.PersonalityFormat.replace(/{{char}}/gim, personality[1]).replace(/{{personality}}/gim, personality[2]);
-                                message.personality = true;
-                            }
-                            message.main = 0 === idx;
-                            message.jailbreak = idx === systemMessages.length - 1;
-                            ' ' === message.content && (message.discard = true);
-                        }));
-                        Config.Settings.AllSamples && !Config.Settings.NoSamples && realLogs.forEach((message => {
-                            if (![ lastUser, lastAssistant ].includes(message)) {
-                                if ('user' === message.role) {
-                                    message.name = message.customname ? message.name : 'example_user';
-                                    message.role = 'system';
-                                } else if ('assistant' === message.role) {
-                                    message.name = message.customname ? message.name : 'example_assistant';
-                                    message.role = 'system';
-                                } else if (!message.customname) {
-                                    throw Error('Invalid role ' + message.name);
-                                }
-                            }
-                        }));
-                        Config.Settings.NoSamples && !Config.Settings.AllSamples && sampleLogs.forEach((message => {
-                            if ('example_user' === message.name) {
-                                message.role = 'user';
-                            } else if ('example_assistant' === message.name) {
-                                message.role = 'assistant';
-                            } else if (!message.customname) {
-                                throw Error('Invalid role ' + message.name);
-                            }
-                            message.customname || delete message.name;
-                        }));
-                        let systems = [];
-                        if (![ 'r', 'R' ].includes(type)) {
-                            lastUser.strip = true;
-                            systemMessages.forEach((message => message.discard = message.discard || 'c-c' === type ? !message.jailbreak : !message.jailbreak && !message.main));
-                            systems = systemMessages.filter((message => !message.discard)).map((message => `"${message.content.substring(0, 25).replace(/\n/g, '\\n').trim()}..."`));
-                            messagesClone.forEach((message => message.discard = message.discard || mergedLogs.includes(message) && ![ lastUser ].includes(message)));
-                        }
-                        const prompt = messagesClone.map(((message, idx) => {
-                            if (message.merged || message.discard) {
-                                return '';
-                            }
-                            if (message.content.length < 1) {
-                                return message.content;
-                            }
-                            let spacing = '';
-/****************************************************************/
-                            if (Config.Settings.xmlPlot) {
-                                idx > 0 && (spacing = '\n\n');
-                                const prefix = message.customname ? message.role + ': <customname>' + message.name + '</customname>: ' : 'system' !== message.role || message.name ? Replacements[message.name || message.role] + ': ' : 'xmlPlot: ' + Replacements[message.role];
-                                return `${spacing}${prefix}${message.customname ? '<reply>\n' + message.content.trim() + '\n</reply>' : message.content}`;
-                            } else {
-/****************************************************************/
-                                idx > 0 && (spacing = systemMessages.includes(message) ? '\n' : '\n\n');
-                                const prefix = message.customname ? message.name + ': ' : 'system' !== message.role || message.name ? Replacements[message.name || message.role] + ': ' : '' + Replacements[message.role];
-                                return `${spacing}${message.strip ? '' : prefix}${'system' === message.role ? message.content : message.content.trim()}`;
-                            } //
-                        }));
-                        return {
-                            prompt: genericFixes(prompt.join('')).trim(),
-                            systems
-                        };
-                    })(messages, type);
-                    console.log(`${model} [${type}]${!retryRegen && systems.length > 0 ? ' ' + systems.join(' / ') : ''}`);
-                    'R' !== type || prompt || (prompt = '...regen...');
-/****************************************************************/
-                    Config.Settings.xmlPlot && (prompt = xmlPlot(prompt));
-                    Config.Settings.FullColon && (prompt = prompt.replace(/(?<=\n\n(H(?:uman)?|A(?:ssistant)?)):[ ]?/g, '： '));
-                    Config.Settings.padtxt && (prompt = padtxt(prompt));
-/****************************************************************/
-                    Logger?.write(`\n\n-------\n[${(new Date).toLocaleString()}]\n####### PROMPT (${type}):\n${prompt}\n--\n####### REPLY:\n`);
-                    retryRegen || (fetchAPI = await (async (signal, model, prompt, temperature, type) => {
-                        const attachments = [];
-                        if (Config.Settings.PromptExperiments) {
-/****************************************************************/
-                            let splitedprompt = prompt.split('\n\nPlainPrompt:');
-                            prompt = splitedprompt[0];
-/****************************************************************/
-                            attachments.push({
-                                extracted_content: (prompt),
-                                file_name: 'paste.txt',  //fileName(),
-                                file_size: Buffer.from(prompt).byteLength,
-                                file_type: 'txt'  //'text/plain'
-                            });
-                            prompt = 'r' === type ? Config.PromptExperimentFirst : Config.PromptExperimentNext;
-/****************************************************************/                            
-                            splitedprompt.length > 1 && (prompt = prompt + splitedprompt[1]);
-/****************************************************************/                            
-                        }
-                        let res;
-                        const body = {
-                            completion: {
-                                ...Config.Settings.PassParams && {
-                                    temperature
-                                },
-                                prompt: prompt || '',
-                                timezone: AI.zone(),
-                                model: model || AI.mdl()
-                            },
-                            organization_uuid: uuidOrg,
-                            conversation_uuid: Conversation.uuid,
-                            text: prompt,
-                            attachments
-                        };
-                        let headers = {
-                            ...AI.hdr(Conversation.uuid || ''),
-                            Accept: 'text/event-stream',
-                            Cookie: getCookies()
-                        };
-                        res = await (Config.Settings.Superfetch ? Superfetch : fetch)(Config.rProxy + '/api/append_message', {
-                            stream: true,
-                            signal,
-                            method: 'POST',
-                            body: JSON.stringify(body),
-                            headers
-                        });
-                        updateParams(res);
-                        await checkResErr(res);
-                        return res;
-                    })(signal, model, prompt, temperature, type));
-                    const response = Writable.toWeb(res);
-                    clewdStream = new ClewdStream({
-                        config: Config,
-                        version: Main,
-                        minSize: Config.BufferSize,
-                        model,
-                        streaming: body.stream,
-                        abortControl,
-                        source: fetchAPI
-                    }, Logger);
-                    titleTimer = setInterval((() => setTitle('recv ' + bytesToSize(clewdStream.size))), 300);
-                    Config.Settings.Superfetch ? await Readable.toWeb(fetchAPI.body).pipeThrough(clewdStream).pipeTo(response) : await fetchAPI.body.pipeThrough(clewdStream).pipeTo(response);
-                } catch (err) {
-                    if ('AbortError' === err.name) {
-                        res.end();
-                    } else {
-                        err.planned || console.error('Clewd:\n%o', err);
-                        res.json({
-                            error: {
-                                message: 'clewd: ' + (err.message || err.name || err.type),
-                                type: err.type || err.name || err.code,
-                                param: null,
-                                code: err.code || 500
-                            }
-                        });
-                    }
-                }
-                clearInterval(titleTimer);              
-                if (clewdStream) {
-                    clewdStream.censored && console.warn('likely your account is hard-censored');
-                    prevImpersonated = clewdStream.impersonated;
-                    setTitle('ok ' + bytesToSize(clewdStream.size));
-                    //console.log(`${200 == fetchAPI.status ? '' : ''}${fetchAPI.status}!\n`);
-/******************************** */                    
-                    429 == fetchAPI.status ? console.log(`Exceeded limit!\n`) : console.log(`${200 == fetchAPI.status ? '' : ''}${fetchAPI.status}!\n`);
-                    changeflag += 1;
-                    if (Config.CookieArray?.length > 0 && (429 == fetchAPI.status || (Config.Cookiecounter && changeflag >= Config.Cookiecounter))) {
-                        changeflag = 0;
-                        changer = true;
-                    }
-/******************************** */
-                    clewdStream.empty();
-                }
-                if (prevImpersonated) {
-                    try {
-                        await deleteChat(Conversation.uuid);
-                    } catch (err) {}
-                }
-/******************************** */
-                changer && CookieChanger.emit('ChangeCookie');
-/******************************** */
-            }));
-        })(req, res);
-        break;
-
-      case '/v1/complete':
-        res.json({
-            error: {
-                message: 'clewd: Set "Chat Completion" to OpenAI instead of Claude. Enable "External" models aswell'
-            }
-        });
-        break;
-
-      default:
-        req.url !== '/' && (console.log('unknown request: ' + req.url)); //console.log('unknown request: ' + req.url);
-        res.json({
-            error: {
-                message: '404 Not Found',
-                type: 404,
-                param: null,
-                code: 404
-            }
-        }, 200);
-    }
-}));
-
-!async function() {
-    await (async () => {
-        if (exists(ConfigPath)) {
-            const userConfig = require(ConfigPath), validConfigs = Object.keys(Config), parsedConfigs = Object.keys(userConfig), parsedSettings = Object.keys(userConfig.Settings), invalidConfigs = parsedConfigs.filter((config => !validConfigs.includes(config))), validSettings = Object.keys(Config.Settings);
-            UnknownSettings = parsedSettings.filter((setting => !validSettings.includes(setting)));
-            invalidConfigs.forEach((config => {
-                console.warn(`unknown config in config.js: ${config}`);
-            }));
-            UnknownSettings.forEach((setting => {
-                console.warn(`unknown setting in config.js: Settings.${setting}`);
-            }));
-            const missingConfigs = validConfigs.filter((config => !parsedConfigs.includes(config))), missingSettings = validSettings.filter((config => !parsedSettings.includes(config)));
-            missingConfigs.forEach((config => {
-                console.warn(`adding missing config in config.js: ${config}`);
-                userConfig[config] = Config[config];
-            }));
-            missingSettings.forEach((setting => {
-                console.warn(`adding missing setting in config.js: Settings.${setting}`);
-                userConfig.Settings[setting] = Config.Settings[setting];
-            }));
-            ChangedSettings = parsedSettings.filter((setting => Config.Settings[setting] !== userConfig.Settings[setting]));
-            (missingConfigs.length > 0 || missingSettings.length > 0) && await writeSettings(userConfig);
-            userConfig.Settings.LogMessages && (Logger = createWriteStream(LogPath));
-            Config = {
-                ...Config,
-                ...userConfig
-            };
-        } else {
-            Config.Cookie = 'SET YOUR COOKIE HERE';
-            writeSettings(Config, true);
-        }
-/***************************** */
-        function convertToType(value) {
-            if (value === "true") return true;
-            if (value === "false") return false;
-            if (/^\d+$/.test(value)) return parseInt(value);
-            return value;
-        }
-        for (let key in Config) {
-            if (key === 'Settings') {
-                for (let setting in Config.Settings) {
-                    Config.Settings[setting] = convertToType(process.env[setting]) ?? Config.Settings[setting];
-                }
-            } else {
-                Config[key] = key === 'CookieArray' ? (process.env[key]?.split(',')?.map(x => x.replace(/[\[\]"\s]/g, '')) ?? Config[key]) : (convertToType(process.env[key]) ?? Config[key]);
-            }
-        }
-/***************************** */
-    })();
-/***************************** */
-    !Config.rProxy && (Config.rProxy = AI.end());
-    Config.rProxy.endsWith('/') && (Config.rProxy = Config.rProxy.slice(0, -1));
-    let uniqueArr = [], seen = new Set();
-    for (let Cookie of Config.CookieArray) {
-        if (!seen.has(Cookie)) {
-            uniqueArr.push(Cookie);
-            seen.add(Cookie);
-        }
-    }
-    Config.CookieArray = uniqueArr;
-    !process.env.Cookie && !process.env.CookieArray && writeSettings(Config);
-    currentIndex = Config.CookieIndex > 0 ? Config.CookieIndex - 1 : Config.Cookiecounter >= 0 ? Math.floor(Math.random()*Config.CookieArray.length) : 0;
-/***************************** */
-    Proxy.listen(Config.Port, Config.Ip, onListen);
-    Proxy.on('error', (err => {
-        console.error('Proxy error\n%o', err);
-    }));
-}();
-
-const cleanup = async () => {
-    console.log('cleaning...');
-    try {
-        await deleteChat(Conversation.uuid);
-        Logger?.close();
-    } catch (err) {}
-    process.exit();
-};
-
-process.on('SIGHUP', cleanup);
-
-process.on('SIGTERM', cleanup);
-
-process.on('SIGINT', cleanup);
-
-process.on('exit', (async () => {
-    console.log('exiting...');
-}));
diff --git a/spaces/xiazi/anime-remove-background/README.md b/spaces/xiazi/anime-remove-background/README.md
deleted file mode 100644
index 1ba3cb5ea0e994e246d57b7d62b8aa5a6331901c..0000000000000000000000000000000000000000
--- a/spaces/xiazi/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/yangheng/Super-Resolution-Anime-Diffusion/utils.py b/spaces/yangheng/Super-Resolution-Anime-Diffusion/utils.py
deleted file mode 100644
index 740ced9943143c7a56a16273044e60d6ab3e9728..0000000000000000000000000000000000000000
--- a/spaces/yangheng/Super-Resolution-Anime-Diffusion/utils.py
+++ /dev/null
@@ -1,7 +0,0 @@
-def is_google_colab():
-    try:
-        import google.colab
-
-        return True
-    except:
-        return False
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/stores/RulerStore.ts b/spaces/yderre-aubay/midi-player-demo/src/main/stores/RulerStore.ts
deleted file mode 100644
index f6ef08bb689d608d17314aea1687066c0eccc887..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/stores/RulerStore.ts
+++ /dev/null
@@ -1,92 +0,0 @@
-import { computed, makeObservable, observable } from "mobx"
-import { filterEventsWithScroll } from "../../common/helpers/filterEvents"
-import {
-  BeatWithX,
-  createBeatsWithXInRange,
-} from "../../common/helpers/mapBeats"
-import Quantizer from "../../common/quantizer"
-import Song from "../../common/song"
-import { isTimeSignatureEvent } from "../../common/track"
-
-interface CoordTransform {
-  pixelsPerTick: number
-}
-
-interface RulerProvider {
-  rootStore: { song: Song }
-  transform: CoordTransform
-  scrollLeft: number
-  canvasWidth: number
-  quantizer: Quantizer
-}
-
-export interface TimeSignature {
-  id: number
-  tick: number
-  numerator: number
-  denominator: number
-  isSelected: boolean
-}
-
-export class RulerStore {
-  selectedTimeSignatureEventIds: number[] = []
-
-  constructor(readonly parent: RulerProvider) {
-    makeObservable(this, {
-      selectedTimeSignatureEventIds: observable.shallow,
-      beats: computed,
-      timeSignatures: computed,
-      quantizer: computed,
-    })
-  }
-
-  get beats(): BeatWithX[] {
-    const { scrollLeft, transform, canvasWidth, rootStore } = this.parent
-
-    const startTick = scrollLeft / transform.pixelsPerTick
-
-    return createBeatsWithXInRange(
-      rootStore.song.measures,
-      transform.pixelsPerTick,
-      rootStore.song.timebase,
-      startTick,
-      canvasWidth,
-    )
-  }
-
-  get timeSignatures(): TimeSignature[] {
-    const { transform, scrollLeft, canvasWidth, rootStore } = this.parent
-    const { selectedTimeSignatureEventIds } = this
-    const track = rootStore.song.conductorTrack
-    if (track === undefined) {
-      return []
-    }
-
-    return filterEventsWithScroll(
-      track.events,
-      transform.pixelsPerTick,
-      scrollLeft,
-      canvasWidth,
-    )
-      .filter(isTimeSignatureEvent)
-      .map((e) => ({
-        ...e,
-        isSelected: selectedTimeSignatureEventIds.includes(e.id),
-      }))
-  }
-
-  get quantizer(): Quantizer {
-    return this.parent.quantizer
-  }
-
-  getTick(offsetX: number) {
-    const { transform, scrollLeft } = this.parent
-    const tick = (offsetX + scrollLeft) / transform.pixelsPerTick
-    return tick
-  }
-
-  getQuantizedTick(offsetX: number) {
-    const { quantizer } = this.parent
-    return quantizer.round(this.getTick(offsetX))
-  }
-}
diff --git a/spaces/yerfor/SyntaSpeech/utils/audio/__init__.py b/spaces/yerfor/SyntaSpeech/utils/audio/__init__.py
deleted file mode 100644
index e8cc4466b27eeda4026e945a5388dca04817e8a1..0000000000000000000000000000000000000000
--- a/spaces/yerfor/SyntaSpeech/utils/audio/__init__.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import librosa
-import numpy as np
-import pyloudnorm as pyln
-
-from utils.audio.vad import trim_long_silences
-
-
-def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
-    '''compute right padding (final frame) or both sides padding (first and final frames)
-    '''
-    assert pad_sides in (1, 2)
-    # return int(fsize // 2)
-    pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
-    if pad_sides == 1:
-        return 0, pad
-    else:
-        return pad // 2, pad // 2 + pad % 2
-
-
-def amp_to_db(x):
-    return 20 * np.log10(np.maximum(1e-5, x))
-
-
-def db_to_amp(x):
-    return 10.0 ** (x * 0.05)
-
-
-def normalize(S, min_level_db):
-    return (S - min_level_db) / -min_level_db
-
-
-def denormalize(D, min_level_db):
-    return (D * -min_level_db) + min_level_db
-
-
-def librosa_wav2spec(wav_path,
-                     fft_size=1024,
-                     hop_size=256,
-                     win_length=1024,
-                     window="hann",
-                     num_mels=80,
-                     fmin=80,
-                     fmax=-1,
-                     eps=1e-6,
-                     sample_rate=22050,
-                     loud_norm=False,
-                     trim_long_sil=False):
-    if isinstance(wav_path, str):
-        if trim_long_sil:
-            wav, _, _ = trim_long_silences(wav_path, sample_rate)
-        else:
-            wav, _ = librosa.core.load(wav_path, sr=sample_rate)
-    else:
-        wav = wav_path
-
-    if loud_norm:
-        meter = pyln.Meter(sample_rate)  # create BS.1770 meter
-        loudness = meter.integrated_loudness(wav)
-        wav = pyln.normalize.loudness(wav, loudness, -22.0)
-        if np.abs(wav).max() > 1:
-            wav = wav / np.abs(wav).max()
-
-    # get amplitude spectrogram
-    x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size,
-                          win_length=win_length, window=window, pad_mode="constant")
-    linear_spc = np.abs(x_stft)  # (n_bins, T)
-
-    # get mel basis
-    fmin = 0 if fmin == -1 else fmin
-    fmax = sample_rate / 2 if fmax == -1 else fmax
-    mel_basis = librosa.filters.mel(sample_rate, fft_size, num_mels, fmin, fmax)
-
-    # calculate mel spec
-    mel = mel_basis @ linear_spc
-    mel = np.log10(np.maximum(eps, mel))  # (n_mel_bins, T)
-    l_pad, r_pad = librosa_pad_lr(wav, fft_size, hop_size, 1)
-    wav = np.pad(wav, (l_pad, r_pad), mode='constant', constant_values=0.0)
-    wav = wav[:mel.shape[1] * hop_size]
-
-    # log linear spec
-    linear_spc = np.log10(np.maximum(eps, linear_spc))
-    return {'wav': wav, 'mel': mel.T, 'linear': linear_spc.T, 'mel_basis': mel_basis}
diff --git a/spaces/ygangang/VToonify/vtoonify/model/raft/core/raft.py b/spaces/ygangang/VToonify/vtoonify/model/raft/core/raft.py
deleted file mode 100644
index a25c22f78c96470e3dca4c25e81683133ae024e3..0000000000000000000000000000000000000000
--- a/spaces/ygangang/VToonify/vtoonify/model/raft/core/raft.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from model.raft.core.update import BasicUpdateBlock, SmallUpdateBlock
-from model.raft.core.extractor import BasicEncoder, SmallEncoder
-from model.raft.core.corr import CorrBlock, AlternateCorrBlock
-from model.raft.core.utils.utils import bilinear_sampler, coords_grid, upflow8
-
-try:
-    autocast = torch.cuda.amp.autocast
-except:
-    # dummy autocast for PyTorch < 1.6
-    class autocast:
-        def __init__(self, enabled):
-            pass
-        def __enter__(self):
-            pass
-        def __exit__(self, *args):
-            pass
-
-
-class RAFT(nn.Module):
-    def __init__(self, args):
-        super(RAFT, self).__init__()
-        self.args = args
-
-        if args.small:
-            self.hidden_dim = hdim = 96
-            self.context_dim = cdim = 64
-            args.corr_levels = 4
-            args.corr_radius = 3
-        
-        else:
-            self.hidden_dim = hdim = 128
-            self.context_dim = cdim = 128
-            args.corr_levels = 4
-            args.corr_radius = 4
-
-        if 'dropout' not in self.args:
-            self.args.dropout = 0
-
-        if 'alternate_corr' not in self.args:
-            self.args.alternate_corr = False
-
-        # feature network, context network, and update block
-        if args.small:
-            self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout)        
-            self.cnet = SmallEncoder(output_dim=hdim+cdim, norm_fn='none', dropout=args.dropout)
-            self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim)
-
-        else:
-            self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout)        
-            self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout)
-            self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
-
-    def freeze_bn(self):
-        for m in self.modules():
-            if isinstance(m, nn.BatchNorm2d):
-                m.eval()
-
-    def initialize_flow(self, img):
-        """ Flow is represented as difference between two coordinate grids flow = coords1 - coords0"""
-        N, C, H, W = img.shape
-        coords0 = coords_grid(N, H//8, W//8, device=img.device)
-        coords1 = coords_grid(N, H//8, W//8, device=img.device)
-
-        # optical flow computed as difference: flow = coords1 - coords0
-        return coords0, coords1
-
-    def upsample_flow(self, flow, mask):
-        """ Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
-        N, _, H, W = flow.shape
-        mask = mask.view(N, 1, 9, 8, 8, H, W)
-        mask = torch.softmax(mask, dim=2)
-
-        up_flow = F.unfold(8 * flow, [3,3], padding=1)
-        up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
-
-        up_flow = torch.sum(mask * up_flow, dim=2)
-        up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
-        return up_flow.reshape(N, 2, 8*H, 8*W)
-
-
-    def forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
-        """ Estimate optical flow between pair of frames """
-
-        image1 = 2 * (image1 / 255.0) - 1.0
-        image2 = 2 * (image2 / 255.0) - 1.0
-
-        image1 = image1.contiguous()
-        image2 = image2.contiguous()
-
-        hdim = self.hidden_dim
-        cdim = self.context_dim
-
-        # run the feature network
-        with autocast(enabled=self.args.mixed_precision):
-            fmap1, fmap2 = self.fnet([image1, image2])        
-        
-        fmap1 = fmap1.float()
-        fmap2 = fmap2.float()
-        if self.args.alternate_corr:
-            corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
-        else:
-            corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
-
-        # run the context network
-        with autocast(enabled=self.args.mixed_precision):
-            cnet = self.cnet(image1)
-            net, inp = torch.split(cnet, [hdim, cdim], dim=1)
-            net = torch.tanh(net)
-            inp = torch.relu(inp)
-
-        coords0, coords1 = self.initialize_flow(image1)
-
-        if flow_init is not None:
-            coords1 = coords1 + flow_init
-
-        flow_predictions = []
-        for itr in range(iters):
-            coords1 = coords1.detach()
-            corr = corr_fn(coords1) # index correlation volume
-
-            flow = coords1 - coords0
-            with autocast(enabled=self.args.mixed_precision):
-                net, up_mask, delta_flow = self.update_block(net, inp, corr, flow)
-
-            # F(t+1) = F(t) + \Delta(t)
-            coords1 = coords1 + delta_flow
-
-            # upsample predictions
-            if up_mask is None:
-                flow_up = upflow8(coords1 - coords0)
-            else:
-                flow_up = self.upsample_flow(coords1 - coords0, up_mask)
-            
-            flow_predictions.append(flow_up)
-
-        if test_mode:
-            return coords1 - coords0, flow_up
-            
-        return flow_predictions
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/benchmark/benchmark_args_tf.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/benchmark/benchmark_args_tf.py
deleted file mode 100644
index c1c2ec16ce550cfc14326aed49a175d593fdc7bb..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/benchmark/benchmark_args_tf.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass, field
-from typing import Tuple
-
-from ..utils import cached_property, is_tf_available, logging, requires_backends
-from .benchmark_args_utils import BenchmarkArguments
-
-
-if is_tf_available():
-    import tensorflow as tf
-
-
-logger = logging.get_logger(__name__)
-
-
-@dataclass
-class TensorFlowBenchmarkArguments(BenchmarkArguments):
-    deprecated_args = [
-        "no_inference",
-        "no_cuda",
-        "no_tpu",
-        "no_speed",
-        "no_memory",
-        "no_env_print",
-        "no_multi_process",
-    ]
-
-    def __init__(self, **kwargs):
-        """
-        This __init__ is there for legacy code. When removing deprecated args completely, the class can simply be
-        deleted
-        """
-        for deprecated_arg in self.deprecated_args:
-            if deprecated_arg in kwargs:
-                positive_arg = deprecated_arg[3:]
-                kwargs[positive_arg] = not kwargs.pop(deprecated_arg)
-                logger.warning(
-                    f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
-                    f" {positive_arg}={kwargs[positive_arg]}"
-                )
-        self.tpu_name = kwargs.pop("tpu_name", self.tpu_name)
-        self.device_idx = kwargs.pop("device_idx", self.device_idx)
-        self.eager_mode = kwargs.pop("eager_mode", self.eager_mode)
-        self.use_xla = kwargs.pop("use_xla", self.use_xla)
-        super().__init__(**kwargs)
-
-    tpu_name: str = field(
-        default=None,
-        metadata={"help": "Name of TPU"},
-    )
-    device_idx: int = field(
-        default=0,
-        metadata={"help": "CPU / GPU device index. Defaults to 0."},
-    )
-    eager_mode: bool = field(default=False, metadata={"help": "Benchmark models in eager model."})
-    use_xla: bool = field(
-        default=False,
-        metadata={
-            "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
-        },
-    )
-
-    @cached_property
-    def _setup_tpu(self) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
-        requires_backends(self, ["tf"])
-        tpu = None
-        if self.tpu:
-            try:
-                if self.tpu_name:
-                    tpu = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name)
-                else:
-                    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
-            except ValueError:
-                tpu = None
-        return tpu
-
-    @cached_property
-    def _setup_strategy(self) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
-        requires_backends(self, ["tf"])
-        if self.is_tpu:
-            tf.config.experimental_connect_to_cluster(self._setup_tpu)
-            tf.tpu.experimental.initialize_tpu_system(self._setup_tpu)
-
-            strategy = tf.distribute.TPUStrategy(self._setup_tpu)
-        else:
-            # currently no multi gpu is allowed
-            if self.is_gpu:
-                # TODO: Currently only single GPU is supported
-                tf.config.set_visible_devices(self.gpu_list[self.device_idx], "GPU")
-                strategy = tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}")
-            else:
-                tf.config.set_visible_devices([], "GPU")  # disable GPU
-                strategy = tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}")
-
-        return strategy
-
-    @property
-    def is_tpu(self) -> bool:
-        requires_backends(self, ["tf"])
-        return self._setup_tpu is not None
-
-    @property
-    def strategy(self) -> "tf.distribute.Strategy":
-        requires_backends(self, ["tf"])
-        return self._setup_strategy
-
-    @property
-    def gpu_list(self):
-        requires_backends(self, ["tf"])
-        return tf.config.list_physical_devices("GPU")
-
-    @property
-    def n_gpu(self) -> int:
-        requires_backends(self, ["tf"])
-        if self.cuda:
-            return len(self.gpu_list)
-        return 0
-
-    @property
-    def is_gpu(self) -> bool:
-        return self.n_gpu > 0
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/graphormer/collating_graphormer.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/graphormer/collating_graphormer.py
deleted file mode 100644
index 58ce602ea28de1a3f5f45c40a9ffb1a0e4f0fdcf..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/graphormer/collating_graphormer.py
+++ /dev/null
@@ -1,134 +0,0 @@
-# Copyright (c) Microsoft Corporation and HuggingFace
-# Licensed under the MIT License.
-
-from typing import Any, Dict, List, Mapping
-
-import numpy as np
-import torch
-
-from ...utils import is_cython_available, requires_backends
-
-
-if is_cython_available():
-    import pyximport
-
-    pyximport.install(setup_args={"include_dirs": np.get_include()})
-    from . import algos_graphormer  # noqa E402
-
-
-def convert_to_single_emb(x, offset: int = 512):
-    feature_num = x.shape[1] if len(x.shape) > 1 else 1
-    feature_offset = 1 + np.arange(0, feature_num * offset, offset, dtype=np.int64)
-    x = x + feature_offset
-    return x
-
-
-def preprocess_item(item, keep_features=True):
-    requires_backends(preprocess_item, ["cython"])
-
-    if keep_features and "edge_attr" in item.keys():  # edge_attr
-        edge_attr = np.asarray(item["edge_attr"], dtype=np.int64)
-    else:
-        edge_attr = np.ones((len(item["edge_index"][0]), 1), dtype=np.int64)  # same embedding for all
-
-    if keep_features and "node_feat" in item.keys():  # input_nodes
-        node_feature = np.asarray(item["node_feat"], dtype=np.int64)
-    else:
-        node_feature = np.ones((item["num_nodes"], 1), dtype=np.int64)  # same embedding for all
-
-    edge_index = np.asarray(item["edge_index"], dtype=np.int64)
-
-    input_nodes = convert_to_single_emb(node_feature) + 1
-    num_nodes = item["num_nodes"]
-
-    if len(edge_attr.shape) == 1:
-        edge_attr = edge_attr[:, None]
-    attn_edge_type = np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]], dtype=np.int64)
-    attn_edge_type[edge_index[0], edge_index[1]] = convert_to_single_emb(edge_attr) + 1
-
-    # node adj matrix [num_nodes, num_nodes] bool
-    adj = np.zeros([num_nodes, num_nodes], dtype=bool)
-    adj[edge_index[0], edge_index[1]] = True
-
-    shortest_path_result, path = algos_graphormer.floyd_warshall(adj)
-    max_dist = np.amax(shortest_path_result)
-
-    input_edges = algos_graphormer.gen_edge_input(max_dist, path, attn_edge_type)
-    attn_bias = np.zeros([num_nodes + 1, num_nodes + 1], dtype=np.single)  # with graph token
-
-    # combine
-    item["input_nodes"] = input_nodes + 1  # we shift all indices by one for padding
-    item["attn_bias"] = attn_bias
-    item["attn_edge_type"] = attn_edge_type
-    item["spatial_pos"] = shortest_path_result.astype(np.int64) + 1  # we shift all indices by one for padding
-    item["in_degree"] = np.sum(adj, axis=1).reshape(-1) + 1  # we shift all indices by one for padding
-    item["out_degree"] = item["in_degree"]  # for undirected graph
-    item["input_edges"] = input_edges + 1  # we shift all indices by one for padding
-    if "labels" not in item:
-        item["labels"] = item["y"]
-
-    return item
-
-
-class GraphormerDataCollator:
-    def __init__(self, spatial_pos_max=20, on_the_fly_processing=False):
-        if not is_cython_available():
-            raise ImportError("Graphormer preprocessing needs Cython (pyximport)")
-
-        self.spatial_pos_max = spatial_pos_max
-        self.on_the_fly_processing = on_the_fly_processing
-
-    def __call__(self, features: List[dict]) -> Dict[str, Any]:
-        if self.on_the_fly_processing:
-            features = [preprocess_item(i) for i in features]
-
-        if not isinstance(features[0], Mapping):
-            features = [vars(f) for f in features]
-        batch = {}
-
-        max_node_num = max(len(i["input_nodes"]) for i in features)
-        node_feat_size = len(features[0]["input_nodes"][0])
-        edge_feat_size = len(features[0]["attn_edge_type"][0][0])
-        max_dist = max(len(i["input_edges"][0][0]) for i in features)
-        edge_input_size = len(features[0]["input_edges"][0][0][0])
-        batch_size = len(features)
-
-        batch["attn_bias"] = torch.zeros(batch_size, max_node_num + 1, max_node_num + 1, dtype=torch.float)
-        batch["attn_edge_type"] = torch.zeros(batch_size, max_node_num, max_node_num, edge_feat_size, dtype=torch.long)
-        batch["spatial_pos"] = torch.zeros(batch_size, max_node_num, max_node_num, dtype=torch.long)
-        batch["in_degree"] = torch.zeros(batch_size, max_node_num, dtype=torch.long)
-        batch["input_nodes"] = torch.zeros(batch_size, max_node_num, node_feat_size, dtype=torch.long)
-        batch["input_edges"] = torch.zeros(
-            batch_size, max_node_num, max_node_num, max_dist, edge_input_size, dtype=torch.long
-        )
-
-        for ix, f in enumerate(features):
-            for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
-                f[k] = torch.tensor(f[k])
-
-            if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]) > 0:
-                f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max] = float("-inf")
-
-            batch["attn_bias"][ix, : f["attn_bias"].shape[0], : f["attn_bias"].shape[1]] = f["attn_bias"]
-            batch["attn_edge_type"][ix, : f["attn_edge_type"].shape[0], : f["attn_edge_type"].shape[1], :] = f[
-                "attn_edge_type"
-            ]
-            batch["spatial_pos"][ix, : f["spatial_pos"].shape[0], : f["spatial_pos"].shape[1]] = f["spatial_pos"]
-            batch["in_degree"][ix, : f["in_degree"].shape[0]] = f["in_degree"]
-            batch["input_nodes"][ix, : f["input_nodes"].shape[0], :] = f["input_nodes"]
-            batch["input_edges"][
-                ix, : f["input_edges"].shape[0], : f["input_edges"].shape[1], : f["input_edges"].shape[2], :
-            ] = f["input_edges"]
-
-        batch["out_degree"] = batch["in_degree"]
-
-        sample = features[0]["labels"]
-        if len(sample) == 1:  # one task
-            if isinstance(sample[0], float):  # regression
-                batch["labels"] = torch.from_numpy(np.concatenate([i["labels"] for i in features]))
-            else:  # binary classification
-                batch["labels"] = torch.from_numpy(np.concatenate([i["labels"] for i in features]))
-        else:  # multi task classification, left to float to keep the NaNs
-            batch["labels"] = torch.from_numpy(np.stack([i["labels"] for i in features], axis=0))
-
-        return batch
diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/pretrain/meta.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/pretrain/meta.py
deleted file mode 100644
index cc35dd3c0dfe8436e7d635f2db507cedca75ed49..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/pretrain/meta.py
+++ /dev/null
@@ -1,31 +0,0 @@
-def download_dict():
-    return {
-        "vec768l12": {
-            "url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
-            "output": "./pretrain/checkpoint_best_legacy_500.pt"
-        },
-        "vec256l9": {
-            "url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
-            "output": "./pretrain/checkpoint_best_legacy_500.pt"
-        },
-        "hubertsoft": {
-            "url": "https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt",
-            "output": "./pretrain/hubert-soft-0d54a1f4.pt"
-        },
-        "whisper-ppg": {
-            "url": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
-            "output": "./pretrain/medium.pt"
-        }
-    }
-
-
-def get_speech_encoder(config_path="configs/config.json"):
-    import json
-
-    with open(config_path, "r") as f:
-        data = f.read()
-        config = json.loads(data)
-        speech_encoder = config["model"]["speech_encoder"]
-        dict = download_dict()
-
-        return dict[speech_encoder]["url"], dict[speech_encoder]["output"]
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h
deleted file mode 100644
index b54a5dde2ca11a74d29c4d8adb7fe1634f5baf9c..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h
+++ /dev/null
@@ -1,370 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#pragma once
-
-#include <cassert>
-#include <cmath>
-
-#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1
-// Designates functions callable from the host (CPU) and the device (GPU)
-#define HOST_DEVICE __host__ __device__
-#define HOST_DEVICE_INLINE HOST_DEVICE __forceinline__
-#else
-#include <algorithm>
-#define HOST_DEVICE
-#define HOST_DEVICE_INLINE HOST_DEVICE inline
-#endif
-
-namespace detectron2 {
-
-namespace {
-
-template <typename T>
-struct RotatedBox {
-  T x_ctr, y_ctr, w, h, a;
-};
-
-template <typename T>
-struct Point {
-  T x, y;
-  HOST_DEVICE_INLINE Point(const T& px = 0, const T& py = 0) : x(px), y(py) {}
-  HOST_DEVICE_INLINE Point operator+(const Point& p) const {
-    return Point(x + p.x, y + p.y);
-  }
-  HOST_DEVICE_INLINE Point& operator+=(const Point& p) {
-    x += p.x;
-    y += p.y;
-    return *this;
-  }
-  HOST_DEVICE_INLINE Point operator-(const Point& p) const {
-    return Point(x - p.x, y - p.y);
-  }
-  HOST_DEVICE_INLINE Point operator*(const T coeff) const {
-    return Point(x * coeff, y * coeff);
-  }
-};
-
-template <typename T>
-HOST_DEVICE_INLINE T dot_2d(const Point<T>& A, const Point<T>& B) {
-  return A.x * B.x + A.y * B.y;
-}
-
-// R: result type. can be different from input type
-template <typename T, typename R = T>
-HOST_DEVICE_INLINE R cross_2d(const Point<T>& A, const Point<T>& B) {
-  return static_cast<R>(A.x) * static_cast<R>(B.y) -
-      static_cast<R>(B.x) * static_cast<R>(A.y);
-}
-
-template <typename T>
-HOST_DEVICE_INLINE void get_rotated_vertices(
-    const RotatedBox<T>& box,
-    Point<T> (&pts)[4]) {
-  // M_PI / 180. == 0.01745329251
-  double theta = box.a * 0.01745329251;
-  T cosTheta2 = (T)cos(theta) * 0.5f;
-  T sinTheta2 = (T)sin(theta) * 0.5f;
-
-  // y: top --> down; x: left --> right
-  pts[0].x = box.x_ctr + sinTheta2 * box.h + cosTheta2 * box.w;
-  pts[0].y = box.y_ctr + cosTheta2 * box.h - sinTheta2 * box.w;
-  pts[1].x = box.x_ctr - sinTheta2 * box.h + cosTheta2 * box.w;
-  pts[1].y = box.y_ctr - cosTheta2 * box.h - sinTheta2 * box.w;
-  pts[2].x = 2 * box.x_ctr - pts[0].x;
-  pts[2].y = 2 * box.y_ctr - pts[0].y;
-  pts[3].x = 2 * box.x_ctr - pts[1].x;
-  pts[3].y = 2 * box.y_ctr - pts[1].y;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE int get_intersection_points(
-    const Point<T> (&pts1)[4],
-    const Point<T> (&pts2)[4],
-    Point<T> (&intersections)[24]) {
-  // Line vector
-  // A line from p1 to p2 is: p1 + (p2-p1)*t, t=[0,1]
-  Point<T> vec1[4], vec2[4];
-  for (int i = 0; i < 4; i++) {
-    vec1[i] = pts1[(i + 1) % 4] - pts1[i];
-    vec2[i] = pts2[(i + 1) % 4] - pts2[i];
-  }
-
-  // When computing the intersection area, it doesn't hurt if we have
-  // more (duplicated/approximate) intersections/vertices than needed,
-  // while it can cause drastic difference if we miss an intersection/vertex.
-  // Therefore, we add an epsilon to relax the comparisons between
-  // the float point numbers that decide the intersection points.
-  double EPS = 1e-5;
-
-  // Line test - test all line combos for intersection
-  int num = 0; // number of intersections
-  for (int i = 0; i < 4; i++) {
-    for (int j = 0; j < 4; j++) {
-      // Solve for 2x2 Ax=b
-      T det = cross_2d<T>(vec2[j], vec1[i]);
-
-      // This takes care of parallel lines
-      if (fabs(det) <= 1e-14) {
-        continue;
-      }
-
-      auto vec12 = pts2[j] - pts1[i];
-
-      T t1 = cross_2d<T>(vec2[j], vec12) / det;
-      T t2 = cross_2d<T>(vec1[i], vec12) / det;
-
-      if (t1 > -EPS && t1 < 1.0f + EPS && t2 > -EPS && t2 < 1.0f + EPS) {
-        intersections[num++] = pts1[i] + vec1[i] * t1;
-      }
-    }
-  }
-
-  // Check for vertices of rect1 inside rect2
-  {
-    const auto& AB = vec2[0];
-    const auto& DA = vec2[3];
-    auto ABdotAB = dot_2d<T>(AB, AB);
-    auto ADdotAD = dot_2d<T>(DA, DA);
-    for (int i = 0; i < 4; i++) {
-      // assume ABCD is the rectangle, and P is the point to be judged
-      // P is inside ABCD iff. P's projection on AB lies within AB
-      // and P's projection on AD lies within AD
-
-      auto AP = pts1[i] - pts2[0];
-
-      auto APdotAB = dot_2d<T>(AP, AB);
-      auto APdotAD = -dot_2d<T>(AP, DA);
-
-      if ((APdotAB > -EPS) && (APdotAD > -EPS) && (APdotAB < ABdotAB + EPS) &&
-          (APdotAD < ADdotAD + EPS)) {
-        intersections[num++] = pts1[i];
-      }
-    }
-  }
-
-  // Reverse the check - check for vertices of rect2 inside rect1
-  {
-    const auto& AB = vec1[0];
-    const auto& DA = vec1[3];
-    auto ABdotAB = dot_2d<T>(AB, AB);
-    auto ADdotAD = dot_2d<T>(DA, DA);
-    for (int i = 0; i < 4; i++) {
-      auto AP = pts2[i] - pts1[0];
-
-      auto APdotAB = dot_2d<T>(AP, AB);
-      auto APdotAD = -dot_2d<T>(AP, DA);
-
-      if ((APdotAB > -EPS) && (APdotAD > -EPS) && (APdotAB < ABdotAB + EPS) &&
-          (APdotAD < ADdotAD + EPS)) {
-        intersections[num++] = pts2[i];
-      }
-    }
-  }
-
-  return num;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE int convex_hull_graham(
-    const Point<T> (&p)[24],
-    const int& num_in,
-    Point<T> (&q)[24],
-    bool shift_to_zero = false) {
-  assert(num_in >= 2);
-
-  // Step 1:
-  // Find point with minimum y
-  // if more than 1 points have the same minimum y,
-  // pick the one with the minimum x.
-  int t = 0;
-  for (int i = 1; i < num_in; i++) {
-    if (p[i].y < p[t].y || (p[i].y == p[t].y && p[i].x < p[t].x)) {
-      t = i;
-    }
-  }
-  auto& start = p[t]; // starting point
-
-  // Step 2:
-  // Subtract starting point from every points (for sorting in the next step)
-  for (int i = 0; i < num_in; i++) {
-    q[i] = p[i] - start;
-  }
-
-  // Swap the starting point to position 0
-  auto tmp = q[0];
-  q[0] = q[t];
-  q[t] = tmp;
-
-  // Step 3:
-  // Sort point 1 ~ num_in according to their relative cross-product values
-  // (essentially sorting according to angles)
-  // If the angles are the same, sort according to their distance to origin
-  T dist[24];
-#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1
-  // compute distance to origin before sort, and sort them together with the
-  // points
-  for (int i = 0; i < num_in; i++) {
-    dist[i] = dot_2d<T>(q[i], q[i]);
-  }
-
-  // CUDA version
-  // In the future, we can potentially use thrust
-  // for sorting here to improve speed (though not guaranteed)
-  for (int i = 1; i < num_in - 1; i++) {
-    for (int j = i + 1; j < num_in; j++) {
-      T crossProduct = cross_2d<T>(q[i], q[j]);
-      if ((crossProduct < -1e-6) ||
-          (fabs(crossProduct) < 1e-6 && dist[i] > dist[j])) {
-        auto q_tmp = q[i];
-        q[i] = q[j];
-        q[j] = q_tmp;
-        auto dist_tmp = dist[i];
-        dist[i] = dist[j];
-        dist[j] = dist_tmp;
-      }
-    }
-  }
-#else
-  // CPU version
-  std::sort(
-      q + 1, q + num_in, [](const Point<T>& A, const Point<T>& B) -> bool {
-        T temp = cross_2d<T>(A, B);
-        if (fabs(temp) < 1e-6) {
-          return dot_2d<T>(A, A) < dot_2d<T>(B, B);
-        } else {
-          return temp > 0;
-        }
-      });
-  // compute distance to origin after sort, since the points are now different.
-  for (int i = 0; i < num_in; i++) {
-    dist[i] = dot_2d<T>(q[i], q[i]);
-  }
-#endif
-
-  // Step 4:
-  // Make sure there are at least 2 points (that don't overlap with each other)
-  // in the stack
-  int k; // index of the non-overlapped second point
-  for (k = 1; k < num_in; k++) {
-    if (dist[k] > 1e-8) {
-      break;
-    }
-  }
-  if (k == num_in) {
-    // We reach the end, which means the convex hull is just one point
-    q[0] = p[t];
-    return 1;
-  }
-  q[1] = q[k];
-  int m = 2; // 2 points in the stack
-  // Step 5:
-  // Finally we can start the scanning process.
-  // When a non-convex relationship between the 3 points is found
-  // (either concave shape or duplicated points),
-  // we pop the previous point from the stack
-  // until the 3-point relationship is convex again, or
-  // until the stack only contains two points
-  for (int i = k + 1; i < num_in; i++) {
-    while (m > 1) {
-      auto q1 = q[i] - q[m - 2], q2 = q[m - 1] - q[m - 2];
-      // cross_2d() uses FMA and therefore computes round(round(q1.x*q2.y) -
-      // q2.x*q1.y) So it may not return 0 even when q1==q2. Therefore we
-      // compare round(q1.x*q2.y) and round(q2.x*q1.y) directly. (round means
-      // round to nearest floating point).
-      if (q1.x * q2.y >= q2.x * q1.y)
-        m--;
-      else
-        break;
-    }
-    // Using double also helps, but float can solve the issue for now.
-    // while (m > 1 && cross_2d<T, double>(q[i] - q[m - 2], q[m - 1] - q[m - 2])
-    // >= 0) {
-    //     m--;
-    // }
-    q[m++] = q[i];
-  }
-
-  // Step 6 (Optional):
-  // In general sense we need the original coordinates, so we
-  // need to shift the points back (reverting Step 2)
-  // But if we're only interested in getting the area/perimeter of the shape
-  // We can simply return.
-  if (!shift_to_zero) {
-    for (int i = 0; i < m; i++) {
-      q[i] += start;
-    }
-  }
-
-  return m;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE T polygon_area(const Point<T> (&q)[24], const int& m) {
-  if (m <= 2) {
-    return 0;
-  }
-
-  T area = 0;
-  for (int i = 1; i < m - 1; i++) {
-    area += fabs(cross_2d<T>(q[i] - q[0], q[i + 1] - q[0]));
-  }
-
-  return area / 2.0;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE T rotated_boxes_intersection(
-    const RotatedBox<T>& box1,
-    const RotatedBox<T>& box2) {
-  // There are up to 4 x 4 + 4 + 4 = 24 intersections (including dups) returned
-  // from rotated_rect_intersection_pts
-  Point<T> intersectPts[24], orderedPts[24];
-
-  Point<T> pts1[4];
-  Point<T> pts2[4];
-  get_rotated_vertices<T>(box1, pts1);
-  get_rotated_vertices<T>(box2, pts2);
-
-  int num = get_intersection_points<T>(pts1, pts2, intersectPts);
-
-  if (num <= 2) {
-    return 0.0;
-  }
-
-  // Convex Hull to order the intersection points in clockwise order and find
-  // the contour area.
-  int num_convex = convex_hull_graham<T>(intersectPts, num, orderedPts, true);
-  return polygon_area<T>(orderedPts, num_convex);
-}
-
-} // namespace
-
-template <typename T>
-HOST_DEVICE_INLINE T
-single_box_iou_rotated(T const* const box1_raw, T const* const box2_raw) {
-  // shift center to the middle point to achieve higher precision in result
-  RotatedBox<T> box1, box2;
-  auto center_shift_x = (box1_raw[0] + box2_raw[0]) / 2.0;
-  auto center_shift_y = (box1_raw[1] + box2_raw[1]) / 2.0;
-  box1.x_ctr = box1_raw[0] - center_shift_x;
-  box1.y_ctr = box1_raw[1] - center_shift_y;
-  box1.w = box1_raw[2];
-  box1.h = box1_raw[3];
-  box1.a = box1_raw[4];
-  box2.x_ctr = box2_raw[0] - center_shift_x;
-  box2.y_ctr = box2_raw[1] - center_shift_y;
-  box2.w = box2_raw[2];
-  box2.h = box2_raw[3];
-  box2.a = box2_raw[4];
-
-  T area1 = box1.w * box1.h;
-  T area2 = box2.w * box2.h;
-  if (area1 < 1e-14 || area2 < 1e-14) {
-    return 0.f;
-  }
-
-  T intersection = rotated_boxes_intersection<T>(box1, box2);
-  T iou = intersection / (area1 + area2 - intersection);
-  return iou;
-}
-
-} // namespace detectron2
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.cpp b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.cpp
deleted file mode 100644
index 0a5b7b907c06720fefc77b0dfd921b8ec3ecf2be..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.cpp
+++ /dev/null
@@ -1,507 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#include "cocoeval.h"
-#include <time.h>
-#include <algorithm>
-#include <cstdint>
-#include <numeric>
-
-using namespace pybind11::literals;
-
-namespace detectron2 {
-
-namespace COCOeval {
-
-// Sort detections from highest score to lowest, such that
-// detection_instances[detection_sorted_indices[t]] >=
-// detection_instances[detection_sorted_indices[t+1]].  Use stable_sort to match
-// original COCO API
-void SortInstancesByDetectionScore(
-    const std::vector<InstanceAnnotation>& detection_instances,
-    std::vector<uint64_t>* detection_sorted_indices) {
-  detection_sorted_indices->resize(detection_instances.size());
-  std::iota(
-      detection_sorted_indices->begin(), detection_sorted_indices->end(), 0);
-  std::stable_sort(
-      detection_sorted_indices->begin(),
-      detection_sorted_indices->end(),
-      [&detection_instances](size_t j1, size_t j2) {
-        return detection_instances[j1].score > detection_instances[j2].score;
-      });
-}
-
-// Partition the ground truth objects based on whether or not to ignore them
-// based on area
-void SortInstancesByIgnore(
-    const std::array<double, 2>& area_range,
-    const std::vector<InstanceAnnotation>& ground_truth_instances,
-    std::vector<uint64_t>* ground_truth_sorted_indices,
-    std::vector<bool>* ignores) {
-  ignores->clear();
-  ignores->reserve(ground_truth_instances.size());
-  for (auto o : ground_truth_instances) {
-    ignores->push_back(
-        o.ignore || o.area < area_range[0] || o.area > area_range[1]);
-  }
-
-  ground_truth_sorted_indices->resize(ground_truth_instances.size());
-  std::iota(
-      ground_truth_sorted_indices->begin(),
-      ground_truth_sorted_indices->end(),
-      0);
-  std::stable_sort(
-      ground_truth_sorted_indices->begin(),
-      ground_truth_sorted_indices->end(),
-      [&ignores](size_t j1, size_t j2) {
-        return (int)(*ignores)[j1] < (int)(*ignores)[j2];
-      });
-}
-
-// For each IOU threshold, greedily match each detected instance to a ground
-// truth instance (if possible) and store the results
-void MatchDetectionsToGroundTruth(
-    const std::vector<InstanceAnnotation>& detection_instances,
-    const std::vector<uint64_t>& detection_sorted_indices,
-    const std::vector<InstanceAnnotation>& ground_truth_instances,
-    const std::vector<uint64_t>& ground_truth_sorted_indices,
-    const std::vector<bool>& ignores,
-    const std::vector<std::vector<double>>& ious,
-    const std::vector<double>& iou_thresholds,
-    const std::array<double, 2>& area_range,
-    ImageEvaluation* results) {
-  // Initialize memory to store return data matches and ignore
-  const int num_iou_thresholds = iou_thresholds.size();
-  const int num_ground_truth = ground_truth_sorted_indices.size();
-  const int num_detections = detection_sorted_indices.size();
-  std::vector<uint64_t> ground_truth_matches(
-      num_iou_thresholds * num_ground_truth, 0);
-  std::vector<uint64_t>& detection_matches = results->detection_matches;
-  std::vector<bool>& detection_ignores = results->detection_ignores;
-  std::vector<bool>& ground_truth_ignores = results->ground_truth_ignores;
-  detection_matches.resize(num_iou_thresholds * num_detections, 0);
-  detection_ignores.resize(num_iou_thresholds * num_detections, false);
-  ground_truth_ignores.resize(num_ground_truth);
-  for (auto g = 0; g < num_ground_truth; ++g) {
-    ground_truth_ignores[g] = ignores[ground_truth_sorted_indices[g]];
-  }
-
-  for (auto t = 0; t < num_iou_thresholds; ++t) {
-    for (auto d = 0; d < num_detections; ++d) {
-      // information about best match so far (match=-1 -> unmatched)
-      double best_iou = std::min(iou_thresholds[t], 1 - 1e-10);
-      int match = -1;
-      for (auto g = 0; g < num_ground_truth; ++g) {
-        // if this ground truth instance is already matched and not a
-        // crowd, it cannot be matched to another detection
-        if (ground_truth_matches[t * num_ground_truth + g] > 0 &&
-            !ground_truth_instances[ground_truth_sorted_indices[g]].is_crowd) {
-          continue;
-        }
-
-        // if detected instance matched to a regular ground truth
-        // instance, we can break on the first ground truth instance
-        // tagged as ignore (because they are sorted by the ignore tag)
-        if (match >= 0 && !ground_truth_ignores[match] &&
-            ground_truth_ignores[g]) {
-          break;
-        }
-
-        // if IOU overlap is the best so far, store the match appropriately
-        if (ious[d][ground_truth_sorted_indices[g]] >= best_iou) {
-          best_iou = ious[d][ground_truth_sorted_indices[g]];
-          match = g;
-        }
-      }
-      // if match was made, store id of match for both detection and
-      // ground truth
-      if (match >= 0) {
-        detection_ignores[t * num_detections + d] = ground_truth_ignores[match];
-        detection_matches[t * num_detections + d] =
-            ground_truth_instances[ground_truth_sorted_indices[match]].id;
-        ground_truth_matches[t * num_ground_truth + match] =
-            detection_instances[detection_sorted_indices[d]].id;
-      }
-
-      // set unmatched detections outside of area range to ignore
-      const InstanceAnnotation& detection =
-          detection_instances[detection_sorted_indices[d]];
-      detection_ignores[t * num_detections + d] =
-          detection_ignores[t * num_detections + d] ||
-          (detection_matches[t * num_detections + d] == 0 &&
-           (detection.area < area_range[0] || detection.area > area_range[1]));
-    }
-  }
-
-  // store detection score results
-  results->detection_scores.resize(detection_sorted_indices.size());
-  for (size_t d = 0; d < detection_sorted_indices.size(); ++d) {
-    results->detection_scores[d] =
-        detection_instances[detection_sorted_indices[d]].score;
-  }
-}
-
-std::vector<ImageEvaluation> EvaluateImages(
-    const std::vector<std::array<double, 2>>& area_ranges,
-    int max_detections,
-    const std::vector<double>& iou_thresholds,
-    const ImageCategoryInstances<std::vector<double>>& image_category_ious,
-    const ImageCategoryInstances<InstanceAnnotation>&
-        image_category_ground_truth_instances,
-    const ImageCategoryInstances<InstanceAnnotation>&
-        image_category_detection_instances) {
-  const int num_area_ranges = area_ranges.size();
-  const int num_images = image_category_ground_truth_instances.size();
-  const int num_categories =
-      image_category_ious.size() > 0 ? image_category_ious[0].size() : 0;
-  std::vector<uint64_t> detection_sorted_indices;
-  std::vector<uint64_t> ground_truth_sorted_indices;
-  std::vector<bool> ignores;
-  std::vector<ImageEvaluation> results_all(
-      num_images * num_area_ranges * num_categories);
-
-  // Store results for each image, category, and area range combination. Results
-  // for each IOU threshold are packed into the same ImageEvaluation object
-  for (auto i = 0; i < num_images; ++i) {
-    for (auto c = 0; c < num_categories; ++c) {
-      const std::vector<InstanceAnnotation>& ground_truth_instances =
-          image_category_ground_truth_instances[i][c];
-      const std::vector<InstanceAnnotation>& detection_instances =
-          image_category_detection_instances[i][c];
-
-      SortInstancesByDetectionScore(
-          detection_instances, &detection_sorted_indices);
-      if ((int)detection_sorted_indices.size() > max_detections) {
-        detection_sorted_indices.resize(max_detections);
-      }
-
-      for (size_t a = 0; a < area_ranges.size(); ++a) {
-        SortInstancesByIgnore(
-            area_ranges[a],
-            ground_truth_instances,
-            &ground_truth_sorted_indices,
-            &ignores);
-
-        MatchDetectionsToGroundTruth(
-            detection_instances,
-            detection_sorted_indices,
-            ground_truth_instances,
-            ground_truth_sorted_indices,
-            ignores,
-            image_category_ious[i][c],
-            iou_thresholds,
-            area_ranges[a],
-            &results_all
-                [c * num_area_ranges * num_images + a * num_images + i]);
-      }
-    }
-  }
-
-  return results_all;
-}
-
-// Convert a python list to a vector
-template <typename T>
-std::vector<T> list_to_vec(const py::list& l) {
-  std::vector<T> v(py::len(l));
-  for (int i = 0; i < (int)py::len(l); ++i) {
-    v[i] = l[i].cast<T>();
-  }
-  return v;
-}
-
-// Helper function to Accumulate()
-// Considers the evaluation results applicable to a particular category, area
-// range, and max_detections parameter setting, which begin at
-// evaluations[evaluation_index].  Extracts a sorted list of length n of all
-// applicable detection instances concatenated across all images in the dataset,
-// which are represented by the outputs evaluation_indices, detection_scores,
-// image_detection_indices, and detection_sorted_indices--all of which are
-// length n. evaluation_indices[i] stores the applicable index into
-// evaluations[] for instance i, which has detection score detection_score[i],
-// and is the image_detection_indices[i]'th of the list of detections
-// for the image containing i.  detection_sorted_indices[] defines a sorted
-// permutation of the 3 other outputs
-int BuildSortedDetectionList(
-    const std::vector<ImageEvaluation>& evaluations,
-    const int64_t evaluation_index,
-    const int64_t num_images,
-    const int max_detections,
-    std::vector<uint64_t>* evaluation_indices,
-    std::vector<double>* detection_scores,
-    std::vector<uint64_t>* detection_sorted_indices,
-    std::vector<uint64_t>* image_detection_indices) {
-  assert(evaluations.size() >= evaluation_index + num_images);
-
-  // Extract a list of object instances of the applicable category, area
-  // range, and max detections requirements such that they can be sorted
-  image_detection_indices->clear();
-  evaluation_indices->clear();
-  detection_scores->clear();
-  image_detection_indices->reserve(num_images * max_detections);
-  evaluation_indices->reserve(num_images * max_detections);
-  detection_scores->reserve(num_images * max_detections);
-  int num_valid_ground_truth = 0;
-  for (auto i = 0; i < num_images; ++i) {
-    const ImageEvaluation& evaluation = evaluations[evaluation_index + i];
-
-    for (int d = 0;
-         d < (int)evaluation.detection_scores.size() && d < max_detections;
-         ++d) { // detected instances
-      evaluation_indices->push_back(evaluation_index + i);
-      image_detection_indices->push_back(d);
-      detection_scores->push_back(evaluation.detection_scores[d]);
-    }
-    for (auto ground_truth_ignore : evaluation.ground_truth_ignores) {
-      if (!ground_truth_ignore) {
-        ++num_valid_ground_truth;
-      }
-    }
-  }
-
-  // Sort detections by decreasing score, using stable sort to match
-  // python implementation
-  detection_sorted_indices->resize(detection_scores->size());
-  std::iota(
-      detection_sorted_indices->begin(), detection_sorted_indices->end(), 0);
-  std::stable_sort(
-      detection_sorted_indices->begin(),
-      detection_sorted_indices->end(),
-      [&detection_scores](size_t j1, size_t j2) {
-        return (*detection_scores)[j1] > (*detection_scores)[j2];
-      });
-
-  return num_valid_ground_truth;
-}
-
-// Helper function to Accumulate()
-// Compute a precision recall curve given a sorted list of detected instances
-// encoded in evaluations, evaluation_indices, detection_scores,
-// detection_sorted_indices, image_detection_indices (see
-// BuildSortedDetectionList()). Using vectors precisions and recalls
-// and temporary storage, output the results into precisions_out, recalls_out,
-// and scores_out, which are large buffers containing many precion/recall curves
-// for all possible parameter settings, with precisions_out_index and
-// recalls_out_index defining the applicable indices to store results.
-void ComputePrecisionRecallCurve(
-    const int64_t precisions_out_index,
-    const int64_t precisions_out_stride,
-    const int64_t recalls_out_index,
-    const std::vector<double>& recall_thresholds,
-    const int iou_threshold_index,
-    const int num_iou_thresholds,
-    const int num_valid_ground_truth,
-    const std::vector<ImageEvaluation>& evaluations,
-    const std::vector<uint64_t>& evaluation_indices,
-    const std::vector<double>& detection_scores,
-    const std::vector<uint64_t>& detection_sorted_indices,
-    const std::vector<uint64_t>& image_detection_indices,
-    std::vector<double>* precisions,
-    std::vector<double>* recalls,
-    std::vector<double>* precisions_out,
-    std::vector<double>* scores_out,
-    std::vector<double>* recalls_out) {
-  assert(recalls_out->size() > recalls_out_index);
-
-  // Compute precision/recall for each instance in the sorted list of detections
-  int64_t true_positives_sum = 0, false_positives_sum = 0;
-  precisions->clear();
-  recalls->clear();
-  precisions->reserve(detection_sorted_indices.size());
-  recalls->reserve(detection_sorted_indices.size());
-  assert(!evaluations.empty() || detection_sorted_indices.empty());
-  for (auto detection_sorted_index : detection_sorted_indices) {
-    const ImageEvaluation& evaluation =
-        evaluations[evaluation_indices[detection_sorted_index]];
-    const auto num_detections =
-        evaluation.detection_matches.size() / num_iou_thresholds;
-    const auto detection_index = iou_threshold_index * num_detections +
-        image_detection_indices[detection_sorted_index];
-    assert(evaluation.detection_matches.size() > detection_index);
-    assert(evaluation.detection_ignores.size() > detection_index);
-    const int64_t detection_match =
-        evaluation.detection_matches[detection_index];
-    const bool detection_ignores =
-        evaluation.detection_ignores[detection_index];
-    const auto true_positive = detection_match > 0 && !detection_ignores;
-    const auto false_positive = detection_match == 0 && !detection_ignores;
-    if (true_positive) {
-      ++true_positives_sum;
-    }
-    if (false_positive) {
-      ++false_positives_sum;
-    }
-
-    const double recall =
-        static_cast<double>(true_positives_sum) / num_valid_ground_truth;
-    recalls->push_back(recall);
-    const int64_t num_valid_detections =
-        true_positives_sum + false_positives_sum;
-    const double precision = num_valid_detections > 0
-        ? static_cast<double>(true_positives_sum) / num_valid_detections
-        : 0.0;
-    precisions->push_back(precision);
-  }
-
-  (*recalls_out)[recalls_out_index] = !recalls->empty() ? recalls->back() : 0;
-
-  for (int64_t i = static_cast<int64_t>(precisions->size()) - 1; i > 0; --i) {
-    if ((*precisions)[i] > (*precisions)[i - 1]) {
-      (*precisions)[i - 1] = (*precisions)[i];
-    }
-  }
-
-  // Sample the per instance precision/recall list at each recall threshold
-  for (size_t r = 0; r < recall_thresholds.size(); ++r) {
-    // first index in recalls >= recall_thresholds[r]
-    std::vector<double>::iterator low = std::lower_bound(
-        recalls->begin(), recalls->end(), recall_thresholds[r]);
-    size_t precisions_index = low - recalls->begin();
-
-    const auto results_ind = precisions_out_index + r * precisions_out_stride;
-    assert(results_ind < precisions_out->size());
-    assert(results_ind < scores_out->size());
-    if (precisions_index < precisions->size()) {
-      (*precisions_out)[results_ind] = (*precisions)[precisions_index];
-      (*scores_out)[results_ind] =
-          detection_scores[detection_sorted_indices[precisions_index]];
-    } else {
-      (*precisions_out)[results_ind] = 0;
-      (*scores_out)[results_ind] = 0;
-    }
-  }
-}
-py::dict Accumulate(
-    const py::object& params,
-    const std::vector<ImageEvaluation>& evaluations) {
-  const std::vector<double> recall_thresholds =
-      list_to_vec<double>(params.attr("recThrs"));
-  const std::vector<int> max_detections =
-      list_to_vec<int>(params.attr("maxDets"));
-  const int num_iou_thresholds = py::len(params.attr("iouThrs"));
-  const int num_recall_thresholds = py::len(params.attr("recThrs"));
-  const int num_categories = params.attr("useCats").cast<int>() == 1
-      ? py::len(params.attr("catIds"))
-      : 1;
-  const int num_area_ranges = py::len(params.attr("areaRng"));
-  const int num_max_detections = py::len(params.attr("maxDets"));
-  const int num_images = py::len(params.attr("imgIds"));
-
-  std::vector<double> precisions_out(
-      num_iou_thresholds * num_recall_thresholds * num_categories *
-          num_area_ranges * num_max_detections,
-      -1);
-  std::vector<double> recalls_out(
-      num_iou_thresholds * num_categories * num_area_ranges *
-          num_max_detections,
-      -1);
-  std::vector<double> scores_out(
-      num_iou_thresholds * num_recall_thresholds * num_categories *
-          num_area_ranges * num_max_detections,
-      -1);
-
-  // Consider the list of all detected instances in the entire dataset in one
-  // large list.  evaluation_indices, detection_scores,
-  // image_detection_indices, and detection_sorted_indices all have the same
-  // length as this list, such that each entry corresponds to one detected
-  // instance
-  std::vector<uint64_t> evaluation_indices; // indices into evaluations[]
-  std::vector<double> detection_scores; // detection scores of each instance
-  std::vector<uint64_t> detection_sorted_indices; // sorted indices of all
-                                                  // instances in the dataset
-  std::vector<uint64_t>
-      image_detection_indices; // indices into the list of detected instances in
-                               // the same image as each instance
-  std::vector<double> precisions, recalls;
-
-  for (auto c = 0; c < num_categories; ++c) {
-    for (auto a = 0; a < num_area_ranges; ++a) {
-      for (auto m = 0; m < num_max_detections; ++m) {
-        // The COCO PythonAPI assumes evaluations[] (the return value of
-        // COCOeval::EvaluateImages() is one long list storing results for each
-        // combination of category, area range, and image id, with categories in
-        // the outermost loop and images in the innermost loop.
-        const int64_t evaluations_index =
-            c * num_area_ranges * num_images + a * num_images;
-        int num_valid_ground_truth = BuildSortedDetectionList(
-            evaluations,
-            evaluations_index,
-            num_images,
-            max_detections[m],
-            &evaluation_indices,
-            &detection_scores,
-            &detection_sorted_indices,
-            &image_detection_indices);
-
-        if (num_valid_ground_truth == 0) {
-          continue;
-        }
-
-        for (auto t = 0; t < num_iou_thresholds; ++t) {
-          // recalls_out is a flattened vectors representing a
-          // num_iou_thresholds X num_categories X num_area_ranges X
-          // num_max_detections matrix
-          const int64_t recalls_out_index =
-              t * num_categories * num_area_ranges * num_max_detections +
-              c * num_area_ranges * num_max_detections +
-              a * num_max_detections + m;
-
-          // precisions_out and scores_out are flattened vectors
-          // representing a num_iou_thresholds X num_recall_thresholds X
-          // num_categories X num_area_ranges X num_max_detections matrix
-          const int64_t precisions_out_stride =
-              num_categories * num_area_ranges * num_max_detections;
-          const int64_t precisions_out_index = t * num_recall_thresholds *
-                  num_categories * num_area_ranges * num_max_detections +
-              c * num_area_ranges * num_max_detections +
-              a * num_max_detections + m;
-
-          ComputePrecisionRecallCurve(
-              precisions_out_index,
-              precisions_out_stride,
-              recalls_out_index,
-              recall_thresholds,
-              t,
-              num_iou_thresholds,
-              num_valid_ground_truth,
-              evaluations,
-              evaluation_indices,
-              detection_scores,
-              detection_sorted_indices,
-              image_detection_indices,
-              &precisions,
-              &recalls,
-              &precisions_out,
-              &scores_out,
-              &recalls_out);
-        }
-      }
-    }
-  }
-
-  time_t rawtime;
-  struct tm local_time;
-  std::array<char, 200> buffer;
-  time(&rawtime);
-#ifdef _WIN32
-  localtime_s(&local_time, &rawtime);
-#else
-  localtime_r(&rawtime, &local_time);
-#endif
-  strftime(
-      buffer.data(), 200, "%Y-%m-%d %H:%num_max_detections:%S", &local_time);
-  return py::dict(
-      "params"_a = params,
-      "counts"_a = std::vector<int64_t>(
-          {num_iou_thresholds,
-           num_recall_thresholds,
-           num_categories,
-           num_area_ranges,
-           num_max_detections}),
-      "date"_a = buffer,
-      "precision"_a = precisions_out,
-      "recall"_a = recalls_out,
-      "scores"_a = scores_out);
-}
-
-} // namespace COCOeval
-
-} // namespace detectron2
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/old-value.js b/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/old-value.js
deleted file mode 100644
index 63a2643864e4f6304de24f77624cbdff24974cf9..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/old-value.js
+++ /dev/null
@@ -1,22 +0,0 @@
-let utils = require('./utils')
-
-class OldValue {
-  constructor(unprefixed, prefixed, string, regexp) {
-    this.unprefixed = unprefixed
-    this.prefixed = prefixed
-    this.string = string || prefixed
-    this.regexp = regexp || utils.regexp(prefixed)
-  }
-
-  /**
-   * Check, that value contain old value
-   */
-  check(value) {
-    if (value.includes(this.string)) {
-      return !!value.match(this.regexp)
-    }
-    return false
-  }
-}
-
-module.exports = OldValue
diff --git a/spaces/yuangongfdu/LTU-Compare/README.md b/spaces/yuangongfdu/LTU-Compare/README.md
deleted file mode 100644
index abe6e7000a31cb61a2771a03a0a725df7a6858b3..0000000000000000000000000000000000000000
--- a/spaces/yuangongfdu/LTU-Compare/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LTU Compare
-emoji: 🔥
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: false
-license: cc-by-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ywqisok/ysyy/modules.py b/spaces/ywqisok/ysyy/modules.py
deleted file mode 100644
index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000
--- a/spaces/ywqisok/ysyy/modules.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import math
-import numpy as np
-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
-from 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.):
-    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
-
-
-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.)
-    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
diff --git a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/compare.js b/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/compare.js
deleted file mode 100644
index 748b7afa514a9f356b3f180a34e9150df3777ecd..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/compare.js
+++ /dev/null
@@ -1,5 +0,0 @@
-const SemVer = require('../classes/semver')
-const compare = (a, b, loose) =>
-  new SemVer(a, loose).compare(new SemVer(b, loose))
-
-module.exports = compare
diff --git a/spaces/zhoupin30/zhoupin30/src/components/ui/icons.tsx b/spaces/zhoupin30/zhoupin30/src/components/ui/icons.tsx
deleted file mode 100644
index 742b489b50437c5b64c86082f2ebc712eeb6a2b0..0000000000000000000000000000000000000000
--- a/spaces/zhoupin30/zhoupin30/src/components/ui/icons.tsx
+++ /dev/null
@@ -1,504 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-function IconNextChat({
-  className,
-  inverted,
-  ...props
-}: React.ComponentProps<'svg'> & { inverted?: boolean }) {
-  const id = React.useId()
-
-  return (
-    <svg
-      viewBox="0 0 17 17"
-      fill="none"
-      xmlns="http://www.w3.org/2000/svg"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <defs>
-        <linearGradient
-          id={`gradient-${id}-1`}
-          x1="10.6889"
-          y1="10.3556"
-          x2="13.8445"
-          y2="14.2667"
-          gradientUnits="userSpaceOnUse"
-        >
-          <stop stopColor={inverted ? 'white' : 'black'} />
-          <stop
-            offset={1}
-            stopColor={inverted ? 'white' : 'black'}
-            stopOpacity={0}
-          />
-        </linearGradient>
-        <linearGradient
-          id={`gradient-${id}-2`}
-          x1="11.7555"
-          y1="4.8"
-          x2="11.7376"
-          y2="9.50002"
-          gradientUnits="userSpaceOnUse"
-        >
-          <stop stopColor={inverted ? 'white' : 'black'} />
-          <stop
-            offset={1}
-            stopColor={inverted ? 'white' : 'black'}
-            stopOpacity={0}
-          />
-        </linearGradient>
-      </defs>
-      <path
-        d="M1 16L2.58314 11.2506C1.83084 9.74642 1.63835 8.02363 2.04013 6.39052C2.4419 4.75741 3.41171 3.32057 4.776 2.33712C6.1403 1.35367 7.81003 0.887808 9.4864 1.02289C11.1628 1.15798 12.7364 1.8852 13.9256 3.07442C15.1148 4.26363 15.842 5.83723 15.9771 7.5136C16.1122 9.18997 15.6463 10.8597 14.6629 12.224C13.6794 13.5883 12.2426 14.5581 10.6095 14.9599C8.97637 15.3616 7.25358 15.1692 5.74942 14.4169L1 16Z"
-        fill={inverted ? 'black' : 'white'}
-        stroke={inverted ? 'black' : 'white'}
-        strokeWidth={2}
-        strokeLinecap="round"
-        strokeLinejoin="round"
-      />
-      <mask
-        id="mask0_91_2047"
-        style={{ maskType: 'alpha' }}
-        maskUnits="userSpaceOnUse"
-        x={1}
-        y={0}
-        width={16}
-        height={16}
-      >
-        <circle cx={9} cy={8} r={8} fill={inverted ? 'black' : 'white'} />
-      </mask>
-      <g mask="url(#mask0_91_2047)">
-        <circle cx={9} cy={8} r={8} fill={inverted ? 'black' : 'white'} />
-        <path
-          d="M14.2896 14.0018L7.146 4.8H5.80005V11.1973H6.87681V6.16743L13.4444 14.6529C13.7407 14.4545 14.0231 14.2369 14.2896 14.0018Z"
-          fill={`url(#gradient-${id}-1)`}
-        />
-        <rect
-          x="11.2222"
-          y="4.8"
-          width="1.06667"
-          height="6.4"
-          fill={`url(#gradient-${id}-2)`}
-        />
-      </g>
-    </svg>
-  )
-}
-
-function IconOpenAI({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      fill="currentColor"
-      viewBox="0 0 24 24"
-      role="img"
-      xmlns="http://www.w3.org/2000/svg"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <title>OpenAI icon</title>
-      <path d="M22.2819 9.8211a5.9847 5.9847 0 0 0-.5157-4.9108 6.0462 6.0462 0 0 0-6.5098-2.9A6.0651 6.0651 0 0 0 4.9807 4.1818a5.9847 5.9847 0 0 0-3.9977 2.9 6.0462 6.0462 0 0 0 .7427 7.0966 5.98 5.98 0 0 0 .511 4.9107 6.051 6.051 0 0 0 6.5146 2.9001A5.9847 5.9847 0 0 0 13.2599 24a6.0557 6.0557 0 0 0 5.7718-4.2058 5.9894 5.9894 0 0 0 3.9977-2.9001 6.0557 6.0557 0 0 0-.7475-7.0729zm-9.022 12.6081a4.4755 4.4755 0 0 1-2.8764-1.0408l.1419-.0804 4.7783-2.7582a.7948.7948 0 0 0 .3927-.6813v-6.7369l2.02 1.1686a.071.071 0 0 1 .038.052v5.5826a4.504 4.504 0 0 1-4.4945 4.4944zm-9.6607-4.1254a4.4708 4.4708 0 0 1-.5346-3.0137l.142.0852 4.783 2.7582a.7712.7712 0 0 0 .7806 0l5.8428-3.3685v2.3324a.0804.0804 0 0 1-.0332.0615L9.74 19.9502a4.4992 4.4992 0 0 1-6.1408-1.6464zM2.3408 7.8956a4.485 4.485 0 0 1 2.3655-1.9728V11.6a.7664.7664 0 0 0 .3879.6765l5.8144 3.3543-2.0201 1.1685a.0757.0757 0 0 1-.071 0l-4.8303-2.7865A4.504 4.504 0 0 1 2.3408 7.872zm16.5963 3.8558L13.1038 8.364 15.1192 7.2a.0757.0757 0 0 1 .071 0l4.8303 2.7913a4.4944 4.4944 0 0 1-.6765 8.1042v-5.6772a.79.79 0 0 0-.407-.667zm2.0107-3.0231l-.142-.0852-4.7735-2.7818a.7759.7759 0 0 0-.7854 0L9.409 9.2297V6.8974a.0662.0662 0 0 1 .0284-.0615l4.8303-2.7866a4.4992 4.4992 0 0 1 6.6802 4.66zM8.3065 12.863l-2.02-1.1638a.0804.0804 0 0 1-.038-.0567V6.0742a4.4992 4.4992 0 0 1 7.3757-3.4537l-.142.0805L8.704 5.459a.7948.7948 0 0 0-.3927.6813zm1.0976-2.3654l2.602-1.4998 2.6069 1.4998v2.9994l-2.5974 1.4997-2.6067-1.4997Z" />
-    </svg>
-  )
-}
-
-function IconGitHub({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      role="img"
-      viewBox="0 0 24 24"
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <title>GitHub</title>
-      <path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12" />
-    </svg>
-  )
-}
-
-function IconSeparator({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      fill="none"
-      shapeRendering="geometricPrecision"
-      stroke="currentColor"
-      strokeLinecap="round"
-      strokeLinejoin="round"
-      strokeWidth="1"
-      viewBox="0 0 24 24"
-      aria-hidden="true"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M16.88 3.549L7.12 20.451"></path>
-    </svg>
-  )
-}
-
-function IconArrowDown({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="m205.66 149.66-72 72a8 8 0 0 1-11.32 0l-72-72a8 8 0 0 1 11.32-11.32L120 196.69V40a8 8 0 0 1 16 0v156.69l58.34-58.35a8 8 0 0 1 11.32 11.32Z" />
-    </svg>
-  )
-}
-
-function IconArrowRight({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="m221.66 133.66-72 72a8 8 0 0 1-11.32-11.32L196.69 136H40a8 8 0 0 1 0-16h156.69l-58.35-58.34a8 8 0 0 1 11.32-11.32l72 72a8 8 0 0 1 0 11.32Z" />
-    </svg>
-  )
-}
-
-function IconUser({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M230.92 212c-15.23-26.33-38.7-45.21-66.09-54.16a72 72 0 1 0-73.66 0c-27.39 8.94-50.86 27.82-66.09 54.16a8 8 0 1 0 13.85 8c18.84-32.56 52.14-52 89.07-52s70.23 19.44 89.07 52a8 8 0 1 0 13.85-8ZM72 96a56 56 0 1 1 56 56 56.06 56.06 0 0 1-56-56Z" />
-    </svg>
-  )
-}
-
-function IconPlus({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M224 128a8 8 0 0 1-8 8h-80v80a8 8 0 0 1-16 0v-80H40a8 8 0 0 1 0-16h80V40a8 8 0 0 1 16 0v80h80a8 8 0 0 1 8 8Z" />
-    </svg>
-  )
-}
-
-function IconArrowElbow({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M200 32v144a8 8 0 0 1-8 8H67.31l34.35 34.34a8 8 0 0 1-11.32 11.32l-48-48a8 8 0 0 1 0-11.32l48-48a8 8 0 0 1 11.32 11.32L67.31 168H184V32a8 8 0 0 1 16 0Z" />
-    </svg>
-  )
-}
-
-function IconSpinner({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4 animate-spin', className)}
-      {...props}
-    >
-      <path d="M232 128a104 104 0 0 1-208 0c0-41 23.81-78.36 60.66-95.27a8 8 0 0 1 6.68 14.54C60.15 61.59 40 93.27 40 128a88 88 0 0 0 176 0c0-34.73-20.15-66.41-51.34-80.73a8 8 0 0 1 6.68-14.54C208.19 49.64 232 87 232 128Z" />
-    </svg>
-  )
-}
-
-function IconMessage({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M216 48H40a16 16 0 0 0-16 16v160a15.84 15.84 0 0 0 9.25 14.5A16.05 16.05 0 0 0 40 240a15.89 15.89 0 0 0 10.25-3.78.69.69 0 0 0 .13-.11L82.5 208H216a16 16 0 0 0 16-16V64a16 16 0 0 0-16-16ZM40 224Zm176-32H82.5a16 16 0 0 0-10.3 3.75l-.12.11L40 224V64h176Z" />
-    </svg>
-  )
-}
-
-function IconTrash({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M216 48h-40v-8a24 24 0 0 0-24-24h-48a24 24 0 0 0-24 24v8H40a8 8 0 0 0 0 16h8v144a16 16 0 0 0 16 16h128a16 16 0 0 0 16-16V64h8a8 8 0 0 0 0-16ZM96 40a8 8 0 0 1 8-8h48a8 8 0 0 1 8 8v8H96Zm96 168H64V64h128Zm-80-104v64a8 8 0 0 1-16 0v-64a8 8 0 0 1 16 0Zm48 0v64a8 8 0 0 1-16 0v-64a8 8 0 0 1 16 0Z" />
-    </svg>
-  )
-}
-
-function IconMore({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      viewBox="0 0 24 24"
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M7.75 12C7.75 12.9665 6.9665 13.75 6 13.75C5.0335 13.75 4.25 12.9665 4.25 12C4.25 11.0335 5.0335 10.25 6 10.25C6.9665 10.25 7.75 11.0335 7.75 12ZM13.75 12C13.75 12.9665 12.9665 13.75 12 13.75C11.0335 13.75 10.25 12.9665 10.25 12C10.25 11.0335 11.0335 10.25 12 10.25C12.9665 10.25 13.75 11.0335 13.75 12ZM18 13.75C18.9665 13.75 19.75 12.9665 19.75 12C19.75 11.0335 18.9665 10.25 18 10.25C17.0335 10.25 16.25 11.0335 16.25 12C16.25 12.9665 17.0335 13.75 18 13.75Z"></path>
-    </svg>
-  )
-}
-
-function IconRefresh({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M197.67 186.37a8 8 0 0 1 0 11.29C196.58 198.73 170.82 224 128 224c-37.39 0-64.53-22.4-80-39.85V208a8 8 0 0 1-16 0v-48a8 8 0 0 1 8-8h48a8 8 0 0 1 0 16H55.44C67.76 183.35 93 208 128 208c36 0 58.14-21.46 58.36-21.68a8 8 0 0 1 11.31.05ZM216 40a8 8 0 0 0-8 8v23.85C192.53 54.4 165.39 32 128 32c-42.82 0-68.58 25.27-69.66 26.34a8 8 0 0 0 11.3 11.34C69.86 69.46 92 48 128 48c35 0 60.24 24.65 72.56 40H168a8 8 0 0 0 0 16h48a8 8 0 0 0 8-8V48a8 8 0 0 0-8-8Z" />
-    </svg>
-  )
-}
-
-function IconStop({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M128 24a104 104 0 1 0 104 104A104.11 104.11 0 0 0 128 24Zm0 192a88 88 0 1 1 88-88 88.1 88.1 0 0 1-88 88Zm24-120h-48a8 8 0 0 0-8 8v48a8 8 0 0 0 8 8h48a8 8 0 0 0 8-8v-48a8 8 0 0 0-8-8Zm-8 48h-32v-32h32Z" />
-    </svg>
-  )
-}
-
-function IconSidebar({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M216 40H40a16 16 0 0 0-16 16v144a16 16 0 0 0 16 16h176a16 16 0 0 0 16-16V56a16 16 0 0 0-16-16ZM40 56h40v144H40Zm176 144H96V56h120v144Z" />
-    </svg>
-  )
-}
-
-function IconMoon({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M233.54 142.23a8 8 0 0 0-8-2 88.08 88.08 0 0 1-109.8-109.8 8 8 0 0 0-10-10 104.84 104.84 0 0 0-52.91 37A104 104 0 0 0 136 224a103.09 103.09 0 0 0 62.52-20.88 104.84 104.84 0 0 0 37-52.91 8 8 0 0 0-1.98-7.98Zm-44.64 48.11A88 88 0 0 1 65.66 67.11a89 89 0 0 1 31.4-26A106 106 0 0 0 96 56a104.11 104.11 0 0 0 104 104 106 106 0 0 0 14.92-1.06 89 89 0 0 1-26.02 31.4Z" />
-    </svg>
-  )
-}
-
-function IconSun({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M120 40V16a8 8 0 0 1 16 0v24a8 8 0 0 1-16 0Zm72 88a64 64 0 1 1-64-64 64.07 64.07 0 0 1 64 64Zm-16 0a48 48 0 1 0-48 48 48.05 48.05 0 0 0 48-48ZM58.34 69.66a8 8 0 0 0 11.32-11.32l-16-16a8 8 0 0 0-11.32 11.32Zm0 116.68-16 16a8 8 0 0 0 11.32 11.32l16-16a8 8 0 0 0-11.32-11.32ZM192 72a8 8 0 0 0 5.66-2.34l16-16a8 8 0 0 0-11.32-11.32l-16 16A8 8 0 0 0 192 72Zm5.66 114.34a8 8 0 0 0-11.32 11.32l16 16a8 8 0 0 0 11.32-11.32ZM48 128a8 8 0 0 0-8-8H16a8 8 0 0 0 0 16h24a8 8 0 0 0 8-8Zm80 80a8 8 0 0 0-8 8v24a8 8 0 0 0 16 0v-24a8 8 0 0 0-8-8Zm112-88h-24a8 8 0 0 0 0 16h24a8 8 0 0 0 0-16Z" />
-    </svg>
-  )
-}
-
-function IconCopy({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M216 32H88a8 8 0 0 0-8 8v40H40a8 8 0 0 0-8 8v128a8 8 0 0 0 8 8h128a8 8 0 0 0 8-8v-40h40a8 8 0 0 0 8-8V40a8 8 0 0 0-8-8Zm-56 176H48V96h112Zm48-48h-32V88a8 8 0 0 0-8-8H96V48h112Z" />
-    </svg>
-  )
-}
-
-function IconCheck({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="m229.66 77.66-128 128a8 8 0 0 1-11.32 0l-56-56a8 8 0 0 1 11.32-11.32L96 188.69 218.34 66.34a8 8 0 0 1 11.32 11.32Z" />
-    </svg>
-  )
-}
-
-function IconDownload({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M224 152v56a16 16 0 0 1-16 16H48a16 16 0 0 1-16-16v-56a8 8 0 0 1 16 0v56h160v-56a8 8 0 0 1 16 0Zm-101.66 5.66a8 8 0 0 0 11.32 0l40-40a8 8 0 0 0-11.32-11.32L136 132.69V40a8 8 0 0 0-16 0v92.69l-26.34-26.35a8 8 0 0 0-11.32 11.32Z" />
-    </svg>
-  )
-}
-
-function IconClose({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      viewBox="0 0 256 256"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path d="M205.66 194.34a8 8 0 0 1-11.32 11.32L128 139.31l-66.34 66.35a8 8 0 0 1-11.32-11.32L116.69 128 50.34 61.66a8 8 0 0 1 11.32-11.32L128 116.69l66.34-66.35a8 8 0 0 1 11.32 11.32L139.31 128Z" />
-    </svg>
-  )
-}
-
-function IconEdit({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      fill="none"
-      viewBox="0 0 24 24"
-      strokeWidth={1.5}
-      stroke="currentColor"
-      className={cn('h-4 w-4', className)}
-      {...props}
-    >
-      <path
-        strokeLinecap="round"
-        strokeLinejoin="round"
-        d="M16.862 4.487l1.687-1.688a1.875 1.875 0 112.652 2.652L10.582 16.07a4.5 4.5 0 01-1.897 1.13L6 18l.8-2.685a4.5 4.5 0 011.13-1.897l8.932-8.931zm0 0L19.5 7.125M18 14v4.75A2.25 2.25 0 0115.75 21H5.25A2.25 2.25 0 013 18.75V8.25A2.25 2.25 0 015.25 6H10"
-      />
-    </svg>
-  )
-}
-
-function IconShare({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      viewBox="0 0 256 256"
-      {...props}
-    >
-      <path d="m237.66 106.35-80-80A8 8 0 0 0 144 32v40.35c-25.94 2.22-54.59 14.92-78.16 34.91-28.38 24.08-46.05 55.11-49.76 87.37a12 12 0 0 0 20.68 9.58c11-11.71 50.14-48.74 107.24-52V192a8 8 0 0 0 13.66 5.65l80-80a8 8 0 0 0 0-11.3ZM160 172.69V144a8 8 0 0 0-8-8c-28.08 0-55.43 7.33-81.29 21.8a196.17 196.17 0 0 0-36.57 26.52c5.8-23.84 20.42-46.51 42.05-64.86C99.41 99.77 127.75 88 152 88a8 8 0 0 0 8-8V51.32L220.69 112Z" />
-    </svg>
-  )
-}
-
-function IconUsers({ className, ...props }: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      viewBox="0 0 256 256"
-      {...props}
-    >
-      <path d="M117.25 157.92a60 60 0 1 0-66.5 0 95.83 95.83 0 0 0-47.22 37.71 8 8 0 1 0 13.4 8.74 80 80 0 0 1 134.14 0 8 8 0 0 0 13.4-8.74 95.83 95.83 0 0 0-47.22-37.71ZM40 108a44 44 0 1 1 44 44 44.05 44.05 0 0 1-44-44Zm210.14 98.7a8 8 0 0 1-11.07-2.33A79.83 79.83 0 0 0 172 168a8 8 0 0 1 0-16 44 44 0 1 0-16.34-84.87 8 8 0 1 1-5.94-14.85 60 60 0 0 1 55.53 105.64 95.83 95.83 0 0 1 47.22 37.71 8 8 0 0 1-2.33 11.07Z" />
-    </svg>
-  )
-}
-
-function IconExternalLink({
-  className,
-  ...props
-}: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      viewBox="0 0 256 256"
-      {...props}
-    >
-      <path d="M224 104a8 8 0 0 1-16 0V59.32l-66.33 66.34a8 8 0 0 1-11.32-11.32L196.68 48H152a8 8 0 0 1 0-16h64a8 8 0 0 1 8 8Zm-40 24a8 8 0 0 0-8 8v72H48V80h72a8 8 0 0 0 0-16H48a16 16 0 0 0-16 16v128a16 16 0 0 0 16 16h128a16 16 0 0 0 16-16v-72a8 8 0 0 0-8-8Z" />
-    </svg>
-  )
-}
-
-function IconChevronUpDown({
-  className,
-  ...props
-}: React.ComponentProps<'svg'>) {
-  return (
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      fill="currentColor"
-      className={cn('h-4 w-4', className)}
-      viewBox="0 0 256 256"
-      {...props}
-    >
-      <path d="M181.66 170.34a8 8 0 0 1 0 11.32l-48 48a8 8 0 0 1-11.32 0l-48-48a8 8 0 0 1 11.32-11.32L128 212.69l42.34-42.35a8 8 0 0 1 11.32 0Zm-96-84.68L128 43.31l42.34 42.35a8 8 0 0 0 11.32-11.32l-48-48a8 8 0 0 0-11.32 0l-48 48a8 8 0 0 0 11.32 11.32Z" />
-    </svg>
-  )
-}
-
-export {
-  IconEdit,
-  IconNextChat,
-  IconOpenAI,
-  IconGitHub,
-  IconSeparator,
-  IconArrowDown,
-  IconArrowRight,
-  IconUser,
-  IconPlus,
-  IconArrowElbow,
-  IconSpinner,
-  IconMessage,
-  IconTrash,
-  IconMore,
-  IconRefresh,
-  IconStop,
-  IconSidebar,
-  IconMoon,
-  IconSun,
-  IconCopy,
-  IconCheck,
-  IconDownload,
-  IconClose,
-  IconShare,
-  IconUsers,
-  IconExternalLink,
-  IconChevronUpDown
-}
diff --git a/spaces/zixian/Zhenhuan-VITS/text/__init__.py b/spaces/zixian/Zhenhuan-VITS/text/__init__.py
deleted file mode 100644
index 11e5586c347c3071a9d1aca0425d112f45402e85..0000000000000000000000000000000000000000
--- a/spaces/zixian/Zhenhuan-VITS/text/__init__.py
+++ /dev/null
@@ -1,60 +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, symbols, 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 = []
-  symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  clean_text = _clean_text(text, cleaner_names)
-  print(clean_text)
-  print(f" length:{len(clean_text)}")
-  for symbol in clean_text:
-    if symbol not in symbol_to_id.keys():
-      continue
-    symbol_id = symbol_to_id[symbol]
-    sequence += [symbol_id]
-  print(f" length:{len(sequence)}")
-  return sequence
-
-
-def cleaned_text_to_sequence(cleaned_text, symbols):
-  '''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
-  '''
-  symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  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/zomehwh/sovits-tannhauser/cluster/__init__.py b/spaces/zomehwh/sovits-tannhauser/cluster/__init__.py
deleted file mode 100644
index f1b9bde04e73e9218a5d534227caa4c25332f424..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/sovits-tannhauser/cluster/__init__.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import numpy as np
-import torch
-from sklearn.cluster import KMeans
-
-def get_cluster_model(ckpt_path):
-    checkpoint = torch.load(ckpt_path)
-    kmeans_dict = {}
-    for spk, ckpt in checkpoint.items():
-        km = KMeans(ckpt["n_features_in_"])
-        km.__dict__["n_features_in_"] = ckpt["n_features_in_"]
-        km.__dict__["_n_threads"] = ckpt["_n_threads"]
-        km.__dict__["cluster_centers_"] = ckpt["cluster_centers_"]
-        kmeans_dict[spk] = km
-    return kmeans_dict
-
-def get_cluster_result(model, x, speaker):
-    """
-        x: np.array [t, 256]
-        return cluster class result
-    """
-    return model[speaker].predict(x)
-
-def get_cluster_center_result(model, x,speaker):
-    """x: np.array [t, 256]"""
-    predict = model[speaker].predict(x)
-    return model[speaker].cluster_centers_[predict]
-
-def get_center(model, x,speaker):
-    return model[speaker].cluster_centers_[x]